{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Polarisation examples\n", "\n", "Below are a couple of examples of how to use `WODEN` to simulation polarisation data. Both examples include details on how to generate the FITS-style sky model, how t run the simulation, and check the results. This documentation is simply the notebook found in `WODEN/examples/polarisation/polarisation_examples.ipynb`. If you want to run it, you'll need a working `WODEN` installation, and `WSClean` to do the imaging.\n", "\n", "Big shout out to Emil for all the help in getting Jack to (kind of) understanding polarisation, and for the RM synthesis code.\n", "\n", "Apologies, I haven't worked out how to get outputs from cells to be scrollable in the online documentation, so please just scroll past the reams of output text to get to plots.\n", "\n", "## Southern Hotspot of PKS J0636−2036\n", "\n", "This is an example of a real source, details of which can be found in [O'Sullivan et al. (2018)](https://ui.adsabs.harvard.edu/abs/2018MNRAS.475.4263O/abstract). Assuming that the southern lobe is a single point source, at 216MHz, the component has the parameters (from Table 3 of this paper):\n", "\n", "Stokes $I = 13.9$ Jy (at reference 216MHz) \\\n", "Polarisation fraction $\\Pi = 0.092$ \\\n", "Spectral index $\\alpha = -0.815$ \\\n", "Faraday rotation $\\phi_\\textrm{RM} = 50.2 $ rad $m^{-2}$\n", "\n", "Ignoring the jump in frequency and instrument in the measurement (it's an example so we can do what we want) we can also say\n", "Intrinsic rotation angle $\\chi_0 = -71.1^{\\circ}$\n", "\n", "Furthermore, to make this more useful as an example, we'll say the source actually has a Stokes $V$ component that bounces around, so we can look at list-type polarisation data.\n", "\n", "First, we'll make a FITS table with these parameters. Then we'll stick it through `WODEN`, and process the outputs.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from astropy.table import Table, Column\n", "from astropy.coordinates import SkyCoord\n", "import numpy as np\n", "from subprocess import call\n", "from astropy.coordinates import EarthLocation\n", "from astropy import units as u\n", "from astropy.time import Time\n", "from astropy.io import fits\n", "import matplotlib.pyplot as plt\n", "from pygdsm import GlobalSkyModel16\n", "import healpy as hp\n", "from astropy.constants import c\n", "C = c.value" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "\n", "\n", "##Stick the source at location of it's name J0636−2036, good enough\n", "coord = SkyCoord(ra='6h36m', dec=\"-20d36m\")\n", "ra0 = coord.ra.deg\n", "dec0 = coord.dec.deg\n", "\n", "##These are the parameters for the source\n", "alpha = -0.815\n", "##hyperdrive/WODEN/LoBES catalogues are all referenced to 200MHz, so we need\n", "##to scale the stokesI flux\n", "stokesI = 13.9 * (200/216)**(alpha)\n", "pol_frac = 0.092\n", "phi_RM = 50.2\n", "chi_0 = np.radians(71.1)\n", "\n", "\n", "##The LoBES/hyperdrive/WODEN catalogues need a unique source ID (UNQ_SOURCE_ID),\n", "##a then edge component of that source needs a name (NAME)\n", "c_ids = Column(data=np.array(['PKS_J0636_source']), name='UNQ_SOURCE_ID', dtype='|S20')\n", "c_names = Column(data=np.array(['PKS_J0636_source_C00']), name='NAME', dtype='|S20')\n", "\n", "##Component position\n", "c_ras = Column(data=np.array([ra0]), name='RA')\n", "c_decs = Column(data=np.array([dec0]), name='DEC')\n", "\n", "##This says we have a point source\n", "c_comp_types = Column(data=np.array(['P'], dtype='|S1'), name=\"COMP_TYPE\", dtype='|S1')\n", "##This says we have a Stokes I power-law SED\n", "c_mod_types = Column(data=np.array(['pl'], dtype='|S3'), name=\"MOD_TYPE\", dtype='|S3')\n", "##Stokes I power law parameters\n", "c_stokes_I_ref = Column(data=np.array([stokesI]), name='NORM_COMP_PL')\n", "c_stokes_SI = Column(data=np.array([alpha]), name='ALPHA_PL')\n", "\n", "##This says we are using a polarisation fraction linear polarisation model\n", "c_lin_mod_type = Column(data=np.array(['pf'], dtype='|S3'), name='LIN_MOD_TYPE', dtype='|S3')\n", "##linear polarisation parameters\n", "c_lin_pol_frac = Column(data=np.array([pol_frac]), name='LIN_POL_FRAC')\n", "c_lin_pol_angle = Column(data=np.array([chi_0]), name='INTR_POL_ANGLE')\n", "c_rm = Column(data=np.array([phi_RM]), name='RM')\n", "\n", "##This says we are using a list-type circular polarisation model. The list info\n", "##goes into another table, see below.\n", "c_v_mod_type = Column(data=np.array(['nan'], dtype='|S3'), name='V_MOD_TYPE', dtype='|S3')\n", "\n", "##Stick all the columns in a list\n", "columns = [c_ids, c_names, c_ras, c_decs, c_comp_types, c_mod_types, c_stokes_I_ref, c_stokes_SI, c_lin_mod_type,c_lin_pol_frac, c_lin_pol_angle, c_rm, c_v_mod_type]\n", "\n", "##Make the main table\n", "main_table = Table(columns)\n", "\n", "##Now, make a table for Stokes V list-type SED. This goes in it's own table,\n", "##where the columns have the frequency in the title. We dupliate the NAME\n", "##column so that we can link the two tables\n", "\n", "v_cols = [c_names]\n", "\n", "v_freqs = np.arange(150, 250, 0.2) ##MHz\n", "v_fluxes = np.random.uniform(-0.5, 0.5, len(v_freqs))\n", "\n", "for freq, flux in zip(v_freqs, v_fluxes):\n", " col_name = f'V_INT_FLX{freq:.1f}'\n", " v_cols.append(Column(data=np.array([flux]), name=col_name))\n", "\n", "v_table = Table(v_cols)\n", "\n", "##Create an HDU list from the Tables, so we can easily write it to a FITS file\n", "hdu_list = [fits.PrimaryHDU(),\n", " fits.table_to_hdu(main_table),\n", " fits.table_to_hdu(v_table)]\n", "\n", "##Name the tables so WODEN can understand which is which\n", "hdu_list[1].name = 'MAIN'\n", "hdu_list[2].name = 'V_LIST_FLUXES'\n", "\n", "##convert and write out to a FITS table file\n", "hdu_list = fits.HDUList(hdu_list)\n", "cat_name = 'PKS_J0636.fits'\n", "hdu_list.writeto(cat_name, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our single source, let's setup a simple simulation command. For this example, we're just testing the polarisation reponse of `WODEN`, so make a very very basic simulatin. We'll run without a primary beam, put the source at phase centre, and run with just a few baselines via a custom array layout. \n", "\n", "Importantly, we'll set the `--IAU_order` flag, which means that `XX` = north-south, `YY` = east-west. This is the IAU convention for Stokes parameters." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "LST: 100.31812162623258, RA: 98.99999999999999\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/jack-line/software/anaconda3/envs/woden/bin/run_woden.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('wodenpy==2.3.0')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "You are using WODEN commit: No git describe nor __version__ avaible\n", "LOADING IN /home/jack-line/software/WODEN/wodenpy/libwoden_double.so\n", "Setting phase centre RA,DEC 99.00000deg -20.60000deg\n", "Obs epoch initial LST was 100.3222997096 deg\n", "Setting initial J2000 LST to 100.0765133194 deg\n", "Setting initial mjd to 60496.1666782406\n", "After precession initial latitude of the array is -26.6814845743 deg\n", "We are precessing the array\n", "Doing the initial reading/mapping of sky model into chunks\n", "Mapping took 0.0 mins\n", "Have read in 1 components\n", "After cropping there are 1 components\n", "After chunking there are 1 chunks\n", "Reading chunks skymodel chunks 0:50\n", "YO ['PRIMARY', 'MAIN', 'V_LIST_FLUXES']\n", "Reading chunks 0:50 took 0.0 mins\n", "WODEN is using DOUBLE precision\n", "NO PRIMARY BEAM HAS BEEN SELECTED\n", "\tWill run without a primary beam\n", "Simulating band 01 with bottom freq 1.80000000e+08\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 0\n", "\tNumber of components in chunk are: P 1 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "GPU calls for band 1 finished\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "np.random.seed(234987)\n", "\n", "##make a random array layout. Source is at phase centre, so the visibilities\n", "##should all be full real and just be set by the flux in the sky\n", "num_antennas = 10\n", "east = np.random.uniform(-1000, 1000, num_antennas)\n", "north = np.random.uniform(-1000, 1000, num_antennas)\n", "height = np.zeros(num_antennas)\n", "\n", "array = np.empty((num_antennas, 3))\n", "array[:,0] = east\n", "array[:,1] = north\n", "array[:,2] = height\n", "\n", "array_name = \"eg_array.txt\"\n", "np.savetxt(array_name, array)\n", "\n", "##stick our array in the MWA location. \n", "mwa_location = EarthLocation(lat=-26.703319405555554*u.deg, \n", " lon=116.67081523611111*u.deg,\n", " height=377.827)\n", "\n", "##pick a time/date that sticks our source overhead\n", "date = \"2024-07-05T04:00:00\"\n", "\n", "observing_time = Time(date, scale='utc', location=mwa_location)\n", "##Grab the LST\n", "LST = observing_time.sidereal_time('mean')\n", "LST_deg = LST.value*15\n", "print(f\"LST: {LST_deg}, RA: {ra0}\")\n", "\n", "##parameters for the simulation; setting the date sets an LST of about 0 deg\n", "##to match the phase centre\n", "\n", "uvfits_name = \"PKS_J0636\"\n", "freq_reso = 80e+3\n", "low_freq = 180e+6\n", "high_freq = 210e+6\n", "num_freq_chans = int((high_freq - low_freq) / freq_reso)\n", "\n", "##The command to run WODEN\n", "command = f'run_woden.py --ra0={ra0} --dec0={dec0} --array_layout={array_name} '\n", "command += f'--date={date} --output_uvfits_prepend={uvfits_name} '\n", "command += f'--cat_filename={cat_name} --primary_beam=none '\n", "command += f'--lowest_channel_freq={low_freq} --freq_res={freq_reso} '\n", "command += f'--num_freq_channels={num_freq_chans} --band_nums=1 '\n", "command += f'--time_res=2 --num_time_steps=1 --IAU_order'\n", "\n", "##use subprocess to run the command\n", "call(command, shell=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Righto, now we have an output, let's read it in, convert it to Stokes parameters, and plot the SED." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def read_uvfits(uvfits_name):\n", " \"\"\"Real shorthand function to read in visibilities from a WODEN UVFITS file\"\"\"\n", " \n", " with fits.open(uvfits_name) as hdus:\n", " data = np.squeeze(hdus[0].data.data)\n", " ##Yup, this is the order of things in a UVFITS file\n", " XX = data[:, :, 0, 0] + 1j*data[:, :, 0, 1]\n", " YY = data[:, :, 1, 0] + 1j*data[:, :, 1, 1]\n", " XY = data[:, :, 2, 0] + 1j*data[:, :, 2, 1]\n", " YX = data[:, :, 3, 0] + 1j*data[:, :, 3, 1]\n", " \n", " return XX, XY, YX, YY\n", " \n", "XX, XY, YX, YY = read_uvfits(f'{uvfits_name}_band01.uvfits')\n", "\n", "##pick a random baseline to plot, they should all be the same\n", "baseline = np.random.randint(0, XX.shape[0])\n", "print(baseline)\n", "\n", "##converts instrumental pols into Stokes params. We didn't use a primary beam\n", "##at we're right at zenith so this is good enough without corrections\n", "recover_I = 0.5*(XX[baseline] + YY[baseline])\n", "recover_Q = 0.5*(XX[baseline] - YY[baseline])\n", "recover_U = 0.5*(XY[baseline] + YX[baseline])\n", "recover_V = -0.5j*(XY[baseline] - YX[baseline])\n", "\n", "##these are the freqs we put into the simulation\n", "freqs = np.arange(low_freq, high_freq, freq_reso)\n", "\n", "fig, axs = plt.subplots(1, 1, figsize=(8, 6))\n", "\n", "axs.plot(freqs / 1e+6, recover_I.real, 'k-', label='Recovered I')\n", "axs.plot(freqs / 1e+6, recover_Q.real, 'r', label='Recovered Q')\n", "axs.plot(freqs / 1e+6, recover_U.real, 'b', label='Recovered U')\n", "axs.plot(freqs / 1e+6, recover_V.real, 'g', label='Recovered V')\n", "\n", "axs.legend()\n", "\n", "axs.set_xlabel('Frequency (MHz)')\n", "axs.set_ylabel('Flux (Jy)')\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lovely, we have some nice sinusoidal Q/U lines, a power-law Stokes I, and a noisy-like Stokes V. Let's see if we can recover the RM from this data. All credit to Emil for the RM synthesis code." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input resolutions going into RM-synthesis:\n", "\tFrequency range: 180.000 MHz - 209.920 MHz\n", "\tBandwidth: 29.920 MHz\n", "\tChannel width: 80.0 KHz\n", "\tdlambda2: 0.002\n", "\tDlambda2: 0.734\n", "\tphimax: 702.920\n", "\tdphi: 4.717\n", "\tphiR: 0.943\n", "\tNphi: 1490.189\n", "Recovered paramaters:\n", "\tS = 15.133\n", "\tPol frac from recovered peak = 9.167%\n", "\tMean recovered pol frac: 0.092, Expected: 0.092\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "def getFDF(dataQ, dataU, freqs, startPhi, stopPhi, dPhi, dType='float32'):\n", " \"\"\"\n", " # Perform RM-synthesis on Stokes Q and U data\n", " #\n", " # dataQ, dataU and freqs - contains the Q/U data at each frequency (in Hz) measured.\n", " # startPhi, dPhi - the starting RM (rad/m^2) and the step size (rad/m^2)\n", " \n", " Author: Emil Lenc\n", " \"\"\"\n", "\n", " # Calculate the RM sampling\n", " phiArr = np.arange(startPhi, stopPhi, dPhi)\n", "\n", " # Calculate the frequency and lambda sampling\n", " lamSqArr = np.power(C / np.array(freqs), 2.0)\n", "\n", " # Calculate the dimensions of the output RM cube\n", " nPhi = len(phiArr)\n", "\n", " # Initialise the complex Faraday Dispersion Function (FDF)\n", " FDF = np.ndarray((nPhi), dtype='complex')\n", "\n", " # Assume uniform weighting\n", " wtArr = np.ones(len(lamSqArr), dtype=dType)\n", "\n", " K = 1.0 / np.nansum(wtArr)\n", "\n", " # Get the weighted mean of the LambdaSq distribution (B&dB Eqn. 32)\n", " lam0Sq = K * np.nansum(lamSqArr)\n", "\n", " # Mininize the number of inner-loop operations by calculating the\n", " # argument of the EXP term in B&dB Eqns. (25) and (36) for the FDF\n", " a = (-2.0 * 1.0j * phiArr)\n", " b = (lamSqArr - lam0Sq) \n", " arg = np.exp( np.outer(a, b) )\n", "\n", " # Create a weighted complex polarised surface-brightness cube\n", " # i.e., observed polarised surface brightness, B&dB Eqns. (8) and (14)\n", " Pobs = (np.array(dataQ) + 1.0j * np.array(dataU))\n", "\n", " # Calculate the Faraday Dispersion Function\n", " # B&dB Eqns. (25) and (36)\n", " FDF = K * np.nansum(Pobs * arg, 1)\n", " return FDF, phiArr\n", "\n", "def findpeaks(freqs, fdf, phi, rmsf, rmsfphi, nsigma):\n", " \"\"\"Find peaks in the FDF\n", " Author: Emil Lenc\"\"\"\n", " # Create the Gaussian filter for reconstruction\n", " lam2 = (C / freqs) ** 2.0\n", " lam02 = np.mean(lam2)\n", " minl2 = np.min(lam2)\n", " maxl2 = np.max(lam2)\n", " width = (2.0 * np.sqrt(3.0)) / (maxl2 - minl2)\n", "\n", " Gauss = np.exp((-rmsfphi ** 2.0) / (2.0 * ((width / 2.355) ** 2.0)))\n", " components = np.zeros((len(phi)), np.float32)\n", " peaks = []\n", " phis = []\n", " std = 0.0\n", " rmsflen = int((len(rmsf) - 1) / 2)\n", " fdflen = len(phi) + rmsflen\n", " while True:\n", " std = np.std(np.abs(fdf))\n", " peak1 = np.max(np.abs(fdf))\n", " pos1 = np.argmax(np.abs(fdf))\n", " val1 = phi[pos1]\n", " if peak1 < nsigma * std :\n", " break\n", " fdf -= rmsf[rmsflen - pos1:fdflen - pos1] * fdf[pos1]\n", " peaks.append(peak1)\n", " phis.append(val1)\n", " components[pos1] += peak1\n", " fdf += np.convolve(components, Gauss, mode='valid')\n", " return phis, peaks, std\n", "\n", "startPhi = -100.0\n", "dPhi = 1.0\n", "stopPhi = -startPhi+dPhi\n", "lambda2 = np.power(C / np.array(freqs), 2.0)\n", "\n", "df = []\n", "for f in range(1, len(freqs)):\n", "\tdf.append(freqs[f] - freqs[f-1])\n", "chanBW = np.min(np.array(df))\n", "fmin = np.min(freqs)\n", "fmax = np.max(freqs)\n", "bw = fmax - fmin\n", "\n", "dlambda2 = np.power(C / fmin, 2) - np.power(C / (fmin + chanBW), 2)\n", "Dlambda2 = np.power(C / fmin, 2) - np.power(C / (fmin + bw), 2)\n", "phimax = np.sqrt(3) / dlambda2\n", "dphi = 2.0 * np.sqrt(3) / Dlambda2\n", "phiR = dphi / 5.0\n", "Nphi = 2 * phimax / phiR\n", "##These are things that mean something to polarisation peoples\n", "print(\"Input resolutions going into RM-synthesis:\")\n", "print(\"\\tFrequency range: %7.3f MHz - %7.3f MHz\" %(fmin / 1.0e6, (fmin + bw) / 1.0e6))\n", "print(\"\\tBandwidth: %7.3f MHz\" %(bw / 1.0e6))\n", "print(\"\\tChannel width: %.1f KHz\" %(chanBW / 1.0e3))\n", "print(\"\\tdlambda2: %7.3f\" %(dlambda2))\n", "print(\"\\tDlambda2: %7.3f\" %(Dlambda2))\n", "print(\"\\tphimax: %7.3f\" %(phimax))\n", "print(\"\\tdphi: %7.3f\" %(dphi))\n", "print(\"\\tphiR: %7.3f\" %(phiR))\n", "print(\"\\tNphi: %7.3f\" %(Nphi))\n", "fwhm = dphi\n", "\n", "# Determine the FDF using the q, u and ferquency values read from the file.\n", "dirty, phi = getFDF(recover_Q, recover_U, freqs, startPhi, stopPhi, dPhi)\n", "FDFqu, phi = getFDF(recover_Q, recover_U, freqs, startPhi, stopPhi, dPhi)\n", "rstartPhi = startPhi * 2\n", "rstopPhi = stopPhi * 2 - dPhi\n", "RMSF, rmsfphi = getFDF(np.ones((len(recover_Q))), np.zeros((len(recover_Q))), freqs, rstartPhi, rstopPhi, dPhi)\n", "\n", "phis, peaks, sigma = findpeaks(np.array(freqs), FDFqu, phi, RMSF, rmsfphi, 6.0)\n", "snr = peaks / sigma\n", "phierr = fwhm / (2 * snr)\n", "# print(phis, phierr, peaks, sigma)\n", "imean = np.mean(np.real(recover_I))\n", "\n", "print(\"Recovered paramaters:\")\n", "\n", "print(\"\\tS = %.3f\" %(imean))\n", "if len(peaks) > 0:\n", "\tprint(\"\\tPol frac from recovered peak = %.3f%%\" %(100.0 * peaks[0] / imean))\n", "\n", "##What is the polarised flux as a function of frequency\n", "# polarised_flux = np.sqrt(recover_Q**2 + recover_U**2)\n", "polarised_flux = np.abs(recover_Q + 1j*recover_U)\n", "\n", "recovered_pol_frac = (polarised_flux.real / recover_I.real)\n", "print(f'\\tMean recovered pol frac: {np.mean(recovered_pol_frac):.3f}, Expected: {pol_frac:.3f}')\n", "\n", "fig, axs = plt.subplots(1, 1, figsize=(6,4))\n", "\n", "axs.plot(phi, np.real(FDFqu), 'k-', label='FDF')\n", "\n", "axs.set_xlabel(\"${\\phi}$ (rad m$^{-2}$)\")\n", "axs.set_ylabel(\"Polarised Flux (Jy RMSF$^{-1}$)\")\n", "\n", "axs.set_xlim(-100, 100)\n", "\n", "axs.axvline(x=phi_RM, color='C1', linestyle='--', label='Expected RM')\n", "axs.legend()\n", "\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using Emil's magic RM code, we can see a nice strong peak at the expected RM of 50.2 rad/m^2, and we recover the expected polarisation fraction of 9.2%." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiple components same direction\n", "\n", "Now let's try sticking a few different components in the same direction, to see if we can disentangle the RMs.\n", "\n", "Make a new sky model:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "\n", "num_comps = 3\n", "\n", "##Make some simple component params. Keep everything the same, but vary RM\n", "alpha = np.full(num_comps, -0.815)\n", "stokesI = np.full(num_comps, 1.0)\n", "pol_frac = np.full(num_comps, 0.3)\n", "phi_RM = np.array([10, 50, 90])\n", "chi_0 = np.full(num_comps, 0.0)\n", "\n", "##The LoBES/hyperdrive/WODEN catalogues need a unique source ID (UNQ_SOURCE_ID),\n", "##a then edge component of that source needs a name (NAME)\n", "c_ids = Column(data=np.full(num_comps, 'multi_comp', dtype='|S20'), name='UNQ_SOURCE_ID', dtype='|S20')\n", "c_names = Column(data=[f'multi_comp_C{comp:02d}' for comp in range(num_comps)], name='NAME', dtype='|S20')\n", "\n", "##Component position\n", "c_ras = Column(data=np.full(num_comps, ra0), name='RA')\n", "c_decs = Column(data=np.full(num_comps, dec0), name='DEC')\n", "\n", "##This says we have a point source\n", "c_comp_types = Column(data=np.full(num_comps, 'P', dtype='|S1'), name=\"COMP_TYPE\", dtype='|S1')\n", "##This says we have a Stokes I power-law SED\n", "c_mod_types = Column(data=np.full(num_comps, 'pl', dtype='|S3'), name=\"MOD_TYPE\", dtype='|S3')\n", "##Stokes I power law parameters\n", "c_stokes_I_ref = Column(data=stokesI, name='NORM_COMP_PL')\n", "c_stokes_SI = Column(data=alpha, name='ALPHA_PL')\n", "\n", "##This says we are using a polarisation fraction linear polarisation model\n", "c_lin_mod_type = Column(data=np.full(num_comps, 'pf', dtype='|S3'), name='LIN_MOD_TYPE', dtype='|S3')\n", "##linear polarisation parameters\n", "c_lin_pol_frac = Column(data=pol_frac, name='LIN_POL_FRAC')\n", "c_lin_pol_angle = Column(data=chi_0, name='INTR_POL_ANGLE')\n", "c_rm = Column(data=phi_RM, name='RM')\n", "\n", "##Stick all the columns in a list\n", "columns = [c_ids, c_names, c_ras, c_decs, c_comp_types, c_mod_types, c_stokes_I_ref, c_stokes_SI, c_lin_mod_type,c_lin_pol_frac, c_lin_pol_angle, c_rm]\n", "\n", "##Make a table\n", "table = Table(columns)\n", "\n", "##write the table\n", "cat_name = 'multi_comp.fits'\n", "table.write(cat_name, overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run it through WODEN, convert outputs into I,Q,U,V, have a look:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jack-line/software/anaconda3/envs/woden/bin/run_woden.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('wodenpy==2.3.0')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "You are using WODEN commit: No git describe nor __version__ avaible\n", "LOADING IN /home/jack-line/software/WODEN/wodenpy/libwoden_double.so\n", "Setting phase centre RA,DEC 99.00000deg -20.60000deg\n", "Obs epoch initial LST was 100.3222997096 deg\n", "Setting initial J2000 LST to 100.0765133194 deg\n", "Setting initial mjd to 60496.1666782406\n", "After precession initial latitude of the array is -26.6814845743 deg\n", "We are precessing the array\n", "Doing the initial reading/mapping of sky model into chunks\n", "INFO: couldn't find second table containing shapelet information, so not attempting to load any shapelets.\n", "Mapping took 0.0 mins\n", "Have read in 3 components\n", "After cropping there are 3 components\n", "After chunking there are 1 chunks\n", "Reading chunks skymodel chunks 0:50\n", "YO ['PRIMARY', '']\n", "Reading chunks 0:50 took 0.0 mins\n", "WODEN is using DOUBLE precision\n", "NO PRIMARY BEAM HAS BEEN SELECTED\n", "\tWill run without a primary beam\n", "Simulating band 01 with bottom freq 1.80000000e+08\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 0\n", "\tNumber of components in chunk are: P 3 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "GPU calls for band 1 finished\n", "24\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##The command to run WODEN\n", "command = f'run_woden.py --ra0={ra0} --dec0={dec0} --array_layout={array_name} '\n", "command += f'--date={date} --output_uvfits_prepend={uvfits_name} '\n", "command += f'--cat_filename={cat_name} --primary_beam=none '\n", "command += f'--lowest_channel_freq={low_freq} --freq_res={freq_reso} '\n", "command += f'--num_freq_channels={num_freq_chans} --band_nums=1 '\n", "command += f'--time_res=2 --num_time_steps=1 --IAU_order'\n", "\n", "##use subprocess to run the command\n", "call(command, shell=True)\n", "\n", "XX, XY, YX, YY = read_uvfits(f'{uvfits_name}_band01.uvfits')\n", "\n", "##pick a random baseline to plot, they should all be the same\n", "baseline = np.random.randint(0, XX.shape[0])\n", "print(baseline)\n", "\n", "##converts instrumental pols into Stokes params. We didn't use a primary beam\n", "##at we're right at zenith so this is good enough without corrections\n", "recover_I = 0.5*(XX[baseline] + YY[baseline])\n", "recover_Q = 0.5*(XX[baseline] - YY[baseline])\n", "recover_U = 0.5*(XY[baseline] + YX[baseline])\n", "recover_V = -0.5j*(XY[baseline] - YX[baseline])\n", "\n", "##these are the freqs we put into the simulation\n", "freqs = np.arange(low_freq, high_freq, freq_reso)\n", "\n", "fig, axs = plt.subplots(1, 1, figsize=(8, 6))\n", "\n", "axs.plot(freqs / 1e+6, recover_I.real, 'k-', label='Recovered I')\n", "axs.plot(freqs / 1e+6, recover_Q.real, 'r', label='Recovered Q')\n", "axs.plot(freqs / 1e+6, recover_U.real, 'b', label='Recovered U')\n", "axs.plot(freqs / 1e+6, recover_V.real, 'g', label='Recovered V')\n", "\n", "axs.legend()\n", "\n", "axs.set_xlabel('Frequency (MHz)')\n", "axs.set_ylabel('Flux (Jy)')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wiggly! Alrighty, what do we have in RM space?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Determine the FDF using the q, u and ferquency values read from the file.\n", "FDFqu, phi = getFDF(recover_Q, recover_U, freqs, startPhi, stopPhi, dPhi)\n", "\n", "fig, axs = plt.subplots(1, 1, figsize=(6,4))\n", "\n", "axs.plot(phi, np.real(FDFqu), 'k-', label='FDF')\n", "\n", "axs.set_xlabel(\"${\\phi}$ (rad m$^{-2}$)\")\n", "axs.set_ylabel(\"Polarised Flux (Jy RMSF$^{-1}$)\")\n", "\n", "for phi in phi_RM:\n", "\taxs.axvline(x=phi, color='C1', linestyle='--', label='Expected RM')\n", "\n", "axs.legend()\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I don't know nothing about polarisation, so I don't know if a negative peak in the FDF is bad. But we have three peaks where we expected them to be, which is nice." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Linear polarisation diffuse sky\n", "\n", "In this example, we'll make a crazy made-up sky. Then we'll push it through an instrument which has symmetric X and Y primary beams, which means we can make I/Q/U/V images without beam correction (as there won't be direction dependent differences).\n", "\n", "First up, make the sky model. This will be a little involved as we're going to make the Stokes I, linear polarised, and Stokes V sky separately. We'll make Stokes I simple point sources. The linear sky will be the Haslam map (via the [pygdsm](https://github.com/telegraphic/pygdsm) package), and the Stokes V sky will be a bunch of Gaussian blobs. Everything will have a simple power-law SED." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "\n", "##We're going to use the EDA2 array\n", "##layout, so we can get away with low-resolution sky model. So set the \n", "##healpixel nside to 256\n", "nside = 256\n", "num_diffuse = hp.nside2npix(nside)\n", "\n", "##chuck in 1000 each for stokes I and V\n", "num_stokesI = 1000\n", "num_stokesV = 1000\n", "\n", "total_num_comps = num_diffuse + num_stokesI + num_stokesV\n", "\n", "##first, the diffuse sky linear polarised sky. Generate it at 200MHz,\n", "##convert it to the nside we want, and then convert it to Jy\n", "gsm_2016 = GlobalSkyModel16(freq_unit='MHz', data_unit='MJysr')\n", "output = gsm_2016.generate(200)\n", "map_nside = hp.npix2nside(len(output))\n", "output *= hp.nside2pixarea(map_nside)*1e+6\n", "\n", "##Set some power law properties, and set the RM to 2 ra/m^2 everywhere\n", "diffuse_ref_flux = hp.ud_grade(output, nside)\n", "diffuse_SI = np.full(num_diffuse, -2.7)\n", "diffuse_rm = np.full(num_diffuse, 2)\n", "\n", "##coords are natively in galactic, so we need to convert to ICRS\n", "l, b = hp.pix2ang(nside,np.arange(hp.nside2npix(nside)), lonlat=True)\n", "gal_coords = SkyCoord(l*u.deg, b*u.deg, frame='galactic')\n", "diff_ras = gal_coords.icrs.ra.value\n", "diff_decs = gal_coords.icrs.dec.value\n", "\n", "##next, make a simple point source stokes I sky\n", "##make 'em power laws\n", "comp_ras = np.random.uniform(0, 360, num_stokesI)\n", "comp_decs = np.random.uniform(-90.0, 90.0, num_stokesI)\n", "comp_ref_fluxes = np.random.uniform(1, 10, num_stokesI)\n", "comp_ref_SIs = np.random.uniform(-1, 0.2, num_stokesI)\n", "\n", "\n", "##finally, make some weird large gaussian source stokes V sky\n", "##also make 'em power laws\n", "v_ras = np.random.uniform(0, 360, num_stokesV)\n", "v_decs = np.random.uniform(-90.0, 90.0, num_stokesV)\n", "\n", "v_ref_fluxes = np.random.uniform(1, 50, num_stokesV)\n", "v_ref_SIs = np.random.uniform(-1, 0.2, num_stokesV)\n", "##these are the gaussian params; all these sizes are in degrees\n", "v_major_axes = np.random.uniform(10, 12, num_stokesV)\n", "v_minor_axes = np.random.uniform(0.25, 0.5, num_stokesV)\n", "v_pas = np.random.uniform(0,360, num_stokesV)\n", "\n", "##now we take all these individual parameters and stick them in a table\n", "\n", "all_stokes_I_ref = np.zeros(total_num_comps)\n", "all_stokes_I_ref[:num_stokesI] = comp_ref_fluxes\n", "all_stokes_SI = np.zeros(total_num_comps)\n", "all_stokes_SI[:num_stokesI] = comp_ref_SIs\n", "\n", "all_lin_mod_type = np.full(total_num_comps, '', dtype='|S3')\n", "all_lin_mod_type[num_stokesI:num_stokesI+num_diffuse] = 'pl'\n", "\n", "all_linpol_ref = np.zeros(total_num_comps)\n", "all_linpol_ref[num_stokesI:num_stokesI+num_diffuse] = diffuse_ref_flux\n", "\n", "all_linpol_SI = np.zeros(total_num_comps)\n", "all_linpol_SI[num_stokesI:num_stokesI+num_diffuse] = diffuse_SI\n", "\n", "all_rm = np.zeros(total_num_comps)\n", "all_rm[num_stokesI:num_stokesI+num_diffuse] = diffuse_rm\n", "\n", "all_v_mod_type = np.full(total_num_comps, '', dtype='|S3')\n", "all_v_mod_type[-num_stokesV:] = 'pl'\n", "\n", "all_v_ref = np.zeros(total_num_comps)\n", "all_v_ref[-num_stokesV:] = v_ref_fluxes\n", "\n", "all_v_SI = np.zeros(total_num_comps)\n", "all_v_SI[-num_stokesV:] = v_ref_SIs\n", "\n", "all_majors = np.full(total_num_comps, np.nan)\n", "all_majors[-num_stokesV:] = v_major_axes\n", "all_minors = np.full(total_num_comps, np.nan)\n", "all_minors[-num_stokesV:] = v_minor_axes\n", "all_pas = np.full(total_num_comps, np.nan)\n", "all_pas[-num_stokesV:] = v_pas\n", "\n", "##make everything except the stokes V sources gaussian\n", "all_comp_types = np.full(total_num_comps, 'P', dtype='|S1')\n", "all_comp_types[-num_stokesV:] = 'G'\n", "\n", "\n", "##create Columns to go into the table\n", "c_ids = Column(data=np.array(['{:08d}'.format(i) for i in range(total_num_comps)]), name='UNQ_SOURCE_ID', dtype='|S20')\n", "c_names = Column(data=np.array(['{:08d}_C00'.format(i) for i in range(total_num_comps)]), name='NAME', dtype='|S20')\n", "\n", "c_ras = Column(data=np.concatenate([comp_ras, diff_ras, v_ras]), name='RA')\n", "c_decs = Column(data=np.concatenate([comp_decs, diff_decs, v_decs]), name='DEC')\n", "c_comp_types = Column(data=all_comp_types, name=\"COMP_TYPE\", dtype='|S1')\n", "c_mod_types = Column(data=np.full(total_num_comps, 'pl', dtype='|S3'), name=\"MOD_TYPE\", dtype='|S3')\n", "\n", "c_stokes_I_ref = Column(data=all_stokes_I_ref, name='NORM_COMP_PL')\n", "c_stokes_SI = Column(data=all_stokes_SI, name='ALPHA_PL')\n", "\n", "c_lin_mod_type = Column(data=all_lin_mod_type, name='LIN_MOD_TYPE', dtype='|S3')\n", "c_linpol_ref = Column(data=all_linpol_ref, name='LIN_NORM_COMP_PL')\n", "c_linpol_SI = Column(data=all_linpol_SI, name='LIN_ALPHA_PL')\n", "c_rm = Column(data=all_rm, name='RM')\n", "\n", "\n", "c_v_mod_type = Column(data=all_v_mod_type, name='V_MOD_TYPE', dtype='|S3')\n", "c_v_ref = Column(data=all_v_ref, name='V_NORM_COMP_PL')\n", "c_v_SI = Column(data=all_v_SI, name='V_ALPHA_PL')\n", "\n", "c_major = Column(data=all_majors, name='MAJOR_DC')\n", "c_minor = Column(data=all_majors, name='MINOR_DC')\n", "c_pas = Column(data=all_pas, name='PA_DC')\n", "\n", "\n", "##Make a da table\n", "columns = [c_ids, c_names, c_ras, c_decs, c_comp_types, c_mod_types,\n", " c_stokes_I_ref, c_stokes_SI, c_lin_mod_type, c_linpol_ref,\n", " c_linpol_SI, c_rm, c_v_mod_type, c_v_ref, c_v_SI, c_major,\n", " c_minor, c_pas]\n", "\n", "table = Table(columns)\n", "table.write('polarised_sky.fits', overwrite=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OK, now we have a sky model, let's run the simulation. We'll use the EDA2 array layout, and set the `--IAU_order` flag again. We'll also set both X and Y beams to be a Gaussian beam with FWHM of 100 deg. This will let us see most of the sky, but chop the horizon off to make imaging nicer. This beam also has zero leakage, which means we keep the I/Q/U/V all nice and separate. We'll phase things up to EoR1 so the galactic plane is down. Do it with `float` precision as we don't really care too much about accuracy, just want some fun images" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jack-line/software/anaconda3/envs/woden/bin/run_woden.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('wodenpy==2.3.0')\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "You are using WODEN commit: No git describe nor __version__ avaible\n", "LOADING IN /home/jack-line/software/WODEN/wodenpy/libwoden_float.so\n", "Setting phase centre RA,DEC 60.00000deg -27.00000deg\n", "Obs epoch initial LST was 63.0347905707 deg\n", "Setting initial J2000 LST to 62.8702463523 deg\n", "Setting initial mjd to 57396.5495717593\n", "After precession initial latitude of the array is -26.7414173539 deg\n", "We are precessing the array\n", "Doing the initial reading/mapping of sky model into chunks\n", "INFO: couldn't find second table containing shapelet information, so not attempting to load any shapelets.\n", "Mapping took 0.0 mins\n", "Have read in 788432 components\n", "After cropping there are 394238 components\n", "After chunking there are 59 chunks\n", "Reading chunks skymodel chunks 0:50\n", "YO ['PRIMARY', '']\n", "Reading chunks 0:50 took 0.1 mins\n", "Reading chunks skymodel chunks 50:100\n", "WODEN is using FLOAT precision\n", "Setting up Gaussian primary beam settings\n", " pointing at HA, Dec = -0.21717deg, -26.74111deg\n", " setting beam FWHM to 100.00000deg and ref freq to 150.000MHz\n", "Simulating band 01 with bottom freq 1.67035000e+08\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 0\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "YO ['PRIMARY', '']\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "Reading chunks 50:100 took 0.0 mins\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 1\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", 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"\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 26\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 27\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 28\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 29\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 30\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 31\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 32\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 33\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 34\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 35\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 36\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 37\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 38\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 39\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 40\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 41\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 42\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 43\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 44\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 45\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 46\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 47\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 48\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 49\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "GPU calls for band 1 finished\n", "WODEN is using FLOAT precision\n", "Setting up Gaussian primary beam settings\n", " pointing at HA, Dec = -0.21717deg, -26.74111deg\n", " setting beam FWHM to 100.00000deg and ref freq to 150.000MHz\n", "Simulating band 01 with bottom freq 1.67035000e+08\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 0\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 1\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 2\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 3\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 4\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 5\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 6\n", "\tNumber of components in chunk are: P 6892 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 7\n", "\tNumber of components in chunk are: P 866 G 0 S_coeffs 0\n", "\tDoing point components\n", "\tExtrapolating fluxes and beams...\n", "\tDoing Gaussian Beam\n", "\tExtrapolating fluxes and beams done.\n", "\tDoing visi kernel...\n", "\tVisi kernel done\n", "About to copy the chunked source to the GPU\n", "Have copied across the chunk to the GPU\n", "Processing chunk 8\n", "\tNumber of components in chunk are: P 0 G 528 S_coeffs 0\n", "\tDoing gaussian components\n", "\tDoing Gaussian Beam\n", "GPU calls for band 1 finished\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "metafits=\"../metafits/1136380296_metafits_ppds.fits\"\n", "uvfits_name = \"scary_sky\"\n", "array_layout = \"../../test_installation/array_layouts/EDA2_layout_255.txt\"\n", "\n", "command = \"run_woden.py \"\n", "command += \" --ra0=60.0 --dec0=-27.0\"\n", "command += \" --num_freq_channels=16 --num_time_steps=14\"\n", "command += \" --freq_res=80e+3 --time_res=8.0\"\n", "command += \" --cat_filename=polarised_sky.fits\"\n", "command += f\" --metafits_filename={metafits}\"\n", "command += \" --band_nums=1\"\n", "command += f\" --output_uvfits_prepend={uvfits_name}\"\n", "command += \" --primary_beam=Gaussian --gauss_beam_FWHM=100\"\n", "command += f\" --array_layout={array_layout}\"\n", "command += \" --sky_crop_components\"\n", "command += \" --chunking_size=5e+10\"\n", "command += \" --precision=float\"\n", "\n", "call(command, shell=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can image it using WSClean. To be clear, normally you'd need to beam correct the images, and direction dependent differences between XX,XY,YX,YY mess with the conversion from instrumental to Stokes parameters. But as the primary beam is the same for both polarisations, you just get Stokes I/Q/U/V times a Gaussian beam after running through `WSClean`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/jack-line/software/anaconda3/envs/woden/bin/woden_uv2ms.py:4: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html\n", " __import__('pkg_resources').require('wodenpy==2.3.0')\n", "The telescope frame is set to '????', which generally indicates ignorance. Defaulting the frame to 'itrs', but this may lead to other warnings or errors.\n", "Writing in the MS file that the units of the data are uncalib, although some CASA process will ignore this and assume the units are all in Jy (or may not know how to handle data in these units).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "WSClean version 3.4 (2023-10-11)\n", "This software package is released under the GPL version 3.\n", "Author: André Offringa (offringa@gmail.com).\n", "\n", "No corrected data in first measurement set: tasks will be applied on the data column.\n", "=== IMAGING TABLE ===\n", " # Pol Ch JG ²G FG FI In Freq(MHz)\n", "| Independent group:\n", "+-+-J- 0 I 0 0 0 0 0 0 167-168 (16)\n", "| Independent group:\n", "+-+-J- 1 Q 0 1 1 1 0 0 167-168 (16)\n", "| Independent group:\n", "+-+-J- 2 U 0 2 2 2 0 0 167-168 (16)\n", "| Independent group:\n", "+-+-J- 3 V 0 3 3 3 0 0 167-168 (16)\n", "Reordering scary_sky_band01.ms into 1 x 4 parts.\n", "Reordering: 0%....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%\n", "Initializing model visibilities: 0%....10%....20%....30%....40%....50%....60%....70%....80%....90%....100%\n", " == Constructing PSF ==\n", "Precalculating weights for Briggs'(0) weighting...\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Detected 62.7 GB of system memory, usage not limited.\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Determining min and max w & theoretical beam size... DONE (w=[2.02049e-06:0.99262] lambdas, maxuvw=19.749 lambda)\n", "Theoretic beam = 2.9 deg\n", "Minimal inversion size: 102 x 102, using optimal: 108 x 108\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "Fitting beam... major=119.87', minor=119.53', PA=48.03 deg, theoretical=2.9 deg.\n", "Writing psf image... DONE\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "Writing dirty image...\n", " == Deconvolving (1) ==\n", "Estimated standard deviation of background noise: 159.09 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 407.35 mJy, 387: 116.39 mJy, 774: 83.59 mJy, 1547: 57.43 mJy}\n", "Starting multi-scale cleaning. Start peak=10.93 Jy, major iteration threshold=1.64 Jy\n", "Iteration 4, scale 0 px : 8.28 Jy at 1952,2044\n", "Iteration 13, scale 0 px : 6.49 Jy at 2003,1791\n", "Iteration 28, scale 0 px : 5.08 Jy at 1793,1908\n", "Iteration 52, scale 0 px : 4.05 Jy at 2417,2394\n", "Iteration 94, scale 0 px : 3.24 Jy at 2374,2175\n", "Iteration 147, scale 0 px : 2.59 Jy at 2508,2450\n", "Iteration 220, scale 0 px : 2.06 Jy at 2278,1964\n", "Iteration 304, scale 0 px : 1.64 Jy at 1517,1441\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 306, scale 0 px : 1.64 Jy at 2504,2446\n", "Minor loop finished, continuing cleaning after inversion/prediction round.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (2) ==\n", "Estimated standard deviation of background noise: 74.81 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 171.59 mJy, 387: 45.45 mJy, 774: 26.22 mJy, 1547: 15.88 mJy}\n", "Starting multi-scale cleaning. Start peak=1.69 Jy, major iteration threshold=253.76 mJy\n", "Iteration 383, scale 0 px : 1.35 Jy at 2090,3070\n", "Iteration 497, scale 0 px : 1.08 Jy at 2617,2665\n", "Iteration 613, scale 0 px : 859.64 mJy at 1136,2509\n", "Iteration 749, scale 0 px : 687.48 mJy at 2098,1258\n", "Iteration 921, scale 0 px : 559.55 mJy at 2475,1168\n", "Iteration 1000, scale 0 px : 499.7 mJy at 1541,1979\n", "Cleaning finished because maximum number of iterations was reached.\n", "Auto-masking threshold reached; continuing next major iteration with deeper threshold and mask.\n", "Maximum number of minor deconvolution iterations was reached: not continuing deconvolution.\n", "Assigning from 1 to 1 channels...\n", "Writing model image...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "2 major iterations were performed.\n", "Rendering sources to restored image (beam=119.53'-119.87', PA=48.03 deg)... DONE\n", "Writing restored image... DONE\n", "Multi-scale cleaning summary:\n", "- Scale 0 px, nr of components cleaned: 1000 (154.87 Jy)\n", "- Scale 387 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 774 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 1547 px, nr of components cleaned: 0 (0 Jy)\n", "Total: 1000 components (154.87 Jy)\n", "Inversion: 00:00:07.834198, prediction: 00:00:02.039755, deconvolution: 00:00:19.873050\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Determining min and max w & theoretical beam size... DONE (w=[2.02049e-06:0.99262] lambdas, maxuvw=19.749 lambda)\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "Writing dirty image...\n", " == Deconvolving (1) ==\n", "Estimated standard deviation of background noise: 1.15 Jy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 1.2 Jy, 387: 1.17 Jy, 774: 1.09 Jy, 1547: 877.75 mJy}\n", "Starting multi-scale cleaning. Start peak=-16.38 Jy, major iteration threshold=3.44 Jy (final)\n", "Iteration 5, scale 1547 px : -12.62 Jy at 1905,2109\n", "Iteration 10, scale 1547 px : -9.72 Jy at 1906,2109\n", "Iteration 15, scale 1547 px : -7.49 Jy at 1827,2167\n", "Iteration 23, scale 1547 px : -5.84 Jy at 1642,2307\n", "Iteration 33, scale 1547 px : -4.59 Jy at 1597,2348\n", "Iteration 48, scale 774 px : -3.87 Jy at 1710,1621\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 51, scale 1547 px : -3.64 Jy at 2419,2099\n", "(6) Iteration 56, scale 1547 px : -3.38 Jy at 2024,1622\n", "Cleaning finished because the final threshold was reached.\n", "Auto-masking threshold reached; continuing next major iteration with deeper threshold and mask.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (2) ==\n", "Estimated standard deviation of background noise: 578.96 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 553.64 mJy, 387: 522.39 mJy, 774: 479.75 mJy, 1547: 363.44 mJy}\n", "Starting multi-scale cleaning. Start peak=-3.45 Jy, major iteration threshold=516.83 mJy\n", "Iteration 73, scale 774 px : -2.89 Jy at 1710,1621\n", "Iteration 77, scale 1547 px : -2.78 Jy at 2419,2099\n", "Iteration 91, scale 1547 px : -2.21 Jy at 1559,2399\n", "Iteration 109, scale 774 px : -1.95 Jy at 1710,1621\n", "Iteration 113, scale 1547 px : -1.79 Jy at 2419,2099\n", "Iteration 127, scale 1547 px : 1.43 Jy at 1491,819\n", "Iteration 145, scale 774 px : -1.37 Jy at 1710,1621\n", "Iteration 149, scale 774 px : -1.09 Jy at 1710,1621\n", "Iteration 153, scale 1547 px : -1.17 Jy at 2419,2099\n", "Iteration 165, scale 1547 px : -930.58 mJy at 2423,2090\n", "Iteration 183, scale 1547 px : 881.46 mJy at 1906,2109\n", "Iteration 215, scale 774 px : -736.58 mJy at 1710,1621\n", "Iteration 219, scale 1547 px : -714.56 mJy at 1559,2399\n", "Iteration 274, scale 1547 px : 571.09 mJy at 2085,1985\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 317, scale 1547 px : -511.91 mJy at 1564,2400\n", "Minor loop finished, continuing cleaning after inversion/prediction round.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (3) ==\n", "Estimated standard deviation of background noise: 329.8 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 372.9 mJy, 387: 335.53 mJy, 774: 286.8 mJy, 1547: 194.24 mJy}\n", "Starting multi-scale cleaning. Start peak=-611.6 mJy, major iteration threshold=164.9 mJy (final)\n", "Iteration 332, scale 1547 px : -537.84 mJy at 2419,2099\n", "Iteration 394, scale 1547 px : -426.79 mJy at 2414,2103\n", "Iteration 488, scale 1547 px : 340.63 mJy at 2085,1985\n", "Iteration 581, scale 1547 px : -270.45 mJy at 2414,2103\n", "Iteration 675, scale 1547 px : -214.08 mJy at 1564,2400\n", "Iteration 770, scale 1547 px : 171.16 mJy at 2085,1985\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 782, scale 1547 px : -173.58 mJy at 1564,2400\n", "(6) Iteration 783, scale 1547 px : -164.76 mJy at 1564,2400\n", "Cleaning finished because the final threshold was reached.\n", "Assigning from 1 to 1 channels...\n", "Writing model image...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "3 major iterations were performed.\n", "Rendering sources to restored image (beam=119.53'-119.87', PA=48.03 deg)... DONE\n", "Writing restored image... DONE\n", "Multi-scale cleaning summary:\n", "- Scale 0 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 387 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 774 px, nr of components cleaned: 23 (-53.32 Jy)\n", "- Scale 1547 px, nr of components cleaned: 760 (-1.95 KJy)\n", "Total: 783 components (-2 KJy)\n", "Inversion: 00:00:13.401442, prediction: 00:00:05.080051, deconvolution: 00:02:21.242978\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Determining min and max w & theoretical beam size... DONE (w=[2.02049e-06:0.99262] lambdas, maxuvw=19.749 lambda)\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "Writing dirty image...\n", " == Deconvolving (1) ==\n", "Estimated standard deviation of background noise: 265.47 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 277.57 mJy, 387: 269.08 mJy, 774: 254.38 mJy, 1547: 202.53 mJy}\n", "Starting multi-scale cleaning. Start peak=3.8 Jy, major iteration threshold=796.42 mJy (final)\n", "Iteration 5, scale 1547 px : 2.92 Jy at 1905,2109\n", "Iteration 10, scale 1547 px : 2.25 Jy at 1905,2109\n", "Iteration 15, scale 1547 px : 1.74 Jy at 1822,2171\n", "Iteration 23, scale 1547 px : 1.35 Jy at 1641,2308\n", "Iteration 33, scale 1547 px : 1.07 Jy at 1596,2348\n", "Iteration 48, scale 774 px : 899.22 mJy at 1710,1621\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 51, scale 1547 px : 846.82 mJy at 2419,2100\n", "(6) Iteration 56, scale 1547 px : 786.3 mJy at 2025,1622\n", "Cleaning finished because the final threshold was reached.\n", "Auto-masking threshold reached; continuing next major iteration with deeper threshold and mask.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (2) ==\n", "Estimated standard deviation of background noise: 134.29 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 128.49 mJy, 387: 122.32 mJy, 774: 110.95 mJy, 1547: 84.84 mJy}\n", "Starting multi-scale cleaning. Start peak=799.79 mJy, major iteration threshold=119.97 mJy\n", "Iteration 73, scale 774 px : 671.14 mJy at 1710,1621\n", "Iteration 77, scale 1547 px : 646.6 mJy at 2419,2100\n", "Iteration 91, scale 1547 px : 513.85 mJy at 2419,2100\n", "Iteration 109, scale 774 px : 451.26 mJy at 1710,1621\n", "Iteration 113, scale 1547 px : 417.72 mJy at 2419,2100\n", "Iteration 127, scale 1547 px : -333.91 mJy at 1493,817\n", "Iteration 145, scale 774 px : 315.94 mJy at 1710,1621\n", "Iteration 149, scale 774 px : 251.04 mJy at 1710,1621\n", "Iteration 153, scale 1547 px : 272.2 mJy at 2419,2100\n", "Iteration 165, scale 1547 px : 216.63 mJy at 2423,2091\n", "Iteration 183, scale 1547 px : -204.21 mJy at 1906,2109\n", "Iteration 215, scale 774 px : 169.96 mJy at 1710,1621\n", "Iteration 219, scale 1547 px : 166.3 mJy at 1559,2400\n", "Iteration 273, scale 1547 px : 132.86 mJy at 2419,2100\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 312, scale 1547 px : 119.96 mJy at 1887,1660\n", "Minor loop finished, continuing cleaning after inversion/prediction round.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (3) ==\n", "Estimated standard deviation of background noise: 76.9 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 87.1 mJy, 387: 77.89 mJy, 774: 67.12 mJy, 1547: 45.45 mJy}\n", "Starting multi-scale cleaning. Start peak=140.88 mJy, major iteration threshold=38.45 mJy (final)\n", "Iteration 329, scale 1547 px : 127.16 mJy at 2419,2100\n", "Iteration 383, scale 1547 px : 101.71 mJy at 2414,2104\n", "Iteration 479, scale 1547 px : 80.35 mJy at 1887,1660\n", "Iteration 575, scale 1547 px : 63.77 mJy at 1887,1660\n", "Iteration 669, scale 1547 px : 50.82 mJy at 1563,2400\n", "Iteration 763, scale 1547 px : -40.08 mJy at 2088,1984\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 788, scale 1547 px : 38.3 mJy at 1563,2400\n", "Cleaning finished because the final threshold was reached.\n", "Assigning from 1 to 1 channels...\n", "Writing model image...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "3 major iterations were performed.\n", "Rendering sources to restored image (beam=119.53'-119.87', PA=48.03 deg)... DONE\n", "Writing restored image... DONE\n", "Multi-scale cleaning summary:\n", "- Scale 0 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 387 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 774 px, nr of components cleaned: 23 (12.36 Jy)\n", "- Scale 1547 px, nr of components cleaned: 765 (451.69 Jy)\n", "Total: 788 components (464.04 Jy)\n", "Inversion: 00:00:18.859954, prediction: 00:00:08.086930, deconvolution: 00:04:21.338979\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Determining min and max w & theoretical beam size... DONE (w=[2.02049e-06:0.99262] lambdas, maxuvw=19.749 lambda)\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "Writing dirty image...\n", " == Deconvolving (1) ==\n", "Estimated standard deviation of background noise: 280.57 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 333.7 mJy, 387: 317.21 mJy, 774: 285.52 mJy, 1547: 219.76 mJy}\n", "Starting multi-scale cleaning. Start peak=4.17 Jy, major iteration threshold=841.7 mJy (final)\n", "Iteration 5, scale 774 px : 3.29 Jy at 2044,2166\n", "Iteration 9, scale 1547 px : 3.01 Jy at 1949,1779\n", "Iteration 14, scale 1547 px : 2.32 Jy at 1949,1779\n", "Iteration 20, scale 774 px : 2.15 Jy at 2094,2222\n", "Iteration 26, scale 774 px : 1.72 Jy at 2094,2222\n", "Iteration 48, scale 1547 px : 1.39 Jy at 1719,1652\n", "Iteration 57, scale 774 px : 1.32 Jy at 2225,1423\n", "Iteration 77, scale 774 px : 1.05 Jy at 2490,1787\n", "Iteration 107, scale 1547 px : 848.53 mJy at 2456,2048\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 108, scale 774 px : 843.9 mJy at 2247,1420\n", "(6) Iteration 109, scale 1547 px : 842.98 mJy at 1568,1706\n", "(5) Iteration 110, scale 387 px : -845.64 mJy at 2043,1774\n", "(4) Iteration 111, scale 774 px : 822.13 mJy at 2200,2329\n", "Cleaning finished because the final threshold was reached.\n", "Auto-masking threshold reached; continuing next major iteration with deeper threshold and mask.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (2) ==\n", "Estimated standard deviation of background noise: 110.52 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 134.8 mJy, 387: 124.41 mJy, 774: 104.45 mJy, 1547: 73.96 mJy}\n", "Starting multi-scale cleaning. Start peak=831.29 mJy, major iteration threshold=124.69 mJy\n", "Iteration 122, scale 387 px : -850.94 mJy at 2043,1774\n", "Iteration 126, scale 774 px : 823.11 mJy at 2243,1419\n", "Iteration 145, scale 774 px : 672.37 mJy at 1442,1680\n", "Iteration 178, scale 387 px : -614.92 mJy at 2043,1774\n", "Iteration 182, scale 387 px : -486.24 mJy at 2043,1774\n", "Iteration 186, scale 1547 px : 531.57 mJy at 2456,2048\n", "Iteration 198, scale 774 px : 547.1 mJy at 2247,1420\n", "Iteration 211, scale 774 px : 437.29 mJy at 2683,1951\n", "Iteration 244, scale 1547 px : 357.69 mJy at 1568,1706\n", "Iteration 253, scale 387 px : -407.91 mJy at 2043,1774\n", "Iteration 257, scale 387 px : -322.55 mJy at 2043,1774\n", "Iteration 261, scale 774 px : 353.73 mJy at 2247,1420\n", "Iteration 281, scale 774 px : 293.75 mJy at 1442,1680\n", "Iteration 311, scale 1547 px : 248.13 mJy at 1568,1706\n", "Iteration 319, scale 387 px : -244.22 mJy at 2043,1774\n", "Iteration 323, scale 774 px : 242.67 mJy at 2247,1420\n", "Iteration 342, scale 774 px : 195.92 mJy at 1442,1680\n", "Iteration 375, scale 387 px : -177.24 mJy at 2043,1774\n", "Iteration 379, scale 1547 px : 170.35 mJy at 1568,1706\n", "Iteration 386, scale 774 px : 159.19 mJy at 2247,1420\n", "Iteration 409, scale 774 px : 124.76 mJy at 1706,2210\n", "Subminor loop is near minor loop threshold. Initiating countdown.\n", "(7) Iteration 410, scale 387 px : -141.9 mJy at 2043,1774\n", "(6) Iteration 413, scale 1547 px : 136.01 mJy at 1568,1706\n", "(5) Iteration 415, scale 774 px : 124.75 mJy at 2195,2310\n", "(4) Iteration 416, scale 774 px : 125.02 mJy at 2683,1951\n", "(3) Iteration 417, scale 1547 px : 123.5 mJy at 1568,1706\n", "Minor loop finished, continuing cleaning after inversion/prediction round.\n", "Assigning from 1 to 1 channels...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", " == Deconvolving (3) ==\n", "Estimated standard deviation of background noise: 48.19 mJy\n", "Scale info:\n", "- Scale 0, bias factor=1, psfpeak=1, gain=0.1, kernel peak=0.000118546\n", "- Scale 387, bias factor=1.7, psfpeak=0.109965, gain=0.909382, kernel peak=2.99227e-05\n", "- Scale 774, bias factor=2.8, psfpeak=0.0273488, gain=3.65646, kernel peak=7.55434e-06\n", "- Scale 1547, bias factor=4.6, psfpeak=0.00622096, gain=16.0747, kernel peak=1.89546e-06\n", "RMS per scale: {0: 76.45 mJy, 387: 64.65 mJy, 774: 47.84 mJy, 1547: 29.26 mJy}\n", "Starting multi-scale cleaning. Start peak=142.9 mJy, major iteration threshold=24.1 mJy (final)\n", "Iteration 422, scale 774 px : 122.74 mJy at 1706,2210\n", "Iteration 447, scale 387 px : -120.44 mJy at 2043,1774\n", "Iteration 451, scale 1547 px : 104.05 mJy at 1568,1706\n", "Iteration 458, scale 387 px : -99.51 mJy at 2043,1774\n", "Iteration 462, scale 774 px : 99.08 mJy at 2247,1420\n", "Iteration 484, scale 774 px : 79.24 mJy at 2683,1951\n", "Iteration 519, scale 1547 px : -79.34 mJy at 1950,1780\n", "Iteration 536, scale 1547 px : 66.36 mJy at 2456,2048\n", "Iteration 564, scale 774 px : 85.36 mJy at 2247,1420\n", "Iteration 574, scale 774 px : 65.92 mJy at 1706,2210\n", "Iteration 594, scale 774 px : 52.71 mJy at 1868,1383\n", "Iteration 619, scale 1547 px : -79.29 mJy at 1950,1780\n", "Iteration 624, scale 1547 px : -61.07 mJy at 1950,1780\n", "Iteration 643, scale 1547 px : 52.56 mJy at 2456,2048\n", "Iteration 669, scale 1547 px : 41.52 mJy at 1568,1706\n", "Iteration 704, scale 774 px : 72.82 mJy at 2103,1409\n", "Iteration 713, scale 774 px : 57.92 mJy at 2044,2165\n", "Iteration 745, scale 774 px : 46.28 mJy at 2044,2165\n", "Iteration 832, scale 774 px : 45.75 mJy at 2683,1951\n", "Iteration 842, scale 774 px : 39.94 mJy at 2449,1746\n", "Iteration 986, scale 774 px : -31.64 mJy at 2511,1817\n", "Iteration 1000, scale 1547 px : -56.93 mJy at 1950,1780\n", "Cleaning finished because maximum number of iterations was reached.\n", "Maximum number of minor deconvolution iterations was reached: not continuing deconvolution.\n", "Assigning from 1 to 1 channels...\n", "Writing model image...\n", " == Converting model image to visibilities ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Predicting 453390 rows...\n", "Writing...\n", " == Constructing image ==\n", "Opening reordered part 0 spw 0 for scary_sky_band01.ms\n", "Loading data in memory...\n", "Gridding 453390 rows...\n", "Gridded visibility count: 7.25424e+06\n", "3 major iterations were performed.\n", "Rendering sources to restored image (beam=119.53'-119.87', PA=48.03 deg)... DONE\n", "Writing restored image... DONE\n", "Multi-scale cleaning summary:\n", "- Scale 0 px, nr of components cleaned: 0 (0 Jy)\n", "- Scale 387 px, nr of components cleaned: 40 (-7.34 Jy)\n", "- Scale 774 px, nr of components cleaned: 742 (266.34 Jy)\n", "- Scale 1547 px, nr of components cleaned: 218 (276.64 Jy)\n", "Total: 1000 components (535.64 Jy)\n", "Inversion: 00:00:24.572327, prediction: 00:00:11.293068, deconvolution: 00:08:03.705232\n", "Cleaning up temporary files...\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "command = \"woden_uv2ms.py \"\n", "command += f\" --uvfits_prepend={uvfits_name}_band --band_nums=1\"\n", "call(command, shell=True)\n", "\n", "command = f\"wsclean -name {uvfits_name} -size 4096 4096 -niter 1000 \"\n", "command += \" -auto-threshold 0.5 -auto-mask 3 \"\n", "command += \" -pol I,Q,U,V -multiscale -weight briggs 0 -scale 0.03 -j 12 -mgain 0.85 \"\n", "command += \" -no-update-model-required \" #-join-polarizations\n", "command += f\" {uvfits_name}*.ms\"\n", "\n", "call(command, shell=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can plot the images" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 57396.549572 from DATE-OBS'. [astropy.wcs.wcs]\n" ] }, { "data": { "image/png": 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4Jojktyxi4prqd5pkK/sWE9WQDMOgSkOoD0gvAqgCaBnMyIN6y5tYa6/TEsrwJAqyrW9yqn8RFGQ9KZdDgqP/DQtAxuM6wCjJN4W7SY3CKBksq2LbeouJq4pEmPareiKsi9CrRllSJyuq5ca5GpIf9Sfbj+KLElliVTy6fETaVL+p/WkjyCpvRIIJobEvMedpMqzqfx6RNRPI4NyqA4BViyJkW192eiRYfdzZhRn7a9SsxlcBna86oF/gqwYHT4KDYByTrDjXk4FDRY7Km+dHaIKyzBRIsNjOtNSXievM8QkulcMUyC4s6AhFUcIcQUPaUiRYVnLltnV9jZqIzJjD7dH/IgmWv8sqrw5x2bxnlMUCjanNVH+pm1GbVZkX01G5VWmdop2Fdgs8a5VWHML9Mem9MhWV00SCVW2X6IPJZzpV9xRxUCS4fj9XjwONGj2pIlZ+/6yapyckecqluW8HRW70ZKKqyMFmM9QyJsTZPLdinflmu1F5lW+vrPgq2xdIMCWNJBBZqAIzPkY2R3Sa9NheEyafdJNqbEY15sByHTYm45l9rKwl1Pvml01DvrZMqaWqRrYNqtmuW5SZBIejBHPmgU+g2kyyb43yCHxISXrSp1FwVBNmG2UpKqOPFqwwmyygGJedTB6UmaIpcE/eeGon+qZjtjEVzlEjTX3MtheQYCMpFchh8L+6/yQ871z8P/7uJfvGJM5Tp1Uy1J3uV2ROnDx/EtVXT7QzJFzl46pQxON9xOddxmdaE3gr1YGswmy6F00B0creB7b7Fbak0CwG6fygq1xUO4jFNFVb00b9fq4eB54+qfgkNj35L040i5SXFb3p5Sw196G4wpQXSTnvOPKiNudFaS4CNSHW9wtIkzWd72/wDwOE46DhZIUSRyDBbjheBOBEqjurVieEOVCPg30BcSyK+42WuS7tkK86Z0lbNnBX/j7R//amv5NeO+l28qOH25Lxcm4CRRdEzCjvclI5uFdIpVDuX+PQoJz0KYiDSi3RTnQrmGyb6jSh8gmsrr5M8CuN0mpbr+o8UCfzW0ZttVV4bQNaAWqz5jITYnEfy/0zpDW6jjjAQQA/UtYdwBfIC3jwLtZej5r2JQIUEfu4HwrCnPJVTpE0N20irULe+Eck2kSGgYxKbBsJXbkooOpDTj0HCa3fswk5zwsVwTYqwqrcwweUC3gi1O/nylGKCFdh2mlHiM2qpGr/ahVZ1QS7/IR1WmaxKrUy3/w0gt7/0RSlOVJR8wmUor8pQqk7/8HvlFK0O010Om3MzHTQbDbRbDbRarXQbLrx/w3XgeM6cCiF47pwHAeO48QtAgAlFEeOHsH62mb4GwFjDL7vw/d9eJ4Hzx/DG48xHI0wGo0wHvkYjz2MxmMMB2P0+z30+wP0en143sh43LKSnUA/rlmoyZxNcK4sNFYCGlPwvDb17ZhUYlUf1PdUdmyK3Ht6i4n8ctl+6PtQzvIi/zmg9jeuUcMGWjIcQdhmrWaoJqK6Ca2tKmxJhq37J7eZV8ZiWyZgUMpvVqHQiuWkMc+owpGPp6FN1f9B8CfJn9cEw9ikyEJICDOeS8wHmJe5Vhgn8FkDnt+AzxrwmQvGAJ+3wLgDxlz4aIAxBwwEnNPwE3xPHSOhGIy6aDf34/YJPBDCQ1LMQAgDJT4o9eFQP/juIPiOAVzHg0sBFwNQyuJjIlSyjBCPQ8hlLEedVvkzm9TylBqtqDOXDMcVZdMt5ZnAT6xK6kzwxW5VrXwWJeE2zwshNVIuAZZ/s8lTHKL2B37pYCJF2E4NMk/Iyym8RdVke5hMWhMU8RXW1x8g319ST5Z05EFuW01WbfxdCUJfISEnb2wGnqlXZ7qbHq90XQQzMx3MznYxOzuD2bkuZme76M600el00Gw1wBlDfzBAv9/HYDDEeORhNBphb7eH4WgzIKxjD743hs84PM+PyS1nCFVeCtdt4Pt/4P34L3/yWTDG4TgOKHVAKeA4FIRyUErQaLpoNlw0my00mw00Ww3Mzc2i3Wmh026j3WnBoRTj8Rj9QR+9Xh97u/vY29vH3l4//NtTnAf9uZDPiQ3BVps0l1M3TYQsFUzLavGjzGKRuI/5Hs/zCTdDrDtfMS4andscUKu8efhBoo5KeesjV+UpEvRGRJXEtSqYzLQBLQnPKLPScalMNkU1mMjlZHIrkmGprpi8yOUV/TcFtxL9eWNo6xEiN0dVMT9WiCKfV/EYOQe8MYU3bmLsz2Hst+B5LjzWhBcSXwIOh47hOmM41AMlHhzqo0GHoK4HhwI0JLHxBzz8jcct+mjg/I0HcPvKk6CUBxZbPgMHBYvJMwNjbkCyeRM+d8HGDsa8CZ/NwPcb8JgDzgko8eHSMVzXQ8MZwXXHcJ0RGu4QruPDEZ9TEdGMSDF18oN6qVRuBUnOEGIFGVZei6Z0SwofY909r1yUkdvVkOBKyLWujrJKtGoxA5Z9NSm+qqBa4m83iVpcv5+rRykiPHn0W1tfx2mYC2bVLTOhN6lYaUzHbDtoX6eg2y5EyOa9cv26PqXLZvuj2icqkyZ1DO12G4uLi1hcnMPi4gIWFmYxO9sFCEdvv4/dvX3s7fZwfXUN+70++r199PsDDAYDcG72HspEhVaQdMYA32cYjcbwPE/a38/4e6qPLUCz2UC73cbMTCsm8keOrODOuwIi71CK/V4P29u72N7awdb2Dra3drC319ccSzbXcTZAG1NGwNb31XyfmdXc9H0ik271+Vcp4GZCXzxInp0ia352FCO3xVHdMytrim7rRTcBmAew8WT71zhwcIXZbS4sTHIzsDLFncxXOILSf7Do5NyCBIu/6Qip3Aetb6NCCY7rN6jipj7FEM2hLYNL6RCRYMKCYJEeGhh6Mxj5sxh5bYy8DsZ+C+AsIJDOAA1niE6zB9cZwaVjOHQEF0Nl8Evra5FEBJ2DwoeLEShYwByjQywSUIoD3CchUW9g7Lcw5h0Mhi2M/Xl4fgucU7jOEE23H3yc4G/DLUHaLC0PUlGqmadRK93UNsKQPXbZJzwnCrVsIlx1hGldW0ZM6J4gI7fNPBJrE1n6ZjGfrt/PlaP0k7Qa3zcbQqzfvzgp1Jt4inVOK+BVgvIquJ3JpeH44AvlIJVNq+H5iwNpE19x32azgeXlBaysLGNlZQlLS0toNpvY3dsPieE2zp8/j52dPfR6PTDGhbojRH0jIIYw0+pIz5qx4jwgvSniK7en+y2pbzQaYzQaY2dnV7lfu93C/MIcFubnsLAwh1edvBcL83MACLa2drC+voX19U1sbGxif7+XdE+p8ir6FfXdYBKddy3Ix6SDOvWU3b55i1np60xvIp2t0wblLTfKI3tPTB64rkaNkihCHg0qsW0gLUCYlJZQj60m0nK9JQi2qb48QmpS2OR9SqWvkpXgAsGwIhPmDESFk/nwPY7hoIvRqI3BeBZDbwaMO2i4o5Ac7mO2tYamM0CD9GKim6eiq46xEhRIOUQIQKkHh3oA+tm+ccBnDYz8DkZe8NkeHMPI7wCEoNkYotXso9Xood0awnW8tEoa1SO2KedDjsqI6ZggkWEAmcBj4vdYObY67CzkMbO5H2P/aT/zW1xHDsqmJrP1Y1blTc6gCFktml7pZiHENSpDQSJc9sTnTUjzzJ3zL1S9ybHZp1iF6fnkTXLjqMdIJBIi8ZEVxeh/VZTbPDNdfX8CiPvPznbxju/8DszNzmB/v4cba+u4dPkqnnjiGWxv78L3o9dHdlEiq4DK51DdT7tUTNHYcKVpiV1aJFU/1PsNBkP0+32sXrse10EIwezsHFZWlrCysoRXvfpeLCzMYjgY4tFHv4mLF69k+iQT2xQpFXzDAVqQWFkQYHmhSWuCrd4vO25FkPdMsIXaNL14X1R16crlk9ybmQQH90f5iWxtenUToggpVRDiIhNbZb7R6H8Lk2yrQDqG4zFNmrX1FhifjNkzkEvG86M1p02oOXWKE2AAKj9ekbCNRi5Wt+6FN3bQoH20G3voNtew3B2g6Q5AC7KuPBPfXBiOMbOwoks5pDET1p17QgDXCUy5Z5o7ya4cGPEuhuMZDLx5bI2OY7TdhkvHWJm7jG57J12Pov4USRYXMniYPgoCGY6CaOlgE2BLh2hMBF9jY+AonTm0yqoj1cfswkipRZCix6ck7CUI6iQ5hhk7FDJcv5+rRwlF2DRBNW2r1sw5S6DkfkRtqvcRYVakpkWKZeSZ46aJo0xyRcTmrAoyLJrgRv6eajKsai/bN3m/fr+Pxx97EuvrGxiNRhUp7GqVMDlue9LEwQNCWSLXblFil/XJJtjd3cPu7h7On78Mzhkcx8HS8hx6gips8sGNgpTFJFhj+i6bWev6JP5mXAxItZNuU7ff5Oe+KjIswmxane2z/N3WLH06C2qEkPTMdhpgHsAmUHRq06ubB5OopRPsq8t3mqq7Shj8gCcKBlYWBnNnLVmITNvlQFhRAKsMKQn9WWUSHJk7i+a14f8NjHFk5ixajX24GBU6pFSfbf1XUxV42u2cOuCgAKGhiT9JbZMR+zNniFjSVh4hyyiLBGg6wYLAHAlIL2MUQ28GDWeYEEljrVDfM6I6S52sMiwjXjCyIMMqYsO9DBmO+zapa4HqPjYtPk14X+XHOyj5ns3bTxdA67BRv58rR0nT6LITVJOyV9Wkt5i6Wd7s0qZdlentJOUS6MhrRjXkImkWVMWIDBNZ6Uu3EdfF1SRKHlfPY7h69Xr8fzXKl/q6sSXD4sLA4UEmxUFffH+MtRsbCdk2nHc5SFn0W0xMM/7fTEOC1X0R90u1qyDm5awI0jg8VVR9f9n13e45NVlAL/2z6kCu4fpF+9JCRBKrJHoWKG0iOYV+AMgqYgZ/XRkyeU2pwjpYmqXLBDPj/5tHhiKi7A9T25PjDf/3PRACdBsKc2GDyXOGAMvfFQTXRF5122NQF6CG55zoXyubIivaUsJElIXjoJSh09xLbxbPgaRMx+0jUX655jWni0odNCxchxm/4XL3lPGaVSmsUT9MfRP3r9A9Qdu2gImeLbYkWPx+sxDi+v1cOSrOI1zEdy9CuclwsbQtNvtmUSxHqi3MipS+XHZyHfQvv0XjcUh5d8V9REIdkOfIDPkgCQxTfLcjBHbmznb7lSdtpv1sFmfSKrTK+iFWZjP+37o6J4fO1P7AyFoM3XFlFfFsGqSD60M+ovvddsGuRo0SsJmsVul/K7dtGViocH0mc2qTSWhevdH+IYkw1aFTf+W+6Xx/U+RO3Mc04VepxYYx0UbGVuSwTe+YzVmsNFmWj0PoZzansjz9pOm5iI6wpRYJ1OeDC0q6CqX9bk1mpdwDYYJiHKr1ylRUgF1Uao2Peeo82vhPK9Ix2cLa1SCHZOei4EJdQuwNcznV4lEREiz/nhdF+mYhyzUKoWIiXAbTunDyyEC++XH03S4IV1EynIU+dY5EgKEODqWDsf8F08Pkl83rV5bYFSG1WT9ic3kZBMRQh7pP1UQDNx9j/sKObJatNp9WEVN9v+z6pLsHphMEqoi5u6md7P7l/eHzIF5Lk9aTxcGTYD9/YpW3f41bFyWVY5koWvvnTgIdyVSVMahYxlzBAsRjtDEJjtVYuW0FsYwJnmZcZLU3RWpCc+iiRELZb43psu43pXosk1ydL2yqXhqstFMHoJoVfgPxJcxLnzeD/20R75LCpDMq6g1jM+ikLlN0ZzdbLvYdTxN7pf+03C/RPFoqYxv8TT7vSv94EbrYACJ0dSq2aSG4HhjTnk0A5bOsaPqlqaB+P1eNQyHCNrlSy0FPkHSEoDoFy1bpVSM/MJVAzAxpcgihqae8bA5LCAVBoPbq0/Ak7aijGEdQ7W+7KJAck5r4p825IyTXjlhPPoJgVeJYmM3zq1OAqzS1z/6eVWZVbaZJs85/WCxj/m2S+8X2HqnupWJzj9s/B0wWHAmKR7TX9yuN6ZNiwjwQVr6dXKWjxlShJCYWwZy0xLWAKnwg5FcBq8mvJcEvbNKtIrXRJoaswqpKgWRS0hQBsIK6NSRDQ/Az/xv7rVDRZWLJND7CumOSfcYtocp/LG/LlCtynUW+1ppt2qBdGjJCmC+R34ruCSGadIRU32zJsLhNgjKolmSdIN8fSksJy/tHXlTSXefK7QIZtkZJsnqzuHkA9ft5GihJhNUT6aKTPT0hVqkrtpPxafsompSqkjeZJRFXK5UywVfVlY4ozCMT58yyaEJi9f6oUbk8VOdvmd9Oth79ubUKMV0RihHOslCnwoqQnPu0D3HWn1wdFbrIvV7URFit2prbUNVrWjQ4eJPtw/VFr/Fyhiq1SN5EUZyM3iyTPRuY/UwnI9w6f0qdv6zJHFgM4KRWgjUkQiTAuijJ8jahPpv0S7oxzDdjRkalzIVSVRbHy/zclFMTlSmXUVoBo3IsbpOnSybz6vi+sx0f6ugJiryAo1iQSAVGA+wUQ0VEacDivpqSAivWZZUiKepPhKqeXWE06NIRsGvccphIEVb7rOpMMctMDsWJsqw0Tk6ibCas6uOpgsDltadaHFCPr26SL6bVIRBf4AIZzmASk+CknxHKqf/pcVYdXzb/rG19MmzPY9FzrifB9vvbjJnZkkAkwfbIv8/sz6u8r/648p8neW2Yy06e0qjcPV/u2j9EMs08YIIV5zoYx+EgmCwWf6UblcUIVfoKTwir9Eoq2EyUDaavub69mT5JQa5s0iExH2L0Z0ChzooqH8+qg6ljMUBLdk33r0w85H1yzLvT7etz7pp+mwSpAFUZgllMRTYFGFP9r+2T+I/2WrK5vhXXGveyqnARFCHFNqqw5RibFuW07hcKc2xleqUCiMjwTbVAWL+fK8cUTaPN5rvF/XCDfdV1F4WZZEwSiKtKZE1ZI1Ki9g8tPnnOI3dlFL4E1ShjtgTUxooAQplJ/E/zMCkJluuZbEEpjwTLqZHk3MXa/UoTR1uSb4Y6BzWkuqe3aKVDkWB86fKH2+8Y9Yv2pYkqA1UJKOQXbFmPWNc0ld+8/mT6YDAnViIkckk0Y4NfJZAiwbljqVP9cqI3m/qZ+p5Hbi38cXVmzTqCy03PzqLmt5ryGTKs6ofUv1z/4yJRnSV1XzalVqLAPcDFPhj8vTPm1AUWBqyjpufUZ1Jdbfx/CwX/koNdKfoS1ynkCL6pSDBQv5+ngIJEOGveWXaib7dvtSbI1dWh3rd4lGk7s81smiD9ZDsyd9WmRJLS65j7VwRVLRSoFyEIsVUM8/rB431l6NX4alFcaS9rTRGAG4IjmEhwuq5kAWtaymmmb4bo2TLMwdQOkVRqUH6BZPrHQbgHosv5Ybl/jVsEtkFqLE1oi6gntibbRftkDQsTYqNPbRG1UlKHVaazohm0cQzF/LBheVX/M/69RSfAOeQWEAiigvDI/rtK09+UwsyF/UZpMlrWCkAub+UjrlamleRZNK8WfKaLkidtjmIL1VzVrxQZTu1f/cKRURm3aM+04FV4LHVqcNwf6b1mUokFMnwzoX4/V49DCJaV9tuzxWQmtmV9NcX9i5pR59UZfVcdVzFlNdie9vlM0h2FqnImtY7an3QaKEswVT7RVSis0/PhzB7f5BGoJ4MpmJZ8zen7puu/rFTr/X2z+2ZVYT3RNpNaMaBbsn81qnPxelSku6y5881H4Gu8dJBSY1TqTkQcbP1Ihd9tzBpV/bEta1O3TU5UZRsq82hVgKuoTlM7hkl8pn+a4EWqfuT9po5GrTF9VhFOzTGpzJlVwalKmTmLRE7RJ6VKWxI6Ym7qo3If2ddYF0AsRJ6JfOYeiOqNt2eVdeXCBLJk2LRolRsNOoKFL7HN/Z8XFEu1jw5xXXkkWAVBJc4sUgE3LRmuUS0mJsJ5KVbUKDbBU0cNzmvLThFS13eQF35+3wKYzcJjEhztI/jP8tQyI9MQM52ZdZGxMBEfVT2q404Tj4TEZcfJfA0UI/rVqsHpvh4s+c22FS2UmMqrAmrpFwz0x1N8kcE0VnbjJkY3j/qQJcOTkMoipvny90lwWKbRzEo5Me5f46aAcUIOSzJcst0ixHZS88MyE+h0BzQphOQAV0KOXy4TzGhXFUFT5fs1KDM2ga5y91X0LQOd6mqrxjM/QxCTcclZEFAuQlBlGROxLkSIBaJXtf9xUL+hThvzaZW6bzDjJszTK+/ULZQmKsaU3A3E69JK6bVU8GUUfpZIZPimR/1+rhyVPQn0KXB0mEztKEa8Exxk9Fh76PwbgewYBeNqTpcjYxKfVRuf27w+lCMGOvPl8ib1SR3i3wRlj0+Fg1Lz8lXcvH1TPsG5lgPpfYpEPLfro/21Il4LRfJq56HssyWLov7VVanYkyFIz1A+unqdnuHWQoYMA5VMhpU+tlXCNoBX2UBBwr5KEqxTxKJdo/ugIAku7IdcFJFymWd6LEFJHEOyYqX2alXCSKlmQl9YlgRromXnta1UcYFiPsamsrpjy9xTiSKvtV6IrBHCBZjc6NPUyZBhbZ806a+K5Ea2QVlTZoTPi+Bec+LfrKwqhIU9FXKtRYx9lN7jh6wQ1+/n6jFl02idieBhIG2GXJYM56d8imDyb9b5YarKqkxMxX2kdkpHes62q+4PFcoXVcrUbWTH1EwIqsitaqc8llk0sPdrnQx2pF1vnmyj6uqsB/L2U7Vng+IWJSpCXgWJnSzX+eTm+4cK5mOyYBw3WXCRGhnkktQyhFgxaa2EzNkSFxU0pLKw/6SCBCsJGveCBH3xBF2RAom4+ebXUZsRqp685pgAA4YJs8051fn0SteVKg+wcrvJJDyHxGmPp8S1aeXrrNsmE2Kx3vCv0XRaWFDJXM+Zsvq2Uu2qzKOrgIVbgnKfvOsmRJF+VhXML0a8YEOzvx0E6vdz5ThgH2FbxUNnHjtpWzqf3Lx6AhSLdH0QiqBEchQ+lyZMI5dtUYKpH1OVQl71mFapAMv7Hb6yZ3vPZMupF2CSbdmFk8lJcB5Mi2pVKbiToIjrRY0ahw8rkmqrukZlJ+hLbi7ToqaSBZRgYwCsjCl0NoBWovYBnIYTbpkA5/Qv439sgqqcwSRbuw+gV7bLmg5Lvr4p4qjzA44gWiepSLDKz1l3nqVx1x2PlUJW0kw3U4cKURRqls3pG/Qv60se5QsuZPosK9xiADVNTuGimHaE5bL1T6VfCv/hmy7CdA0r3OSzsyKqoAo600tR1dTtZ/KrDFBV5FdCaOqT14aOdJjqUZXLHwc7qHy49Qq32Uybc5lUTcOqwHTc+efdrh9219BBwuwPnu6vTYA2k+9x+vqyvc50xDtvDKu5jlWYxHJEfV8A2XG5yR7DzJv8UwCf+9zn8KEPfQinTp0CIQR/+Id/mNrOOcfP/dzP4eTJk+h0Onjf+96H5557rsIDrmHEAU3uOHXiT4wyPsuR4lqgzaBuSZGkDrjbCgISua34t2A/hRoZK3aKSNCGtnnYTqw6ix8VtITWNRNpCyWzFPnVnK+ortzAWdL5kgNAJXVLhDivr5bXQdSerE5n+iGce5uPNaTrL3VdiGMbmUwL36PgTlU9i43t20IwUxZNlycliZWSYMbSH4s6xE+mngn6VxgH/H6O8Bu/8Ru488470W638da3vhVf+cpXKj6ww8MhzsDSk8S8CWd5RSXPd/XwA9sUmWybSK56gq0zw67KHNgWCcESz7eOvKeviTwyXKTPshFRlgTKfcien3zz4unDZL4eQBxPvU9q9rzorsf0+dCT5oQE669XfTtqUp7XtwDVKPwvd/VWfumX+RTB/v4+Xv/61+M3fuM3lNv/2T/7Z/jn//yf47d+67fwyCOPoNvt4v3vfz8Gg0EVh/uSxrQmZxObPMuET0X8tETQQBQ1xEd1faojKzux2XNsAh2RYeET+OkJpMQfJESF+cE21YRTZf6sUpongQ0JMxFtU5m8c6KBkiTKRJU4gSJMnGSbrLhn2i1AiHUfRT/l31LHqexH9vhLk+FM+4bzJBFi5XM49lXP8Z/VjItxYSr6La+MDrZWKZb15D6XLImvCF2E68PCQb+fAeA//sf/iJ/5mZ/Bz//8z+Mb3/gGXv/61+P9738/rl+/PoUjPHiUsn0ploKoWL02dZXLw2q3j8mUUWWirTbnLd4nnXlq0ZRMMpkshqL9L9aGzXUjj7F9Ohz7c5zeJx9mc++DMIHXQVYZs7AJUjVJwCvTOTXluha/m67xaQS2M5vx16bMB40PfvCD+OAHP6jcxjnHJz7xCfzDf/gP8f3f//0AgE996lM4fvw4/vAP/xAf/vCHD7Krtw4Esz1dWpIY8rYy/n0mVD0xlpAXMTo/Oq3k/yv0g1OJLKUUYPWkMiDBijZ1PsAmk1mgtIKTQU57StNvQ3kTckmgrdl6WVPxImNmESxKm0c4048J/NlT9aRNwAOTZej7qfmdMCTBtqhbrF8pMqy4jxTXiZiyKc/MOrPdMK6ZFEmllGpBJMhzw1CUM7X9UjeJ/rVf+zV87GMfw4//+I8DAH7rt34Lf/qnf4p//a//NT7+8Y8fcu8mRyU+wpNEWS2qhpZHPlGySdOUT4Z1UJFlXZ90Y2JWQqejmheDfXTgCEXU3bx+RuNpQ07VZNCGyN2cKG/CK8P2uG3TK4km1EUW0aaxkJNPwqtZ2JAXpMw5lG3bO6DrkU8YjIMHk4KdnZ3Uz61WC61Wq1BVZ8+exbVr1/C+970v/m1hYQFvfetb8aUvfakmwipEE76yOTDFyXwOybSuy4CJA1kBegVL+F85+VUFa6KaQFhx/0TVV2V2mRMES2p3KhD9P1O/Z4NU6ZCXt7e0D7E8pnE9JFCEqQtQhfdrkeBhKl/iIn2SwT19oCzTbrb76IiUFGk68g2Wia/2ngn9iVNkOG7TS5fVIArgFZNhzf0FBPZ2PPwLcQEu5YKQPF84dVI2emVzh+fmQJ40mJVGFc6Q+YMImnXA7+fRaISvf/3r+Af/4B/Ev1FK8b73vQ9f+tKXyvfjJsKUZlYm011bqE1VbwbYKGN65Pln2pJguQ41kVfXZ9OeqS21+W0RVBXZt3iuWw7VMZtNzk24Oa5JG8gmxipSWnUAtbIkWLXd3L8qz0O1j0WzWfpNRoIBEMYmNL0Kjum2227DwsJC/PmlX/qlwn25du0aAOD48eOp348fPx5vq5GPUopFVaa6IWTfQ50Pok0ZKzNOQ70pRMRXMIOO20hVIBGa0DeTeMP0RyLccZuR2asNgUwpZYpgUTYQ29NFZ87soyfKpX1gAa0p8kTBwWz3KzLuOcgbl6TdnKBgIqxMf6XFFAv/ZzFaudKXWNWG4iO2qVtkSv0fX+8KFwbF96mlCSvgA1wWh6EEH/T7eW1tDb7vv6TfwVNYlhT9J/P9GEXYqEOT5fgsYz4rIjmm4spwMRPisubfukBTxaPX2itW0VgU73PiRyr20QbFInhXCf01lB85u8wDedL9A5jG1j4dVf51oNonvnfB4sjm2cBdRZ4JaiuCqlMoqWHjBjGJD/tNAOYDE+QpjCZ2Fy9exPz8fPxzUTW4RklQejDKRAFUNtlVRVo2IY+EiCRYYwadUfQiNTjVEUUUY1viVYAIWUGnjIlKtzhuuvZ1JuG6MqpyKrKWe040irCqjqJm4zqlPA+C+bSo8qr8iVMm1LGZsFldV/dVEeUZyKZason2LOQlTvpqaaFA3cTkOVSGM5DNqMN+qvzxZQU5pRwD5RfgCiizU8trfhCo38+VY8r2OTLUStzBE5kI+WbP2bL5ZDjaZgtd9GRzHTo/WnvoiUNxc9FsXyPCaGd6e7Oo/XYk5rD6Wr0/crk8v3nXePra5FZWDsmx6U2W9W3lXz9B/ZPkENf1V19GXbYcYZ90Ee/gMT8/n3rRlsGJEycAAKurqzh58mT8++rqKh588MGJ6q4RQmMCrUOuz/G0oA3gozGZVfn7Qk1CMiQ4Ksd87T66Phon9dOeLMvmp6Yy4v/CNaBOa6TwozXl9s20aTnVLDoPLEOI88iwhQm2SeVV+hNL5z3XZFrnpx0R4tBUGkBsLh0RVC0hlhZrlPex4XotklopMnlOLWforkmTSbPcds5zR9u/yEWk7AKhdI9kcAv6CNu+n48cOQLHcbC6upr6fXV1NX4/3+ooRYTlyWRxv9BsfWlUMXEU69L1R0/M8+usjpDo2swG4tKPSx4Btw3+VUylzVMr7UxhTWbchFBQStFouGg0GnBdB27DhetQuI0GKKWglMB1GqCUgFIXlFI4Ds24xzmOg+PHj+J1r3s1fD94cDHGwBiP//o+C78H/3veCJ7nYzweYzz24HnRp8yDr8x1IxOg6q49M5E13Wd2fdCRYDGqdLpNneXHQZhDi8c8Sb3msZtswaeMK0NxBOZT5VecqzQXu+uuu3DixAk8/PDDMfHd2dnBI488gr/xN/5GZe285DCBKmwTaOrAybCKBKvISERyVP6+8f6yea5Eqpkc/dkcJEj03UzlAq7YlzqGdrI/wflInXMvO0YaRZhzgJMGOCdgjIL5DJzT4DunwcIAJ8FH/M58gKefMQwu+v02NjaXQGNFmIMQgBCefMADd2LCQQkDocFfShkIxsFvjgOSSRAhLZLIRLRCv21t/mSNUl8Yku8wkCXE5g56ejIs900g30FDOcHFDNtLPTM0RFnl+w/A/Ny7Ca1liuKg38/NZhNvetOb8PDDD+MHfuAHAATz5ocffhg/9VM/VbofNxNK3/nT89WzMa2GZZkiBCKt9E6CMvvb5A/WkeBJcNABoQghaLWamJnpoN1po9VsotlsotUKPs1WE61mA81WE+1WC27DBSXBA49xBi8kpGNvDM/zwHwGxkUCCzDfB2M+GItIFY/bJoSAUhr/5jgOHMcBpU5IqimoQ2My7To0IN+uGxJwN67T8zyMRmMMRyMMhyMMBwMMRyOMhqPg9+EIg8EQ/f4I/f4g7k9xqMhw9LvtuJcxtRdRzErA5vqUF1uqv6bzj9k+6vtB+A3fJKjI9MoWe3t7eP755+P/z549i8ceewzLy8u4/fbb8dM//dP4J//kn+C+++7DXXfdhZ/92Z/FqVOn4pdyDQ3KBMqSYFKBKifDeWa2yCHBEQpEP84owSEJDnx9PcAbZuuOlCGdH6jtmJQduxwFTad8WwXI4oDPXHheGx5rgfkOfEbBfMBnDhhzgr++A583wLgLxOGOOCgNCCmFB0JJQl4pBSEciMmsE/wfBu4BcQKCjOjjxP3hjAERgY7KcCf+P1jIpuCcgnOxL0F/HOqDOuFfMg7+Uh+Ow+A4HhzHh+P4oMgxx7aILp0Lhbm0eJ4Km1CLvt9Im0vLyJhFh2QYAFJRpXUWAxCeB2IbthHAoXlmaK5lmegWUaQzbZUNHGiDqE/MPxhl+IDfzwDwMz/zM/ixH/sxfMd3fAfe8pa34BOf+AT29/fjKNK3Og7YNDqNombEZvNd+/r05YqbO9qp48UxnZQy0/ddbLWa6Ha76HY7mJ3totNpC58O2u0WCCEYDIcY9AcBgRyOMByN0Ov1sbm5jeFwGBLKcajGehiPPYFIpomTTZoeAHBdF/fccycee+xJeJ7qBZPve+o4ISl2m4FK3Wyg1WwkRL7pYnZuFu1WC61WE+1OG+12C5Q4GA6H6A8G6Pf66PcH6A8G6O33sLfXw/5+D/3+ICbodqjWMiHfx33CBSJEwa6Sespdy3bm9llMYh6d7xue9CP6P0uwi/g8v1zwta99De9973vj/3/mZ34GAPBjP/Zj+OQnP4m///f/Pvb39/HX/tpfw9bWFr7zO78Tn/70p9Futw+ry7ccihBWW4I7VRKs+E2bdsigsqWIRVSOSlGhRfNakQTHZtGG8dCRa/EwDNGbVeXzQJiXJcOK45VVSc4BxhyMvTY8rwnPb8DzXfi+G/5twGcOOKFwqA/H4QFBpD4cMoLrjuHQQUgsSUB4XRKrsiRUb7PHKgUf05BJxgj29hawsrwjKMJI72MiXdwLjzFQozkjIXlvwPeD331GMfYb8IfBWHi+A993ABBQ6sMNibHreHBcHy4doOGO4TpjOI4PYrFYkwvp3FmbWYv7ywhV28R/ONs/5TWsiiqtMwEWTLJjMqzsdEXpvlJtB2Nm+8xREuZJlGCR7B6GW8gh4y//5b+MGzdu4Od+7udw7do1PPjgg/j0pz+dCaB1q6ICIqw3jy3jL6uC2V8wmHiWmVBXnT7FTNRt/CTzytq2bQvVsQYTft14cs7Q6XQwPz+H+fk5dGe7mO12wr8zcFwHg8EA+3s97Pf66PcGuH59Hf1+SP76AwwGAzDGLRYQzMdUdLEgf4zyyR5jHKORj9Gor9hXfc1HKri8KDDT6eDIkRXMdmcwM9MB5xz7+z3s7e1hb7+H/b197O31sLOzh/39nrDaLaKIOqy/JlWqrN7v27Y9xMGxVPWZI5ur+1kVphWboFhe8Wru/apx0KZX73nPe4wLQIQQ/MIv/AJ+4Rd+oXSfauTAMMHLVWJ0Ck+JCaMulZJ17l1hm5JgiPuJJBgISYGXUYLz2i5EZFUEuKAfcUyOFP2IFN2x18J43IQ3djD2A9I7Zm0wRuG6DG5I7lxnjEarFxLAMVzqgTYQEFpTsCzbgGCqCNw6ZdVEcm1UR+KCEMAhHhywcB8v/IRQWRlwwA8Jse878MYk+Ou5GIzn4XnBQgEhCMbMHcN1PTTcMRruCA23D9cZx4sAhQNh6VDEjFplwgzYqdg2ZFiq3yblmbG/BZDyD65QcT20WAcT4rBcl37qp37qJWMKLWNCIjwtX7hybYptT24KaldefZyTTq7NZHz6ZpUB4ZmZ6WBhYQ7z87PhZw5zc100Gg3s93rY3dnD3t4+rl9fx/7ZC9jd20Nvvxf7z2YXBqow7Z42Ucgj0DTzW3Asidoog3OOwWCIwWCIzc1tZauUEszMzKDbnUG320G3O4OVlWXcedftmJ+bA+ccu3s97GzvYnd3D9s7u9jZ3sPu7l5IIoq4CUwCSwJcKAiU3b5F0y/ltTV5XvKXKA7B9KrGzYOJ1eAqJpeaCbeyfsUEOYoErUJsDi2m1PEGAQn2hoCQAilJe1TA3Nni+teSYEsw4sIfE4y8NsZeCyN/BmOviZHXAuc0ILjuEE3aR6e1h4YzgtNgcJ0xqGuYt+lSR5miP1sEl8pAJmjERZ5lshK0EbY7Ttdl2y4C0u+6PlxXjDrspf5yDnheoKCPvQY8r4HBsI3d/Xl4YwcAR6MxQtMZoOEO0XBHaDaCv4TozdUngbgAI1sJpBavVOMhk2NTvuEIYiA2lh6rlBlzUR/higlu1I7JpUP8PjEZnkL/jajfz5Vj6qbRpsmmPsiWbh8dCUkQpWgJyHCyjw7Vq0JFgk1V22aAMmoywezcDJaWFrG0OI/FpSUsLc7DbbjY293H9s4udnf2sLp6Azs7u9jd3Y+DTYkoG3HbXg22O7Z8NVitTOZfB8m5JZEPE/wJIxEHYIxjb28fe3v7mbooddDtzmB+YQHzc8GixMlTxzE/PwcCYHt7B5tbO9ja3MLm5jZ2dnYKB/Oalom/HqZzlFWi84PJqVTYqu89dX3ivV4uPZm5/oMGYTzONVh2/xo3Fyad8IkTy6KpTlLlVaalutyyOWqjjdmoNgKySIKF4FhylNyIBGdIhw7KoD66SMr50ayBgISNvRaG4zZG4Wc4boEzoNHw0AyJV6e9F5AwZxSbFmfS/NiYZBfw+ZwISnN4GpLbUfp3lWl0RIKj7yIZzmtXle7KoJwSAjQaYzQaY3SQtgbjzMPYa2DsteCNHIzGTfQGsxh7QTqaRnOMZmMYf1rOftb0uyDMuaDlqNKKuYCKrIpkOKctJQ2TzKZtgnZNS+2N6pZROpBjXtToqMwBhP6o38/VY6pPPLNJs+lETisarBq2E1iZLOjIz8EFwrHzhQZE30Uaqo1LWDmyhJXlBSwszINxhu2tHWxubuH8+Qv45uO72N7etQ7wVIYIVkeAi5jrph/h+eRd2DPsb0SC1ftUT2YY82OSfCVlakwwO9vF4uI8FpcWcPrMCTzwmlei1Wxid28PG+tbWF/fwPr6Jra3d3P9j8tet8UXk8xjnrfQBaRNrg/k7WOB4mNQZAHr5jjGGrcmctOkHEQdRdLtiPXnBIaSzYSVeYFVxEFQ/WKT6Jg4JupXnumzMspyHqQozXHfQ+VxOOpgOGxhOO5gNG4DlKDZGKDpDjE7s4Vld4hGYwhKzM90Xb+sSLBpzCwXK2J1UbXgIUMktgcFU58s1VtCgGZjjGZjDNJKn8ux18TQ72I0aqHXn8XmzhFwH2i4I7SaA7SaPbSbgYqs9K+GwtdbMeamdExaQmgIqhVfVaIKLNcZRVVXmE1r61cshNmmRlJBSWynFRlaNQZRHw5aFa5RKSYkwnqfwfw8oNWShtjnTwrEIxJAuawK01HFypJ+0xjZkWDHcbC8vIgjR1awcmQZKyvLaDYa2Nraxvr6Fp599hw2Njawt7eXIUplj798RPEyyraasKrPo+2CSJaQlUMxf9oIZnU2uZY559jd3cHu7g4uXrwUl2i3W1haWsTyyiLOnDmF173ufhBCsLG5hfX1TayvbWBtbROjkbTqbjyOaZvq08xCA4dk7ljAtHlyM+pqoV4suDkU4AyYP1nX6gnBzY0p+MXp1KciKrRWabaMkpyBrPyKiEhv5BfsDdNtVJhKJwPh2BgjGI47GI46GAwD8ss4Rcvtod3oYaG7jlajD9cZZYkSB7itemtSgk1mz7a/i9srNP+NYePraqsKi/Wo6hTJ/gTHQgjQaDE0sAvM7AbN8aDK4aiN4biN/d48NraDgEOtRh+tZh/tZh+tVh8OhWDqbDBVznveKnItZwJeReNgWLBQmWJz4buWDJsQnlcrU2bJpLkMikae1pXXu2wcVNToCfevkUIFT/syQXqqhg2pPriJcXoSPulkt9iCAaUUKytLOHZsBUePHcHKyhKGwxHW1zexdmMDTz/1Ara3d+H7orkpy1EL8wmdeTynQYB19ejMnFW+u/prwkiyZII2BfP3ScZzMBji6tVVXL26GtZFMDc3iyNHlrGysoTXve5+zM3NYntnFzeur2H1+hrWbqxjNBInEQdJHIVx16rtNjD57R8uJnveHDCJ5xO+aHn9or2ZUNSUOWNGrZpwFw2sNam5rW2AKl2gLFXgJxUJlkyIbSJAF1GDA+I7g/5wBoPhDIajDhwyRrvZQ6e5i8XZ62g5e7lKrxY55zfVV905sT2eoosFeapwHomtwmR7GhGNVc1oxiYIuOXB7eyh29kLyoam74NhBwOvi/XdRXgbDpqNIdrNHtrNXbSbPThUeCjn3cemHNtxH/V+xGlV2NJFQUeGM+VFawg/RYZlqPY3EdlEXdarwkVdRAqR52mlaBJRv58rxwE5g+jMk8upwiaT5EknwXZqsb6NO+44g2vXrmM4HEGemNsoWMWCDFEQQrC0tIATJ47h6LFlHFlZwng8xur1NVw4fxlf++rj2NvbD/dJK52yn26+ijod09/0MSWYnqonmLdnTG3NyqT++lL9rvZrjTDJ8RQZG845dnZ2sb29jRdeOAsgSHN17PgxHDu6gte97tWYm53F1tY2rt9Yx+rqOm5cX1f6gU8L73nP2/GFL3wdvld0Aqj29y6KIvef3hTcfkGnqF+6bb01aogoSoIjpHNwZvPmFm5b+M04OY7VK1MUa9mnWEFa5YjQCAjH3n4XM+1dODolOKzPNp9rXv5gzoHhqIN+v43BqIvBqAOH+mi39jHX3sTRhUupiMNBW/bPwCJm2VYkuAqIhKswWWYBIS77ajSpwqVSHVmowoJabZWrWTJjDkyrA//hORqQY893MOw3MRjOYGPnJDzPRbMxQLu5j05ngHarlyyWTGCWa/Qjjsp4Hq5u3o4TRy8nPC+lzgrjo4terUCqzTzFP+/4dCbSQCVm0uI43YqRpmuYUeppWG4yryLDRS/QvEBa1QSrsZ0Mp2qjBG94w2tx222n8bnPbWM4HCjLyf0tg2aziRMnjuLkyeM4ceIYCCVYXb2BSxev4Btf/yZ2d/c07elQdOyqhpkAA5OYW8vg2jZS7Ukk2D7Vz/SRHzgqf5/hcISLFy7h0sUrAAJz6qNHV3Ds+FG86Y2vRbvdwvXra7GyvL/fq/YgBHS7M3jt616N+x94JX7v3/4BdnZU128CIixW6BAFzbOB7PdfngyLzye7+9uc01yu+2BAOANRpuqy37/GrY+y+YQzZDckXbl1ySRCIsM6Ahxv0/kDMw+ce1jfOILeYAatlU04DU8zeXbSEadtTLKl4/J9B/1hF73BLPqDLgCg095Ht7ONI4tX1WbOqnqhJ+A2ONRgWFWYlqsiQotgY7VfcZHAWcp2JRJX1kS6YM5h8Xy5jg93to/Z0KTa810M+i30R12sbZ0AYw7azR5m2nvotPfRoELKshKkWKt6Mg+e38TGzjHs7C3j7jPfRsvJmQvooleH0JJui6BlpSCqtBOS4uh4bBYQpoX6/Vw9Jn5aFQuUU8UJkOuoUrG08enMKqjNZhMPveMtaLWa+Mxn/hd6vSCqYJEoynmYn5/D6TMncPLkcSwvL2JnexdXr67iC1/4CtbXNw2mzfkmwWbo/cD17emV0FwCM1UV2P641SrwhA9ROfBT7r2jJkMqq4W8ccqzlhgMhrh48QouhsR4fn4WJ08ex+kzJ/Hggw9gb38fV69ex5XL17C2tpEbeKsI9vf38Nu/9X/jwx/+fvzkT34E/+E//BEuXLgcbk1HjLYlt+r8xVnFdhILkkn8fs3n45BV4NoH6aWHA1YxSvsGa8u42mNIpUMSEZIX3+O4vn4KnBOcOvIiXNdTE0yZBIe/GSNBUyc2bd3vz6I/mMXAm0GzMcRMex/H5q6i7e4lxNfm3hAJfvi3KCEuHLyrDHRpdqqGidgWIcM2fsbx/pJfbR4Z1kWi1pSxCYAllnMdD7MzQ8zO7ICT6xh7QWTq/f481rePo+EMMNPcxkxrF21317jQooNMWiPC13R6ePXpR/Ditdfh6XMP4p4Tj2O2s5PsqLt/86JX68aryHkqiglV4ugY1IsGfiXqcy7q93PlmPpTrJi6VwZR/fJkN63ymNrNTkrtJqKEBBGY3/Wut2FnZxdf+Pwj2rQ1NkRFxuLiAm677SROnzmOmZkZrF67jrMvXsCXvvg19PuDTPnsxNzGz9SGlBclztl+lUERhTOvvLYNzUJBNv1VljgdvIKuPkdqNXGy8dnZ2cPOzh6eeeYFuK6L48eP4uTJY3j7Q98BALh8+RouXbyCGzfWwCoIxz8aDfGpT/0+PvCB9+JHf/QH8Yd/9Gk88a2nc0hw/vHYBkrTI/s8EZ8XxRYzbBYv8haKDiD1AWOYLE9hveJ8KLjJx92onhjy6ookwSodEoAoEJY3JLi2dgatRh9Hlq6AggPyHDtXqXYyKW45cTAat7Hfn8N+fw4+c9Fp7WF2bhdH29fgOuKxWiiKWjIRWSaFh2pBiA9UCa6KDHMPKKtyTYsMF0XB8S27WBGYUo/QbGxgcW4DPqMY9NvoDWZxffN2gHB0m1votrfRbu4XIsW6RSmXDnDfsS/h4vr9eO7yg7jz2BNYmr0ekl1DxGdhASFFtBWpm1Tjp0qvVDTgVQaK56RNkD9jmweaR7h+P1eNKTwZ1eqhaKpYNnevmfzIZtNZMqAjxFF/1IRNf9EsLs7jXe96Gy5evIJHH33C0DcT0v1cXl7Ebbedwukzp9ButXDlyiqe+NbTuHbtujE3rP3EXD4ekRBX7wNclARXaSJa3O86u6/Y5uGS4Ah5ZuzFrQ3y4HkeLl26jEuXLoN8jeDo0SM4c+Yk3vKWN8BxKC5fuYZLF69idfV6DinO96H99Kf/DJubW/iLP/BBdGc6+PKXv54pUzw3eRXnKj2e9nnCZXPp6DdbZBf0atQojJLpSfJQxl/Odh/Z9Fjp46pK4xNGhB4NXKxeP4m5zhqWZldBIqMgwQfYREZS+X1D5Xc46mC/P4feYA4+o5hp72F54To6rSQ3rDG9jawwG4KAqXwvzfljFeNahJzZEDPZZFj+rWh9gESKhGecjuCqlERdWRWmSYYrhs3Ch0MZup1ddDu7wLyHwaiL/cE8ru/cAXCCmdYWuu0ddJp76vy/8r2T6UOoglLgjiPfRMvdxbnV18IbP4WjS1fU5zkVXyDnOokgkmHVOaqCDFuQ4Oi32hf45YNSRFhPGu399sqS4UydxnoS0pDtb5o4F51kHjt2BO94x5vx1FPP4emnny+0r9z2zEwXd9x5BnfecQbtdguXL1/DY48+iWvXboBVmoqAWSip6Qn/JGTS3I4N7MtXpx6LCwN6S4AykMlTfj2yag/h/7z7bzqkiXOOGzfWcePGOh599AmsrCzhzJmTeNObXgfHcXDh4mWcP3cRGxtbhr5F/6sXqr785a9jd3cPf/EvfS/m5rr4zGc+F5epegHCnKoqv8+HnZZpKqhXnF8yyAS90kET2Cp3mw3yogXLQa0U6rCWBKvMoZmH/r6DG2vHsdS9ioXOaiCp8mxbNosDI9bBXm8O+/vz8JmDmc4elpfW0G734PASvqgG1dtIfKrMVVpGjZT7lqcGqwJmideCjoxyD4iyB8iKrqWSqNxXxGGR4UnbzbleCQE6rX10WvtYmb+K4XgG+/15rG2fBgdBt72Fuc4WmnQXxNFElgbUpDXs94nFs3BdHxfWXoMRb+P08ov6/skLPchRVhXn0xRgL/7d5t4o+F4qRIYPMo9w/X6uHNMMHQjTZHyaE8g8f8h0fmFAT7jUdZw6dRxve/t34NFvPIGzZy9q9jWrP65Lcdttp3HHnWdwZGUZ167dwLeeeBpXr1yfOFpvbuAkUdmacmqWcudZ5d8ZEaX04oaJxE8GWxNkfXkdor6rTbBV7cnWDmLbWeXajDKqpN4ceH19E+vrm3j88W/jyNFl3HnHbXj3u9+OwWCIc+cv4vy5S7HPfJE2nnzyGez3+vg//spfRKfTwZ/8yWc0xxGh/Hk3L4rYLkKYkG+tot8vQfzMmrJ1NGEsUM8m2L/GIYExyCk8yqgbJhKshS44kPB7Ooq1gZQqIjen6opIsEiqmAf4A/T2O7ixdhRH5y9itrMd3n7Fpjo+o+jtd7HbX8Jw1EGnvY+lhTV0Ovup1EacuBnyWiSSMyD5izLfnKLJNncsoCaIVfv02kSHlgmzDRFUEdxoP12boio8SbAsFQksKkjIx1hFICjTvShZDBDmgRCE6Zd6WJ6/hsGoi739eVzduAsOHWO2s4W5ziZcR+UvrzDnF87HkbkraNARXrzxBvh+E7cffTplJq2Muk6dpE7dgkBBX2EVGVYS55fQO6l+P1eP0k9F21RFMWHRkBszsuaItsptUlY96UyrcuY6xYn/qdMn8La3vQlfeeQxXLp0VdNn/TEsLy/innvuxG23n8buzj7OnbuIL3/pGxgMhtJ+eceZJjT5qneRiz9vTKapPNqbkxPhoZkQSn2/qjUtLUcoZeQt2ADyNTrJwsVkiwZ59+3ajQ2s3djAo48+gZOnjuPOO87ggftfidXVG3jhhQu4evW65ONqVljPn7uET37y9/GjP/ZDIJTij//ovwsly5gXlzVNrmKxqAx5PiQwNlnz9Yv28CDnzwxJcREybEOCM/XpggNp+ylsk02fxYm0jsyJJBiIleD9vQ7W1o/i+MJ5zLR3hfIK82pJceYcGA0a2N1fxF5/Ds3GELOzezi2chWOE+Y7hVOZkphSgSMCzPwgpVNcxgGoC6Lqu0Ve52S75lyofi9jhZZnHm1Tp6xy5Y1zRLBF0lw1AS4DVb8PQn1OmQ6nTexTSjG/hl6vi73+Erb2T6DT2sP8zAY6rTCom5hTm2Xz+0Ykc6G7hvucr+C5a28GGMftx5/RLm5xRGTYTVRhmwBjFlAR34n9iG9m1O/nyjHR8qA5fQggqnflUU7lsyU9JvVUriMiwY98+VFcvnwt/DW/Hdd1cNttp3HvvXdibm4W585dxGcf/nNsbe1gclKpJ/oJgZIUgolMjqepIGfrNkXWlfP9hv9YnvtJiGx233zzWr2vZ56/afoanWRxQ0TWLFlvFlxsUcb3fVy6eAWXLl5Bu93C3XffgTe+8TUAITj74gW8+OIFIdib+f6+enUVn/zd/4iP/vhfBiUEf/iHnzb2xBxNu+wLYJovDrl/9UuqRklQxbNZUIhtyHApJdgSyrZFgiqTVXliLPoDi9tCNXh/txGQ4MXzmGntptrQpToCAB9N7O3NYXd/AZ7XwOzMDk4fO4dmY5TuT0S4pAm8HAEYSKvCJoU3RYK9IYg/BPFG8SSeu01wpxVM7N3QGKSKc6QjxjoyWJYkFs0nzFlIcDXHVqmrmGW902rTAlYm8yIUJroyKaaEY7a7h9nuHjy/gZ3eEm5snwYBx9zMJuZmNuGScDFGlYpMiAI9297GfSe+GpDhVeDM0WeC21NqNwo4Fy11pMhwBcgNqiUvEKr2Vfwe4SVLqmsAKEWE84ipyQywiDmjuo4yOX6j/fKIhm47ITQwh37bm/DlLz+KK5YkeGamg1e84m7cddcZ7O/38MIL53Du3EX4flFz1jyYfKHTbZh9Im3amSbEvuvbEglwpv/h0zYQHvUkesIltXSThdMiJRDLyecmMmEnkM3A7dTuoveKvt+245UtNxgM8e1vP4unnnoOJ04cxz333Inv+777cPnyNTzzzAsKX2KxrgCrq+v45O/+J/zYR/93/MAPfEBLhnX5lYFpumJUaY5fZV0ToF5xfmlBYS5dBUqpzEBmch2RYHMgKEkFjiICcw/wBtjfb2Nt8wiOL53HTFNBghX9HHsNbO8fxW5vHk13hPnZLXQ7u3HQq+geSJk6a8hw9niz5FiHiATTcQ9k1AfxBFWT+UATAFpWHhHKRYSiQbDyftcdt4nUFA2oZTN2NsdVgeJYCFNSfk33RuY6k691gcTJ16XrjLE8dx1Ls9fRG85hZ38ZW3tH0W1tYaG7hlZDcmsSFP6IQM621nHf8S/h+dW3AkCgDItlhX6JPsKmZ4ecxqkQSgTVuqUCY9Xv58pR8MlAhb9FybAe1UXjVZkx2ptv63D06EqsBCckWN+HpaUlvPKV9+DMmZO4fPkaPve5r2B9fT23HyZkxyjfZDNNbETCNVFXBEzzhlLVLRxDSIKzPsRFMRn5iP1mBdJqU17dj+TcRO4Eoj93VQHmouMtNl66cbLzoeWc4+rVa7h69Tq63Rncd99deM973o6trS0888yLuHLlmjE10OrqKv7NJ38fP/5XP4y/sN/HZz7zv6T6p3Mtmp9N2XtLdy1N9oyb9gKUhPpF+/JCrMROZzKYqMwW0w3Rl1I0eY1zBQuRgUMluN9vYm3tSKAEN7czJs8y+uNZ7OwtodefxUxnFyePXkKrmU1HmOpWAb9fZTnRjxECeWF+oAhHSrA3BvFGSEX5pg6404IYgbewUqiDKgCWDnkkr4hfLEkIFQB7xbgIVIG1xLZtkZdDuAiq8BWWkLvoIqnEquuTMA/d9i667V2MvSa29xZxdeMuNJsjLHTXMNPaTZtNp3Z2Mdvexr3HH8Fzq2+DuzbCqSNnQZiTtmAQ+xNBdf6j3+QxmlSVNajCtxTq93PlmOISWRU+jXkoFjAoL+2KajK9tLSEd3znW/Doo98SzKHVOHHyGF71qnuxvLSEF1+8gP/23/4M+/s94z5loDZ7LkLo8ibuefXoFdGqlDd1nUX8NcsppGlUodDlXY9U+J74OBOSkOF0+SzsSKBZxS6P4j60+/s9PPbYk3jyyadw99134A1veA1e/+D9eObp53H27EUwpjYRXl29gX//7/5/+JEf+d/R6/XwhS981TpOQRp2i2X5Sn/xYy+OAybBNV56CNXgyoiuIRWTrMAYSbCqDhVBEElwVI/XB5iHQQ+4fuMYji5cwkx7D4BaWea+j95oAdt7yxh5Hcx1t3HmxFm4bglVtyiJESfw0tiR0LeZMD8kwqNYEVYuC0rKudhHrUm5KrqzeFzi9wLHJo9NZtxNdcnBsJgHoJH+vwg5LnJOdNeYkUxWQIYLBoLSBgtToCgZNqHhjnBk8TqW5tex21vC2vYpUMKwMLuGudY6COEQ/YcjE+fZ9jbuPvo1vHDjLWg4YxxduRaYSJsayznHKTXXoHSbVN+MiTRQiAzWZtEvfVRGhPVqlXmyWH5Cbq6zDCGTyfDsbBfvevfb8PRTz4XRodUE8sTJY3jNA6/ETHcGzz7zAr74ha9iNBpDTdSrge4YM+a1UrmsP2pE9oqTqXQ9xVDEN9tk3iofK4dfMckzI+6Thd2absGAEBr7ORPihMeQVofz+mD2jdX3wx7pcU63UYYQMozHDM888wKeffZ5nDlzCvff/0q8+v5X4OmnnseLL54PCXEa589fwn/6z/8FP/RDH8J+r4dHv/EtqS95mIxYmp9zeuiibqfH7iZZqeU+YMwHnbf/TXIcNcrDlHdY/p1EKqUF0c7Jm6tUU1UkjnsYDRiuXz+J5dmrmG1tICbBoiLMgV5vBlt7R+D7LhbmtnBs7hocysJ+2yt0uv6p/IOtEOVnDVXhuM+UBpFdqQNQauW3natW5+X/FY4/Q6xhd0xiGWXUYLm/KjIc+wgL/TOZT1epJFdFdvOiIU8JeWNeFA71sTi7hoXuOvb6C9jaO4KtvaNYnLmGuc4WiIIML8ys486Vx3Bu7Q2groeVhbXEL1gk66k8wwUioYvQkfu8VEuANSG+KUlw/X6uHAfgNAEUV4dtyqkn3+rJZl4bWR/aZrOBd7/7IZw/f0nIE5y+gEQC/MzTz+OFF87B83xlveq2i1+QYi7mpI7J/VHLYJL9y6ibwTkNVVPFUmNCgtX5ksOWpf+LHoN6DPOij+vybkckODUekp+zXtEsrsSbxjc/t3J24SFL6IpfE4xxXLhwGRcuXMaZM6fwwGtehVe/+j48/fRzeOGFLCF+5unn8V/+y//A//a/fQ92tnfx4osXirQGG1NmwPYeKbCybOHDr95WRcAve9TpGWoAMJNh6ImvlhAbSHDUVopsEsEvODKJ9voA9+APx7h+4yTmZ9aw0LmRaYMRF/1eG1u7R+B7FAuz65ib2Qz9fx1weeqjITCy0laYXMRkVyCXqgi31AnGzW2CeKMgSBZ1gr9uE9xtBZGtVWq3KseysU9ZAqw6LuWx2hCD8DyaEJ/nvBzTgJnwqgj+JDAp57ZE2Zb0TjmXcRE/dRMI4Zib2cJsZwv7gwVs7h3D1t4xLM7ewFxnE4SPU2R4ee4GPPYULlx/AE3365jr7gX1qCqv2h2jIuJaxK/4oMly/X6uHiWfHPZmp5OoH0XJ2mTqcrKv4zh46B1vxfb2Dh5/7MlM6ZWVJbz+wQcwO9vVEOC8ttTt6sumA2FFZFiXRzdL0KaDyQJv2dWpBgPn4ThkfITV+5ZR8ZI6zQpuXFYg4vL+ur6IJFjMjcx5dD2ZrpFixLNYQC079T8viFde3XKdly5dweXL13D69Ek88JpX4hWvuAdPPPE0zp+/lCr32KNPYmVlGT/0//5/4Xd+599ic2ML+gUhuV9VkNdyKHePHPCLq/ZBujXBWHbsC5hFF4oqHe9koQargmOpIBLviATH6mUYQIp7YOMhrt84ipazg6WZMIWhYDLcH89hY+toQIDn1jHXXk8CYJmCclVNTCQSHH1Xk9mABAd9DFVg6oA1O0HUaLdVjjRYKt1if22hjYYt+UPHkMxcC5FhG1StEB82xHFRKPZxMcMxZ1RYzTm2Ic2EALOdbXTb29gfzGNz5wi29o9iefYaus318JwG9R9buIiB18WL116PV51+JO2Db7qOQzeBUiiyn+YdVabtA0/VVL+fK0eJp0bxiXdWNYt8I21JW7qebF3Jb/YEUDyOpD8A8IY3vBatZgNf+PwjqT1mZ7t47etejZMnjuGZZ17A5575UgECLLZrMxlWl8lPfZQd12liGkTblE4oOb/i/2lCmG8mbGcKrlM/tYG6rCJWR6DGsSNwoE8Flae+5hNTs99sseumuCuCvu9RPZcvX8WVK9dw++2n8drXvhqveMXdePyb38b11bW47MP/889x9MgKfuQjP4jf/u3fw3A4iuuoMoK07b2krv9g1dwaNaYFLpJU1e8qaEyJrQJPiVGiw8BY3BtgY/0o4Ps4unQheA+E5Hbkd7CxeRSD4QwW5zYw390ICDBLSHAuFMQjQxJ0k95UEKAsCRZV76je6Di5UEf0PUqbhJAEm0xfM1GtDcQpA+l45HqN+yvIbdwfEzmQLQC45CNcBCIBrpIMH2LapBjSQsbECm9JMiybyc92dtBt72B3fx7ruyex7RzBcvcSOs39sIyPM0tPYziaxfNX34BXnf4iHNEfv6QSnElpVMD3OUYFJFifCu4mNKWukYuKWQwVPjYTzqRcnv+nHlnSk+cfGX2ydVDce+/dOH36BD7/+a/EJLfRcPHggw/ge97/boyGQ/zX//ownnzyGYEEJ8esaid7HHZELIHteKYhl88bT3VfDw+mc5nuaxESzFNldcdsSqUV9w0UlLjJh7qpnMa2Cqy8YGTn6yxvS997yW/6Y0jvdzhQ+a9H4Jzj/PlL+G//7bO4cOEKHnrozXjnO9+K2dluXOY//ec/xmjs4cN/5fuVdURm8hlfcsO5D5AelyL3Xl5k8JsekbI4yafGTYVJFQsVCeaROa8KopobIRXcRpHyJSJ7RNo3JMHwB9jZmsWg38TxhbOghAPUgYcm1nZO4fL1O+E6Hm47/iIW59IqcFQ3l+vVQadQ5pE7wd9XjgytHYOQ7PJGF6wxE3xa8+DNGfBmNzaLFvtkJESCuXOmD6qPUGeuv7NmX3lbVJeshqd+l8eJ+4H/IvftlXnTOBBXfx7z6pwGCY4ioUcfoFD/TPmok0LZY86UyfE1l8vr/icEmJ/dwW1Hn8FMawer2/fg2tadGHvNMO+1g7uOfwMAcHb1jeDRfFk49xlQN/VM0X1X/T+N3OeFMYU0dRnU7+fKUfCsZSeT+qqqDV6jC6CkryNLDMx1AMvLi3jd6+/HF77wdfR6Qf60O+44gw9+8LswvzCHz3zmf+FrX3sc/b6UW03RDxUJVRNiHVRER0ccqr2wbyYyDNgRYnl8bRZE8toEAvNrrlpsEf17JR/fIv66HH5MeuNPalVRT/IT2PuIp9NpFVtgifaf3Aqg2OKV7/t45pnn8V//9GHs7/fwPe9/Dx54zSvhOA58j+Hf/t9/gOPHjuJ973untp3oPMrnUo38xYTyuAXIMOMTvmgnCORRY2ogzI8/VpDTKglm0EVVYLE+1e+cuuBOG3DagNtO6gjzBMMfYLDvYGtnGccXz8F1PHDiYGewgkur98LzGjh97ByOLK7CcVTBcgS/YzIhGbaArYoaEQ9O3YDwNrsJ+Y0+EQmW/XlVpBsaAqzoU4aYqvqpIr4RUTR+0u1qfZBNZcqYqU+qBhclwLZ9VJUrm85JhyL1hItDqY/YNeG6VEH8nRKOpdkbuO3IM3Acjksbr8bm3jEwTuC4BPee+Br2Rwu4unVP8uxRkWGZ9Fo/b4TtOcdVBQ4953D9fq4clTtUmMxa9bD3dcwGixLrsNs/guhn22w28dBD34EnvvU01tc3MTc3ize+6XWYn5vFo48+gYsXr1gek13QJDsyEZnApus2R+fWtR1sv9lIrhpqc1IbE1WbcS0yBqpcvhwMnOiuQ1ukz4fo72xSuvN8srM+0+brUfZPtkV5Mlz++huNRvjGN76Fc+cu4o1veh3uuOMMHv3GE7h69Qb+8//3T/HDP/wXce7cRTz//Ll4n+h86VClaX+xyNW3wn1Y45aGQZ1Q+QMbJ3i2kaELqE+RWWxKBXbaQodCEsw9+EMPNzbOYGXuClqNPkb+DG7snoHnNXFkeRUz7b3YXUaGMq2QLl9xBVCaUWeCLkmByEyBxKJ+QiLBYhtygCqNabZyiPIWRgzkULeoYjXVjkyjkx8AzgJSDJb2Hy5q8lxWCS6Diq8f62ar9ocuY2YswHF8HF24jLnOBtZ2TmNvsIyVuUvoNrZxx5Fv4uz1N2Cus4H5zmbyHNHdB0xYJLBcbND66obHFW+fZj7hWm29JVHqTsozG01AQTR+jmbCHP1W3YQxr89vfeuD2NzcxnPPncWr778Pr371vTj74nl88QtfwXjspcqmYZhsGMiFvV+lmgzryxapS79Pcb/PKpFPXPJNoNXgild0WiXN9kFFeGOCpUhxZH9/6CwU7BXlMkp02fI3i7/rxsYWHv6ff467774db33bG3H1ynU8+ugT+MLnv4q/9IPfi3/xG/8Ge3u7JSNk56Oae+MmJsOMT9a1esX58GFhomcTHMu0bwTbKNEyUqmOIhIcRYf2gzzB4B4wHmJt4ww6jV3MzuxgY/8ktvePYm5uGyeOXE5MoFX15x5IThl5Iq7wA46LqsyIC8LUZ2NQK11fUoTZsyeUGfU451jEMSJucm0ZIz5LfsIsNI1mPkBJNpjWQQXDypjta9oUjzmPDFcQiC33eq4i0FsZMizt0272cXrleez0VnB9+050W9tY6V7E0fnzOHv9DXj16c+j6Y6CmZghvzgH4kjURciwiCR/sIIMAyniOnHQq4MKmFW/nytHqdmczSQwr4zKR1fvu1s9RHLzylfei7n5WTz11LP47u9+B+64/RT+nz/7Ah599ImYBBeDHTnR+aZmTX3VZtKqffLBpM+tCdFM11YFLqqGq32sE/9dxr3UJ2vWrK9L7JP+fJcnuUUht12d68N0wDnHCy+cx6f/22fRaDTw/ve/B0899RyuX1/Dhz/8oaSctKARmbSrfaoj2C5kRDD5WZuuuZvUTLr2Qbp1QelU/dRUptXJZNMcvVb+RL9nfIKjCNH+AMQbYntnCZ7nYL67iSub92J/tISTxy5ieWkTxM2SbZNJZ1mo8xsbTC8zZp9SFOwCSPnUWpgbp8oK5sqxabw3BPGGMJo2x/VZmNLLJCVWryVTahU0Ztuyr3HchqqfVZxr5qX/qrblYYqpkCZBFSmUVDAu2hBgobuOM0dfgMcauLT5aix1r6DZ6OHsjQfBWHh96PyFqZP2Fy6puHOF2pz+Lf28LBw1HxaLRFWjfj9XjkrfmHmqmMlH10ZRA9TqnBpqshdNhoFgYru4OI8HXvMqrK6u4T3vfQdurK3jM5/5HDY2tsI98oIJ5V9U1fhVqqEa0zLmz/n7qMfBLvDQrQi9/2xAgsfBh3lgzIt/U/kpGwNk5fqtFvflTSPfTD/Pd7448u6Z6jAYDPH5z38ZTzzxNN72tjfi+edewMqRZbzr3W8DkDwvSGoM5fHMI8OTxjswLTrdhGS4ftHemjiIQC0R5KA/qX6kCWJqwiz47nG3pSbBoUk0YR6GgwY2d1bQbu7h6sad6LR7OHniMpptfeRbAMqgQek+FiClRCDteVAFkVK2r04tJPvt5hFgbVmZ/DI/fc64B+IPU9tVn0yAJ9Un7otAmmUyDGjJtngsyvGMtumut5sJVfcvuo7l61n3exUo41+rKes6Y5xYvoDF7ipu7N2NxZlV9IbzuL5zJ4AcMhyiUjIs/KYjxCYyrF0EPEgyXL+fK0fpO0ht5imbAusGPChrLpNtL3+CXswUklKKtz/0FrRaTZw4fhSf+9wXsXZjQ1NDEVPGoopSuozeXNw+qNGk0KeASTBN4lu1aXZxM/Q0oRVJY5K6CRoSOzmRzKrRpnqz14aO3JojiRfp9831MD179jxWV6/jzW95A1ZX1/Bd3/VOPP3Uc1hdvZEhwabAaxFU6dmi38tf91mlP/nfRJRvrrGu8TKEGNjH2kwx6y+cIqpC+qBUvaFfMPMYVjfuAIOLwXgeJ45eQrs9Ahcn/zwnFZNq8pwi5q694icdU2mlzVZFlqBtTyabQIaQJnWk2+CHuQ4XmTrLfqKZcopUS+J5VZlMK9JITdxXDTLpsKrwGdbtb/pdc6xiWqQ44rPttZtzrdrmH56f2UCntYu1rTNoN3dxaeMVWJi5gU5zPzCj17VpuD9VSmyuy4Zkxh2Vyd4XB5wbuMahouLHoKh8qCdvaRNMtSJlg/RkNq242CmUFG97+5vx6lffiwsXLuN//I8/05DgIuTXpPzYIy+fsqlsGfKYN07m/4u1baMcp9IIWVwbtqp0EOU5Gy4kSwbV46wOZqX66JA1dZYtFCKiFkejTkWlLpoarMj9lVdWfd+qx11H+KqB7jz3en187n99CV955Btgvo+/+hP/BygVVXn9fZWvipvM1PXPumqsJQ54tsr45J8ahwZbv1/bcqkJoTzZlifmsfmhm/wvq0tiYCynDTidZJs3AAlNolfXTmGnt4K5zhZOHTsfkGCRbByUOqhoJz83b75SboJSGc60kY3kLCu5GYU3047dtkLRxg8CNue+ihRMOXXL6ryx7Twf4kkVXsN+srvAxK4DJWILNJwxTi4+j+PzFwAAz175DjBfWLhRRDq3Qmw1kFVrldetRiEOtuWbSR/6/VC/nytHiRmWybcuQpqUyrA1g5bbsvEJta3vTW96Lb77u9+BP/vsF/CFzz+S4wucR3DyJrmyT2K+6aj+OLN90Y/LYSjI6uOTyUBVarL99WP2t7UlLCIZLkdy7M9J6iOkaMpGhgbS14TNIlMZJbj682eG+T5RP1s4vv3tZ/Gv/tW/w5Ejy/g//8+PgGpMRjPkNzfF0mQuEuUw3UUFJTib/FPjUJGXciSVbkT8X4PMpE+YsGejT7tm0hf7yzYAt5N8BwKF1/dxff04VjfuwKkjL+LoyjVQl+RP3iWlOENGRHNolZ+pJTnQpgKKoPI3VU6+1e1ZqXWWBDhVXveJ280nvaZtuWlucmA8vzpf4ky5gqRWV95k+m8Lgxn51FCASBchw3nplADYkWPHwfLsVbzyxJfQGy3i2WtvBw+v28RMWnEvKfukaE9xXU8C6+v5IFMq1e/nylFwdmUmWipSkJf/VYXp+NMGdVJK8OY3vx7vee9D+Mz/+H/w6KPfqrwl+2NTjacdUc5Hsf0nGXOzr+X0SJOqXnNfzGRYrLdoxGFV4KtsHWlSIy5eiH8JnMx21TFkkVVr9f2u/h6r9jyr+2rbxuXL1/C7//o/4tX3vwIf+tD3oN1uQT02eQsZemU4v2wat4zvNZ9wtZnXK86HBZuJm0yClTAFhVHl91RMkI2T51AVJrQNIphJM05wY+MEVtfvwJmjz2BlfhXEcexIsAkpP2QDIdGkLYqbkVMU5U24pxSoSKxbS4BtSYFqu4EwR21WoojJ40Oo2opAda2WJZY6P+U8Uix/V0Cr3h80ZIVZozbrgtjJZZT/60yPLZ5BczO7uO/EI9jYP4ULN14NnzkJGQbU5yc6LlWbKhy0YksdHEichvr9XDkq8LLP92Gz8cnLlrX1o9O3L9fVaLh46KHvwO23n8KzzzyPr3zlUWO/y0BWPVXpbcr4Gap9PE0mmpNNupO+M6mdpF6bvohBoeL9LNRwm/7p98sqp0KpVHnTeTD5KZvTf2X7aTqmvIWEpC27B3uxa6sY4TdBn16s6LVYzcvkySefxle+8g285rWvgu/7+PM/fwTb2zvW+4vnX3W+bc5tjRqHBRVRUZHgDHkuRKbTgbCsVSbaCAgwEPs3+j7F9Run0dtvY3F2FUfmrwCQTKolk1Tr9kypd4oqwRlSqP69SN2FECpn2sBUZQhA5Kdr2jfaJlwfJnU48pUV03SpfDGNUC7IKPzCxfOriyY9afTnAsRb2ccy7U4jP3GO73RhP2IbKHx9j8xdwubuCWz0zoBuAMcXX0TTHYCwqB/qa0YcE07116C5P+lr3VSHyV9Y28catxQqncEVjY6cMf807qsjwQlMBKDTaeO7v/s74TYc9PsDfPGLX4Xv21+8RfxQq4NaedRtU5U19ztreq5uiwkfe5hI8KR5XGXYkeAs8q4/G1PpYib7pvMXnqcwBVNRX2l7mEm5Xom2U9RvJvzJH/8PMN9Ho+Hgve99CCsri9rxNC3QVDH+t8qY1T5Ity4m9l+zMacuuF+yv8Zsk3sYjx1cvXYSjBM4dITjSxfVSrBNBGexDXHCb0rjY4vU5PmA1b9YnfXMZtCT1F+oH3q1WeevaQOlMqnbVz6/qX0MUaoNKaPS/Taf47y8z9bXxzTMpsWUXSUWZHQKsXxM2tRiFrjj2BPgnMD3Ka5svhKDcVdoR33NpCxRSJhiKfqtChPlEqrupG4BhVC/nytHwTNe1HeuKjPfySah3e4M3vtdD2FtbR3DwRBnz57H2toGAJlY2vfXdmIclLPx01T9X6ytqKyJOJVVovORJs2HSYJV+yT7mR8CKhI4HdPutLKoC9ZUVduTkTBZ+U+bdhddAMsn9tWN997eHh5++M/xylfdi6eefg7vevfbcfLk8VSZlIm6ELxMht09NY3FguosPeyaq1+0NdIoQ4LFQE+JOWUyKefcA2cDjEYcVy/Po93aAyUM83M7aLVGFn3SEF5AT4ZT/Tb4DCuORT6uFMQJuyX5sPYFFghwigRDIpwV+kYWgokYV0HwZFNvGMbOtNiRc45l5J7zEHm+s8bznOc7fJOkizIGbdMhY2mSjSnQdAc4ufQctgYnMT9zDVe37kVv2IGMiGiK+2qfSbbpn4xWMRYcwBR0a5qo38+Vo8RMzZYMV0F+i6uQMubmZvHe73oIVy6v4uLFKzhydAXf/OZTFnum+1/FpFZFbnSRfrPf8/qRJp/yJx/5PrP5qnj2+CJiQSASpuyCQ9nounk+6FlVW4Vsn2RyZ9s3+zRNSb3p74kqnBrv8H/xPJv6oDLJN/VDj7xFrmJQXfvVQN+fRx75Bra3d/GK++7GV7/yKB566M247bbT0n4mQl/etLvMQoEakz8La7zMYMoXqTILzAS9yk4+M/sIdaXIgIoURb7AxAXYGGAeRj0PVy/NYbazhU5zDyOvjcWFjYRcqCaWtuaiJjJs6zMsgvlqH+E88mnMS6qJDi3WKwbE8odpFbji4EDqPppzDWesEML+6FI42fjZGiGTYVV9lopvBjnEcxJ1+MChO/6C5NoqbZcOJmIY+iwfn38RrjPC3mAFR+YuYnX7HuwPuup9pEUmTp2MKqwjo8ptxgW+W8SCq8bEKHmmdZMynaJqXzbPZzOvXpH4dbsdvPe9D+H8uQt4/PEn8OCDD+DbTz6D0ShZbTZPUM3DU8YXU0141WWL1g3AkvTqMalaq1NsA3+w4G8xYmDjWyuXY4pP2EdwCwJmNhdXm5ibYTSbFhctZDIcfTQkOButO71NH2xLce8UJNjp+mRMasWgPn9yf/IQlfmv//VhPPjga9DvD/DlL38db3nrG3Hq1Mm4jC69UvZ+rVapvxnB2eSfGoeMiATnkWEFoc2FyRxVVTwivxJRGA08XL26jPmZNSzNXsHm5gKWZ1fhUBYrOim1TWxXFR3X1sfSpNQqovwS5qVJcBHiKU+0c4JQZdRfb2gmwBVCNkUtY3KtqgNAZkxT21MkjYW/p/2ulQsFkEiaKUKzKoKzIarzNIirTZ2lzO2LRKauigRPCJGUUsfFmeVnsbZ7Bxq0j6Nz53B95y70hnNx2aCgnKqtmEl2dC0WMmMOyXCpVHNTQv1+rh6FWA/nMokwqRTlFd1i6qB6Ytput/Hud78d5y9cxre+9TTuuut2OI6D558/G5exI2N6UqSD2ddWjywRSNdnMgHOECRRibU+zebj1JnCyueKS30nxAUlDTi0BUoaoKSRSgekP89FTJTT15vqGlKZHyem67rFGjVMZFgePx1hVx1Phgyn+miDcgsZRYid/SJGtt95+5YnmGbCfP7cJbz44nl87/e9D1eurOIrjzyGt739TThx4piyvL6Nw0NwLRyAWVNtenVrQ0N+TZM5XR5ZE5TbRbPEVNRfQQ0GMB4D166uBCS4cwm72zMg8DHXWQ/7qjE3VZlA5ynEtilldPmCyywWAGoSrILG/FnpB5wyD548n6mqDm2dKhJvIvZF+qVaaAh/05JfBUnWEuMCmIT85QXIMhHdykhnBcG+clH2mstEoA5I6WL3BmbbW7i0eT9m21s4Onceq9t3oT+aNfRB9lXOJ6vaZ5Zpf40yrPKDPxDU7+fKUToknRhRFanIwuUnizaKlA2azQbe/Z634/r1NTz+2JNwXQevee2r8PWvfROM8YIpacxIjwNF/vHrt9tFFs7WJ5siJ+VtlPnifTFBRYIJHFDqxn3jMVn1EY1Z9Js9ade0r1VMw/4IUaPjvvJoO6C6FqLo0vo+ssx++gjKUn8LWAaIpu/pPkxuwlM2UncVbU2HhGfx6U//Gf7m/+ejuPvuO/HiixfguC4eesd34M8++0Vsbe2Ubi9dJnst5OMmW6Itt36Z3r/GrQPmK5UU7cRQzh+siRYd/+a0UxNg32NYvXYc3fY2lmaugHkMW9tLOLZ0JXwGp9tRqnsysY0i4aq2RVCRFAP5kH1EC0MzrqZ2bSJBmxYtdOcsd5I+BXVZjKhrpagxH4SzgCyKDxEFuSYIry9xW9zedM2QTRGhbduW66i8z/J9MIHJ90TIWDyE7UhRpDl1cGblKTx9+SHs9pcw19kEQxOr23fhFH0WjWYB83Zd+wrXjyLRoFXlDyVadP1+rhwVGsGrz06+X2nSDbOSZgdKHLz97W/G/l4PX/va4+Cc4b777sbe3j4uX15F+UO2VYX1JrUqFPchTE+6VcQvMD92Y/WVkobkm5vsr65/sssi8gcGaEyCaao/oXk0LM1TVG1oTJbVPtHJmAAkvS9nQdAW6JRXgUhniLxawVfD0pogJ1BTvul7WgGfrglverzKtleND20+1te38fjjT+N9f+GdAIDz5y7iqW8/i3d853eg1WoU6lPWSiOCzWJYcbyUTbFrTBEFTftyCZFMMg35g0HcDAnmzMP11SU03T2szF4E5R62d5fQagzQae1l2yujbqn8gQtGzRVVR2N6JBm6IFopMicSbMnHVlSAvZGgjNqpv0bf3Uxf7UysC/kHZ47VULdI9mN/X65QmRW+roJarFKSrVVr3XjklFcRx6JkUheAqnDaJWXlU0i7ZIBVn0USHP0V9ptp7mGxew2Xt+4HAMy3VzHfWcO1rXvAPFLs2gZKLe7Iz8lksc9yfmJyRalx02Jqs89JJ+FZc1IN8ZHaeP2DD6DT6eDLX/46OOdoNJp45SvvwRNPPGuojxk+ZVDMlLpovVHfZHIUkc/IDNmhTTi0GRLRgIRmYVqc0H3SSKmuqkBBESEmFHSKhEcmidGiQDAWwXgQ4sR+ysl+fkCItWRYID6FTM1FqM3jiYJoZ45LPs/a60tvBl4edveGyQQ9gPkaAvLI5yTXTdLmZx/+PI4dXcF9990FAHjqqeexurqGd77zzXAckrN/3kKdravFTQ7TKbf91Dg8UJp8IE3uVITYRg0WzItjHztdMKuovDTR5b6H9RtzYIzj6Nx5UO7BZw52+sewNL8GOFJ9RU05oz5OSAIKEV8R8X6SeqUgwalJvGDCK5pAV0VkTfsUIrg5MKll2vpkMsxZhvhm+hNvE48jG3RMG4hMHgebMVSU0dZdNfLMu8XrXnf9V3RvGLuZEz1bCyHw1amlZ9EbLmCnvwIAWJq5iJa7i2tbd4AJC0XphjVjo3FNkFVeEbp0Tbryqvqnjvr9XDlKzcrKKjh5aW7Udef7w0b73Xnn7bjzztvw+T9/BJ4X3Bz3veIubG/v4vrqWuH+BshePfa+xXll88mBuj8JVORPpcDGKqzWZLrIHWNDCuy2mdtQ15OnticRqt2YADdoBw3aCRcJmqHJtokMm/tSDhqrAIFcm+4tWxKuS8U0LSh9nTO/qca0qBXE5NYKe3u7ePzxJ/Fd3/UQomv86197FD5jePDBByz6OC2ibsZBKOYxeAWfGjcFlCRY58ergjR5TgWuiX3r7NSgvd0Z9HotnFg4CwdjAMD23gqarSFaM36x+mxhqssyeFFpZNRhydw5FZAr7QMsR2C2DrKl7Ut6e1bFZakP8cYg3jjze+pjPQ7qtlUqODhLbw+DhKkXCyRCrBuTVNt2hDmXQB8GDMG9CkFBiAvfc5OkCTLcU+3GAEvdq7iy+UoAgbvasdkXAMaxuXtSuXikwqRpjNS+xAbrmoO8Jur3c+UoNLsihOSS1KQszXyKd8V+gjg728Wb3vQ6PPLIo+j1BiCEotls4pWvvBdPPvmMtNfkyyJFTJrtFw7yykhmqCoTYDgZBdZkgpxPkkwk2Y6YRBGQGWdgoqk8ij88ipiQR4sALmmhQWfQoDNwiIsG7cClHdCYEKvGR68K5/XFxh1AbdJufztmF5XS5tr6tFmTXfu6RSg76JTiovuZxz2vns9+9gs4enQFr3jl3eCcwfd9fOmLX8Vtt53CmdtOZdoo9+wqahVj+xzRqdY1aqSRqwTnQSLAuSRYOUEMyMpoSLGxuYhjixfRoAMAgM8cbPePYmk+SZcU1adV2YooW5MQ6rJqMCSSKZn0ihP5tMqZVoFLE64c0pzum0B8RfJpa5KtIMWyT3O6PXW/UqQXLBgbfxh8VAq1jhDLbdiMS94nPi4LQmy7aJGDA0u1NCV1WPtMsMyvfXr5OfRGC9jurYAwH5RwHJ9/EbuDI9gfLuRbK4jBr8TFvgpz/B5IvuAaB4YydgyK/4tMrIuqQGnoJpbLy4t47vmzuHb1evzbvffehe2tHVy/vmbRzzShkNuajiITkcliSI0BZ4BtQB9pJSgmTQUVQ3WQNMRBpcT6ORgYPFC4iW8tpJzEkslvGgX7hmSBghIXDmnApe2ADJMGHNJAg87Ap8Fg+HDA+BBMubKoPz8mEmzzm7oevamzue0sCbaFKbiYrlw6QFx2PxsyWhzl7hVVm/v7PTz62BN4z7vfjmeefh4A0O8P8ZWvPobjx4/i0sUrcVmT+bktVOOlRtFn6XTAGQFn5Ql37c58+NCRYFUQmGJ1FZgyxH6fHkb9LhZmbmCmsRNv3u4dQ6s1RKszVrSpCIZlQpUq8gSQzYHFsYtJHNSEMYZhkq8y5SwUDVxSclXBuJTETQgUpFbEWNaPMqrHFJQoVZ4nRJwa3pnRvhQAcZMxqJpAKoJxRSQ1vj5t7yNhLGxQNl9xYURB5sJ6C5Fw6kxFCW26Ayx1r+Dq5n1YPHkdYD4apIej3RcxGHXRbWwA0nOMC+SXMD/uW+r/EGVIbFIPje+hVPCsKY2Fsi/1+7lyFLWHAFBk0ltk4qoypaSG7WlcuHAVuHA5aZlS3HffXfjqVx9X9iMb7Vnss5rM5E9my1xh+n2isTW2m4lYG6mBfhwlPclDK+0qEddo30w5hX92lsBlzXEJARgfg4Cm9uGcgfFxZp+oT2WR9DNRwcUUUg4aoHDRIB0wh4CSBsakD5+78NkwCJqVY9J7cCaqkz+tTGbU0zSXnt4YmftcpN0//9yX8bd/+mO4/fYzuBgS3yuXr+HK5WvIe26VWSBLrv88Qm+7YDdFTGowU79oDwXa/JjSRFC3b/ZHKfJsHORG72cs5/yNJtZznQ2QVjLJ9tHAzv4Sjq1cNfYrqMQif+i0YTnJlSPLpghgZP4r1KcioinFVgSlSkKZu6gR1qNWdeX+yPMBsX39IkpARFmqj/E1FRL7zDWWGauwrD9OtyGTmKivCMlwtC+y13Gez6eqTNJvKbhTitirI0eriGSqnEDS5GOzRaptU5T0EjCR7CIkOUWqS5DEkwvP4duX34394UJAfAHMtjYw2wq+m55p8WJfWM4UHXpSTLNuLer3c+UofAeVi24sgim2685MNGm0VcYS3HHHaYzGY1y9mo0ULSqhcnqdYFtx9Xia0PWTgGaIa0B6KRjSKQiYkuRJ7WiOJ+OHnCK22Ym9GFWZhObTUcofMadyfkqi5PjFYy8KOaVTk3RASAM+bcOlLYxZH2PSg+cPwKAm6IcFO9P1NOSx1PlUp8Ylh2CpFk1sYIqWfpjY2dnDs8+8gHe96234vd/7A205OTK4+niKqtVl1e0DUow5ASZYca59kG5elJm0ZUiMMAkVJ+XyJDrKwauKirvXW4bb8NFu9QQSI+yvmtznEeCi/sBSCpeJYKorFRBKTYAz/roq5PnlioqsTH5VZrwCYgIs/w6AU5rdX6dMR/WIpD3uS7INSD8mCPNBuAPCQ1WYpBXqFLlBQohJ9D0khDbXd16ZLJEXzmukEitImCk/cIYMq75XbW6ryrl9i6Dd7GGucwNXt+/Dvce+pibSqeeQ8IwKVW5OHXsyXISsC6rwoaB+P1eOA74zmOZ7ADXhsb3g0uVe8Yp78MzTL8B2wikGYirm16c2L86b6BcldyoyHGzIlmMYBy8VQYENiorKcNB3sUweGRJNm7NkWC4cliVRH5PJv86EVzUOZZVLDh8cDD73QEJVuokmAICigTZvgYGhSToY0EZ87D6j8FngqzSZP6xlP7mcj7s62I6n+rxnF6rsIjrbHcOk13+27eJj97nPPYKPfeyHsbS8iM2NLWGLiqgqorQXJLP6cz2pu0mNGlmkJocWqjAA9cRSBZWipchbGpFhsX5OHGzvLWNxYROiy3vG7HQS4qsqO+3gWGEbqUm3pALrFWGNybIlUiQ0Va/YtuKZolOfxb5EbUQ/aEhDmqTqVeioHrk/xGdBP71xTIRFAhyrweH4imQ4KKs9BKER6ZpRuUSRyE896XuKFAt+7EoFVUFuTbmHU/tVRYZVxyX+psvRXREy91EJVfjE4gt47upbMfLaaLqDXDKc7kCWDOtgq+qmTaHDi42xw1GFa1SKiYhwMZKSJYymCbBu4mszaT5x4hha7RbOn79kLDdNVcpkSq2K6ltUac9OqEWlNZys8zxin0y4Vb6qaSLN4jdYER9UcBaQ4WDHDPSpgNTXlzxWOoWOc4AxLzlECozRAOcMlFNQuGhxFx48EOKA08BcmxMGRsbGIFfTJcPp34ugyOILkCZysptA+rrJI2Y2VhTmfuU9C8S/ekJcrP2rV1dx8dIVvPOdb8Uf/9F/l7ZOQkSL9qWsQjwd1D5Itz6IQLTK+sSVQmxGCgDZyTCnLnqDWTDioDuzm7wTdBNaHYGtwjxarHcSQpAydRX6G5FgiQCr1F+j2bJF+1lfYIkAawk3y+4j1MujfSiNCadYB1fkV03KqfuvfLIwH5yH4+iNABIS3qgdmMmwyQ8ZEK5nxXnOEN6ojECaVebeVqS1KLm1cmOo4NqfEgG2gjiGEmJSSVzMtTfRae3i6u4rcMfSN437xSCSVYlwnDaE9VYIglW/n6tH4TsqPy2KCmrVVG+aXB6cM9xzzx148YXzYEyeYCYq6EGnmLGfHKfLqdQnIkSCJsQJoy/ThLBqzIrVk+2sGS2BAyrmfxR8ejn38s1oQUMlmMEUyMsqFZDBfNdMVhEQW+aDk8Bn2mUOGDz4ZAwKFxQULnfhw4WLFihpgJAhCBzwcBKXn7tXjyLk1naBqEoSriK/qu127gLFTKAnud+yymr5Mfnyl7+BH/iB9+NP//Qz8D19n1Lnp6CViRxoTNyWQP2sypY5ALAJTa/qF+3NibxJthhtVcAkk8PAVzA9+dzZX8J8dxuEAJyEvoSysnyQfsCSP3OpoDc6xSsivqICrCO/unbLmHSKPr9hmylCDGRVYPH/0PyTUJohxCJSgYgYU5pRE5k4y9dhRNZ52EdvDOJE9YVlQ5VZRYYBg7VnjoWDfA5S5RT+t4T5QVt5qrACRcqqYL3vQZJcxfWntaqQ3Ss0EMnw0blzuLRxP84sPQknyjJSJjCVEDwr0x82Sb2CmTR1gBKZUAqjfj9XjoJ3pTxZK06C88wKJ5noc87Q6bRx8uRxPProE8ay6Xbyj0vdr6qvKJGYSEQdielylqiGJJhAm7Ilb1xFAkzDlEI03MfnYxBOwZgHBoArH7TpRYaYDMNMHKogSiqynCwKUPjhmIyZA5+PMeYDAE68NM2Q+CoHfst+pl5j+wXN4m3rK1+P2VxYJMCxS4CQQiqzUESyZHjSPhZdUDAvopW/D5/69rP43u/9Lrzhwdfgq199rFD/1MH2IPymHiPxHi0+fvVbrEa1ECeHXCKkqZRJhet1MxFbx14Dg+EMjqxc1+6TwgGlksklwaZJcopcRvlwxfQ+6ZRF1pGabaBTXVUEWEh1lBdNOpUzlYaEGMgspPCICITb08TYYD4t9jMq6zOEplwA92MVOiJGGSIrEJjcqNQ6Eqzpo1JBFkmxEAgq15w+5csqmf9bQlte5fcrKaFTR1ECqSorWX5E53x5dhWXNu7Hxv4ZHJm7lI3SnLdIR1xwqrjeJULMIVy7tZnzyw4llqdsJ2JZFaSQSW0MlXKiBiEUd999J1ZXb6DX6wvlbSabxc287ZFPQo0QlNU4f7FAYqIURZz7UJFo28l2lHLIJa0wB2/wsKB8DI8NwYkQ/EoZcCxLhov2Qdu3EoosALBQwWZg8HwXPvcw5Hvw4MMnHQCADw9jPggUZOjz/1bpwzw9ZMlhVm0WriUkvuQpf3FL6ww7JXvyRaR8MlwOnDM8/vi38aY3vT5FhLU++YWgVtHFYzEfQ3lz84nACRJ/hjL7V9eVGuWgJQ86yOV0JNhUH/eyl2s8yQwmubu9Rcy099AgQ3Cog2wBgOiLqUTedlV51f/cM5LgQtFvFSbRiSIsEFAFEc5EaxaUV6Vvbx5kEux5qX5k/sqIiC1NSG5EisUyCXlgSZ9TvsosfTxSZOnoN8IYiB/0m4SKcPwYYdFibWIiHfcnOlYNRFIr3g/KwGVRWeanyXC8MQnIVdhEWu7XhOpwUIlhf9W2CsixMiAeMBUCyakDCh9L3VWs7d6OI3OSq6Nh3JVp4nJNzhULKtI1lh906wBcnOr3c+U4UMc0MZUNICsquo8Mk7pIcPfdd+CFF89LW7Imw3kQFRtbdUj+mMpmoSBZYEqD/oQMOzGRiVRcuX5Z/csq4WkiEyjBDbi0BZcEH4c0QC3HIUWyLMZCBZ31QELiaHw80XbxuorrEYJ7RebdjI8x9PfQZ9vY55vo8x302TbGrA+PDwO/YkEhFtvX9fNgYLonRJhM44XaSCP4UDe+fiaFuq0i5Lh4mWLnIKvMAsAjX/4ajp84iuMnjsbbivrsB2CKj/p5VubeOKjrLfJBmuRTBL7v42d/9mdx1113odPp4J577sE//sf/GJzXb+xJoEpXk1sG0E+wM2qgG0yMxd+5F39ISuFxwYiLvd4i5rqb2fbET5VgXiESTJiX+RRpQ1aBSUxI/TQ59sYB+fPGIN44/j0hzyz+2B9ruL831pNgxgDPCz7jEeB76o8nfEajoI6wrySsP/moj0k8rnhbaj8GEtYbK9aRIhyZSxtMuokwxrqPyjQ9k95KXrQQt8lIRQCfwJS+Cgj3Wuqjw4T3mYq42/6WC8N9dnzhHPaHixiMZsq5aYiLeiGiVHPJYp9kMh19LHGQvsUH/X62xblz5/ATP/ETqff4z//8z2M0Gk2lvSoxwVunqIl0GmazQlU50dxVjWPHjoBSgqtXVgv3x9Tu5DCr2CrkTXpF02UWBseKlGLOIUTj1CmpTPoebKeEghIHFA4cNMDgG4mmPuKwvZpoCn4lk+B02eA4tSl6QCUyHKjmHhuAsxF8PozLeGwIxr1AFY7qk5R4m2OzCfqkKj8NkpOXuii6XuLfxfESIo1XhXLHmjYxtgmgpq9HjZ2dPZw7exFvfcsb8cd/LAfNyqsn71hki5Ry5tyJgnwA5JBRTOaDVKyPv/zLv4zf/M3fxL/5N/8GDzzwAL72ta/hx3/8x7GwsIC/9bf+Vvl+vMwQTNxLvtJFZSsnN6lIfsVJb4o0KshBbzgLQgk6rV5+f1ST6aLpjlRlBRKc/JaQ4LJ1i1GilX7BUTvMHLxKibxJtlSXbApNQjIcE1AukFFle8I7LgrFzGjwe/gR09Kk+6JRmgUT6owJatwvJyHK3AfcUNGNFFpRqbUaN+lZLbWbMVtHojgHfsBZ9U9O1RT7C+eRYRvl2LC9kIKsCPalrrQaE+rYakIYW+1zAciqxzn3XbvZQ7e9g+u7d+H2lSfNqqwhjdZEZPVmMpk+4PezLZ5++mkwxvDbv/3buPfee/HEE0/gYx/7GPb39/Grv/qrU2mzKpR8a8qTwTLkwGQumW8GmzXJBW6/4zacv3AlnixOmn+2OOwWB8rkho0QqMDFlNloPyBNbEQiGpkDM87AuA8Wpi9gYRqi6BPtlzVzFyf85uOzCVSW8okWSLDoyxr4riakXxVROkOGweCzIZhP4JORdOzjgCxHSnzmmil/DeX5EZc1/dVd4yp3hDxTddEsPJ1qK0K2rcn9mdP9NfvuT0qG9Xj88SfxF77nPcAf//eceopd20Fd2eOwN3s+JPPoA8QXv/hFfP/3fz++7/u+DwBw55134t//+3+Pr3zlK4fcs5cfZPPRrJISkGBOJVWJJ+qYrAQDAKiD/cESup0wSFb0e1SHaZKfMs2uTjEWUztlSLxq0hwTN0EVjMCl3yST6AwJFtVelfmmaGosb1P2yYIE+16iCAPqCTElSZ9F/1+KwIdXnDKGpFbOB6yEfC1JhDnIIxy80AljIMQHZwIJB0A8gLtI+SErTWCj36PgWhBMpFUkWDTlFsvHxynEz4iOQybDQP61aSLDFiStsDm1DSGu2p9YQRiNpDgHsa9w9zKubd+DM0tPglK1iXLq3AAlVW+7yNJ1uqQsPvCBD+ADH/hA/P/dd9+NZ555Br/5m7950xPhEjPHKgiliQSnv0fmvyLkdCoA4DgOzpw+gQvnL2e2qf4v1tesSWrWj3SKRDtW7oqbGQPIjJ+IiPxAMB/2+RAeG8KL/rIBfD4GY56WhARIK/YxqVJEzS18DII/a8o0XPJ5lfcJjl8+dx4YH8JnQ3h+PyDGfJiQYCBRgkuOed6x2CI9tuLfya7xhPAKH8F8PB2BXG+JkW/em70eVH3RH0NVJDB7DGK/n3jyaTSbDdx3392lW8i/N2TYXwcHs5AXIopKOcmnAB566CE8/PDDePbZZwEAjz/+OD7/+c/jgx/84DSOroYOIklIKaaaXKtU+MiTTmmiyBjF/mAe3dme/WQ+qtsGRSa93GDyLKm3qY/YnKgA5wXG0pFgwYw5MVsep8rHCqkJohm1SIK9cZoEx+bOPjAK/4ofxoTvXPjNSyvK4geCiXJ0LKJZtbBvqoxA1FNm2+HxxP2PTa1DpV0yI5dNnTPnB2nSG/RhlCLBaVNqpjiP6fOfOvfi/5GJvPxRXV/G82m67kqQ1oNOlWQwLRYXxpLy5nuXUwfLc5fh+U3sD5eNZVPnUjQXL2EWnjKdlvt8mKjo/byzs5P6DIfDyru6vb2N5WXzObsZcEA5Cmz8Gs3qUpDSRv9COHnqGAaDITY3t0v3Mh9ZdaaIEmVWsjTHJgTGkoMaAQCL1DtDkCddX0SlFBxgCKJDewxghIESGtfvs1GoJqvUYLvjLA9J3bYM5CSWlU27VWb28nFllXczoS9r5p4pr/VPzravi8IuHrd8XFGEcXEpP60CZ6+jyYiYShHN9lXsS56PfVWm5FE7vsfwwgvn8IY3vhbPP39OW940LqkgWMJ5qSKo10GCcwI+QTCOyHp7Z2cn9Xur1UKr1cqU//jHP46dnR286lWvguM48H0fv/iLv4gf/uEfLt2HGvlQmgpKqlVuwK1U9gK1SSKog/3+AhqNEZoNS38xeXJsUq6Imy1j5dur7qtVQCzAoAJnyVleFOd0Gyxl1qtTJzP9jH2DJX9gzhICHJFdnRoMhEqwH6rDSJtKU6aeqkgkNl0vTbZRqvZ7jky2Ix/hMH0S8cbgbiMYBw/gUQRraquYR20myq1yoSLahrQinArMJdwbKmVYRhJQKwooFeXY9hFFm+bSb8prL3NPqq9t4wJTjruDFvI+RUm14npVmVLnwaUMc511rO3dgbnORraAajEk6rJwnpKN+ueJyp84N0BWmYB2JVHV+/m2225L/f7zP//z+Ef/6B9N0LM0nn/+efz6r//6Ta8GA5UR4axCa+sDHMFmImyaAN9x+xlcuHDZWFY/IdWZ9BaZvOon+3mTdu3xyqbBQoRfQCDBgjlr0peg/zHpSfVF1x8Wq4GMeCmz4aAdtSJc5FhVUPmOqqJElyUU8j7cgsybolRPSsKKHofK5BtQEHfpnov9xCXCS0KTcnGGJZNgmYxn+1zU19VMhmUUJcNVEM3HHnsSf+kvfS8clypzCtuc9wyp1/rRF0e8YDZtN+GKfJBsX7S///u/j9/7vd/Dv/t3/w4PPPAAHnvsMfz0T/80Tp06hR/7sR8r34+XGSoJ2GKqg/n2Cq1Uz15vHt2Z3XQZlVm0qn4V0ZW32cBAjlPRoXP2UQZUYmm/YFkNTn6LlGAvRcgApMivfIsTxW8pc2iRBIvqbWwOLaq+PJkNp+pDuNavIsHhX8bU3hqi/7F8TD5D7GMskXwtonIRGRbTOFEnMJEWx0SRTicmq6Jvcli3Tu0Vx1qsX85fnKpfRagkchzsKxBiHRnWjoefHKcGqut3osjUVQeuk3yIU2Q4x/+fUwfL3cu4uPFAfGnIBDVjHh/9joqejTkI2jgA9b2i9/PFixcxPz8f/6xapAaChepf/uVfNlb51FNP4VWvelX8/+XLl/GBD3wAP/RDP4SPfexj5ft6QKjgSs+S4Ox2nZ9cGmUnja5LcOLEUXzrW0+lfldNlouRELvJe1rVVRN4ubyqDrG8TDooaaSCG8W5bjkDCwmqrOYlvrPqfstqYbBf5CMrl2ZKoioeXx4ByBt322BKuu15/qo6U+l4u0V/q1IipwUiRA+PyK0cUEwkw2kynSXBasiEOP96jurP7p+U0Zl6551PG+Qr+BTPPP08OOd4xX334KmnnsvdX9w3QfgsUN5z8vMhM5uEzfPmVoHti/bv/b2/h49//OP48Ic/DAB47Wtfi/Pnz+OXfumXaiI8JZgiSmvzCsvRoaGYvArpkqJJuM8o+sMZrCwJuYNlElzEBJpLqmxUl0mtUkWKzoN1OY3ZdLgtIr+5qYzEOhA+QmI11c+Q4VhZVZFg0R84MnP2/OAR4/P40cMZklAYlCcNyKSYcsB1gvpcN3nsM6Ei1fGIPsYM2e3iMUaKMLLlSFgPp04QRCtSh91GmMtYUFR1ynBcl7g44cfjGNcjjL9IgHVkGFDcT1L+4VS5KPWXQIbj3YQFGXVaMYksFyF4ZVVhcX/dJsP9m7rfVCqwJRlenLuBC+sUu8PjWOhoAuLK6n6VBDi6NixV7Jsd8/PzqfezDn/n7/wdfPSjHzWWufvuxKXsypUreO9734uHHnoIv/M7vzNpNw8EJfMIpye2+eSASX9tiAiLVavALNpPTYzFNo8dP4p+v4+dnT2hDcRlZWQn2FWSGzvVSOyHHokqKZJjcb+IBDM+RobIcEFF0vVFmVLGTi0Vj6VMGW39GVKUXAspdVNjymsTwClZULDLd1yWAJtIu+245C4gxKbzbmrBRPT7BTzFtRHVkL5GVObUaWSfAcUQ7Z+uQ3df2JxPE+zPHcW5sxfx6vvvSxFh3f4q//RgHmeTh/nmXVDhDBOlWODhirPti7bX64FKKpHjOGAHaG72kofRvDkdeCiaLCvzCIvIUVijunuDBTTdERruuFCXk8pyJvBFCfAUJrJy8KXk/5AEhz6umVRGqms8UkIhKp40JmPJsUn+wHmm0D4Hws1iyIQoMDSoiRSHYyaSYSSqbVBOJMM8MbVOOqxWg6Mh0D1y5GBWHgJ12G2Ae+MsGVZAXpxISE2abBvJMAQV2FR/iLiccP3GhFjVSYkUGwmxeCyphasD8nYsAJ21hTJHd6yOZ8tTCsy2NrGxf1JPhAsiN8/wTUh6q3o/2+Lo0aM4evRofkEESvB73/tevOlNb8Lv/u7vZt7rNyvK5lowbi0+YaUZ1SqCbNqrauP0qRO4fPlqbr9skZ54yyqNXgGbnPCpy8QKn8LMOTGJzvp1ysqu7M8o9z+1n9yPksS2LHnRLmBYm3mrzwmRLia1CXQ5kleZ36pWFQyuPfG6SYJ50TA3sBv+Dfy7GYLJZ1A6MW0Xrw0iDInoW5zqU+Z8VElSsoT4sJT3bz/1LL7ne96d+k3VH929FJQFYmU4hex1pV6UM6nGBwBePOBVZv8C+NCHPoRf/MVfxO23344HHngAjz76KH7t134Nf/Wv/tXyfXgZwpg+SUcSFCQ4Q4AzKXIiQqYyJc5OLPu9DmY6e/qO20zei6hZMeH1kPjxRmmNPGUftXWUQCY3reclqq0cHEsHFRlO9S8hnykSHAXF0qjA3ENIggU/Qx8ghAOUhyo04kdQ8GqJSHFIrF3RSoDG/Yj9jmOzZoEMq9TiaF8aNaQ5Ruk34obdiUiwSIYBpWIb/y78Vfkqx2Q4LJcxk479fvWkO6nLT5PheINrJsRiO4CWRE4Vk/oGW0JJhoHsMyEsMz97A6ubd2YtQyKYrAJSDUeLDMUiQN8UavABv59tcfnyZbznPe/BHXfcgV/91V/FjRs34m0nTpyYSptVYeK7ySaoTtZMUp7sJdtkJRAwT4oJITh56ji++MWvKraaJ5QqoqT2VZ5UAZP7lB6PPF9HDj9+YqYJq59LBlP1KJW1LLkyoZyKqR67YkqffC3YWiPk9U3X12w/TfWU6Yd8/ZkJl+J+kRYnCAnIsENcEOIEbl5h8LNgwcTLpJNSkW5Zma3CL9sOyXVSdUAsu7oYnn7qOXz/978fp0+fDBfXkjqyMEXM1lliZEmwqT8vB/z6r/86fvZnfxZ/82/+TVy/fh2nTp3CT/7kT+Lnfu7nDrtrtxyUaWQ05WQzz9TkVyLBmYl33oQw2o8DvUEXJ2cvKsoUnH7kTcZTKZAEEuwNEwLMPGR8N41mnRrT17gdKUBWHAjLT6Ide+N0BGfRlzZVl/S/igxnTHxZlgTHkaBDX+BIBfaQEOCQDCfjAMAngSocEVrKY7WYuAiIrQMEhJgnJtNSv+L/pSBZmXLi7xRBJy1UYXheQoYjshqRYUBNhixIcDAO+e+t1L2jg0lBFq5jEqvyah9ifR8UgbZuUuTlEdaSYQWWu9dwae1V6A1nMdPcShHZ+Lu8uBc3VILMFwjo9XLHZz7zGTz//PN4/vnncebMmdQ2rvPPvEkwERHWEzg9adRP/tOETPZz1WF5eRGUEKyvbZY7iBykyXCCySbqWTKc174q/6+ODNr7WadJVqTKi2TJVgk2k3nz5D/6zXEcNJsNNJtNtFotNJoNNFwXjuPAdR04rgPXdeE6DhyHBv87LigloJSCUApKCAghIJSAkvB3QkFo8Pu9996J7/med8P3wyiSnIPzYCWcM8BnPnyfwfd8+MwD8xk8bxz85vvwfR/jsQfP8zEejzEajzEeecH30RDjsSK4irUpvJ5wmUHj/NKURMpwNBH1E7N6Llx3gnNY+p5MylQRgEoP9WKYiJTyPWFf8sl98PtoNMalS1fx2te9KkWEs1CNne4cl+n74ZHgyaNSFtt3bm4On/jEJ/CJT3yidJs11EhNxHXBhKLvjno6EOUOTiEmFFmFlQsT+cFwBpRwNBsDEIZs/uGqIKrAAgjzEjXYGyJKdZQcXDDRzTN3zShBUj2xSXSUmkdFgiOzZW1k5UhJzR4eEUljfMwGEjxmiQIcEWA/UJK4GGyHE4AkxJdQBo6Q4EbEmHAwxsGpA9ZogqEJ322BoQmGBjhcMLhgcMBJ8JfRRvjdDcklBQcBpwTgJPgeSs+cU4BRcOpii9+J8/53ghIGwhmAgMwTBKbdBD4I46AcIJSDeBzEISAUIOPoO4PjcBB4cIgPSrzgw4dBi9EzOmNWLI2vdE3EkadDwmXrf2pSEeOFBpkQFyXDtrCxrJjG/SnDRIYNcF0PndYuNvZPYqa1l5BbqrmP83y5NdApxTLxjus+wMjRB/1+tsVHP/rRXF/imxWVXPF6RU2nimT3F8mwCDXBSto4cfIErl67rllxyE62q5xcl0ORm0VUQdVkUpc+pyxUprF546QjeI7joN1uod1uodNpo9VqotVuotMJ0qg0m82Q9DbQaDTgOA4AgvEoIJje2IPneRh7PnzPh+d5IREdYzQaYex58LxxGGODgzEGxjk444FvLOPx7wDQaDRx7OgRPP74t+EzHwQkWGghJPhOCRxK4ThuQLRDsk0pgUMdNJoNOJSi0Wyi2Qj63Gg00Gi6aLhBGEvOObyxh9FohMFwiOFwjOFgiOFwiMFghMGgj+FwhMFgiMFggOFwZHEd5pNGHQhxQLj+4a9Whe3u21Q9OVYNauiOK03GJ1twitqx79vzz53Fa177KkMJvRWJQtZRbstfRMzWf2DPKxZMTMvvX11XakyODBmOEE3ow++pSXI0GRcgTrpJVFestCXlxQl6b9hFp72XXdAWg2SlgulIKU5SHdBMlGUSLJlDRySY+MNEuRXGIPB/bYXHqDCplRGnaBIiRetI8GiYENWItAJIpS+iJFFHk5C4iCMtx8cpKatiZGiZBEeCuBdOmj2BAHMCzggYa8Bz2vBJCz5tw3ObYE4LntOC7zTBSBPMceGTJhhtBiSWAg4ZwSGjgFzCA+EeKHxQ4oNwH5R7cDEGJX5IaDkADoIwajaiqM08ILCEA5TCp230sIIj+DYcsMCCkwWGyRzRXxecUzDWACcNMDTAmAuOBhgPvjPSACMN+GgGpJy7YNxFFIiLYgwH4/A4xnCoF3x3vOB/x4fDR3DA4JBxauxlMpy6LGQF2oJ0JaQKscl0KTLMksBbueTYpIwWJcEW96iW5OrUVvl3ad/5mTXs7h8B8FxYf1oJTiE2tdYTYpn0RtsyZFhHtg8a9fu5ckxEhPU+cDaQTV3VECfqKlPm48eO4OzZCzntpGEMolRy4j29SWpW9TUFMioW6VhNRGyOhRCCdruJbncG3e4MZmY68ffOTAeddhuNRgOMMwz6AwwGQ/QHfQwGA/T7A2xt7WA0GmE0CkjtcBh8H4/HKR8G0/GYVGvVMbuui/39HlZXb8ALJyS5ZunWpsoMjkPRbAbkOCD9bbRbwUJAq93C/Pxc8HurhVa7iUajAd/3MegP0ev30dvvo9/vo9frY39/H71eMFaBymxS1ZNc0owzEIzjsWF8nLImSA5Ed441JmMGMjaZCbOdMjwZ9BYqKjz99PP4ru/6TrRaTQyHYt5TvaWDHuoFvggm03dAuu8PyG+aMzJhMI7prDjXKABFSp5sGVVQIITKrWSqCaQmvUE5XzFpFSakzMdg2MXC7Ea4jzTdkMmwiQTLv8kTbg0JDv6GrJD5IN4oPbl1m+GxD8GdVr4PoCp3sDdOzKN1JDgiqkBWOWJAKndvPD5Ipx6S941MrCP/3Mgc2gOYRzD2ZzHmXYzJLMaNLsakC4904dEOPNIGow2AcbhsCJcP4LABXN6HM+6jOdqBiyEoH8HhY1AM4Tp9UDIGdfwgcrOL5LEVrXTEjzMS+BJTCvjh/6IPcfSdk/DYCHzWRpPuo8uvw4n8zwmyZuSEhnbRbvCdUHDihNGsnUDZDdMsxaeNA4wRMO6AwYXPm/B5I/hL2vB4AyN/Fj7a8MYN+GiBcQcUDI4zhkuHcOkIrjOCSwPS7NIhGs4o8SyQSZaKnGmIX0CAkSbD4j425KsIGVahCAkuSJi5sEhWCIryCzNrWN28C4xRUBr4CisVXJV/OKBU9HXkllMneT7KzzuBGB+k73D9fq4eBe8WijJqkT0UpNUwSXZdB8sri/jyl7+R+r1Y4Krs5PYggkHZQGlGXGLsZXKfHh99fZRSzM52MT8/i7m5WczOdTHb7aLbnUG70wI4R68/QG+/h/1eH739HtbWN9Db76cUT1W/kyBP8vGSQmm0dMp1mQUNm/Kma8v3GXq9PoB+pj+qNlzXQafTwUx3Bt2ZTvB9poOlpYXw9w5c18VwOMTe3j729/rY29vH7u4udsO/o9EonBQwMOYBFODwYxWYw48ji6uQN86qCM42FgI2ZvT6AFHFUE6N1uPGjXX0en3ce+9dePLJZ8Jf9SQ434c8bzHO8lo/xCBiNW5R2OZtFcG9hAzLiCbYvhdOBCWyHJlGA/DRwGjcQru1n+xP3KCsjhRHZcrAoHSRUL1NpS0SIjHH+WB1baf8OiPSO0rq9EZqc2iRBIsEUEZEghkCs2QanrfIZJpnzyFjBGM2g5HfwYh1MSYzGDtdjOkMxs0ZgAGuN4Dr9YO/oz5a42243hCOP4DjjUDZKHbLJZTH5tEgPPyfBX8JBxgPOSgFAQvIQTQlBA+JO0JSHBLzeDhFJZsLhD5cAIiiUTd4uBIjEX4RNGzYFE062i+89gkAhzM44g4Eyb0RE2gn/j+Ir9GCx1vwWAtjdOD5LQzHXYzRgc+a8OHCIWM0nAEadIiGO4q/u40xKPdy3ftSRFdnlWEgwyp/YauI00UQBacqG709KiKrwwKxtDWPnmlug1If2/0VLHWTgEwmQmpLfLWgTpoMK/pfqt4aNwWm7AyQNW+0UxttUo9QHDm6gl6vH5KPNEG3I8M6s8yk7zLKTrpVx2Prc5vap7SJbJYMR783m00sLs5jfn4Oc3NdzM7NYn5uFjMzHfi+j93dPezu7mN3bw+rqzcC4rvfQ78/sHKCF5VbMcJx5Lsa+YQHEY4ZwBNLANN5nCRFk66Pcr3Kspzlvtxs+uN5wdju7fUy9Ud9ajYbmO3OYXaui7nZLmZnuzh2bAWzc1202y2MRmPs7OxiZ2cPuzu94DztBAp8VJcYWTw6Pnlhws7XdXLoF2RE2Lddhqjb4NLlq7jn3jtDIlycBNvDngQfFG5WH6Qa00Nk8plNFeOGE+2QKEaXO3EBBBPw2ExaBHUw6M+g4Y7gOn75CTltAMyQdilSfFPHIgbNMig1jNkpbam6k8BYYnAsEpsq+2YSbPQjlBYGmQ9QBg9NjOgSRpjFiMxhhFmMMYsxb4NyD02+gwbfRtPfRWe4isa4B2fUhzMaAz4B95zAHNpzAjWJBybSnIWmxghIMOc89EUOfYQ5C0ypI0IcxtIIrpWQDFOkHmHpKNMIjl1ISRSPe6wOhwsAHoJyDkPs2KxSwcWhkoViIIkmHZHajPqeNpXlqv1DckPho0nGaJK9UANKEynOAUaaGLN28PHbGHkz6I3m4PEOPNYEpT6a7gBNp4+G00cz/LgYBDFZTD77ool0DlLEVyBrlRLiCUlwXNSS8Ir3tai2Uwp0W9vYGRwLiLAqgnQcwyBHqTVZmcT9zfoFp+qOSHL+EVWC+v1cPSomwkVMdsX/zSRRPdlkOH7sCK6vrue2nYXNpDL7pM2fdJuPSYZu0m4mzeZJuYkIOI6LubkZLCwuYHFhPiC/C/PodNro7fexs7OL3d09XLl8Fc/s7mN3dw/9/sDYLxuIKYoIceHQFhzSgktbMSnz2BA+H8JnQwQOTnZkeBLIJtbx7wpFWibKqoWdvMUeW8VZbG80GmNjtIHNza1MWcclmJubxfz8HObn53DkyCLuued2zM7OwvM87OzuY3trB1tb29jc3MT29g7G46w5eZH+qY5NtXBV/JwprB+0ZullUOzaPfviBbz5za9HWaVaFZk9r3zRxbKpofZBuvUh+pumftebAMrbjOqGPGlURJfuD7tot4eTTcJNJFjRH3WEWi8xcQz/imMjH6dysht9V/gFB8RXigwtRoi2IsHB5hGZxxBLGPF5DOkihpiHTzto8H00+R6a2MUsv4QmC4ivw3pBlOcxD4ZgFATF4iMX3KcB+fUdgBGw8C9nBOA0ZSLJ/VARdvyEELsIgmexZPGUkIBQEzckw0KE6WDsANCQEEdRpqMcxCnzaPHAeVCGUsBlQaopyoKPDDEokYkMRwNqGuzQ5zciMpn9DYgEZUJGcOgIbboTzKaFa4pxByPeDQiyP4PBaBY7/lF4fguU+AkxbgzQcntoNoegE5pIyz7DhVAkRZktLIhmXmRm+R7k1MFsewOb+yfVO9iaKcvHqkvJpOnnQZtEx6jfz5XjgLNv26g/elNlmegdPbqE5547m6mjusljUTPwyUw8dYhUVELC6MDieChSKHHOQCnB4uIilpcXsbQU/J1fmIPv+dja2sbW9g4uXb6K7SefCQmSJ7SVxaTKV0SCXdpBg3bQdhbQJDMgoPD5GCPaw8jfwwiA5yfHoyLD0zYRzVWExWtC8dacFnEHkDr3vsexubmNzc3tVBmHupibm8XCwhwWFuZx8uQR3H//vWi2mtjb3cPmVrDP1uY2Nje3MBrZTTbVi1lZq4+bDzb9Si9iPfPsC/jAB96LdruNwWCgKJte9FAvguSR4eR6tuvvIZDiGrcuLEyiTSQwBabIHZwJFpROuTQYzWJxfj0pYDvJtp2QK9RgY7WCv59oAhuYxTpI5VDOAUkR4kQRjskwZ4IaLJDgkAgyUIzIAgZ0GQOyhCFdwojMg8BDi2+jyXcwO76IFb6NJtlNfGalelL5gT0EJNhzUiSYe0FkZu7RmADzOGhW+AyiDJxxEEZAHMFXmZKA9MZkGIES7DnBPtElJuUk5jQwlSYuTzzqfOFlKT/iOJJx8vyQCEd9iMyXhaBiKrBokRe5ZIhHarFgOh2ZUxPBXFquPxVZWhVkSeiDAx9t6gckuZXUxTgNyXEHI38GveECNvun4TMXTXeIRjMkxo0Rms1hcO4rTpVUqVKcchsw+CfnEU1IarEcSE/AQncNVzfvhcco3HDBRCalqgBY4r0u/m5NaHNIe2E3lBo3BUrcBfkmwyLygsGkoTNVzqpNlFIsLi5gbW3Dol4Z0yGsSd1ZqIhCXiCohJAlpsSUuKnJN+MeGMbodjtYWVnE8kpAfJeWFuF7PjY3t7GxuYVvf/t5bGxshGbkxYitqax9gKngGFzaQttZwAxZQofPwOUuPOKhR3bBKYPHh+FYWXfPus/KvuYGMYoLBmVl5TN8V1cVtTtuP1qB15Cl+P8o5ohwHD7zsLW9ha3trVR97XYbS0sLWFpexJGVFdx7712Y7Xaxs7uL9fVNrK9tYH19A9vbuxmTdzPBA6IFq7zrypYw69Js5S0w2EY4N/voUmxu7KDXH+COO0/jmadfUNSUfi6V8fkthgM0ja6DcdyS4NEkD/pJYOp/4bfU7xEhpFHwIWESLkBUX2USzBjFyGui1erHdQbb9VGp0wcjTIDzItPq1OAIoULGXQS+vHGfhfGwNPsU1WClSbQYxZkFfrWcMYzJHPpYxsBdxpAuYUgWQOGh5W+izTbR9b6NNt+Ay/tpE0uVkRkPyG/8OmIIokJHZtCSEiySYObRmABzntznJHyRUccHZwTUjRrzAc8JFV8/IcOcB/vS5D1BQkLCKQBOQFiQwSEgxIphzawL8sDHeMyAiIxT8Tkrmo4LP8WbaaLAy9G2U+3QJB1VpLpSljKn5nI6HJEwy6beqnsuGhNEBDnZRuGjRcdo0V2gEZYnDjy0MfRnMPTnMBjPYqc/C4814bojtBs9tFoDtBp9NN2BIgq7ITOEdJ8R5mW+x2Wia111zylPYrau0sG6RKR8h7PKa7uxB0oY9gdLWJhZV9WgTaWk3W6Ajfob1FcwGFgJ1O/n6lHwarUjkEWCHSUoVn5xaQGj8Qj7+738wkrYqC5plVWFor7P0T62iqGoglLigpIGHOpgcXEOSyvzWF6Zw/LKPBoNio2NDaytr+PZZ1/AxsYm9vf6BRciikHru6sh/VH/XbTQ4m10WRcOKMbcg089jMgMRmQPXqh6qxcFpovAj9lNHVvsE8xD4qsJYib2UXVdFCWKWRKsMkuOOxD3QwUOhsFggKtXB7h2LQkw0Wo1sby8hJWVJdx222k8+OBrAAAbm1tYX9vAjRsbWFtbhxdFPFUQczH1me0xTgNVk9HV1Ru4844zGiJsbiMbvdxs+aIvd/CofZBufViZ7WlIsGp7hHgSLflayhiO20FkXcfko6tJmyRDpSIVjD4bjAcA6ifEJGdirDKLjr5n1GDBJJozjgFbQB9LGNAl9J0jYHDR5htoextY8p9Fi22gwfbi/LHBMYV/xUcDl7aL5JcBUX5g7oWkV2EOzT0aKMOMBH8jAhySYSA0jSYcPiOgDgPjJFCHGQFxGSh8cDiQ/YYRmVQD4D6FmJMYlAEuTxHiFKRHHafhsXnhgYfnK0WIVemkOMs+NuXo2iJixZcGpBhIEWIAiSoMpMkxBDIc15+9xsXXsrzwEpcRiR730aADuM0xunQv/t1jbQy9GQy8Oez35rDhB+bArUYfrUYP7eY+2o0eKCZTijPkVXXPyWRYQYJt20pBYVkiq8Ly2FEKtJt72DMQ4fQOikXAAjgUE2gN6vdz9Zhg2SZNGOOgR5JJqxnFJn3iJHtleQkbG1uGfpWdUJbbzxxEy6QeZbfHdYZjSCnBypElnDh+HMePHcXy8iIYY1hf38T16zfwzLMvYG3tOnx/BB6v6FHj+BcxZa2SuER9ovFfAoqkr3FALZ7dxwSReJb1tYxIsBjIK6o7CuYVRMvUj5uKEMtkWOyvqUwauv5HxDTuQNwP+dhUdQ+HI1y9uoqrV1fD7YHf8crKEo4cXcYb3/RazMx0sLmxjRs31nH9xgbW17bgeR5IFIOTRMcBiK4MxZB/3+r8k/V1TYYrl6/h9JmTUM9Q1bA/9mRR8aYzKa99kF76MERQjdVgpNWkDAmOvkuKMwAMR220GqFLgawGqyBHn54UCsVOzAEqBgUzmkRzT+kbLKrBfOxh4M2jP15Ajy9iwOZBMUab3UDHX8OS/wxabBOUR+mTEBNa5fqXae06Un/F3MB+aO4sB8XyFX+j7YIaHE2MCeGAHyqhlIEyBsIIKGdgosk05aFPMQMIj4NtBX0Oo0z7EdlOE+LM8AqTcu5TwBf4lctD0h0dO0fy3AwHzAuDaslTKF3UaUqDlFRR1Ok41ZKgEkO2mmAxYc74EhtcCdJ5uqkxojGQkGaxXpcO4LY8dFs7cYCukdfGcDyDgTePte3T8FgD7UYP7dYA7eY+Wu0hKM2OdSWwjAxddt8U4gUrtYn0TGsH+8NF/X7i/wqoAmDlmm/rCHGeyXSVqN/PlaPQW4dzromWmzyF5Ii0ySS/zOirCQ3nDMvLi9hY31T0I9unSVBmkqo3v7Sva2FhHsePH8Xx48dw/NgxcEawtraNy5dW8c3HnsPuzn4cZIpxDywnEIcKNv7AVZDgyIeZcQ+Mj+FjjCEGcIkLDy58eBiSAXw+hm9I9WPfVvFyEQGnpAFKXVDiwiGB3RLjDIyPQeGCMQ+c+FI9LP4uXv9FFiLMlgU250AkuiytJBQA5zyMQr2Ls2cvgHOGmZkOjh07imPHjuCNb3gNut0ZbG3tYnV1Hdeu3sD6xmaQuinux832pBWeTwWI6oWLl/GGN74m9Zu+/ixuJpW3Ro0UUpFlfXulJJrs5Zg2D0cdtNqyb30OqiLBGsSqMPfUCrgAYxoWDoz8LvrDGfRHC+iP50D9IWbYdcx753Hcu46Gtw3i+cGtrzJlFtRcyKaKkX+tqK4iLC8T4MgfmFHEkaBlf+BwO/ODCTTzowW4xEQ6Pj7CwTkJnpOMBSbT3E/U4SjFEmFBsKzw/5h4iubSUaRpkRCHxxY/ElPHHoag8kiipLrh2EVqcESGGdJm0yrfYcbSf1OBtkICzIR9hQBzRCiXiSxtkZs7IXHRIkzOOyAkyroo0tH/BEDb2UerMcA8NgAAY9bBYNQNiPHOSXibDbSaI7Rb+5jp9NFq9jXz9uqQRJcvcQ8LC1VF0G1vYWv/mNAHwQrGQH7lVGlVqr11+qRbExO/eZIJPQ3CwfN8JXjSaMuEUKysLOPCxSul+myDIqpiitAolfBE/VEjSJNz/PhRnDx5DCdOHIPj0kCBW13Dk088h53tPhzaDIha2A8WmQ6LfdVO9Msr3Xl+wNpxEVVwBGTS40MM2R4odcAoA+UUPhljwPcwZHtgfCwEAEuQ10Ye6cztH0lMoilx4ZJWONYOGPfB0QLjYzASWTwED89gEcIL//dyroWy1gqm8irTaxq/+PIWBnTjLP7e6/Vx7twFnD9/CQDFzMwMjh07iuPHj+Khd7wJlBJcv76Oa9cCZXl/f6/IwYXI9qPqBRlz29lzc+7sBcx0OlhaXsSm0voku79pvLPHIz8XTOfq4Mh07YP0MoGKDEc5hMMi2jOpyBssYuDNYK6xY9+XSUiwrOLEJsyCgl3QlBpAogYDYB5Bb7iE3mge/eEcOOOY4WuYIddx1PkWmv46CBsF7cRBspAEs4rIr6jmhiQ2o+5QBuIw8Cinr9glmQB7TiYadFCOKFVgFpFmREQ4rQoD4buQcnDug4ZlKGMgPgVxItPhIN+vSIy1pFgkxMIxRf2IybATEFPOSTxmhCG8CCM1OPkT1E0AhBGnxbFS5WuOSZIQdEtFiCVSTRgDdxtCPYKJdLaVpK2QDNs8DSPVOM5rDSRkTuEvL6qZDdpHY2aMOWwB1IHnN9Abz6M/7GJ1bQmcUHRaPXTae+i0emi42eCYVQTNsq3DJn1SXMagCs91NjD2Whh5TTSdyNc53yUkGbvoB0X0aMBS/T74yNH1+7l6THT1q6L46lKpyFATrCyxU/lHOg7B7GwXW6loueq65D7YTKiLpi/KqHtQpf3J1jk/P4uTJ0/g1KnjWDmyhJ3tXVy9uoovfvGrWFtfA2M8VioJccHCG5MLk27GIxKWVTjzjs02dVNeGWMO2vA7gxcEjmQjjBEEUBnTASgCojlmfYxZDz4LzLtN5seTQqUGR4iUYEoacMIPCxVgzltB+XghwoPPx/DIEB7rhz1OUj+V7VveeUmT/6i8fN8l23T3n659c9+AXq+H8+cv4sL5KyCEYmFhFsdPHMVtt53Cg2+4H/t7+7hydRWXL10Jg9mRTD2APblVL5rZqum2yB73cDjC7u4eTp8+kUOEo/11toymeyZ/oSJ9XNMnxLUP0q0P68lZXhRaebuurPC7hyZ8r4Fmc5hvFl1x1NoIKhJsGpNMDlcOjLwO+oNZ9IbzGI5m0KR76NI1LLbPo8M3QMeDMG3SMHjPRRGPGcuSYC80/Y0I8FhIbxSquCRcZCUui82OiRuSehopq6HKO3Zj/185EjSAVDAs7jsxAY5/i+7RmAinlWFKfTCfgjoM1KHgDgNxfBCfg5CIzDoA4aAuAzeRYhUhjs+d8N2nMQkGCVVkhmDfSFVOwQ8euSIZzgMlWTU5GAi12XR0fXjjmAwTxqRgWJp2BTIctK03n43Ir+ovICiNEilWWXO4zhjzzjrmZraD63jcRn/YxV5vAetbJ9BwR+i099Bt76HV7AOO+R5UkdZKok0XbFNG0x3BdUbYHy6i2R3FY6Mkp/L/kdm66dmnWWCTEbd3UMGy6vdz5aj4ahYn5glU+Vp1+WFtiNj8/DzG4zEGg6Hwq1rVEaGfKNsqdVTR36jurGmsCkeOLOPMmZM4dfoE2q0Wrl9fw4ULl/DII9+QIjoTEIFARD6qQELcIt/VFLmVFMJ0X6slZ0XLROUYH8PnFDw0F2J8HOQRZiMwPowJP2BWeouUyfRDZXaPyH+MgpIgYBcBhQMKGgbwosSBE942PvEw5gMMsReo2H7kP1yM9Miqtm5hQY5azRG+tDkE06fstTypmmpS3Tln2NrawebWJp5+GnAcimPHl3H61Ak89NCbAQJcvXodly9fw+q1G/C8cWrfYlGgcxYIUuMsly1nsr2+sYWTJ47hiW89rWxbTVLtnidF7pkDBVeoVIX2r64rNewRBHHSTHJMwWJEsiuqI6rJaMq3WD19GI+bcBwPjlPwGsqLDm0D2X9ZJsGqiNNRu76PwXgW+8NF7A/nwRhFp7mDudYaTsxsoMH3AnPhUR/E85D4C7OECIeRoiNzaJEE81EQ0ZmN3IDIjt2YqMZDQDkwDogkcXyQsQsxMjNEpdd3wKJAWKKvbXQNhGbQzE/U4CiKNJBMiEVVmEXRo0lAgsH8kGSHijDhQd9Cs23i+uB+QIyJE/RbRYojdZuH+6fV27A/Tmgm7jvg1AuDbIWqcGoMhPPnC9uI5sGTeoWIkahpeO2nSa/u8R37+4qqLQxWE1A8ClVkSlCOM37Hgq9xql7JvDd2VUhZeXggAFrNAVrNARaxDoYG+sMZ9AZzuLZ5Owg4Ztr7mOnsod3qZXyLdYTU2gxaE23aRhW2QavRR284h6Xu9RRxTZFh1ZiLzzyhj6qo+zdToCwA9ft5CpiICKsncuUmnJm6U2al6TYWFuawvb2r2Evfbj4Jjr7r1OAkj296Y1qRypqGcxw9egRnbjuFM6dPglKKS5ev4Btf/yauX1+D7/uZvqn8RwOiDfhheiFxG8Ayk+W8xQjdQkTSawsTd6FMLqEJlm5D/9tG2L4PDgafj2P/YS5GECH5ddsG0opNfi2eAhwMjIepIkhwjAQUDmmgwVtooAkKGvo2u+A0IPeMemD+OFOXjkDm9VmuB/E5FB7ScSTr8H/prVyNWpquL7oW5RkA5wye5+HK5Wu4fOkKCCFYWVnCmTOn8ODrH0D7bW1cu7qKixcv48qVa/A833gNlumzmQyb91O1e+PGGo4eO4pizzX1M8TOMsbcjpzWqkYNJURfyBA8zJkbbNcHk8mQ5VQEVz9rDi2R49G4hWazguBXeSaKijQw8vfgf19bB+dAf9TB/nAlDrzTbW3h6NxFtJt7oBiDeKOgDk+YFEf5YkUyHOXBDddCZSVYJsEsUoWlSW2kBlPXScyPo/6G5s9RGiQWBsKCYO4clYt8gJlPwX0Kxpy0GhwNo8JXOOCePhwapkHy/ZjoAoiJMPVpQtp9Dur4QGhaHRxLaELtsgwpTh80B+dBsC+wwHQ8pQpnjcyy647giWdYegoWthG+KFkS9CtQkzXPXPlnz0u4FmUgbiPOPaxUo6mT+BoL3chAsrqICHcc0E2TqinlykCdNBlO9SFJkUQxRrezh25nDyvkBgbDDnqDWaxvHYPPHHRa++jO7GGmvVddwC2bBa2SZLPV2MfAm09+MAS7yuZ89tNB8yz6pwywVeOWxwRvqWCiqZ7Qp1WSMpF8TQRnfn4O29s79l0tiLyIyql+h0+sKGJuhKNHV3D77Wdw5sxJgBBcvnQVj3zlG7hxfR1MXt1TkAF1MCWFGbaVGXdJJdiCDKuQ6mOoqlLahENbaNIuGnQmMDmGHwb7St5sZpNiM0lQ+bfGxyIo53IbsSk7EmIeq7NgoOGELNKGXe7CgQsKCg8eHDRAiBObsau4SoqMG0yzjSAU4Cy7GMNZYmGmeXepIlPb+GGbfP6z45w2A+acY319C+vrW3j88W9jfn4Op8+cwKvvfwXe/OY34MqVa7hw4TKuXbthEeytDJGffEFudXUNd991h9CH8kp7/piL9y3T7jNt1D5Itzh095LBxDnKpWtKIRSoVdKUQTEhHI9baDRGRXocNqDJVZoXyTWCQg1WKTucA4PxHHaHy9gfL4OAo9vawvGFc2g39tKLiYrnKRGDMGU+iRocfAR/4DCtEQvV3OA+S+fzBRDn9I2U1vDHsD/pfaJ6UsMolAlIcGAa7bNAHZYhk2fxd0YCchsR4lgJDs24feqAOgyEuEEOYifKySuUpQzET1TulPm02A8nim5NwQkFfB6qpDw9lYmnC9nnDI8DdSH+m0yHeDhfi1aOo8VSUR2WA5cJ7QIJGQYCs/gw93DmMlHkGiaK+1JnVi2S4aCctIAlmfbG3+UgdgbXB0KATruPTruP5YX/P3t/GiNLcp0Hw8+JyKyl19t3n30fcriTIsVNFq1P+iSZr0ADtuQflgEJBmh4lSzJskz/sS3YNGzAkmEDtizIEOw/hgxTsgW8oD/KWrhpRFIUNaSGQ85wFs6dmbvf3qq7uiozzvl+RERm5FpZ1d33cq7vuejb1VWZkZFZucQTz3OecxVJ0sPeeBVbO6dwbfMclgYjrAy2MBzsQTWx7ccQ8zDFw/4IN3bvLjXQ8X4xZ5TvJbcKDN95Ph99zAWEqWI9V8e6lN8L5NIdrqX6gV+xzfX1FVy8eAVAM7O2SBmTZjbYvXby2Kbtrays4MEH78b9998LHSlcuPAannzyy7h27UaByWkf3OZ98cuVXbjboolFP0w07nPtNorft89vjtQQfb2GJbWBJaxBI4KAsa93sU+bzk26rc8tkyYzJhEaQzh7SmbO1pwCCkiEoSiFckA+UgMAgCYLgiEAE9sfMRm7XQvCg5zxRcOD8bxUUTOoD2N+E7HqsawCXK4wz11iZ2cXO1/fxTNffw5ra6u477678I53vgX9Xg+vvHoRL3/7VVy5cg0i0iA7nj2xM/u6r5MyNx+Li69dxYmN9dL6i3+Pi7DcmdLEfxfHPCYRAQ6Xg3SEnbkT80UDCPaDtjrpXwEEqxnS6A4xTXpYWd6v6VsNS1weuB8SDNcOot1603SA3ckpjA5OQUBY6d/A+dXnMIhGgA4HtTkTXZFXZsxQAH5DWTQQsMF5jV9xrKykqgDgSLFlc91tIRzkCjcPtMu5vmVWOQTBJtUwRoNLjtEeqGZtiAIHfVMsWa4waw5yg5EBIyKx+cPEYG2ZYKVNAIJdTnFk3yOdM8j+c9ugWMm2uLxp8fteNZvK8oiBKhgOgDCRlWeLcn+Hy3AJDGcguHR/z0y1grfT1Mq8AwMtCgCnlN2pgQKAzfajBgSH5Zay9QKZbwEQ10h77Wf1YLhNzkwE9HpT9HrXcWLtOqZJH/t7S7ixfQ5mU2F5uIuVpR0MevvZs7+pvfAa7CSf7soG1xhmLfV2cDF5pJGU7xzuGN5KgNs17jyfjz6OIEe47sHbDIa7gIH25Rirayv45jefnwNY1A9+m95rY1nLA/ReL8Z9D9yHBx64F+trK3jt4iV85Stfw6VLV8B1zoWHibb9rTUeqzKA8zC8Tcs2O/rWgShlmWC9ghV9Gif5HNZkGTEUpjDYoQGYDFJ1kBtlzXGNLwQsfD9rjhlLAmEDggaTvekqimAkBStrzsHEWWmlqewjlUkjCF5kH6rHNpfll2XxLAm8NL6OTZ+P1VcN/fF9KYLhcv+7MMw+dnZ28fTTu3j66Wdx8uQJ3Hf/PXjve98JZsGLL34bL774Mvb29uf6fuc/F2YD60uXL0MrhVOnNnC9Uq7tqKOi9cvieF2z78T/bdE62JunJmYN45SkPcTxVmm5Fql0+bMmMNw1gtzg1ETYOziJ0fgEpmaI5d4WTq9+G0t6MwNroHx/s9zL2nY9E1yXc5iD4EwaLeXavsE1TAwVWeBLmjOjK0Yuda4YW5VCSnLo/H0niw5AsP0dgUvthWA4B8TkdlfcRLZAGQuK/ToewCrFIGYoEussTQIm7VjfPJ+YUsccZ2BZZSw3KbscKzdZkEQQ/x0wAZFBNU9a5Z8DgFDOmvtlIwOIZbKhBBK1gWEgQ7sMVM208o+zY5cmgFIWEHsG2DG/GcitA8Tla682b5+L0uiGUmeNwG1GeTO7boOEGMAgSjFY38PG2lVMkgH29tdw5cbdUCRYWdrGytJ2rfv0UeT9VvKHW+5Hy/EWmDVSHqCniuXaujo61x1T20CphviduG3jSM2yivmI7WB4PpMcIJcKEpaWhhiNupZomS3r7GxYQ5YVFmGcOXMSDz/yAO659yyuX9/EC8+/hAsXXkOSLCAJmzc6G1fVg+FOm5gzv7LJDMoaTEXoqxUsyzpOyApORj3EijBlAaWECR1gokaYqj2wBA7kFXOyKgib51yyYJKytitsapb/yhDn/gwABra+MEtijb3UJAPCqUyQ8oGTeCf1x6ATK9vENOa1jTX1Eal+1ne/TcNTa6R2KLft8BjpArgl0q6kVREM++gGgJtB540bW7hxYwtfferrOH/+DB566H78hQ9/P65dvY6XXnoFr7xyEcZUndHbt9cUdSqWZpbXpIz9/QOcOn3UQLj+/lZ9fQsA8CGlV3WSxTtxkyJko7pGyAYX5NORlWe2DQJLg3BmguEeIj9InmdgPGvw3pEVFgEOJivY3d/A3sEqBvEIa8MrWO5tQaPm+RxKHms+o+AnN8jyf3MAkBkQydhgpDbf1YPh7LpQHkhyZm7mZc72TqQtyVxyes76WM4Zrux/7hQdguDUl1AqAWdFkr1XdqQlskBWkYBIF99TFhwTiQW5RqB17nmSgWBiQElBQs0qcowtZywyKztZwEkEMjGUEMAMcXnIBTOsktFXZh6kGDB22wLLuItng9MGMMxeel1WHdU8s8qPijr2ty1C1riDG3sueZ7h7h4u2xCt7GyDZwARMOgdYNA7wMn1K9g/WMHu/jq2Lj+MQX8fKysjLA1H9vw4AhBc7mtbm8QGkQa0SjGeLqMXtdctn8n0HnYC7mbFnefzkceRfevd3VubQG63Ad9wOABEMoflo4g2Q6EQaMVxhIceug8PP/wAev0eXnrxZfzvTz6N0WgP88olm0HpbNa31nzJg8RAhlsHTtvAcNc87nmYT4IGQUMjRl8GGJDGUkToKYI2QN9oxNKDdgZacMZU1aiC4LA/dS7LjX0KjmMdGAaqEmOAIexziNNCLWcjE1tLWNICM9v2PdXXFy5+X3Y23hqM9dQyenoFPSxltY2nah8JjzHFKJvIzlwTW/rQdlxCEFw8ptXBcB3b3A5K278XEcFrr13Ca69dwmDQxwMP3IsnnngM73zXW/HSiy/jW996yV1rfvn6iYPytroZhtWBYXtf2tndxelTG3i2IUVhcSb6qJY72rClVBbf9h1Dr1sctbVQTcaQdJL+lViY2nVq3ktNBCKGVsfEoIRg2Eklia2DMyeC3b0N7O6fALPG6uAq7jt5AT3lZNpNpWtCNrjOLbYAfgPQW5cfnMIZZNWXSQJQzI/16lYmiBIwIhCrLPuBs5JCYe5uy/XlXKF9TnAIgg0rMFevbb/HXhZdV54lBL9ElvnViqGUgtYMZoZSDGaVLQtYIKrc3+yAc55XbKXWSrM9PjEgqYKZxNaN2pWUKpSTKucVlwf1rB0IduMgJtsWAjCsHN5hWDCskLPDoUS6SS4duqEzV66DguS5BhwXP28xqnPX8LHLdetKDTW4wy8tH2Bp+QCpuYbR3hq2dk7h+uZZrC5vY23peoEl7lxiqYXx7VJPONYTTJIlANfnbr+6wbR2wi1kl2+1dPrO8/no4xBAuF1W2LVOZh6zlxNhLC0NsD8+gIgsLBdsA0p1n21srOPxxx/BvffehRs3tvH009/EK6+8mhn8HNaZt9G5WapgJpTKhstRII+tr2Nc3F6dxLVLFCY8ArDVBKK9wzF7IyoIEkuuI2HACCNVKRjVG1Wbo3W2jDe16rQfCn4+uE0qnrUvnPdfbB6UCIMpLTCmli2tguA2yXN9FI+tdavW0NTHQK9jiTawzCvQEiGhKca0j321iSw/mQ2AbhL4WUAuBMPiDLpmsc2HAcHlODiY4NlnX8Szz76IU6c28OhjD+GHf/j7cPnKNTz33Au4+NqlmrWqCpQw6iboihND9WB4Z2cHGy5PePZ3OFtu3T2K/ey2/aPYbHHgvdD6d+Kmhygd1LSsuRZrSrE0DurqlnGMsZSZ42y5COk0RqRT1HoIFJymj2gOXlJMpgNs765jf38Fg3iEjZVLWIlvgOBZaTQOhFvLpAROsTkbbOrzg1MDGMsGZ8A3ywsu1grOSguFzsmptmMaFUiVOQfBvs4vkVjGk+omhHODLJ8T7EFwajTSNMpALgtVDJDYfSag7HVeTilghmFBcBQZm0PsQLDWViadG2oJAB0wwwKtLVD2ecU5U2yNrIQVzDQCpX2QNlAR23JSgeu0lz6Hx89LpUkxxLj7l7K5yZLqDAxDyI6VUqCQN4ySkVYFBKv81i4diZvyNVhmgNtKmh02GlzdK9EEEGcw0JE2OLG2ifXVTUymQ+yMTuCVyw9j2N/D2soNDIbToroiZFqzEkeBBLqprFuH6EUHmKRLxTcdmC3W+C212bR/ZdWJA9K3GgBncef5fOSx4NOoecA5qzzP4ZgUYHl5Cftz5g7m0TyILA/8AeDuu8/jDW94BCdPbuDFly7gd37n09jZsZLsMiAMwWBnqfWc4Y2nFMWOvfOsZOpkuam7n3MtW7fI9pr6LDXfeSWckzZLgkTG2KMRdngAlRBiIkyEsYMx9mkXiRkX6gfXNleTB9tYnqh1/+tA0OywJYxSMBiQHPCU+1UEwfMBo3JbSkWI1RBDWsMJPokVGSICYeJY6VRNMKX9zmWkmoPhSyKF127OkLc/lI4zh/X69U1cv76JpwZP45FHHsR3v+cdSJIU3/rWi3jhhW/DmO4TW/OqBwBge2sX6ydW57xmm773bhMUZYfvutd34k7URSt7MUtiWXBe7i519OxPaga5LLopZoHgDvJEEWB/r4+d3TOYTmOsDm/gnjPfQk+NHUtsgU0GYLtEzXK5/Dkom1SWRfvawQzHCBMksWwwT6Ois7MiAKbCbNq285zgLDc4AMHClN0+RHQhvxew7KhhK6cOmWAPglNXT9gDXdsd20YIfI2XYwuBkbPDRAJNDK3sj2GFSCtIlEK55X1OsQ+lcuBOJDljnHoZdQ6KxZWDMkkMIoAoAmcmWyYv01SqUVw4LxAAYgaQWhdr2xlXy9iZk5Hm3Egrw7sNztIKFZVFeI5UjK86OjcdFlzNvX7L8mGZpa5BBAz6Ywz6Y6TrEXb31nF1616oHcbq8hZWl7ehdPM2W+sJZwC0veZwHI0xTYc1nSuBYd/mHFEB0uUo3zO+U8DynZgrFp6WLTIUqjZ3sByHBcHemXmvIIsO25lTalzTlyjSuP+B+/H4448giiI89+wL+Pznv4jpNKmXCQeSZKB5AgCoPz6z2OBwOQ+CtbJSYkUxRAyMpDCiYPFAEQx75+au0bnfM5hUANa/AgzDU0zMCKPoOqCAA3GsJqYY6W3s8yYS3m/MsW3qg+9HmS1rlonX729dOSMBA6RANeMV7zBdt8/F/O3m414F6gGgFnbt5PnVa7yBDVnGWhSDAOwbjUQMRjWXcNdzKny/DRy2Hfvjiuo+KBwcTPD009/EM888i7vvOY/HH38Yb37zG/D88y/huedexMFBjXnHXJMd9cttbm7h/gfu6dDPuuPR5RjVsTvFcls3EwDXSSPnXf9O3NpoHSCXB3aSAqgpvRKGe788SC7/naYxoqhm0OqXC5dfIB+PmbC7u4qdnRWAGWsrN3Du5HVEPEahZJKkFYa3bnKgyVAnrBfsXzfKolMDN0eaM8CpBk99reDwWAcPlKBUj1/O1xdml+NbAMFAQQosyKXMvhawd4Zm1khTjSSNMhBsxwiq9vrM2GAht5z9zVIHhAWxMuDIgmoRQhTlzLDdNbufxnhZtTPdYgtgvYzam21pxRDYvqfT2IJqJVBpnoMMJ6P2TLGE7LpnyJmcNNrPGLBj0JQFxEoBmhsBMbJHrwPEQACCq7Llxr9vQSwEqgPZ8DwAuC6iKMXG+nWsr93A/v4KdkYb2No9g7WVbaytbEFrP0YqSo9nOU83hb92+9E+tvfP1S8UgGHb7vwpG7VGWj4CtvhmGWrdeT4ffRxanzSXAdOcDFxdDIYDjPd9Unx1sDwL+DYZ+8RxhEcfewhvePwRjPb28PWnv4kLF17NnJ/LA9Gj2JeuQRkosiA4oj4iNYCiCCIMJYl9EBPDBCDtKKIRBNWZPEmJHRYGw7l3ssJeeh2pnmCPhlDQMEgwMSNMzQgpjzN5cZewADjK8lotY+n2PehHka1n5LO81f0rAA8vuQ7uGY3AryVXtdt50nyMI+pjSOtY4xVsxDHW4rxO8W6qs5rC7KXZh5Lp56wwAIR5wf74zTOZk6+32LJ5FNdhFlx4+VVcePlVnD59Em984jH8P//PD+DFF1/GN7/5Ivb2akq3VKKjvA3A9vYuloY1M84tfQTKOdbhvrUrU6rLLp7KsEjcqVN4m8Usl9o6QFo3EJ010KYIxmjEcTAh1TS4ngWCVV6aBpzAMGFnZw07O8uIowQb6zewPNgGIbXuvU0RyJuzTTfJHNtyCdtk0cyAkZwNds7HHtSGQJi0ATHZHy+DNhqcKnBqlzdJBONqDjc5R4eGV2HNYCuHtk7RIROcss0RLgNhLoFpD4A9GPasMAAoL4kWRjnNkBOCVpzlEYdH0bPBXlqt3HJaGzApKM1gz0wbBTONAVgZuHegVppdnWLrPC0mB8QWhOZGZGKCSQtF+XuKIT7PODI25zgyOSAul1uKADteMECkHesfAN7AlI7c95C9zs6pECxXmcXXQ7meuigD1RDMKhKsLO9ieWkXB9NVbO9u4MLFB7G6vIO11S3EUX0e7syoyRMWZe83qYmb1wu21TbxVfke/D2qANrrAXWlzNoxxp3n89HHkVukVfNZS5/XMD02ug3wBv0edrZ3FuxdtS9xHOGxxx7F4294GDvb23jyyS/j0qXLleXqcvUouOMt7tbbva8AQNBQFCOiPjRiMLkcVkrAFIEpQThwzvs6e9DffZncydi3L8LInIvtU8Ft3IFhHkOcsZSviSvCYElheGJBcIPBUwGsOgZYUT9zUlakwMKBg/KkBQxX22w7Dk2S99r1nVx9XultbR/cMY7VEpZ5Bauqh7VYYTW2Y64DQ1CwOdgsifsO0hqmefbkUNmZu+qlMD8IOx7AVt2Xa9du4HOf/QJOnDiBNz7xKP7CX/g+XHjlIr7+9LPY3R1Vlm+O5uV2d0cYDPuV96vy9/L7822nHLNMB+/EnegaeZ6vrr4Xxgwmplw7tPy3YY2BanBv9cvNAYKNIexsr2NnZxW93hhnz1zBIB6BJHWsb9DfBja4lfGdNwoS6ZIsmnODLDbaAdzIukZnO6Scw7IbR3gQ6wAwu7xiZl0AwGXgC+TMkF+OWcMYZdlfL4tmjcTkRlk5y4tCu14uHbLB/nd2zEigRYHFl1GCFaAJIdIGLAqE3CjLh88v9uBXufxkn/OsxUmkxe53ksQwXkLtzLaUZihloGJjSy9FBiQEh3CzEkwenArse9lcbiClFiUgo0Ca4csuUWQsAA7LLcGdquS/b5ebyVx8FDEDUQRKE+e+ziDvCh2WUvLHESFQ/g4AwXUTQAUWdDE3aCJgOBhjOBhjMu1je/cEXr10P5aHI5xY20Rch127guMADMd6glR6jRMKxGYx4J01UFy3cO+4U1bptogFgHAzSAkjBI95lJftOmDPP+sP+jg4mATtNUuj2wbj/X4fjz3+MB577GFsbe3g85/7Eq5evV5pIzSnKpSVAVdYyJsVivILXrk+ZaUNoG3pHxeHyRVukmdHaohI9R0ItQ7GRiZIeJyBWkZaYEo9Y8kSnhfN9W+b+xBBqz4iNcRAr6FHS5mUeSIjTMwIKSsYB7zD42B/S2eJcLjfde955ni20mT2fhYbt+ebVj30aAlDHmIpUliKgIEWHBg3OALDILU1jiWpAP887PVTKwOvBW3dJgya1p+tIij26yhie3sHX/ijP8GfLT+LNz7xKH7wB78XL194DV9/+hsZQ1zt16xtK9f2CL24hyiOYNLF60X76GJUFjLARdPBm8AI33GlfF2GNXUKAFMLE5x91gJKW9mqhrw5w7Y8TiXmBMHGKGzvrGB3ZwX93hjnzlzEYDDJgXpwinnX6No+tv0NlOSu3Qa1VAHDcCZZlEujPRuceolzYNJoiuWP2JU4ykoeuTzfughr/YYAmJnAbEGwl0V7EBzKog0XzbAAywp79jcVBZNJpO1yfpSlXLkjJQopS8Fsi8UywvDLhcfLSarJgV+tGUycAWKfNyxO1p0mERRTziJnpZmUBeLaHh9lFERbIywPmgGNMJ8pzCG2JZgC462Ic2be1yFW4iTUAmKBsIB6vo3QaEnZH+NeezDs5NceEHvX58wYy/el9BpompgKGeh2RrlwvTalOTTlu87jrFwT5QmxLJwLc783wdlTl5EkMbZ2N/Dq5fuwsrSLE2ub9akUTRH204HhODqA4ahRnZ6xwHU5w4V9mM3OfyeA4DvP56OPQzHC+YCOawZ3VQZ1MVajKMEd9PuYTMJagPMNDLXWeOyxR/DEmx7D5o0tfO5zX8K1qzcKy5QBQwiCvUmVENsZTCRoqq8a9ruu3aaoz9FkgACBgZHEAh3KP/cy1lmmRotGCIL7eg1DtY4+rUCJglEJDmSEA9rBBDswDJBwBSBKNmFQAoVZTmzOBjeBNkUxenoZQ72BFZzCiqwhEo2UDEa0g53oCsZpzlK3guySwdUix6TZobkIJsv9aNpevv8RNMX2BwqabAVkFssIJyJIYJDIQZ5bXZun3N7/cswD8hYHwfMea3/vqJexh7G3N8KX//ir+MYz38Kb3vw4fviHvw8vvXQBX//6sxiP2+sMNsXuri3ZtLa2hs0bWzOVE80517NAdP294nBy9/njjvTq9oimgV0ZBNct04kxzVyo3SBYUhij89JJs3IOPcsSgGNmws7OCra3V9HvT3Du3DUMent2WQ+COWCDZ/SzLFmsc4Yu73/dMlTOCw4ZYckZYbDKrh82roxRGuWlk0o1gCXI5+VACh0uGzKs/nNfBomz19bkKk0jC4gbQLBxOcce5HrwKwLLFgOWPXbb8nerDAoKQbFVIyXBZa5h+1E5/p4JdgZbnsVWypV5CmTTBNvPJImhHVuslGS5xx64K2cixtqy68rVGc6OkwPkBRDsgTIpKHH5xcJQTEBkbJmlDATbPGLAOMBaAsMsTnKtgt8qH8dUALHKAG0ZAIfMcN01Vwdya2sLO8BZe83PMsgL+nAs4C5gU+MYOHPyCk6sbmJz5yReuXQ/Vpd3sL62iUhZgitjoLNrtt7F2b6O0NMTQAgp99BTNXXCj6j/TSD4ZsqigTvP5+OIhYFwOwiufz0/k1K9sfb7PRwcTDrJfYvbJ9x//91461ufwGQyxR/+4Zdw5fK12m0U18+BmXI5qYAFLIwUJNoC1I4D3HmiKBXmTErMkiB1n7PktW2PW55tQegKltQGNuQsVngZCoQEBjtqG1DIZbqUm3bl+1DcryxqQHAYOcPpWFK1ghWcwhk+gw01RKwIE2Zscg+sGaxTW9tX3Axgh+PSxVirLrqAoqbttxl42frL9nOGIGHLBBshjFNglBqMaYxUJhnbXo1Z53ZLv2cA98MxwbOj+j3M0zZjb2+EL33xT/GNZ76FN7/5MfyFD38/nv3m8/jGN76FNO0yA523y8w4mEyxtrqCzRtbtdsrr9OVfa7uZ/O9wk7uHP9s7h0zjtdxZLmRM665FhBceT8AtG2GOiIoAuHy+jUlVMJ1R/vr2NxaQxQBZ89ew3A4BbiU/9si02wyySqAYK5eW1R3zNx7RYk155+Jl0VzLgrzIFisvBmsLLgNHaCN39+81q9nhD2za7siIA8A4QygvKO0EIwDzcxW6uxzg0MQbFhVQHDZCMu4HwuIcyYYyGXTigQg+0TQsAyyZ5GVu+cZDpYNjy3Bsb4MoxixNhCQZYM5N9ECAHLsdppqsCAD0CowFUsRQYmBVlbCLMbWJSaX+xvWL7bb938HjtNiQNqz4chYYfGu0k4qDQeSCZyDYRFAOyAMUwLDBESRPTd0ZIFvCIiZrWw6iuvBcDl83W93buavdT0wLkeJFc4mrLoC4+MId93HUYqzpy5jmvSwuX0Sr1y8D+srm1hfvYFOPQvAcBSlIGJM0wF60WwgPFdaRJ2c+hbLoe88n48+5gTCValz0wA8zGmtspTlgWM3AKeUQhzHmE7tyd6VKTl77jTe+Y63IIoiPPXU07hw4WKpL9V+FPNEQzY4yJ3I5KbdL4xZEwJl5+PCZ7BF5lOeQBHbvGBhlyeagrlomHSYCPvhQagiW8pnBSdwQtawqnqIiXDgBgMTPcaERiAaA85JvO4unxlRlUBymyu3/9znR6/IGtZpgJM9jZ4iHBgFmQ6xzyvYp342adGGGzJ36GBb85bBaoq277EL+0yksomOCU2xZwz0lBARMDKMbTnAnh4hNZPi900qaL/IpC6SN17u77yO1M3bqDkupWvj8G7JjN3dHfzRH30ZGxvreMc73oKHH74fX/vaN/DSSxfmApWTyQSrayuFvoX99tuzUZ8S0v26bJay34k70Slmudg6BqlzdBg4M3oAqF4a3RL74wFu3NgASOPUyW0srST22THPqV8GyHVmNiG4bXB/rbZr8h+gnhFmOOBLVmLrHZ4DGXO5DJIHsxYAq0KNXyshFhDlbs1iPAhWMCZngCvS6MAUK39dBcH+d8IqA8McgODssJCAfS6v2xeBA75KAWxZ4rq60Qq5QZYmQuQcpj077D/LQgipUZgkPUSSQmurdNPuuPi/RQjsWWSl8xzkrF6xn9govq+YrDEXgju0KEAbe7y96ZaXShfMsBwYjuAYYddvEq8bzw21Ii9PVhlbnAFiAEiTChiuyyXOFBeoYZELINkDY2SssN21wAm+rN44YjC3kOO0pOhFKc6d2sfBZIAb22cwGq1iY+0qVgY3as+pptA6RWL6tc7wN8vN2d4jF8xDvhO3NA4hjS4CCKCGNaqd2+GG17Mjimx3PRDOtt8AXJaWhnj7O96M8+fO4Omnv4lvfeslcM2McFs0DvwbJMhVZrFw2z1UeHaRkToWOH8/NEwq9P8Q2y6DUCIFjRix9NCDxpK2kl0AiI2GlhiKdGf2sAqAZzOAdimNSDRirRAToafswzgmgpYIGnFpwiKc1KDSJEc9GPZ9bItZoLANBFdY7xo5PAsjxQT7NMImx5gkfSgAYyTYUtsYyzZSnuTnYgEE+zgckGqbpJi57hwgOFynztSs6/abwPPm5jZ+//c/j3vvvQtve/ub8NjjD+Mrf/I15wvQ1Me8nWQ6Rb/X67jN5kmBecHwrYg7M863aSziWquiPL+xPNgtvS8ZK8j1smgPvt0zKkki3Ng6jYODAU6cHGFtdQ+k3XohsC2zMuW/Gwa6jSZZAbD1LK9kcu6qrDSTRaeJdagOQbAR2x0G4EoYZdJmz0aS5KWOGkAwO/DqwwNFwLLDgJWNl1lgD4wNK7Bjf5OCSzTZ15KbYXnw63+nrDI22EuiAUA7Fjhyub0+95Zhn6NgWFdnVJlgH95pWpM12dJKQQtDc75/PoQVDGtM0hhGCBEbRJog2hQGqn6ywK6vi0xwYLDlQbBSxpZIEuvuFY7KhCVnhINyTPlkTqhi8K7R2c655WGpcpgcINsvzgLtEiCWKLbnkjPUkjCXuHDwdDWPGGgHxr5voUy6BgwDC4LXY4pB/wB3nbmA/b0hbuycxc5oHafWLmEY73ZaX5GBpMG47rDgd45JwsPUKV4k7jyfjz4OnSPcnoNZLr9yuIgiDRHJSho1hVIKb3jjI3jijY/hwiuv4ZOf/L3AYKsu2hlaIp3va5iX602QZuajVgf2QBvIDmciPbtedSMWxwaXS9ssKpH2A/q67ypsm4lhxMp1oWy+aureZza5aVOlnZw5mxdYZccaBgyDCU0wZcaBUWAQDoxgzAaJmsIgmZkrHe5nGQy39SFbfx43aTSzquXvLGfLrRv0hEfYUzFYMfYwAABMcIAxdnDAOy4/2LUp1fZn9atpmS4scFvbh1mn9vjMYIebHMHL67zyykW89tplPPbYw/hzf+69uHDhVTz11NOYTutmcvN2pkmKfr8eCHfpX1N/qturTiLdbFY4zFNcdP07cYuiQy3TbIDclldYiiYQHAaLgi2V0759lhjb28vY3j2BleV93HvPa9DxYgPImY625Vy+Qhmk0mdevgpAlMql0GUALOxqB+eyaDTk7ZFikJBVn3hSWYoSZw+CPeNLxJlrMlBUc4dMsF+XhcCiMjm0B8Gpa9f4/GHJ2d/EfZ6D4iIIht0laGXvSARkbLESsvJhymvt1k3bKTh5syhExBbcCkMzQZHKzhMvzBEn76akh1gIRiuEg36VMeb+OVGcKCiUadJ5iSZhghJbbsmbivr+2n1gCAuA3H2aXYkr1UsBRDlDrAiIcuAtkR0DkT8QEdsD51arOziUJpYdjuLiuReEuLxiC5bZTtQE7DDBM76mCozrru0QDAMFQGy3dwyguKb8UOviBKws7WJpMMLO7jou3bgfy8MdnFp5rZpuUQpFBoa73UOazLJq74F1btPHlUvdMe48n48+5j77Dzcwm3e94vJRFGX5fU39OH3mJN7z7ncgTVN8+tNP4vr1zQX7mofAAAJnkJVvkzkNnKM7tNMBBIcRgmBFccbKVturB+KHYYPDsCwpwJIilQn21S5GGAIG0IZwgAQjNcJERjY3l/0U+RFFZqZlc6QnPMKeHmHTDJFOB4hJYSoGW7SPEW1javYLebNNztl1YLjpmJWPbVfmuA1Q1k1chIZiLCkS3sc+gEQdQMM5qiJBwmMkvA/D05msaReX7EXze7u5ULf3oy3qHZQPF8yMb37zW3j55Qt4xzvfgg9/+Afw1FNfx4svvty4TpIk6PXyEkrl4za7X3WfzzoedyTSd2LxsEY9zSVFygPmyrp1URmw5/m/IsqywS0xHvdx7cYpaGVw17kr6PenKNQMBqoy56b3aqJO9tzKDoUoM5CTh87QnhHOwHCaut9B2SR2zF84QFWc5cASOcdiZZlUCcBwDoyVyxHWNkeYOGM/fZSl0CyUA19njJUahYTz1z6f10iRCU5YIXEgOJdHu66T27Vwbo5VBoBVh8G073dEgpQUNDNiX0PY11AOyzixgmaFNOmhJ0DMzjzMSaqjyIA5B7vZYQ7YYKUYSgkkK8mUS/U1MdgEz2N/bBVnrLIY2PrEfpenyN2lIwNoctJoziTUpNmBU8cOswBgC4a9+VaZHQ7LLblzpXDs/ISW0pCongWusMQhMG4Cw0AFEANodn3uGJX1qfR6jtJFSglOrF7H8nAH17fP48LVN+Dk+mWs9q9XJ9ncPilKYeSYGO46t+kSGG4C13fi9RFznTllUxefA1qbzzpjkL3IoNYC4WAWN1g/iiK89W1P4KEH78Of/dk38NxzL+KojGVELBNcVlVbNrbJqKimjYWWsSBYq56t3ev22UgCKMB0uPhmyXDLy4VuyPl3y2BOMTEjm3+rNcayBCUKCU0xwhb2eRMJj3OZdq1MvL4PRRl5M3AzPEFCY+zSFYg22JNVRBJhqqbYp13s8XVMeVSpTdwl2iYOurLGde1Vpd8l4AsEYD8PlsQNmtjlhatqTngL833Y8/KoQHDt9d+QTtHUjzYwvChI3N8f4w8//yXcc89deNe73oaHHroPX/zin2I02qu0nyQJ+v1q4cNu97AmOTzQFQzfzLhTnuH1HWUQW1dDuLjAnPnCDcGsCk69YRhW2NzcwGh/FRsntrC2Oqpnjgt1gZOaBWzUOkY31fqs7WzD52FeqGeNQxBs0lwWLWLLJvkc4BAEe1muk+RmrHDDpexBcFbSiAEPiAE4UKzcZ1Rggj0ItiZZziyLSznBzgzLs8OJk0QnTAEYztkiTQKtAE2236LEvudZWM9WNwDiDJwCSMmvq5AIQ5NAGS+zDiTZrDEwGmkSYyqEAadWzs0KvSiFYZVJxkM22LjjozJXanafGceeWQDPrAt3UwomMTOHbp3XJ67cATNW2IFbX38YLgeYYUs6wLg8YTeeYVMDiNmCYaT5++GmvNs0AEoBaTHZagXGdaqPMiC+GcZZ89bxVRpxlODcyQvYP1jF9e3zGEXrOLN2AXGUVK5f6y7ecN/DnFLplnthGxi+WXHn+Xz0sdDTr1JeqIlFO8Qguy6iWNc6vp47dwbvec87MNrbw6c+9enKQLY9Zg00/QC8DvBz7XGYb9+q4Mi35c2htOpldXs1xRAwlCQ2P5QYRrh2/aMMEQYjQcpjjFOAdYp96oNIwSDB1IyQ8BipGRcAaB3LWicHrgPL+c6obHmRFInZgy8jtac2oUjDuOMxNSOkXOxDuC1p9mgsbrKGeS+bfM3Xzgzw5vJ760CekSkMnEGcA75S8523tl/bp8WY4Hmk0F2k4uGybU7afpkuk2jzXIOvvXYZV678Ht761ifwgz/4IXz1q8/gW996sdD/6WSKXs8D4fnZ3dA8ELDfY27mNvt8KhhyHfNz7E55htdvFN2e9WwQfJTbFqplhPf3h7h24xR6cYJ77r6IOFrQtXWWY3Q5aljuyiDWl7YptMX5+k0lk7wsOsgPRkmySCQQxVDagrEyk+nNs2p3271vjP9cFT4zrCCgzBjLiMpA8NQEQLgEgI3kwDctgGALhHNGmKCFEJNYohuWDFUgqDkubyKxKbQeDAtlkulsXzwrrhUUa+wnMXqesWaFYWTBfE+nudEWAhaYJHOWloZc5TAUvMGXuycHZapCQ63wqIsoKG3s9xg6SwMglQIpWddphkP/BpnxlqfXw/JMYbmljP3Nf5NTJ4jSgGeEyzJoB2Kb8obrwDCAIiCuM9ICDgeQDzGpJirKJdvuel0e7mLY38ONnbN49dqjOLl2CavDTZAY+FrCShkY9Fpd8GeB4bJKprJP4nOrq23dTDb4zvP56OPQ08AFeWkneeB87EY4kNZKwwQPLaUU3v72N+HBB+/DV7/6dTz//LfnaFmVXs9ires/L0cb+zd/BGCY+uipJWjEYBgLHsmAKQJTYrcnVeA5Lxsc/h2ywlaym8I4hpIlgSILDAQGhqdFFraG4Qy33+TWPEuqmwFyY6XaFEidfGkpkRTcMvvowVRTNB2L7O+OkwyzTMDCiQAroYsyh/JCX0t54HX1j7uA27p9myfmZV5nMcaLKEX8d9Fdkl1+v34f0tTgK1/5M7z66iV893e/E/fccx5f+tKfYm9vL/s8iquMcHE77RMTRZO20E1/1n3x5rLCd8w4boOYAYLLtYS7tllso1gWKZPxuveZCTe2TmFvfxknT25hZXkE6iq/bGGDDxXe4As5g1bcbhUEU5pYOXTGBnswDMsEp65EkjOvQg1bQ4qB4FlVHoxap2PXN88K11xHGUB2INobY6UlEJyEjtAc5gZTBoanxv42gowRDqXRmmyXYyEYsUA2Iikw+SG5VJ8bngPfiADNyjLCzo3akqhWui1C6DNhL9WYCNDP+q0w4BSpVog0Q1PuOm2NuIIxojbW5VrI5vmSc+kmC7ZhVF4zGDkgJ2JAibv3mYL0m5hA2ljZupOsk3eWRgpMI6CXglLJJdJOHQ3lADGQg+IKIHbnhTdLCxhiC4ADZrgkk86W8b/DkkoIWGJ/6tUB4qZYFAwfkcIEQDZhpRTj9IlLWO5t4dr23dg/WMPp1ZcRaS+NNmDp0Nd5mel5170JgPjO8/noY66ztclMaTEGFJh3cGel2PYWsLa2ive9/7vAxuB3fuczC7DAYTSD3HkG/03s3+yokcoGoUhBUwyNOPstYBiKQTRxg2oLlNpA2rxApuLg68Awg8EmKYGvtAjSMgZsvmNRObdqJMkS9KEYR1M6qi66As36CI5hKW/ZA2CdMf75JWnE1kO28nebMwwHjDNmkXJGfZ59CN+bR0JdB8Lblq9uuzxJU73O5k2bqC5bnQjK228HlVeu3MD//t+fxjve8QR+8If+PL74hT/Bq69e7Kwk8FGdRPNqAtNQUm5W3HyJ9J14ncYcILg8GPaD5C4GWtXtRtkAfjLt4er1s9CKcffdlxFHhygr0uYgXf6cTan2b1D2qNDXqiNvZRDLnJddqiuZxMhl0akGOJBItzC9HtiUw8o78+Wy5VEExj6v2LDKjLYS5wydSg6CU84ZYesMjQILPGX7O2Gr6K0FwkIwJOiB7N9EjQ7R5VskS76sJkIKW0bJAmG7TCrI85NhWeBRqhAxMFVBTjPb3z02iJWBVgJNDNEGElDUNj9YMrBgWXfJcoM9Y0ylnFzlDLbAxk1S5G5XCsjAc+YuHXHwlEmBVMOaZrl8YQWXQ4z8tq1KLHEZEMO9ZmTSaXsgXQOBmVsog/Z/F5jh2vJKOr9+/DijptYwgCIz7Ld92OgIQgussN+268dw6QD39L6F6zt34ZVrj+Hs+itYGuwiNAu16wTQpkZJ0sQQt7LC5Qjrk9+J13XMPW0ze9DcBnTrBqzzDO7sTe6RRx7A29/xFjz37PN4+ulvznSRnjcWNfWxcbSSZAAuL5TBMJYR7li3+HDgLWgnAKOhmVM2uK8BSX69cj8W3X45ZkmDD1U2qgMr2bRe299ACUiSgqI+Ij3AQK+jTyuIyBoypTLBVPYx5ZH923DpewjA8IwJkMNG0/cb7kfd8sXPq3W4bZvGHe8qGK5re3bUn3OzlAZhpGmKP/7jr+HSpSt473vf5Uy08rqe4blfF+VJNA/4pTLyrva7PW4OGL4z43ybRJtRlu67PMS8PBI8i7TgwE4EgADbu2vY3DqJ9bVtnFjbzEsizd1g/aDZ5wfX5gn7aNuHTBbtyz/lJkN2Xa6CYAlBMNud9Y+gQBad/zjZsyunJKwgjo3MTLKCnGDLTDKUUpkc2oNg4/KB/XsAsvJJxZrAuoYBzl2hiwAY2e8QCGeHCNYxmgVgZeFVpMhOvNLs5BCf9xsywh4ER64NewhzRpqhkDKwbxS0ISSaC9LugauFnCqFSDFiZcAg9JBa2blIxgKHrDCgUTUec8ypL01FAiUGWhE0UtiCTRYMexBspdFiDc9cTrcC4PPBZQorY2abUw372MtLLGUw1T8/3O8wh9gbtgWS/QzQlqTRYYvh6zYwnJ3vkoI4mBQrsb8VR+nweuoCipuAZBu47MjUasU4e+JVjPrruLJ1H1anN+zkh1KoLdt2m8Wd5/PRx4JnTdNgrBkgFM2Qukc4oNU6xgMP3ofhcIDPfuaPWmqAzhPVwXd5u+XPmvu4CBhpkACD7eyikyEbSQpAKJVJZkrV1k7XaAJ/dcZZoZQ57G+2TgcAPA9wWwTQH9ZQaZHc2Tp2tQIenWScYEFwrJexHJ3CGs5gRdbQ5xgMwT7tY6S2MYKdCGGyLLhAoTwhf9QguKujeddl/XIEDeUeVJnkXhQYCcJc2cWBfbFPIeieZSpWtx+vvHIRW1s7eN/73oWHH34Qr1y42HIva1aQlPPfF/OquHlssDAOmYN0hJ25E52jjsEtMB9ldseBYMmuSbcOuoHhslMsM2F3bxWpiXD+7EUM+m0lC1uiThbd0TW62L8iG0xNg/lS6aT8/TITXGSEM2l0BnQ1xGgLisVJpoXARoONq/nLCmwU2Dki1/bb1+2FA7w+JzgYAHOWH+xdo70ztJMbu9rADMu6hiB4aoCJA8GpeCAsMBz2AdBCFi8yoMi7JQMqLxZUPFz+tydCrZ+YBc5kGeUyq8yhNBtAKoQDQ9BGkIoCa/sEDV22jSL0dP5dKugsV5hZKqywxZbK9cXfg90zwp8b7piLY4o1UrDKSy2BBaYEiBUAVpJBWoqMVQVExpZYYrFya+XxnwQolpHnDRNyQBwaroXguMgGLwSGw78L538xV7gcXRylaz8/Sol0TawMt9HXI1zeuh+jg5NYGXbHA233t7lY4VsQd57PRx9zfsvdQKNftsAkCVrB8Kz2lpaGeP/734VBv4dPferTM+oCz4r2M6EJEHZrd7b0u66tpgE0IwGJQsL7YMrzctmZQ/m82KOIWSzcLFflwt8tpkdl4DFvvm7bOmVncxtHU3bHb7drW7UMqgfBqodID7AcncIp3IMzfBIbUQ89RZiyYDvtQbFCQhOkamLLUhk3GXKLJvS6fMdNOcx++UwO7uTBjBQKMYxMChMJdXnjYTvHJYEvhsJotIff+73P47HHH8ajjz2I8+fP4tKlKwVgWxftNadnKWXCuPlPrTszzq/j8LLo8oC2YeAnASOc5RQuADqTJMa1G+fArHD3+VcR6ZrtLTKonCWLLsdhnVwDp+js7wIbXAbBCr5sUoENdmwvGwd80wgm1RYUs4IxKnN+BjwjDNew/z4c8HMS6MxhWciyqZI7QhvJP7dMMty6uRFW6sDvhAlTxwQnDKQsSAWF9A8SK91NmEDKssWaqDFFhJEDYMMWqPu/Ifa8ikgs4+oAMVwfrWQbSMmuM2WAmBBluLFBSu7ArxGBco7SHOQG+5JMFuT6ften0Wid32cNKxDrAkXuTb+sMk87MExAquyoL3RLF+cuHRlAyBIaIaYiZJMKRVbYAWIvkfbnHVAEs2iWRttdqk7y5CWUArBbZoX98pUDfQgjrbIU+4gjjqa4+9Tz2Bqdwc7eGYwnyxj250mTPOK4SaaEd57PRxsLnZ1NZX6A6iC1LKntCgLDNs+c2cAHPvAebG3v4saN7YVA8CwAs7gUsxz1gHZW1Jcssu1ZEyqGoWkBCHq22E86tDFp84CH8rGYZ91ZsmGbE7uobDUHEbP7VAbE9UztLOaxTu7ddC415TcX2VNrfhbpAYZ6AydwDmf4JO7u93GyT4gIGBsCHfQwTofYVUs4oB3HIMfgTJpejS5GY+FytX3umEc8rymcNwEjl+8OuLEAAYz0WMBtKB9fPBSYGa++ehFJkuADH3wPnnrqaTz/rZcWUjzUT/jVTQ7emba9E4eLpvzfanmlgBHOcgS7XzfjgyGuXD+Pfu8Ag8F+EQQfsVSxIosug/YGENzIAJXZcgDNucHup6ZsUsbQeCDqWGCTRBAHhlMHho3RAaBtH5T6ZTwDHAJg77jspdO+zYyZdT/ejCpxzKsHwFOWjA3OWVyBIgLB1gpWIll9Yf9DKBKXZRCcOuAtsAAXbnlDBC05IM6EwpKv47ch7pao2AJnyyiTe4ZY923N2jLXxNBk2XZNvoSSyw2Gy/EtHefQiEz5esOKECEFkQaTAJk8GlDagI2yEwQwFgyH7SGC8rnhmnNmNjKOEpfcP9TT5Ay7VBkEI5dFV1hhd366ToGYAwDsWe7cKKvpOm5khVuijRmeyRrPYlcLpc+6XdM+FAmWBzuYJENc2nwQp1cvYHV4o3Wdo6z7e6eG8Os/Fn5K1QGJxoFhg4Nwe9jl77//Hrz7PW/Hn37la5hOp3jjGx+dv69Bzt4iQHyeQemswfw8stO8PQ4AUPH2W87fPEy+aPk7rQOv8zoS1+XNzgJRzftQr0iojxBcHB2oqJOFN4VlPiN4529FkQXBqo++WsEKTmGDN3Ay6uFkn3Ai9nUaFfqKENWCOFe/u9wvycsKzQKVi8q+6/pS6YM/1v68pGZQqEjBuHz3JsO3+Sam7HdecB8PJNGzjknB1bl0DIkIW5vb+Mynn8QHv+e7sbq6gq8+9QwOU5OveXKues3kr4/WD6EuDl+n8Gil+neiY9TKGhvKCqkUQN8tk+bvA/AlSbrE7t4arm+dx+lTduC5O1qtX3BeRqhcNqmFDQ4NvmZGw/HwNYOpAnrLbHDACFf67O45Dhiz0RCjYNIIaRplkmhvcOWZHaoxn+IAKHtW2LDNGfbvmwz45pJoBgog27PBiZADqYEc2oFgC0DzKV8WQUTOKdqxzx7YikNY5ak7D2h9zm/44xcgALHKAbEH0yy5UVfWjkulJcAxyISYCCkppCRIstrEjIQ0tLKssFEKxL5MlcsNLh3fMiiWoKwVs/bYNwfFbjmljRWFswKULaVk84MVwAIhsqZZLggAFFtptGeAs8ZcnzKJqwPEvv6w9+pSNaxwWPKrkNcbguKOrLBv9wiYzEpecZcoXdcVENwWwbUsIPSjXZw/8RIubz2IJO1jY+Vi7VjpyCJQn9xMMHzn+Xz0sTAQrnPzzT6j+vqY84K0N77xUTzxxKP4w89/CZcuXcZdd52D0oe7YNuY4S6D0lltt8WsQX0dU5cB3CNiy7pIko+aWW5uswpsqw7R1e+rC8ibJV2t69eRmYsFzK9WPcRqiJ5eQQ9LiKmPWPqI0cMKr2CdBliNFXrK1lpk2IHKhAUTJEgxQcoHNp+2Ic+1zWTqsEx+tyhto9YkzMDfblgYihS4ZrnDM8M5GO7U15bI+6IQRzHSNMW1azfwu//ns/hzf+69WFlZwR89+WUYYwrr1EW3iZtF1jv68IPww6x/J25NSGmAW/tZUEeUOHdPrkR5MBvkGIsAW7unsb13CudOX8RwKcHe/vJisrumUkmcFgB56yB53pIoBWdpRl4uydSzwdl6pR+pcYj2IJctM5lJm50k2v/tB6Q+d1WcARMHINlkkmjUgmBvlpXVFs7AL2WOzEkgiZ6YXA6dOBCcimTsKWDxmj2aTlItgHZglUAgNxnnl/fg1W4LLue33onaSqztT6Ryia8R25D7VfwROAk1IRVBKoQoMwOzINgwZaywzxUGOMsNzr4arrLwRALtVQypNWsmIzBkc481MYQIQsqCagULYJWVkIsz5HJ7YoEyiV2GlT2fHL6tzM17QGxgWeI0AMPsJNa+PBTnTHOh/+F7c7DC9ssLmNoZrHBncIsGhjjcVs31Wnt9dwSXwgKtBcPeHu4++S1c3noIienhzPrLUCSLg9QGJjsDvqHRoNLIZlGOMe48n48+DqVbmgVqs4FkZl4zG1z5geQ73/UW3HfvXfj9338SW1vbAADDBkrRTJnzPNE+0Ly5THB5+UKb5fY75PF26VOXgXbX490GrP3vNmlxeZ0yM930efe+BIxfSbZcjlCqHu5DuQ+V9ZCD4FgvY6DXsKJOYZ1PYVWWMKAIClYWFinCUGtosgOH3VRhysCNiWArnWJHbWPM20h4nMnj29jC8vvzGFktFg0MPamKCkSEwZzawQOs6Zd/3zLJNaUMuuZiV/a16bvvcj1XWWUA0JHGZGJTMnze8Ac/+N343u99Pz772S8iTcsPwOK2ypMs83w3twIM34nXX7SVTCo7JQNusJxOQKouLzBqbEsEuLZ1HvvTVdx17iJ6sbXLDU2eDrcjRQC8cN3PpsgYZM7/DusFh2xwto5/z0pcC6WSkLMsvjSSd41moyDOIMu4/GAv3eWC4Y12m1HO9bnIGhe6X2M7758MOUAmlxMc/lipctIAgjkAuP7uRGxfW7m06yZTgWX1+cAe+BbBsMAbbCvASZltClAqea6wPWgNX5d7YpgQ5LNC4vJ2U6MKrLAiyey8vIImNB8Lj6c32RIhlyecZliGlGT5wgqAuDrDYHdfBkPC9BYiwDHSOd0d/Pby6PIXlwnkPHh2b3qHcuX/dmNkuGs9mKCx76mcFV6U4e0gkbbLzc4TbgTDNcs1bqNjsCiQG0P0ognuPvkcLm0+hMtbD+Hc+kvQNeOLNha3wJJ7MFzqe1iz+dDeBHfilsacQDhnzhrllc4EpwwkilEW1hQHit/17rfj3Lkz+D//53PY3x/DDypNmkKr+QftneTIHYHeUQFw317b9omUmyXlHFwABYCxSJ3eJgloG8u/6GC8rb2mfHLfl7a2ynm3eeT50mHUtdd5ImfWssH34qXJtiTSGtbpPO4y53EmGuBkX2Hg7q0TY01B/ABilNiBwV7K2EqnuKxuYAuXMTabSHkMX6c53+cq417Xz1lAqmuqQJf8er/97Hwt9Udg7AS3M80KQXAZ5DeZnpX3q/v1OP/5G+5XpCMkaZptbzpN8JnP/BE+8MHvxp//8+/DZz7zBUynIbPVDMw75ZeXrtF8uZvwsA1zHhdc/05854VEvcp7ZRMeABUQHA5mLQi+CwfTIe4+ewFRnC9vrZSo2k7nDnqJdun3UUWZBfbvMWeS6MrAuMAG57Jo7wptyyMF12pYnzYolZQ5GGdOxkVAVpQzOzDs2OAyA6QgYKIMOIYmWUbyMkmJOJdoZ441Zas0srnB1mTKA2DLnzogLALl5NZCBIFyy1l4GZGdxA0jZdsdI8gl2IEJl5c9e6lzRJZl1s5NGnD+UG6fCEXA6B2ofb5zSgTNCgqAJgXNColRINIWCJNUhUqo5mV7IKw86+rygskIiHKgF0XIdN4+X9iDY9RIpG2+vQK0ZXYzeTRq8H44Z6x8vz0jXGKFfb9Rw/RmcmmFrNSSHy/XANcQ7BWA3zxguBxzAvC5pNAtIaKggmtPK4O7Nl7Apa2HcGnzQZxffwG6bsKvtW8lMNy07XJpquOOO8/nI48FGGEPQJqX8GDY5zN2l2cS3v3ut+PMmdP4g99/sgCCRRjGMJQ+Wn37vEznvKZaXaWSTYP6ChhGGQS2t9u1j50A3wLRFVzPA4IBB6Zczq2iKNsHlhSGJ4CkzqDNHrdy/d6m/jQZedUu27BvBI2I+hiqdZwyp3E+HuK+ZcLZQYqBZkyNwlaisTUl7CbAfio4YMYBG+zIAbbVFrZwGaP0ChKzB8MH2X7U5SeHfZgn/WCe82ee88o7QxeDnbzP9U/Cdjl4XY7mfTms+d08bKvWGiYtPkiNEXz+c1/C+9//Xfjz3/cBfPoPnsRkMq1Zuwrk6/paH8V1KRgAH1fccaV8HUdDCSUAtRI/SAoyubOrZZrygXAZBF/duhvTdIDz57wzdP55KyPcldUtg+DDssHlkkiFzwIQnCY561uRQ5dygxlAaiXR4sFKS+TmTZS9Dj/LcoFBGQD2rLC4HF3ATjQon58rgFCx9q8HqyaoGZw4AGx/chCciBQAsH3tXRosalUI5ch5bWLP6IZjwKyssgO+HgzbHGRfAsl2logQuTxhL5PWlLtEe0Muu8+ufeTtJ0KAyY+hIg3ynlRGoLzrtqLsnAzNxMJQJNZtmqhw36s7j+2ZXjTPsmCYc5srZ24lrECakcnmGe6ABQA9eDT5SYJspxWC886xwqVTuSKTzmTRASvs5dQd5NELgeFydHSU7gSAwxrHTZ97AIoIRMXllGKc33gBVzbvx8WtR3DXiecrYHhWbm9t/nT5PnrUipUZcef5fPRxCGl0vdQwdGr1ucJdE9bf/Z634/Spk/j93/9DjMcHWft+oGjYQB8yR/ioYhHDoS5tNoHhKripAg2/7KzBfRPgOy4JZquEuYW5beqTNZ2yNXj7egUxDaERI8UEU97HxOwg5TGYp5X0LTdX29j2bJbRnve16zvdkzfH6skQy+hht+qjBQABAABJREFUvUc41Tc405+ipw32khgHLq/owAh20hQ7coAR7WGktzHi6xibzQwEiyuPRW7w44Hmkbssz9lemYWumqCFUQW8XRjtOvXIUUb9caxuU0e6kAvsP2dmPPnkH+O9730Xvvd734c/+IM/RJI0PRhnybfzPlWXP75jUI47D9rXaXQAwQU5X2XhoJxK2VlagKub92Bqhjh/+mVoTZUB4aGk0XUs8AIDTJvfawr7V9jXwBgrZ4L962Ld4WL/GF4WLU7uLN4lGFiIZZkFgr1LNICg7i4yMBw+3IrmWHlt3qnLC/Y/HgQnwhXwa2AsKIY4bp+gQIglQmwUElaIlULsHJxVwy57MMwBEE4lKLwkggSwYNj9xE7w5pnfpuNlPCJWKIDhSh9A0MyZCVbGyAc8s4JkEm9yub+2JBKgVD0gjgCAbekk5UoukRBEiXWUFi9pDiZJUg2KnPJCKN9eBohzppj8334f2TPDnA+5G1SRFVl0mRVuiwAMAyhOiGVmW7PrCfvttoHho2KBAWRgmEXZ76xk9Kclxfn1F3F5+0Fc2noYd514vvG8zfoetg10YntFaXdmHT8ovvN8Pvo4wtoG7ECvajT1KS8fxtve9iacOXMKv/e7f9hYHilNUkRRucvtbMtRRFfQcZSy6fpokktmd83OLR03GO5i1tRmttbUJ1tGqI9+tIoVfRZrOI01XkMPESZIsKO2saUsmzoVzpjhepay2I+mnOQqMMlVEbnJl50ACtdVLhvY33YYQMIKU1bYTxV2E2ArTbApe9hR29jDFsZmG1MeITF7YJ7Cy64JOmOEfX/C9IOmY1l3DMvHeJFo3l7Qv0wZ4gc3VZfzQh4e5Xnbh01DmGfdLiZ5/X4c3JeKyzMLvvCFP8YHv+e9+J7veQ8+85k/qgXNi8RRGbjdif+LowSC/etZBjJ+0CsCXN8+j0kywN1nX4bWBlIzdCDFYF7gXO8AgjsNnmtkzTm4bSmfVC6XBCB3i/Y/DpykcHN61ggpA8RArVyRpV7GGDKPHgQnvsawM8Aql/jxvz0YJrJgLtyWn2609YPzvF0rU85BsBFGCg+E2QFhRkoGBinY3f8UFDQixBKh5wCxJgUNgiJyeb/59KUqsR6+PwIgFQuxfSRCiEhBk5NhawXj+hi5SV/PELPYQ0/ij5n9nKEKA3t/nHpi0+gUFY9Pxq4TICTQYFt2SdnfHhirFrCgFUEJQ8RAhKBCZYSTWQuTBcasbG5wqgElmXRegqGEVT1L9rdlj4GsZJJnhe3GCxM1tUA3A8B5uSX/d5nhrJX/ltnhIPx12NU4qwyejxQE+1AaLBG0qrbt9/ns+ku4tPUILm8/hHMnXoBCg0FfGIE5INDADgOoyx++E6+vOBIgnMsQkYHhYrSDtMcffxgPPXQ/fvd3P9taI3g6TUBE6PV6JdZlsQHiPDLnww5I53Varj+GbX35zhkkN+c3tvexi6SXKIJWPQz1Bk7L3biHTuLUIMJAA2MjuDpZgqYIHBmwpEjNuPUmVZYV1/UpPL8rue01INTnKCc0wRQGo0SwOVFg6UMRsJMoXBoTLk2muIItbOnrGMs2pmbkjLGmEC/tBtBWC7duosC/37Zc5Th0zCOeJeO16gWdr+PHIdm9IWijllFvAqU3jxGti36v51I1gGJ/7D4wA3/4+S/hQx96H97//u/C5z//JeSljtr7X2cWWLf8zTLOujPjfPtFbqQVOMQ2RWmQuz06hf3JOu468+3cXRdA2VFVKed8LGQBiN9G06A57ENXKXQHQxqqMLz57yobHIDfOlk0kIPgDACTY4VrAG7NRAApQR03wCUmOATBJjyO8EywZODXVuMJDavgHJYpkymL2Nxd7/ycOnbWg2DPAIcAOKXU/g6YrQgRNEWIpYeexNBip3i1aLi7va3zCyt5DoFxZZ9hq/D6SIStAzRpiLH7mbB9FCiXRyzu2SFkWe6IPFFKmdlXdqxJ7HHVQCScGWeFues26znQX7vvQsHmIAOoSKUL37FiaDEWLIfKA9IQEmuqpQSSikXujhGGCSZFHAtMvrySbblYa5jdF+/zhYGiRDrL/3VvVHKGi0CuiakNJ8RE6aJUGiiwwvk63UslHQv4LYXhCFHcXP5NkeD8+gu4uPUoruw8iHNrL4K6srcd5d43K+48n48+Dg2Ey/mzHgwXo3nwdt99d+PNb34DPv3pJzEa7bl3w8FgzgylaQoRCYBw18FxFYhX+z0fgzQvsC2v3/RZc5/ywXS9lLOeyewCfuYttbNoNIG2phJGlfUd0IrVECs4hbM4gfuXItyzZLCkGXtGYbgXI9k7iX29i6kagdnObtflZHeJ9txld16RsjPVLi9eYJDyBAdqhC3aQW+qkUoPSxN7M91NGNfSA1xV17GFyzgwO0h4Hyyp7a8Es5UlY7QiUGrr24z9Cs6vecy0ms6ppu+tAIgDMAxBRbY+O45f/dEU/X4fe3vj4J3q+ZSmjM985kn8f77/z+Htb38T/vRPnz70drt+T0cZLAp8iFqDh1n3TiweQqqR6c1riIYDWF324KkwHrv769jaO4O7Tr+MOGpnUXz+HRsFFYVy5BpAPAcILtc5Jk4dmM3Xa2R+W0FwUH+4An5DJpiRyaKFcrArs/ODFRX1ceWawd4IyxtjeRBs3DaM2DxXXVNruC58LV8vA/aMqsBLlHMQnDoRdEoGCaZIaIoUKRKawATPIE0xNGK3TA9KLEsciXaMsUIkGjEUBNpO2wbMcPikYsdAp8H900BZQMuRZYRZQOJYbQeGUxDAdiLAS4x9fnG+FRviJmtYTGHywB977d6rHjs78WBYZVLp8gSGiILWKUQIkQdS7lDZdRRIKYi2daSVEttIqi2I9alZ3s2aKZNNU/4F5gctdJCGOyeV21/mRpl0GPbar7LGTaA4BMN22UAiDVTW6yyZPoYQFYE4BbNCpNrvT0oxzp94Hq9uPo4be3fj1PLLC260VFLJ339KKRnHFXeez0cfR3b2FiWNQBeQeuLEOt7znnfgySe/jBs3thqWym+jRArJNEWvF2Nvr+uguNqPoxhQds3FndVG23JVYNu8TFO+ZlfQfdzRCrbacojLxwAakRpgKEtY1RFO9wXnhxOsRgl20xgHZohrkx6WeBW7agCiXadD8g7FUpXnNm68+DkjhaIIZXa+0EdhiKQwMsEB72BTxUgpxW6yijjRSMHYp33s6E3s8XUcmG2k5gC5c7LdUt1xac/BrVtuxu51PD/rcltnybFr28qToGCHa/UTOMVtl7cf/t1l+7OWLbddP2HS68cBI9wcSZLic5/9An7g//u92NkZ4YUXvh20W6+MOex95E7MjitXruDKlSvgEuB529vedot6dPwxr4NpefnJdIDrW+dx7vSr6PealVp+oExk5+0YPQAt18ohmOAyCKYC0DVFNrhNFl2WQodssJTfKxllheDXl7yBZX4LrtEtUXaT9nWAPQj2tYOJ4IyxqMACC/J819CRGgi6KzkI9rV8JZNDSwUET2mKFBMkcuDgqj1uWiwQNhQjRYoIkQXAFCGSCBqRK70UOZSqM8k0kEunFQhKbJGbTJJNtiySBltzLlhpNLEDuhIagnnW1v5lINBBwicRWUWSA7xGCLFiVwcYIBB0MPOjyeYQe+doe+xce/63AogFlDpndHL76Y6EEmMdpT0YVgJWAjIMUgJJ/aQJF84Tm83GthiIzyF22xJ2cumyg7QHv4BdQHkwvGippHrX51o36dA8qwtDekwsah0TbRBZRlhPERpo5cvlk4NaGZxffwGvbT6Onh5jdXC1U5pIY9yRRN+SOOrn+UJAOB/8F12hq2C4OXq9Hj74wffgmW88h4sXL8/YYj5YnU4TxL0+gF00DVqPL1e3xMIdkVSxK+PT5hYcvjeLHWw2JjqaOMwxqWPAmgCXzZOys+VaiXvY2lliJarEoAbbQHN5pfA7FYIFtm5AYFnf6kRPGRSKMAxPMMUIAJCqCXbVDSjSMJJgIiNMEpcHLJNMSl3ub/N5fPzAqItEd9YERh2bHKoAcq7EjzAXj/prv/y6K3AuLhdFClEUYW9vr36VUuzt7eMPP/8lfO+HPoCdnV1cu3bDfTKPKiFXgBTXuxmTVocrz/CdJL368pe/jJ/4iZ/AM888g9C1VkRARKVc7td/NJlgNeX9VcCyG1ymJsLlG/dhY+0qhoP9zttXPk+4Rp64SIQD31oQXMMG14HgChuctTmLDS6B4ELnvLzVyXNbTiUib7CYXxu+nE8IfNkzuR64CsBk8021k/0WugoqdI+BTDCdJWYIHDPsjbFsTnAoh06RwiBBIgcwSKw6SQwUaXuvJjeRTAxDCTRiKElhnGQaZPNqyTlDsEvo8XfTDAiHfSeBQQpDlinWUDDCmDJDiULK1lArgW1A8kPuDMOo8NUpUrVPJCZCRIzImVspB3zJOUaHhlph+MkJRQJmAbNGmqFyC4bh/vflldiVcBLtrjUlFtxC5fnBHsu6cQMpWJdpdtbXZVZYuS9RocgCd2SEy9GY61qOMvM5Lxg+iiiY3tXfT5gjQAhRi2Il3N+4l+Ls2ku4vP0QYr2HQRzc3+rqAdftp4T3pZvHBgO31/N53jiu5/mRMcLzgqr3vu9d2NzcxjNff67m03AQWIzpNEG/V62H2LX0yuFydbsPQhctY1Red17Wsj7H8ublF3aJWTmns/ppJMFEHWCUGmxOY6we9DFOI+ylGlsJYZSmSGgKwwkK5XkKw4SgPxkA9b/dOlI8/llJsDqA5eTRfjsiKVK2rEjKE2iKIGAYSWB4CsMTsEwzoE3QDngfHaN788Ma5hVqXqCqTshfR9kkQB0jbmP2sThMmkPbvcbH0tISAHRihH1cvXodX/vqM3jf+74Ln/rUH5RqDFev8eZ668XlpZp3cuRxK3KQXn31VfzCL/wCPvnJT2J/fx+PPvoofv3Xfx3vfve7F+4HAPz1v/7X8fjjj+M//+f/jHPnzmUs1e0YJA3ncjCYbXVADcyxrmzdi2G8i7XlG0CLR0F5fR0aZjWB4br32wxn2OQgONyPkiSxAmpnDUxbSyuFDLFj8ILBJylxVC1DoCwIJkaXY8Wc1wYu1xguLBfkCIvYmr7hZx44h+GvvZwNdmA6LJWU5QczUrJeFIYS+2xyINhLo40wNMUZOwyxUmmDBOJUPkQKShQMNCKIA9wC7fqr4MG8d6JWmRoIsMAcSGGgbd+cy3QqZNlh64plJ77dOopcDjGs4jiBZZAVCIoUCkXsFEMRFZ5NZYm6f08BAbOeTz4YVq62sAI7EB0+Z+x7Gmwcy2w0rHGWgpAzq/Jg2BulcQCGyeWeN7HCGQjWyMopdRxzH7a2bWNZpeMGwx2BZWoigGSmNDqM4XAfG+klXNl5FPdsPI2obhZr1qQhakDwTQDDr4cc4clkgve+97146qmn8JWvfAXveMc7jqTd43qeLwyEm1hhH20D0sceewhrqyv41Kf+YO7tHUwmGAz6qBsk+uXmjTZgNotlbnt/nlzKLutmn4f5MBno4AYQ3JUJO/poK420aBssCRLex57awhWsob+3jomJsBRFODDAlQPGNRlhRFtIeAyWJANb2agg6I+VUkUgaKhsIGhnvxmJfUhl+b9wA58iK+xlv6ETMiOFApDyGIpy6xHbnxQsKUS637gPC3qbzsU2WX4+KTBPXzi7L9jjlA+WgHDSId8GI0WeQxxOhVf7O3v7xb50Gyy0L7e+voY0SStgtq5vYTz33As4e+403vOed+Dzn/9S9n6d0qHNLftmT3jc7Aft5uYmPvjBD+L7vu/78MlPfhJnzpzBc889h42NjYX74OOFF17AJz7xCTz66KOHbut2igorFOT4bY/PwZgY5zdetudiacBbyQcM/tbawJhSu7OMufznHrC3yQ05zUBwuC9HNgANr7WQES702YOTvBgPkQPEbU2HTtEB+GWUHKIhYKJMBp13J1/Gm2mJlNpD7hgtQKGer3dtZgiYcr/o1LlEG9hnJYuBiGPSydXkFQbca8sU20NA5J+V+T9nRQURvx9uv8iWY/JmW0psDnDlOMGbeomtiQw7ziEHfgHLDGunFiZYkJ2yM+1iJ8dm5STaAsW2TFNYsgoNtynlwG84CWGfTbYGNAuD2D/78zrFZJT9IQ2VMqANIApiNADjJNE5M2zrULthm8s9Jzb5XEpBFMRzgU5Ryi5/REB1oRrDNUDZ5/R2Wndmn2w702kMrVJLjs9xH1hfuoLxdBXXdu/H2bUXoaQ+B9r2uxkEz9Pno4jXAxD+h//wH+Luu+/GU089daTtHtfz/FCMcBkMA7PZmbW1Vbz1bW/CZz7zZEu9TaA8OPXtjvcPMBwODtPtzhEyNYuUc6mYQx3SkCtkLvP3gHyet27AXAMojkESfRSGPnXAuZqLykjNAfboGq5FMZgZm3vL6JNGIowtGmFTX8WeuY6Ux8jclz3AIjcnTRGIIiiKXV3iGMrtAwtbwyq227NALXBWbGLwrMbJLeS/jxRGwrrDuXFXwQ2a6qXcrbndHSTuTet3ySGf57uUwj4CgP8bQAPTnUmssz6EUmkEr+c7X7tcq9UJrubtbGysY28ONjiML33xT/GDP/QhPPTQ/XjxxZfz7QeTJrYjZda8Ohlg17n9GM1/9a/+Fe677z78+q//evbeQw89dCRtf//3fz+eeuqp/3uBcE3ppDYJ3yQZYHP3DO469ZJ1gS5dEwUQTNXhg9bGMjRh1A2aQwBcBssdy5FUB6J8NKDYy6KB/BL0vxWs069jNQUAmCxDHIQHTeXIwDACQOabpryur3KS6PK6Ya1mCUC0L51UdozOQbDAQJDCVNhgpqaxQ/5sCdNaKofLtcGODU4h0BCkYgeYHDDDvpXIsb/ssp2V+2e3aZe3pZTs12HIM8K2fnFmkuWAZOScsTUTEgKIPfPsgLCy7SkRkCgodowuI8sR9vnCEoDg7Nj7kRbbPGTlATBrEIk1xiKBGA3RbCXSGSvMgFIgtiqCAhgGYEsshecx5VLoRWJR8OvWK9ce91FbY3iOyMqxNYHhBa/bJOkj0t1JBR9EwJm1b+OVG49jNDmJtd7VvB+lUnN2haJBVtbOUU7E3QbxyU9+Ep/61KfwiU98Ap/85CePtO3jep4vBITrB5p24FbJ7wyWJSJ893e/E8899xKuXd1CfQ5cGNXB6fjgAGury4W+zBPzgtOjAMO17XbsQ9H8SlnApqJCrqV1Rk7sg7nmuL8eoo09LjDgjqmdmF1sg3Ggd3FDrSJCHwY2//Yg3caU95z8OC2BDYItwaShVQ+xWoJ2YNi3bySBEQvAhY2THOY1cavRoIpw34e3Csn2JzOOqgLDcD+bjk3TcWprY1bUTbzMkuWXt13psx0lAojcjHpLLecFJ1Bmr1dlmJsn6+rbWl9fx/5e9zzJMKbTKb78x1/Fe9/3Lly8eBnjcRdAXQ+G7aTQ8Uuj/cD6MOvPE7/927+NH/qhH8KP/diP4dOf/jTuuece/O2//bfx0Y9+dOE++Pi1X/s1/MRP/AT+7M/+DG95y1sQx3Hh84985COH3sZ3YpRrBjd9VnhfgKvb9+LEyjUMenNO/LhBbhSlRUZ41jpNYHie6Jqf52WlndstLavEITJxINhJfFlBlGRgOAS3nIHW8L5cD4JtXi2DYGXQFADewro+nxhVoOwdo1OhrH6wcSDYgCu1grMKwmIK93vPBCvSTsys4aoHB+8V7+Xs2o9gSzQp0g7UhyosuDxihQgKkAi2uBEhhi3H5PE/wxpjsUjGHBOAiIBIEaAsOLaSb3JMMiHhHHCXmeFCf4Wgla0h7I2zPCD23wmC9Vjy7yR8HbL7SijLE1Yp27rEkZ1YEXIQvw4MzxNKNaj/qmHzlNVi8uhyjrCLIjtsQeOhnaMPASQn6QCxnuZtzHEfiXSK0ysv4/rufVje2KytRQyg9jjcqjiq5/POzk7h/X6/j36/f6i+Xb58GR/96EfxP//n/8xSyo4yjut5Pue3mwPTRQauDz98P3r9Hr7+9LNzbhPZdsf7+zh79lT2aTvYa2fDmj7LGbvjAZLzyMh9nwAFrfrQqoeI+oiUZcWNJEhpAiMTGJ4ASAtg2K//nZRPWmUPm6MO2ImkMAxMhJHyBGPaytjclCcwPAVLFQRbxlUj1ksg6mGg19GnFWf8oWAoQYIJprQPYYaiBIzU9hV5DnC+DwBKgN0bbNmF7HS1B4XdjLDqpbOFY9JwXra22cRityw/TxRMxlDqv6SAzR6zxzPIs/YTOXmOcLHUVS7PPsw53L5e0/Xnt7e2tord0ahx3byfQHHizn528eJlXLp4Be9811vxh5//YrByywRHY2qFG/kdYxyV9Krrg/aFF17Af/yP/xE/+7M/i3/8j/8xvvSlL+Gnfuqn0Ov18BM/8RML9wMAnnzySXz+85+vnZm+Hc2yCkFRtX4w0DhQ3Nk/DRGF9aXLWJSO0tpgMq16eDTGPGDYM1aAK/vU/bsTpaxJlnfarYtCfnH4GrkVBAlEOTDMBpKVxSltj51cmW29YfZ/O0lzOXx+KshOuYY5wNYxugiKw78ZxVuCB5C+fnAigkQYKQxSMgUWmGcxwVBZ+aQQBFcOHfL2UhjL6ooBe/hMlOUpK2duowNFlIJCjMg9Gfy+CVL32x4jNxWofM4wQXv1uvtJYWXTiRBIUATBRsMaYDk3acXQLIiUQaydo7Ri6AZAnH23orI+WYbY5/wqsLG5wp4VhlKgNJwyMBkYPnTMa5RVZnwPGwuwwvXtHO4enKQDxLrqal+pj9wQy/0t7I1P4NroPpxb+Vbep7bjRBEK9586k61jiqN6Pt93332F9//JP/kn+Kf/9J8eol3BT/7kT+Jv/s2/iXe/+9146aWXFm6rKY7reb7A047RZWBZjn6/h7e+7c34ky9/bcHO2u2OxwdYOmZp9Lz5wPPEvGZdNiwTrFUPPbWCod7Aksp/+noFmvpQFKPuK71VIJiyx1pVVlUn+y6/rpPS5uunYJkgMXuYmh0cpFs4SLeQmD0YHmcgOGRxrRRao6dXsKLPYgN34Yw5jzPmLE7yKazwOgZYyR769Ts15zlQYh/zUDU/wWoNkvbCcSJV+CmsX8Moz2R4qfgdtH1/s9avBuf52sLOlTRtBcHt0Q4im6ItJ7rMFPu2VtdWMNqtOkbP7mvej6985Ws4d+4Mzp0/O6Pf+XdZ/vtmRV1pl3l/APugXV9fz37+5b/8l7XbY2a8613vwsc//nG8853vxN/4G38DH/3oR/Erv/Irh96Xv/f3/h7+2l/7a7h48SKYufBzu4LgJidohKC4NIBNTYTN0VmcXnu1MvCfJ7Q2MOmcg+OwL/51iYGpY5vK+1lhhetyJGcxwmFJJcDS5Fl7sADYg6Say9IDX+/sGl4PTSyOclm1mhiRMhaYKYNIM2Jt2cqw7I/PC65rLzTJsnnCuXmVAVf/EWcgttIv0pkkOgfEUSZj9qk9BRBMxjHPxtYrFkbifoxP4HLH1LZg/8UBYPYmWwwgYUYigolYN+kJMyYsmeQ7dMg2jgW3rHD+M2WFqdGYssKB0TgwEfZNhP00xoGJMDERpkZbFVjpmGasMOqdpfPvvZh/zEaBjbZSaaPBqT8v7G9wUI/aBSnOzrHil1q7yWoch2GTpNUflK61sLRZU5+Ooit1oJsNkiRCFLWUd0P13pD9LSmIgNMrL2N/egLjZGVGH0pKG6WDycajy8lu7cMRPZ8vXLiA7e3t7OdjH/tY7fb+0T/6R7ClyZp/vvGNb+Df//t/j93d3cZ2jiKO63l+GC2D+12UFTYNDt/0psdx9ep1XLp0taGdbjEe35wc4S6Dz1ny5kVyLNv6oylGTy1hQCvoyxAAMKWpfeyoBEbym0FB5nRMzHYYRwG2yyDPRpVhA4qy47KRrs8JzkAw2ZxgrexkwUCtYYVP4jSfxDLFiIgwEYYWhZRSqBrnz4ztrUQV0JeXK0u7Q5azazRNDhxlVCYnyhMRDaxs7XKl78oy4gBLUjke5XvHbPZ6MRDcJer2b2V5CZcvXUNd3m64Xv227TqTyRTPfP05vOPtb8b/79IV11mVqQZuxjV6s+PChQtYW1vL/m6SXd11111405veVHjviSeewCc+8YlD9+H69ev4mZ/5GZw7d+7Qbb0eopH5CAdrgGM8cgZ2c+c0hv1dDPvdSoQ1RaRTpF2l0YX+tbDBHdmWueqBdpFIc82EgAIAQV4TljNmzpc0EXFAp2bwyTVlTyiQ4IaTELl8WsPeQq3014fPB561CwK0plT4e7WHqlm/Akm0prgAfivbgc05hgCpS/2x1lza5vWWHooc9EfBlV3y0uGs707SLTlvrb3JloiTiObg330jLr+WkBYOjsqeuIoEmhU0CVLFMCqfVCASaGGX21t/zJipkYy1EyFswS8JSClQEkEBEA9wIwDshO3KGWgcIqxU2Y0PmLPX5c+K732HsMJN12t4n5qxbmr6WNVbeX8awu93HSiOtMH68BKu792He9afsWq/kBUOStDdLrG2tlZ4PjfFz/3cz+Enf/InW5d5+OGH8Xu/93t48sknK8/5d7/73fjxH/9x/Jf/8l8O010Ax/c8n+sMtnKQshSwyqKUTXyWloZ4+OEH8Tu/89mgtcWA0/7+PqIoQr/fw2QybVzusE7SxagOgGcBv0OZRtUAgsLMrMS2dp+LqWMwvdnSTaiwcqxR/301AJASaCo+yMObvZV1aYrRp2Ws8iqWKcZqFFkTDkM4MH6G24DFlpGoywuuyxkPGUSfE1w1ouIC81jn/F4LNCsguD2n1S8770RMuK7fjt0fL0k0CBUhbU7UVZOzXE5ePD+58b5RH/UM+3FMEPg219bXcP3Glnu3GQzPiueeewGPPvogHnjwPrz00rdbt5nHPCz50cRR5SB1fdB+8IMfxDe/+c3Ce88++yweeOCBhfvg4y/9pb+E3//938cjjzxy6La+00PCc2eOnLYk7WN0sIF7zzzffWO+/dIgOIpSMGsYtrmXc4UHw7NAMVAcIJeBcg1wzuoILxIKyOBKNn/p8oUPGWHeqlZckOT6OrbuU7AJQHKDaZ5asEvsVDk+V7hWoQUNJQpM/n5tj6chA4Z2mnW4eYIIkWgY2JxnVeqv/9sJl6v9cSDYMslWbm3XiTJHaXHycQv0LQPue+zBsJVJA2CARdlnvTu+mgQxK7Bz3tJKEDHDkLKsf2CaJXApKS1qCW9mZlhZh2ijwEo79YAChYA0AqiLtJ/RXJGrXFMYyPLgLSDW2WfZ+eveO1IwHESXXOGZrtEFN/uGSbDgvWnaRz92E3gz0isKQLZkyHdi8Bp2xm/D3nQDK/3NbDsEN8HYkDN9s+Nme3icOXMGZ86cmbncv/t3/w7//J//8+zv1157DT/0Qz+E3/iN38B73/veuftZF8f1PD/yb7WuJMib3/wGvPLKRezs7LpPigPpeQaxaWowHh9gZWU5AML1rGGn/tb2oYnZ6db+IgPXislYYTtFsMBkSx4oqEyWlP3MyDtctH+LRtM261i/WQB4Vu4sELCRJbYtlw8TtFhbjogoGzjYfCSDBFOkMkHqcq5FbM51E0tZBmOUMc8RNNkJCiMp2NUPZtjySeG63c3SgHnOw3IbddHsKp2XlArzee1gKQlALVfY0DYwXO57EwiedT0e9Tnc9B0opbC2uoIrl68dehvMjK9//Zt40xOPNQLhuskVIL9ebk4d4WYJYNf154mf+ZmfwQc+8AF8/OMfx1/5K38FX/ziF/Grv/qr+NVf/dWF++Dj8ccfx8c+9jF87nOfw1vf+taKucZP/dRPHXobr5fwA1TitDDA3Nw7h5XhJmK1j8MOCbRmKGWQphF0r3mi+qhDlM4G++SZnCbg2xUQ+xpBIWO3+DyYXV0JFOc5qP6u49lgC4b9+IgQOhkbsn97yVEIhsOr1dfbVcFr7bjdSDSErEuzAQpGWfkYwsDiPq6wxCEI5tIYxeJPhji5tYGCdudT2VhLieeb/bFVmdTZBCA4ccWdJLsDMrjmeeBwb1Y2Srn3jABafDYnObDsgC8JjMrzgLURxMpYAy1h+Pxr/3lbWBUAbK3nLFdYLDss/j3rLE1KwZtngRVA1pNaWFlwzDYH3Q1fcurfU98AsioYIRgGHIup7G9WNakBZiYYbgXJZSA4Bxt8aEOtmkhZIeUYw369h8c8oYixMXwNm+O7sdzbzImKMhh2UakhfJPiZj+fu8b9999f+HtlxcrMH3nkEdx7771Hso3jep7PdVZaPXj9gNGDBfuH+00Kg0Ef999/Dz71qc9Wlq+P2U+a0WgPKyvLuH59s1O/iwP1Lk+yOWWrDWVhFl0/DC8rFeEMoE2xnz35UkyQyEEGTtCQ73MrHKQbz5UO/WvM5ayRIlfWF2RgOAPBwbQqk33AjtmAjDXuGHGKfRpjjB1MeYTUHMDX++26r0QRIjVEX69hSW2gR0uZCddYdjA2m5iaPRjmYrs1N6Z6mTjQxIiW++J/t8md6/KIMzbbgWCbe56XsTKurBQjaT3HQzAcRmOfZ8riZ7eRr3N00umNjXWAgGvXr7cu19xusS8vvXQBb37zG3DPPXfh1VdetW8KO5O1DveCBibo9Rzvec978Fu/9Vv42Mc+hl/8xV/EQw89hH/7b/8tfvzHf/zQbf/ar/0aVlZW8OlPfxqf/vSnC58R0f9VQLhuIJqYGHsH67j31Dcry881aA0kknGUIk0i9I8KCJfzEUPGp8IG59V3qbwuHDMc5vn599pC5e3V3XqkJHe2DKADuorBpKC1A1bM7nkkzinabcIDY+ICk04O+BhlU3dMyyjWAmvJATABBLITvmJLFlnuNgILZwaTHvAaSZwSip2Q2N6X2RlsGSQZoM3ygsOygsTQFIHBiFz7fsK+ekiVA8IRWAQ9RDBQtvxSllPMSG3PAKBgrhVGLovOIwVlINsfMX9U/SE0QohcXzQrRC6XWBNn6VF+goLdMWXJn1R5GSsFIpPlBivNeV1hyllhpRhwueMgQlZSKdwtzwD7IWj4O1umDH4d41snj/YgDsjBXAcwXBsBCF6UTS6wwW0TVV2k0QAm02UQBL3oYKH+lGN1cA2b43swTtaxHN0AkB+z7PgBVfB7G0mmv5PjuJ7nh5yeqWGnSk+KRx99CFeuXMPu7qi6rF9lRn5scVsOCGcllMrr2btGwf25Y75vE+A46ujqWh2GZRQVEtoHlAXAgHWNTngfKU9s7dtye4FE9zsh5gHBdVLZzqZNhfFCCP4MprKHkYoABg5MDIZgn8bYUtcxNttIeFx0nQ5yOOuYew8etepjGG3gBN2NM3wG6zRARISxMdikPVyLLmIHF3HgWNTQxKqp/bpoYsbrjk2ncx+MfPoZ8Oe+ZbUjRGqQAWHiAwiF/c/VHd7cybex+IRQN1DZHIekbYI4e/Y09kb7MOki7RWVL/Y18I1vPoc3vvExvPrKqxAYO0mTHX9unbGd1817kTgqV8p54kd+5EfwIz/yIwtvsylefPHFI2/zOz7q5HvB4DKs47lzcA5L/V3EUQBaK2ZbLYPemhzBKE6QpHHDCotFm5wyZIMBD4BrrhMHECrAt8lJWilA5QydbzuTSIf9UwxSkv1WmiGaocUCJKSAUgSlOAPFAscI+s0FbsXKb488EFYWREPVptSUg2CBW0SAJkJMGixA7CTMTBFSBxcBr+wxltGFceDSwCABkYIS7fwvdAZ+Dex4I1erWECtKYaQyaTUdaHIguBIODuoBDuNnzq+OYWp5DZ7mbUKDLVyY7B8v8vHIgxbezn7AwpA7EBwrBjaSHaMlbIlkOzkBlfatpMbAKBBAZMvrCAksGWzvImVsgZZASsMjQwcE7nccgWQTbJ2EzVw4JjtuQhXBoyqgBiF/GAuAtYMMBfBcKeYQxJ8JMxvDbCsuwccTJcRR5O5DbSb6pQrYqwPLmFr/yyW12647ZoCGA77eCtyhm/F83mRePDBB49cwXZcz/MFz9b6AXd50KsU4ZFHHsQXvvCVOdpuGujlLO1otIe19dXK+136VG6/UPalEyCvymHniUVAqX3QWElqwmMYSaDJDjTYSVUNTwv5p8BsEDxLnjyv/HTeEj3l7ZXbKi6javrWgZH04AIGLCmMpJjICCkMJnoMLTGYGBMZYWy2MTE7SE3uOt3W13IfYzXEqjqDu81duH8wwNkBYaAFu0mEi+MYKiEk+iAD2iJFAFpXXqn8nRa2W36fuk0UVNqSvD3fhmfRlauxrKCt2I1iKErBzhilGv56rE5IlfexPuqPwfFH/STY6dMnsbW9W108iDZjt7q+v/TSy3jbW5/A6toSdnZ2MzC8yPVzHNHkSDvP+nfiFgRFlQErsXG5gdVHPVOE3f2TOLc+R25wWzhQHEcpknmdozu13zzo9GAYCNgvlJibEPAWAELLYNabGQVguAqCHVxTAtIM0gZKK7BWVgLswLAHxSJkQbEQOEC1hBwEK+XEvGSXj7VxeddOWg37UxqaWxaYBMoZT5EDwpoIWggMDVsBuVgHOJ+UNUV1HwAWjYQOoKBBUmSRQ7Ihu38JIKRB4MY0agUFdiBYibXhSqFgYNVavuaxl197n2qFOlMtz9bmv/0Cnh3X7rjYCQjJZOXs5NIJK0SKMTUaCl6ODkSwZZW04uy7C+sGZ3WF2abRAOzqCOcPR+8oXRfCbl9YATo4sdidd3WPg3JucBsK9I7O8AD4EGDY9/kmOCN3jYPpEnozHKMBoJLfS1EVDLtjsja4is29u5CYflaWKQTDFRVK0+tjijvP56OPeescoDl/1s0KBvqhc+fOgplx+fLVhvUWi93RHu65964Z/ezOBpfB8FHE/ACgtH4pv9K2lSI1DEUxDE2z97OHl7e2nyMntI6NbNqHeaNVOtvA5IcPYQvIoqx+cphza2QC5tSx4O3sqd8PZgNBiokZQcweJq4WM7OBkQlSniDlsT2Oc0r3CRqxWsIKr+Ns3MeDy8ADK/tY0gZb0x76qof9nVXs4BTGahMpj93Nuft3BTSD4O5lnWpUHEBmfEJuEOG3rci5hiLO/q7rU/05l4PhRWKxc+9oQfPp0yextbWNedMlmkLAmE4Zr756EQ899ACeeurPaper3D9yQV79/MMRxutlxrlrvPLKK/jt3/5tvPzyy5hOi3LdX/qlX7pFvbo50TRoFRVhvDeEVikG/YlFjUeUvxdHCQ4OjqmyA6f1ZZJCR1ilc8lzTa5wLStcDlXWpeZgmBQQEn0ALMtHlg3WcZqzf6gOsirlTBwoy92juQCktGYYdnVv3Q+xY4ZL6cuFXchAsGeFrceAQNuyRgV4Wb23CYzNqeUDNwFaPZdYDBRpe68i2wcrmc6Z2VA+nR+vGOSURb7skoWgPrnL5e76sQkIETTKVYyzlFlBxuLq4LdWAk32B4B1cnZtcDCvYYSQssKEbB1gMuIAh60x7L8f/135WsK5PNqmWBERtDYZWOkCGTOW2DHJFnQRfMkmexpK9dHWlCfcEKEMepYk+rBmWgVWeEHA3RjB9TxJltCLxh061P3eplWKpd4WdidncHLplbyJEAzfwrjdns/zxnE8z4982jYsH/PA/ffh5ZdfKy3RXbpc/dz+vbO9g7W1VTdb6p2s64FV536XBp5NdVzniUUBQLZeacTrWVD2mplgH7uUtSm01eHYHDUTV5HyzpDCEkWI9BADfaKQc5vQBGOx7K3NuZ3Ag+ja7TpjJyt3NkjMPpiBhMdZvzyoFkmL/QzyjNuAEJEt0TTkIVYjhVP9BOeH+1jqTTEYD7GVRFiNNIbJkpMa6xYzqfbj5vtV+LNuIFM7EZMztn4gVefujHAQQwCXHC7L+eh5iaTDgsUmwHncrHD9dk+dPoXnnn0+WKZ7NDmACxgvvnQB3/2edxSAcJ06ww/w8nshNTDxd6Iufvd3fxcf+chH8PDDD+Mb3/gG3vKWt+Cll16CiOBd73rXre7ezQkVWXAY1hNmg93JSSwv7dr7wLxuqCHDUpJHx70E06RXACdzBdt6pcSpkx+mswefZTAMIDMNckxY/p6TSANFSbRwVSKtvO2UA8NBNzJgRGJl0RGDjAExQcUpdDDgDI+uuHq1/rcJ5NEhAFYkgGMxMyMtCAgCraxzctP0o12OEJGXSBOECALtFLlOmozI1guGsgBQdDYR6p8DRjy4DfsZeG5IcYLU2mQFILUmhadswKWz3ZZgOXL7Y0FwDIWIFCgwubQ50+SYZWRjT+VAOcGaYmlviuXNGNwWwn1IhaBZISXBNICwVq5uDbQAL4e2UmbDKssjtkw/2++Vy2Od4KbdkCccyqMt8PU0d9BQht6bwW+5XFLnOAxgPWqwW4qm1IiD6TJOrl46VNvFe4ZjhfvXcG3vfmwMXwHJ8e7bnegex/U8P1IdXmbSAwWtNO6+5zwuXHi1dflZILguRqM9KCIsLy8F77azgsclrzxs2379JiBbd3zyddLWdevWA7qB4Mq6HdZpZaJDGXFhuRyghWywciB4OTqDs+ohPMSP4o3yIN6I+/GQeQjn5EGs6LPo6WUoigvbqfthd6wgDJYJUjNGYvaQmD0nhZ5kjLrtpCqBYGT9bAsv5rKOlfZBWchJ8s2HMugGECvg2kEEwYLu8MefK3U5zN7tuXqe+HUiB8yLN3vPEnvnUG+o4t/PpN0dzv/5rhOu+QmOQY1yYZGo9qm6rVOnTuC11y7Xrtsc7ftJULhy5SqiOMbJjVP2uAeTQtX9Ua33yqOOCmO1wM93SnzsYx/DP/gH/wBf+9rXMBgM8IlPfAIXLlzAhz70IfzYj/3Yre7e0UYXYz/lS8RF2D9Yxcpg+2j7wCniKAEzwcxbT3hWWZVZn9eEZ7VEqXwwWwcUmnKEAYeqFDIHKj8nqiT4cZLoOAVpk73WcQodGagoRRQZKGVdtcPfnuVtcib2RkvK/dYql0Z75+nyukRApLw0GhkrrMlPwRIil/erHcyMqI9I9W3NYIrzZ4m772fVFMRPGht3eGw7CsXflUMczCIQFJSo7HfFVdr3ESoDwZoUNFEwYrBMsC+nxIX17bGJ/PFxP1T425lpSc4KGyEkrJCIzRlOWCFhnTG8YemafELDPec73Psqn3ujNecsDccKtz5C6upbA51Y4bpoynPt9H6L+dVcZZK6RrANZmCSDrA02Jm/HaB18m/QH8FIjMQMZ7dzk0Hy7fR8njeO63k+5zRwmzywKIM8deokmAWbm10ftPWSTSCXt2a9YMHOzi7W19cwGu117F+1nbZoAnWNrrsz8kmb+lPfvyLwKki32/JiUexbUxmWpnXq+lZXDmvemBd4e0l0Ty1jnc7jPjmP+5Z6ONUXaAI2pxFe3T8NCPKcWzMp5rsWGmzrc5VRn+XYXCtZFkbKE4zVGDvJOq5MIvT2VrAcpdhJYlybaOymKca0D8NJ/v1IdTuF9v3r0AHbA9fSOpLlbIUsrXsd5A+HLtp5beD8+y2060A0ZyyBybZVPABcc5yrx6vpGpzlgN31/a5Rd+2W21xZWcbScIhXXy2rWpoiZ9vL123YvghDmHD50jXcdfdd2Noa1W6/Po4fDN/sOoXHGc888wz+23/7bwCAKIowHo+xsrKCX/zFX8Rf/It/EX/rb/2tW9zD44tQ2iiOFfavDw560CpFHE1qFQaHMbtRShDHKaZJD1EX2WJhMN0+eC4PzgtsTmOHAjljnbTRg2B2jHAINHxesWeGlbHKF3/rVgAptkiTFRAZKG44/5kQRdUBaZpGnQan3mnaG2pRDXC2YA952aRMFm0xlxGbP6xFQ8EgEgszYxpkz2lbsTeBQNmJz1LOMMFmGWuKoWDTlTTF0O51gR0OZNEZUCbtoG3+z35uOVzlRMFWtk2IYM2+7H5YMG/bBrJkcOT8bjhfMSssYPbgwrLC5FleBVevmGGYEKlSjjAjywsGUPt9+Ch/v8Lk6gobyw47eTTKpllNoBdAxTCro0S6NY6Z2V0kMkBdum6n6QAsGkvhZN4Ck2V1oYgxjHawl5xAb1i6h1Vc52+uVPp2ej7PG8f1PF/gqqlja6pMyrnzZ1xucLhe927UGQeFsbW1gxMn1lpYnWaZbNcIWTYPQMK+tkqPZ2ynO6OUgxegHrg25vjMYOFmgeBZcRiWatZ6imL09ApO8imc7/fwyKrBE+v7eOPaPh5dTXDPUoRTcgJDWoemPmadyh5cE2lEegmRHiLSQyi3bh3LOA+rLjBIeIxtXMNr6QjP7wqe2e7jq5vLeGa7h5f3GJdkB3vYQsL7AWitjzpQ789BrfqI9ACRGqKnlxGpIbTqlWbx7fq2IqPJ2iKKoCiGVr2sDa36UKoHRT13jCJXP1gXgHEogy4PjopxeAlx+Fn95+E8/3wxKwXCt3/PvXdjb28f+/vF0gwhcJ51HpeZ+vC9S5eu4q7z54PPS2x+DYN/J+aL5eXlLI/orrvuwvPP58ZQ164dvjb0d1xUDGCi4sDNAdzxdA3D4Xi2dLk0KJ7N8tj2e/EU02mvU5ez8G3XyaIbttt5EKp0vWQ0BMHzhgJIMxAJKDL2R7OVSEcMFTGUNha0KgZphtL2PcsIOzZYG2jNCN2iQwYyBFEeAHtArFAFxsrVpfVMcESAVlVGmBx/68sYRehnrHCkBvY5ET5TpHjfz0Atxdm6MfURoe9Y5kB2nSmZdIEFzllhKh1aW/c4hkJMGnEAgi3Iz8Ewi2V128IIOQaZGkeHth0Lwj07bNj+9t9DzgZ7kywrcS8HqWqHxEmiJWN+3Y8oO0sRsMKF9QrDbM5/Cp2fcf7WTCB1XbayH4us2/T+wlLsFHvjFcRqggjT7L2jjKXeNsbJ+szlROnvuMmD2zWO63m+oFnWbFB7+tRJvPJKmUmZb+DcNsjc3t7FqVMb2d/zME11y9bnU6rS337Zap/rSv0cVxxmG/MYabWtOw8Anp0LWwWXRAoxBhigh/UecKqX4OxwH5oYwAquTSKsqAh9GSJSfSQUwwqTS0ZX5GXEffSiZcRqCWvx3RBSMEgw5f2sbjBjMhOUlfsZfmZkgn3exCUdI0nP4druEjQICRjbtIvrdAl75ppz+A4KszexwcE+FIzDAgkbYGfdjSik7Jnh1PXTs73+PLbrx2ro6gNrt+7Eld9Ks2vDl07yZllN+104BgXFQDN7PqsNoO08LZ6DIhy81wSYw6iC4KrSwb6+5+7zuH59q9i/Vl+DahvNwbh+fRvvfNfbQKRRLjMQMvvFvi8wYJ8zbiczjve973343Oc+hyeeeAIf/vCH8XM/93P42te+ht/8zd/E+973vlvdvSMN8tda2SG1Jg6mQ6wt1dTGXmRAV8Mex/EUSdKhhFJ58Nqxbnu1nQVYmVnrNDFs5HI43UfEAons8gTYHGEmmFTZe3dkbE1ZZQ21FNnySpqtqVIEwLA1WvKgqmjCpArXlM8TJmcGRZLLfYny3OCIBJGyrsjM5MBz9dq0zKsFtNaoWMNQAoJCCoCFHQscssK2okDkgG9MfWjJWV5rgJVCw92ng/u6Z4PDey6Xpd3u04gUekohCky/snUawK9fwt4tCYnA1hT2YBZeDu1ykEWylRgogF6b1mQNzRi5uZl3jC6zwORM08IIyykBOn8iB2WUhHJW2PrNWtMskMsTZiAzzPLonxlQXDlPC3WEgU4sr59QOrQj9BEDwlo22L03nqxiEO8V3jt0BNvpRyPc2L8XrV4Ht8A863Z6Ps8bx/U8P0T5pPYB2YkTJ/C1r31jseY7xPb2Dh555IHCe3UAd5bzcbOkOS8hUwmqB8P17czeZvkzX/KnyzqHKXfUZeKgS23aRdyly/vZaHQFQcpA4lwdhfwMrf1cI7IPYz2wxhXe8CoALIr6GETrOBHdhVU6jUf5TVAMTJBiS+1gS13BiK5gkqIRDDeB4BD4GZ5ggh2wJJjoEa6pJZCTl01khKkZZeZebd9nY764m8vXZPc5A8JkIMxQxI1ljTwI7qllDPQ6epT3bYp9KBpntaiJVJYnpiiqTGRkEm2pvn/Y6NpGppAoyP/L102zrLrL+Xr27GlcvXajcdvV7czedvj57u4IEMH6+iq2tnYqbcyTynGUcTtJr37pl34Jo5GVnv+zf/bPMBqN8Bu/8Rt47LHHbkvH6IozLFAZnIoAyTRGf3W/vbFDDGp7vQT7+8vtCzVIoruywXURMsRZSaVFB6plEByybgp5BZZIQCmAyB5vUgJRzkDL1wNWvhatBcFkpFBTOAdU/nmoMpaTueourZVAC1vmNGCJPQBOSaAVQYtlhRVcCaFgd8Q5P+S7pACK7cSgBEogsqoif2/2k6ReDh1TH7H0EaMHJQpMFjhDAIYCKMm2Us4Ftv2wvUjJIHx4aVKIiRBlPzkg8R5ZPkI5eNauEBJ2kvBATh0+Hsmt7I+uLWklDgTnoKMAjsWy6TmgyJn4yr4JgZjAxsu/jX3+KO8S3W6aldUUDsPXE2bK5dHM9npnhdAcjoC5zLMqZYJmxVE4zS8IJsfJKga90eLb9VVWamsWG/T1PgQKCQ/Q0weVZeriZsikb6fn87xxXM/zYyj2BwyHA/R6MXZ27OCunm09XNy4sYWVlWX0ejGm06R12flLGeUguGxAtIjU2K/bNbosW7cPXQb4XaSci4Datu2IcAFMdck1FmFMZR97dIDN6RCXxz0AK4hIcG0SY2tKmAhDkcJQrUNT7Iw8EiS8D8NTsCROYr2Mtegu3MsP4oxaxjvWhxgIY2wGeG1/Gd9OlvGqhgWFaQLv0jvzWKEKxlK2NYgTHmfnkMBk/QlB+iJhz0d3Xma5Ve0MKlEM7WTUQ72BVZzCkC0QntAB9ikGFCBBPlc512smQD3Edd12zrV9B2Gec5Udrq5fBsuz9unc+TP43Ge/WFo/jLb1q32pBmF7exdrax4I5/cXf43cCjAsyAfdi67/nRIPP/xw9np5eRm/8iu/cgt7cxOjhRVOTQwRaswPzqJhMJyVRvHbKLlGA0C/N8E0iS0TqaQdzJYl0Q0x1yAzGFx3AsRKAaKAIOezQDlW3Q4BJfYQswWVdhsMOAbY1x2uyx0lxSCloMRAOQtkw7mbtG2qWLMWQCaD9tJorRhaVA5+Saz8WQm0WBY1UkAqWXds2w5+MjFSpBBXtRdwrK1LghZiiBoADPhvxo6LLIOsKc5AcCR20lQLYJA6lJmCobM7IUFDBbWIU0ozibSCZYZtBjKsfNuB4NhJu32dYCEBBZJo7xKtAqCcCgCmQi3jsszcVkay2xPHe4sQxLlAe1Y5BMP2c1ceiYrfcTl3W5ggpABTnSYV5wpeqErl5dHFL8tacDDsMW197LCdREJ+aVswXE5xKJUC6jjp1coYlyfcDgmSZ6VhjKdrOLl2xd57Ssu23itCNZ5frg4MkyDWY0yTfmcgfDPidno+zxvH9Tw/xJlaZPPCwdryyhLG4wOk6fHNjkynU4xGezh16hQuXpxtnz5rMFk38Mzy+xyIywFPzUXTIhvuAhRn9a1uO3WyzzpQMe9AuiubfRhA59dv2hY7QLsZXcVr0wGwO8S1SR+RAvYS4NrUYF+miNHDSTmXzTQnNMUo3saebGJi7ETMUG/gNN+FB5eWcW4oePTUDtaiMXYmfTy3uwK1tYZxch5jtY2UDsDSoUC7i7KZlpVIMwwmlfeBtu+y1C7yMmRlE7TMsMpJsKyTc2iW5dezbsSKetCqh55awTI2sMbrWIKt83kgMUQxUkyQknUE9f1kMa4f1jE6dOHON3I0EyvhcnOfr26yoa7Ntm209avf72HjxBpeePHlufrSNXzfRnv7WFmxzFk573pRc7o7UYytrS38j//xP/D888/j53/+53Hy5En8yZ/8Cc6dO4d77rnnVnfv+INNAagmaYxI2wm/ViCcrTs/MxxFBlobTKc9DHp7s1cobbNcMqkrCO5knhWGUvmPHw5lOZhsUVddTWHAMnIaFhADEHZo0wMcV1e4HB7IejmwUlKT8lkEwQVpNAk0MSJlPzPMMIoQCSEWQsIuF5gEsbL5rgS4vFpbh9zAICUDgxSGEgtckT9rMnZYGEzG5gsXgLIzyUIEBeVMt/JzTNx7QoxIIjAsU1z5vsAWgpMt58cWimafeyY4VpTVB7bfAsE/TX3OMAWMMIOs0RUA/yANc4nJyazFpeba3RbXB2SMMIL2xOUQZ7JoD5CZahX0Xg7NRtlLyIFhUpx9txaHW3l0WXw4tzza182uC3dNZJd8Re3QAojnLa2GOUHwzBSFct3wCNO0hyTtY3V4I3uvcbKtId2iCQSH949YTZByP7sPNqls7sTNi+N4nh8PIzwYYDyebwZlkUHw9RubOHnyBC5dutKwxGwJd6EPMwaeXUrAzAuC/d9dXKG79HHWtmrb7MiWN5n4zAJ3fv/qWOGyW3P2uTCmvIddvopXNLCXnMHKdIjI5dxO3YP7pKzgRNTDiZ5CrIADA1ydnMYl2cG16CKmso8l2sAGL+OuIXCqP8Ub7rmA1ZUtjHZWoV+7D5vTE3htaw1X1RIOVAQ2VSA8D1MuAZhsXa7huywfr+xYOTBqJAXJJHf5lCTP8Q1LVLiySj4vuEdLGMoShuhjSJGFzpKXuiiC6RiEJHufxYAlyV1EWyX0dVLhfN+aotu1nzOtZfC7iJqhadsPPHgvxgcTbN6wD9rZbHDx2shl//Xfr4/9vXGpDBwQOnCHJlrhdo4zbqccpK9+9av4gR/4Aayvr+Oll17CRz/6UZw8eRK/+Zu/iZdffhn/9b/+11vdxZsaxCk4IUS6XUVViHCgOMcAsN+b4mDSnx8IF7bdzgrVAd9MGg44V9eGa8ZJSEVpO9ZPUwCRpd48uEjTnA0u1BT2L129VyV2NNUwpm9zFC5/XscEh8tpxZaNBCEWwGjlgJ9NH4pdjWHjQLA3zQrD3ulTTGmKJHiWeCWQP2I+p1fIl+EzIGo+B7y0mDK4GoihywrfEtS0rGwu1yb3Y42+8n0Qgp2jADljMMsUq2Af7Vfm8qOdHDok9TVZRtaS0wQSu754sExF0zLbps0TNqxs3WDHCisU73mZKRbZEmJamxwMi2QAWbxBll+v7B4dHuYyK8yMCnLOlvXn54xJ6iCNohI17x86f7g8qXYIGfHueANRNEUvmlY+K9wPakBwHQBumjyL1ASJ6efLzzgGhz5GHeJ2ej7PG8f1PD80EK4beC4tDR0QLgLRsgmVjRKo6iwFVLhxfRvnzp1p/HzWtpqjOICtOAQ7I5vDMq9tQS3bz6NdfrlInnJX07GmPh1W0lnOuR2nm0jVAfb0Jnq0lOUl9WWA03wS5/t9PLYqeGBlD+vxFLtJDy+OhvjGzgbUlHBDX7fgT2msxIJBlGD95BZWN64jjlOc3DyF5WgNA7JlJOaN8n62uSk3faddctvt6xQmICaY7GCWnVEXc5pJr8NSS8o7RVOcydJSsTPvCRwrgAQsSWaY5UExSQrl+maCz+cFuYeNMsC0BhazJ53aPm/r7wP334srV652areu/WIOfP12CBqTSYq19dWGz0Mw7Ns9fob4dspB+tmf/Vn85E/+JP71v/7XWF3Nj/OHP/xh/NW/+ldvYc+OPoRUPhALWZzS4MxwBK1L4PaoctsCmXS/t4fppA/Un9758kAja9M52vZBqfwzNrD5lEWwIHCHzDNraWov2yhCnpMpAEqsm2flAJvjSXl+cDlIWfBSBrge+IZR51BMsNJnKTn3xGIgAutw7ECydT0GEif/VVQEiwwBE8MgyX4A2GegeJbVgmIjidu+yilX3/fgHyGfwMsOPRSMe1bU5QeHYLjMBvs+h0C3rZpQduwAmAD8+t/huqKcRJQF5LCn9z/TjnTNDcvcD4oTFCErLKLAwlCZuZlk63sw7POFOwMRbskTBtrBMOwEULZWXb67Uq1g+FhAXcf7zCxZ9N7BOpZ6u3Nvfh4QDABaJZjU1RK+hWWmbqfn87xxXM/zQwDhusGhHXxHcYwk9Xl7yHIu62M+1jaMGze28MSbHpuzjSIwbmORKuBPauqnHkGUWVGgyARRDehuMtWaZ1t1gK0OxHZlq8N255V01oPvFInZg+EpprznTDpi9NQKFJ3HEsW4fxl456lNvPm+l7G6sYW97VWcfeV+ME5j78YqJmzZ3RSCA0NIjMb+aAmRmmB/tIT9aQ9TJiROAtbM/NXLusqsdp2ZVpXVy7/TbPlMslVeLmeF7TIWDAsV60SH+cfh8Q/rDbMYTGmKsUyQIoIBY0/tYR87mPAICY8zIy8iBUZayZHnGkZ4nu961kTLzBSGbLlc5laMgDGW4jHKP58VjLvvOYeLr13usGypfwGz0hY+vzlNU8RRnJ8jglL1DFVYPs/UuxNd4ktf+hL+03/6T5X377nnHly6NDul5nUZDgTbsh7FR7yoCAYxlKp5jh3VwM4NYvu9CXZ31w/d3DwlkvI+mO4yaSeNFlbwpkO2DU/B+WuuBgT7+40/ngEAJiUAsQPA1QiZnbw+bcOkWcgoCzJWWIQQudexcmk5QkiEEIlCLOKk0t4sy+UaB4jWKn4CFZP3yCjdwzwjXGCPndu0hjdpjPJ2kANd7yTtX/vPyu+X++dBcHa4kauEBWKV62Sds9jdO9kBec8G1zHC/msjEEgEkTPgZLKTBFl5pJANLucJC7ljVWSDs9dsE5fJmW+R+/tWBDnlg33dAH6PSBZ9M2J/sorVweah25l1f1DElWuykl99J25aHNfz/FjO8oP9KUzaxL4UWY52tqUJ6Nl1Nze30YtjrKwsZU5ieYRMDDe8H/SvfNMXDhIqchlmExt8VOFZvBDA2HxQg7LR0rwMWPZ+eDz8Mn5bNeCuvE6dSVR5mRy4FcHNvABZJIWRFOxuRor69qzVwJLWONNP8dDpq7j7Lc+hf/8NnLi8giSJ8e3dNTwf9xFPe9inEbb5AJfGPWxO+vjmq/fhxJWT2J0M8K2ddVwaE7ZohETGNbLf7t9zPQi2bs3KyZQBgCUFcwqGNasJ5c+F9cNJmoyltcMIUwF5dbV9i/VrDRIc0B6MSqERIcEUBxhhnzcxNSOkZmzPMQSuoaJqt+P3t461DI9bl+uknkmtfl4FmW3XuN3/Rb/Lc+dO44tf+JNDXeezVBFECuP9CXZ3vXtvfr8L6zeHzvU3wzjrdpJe9fv9zLQxjGeffRZnzjSpiV6nQVERBDdEpFMrCVXaSYfTwiC4Mb+v3GY4SG4wzEqNRppqRNHsQePMGsXzRDhQ9QZCju0tSKX9PrGpZ4e9XNpLpX17gLtcCfBeKO7SJMUQxTZ32OcMK3bPdie5ZZXVkGVWrSxNCIIVubugA8MilDPCWiESRiyEmBVSCuoIF14TlJA1qAruJywGioBUJlkOMGCfG1YNlGTsMAs7kGerByjPFoceT0FOsAe92T7V8MO+urF25loKRbmz/26MwEq/2TO3AJRAOTAcrhKCYAuX6yNrZ0b4sktWmu5YZcDlCeeviaqllY48VHbCIc9zd+/7utnudRYzmExfE7cTG9ySB5wZ6i0QhftADdhkBg6mK7hr46XgzcXuHbMmyyI1QRz1F2r7uOJ2ej7PG8f1PD8UEK7Nf4XGSy+9mr3uyiJmeZEV+XQz68nMuHFjC2fOnKoBwmhcLx9A27brWWF2cpi69xeXhbaZZHkQ7GvFqgw4WemrYQBIs9nbStszQGbtd9HBRCxs3zNXoau2ZwopmFn265YZ5nkk2SEAEk6zvhoZZA/ZWAl68RTRyj5oNYLeG6M/mKDnZuENUuzTLlgxXh73cflA4/mrp7CEdewbhVf3FV4cj3FDX8ZBsg2W+ty5ppzYpv3yIDjWyzY/V69k0utExpiYEVJWMDwuAr0ayW+xI7ZWMmXnrw2WYv1kN1mdtc2SWKAPxkEwwEl4jKkZITF7YJlmbXjQbb+/UhdmXtOzJ2hmKTG6MMNlRUQ1172Gxe+QD3/mzCkMBwO88MK3W/vQ1O48ceXKdVy5cj27V5Jzay20DYPaMm7HFH6gd5j1v1PiIx/5CH7xF38R//2//3cAABHh5Zdfxi/8wi/gL//lv3yLe3c8kQ1iVVEe7Qemaytb9r1Zp2yjZLJh2FACw0oJer0pDiYDrEQd84RrBqW1g9XDSqnLkZWMCcAwkIMLhk1WFQ7e84DYUZBeW5uVTrJgWEUMMQwxxQvbGy2FrLAPInEpyEWTLP/bjmBsvrBh5cyfGBExUlKIFSMRQsyEqSurZN2kgZgIMSLE6EGLTTWy0miTgWHjyugBbuI29KHwiiH/rFSwFQbEKZVKKUCM6rNRuecXI/eU9oyw/d8afmXTnJ6QCEBwKpYVBpFPq4UgB76hnDoE1AQv/S69f4jblqqRwhc+Jzcp4spoUYN8PjNgm73B4u/y+6VoKqGU3ys6gt85YhEw3AiCg/f3pxtgVlgddmCEsxpnNrJ7SdlVvlR2zfbFYLm3heXeFoAiA3wzyiQ1xe30fJ43jut5fqSM8Kyc1Fk5c83RDIavXr2OM2dO4cUXv13azqyogmG/fnGZYhwVCM7aK7BcgbGRWsnqxPqSQFMAqfHgMrhhBGxuYXtQraCl1RisQeJLFCFSQ0SqD+Xr2LqHpOGp+7sKhsvHoO441oJhcA7wHOPJkmIq+9hngxvTCFe2NnD6+XuxMr6Kg2sncPXaKVyf9LCbMnbVDvZ5E/vYBOsEV5MH8PSNFD0mTNhgU0a4oi9i11xBag5yRrQhz7k8GZBHmrG7fj8jPURfr2FVn8Uab6AvfTAJ9mmE3egGRuYKJlLH5M4XRWm2d4fMrxkRtq6gfADj8opFDIykSJ0c2oPgfH1t/z7EPfO4VBNh+03nU9vEyqx4/PGHce36DUwm0wpgr7/W69tdxK29WcURKlKON26nGed/82/+DX70R38UZ8+exXg8xoc+9CFcunQJ73//+/Ev/sW/uNXdO/4IADCAnMWdBSRnDYqbJJN+wOq2OeiPcTAZYmW5Bgg3lE0qO0Y3bt/LeWtLPNWsXxhYdywbUwEQqsgIA5YtVgqFRFMSkGYobSxDqJ3SawZgqmzeyWrbolJOie29L/LllJQgUrYEUUyEnlLos0ZfYvRpgIQmMEicEWPiQKeBgs5MEm3azBQsFgwTFJTqBYdJZ5N4KgO6aATB3qsi5IVj6dlSUNCISEGVkKkvieRBsMlmaHM2OD9uVTY5/MzXVFaQrM6yIoGCnYCgYNKhsj4ke5+Ql0uqLZNFdkLEl1UK/7YL5OuIKBAMah+6yg3zivPf88csSXTL9VCQVNcB3Bq2+TDMcN5u8V61M9rAsDeCUlz5rPa6bwPDrt9195DaCbgmRv0mAuPb6fk8bxzX8/xIgbAdfOqA4TI1nzcDoUVkf1euXMd73vO2QpvzGzUFksQ5nGe7bKNpmbptWEOjPnpqBUO1jghWkpGSzXM1koIpzUrc1G6vNBtb93k9kPN9ama8FfUR62X09QoGtIYeWbfbqezjQO3gwGwjMQbkgZ1wI+Psj3Olj6VnSVZGKOgTc4qJjHBdRvj2aANfvXYW6dMaJ17Yw+7BEN+4cRrP7Ua4YnYwUtcwTjctw6km2KJreFY9D2ENUQZj2cFBuo2J2QUH7pnFY5L3GVD2e1K9jBVnSewgAUl2wyWKEKshVvRpnDHncVatYjnSEAi20xVcwQCsTSY5E6m6H84bs/LXxX237Bj8sLZxeHwz9nHG+d0ki551/WSS3zmutXB75XaOGhw++OB9uPDyq5XtdI32Emc2BGYm+2/vY2XJ3vED4dsp1tfX8Tu/8zv43Oc+h69+9asYjUZ417vehR/4gR+41V27OeFrinauE3q02VLD/gFubJ/stvC8g8nSAHehqAx0a66vEAxzyAjDARMFX8iWFADNQGQBDbH9UZEGRwYqNVDKgILvg8gmt1pwxFBKZd0QoQwMh4DYGzb5nNUyWI5IMEUO+mwJIqCngb4AA1ZITIyEh0jU1JolkmWE82oENic4deCXZQLDY0BSa8xm/LNOZSaWihQguelkOS8YgKsZrCr380g07NPHZggTLEtbd4eUgq2Wa9cdgjLjW3bMzt+XQC4u+fbcNuuBrf9tJx08uFXK5DWeQ9Y3aKNcTqvcPtWUl6pbrrLDQCMTXBdW/qyq7x1FHAIMd02NGB1sYLl/oxsIzj6sguFsnXnzfVvcr28lU/x/QxzX8/zIc4TDwXiTu2xzbl9TNC9z/foNDIZ9LC0Nsbc3W35VBMlhP7qD4aPK0yvIjp0pkaYIPVpCT4YYyhKYGBMoJHQATRFSt5yU8m3qJNdlRtjLdT2QUxRXHIENT1BhN916kR5goNewqs7iBJ/EMg/AYOzRAXbUUrbtRLgB2DV856U8ZX88alljWNZyyiNcjS7h+fEAfH2Ii+O7sBwxxqnCq/uE58djXNQXsJ9cR2L2ADDGSYIpj3A9eRFiVA0gbDb58iA40sOMsY/ITVTIBFMeYWr2nHydoShGrJawKhs4q1ZxzzDCiZ414bg+6YHH69ijEQ7UDlLKmejiscqlsuE4p/x95++X6+mq7LxSlNfDFphM7lbeLjmWv84opbitw4Hg8O9FgCx1OFfq1umy3D333IX/9//9Py1L1Bx7n9rRmDOdRyYNd2C4ju0t5uqHE4jHnHcGJ706hHzqO1F69T3f8z34nu/5nlvdjeMNSQHEgZlLIFX2tYQlbWZzjyH6/QMkSVzNE25wi24aDBcGroUVOjLcAHy5pMYIcoAbB7RKNYNlRUBknZxsHnJqQbAzwVKpgtIRSLOVNGuGFusm7PN9LbAVALoAhsMQIRhRMOx+xKavlMGy8oxw9mPr8fYUYaAVEomQcB+JLCFVqS2NR5zXpXeqIZ/6ZNNvXIk+MQDYlRKKkdIBIt2HQQxyMunaw+f+aURQpf3y8FiDEJGTRlMOThkotEvZb8oAsyYgUsVySwplwJkDZQ+CtZIMHGcMOxyjS8gYd0UCpdiVbXIMvDYOADO0Zgt4KQfBoSwark241K2KQsDnlId/H1N0Ar/+unLXWWvJpZY4FDMc1AhmBvYn6zi3nqctHQZ81kmlO0Ut4L85IPh2fD7PG0f9PF/wzGx6mHAte1E/+Ow6+G1fN019nvDJTkC4PZol2D6O06zGghZb1zVyheqVKCRkHx1EOgNkyt+Yaoy1GAnKzG5o3BSpIfp6BTENEdMALAYpJpjwCFOMkPIYQG7M5ZnPiPpYUhs4aU7hNK1gJbY3gt20D8UKU9pHovaR8hgkbbLs9uNcZKy5AgRFUkzNHnboEr6tgdHBOVwYLyEmjYkk2KIRrqnXsJteynNfAVfpIsE0HcGY8NzsZn6mVR89tYyh3sAyNjAUC/7HtI+Rvg4AVuoMA0URIupjyEOs9jRO9gWneraQBCPCVhJj2axgm/oZWK0OHqrHqY5tDF3ZPZtLAXMdq6UMtGdmJ1QaOGbGTPUgu25gs4j0d573w8+7ukp32VabYuT06ZMYLg3wrW+9GLzbts022XSTjLqpbjjXgmEADcsfUxxSenUoPf0xxO/+7u/il3/5l/HMM88AAJ544gn8/b//929PVtgB3boBazYYPYr82o6AWmuemSfcKoUOBsFAdfCeDT47S75Vnv9bkErXgOCyOVb5tVLuclYW2EQaYAFFAmGxw1XXlko1JGKoOIVKIijN0FEKYUIUpTDGllUiIZfza5lhY6qOxIUyPgEYtoC4eO35XGOtBLErqZQqIFGEgbJgeCJ9TKSHlAaZcSO7g6Qyrwjt1FnKgWDHyWYmWhZIMwyEjCutnMufPfiNJHK/Nag0INc+S5gAciA4A8PI5c5EgHbOXATK8p8jZVnviCwj7gFv4Xj4ry4DznY5cq+98phQZGLtMjlI1oodCGYLgDN22P5NqiiDDuXTHvxmbuLEFcAbAufaKO9YqFSYJxpAbeX+0Xa9h4ZzxxXuPjCeroJZYXl4Y771O0+UNezDdxLTe5s9n+eN43ieLwCE58uXmzWAbf+826Dv6pVrOHvuDF566UJNW0UWsjoQDlnh9phn4D/vwN2vI2JgkCBFCk0pGIyUUitZEvdQpdixfBEiZ6wF2EFyyhNnrJUzu74fimJEaohhdAJrOIsVWUOfexAI9tQedvUmdn07pnjslYoQqQH6MsQKhliNIqz38rycUdJDTy3ZvnU09ikM6t3+eMbahzXk4KKsRWyN4YN0CywJ9tUmLqslEBQMJZjwCJN0BymPwVxkpgUCca7N5Wg3MougVQ89vYJVnMIGn8QybI7USIaAAoxKkKo8VxoAuCHHKLwVdTn3q3nWqvK5l5CTO45a9TKZfY9sLbxEJjiQHcuGu/OrMMte03bISLZF02TCUUwezQOWD9PWG97wCK5ds/nBs6Ntoqdt2RqlQ4MZW/b5TZRE3051Cv/Df/gP+Omf/mn86I/+KH76p38aAPBHf/RH+PCHP4xf/uVfxt/5O3/nFvfw6IKOIEWgkbkJcmrnZXda84SbIhyUhtsq3bcrTHFZLq00oCx7I8pky4lSzWA4fF0Hhit9VbbecBYpyAFFEgMwgeIUZDRUqqDjFGwUIl+ax2goxRbUGrs/FuhWNyXIJdEZAGb3mqnAFmUgmMQaZQWscF8DU7bu0n2J0ZcBppRLpLMJOIqhJMkm21Nj714ixltauRQnK6k2SKARQ7sxgAfBBJWB4Fgi905+n/Bw25tl+aeQZ3kt7HVvwDpgedCsye6PB8Gx8rWTG74u9ztkbNuWDfOElQPDBM8Ap9DKgWHHDIeSaJX9HeQFo4YJdu+R4iJQVkBuwoaczj7CaGJ482vFfR6C4RqX+COvq1sG2CrC9sE5DHsjRJ5RnwVQZwDgtvXrag5/J8Tt9HyeN47reT4nEJ4NGufJ/W0fuFYBdVM7Fy9exgc+8J6aT2bLE/NtzWaDu8ascjA+vFQ1ZH2MpEhlggMaIXG5walMkMgYxplUWJZviIFex5DW0BcLciY0xtjl6npjrXyfFJSK0NcrWMNZnOOzOKmHWNIKRgTb6RBXJEKiDpDyBCnGrfvY6V7cCDhKbDVFUKqHSFnZcaQGFtQ6kw5r6FRcT8TW0z2QBFPaKzLiQU1df5zD3z7aar6WWUhy9RJ7WMKKrGGNBljRjpVPCRNZwR5ZAzGDqZ1MkAnGtI/ddA1bU1fzCcBWQtgzKSZ0AMPJ0ea4eom96qOnV7CkNrCO0xi6ovBjNYZSuQkKGzfRgCIILgLLxa+N41RQHMe2Hn30Qbz04oXSu3X3vbrjMf9EWdfIz9FqbtydaI6Pf/zj+OVf/mX83b/7d7P3fuqnfgof/OAH8fGPf/y2AsJZdGBrKxLkGWVV5tpOaYA8HIxx/cYZiDX47R51TFN58M25YVbboFaUtnm5Kge2dWCYmoBxYx/dPbOS32nBMMAApyCjoOIUkipok9gas7BgjBUjpQjkZpXF5QvbnNFwAtTVAAoBpJdJO5bIBC7UHsRpJdBiwXCsgIThALE1z9IBW5tSjJwy9xOsDKMSTHkfU9VDYnpgnkIkdZOuyvXFAFSctKsDwT1EGeAFPAgWaMfzWnbWOlxr5STMCnkdYUcPezfoMAc6VoJYFXN8s5rAs7/NxvAGWVRgg00mldbaFJlgxwaXo/Be4B5NnhX2sukWA65Dh1JzyZsXlUNX21lQHh3cB3b3T2F1aat+uTbvnEXBrFuv4ipdNhjLjtERu9nfiUIc1/N8rrPS5q7UMcLFgXJdzdniILHMtjUzuHa77bew69dvQGmFjY0T2NraKW2nOYexCoabY1EH2nDZ+rzOgIWEgXH5poYSaIoDJ0f7Y90g+1jWp3ECZ3HKbGDJDUb2OME1dQNbWlmpEk0Ks8qKIsQ0xJqcwGk9xF1DjfWeIGGFqwc9JAdr2FXrGNNmLtV1hlYWpCdI1ARjTLGXRmDx0miDMU0xlX0LQmeYNoX77k24+tEqlvQprOMMBrIEBYWx2seuuoE9vo6J2UFqxgACgy1JIQIIFdniwjaCc9TKyBebDSOXY6sphmJyM+z+uJJzw8xvjswpEt7Hrr6BK7wMtb+KnakGA9hNU1zFLkbYgpFJlp8c7sNsI6gG9jWTwPfQVytYwQmc4DUsUQ8KhH3uAwpI1AESHkNRDC5MTBSlu23bajtWXaJLKaObGUop3Hvf3fjsZ7+wkKLDx6zvrix/ztbrkHdNRI35d0cVt5Mr5dbWFn74h3+48v4P/uAP4hd+4RduQY9ubRQGo2y61+6dN4cuWy/CoH8AwxpJGqMXJxVWN2xfVFTtU5v0siSdrgsLktmywhFAabA9oPBEEASD3qZ8YP9ZJULmWuzxjazVMWkGaQNy8mjtrhGjBGQUSAnS1LPBrrYwAtbSNwsLyqQEkFiKeYMejOnKT15LOPsBIRKNmHpOiWbTrTx4VbBljib6ABM1wlht2zEKTyHCUCqqVYF5ENyTXgaC+4gRgaAz8OzKRIEsPCabH2zlyZSBYM8K+4OhBAUgbEG9uNxgqYwi655mSghwRmTk3KPbwkq2c0CsHBPcZI7llyPnJk6qAdwG5bayskqhNFrhaByjUWJ/y+C2sVyaN88My7FFres0RRMY7nIfShFj72Add5/6VqfrPm+7432rYUKs0/oFMHy8cTs9n+eN43qeL+yaUQTB/vdhma3mvL62YBZcvnQVd919rgSEMbNPXRym5wHBZYlmmfFtcsy1ry3LKcJQjg32bfh1FWL09ArWcBp3y2mcG8RYi+3s6PY0gp6eRqKmmKiRM4nyfbChEWMoPazFGqf7gvVeiikrJKyxOY3Ql0FBmuzX9yWSxmoHm6oPYcHAy6rpAFvqBiY8QsoTNJcDqlMLROhHqzgR3Yf71P24d2kVJ1di6J7CgSJsYoqt/hbG6jpETaCUgY4U4lgjimIopaCUgtYqe23/1tDKulB6GkIrhTc+8ShIEYyxeU7M7H4EwoBhk/3NxiBNU6SpgUkJkBg9XsPSdB3LyRAxE5Ipo38wQZQIaGogaZrVV5yaPezRdVzRGhM+heHUsrJ7ag+7ahN75jqmZg++FFZdTmhdlM/ZkPkO2esIfQxkCUvUy9hrNoK+DBCrQWa+5oFVXW3e8nbbYhEAXNmHWwiIH3nkQTAznn/+pey9+rJvnH0GdOlz+6RePQDOt1X8rmfvx2Ej1JEsuv53SnzkIx/Bb/3Wb+Hnf/7nC+//r//1v/AjP/Ijt6hXxxyLsMH+d9tArssgr4axVcrKo8fjJfTi7coqZfDrB8qdAbEbFLeywj43mI1lgmexvR4Eh7/Ln9ct74MFSP//7P15sCzJed4H/zKrqrez3/3OdmcDZgMGg40gQJCACECwQBGMkIKK+CgpaAnhkEL+g7bkCFthO8hQWA7blC2HP0X4k7UwZMlhfZZEUvy0EiIBkFiIhSCWwez73e89a5/u00tVZX5/ZGZVVnVVL+ece2cwuO/EnT7dXZWVVV1LPvk87/OmmbRVhGnGCqNExgjLQJEmAaljL21+cO5GrAvNBsKYcSkhJgGVDSE0UgtCYQBzaOsIh0KTYJjWTHUrIBTSlC3SIU1aAOZv3aTpyZx1pIhbMePWANUYoMMRQQhBKGmEDZqNDs2oTStq0wxbNMMGoQgIZURDhoQyIAokoZQEgUBIw5lrbXQuQgrufnSDSAoCbdjrAJBaI7TJu05TjVKaNDXjPldLSSpFoFJkqhFpauo2Jxo1TolHmiROSUYpaaxyLyqXe2xnQ7S2bHP5VKAEbkslkyZkzx4bXHaJnii15CsJPDa4VhbtzrWqf9OizGLamAXcyt8fFzN8mOj2TxLIhKVmfg+ZpQSZiGNcttbA7xbH2+n5vGjcquf5Ee0jy27FQAXDMSm3tHOAlQM///38OX/Xrt3kgQfv49lnXlxsF6a0OSvK0tpaCbgHcMrbqATDOiGtWF6KiCBo0qDDqlrlRCPifBvWGylKQyQDDtKILdUhsHnEeRTlyNLOboYCUjuTOsE22dwfoY0UOVEDBukOKkgZBQMi3UQJxVgfMFI9hmnXzhLnNzopJZ12h06nQ7vdot1u0Wo1aXdadNod2u0VVlunONM4y+lGi5NRypIeoeOUvQPJlX3BpX6bq0qzO7xG/+CAOB4Rx2OSJCFNVQZm01ShFTmQVcXfJAxDgkDyb//N7zKOR/Y4aIIwtAA6JAzM30JAEASEYUAUNYgaTVqNZYJmE1YSZCBohE3akWCl2eJcq00qT5OkQ0bjA4ajEeNxQjzSxCNIRgnJcJ/BYMjBYJ9+f5tR2iOdOnFQPIeAWqA8TeatFkBPt6P2b/Z3RZ/fTED82GMP84ZXNsnFPBMEh+3v5L2j6j7oxw/zY+z2xP/2v/1v2d+PP/44f/Nv/k2++MUv8uEPfxgwOUVf+cpX+Gt/7a+9WV285WHke+Hx5+3BoeSNnfYB/cESa8tb9QuVWOdaQHyUsMfDZ4Jz8yxVAMwZuCiDXA90TAyGpQQtyZydMiBjWb8gRQSSoGlAku8hobUgSCWpNDnDwjPNEsI5y8sMRKVSmvxUJQmkRqdmW0K7BAqJKrDBIsNSxgzKPJ9bS4Kwtcxau03YFrTaDZaabVbaLTqtiGYzJGpIlIBxqtgfDtmP+/TjfQ7GfeJkTBKnqFQzGMUMEoWMR4g4QKqAKA2RqSTSkkgLhDZAXSmN0ppU2ydBAI0w4LnPX6KhoSUFzQiakTCqq0CghPmHtYmWoaQRCRoNiYwEMhIErQgZSIRzz4oCRBQgApN3rROFGqfoOEWPUvQogXGCGMXIcYIcxxAXPSIcE5yfAl4t4QIoNiDWZ4NFkCIDlef/SlXIBTYNurzgnA02TLLHBoN3Xk0i9gmQKqVRQcxjojUN8JYn1g7JBh819nqnWG7vIcP8PpFdd/OYYc0JWBdlgxcG43diobgdz/Njr6OQlXthckBblF/6Mc8gcrqE+dq1G7z/A0/SaESMx8ecdzlHHKacS3lZTYV7sVbgSt/YMjhSGMFSKxC0AkUnUCQaWoE0s6kqLA7MnbwZZdyhiTlINP1EEghIlKCfwFgpUpFMSHUBUzpBjRhjnIfHomeYY6FptgOW2wGnOifoLJ2l02mxtNRheblDs2nY4+FwbEHgAYODIbs7Xa5f24a0xWqSMI7h9ErMvRs3ue/UDQAubp5hb/MU+1uS19M9rqlLDOJtUjWoBX5VkyvusyCQxHFMv28e3n7tXHeMC4fe24Zz3G4ESzSDVVpihZCmKeWkBwzUHgk9RBATNQTNZoNms0W71WFpaZml5WU2zrRptVZotu4iCARxPKLX79Pr9ej3D+j1+vR7A/r9A/r9A8tae12c4zrRWqGFqU+cMGIoDjjQzSx1ZUjMQB4Q62FWGsPf13m3c5iokwRXTRQdBRDPW0qpHA88cB9f+9q3jrCNKln55Har961qP9XEd7djguCHXXr1t//23y6839jY4JlnnuGZZ57JPltfX+cf/sN/yH/z3/w3t7t7b42YJYuuYIcnAHDdALki2q0DtndPopRATnXDnSHBnsfcZlZkzHLpOvUA7ySoqAAZXmRgWkpQPktntyHB5YTKRoIa24+kYw8bxjQrUMZRWqUoVRzzmHrCCinN9RlIRSDMvxQDioU2ubdKCGOeJkC1GjQaTWg2CZsNlpoNVCsibYaoUDJKFf2DMb3BiNEwJhmkJLsp+zf7jGJNmCp0rBgPU/pjxX6SsqMGdOU+PbFn6gybHhCKJpFu0qRFU7do6oiIlKYIzDNWSgJn04xJt9MaUq2RoUAlCjVMSBQkgUCOzbFzsuVUm39K58c8sni3IXVmlGXaNkcutW7ZWghkQxI2A4JIErYComZA1A6IVhpEZ9pErYCoFRCGApkmBOMRcjREj0ek4wEq7aPig8y4tC5yqXTO7udAueQQXVU/uIoNlhTZX2lRcvmzecFvRdQB4OzzKdf4LZm48mJ/sM5dJ159y5lX3e74YX8+Lxq343m+EBAWNU4Xfv3SeditxWI6U+zYmMFgSLfb4+zZ01y8eGWulo/Fzbamf35d0YWPRwZO7KyXLR9kjKCULXekGKaaQSLoSWOWMUgFI6WIxdgAV3LAoVEolTBWB/TkPttxh+awwUEiSTXsjBVdPWQo+6QVs2tRFLG2usLa2jqra6usrqyytrZCu90ClAFy/R69/T47OztcvHiZfv+A4WDEcDiqzJuUssFSdJa2gE5bcTYacuH0dc496Ny/Ndf6y6w3lmkf5Cx3udRQ/e/ogInbn9B6eZZ+D9c3qn8vbV2rUwVjQGlFLAfZb5/qEbEakKoRajRGH6jCBMakE7MiigJa7QadTofl5SWWl5c4d/YsSw91WF5aIggDBoMhvf0ee919unv77O/32NvbZzgc1uwv2TFWOmGkeuzLCCUVB9q4ag/FAT12GalelndexS4fNxie5Yo8bb15zPYOOxHlYn19lY2NdZ45pKKkWkIN1ekA9ZM11ZOFps3D7NdhQmmO6Ep5jJ05RLz66quzF/pRCR/QFpiOZOIz/zsf9FYywFX5glMiihLCIGEw6rDUnnSPrswNpjSori1tMm+es8f2gpVIV+Ud19ybJuTQ3nGV0niH+WZIWRFcl+tpJNJaCUTo3buUMHnDqTRu0u41TBFpUVobAEpJa4wlSWRgauAGAt1qIzpNdKdJ0Gmi2y2CZoNACcJ+iuynHPRTht0Rvct99roJ3f2E3YOYgzRhTIpCERDQESErYYgMBVEIDWlydWUkUDog1S1SpVBSMRQmhxiYAMFNIpoioCElkRBmot4jNKUSmUGWEAbEC0SBfYX8ca/JQXA5jMxaFOwE/UG/0Bo9MhJpJUAJTQKMpc7yiyOpaUhFo6lpdQJkR6KXAsSShBPLxJ010nbIqAFhMiAaHxCNe0TjAxpxj3B0QKQHuWmWndiQjhUOU2SQ4htlzR0+E1wliz4k+K0Me33XAuApk2S3AhAfDDvESZP15U3zgd/2IcvBTbhiHzJuuzT6h/z5vGjcjuf5woxw1aA0N0eqzv0z+Lk4uzkf4zP9wi4PCq9cvsbdd5+fCoQXKQMzOWCtHszPypeeJp2uHNgKCVpVGlAoHTPmgK7ssh23iIYR+0mA0rA71mynAw6CfZJ0mOWqZtuyZlwHYp8dsUw4kvSTAKU1+2lMT/RJGLG61mF5dYXVtQ5rayusra7QarcZDkcWkPW5evUKz7+wz37XyH21ngSYsyTj5eUyE4rATgC4GVRAalHZTrWk1D/HvIkAbf7yj/NUQOYzmIIMDGutTK1kC0wKTtXuGEwoIYoM6GicMBqP2NvbLxwDF61Wk6WlDqurK6yurnDu/BkeeeQhOksd0iSl2+3R7e6zs7PLzs4uu7tdw3Lb/U/ViBFdtE6JgyF9YXK/Ukx5qXHas2We6nNWF4l5neGnnQuHMaDz35fB8CLtPvmex7lxc5Ne7/C1yGdJqIsx7doou6pL+9mhu7ZQ2Cy4I63/ZsdP/dRP8XM/93N89rOf5R3veMeb3Z3bFiYHFoSyd7ppg9KqAdw87tELgmAX7Xafg8FSJRAuRBUrPC8IPmqN5CqDLMuyAdVMm8s5lqoCnNhHgc3/NCC4uC8S0BkYDtBpSqhjRKKRZeZZCJJwibi5yrixRtxYJm0sQdRAjhJ0f4zuj2B7H9XfJO0pxgPNQRwySAN6iWSQSg4SQZKCTrQBqEKitUYhjXmWsPV5LRvrzKpCAc0AGkrSShuMtXuumOMe0agFwU0pCKXIavgqzLFxUmmsdDsUILKayUXwmzlH29dA5J8rm+Nbdfdxn/n1gwOZm2sFLo9aKvM+TaEfI4cpwV5KIBPCMKUZxkRBQtiGYDmAThOWGoxXNxi070I3IwKdEMV9onif5rhLK9mhmXSNLF7oDARXhVYCUb78JrgWUWSD8QGrZYN9ZtjmB5t0iQrH6OzcLppiVYLgBUBj3eTWYWJn/xydZo9Q5t45CwNQ775SJX+eCogrfAKKDvOT7d6qeDs8nxeNW/08PzZpdKUBzgSzpGqXnYyiLLA6nzb/WwjJpUtX+emf/gmklCg1zyB8OnOzWO5fNUibBvirWLJsOVF9nJROGKc9usEm1wgZDldpiRClNT1G7MhtemqLWA3y9T3Al+qYod6nJ5ZoiJBwbYWl9TZL64K7T6wSrtzFKO6xtb3Jzs4Ob7xxmb29fXr7fcbjHFhPY7kna95Wh3GiHjGUB3RjxbVBiyubpwksEL66dYobgxa9WDMSI9ISsK+O/BwzTK7XT61waMJnasugSaAyRt1f14Hhcu3dwvYqjkG2zBSnZygeq+HQMOlbWzuFZcMwYmVlidXVFdbWVjh//iyPPfZOWu0mvf0+2zs77O122d3dY3u7xyjZz9yhTR9SUjUulJiaT2Zef93OO8FTLs9UXl7UnPNHjdnXsOSxR9/J88+/Ulh+aumzGnb2KPnCbsJkQlWjqxjjOzEtPve5z/Fbv/Vb/Mqv/Ar33HMPn/3sZ/nsZz/LRz7ykVpl0w99qJTKR/pxDs5mGHFNi6VOn+s3z6H1Dap+gvLAeRprbT73l51tbFNeZqZhFkxKTbPXIsuuAWFBhiiwdZYRDjVojaipqCDDAJ0qgijJWcxAkDSWidvLJK1l4tYycWsJEoXYHyG6Y8LNbejegP0EFVvDLUydUa0lMg2qHZ0tYIxsSaWmlsZfw9XzFcKY8ngANPeYMk+KEEFDm8oWyrpLRzrC2DTmILgpBZEwINiBa7BzA7ZF0Jn6150b2oLbROVEaJkNzvom8uVN/718Xre/rq5yBQD2nbUjqQiFJhIKaUs7SaEz0ywByDgh7A0IB3uEe4lxjw4UQaRIOy1Uq0m63GG0ukG/fS8qConSHo24SzPeo5ns0Yx3CN35oASFn0mRv3d/15lkzWuYVTgB5jTJOiQIztupvl8sCpD3+ifZWL628PaPNXwwXDFheCdP+NbFrX6eHxkI+1K+6bLow4DgxWJ3d4/haMS5c6e5cuX6jKXnY7CO2qeZpaG8yQIj+/VKEQgDXMqgP1YD+mySBjFduWxMq6Q1rdK5aRUlqe/ycoczp85y7sw9nDt5jlNrJ9Ejwf7OgO29Ltefu86V7YvsdG8Qe27G/r5MzX9cgEk0+61I1Ii94CZX4xO8sL+MEGe40ltFCM31QZsX9yOujod0gx3SZDSFCayeJDHbcoMVkfXT1KVv2tIPuexUWzbVtJhMAGknUc9Mq2pYcHe8/P6UZdfzMuZ+pGnK7m53wh291WqytrbC+voa6xsr3P/AvSwvL9Pr9dne2mXL/tvb66F1fk6VJ6um9aUavC4Ggp2r9QRjawcy84Lh8rl4+DQHaWTp50/zL/7Fv8FXc8zjJn9cYPhW3hcXDaXFEaVXbz7Q/MVf/EV+8Rd/kdFoxO/8zu/wL//lv+Tnf/7nSdOUn/mZn+Gzn/0sn/70p2m32292V29ZCFseaGYcprbntHCD3FK7zcYIITTD8QrtplHCIMLDsbhTBtJHGpBOA8cV+ZeFM93VKQ5D006YmNdEmzJKSqGVNBJpbU2RtEV5QUraXuJg6TSD4CTDaINxtIpMYsKDHo1Bn/bWTWR/AAcx8bjBaNxkOG6g4witA1IpczAoQNtJ36yOrlNoAxJNKE3ubKRNHq1CErscaWtiNVYg07xUYKwgUflTLLDg17QriQhoEtIQAU1pPEsaUmQ1jH1ZtMAqrZR5p/MCD1n4jPDEz1FifxUiS3ySWS1mnafYiukAOLBscCQVkVAGEAemZrBfOqkqnBRakhKO9hHJLsEwJYgSRJCiGyHj9hJJe4Vxc5Xe6t0kUYco7dEab9NKtmmrTZpqL5+gUYKJGfeKqGSD5wifAa0skeTHFBB8GPO8ReTTw3GLwbjDQw4I36Ic5OOql3yr4+3wfF40bvXzfOEzuMhwlvPh3EKqIA1dLMoPIrONqty5ScZZcvnSde655y6uXLm+oDHNUcMdi0mmuir8SQONQooQIUKkiOzfOYhSGXOXGiZPpSidkOiRqfmbsb3G6CpVYzQJGxtrnD59gtOnT3Hy1AkajSbd3QO6uyNeeOF1vrfzBqpv1h2JIQO9xyDdLxidlY9fVe5zGUjNaxbkJMYH6RaXwovQv5e98RLrjRWkgO5Yc3U85Iq8Rt+y3JU5rXWyWDtTbFw0UzPBIIzEWoqIQDaIZNv8bRnTVMfEYkCiBqBGaJKcWfbB2xwAuGqfZ7Gus6LunHIM8rVrN7K+NRoRJ0+e4OTJDe66+zTvfvKdCCHZ3t5hc3OLGzc22drczUy5Fu3LrOUrzw8LgqVvxCGUGX/eIkZ4VrznPY+zvb3H5ub2jCWL1zdUM7XzHcfyfc199uaHkxkeZf23SjSbTT7zmc/wmc98hr/7d/8uX//61/mt3/ot/tv/9r/lF37hF/jpn/5p/vpf/+v8xE/8xJvd1eMNnYAIF8+Dm7WcA63u+lVJEfAWcnqL3wkBS+0+/cES7fYgX9a1pZP55JSl72c6x9YYcNXmCFe2MQmC8zZU/rfPCmeAOMU5NwmlAWOENWyscRCcZRCcYhicItURjcE+0cE+6/svE/W7Ru6sBDqVKBWgUkkszbNKCAP9HFuphedyUVYsUby7CJFLnhtSZNe8FoLEujiPlGknVTmLmyrzeWpBdmgn7W3RPiIkTRkQCWEk0ZIsL9iBYDcMD4QxvgrsbVUJky8sRd5fock+c+sJcvOssoGy8pY3/TIMsJF1a0KZS6DDUo1l6QCyVIRCFczIDBguukTnx9I5PqvMIMvlBZtUL4VgQDQ6QCTX7LKaVEaMWuuMGmv023ex3XwMITUttUVb3zD/1CaBSrHJ4WQ0cZVJVuF8rZZFT8Q0d+jbEPMwxtvdc7SjPs1GjTfKvJNpc1zrPwys7tvp+bxo3Krn+UJnvtY6o6GLA/LSAM6BppLstByTJkJuO9MHldMGyxcvXuGnPvbjSPkdU2eupo15Yx52p8xKVsm288jBr/lDeccrQFowHNibksu9USQ4p0KtbYklNSq0vb6+yqlTZzhz9iSnT50A4ObNLW7e3OS5515id7cPOqARLNGQyzREh1A0kUhiPSLRIwOmVUI5T9EPHwyXP5/3+DkgrXXCKNlnl4skwYit9ASdg2XAgPP9YIdeusko7ZKq0eTxyzZUZNSLEwkxWAOz3AE6IpJtWsEaTWFYdYkkliOGsscg3WEsJEk6MAO0KZNo8zCqVRME8+bVTvt+2rEdj2OuXrnK1StX7bKC1dVlTp46xenTJ/mxH3sfrVaLra0dbt6wwHhrBzWPZPAQ4YzO/Ikes0Pu+KRg2frqYzAJRI8eikcffZgXnn95xnbklO8Oe49x7VT/hnfi+ONDH/oQH/rQh/ibf/Nv8vLLL/Nbv/VbXL169c3u1vHEFBDrD/AKoPiwbLAF2sBCDE2n0+fm5ln0xibC1v/NO7k4O3wYSXQhZDCdBZ62HoCrS+y2F0a2FFNqQbCChkarMaPGGgfhGQ44y0CehlTQGm7TGd5kffclomEfkQpUHKDSAE2AbghUKlEygJis/rAPygKpDMujzD0+VTIjEh17pLycW7AktDBuy5rcWdkww8bFWWlNKgSxFoTKscqaWOdAWCCICAkRBEISCUlTSsKMAc5BsAPCPnCV1gUa6eTLxiyrYHjlH/bS+qYPbl/NndQeCgJ8wG8NsawxVuRygT0mWOLyhlX+av928mjHCrvySXm95xwsi8C+lkolubxgVyIpYExneIOl0TVEX0OoiJtLjFprDFsbdJs/hgojWnqLjrhJW2zSYn/yybCAJFr7wNj/rLxMOeZxkT/G8CfE9vqnWFu6mX85T91wG7cM2P4QMMc/CnFcz/PFGeFKR9l5BqhmmQxEHQGcun648Nva3t4lSRLOnDnNtWumDE8dGK2SKs/crp4EYqLE7FR9V2neUxjsywwoBBYM5+2l5phr01fHJjebDc6fP83582c5e/Y0QRBw8+YWN27c4AdPP8fu7l4mj3KTDlIIEjUy/ZKKFJN3m+gRsTog1aOMdZ1Wm7Yc03IYqxhbt5zLxR0mKYka0ZeblhUPSFVs+qTGGQiuzP3N2Gpb4kguEclOts1YDYjVgXkgicCWfYqIZIe2WGVVbdDRbUICxiT0ZI+9IKIvTM1LU+s3mQlQFzGCWnSCZ1a7PmNvvrAv3qBBA3vdPnvdPq++Ypy5V1ZWOHPmFKfPnOShhy4QRRGbm9tcuXKNq1evs7/fm9mHRY2psvVwNT3tJIUO5nBYP14w3Gq1uOeec/yrf/Ufjq3NWVF/PzIx3Vvg1rPGbyczjl/8xV/kc5/7HD/1Uz818d1DDz3Ef/6f/+dvQq9uXSwk7buFA9m6aDUNqzMctWi3hnkfPECsZYgThc6dSzgHgNYyyNt1H7q8vyowXFeKpjCREBTBsFJmnTAiTSP66d30oxMMxAl0Cq3RDTqjG5zoPUNj1IM4QKcSnQYoGaCVRATGNVkLjUpl9vTVSiCUA145KHOvWmijthbagkELXu2/goMyBhwqC1QjaWTPgRCk2iQTxVqBBmndnLNDhgHJBgSbGr8BgsgCYB8Ehx74df8C7/aQsb0KU34ZY5alSssIjGu1zw7PIrYkufTZgN8cABcZ4RzsOlAsRBEQO6Ab2HJIUhq2NwjSrARWVifava8qlVRROskBY4GmmXRpDXdYT19CjzRJs8OgcYZB4wy70cOooElb7rCkt1gSuzQYVO+8Xwe79gAFE5LoiftHWfFh41aCYH8b8VhyMFrmgfM/qFnGguGKSbRjB8HH6cp9hHg7PZ8XjVv1PF+MEUbZ2ToHhqUHgGx+h65g60psyuIgeFJ2PBl5m5cuXuXee+/i6tVZyfWHG1RWldepA4vF7w4/iDWDZ8HGxgbnzp/k/LkzrG+ssbOzw5Ur13j++efZ3ekaFtxj5P31wRY5UKZfqU5IhBmYpDoh1SMP9DnZmvS2Pz/Qq2Ns/chZYYXWY2KdGDfmQjmu6lq35RBCEsgmnfAkK/K0AbeqjUJzIA7Yi7YYih3LuAcIERKKJi29xKpeZk02aEhJrDXNNAIJqczLC5ktzwbD045J+bsqlnia5HraMtPk4VXhvu/1+vR6fV555XUAVlaWOXv2FOfPn+HdTz7GcDDk6tXrXL16nRs3tiZqG/v74totMNY1/Te/fVpoo+rvWx1PvfcJdve6XL8+y1PgePs0zURv2ue3I95OOUh7e3t88pOf5MKFC/yFv/AX+MVf/EXuvvvuN7tbtyfKMma8gW7dQLbSBKYoWc4GwT4rXBUV2xDCsML9gyUDhP1lVVLNCpelzWUmuRTZ4Nhfb4Y82md1DxtaC4Z6g4N4nYPkBGPVpim2WBKXOaGfpzm+gUgUOgatJZpw4vYspDHVMpHm7spK5CV5hJHfBtICNKUwWT/asMEKUoLM3KkcJpdVoMmBqQHDgtiyyq4PGsMAu7uRsEfJgWMjOXY5wCJjgKuYW39bWd8EJFoQBJBYVjiUkMrsawL7Wc4s++WRDODP3KQRCJ3XEs5comUuga4CwYFQmTQ6kEZyHkidsbz+axA4MKyyf+a3SL3fZ4ortGOGvfrBQiqcszh2XSk0DdWjmfRYF6+iRcCYdQ6Cu+iLu9lM303ImA5bLAVbtEUXqZQ5slayX+UQPfFZHQh+C8TW3nlajQNa4ewJ+Znh3QPcvh4FLB9HG4eJt9PzedG4Vc/zQ03rlCXPJvfS3TuPf8BYVZJkGkv5xhsX+djHPswf/mFQOXCfFtNyXGulwhRBwKFqB3vtm1I/hqnVQnH2zAnO332K8+dPgVBcu3adF196latXrjIajYuNVIBg16b5w7geqzRGiYTUDlachFipsQGfpRrGk6A6b7d2WxX7V5Vr7H+vdb4/U4+hkBkrLJBI0aQZrLIqz3FPejd3N1qsNswFvzte4ep4javNq4SyRSibpPbJGeqQBgHtIKATCiv5ajBKlxkEayTSulUrSGc4Gy8K4BYBwVPPSwcsKyYrFgVU3W6XbrfLCy++RBAEnDlzirvuOsf73v8eWq0m16/d5NKlK1y5cq3gIl4VE+dFKS/e7795TZl9/5hMP3DvDwMe3/Pk4zzzgxcWXu8oUc6Tn8+/4PZNDmh/LH7I9d8q8Zu/+ZvcvHmTf/yP/zH/6B/9I375l3+ZT37yk3zuc5/j537u54iiaHYjPyShZ6XwzALBU6Ls5jyTEZry/XKnx/XNs5zc2GJuw88aIFsXPhg2zLL3NyVWWLr7DxNguLIUjRdKCw6Sk/STU/Tjkwid0hFbrIevsySvEcY90GNgVOQDpoVQJscYQCuE9kGwythGKRVBkBqmN83b1VqYvFjl8oexhlGGxZWQTUEaya/wwLCVSQuBwjwPy90NhcwAcCSlXTcHwGLiKObH1THRvmlWqDWpBmlziUNJ4bwIpWGJI+v2HNrvFEYObZ7ZFdu0gLtgiJWZZ7nj6Y6B+dscoxwEu/zgMhsc2GMvg/zVrxnsZNEufABc6KNlgwt1hZ3LuLRab/tPAE3ZoylfY0NeQgVN+sFZ+pzhRvI4adKkE2yxFG6yFG0TlCZCdEnJoGXwlgbBADv7Z1hfvjF1mWms8KzwwexUqbVfpqpqMuE2xtvp+bxo3Krn+ZH0Dc4B9jBRBLiHZz+qBr/b27sMBgPuuvscF9+4XLv9uvVnGT5lyxVcf6W3P6qGGff6rCnlneZS4SCQnDl7krvvOc3586cYjcdcvHiJr3z1m2xtbqH8GmilGfl8f3IW2pdiGwYWC5gNIM6PiTGG0jUlHsrhg5qiIdJ8EwHT5MEF+XOW2+ueCq6BXOIeBi06coOz6VkeaLd5aCXlXMuA6uvDBq39Nio+S0jT9i8mJSYWY1Kt0ZhZcoQp9dAkoqlbDESTUDTRQhlTJ5tvPKtUkn98pi1TXq68fJWkfP6JllnX1fQ20jTN2GDB91ldXeHuu8/xjnc+yAc++BQ3rt/k4qUrXLl8jfHYGyhXMd1uEoEUtMl5L39/u42yNk6sc+78Gf6ff/b/u63bLcdxpIrcifo4ffo0f/Wv/lX+6l/9q3z729/m137t1/jzf/7Ps7y8zJ/7c3+Ov/JX/sqPTq1hB1B9YOmzqzIElebS01sgtW42h0iRcHDQZGlpNHXZgnnWLQLDWqZ5jm9hf0rSaHsslJYcJBv0ktP0kxMEjFmWN7ir9T1a7CLT2BzD1Ns3373Jf3XbVZNADjAAyf2TCmxuqgwMExwW6qHmrsaplpmJlhQeIBYWIGrLNAtLRGayZQNGm1IiFcQILMdYywBH0oBfvz5w5a6AZWINqJV2+9rmJoeBJhSahgQVkLlfu+UjAaHM91HZvGbnrpxWMF3CsdNQzPElB8DZT+QfK3vsHAssPTY48JhgmYHgkkS6ghGurhHsrROmEChEaOsNSztM8yl2zxxLipRleZNluYUWzzPWS/T1eXaT+7gRP0473GW5ucNSYxcZ1pxfzADBt0gWLUqpEFUxOGgwHC/x8Oq3i1/MkS5RD2jrlCF5LeXKdSvqCPvrZuvcIn+VO5HHrXieH+GMLpZBMUY3xfxNbZ82VWVX3OtxgOGqdV9//RL3339vLRD2+7KwW24JAEsRZfm9bkCv3CNkTkAthODMuQ0euP9+7rrrLKPRmEsXr/KFL77A9vZ2Jm0GiZQNyiVosrZKnymVoIihBHKFB0YKpYAKstagBELzmAXEymBtGmvp/37CG5G451pucNXMzMQAI1u224hkhzarbMg259qah5YPuHtlDyE0y/tr9JMVttIWgYgQIiDVB8RqwEGwT1cs00zyh8FYKVJ7nCQBQlQf6/K+LXJ8pi1XXVYpTyuYHwznEwXVn5f6MkPS3O3u0+3u8+yzL7K01OGee87z4IMX+MD738PNzR3eeP0iFy9eJUmS2skkN9lSxRZPM2jz+72oPL3ut/vA+9/Dtas32Nnenbu9RWPe+8v0e+HtfbhqRJZfeNj134px9epVPv/5z/P5z3+eIAj4zGc+w/e//30ef/xx/qf/6X962+UL18a0gW9NTuDsNudfR+iE5c4+/f7KTCA8uR07kC0b5lRJtHUyAYahBIIB4YFj/9j4AFiLkH6yQS8+Qz85QShGLAc3uLv1RzRlD6lVbpAFxVelmGXVKqRG++NvywpnAFkqIMC5EMvUSHEDqwEWQiOSEKU0SgtCmZIImeW8hlIR2dSf1ILhsgOkY1ubAUhluOPAOkjnXiOikAfsHKH9vF1Rw1gFwjhEm1xdnUmkjbRZowJJKKEVaBLveGVA2BpdBYKsT7EWaG1k3q6PPvB17K+TPftscHaoS2ywMdhSBQMsKRz4TTMmXgbzS6Infm+XGwwFSbSQChEYRliEmBG6xICw0Do/u+eJLI6bmqJPU77KhnydMUv0OM9efBfXx4/RibostbZZbu7VF3SZdg0fgQGdledfpTARKmFz7wGWWns0Qk/1OKdnwHR2dxIMH0XefLul0W/X5/OicZzP84WeeGbwqmsHdloryzrmYKvuqsuluovLN6cztubz1167yONPPEKr1WQ4XPBhWwo3SC0bffnljkLZzJZPdUyqpGUPq/NKHSu8vrHG/fffy4X77kFpzeuvX+F3f/cFdnZ2C/tjwoBuKUMC0Zww1ZJ2utGBJKVTUjkiVgNSNTLPZm1YOcPMmZJC5YlU4Vn0TzKx0/N0C8esBqxVsfF+rrnbhgNLUjYIZZtGsEzTul0DxHpIrAekOiaSbSIaNKWkHWiWG2NW2wcArAzbdEJFJARCC7ROSdUYpRPzsA9CUDCI24QIYhR9MSRmjHJGZd5vlu+Iqj2/68pLHW45n0YoguFyVJtyTbKzi0b5t+33D3j++Zd5/vmXabdb3HvvPTz08AO8731PcvnyNV5//RLXrl3LnNv97ebycr//izPC03LzC8vVgNHHn3gnX//6tyeW9WPevPi6yb5p26/v55s7q6zt4PIo679VIo5jfuu3fotf+7Vf47d/+7d58skn+c/+s/+MX/iFX2B1dRWA3/iN3+Av/sW/+EMPhMUi14/HBtc6SrvljlMGaAeyy519Lt+4nzTdJgim93uipFIFGPYj2x8rlSznDJdNs3TYMNJIlRZyirWGEWt043P0xqcRImUlusnd7e/TZB/hkKt9rppte4C4jh3KbucWAGVO0CrLVc7coV2t4VRmIMuZNLnrTEqNSAxITtPQMKVSmhq4qQXBSjK2plFagBKCVBjHZmlZYYGRIaMEQrryRib/VqM9htYZbBkw7AyxwEqVoaApc+0W8pEtoDX7pNFaoIQB7U2pCQPzmasDHHpA2G0r0ebYK6lRyqBf97gxoJZsfXdHqqsD7LPBOfh1AFplUujAk6XnEmg9yQZPYYZxANhfp0oSHWJnJ4IcBPv/pkQjHLMhL7IhrzCWy/SSM+yN7uLm8GGWmnsst3dpNw/ya3vOiaxF2eB5ze7KyykFu73TnDv5mvfhMdYP9sDwTCD7FmN5307P50XjVj3PF576rRt8u/w/P88vF9QUB72HyZ+dDsRK7WvFwcGAra0d7r3vbl568TWvjRyEHYZZKoPgQDYIRZNQtjJQI9QQJOjUHJNyHlSz2eD+++/jwv13s7TU4dLFK3zta9/i5s3twvYgP1au5IyrfdsIlmnQIRARAbagvZa4/xSKkRww0j2kiBjRzXM09eSFn7HCovhZXYmr8rrl5RzY8YFeGfD5AKEIFqR3zIwJVjNYZS04x4Y6zZJqIRCMiNmTuxzIfZTdp1gpBmlAd9xg72AJITT7cYODRDJSKUqkpkyUMpMjzq4lliN6YpXAXhIxY4b0SImnn68zwPDEZxXnXd1yeVRPpFSBtvJnao6cmdnbn77twWDIiy++wosvvsLq6jIX7r+X93/gSaR8itdfe4OXX36d7n4321a+bg0zPQXglq8Lt+yiLtz33Xc3q6vL/NG3vz9z2UVjofvKHJMlxbgjoV4kzp8/T5qm/MIv/ALf+MY3eOqppyaW+WN/7I+xvr5+2/v2pkWVJHqe5eHYQHHU1DSiEf2DJVZX9vMvdFI5eHYD8AmZdIVxVsHExgPDkAOiCXm0+04GJKrBfnyGbnKORDVYbmxzbvkFWmHXPJeUMlk5osQsLTBgdmDJgBptag0nppRTDoYNC1xYL6tTK5D2mSdSmzucmGMUpJIoSEnSgFCmRFIylpJGkNoySSLL01VCoLXJEw7t8EgGBo+5kkomDzdneUUJBDsjK7ByZvvPgVLf5Cqw7s2unq8DilobgyxT11iTSpOu5NZ3rs+uDQUIO1hRaNu3vA6x9F59d23I76DF/OA8Nzhzj/b+FcokWTbYL580DxvsAK9jgw37qyYl0aEVOGS6bk8WPW1CNctjl4XPIjlmvXOddbnJUC3TG5/h5v4F6AUstXdZXdojkuOp7d6KmLimvdgfbJCqkBPL0xWdE3GIPOHKUOniTO9bDDC/HeNWPc+PJPZ3+X4Z+HXyXS+38zAxvXzI/PHaa2/w8MMPeEAYpmexzBs5QxiIyBgwiSYBEYrUgGClUDJBqzSbKDh9+iQPP/wg99xznps3t3j+uZe5dOkySVK84Ao1hiE7jlJEhLJJI1hmSWzQ0ks0VYuGjhAIQiRBVpJGM2BEV+6xL6U1wnJu0MaVuUr67AOVupJH8x6jKjDs718ulTbLVwEkKUy935XgDOfSuzgfLnGiaQw69uMWN8ctrukGPblHqhP21JgrBx3awRL92EwQ3BxFXB0IunpEosekKsY5Y8fWlToNRgxll1A0s/6mOibxy0k5+W7NOT5vGaFpx3KR831afvsik00TOd6VfZjvuul2e3z/e8/y/e89y5kzp3jggXv545/+ONtbO7z08qtcunQFpWrY7BrAPy0OW2/3/R94D6++cnHSbO4YYsI5uy41ZIpaotyeidvDFr+dXCn/9t/+2/yZP/NnaDabtcusr6/z6quv3sZe3bpwpi+LLH8sMa+k2ltmealH72ClCIRnxAQ7XGqzsKx9zawkSoDYB8FoOEjX6I7P0Y9P0A732GhfZinaRjj5sXfpVUovp5ZhckDGgCHt0GJQEjkqiVAahWGt0RqttAeibJ1aLbPc1VQECOUApSAME1JlZdHS1c5VpEKSSmVdTKQ1xTJ4y50xfrEBB4BTDakSJJYZdoZbDnQ6oOuOqwv/ewNkycBsaIFxBoTttqVlraU04FZYMFs2vAown2shSIUmyA69yHKPnTO1A8N4rz4ADjImuFQqSapMEu3Y4CAw73337plhf3OEytnjMEUEqiCJNuhdk3EKkkk22J1PM6Jg7OYt3wwHNFpX2eAmB8ka+4MTXNp8J63mgJXONkut/bnl3UcJn1muuqa39u5ipbNNKA//vPOv0bkdng95P7yd8ui30/N50bhVz/MjZ71rUg8keFHjXuw+qx+oVw325h3sFte7+MYV3vved7OxscbOzt6cbcwXpnJevo/un/TeA0RRyIUL9/LggxdotZu8+sob/Nt/+zv0+9X13yZAcBYGrEoR0aBDSy+xolZo0aApAgJE5uIoME6KB8pIfuNgRCwHxHKA0tI8YCZAcJHNnehXFXCbwoZW7lPF5znwrgYAQkgi2WFVbXAm6HDfkuRcOyEQmr1xSNBvMBquMdJDRmLAptxGDAXDtMXlRgspoDvWXB0Pudm4yTv0kgW2STaRk6R9lI6J5YBANJHePinnpu05aU/0s4aRXCQOXYt31nZrrsuJxTwwPL+Tsbd+hfz3xo1NbtzYJPr293nggXt517se433ve5JXX32dF198lYODg8q2Dgtu540wDHn00Yf59V//N1nfK/txSFn0rHWzZQ6xn7fDVEtTzAA4zPpvdvypP/Wnsr9/4zd+o3KZMAw5d+4cn/rUp/jZn/3Z29W1Wx6LguFji0Xyi2XI0vKQ7b3TjJM2jbCmHmpFZAPnqbnOk9LpIpA1QC1RAb3hOt3BaZQOWYmucd/ad4iCkV3Ozxl2EmsP8DqTHdxxt2WYpEKHkZVKOzmr8AAOFrymORsNOOcqB/JMv0UOvKR1kQ5MaSUnH06Vu3dLDwTnbseRVKRaZYxwKizrqjH9UkZCrLyHv5FEm/rCse2aM6TyyUpBXtIoECC1IND5fcA5QTs5dCByBjW7mwmNEMKWT9JIpbNawo7d9eXK7pgHWZvCLu9MwfJ8YRc5E1x2i87lz1IqIgt6pSeJduZYho1XWf1gXxZdjuz3cvWFQyun9kBwxghnkmocci91fjYI9h3OzbLBxPnrvhMCOs0enfaQVG+yP9hge/8sW93zrLR3WF3aJgzKSovbU3c8SQL2+id58K7vz71OXU5w+T44NXd4hmP0WyXeDs/nReNWP8+PcGarSkMEwAPB7iTM82uzRfy8y4WkmUWgPC2vLkkS3nj9Eg89dD/f+tZ3s3679Y4SJs82Z3Y0KpPRahTtToNHH7qX+x84z+7uDs8+9yIXL15Cpb5Jgyy81uXVOqAlMAy0FAGhCjGiaEkkJJF1cYyEIJAQK1P6oKkjIt0sgPNC6aGC43XxuFQd21xenQP2cnklE2rK7523lR/PKqCNBf8hTdVkJZKcbKacaY2IpCKSTfbiBjdGIRLJWB8w5oBh0Gcv2aATt5EIBmLMbrDNQG+R6rvMdoUEnWZgWCnTn1SMCnnX+T6n+DWNs+O4YMw672YeE7fpWtCaf6+ZnKiYF3wV21UT381eJ484TrJ84jNnTvHwww/wMz/zKS5fusrzL7zM9tZOZXtT5dkzGOxp5ZU+8IH3MBqNeP65l2rbv5VRpwiok7ubuH0P47fDjPPa2trMZZRSvPjii/z9v//3+S/+i/+Cv/E3/sZt6NntiTITcttiHjBsvw8CxVKnz35vhZPr8wPhuWIi1zkpsEKx6tDtrbM/OElT9tjoXGY5vFmbQ+rWy9uXGfh129Mhpnardd0miSEMc8dZB4YDA4J16GxrLBgWVh9sQS1JgNYe0JIuPcuCpiBFKJGVzdJSEQQJgQxMrdvEynwdoFOKSBognE89S1t6Kc8FhpwpTxWILAHYvPjjvkyKLHIwL4XJJ3aMrjO7Cm2Or2ODfcCqtGOZDUObU/V5aNtvYddzADwUAi0ccBZZv+pKc5XZYFdLOHRlkQog2GOIJ9jguhzgohFWQRJtQbAIU/OdA8GBlxuc/ZPFf3XPw1JZr3KZLx8AV0UQpKyt7rK6sstgtMR+b5WLN97JUqvL2tImzcawdt1bETe7dxOFY1ba1eMCmDHZd0zy6DJofiuUToK3x/N50bjVz/NjmuLxB28eCBZFpswM9Mgls4eS3i4GYF96+TU+8dM/yXe+8wOSpPriOKyzq9ZGPivUECVSAhGxtt7h/ofv4fzdJ7h06Qpf/MJX2draLICaOjBSOAYOrJb6pXSK0imJSIh1aqTQCpSQKCRIUBYEJ1pjqwZnAH3eyAHv/Mv7Dx5fnpsBeRHi3K61Vmjh1ARJcQLA22//nHAz1FWRkBDrAbE6YMAO+/ImoTQTACkxcTpA6xHO3dz0y3tYYGoYCy3Rwp0nORCsLOtE9W9ZJX2dBUAngJv/W1WA7+kMbvU2qyed6ieGqiTy8wD5aQzpzZvb3LixydJSh3e880E+/vGPsLuzx/PPv8yVK9fQM4rczZIdzxPvfd+7+O53n1l4PReLT6JNHvO6fpcN+crrH2cu89s5fu3Xfm3uZf/Vv/pX/JW/8lfeVkC4HGYQV1E6aZHISozMGDZMA8P+5yJkZeWA6zdOsbG+Y4gXZdlax/gu2E+/b1Ws8XDcZq93goPhCkuNbc6vv0Bb7NqcwMl7z1TJowPD/vbtex3mWE4446zQvqrEMMI4qTEgU0iEAUPeZLnEPM+FEkjbjragUWthWGNtwBz2s4y5dKyrZUADqQm0Mc9SWqClRmttcLeClMkBspAgtOmlOzx5NQcnfTb74Nf3dZjUfR9JbUsn6QkQjP3brScy1tiCX11sV5IP5p0btBKmBrLLTXbk+8RPZtuXeLWCPRAcBWkBBJuJBc8x2mODnSy67ECdHztVlESHkznBSLuOtLnB2bxnKTe4Lgr5wBYA+4BtSskfZFi4PoSATqtPp9UnGV9nr3+Cq9sP0GiMWV3eotPs1U4u1EWV7LnKIdqP7e55Tqxcy+4HQMEHwF2Th53sq2WFXTt15oF127nNrtE/inGrn+eLuUYLUcNQ2HxQxzpaICMoso3Ftm4d0+EPlnd39uju97hw4R5efvk1YL7BZH35F+fam5hrVIEWijOnl3ns8Yc5cXKVV1+9yL/9t99lv9e1ObleuzP21zkTV7F5xgk6ZswBByICCSPdINIRkQ6J0pAolYRCkmjFkJgDccCQHoka2hzhIpAr75sDfQ7wFUsYSdcREJPMdV3uo2F1m8ZYTDaRIkLrlFQnpHpkSjzpGGd9kT+PzfaUTujLPvvJKlujgEg0CaWRRu+OoadHjOWAOD1gnPZtP/aQMswAttaKIJB2xtuwzGUTr6zO8rTfaGoOp7fYEXI5J9adU85cFUUDsnLJM8d0q2xyqgq8Hzf4EkJycDDku995hh88/RwPPniB9773XTz55GM888wLXLx4dSogrgaL88Xdd5/l9KmT/OP/858fqu+zj4XfJ+W9VgPbyWXL59Ptl2VVJyostv4PU3z0ox/lAx/4wJvdjdsTnmwSONogrqpk0YLrNptjwiChf7DESmc3+7ogf56zj+UBdvY+TRjGq+z0TjMeN1lpbXLvyWeJ5IF1iw4yhmnhXD/pMlHJzLaM+7TMPtNKGTCsHRDWVrptSuRotGGIkaYfUICkQglEGmSssAyEHXtIhHVX1kIgtWGBg8CW88E3iMrZ4UhaEA0ZOwxUlj0y5mCCSGiUFLi5cYGROzsw7Fhet8+BBbEiky4byXNQ56QMGWAPhUYJDVpkyUhKg8T0WUGW16us8ZcvFHRgt8zY+hMDTjJujsckExyGCVkNYc8gq8AEO7dn7LZkCRB7bLAMUiuP9oyxSixwNk9dhTaFnAC12ruWHQucv9aD4HJUTWyFDcXJxibra9vs9zfY2j3PjkxZX7lp84inNHjYiTZg/2CNcdzkzPqlyu+rrs9D5+fW9XMKsK51qL+N8aP2fF40DvM8P1bRf0F6yyRAmBxEVjEdOUM1V57dHMu8/NJrvOMdD2RAuCqqtlclFy6+T9g4tc67nniMkyc2ePHF1/jKV/+A0WhUKaWdbUjkbbskkdZaoVRCjJGQaalIxIhARgSEBDoiJCTSDZOpLGAkhhyIfYaqS6wGhdxYwZSLvSR9FiK0/6Rl+skAVFkCPcGY2nXDoEUrWKMlVolEE4Ui0SNG2oD01E4YSDuB4syqwJSj6ss9rqcryN4y+3FIIKCfaK6PRmzJbQZqz5aJGmSATqiSIkE2vCe950ztJh6ynfBY+dofqAacHhI0TqsnnL2vOR/rwpe9C4JsYqAIhFU+ESEmpdmHyWNdBKQmieKFF17lxRdf4cL99/Ludz/OE088yjPPvMAbb1yuBcTz5u+W40M//n5eeeUNer1cijldkjxfuyaqQHD5/eS90bSbj+QOC/KPK37UyjOsr6/z67/+6292N259yGDCqXUh8HfUUkoVA24hYGVln/3eSg6EM6fnxcBwuR6pFgHDYZOd/dOM4ybrnZuc3XiDgNi0p0KyQj912/DNr+qAhf1cu9xhe0xzs67UyKQdmHHIEQyYwnIHdhJWhDkY1krkOaaWFVbKllJyealKWmbRALZEhJWyXWENoZTSRNY0K9TC+IYILMjMmVbAODNLwwhHmK6nOge7rh6wY3DBGG9pjQGCgiIL7P55oDk7jMKB9XLfDSusrJpQkJt7BVIhtUBqwxVLbcsqkd+NXT1h85mVQltJdGjlzmGQ5tJoC3jzUkk6k0RnbHDg1Q4u5Qf7pZQQKgPA5r3OQXCocwDsOhy4PHJZ/Oefa1XGWVJWguBZsuhZEUjF+soWq8vb9A7W2N47y+7+adaXN1lqd+diiH1WeBYbfGPnXlY724ThMZZKuo1xO4Dxj9rzedE4zPP8yEA4H0TK2otiVrmUPOpkmnVAdL64ePEyTz31BCdObLC9XZ93UBdVA+WTJzd417sf48SJdV588VW++tVvMB7Hhf4eGgQ7xqsAhg0b7N6mOiEWBwhhTLukfQ1ElG0rJSZJR4xVj0QNUHpca/hUALWWKczl7SFh0CaSbaRtX1lHZVOP17kwTzKZQkhC2aYVrLEiT7OqNmipFgBDMaQvesShMSZp6jZN3SIgJBZjDsQ+A90l0SOGep+bwTVGaoPNgw4SyYiYXblDl5sM0y6pGmVSa7OfgX0gO+AnKYqrHSgxH0+WjgpL4LMoMZ8XJFb99rNygcs59MU0gtLvVdFGYZsWBEsRFs4PLexkgyRzN59mZDdv+sJhQJzWgtdevcQbr1/mwoV7eOKJR3jiiUd4+unneOONBUso1ESjEfHYY+/g//n//iv7SQ4+D+tUX71scQKruGyZHS5u23+d1fatih/FHKS3XVjQKlSKDiYf82ZwOn3QVgaWtyqWlvps72wwGjdoNjAyyEOCYReDeJmdrgHAa8vbnD1x0QAbpQqXUFUe4LyD2UonWgdAACEDA1YdMCn800YqnQChRiROTm3BsFQGBIcpaDHJCmMHxEqYbSjTpvBYTxeOHZXCSKEDodEWnEbWNCvQxhXasazZ7gjDOEfCALxEGWMqiQG5kcxBsL9NH1CHmRQ670cZAJvPpVleKrTUBtwqae+YuUTaRVZKSZj+CwzDLbz+O9l2lg8sc0l0FFhGOEi9WsFGAp0zweZ9xuoKbd5nbK/K/7aTE5lDtcwZ4QwAV4FgnwmW5E7RYWByzEX53JElsCvJ5NBhI/8eMhBcYDIPoeSQQrO6tMtKZ5f9g3V29s+ws3+aE6s36FQxxKXrdZ77SJKEdA9O8NBd31u4f3Vx3F4Jfm5yQaKt1G1jh+88n48/juUp54NhE/5AcHJmZ9oAbxEzoVm1Tl0kScprr13kHe98gK//weJA2LWntWJ5eYknn3ycc+fP8MLzL/O1r37TA8DT1z9suFxdITCgU6UIxsbJsZRnbUBxZNdLUTrJyyaVWa8pQM6BYCkaRMES7XCDjjQlmySSsRgz0j0O1A7j1ABtrSdL0QgREsomLbHKmjrJSb1Cxw7MhqrFUC2TkNKhwYmowXpD0AxgmMLNYcpl1eVGcIWB2mNf32QgumxaMJcSM1I9A/TToQXAPsC0NyqX24Sy0ugaebM15zJ9bmc5xkAm4zZguwj6K9UEFlAuMgFS1Sc/r9qPvGa31xePyc7PmSIgLp4fHugisO+nS7+nsdLHwWIqpXn11Yu89tol7r//Hp58z+O885GH+N53n+HGjc1DtyuE5EMfeh+93oAXX3zV+6YIhosxeT8rt1lcLl+2aqLDTTTkYHget/FJ4Hwn7kRtFOraHpHBhUomJwPIVYY00wbZfu6wTrJlA6lYXurT7W9wunGdrDbwIcDwOG6ws3+GwXjZAOCTlwikAqysV4Y5CPONrrDsZFkiPVepmtngWcsA4YBMaH+TBJs3DGDBsMTIpCUG2GoDWh0LSSKRgWGFwYAtc4uwRlqeXNd3WIYcEIeYlKdQCFKpDIgUppZwWgKbYHKLURanCYHSOsvBdeDSSaQdWC0zvhMu0XgA2H4f2nJQTalQgSJ2pmFKkuLhRMvWRlJlcmwnl461QCpZzCGWyuYn53LoUCpCmWZMcBiklSBYCMcK25xr3ym6LPF2DtG2JJLLDcYDw1mRZWlPfweAgWy2oFw3WEoDiKEEch0Aztlgd66Z70vX/lGMnryyYz4g3to7x17vJBurN2g3q6tATAufLb6+ey+NaMjq0m5xuzX1wusbrcAcx+ikL8r32DvxQx9HL5/ksShVEuJyzdUcGBQHj/MwTdOXyQe0VfHii6/yH/1Hf4zvd57l4GCwMKvcaDR4/PF38OBDF3j99Uv8m3/9OwyHuZveVEbuGACCy3M15YY8QFKqW+jAk+lDkEm0VfnmMCXv1N+HQDZphxusi7s4mZ5iVbQIEIxVyrbosR1E7Hv988GwcaoOTC1g0aStmnSCkJVQIgW0UkmiQxpScK4teGh5zAMrXZYbI7rjFi/srfL07gZyILksY/rpJkO9l/dVKwP0y4y0kJSfUZMxCTCkbBDIJg25RDvYYIkNIhpoFAPR50DtMEy7xGkfxWgCDJdj2jkwjV31S1o5ECxL7LQWyjhdk+f35p1Rtb/vIjF1omSO9mfnLU9b1wDiN964wjve8QA/8RMf5ObmFt/97g/o7u3XtjMNoH/gg0/x9T/49sxtm5it2phcrsTq+uZvuIkZV27F5qlP5OQV2eE6FvlWhqaomzjM+nfiTQiVUvlIt7XjswElk5LEyrZ8Ix0Hfo8BXBf7ZWJ1pcuVa3exsbpJGHiDyzIYdquW3qdpwE7vDN3BSVY6u9xz9pViO1WblwEiTYrv3d/ecnUg91AMUMbuKQOIlYDEteNArZEnlVlh0gAZGqMrpEn9qZM5+rms0oJArQ2STJUBtyGKRDsnFyMbLhtmuXeB1AhtpncRDhQ7kyztb9hImO1b6ZYlv4MZ0JyD4MDl7EqT7yuCFGWZ67Hdl8SCW8cYR+5VqmzKORYaqUwJy7zMk1d72LHBjv31SiVNGmPlzK8MVM78+v9KbHAGjoVGhipnkS2LX5sPnAFgaV4dExyEOQguSJ3tkfRygifygsuu0YeNqnXtdbK6tMtye4+9/kmub99Lq9HnxOoNGs0Z6QYVoRRs7p3n/InXD9/XGTFvDeFFQLNjg29n3Hk+H38cqXySiWqGqChRteIWKz+tG2CXXYbz9hfJzauOXq/P1avXefjhB/je956ZG5wKIXjwwQu8+8nH2Nrc5j98/vfpdvOBeDYRUCMprdvOPDmJleuVGSdvkC0yKXAykQuabcfLDa52EXZf5sxoR25wMj3F3eEyJ5uSSEI/iWgMA7RSxHJIokakYmRdl2fvl0AQSU1LSE634Im1Az5472vc/8QLtM7uMLx2gnu++ziS+9mPV9hV6/TZJFVjUjWiVn7q9lsUv8t/G23PzaLLNRb0N4NV1oO7OJ2e45RcoiUlqdbspTHX5Qpb4SU0KXFaYmNL+3sYmW22PxVMcpafDQhhJjh8SfNkI66slTPEksal2ztPtU5RBYOsSWfxScA3301/2kRTney36pilacpzz73Eyy+/xuNPvJNPfepjvPzSqzz99POk6fSUCb+ddz/5GM1mg29847sVPZrnHlPV7uQ1Nh0Em3uicYS3kj9LCzhH/QxMZ+uXz6FbD4jvSK9+eGOi3If3eZWDa2U45rYODJf+ntrGnJ83GjGt1pD93iobazv5MmVmOGvH7I/W0O2fYGf/NK1Gn7tPv0IjGtcO4CcG5iLMagrjDYCL26mQQE+LOpmkn9fpyiq5YrlJalhXqWtZYRmkpIlBUUJqinOf/vMiB2vOFEpJYZ4rWiClQGojgQ6FyReOtSDIjLNyNtcvZRsI7TG+ZPJo/44krNVkJo1mUhadHQ7yzzJnaakgMM+0VEmzvgoKOppAaBpBSlOmJsfXPjEDoYhFgKurnC3vGWP5pZJC6xAdhmkBBDuzMWeOJV1d4cDm+5Yl0ZmLdDE3OGODA1VYpzArUAbBZQm9BcFFkOtAsaxmgxcEwYdOgZABkpSNlU1WOzvs7J/m8s0HWVvaZn1tGyl13pcZ18529xwgOLVWkQblXbe++uJYjatUmq/vzO38yYdSlLd1O02z7jyfjz+OQRpdBMSV3xsnCCs/WmQgN8+y87f3/Asv89GPfohnnnmeJJl94m5srPH+9z9Jo9ngD/7gD7l29cbEMoVB6pxTLdPcqP12Z+Vs5ptNC69uwsHvj0AWHKAnw2PlPYl1KJu09BKrosXJpuRsS9MIFPuxJFYh+6MOu7JDYCW8VfuldEysRxyIAftpBESEQhAIWAoFq5Hiwuoe9z/xAp2fjRg98Ck6r3+fd/AMV/Y2eGH/FJ3+snWbdhMs7ryS2T45NlyKKJOJF8ygsuNVLA3lgGYgmizJk5xJz/NgY4W7O7DaUIxTybVhi+jgDKlIiOXAAPIpteoWMVuq+o3rwKIUElWa2HDAWTvXlYltWlMs21+lVdaOYdPzV7NSmVGePZmzCOM9K6r2fTwe850/eppXXn6d97//Sf7EZz7Bd7/zAy5evFLZRnn9D3/4/Xzvu8/UllBbPKpKw5UnXbDvHQi2k4LaTlphmGHXThkMZ21NgOE7cScmQ1Qxwj6YLYPguUCxx+6U2qw0wPEkz4uC4bWVPW7ePMna6m4OmKbIpIfjFls7Z1BKcvbEJdrN/nz74kKGZkyiyMGw241sGW/gbT8qSiOn3ONUWrFNOSnwyOSyaQ6Syqyw1Ggv51QIk+SjVbVxjjN2ChJjBOWAqdZmICy0ROkUrSHRgqY14DG74+UIk5tggfFyyr+jIHc2ZLZACezdLQeuVe7N/veBBeRGIp0gAkUqJYGSiNSCcLt+JAwT3LAAVdhtJd74IyE/zM4krMwGu7+LcujUGGM5gyy7bFYH2GfaA8P0FkBwBnpLbHCQA2PvR8rPCQeCfTY4DNFhlIPi7ACWSvr4Umkv5ir947pSBYbnYUUtSA2ClFPr11hd2mFz7xyXbjzIyTUvf7iqLe/auLF7LydXr07PRLD3gltax9ebxNIAUk4wxFXX/6Ec5+/EWyYOBYTnclidg2GpdGv1ZKF5TDI2k9ucvb2bNzbpZaWU6iUYYRjy7nc/ygMPXuD551/iuWdfIk3LJ/nk9ucZ/JdL1LioAqfTwHD2mScDrjPCyo6VrvhsSjgwGRASCcMENwJFQ2oiCZEUBARIqgEBmJzmRI0Yyi67MkIpxUHapklEg4BANABohjHNk13ie34cedfHGKuU5pkvsNwY0Q6goSNj9JTtSxG0GflwkzBo0ZDLpkwTAamOidUBsRqAjAvHuaBAICCUTZZZ50ywxH3L8NDykI3miGEa0Aw6HCQRO6M1dmWbkehOtFEXhy8/5LG5mMmkilKXc7XjjNa0MOep0nlZKaVjMja4sq9uSFFSI3hxVKfjWQyyi253ny984Svcf/99vPe97+LBBy/wrW99l37/IL9eSiD+3nvPc+7sGf7p//2b5PcJf5nD7TPkCou6a89dn74iwS2f55HX7fjxSNwXDcUi/Hj1+nfirRNmIGcBX8V3lTGtFjAUAPZURskH2zNYp1ajRxCs0+8vs7K8X1zPA8MJDXb2TtHvL7O2vMXayjZyyqRkZUywTBhTMdtOGRQLt79zgGBRB4B9QOODYanI80KLJXgyVjg0rtEikAXTLKEFOi3JmS0IlhbEOYl4QoDCAF5pHyRaC5rayKaVdV5OtC6A69pyR+RML1gQTMkIC1u/2OUCl9uwoDK0dY8lmihQCBSBzmsgJ6nM7pSBBcLG5Tkvo0TpHuzSTgKpTYkkywYHXo3lIFAZCA7DpMACS48VFrZ8kvTZ4jDNALBfKknYUkllNtj93gWDLD8fuJwXXPjns7w5M6xnsJa6zA6LsFhT/DjCuy4a0Yjzpy/RO1hjc/cszWiNU+vXprpA7/U3GMVtzmy8cfS+lO4Di7niq8K1m3FJdei8dP1rGaDloQZoC8Wd5/PxxzFeDcVpzhy8ualWmZn1lNmUuli0XIyLqkG5a+P551/iXe96zJZSmhyCnjlzig9+8Cl6/QM+/9tfYn+/N7XdolHYbBA8X+RzrD4YLm83c/+dwspXGR0VYzq40ShixgxVQj8J2Y+dNFpwkGhGxKQkmXmTzyo6aW6iBgwSSIOYkezRkB0auk1bd5DxCXbGDbYOluhfOsPac98mTsY0XnuO7uXT7I1aDFJIRFqcDBAyB8EYEGxMvdZZFqdYUstENIgZ0wv36KktYtHNlnd9xB1faRy3I92gE0pWI8Wp1oAT7QMGcUR33GAlatIcRQREk8eKyd/IP65V9XkLv1NpsiNnrBWKOFsmF6+5es9lqV91H5zRmqnz7ZjkUgmsqefndECYb6d+UmSRmAashZC8/volrly5xpNPPs6nP/1xvvvdZ3j55dcq1/noT36I5194mW63531afb679uuWmSd8o7R560lnagp7r6x2zp/vvnnUuFOe4W0UC8oHC4PnWWC4LnxW2A+/PQeQPRMcIWB1ZY/u/hrLSyUnWrvcYLTCze1TNMIxd527SEPaMmhisXrDmfmW+8y+Cvf4KoHiDAxn+2LvFYeoO5y3gQVD0gJgOwCXhqEGywqHaV5GKVSI1JhmGaOs3K2YVBp2M5CkMjBMcBRnUkghDMBNlVNPVXRPaBItshzb7PjUsM6Fu5EFrYFd3jfCcsxv0bzLscJ+7q0BulKmBrALUw85EJLUHJSCxDmwpYsCodFaZuwvpFmfA5mXS8rYYJmzwdnEQVAljc5ZXulqCQe5i7QPgBEqB8KhsmDYgGTn6D3x6M9ArSgwwUU2OMjBb2GdoMgIv5nhM6bAytIenfY+23tnuHTjAU6u3WC5s1d5zt3YuY+1pU0acmSvicX3Z9p1OAsMz/M9kAH+bPkyQL5NucJv9efzv/7X/5q/8Tf+Bt/73vdotVp87GMf4zd/8zdv6TaPGscChGsHrRlbGeTvveWq2ODjiGmDxUuXrvDkk49zzz3nuXjxSrZsEAS8+92P8uCDF/je957lpZdyZ9lpDsHzMMBF6eMiF4sB1+X1MiMllzcqmAQ1Nf2t6yNUSzoTNaIvd9gWqzSGAbEKiaQBwVvxiK7sMtT7JK50UYlVVDpBgi3flDAWPULZJBBNmnIZNKz0z/Ds7hqnnn6MR+IX6Zx+lv72Ki88905e2FvjxlDRE13rDO0dj+y4hgSyQStYZZ1znE3PsB40aUrBSGl201Wuyyb7QWDKTIkQsKZeHhhWOkVJRao1sRIM05BREjJKQ8ZKEitIvDzaKimxf7zrj2sOmv1lqyXSCRCiiBE6KLWT/+YwDyudT1ZpksJ38wI2fx9cHNd1O7GNmoknF3Gs+MM/fJqLl67xYx98D/fcc55vfvM7HBzkNYI3Tqzyjofv5x/8g/8ra7Pczjz9qP3OpSXU3Mtyybq0+doB3gi49tj5qQy3O6p91Rdb/7DxP/wP/wN//a//dX7pl36J//V//V+P0NKPdkwwQQvEsTisOmZmVqmWkjx7aanHzt4Gg2GHztI4W0YpwfbuWfr9JU6sbbLc2UYI0Hh5z4fps+9KS/HczUCx9MAwzA24XWgpETbPc+KSTmuuNMcektcWNn0SyMiWTcLTraTGTTrVwjKXhuFUSqKjBJloEhGgtCCQJv82KaUyCaEJ0oDYDrQdGFZagCdNdiC31FXbX/dHLn12OcBuG/462XuhM5ZWSkVgtya1QoiAVGnraSGsE7Qz23Ku0QJn4KW1AZxauxxmnbHHgZM3e+Db1QjOpNHWPRpZzBN2LHAdAMbJp706wu7zTO7umGDhuUOHk+W1spzgzBCr9JzwwLFbdh5WuNxGbRwDuA6k4vTGNZba+2zunKc/WOHUxjXCIMmuoYNhh/2DdR6971v5isdpyLdISFnYdqX52DQDsKrf6RbEm/l8nhX/4l/8C/6T/+Q/4b//7/97fvqnf5okSXj66adv4RaPJw4FhKuBGRQGeNmTo/rEyNevkjzPDzQXPSVSlfLscy/w2GPv5OLFK2it2NjY4Mc//D7iccJv//aX6PWm5xrlRkvV8spquawDPnaQewjwYdY3x0eKCCnDgimWK5VkGMTZTtzTB/gmFzdRA4aqy6a8QqoT9kZLhASMSejKLrtcZ5R2SdW4cAz83F3nJq1FQookUQOkiIjlAEKI0gbNnQ1ifQ+v755gtTFiP27wam+JH+xKLia7dOVNEj2aYEDd/ksR0RTLbKgTnIvanG0L2oFmkEpagzZJfJIkGkzkD+f7m5LqEQNxwE4cc23QpBV06I4bDJXkyiBkZ6zoyR6JHqHc4KvmGNaB4LqoksFnrKJOoJJdrP+Nq9QUvvN4oa81v9tR96mqH9NY3jpVQrmtcty4vsm/+3df4KmnnuDTn/443/rWd7Pc4T/2xz7C629c5vLl6xMTROU2F2NbFWXH5/J+FNUcQaaQMBODufkZMHFeAxOTJX6/347xzW9+k7/7d/8uTz755JvdlR/qqCyfskDt0LlB8Dzy6DIgnsEyS6FZXdljd2+dducGQsAo7nDz5kmCIOGus28QhcnhRnMFObTnnl2zvz5LbKTlnpQa5mOAZJAv5wbaLvz1/b99cZn0wDCplR+DVDmk1kKDlFnZJJQgDBO0FgRpmjPCqTWR0jX34NTm6ipJqgWJyxsmZ5EcKM4mBUrHqwyKfXdoKMqsXX1hIdy4SGfrOKZXWOCbCmn7kptwFbatBVqTAdzAyJ4yx+yJ/GALegPnEG2doWWgkGGSMcLSGWGV2GD36sAv4JVWsixymIInoUZSkkV74LeyXrAHgkvnqO8WXVU3+K0UnVafu8++wvbuWS5df4DTG1dZau4BcO3mfax0dug0usWVCt4GzsSqWl6dsblT0iOmsb7T6og787HKzyvjuDxIfvgiSRJ+6Zd+iV/91V/lc5/7XPb5448//ib2ar44NCNcP2gsgeEjtFeVi3wcA8FXX32DJ554hPPnz9Jut3jv+57k2Wdf4LlnXzKziVP6UIwye1fnupuzt/mAN3+YzzTDqjgOztzJsKtGqpvqmESMSNSAVI1wZXXKg/4yOJq23VSNGKVdtE4ZBwfsiA6BiEh1zEibGr7jtI/SI287YWG/820acK20QjFG6ZgecDWENE7YubnBC91lWsEKw1SzOUq4yhY35EX6yVbGCE+ybwGBCGmIDss0WW8ITjUVS2FKPwmIlWQ3abKt21k+cGE/M9A/oqc3uSGWifonGaQRS2FIrGBrpLiS9NiTm4zSnmFjyZlg/7eZFVUAx72vB8M581v32xXd1ottTsiuKztWBMHz9G9eNviokt5Z6ydJwre+9V2uXLnGj/3Yezlz5lR2nf+f/+if1a43j2KiDG7zyPiY2r7mf7uikd5yFb9XGUS/GaE5ovSqPuu5Nnq9Hn/2z/5Z/t7f+3v8d//df3fobf+oR3lAPC+onWe5QznLZivXyKVdeIPc1eUue911BoMWcRKxs7vB+uoWayt7iEVzgctRMv2aNAgqMcwqzQy1kO7vQ5jiWEBsBtQlAOzeewYQQnpY34JhM6BKclBpGUedKkQcoGSe9xtoU24oTP0SS24yghw023DDHik0oZAkWhKWgLDKXKWL8sqqz8AyzMK5RuuJ7yRG5hwK4/xsltEZYyuF1c94LDb2vdueD45dBBaYOubYSK3zcklSuBJJ7l+eTy1lnguc5QdbFjjL97U5wBnbCyYHGHKmOHSssSqywSHGcSwMvH8WBPuS6DAqgGBdwQg7gPZWBsEuAqk4feIqncEKN7fPMuy06LS67PVP8s57v129UmHSKL/uF0rvOEQU6jSXPp+nXvitjuN6Pne7xcmHZrNJs9k8dLvf/va3uXz5MlJK3vve93Lt2jWeeuopfvVXf5V3vetdh273dsQRpdGTg0ATORiu/m7RAd7RB4T+ID5NU1588RX+5M9+iuvXb/L7v/cH3Ly5tVB7syTSvumXz97muZ1BxtxmgDhrs8hcFYCLdqqakFAaeXFIEykCYj0kFoNsPaVHdhUfTPlNzZa3ap2QpAOUSojVgIGICo7DqRpnjsNuO64erxRhAaRnpY+s67NSY8Z02SclCUbsyg3a4yUCQmLGHIguB2qHUdIlUQMKtYIrQmqJFMIqjvK8pNpbhssztsfL5DLvcjO8SMyY7cEaLRqkpPTEATvBTfrpVtaX8u9duYkSk1sHMqvWq4tyaZ5sXwrhqzQm+zG1zwuA+UXXm9rmHJNhxZj87sqV6/z2b3+JD3/4A/zFz/2/uHljk9dfv3SkftX1ra4PdetXH+/Jyan6bR7m3rl4KH1YU7Z8fVjsQfuf/qf/KT/zMz/DJz/5yTtA+JBROwibAkBvx8BtVh/KTI+UmuWlLhev3Eu7NeDcmeu0WjZ/sHRezlUOqi7qmGCPOc4rDQDaMsEyyBjRSifpie1IA3bdK5hXbUFwzQVXuPwboBNtALGMjQTYAjGdpOb4xhqtBEFopNNBKlFhWho0m98hVZJAmprEUipCT1qcCk2gzXdaCxLt7lFkoLMKIPuSTcP0akKPES7KqY1sORSKMHAsrUZZibQ0w33A5v8KkfXXbdMBY5dDYvKL7TqeK3SWD+ze25rBYWDcjoPQvRq3aj8XeIIFDs33jvl1wNfslMcMW4aZ0BhmEdpLQMwAwRnDWwGCSy7QkzWFj+laXqCduUuy2Vhq79M43efGzj1cun4/neY+y+396Y+1BUCwv9yhjod3rfps8Fspjuv5fO+99xY+/+Vf/mV+5Vd+5dDtvvLKKwD8yq/8Cv/L//K/cP/99/M//8//Mx//+Md54YUXOHHixKHbvtVxBCAsS6+Tsk28Qf9i7Eb9svMPticHka4fy0vLPHD/BVZWlvn3/+4LtSB43j7XS0YNCA5kw4Bh2/dUx6aUjSIDq35blSZh7m8v3zgUTRqiQ0svIbUkoc1ABKQyJtUjVGqBNjkYnhZVwN7k/Cak2pYgmlhGTYDgKFiiFazREqtEwgx8x3rAQO0xSrvEab8AhmMgVWMGcicDzkorUj0iVaMJAFyVy6xRxGLEgRqzH0fsjgPGgeQgFXRjzYEek4hRZuqV77S/Pwlx2mdfx4yCHrtyOTPGGusD4uSAsepbl+UcXFaB2wKLSPHzY4mp5+ZiQHaRPs2bb77o8rMk04u4Uh8cDPj617/NH//0x9nZ3uXM2VPcuL45t0x7Muom9Gbtw3S1QF16yNsl5n3Q/tN/+k/59re/zTe/+c3b1LMfjZg1gMuZj3LJpQXAZVU+nw96D8HgxnFE/2CZOI645/wlA4KPIRZitO0AvyABVimQetLoNBs4F9iiisG6tmVYKsOxwv5toGJ4JRqYEkqJBpmALfFTLF8lQJl84cDKo80/aV9TlBJZLq0UpkZxIFS2LYEmQGSgM8w8MWxXLPBMLeucun9KZsfKscDOpEpkk9N5rnD2vVSE1tAqL/Gos2Wyw6QFAoEWAuH6oKSpieyDYJG7ZQfCvlrjq5z5VRkIlvZVBIogSiZAsLTGVxkgztheY1KWAWHXVweSw9wgS5hZeQi9vGBnjmWl0VrazybygyfLIBXygsus8IRj+a0BcpXXU1W5tVJEYczJ1Stcvv4AnWafwahDO9qvXf6WxbRc30PGD1MJpYsXL7K6upq9r5uk/q/+q/+K//F//B+ntvXss8+i7CTff/1f/9f86T/9pwH4tV/7Ne655x7+2T/7Z/ylv/SXjqnnxx+HBMJ17KHHfM0xAK4eOM4/KFxsQGvaPnXqJD/x0Q/y+uuXeP31izz00P1cvHh17m3Os728pm2evxqIECGC7PsE0C75yDNgKjO3ZfOpfLmAQESZ+7JAEosxsRiZMkOZKdKUSYUSkDPhQN0kwNd6jCjlFxVKEImQULZpBxusiXOcVKdYwpRH6jNmR26zI65wABkYBlDK5g8rf8CTA+xpZWQcw56oEQO9x65cozWKiFWDViAYpsbUa1vucqB2URZU1zF0yk4gpGrMUOwiRZhtx9Uj9nNzpzG88wKtaRMuteDJY7PrS+xMZ4CP24G4Lse3bjvzmrr5YLjqu6r42Mc+zCsvv8bv/M6X+ehHP8R3v/MD6xQ/a7/9e1D9ZIGr91u/D5PrlnOCzX4l2bqz9ul2hWaCeFt4fZjvQXvx4kV+6Zd+ic9//vO0Wq0jbPVOlOsIZ6xIhSy5FgQfe6cWM8saDNvc2L6LleV9lpYG7PdXWVoaVK66CBM1EwRX9LNskmWOZZAzwb577Lz5wlLVD7QloIollLL8ssxU3kpsEzWxqtYSkVpDp1SiAolUCpk6+a8g0AIdGJbYr4lr8moNGJbCgWDTtvIYZVeqSFugnGpJkkpiJZGSgtu0A8GRq8UrHBOcA1wHkiOZ2seYBY+FQ5Czyg7Yp0pmt9i8fJKTQ5vSSmGQEoaG+c0NsXQGiJ0EOgxTrzzSFBAcqiz/NwPAokIebWXQZjl7aoUYEOwzwTUgWJdB8ER+8Azzq4rvC6WTbkfMMLy6vnWBteVNzp14nWvbFzi5cpnVzs7czbv9mAY8fVZ4YrnKyaqg4AOwaNwuEHxcz+fV1dXC87ku/tpf+2v8x//xfzx1mQcffJCrVw2O8nOCm80mDz74IG+88cZhu3tbYqGnoHPhm4wiUHNgbtJQalrUyyIXNdgpL+Pauu++e/nAB5/kO9/5Pi+/9BpRFPLOR97B6dMnK1nhRQekdXJRKSRCBLlMGBA6qJaCloBp7jo7fT8l0kqD651oXX+AOYBcbnjmg/FpqQlSRESyzbI8yfn0HOeiNusNm48QN2mOI+PKHBhGPFU5kPBzYOvq2VbVADbrJqRqzCjtshvcQEnFfrxCEEtiErpyjy43s1xn85s0s4kKt30j8U5Mex6TcSuBSZ2aYKps2f0monieFE3rpsc8+zSb3a2euFo0f/goUQWgl5Y6PPXeJ/jn//xf89prF+n1+nzkJz7IysoS3/nOD2a0qEp/lyYPCnn+s+9BVSDY77v/+lYJldUUPfz6MN+D9g//8A+5ceMG73vf+7LP0jTl937v9/g7f+fvMBqNCIK3ljTthzKqXJwdOPRNaVwpIzeI9MytptYLXiRqAGxvsM7m9ilObmyxsjogTSUXL9/DcNSk1ZyDFfYHovMOZN3xqGS3SmBYpjavmKJEulCPuJ4VLoSU4KS9WfkkKzBWYnI4JIXVNBqgnHtGA0oY2bRzLPYcjV05pZwZFkgtCAJTO9gxwkjzHmU2Uc69dQDWhbYgOFWCMSFCaMapWdKFD4LDTKqcg2Dn+py7N5Mxx9nPk33nPtMoZUG8ymsIO2Dv2OAoigmDlChKsrxf1xZQLJHk5NFRcigQbPKALfh1fQ89JtjlBUtZlEM7EFwuk1QxUTIBzMpscMkTYKqx062e/JoR43GD7d4ZHrzrB6x2dojCEdd37iNJG2wsX6/BGCbK+zIrb7fyu6rPapj0aYC4uu3bkbp0PM/neeP06dOcPn165nLvf//7aTabPP/883z0ox8FII5jXnvtNS5cuHCovt6uOLYrws9lrZLh5gNGM3guDl7rQfCsmBekPPzw/bz73Y/y1a9+k2tXbwAQxwnPP/8S737yMX73d748Vzt1fZjWX6UVsuLcmyV9zr/Dkww5pjc1ebcyZsyYUCsSkWQ1fY/a5+qVpgNsISSR7LCs1jgRNDnXFpxupQhgaxQQqya9dJ2e3GIkuqTTFABlo6aS8VY551XpmHHap8cNxvKArjQseUrMSBlTLzdjG8gGYdggkp2M8U3UiLHqkaRDFCMjXc+AiunP7PJExeMxLWYZltUx9dm1o+uWq+/TPH2r65evVJi8dqu3M48c/ziiDIY/8YmPsnlzm+efewmAzc1tfuc//D4f+9iHaTabfPObf4Q6SpIN7vqBKsO8PM9fVgL1yn04xO94qyKfkjr8+vPGJz7xCb7//e8XPvsLf+Ev8Oijj/Jf/pf/5R0QvGDkALaGkSmzwzUlVwptTXxvVQx1A+pZxlgVsdc7yW53nbOnr9PumNSTIFCsrnTZ2V3n/Nnrtr953eFCfmIV4+P1fWqfZQUYdhMACjIn2Mw91gq5ZM4OT3WRdiAF8jJKjhmW0rDEYWA2I0vtFOrH4kmotZFLCw1hikglIg4xpXzykj+urJLUqWGDPYm0lAYUI0F4YBKYMONxLK4PhlMliZPQAJfEHLtY5RJqV+c3lIpIpiYX2JYvKoDhTFIsJky1/D4JV/pIOBfswCqF8jrJLv83ChPDBkdJVhLJLePacrV9pZVEHwoEh2kOgL3ySNYexgJgciZYyum1givY4MI1OEW2O7Vk2iLX4xzli44yIXZ58wE6zR5ryzugoN084O5Tr3B1635SFXJq9fJUMHykuAVM8O2O2/l8XiRWV1f5y3/5L/PLv/zL3HvvvVy4cIFf/dVfBeDnf/7nb9FWjydu69TQbPB1/D+R1opHH32YRx99iC996Q/Y3t4ufP/iC6/wjnc8yF13neXKleu3ZPtapFleKRjQ5gymfFl0MXLgl8uUizLMRI8Y6h4ICIiIxYixPiBRQwPkjvNyyZitIiAtgCUHhnWDhpQshZqV0Dy8RqmkEwqixBhoTQMH9RLe4vHIzMdcHrMy1YETNWIoumYvbD620jGRaCJEQCtYpyU3WNEbRLqBRtGXPXpyiz5bjNMUYfdNk2Zs3iLAbu788hm/URlY+cfEL4VUn39ajFmGVHX9KUv3y6Zus9aftk3/s6Oy72trq7znqSf4v/7Jvyh83u8f8Lu/+2V+6qd+nI985IN87Wt/SJrOI2UqAtqqMkflqPrNiufRZM3v2zFh8FaMlZWVCUfJpaUlTp48+ZZ3mvxhiGOpCVxoLyn8PRUMZwtOMevSsNs9Qbe/zrkzV2m2iveFtbUuly7fw2DQot3sLdbZinzFWkDs3ovQ9H0CcKfZchoQaZIzwliQWwCw1nFaSvO5L1sNyfOLA+fk7K2rpqEAe19RIEKNVhpSPzfVgTRzDzIAMMkGeq7+sLYSabPpnFUFKl3f/ZxdIbRhhIU07xNtwa814tISySQINrV8nXGVLrQNoIW0W6+epPRl3O6925dM3m3Z4CBMs1dh2d+yU7abLCiXR1qICXb9cIywKw4gMQOV0GeCnfxZeozunCDYA8B+PvBc17ZL76rINZ4nCqXGqLh2FojhqMXO/mneee93C59H4Zi7Tr3C1e37ubF7H6fXL05Mihw5Zqo0KiToJZf5bLkq2fVtYIPf6vGrv/qrhGHIn//zf57BYMCHPvQhfvd3f5eNjY03u2tTY6EzWhzbNE15EF0c5B8PA2LafOJdj/DQQxf44he/xu5ud2KpOI559pkXede7HysA4cUNdSbDAUXlcmEzGWxqclXdgFgXAZcQYcFp2qyb2PbS7H2iRozooaV1w9YxsRoQq4EHtE1UlWop97XKsdqt646FKAxqFIKivFlrRcyYsVIM04CD1DwUhymMUkhJURkQKEZ9/6pl25NtmHJPKaPSpIvCTSwEosFqcJaTo7Oclh1aoSRWmp10hZuygQrNpEXsDMD8XKU6xu6QEw6mlFO1PN69TgOeGRieAoLrzLzKy84NYCf6NLlP9W1NUQDMiGmTaH4/PvXHP8alS1d55ZU3JpYbDkd84Qtf4Sd/6sf5yE98kK98+RuZwYMfk+y+6fsi9yX/N9OauUH0vBMatzK0PmJ5hiOseyeOP950tqOGIdYadvZO0jtY4fzZKzSimPKQJJCKtdU9dnY3aJ3pzcUUTQzY5zDwMSsuMBwSYVE2bYFtzg6nBTCM/VyHIBLQYVSEm44hVgoIqgfVysmnJYSWes0YSA+YOVl0oNA6L9cYAtqVIPLAtpS68B5qGGGvZq7WJv9YpEUQDaGp4YuRUwfWFTqUKZGr2ytUJchJhais8GDatm2WWGm/f0FgcqHDyLDBQZggI1v6qGodJyO3dX4PK4eeAMGOBXamWA4IS5lLoueoFez9QJUgthIQ15zD0zwB5gW2dcsVlBlVEmMvLt94gJXOLsud/Ykc/zBIuGvjJa5ZMHx2/Y3K3+1WRCUrXJNrXZZEz1NW6Tjjrfx8jqKIv/W3/hZ/62/9rVu2jVsRh5zaKefOlfJJK6KcW1fdZsV6RwCijzzyAA8/dIEvfOHLdLv5bHJ5cPnyy6/xyCMPceHCPbz++qW5Bp9VUthJ4xzDSGqVGmMsb7ksH7UiN1QIiZQhgWgSeGZNrgSR247SMbHCujkHaJ2S6JEFzclMo6lZ++TvWwbO7asD80olKEbZ+rE6oB/22EnXWBq2GKsAKWBvDNvxiJ7cZ6R6E4z1pMmQKr2vDjExgZLLmYtGXpJARkSizcn0DHeHy5xrSzqBZqQknWGLdLzOMOgzlj17nJOJnOhyjvXcINg/tlZmLax+asKJ2xkooWYDz/K6/ibnYJurliuD80mX7uo+lVnqYvi/b32/ZplmTVvm9OmTPP7YO/gH/+D/rm0/jhN+//f+gI99/CN8+MPv52tf+9ZMmXTud+DvB9l+1IH0Mhietk/zxKHSGQ4Rb7b06otf/OIRW7gTtyPKA+Mq86psmQowvNvdMCD49GWiqP4aXF3p0u0ucTDosNQ5mNqnMmNd6IcHiCvZ7CrArpPJ/XKssJVIV5VUym8XOTDITbcUIoxKjtTKNOgMl8q1hs0ftj1tZNQiZ4B1oMz7IEWkgZFFYzy2DFtqBvWR2y1PTpwqiZ+B4ECqX6/XLeu+V949uFjPNyG1hpouJ9iBYMcIixowixQZqysyIsKxwPl6pvavJ3EGY4YlNUGYEIYpMkwyR2gZeMe1FGbCwLhAT4Dgkkt0wRTLzwkug2DHAvtA2JdE+2yw6bwHbktssA+CKwBw4f28IDhb7xYJQmsAfX+wxF5vg0cv/GH1eiolkHDuxGtc3X6AG3v3cGbl1amTX8cJQBdizCuZ4tuj6nqzn89vxzjSlVAGBTCNNfNP2FvxUxQB1AMP3Mdjj7+TL37hq1NBMECaJvzgB8/xxLse4eLFy8xSTE4DjGUw7CYNUl0eNKuMDQYzUYAHOAPRJJRNQlt+SKMQaggyZ4eVK0FUkF0ntuTQYse4fgLD9CmQTRpyiUh2CEUTjZFmj9IecZrLvWM1YF/d4IZsoEen2B5HSCHoqZgtucMeNxmr3kTt4WKU1QJQBUBm7otn8CWFJLDu3cu0WW9ITjYVK6FiqASxCliOmzR1G2ml234uLhRBy8KgZMLYKsxKaxkH68CTcVs3YT0fGK4EYSXDqlrnZiavXQfQ8889B29vvTpzuOkOyEe/9uv25Y//8Y/x8suvcfnydBf4OE74vS99jY997MP82I+9jz/4g5oHM0XQb7Zd36c6MFxcb9bDsjwRdPtDa2qB+7zr34m3VszFBnu5rBMxR95gVRQApyeX3uudpNtb5/yZy0QVTHChW8Ssr+6ys3eCTvvAgEcnYS5tq9DfrIGgyBJP2xfXpkpYpIyUK6nkg+GsX/5yYEBwEpt8YccMZyWUlNsZu4LHFGeMsbIgS5iLLdSINDXMZhqYv5W5OxsjLFmQBYce+6tUgPRAd5n1VaoIhrN90nk+r7Yu0gCSgFTn0uWJWr4WxLptFNoUAmlLQjkg7Ls8+2WPMoAsc7l0Vg4pTAp5v2VJdL49nTG+WZ1gHwSHaTULbHa0IImeAMFhYH4jHwQXmGCPDXbnRh0I9gFvVR5wlfS5HIuA4MI2jg8sX7l5P+vLm3RaB/XXKhDIlPMbr3Jl6342u/dyavVi4blbCX4PUaatqr1FzbiAfBLsdrlG33k+H3scy1nujGPcyVoesGf5dQsYYB0lX/D8+bO8973v4vd+/w/Y3d0r9KMuXnvtEo88+jAPPHAfL7306kTf52nD7zvkIKPIkk1+n63rAZEcuEWZ6ZMUEYEHSpwU2Q8z4VBkHw8bPghuBqssyZMss05Tt1AoBuKAXrhFHxinXbROSNSAQbrDVgCDoE9bLyGQDIM+A92ln2wZQyo1rgHf5YmE/POFwGeV9NpKkZ1qKRD2YWgf4lkNRBF4YHDG710hBZ4FPt0xjWQ7m1gArLTd1CpOFWg9zndnBhgu7PocIGoiv9tjqGUph9uVjKJCwVDVn+r+ze7TUdQf9957Fw89dD//+//+j2Yuq7ViNBrxpS99lU9+6mO8612P8vTTzxX64acFVJU2q2Pnq/KtJ+XtZfXDnbgTb5GYNpj2AOQiA+Qy+9o/WGZ3b52zp6/QiMZT1iQDoyvLXfb21+j1V1iZVWZlSqkU4fquUvs31bcmleRscNmNuuAS7bHC9vgUmF5yUFxgf30WMIxAKTOQdmA4A732NZBFUJy1pRGhhkQjohSRpMhIZP3TSiGkRPlg0APCaaKRMmd8fVOpzGlaCZRypEcurTY1hu0+agGhbUPl97VAKqIgNXJoDwT7jHCW8ytThFCEQZIBZT8v2YFgV/rIAVlp+y1ldS1g07YdcymZlzlyQNrLEy6A4EBNyqC9/massBsqSIog2OUDl+TQOcgty6Nng+CFwK+LQ4Lg44xuf41ef5XHHvjWXMsHYsT5jZe5sv0Odvtn2Fi+Mfe2asFrhWy7vFzZU6HsAl8GzT9MdYPvRH0c23SP76JaHCSam3mV2dNUw6RDDopXV1f48R9/H9/45nfYvLk9N3DSWvPd7zzDB3/sPbzxxiXG4/jQ/agCbDlLWV64HkTkwC1Ao5AiyHwxXL6wfxxvhWzSlUTqyA029BlO6nU6MkRpzb5aRkpJEoxI9YgkNZLsOO0bmbQ8YF9ESBGQpENiNSBVY5QeTS2xUwZp/udVUalCEHICDGsUioQBMftxxM5YMkwFw1TQjTUHOmEkB6Qqnjhfq6TbvhS4LMP2AWX5N3XHtC3XaLNKZGstj+SQgTATNyOt0CKZAFbzmioVSnDNfQ5LAtlEijBjqTUpQksrgacSDPvrzwN4F8sPny8+85lP8P3vP1tZBq0uRqMxX/79r/PTn/hJer0+r7122X6jasGwi2lS9WnrHTYmz79bFybz/wjlGY6w7p04xvCA6yzTLH9wnAO2OQZ5VW1OyckdjZvc3DnHmRNXaTX6TAxBvHJNfggBJ9a32do5yVKnhyTO+l1bT7hC0pwD4CnbrWivSh5dXi4bGMsgB78VdYaNgZY10oLsO8cq14djhjEgC4xMOk2gYVhh2UiyqTYlNUIJW0M2H3iU2d3MbMqCSt9YSqXSlGtJAwuKlV1eZKA4O5IxCDSplFmbUmikULaWrynnBEWZdWZ6FYCUijBMCTyGz2eFhcwBseunlCnYnGjpm14FJsfX33cReJMi84Dgsiu091MUQLCp+5T/mwGCKRtlhY2CMdYE8HV/+wB4Ebb2ECD4SGywd+9QCi5du58Ta9dpNYZT2WDzmfk+DBLOrr/K1Z2HicIxy61d26/ZAHSeZSpLndn1tP3b/76qvTcDDN95Ph9/HAkIVw8GqwbDOTN660LRaDT56Ed/jBdefIVLF6/Mvabbh6tXr7O72+Xxx9/Jd77zg7lYH399E2b/nay2Lr8ScrDiPnNtG9mzyQk2JjsKRYrSKUrnbsZF59lJSaUbiGfbmZZ/WcVsCmcw1aTNKht6jVNRg5VQkmiIYskoXeNAdhmJHgkDk1erx8RakapxAaTX5kWXGPe6OsLVHa8AfFXH3ZqLxXrEjtwhHAfEqkErEIyVYjsesyV3GOgusTqYMBubPHb57zzp3mwAZSAbhKJpc6uDbJlARDREhxW9wbJapk0DheaAIbtSkgaxzfWOjXt1VgP0eEBw1USDUSGYHPBQmj6bZVNSApCgVWr2uyTZrosqifR0EHw4Jvmpp57gxMkN/vE//mczly1Ht9vjq1/9Jh/96Ifodvtsb+9m/fAN2RZxdS6D4cXjzZNFgxlbH6W61BErU92Jw4ZKgbAIemdJmiu+z8DlDOObqVEhr0zTgBtbd7G+sk2n3bdf2rzcGgDsA85Ou093f43d7jonVm9WyqOnhp8/jCdjluR9yDpdkRtc01ZleM6ys6WV9h7hwLIvha7cNkX2USnjTB0pIM5GYFJqdCINSPSeA0Emd5aEJIUcYAci864ZwKukIlUyA8QAKjUANU09Bi0JCbTK8oulKAHXUukkv06wDoz8udEYE+D9Vhk4z4G6YYQtWBUlEOyZX9XKoqUGV3d5CggWQUn+nK3vDhC5JNrPB640xvIBcQUILuUDzwTB0yah/Fgk39XGooqPabG5d444aXL3qe/M3aaLZjTkzNrrXN+9nygY0oyGC7eRxTw1hf37YRkAVwHmN8GA8M7z+fjjVmXLUy9xnbaOi/lZJX+g/8EPPsled58fPP381HWnsU/f+aMf8KlP/RQvv/wGvV6/dv3qffLvjtVguFI267G8vplWqqWRNlmQ6vJwlUpKIDiXpZf7uEht0jIwN6ymKXfUpEVbhCyFkqUIEgVjJWmnTSLRRAoH5O3+6gSFKmjP6uW4FL/PpOO5k/as8PNaM+M2gWnLtpuoIYkasccNYpnQTVeJkpBYJPRkl322GKQ7JOlwArDnx9GfdKgKmeX+RrJDS67SEB0i3fSXoKlaLOsl1kSLdhCgtCZMBbGOGYgugYiIp2zlsFH4DayRmvut3cSHtJJ8s7fu3HMMsT+YLB+H+mt+9j2g2tW8rlYvmN88CCWf+MRP8tWvfJODg/kekuXr98b1TX7w9PP8+Iffx+d/+/eI46SwbLn/9fePSbXA4rHoffNO3Akv7ECuCgz7n+WsRwX7Wlebd96oAMFaw83tczQbQ9ZWthdsz5ZvAU6sb3L1xl2sLO8TyTmv9yk5gPkXnlGWnsJY+TJrlVrAnB7+WJW7kbHKsgiMoQiOlf08q0GsEQ07Qo4Skx+caLSU6CQwi2sFJHkJJSXsfco9N1XGrPqlibQy9zOhArQ0jHCaBpl8uixzVirIcosdC+xLnP3yRz7bSySQgaLRHCNVseRRVf3fLD/Yl0pbg6xM9lwDhBHKgF/36ucEl0FwWAK+/qtwDtEO5Ir8N5oAvxbs2trBORguSqFrzbDc3x4ILig53ORV5XlVNcl0eAA8C/y6SJTk2tb9nDv5BpEcTw7t55hs6zT3WV+6wY29C9x94kWkVJMTTBWTYtMmoeZhiwvrz9vObTLMuhPHG0cGwsczWJuHVa7fvhCSBx+8wIkTG/z7f//F2mXnkV92u/u89tpF3vPUE3zly984QltFMDy5XvWgWWsrQVVYOXScSVRzgyyTBzydsayRaM/xe1WBZ4Ui0YpYaRIlSDWkWpOQGsBbCgNo7Y6496VtzDqGGQjOwBpFEFe1jp+v7oFhrROUGqP0mF5ygwO1T1d2CESEIiXWA2v81UdZ521/G66CQ57nOaXfNr+7KZdpizVW1CpN3UQgTPkooYl0SJuIppQ0pDmekQoIyEtmzTsJMK0ffhR++xpmdvY27Xk9JxO8aPjb16SFlIuq7Xz84z9JqhS///tfX2w7pWPz/PMvcebMKT7wwffwta8686z8t55ful0EzJMTdnXtvIVA7xHNOKrdlu7E7Y5axqLsjmzlwoWoGyiWGeQ5TbS6vXXipMFdZ1+fmLStK69UFc22YqnTZ2f3BGdOeKqvzLBmjgG6XabIChcH0vMO9P2oGjRXDbgL9YanRR1T7H8vpQVr1jirYS9eZQSQWgkDDnFTuPl+BlqgZIBK8wvWAUrpscLaLieUQqcGOMtAZbJp50gtpUYkAako5R1bVjiTQ3tmWRmbKxUiBBmkhFGMJMnBrywCbfOZyxG2wNrJpoM0M7+iwpAra6cMgh1odnJqSREEu3/u5M1u5yVzrGmS6NAC2AwMB+iwMQmCKwCwKdc1XbpclSYwLwCeWLbCPX1au3XXy7Wb9xPIlDOrr1d+P2+sL11nOF5ic/9uzqxdrF5oUYUITALxqvxgfxPunnpI88BjiTvP52OPQwLhSRnutLjVZT+Wlto89dQTfPkr32A8nmG+MUc8/fRz/InPfIIzZ09x4/rm1GWLA123j5OAtBiTEtusPY9N1SpFkUzIix0IrmYsi3HUY+/coYeyT49VmnFArAI0mr0k4UAMGOsDUl3kL6cBpbnr8QrDSPtsYcaEe9JcxwY7ICeEK9VVyrHVCUonxOkBKh0wlr1skiF3204yEOyczh04c+C+zBJW5TsLJCFN2rpDR7dpEyEQxChGOkYiUECsNVJpEq0Z6ZRYjEmJrQQ+zY5DXUxzMK9artYd3MnyRWrl0GYfjAqhOmfbb3uaUdS0mOoTMKPk2vLyEj/+4+/jN3/j31XWA140vvGNb/PpT3+ce++9i4sXr7A4OF180slfb77c6VsPmO/kIP2Qx5RBWi0rXCEJrMyxzdrxDLBqBpIuxnGDne4pzp26ROBq3ZZr/c69bwkba9tcunovw1GLVqNXAABahp5x1WQeb134+zMTBKtkgg2eCoIzI6wZzLEFurVMVtU9zrHCUkKQgznCHATrJDAsp1IQpIU7sohNaaWiPDrNAabbjAW+OpDmVQukFIb9tSxvmlh21+UTu9xjj/ktMMKZrNmUVSIUht2NUgKdZmDXjzJD7ABw2QHayZ6rj7OeBMFhmoPg0IFrDwS72sBgAG927EXpdToI1hkYrgfBZQAMHiiuk0TbqL2eZoC2bL1FammX1i9fN8Nxi5t7d/HA+WemE6VzsMJCwOm1N7i09Qj94SpLre7MtIPCd1Ovu0mn6Mo2yvEmgeE7z+fjjwXP+lx+Wifhq3fKXQSQzcFaemDk/R94D6+88vpU0DoPo+P6NxqNefaZF3jvU+/i85//Um2d0clSSfMxhrXb99hTrU0JAcMo58e8Stp660KhVEIiRvTlDlsyItbrtGOb0yoG7MotRmkvK+kE1eCn/FktW+nJdbPaxTK0gNhNBhjQKryJhxwE23btVL/5NTx2VyuUGpOqYtkpB7B9JriOHZ0Ale69wOtTHhKROVNr+99IJMQ6IVYRgQpISemLIT25R6wGEznK0/oxKypBsHCs86Q8PSVB2mVVltvtgHn1Nqu2Mc09e/HJmcnr6md+5lNcu3qDH/xgeirErHBAfjQa8+0/epr3ve/dXL9+o2ZSbfYkYF0ZpWlu2tN9B25v3CnP8CMQloWdAMMuHGvqOSTX5vH6URocag2bO2dZXdql1RzaNqeYW80BjMMwZX1li+3dU5w/3csZZn9A7f09wYpPGGU56fjhS7Bk7djwQXBtSFl0mPbcoguDfJ8JrmsvM2nS5paSYgCftvJerSy7qVFSm7uYM5tKVAZuq1hWs11BIDRaK2SQm2eRGiCdgVulM8l0diy8vN7sfcmYS8oUHZrySUGYEHiMsNm/iklC6YPqChBc5ZTtQLB1g/brBBfLI5WMsbJjLKv/9plg93cNCHZs8FQQ7AHSiTrAeMC1fG7PmJQqRy0LXDaOm4NtLYBhGXDpxkMstfZYX9k+lvnbMEg4uXKFzf17aDWeI5w3eWxOk6uJOsIVOf6VCpvbbJZ15/l8/HFks6zsb8r1NivAUK1hjs+kTrIq0waF9913D2trK3z1K9Uy5nkHlGVQ++KLr/DAA/fxjnc8yPPPvzx1+UX6a6J+f00jObAq5/nOkgbP289511XEpiRSAjpQDEWPSDZRKMb6gGHaZZz2SNWosC9TZbkV26kybwpkk1C2C/WUUx2T6hGxsE7G1lDMgeBy7nV2XpZYd23zpSr74BtwzWKvfeZY521rFAkjYsaM7A1boRmJmIE4IGaMkopQh0gkCsVIDBjoPWvWlVA+N3zTs2lRBraF/lIGqHlecGbMpmOUvy+eEqFqW9Pyzf1t+H2aLLWmzA1auPWns8EPPniBRx55iP/j//gnU4/FvOH24eIbl7hw4W6efPJRvvWt7x26nbqSWsUc83om+E7ciUPHVMbI+86XJFeAUFECdocxhukdrJKmIeurW/MDzTpA7K2/urJLr7/Kfn+d1eXdfBlfIp0x3N526wBEXWQD4inu1KWoKslCYTAtJ8orzQTDfihlnv++ZNoBP5dXa4GsTi1jrIQxwZIGbGqhEEGAkBodmPrDOnGAq8iyAhCAVhJXTskxxEI4Rtjc01IlJxjmKuDr5/xmADYy742M2j5Tp9QANt8rCvnAHgj26yE7YJuBYGuKlYHgMCUrh5SxzXgg2B3nCjDsM8FzgmAdRpVyaNPJitJI8563h837dfeBI9YNdtfJ7v4G+wcbPHbhW4XPjxrLrR16ww12989yauVS7XKzcntNnyqOZQUYnvjbjwlDrTvP8h/GOORZPymBdH9PAgf/fT1ILrcL3qCyBlwGQcBTT72L7/zR94njZA4AWh1VA9E0TfnWt77DT/7Uj/PGG5c4OBjUr18CGdPAcPG7ycGyxrK8VZMMFYyeW2/e/Vo0tE5sTVsDhoaiS2Bvmoke2XJI1g16yvbqANNE3qrHBgeyQTNYpimWaYg2goCEEWN9gEASM0Do/HgLL5cYUWp7AVayLLcuLu/lZvtKCALTdzcg0Mb5+UB0UVIR0UCjGIsxI91jrA8KDLpGkaqYRI1ILCNc7lNd1Lk3T7CMHjh1bHvhmNm2HLs/6UzutVV1PCsmM4QIi0x9to0cWE+AYdPZ0j7kbUsp+ZM/+8f51re+w/XrN2uOynxKFfedv+y3//B7/InPfIIXXniFvb1uad3FmODsu4KsflJJMll+C6runbcj1BG3dmco8BaLqpzDOeKopUGUkuzsnuDU+lWCWezNRN5yCQwXSq4kSAEnN25wY/MMnfY+oSPSLACeAMMuKoy8ji1qWKSq0NIpcrz3Shr5sg+amSHPzLbtSvdAlt+aAWM8hlMjAoUOBDo10mAVh4hUoYMU7dUA9kEmYNZTAmw5JVfOyNQptvLrJMwMtbJ2KlyeJ9hcodGBtI7NuSS6LI0u9A1mgmDThve3d1xw+1cGwV7NYNsAmRzaHWsoguDAA8DWDCsrjzQnCJ4wxYLiNVA2yDqC2VUh5gXBc+bgKgWXrj/E6Y2rmQrkuEIIOLX0Bpe2H2W1fZNGOMq/LPdtRtmjeeOtVi/4zvP5+OPYXaMnB+U+A7JAO1NBnBlMPvzw/QyGQy5evHrsUkIhJDdvbnHp0lWeeu+7+OpXvjnXetNKyvjA3t+Oe3XsbwaG3TKZeVIRTNSVyZknb3QekJyzngmpLrKvjq3TGWjKB/LzRgEElI6JFBGhaBKJNm1WaasOASEpCQPRRgRm5tmxp3X5lc4R2YwoJEw4tRT31ZdkB7JhSwrJTCKcqjGKGHSSGXE5EGzq74bZcU7UkKGERIyy3zVRI2J1QKKsIVfFMfHzwMvH9LA53zkILu5b4A0+3W+s7GtdLnqdKZtfrmsa2FYkdsLIvHP7bdpyx8HfQnF/P/7xjxBFIb/921+q3d8qk7h5wfDBwYCXX36Ndz/5GF/+/a/PqfI4/G+TrT/hHUDp/e15hN0pz/AjGFWD6yMyOHu9DaJwTKfVm71wVW5i1fa9z9qtAe32kO3d05w5eQ3wmCfp1fL1Vp8ACvMMiMvMEA6ggpZkplvZtqrAcI3BjpGcy8xAK2OHfWm3vw9lsyzfSVpqC4aNyRMpWY6wCH2HXY3QAq1SdBAgQoWKQ3Qi0WmQuUr7ADNbVUrDCAuNTo0bdZC5QedO0pkU2fYrc3Uum1xBxkAru74MlJF018Q8ILiSSc4AfVpkhLOc4CITnOUHZ+vXOEJ7OcE+CM7/XgAEV8mT583vrT1gFW7odds6hri8+SAAd51+rfB5LSu8AMgUKqURpqy0ttjpn+fsmt1Gwehuulld3ed1Jlhzg+DbxAbfeT4ffxz57C8zldVA6GgnSNUgNAwDHnn0Yb75je8cqe1Z2/rud57mT3zmE5w/f5arV68XljnMoHcae5y9MntAPS3/cp5YZNCe5S7rMULLyu8WjVkTHQZIBUSiRVO36OgOTSJSUiIahvGVCq1TjIg4KfU1l/WWQ1QZlLnvHAgPWjTkMk25jCRAkZLoEWPVY5z2SRWWxchBXyCbBgwTIK3MOFYHxBxkQNo35Vp08qA8aVLcn+IydesbyXmDSLYJZasgORd6RKyMU7NxJvRqGEMFqz4JzgpsumWC3TExxzo1y+igkBOfhXZ9rd6HtbUVPvITH+TXf/3fkCSHV4FUhX9dP/vMC3zmZz7JyZMbbG3tLASGTSPupR6QV/2G/jaOcp+5Ez/CcVgTl7IskBzgmc8qchRrIlWSvd4Jzp68XHstHwcje2J9k8vXLjAY9Wm3BmaC0iu3BN4+LMQClYyzfJY5WyYogGG3rQIYdp9N+01kYGq0W0Bs/i5a2uQA2d4f3C2hANSUrSusEaGGRBmGV9q8YQcEbS6sUAIdpeg4QAQpOg5RqTK1h21kgFJqC25TEMKwwEAgVYEdloGw8ukis5yVZKqSXTtALMx2RKAQ2oIPXXHy+GZbdSBYeu2X1q0FwR7wLYBgJ4suRx0IDiNbGsmCYK9M0jwgOCuRBPUKhjI7nO3flGuq7rtFrsMZbtKDYYvNnfM8eJczyJphhHVIpnVj6RoXtx5nFLdphvt59+ok0XOYZRUcoY+pn3fihyOO4BqdO8XOMkK6FfHAAxc4OBhk4PQ4oorpGo3GfP97z/L+97+Hf/fvvkCappPLT51hWYTJcdufb9A9K2YBrVlgeEKOzeTAvnbbM2TSE8tM2V+pJQ1CmiIAAgIdoFCkMkYFKaQm29cxrBkI9spM6UyZUHywTu6PAYqtYI0leZJlvUakjay5L3r0gi3joqzzolEulzmy+cyuD4kekagRqR7ZfOZRBi79/FczyMlLRWWHZE6AO39Iy3BHhLJFUyxnQDhhZCTLIiJlXDg21fWcp/fHgW5/YgDMbKRw53fFdeN7DVTlmX/25z7NG29c5pkZBllV5/YiYHY8Tnjl5dd55JGH+epXv5mt776fjPK9I9uhhfpY3ZZb7vawwpoZt7Q51r8Tb6Hw6gjDdFlgoS4p5APxEpAruEZXbG+/f4JGNKLVOJi5naNEGKSsr26xtXOau85dNKaK7jlQBsSH2GYZEGeu1O57LDMcmO1m7LA93g7ATlwTPrBSKl9+AhBbtjiJTVuhXd79w7LCgd+eBpWausLufqHEpPwXzHbCFD0O0VIjUsMSa1UBQN3PLyXaKte1Mk8pLSTatqmVAAeEnUu4LW1UZnELISxoDhVSWxk2eqIvmVS7BIJlkOYAuKL0Umb+5eTQoQHdbhQ8kZEioSCLLrtDZ595/6pAcNjIagXPbYwF9WC36vNDuj0fJxMMcPHqQ6x2tllb2Zm9cB24nMPtPQwSVtrb7B2c4czq/tRlyzFhfuWpPapz8icl1ofxSziuuPN8Pv44wlWQg+H676F+4DYfczzZvtnuQw9f4NlnXpynozNjFov2yitv8MADF3jiiUf43veeq1imWu5cvKNO7ltR3lwli5xn0OuvV81yLho+UJ2o38v8DOY82ynIwbPIDZpiPSQRCYlOgZDI9q2tm8R6hZgRSiakaYLw8lvrpL2V4X47C95C2aQlVtlQpzmhl2mIgBRNV7VAQhKMTPsKpAyJZJt2sEFbrNLUbQBGcsBAd1E6JlEqq02sJ3J/j++GOs0ky2ccAxESCmNAFhCZ5YRC2hrGExNCM/o4CeaKDt7SSqMXPSfLwPXJJx/jvvvu5u/8v//hXOtPMOcLTia89NJrfOYzn6DTaRc8AiYBdZU6w4H5fJ2qPhxVTn0rwkivjlCe4c6T9k0JoVLD8nkSv6q6l9ln0wbP/kDdlSKiyIjWgWGtYb+3xonV6xNs8EwwOss5uuK71dU+vYNV9robbKxtU85lrC0NUzfgrkpZ8coylUs0ZbJoB4hlMgGCJ0onVwAelARlf7NkXGSZw8hsO/HaKtcZ9j9z+6HMxKPvmlwwgUIjEg0yMd/FAbl0usjqaiVNO8qCR6UtO6zRTtpdBaCZwd7mXTFMbZQg0jSbTRRVbGxFO7n7c84858t7LLHUiEAbIBxSOh7ks5gSD/hWMdM5GHZAVxfAsGeC5ZZxu1rFArvwSiRVscCT/bhFIHjB1IjNnTMMBh0ef+jZ0nZml0eq7duUPqy2b3Jp+1FOpBFhEM8lYa4ysqsFtXNIrGd9dyvizvP5+OOI00FlEFbHlMzLDs5iPMz3p0+fpNVscvHilblZnqPGN7/5PT75qZ/k8uUbbG/vZp+7fk/Kvyb7lElO55LEHlFOfgjgMdGGf1x1zeelmGDhKgB0WQ5eBsMOyCZqRCwHDGWfgWgR6RBrpUWTiKZu0RLLJGJEKmPGaWy3WWKCK4yefDY/z/WVlr2MaLHMiu6wHjbohIJYATEM9TJ90SaWhumQIqIZrLLMSdbTDTq00Gj6YmhAsxwRKwuiXD4xkJuCFdnxo/5m84YirdzWhHTfvS/117VSXi+fODETFwBKK6Qgq42cu3ZPD39bnU6LP/GZT/KlL36N3d29eXfzEPeGfHmnOHnwwft5+uniw73uvlOYNBBkedCzJtuKYe4pbhu3GyjfKc/wNo1SnuuEQRVFcJub8nh1epkEw4VlgcFoCa0Fnda+PZVvLXsiBJw6ucXVa+fotHs0G+MiGJ6WCzlRSmnymACZ5HoCDPuLWGBdzh2uZoSLstjChIUyrtJldlgk2PrAsgB+C2WWkgQa/rExLKlWOhuiZbJfN9ySQOKYW4WWhoUVqnRfEtqwwmmAUBotLEhUGlH6iX1A7MurK6XLwjyEpQXVMkiRMkErYRyfq0bu5bb8esCi1L5dvsCGh+QgWIjJ4dqsWsEV/5w0ugyCJwBwHQguyaH1lJrBR637e2xMsL3OxuOQK9fu5a4zr9GIqsoOHjKmsMONcES7sU9veIL1JU8ZWmJuJ4Bq3cRRYZnpINgH0LfbTOvO8/n44xh1EYuD0XI+3GyDLBP3338vr712kTTNpbC3FgxLut19nnv2JT74waf4D5//CspdTGV34izqJgbqAHD1vs+WGPvbWUw6ucgAu5qZd1Fk3epAXZ2RWDUYTkjVmFHaQ4qQQESEOgDdIkIigUiHRKJBSJMxB6YdlecG64qZfVPP1+T0Zn0v5PoawCeRREgiKWhIs1ZTSAIdIi3Ycexqgw5Lepk1OnSCEK01gZKM9AoD0c0Mu9w2XT6x25ZG5aWg5mCx6363MnCqBLpaWVOshJghykq0E0bWwKsEUr289WLkkxaTkQM5hXF4TW3Ol+9IjQeep5m/AfzsZz/N3m6XL3+5ukxaHtXn5WHj9dcv8eSTj08AYfDvO1OudVE0BZvs5/Q+Fo/vW4s5vhM/HFHFekx85uXCFhd0g3UPAJcHfaV1eoM1ljt7i7PBfl/mWdZbrhkesLayy+bOWe46czHfdqX5UJ2ku2K7pTqqZTAMFWwvTBppyZp6pD5Ycqx9MraMa5Ed1iFMOEsrZR2nrbzabcADw8gUker89uHAHxhgK7VdT6GRCBLLTnv3Gy0sOLcMcCptX7QtrSRqSx3l/fBylD3m1oXQ0phpRYktOSULbPYEuPZk0C7vFweG7e01N+TKmXAhMcfXAWDfBCvrawUQzt7bRnwA7BljTYDgbN0ql+f5QXBtPnCdu3pVHLdTuo03rj5Mqzng1PrVW9J+XSy3dtjrnyoC4aqYxUrXTDgUAK5vTEfFPbQOVN+Jt3zcmqviiDEd/AnO33W2tm7wPO0cBjQLIXn++Ze55567efyJd/KDp1+obH+yPNLi25rbxKpmIF4nOa4FUDMZ6ln7UA3AF2GlywDO/Z0oySiV9IMIKSUoaNO0gBZCHRLICFmYkjZA0s/DFQR2/wRSRKaOot2m0sVJGI0iEQkjEsaqQZBCrGCklan/S5oDxmw8ERIKSSQECEGgBKEOc3Btn77OiCuSHQJhZMmpjkmEKZvk6jHPkx+8iNmWAZrW+VpLYiVRIi30IdU2n7lUM3iWMVsd4HafZ0ZmXu52XWmnqu089vg7ecc7HuT/87//oxl7WTdZM3uSqe6avXLlOh/84FOsb6yxuzPJRJcn4cr51K781PQ+LzaRdqtjVo/nWf9OvMlRYqEK+cET+b6OjTTXqYAJ4Jwv68BwccBt1gnRGg6GK5w/+Xr+ZSbTnpJXXMFO10aplJKLtdUdDgZL7O1vsL66MxsEl910q+SYBZBBEQzbfSvkEZddpUtS6bzdSbYwk7KHDcsMG/MLETZyRhiKpZZ8+bpS6DCaBMNSQJJaTSUVtxUD1x0Ytrle+YXsy52VhNQC2CQwQFqLfJvlcke+hNPP2/XydfNepObzMEXoBKGMQ/XENrz2MhY4UAYUTzO/gvyzwJWbkkXQ68cMAFzFAmuvjUopdFVMAcG110tdyaM6BvUwAHjaOt42tnZP0T9Y5dEHvr0YFpxHMl1Qn0wuu9TY42b3HsZJi6bsV7Zby9iW5OpVUVh3np27DWD4zvP5+GPBq6NucFbHdhxX5O2dPLmBQLC5uZ19djuk0QBKab75ze/xiU98hEsXr7C72y18Xy6PlIPU6qgyA4L55bFFUyH325i/64zMynErZNo+qJ0A41PMkArLWzYRNWBsP5dBABISvUxDRyQiJRHJJPOsJ+XQPuMrZQDaOhkLUDoslGBSOmake3RFjyAJaKchiVbsM6Qf9IhtnV9ly0qlxMSMGemUSAmMitrUDE5t/UyTMxsQBi1awVrRqEqPGLCXAUQ1h3R4dv3uinW0AhKUCogZkIq44OacO1pPlm6q+q2mM9NYhlsWdIEOGM47SdVqtfiTP/NJfv/3/iC75m+FXHhyAsv+pRQ3bmxy7uyZSiCch6mBLIQkLy2WP7Imc4TnAbo+hXP7Hl93pFc/vFEe2M2U71kAWgDDdiBpwC35oDtjIZNa463heAkpFI1oOB34HjamMF5SaE5t3ODqzbvptPvTJZpiQUDsPreXYcaQe0C0zq3ascO1rLBvlmSBcwaEwYDepMQI4wCxu0/nocPI5NWOMWBYJmaAnqTVslBP3SakNqZZDjQrAUHhBm5k1Era3ODJXEXHDGff+RJrzzwLv2SRO1CZa7TKmOqp7XgssAiKAHjC+bku9zcDu37JpxoA7D4LQ/P72RrBZUn0TDMlEc40xppphjXvtXWLWGCAOIbL1+7n3MnXaTWOt2bwPDnKUirajT6D8SrNVn/m8s7J3f1tGin+XpnDu3ePm10X/PbJo+88n48/jniF1A3Oqgdw0yTMs5kTE6dPn+TGzU30IX7NRQBzGcy6Qe7ebpfnn3uZD/7Yk/yH//B7qLS+35Wle6bufzUILoOcibIzuhp0zwMYqgDUUXIT/dzfrK05wfDENjMwPGIsTDklJCihiIRxch6LMbEekurYk9yW99GxwYYRDkSTQIZFRta6OxvpcMxA7bEjJbEY09QtlFD0ZY+e3mSU9kw9YVtXeaR69II9AhUSqxYKzYEYcCD2SdK8XrCUYVYbuSmWaeoWAEMhSaVlZMUYDnmjmpZ37Y47GhQjtEozptZ8l06A1HkBdn0o76ZbBITT+u/Hz372U+z3enzpS1+buexRo+7+dP36JnfffZbnnqs256suiVS9v3WAe3rcmcO9E7OjUm5b9b2NDPxOgGEHgvNc4lpZdCmGow7tZt9gjor72FzgeJp02fZnsmErkW6OWF3eY3P7DOfPXEIEU/KDZ0WVTNoxbiLEryRYZoirnKWBzF3a7/Pk70Imq87qIUtVANi5WZfM5NGZy7cz1Wo0IHEgLsmZ4Yn9DCxb7IFUd6hL+bnCGmT5gLhcMzhbNgOepROhnMObfW/l1UFq2GFAK2nyj5WAQOUMs2OXQ+sU7UBw6AFg9/tUAWC8v6cBYHNACixwxiRaEKzDMKsTvHA+fBXwlcH0HGBZc06rZPKzWxEeQH3jykM0G0POnLg8Zfmae8bUskrzG3W1Gz0GoxXWOjemg1F3bVQZ1U1ZHm4v0L0Ttz8OccVUsG+lqBro+WxpASQtZCJjGOEbNzZrlqlnTg47cJ4Y5Ap45tnnuevuMzz22MM8/fTz+XcLtjdPVLn4+oZb5q3ywHBp/YkB+oztWRB1GPOmOnBb1+dpzHC+oEIxJlWSMT0EBjSGNJEiINZDYj2w+a0u/7T+HBBCEIgQRJQxstINSCw4TNWYkXV8HsleztwqU0c4UYOsFFKSDhgh2RcBqUzoWdfosRhwoHYYqx5KuXJJIUIEBEQ0dIOGbqBQBES1gLPoQF7aL608zRfecvmzv+5c0XpcqUhY6PeeS3VQBITl9qet+8QTj/DIIw/z9/6PfzJ3n6q2XRfzllja3NziyScfW3h7Vfs81zlfG7dJ+cId6dUPbdSA4Iko17WtkiZbQJz9XRqcVm1jOG6z1O7a7/1Buu9aPWmwlff/ECC4FOsbXa5cW2Gvd4r1td3jAwYOBGeMMVmfqtjgAhguSFY9N2+/7cKyATCCsIFIxiCtC3U2MJfVYNjJpa2pVqW3bKX5FGQllpLUqqV1cX17Y9DSOk0LYWTSdZHl9k6CZefe7OfyCrTZbpgXiCnmaE46WU8FwT4AhmoQnPVJTr6WWeCyHHpaXnC53ex9BVtcB8wWMXlzn9UByEXyiKeFL4neXme/v8ajD3yvXhF8GBBcEdNAaCvqsds/ZciqWQ1Ny9OuW35OMGzaOcKxnTPuPJ+PP470hDiaPNFIAxdtY2Njneeee6nim0nAcLysUd7fNIWvfe0P+eSnPsq1a9e5eXNz6pr1zNp0yWMOgmVp8oBagAOLA+KZuZ9Vv5GQxc+FrAC3+YRI2THbtV9Zg9pzcs6WteZZY9FD6djWww0KbO5kbmvu0Oxtwf7fmWKZZQIRkRCg9djKnmNSNSaWA6RljjUpqRqTWtCNBY6JkgwSSIOYUDTRKFIVM04NaDamXW6CwbgmJyQEIkGhSEVMqmOUNjm8k7/RFDBM3XlCtuw0Rt7/7DijMn9+DhDs1lteXuJP/uyn+MIXvsz16zfn3OrhFQyFY1ECqt3uPlJKVlZW2N/fn6vdOhDsb/OtGlpXj5UXWf9OvLkxtSzIlOX8XNYaUhe/ZJBf6gVgHLc4sXpjzj4m9cxXlTx5DhCMDJFoTp/e5uq1c7Q7Y5qtBe8LdTnC5X6VATFkLLF/7PJyR/6gugrIlH4vlWaSaiOrVvkyKs3BsBflbRZyhrN2VS6HLtQzFubClyJniP0bQWD2VaQGwOrEGl7VlEzS9nElUGZ5JQsAWJSl0Ri5tAiMn0W+TZG1VwAi2WNRFx+RZW4E6kFwOQ/Yvc4AwU4ObRjhaBLglnOFZbFusPmsWgY9WTd4CiAulQqrjOOaCPKui9G4waXrD3DX6TdotYZzPXrLE2nHFY1giFIhqYqQYn6AXXV/rM4lrs9l9u+ft1Mafef5fLzxpphlLcYE53+HYUC73WJ/v3eIwaS//GIPxmIerolud4/vfe8H/NiH3stv//svEMfJRN/LQENU36Er++PAjRAhfk3W3AQqyYGlG3R709F1x6fMgtWB5myZilzbwt/+ch4YLkY9m1nFZk+AYQs4FbFJc5JJzuJqUzvYAMhkEoCVpNqgLThxbZpQOs/r1NZVWSBRepT9Br582N8PpcbEth+uX3m+bZIdE6UlqXVrDkRkcoFJGVtG20mzTS/rmVa/pFG+bzID7OazwEq355uwOmy96LrzaaoEumryo7TOz/+Zn+XG9Zt8+fe/fiTgOOtcr+pDeTmlNPv7PVZXl+n1ZuchFdqt2dfDxe2Zy835mMOvfyfe/KjL482izApXtlEspzSNcUnTgFQFRGFNbm5pe7US6Trn5oo+mQ9KxkEypNkYs77e4+bWWe46fw05y9G4LuocrGuZa/OSSZvxjn9NPWay5YqmWzpsGoMsaY+bY4WzbaVFFtixwxQZaS0DC0BljtSr2FAHjpPEAmGXm+sdO2VALanGDYmy0kylcDLqDBBLZzpkQXCos/d5XyiOShUZWM5k2X6Uc4KzjXslkabJoadJof3PfWMlZ4pVeA2K7dkogGD7WgDBvgy6ShJdB4LLOe5+fvu0iZyqZeYJb3ml4LWLD7LU2ufMqWuLtQMLs8GzQkpFGIwYpy2isDpP2bG50yYHDwtkb7dk+s7z+fjj2IHwYgM9w7LmMZ0hXVrqkCQJo9GidcrmAGb/f/b+M1iS7DwPhJ805et637ukEy4AAQAASURBVHba9/R4g8F4gxkQGIAQoV2KWn27S1L7hTaCH/hDIYZClPSDVDAkiqEIBSMkUhQpGjBESlyJBhukhMEA473v6Z72/npf997ylZnn+5Hu5MlzMk9W1b3dM6ino6JvVR6X/jzneQ0DkY8s/dvFC1ewa2oC999/N95775NQXy4JEadLEpBVWuGDBtUzu1JBFMt+XykAwBA/IbmM39/Yci7pYv2VvbetBb6prt92QMmOKBdQMp327X0yYAEB/1bWt5UlifSYXZNvw2pAsYgXNZnA8lRgN2gUIS17MkE0QPEJrttO4Bg4YzMtwFJaVF8G3BROxLkWTOLkFVYBDfZDu0VqdgAuy4BL0N12g8c7HGTK9X1WlZQXDMzt31Z97XpsiiLhQkkbVhqxiymBsYrruHj8iS9jYmIc//7f/b5gUUxufDJmz7xx81Ct1pDLZSNqBs8TS+67pwL3DJt6iEBAMQsrFSIVJPQ7L52S668KxgTRIbhGS4WuNqGqHV6jtC+uN3BnoVl2Aq/oGOjfRK1ewFppFKOjpZg+W/HqGqtas8G1Au0h6A/MlrMocsyANrO2fbh901tXaQ74A1MkWKGCYXnl9CAxtp2QqfekS940wEsRY1k2IQZ8IuwRbgLAMZ9Wief/zKwPe6OwfZ4dSywmojNtOKY4w1GoRQT6sWq/5t2xhLdzwSO+USqwV4bzN1PeC47FbGN9UAMkWMuAtqKgSbCHJCT4JmBxeTearSyOH/pEus52qcEudK0J00z5jIaj4spYyHDR8w3+wiNp9IjAN/7kjv2N/1IMRzuORzqdRr3REGzt/gRRFNXZ+w4L7777Mb729aexb99u3LgxzyjCPhkWI0I5U/y8trSvrarqsCyfKAXMiCkyylOyWcQG76EIsKtQB+s7frnUGHwiDsie2yDUwCJCMAWUY2ZMpeTx+mSHzrkWCbHNli2rBU3JwFQankmyaTXt9inVl8CCQlKw8+HqgbbZfVYoouqScjd4l0IAAgOWZcBQ7GvYUgxYxAyQ8EDEa9cHVyDD2OfETsmkqrp3jlxfdoVo9rgYX17abJ1nweD2x5oLs+XjwCXDEcRWUVSMT4zgmWcew1/8+d+gXK4wJvZ0Pf8474Spca3WRDaX54yFB2oxR2Dd4uPWI7ZWh6ZXndTtoYsQkGLhhJANmkXDVatU3VZk4UxuvcjGJixTgaqGfYmBsPobGzBLMHEW5lJ163h+vICi6BgbWcLs/G7kcnUUChFRbdWUTYaBIAmPGic3kBH7A48kGz5Jdo4nDwSOimqZthqspwE3pzCdO5iKchsyjXb/dhVhXQ+bRgfMo51tCkWI3Y+nbDvm04ZpP77czlSfpHpqsWGPQqGUXajOoVNhpzHyD6jdluCQK5QxFtfwy/2w+YG5EaIjFGDO/3Q+YBIgxpq3jR8Fmk+CWQIsrQQnwTYEzapU81hcncL+3ZeQ0qlgmxHXcWKELEBiiCgxoKstGFY6uhyLCJNn3u+x46Dvq21E7/3cfXTxTulkMkqTYRFxs5BKazBa3bjZ2rtY6Ym9S7zq9To++OBTPPTQ/Vhd3UStSqnVShwZ5iiLHoJExfV3VRTbRNc9TBYAECPsU8sxreX5hPKC9/BT56gBwuW2YVkaLLSCYwio0v6+eG0Rdl/5YAkc69vK9WFmiDu7/7b627DVWxgB5Y7vnxus7y5OEIqs0scqDrbi3HJyF9sPVpMwKaAY/2uuX7ezf+45URU/CrblKNzsftPjtBdYNO9ve99c82+7FVEwKW8MEgTUbzuKENoXs6ar+Ls/81M4c+YCzpy5yCXsUVYa7HjajXzOQ8toIaUneVyyz7HOybptZbD9b7FeeobPN9gJGy93sBTo9ECB9hwTaSansGUSaGgl7ytK4YqaXPMUWuaW0zUDI8MrWFkZQibTgq5HRZVN2fWJwByaLcsdr/M/qwYHAh9Rx80lxSyIYRNePeM3gUYwx7BHTJ1ni+WQYk8lthViYrSg6CmbJFsW//qgoBgthhAzE3zLgh1YyyXKrj+xN1D/IeBN6ygSrMKeedKE1T12Ljl2zbLptVLNaYci2v4xhRwJptMhAZHqL9fnlzKP9v9WPcIbqEd9/1yQ4JiAWqal4trMYQwNrGBooATb1KANMszxhW8bbkYOxeSGsokdA48MR5FgluhSKcx2Cr33c/fRrXCKAPjqkov4CK2ii8n/XVVVmGY7Fx1NtLtz0dLEZ252AdM3ZvHIow/g5ZfeAYiSaAIuJsn8CLN2OiDLNpm23EVZV4EUExcgTEB5gZMCfVEkWFPTTpAqezwWsQAVIJbpjdVu1CGqjh0XT9HkkXSeek1fN6w6TPfFts0G6wrsMTFgWX4AL3459pgHibVfl0VwH1zf4sCQYcEiFjTGfNvzBXf9oxFc0KDJrKvOq6pup4NS7IjUNloxq37MIgt9fTkSAh1sS4ROzX6D5FbF3/pbX4eqqvh/v/cD7jjdv2nVXaaPbpBhYllQExHh7r0cu0XmZUEJL23X72HnoVgmFDpwkWPmKhs4C0BYFab8Wu1O+ObDimWCEAUgoqAyjl8vjyTTEat5E2mRuTL1uz8Bdy2FdMCyFexifhO1WhbLS/2YnFiCoglILOCTYbbfAJHl1OcSD1E/zu9KyybFXPKggyiGT5q9foIqPOA8sl1ybKk+IXa36SkQNwK4ZQYiLwcIXqg9Sg12zaYBuDl8bV9iN7AWFWALzneV2IctaORkzzo1BSGzZUtziKzi/+aN0/nfVaAJYV/RPgnWtWgCzCO+AR9gzvuMZx6NIOnlpi+jA2OJ/IEBeRLMWzRi7w8ZEizjS8zg+uwBKArB3qlrwfHIBLGLHEt0MCoh6PufmPBNEyT6SzAeIQnm/LYT/sK993P3kZAI04RSjGhSFTa5lJ1IW5YFlX1ASkNeSYqCaGL98cef4bmvPoG7774dn5481+YYw7DNjm0TWy8KMgBVSdnRk1UdClG9iMm0eW6gnQSXP089tFXHFHQ14/8Ox5xXsWAG1GZnrJ7JdJjMuPvG7iu9nS4vJMO0esqakXtgCLbnvxuuozhvagV66DhEIUi8g/7Q9HkL19O8rUS1nEUFKhAaROfOWZxQMtDVDDTFTsFkklbYr9idiQh8ed3/2yVcUfcwTVqjArjdd98J3HHiKH7v9/4LDMN/znTD7DlJG1GKtwKC9l8jcs/OHnroCgQqhVQOTV4qJW5bemDipwQ9USMhlU9Ysh0ACKV3Uk1ANUBg+/WPDC1jbmEK66UBDA9tOGUoohqIBu2YSfMm+bw6ge3ifaLdiojXbsomxEDwuLuqtGOGbretAUYDcCPjei8OihwbzRAhJu53d4LPU7UAT1m2DbtUXz1my1v++xaEIsuGGSTEBjwlOBCuxCXBum9u7O2fZ75M/04F7VLhq88hRZgiv27bLAFmFWDAN3tmVV8GhNru+Qc7BDqkCNM5okNRoJn2OyHBnULmHrQMLK2MY6s8iKMHPhOnSgrV64wUCkklc0+GysXlEt4m9PIMf37R1p0lIizhibQcIY4jw2450zCgSt+F2wceabAsC2+/9SG++tWnsLK8itnZxY77cX09CVFtM17HXNUlVqpnCtuCoqhUvlr7LRHlP+vtC/XCEanFtgKtQnVIo+ooj7b/rB/RmiapNHEX5TmWCUjGDcTEKKR88+To64T23aXf0rbSGh6HjeACTtQY/Y0UUWc3KfZxhWLPaYKLCkHi7rYVMIlWdGiOSbTrv6wh5ZlFh3yOBWTY3R/bhJoTGbtLEMUFmJgYxfPfeBbff+HlQKok3nGVI+vJrT9E16E7hlQ6hSYnPoHo2cW/PpKT4Z1WgwH0fJB+HMCaw0aoxjZpdba5wf9cEuxO/gEoCoHlmrJEmUc722LJcIzS5Pooe77KluHth6LSY8tCVYHxUZsMZ9NV5PM153ZkVFvPx5giwzFg42ZwCTRTRgEATacIMRBUkN2/awCyjlmzHywrMNm3NBCNVq5M6sMhxKL9sCxbMdZT/t+i8pYFRdcdVdgCTAN+kC2HEIPxIQaCii1LhC3FVnV1+23oBe6i61ruM9QKP0pVlWpbjyTAAfWXJbju7zy4ZfS0R4CJnvYVYZoAAz7RZBXgKET5wXcTcTm7nb8r1TzmlvZi79Q15LIRfvaBNmhFVdJcug0i6V7zlqUhpVaTk2DZPp3Fn9hI/DuA3vu5+0h8h/HVXPe3oBIVhniCKqMM1xsNZHOZyDLtoFsBd8rlKt5//1M8+KW7Udp4DeVytcMx2RZAFlpQiBM92ll0d/1BXYJqEQuKqtpmv2hF+ibzIgcHAlMx/q+saS8PriJKoNrk0mvYJ19cM2+O3zU9Rq5fLlOGj3jSoUALkGA28rLbP00Q2bHK+gZHj8Mmw+6igqcuC32paTLsEGLY0a9NAfkLWhTYx4YXDCtQZxtIGH0NEGIhlUrjZ/7ut3D+/GV8+MFpYdkg+FYPYTCWABFjikM6nUJ5q9zWcQouHHZ2TBVFwXbnP+ilZ/gCQOTrCAgJauQkzzGtDeQ71TO2OatlQrE0aDqBSfjv5xAJiCLKEqaWARJsNOyxE8P53wkcpWecfmzf4bRew+jQEpZXx7ArPW8H+3HJsChNkghuZGeXpNCBFFUd7tSKWIbzndo9y/DIsqLoXkYjPnKeYkwUX/2mx6pYdKCsjL2/blAtwCEZKoBUaELvwQqmZrLrOT7GrFmwl2apBegpx6fYIcUuITYoYmxRpDag2uoMEXbee3qUT7tLgt3o1e7PnHYVNdgHJ/AV6DRI1He7HOfE0MGvnPzBNAnmEeCOsNMRohkSbBiOX3D/GkaGVnd2LDwIng2mpSKjta8Eh+6HGGKeyN2ky+i9n7uPju8y+Ukeq9bxg/FEoVatIZ1OQ9M0mGbnZgisv2GnZJgQCzMzsxgdHcDDjzyAH/3w9UT7xw9u5ZAW2GazqpKCCh0WLM8v1FZsg8fDJsN8Euz6l/qKra0i06vTBKYT6dgh5I5fqwJXbbS8YE92u3aEYlXR+b7KlC+yzHEO+MYKyLBIiRP5GwePg/8Qc9t2g04p0LzFBaKYTjRpKgUJrQwL1O5wh0ErCPtvkyKF7LHkP/C9SOKK6vlqu+OwYHrHnoBJJyWAT+xNamycNE1tq7Mi2Nf13/7bXwMhBN/7qx9wS8n20e3I0Wx7hUIe09NzHbXZ6TOmm8G/evgxQ5cmbZ6K6ylfcFRUDVpagUnS3iJs0jzFdoOS/oY0CTYbno+sq1ITHY4SqoHAJ+fFQhn1RhbLS8OYmpiDoiGsDCfNs+ocC4UlxwCUkOrrl/PWmyOa9kiyq8QjC6hGQGVzfbkVS7N9gQ04+2/6k/mAgmyCIBXwfQxHmXYXE1JOH4zZsGUCesojwzBaUFQVMFSf9NI+xjRYtdaDQ4RVPVzH8t9NPgl2fuP5Amt6SAkO+QDTJJhn9uwNiw2CpXKVYDY/sH9+JH1220U3/HRZONfX9dlDUFULe3ddjR8G557Z7rRJLgwzAz0rSKsaZZkCeZPmnunzFxddW26KnuQlnfzxleNGo4lWq4W+vgJKpc2E7cWbactM/sXwVbaTJz/DM888hvvvvwMffHAydvIaGRHXISVuvliLqkNAR/7VoMJyflO9ZR9WZeWl27GDRzk5eok4RzOBCQLVSfVjE2PPxFhRoSLlkWqFQ8SSkuFA3wnPDf+Yq7ClVzVkTu2af2uOymr/ZsIksMk9sUBgBBVzQGBqTO07Z9zEIdjusQSc8ww3WAnzwOWMFQAsYkFVnJRQDvk1SQsmaXk5ib0mGD9l1+w+eLws6u/gmLcDDz98Hw4fOYDf+90/gWHIvDCTmEXLlg8TTN7+9hULKG+VY8loVMBA97ekxzM64nb30TO9+uJApO4mJcZuyqQgQXCUT8sAUQEtZUABQcvMIK03kvUVN5GnfGeDfsHOx/WNBTVhVXTKv1b3yMbwyAbm58axtj6CkeHV4PQgAQlWGBU48N1dKHBU38CuujmRVd1Xh3lwFWM4ZVTYY+OoyHR+YaJnbDKsAm56pcB1wJhQB3ITg1KMaPLr+cE6vzmm0x4ZVlWfENMKsUtgGSuzkLmyu83SAOjwkxM7db10Ua4ibTHXYgISHCK9AhIcCIBFm1MHcwOzAbHs/ro0tZb0108MXnow5rpfWJ5CudqPYwdPQVUMcOlCzH0rHUm6A5JJCNCyMkip0WbbRNVCZFZIbmPG0zON/mIh8R0Wl8ZEfpIXVIX9euH6dJsbpS0MDQ0kIMJsu2JCLO33yYD1fbQsgrfeeg9f/eqTOHhoHy5fuibVTsgkF/5LhDgk1+1FIa7vbviGjQpW5kZ/1tWM72NMWjDQcA6NZfsjB3xnfVg0YXJURM+01wk05ZkTKyYIMWAFlOZwWqd2lK7IAE3MOQ71JxP8ylkksAk/rw8zRC6FxEsJB/EixIKF4LF0FySijwWtApsgxLTPHWxV2XSOd1Dh9ccR2IfAdllz42QQndvbbtuL5776JP7qr76PlZV1ZjwJrCiEJFHmGMrd7/l8DpquYWur4pXlWltwzP6BzpTz7VqEiEIvPcPnH4knau7EL4I4e/mDAT/ljxdkClAUA+lUHc1WFmm1EtyeAEG1k19fcVIPeYGiXDXYI0v2u8dVjom7W4oOFQbGxxYxt7Ab6XQTff01al87U7BY0ssjwYqih8lwFFQdoRzHjAroprSiybDnG0ynZyKGT47dssz/wb6ZPLm00kkp8CFCbJmAYfAj7rLk1G1P04CmCuhpezBuHS9ytRpaWKbbC5Fg79hwokHT5tDC78H95kaIjiPBwsBbYZN6D6yZ/naR4QhsbA1gYXk39u++jGy66o+jDcSS4YC1QvI+WmYWUFSktHAMD1bN55Hh0Bgo0GWFdXcYvfdz99H23dXNCVq0khx88K2vlzA8PISrV6fb7C1eHY5C/KTW3l6r1fHWWx/gyScfxubGFpaXV6X78MqxPqLEJqkq7MBYtvBLB6Xy/VhZhdtVlFVVd6IM29GGAcAijvkTLFimHXWYKL6C7KqlgePAEC2VMrWGAidXrgGT43vMiyTtjjGJ723kwgttguztiwrFedWz0aeJY1Zsq6zub8x2YjmLEjGrhfQiBkWCXTXeI0ju+YJ/vtzo31H+0HY9e/wGGlCIf/65ZJobTMxetBGpwEmRZBFsYKAfP/N3/xbefedjnD51PrJNF9ygaQnuV35Z8bOALjM6OopSaQumSbw6LMnlL+QFt3XufiEz3s7hL8+0X7+Hm4coEhxMi0SZucYhEOBKcybF4elDJlVDo5lBkXEVlo0SzZ0wsySAIr6Bj9EK7I/iqnV6JkQuUmlgfHQRi8uTSKXmkc1R/sIsXAKgOISULsMhKKxPsNeM5ZPggGLsmUpHkAA3rZO3hhcmR0QNE1rvmHttuwoxVZad7FN/h4ggqyy7wclYQuyqxR6ZpQgxGyna61wDMTWQdBqEKAhErfY+sAmxyjxlZIJiuf8LfIJD46F+C5FhQVRoNkcwD1JqMavYRpHhdgiqu6DC8Y2v19O4PnMIEyPzGBpYB5svWK79mOBXMc8cLuEUjKFuFJDRK1AUPsMLnDNi3DKEtl303s/dR0IirCKs2MocVvoJngThtpeWVnDvvXdCflLYbt9hJCULKytr+PjjU3j0sS/hxR+8ilqNs2IVAn+/AimJYLjiLWwx1lUV/cBOAGOa6/qWOqbTupqB5uUzhE3Q0AgEaqJ9ZxVF9QJ0WRzTWTd9j5vL1iIqDAsgiq18Svt6UueKZ2bKC6iVFIF2HR9fz5xZMWFRKYgsjp+tT/j5Sm+ovEOCNSf1FNu/G+3bTukk3k/X6sANoOZvc3NnBs9/3L53QoKTWITQRFBP6fjf/4//BfPzi3jxxdc4LfPHxLYd7id6vzohoWNjw1hZXuP0Fddm8F6Wjy7dQw/tgxvIhSK+ATJMg6sGUxNlyqRWAfzgTYA3Sc2mKyhtDiUKJuOS5FgTSmLwy3hkyfeJVSzTvvtUDYrRoMiwXy2XrWNocA1LKxPYNbUIPSK9sLePCYgvgJD6a4/XaS5KHXZ9gnmTf15/lMm0HySLOqbUoodNfn3iDFVjyDOl2EaQQC+nMUOIiZ72fLZt1dgKEuHAvtDvTs2OpK2nQBQnUJdzbm1zayuoEvPaaZMEcwNjuceA/puNCh2DSNIro/LS91+3/YA5MAwVV24cQaFQwcTYrL+BHqvsOKhnjNT9HbU9ImVSvdWHbKrM79+rQCnwCY4jS5pvpkl0D9uHthThoBoia47oOeFElI1va2lpBfl8DoVCHpUK5+IX9p1kkim7Tz5EE+2rV6cxNDSIJ554GD/60RuBIF/iSTClOhFH0aQWu4jjg2uRlv2iYRThJGauKjSYDqkC4AVqosmbpmS8wFyEmFAVwGSCOylwoxg7Y6ECQW0n2CjEIQTIquqoo2pAcbfJsAGLqIBD3lUl5R1PV2Fl8xcH2gUQuFaIRZUJHkcAnj+v/bdPgqPMumkTfI8MM4ugtK8vW5937GSQhChHEU5FUfHTP/0t6JqGP/uv3+OUEPcTfy0FFe7uwcLUrgm8/95Jbxz+8ZBTlEVlbtXAVxbQmQ9S10bSQ1eR0DfYJ8x0fl6d8hc2gifbMpHLVLBkTKFFstCp90p8Xx2aJLsqsUuYnPEE3uRZAMiC9gPuL26i2cxgaXkck1PLUF3F16Jy+8aAFxka8M2iafUXCBNiepsQbB3R49CCbQrtBNQiFAEGKOJrNDwyDD0dWETwxsSaAtNRkeGQaaevYMomP22T1zfTvuKcowABVdIgDR1IZwBnbgOLilztEGFubmNRlHSRT3AMAQ6BJcTe72IFOECCo0hvt/yJ4/oK5cQOlrOg4/rMfgAEt+27amcpibLQ2AFizkOAmBKg2hzERPECt2zXiCsdLV3m921E7/3cfbTtI8xHeNIXNh0UnQY++WTJomGYWFxawd69kzh37pL0uOXJME9p6uzS+eST03jqqUfx0EP34u23P+SWYf2MQ/1y/Dvd9EhshGFRoCbPDBeW7bfrRYM24EYtttVE15TWDSCleyTXcgI8uVGi/Tqsb5QWImlJwPO3DKmEHH/jYCWOWTIUirzSps921GyL+g64x4yvsAZJsHg/3BRHtkm6rZy7JNjyVv45Cj6n7aAfbTKS2w2fVRmIyPBTTz2Cgwf34Xf/439Gs5lgkiy9oNLZfvEWpoaHh6DrGpaXV0JlxGNIvpAmh515hfXSM3z+EafIhrazZR31M6SGOBNjBbANUdSgmqupJrLpGirNYQwUgulW3DJdCyTEQDFaUJoNnzAZLRAmajFJGyBa1iMBigKMDK9gYWkPVlcGMTpagmLJP5t4KrFLjNlAWfTftEk0rRCH9okm0YHjRpF1wPaNdctYttm6p9jTdd10Vzr8CNMquKaskXlxKZLtEWq3DZb4UrmM7e/8a48oThTmdM5Wh712LDsIV1xuYxZMpGuaBIeiYLcD0XXMUyKTttEO4vqKUUPnF6dQqfXh6MEz0FzT86jgcYnV1QhVWFINZk2a6808QCxk9S3+EAXPQdcFgHUNSIwdJsFA7/28HUh8F7ZrYhiu114bAHDj+gyOHz+ckAgDvIlktA+hPKLq28Gz3sdzzz2JO+88jtOnz8W05k+mXeWSB1GUaX9QqqdMuu1algFTaUGx6sFgWaThEOIgGbA/GlTFVX3hRTr2+iOASZFx4qrVxM+/yw1yIYU2FyIU6vg50bX9tEMpKIoBOBGs3bHZ/tew/ZoD5s5hn1vX55obJTiwr6pnOq4pKeiK7zxHm2ALd4PXPlziHrMQINEGEG+O3g7Y+/3Ou47jyacexn/9L9/D6uq6VBtJI4Mnv4fF++e2fdttezA7Ow/L8heIOmlXHnELhz30EI3IvMA0pE2Y+SbXXoAlx8S2WNjCVnUYA30lQTuOv3CMQk2iTEJdlZJoti+wNx7LJsNUHluVmuhaAJAGCLJOOzpUhWB8bAlzC1PY2OjH4OAmNVhqsi8IfBX3Gw8hH2FK6eX5Cntt0umYQiTFIcdeIDM3urb/P+tH7OWCBgCFYwLKC/xkUXmMLdfn0q3ntOuQ4oAZvuWoZ1w/XBUgKoiq2qbRhHjkmVimr7zx1GD3GHF+Z82gpQhwF1REOsVYW2Q3rk6Xg2etrA1jeWUMB2+7gmzOCr52kqYTcyFLMGWjtAeCatl/b9VHUUivQYnKQQaEniFJyO/n3ae4h3i0dTeFJ8g7a/o3MzOL+++/C8PDQ1hbk5tUbxfkJt8qGg0Dr7/+Hr7y7GOoVCrcYF9B8iAymw6WBxjyC8YclqlrkRZMSwVRLCiKG23YVoXdQE3umEP9wYJF3Fy1pq9mMsqqm1aJJdbueFg/WbquO/6gQs4Hm2dYtN0nwBoABYqiQFVSfiAvBR55pZV2b1wchd1bJAjkYnbG6y5AMGOxA4rRYxK8cAVqsDce5niz+0uPMQmiyrPqO1s2ymd4377d+PZPfR0/eOEVXLx4JXIMsgtTMj7KtBUBzzc6DrquYd/+3Xj11beZet0hqXLR0tl7YPuJsW1231n9Hm4dJPHXDUAUoIdShUPqrqohX6xjdTOHullAVnOiR0dFpY6DSHlyyDRxSLECOOqhAbTsNICKowirqgaip6BoGYcQOUTF8cHU1QYmxxcxvzgFPWWgWKgmGiJx/I89n18LXFVYZY6nb4HE7F8E0VFo01QtaHbNTYejuBGn7eBIRDGcv10VOOObu9P7JIrY7bXvE2K3rmcuTYwgIXbLcEiFF9EXafvdqWggmmuu7avJxDJhk302BY47D6LGRYMlwBLXIJ0qqiN/YFkSvMPEl8bmVhEzs3uwZ9cM+vtk3Q0jIIzAzPPvb59gm5aGcnMYuwfOiMtFkVj697jgXjHgRkffJvTez91HwruLNvuLPxXiiV7cBN2f/PHaMAwTV65cw7FjB4WmxtsBdyzJCIZfdnOzjDffeA9PPPkwKtUaFheWQqXF7bO/WdxjEySZPCJpwbQasJSWp5LyohV7ZtfeNrttOxq05ZFcL/ctsUBUC6abv5huM9Icnq9qi4igG+2ZJWXiKMtBNViBQplLO6bNjh+223fI75YlwTzC6UUbccbCIbN2MC4/lzLbhlsv0O5NhBxRiy47NNSP/+3vfRsffHAS7777cWQb0cGtxM8d+nqID6olvw/79+9FuVzBmqNgx7t3yCB4zSc5xjsFQjo0veq9aG95tEOMuSqht40ie6qtCm+WR5AdqAQnmCIVmCHKkellqOBORHUUYTr6Lx1lGICiqkCzAbVZB9GrTp7dBogOnwwDSOs1jI0uY2l5DLraQDbn5yOOPjD+gkEgIjTjO6wGzKTtsaqw3WNYVditb49fFIXa/zugQruk2CXJnqKXskkxS4gBXxUGxATYNW13v7sbXELMM5cGQiRYZKpKLBVQFFsRdsoHfI7hR6EOmOzT4wCcshQoX2e3r1hw0kWJywbNvBOb/sv6Em8DavUMrl4/gLHRZYyOrNk/suRUgqwm8vFPQjhdv3pOna3GKDJaFRm9FtrG7StgORIzBsa33v5NjSa6O6Qa997P3Ucbd11wsuoiKkBOe+bU/qST59t44cIVfOObzzlBs5Kt3kYj2sevU3KyvLyKDz84iUcfeRA/eul1bG6EfRuiVDF/4mxPqGnTac8MmFIqAXCCaNn1CYzAHRUI1uQEY1KICpOoXqRoOkUPrSATGHbqpcC5ooJ2uabKoQBTYsQpvbxjRCB3TdrtuH7OwXMcIu6MWbQN2vSahgpWiXbbNCmTcpO0bPNxnu9x4NjIkS9ZNTigkkYcx6j2Ze+BbDaL//NnfwY3rs/i+99/ObY8SwrDFhJiK4Juwbs2FILjxw/j9Gd+eqfO3ShU5v9biwC7CN8Ryev3cAuiA5PPKBLsgZrE9/VvYm5+D4aLKeiK6U+muRGPBUQ5MsCQQ4YdX1eiN23FV9dBB07yCLHRAoymE2jJAOC4qNCqt2Ugn9nCyJCGxeVx7JpaRirFBhgSh5amiW8oRZKiwyKGbRVEWQIpiuYvjVFrZAEfYjBE1xuLaCQ+EffqKobvZ2wZPiF29ptIXBpEMeD6G9MKMs8q1d7mp2oKkWBuxHIVUNxoz868gibDVD0C/zz4apwW/s0dDxtBOm5fE5LgEPllzxe7nZO2SJoEs9dgEp92Ci1Dw+WrB9FfLGFqbJr/4E4Qydkun0ANFiDWDFnVYJkWNmqTGCnckG6XNzYptfgWQ+/93H20vfwUl96ERme+wUFC7PZbrdZw4/oM7rzrON5956OEbQbHJmvqzR9bksvKLnv9+gwKhRyefOJh/PCHr6Ner3NLByfeDnkl9HGgyLAzHt8HVqXaUWGhFTClZskQSyjoyNSw/HPoKr28KMeu3yw9/k6JSnB/g4gz5XVTQNFt0f/7bTiqMG+sMWqdO76gKqyDJW6m4w/s9mGRFkzXj5r4/r6eeXiEKS9PFQ/XEaN7KqSY0Kmqiv/j//xp1Gs1/Lf//v9KtxhPhoPlOjUHF+HAgdtgWRZuXJ8JbetWXAH6/vWugRDx7722emgDbL7WDiAkwQGT1+C2dKqFQnYL61tjGCte5TfcgTkiUfWA6a1FDChGEyRdh5J2AmaZLvlmnhFeCiHHVzmQJ9hAX6GElpHC4uIIpqaWoWnJ7kFZP2FXIQ7c6TG3fCDdEhONmi4DAArtTwyHADvm1L5KDN90OnKnKBJN+RoD1Fo6m67JMp1+GB9kYnBJJoEK4jh7BqJTwz/PbD0eubZ/41/70pYQdLoo+m9qX3l1hGCJr4gERyy0hHJYe20xgdN4YK4RywIuXzmEtN7A/j1X+Y8KmrzuUIRoWV/czeYkVM1CIbsJiFzMAlHKu0RseQfqJgTM6qG76JodRvuTQvoi4qtOokn76dPn8Pw3nsPg4ABKpQ20O3Fsb+xR5sc0+BPazz47j0Ihj6eeegQvv/wmWi0/ejALNqJ0sAylPjomwPaqM3X8YFGk1F1yts1wo4iq3Y8RCB5FK73sC5g45tQici3yDY4D75ioqoZUSoeu60inU9B1DalUCpquQFU1qKoCTdWhaRo0LQVNTUHTNAyPDODw0X1oteyIohYxYRgNGIaJVqsFwzBgmC0Yholms4lmswnLNDmKNn98CjTPRNo9H24ZE4b3QrFNzFm/7LCyK7q2grmFk6NdMhzVH93m3/t7fxvFYh6/+x//GKbRncWQZOAvJMhYsWiahhMnjuLkyc/soC0d9imDzhbkuguLAFYHxledpHbooftIYgYdKJvATFOxDNs3lMJQ/xJmF/ZjIJtFWucs9rYxOaWVN+9vJ0ewlc5BybZgWZZ9JxrOOzWdAUlnAD3t+RPzG/fV4aHBdRiGjsWlMUxOLEFViU9CvIjT7U+faDNpVdFDZFiBzg2a5fZLv3ujxhHYpgJw2nVVYoBRM3lKpWtW7Qbiok2rKYXYbstNn2g/QyxLAyEElqXCMu1FeWJZIEQBgWI/LJy/iaWg3upDqTYFVbUcok2gKBZUtKAoFjTSgqKYUBULqmpA0wwobipHWnWWNX3lgCbAge+C49xxFHRZEuz+H1XOG5SYuFoWcPnqIViWgsMHL3VMguPUXtlI0bJk1bJUlKpTGCteR2yQrATtblf9bqP3fu4+OibC3YjUyhI9PoLqCQDUag1cungV9953B155+a2E4+gWZCbZYfUcAD744BM8+tiX8cQTX8arr74D0zQD+ycOHhVWwQJ+sM53VXFMmhW3jK1+hnxRESTNgb+JO/6geXowdZJDACXIdRTccWSzWRSLeRQKeeRyOWRzGWSzGeSyWWSzGWRzOaRTKWc8BC3DQLPZhNFqoWUYsCzL/pgEFiH2gh0BdC2FTCaFwYE+mJZhP0QVQNdU6LoOTVOg67r30XR7PK2mgUajiUajgUazhXq9gWq1jmql5vxdQ61qT8AAeNG4/WPuEB3HRN1dmHAja7eLsKVFMsIXuNZEZuhtEOb/5X/9BnbtnsTv/e5/Rq3WDG3n9+WPn9cnrw7rMpE0wJbID/nEiSOo1eqYnp5LYI0g3h8fUc+37qnZnaKXnuELgC6pwVzQZs50BOFAmhMDKd1EX6GE1fIeTA5cgqJJTDdoguISU95EnE5F5HZvmbCydu5gS1Xt6NGA7XOazoDoKbt9CeKiKMDo6AqWliexuDSGicl1aVs2ETFlA2Wx20JL5lSHLClmyXDcGGiV2leKKVLM+BUH4JZXdRDTgGloMIyCbW1uAGZLgWlpME0dpqnBNBVYngpMoMKAplpQYEBV7P8VECgKgUJMKAoBiAFiqTCtNBpmEYplm1fZ/pAqiEOiLaLZ3JmosBwnZA0taKoBTTWgogldbSKl1KGrTehqAzpq0NWm3Q9DgLmLIjxfYoGvergOdS9QwdgA8BcZZMCqve53lxCz22NI8LXre9Gop3H00FnoehcXW7mmx6y/sch0mvO7YD/WqruR1urIp0tJRyiNtsivqgHYftLcez93H20TYXFAJ/kbizWBtNt122FhMeXsMmfOXMDXn38Gt922D9euhc0Yk4wl2USUT25l+vH/VvDO2x/iyScfxqOPPog333wPFme5xjWD5ZHhEDmAT4IDv9O+xLQJLtuOG+wKDOEVKL20LzKtmkbtv6KoSKc19A/0YaC/D/39fSgU8ygW8sgX8tA0DbVqDZVKFdWaTTZL65tYqC+hXm+gXq+j2TTQahkw3FV/hqDz9k3X09i9Zxxvv/M+DINP0GioqopMxibh6UwWmXQa2WwW2VwG/f19mJwYQy6fRT6Xg+qMuVyuo7xVRaVSR3mzjkrFJsxuJO0wCba41w59PcYTsXgCKHNtJzf5DY/r+eefxbFjR/CHf/BfsRHh/x5Hhr3y3MWgIDohkKw5cn9/P44cPYSXfvS6sE48CY6Cu3+3BunlweowKmVvxflzBl4Aq4ho0YHvlL9oKIq0qmFwYA2ziwdQaY6gmNvw+uOVDXTPmi1HQdFBdICk8951q6galHTTa5voKVjpHIgTNVomqJGdVmkFC4vjWF4ZxvjYmkfmA0TTJZgCn15Z5Zgmw4B/D9L+wjQhFpFhXn/CMbivfa8zuz3TVNBsZdFqami2dBgtDUbL9islhEBTW0hpDWiaCU1rIZ1qQNca0FQTqmba/6sWFNLyVTsv8JHrm2wGvlsGQaU5jPGhOagq8wRy/Z3pVFiWCcvSYVo6TKLDsnRYpgrDSqFhFFAxhmAaKRhWGgQKdLWJtFZDSqsjpTft/7UadN30xhhHflnivF05saWQ1DfYMjAzsw/lSh+OHjyHdFpCqW3TJDpR8KwEaBg5bNbHsGfwjFgN7rYCTJs+8xYYVbXzfMQJ0Hs/dx9duItllJAwoogLvZ1Xh4ZhWPjwg1PIZrNyw40ZU7dUGZm2CLFgmsAbb7yLZ555DF966D68+85HQv/HOPiBmczAxe76H8bVs32MfdLL8xsOVgyTcB4KhTyGR4YwPDSIwaEB9Pf3IZ/LoVarY2NzE5sbW5ifW0KlWkWlXEWlUvXUVXf8suCNwSdfhkNEOWmdOCAEqNdbaDRMqIwflUtm3Ws9m82gr68fA/2D6O8fxMT4OI4csgm+ZVoolTawtr6BtfUVrK2to7SxFnte2lWL21mUYX9v5z548qmH8cCDd+OPv/vfsLi4nLi+jW75xYZdEmTU7VRKw2enz6NU2uRu74wEs+ProYftBWvm6SJJSiW3bCBSryDyMzs31XQFI4NLIKbAv1jVhWaqvrmtxDRF0e2cuLDvLEVPQ6EWO4meBtEyIHomEXlRVYKJyRLmF8awsjaC0dENqJJRfnkRo4Up8+h6igZCTJsYs0G3GELcrnm2T6J1tFoWGvU86nUFzYaOVkuHYajQdQuptIFMhqBQNKGnTOhqE5puQnWDb1lGkJB5xEkBoNkfjxQ5kaWd9Eps0C2olq2EqBqg8lmOn6bJdFygCHS0ALQYiwQ/wBYhgGVoaFkZNEkBLTOLaqsfrcYkWmYGqmIhlaojo1eRTjWR0WtI63UoIJIRpmNM7dtVggH+sW3znM8tTGF9YxiHD1xANl2VewWJLDLaQSAoXkyb7Ll0q1k6hgszYVeLJEqzLHi+v12MvdDDrYMOibC8KWJytTW4LsqfwNpl5ufDaYhk0Y2gQZ220WoZeO21d/CVrzyO++6/Cx9/dCpUJkoB9HLZ0oGl3BVOyhQ3WIdVlOPJsAwxT6VSGBkZxujYMIaHbfKbSukolTawuraO6Ruz2NzcwubmFppN/yG/3Wahdu5jCyAuETYiFWw3iJaqpKCqupMD2D7OFmnBguGplIRYqNVqqNdbWFstQ1dXkVJz0JQUVFVDX18ehf4U+gaz2L9/D+66+yg0TcH6egnLyytYWVnFysoaGo2wSi3cH0EQKbnyHDNhJviWiAyLfn/wS/fgyScfwZ/91+9henqujX0IojsBvZLXX11dx+qqn5tcPAax1QoP7T8Ldx7E+ddJ/R5uPuIm8aFAQxHl4yaUnoLr9e3/XShU7fa9vLVMPSDct0X7fRph8srJcUxU3ZvREEOzcwZT5IHomeiARgKCoSkNTE6sYH5xAutrfRgZrYXKuwSVJaZJiapPfv30SgCEhJgGz6eYLWdZQK0C1Os66nUNjboCiyhIp5vIZAwUiw2k0hWkUiY0jXD6UWCbVLsWAMxm3iNQ45App1wg6JaqgigqiKqDsIowHYgLYfNOxTL980EMP6+0s01LmdBQRRZOhhHnOrAUHU0zi6bRh4aRQ7k+jFWzAEIUZFI1ZFIVZFMVZNMVaKp7TW6TrzDr/8tdYIiBoNzS0iiWVidxcN8lO8DULYTQs0VAggEgl95CzipFvtrbJr/tBu/b4WBZvfdz97ENdh1RxNUHrXqK88YG24rKL0uXl4H8JFuebPD64Pk18vqu1xt45ZW38JVnH4dhGDj16VnBOIJthfpkj5kbldj1T41YUOCNMy4qr6oqGB0dxvj4GMYnRjE8PIhatY7l5TXMzy3is9PnUCptwrKi1c8ocH1GGdNu0fgC9dj+KZ9mERl2g4/pSsabnFhEhWG5iwwGNRa/fVXRoCkpaCSFymYTGxtbaF2vomXVYFpN5AspjIwMYnR0CHfedTv6+/tQLlewvLyKxYUlLC4uBxYKZCGndMco/RL16eN877134fnnn8Vf/dULuHjxWqK2fPD9Z2WtIkILPREEWxbyJLiz4yeHnSPPPdOrzz8Ix3+RB1YZ9r5ziGb7Y9G96NIK4E82afLt/h0yS4wg6AKFjKi6l1bJS5ME+ObQXq5bDsEWkBldNzE5voT5hQmomoqhoYq3jSWLLPlNogbT5V1CTKvDvPYBMTm2LKBe01Cr6ajVNDTqGnTNQDZnoFBsYXi4gVTahOIGzooI0BUcJMJk2DLCgZxcMucqi6FAXFR91X4fixRhNzo5z+yWfuQoFMkGELJSsLc514CiIaO2kEmtoUgF+jLMDOpGH+qtAtYqg2htZpDSmsimK8hltpDL1qDGKYNeECzm/9Bg6BRegmBY7vFr455cXhnB3OIu7N9zBf19WzfHGElWDY4gwXEkdbtMk0OWMBxVeCcDavXez91Hl4lwstPDkq3YdDgSkJkQiya40WaPvpklq+5E+XKK+uH5SlarNbzy8lt45pnHAEJw6tQ5bwzRx8a0c+JyggGFcwjzSCSfhLBjdpHP5zA1NYFduyYwPj6KRqOJpaUVXL1yHe+8/SGq1YgE56J9SEAO2IBeUddPgETT2zlqcOAaI7aVlqKotrKrpKA5QbAMAKpiwVKCeZhdqJy2WZTLVZTLZVy7dgMAkE6nMDo6ivGJUdxxx3E8/PADWFsvYXFhGXNzC1hbK4WPQxsLC1x/45hFlih19u57TuBbf+ur+Ju//iFn8UZuPEmUbVEb0e12p80kwbhkysqP79ZWkHu4dZAkQrSLQKRdXrAq92+vgh4i0B6hdNrgKcRcMgyKkLY7mWRVMDfHcJI2RATDM29tIZUCJqdWsbAwBigahoaq8bl+OwCtDrtkWAT6adVqKahUNFTLOqpVBbpuIZcz0T/QQm6yBl2zyanrXxwVdEsERdGDZJgmuTTRoVP70NeSm4pJ1X11WFPshWmeIgxKOQbChJhaRHFTQ7GEONiYo65z0iIpAFKaiVS6hD6UAACmpaHezKPe6sd6ZQpLG2lk0k3kclXksxWkU/wUmNJgyTAPbVxfKyvDmJ3fjX17bmBooNT++LYJAQIpIsE7HLWZdQOh4T8re65NXyR0assBnmIaP0GnJ9nibSLwJ5BBoteuf20nSEKIWTLsYmtrCy+//Aa+8pXHYRGCz05f9NqlA1MRJjodTYbdbX67dPqkIMITcj4B6O/vw959u7B71xT6B/qwsrKK+blFfPLJZ9jc3AzsPw9JlMpumo6G+lUUKIpuR66MMBVmf1MVPxWShhQsQe5F23fYvw4t5//gxwQbJKvVMjE/v4j5+UWcBJDNpjE5OY6JyTE89dSjMAwDMzPzmJmZw8rKGpKk9ZEltWzZuDJ33X07fuqnvo7/8Tcv4aOPTkuPJT56c7isaFxxPtbx+xOt7oosD+LGwfbdHVPvnYGFDlecuzWQHhKhHRLMQ4DkSpKkAPGVJcNMfRqsUstVb0X14ybOMWbgdhmK2FEkJZ02MDW5jvmFISgAhkYoP+SI/MG0GhxFaFmwZJgHQoBWI4WtsoZKWUOjCeRyJgoFE6PjBtJp4o0PCBtGBcap+mmbeAozvzxFhoEwIWbz3ArVYSdNoaKBjT8CIOxbzFoQsObTDCEOtSfKj+21558zTQUKehUFVDGs6jAMHbVGEbVWH+ZXhqGqFvL5Kgq5MjLpmjiQ03aCuVdX1oYxM78He3dPY3iotKMPZiJQ7sUV2iDBTP5obh2RywVvW6h9FbCsSGIsbHsb0Xs/dx9dWMJMeliTBJvxVdjwRFQwMaaiHcuSYTniFaUQBYmkLCEWTY63tsp4+eU38cxXHgexFJw9eykYnVkAEQG2t8Wdp/BCRqGQx759u7Fv/x4Ui0XMzy3g3LmLWFhY8sx26fF3a6KfhBDzfZ05yj1U/6Pontrrw1Z3PT9ixTZ1domtRUyvL4uYHtnljh8mTNLycg4TYsEkLZikBcsyIkzU/f2v1eq4evUGrl69AVVVMD4+hj17d+PRxx4CAMxMz+Hq1etcpdjb5y6YB4tw513H8VM/9XV8/3++gg8//JTaIrcIBMgvGHUCMRkWtetfP3KQL9s+GY632OgmCOnQBylR7uUebil4JNZV1ySiSYvagTOppSI026bJuucvrFhGsskpB+1GqPWINWvC6oJOE0UM71ZPpeuYnFzHwsIoFFXD4FDQAopHiAkxpU2jRWDrN5sKtjZVbG1pMAwFhYKB4RED+bwJzSuqBUyqXRNqN22SO9ZAXmJJX9dAPUX3FVuaEEeRYSBIiFXFMY3WAVXwDGFVXoHvMM1DCT0OGrz9FFx/7CKMrhvoS2+hD1uwiIJ6I49KfRCLq7uggKCQr6JY2EI64wSQS+o/LEqNRG8TbQewujaEmdk92LN7BiPD68GNouPBQwc5hANkWNL/tutKcFQbt1h+YFncyu/nCxcu4B//43+MN998E81mE3fffTd+7dd+Dc8888y29dkNdN1HuB2TwjjETyApxdPx+wxCbHoZPbbwBFRO7U5A5ASkYHNzyzGTfhwAIskwf0zyJJgOsqVpGvbs3YtDB/djeGQICwtLOHvmAubnl2AYZqjediIQ/IuBONgX0wa1kGKXVWwzZ1ULHHu7FBVN2rmeLNKCRXSYlH2VRVqUyTl/3ISYMNHyyLVhNWARw6lnBM5J3IKNZREsLCxhcXEFH334KUZHh7F//x48/fRjqFZruHZtGteuTaNe982z2rvn+CSQNfm9/cRRfPvbz+PFH7yC998/SY9UWIcH+vhvhzVANIL3dtx15kPe1eLzip1ecf71X/91/MVf/AXOnTuHXC6HRx99FL/xG7+BY8eOdTCKHrqB2OjS1ERXNCl2TVkDuX9V10Sa4+/JUwpFQbNC45GY3NKpk0QkONBmWL3MZAxMTpWwMD8IRSEYGJQ3jbWI0ZYqDNgWmeUtDRsbGup1BYWChZERA4WiRbktBtXjoM+xmAwDbZpJuyqyo/QG2ooiw0CYEEcR4cB58NulTZ8D5tNAyPKAC0nyK6qjKgT5bAX5bAUjio56I4dybQjzS7uRSlkoFrZQ6KtD13aGeK2tD2J6di/27JrB6PBa+w3xUnOJiK/gvotShmNVVtngVSHLgDaDXnHb9lXhWwW3siL8kz/5kzhy5Aheeukl5HI5/OZv/iZ+8id/EpcvX8bk5OQ29twZtiFYlg+RCXMydcOenHv+xJITZZ/4dDqxljPzDhLaoCopS4jZCfXGxgZeeeVNPP30Y1BVFZ99diGwXTwBj1GiOcekWMzj0OHbcNtte1Gr1XHlyjW8/sY7aDabgfKyqlYU0YszSY5DkASroeNLCHyyySyMKIoKVdUAQkWBhv1yNml1G6ZNYokK07LN3yzF8ra5pJa3jwQWTIrsWsSCSRowrUaIBEvvs3d9ESwvr2J5eRUff3wau3aN48CBfbjzruOYnZnHxYtXsLpaYmonvd8A0TXkmkP/8MXX8O67H3PLy5o+u+C5CUjf5xHXUnwb/rOIV1e0wBR3jKL6TvocC/Yb3efnFa+++iq+853v4Etf+hIMw8A/+2f/DD/xEz+BM2fOoFAo3Ozhff5BmQJLm/lREJHhUNRpHujAVJpOmc06BEYNp1yK2gceGRYpT7HmmQo1nigIytBk2CIKhoZqXDXYJb60KpyUDDcaCjZKGrY2NegpgoEBE7t208ovs2uMesymY+KRYbseq2Qn9COm0u147bJk2AWrEMfZE9PngSbF7iOR+NdGW4TYrRMKoCZPghQFyGVryOVaGBlcRrUxgHKlD+sbw8gXaujv20Im3Yg3nY7KEczb5hzzldVhzMztwe6pWYyOdI8EJyXAgabc+1BEUKm+Qs+nBKQ2ybNtW4JbqRqAz6fK3A2srKzg4sWL+P3f/33cfffdAIB//a//NX77t38bp0+f/vEjwnITUCApSRUTa7rQzgeWifMF7IQMl0olvPzy63jqqceg6SpOfnKGqSVhXio4zoqiYnRsGMeOHcLExBhmpufwxhvvYHl5VdhWWwGa2vDZZsfJJzk2CVageWVY32l7AC4ZdhVh3akXJK+KokIhfsool/DaTdi5E91+LNICLx+x3b/uqMauKbURNImm63RwvZqmienpedy4MYtCIY/Dhw/giSceRrVaw8WLV3D9+gwsS/zGjYywzfntS1+6F89/41n8z//5Ej7wlODwtd4NtEMWZcqHiTZvYS64+BZuu7N93DnVuz3stOnV97///cD3P/qjP8L4+Dg+/PBDPPnkk22P48cZItNmKRUmKWgT4ii45sV0OVUL+3oK+xH79obIsFNeSIZpwscSLBpR+0QMZDJwyPAAiKVgaLgaIDksMU5ChgkBalUFa2saqlUNxT4Lu/Y0kc2SxD6ovNzELBmmx+zX02NJcCjSNKXycsmwCy4plnw20n7IbtsUCQ+QLwDBqNKMH7Gwjw5UQGJAVXUUcyUUC2W0Wjo2ywNYXJpASm+hv28ThUKlO77Ezj4vLY9ibn4Ke3ddw8jwWvuvqbhFj26SSE5fbBR7ri/wTiq0jiosXXab0a33sxvbx0Umk0Emk+FVkcLIyAiOHTuGP/7jP8b999+PTCaD//gf/yPGx8fxwAMPtN3uTqANIhwdXCZZtFTRxSUz6RQrUKwaLDR7jAnAIx3FWGLSLtsmXxnexMsvv4GnnnoEuq7how8/bcvOn+571+5J3H78CPr6i7h08Qo+eP8k6vV6W2olty8mB3HUmOSjePP6sUmw+7EssSm4XUZx6ujeGFVYsIjKNz0nFiwYUBFUjAPm+GwVJ3qIu1+WZfjEGUESLIrYnJRQVipVnDz5GT777Bz27duDY8cP4447juPs2Uu4enUalhV//fn9hvt+9NEv4dnnnsD3vvcCPj3JLsZ0ju4Gk4pXbIP3YzQZjkJyFfrWB0FnVL9TD6SNjQ0AwPDwcIct9SCDrpn9yRBiytcWiu4FQPL4gOdbLA6eJSIyoTIOwSYh4h1hDi0aO0cpVRzilckAU7tKWFwYhmUpGB0LK3406Y0jw4QA5S0Va2sqWi0Fg0MWxicb0DuULeLIsF+OT35FqZXEHfqm0sKo0i7UlP+7ogPgqJ68COYR8NRhihCHrofQOLaHYKVSBkaGVjE0tIlypYjSxiDWNwYx2L+BYrEsR4h56cKc3+YXJ7C4OI79e692Hh2aWlCILSrj2sCtF0GmGRcKr7zo3NDxCESIWmyjSW6bRLYdS5t20a338969ewO//8qv/Ap+9Vd/te12FUXBD3/4Q3z7299GX18fVFXF+Pg4vv/972NoaKj9Ae8AttU0mgUvSrKUyhsDz9xQkEan3YBOcqqUyNQ7rCqx/UcF22LHubW1hR/96DU8/fSjeOihe/Heex/BchKCJQmKND4xirvvuh25XA7nz1/ClSs3YBi+stmOcsvrhz4f7UBWQfc+bj5qj1wK0iZJtEmgQiEIqMsWMcKplujzqjjk3yHHtH+zGyXaLhi1IMP30U0CwzBx5cp1XLs2g917pnDHiWM4ceIozp2zz7UVkUSOZ8kAAE8/8xgef/wh/Pl//xucPXtRuj67LSlJTF4nyp9X7M/tk2G2TrzPtIvwOPlkPLxPyZTmz1MgqnZWnC3Lwj/8h/8Qjz32GO68887tHN4XFt2ekImIciyBprdbvgk0URGczCu67TPsRiuOIlyMKiTlN8wbDxBUE6Pa8PyIOfldHaTTJqZ2b2BhfgjLSxrGJ5ohc9w4MkwIUKkoWF3RYJoKhoZNDAzYvr+d5P4M5iAO5yWWQYAEi+pFqNsBci0MXiU+vqF+RG1xSFxggUSuh7YQuA4Fx0JFC/2FdfQVt1CpFlDaGEBpcxAD/Rvo6xNEm2aPN/N9dn4KKyujOLDvMgb6g8/c7YTY99dX5QPl2OcS7zoKkGAtnBO6nejNXnRw2m1CQFxdUrwDyu7NxvT0NPr7+73vonfzL//yL+M3fuM3Its6e/Ysjh07hu985zsYHx/H66+/jlwuh//0n/4TvvWtb+H999/H1NRUV8ffTXSVCIsned2ZIEepquKJqAs55aZdohyXkiYu2Jc/Xj6BBoBarY6XXnoDTz31KB599CG8/fYHME0z8ri4GBoawN13n8DQ8CDOnb2IixevwjQlovi16c/bicInPDcxqWxYFVqBCqLw2yTEtANXwY7o7Kc18uux/QTzDPMJkk2k3ejdwRzOBIy/csC/OexH3YlCSgjBzPQcZqbnsHv3Ltx113EcOXIAJ0+exdzconQ7X//6M3jwwXvxX/7LX+LK5RsR/cmQuLgFMH6d7qis4vsqbKUiJsPxJLiT50rnCyHdgEXsu6GT+kB7K87f+c53cPr0abzxxhtt9//jCsUyoYhcIWRSBlEQklw632kUPJ9k2uzYfb5SZJg2a4WRLIo0u088xTBqPwDfVJtHhgPm0w5JiyDMqZSFXbs3MT/Xh6VFHeOTzdB82iWfrM9wraZgZVlDo6FgZMTEwKCVaC4e528cDJgVTsUkpQpH+AsrCVREe0AR5ujdBHNNxPmNh8ibJCJJMOfaUoiBYqGCQr6CajWP9Y0hbG71Y3hwDbmcXPolywJm5vZgvTSEg/svoq9Ylhts3L5xzZUTpkMCcwzde5rTBi8GAfGeH26MAfd3v4403OeEMI4Bx/TZsm5ZMtyt93N/f3+ACIvwS7/0S/j5n//5yDIHDx7ESy+9hL/+67/G+vq61+5v//Zv48UXX8R3v/td/PIv/3LbY95udI0Ihyd68cpKJ30lS4kSVqLbBZ+giFTh6HrR+8GftDcaTbzyypt44omH8dRTj+CNN95FqyV+KKTTKdx553HcdmAfLl68grff/sBLfUSPgx2raB945UX7IfbtTd63CIHFCI8oa87ftCrtmz7b6Y3g/e368AbGIHjOcM2uAybOFoKiHT9VUhQJjttPGdDlZ2fnMDe3iIMH9+GBB+/C0c2D+OijU9jcLHtl6fECgKIAP/3T38LhIwfwx3/8/2B6el7YT2C/BNYBot/i/OuTIe4+bJcMB8dMI8k55PfFGyOLnSXHBB36IDl1ZVecXfziL/4i/vqv/xqvvfYa9uzZ03b/PQjgTgRF5LADs1D+ZJZPKtw8woSeFhBDPFkO+TkH0+UoVF9yY6VNqxG8vdho0oC8UglA1wl276lhfq6A+bk8Jqeq0DRxfmHTAFZWdGxtqhgaNjC5y/ACYLkqsKrokQpukqBbLIRm0ALSS4jBJTMEPtkT51IW+BvTqq77XGSJddw+BtrglKUIMe+65AVg86rGEECpa4+30GLZZumFQhX5fBVb5T4sr44ik25ieHgN6ZQ4YJZlAddu3IZypYjDt51HIV+NHwN38G0sQlAuB7HkOJAOybX4CM9X6ecHL78zceslXXDhjUO6zs1flOahW+9nWYyNjWFsbCy2XLVqX4Mqs4CgqqrnmneroutLHnHKqMxvUfXdTxAqkpBgGQVHdmx+u7z9Dqt8YiLIX0jg1Wk2W3j11XfQbLbw7LNPIp/Pcce2f/8efP35r6BQyOMHL7yCU5+eTUCCVc5HjCg/X9F563Rhgja1t4gfjMpTdr1+dYrEE1jEsKM4kwZMq0n58LoEyK1Df3wTbLtx1fuETfIt7+ONUSpdUrTyyCNvcebI/t8mLl++jv/xNy9hbW0dzz73OE7ccYRaefZ9g9PpFH7u538G+/btxu/97n8WkmAWUvdLl4NrhY9BXHvB+0o8Zgv0MZFFdJtx/STvbzvAG1HSD+CvOLsfEREmhOAXf/EX8Zd/+Zd46aWXcODAge3buR8z8M3/zEQTQ1tp5k9e6W1sGcUygmaRziek+rJjaxfECCh/QrBjoSfW7ngTTrbp3LuaRrBrdxmqQjA3U4DRUkIE0LQMbGxouHo1BdNUsP9AEyOjRigKNE1yXUIs2i4Dt7yiaNTfemgfaBDnGHkk2GoJPvZ2QvwPC/d9Gh5YBHmNIrZJETC91b1PFKLKhH6PGhePNLrkUAH6+7awZ/csUqkW5uanUNoYAM8TxjBUXLpyGLVaDkcPXZAnwRaziNEOoQTk71FJEsyF58fvk2J6oa3r6Ywk1V/2mUf/vlPo1vu523jkkUcwNDSEn/u5n8PJkye9nMJXr17FN7/5zW3qtTvoUBGOUlrlVFIf8WbUyZGMBHeuGPP2OayGi82p3e9BM2m6jgvTNPHWWx/gvvvuxHPPPYHXXnsHpZLtH5LJpHH/A3dhdHQYH7z/CWZn50NtyO5rcKzhfaEjQicNMiYLESlUFMAiLVtdpdReHxTpgRYkw5ab05d9NPiRqAP9OWUVBSG1WNbMnk1D5SOaAEe1GQdW6TSMJk6ePI3r16fxpS/dg717J/HuOx9hfd0OUFQsFvBzP/8zAIDf/d0/Qblc6XgsIT95iP3HZVwNeP2Hg18B4udP8Fkje72KFWt2ISQe0aow+xy5NUymtwvf+c538Kd/+qf43ve+h76+PiwsLAAABgYGkMvxF/l66AK8lETiXMH0BM8rF2cezYnezG/boEwdoyfliXyBo8COxfFd9pRhXh9WK5EqDNjz6sldNSwvZTEzXcDU7ioyGXsfTTOFpcUMWk0dE5NN9PUl81x11eF2lWBaXY4LnBUgwUaNMimnzc/dIFhsR8EcxTREUarDBbtwztkgXW2qiu2YB4cgUIYBAKoOTbUwMryGQqGC1dURVCoFjI2tI51uAcRAs6nj0pXDUFSCo0cuIKU25PoM7Uzwtyh1PM6UnLs9jgQzKZOSwmtrB9Ml7STZ/TxidHQU3//+9/HP//k/x1e+8hW0Wi3ccccd+N73vod77rnnZg8vEjsaLEuMTiZ90aaV4gl1tClv+2ROpr9gH+E+6Ymw+HgQQvDRR6dQrdbwzDOP4a23PoCiKHjooXuxvLyK7//Pl9FsNqny0ceVNfMUma3yiA2ARIRYNBaWIAQWMQRmo4TwTZZpAmx/FCiK4qnGbvojevxsHW8Mjg0eIeD477Dj4oxFSILD+xnnC50UPNJVKm3ghz98HcePH8IzzzyGU6fOYm1tHT/3cz+DtfVN/Omf/HnIeqAbkN0n2WBpovs9SIhdxC9SJemz0wWfZGTY7V8Rmu13C064t47qJ8F/+A//AQDw9NNPB37/wz/8w1jfpB62B7GTPhmz1Yg2FMvwzJoD8Mgz41/oBMYC+Govb5soWE9UO4HI1zzCHxdYCwAdUXpsvI71tTTmZvKYnKrBshQsL+WQy5vYt78OTXPfKcnIQCfm0G59ermQJsP0fgRIsFkPK+YK5V+tut+TLxzYA6GDkjUjiwrrR6W/Ys2n2yTDHUN0DVHXXjbTwK6pOZQ2BjG3MIHhoRI0tY6r1w4ik2ng4IEr0FQS//qKIcE8Eps0Z3CkX3UMCbY3BgNkcfu0jARqskbd6wnIa0SapFuJBO/0+zkJHnzwQbzwwgvb1v52IeFdLTc5lS3bbcgTPRp8tbbdPsRgyW24b7GfYvSE/dy5S6jV6vj2334eRsvAj370Oq5dm+6K2SnfDD1ITv28u0Glr91AR7LjFpmV230yJrBQQ2OzP4xvEtWGbwbtVnJJarhscFz2mGR9Xnnm0+73bpLh8G/AmTMXsLi4jJ/8yZ/AnXcdwwfvn8R//+9/03WfDtGx6Lb1gAv+tcdTijlWDjFEN8oCIKmfc7SPdbxVzXagW8E4ZPF5ioT9eUMitSVhQC2/E58weqpxVCqluHzAgbIOaYhLn+IF1YlXjoXbo5RuEbmLIaSKAgyPNKFpBJcvFaFrBLv3VtE/4JYIR5GWAa3otgtfDdaCMxIVIKYBYjEk2KwHzdpVDYBzvOlHa9SYOAHNZHIVR++IHrzeolRbWTKcMFWTB9lgclELKs42RQGGBkvIZuuYntmN0uYgxkeWsG/vtG3JG+ufy2zvkim0aCFJSjHnLKDxfIKD/QfNuZOqwV807PT7+ccBXVWEo9WNbpgeA0knhu7EdLvMsDubyNuTcHryLBO0h4Wua9i9ewql0gYUAAOD/bZyxCi08vD7jfL99cYbMUYeIen0OmCV9EAqIyfQlaIAClJQFBWqojsqLwAokecs5AtMjxvhB2900KOdI37tmL27OHz4APbftgdXLt/A5uYWcrksKpWw35GM2Xbcse02ooin2CUiaAovalN+IUd0v4j6D4+P/m27rpEevtggqraj+SyDnQsIQMSE31WFA+lNAF8p5gXPEqVMiiPXVF7hAITk2girwjwy7GwnTuAjQimrivM7ITqqFR3ZjD19bTY1h/gyTRE3tkU0IaaDZnViIg0EyTAbSRquT7BZh9IqQzEa9vHzFGDNW7ggmsQYmJRZbROzOMSptnFkeKfGKYnNzT5UqgX0920hlTJhWRpUxJhEt0uCY6w42O9JgooFK1MkmPUHZsyqvecZhwTTz7rAsy8qSnQUIlRhGrwo17eSatxDMnRAhKMmeUkncjThC/r9RU8K5YiiWBnqDHHkOjwRlxtvmAzzywB2YKMnnngYlmXhL//ifyKdTuHxxx9Cf38f3nn7A7RaRmKVKtgvb7y0mW9wPHGQUe2jzIRF5tp2WdPO/0ulPnL9h1VFhaZoUCIyCUYpfX5QpiCJ4l2jceRMtrxMXmzRfsiej2996ydwzz134K/+8n/g1KlzuO/+u/Hss4/j9dff9fyGZfoUldsJUidjxSEeV/RCjzwChoZSNdq7L7cfOx2VsofuQqgCy6i9vMlcQpVY6HNMk2KGuLK5XgNm055ps+97HKX88rYFyHBSSKp8LhmmYRoK5uez0HTgwOEtmKaK+bkcWi0dExMNQAsTWTbHMIBA+qNug1WGTboPs2aT4GbVJsIOGSGqBqJloKgmiJ4BVAM8H2vPF7gbvr7dRFyUaRo3gwxbBizomJ7Zi9LmIG7bdx2DAxtYXR3E3PwEJscWkE4LzMd3gATTv4eUYGYBiyaygWG5JNhZUPH6cUlsAhLM++720Q2CSrfT9UBdScfSez93HTdVegirJ2ESGT9RZMkJ/Xt3kSTytNhXubOJNz2pz2YzeOaZx9BoNPHqq2+j0aijXK7gRz96HSAEzz73BIrFQqL2wwgeXzrarhuRGYiLwCsHtz5XPYv193TLyeRGplRhEfn1PDHoSNQWlxR3g8zQ5tu0GTfvWPCIdFwZrw/nk8mk8ff/r/8Nx44fxh/+4X/Bp5+eBSEEH390CufPX8bTTz+K4eEhuXEn8gVXmc/2Q6TGbg8JjWozvN/R98zNeTxbwau/rU8PtxhiJm+yk8XIcqIJNx2tlo1c67Ub/o04k+RQzlnG1xdAaP8CEaup9qRT3dD/C7Z75ry8FEMAWi0Ts7NFpFIWpnbVoGlAOm1hz94KLEvBzEwOrZbCJbd2Dnr/WFvEEJJgWXLstsF+AL6JtR8l2oRiNKC0qlCbNShG0/6YjXjzXBFYX2QgxqQ51Z7fcRSS+PvGmXvTH1lE7K9hqLh0+SC2Kn04cvAihvpXoRADI0Mr6C9uYn5pCo1mOr7NiGuj48BfvHYi8gX7FVwFOEiCAYHbAgm3L/W8YtMxsc06FjSySFp+u9B7P3cf2zDTkm9SNtouH2wZi/l0H90IjmODb5qZhFSk0yk89fSj2NjYwptvvhfw6Wy1DLzxxnuYn1/Cc889gfGJ0dixsaa1QcIXHLMCLfhR2iM1gZRCiY6t7zud/JwwxxsqgnmAg/vvR5f2A2vRBJlHhuUJVpAUhhcZWNLI8X2WMfNmyo2ODuMXfuHnkclk8Tv/4buYnV3wygPA+fOXcerUOTz51MMYHh6MNY9n/3aPD39/txPRx+vWQvi+jz6fOzf+3ov2xxN0apDARDOJokI4wXF4cAkWQ2y9SbU3iY0gw279iDEKCXZSCAgFS4bdiMuGYWJ+tg/ZrIHxiWqgvqYBU7vKyGVNzNzIoVZVhWQ2ZK68DaDJsKJoCER5ds+Jo8h5JDiQNivCH9yFm9qKSnElHlD3gzQK0Wnwq06Vbs7CULWWxflLJ0CIimOHziCfLfvdKcDgQAmD/SUssGQ4hgS79xdvkSgKUrmC6ZRsVPlINZhGlxbqROCZMgvHIgtVhWzapW6i937uPtq8i0UT/eiLop2Iq9tl1twuZM0Z48uJt8X5e2qahscf/zIq5SreffcjL9hMsB7ByU8+w+bGJh5//Ms489l5nD17IbY/ubEzdbyIyt04LmG/6dg2odrm0FL3t2tqrTlkLRzgy7XNc9VlNrq0VxYWXH9k10ya7Udq/J7Ps3hbYHwdpNO56+7b8a1v/QQuXryCv/iLv4Fp8N0PLl26ClVV8MQTD+PFF1/h+gzHwQ325ZuDs64ByfdBxiojfP+Ij9d2+eTyx8nuf9R5vNWIew+3Omxy4rt+RCkhbD1Re17ZQECqoF9e1Hjk+jeCUzORzzAQVGtFgbiYdmQCaHllXDNoL0gX04/rJ0yRPzpKNBQdpmFiYWEY6YyJ0bEK+OmDgNHxKtKZLOZmcxgZbWJoiIT8hoFoMkybUMf5CtNpkxJBkEfaG1/EsRWlT3K3JYaa6j5RjguwBeyYafTq2jBm5vdhoH8d+3ZfD/MsZ5wDxVUQy8Li0jh2Tc5DZ9MoxUSG7gpCqcg4BJgZCxSdaxIdsPJgzaI7AeUrLEV4Wf9g9wR0OXhoD7cWEhLhdi6G5D6D2zkxjfZnlvftC0KsmHVifimq+8ADd0NRFLz99gdcEkzj6tVplEqbeOTRBzEyMoR33/0QrRb1wAqRCZnjJDF28IJoBUmuOMgWNQ4qKnUsXNNqSgm0179MWAQwiWV7VxDikVzeOQqS3ngTWptMRQ+N9nWmyZdIVW0ffHKlqiqef/4ruO/+u/DDF1/DO+98GNvShQtXUCjk8cQTD+OHP3wNhhH9QqXPa/w5EyvoSdwExCRTPL6dRvIAeLztOzfung/SFwMiItvNNtjJqpdnGAA38q6ih9sEApEb3ABant8gon2G7TzAOmdiHowmHb+vbjRctz4EZNshYi4hVoPBsVaWh6EoBGPj1cB7gc7P6/oS9w/UkclYWJjPolEHxiea0BKcpih/4rh6gE+kWSiKbu+PqoPoGRAjEzienpmoq9gruv23oodMmGky3HZ06KiUTNtF9FxEjVk2SnQMLMPA9MJBrG+MYPfkNMZGVniFAl8H+tZhGDoWl8cxNTYDVSWh8XLNll3Q93ICi4qoccmmSZLuS2Qp0cXgVMJ26FUIySBaO4He+7n76DLjFPv88rveGdVDbLbaGdmTgZzZozwOHtyPqakJvPnm+7AsItXu2to6XvzBK1BUBV/96tMYHOz3xmaDPhdiH05Zs+2wyTPbfnT9qP54AatC/rUcFdUiLacukVDq/Y8otRGAsJJ8i6JQyOH/+v/+f3D77Ufw3T/6M4cEy12Ln3zyGer1Bh58MJgQXXQtKNRihCjvcxQJjkN04DDfTF3WBzj+nmzvno0fp+j5ePOVYNKh2VXvRXvrQGaymDjFEm0GGdVfnBksO0mPylPqEuNQnlHKxDpuvDIfUBNyz0eW7wsMq+V8fJ/hzc086vUUJia2oCiMLzG9T9R4szkLe/ZVYVnAzI0sGo2YVVW2LUYxTqL6unXpOh6RVnRAy9pEWM+A6GmQdM7+6GkQzfld1WNNjImbj7gTuMf7VkOH+9VspnDhyh3YKvfjyIHzYRIs8qtXgJGhZaiKhbVS2P0t4DbAu7di7+Woe5eTQYM2gxaQ4MDiiXCscsez6/66cUSXlect66aQ4977ufvYhllXUgJ88yd+cYGvREgy4e4GCoU87r3vTrzzzkdoNOITztNjazZbeP21d3Dt2g185dkncODAfqdU0OdUVVICH1V+234gqSBoEsTze2V9Sn2Erx8xoaLKCNqn/XotYnrjDRNqZp+oY+eq27xgVrz69Hd6H0R1kiOKSAa/HziwF/+/7/x9mEYLv/3bf4jp6TluOREIIXj33Y8wOTmBffv2CBZegtdIJws+7ViMhH3ZxcekvYWpqP7E5Vi0/5zY2ZetpVgdf3r4MYBgEs36GidSbii/YXoiHiTDbOqkGDKcBKzvMQuRn6tloNnUsL5WxPjEJlQ1SNh4RJAmw7oO7NrdRLHPwMyNLLY2fZVXFOAq2H44uBYPUWoxXUdRdVu11nM2GU7nQTL9sFJ5WKk8SKoAki7YEaO1rK8GM8ckETr116XbkSDnHqLGKUtyWR9oGV9oABtbAzh35U5ouoHjBz9FIbvpE18BAaahKMDY8CIqtSIqtYLXZ1SU547vk3YC5oFPggPjodqVfWZ0Sob9SNSfn3dW7/3cfdxiMe1vHrplxsyfXLfv00njwQfvwfVrM1haclcMxWbdon05c+YC1tc38eUv34+JiXF8+OEpmKYFBZo3ftd81/4h3AaB6fjYuj60QfU0GPHYVQipBxblT+ySYN/fVqA0Muqr73PKwlWItUB5+3/HrEQi4nNA1Y4gebz68teRbSZLm87KRUkP9iPyc33uq0/ikYcfwNvvfIgXf/AKd/y8cbNl6vUGPvzwJB544B4sLCwFTOuDCJrWy5iUtwuRSbu7Len9JvY9jq7DXxQQH4N4n+TO/ah76IELx7SVl96IZ2ooPcmUMLvmplSSICq0f6+Xs1bPQDHAKMqMmTSnHRdRaZfo7XbfYAyQqLqeP3ELRElhdWUQxb4yspka3KkV6yPrkmH2N9sUGRgZNZDJWlhcSKNaVTEyZiSKx0ObPPP8hkUkmjaRdgNmqWoWRDVAUn1OxO0yFD3jHyOFIpwck+iuQGR6LBNpWgY7ZVbNngcLmFvch5X1cYyPzGNybLbtuEu6bmBkYAGra2PITW5CA7UIEyCXYZPpuHshBIFfcCRxpf2CRYiINt2RCbREiiUuEp6MXg7hzzduvhzbxoS1ExVWZG7Li44cp/qITXdZdD6ZnZoax+DgAE6dOhsaB/s9KsqvoqhYWFjCCy+8imw2g6997WkMDw+HlDLvA/4EhyXBvL5oUhpW4HyT5kC7bfqLum2K6nrKNSFIQoLptoKm5O2MTwy+qsn7+OXZ+i4GB/vxf//f/zvuvfcO/Mmf/rlHgnn1eL/zyszMzKO0sYnbbz/KqR19vcWZUYu+twuesh92iRC7ZiR5trTzHIoeG42bQ4J7USm/+BDl3KQ/UXWTKr1Jo7Sy0aQDUDWEIkkDAWVYlDpJOmK0KBCQgCBUq3YqpKGhLb9PymQ6pAYzUaYBn6QWixb27W+g1VIxfT2HRj35c5E2eXbbdf/nbWPNq1XFVoRVLQtFLwKpPiA9CJKxP0gVnU8foOW2jwTT/3cKVmlNqvo7aEtRpdqrN9I4f+UOlDaHcGjfeeyaEJBgGZXZ+a2Q34Kut7C5OZBoWNL7EmFCLW5cD5Ngbn5xyXYTWpYkIsE3Ifpzu+i9n7uPbVeEwwoIq3h0C2IVRaxOiccSp7ZFtb0dkWjvvPN2nDt3MUKNE0+aeWOp1xt47bX3cOzYITz99MM4e/YyLpy/FqjjK7waZU7M7zeKELABoURRMMNRhsUQqZfChQ6YIEQFnIdA5HipYE/8gF/u/3L7HE3u44lpVFtsnXvuvRPf/OZzuHFjBt/97n9DvV7nlpNtk8bJT07j2WefwPnzlzim+d1bmEpal6vKS5nXJ4GMyixaEOLjVvUrF6e/kq/fw62BOELbbT87t71ECgkV2RWAT4ABb8rmKr1E1T2VOBQ8i/5b1WJVr/DY+UoyUYGoSNKEAKVSHwYHtoIBixgVkFWD2SBSChXVWdOBqd0trK+lMDNtR5UeGGwhLihjsL9wIC0eXOWYDaClKDYRdo8NUfQQoe84CJYseO13W82V3Ie20m85WFkbwuziQRTzm9h/21noupWc6HPKq8TASP88Flb2ob+4FlSFEU14eZYQAYjuZV6btKWDF/1dQILjnhGc/MFSiPJfjkIcGRaYT++0Gtx7P3cf3XLKcP4XT+r5ZLh980V+/2KIJ6RJL4r4yS5P5elkwj82NoJCMY/Ll69Jjym6T//38+cvY3lpHQ8/cj8mJkbxwfunUKs1ggphwAc43hSYVYNlTX9FZCXKzJZH6uL6kgqixFV95UxWeeRclvS0a2qt6zq+/e3ncfz2w9JRoZOiVNrE0vIqDh26DZ99ds77vduLPlHgLRAkI/ViU2Y+4hfu2l348qJsS9S9VUlzD7cmuASX81ssGWZIalLfPanyPPXViQJNR4RmOWCADLv16P9FxyCJAuUQ6hAZplCrZ2GaKorFCgA9SHAtw4sQ7Y2bNZd2yrC/KwowPNJCLtfE4kIW1UoW45MN6LpY0WGDXokWnVliTJtRs3XcMSl6katkA74HlYgQKxwS3TWweYx5qbW6lB5JJhWXWw7wyaVhqLgxfxhb5UHsmriKsZE1R82Nru8NL6JPt2w2XUdar2Jrqx+DfasyuxPZJ4BIk2W/Ht+6JJEKLLmoEZu67SaYKHczgnUPNwcJiTBrTshOzpKQ2+2a2LVPOHgEqpMJLq+/JP6HLm67bS+uX5uBYZhU/WjCJDu5VhQVa+trePEHr+O++07gqz/xOD755AyuXZ22y3BMoGXVX97Y4gJridqII8N2GcD1uWVnTX7/jiLMUXzZ8x8eR+fHXFYhFPko89o5eOg2fPvbX0Oz0cJ/+r0/xeLicuw42LZkidblS1dx//13B4hw5xYQyRTyqH49chmp6ochdwzs51v793d4P/k+xkEEzda336zJAqB0YD7Vo+w3F4EJopQZcgQZ7nCCJ+9nHA4+5cIjWdz2HTJsmQjkg40iw0lAq9MuGWZQLhdQLFZtQYlOpwS7fBQZptMuuWSYRTYH7NlXwcpSFjeu5zA2ZgfV0pg2eeQWCAfIEqnDdHn3b0XRudM6RfPHSixH/ab2ySsXIPZdVI95puo0AU6iFifMuRtHhllf3I2tIdxYOAJNM3H0wKfIZevSBJj9ne6Xp9z2F9awvjmOgeKqZz0Qq/DG3fsJ/XZFkaG5/Sdou9tkUzaf+q2G3vu5++hAEY5SZ+UmrUkRMNfltsX3c0xKPruhcIkm1knMfgFAVRXs2bsLr77yVhfNLN2y/hhbRgPvvf8xds3O4YEH78bu3WP44INPUau1vDpxffCUU5b48nyx+W2It0Wrw3b/4TK+sifyAZYZA9ufTB3W/1aKMEeo4y50Xcc3vvks7rnnBN5792O8+OJrsCz+NSx/7qKxsLAMPaVhaGgQ6+ulUPvJ7514hVbu2qbPn1sR1G+dLVIE+xFbHYiviaRK9M2DpVhQOogsad2i+/VjhwQkME4ZjpqcEldlFQTiCvxGDCiWMwkV5R91wOYvFpk6h5Th0Bgp4hJHAjh+ySSgToN6feqwCFCt5rFr9wrVRmdkmAdNAyam6ihvGVhZzqNc1jA23oAuMYNLkk6JLe+Zc6vO+Cii643d2eZC4SwWJIKaAhCRFSPGX1tYPgahnLuR90O8MmwYKqYXD6O0NYrxoVlMinyBef0nLeNct/lMGSvmLrSMDDJaRViO+xsvr3BcMCzGBYAlwUJza6rtqPZl078lJbG8Z11kOzFRpXdSFe69n7uPNp9YYpJhY/smeWIFhU+CO28/CaJNxGXAjn1waBCEEKyvb0rXkQd9zOz/Z2fnsby8gvvuuxNf+9oT+Pjj07h+faY9EiwROKxbijvdp8iPKmTC3abf6M0yU6WP8cGD+/BT3/4aDMPEH/z+/4PZ2VnJup3BsiwsLixjYmIsQIRdJF/wijc9DrTv+JFHI0hWuw//+SavJEf9bkWWS6LYdwMWLCgdPMN6L9qbh058frdjIqd4xDJIkEN9CfKO0mbSAChT5aDZJe1LbNcz/f8pEu11lyBoFr2u5pFhZ+rUqOtQNQspreY8ylKBcceRYRasybELt16xz0A2t4WV5QxuXMtifKKFYl93zptIqRWNKTxIPdbEONRHgHzGOEDLjINnFh05IJfoueQ67KsuIsRRZNhWgQ9D0wwc3X8ShZxNSglnyt3NdEaqSpDNVFGvZ5EpVLhlZNqxvzMkOO7cCkgwt48ogi2xcNP1HMJUu4kjSzsk2R7TNvvKo/d+3g60QYSTHUTxJE7O31IOMiQ42F8nk8tOTKeTqrojI0NYXVlP1IcM+H7bNprNBt55533s3j2JBx+8F7t3T+Kjjz5FrWYHXpIjIyz4ZtVJ/CPDQbcYNY4KtsWWt3+j6rcZ+Vme9IjNyEVkUcY0P53O4Otffxr33HsH3n//JH7wwquOCpxs8amTa3htrYSRkaFEdaKxHQ9mkVl7O33xyLp/njsnqrxz13tZ9ZAMSU2iuwWeKhxZTkahZJVjWtFlyTBATb61AHnlRrtNkhaK+t8N3qUAIIq9D41GBtlM3V98tRwLKjUVHLejnInIMI+E8vyIAUDXCSan6ihv6VhazGJrS8XoWD3SdziqbbZ/EfElHKU4qnwkoupYLc7ju02Cwdaj0xnJKuUR6jB7HZpNCzNLR7C+OYbxoTlMjV0LqMBdyXUdg6xeRr2Zw0ABAgWYMwb6muQptd0gwcLKskHK+BYncej6Ah8r66sqQ4Z7+LwhIRFOpt7Q2D51pjsmn0nMldnfRX677AS5HdLc39eHra0ys6V9xZ01043C7OwClpdfwn333YmvP/8VnD51DpcuXXWXxBMg3rc4bqzu9ygyHJeKKVju1iUaouvkjjuO4fnnv4JGo4nv/tF/w/T0fFf6S6ribmxu4cCBfR2Z/ycZGw3xAky0qsreM/wxRll0iJ59fFPp5Kq4qF947e2UKtxbce5BqCB1ONGLmshGmlWLyLAzpoDvJBzCKvIfTQKmDUU17TzGqIMgi1ZTRSrVCJmI2mTOVoe9IFgxZJhFKMo0Q2AKBQPZvQ2srORw41oOI6Mt9A/wI0u7dd1+WfNmthxdhjcmXnlpNXi7ySAd3TswAPmxhtsUq8PudbZaGsPs0gHoegtH932KQp6dt3UJMcQupTdQbfRx6kUtPiTz1fXuU06OYFkS3A5BFT07REqu1D7IgiHAgYCAFBnebvTez93HtqdPAqLMmW8u+JPV7ppai/uRK5fNZrG8TEcApE2Q+eaZcQGr4kD70DabTbz77keYmBjD/Q/cjdtu24sPPjjJNYu1wScMnZJgEWSiUUfVaxdJAjF1ioGBfvzUT30N+/btxuuvv4fXX38PlhVNNqPIaDvkld5erzWQzWYAiI9/N8lwFMLXfTQh5i0EuVHOfYgWSuIXApPvs/x16+3fNsfL6qVn+PGCdIRnmYljjDkprx9Cq7mBDQy55PQTUGlVN29p8PdI4kBviyDo3pAAKAZA9AygGjANIJNuiMdLm0hzyDAbKZoHlhDT0HSCickqqhUdK8s5bG5oGBuvIptTuUqtiBDT20Q5j3ntyEAYHMtqMd8VoFuLfVEkmAM3LVc7qDezuDF/GNVaHyZGb2BiONoX2B9jG4tNEuRR11owzJScOTQDWZ9cu3A4RzBLguN8g33T9C6ptgmiwovIMJdUC0hwoPwO5SLuvZ+7j20iwkkO9PadFH7amygkI3AyaVs6JQOapsIK3JR8M9hOTbS5UbSp47G0tIofvPAqbj9xBF959glcvXIdn356BoZhePWDyniw/STqYTsLD7KKmQKFIUzifiMDd1FRiYOQGAOnXdFCxlNPPYLHn3gI0zfm8O///R+gVNqEF/QrJop2t8mo255pmlA1RrnpkquBCDKkXjqQXkxE7uSIszDobBFoJxYUevgCQNU6Umwj/eOSthsTbGhb4ZLhAMnVuRP1ANwxWyYVjdqZtLsTdtNw1GYNMABiEagwfBXSJcOurzAFlgyzv0elHgrUR5hc5vIGdu+to1QqYnamiIGBJoaGDaiqgIgKHkki82fvNx6xiSCZkT7BsuhUQU6i/iYgUpYFLKzsw+LabhTzGzhx8EOk0xFBvgKVYxZn2kgN5JJO+1pizAKkAnFFtM9ZhIkiwZGpmNy+uk2COYjz+W1HGY5ON9d7V38e0WUiLDPh66ZvcBjxk0YZH2IbbDqbKL/SeCIg8lmMJiwWIZxt7R23uIjMHpEQpDOyLAufnb6IG9dn8cCDd+Mb33gOn376Ga5dm+bshzwZptGNQGfdMiGNSs8DBI+TdDToGBLs4siRg/j6888gk0nje997AadPnaO28lRPGdP9aIjKhomZAnDS+LDnt1tEnL+glfwcd0LUxde1zPOkPd/t+Ha3B72olD++8NUNihC0m4IpKRkW+G2G2ubksA1sp8mcQ2jZ4Fre+OBP2gNlBGaWnp+i0YCiA4rVAiGmPyZugCB/myjHcFRKoSgCTP+mqjqGh8soFjQsL+exdb2AkZEa8gUjYC7NRn5m2+kG+OSbNjNn1GBiAESBbfFicN8vOwYRGaaundLWEGYXD8IiKm6buoChgYi8vd0gegESGXOeCKDQ75EkJJg+b6wfOXsvC/IEh6Jv02Dv3XaCeHGwHamVbtV0Sr33c/fRBSIc5VcnKruTSDI+F4yZp1B95betqqqXxqY9+Mep2Wgik0130JZcPzw/R4BPQra2qnjl5bewd+8u3H337Th8+AA+/vgUVlfXI32mg+2F0a0UP9vtT9lu+zLjHxkZwjd/8qvYv38vPvjgE7z0ozfQbLYEpdtfEJHzeeefk2w2g3rDMQdk7oHAQoHQ9SDYpq6nYBj+PvLNznnuFd0IVBUNmcjnYXy+V4UJrI5elj3Tqy8IOghKw6Y+CoH3exwRi/RxpNIbwZmMu+qunnHGZE93LEuBohAn7y87UTc9ZZ0AUFROsC03kJdlQlOasJoOebMQ7YfsEW7ERpLmmTBHBbiiv6czwK7dWyiX01hZySBVSmF4tIJs1gi1JYwULUuKJcyoAUSfu6i+WD/vBGNhERc5PGAezS6YON+r9RxmFg+hUuvD2NA8JsduQFctv1wb4KYF4y7ESJwTy4RpqlBV+UUNYUAsx3LBIgoUzbmxOAHqAH/sXBIcF3wratEh6THllG+X1N6qZLj3fu4+OiDCSSd7nZRPeuKiFdTwRF2sgrGTcn6d4KT83nvvQL6Qx3vvfoxWS/RACvr4isZerlTQ398nGHc3EfY5jutnenoOs7PzOHbsEJ56+lHMzS7g00/PoFqtdXWM3UyxRNp2sAwvGsj4ZcuqxOm0jq/+xNO4/767cPnKdfzWb/0h1tdKgXIyJrPdIIVRbRBioVDIo1KpMVtE1zML37RfURTccccx7No1gRdeeNluXyL4nay7QhziA2/Fo5NgeDxE71+cCXZ3QGCCdEDm2dzhPdzaaCtwTDfapKJMx5WNLOOSYCCcT5g22XTI39rGGEwTGBuchYag0ubmDA7W15wI1syYjAZSqMBo5aAYDRAdjok0EEilxAGbVglAKB8vEFaNeQRW5PdbLBrI56solXKYny2iUGhgeNSOLh3lm9xVEsw1kaUWdqUjN3eXBNO/Rea6BQBVg2HomF3aj7XNCfQX1nHi4IfI0GbQbfnjJiGrMcSRQstII6U5x1giVy8A7vEjRMFaeRfqrSKmRi5D0fTQ4lViAkz1FTkeZlunhFREatt57sWmgttm9N7P3UebRDj6JHR7cpgM8mPjgyEb0ml2XKIMfPbZBTz44L342teexjvvfoSV5TXwTIXF/fvlSuub2L9/z7aYnEYhvn37bW+aJs6cuYCrV2/g7rtP4Pnnn8WFC5dx7txFGEZnk/ak6nF7x6QdcuEvfHQKVVXx6KMP4tHHvoRKpYI/+dM/x+VL16Ta7uQ66MSMfHhkiCLpstc1DQv5fA4PP3w/UqkU3n77Q39cQt9rG6Lzzk8Hxler+fd0vM940u2iOnHnLF5V76GH7mJbJ3Os+kv3FUeGKX9abpAtKhq0F0XaBd2eS/pUHUMDq1hZm8Ds8iGMD84gmyp79bm5YfUMiGUClub7NjqT+YxWxnqt3yYClgaCOoCs+LVCpVWiyTAAcdoigQl1FGn2ulOBocEtFAsVrK8VMX19AANDDQwO1qHxDneUibZEWiUP3YgM7eX2lVeT2wl4FVXHsoDF1b1YWtuFVKqJQ3s/Q39ho+324upF+tlGmRlT9RqtHNL6VjwJFrghAEDLTGNx6zCIomO8/xrYSOTCcTIkmEeAA7+3Y/bcpg+1VFBAxiw+aTTqHj6f2JGo0cnBN4Ok0elEMWkgLb5qyyMBKppNE2+99T4OHz6AJ598BOfOXsTZsxfB5rKlIZogr66uoVgsIJ/PoVqtxZaPggxZSNq+O2mv1ep4992PcPHiFdx99wl88ye/irNnL+LSxauwLDkVtlM1LC69Er9Nv+1kZra88STz5X7ggXvx1NNfhgIFL7/0Jt5//xNv3LJIvs9ByCqsdJnxsRGcPPkZVSoZIdy1exIPfeleTM/M45OPT8M0zUgfdb8P8XGJC5AVXS6IdoNVyZLcMML34M0kwLbZVc8HqQcGcRNIBgFyKxEcyJ1ccqNHBxrm+eH6UaTtNkTRpg1oGjA+Oo/N8iDm1/ZjqLiEgfyyV5dLZNzI1gAUC455tImsvoFW6zYYDRM6GoCOIBlmx+umVeLkGObuL8ef2P3Og6hcKmVhfGIT9bqO9fV+TG/kMDhURf+A4cX3iUyNFNggCKglZboroQZbBsCz3Iog6d3Oz2tZwEppEourewEQ7Bq/jpGBBX4spCT+uzGICzLl/8YPQkYIUG/kMNq/QrXJJ6Phvu1y5cYQlrf2o5grYbhvAapCQJAgYjxLgkOLFWJiyUufFmqf/vtWydu7Q6mTgN77eTvQJhGWDRiz/UhKCNs1s7XB2+dg4CKfOACXLl3HysoqHn74fkxNjeP99z/B5mYZLAFz4e+L/1uzaWF5eQ2790zh4oUricfOgk+Gg9uTgiYxa2slvPLKW5iYGMPdd5/A0aOH8Nnpc7h69QZIKPCXWI0LQ+DTGpPGSJ4YtqsMy7VJj+HEHcfx3HNPIJ/P460338dbb38AU0I9TxJ5Ox5hU3hef+zfA4N9yOYyWFhYStxvOp3CvffeiV27J/HhBycxPT2XcMw7YBYcSYKTXK/tIO4a3JnnrAXSUV/Wdud36uGLB0rlEaktYtNoKlAVrRbRfzPkUlGAgb4SsukallfHUK0XMTYwi7Ra5ZhGO4ozPVYHKaWBrLaJWr2IPq1kl3HJsKXbY1LBJ++gzJ1FBIUiybIpi6LKZbMGpnZtolZNYW0tj42NHIaGqij2NQKqn0x06EgSzAbDimkLlgFu+iQmajcP3STBlgWUtkYxv7wfhqlhYnga40MzPgFO+ljsJAp75MITJ08ygEYrD4uoyOubUFgBgqfIUjAtHSvlfag2+zE2MI1CpiS8boXHPIYEB8vGRcEWkOAEbcQhHIivDR/jHSTBQO/9vB3oQBGWOxFBcteuytGpT6DQTolbOi5YFI/c2C+RsDJXKm3iBz94FXfeeRzPPfckzpy5gPPnL4MIoiLyyMX1a9M4duxQgAh3ohiJ1PTkuWDFfqFLS6t48cVXsWfPLtx19+04fvwIzp67gOvXphEK7S8gZTJRjGXS3wTVRrfvTomzuC92jC5O3HEMTz35CEZGhvDee5/g1VffYQJhxZNxmfGxYxCfx3gyzOLgwdswMzMP0zQDfcRdj1NTE3jwwXtQKm3ghe+/jFqtHjtumXaTQ6wcy0d951sQREVMj0aS622nfIRD3paJ6vfwOYKMutJ2ICA5c8XYPmnzaYcchlQk1kya479IAimOdGRyJnZNzmK9NIjZpQMYLi6gP7cUIIVuW34EattnGM51XsysYqsyir7Mol0eAFTT8Rl2x+82podUYTaCcyiis0smYvINS4MYyOUM7NpVQ6WSwfpaAetrGQwOVVAsBgkx33dUQIDjyK+ozRhS1Q3z5zi4BHhhdS+arQzGBmcxMUIFwpJArCkzb1sbJr4eGDKsWCbK1UEU0+tQFGp+KaHIlhuDWCnvR1YvY+/waWi6Xz9EFDsZswAu2eQ+L7zo7pxgfF2ATIwCaZ/gHSDFvfdz99GlJ2twAi83qY5TPqKX4MQTZNlJqJwpJV2WJWlxaqQL0zTxyclTmJ6ZxZe//AB2757Ehx+eQqnk+5pEEcDp6Tncc88dmJqaxMLCErcP2cBNvLyrcki+kKEoKmZm7IBa+/bvwYnbj+COE8dw7txlXL16w4msHd1mEtUxqpyiqMyiBi86cpSJdPxYRSbKd911DE8++QgGBvvx8Uen8N3v/lfUak1Be+2RYVGqJ3dbtFl8uF3escxk0jhwYB9e+tEboW2ietlsxr52d03gk49P4erVG9wxxv0m6k8G22lmHB8YrBMkM7XvoYe24PrFSZobbkdwLQD+JFs0GeaQYXc8AOUzDCAUddZTmyk/TIeIqirByPA6CtkSVtYmUakVMdI/h0yqTvXL+GNafiqlvvQS1sq70GjkkVWbXmolGGDIcPR0SxQFOkSIZRFDnBUFKBYbKBQaKG9lUVovoLRewOBgFcViOagQJ02HxCLJ2GnT6ICKuT0EeG1zHIure9Ay0hgdXMDk0HXoepuBrGjI5CSO3W7EXjf2GEwYZgpb9UHsGTorPN6sympYKaxWbBV4tG8axcwaPOdx0fXDPieiAltxrB24KdpE4JDgTiBqJ8kz7VaNJt1D++iij3CQuLZLJoJl20X7ptttBcCBBRD7xUKIeHK8urqGF154CXecOI6vPPs4rl69gdOnznIjS9PEwjRNnDt3CXfddVxIhNm6smQ4Gdo3m75+bRo3rs9gz95duOPEMZw4cRTnzl3C1avTMAwTUQSQJVndis7LGydLhqNzyIbHSZPPe++7E0888SUUC3l8+OGneOXVN1Gv16FADZFmej+TjF9GkeXvi7s//LJhqDh+/DCWl1cDCzgs3D4URcHhw7fhjjuPY3FxGS98/2VUq1Vh+SRIev36fSRfyGkf20Fa2zHfT46eD9IXF1LKhkj1odAtMhxnmhjZD2vu7JZ1zTNB+Qqz++QSaYrEZHMt7J64hvWNYcytHkJ/fg1DxUWoCC8Q0KqwqlgYyC1gtbIbuzIX7X6dSNZhMoywKswD7efLyfkrBZGSzCqEAPr6yigWy6iUsyiVClhfz2JwoIJCcYvvF0uDlxNYFuz+C57tsqmDAoi4Pi0LWFkfw/LaXhiWjtHBeUwM3YCuM64xnZJvGbLHHSBHdY8hxKXKKPLpDWTUSuzjmxAFG7UJrFd3I5/ewN6h09DVFqDYacM8c3xe/m76nhKNmYbEQk5HxLKdVEu0cutc4G2TYVX124u9WTpH7/3cfWxDsKw4c8udOgkyivT2+DkLJ+rEgmkCp06dxbVr07j/gbvx/Deew6cnP8O1a9ORJPXixSs4cuQADhzYi6tXp6XGEEWGZcfczQjVhBBM35jFzPQ8du+ewokTR3DHHUdx+co0Ll28zDWXlRuLWDmLNSOGGwTFlFwkoGc0rN0bkE7rePjh+/GlL90DTdfxwQef4PXX3kaj0QSBxVgPxBNrb5wJzmUUZE2Z2WPd31/E4cMH8eKLr3r1RcdqdHQY999/FzRNwztvf+j5E3cL7QQE6y7i3CzaWEzjXtvb94yKHAusyMjdMvV7uPXATvIilY0I5YT+e1uU4Rhw+2XNpd2ycMhwYAHACSxEmys7/ys6MDK0ir78BlbXRjC9fBjDxTkU82VfxaIm3kTVoAAYzC1gszGJSmMIxZwfsTeWDPNAE2QOIfb2QxFvC2yPigRNK66KjmJfHYViHZVKBhulItbW8+jvq6Kvv2KTxHbTGEkRWZYUt0F+Y7YZho6l9d1Y2ZgCCDA6OI/x4WnoWocEuJsKYZSpOIcMK5aJRiuHrfoI9gycDo+JuVdqjTxWyvsBAJN9F5FLb7U/Po55thBRvvDcfiRiBUQ9fxiC7EeqjnC17IQM241L1e0Evfdz97FNUaPFvqO3FpIrSy4URQVI8KKyJ7OA0KybOhblchWvvfoO9uzZjXvvvQNHjx7Ep5+exfz8QqhPRVFhWRY+/PBTfPnh+7GwsBwijd3ybWX3kx5Dp23T7czOzmN2dh5jYyM4euwQvvnN53DjxiwuXLiCUmkjATGXI5K874ExQqN8J6JUYRdW4O/BwX48+eQjuPOu4yhvVfHGG+/h/Q8+hmlYMdGQ+ePajnPAmsXLkmFFUfDgg3fh8uXr2NysCNvs7+/DXXfdjsnJcZw9dxHnz112zN+jx3VzIeuPLOs/nBy0S0T8OHroQRJJIqu2q1x1AVGTT9HvQjIMSPvSembSlM8woIMoBtKZOqZGb6BSLWJtcwqbVQPD/UvI5fz3Lp1fWIWJ0eINLJdvQzZ9AZpKAv0IyTBv7K5aDPBJESeVEg9JlWQ3cJdrMp3PV1Cvp7GxUcTGjVEUijUM9G8hnY6PJu2hXUU1ziQ6wbWqWAbqzRwW1g5gvTyGlN7A5Mh1jA7Me/ylbeW303smMeEO+/sSomB5czcGsgtI6w2OUmt/bxg5rFX3oN7qw1BuDgO5RajEACz+fRYZwdndLkuCI5CknhRJ7WBx7mYs7PVw83GLpk+SwU4qJawyQ/mYcmNeySqSKmZm5jE3t4jDh/fh4Yfvx/p6CZ9+egZrTp5WelI8P7+I2dl5PPTl+/Haq2+HAm51Soaj2hG1K6MuR5k0Ly+vYnl5FcViAUePHsRXnn0c62slXLp0FTMzcyEixSPBIl9fILz6RZyIey7JIzAZEpwMR48dxJe/fD8O3LYPM7Pz+PM//xucP3cpNGb5AFDi8ydPhrvnW0qIhTvvPIF0Oo1Tp86E2lIUFblcFnfccQz79+/Blas38D/+x49Qrze8+iIkI3wyrhedtC+H7pDVJDEVdn711oIJdBCMw+oF47gpIKoWqfJuq4IbGeE2vs92xhaVbkmxnFdzwJxZD+U8DZhPe+PVQYgORTNRLJSRz25iszKKxdJ+ZKp1DPcvIqNVAlWIqqGQ20S5VcHy1j5MDF237yDax9jSbBU6bEgUGHtALXYJMY0k5IkTjMvrKiYQlqIAuVwTucwSmk0dm5tFzM2NI5Ntor+vgny+BoW0/HFHjVGQ8za43XnmENP7s10SrFgGLAvYqIxieWM3ytUhFHMlHJw8jb78ancEPBn/Xha0f7ok4ojiWnkcgIKhfDALA50TeL22B+XmCPozSxgfuARdNQRz12B97vOEsb7gostqvjhivMS7RnKRr51n483wF+69n7uPm0SEd86MgEUcIYkOwgX4Yw8HXIrsi4koHWjZsnDhwhVcvXoDx44dwjPPPI7FpWWcPXMBa2tBf8yPPzqNZ599HHffcwInP/kMLLaTDPPAkrN2+y6XK/joo89w+vRFHDiwG3fddTvuu/8uXLs6jStXrmNrS858J2rcIVJMkeGo9lgUCnl8+cv3455770Aul8XZsxfxe7/3J5ifX0zUjg16kaUb90O8r7ALGWK9a9ckjhw9gB/98A0vUrTbTz6fw/Hjh3HgwF7MzS3ihRdeQbnsTxC7R4K70UZniwPdU+iDzw8Wt4Ia3DO9+oJBwue3XXRErmUV6078lS0zEEk61HeoL8fUU9EB1QABoMLAYHEZffk1lKoTmFs5gFx6C4P5OWRTNcdX2MbIwBzm1o5ivTyJ4b4FL7AWACiGk2c4ytVfpcxHo8ynvfIcohzYf9+c1kvVlATOMUunDYyOljA0tImtrTzW1vuxutKHYrGMvr4yUpoRKM9rI/SdRwrpOVObKm2rqWJ5/SBWtyZhmRoGCks4vucd5LOV7t0H3IBubZh/x4BLQimU60PYqo5g9+AZqAoJmAG3zAzWq5MoN0dRSK1hb9/HSKXa7J8ltkkDt/HaZMFEiW9bbZd93lH+vO0+w25G0Kze+7n7SPhUlJnMJ2mj+4Q4iWLT3mSW3lexKs0jAcGoxWybQKtl4NSps7h48QqOHTuCp59+DKtrJZw9ewFLiytOmSbefPN9PPfcEyhvlXH58vWE45dHHBnejkl7s9nE+fNXcf78VYxPjOLQoX34+tefwdLyCq5euYHZ2XlYbH48BH19bai2mTplvs7zz01C3I8dP4wHHrgLhw7dhrW1Et5++yN89OGnaDYbXhmRGh4N/rXSPb/aZEGW3L6Hhwfx8CMP4L13P8bGxoa3L319RRy//Sj27d2F2dkF/PCHr2Njw12oiO+rveumk2PRXRJM/27vC99iRG5ct545tEU6XHEmvRXnm4lQVNZOc5by2qa+dwshQttu2wzZUywjqA5HKlH+BN9LlwT7btBUYKR/EQO5JWxUxrCwfgiZVBWDhUXk0vYCoAYLE4NXMbd2BCm9gb7curMvfoAhW4VGjLdFjD+lu49RyjG7T5Q6LNpnfn2fjGsKMNjfwEDfOmr1LLa2cpidnUQ2W0NffgO5nGkTMpm2Qz7BtP95MqLlqr8rpUmUKwPIZqqYHLqO4b6ZYAqk7VgU4ow1iiC51zivjDCYHScFUq1VxPLmPkz0XXbyX9vbmkYWpeokKs1hFPQV7Ok/hbRWc2qG4wTwBym/T9KphWjwokGzzytJMsx9HtHthIKoBd+1hO6PGZ9svzuJ3vu5++iiItzNiWq4PZmItzJqV3SEXn77UeQ6GG2YP66kPq2NRguffnoG585dxJEjB/HoIw+iUqni4sUruHFjFltbW3jjjffw5JMPwzBMXL8+E9GfuB+Z8jdzgr60uIKlxRVksykcOLAPd9x5DA88eDdmpudw/foslpdXwR5PBRqg+CbSLhn2t9MPkCgbNXumsnv3BB544B4cP34Emq7h4oUr+KM//DNMTy+EygJxBDaZOf/OHvvg9Ts0NIgnnnwYp06dxcyMbXI1Pj6Co0cPYWJiDDduzOKFF15FuVxx/Ijp+yroY01j568nMQmWN1ffTiRbpOihh0Tg+PlFTcRlcEv70fGi3YIiw7wqliEkpjQZdsvqmoGR/nkM5uewUR3DYukAUloD/fkVFLIlpPUGJgavYbF0GxTFQjG74ZElmlgT+pGjUOooHYBIFCmYSh9l13OjUHMUZNbfWNY8NyIatAIgny0jny3DaBJsVfqwXhrGytooCrkKivkSMuk6lDbm64plSOsq5WofVktTKFVGQKBgoLCKo/tPopgpOfvAnNRuXLuBFEQJgocx6b54iLsn3e31VgELpYMYyV9HIbUGQoBqawCb9QnUWn3oSy1j78CnSGnuIr0kAUZ4QcoPMsUZWzumwR26UQTGxPmdyCx4OeCS4Db67eHzjVvYRzj8ZuosYFDYLy+8Xbb/oAIkr97xCb+obrNp4rPPLuL8+UvYv38Pjh07jLvvOYGrV27g0qWreOPN9/D4Yw9BURRcuyYXSTru904JQTupgHzwLQvq9QbOnr2IM2fOY3h4EPv378Ujjz4AyyS4fmMOszMLKK2X/TE4ZNgeR7SPst+vj9HRUdxz7wmcOHEEQ4MDuH59Gj/4wSs4dfosTIMuG68CygaloiFtbs8txyP3MufCPfYqhob68NRTj+D8+cu4dnUGR44cxOHDtyGdTuPy5Wv48MNPnWBtya6Vm696yvruy+Hm78/2oGd69cXGLTOZ2ylCTfnpBvyBmUjSJOp25qhTmmpiuLiAwfwStupDKFXGsba1C335VfTnVjE+cN0mw9Z1FPKbXDLsIhDBmiasUemVeGSYu/+Maixlwku1FRMJWteBoYESBvtLaNQ1lKv9WFyZgqpaKOa3kM+VkU41oYBpJxTVWg2YRnPVW8tEtZ7H+uY4SlujaLYy6MuXsGf8Mob6lsK+v90OAidBgoX3l2Q6K78hvlVAvVXAfOkwhvKz6EvZ1gkbjQkQono+wJruxkjxF7245FcmFzBne+h30XHmnL9YJF0oYFIh8fY18pl3Ky/scdB7P3cf20aExQQgiRl1t8CaJPMuBLlxyfjE8slKMhJMwzAILl++jsuXr2NsbBiHjxzAN775HFZW1nH16g088ODdyGYzOMcEaRKNKQrypuXR5qY0IU4WYChKpQXW1kpYWyvh5MnTmJgYx/7b9uKpp76MZsvA3MwSZmYXsLqy7plK07MNRdEBL59skLhOTIzhvvvuxJGjBzE8PIj5+SV8/NFn+OijT1Ct1iUXC8RKqAhxwdRsxJnm8tDew25iYgyPPvoA5uYW0D/Qh7/1U1/D5sYmzp69hBs3ZmBZ4bzKnq+1tBl990yV468p+fPQWYoq2efazXj+JYP9om1/8th70X4OsV1RozuZZAqIUKJ2PYWVQ4aZ9llz3FBwLReeCadNVFXVwoBDfmvNIrYqw5guH0MuVUZfZgVLm3sxbM1joLgWIMOgxqJ4fbqD0UH79wr3S4ZcsWbU3n4IzKlFJDgqrQ/sfchmDGQzyxgZXEa1XkClWsT80h5oaguFnE2KPaU4ZO7L8eF2/q/UiljbnMBmZRiNVhb59CZGB2Yx2j8PXXfTYcVMZ6WvmTaCYCWBu4AR52cbiJztHIfGABY3b0M+XUKjmcf1yv1IaxUMZ66jmF6DojnzLgSVTmkrDtZUmflNGKBOxu83af8MwuSc856xLI8MA9HBrLjHJGa8NyM4VmgMvfdz15E0cgLYSVz7yh9CbYnQTh/Rk1eROWKSfngT2qT1k2N5eQ3Ly2vIZjPYv38P9t+2F5l0Gs8+9wT27tuNV15+C61WTIANCUSTV7EpcVyb8ipadFuWRTA/v4iFhWWoqo7JyXHs3jOFxx9/EJZlYW7W3ra4uArDcF/UGhQoUBQVuq7j0OH9OHbsIA4e3I/+viJmZufx4QcncfLkOVQqVeE4xL6hyfbJS5vD9WGmsTPmsw88cCe+/PD92NqqYGJiDNevT+OHL76GzU0/UJko123wHk1ieRGF+BgC3cxz3Tlkz1Ey/+EeepCBYplQrGhb1Eiz6BgyzE2fEoVum6Gyv7PtiwJBMWRYqn030jNTziPHDBkGAJWYKKQ2UBjcgGHqKNeHsVUfhmnpmFk7hnJ9EVODl6HqQRNjgDK9dvrwo0fHkGEg3p+Y3UYTY5YMJyHBEYRQUYBCroJCrgLLUlCvp1Gp9WFxZTcU1UI+W0E+W0E2U4OqWo7puvMct0xYADYqI9jcGsFmbQgtI4VibhOjg3MYLs4irYf7jo0wHeUfLO3bKk4XlMhlgOPzG9mtBSyXb8PK1n7oWh31Vj/600vYkzuNtFoOlGXNfdt2hYh5FnTDRSKOWMYqwDw4ZFhYP1Q+2XFJpDb38LlAx4owawqbbFIaRYjjfG3desFJd5g0d3+SHCRC4W30ePhjsAJlCSyPBEWbu/qo1xs4f/4yzp+/isHBARw9ehsee+wh3HvvHXj3nQ9x+fJ1LCwswjSJ1247CwryRCP6WMi0144/KSEWLMvA3NwC5ueX8OEHn2J8bAxTU+O4867jePiRPFZW1rGwsIxWq4l9+3bjZ3/2pzG1axymaeLG9Rm8+cZ7OHXqHOp1N0dkcsuAWw1xKazs8SsYHR3C/v178dCX78PQ4ADee/8TnD59DosLy6H0XGK0o1hH15UxO6f7EaEzlTdqTIgcU+e4eSSYEAtWB8E4uhfkrYdugp7ACSegAjLMK9u14FYdgjchDwXQoQhlyEQaQRJFqEjP/P7EZNiFrtYxmJ/DQG4OTSOP9eo4ljYOYXVrD8YHrqE/t4ZstgLVGRdNiAPqsBJuOwQeWY5SR2mTapFvcRSxTqiIqkoL+VwL+VwFhAD1Whq1ehFr6yMwzBSy6SqymQoUWNiqFHHhxl2oNfugKiaKuQ1MDF/HcN8SUmqTGgMbAGmbFMpAXWdBPcI/NYAOIisDtltXvZHHZmMUy+UDaBo5TPRdxnB2BrnUhq2qRyzksPdFW0Gt2DFRzw2ZgGCJwFGeg9sTvFcoMsxDVH5ymXJsmZ0kw733c/fRNdPozkgBS4hlSbD7f/s+rjK5SONIZHSEaBZBc+hoMwUZs1mgVNrAe+99jA8++ASPPfYQTtxxFLv37EKz2cTKyjoWF5awsLCMUmkjNF6ZPMAygcqSQJZEsosrcflWbd6mYnFpCYtLSyheLmDf3j04fGQ/7rzzCIaHBzE5NY5avYHXX3sfp0+fxfr6BizLjNin4PVFL1q4/d7qpq6Af8zz+RwmJ8cxMTmGifFRZLNZ9A8UMTe7gD/+4/+GSrka3xin7bh+O0c3FNJ4hTm+bhi3RtCt7sF+JnXwou0p2bcc2jKNdNDtSV7SXMfSk1Oa4FEmw6EAOpSJNE9FDJFi7j4wZFiwX4oCZFJVTA5cw1jfdSxsHMF6eRLl+gA0lSCXKiObrSCX3kI6bQQDc4ExlebtrwtvCiURCIv1L5bxB24HnLZUYiCfNZDLVNFXKKFc68NWeQhrG6OoN7KoNYvQ+5YwMXwNQ8UVZDO2GXVs6ivubxF5fEXbk5QVKe0ciEyK2eulZWZQbfaj1iii1uqHYeloGVkUUyvYM/oZUlr0YoDMfS7y/+1I4W4TogW2EEQkmCW7dDnGd7idschiJ8lw7/3cfSQiwrZCJDuxlVM2Zba3E9iqnX6iwI5BflWFTyDpizFKDY5SmFk13rIIXnvtbVy+fA0PPngPtrbKWFxYxsjIEG6//QhMy8Ly8irWVtexsrLmkEA+id/JXMQ80ISTJlOiYFzpdBrDw0MYGhrA0NAgRkeHkEqnsbZWwpUr1/HeuyexuVXBT/+d5/Hh+6cxNDyAJ598GIoKrK6sYW19A+tra1hb20S1WgMLd19oEhw2kU6mjPLaDCK6XZn7sH+gDyMjQxgbHcXY2AjyhRxWV9axtLwMYlnYvXsKr7/2Ls6evSBUgGWDd0WVkbV0kIGsRYG4704IcTTaIf7tWdNsH0iH6RU6rd/DziA0eYv1jQxPLJOaSLpl48hw2+DlO3XJMBAgNyESzPGPdMvwCHGADAOwfX6d/aJJkTMmTSXYPXQBA7llLG/tQ1qvIqtvotHoR2lrHIoKZNNlZDMNZDJ1pDMtvu+waD8B55XBmdZxSWH4Jyn1me1TAlaLoNHKotEcQLOVRb2RhWWpyKZr6M+vYXxoBrpax9X5ExgbnEejlcPi+j4ACrJ6GZlUDRm9grReg66Z0dGoOebLhDpHLIQEMKosb98ljkfAUoEATTOLequIulFEvdVnK+SpCrJaCQBBtdGP8f6rGEjPOgowp1HWD1hAiBPd65BblEpSt9PxBCAitqrKJ82UQhx5bNy6EsSZhd1uFxeSRP303s9dxzYFy7q5vo7BMcj3F++r6SOK5PFNooPm0F5ZSRLsfo+PgAzMzs5jZWUVDzx4D44dO4RPPz2Dt9/+AMPDQxgdHcbo2AiOHz+CVErH2noJpfUtbGxsYn19A5ubWzCMVpfz2IbRrlKoqir6+4vo6yuif6APA/39GBoaQKGQR7lcwfp6Caura7hw8RLWVjcDwZ1Segb1WgMXL16DYRggxEL/QB4jI4MYGhrEiRNH0T/QB6NlYm19AxulTWxubmFzs4zNzQ00m82IkXWX1Ina9cgzU09VFfT19WFwsB8DA/0YHOrH8NAgVFXF2to6VlfX8fHHp7G8vIrBoX48+OA9sEwLL7/8JtbWSgESzC7SJDlX8ffQ53U1klX++fdzJ7jVze17+GIh8USUnmTGmB7eUmCjK/PAU8lCUaXjgjHpHhm26zhtMuS1mF1HNrWFlfJ+bNQnMFKYwXjfJTTMftRbRdTqfVjfGgGBjky6gVS6hXSmiXSqgVTahKrGuKywptIhwp/A7FnUjoD0WZYCo6miZWTQMtJotjJotLIwjBRSehOZdB3ZVBkD+WWk0/VAzmHLUqFrBgaKa1BV28Kr1dTRaObRaGWxXh5H08hCQwvpVBVpvY603kAq1UJKb0CD/34OKfOMqXy31Ls4FdUj4kRB08yiaeTRNHJoGHk0jAIAIKNtIauXMZJfQS61hXqriJXKbdDQxJ6BM8ioW+H2eH7AgsBYovRHMseg2yonX+2VfPZwwDX3FpHhUJvdDxB4S6eW60GIxEQ4eTTh7UTn/sA04WMn7/xIt/Gm1HFjYU0ToogzXZ6vRiLwm4tGo4m33/oQu3ZN4L777sKhQ/vx0UenApGli8UCRkaGMDg4iD17pnDHnceQzWRQrlRQLpdRKVdRLlewtVVGtVpHvV5Ho9FM4DsqVnBFZRWFIJPJIJ/PoVDII5/Po1CwP319RRQKBRiGga2tMjY2N7G6uo5Ll6+itL6BZpMfJMxNoWRH2iOBSNYbG1uOyfhVADbRHhwcxNDQAAYG+rH/tj3o7ysim82gXq9jY2MTlUoV1WrN+dRRrdZQq9Vhmu5LL25f2/M1TadTyGYzyBfyKBYKKBTzKBYLKDrHxrIslDY2sVHaxOzMAk59ehYbG5uwLPt85fM5PPDA3di9exKffXYeFy5c8c6laOHDVebjVG3vu+C6T07y4t0l4sbi/iaPuIW67TGD3+5Fp6SwPZB6plc9dBc8n8XtMicM+Qi78AJM0b/JqlxR5sbhCTD9lgyoxMTORTzRfxmVxiBWy3uwWR/DaPEGhvKzTl0dLTODutmHpllEtZxHqTUMEynougFdbyGlN6GnLOh6E7pmQNMMaKoBRZR2iSWvZkTEaYVvYk2IXc0wszBbKgwzBcPQYZgptIw0DDMFRbGQ1ptIaTbp7c+vIJ2qQ1MFwoHne8ss7hIDmZSBTKrunSPLNNFs5dAwcmgZWWzVhtDaysK0dGhaC2mtDl1tIKU1oCs16FoLutqEplse6ZbJ1RurqDIgBLCIDtPQ0bIyMMwMWs7HMDNomlmoioW0XkNaLaOQXsFI/jrSWtUx/TbRMjNY3tyPamsQw/lpDGQWHeU7aH4f6pvx/eeOWcKvt9uQylnO+V3WlznR84RVg0V1hUrzNkXVbwO993P3kYgIK1LZ0XfCZ1LsJ9qOLx8vHZI43Ys/WQ6RAMlJN58sJCUJcpPnublFLC6u4NixQ3j6mccwP7eAU6fOYmurjK2tLZTLFVy/PuP1n81m0D/Qh2Ihh2KxgOGRIezfvwf5Qh7pVAqEEDQaDdTrDdQbDRgtEy2jBcMwYRgGjJbhES9C6OMJaJoKVVWh6xo0TYeu60inU8hkMshk0khn0kinUrCIhXq9gWq1ikqlilq1gfn5JVy4cA2bm1tODttoBM+j6z8MENDKpxk6hpZlYW1tDWtrpcDv6XQK/f0F9PUVPJI+PjGGfD6HfD4HVVFhGAYajSYajQYajQaazZZ9TEwTlmnBNC2YpuktViqK4rzoFCiKAl3XoOsqUqmU/XcqhUw6hWw2i2w2A1VVYZimfVycRYqlxRVcKVewublFRbpGaOy3334Ehw4fwMz0HL7//ZdRrTZg+5n49la0qXaSh6XsYkd7iqfsogL/nkzebhzx5iv07t/REC+AxEdn37mXl70/vWAcn0e0RS6TpAzhTBS9CW9UVN5OxpLExDKmHOsrzM0LG6X8xgVpCrTvtxNcOraPp6IAxWwJ+fQmNipjmFs/ikJ6HUP5OaT1BtKagbRWARQdRNVsEoosWlYeTSsHw8qiUU+jYhbQMlKwLA1QFWiqaZNiDVAUC6pqQYUBRTWhwQBAnLubAApAFEdJJCosooAQFcT537JUmFYalqXBNAHL0qDAhKYaNiFXGtD1FvKZGlKFpq3Mqka06bJ3Pjimx8SOIK3QzzvqmKsKsYNrpYPvOtPS0DKyaBoZm4yaGdSMPAwrA8NMgUCBqgCaakBV7QUDVTGhKpZ9jJz/FYU4fSvOOVMAYh8x+/iosIgGYhFYRINlqjCtFExTc/qwoKsN6FoDKb2FfHoTulL1CDp9XBTLBAhgmjrWq3uw2ZhAMb2Cff0fQ9cF1xnHfD/0N90+VYerDAsCam2XYh7qPwbdHk9gDDHPFRFpZsez0ypw7/3cfWyTaXRSMiw74WvftDo+X2t7fnry6YWS1Pe3teOXypJ40yQ4c+YCrl69geO3H8ZPfO1pTN+YxZkzF1AuVwIRsOv1Bur1GpZ4e6WqyGYz3ieTyUDXdaRSNqlN6TryuRyg2GRfURRAIVCggIDAMAxYpgnDNNFsNlGvN2zi2Gyh2Wyh2ah7RNJVV3n7kwQ0ESfEAgixCXCABMdfU81mCysra1hZWQttUxTFJvLplEfmM5k0Mpk0NE1zSK2ObE6HrmmOOg37BeswdALYpNkwUK3WYBgGWi0DzVYL9VrdOS91GIb8CyGdTuPIkQM4cvQg1tbW8dKPXkeptBlZJ8pvOY7ItudH34m/bvQCVWd+yfLPmvbNmZM8z3bSraSHzzO4QZsYlSvpxFJ6QiqYYIomi9EqTswEM8lkVlQfEJsK0+3HKVoiQiwcl63aKpZddKhvEX3ZFaxXpzBTugN9mVUM5ueR0pogKqCYdiAtVTGha3XkUrrjC+pP4SyiwDR1mEh7/1uWCoukbOJmaTBNFYDimbbbrx8FUG1rLJcMqqoJVTGgqiY0ddP534SmNH2iK/TxNljm3z54CxUcaKoJLV1BNl1xCCYV8IwApqXDQsb+39JhEh2mpXuk37Q0EKTs786MxakNEAtQCFTFgqoY0NUmVM0m0Rqa0NQWdNShqS2oiuQ7wzJhWDo2quPYaEwip5Wwp3gSGa3qLCqB6+tL0x8p1TqKBLvf4/yA4xZ/kqBNQivzTAuANo8WqbwyarPEMe5WKqkebh4SEuF2TBuTtiea8EX70PqT0XZJeJS5aqcT0OA45QmCFRspGRAr3SzJqFar+OjDT3H+3CXccedxfO3rz2B+bhHnzl3E2lopQIi5vVmWZxLcLsTqWbDvbpuL2m35xyueBPN9QsOwJxM2UW1QfYUhJkyyptKq1JhyuSyOHjuEQwf3Y3V1HW+9+T6WllYEbUaPMz6CuDsuGsFzHIiSHqjPJ7K8fnljCyNuQSu8L/FtRvUlQhKLFfnn3U6RYduF4ObV76EzJInInBQi4spVW0WIMjPciQllknREcUSMLhe3z4BHXN0gW8QyPUKspwyM913HYG4B69VdmF67E4VMCYO5BWRSrgJqAkQDzIZtvqxq3hg0AJqqAaiB6GGiLDLtjvV9puC1EUWCXcgQqMDxJY6NsYnQc04yZRP32lQAXTMcFtmQasdDRAAsHrmUWWBqmWmU6nuw1RhDVtvArvwZ5LSS0xacaOF8tZeI7p2IfpMsesWSYPe3uHOZIHo195kUtwAl1UaE9UoEbnVS23s/dx9tKMLdmozJTWjjy9oITrDDainfhDHJGKLKuX2C83cY7arOXt7hREpbGJVKFe+++wFOncrh2NHDePqZx7C+voEL5y9jfn7BM21mx9qZ/2VcnThlW1xOFvb4idNGJ+azLsRETtx/GFIeB5H92+2OjAzi8OED2L1nCgvzS3j55Tewvh6tAIvbDI5Njszyx5V08ScK/Ps8WRtuO+E22faSkNTkY2gP2+124lopdHKfdUsO6iEp2vJ/lCGm3TJNpJWWbrQZQUC9/ZRIaxOqE+pHpBpzSDNXEde53wP5gy2AqEAqbWA8fQMtcwEb1XHMbhxHVtvEQG4RBW0VqvtMVjW7Pq0cOmbUiuocFzXq+OheHfuH4D7SEbNj0xex6NAkNlivfRIcC1EKJOr3bpjlEgLUjT5s1CdQaQ6hkC5hd/E0MrqjYAvXvsMBsALThW74/soQW5k22iyTPMI0dbCo6PWyiIqG3U5e9J03je69n7uNbTKNttG9/JpJVV63ThDtjiFecXX/7mTy69YVtxlHKKKIIy9ada3awCeffIYzZy7g4MHbcO99d+CBB+/ClSvXcfXKNCqVaihStZgQBZEst/Ktj06tD6L8aDtRJlMpHfv2TeHQ4QPI57K4eu0GfvDCK9jaKnPGG+6TVyY41jjLhHbuzc8Tuk1qZcn7zTuunQbT6AXjuHUhk74oAHeSTBFXqbpRk2tJEiylPrcBuf1OkAYlFKVZTIgDv1umTTKdn21zab9OSjEx2r+AocI8tqojWCnvx4q1B/2pBfSnFpBSK057qhc1mOhpWzF2jpGiBsdCVD04Ls4iAascy5LgyGBiUfDqpSO2RSNwTkUqrky6p9BigMCkOGoMVFnT0lBujmKjuQumlUJfZhn7Bj9FSnOUaW/aRwkO7rnkkGD3O72/Mr6/sfvA3q9x5r9xbgOdui0Ix8laClhi0+cIJHr2uX9zwF1U3Gb03s/dRxtEuL2DGCZNURM90YQ7iO0lVkn3k2/WKSbO0ebO4fJh0Ll2ZcfDG1ez2cK5cxdx7vwFTEyM4dCh2/D8N76C5eU13Lg+g7m5RS8i860S4TaOPIqIJxE4L4nak89NK1brhYsF9CJHgjRFqqpgamoS+/bvxq6pcWxsbuHihSu4cWMahmEmSgMWFRU6eeCpTvx9XcS7BHRLFd4+yDwLZNv5Ii8y9NBVWCaiXumRE88octq2kudPJEOm0xFgo+B2M1DOTkbNpQlxIP0SQxJoMhz43amvqgYGi0sYzMygVstjqzaKG5V7kFPX0ZdaRCG9Dk1rgegpvw2VMaEGAkqx32eEf3QE2ia9gT44aZ2I5fwusJ6KO3/ECJVLGgE6PE6+6iq6nyyioNYaRLk+gkprCOlUDYOZGRSzJaiK5fsAi9rhqJXcvgIqqcCkOeKe55JbTpnIccSR63bQBSuUjs+5C06dSFPuHcgj3EP3kZAIJ5uYJYuoSvfRbSQh3EEkJX1yPpXi8uHFAjGkiI6QmNEKoOs3S7CwsISF+QVkczns27cHhw8fwIMP3ov5hWVM35jD3Nw8DMMQtO2Tk6SEOYnZdZJ2WaWTDjsvT3KdbRTBDJrTBvuLIj80QXejM8ucR1VVMDY2gj17dmHPnl0wTRPXr8/gxdPnsbm5BcDikH62bcYP1yXvsWOIJ6c25M6L2BRZHtFkOPkzJHw+o0i96LqX7TvJeHmWItsLQkygg2g3t8JCWQ9BdNt8jyWoYpNiARkWtHWz/fPaVpsj8++aHhkGXOWXJVWcqZjnT6zZptMACmQLRXUZpmai3BzDRn0Ky7UjyKc3UMysIp/dgqI5/QXa16C4KiPjW9x1FStEyjonB7IEmFc2Seqe2L5pYkg3RRRUWwOoNAZRaQ5DQwvF9DL25m4glWqGTJz9dD0xgeW8a0dsVhwJCXIb6rODtjtGF67FtgLzdYIdN43uvZ+7jbZMo+MD5siqILf+CZEj80l8d/3yPLSTYibO9JUmOjQJ9kmZ4wtKfHJUrzdw8cJVXLp4HcViH/bu24XbTxzFQw/di+XlVczNzWNublGQssdtPzyWbmAnTax55C8uty4ffN/bqOdZKqVjcnIcu3dPYWpqHC3DwNzsAt58612sLJekxhruX6yW2vsDhF1I4u/n9s+xmBTG9RUmw/LoFqmPKxt/fXT+zOg2Or1fey/aWwvbRS6l22XIMK8NYf7TOMT5B7cBXt+J22NTMTHqsN9uPBklcERdz9xZha43MajOYig9jQYK2DKnsNq4DYv1HLLpLRRS6yhoq0gpdmBL25dY9feF9iumCHLsfiYIrGXvd/skWOoaSOLLG2nWnVzZNA2gao6g0hxCtTUITTGQ11cxVTiDrLblz3/Av8ZZRC4mRY05oX9skt955br+PEnSXhtm0DSiIucn3q+btGh3K7+f/+W//Jf4m7/5G3zyySdIp9MolUqhMjdu3MAv/MIv4OWXX0axWMTP/dzP4dd//deh69vqqRuJRD3TTtrRJKAbJpKy4PeVhKTEmS8mCfCUBJ2ozdL7JlD9Iusz28rlCs6dvYzzZ6+hUMxgamoCu3ZN4Z577kC5XMHi4jIWl1awvLSGVssIBFISKaehLtuyHnARpa6ximjyyFRsft04BVcuxVD42KiqitHRYYyPj2JiYgxDwwPY2ixjdnYBL7/8JtbXS6E2uH3I+i1DlC84mVLbzoM1LrhdFOhrpZ2FI7odt4122uqk77h9FVtIbP8zteeD9GMIns8hOpwc8pS0GLPPbuYv9dolBtcXNml7PCQnyEE/Ts9kOsqnUrUVYegZgBje01IxWiAAUiowrC5iSF1B08yi2hpCpTGEldY+ZJQKctoq8uoqctoGVMUMmmE7JJjoKdu/WE8HjxXtM6voHhnvFoLqpJ2qSLFMP48w2z8N1gw6iR+vCIL7ALBV37rRj6o5jJrRj4ZRRFqv2bmfc3NIKzb57VaO2ZASvd1+8TxwAlN1BQn3RVbNTXK82z43EYtvRN3+QFS38vu52Wzi7/ydv4NHHnkEv//7vx/abpomvvnNb2JychJvvfUW5ufn8bM/+7NIpVL4V//qX23buOKwzRRcHLyJhgwBiieB4Yk0zwRzu3xc5dK9xPcbp+7KTLy9iT3jgxrqC1oolDpN9lz12DYptm/+SrmBSxdv4OLFq9A0FRMTI5iYGMXdd9+OYrGISxev49OT56nxak7OXkCGDIfRmcmr2IdU5loIm3rLkkwevKBjzrF0jw2gYveeCTz00L1oNVtYXFzGpUtXsbS0glqtDoBR+L37hR1nvGk2W07Gx3xnIHMe+OU6I6RBQtzu4lS3rRR4gem+6Pit3/ot/Jt/82+wsLCAe+65B//u3/07PPTQQzd7WJ8fJJjYdV0BEvnNSfguun3sqP8uA+k8yQw6MumOCVAU2OaS1lTBLsoeW6dsCsBgagmD2XlYLQu1egG1Vj9WmofQMjMY0q9hNHUZikNwiKpC0VMg6SyIZdpPV5U5H9443d+Z/RBFXm4XxPEV5v1Od8MQYNG1KjJtDl2ztC+wc3zcNreaY1iqHYGmtpBLb2Egs4BccQO6bgkJuFQu2oClgBm+Di0z8He8Yh/2rw2nRJIkt2xgKqq9bXdh6IZVxnZgGyxQvkj4F//iXwAA/uiP/oi7/Qc/+AHOnDmDH/7wh5iYmMC9996LX/u1X8M/+Sf/BL/6q7+KdJoTMG8H0PYTix8sSGYyyPgpSkYhptGJmpRsQilHwtpJK9SNVERJwfoGi/KJyZI9wzAwOzuPmZlZAEA+n0cqlfH68ibzjoOTS4blyUP0wkiUShbsQ0SGw36vUWS8/TRHvDE7/TmLESvLa/jhi69jc3Mzug1JP+UwwscoKpJ1EkQtKIiCj8UtekVbm2wPKZSNyi5/794qiwzJcDNMr/7sz/4M/+gf/SP8zu/8Dr785S/jN3/zN/G1r30N58+fx/j4eEfj+XFHpOoqqaJ1rJ5EERB2TEnQoVIW2b9MlGEAUPSOCLEwv6/7h2c+bge4IqkCCF0n4I9sQFE1KEYTmlVDX2YdfakVwDJhNFUQy1aSPaKnqnY/quqTMVB2Uw4Rc8di57ilx847b/6xSKSOEv/ZLn09MERR5HfLBrwK5ONl7w3LCrYJIK+uYW/xY6T0BqBpwmsudB0wpv9C8u5+F4zXjY4ce51Fmnh3+N6hIjTLXu9C8+OY89QJpE2eBQshtzq69X5m55qZTAaZTKajtuPw9ttv46677sLExIT329e+9jX8wi/8Aj777DPcd99929q/CG2xr2hT4Z2ZRAXLydRJqsZG+UCLxpGcBLuE0W6f/XQGzy+YQ+hd9cseS1gJo/eDEMtWdekPp16tVncCN1Hqp7d/ScEz4w6bsoo+gbET97rkmU2r3hjliHl3zg2LRqOFzc1yfEEHhDqPnYLd/3aUUbqNqOMZdZzpfuP6T2rCLLs/9PhE+xK3j9HXSJzy7dflW4fsjOlVp5+k+Lf/9t/iH/yDf4C///f/Pk6cOIHf+Z3fQT6fxx/8wR9swx7++EFxSEJSEiyNuLYcRbNbfnWh/WA/bBkaxPA/IsiSYLo9IHyMk4DZB8UybJJM7RNRdRA9A5Iu2J9sP0g6T30KIFoGRE/DSudgpbMg6Sygp6GnLaTThh1dWlU98hv4H4hVMEPXEu/4WyYUoxn6zv3QbRISf24DxyhMLNl+eddDaPzuqeQp9JYJXW0irdUCJJin+BLG9zqqP4Ues/thjgksyyew1N9JLDq8uiy6YOrczes98fNI8nkiPUZZtT2ij52waunW+3nv3r0YGBjwPr/+67++7WNfWFgIkGAA3veFhYVt71+ERIqwoiiSE9BkPobbAbEqFlYR5fcpSZ98NZmvPgYnvp2QmxCh5U6qk5hc2scsXI5qw/PncVQ1mCCEIQ/Q2pogC0kTT+F0ibJAISZEBbXOTh1vuiRvgUXOFD8a1HHkKMvSRC1BpOmotuP8mF3ymCwY2M4qnZ55N/e+kzGhFpftzE+9G+BbLHyeILvi3Gw28eGHH+Kf/tN/6v2mqiqee+45vP3229s+zh9LREzYeGbKUqowR2G7JRBlqivpPxw3wSWqxm+LzTEsgkdYwgTcVmGZNiiVONCn6tQ37N+JnraJlZ6GYqQAowXozv/u2B3/YO9DEzh339o4t7KkwH/9Ctx44hQ+AdGly4oIoQKG0Fom4KjkCtW2vZ0SBygynIj80GONI6cxCm5UULfYMXXT3xfyViM7YlZ9C/Z9K2J6ehr9/f3ed5Ea/Mu//Mv4jd/4jci2zp49i+PHj3d1fDuJNk2jZUyGRWQ4rDzKTPR45drzDUxKXtqBnHpMl2UVp25OfiMVOPZ8xJwzXgRltiwhgIUWFEKtlFJm2HL75xPHsDpthX2IFE7gJ85Y7TzCfpsKNMqPObyfPB9zf7sKnppOlwvvE103vE0GnfgphyFn/i9Xf6dMgSkTOuHikwwxd8+DiBB36zh38hzZeTLcrfQMe/fuDfz+K7/yK/jVX/3VUPmVlRWYpsldKT537lzb4/ixBzsZThhIpu2gPxyzx+0KiMWDR9xkx+yqvy6hFPmixoyFJsOBSbfIjDl2YcEn0YE8xEBw/2gCruggOny1zDJBtIy9D2nDUxtZU3U7YJYdKCtwrhWXAHMIOkuOI81yxdtCZDuqDcaX1uufR3QZ4qo4JDeSYKp+qiraRDwUYZvaZ6JqYdIcgegI1R26KfHaZtuUJcEJyHKS50PXCGkbz5HgfXmLLtzFoFvv5/7+/gARFuGXfumX8PM///ORZQ4ePCjV9+TkJN57773Ab4uLi962m4WERDjaLDRMTNkJv9iPUJYMu/2Ix0ejk4eKvCklv65YbYpDu1F4WcgGHwv8zqiNAXIosS/2NWCrrIQinLz+kviE22CJretXQ69gB1VhmoBbpoHTp8/CsuzzE6ynAV4+5SAhpsmwyCSfDUwm2jfRvoh8aVlsny95EiuB7cFOEL7oZ1Q3j63cvQLQ55S3QOKTYbvOTvgx+dkB2q8vv+LcwzaAOyEWTPwECltHk1WKqN1SKky3zKHj+qD8hgEESTGdUknm2HgEkBmfZQCq24YRUIeJqvuzO8v0yDhJ+X8DDDHgpHIiHsEMK40k9KgKBtj6/7P330GaJHeZOP5U1eva93TP9PT4ndkxuzPrnTS7MiuEVgYhLUI6SV+JFXDcRVysLuBHxMUFhA4UXBA6EwRHBHECDiNArIQEMiCQWVZa77Te747v7mnv7duvqfr9UZVVWVmZVVnmNT2Tz8Q73V1vVlaWy8wnn4/xLabIEF1Dx7bOcViGDssJyOESUsG1chVQcp6E6NIkjjyHbj18MkzX5ZJhqj7uc8xTy0X3Vco812l3GCFmzy8Maf2CGwQeGc4q2naUSh84dpxFs7ZANuOzLHbs2IEdO3akOJ6HkydP4vd///cxPT3txv64//770dvbi+PHj2dyjCRoQuKmeIRSxiQzOrowuy3KNJndP4vOI6heh4GcM0uUZIiBHAmWI+YitTHMFJnfnjCzYn/ZcJ9zMWgiy7ZHgw5LAzTLI8OmaeLVV9+A7kwWNI1RLUSk34qnqovyDLv+rxwVPs5CkKgdUWX4SPusiyw/ZOrnuynwzfCj3s80JDbqHJLU5yGqP0tm2dI42Nc/eWQ44scsu+K8fft2GIbhrgwTTE1NtXSV+JJG2MRPhrRsJQjMn2Un3okiWjPH9AdpCiHDeo5rHh2Mvu34JGPT3kc3HFLsKcfkp8/8l22mqP6Aylu0VWmTJtLB62IZOfvcqcBb7jXgtsBrm65Z2NYz5btulu4nwwFllmwH7NRPZj1g7hwwfTbrfqWXUXktpqwMSRKS/jC1PIzMRinX5Lu4UZ9TmEQnIadJ3p2w48gG6UpEhrcIshqfG4GRkRHMz89jZGQE9XodL7zwAgDg8OHD6O7uxl133YXjx4/jl37pl/C//tf/wuTkJL7whS/g3nvvbelCeaZEOJ1fXdTkMbnCmhSNjOzMU4GiSLDs8WXKuf6VVC5Zvj+x/LF4RI8cIw5E5+3bHoi+rHPbEnoc1F0yLYqgTddnL1I39jmUU0XjvDfxlNZkiqyISPKUdR78ZsyyUa+zhSwZptsRvcjD/i5ygWAtD/yQMfPeuigUCrj55pvxwAMP4O677wZgL1o98MAD+PznP9/axm1FhPhKthrBVC5+E10WkelwRMj4fBOTYRq0yTRLhmkQMizyLTZrXlusmqe+6nVYuSLfp5jexvOJ5pg+u20BMc22CZ5mekSPdxyiHkOziTn3GRSZz7uKtnO9tZxHhmml2leWVXEpSwT2GPDK2udnBJ8xlsjyLClknwWBH29mQZV4Zs8NUIEbqdAmOYaMeXXs6xy1ALjllOPm43d+53fw13/91+7fJAr0T37yE9x5550wDAPf+9738J/+03/CyZMn0dXVhc997nP4vd/7vVY1GUBTFGERxCaAwckhb78ItdA3+W/ERDIeIQonl3yzYbl94xyDT5B8SqqARIkJM70v1W6aBDiLV9xcxjGjbPPqYYOOkbbAgqsKC+smqR44UbD54CvjvJzNUgsSEWWDgbHkrhfvPsrsJ0bU8853B+CbmfP2SQuxakz3BXHfEV59/G3RfUzUPQgnw82D/SynWHFO4L/0m7/5m/jc5z6HW265Bbfddhv+z//5P1hbW8Ov/MqvJG7H5Y5mRDCVAZm0RkasZiaaQhUzxN9YODnOyuw5BLF9H0VkVzbAFjknqwbNdEheDZ46zLaP7Ea6lqggYYwyS+qwFdNc0FSbgPgmAwAMuH7G9D1gr5PvWH6Tes2klGaqrHetDZd0+6IPc0iu134joDBHgvJRjiwXgcigViFkNvQ5a5ApdFq/3lQxB1K0iddXhO6TgAz762t8P9OK8VkWX/nKV4Q5hAkOHDiAf/3Xf21YG5IgJhGWIZb8yWhy000RwieejfU1ZIlJNseKNnHmkxH5aypjQk0fx95HTJj9KXeiEDvacYQ5Ni/qNrcdvujV3jUUR4vmHy8MYnIeXlaUYsh9X5pEiLJ5X5KqyVlD9jkP2zeu8p/sPNhnNooMa5qGBo5jDtINtEka+MlPfhIzMzP4nd/5HUxOTuKGG27AD37wg0AALYUQmHXAdHwrZUhwwuA+yXLlZmcembYtSeqIna6GV29UIC0asmSYqpuQYV87eIomIcmOCsxVdan22hXaRJTkMrZV6AgyTKpx2+T8HfOe+Qiv7xhEUa85RMsh7CKSbdV8ga247XCuFS+1EvfvCMSLKC2Yg3DqSB7ILsE4xeQOjn3MFPukAUuUIwkw+3sCFbs559j88flSR0JFODsfPXkiJeuL2D6+dkGIJ9fyJJj8npZAiMxrw5V6mnwGzTz9SiwPccmwaH9ZEsxTsj3wlUteWRGRSmpG7kewXWJz5iRqYRyV0wNPaY+D5OQ6nISKzayzJNXp64pjyRF9ry9t82gA+PznP69MoTNAFoqVdF1AfJ9ilmygARPIGL6dYeBH4Q0356b39Udgzom/4yGEDBPiZ//BBL9i62B+13QRMWYn9TkIlXSZe86UCQTlErXTV0eOX0ZE3jVOm3nnwLZfQIDTPKN+C4gIn96Y+YHZ42T1/vDN2KlxJyNSLDpuWJ2iBYEwCxGpa6PMnS97NMU0Wn5CHDSP9oNvfhksA4iIHj/3aFzItINGuHl3PBIcH9FqvNwkOxbpE5gLS+0b4tMaHqAL4F3TsGOHPZvxnhExyZYrzz9+UjIp94x7970REZvTW3800qWBoBXkMtg3yV2rJpHhlME40MBgHApiaGYdmhnvvoXlIg3bJ2BSmvFkkj5GGj/nsIlwbBKcghRL1U8hoBhzfX49P1rZeu06/X7KbkRmJkUTHak5oBqbdU8NDjUllTcV5ZIhejGAUoDZen0+0xJ184gud1tYHQykzJVlVF+eTzL7HUHK9y7uuXq+1XqgbJaLWXHqTOUCshVJsBqfM0dTiHDYJDt+mpj0porhKY9kfQvjkPtwsNcnSxJMw0+ORGRe3u8x7N7JBskKI2wsmeZDTl2n2xa3XDSyUmjp78MIUlwyFKYit1ZpjCbswfYlJ9giN4DwZyjLIICyx5eruzFoZx8khezgqV9+tYomj5kG9iEgPqQRdSdSdzJCpD8zD2EEBoAoenXU8V1CyKsy4f0JBO0iJJCtn5SnN7LXRtA24SILq3Szwb+AoAk5VVZYv7OvzL0LJcAZR1umjxmqdDbA+iLxu8vbj/arjpPCKQUaEuegUX2IbuBy9xHeqsiICMuRvWi02vQvjW9hOsinhIp3jViyFyTDYQj7XtasNrw9YfvHIdNxkR0JljtOdJ3sAkQcMiU+dvBeh/gup158CoP4fLKyWMgS0eScIAnRZRcmRO9jq9w8lA/SpY6wST/P7y2NKWSi4FYCRJFhv/9t3T95j3EMFxwyzoMoInLg2LRimYAUs8dKShT87RVHsOapxT4lmCaxbuVBtZYOfAX4ewjNrDs+zvQzBu/6uGSdIbqc49DnEMdFIJQES6Cp/q+tUjETvk8yiFrQiQwulgRhdelGsuusfIS3JBqkCId1JsnJXTqVpp0Qx2w2HQGQNXmVy0UcXl68X7gJrkgVDFfK/W0Q+wEH25XG3JhfJ00y+X7H3jnyLQvEar2/XlFqKdG5NTZSsyzEzwON1qQQilunTLv4z0OwjMhlQkEhPiwnSm4Wwa0A+NQnbh1pJn0iZYv17eWorFHKWpKJuswEO0q9libDBEwUZC44pr5aim4wmCqIIps8ELVYN4Km0IQEWyEqLU1KdQMAsQQgDaLILU1MYZNhOwCWp4jTJDjMfzuOytoQ1ZED6WcyJMBTHFcGKdN7UbkQ5Zl3vEYuBPACXvEW5qTvY5Put8LWQYZEOKlPpGi/cDLIBm6Sb0d2kE/JIgux+bUMeQsL6BSWxibMbJ1XZxQB5d+baDNrERmOgsh8Oqrtcdoid41k7pffJD0q12xc8AgxnwwnR+sWpMKeoTjnF7VQJ3MMGci0Q9ZSoJnX2Uq5aKxWnFuJTMyZWd/fjCaPvrbxCK/oWMw+PB/iJAjdV0ZRDCPoDBlm4ZYVmUxzTH0t3RD6wQbA1CkkwWFgTKfdbaRdPHNkgKvK0mbWbjomirCyZVkyzCXBnOsaJ+BVomcnKvBVTLjnxrHEEJbNEEJSzJLhhL72AQh8n8NIrswxmx2VOgBmMadxUONz1khBhOOaBzYLMmaGfNNMQH5i39j0TEBcEkzKJSXDSUATUBlFNh3E9y9O6iJZhF1zNghYVFn+NTED5Uh9UW3i1+l/7rMJfiVWuBsPEUmUUYZFz4rsfqI+JL2aHu+947W9GfcgrReRGmhbjaQTZuFkkqcQcSbvvIkzSxC5bUtAuOOcH0+VFe4f57pxyDBAXUce6eeVlfQf5gUREpJsRnEOEnVJQs0pJ7OAIPTxNusBn2MfeeWY38qS4CgkDuzEM5mmCLGwvQ3K6xsF6eBbDALvpoicyy4CxFHBk96bFqKZcQs8qPE5a0gRYcuJMmb/JJPBZBMz/wSdnmRbdo5MAPzJrum2g94WjjBVzg+v7vgdVygR4kRo886T1y6WfMg/tJqmRZIVuj7LqrttER9HpITaa7ZBNZa9jn4FNP6xwkhJsC7/NUh+LcPhnbeFegT516hui//c2/WQMhb418FkytJ1su8L34IiuC147TyQOqOun8z7wiOQ/PtHP5Ni4plkcpF0QiJ7ftHw+tHk5+jvixsJNVhuFZBnYWWNb6Jq6c69ZMisbY5KPWe6TpXl3H8u8QgjUzV3Imxx6ktGRGuSE3pyPLq8BPGLMH/ml+W1ieTojTi2E2DH0nQAVT8ZJmorr+92bIt933EITLDeKr8dvraGg/XRDbRPQIRt1JzjcPx7metp6Zb9U/O3WbNMufsU0Q4uOMRV1sTYRw6pemQUzvBrFa+cnwTT51On2hisO/wYNrz+QQ9u4yHsOxai4woWyRIvakSB83yGgT7/lVXnvVfj85aCFBFeWVkBAJjWQmOvf9y6G9GWZjxfjTpGknqj9olZpxX4JcO2NKuORh1SoqAVp1ysg0ugVfesGXW2G1Ke48rKCvr6+rJpi4NCoYDh4WFMTk6mrmt4eBiFQiGDVilEgYzPR372qRa3REFBQUFBjc9bC5olsXRhmibGx8fR09MTomYqKCgoKDQSlmVhZWUFu3fvht6A9BXlchmVSiV1PYVCAaVSKYMWKURBjc8KCgoKrYcan7cmpIiwgoKCgoKCgoKCgoKCgsKlApWvQ0FBQUFBQUFBQUFBQeGygiLCCgoKCgoKCgoKCgoKCpcVFBFWUFBQUFBQUFBQUFBQuKygiLCCgoKCgoKCgoKCgoLCZQVFhBUUFBQUFBQUFBQUFBQuKygirKCgoKCgoKCgoKCgoHBZQRFhBQUFBQUFBQUFBQUFhcsKiggrKCgoKCgoKCgoKCgoXFZQRFhBQUFBQUFBQUFBQUHhsoIiwgoKCgoKCgoKCgoKCgqXFRQRVlBQUFBQUFBQUFBQULisoIiwgoKCgoKCgoKCgoKCwmUFRYQVFBQUFBQUFBQUFBQULisoIqxwyeKLX/wiNE3D7Ows9/trrrkGd955Z3MbpaCgoKCgcBnjIx/5CDo7O7GysiIs85nPfAaFQgFzc3NNbJmCgsLlBkWEFRQUFBQUFBQUmoLPfOYz2NjYwLe//W3u9+vr6/jud7+LD3zgAxgcHGxy6xQUFC4nKCKsoKCgoKCgoKDQFHzkIx9BT08P7rvvPu733/3ud7G2tobPfOYzTW6ZgoLC5QZFhBUUFBQUFBQUFJqCjo4OfOxjH8MDDzyA6enpwPf33Xcfenp68JGPfKQFrVNQULicoIiwgoKCgoKCgoJC0/CZz3wGtVoN3/jGN3zb5+fn8cMf/hC/8Au/gI6Ojha1TkFB4XKBIsIKCgoKCgoKCgpNw8/8zM9g165dAfPob37zm6hWq8osWkFBoSlQRFhBQUFBQUFBQaFpMAwDn/rUp/DEE0/g/Pnz7vb77rsPO3fuxHvf+97WNU5BQeGygSLCCpc1NE1rdRMUFBQUFBQuOxDVl6jCY2NjeOSRR/CpT30KhmG0smkKCgqXCRQRVrhkUSqVAAAbGxvc79fX190yCgoKCgoKCs3DzTffjKuuugpf+9rXAABf+9rXYFmWMotWUFBoGhQRVrhkceDAAQDAm2++GfhufX0do6OjbhkFBQUFBQWF5uIzn/kMXnnlFbz00ku47777cOTIEdx6662tbpaCgsJlAkWEFS5ZvPe970WhUMCXv/xlmKbp++7P/uzPUKvV8MEPfrBFrVNQUFBQULi8QdTf3/md38ELL7yg1GAFBYWmQrMsy2p1IxQUGoXf//3fxxe+8AXccccd+MhHPoLOzk48/vjj+NrXvoa77roL3//+96Hraj1IQUFBQUGhFbjjjjvw+OOPAwBOnTqFw4cPt7hFCgoKlwsUEVa45PF3f/d3+OM//mO8/PLLqNVqOHjwID796U/jv/7X/4pisdjq5ikoKCgoKFy2+L//9//i3nvvxW233Yannnqq1c1RUFC4jKCIsIKCgoKCgoKCgoKCgsJlBWUTqqCgoKCgoKCgoKCgoHBZQRFhBQUFBQUFBQUFBQUFhcsKiggrKCgoKCgoKCgoKCgoXFZQRFhBQUFBQUFBQUFBQUHhsoIiwgoKCgoKCgoKCgoKCgqXFRQRVlBQUFBQUFBQUFBQULiskJMpZJomxsfH0dPTA03TGt0mBQUFBQUOLMvCysoKdu/eDV3Pfh2zXC6jUqmkrqdQKKBUKmXQIoUoqPFZQUFBofVQ4/PWhBQRHh8fx759+xrdFgUFBQUFCYyOjmLv3r2Z1lkul3Hw4DAmJ5dS1zU8PIxz586pwbYJUOOzgoKCQvtAjc9bC1JEuKenBwCwo+M26Jq9i4m6+70OI7CNB1E5sl3jWGpbMIX1kfK6Zvi2m1aduy9dP7sPC1JHnPJRZURtIe1k261Bd+uMe23o+sLgHhv8a8at1ymjO21i20bXYcJ0y9H7WpbpbvHAW0EzI+8F7zxlngn/80MfW3w/eLBgcp5FHZpG3WPu+bqt5dYbB/Sx9JD6SDtF99n3jsC7hvQ7y14PuhxblgZbji5Lf8drI6+/Ydsr01eIIHrPZN6HsGsqal+cd5N+tkyrhsn1x9w+OUtUKhVMTi7h7IU/RG9vR+J6lpc3cOjA/w+VSkUNtE0AeRbyxi5fP9BqxGlL1PvZKmR9DjL9iWxdSSB7fBpZt4Vtgzc28r8PK5vl887WneY4Sa9ZK9/fsPNP8txEIcvnKqv2hV0D2e9r5pQan7cYpIgwMbfStRx0LQcTdeicXXnbvO/oSXDQfItHhmmCwW2X4Dtdywn3jSLDZIJKCL+/Xj7RtUlwLrQsPfFlv9Ogu3UE261xJ+mkXNiEmrQpiiDQ9zOsLI+0sPeNbr9dr8HsQ8il5vtJt4c+R8AIPC1W4CSCzxNbRtMMp20GLHLNNA0adFgAdF8dBkyr7tuHbZvXPrjfk+PqmuGWJQOb6Hzd9kkOCmFkSzSI8ggofY/YcnyC67WbtzBAL3LpyPEXHDjtIPVGEWH6+Px6wq+hLBGOu59MWfZ8eP0FD/xFG3u/RprA9vZ2pBpoFZoL8ixomt5WRJggaZviTpTjTIbj1p3VdW014U9zfE3Q78rCcsYa3xhvmf4FY8G8jRCQsPuQ5h6x7ciybiDZdW/WuyxzbRuJVr8TjYAan7cWpIgwjSjVlwd24qrDENYj6gh5JCzJKpCPqEWoMuxElKf6iuoIq1v0neh8yHHZ7+O0H0h272T3jTqW7LFlzjG4osypWyM/dLc8W86y6m45k2HO7D4a5/rT37vPlWbXpWvOsakVRFb5BzwCFrXow8JbsKHaZfEHM9lrTxYu3EUWiXvOI9SB+0PVJ1KZ2fJJEaYwh+5HXU/Rd7zvRe8gWy7tirXMwldWsKwaLKuWan+F9oJokhulcGSBpBPtRqhQvLqllNwWk4V2gEX11zKk2AoZP9KowFHl2vEeRVkLcfdpoPotOkbg+wa+g6JjNHIBLC00TW9KnxkGNT5nj1hEWFY5jHqQZSbmjR4Ew9rIJTzURJQ3WZZ9mUUDcJzzZSfDvDqTkmCZNtGrpyy5kTkW6UjimHaHkV/etbcoRZcuHzim5e4UqNtX1imnEeJKlTGd89E1pxxDhum6TIY4089TXDJM6qOfx7STAZYMR4EtF3lPBc8G7zmKC17dsu+57OJYEiLazIE6C1hWnb+4FGN/hdZDpg9wLVbamBA3GnH63SjlsBGQuTdNb1OKRXVZEpz0maT3k7kurSI2US5XgfIZvz/tQILDjnspKsVZQI3P2SO2IkxAK20s2IEli0luFHiT4LBj8ga/uC9+3EkxTykLg+8axjgWr94oFZ49Hvk7yp8nLlgSHEfVDii6bh3+NumaWyCyPCnLnpdMnTQJtlBH3apDgwEdJnQt76jM4kULrz3RPuhhoC0GXOIdw9+JHWCjyDD7XGQ1YPLU3LgQKcG2f224as3uIzwGda+aodASNGtiYlo1mClWjdPsq5AezSRFcUkHvU87EeIki5DNQDuqoWFqcBqC7KsnI3Iq83y2WuVrxbPXalWzHbFVrokan7NHLCLMTlDDAjjxyLAMwjoE0QQ5jckvQaNIaVwk8iWR3IdHhnmEJiqgEsBXhX3msYJOhaeMis5DZNLsr8cMDL42sdT9xFBAmtm/vfr9dbokl1KOaRLsnq9GzKyr0KC7CrJbL3Mu5HsemW0UWFVDZE4dRxkOQ2xVNsKEWrSwFkaCecfk+UfHPV+ZBQx24YAl5VHbRfUoKLBI7I8bkwzwyrYjwZWFzDvfivNqpmIvbEOMhUm2bBJinCRgkazyG0aG09SdFu22ENMO40y7XROFSxexFeGoCNE08ZB9kEXBfNrhZUyKLF7iRihPNGENU/RpuOVDBqgwEkzXRxNgmhRG+eBmDS9wB8/MPXi+Lum3TOi+4B6s37HpM40WqdL2sXXXT5k2k44ydec9C+y+WQwgSchwmqBfIp9jUdsAOfWYpwRnsXgmC5l+jEeoW9UHKh+krYlmB9cJ+z4tMWk3yLZTljTGJYhRx3ctrRIo9IFjhZyDHlEnu7CsuQuM9FgvXnSJu130fZhPvExQrFYsPCji1xqkXQBsNtT4nD1iEWFelFgCdmJJJnGNmJizCDP75aHR6QCitseFyCc4TJEPA3u9ZH16pbcL2mBadZ/Ky/rgsuppGMjzpWs6TItPbMOjCIuPpcGAhbo7YAujMTPHZmGTZtH2oGLNC4bkV6CDz0FcX/S4oMlwFGR9ihsNmWvCgu5bZAObpHGtiFJ/o47dSNg+SGkGWuWDdKkijqnuViG5UZDytY7pzkHGmEYirkIvOoco8isqS5PipOeb1k84CwuJZj7LMmQ4bXsa5RucxSIMry2NHP/agdjGhRqfs0dsRVhkNssjo76IvSke6KzUmzQvVLOUGZ8qFhIUiy1Lfy/K08yD0G+YE+1YBlL5UVkSTG3XnNRDLtnXvHRHAaLsqK26a47sDcQif2ebvJq+svzzAPeYdP00EQeH8MpOINjAXjTqkoOWbCA32oeYN1jxVtZlffxliXnSOtLAp6gnmZBJtCuNgit6b5RCoLBVITNhbxfC3Ao1Lgk5TDJxT0MKRWOYzCIgPdbKIGBJxjO/D7leXOsuzvMl+8zJuAvEuR/SPvQS4lGSeyrT1iTjV5ibBJCeFDfi3dyKBFihccg0WBbPbFoqdUmEGiMqyx5b1pewGZAJ3iWakIuuGI/g8syYg9v8x+Ydlz/omNKqlayKFgZCggP7UqmQ/OWdZ9ACDJ/PK29ArLv765z6NF/k5bqvPrYjJmVJTmJD02G5ixceyRYp9jqn7jSrdBrlWywiw/Tx7LaHq91ueadMFHmMCkzXTEQpv604Lg+tCroVBcuswTJTrDin2FehfdGoyWMjfIuT+HqKJtztQNazuPZR19lCPUAmWVcgr2x0e0zfYjrfdUpoTRdwr5LJNCJ2d+LVH4cMZ3n9Sb2R5WMQ4rSIM3bFPWYWpJj3bmahXGeNpviTq/E5cyQmwmGI8iPmoZWkVfaF4gXXCSsj+x2r2MUpxxJcC2agnfY2cV2i45iMckuUS+4+giBQgDfhNwkZ5fjO0iQ4SOSCx8zlDRQKeeTyOvL5HPL5HAxDh27oyBkGdEO3/9adjkmzAnXU6ybMugnTtGDWTdSdT61WQ6VSQ6VSRbVaQ6VSgWVZfPNY4lPkDloC9dq9UGDKx5tkcFMVUSmgZMgw2ZeQYXIeUSbwaTv5ML/eRqoxcfIoA9F+x9wo3bTiLAiEFYY4ptINh1WzP2n2V1BAPBKZBeEMm+TKTMibTYajVOFmK1c8MiljQcWWzedzKBWKyBd0FAp55HM5GDkduqbByBkwdB16TvPGZ2pCYFn272a9jrpJxuUq6vU66nUT1WoVlQr5VFCt1tx9Yp1rC4O7xTl2lu6ForojyzUxirdw3wwW1BtNgpvyvqrxOXMkJsKyvqhAvImojGraClWYVy85pqgMe43CVrS8+oLlWZNnXgAjVgkmL6Sp8cvT9UWb/9R9v/MiIYt8UnUfwXWOy5gS0wQ4lzPQ1d2Bjs4iOjqK6CiVUOoootRRQEdH0R5U8zlougbLtFBxBsVatQbTNFGrmajX6y6xNU0LFixnIqNhz94hXBybBmChUNBh6DpyRs4lzoaRQz5v2OS6YJNrAKjV6tgsV1Aub2Jjo4LNchXljU2UNypYW9/A2toGKptVmMy5+663Vfep2Nxr7S5CMNYWWpBwsfvAua48MswnZ35TaW6dEJdxv49hPh3Yt8kEOE3/wkPA+iIFmW0XMqyCcVx+yNIElLdvU5SSBGa4rVZ6geb4C8eFiBDn8zl0dXeis7OEUqloj9OlIjo67HG6WMwjX8hBA2BaFqqVKjY3K6jVaqjV6s4YXbMJrvM3TbT37hvG6Og4LMuCXsjbY7JuQM9pMHR7jM4X8igUCs7it92+atU+Tnmjgo31MjbKZWxsbKK8Ucb6+iZWV9dQqVT559piQpylybT0cSXm740mdc2+7o06n+bnFVfjc9aITYSTrMrEIcSyk9AkE++k5hVpVV52m4iQ0r+zZs+i4/AIeiAnbkjAIxEBYtXgJKBJMOCdt6EDXd3d6O3tRndPB7q7u9DV3YGu7hKKxQKqlSrW1zdtolmuYG11A/NzyyhvVLC5WUGlUkO1UkO1yh/YAufonEcuZ+DQlXvx+ivnUasF/dl5MAzdJcXFUgGlUsEm5qUCBgb70dFRQEdnEcVSAbVqDWurZaytbWBtdQMrK+tYWV7HyvIaTNNyTKjNwEKAL3o2x5eZB953bpRqigyLroVHlqP9vaJ8dOjBXNZvnbQhLeL0A3H6C7pe2XZmQWJp6wkFBYXmo538hbNWmeRcYTR0d3eir78XPT1d6O7utD89XSgW8qhUqlhfL2Njw/6srK5iemYO5Y0yNjcr2NzctBenqTFWZu6Ry+Vw6Mq9eOnFN1CriSfr9HXKGTYpLhTyKBYLDiEvobOjhO3bt6GzowOdXR0olYqoVmtYW13H6uoaVlfXsbKyhsWlZWd8NoX+xI1G3IWiNIS4HQgw73iNJpNZm7YrXFqIRYTjDBA8k8EoP1WZ73zBpDiT4LgRpEXHpetNUycPUX4xUQpbnLQ2vE6GVYLjEF6NIbf2RvgUTrqMYRjo6+9Gf383evu70dvbiZ7eTmiahpXldayu2Grq7PQi1tbKWFleQ7Vao+oSmLBl3CmJrqVtllVBuVzByvK6sF2GoaOryybzXd0d6OoqYXBHP3r7umAYBtZWN7C8tIalxRUsLCxjfn7RNumCGfBPZhV095gcv3x6UuBL2cT4QYvILSHDScCzShA9s6Ic5LzyjfDjTRrsi/dM8Mycw4irTK5h0T4tIcRmDTDlFpmE+ysoNBlJSGMWk/C4EaO57QgLApUhGWbP1zB09Pf3on9bL/r6utHX14O+vm4AwNLSKlZX17G6uo6pyVn3d3p8pusNbIuY5yQFfb3rddMl5IFy9HnmDIfQe8R+x9AA+np7YOQMrCzbpHh5aRXz84tYWFjinqcIYdZSjYLICi+qbGi5S5DspT2ntrsmanzOHA3xEZZBEnLJi0AtIsNRdcSFKP9uVAck41cM+F82OkAR2SfqevnapPl9P9l2JCXAgH2+QZNfuMQrlzMwPLwDg4P9GBzsR19/N6rVGhYXVrG8tIYzp8exvLSGtdV18Nx6eJEj6cEk9kQn4hxpE2LRfZWJulyvm1heXsPy8lrgGB0dRfT2dbmfAweH0dFZxMryGubnlzA/v4zpyXlsbGwyJuOc58Wi3wPGtJ3JXyyKRE2fl6xPcVQdrCochqh3RjaoXJZg+5ZAnxKT/IbtFwetIMS26VXydivTq0sLzZoItoOZMsDvn1rdtqwWhPP5HHbtGsKAOz73oLJZxcLCEhYXlzE1NYulxRWsrq77/G7j5NdNSoAbqXzXa3UsLa5gaXElULazswN9/T3o7+tBf38PDh3ah87ODiwvr2JufgHzc4uYnJz1ke04acR47cka7e4/K3PsqGskWtSPqjdNm9oRanzOHg0jwrI+xEknuVFkOC2ySBkTp07Rqq9oH9n8rjwiSUeDptsvmrSzJJg9X1tRtP0bS8UOHD12BRbml3HuzDjm51awvh5crRW1VWa76JxF5JXAtGyzcdOq+4hFMHcvY5ZOfU9HnI7qKMl139jYxMbGJqYm59198kUDAwO92DbQi337hmHWgYuj0yDXUVwpAMtLGcVTEtz8xSHxQzSK/EeRYZnV5jTvYtRiUdYWGbIIey+iiGlaAiyqz2zGIGbWADNF+9WKc1sgTTqvTNshGZ33UoYov25axFWLS6UiDh/Zj/n5ZZx66zzm5haxvl5uOEltZ3Pb9fUNrK9vYGJ82t1WLBUwOLANg4P92H9gDyqVKi5eLLt1Z9meVj/77Ur4RJDt15pBglty79T4nDlapggTZDXJTUuG07SB9dmISt8iRbCpjjPKPziqPjZdAfH/dX1TBQGWSIdDEwJd4zwyDuEyNB3l9Soe+cmLoW0Agh2ISKUW+a+Kgj/RCBBc6jqF3iOKQAaDIfGPxSKMKFswUdk0MTkxh8mJucD37D2g93PVdwsAee5iRkAF4At6RpNh9vhxEJZqSeRvKxOEjv47jdtDVotlWZFg/wKLpJ9zxgRb4dIEnYauHcjwVkIrfIRZaL6xKry/oclwVMaBlZU1/PiBJ+O3J4YinAR2ve2zGLJZrmB8fArj41ORZRt9bRqBZgXCYhFGHKMWL1zrswb3Z1vpPipkg0yJME/Z4eW+TYo0L4Foch43tUoY4qRh4vkgR63uinIjBqJVCwZFWgWWzVtLk2CeXyd7bBmCyyOJPILLKrO88/Bv95cL+sVqdgRpajsv7VNgP7qMQ4DrVvhkKYnfbdTky1bkPSXYDqjFOzYn/QcnwIYoHRa7ICKaGNLvIy8dUxzIBgCRfWdlokFHBfBiFwiyBFtn2CJEGtP1RFArzlsaPD/3VpHhraAGy6iHaX2Jo1Rg1g0mThRpWVcn3vcyhJ8+bxkFmve8yaTnC0OoD7VgTtmIIExRC/rtjGYF/pL5PszUP4oMZ9GOLPZr2b1X43PmSESEo0xQgXAVM02Aq7grtUmDcom+D4vkLOsDTJPXQBAhenVXxiTY8vaj92GJpUtYEgT1ISRY0/RIIuFrmmCADiO3vG28NEChaYR8baB9o+1SllUXXgfhdpfoeXmCZVXlMLCEE+BMZDmTCE0zAAtuJGrfdyHvSMB6wSHDgcBXzLWXCsxGleE977z3QxjEi4naLAq6FxdxU7mJzlvkTx6XsIque2tRR7pcg+1wDpcnRO9oFBluRA7MrUQStipSmyxLLkDSSBOzI3D8sIV/yX6El+qJrr+hAatS1h8rWnQbK5VJgtQB7dVHbAkSDECNz9kjNhHOYrW5ET69cQhaEsicY1Kz50jT5ghzYFZhpcuHEeCwAFhRE3pRDmIZpZeun94uInTsvqJzYqMvx4EM4aEnDZH1c/Ik8xcAvN/ZoF2i/L12vlnvGBoMobpr718PvKe88+WRO5oQi6Iq864RXQd7DPZ6sJYkaU0TfYHhJBcJZBYPGoV2MMVU2Ppo1DOUhCS30wQ3Cbj9YxPSvHDbkiJA1VZFkrzKFuqZRPAW1p9C0W7UsVuNLBZCeFaE7dx/tHPbFJIh1h1t5GQtDTGmzbCzTnNEPixkjyM6L7oD4UV0po8PMGbNzHb6bx7x5BFGkuLI0PIwtLyt+DofFmyKHF76JdGHbYtbDxOwSqTwxosQWA+ovWH3kD22qP2idoWdM68NUXWRa0I+Ye1xVXrnozP30PehFjwIWBPpsHYBCLRJBt61CC6OxLEkyXKSF6f95FmXftdDFo+SEPBWQTNrqT8K7Yc0z5qm6dITwDgTRTWptF1ZyIdFO5DgLAhgqyMSp90/TR1x3p1Wg5wr77NVkDSYmQwiI1s34T43e3z+8pe/jOuuuw69vb3o7e3FyZMn8f3vfz90n29+85u46qqrUCqVcO211+Jf//Vf05xyw5Gpj3CjVdmwYzY6z69M/TI5UcPUnyjfXl476SBXMibIhADTPr+sWTWt8vkJk4SpNgMe2Q1V3wQ+u6yKKKP4ylyPRk4iWJU2roosUtFFf/NgkrzEpG6ByXZo26hI2eyzRsh2GNEPiygd5boQ1qfILJ6J2halwrLPqiiydSyTQuoZtlX9um9728CsAWaKNikifMkiy4meNLEOsVxpB8gokYTkxokYHRVAMqvrINP/xCK9sgulW4BcJW3jVvCNJ9gK90GBQpPH57179+J//I//gSNHjsCyLPz1X/81PvrRj+L555/HiRMnAuUff/xxfPrTn8aXvvQlfPjDH8Z9992Hu+++G8899xyuueaa5O1uIBoWNbpZpDiruuMoVGnAEsso394o0GQYQGCCbcH0kTGaBId1xDL+oHHbyfvdF3CJp0xSPqyieqNMmv3bxc677CSFt0Iftg8p7yPuIYsYNKL8j3lRvXlgrwUJ2mU6+Yd55tNsG9lrSkcXJ3WF+Q6njT5N15uGKBPEJcNhJuRJSTCvPfS72lZQRPiSRdiiVSP8hIXtkCEKCfydG2nGnYS4sGMIjxCz406UWXAWBLiZi29bkWilVYC3ApL49bLnlkWfkaV5dCuftab1n00en3/+53/e9/fv//7v48tf/jKefPJJLhH+oz/6I3zgAx/Af/kv/wUA8N//+3/H/fffjz/+4z/Gn/zJnyRvdwORiAjHncClTZEU5XeYBmGDSloSLPKj9R1f4Nsb1TZfHZR6RZMlVw10oFMmspFRMgXqLd0mVu1Ncm+iVFpCEHnKKmsOzju+f5Lh98H1nwvPfzdep2bn75VYXY8gZTQxjZviSHQfXL9iXnnNEF5TAMF2aUEyTBCnX4gMtgZ+ugTWVF8GSZXhsPpYRCn4PLQtGVa4LNGMyVwaEtyuiFKF2QCjdsR/M5L0bjW0m1pPIBsZvFnIiizLRGHOCu1O8Nuh39pKWF5e9v1dLBZRLBZD96nX6/jmN7+JtbU1nDx5klvmiSeewG/+5m/6tr3//e/Hd77znVTtbSRiE2GZyR2PPCU1YRaZt8YNbhNHNRTWy0TCZcH1b+QQ3bC20AGuwtviqYciiPLRyrRR9DcBz+RZhjyL2s0SMXpfXnCqMFIjbjP/PMl2dlKSJvBGXKVatA8v36/dZl7gtCAx5X1PQBZKwt4X1qfWvRcMGea1iV6UoXMMh5k0s9/FJa9h71gYGZaBKBeyCFGBxtx624wMa1YNmpXCnzRVREuFRiNJgMvMjn2JTSaTwjcWbUHFNC1acc4i5bHdwWtjVJDStAi4y7X5e5v2/KNyQbfT+Wc1Pu/bt8+3/Xd/93fxxS9+kbvPyy+/jJMnT6JcLqO7uxvf/va3cfz4cW7ZyclJ7Ny507dt586dmJycTNzmRiMWEU5q6kgPvKJJcNwgOUmIRlzQSnbajodnGhyInkuCEUUowmEBreKS31BFXGCKGxXkSnxMcQCv4DGc58VJD8SuqNNlSDm2PTxF17L8sqiIBNPb4hBiWhVuxGQz/H55v4veVb+FgHcviSosExWafm5l0kSx969RrhKidtKLcHFJAFtHGqUqigzTaCkxNk3ATHF8s/0nlwpiNEoVTuoT3CoEXUTEpCDJoqmFemAcbta5y4oFlxqaFeE5LXFqtYkv7/dGIo15dKNU7ziLAE0lyhmNz6Ojo+jt7XU3h6nBx44dwwsvvIClpSX8wz/8Az73uc/hoYceEpLhrYZEptFJiZCwvgb7+TYaYYQ1iswT8EiwyDRXp4IX0T+lSTBjgh2GtBPyrO5JVD1hZskeyTXcsvRPS7DY4a4SRjyf7CSIJvFRbZYpJ0qNxKrrrIk8DZFPLzGZFvkPOzsFlXpOm8JMmO19skmLwCO4ItALb6yLhiiXN3uctPAp47Lqc5upxAqXFpqtCmc5UWy1+W2cCbLXxwe3ufWFLJJvJbT6vmxlpM8HzUmP2cBUUu2CRi4YNDJ2QDuARIGWQaFQwOHDhwEAN998M37605/ij/7oj/Cnf/qngbLDw8OYmprybZuamsLw8HD6RjcIDbmDcTvEpKmTeANIs0GTyqiUQGx6nECqHIYEW6jDQh2mVXU/9t9+U1VdM3xRoHnHJtvsuoPpeUTnFnVdRao8u2+SKM+A2E83yqeXp/TSv0eRYLJNLn1FnTsQmZYZ+uG1W3gvqNRQgRRRISmj2HtOf0/DTbXEfAC4kcbZbeyxeGgUmWMJblzwCDEBfZ50/c2enBLyLLvAlRVU+iSFVkSHBrJ5vptNusMIhzeG+/t9Wg0OO24z5zXtRGS3OomTJWhJUhMFs3eIAnLy5yVh2EqkbiuYtTcC7TA+m6aJzc1N7ncnT57EAw884Nt2//33C32K2wENixrNmlA28jhx28PuF6UMxT02z6Q4LAAWawrN8x9y97PgRO8V54cVDaKy/sfsvlGr1FEEPKzuJAgLYuUnlbxBwAtQwjeHDp6rrN+IaNCRGdRlA22FQbTYQKeg4j2b7N/0M+SaPmuIBBvki+0DbP/idKow+6yTutl2Z1E/izDTZhHiuJPw+kuWBDdlsmrWU0alVEr2VkDU8xTlNxdZ/xaaVDcCokVYoH0n8Un6OLIfd3vI2Bl1DTQYsYncVkEj7r8oho1Mei92nzhoZIC9rKzIGoWWtK3J4/Nv/dZv4YMf/CD279+PlZUV3HfffXjwwQfxwx/+EABwzz33YM+ePfjSl74EAPj1X/91vPvd78Yf/MEf4Od+7ufw9a9/Hc888wz+7M/+LHmbG4zUUaPDBtN2WmEEBIpjDFPJsHpoiAII8RRSHvm1txOz1/BOxo0EzSnHqnG06kzANbmNuJ9xB0pWrYwLGbNntqP3TULc62KEfh/XRE3WRI4+Fmknbxs5T9lrJHsf6BRUrNm0SG30zO3htC2k/pj3VGSCLgv2/dSgQ0d6FwvR9YwK8MUiSSyFMILfdJg1wJRY+QjbX2FLQGZxJc5Et10mrbx28M4hi/aGEY3EiwgtDGgWB41uYxwy3AgVmTdOtwKiuYbstQl7RtvlnRWhXYOctey6NXl8np6exj333IOJiQn09fXhuuuuww9/+EO8733vAwCMjIxA171rcfvtt+O+++7DF77wBfz2b/82jhw5gu985zttm0MYSKEIbxUfNnpyLDuZDZtQizr+qMmvq9JyCHCkf4fmnygbWj5gvinKQxzXPFkGMqvGMscS+cjKpjJiV9x5JNMXEdsyYcHimiOlIf+RK9vM6mzwGPUAGWYh608cBfZ8onITkwmZqAwdeZpOw8SWIZGTufVHLCjQC1QWTJiI9y5HlY1DcuMEvJKF3wxbd85QXEZBodkIU4ezmhA2+hlP0s6ofp+OI6ExC62igJS8gITNRlIBI84iXVZkJWyRW1QmCwRUfEllFcjYPD9mhHFhKqUEwT9l0ei0a+Rdu6wJcIvwF3/xF6HfP/jgg4Ftn/jEJ/CJT3yiQS3KHqlMowkZbofVSxk1KKliJHr5onK6+nwyBQowz1eIvGge6QkOnGFKriwJ5plWk7+T3E+W7IvbxyrX2Tw7SczPkpiKx1fEJXIYMmRYBDYqddJJFBtYKyzFT9gxSORpmgwnbQcQJK6sxYbum2yGp8uKc32iAnBFpn3imJfz2iM6rn1s3f1pCtquN2HSrJl1aClMrzRlGr2lEKe/v9wmgVF9CL2IJ6/OJbOsygIiNypZtHqeBzRHmd2KJtmy72YcQt9OUCTYaYManzNHZj7CUZ1qmDIbN3VS1L48ROUAjnrJfD7FnHypAN8kmjW3lYlUrMGArulCwhuIxssQ4MBxQ0hwUogmCFGpn/x1hOftTToYidReCxoAC6EBwqRIa3xSLKMayw5QWfgTu+1izKXZiOR0+wL7WqYTrC08J7Hd5pDIzkQttuDmGmaP49ajwTGD9hRiQOziQBTkRoG3CJZkgitDcEVqceawUvogbQFLIQU/2mExWwbt2sYkk/RGnkurLUhklct2IDcswuYdsmN0Vr6tyU3r5UzKsw6K1473MytIpVFqxnunxufMEYsI28qRf5ckJtKNyiPKQvRSsp0UN2Jwggm96DvLCVIlMoMmYF80Eq03LJevTCor1mRVhgTLmEAlMa2SydubBMJ7Lav4MuZH8qur4WZvcSEaaFmzJtafOOy4UfdbpA5H16v7yLDdrujjsDCtIBl292EXNSzTR4bZ+nkKcprAfWHXQWQJIqtEt3qyqqDQTohUX1tEgsPaFWXl0a7EHWhc27YyGcpSCY6TZitsf+H3EULCpRxsrJmIk/+8nd93BTFiK8JhPoVZPghx60obgt7dzhwziuSzymsgtypDglllmijANIgaTEM0uMqbtIWleBAQaMcPWZ5MelGpafNeYaCrCJWeLcMrF2cRw9vHih5kEg5iiaNuCvzNwvyUWDNpGnECaQFwg2nZ9drf0ebSUYsDPF9i2Xzj9vvhJ8N2mwQTSstThnkEN6gg8/2Tk8ALIha9uBBFvuM8K80wiSbQTDOV+ZRmqsnAVkQ7TOR470Sr20TaIEtw26G9UdgKbbxUEFcdlk3ZqNB4xCHBzYIan7NHw9InZYFmD8wi82eRKsQLgEUUsmDdHBJMmUAnaSPte+zVm53vB3dSIjCzprfzfICTmPKKcvuK2ipXZ4hkGdKGWAOZpLIRXkbOPEsURCvat401P/YWcUTm0jyCSoOdKPKsRUTPK02GvbIh95oiwwQiS5Mwv2O6Tg1Bs2weZINiJe27mmL+HNqAesqolGqSppAc7UrS2rVdNFptZbKV1eBGIowMx7lmWRDgOP7BcVOoJTWPbgffWxrt1h4f1PicORIRYZEqvBUGChF4JJg9H5rA0CRYVM6rj39daD9gtk6NUdY809N6gIBLnV+MsvSxo3yN49adBGHEUaQE6rrhC+lub9Oh6RoMw4DlsDrTNGGF2fMydctGT5VRNtIFu/KbUPMIsafmyvrR+8kwgFBCHAZaIQ6zqhA9682c0EUFwhK1pVkLdUQN1mBAa8Lx7GAcyQdaFYxj66IdVGGFeEjSV2Z9n+OSH03XoOs6LMvrZwzDSDw+bwWkWSSQIcA8t6ow0htF9ujvL3X/X4KsI8s3Amp8zh6JFWFChmX9g3UYTfMNFkHGfFREgnn1sASDJiNhQbE0TXdTIOla3tceC14aGtpctW5FE1JuJOkEJJVn4u37XkLRZMFbDKA7V8PQUSgUUCzmUSgWUCzkkS/kkMsbyOUM5HM55PI55HIGCvkCcjl7u0tunYGVfKABmuZ1FpZlQdd1XHX1ldCgwfR1BhpM04RpWjDrdZimhbpZh2maqNXqqNfqqNZqqFZrqNXqqFVrqNZqqNVqqFSqqGxWsLlZxWalgspmBbUauffxzfXTrkSy6nuUSV9kGizfApHfXJoF69tLT7bCUiyxeY7ZdqedsMmmR4pj/qi55DR74tC0oFgKChxczmRYNNlvV4UozSQ8zn02DAPFojM+FwooFgsoFPLOmKwjl8vZY3TOQC6fQz6fQy6Xg+6MywY1NpPx2YZHcv3jMyMmOONz3Rmf7b/t8dkel2u+cblatf+uVGvO+Fxxx+dqlZ9HtV3NjeO0Szbvcbs+z63EViDBCo1BKtPotHmE0yhiSY4VtY0lwWGEkBcdmRAQNjUSTQZ4JFi0oBCVhojUGdY+3nlGgaiIPmVYEAU76v4Vi3mUSkWUOkro6Ciio6OIUqmIjs4CSqUiCoUciqUiDF2HBVDEctP+vVp1B7iN8qb9Oxn4ajZZrddrqNftn6ZpwbQsd+CkkTNygAb803f/DbW6PRjqmgbd0GEYOWeQ1uxtug7D0GEYhjOoG8gXCs5gn0NXVyfyzoBfLBZQLBRQKOadVW4LlUoVm5tVlMubKG9sYmOjbH/Km9hYL6NcLqNcrgTUVVlT7LBBjkeGRaDvIe9+u8dziKrnP0zawZBfNuAbJ6q0KN+w135x3mH2mGFqbmT0b+pa8wJrZYGwyWac4zQ13YUyvbrssVUCPmWJMMVLJp5Fs5FFP1UqllAs5dHZWXLG5RI6SiWUOoro6CihUMijWCzAMHRYFrC5uYnNzYo9NleqqFar7uLwxnrZJqHO2Fyr1WDWTdTrpktcTdNE3f3dH6vDG58fCIzPhEDThNo3PjvEm4zV3V2dyOVz7uK6TdwL0HUNpmmhUrHJcdkZjzfKm9jYWMf6+gbK5U2sr29gc7MS25UqS6Ql5lyFuE2eXRrt2CYR2oL4qvE5c7TMR9glmyETT58axUm/lERhZgknMfHmkWDykxcAi21fsO1+EgzwlWCaBIuU5jjBqnj7sWpslL+uyKSW145czkBXVwe6ujrQ2V1EZ1cHOjtL6OzqQFdXCbpuoFqtYmODEMJNrG+sY3FpCRvOYGMPqjbBJQOPZZnCY/rP2fT9JOB1rqZRh1mvO4N3jVs+bsJ6tnw+T6vaBWpyUURv3yA6SiV0dJZQLBYBABsbm1hbXcfa2gbW1taxtur8XLOvDe9cZFMsAX7rBBbiIGb8RRlWtQU4KipP+Zcgw+7vgujTYX65cfsB1jolEEU+g4l/nHydMoGwmpn7UZleKdBox0jOWSOWn2YGwY/STv5lJ+T5vL1o29lVQldXJ7q7OtHV3eFu03UdlUoFG9RibXljEwsLSyiXbdK7uVlBeXMT1Uo1VZtZBDIC5CyYdZM7PmeFXC7njs+lYtFZpC+is6OEvr6dKHUU0NlRQrFUhGVZWF/fcMbndayurWN9bR1rq5tYW1t3x+es0S7KNPucy86RtqIZddukRpKAGp+zR1sEy4pSYdiJbpYm1nGUWF8ZmEKCQZNgyyUkOcfHzybBNAHnBb0St1cu8JSIBCdFqSOPnt5u9PR0ore3G909nejt7UKxWES1UsXaWhmra2tYX9vA+PgM1tbWsb5mD6z1Oq0i+wkIC7oTZf1/404+goQ1QcAuJm0RXTev/dVqBbVaDWtrG6H16rqBUqmI7q4ueyGhuxM9PV0YHt6Bru4OlEpF1Gp1rK6sYWVlHcvLq1hZWcPK8gbWVjekV6rj3P+whQ/WSoCOEyBjbh2Vbziw+KL596VzdXtlg88ELw1WlA8wAa9c2CKd6DzjwoTZ1KjQkVArzgqSuBTMqBs1aZdRmLNSwzo7S+jt7UZPT5f9s7cLPT3dKBTzqFSq7gLr+toGxi9OO6RuAxsbZVd9TXIeSdEqolRzTKdF4zMZ73VdQ6lkLx50dXeiq7MDvb3d2L1rGN3dnSiViqhWa1hZWXPG5lUsL9k/V1fX4wflbHJKUen0kDwrry1IdMOwlUgwADU+NwBtQYQBsX9eXNLLm5RHBbMSlXPL00GEAmSBR6T9JJikSdI1PRAYiJBgGXNnIDo6sN+n009AZRSlfD6H3r5u9Pf3oL+/F7193eju6UDOMLC6uoGV5VWsrKzjwrlxrKysYW21bCu5nOOKOndZEzR2W1inHNY5J+20faQ9hiInylPt32ahvFFBeaOC2dmFQB2GoaOryybHvX096O3txt69w+jt7Yam6VhdWcPy8hqWllawsLCCxcVFlMubmamGYYG96KjSYUHzeCnACBnmlQ8sfJD9KTNs2myakOHQ8+CQYZk+RWbgaxQBYP2DCVFuF6VAoT2RJHbD5Y7EY0NGREDGkolGPp9Df38v+vv70N/fi74+m/zquo7V1XWXmM2cXcDK8ipWVzdQrYqVXFmLq8sFJPeuadpq8Pr6BmZm5nzfA4DhmF/39NiLDn19vdi3bzd6erqgacDq6jqWllawuLiMhYVlLC4uY7O8GTheq/r0LOKRhCEOWW50W9JC9aeXPtqGCAPJJ5ahxCrMfJkTJZe3L2smTNRgNiVSgIRrXkAdogbbZIAyh+ZEnWYRteouc92C0QR1FAp5bBvoRX9/D/r6e9C3rRtdXR3YWC9jeWkNi4srmJycwcryGlZXNwIBLHiIQ4LDlMQsSAY/x7CVyeATRb7Tol43sby8iuXlVYyPz7jbdS2Hjs4Senvtlf/+/h7s278T3T1dqGxWsLCwjKWlNSwtrmB+fhnrEco0EL6YEgVRnICwoFr28w9A8xaWeMo1HaWaNstmyTB7LjRo5ZglwPQiCiAfVCssl3oc0O2PCo7lkeHGT0w100qVa1C7RKK8blXILqpmfaythnYkefSiabFYwMBAn0N87U9XdwfW18tYWlzB4uIKJsansbycVIVsDQnOYmEaaHLcBOZ49VodS0srWFpa8ZfRNHR2daC3pxt9jphwxRV70d3dhc3NTSwuLGNhcRmLC8uYm5/D+nr0+Ewj7sJJ2AK+LAFtBlFtFRkOO2a7EmA1PmePzIhwIPAU49ObyJ83pRostY8zEZeNksyqVrTyS0NzUyPZJNjQcj6z0qTtlQVLLDRNQ39/LwYG+jA42I+BgT50dXdgbXUDCwvLWJhfwvlzF7G4uILKpreCHI8UiUlwwB9IIngZd7+QDjMNMZXpiNlBRdb3i6di2/XFD8xkWRbW1zawvraByYlZ91obhuEMvD3o7+vDkaNXoL+/B5ubFczPL2F+bgnz80tYmF9Gve6RT51qCwtRjmJuu5hzYQNeyURZZiNe+trH+ChHBdRi2yW6v9xtGQ+AssHKRPs1fUA260jVNSnTq7ZBM0nxVkK7kWBN19Df14OBwW0YHOjH4PZt6OrqxOrKGuYXljA3t4AzZ0awuLiMSqWa+l5uJRIsXFRvEDEmqnASWJZl+xSvrmNiYtrdnsvl0NfXjf5t9sLGsasPor/vOpQ3K5ifW8Dc3CLm5hawsLDodyWTtHgKLdPE+BI04s7H2kkZbuu+Uo3PmaMhirCMT29WvkWyQZWA8CjXcf2C2Y6SF1WSR4IDwbgk/CuTXCfD0DEw0IehoUFs37EN27b1wrIsmwzNrWBsdArzc0vCVAJ0e8JIUrCt4T7ArYYGzRkUguedpr2t6MDpa12v1zE/t4j5uUVoGAdgPwP9/b0YGOzDwEAfDh/Zj2KxgKWlFczNLmFmZh6zMwuoVYPPfpRvsWghQ9an1q8Ky5JZPhnmQeRTzEL2vrFqcVyz8LjYykqbQntCmU7baIdxyTB0DA5us8fn7duwbaAPpmlhbm4B83OLuDByEQvzK6FmzY1Cu5DguGQ0K8IXRryT1l+r1TA3t4jZOc/M2jB0bNvWj8HBbRgc7MfRY4dQLOSxuLiMmZk5TM/MYXZmDpWMg5QlQVKS2s7+xO1CuhVaj0yIsCi6cxRoksWLCh04TooVI54KK0rjElC3OeSXjQhNYKfP8cyhCQmmz5VnDi0VOTsk+q+u69g20IsdQwPYsWMbBgb6UKlUMTM9j9GRSbz4/BtYWVkHLL6TfVjqHBphBKlZJJh+DprdyWa9uiqjCvvOV+L9Im2s101npXnR/a7UkcfAYB92bN+GEyeuRE9vF5YWVzE7u4iZ6XnMzS4GFkdkA7SxoMmo3S7+vdKgo5DPoVL1onCGRbTmRa9mwZLUyPIJ7mkYEY9DZHltc6Nrt4K0WClXnFOm1VNoLLJ6rrbiYo2u6z7FrZnHHRzsx9DQIHbsGMDAYD82NyuYnp7DhQsX8exzr2JledW3T6Mm6uHuYluTBNP7NVL9TFM/e071uonZ2XnMzs672zo6ihgc3IYdOwZx3XVXo6e7C4uLy5iemcX01CxmZ+cD43OaebHsvrlcLnEk77j+wmSfVqHtFwrV+Jw5UhPhpCQ4DLx6wgImyYIbJIsiw2HKJ+0PzPMF1jQd3d2duOOOm/HCC29gZnqBO5knJFjG7DSKBHd1dWB413YMD2/H9h3bUK/VMTOzgLHRSTz/7OtYXV3n1uprOwmAlPDlaGWwh9R1cCJC0xCZSPEGwzjPJS9dT9LON05+Swt1bGzUcXGsjItjUwCAUrEDO4ZsZeKa646gp7sT8/NLmJycxeTELBYWlpnjiXNri8ASYhoadHT3dODf/8e78W/3P4mXX3pLYmGAH706CkkVsSQkOS5EZLgV0CwTmmDBTHZ/hfZGyxZZWoieni7cfsdNeOanL3EDFGaN7u5O7No1hOFdO7Bj+wAqlSqmZ+ZwYWQcP33mZaxxx2c5NOL+tUt06Ebkz23GcdPUTa4TCdA1OmpbdZVKRQwNbceOoUHceOM16OrqxPz8IiYmpjExMYWFhSV3/0aR4f7+Xvzaf/wkvv8vD+LVV08lOkbWbUqLrawGq/E5e8QiwmkDxNATyixIs6xZtIxJtGw6JBqu36OmY2jnIN729utx7uwYZmcWg0RJ0EaR2sZLf6TrOrbv6MfO4QEM79qBrs4SZmeXMDkxi5dfegvLy2tSyh1NwpOSg7COXcbUtZ0QNnBmRYZZBZtd+Yya2IgiUYvKyE4GNjcrGBudwtjoFHRNR6lUwI6hbdi5azsOH9kP07QwNTmL6cl5TE8tRJrSh4ElrSRo3NrqJh5+6Hl88MPvxNDQdvzkgZ8y5xXeV4iItiiQVitIQFRQryg0Nb2S8kHakoidYi7NAlyb9eFRGB4exNvedgNOnTrfMBJsGDq27xjArmGb/HZ2ljAzM4+J8Sk8/9wrWFlZc8u2IkhRM5Xg5AGxsgnkE5cMxyHBWdcddq3K5U2MjFzEyMhFAEBHRwnDwzswvGsIx44dQr1ex8TkDCYmpjA1OYNaLfv3cnFxGY8+/Aw+evf7MLRzO37y4ycyPwYPrVCHt8TioBqfM0dbRY0WgRcwiYaMvzHrm8v7zv49Oh0S4KnAGgwcPrwf11x7FC889wZGRsahI+fLg8qLOE3DJsPBNpuWiVzOwPCu7dizZyd2Dg+iWq1hcmIar7z0Fqan51CrhauabH28v9MQYhZpB1T2XmZlAk3ul+/GhJSlj9koZTgukpiXxQ38US5XcOHCBC5cmICmaRgc7MPOXdtx7OorcMtt12JubhHjF6cxMT6P8kYwHUQURItSLz13Bguzq7j7E3diaGgA3/rmj1GrhUdUZ59ZmmiLcvwG0kJREaXJQl0g8FdIHmIZRVqmLVtiAFZoe8SxEAEuD2X4yJEDOH7iCJ756UsYG5vMtO58Poddu4awZ+8whoe3Y3OzismJabz44uv2+Czw8ZWd5IeNJ3HuXVoS3GgXJAt1WBJjc9w6ZeZFSZTgrOqOe103Nso4d24U586NQtc1DA4OYNeuIZw4fhRvf/tNmJ1ZwNjYBC5enESZStcU1d6oecuTTz6P6ek5fPwTH8DQ0AC++c3vw4zhYtDoQKYKCmkQmwjTqX8A/4SOjg4dZk4YRw1O2wGHqsGM6bHIx5VLgh0f4OuuPYYrDu3F44+8iPn5Zeha3qcyy66c0yQ1n89h1+4dDvkdwOrqBi6OTeGN189iaWk1lJTxFGY2n7B7HiSvKocQ29/rgWvE1gHEu0dh14OOLMyWFU4GJI4tVGk5JJEuS3fecQglr+NuVcCINOZelmVhdnYRs7OLeP2Vsyh1FLFn907s3juEa687ioWFJYxfnMHExTmsr5cj2sFfaKFJ5NjIDP7m//0LPvGZn8Vnf/nn8I2/ux/rGxu+NEte27xo73EWccLKhJFh+jzY7UnTKfGecx6aqgYDgGkCZopJaYrUDgrZQZpsxSTDSdXgZk9oLcvE9TdcjX37duGhB58MuHkkRaGQx+7dO7Fn707s3LkdKytrGBubxKuvvIVlx8+3WT6RkZZECQlwM8erZpgkC1MIpRkfBeQyzjwhDUzTwszMHGZm5vDSS6+js7MDe/buwr79u3HjjScwN7+IsdEJXLw4gfX1cvS1iHg/z54dwf/7s7/HZ3/po/jlX/5FfPWr30VlsyIsnyWyVIe3PKlW43PmyEQRZslwGGgSzAuURSNpR8GS34AKxDE75pFgXmokTdOhaznoWg633HINdgwN4uGfPI+11Y3YEZbp4xqGjt27h7D/wG4MDQ1geXkdF8em8PLLb2F1xe9LFEXKCBnmHZftBAlp5pWVmfAkzeEsAxmiwK40ynRyPrIb8byGrWSmWWENtqP9O+fyxibOnhnD2TNjKBTz2L17B3bvHcKJaw5jeWkNo6NTGBuZwuZmRfguhKm4ALC8vI6/+6sf4OP/38/gl371Q/ja3/4Ay8trrpAvIsROpTCt+C4contFTzLDVOEwpCXn9rHNJptGq4H2UoJcipXoxaQ0BJj3e0MnpJqF2267HgMD/fjJj5/AmkQ+9TAYOQN7du/EgSv2YGhoEIuLyxgbm8SLL/hjcTRC9Yq6f3Hvy+VAgHnHCrqrZeCelzjNUvbXeX19A6dPncfpU+dRKhWxe89O7N27C9dffzUWFpdx4cIYRkfGnfE5GRleWFjCX/z5N/DZe34Bv/Zr/w5/89ffEsSiaQwauZi2FeZgANT43AAkIsL0RJOQThnz5DgkWIQ05kD0pJlVgL0yfhJMr0QREpwz8jh58kZ0dnbgkZ88j3K5Ejgej2zzsH3HNhw4sBt79u5EeWMTFy6M44XnX3cH7qQRCsMiTPOuH880Owka4TsWRYiTmE/zCC6d9oqAdLyWZfoWIJLcl6w68EYNBGHPKllcIe9fZbOK8+fGcf7cOPL5HHbv2YF9+4dx4pornUjlU5gYnxFGaOW9x+T+bpTL+Luv/AC/+Mn34J5f/Xl87e9+gLmZRXsfJ7gdz1/fsuoUGY6OxC7Tj/DIsGwftGUG1hbjS1/6Er71rW/hjTfeQEdHB26//Xb8z//5P3Hs2LHQ/b75zW/iv/23/4bz58/jyJEj+J//83/iQx/6UJNafWki6/47inQ1oi/TDQ133H4L8oUcfvLjJ7CZQrXaMTSIAwf2YO/eYayvbeDChTE8+8wrWF8PEuu05KYZirmojZcqAW6H47JoRkaNcnkTZ8+M4OyZERQ4ZzvgAAEAAElEQVQKeezduwv7D+zB9dcfx9TkDC6cn8D4+BTMBKRofb2Mv/zzb+L/+8xH8Gv/4ZP4m7/5NuapzBSNRhp1eMurwQoNQWpFmDaVls0NnIQE80gmD7w0SWxU6DAzaFKOJsGAZw6dM/J4xztuhm7k8OhDL7opX1g/YHIcHjq7OnDFFbux/8Au5HIGRkbG8fBDz2CRY7olMr9hVeEoYkYIsK7lA4pZ3aoCVrDdUYG3eNctrEzaTigqx3K0AqtB0zTq7wQdacKFibBjbTXCxN7narWGC+cncOH8BDo6ith3YBeOXXUAN9x0DGOjEzh/dtxnlkjn1Cb5gAnIs2maJr75tQfw4Y++A5/93Ifw9b/5Iaam5wLH9p5VfkAqehvPMiQuGZbZtxH3s5mqsGaa0FLM1bSYk6uHHnoI9957L2699VbUajX89m//Nu666y689tpr6Orq4u7z+OOP49Of/jS+9KUv4cMf/jDuu+8+3H333XjuuedwzTXXJG/8JYpW+Nm1wh3EMHS84523wDRNPPTg04lSvnR1d+LgFXux/8AeGIaOkZFxPPiTJ7G4mI1pdRiE1ikp718rCXC7ENB2QFOuN/OsVCpVnD07grNnR9DZ2YEDB/bgxLWHccut12J0ZBxnzoz4nm2ZZ61Wq+Grf/sd/OLH349f+ZWP4ytf+UfMRQSha+ecwlsJzR6fLwdolmVFhulbXl5GX18fhjvfgZxe5JYxOUTQ9z2CBFWGCMtGhmbbwQbHooNUhfkCk20sgdO1HPK5Et7xjpug6waeePQV1OpV9xiRCrBmYnh4Ow5duQ9DQ9sxOTGLC+fHMTk5C9MKzwuXJm+dBsMlwTm9ZJt1w3An63VUYVkm6lYVplX1XScC1syaXD/etaPPIXAJYpDBpEGywp6TXM7A3b/wPnzn2/fbgZgk2shGeQ4rL1KYefX6ysUgOI0zC5J7xsIWR9jz6N/WgwNX7Ma+/cNYW93A+XMXMToyiVotmIKJb6Vgl/nQR+7AlUf24utf/RGmJmedhap64FklzznJ2y1jIk1HfgfErh10++gyZLsoYnWWwYh06DCtGkbXHsDS0hJ6e3szqZeA9PNz3z2A3q7kbV5eMzH40QuJ2zgzM4OhoSE89NBDeNe73sUt88lPfhJra2v43ve+5257+9vfjhtuuAF/8id/krjtWxHkvhVye2K5YzQaaeI3JEEuZ+Ad77wZtVodjz/2bKx8wZqmYffuIRy6cj927BjA+Pg0zp8bw9T0LCxTLpJxFpP8Rty/pCS42QQ2l8vhFz72AXzrH/9VegFjKyl8zSaBUddmYGAAhw7uw/4De7C8vIqzZ0YwMjqOOgnAKnltf/HjH8AVV+yVIsNZX4O497/RgoRlmShXk499YWiX8flSRGxFWDZKarPTK0QFxRIFeqLL0ASYkEgfCX7nTdBg4PFHX0K9TshgcEJOo1gs4IpDu3Dw4F5oGnD27BiefeZVbJa9jp7n85t6BZhSkj0lOIc8SjC0vFtOtwzUtaob2IvXUUWR4CiIg1UFt7NlRavjwmNFqMYy7ZI5Dm//rbziKXovWILMWyTx6vCbHC8urGBx4U288tJp7Nu/E1cc2oNrrjuM0ZFJnD09jhXK951n6kz6mu//0xP44IdP4tOffT++9tUfYmpy1gmgFVSHbWXYcN5J8fnSfVgUCSbtCw20xfSLPAuWLaP8m2bK9Azp3oGlJTs35sDAgLDME088gd/8zd/0bXv/+9+P73znO6mOfamjWcpwnGBRWbQnKQkulYo4dGgfDh7aB8sCzp0dwdNPv4jNcjxz6nbs95MQ4JaZLZO5V7QuE9gPaH9CLP98sOXSWQGEXZf5+XkszC/hxRffwP4Du3H4yAFcf8PVuHD+Ik6fPo+VlTWp6/qP//ADfOwXP4Bf+ZWP46+/8o+YmZlP3GYFCbR4fL4U0ZD0SWlIcJJcwzxzaH67gvUR8uJ+aHNjhwQbRg6333EjAA1PPGaTYNbcmj1OT08Xjhy9AvsP7MLszAJefOENTEzMgAjwLMHw+Z+yqmRE6HuajBCiQsrrmq2QGVoeeZSQ04rQnYjXFohPZZ1j+pksKb17PpwONIwIxBnIZHMwyhw3zjFlzLwTrdg3KYVJ3FRKQPRzwDOjZ9+JWt10/Yn7t/Xg0KG9eM/P3orZmUWcemsE01OzdvsEC2wWTHz/e0/g/QA+9dm78NW/+lfMzS06CzxBcm5ZQcWZl6oolhIvSKfk8yGOWCTk9RWybWh65OgMsLzsNyUtFosoFvkWRQSmaeI3fuM3cMcdd4SaOE9OTmLnzp2+bTt37sTkZLbpcS5FNJoMx7biSdkew9Bxxx03oVqt4fHHnpPye+zr68GRowexf98uTE/P4blnX8Hk5CwkDOQaisjAWDKBzxIEw2oFAc5y8aCdU+zInaeoDNnemHOzUEetBtefeGCgH1cePoD33fVOTE3N4tSpC5iZnous51v/aJPhez73MfzFn39D6EaQtVjQzvddYesgcyIsk/eTgE631Ijj8nIGB8t4KY4s1N0Xy1WDdQNvu+0GFPI5PPbIS6jVgoGxAM98ePuObThydB+Gdg5i5MIEHrj/CaysrHHaGyQYIqISh5CyZsx2micdhpa3iQN0GJatCNuB54ImSK7qy7QxTA0OC2hFT/YjTb8izlV2wOaZVtvt0Jxr4k14ZBdutkKHG3eFXKa8zKIIL20XD4sLK3ju2dfx6itncOjKvbjltqtRLldw+q0RjI5MAtRzRKvCFkz88HtPoFh4Jz752bvwt3/5L1heWQEscaA3mfvKU4OjUsCx2+OQ4S2BjFac9+3b59v8u7/7u/jiF78Yuuu9996LV155BY8++miKBihEoVHRm5tv/qnhtrddD2ganng8mgQPDQ3i6LGDGNoxiPPnx3D//Y9yx+c4yPqcZcgwgSyxaBcC3IxAUe2EdCSYLZPMLD7ONZmfX8T804t4+aU3cPjIFTh58gasr5Xx1lvnMDo2Eeom8K1//AE+9akP43Of+xj+359/A+trzYsmnQXSChJNe/aUIpw5MiHCsoosz2SaJcNxiXEgVVII+RURTdYnGPDU4BtvPIG+/h488uALqFYrQh/ancODuPr4IXR2deDsmVE8+8yrKJfLvvoCx5UgGElUWUKGCTERTcwt1GE6pt3E7zKgrsVUhoUBhEhE5hQqM71/IP+vIJ+fzERBZMqf1E+ZB5kUGFKBm0LqYdOU8CwL4pQP2zftfdzcrOD1187irTfPY9/+YRy96gCuPnEIp94cxYXzEzBNM0CGAeB733kEH//0z+JTn30//vavvoeNchlkTYO+hjxVOAps38NLmSSTQkmWDLetqbRVR6hdeeT+9nM1Ojrq80GKUoM///nP43vf+x4efvhh7N27N7Ts8PAwpqamfNumpqYwPDycsNGXL2T7t7huKnHbkGQiedPNJ9DV1YEHH3wq1Bx69+6dOH78MDo7Szh9+gJ++vRLqaJJE7TaJDrSzzcFAW71uSVBO5Hh7EgwWzbe+SW5JuXyJl55+U28/vppHLxiH46fOIJrrjmK1984g/Pnx4SE+Bvf+Ffc88sfwz333I2//ItvoFIJCi1KFU6JjMZnBQ+xiXAz1Q5eoKywSSaPBLv5jR1ySBMpogbTJtG6lndJ8NVXH8HuXUN4+MHnUalUmXptgrhzeDtOnLgSXZ2deOutczh75iKqtaq0zy9LNOMSDN0lmH7TVFadM+H4MsMENPtc6qihjirqlv2xCbHIhzrZynEcAiyjKALw3cfQYycwBebWExIQKw7CgmmlqScLBPydOZYAvjakSCNFo+6YTV84P4Hde3bgqqsP4uixfTj11ijOn7MJsUeGgboJfOvvf4xPf+79+ORn3o+//at/gTv/ZcYGtj8II8YyC3BpfIh97UhAgLdi1Oje3l6pYByWZeE//+f/jG9/+9t48MEHcfDgwch9Tp48iQceeAC/8Ru/4W67//77cfLkycTtVghHo4lR3AntNdcexdCOAfz4x0+gyozPBIQAlzqKePONszh7diRWEK2o9jYKWUzuk5Dg1pJfE3YnbiKcIEabhreaGGVPgtn9siPDYWJHvVbHmTMjOHt2FHv3DeP48SO4+urDeOP10zh//mLAAsM0Tdz31e/gV371E/jML92Nv/6rbyVK0bTV0MznTUWNzh6xiHAzSHBYSh6i/gZUYK6fbnAbIcMsCQYon1o9Bw069u/fi2PHDuLhB5/HxsamLyiWhTqGdg7imhNH0dXViVNvncfZMxdRrwcVVfbc4rwwATIrCFQUiLjMKJzEB7iu2WmSyPcm6i4R5qnB9HWhz8H3fQxTZ1miyzsn9/gUyWcjBrNw/aUzXIFsZECsLExzsgliljx3dZx7TMOyLFwcm8bFsWns3jOEq44fxNGrDuCN18/jwrkJwNLcRbBqrYZv/N39uOff/xzu/tid+NY//Jg03KkrzCpELpp0oxD3/jbLf7yVuPfee3Hffffhu9/9Lnp6elw/376+PnR0dAAA7rnnHuzZswdf+tKXAAC//uu/jne/+934gz/4A/zcz/0cvv71r+OZZ57Bn/3Zn7XsPBTSQ3aMPHBgDw4d2ocH/u0xlMubge937dqBEyeONoQANwtJCV1cAtw68ps0MFS0MtoOZLixSGYqnQT286FjdGQCY6OT2Lt3GFdffRhXHz+MV185hQsXLvp86yuVGv72b76N//AfP42PfPS9+M63729KO5uBrRwQVUGM1KbRYdGaeQiQtIhURlHH8eeRNaT8gsl+tEm0pumuErx9YBC33HINfvrUa1hZXvOR4N6+blx33VH0b+vFW2+ew7kz424E6ajI1OQcwzroMCLBmjwDVOAfkpuVyulMn2vdslfMTc1/vUnapLpVkzJ/5QfBis5hzLZXBlwFj1L9RL6h9HFNyzORTpY3WOz7nAaybZHteF3lPeXgH5cEs6vJIjIsEzmZkL7xi9MYv2gT4hPXHsbhw/vw6itnMDk+7/YD5XIF3/zav+Gef/9zeOe7b8JjD7/oWQtp4WQ48pyoPoFnHh0FEdFOQoLJTw26bc3RDDTZB+nLX/4yAODOO+/0bf+rv/or/PIv/zIAYGRkBLruXb/bb78d9913H77whS/gt3/7t3HkyBF85zvfUTmE2xiyi6hRY+Tg4DbceNNxPProM1hd9fsh9vf34rrrr0J/Xw9ef/3MliTAaRCHBLdmQp/lMcMJcavIcGPVYLaO5p6fZVkYHZ3A6OgE9u3bhWuuPYqjRw/ipZfewOTkjFtufb2Mr33tn/Crv/JxvOOdt+DRR57x1dMq8+gsnomWk2HlI5w5Mg2WlSZtksgMWlg+pj8yTVJZn2CaBHd1duPkHTfg9VfPY2py3iXBxVIex09ciX37h3Hm9CieevIlVKtM/l+WSAp8TNOE/A8jwfTvNBGwLDs6dM3ahGb5CbJpVV2V29d2iUBedGTqKMiQAJHpqm9fmvxGuEkQ9ZiQYXt3zbnf9s7S/nEZpgSLQ4KbCRkSLPPshinDPEIsSjU0fnEakxPzuOLgbtxw0zGsHt3Aqy+dxcL8MkyrjoX5FfzTPz6MX/h378HszCLefP2CFwFdQIZJWiXdfU/8A2OY33QSUkzAPv88M2eTcx1agiYPtDKReh988MHAtk984hP4xCc+EetYCs1HWBqfuH1cV1cHbr/jRrz04hu+aLYdnSVce80x7Nm7E6dPXcATjz8XGJ+zRLMmwnEm+NztmanAMuVJO5vdd4kJoVKGswHvOo6OTuDixSlceeV+3HbbdVhaXsVLL76OhQU7YvT01By+/e378fFPfBAz0/N4882zDW9nWiSxwGr686WIcObIPGq0aIIbhixJMK0KuybAAnNou7xNgDVNR94o4uTJ6zE1uYAzp8ecgDsajhw9gKNXXYHJiVn86IdPYH1tg9OmEHPMiBWkJKmKuCSYQ9ZcVYlzfJGCzRJcNiUTQaZKr+/44rpohZZH+tmyNBm228+YkadY3UuyoJFIlW7CCqTo+bMEFhtsmTj5hu19ws/HVUHNGs6dvYjRkUkcProfd7zreoyNTuO1l8+iUqni3NlxPPTAs/jQz9+B2dlFzM0suX0HayFCB9AiPscaoq+tTYDhqLLBZy0pOVZQuJQhG81YFISL3W4YOm6//SaMjk7i7NlRZ0ETOHbVIVx11SFcHJvCD3/wCNbXg+PzVkbYNQrdLxUJTjLepB+jLFgBoQJoAdnYUmi+Muwe2TRx6tR5nD8/hquuuhJ3vuftuHD+Il5+5S1UK1W8+eZZPPjgk/iFX3gf/uz//T3m5xbdfVulrF4SqrBCpmjo28MjNCKzaBpxSbBl1X2f4D4eCTbNmq+jJSTY0PK49rqj0DQDLz7/FiyrjsEdffiZ992KvfuH8dgjz+Ppp14OJcGkXtmOPMmLRNIh2XX6SbDvpxYkJvTHDo5Vl1J5eeoe7Z+swc5VLPvhnQ/98Y5hCD/sdRAdh5Txk3bD/bDXT4SwBQVZaMzCAXu+svs2AmEBsdg2JGkLee6Solar443XzuGBHz2FYjGHn33/23Dg4C6YVh3P/vQNnD41io994j3I5XT3+bDbyvq41917aVK/y0BUlvRjdL1R+7JmzjJmz03LJWxazqpz0k9rc7FeriCWTUndQLJEnH6SN17y6rjhhuOo1ep48YXXAdipkO56/zuxZ89OPPTg03j66RcvORJMwM4tkpDg6P1M6tMcyJ6TXNvF+zYL8sfK+v1sLSmrVmt4+eU38aMfPYrOrg588APvwhVX2JH/H3v0WZw7P4ZPferD0I3G9UuXDTFV43PmSPVUxvUPls0xzCM+wv0iSLOnejok2CF/9ITB0PLYu3cY+w/sxlNPvgjDAG665Sq8/fbrcO7cRTxw/5OYo1ayvPqDRNLfNokBK05woww6T1GbCTHUNV1IfOlPgABzCK3Mx66bT3bp82Y/OkV0xSmbgmRY07QgKRYEHcsSLAn2fQe54zeDDNMf2TakjRwd2S7qHV9fL+PJx1/GMz99FceuOoB3/8zN6Oop4fv//BhM08KHPvJOAPCRYV59PHNsqbaQVGMwEbaoJ0uGyb+wck0PlmVa6T8KLUdaUswjXzJkJWvfP8AOjrV7zxCefPJ5FAp53Pa263Hy9htx+tR5PPBvj2N+fjGzY251xPcHbi75BeTmRvH322pE6NJTuddW1/HoI8/g2WdfwfETR3Dne96Onp4ufPtbP0QuZ+AjH3lvq5uYKVqy4KjG58yR6C6aVj02CW4G2EmqzySaNod2Vc8cDC2Pnu4u3HTLCTz3zOvo6e3Ee953K3L5HH70w0fx1ltnAz5sQjIZEvwj7HtSJxBUbr02U0SKo6zKguf7y6rAUiSWIcBh6q1oIYP+TqSQCkkuE/1XpCprAsJME35ChjWNT9RDr2fMjjDsfKT2j3jG2h1xVOEwgjo1OYd/+9GTmJ6ew50/czMOHNqF7/zDT3Do8G7cePMxAB4Z5r0rXgA8uRzosm2kCTLpJ+m+MulxmqYGAylXm03lg9SGiNNPxSG8WZJfUR09PZ244car8fRTL2FgoB93vf8d0HUNP/j+Qzh9+oKUj/nlgngkuPkEGMhmrBI/cwL3nbYdHy89MgwAFy9O4Yc/fBgL84v42ffdgQNX7MU3/v5fcPz4Ydx403G3XCuIZNgx47n4tejeqfE5c8TyEbYndBGhemFPQNNGkw7zNQ7LF0xPtGmTaK9ew/MLhk3Ebn3bdRgdmcTwrgHs2r0dz7/wKkYuTHDbKZuXlg0KlNQvIRAlWpRDWTAA+P1kSZuS+fryTLF1RnUVQRThmbefWOENBh8TPWv08+T3G/c3hM4vTSwF2nfQbD8kUYPjpFkivsK0fy9BvW7itVfOYmJ8Fjffchy7dm/How+9iDt/9mZcOD+B+bll5/mAMHiW3R47LRMbKCPu+0oH02LrovOfRwXk8D27l+hESaH5aPe+jfe+6bqOt739Bpw/N4r9+3dheNcOPP/cqxgd5Y/PzUK7Xct4/sCtaXcjrhd/XmViaxFMHc24J2l8Y5PsV6/V8eKLb2Ds4hRuvfU6lMubeOThZ/D+978L586OYXFxOVFbFBSyRkN6C5aYiHyByaRR4yiRPIjyBdMkmKi1djTkGkzL8wmmo0PrWh66puPY1Vegp7cLw7sGUeoo4P4fPYGRCxO+TjvM34aNMhuGpMEtkoA36WfNm/2qaLiJMaus8n11de6H3o+nuMqY8PlMiyVNmWVJBK0Ms8cKrb+B0Z/9ptSMP3NGK5EiZSeWOWSEe4AIrOWDyBJCBgvzy3jwgWcwP7eE4eEBzM8t4yMfe7dvsYa2YvC3P/pYOsLN9H3nBb/6KwoAKBP1vmUkWK04X7Jote9wXJy45gi6uzqxd/8uFAp5/OiHj7ScBLcbGkGCs1b9Y/mMM5Z9WUXQb6eFiyB0NJq8t+rdn5tdwP33P4rFxWUMbt+GxaUV/OLHP9Dydm1ZqPE5czT0CcyiAxOpwcFJpp+Q0oGxTCeHLgAfCR7Y1o+33349isUc3nrzHB55+Fmsb6wBCL6c7ISfTbkSCI7VwIls2IKBKGCYaP8kBNjQ8pSJNJ/E6gxxC/v4j9lY018LfDM6WTKcVact67vnD+gVvKZJEfd6yiziyPgYyyIuGa7XTbzy0hk8+fgrmJtZxJ69O/Cun7nJ99yyZv1xkfR9TkqGWwblg3RJI6rfaNTENKpvYPuYwe3bcPLkjSiWCnjtlVN49NFnUC5vZteelGSvLYKSSZPgcDPo7IJWybRFUA4mIBifwwhxO6ne6Z+JxhPiJEg7/6rX6njh+dfwxBPPYWZ6DgcP7cO73/029/tmvUdZHifreAjSUONz5sg8fRKBq9KGTIhNhlj6VVj+5JF8FzZRJimSiGm0fQAvQrSu6ejo6MLHP3kXNspl3P/DxzE3vxh78p6FeVTUixllQkqbUYaZjNPl5drFBpry/03aHZY6hv4uyiog7FlgwX4XZorvuz4xB0e6HazpftR+oraEHSNqf7sOQ7ggEwdJnl2fn30DBi4v17PA/J9jHs1idmYR9//gKeRyOdz1wdswNjqNs6fGHDJsl7HzSiczPWafd95znSbfsIJCM9Bo097QdIIS70ZHZwmf+tTPYXl5DT/4/sOZm1FGjS9JU+I1NUJxBjmCk7Y3jZkttz5Jc2aRW4msiXTW7RaB58YVD3Qb23TBNAGmJmfxr//yIAqFAj5y989iZHQc586OtrpZAOTmArz72axnSqFxaMjd45HgYMTU6GBTdKAZvll0+Oqy/dB6K6GW4wva29ODf/fpu1A3TXzjvh9ySTBrjipaLfUNgmGrlhwzn4AaSh2PF705aYAsth1RoEkwawLtmpfDiDXZp8uSfXnbvDaIlePoxQNxgC7LsiLNb6MmarIm3LJl4qY9aXSUZhnIqCk8lVj4vvr6Ci9wXPC44RYPplXH5mYV//Sth3HqzRF85nPvx/Du7W4gNAABk/6wcwTCF/PSQnZxhRddumGwzPQfhbZH2n6MhyysQfr6e/CZz34Um+UKvv61f24qCY5Thoe4fXlSZGEO3d6mwmLEU4Zbi2yeBR3RSnHjrTyyur4bG2V8/Wv/jDOnL+Df/9q/w/DwDv7xMuhLaFwyZFWNz5kj8ydD5MebtA6aPIomwOKXJThJH9o5gPd98CTy+Rz+8Rv/hkrFM5tOre5mZA7NBq9qpq8gS4JZX14gXAUOgwx5jkuw3f04EarD0m4BiCTEQLiJNEvOs4pG6O0jbj+7UJMEDVF1ZSaZYC080g92gYU2y8TX/uZHWF8r4+6PvwuHj9o5DVkLB/b3gH9awj6hUeS5KWTYSml2paL4XlaI6w5ByvHKDw/vwAc/+G7omoa///t/QbVay66dMU0Zs5j4Z93HxjEtt8FXsJpJGhtjPSTbfv75twLZk2IZgizRrhjziLTXzpuPW/jLv/gmNjcr+MS/+xCOHL3CX66Bi888pJ1jN/WZavL4/KUvfQm33norenp6MDQ0hLvvvhtvvvlm6D5f+cpXoGma71MqldKcdUPRMIYlaxItLMOaQ3OUYdef043UWmceSP/q7MFD+/D226+DWTfx1BMvY2lxWSJoTbwASuJ6gv64PlNTQQoj9/uY0aJjt4/xB6bzsNKdd7NMPmnVmEeOZQNmxTomE6CKhci0OajsS/pgO2RWRGplBoO0hDjtc82zgpAlw8Jcxcz5JH3GK5Ua/vWfH0N3TyeuOn4AN9x8FBoVNFxkOcBaoliWCRNOuqUGD9BsmqSsA8ZIQfkgbUkkWmxLqQonnbDyyPCVhw/g5O03olar4bFHn8XKylqiurnHS2ECnBZZqcNh1zoOCc4C7aCucUWXLaJ2NcNqoFGI47YW9l25vInv/fMD6O/rxrXXHsONN52ApkVnpRGhERYucdC0BaYmj88PPfQQ7r33Xjz55JO4//77Ua1Wcdddd2FtLbx/7u3txcTEhPu5cOFCmrNuKBrmI6zDEPrPiUA/RLIpmGgSzKuHkOGrrz6Kq646jJHz49i1aydef/WMsA2apkcOOjxT3bQvgc4hd2Hm0FJmzhCnpWLrpnPzEjNot20NIsBhkzf2/FifY+LnJjpHsZm6TcJ4BJJOM+XWx/gK89qXNg+wXQfnWY7wm6X3bfYqahYQ3Qcg2j8+WJf9LNCpil5/5Tyuvf4wuro7sG1bD259+3E889TrqJtMZHtOX0PX49sueZ1FvsJsvVHplOhyCgoyiBvToNUE4sSJI7jy8AFcOH8RQ0ODeP11/vicBFmoWFlMpJNe56h+vdkkOAka9Yzx+s7g/UrmK+xzjWvg2JrV/LGZkCG8YedFrv/zz72G6284js7OEnbu3I7b3nYDnn76BcA0QucGWUKlKAzHD37wA9/fX/nKVzA0NIRnn30W73rXu4T7aZqG4eHhRjcvEyR+AnimqMHK5XLMspBJn8TzoSXRor0yeehaHtddewJHj16JJx57AXv37cYLz7+BWr0aCLoVJ5om7+Xm+QuHTUY8VTOYnzcssq2oXrIPuy9PmWSjQRtaPuALLFJjs4JM+iORuhpQiAXqK61se2U9FZWXQ5mNIA1EK7++7Sn9fNOowzJl4g4uYavXSc8/C0T5C5P35If/8iR27dmO8YuzKBYLOPmOa5HP5d1ybCRnf0q2ekAVjkNITQQtWUi9XPLdDmRXKcJbFj5Xlgzcahr9LmswcP31V+Pgof144vHnsX//bjz33KswM0rx0W7kIu71bAYJbpbVSeOikcucb1q3t/SuSJHH2ILqcBhkIpADwD//0wPYf2APzp4dRXd3J97xjlthGHqs651UDd6SJLjF4/PS0hIAYGBgILTc6uoqDhw4gH379uGjH/0oXn311VTHbSQSPQUs+ZUhw1HgvTA8EsPd1zGJdqNFUytSN9x4DQ4e3I9HHnoee/buxuLCMiYmZnz+oWwUXlkTh0BwIAl/UxGp0KlgPnGCYhEywCMELDH2fcDPB0wHw8oKYYSWBc8kmiXkNBmWHTzcc+aY3rCTR8BPhnmEOOwTVq7RkCXYsoOMKLCXbPnI+gWqfJQaHJUijCaai4vLeP7ZN/COO6/H44+8BNOy8LY7roFhBM2Qw/4Gsvf9DQsGyKJZg7aKxXH5oVWT8BtvOoE9e4fxk588gX37d2N6Zh7T03Op6kySCkmmzqyQ1bVOQ4JF5FeGEKdpvzDmRlofzQRkOMk9bTQhvtTIcBQsy8TszBxeeO5VvPe9t+OhB5+CYei4/Y5boOvZWGEIv9uKJBjZjc/Ly8u+z+ZmdGo60zTxG7/xG7jjjjtwzTXXCMsdO3YMf/mXf4nvfve7+OpXvwrTNHH77bdjbGwsq8uQKTJ5EnjKRlCFy/6h8+UO5pDga665Cgf278MjD70A3TJw+Mr9eOXl0055cYAP2qQjKrUNb9DlmcyKBwAjMQmOC5qE0gSYpJUytLxLOOOQV7Zu3t9RkFWeeVYGcQcPQrTY6x5cJPD7DEf59MoMkmkGUlnzLFHbsgRXHc6IBMdrR1RANJsQP/bQCyh1FHDDLUfx9OP2yuTbbr8Gui72SeKZ3MnCl/5NoArTbYxqw1YdtBW2Pho1Ob/uuhPYtWsIP/nJE8gZORy8Yi9efumNyP1EEesb6aPXCoLSKJ/gVrtYtJMyHK6Uh6cAC5sLpMHlRoY1Tce/PfAEenu7cf0Nx/HYo8+hkM/j5O03QQsZn+n9Yx8zg4WXlsTvyBD79u1DX1+f+/nSl74Uuc+9996LV155BV//+tdDy508eRL33HMPbrjhBrz73e/Gt771LezYsQN/+qd/mlXzM0XspyFM/RXlrGXT5AT2izF4ESXXtEyPzHLI6tGjV+LKwwfw2MPPY2NtE9dcdxQjFyaxvLQqfSxR+0QvXpyXgnSgPrIlQYKFgZcEptA88mtoeR8BZtVWGcIr+pvXxiiVV2TmzJ4LXZ8MAu3UDGjQ/NeFY4LuDxoW/Hh18gdDHmnOJMpzHFOhDAJxRR5DUu2Ocy2iongnRaVSw+OPvIST77gWhqHjqcdegWEYuPVtx10rAdlFHxph7gNpouXTaDoJVqbRlxTixC8Ii5LPr1ve4oseR6+6+jCuuGIPHn7oaZQ3Krjuuqtw9uyoMEBWo4nuVoLsNUhDgoV9UZuTtCQBtJKSYRphY36S8b+Z17kdYotUNit49JFncOd73gYAeOSRn6KzowNvf9uNofs1+3kUzfGbSogzGp9HR0extLTkfn7rt34r9LCf//zn8b3vfQ8/+clPsHfv3lhNzufzuPHGG3H69OnEp91IxHqKRBFWAb5ZYdSDwc89yvPPq/vqogmwadVcNRgAdD2H/fv24sQ1R/Dkoy9hdWUDg4PbMLRzAK+/elb6XHnKr+j7uC8BL0K03H7B8jx/YALajzssF7DQvzZEpY0iCzyCICLoIgISRbA1eCbcYSZXdAAwQAv4UXPbwajDvnPjkOMwwiy7j7/dQXLdKLTb5IabPzim6TDP1/e5n76B9bUy7nj39ajV6njqsVfR1dOJa687EnhX4sRAEP0dBbZ9UdCdfw2HmcFHYcsiy/6AtdICgAMH9uCqY4fx8MM/xerqGnYMDWJw+za89top7r6K/MogWuls/ERd53z4EMWbyALR5xl3gTLDXLYxCHG7jctZg114e/LJF1CtVPGud9+KarWGhx9+Gv3b+nDddVcL90903ATPmewcvylkOKPxube31/cpFovcw1mWhc9//vP49re/jR//+Mc4ePBg7CbX63W8/PLL2LVrV+x9m4HYTwQ9YYvj3yYDelJoMuQ3UFbgE7ytvx+33Hotnn36dSwu2OrvseMHcOb0CMqb5ViKU9iLJjM4y7xwsibRPBIYSkQ5JM8+TpAAA+HpiuKYLfPU5YCiG5NwhF0HclxybjyF0m8K7leESZAw8aKBnxDHIfIsyY0yNZchxI1CmE9zlCli1IQ1q4lEFv3Mow+9gOtuPIxSqYBqtYanHnsV+/YP4+ChPcL70kxFVnSOTSHABIoIb1lkZVUQx/VB1D/x+oOBgX7cfPP1eOLx57C0uAxN03HixBG89dY5VCpVd7/LnfymyRXcfBIcRnrF38UmMjECEkSn9JM3kbbry1Y1vdzJMO+8TNPEww//FLfcci0KhRwqlSoee/QZHDy0DwcP7m96vBWCtjN9bvL4fO+99+KrX/0q7rvvPvT09GBychKTk5PY2Nhwy9xzzz0+Rfn3fu/38KMf/Qhnz57Fc889h89+9rO4cOECfu3Xfi3pWTcUsZ4mQnzFEU/rwg8L0vHwlBt3OwkEJaMGaTpKxRLueMcteOuNC5iamAcADA72YWCgF2+8ccYfIToBuYjyB47al0ZcX0j/vnwF2CW1jJJJdx40AY4bFVrWvDmK+MYlyDySzAucxcs1rLEkVAuaiPPa41eRGR/ikI/vWJx6eSmrRARaeB9ClOSsEXdC2mgVJ8mgRPctb75+AUuLq7j9XdcBANbXynj6iddwzXVXYnBHf/B5EXSRsjmF46wiy6jCCgoikOeokSb2SU2kNU1HR0cJ73jHbXj55TfdgFhDQ4Po7e3CW2+ebWsC3Mx2tSsJ5t972Wcq5bMXdv0FhJhV8RoRPCsNmrHATdBsop8UL77wOtbWN/DOd90GAFhZWcMTjz+HG286jm0DfS1u3eWJL3/5y1haWsKdd96JXbt2uZ+///u/d8uMjIxgYmLC/XthYQH/4T/8B1x99dX40Ic+hOXlZTz++OM4fvx4K04hEqnzCLuTuAQvEi+lCLvdF3SG8zLTE9e3vf1GzM8t4dSbo+73V504iFOnLqBS9UdEo5W2eAOPHBqlIhGCRudZZskkTYABv8kmTRzpv7MYLNl28L7jKVv0kYWKGCevNMlV7TsuZ1/TibegacRXOgc9LHG7RY7p7G+J28ZePyk/b6aMxdxHUpcsyeWVi7J8iHr2vbYmz/9I52qMmxPQtEzh+Yvy7srm4330oRfx4Y++E0889grK6xUszK/gtZfP4eZbr8JDDzyHSqXmO4dA25z8wDYZTp5jWwM/16vseTQMFtx3IPH+Ci1FFs+Q6N0Xboc/lzkpZy9Ianjb22/E5MQMTp86735//MRhvPnmWdRqtUB9shD1YVF9XDOJiCyaSoLp8okWVOPuo4Ntc5rxJQBSD+vWRr0LUTmGw/ILNyKvLfvOXA6IUnMfeehpfPBDd+KRR55BZbOCmZl5vPbqKZx8+424//5HUa0m7yviQsa9E2iyct/k8dmyond48MEHfX//4R/+If7wD/8w3oFaiFR3L0uTAZYER6VHATzVRtdyOHLkELq7OvHCs2+63w9s70VffzfeeutsYF9e1OgoM082UlySlXdezmCZ8+RBaNLL+ACTOmgVmOePG8fsl7cPq+D6vnP+8QJI0L6P4abDwYjRrCrNa7P/ey1S2XYVdOq8RNco7vUTqcSsopz0mfCulZxa3IigXjT81hPZD/hsPyHjy3PqzREsLCzjjnfYqrAOAxfOTmNpYQ3X33jMv3DEmGGR86EjQYdZvtAIu2eyMRaaQZAtU0v9UWg9wtKBSY89KSd4ZP+jRw+hVOzAc897uSSHhgbQ09OF06cuSNdHj9uirA9sWdm62hWZk2Cegto0NTTB8xS3bZzzi6MMh1bdxs9JFLaKKvzSS29iZXUd737Xre62N988h+XlVdx444lUdccZPyNdMy1T+Hcjocbn7BFLEc46MhqriITVzeb89ZRgO/Ly2MgU5qaXUat5L/PRYwdw9sxY5ApSeNTAeOebZKIapSLKrO7zVGCWMNLlRD65UeaZInLK/c5HFB0l2/KXMTXTXRXVoTsaW/B4nkpqK8Oi62FwVDbT/S4PDQYM5GEx+9Mkhnxjas4KMXRIRPGPBVbNpxVlyzm/KCIko+YTMhw3GrPsSjXtRyyDRqgzllWXUuJpPPnoK/jAz5/Eww++gHrFXvF84dlT6OiwA0YQawP+8RwyrNnlslRwTavue6daog4n8CMK7K/QFgjrG9JYA8VV8s6fu4iJ8RnUqfH52FWHcOrUeWk1uF0m6o0C7/wyJcFR98syfWpqayxTMhxoQ84nVPkN+c6uJztluN2e6Uao3jRkF9Yee/QZ3HXXO/HAj5+AWbef26effhHFYiHV8bN4pkP5QjPIsBqfM0eqJyLNAxX1QvDUMbbToHPw1muam3rBgonung7sGNqGM6dHuUqXSPmVUXzDlONGpx1giSrt+0pUYJvwsUqoX7X17StQdWU+ZD8DOeeIefdDrrlh5aFbOnJWjtKGnX+W/XGJMucf3VYAvnMIDXTlti3vXhMNGgwtB0PL+3yEWfXcbosR8BkN3FeOOswDW4b1fWYVYroddBn395AFCB6y9icOBCRLGcCCNyFoVColAHjz9QvYWN/EzbcecwlvrVrHyvK6W4ZVhllYlucnnGVESX78hXD/TwWFRiFOSiVRGrlKpepLjdTf34vBwW04c1pODW41YWifoEUNIsGxIX4mogMaxbiWoSKFhKIvrQzH8xdu9fN4qePFF15HrVbDbbde725j+5BGImzur3DpIXHvrlEExa4oWtUE4vkS81QelnzaJNAIlL/yyD6MjkyiXA76BtNtCiO+spFxZclwGoSpg2wkaJ6ZMI8AE9CkU8qUl67XIZo04SWkl3x06Mi5RDnnflhCLDLTFRFiUbArUSRow8mbzCvLEmKWDPOCKPGIath14157jjl5FOjnPY45dRywvn7+44eY/gsiT7cjnv3p67jh5qMAxP2SKD+w58IhFzSLGzAw1AKmLlWuIbA0W+5O+rGU6dXlgqTBs44eO4jz58fcSNFhuNRIR/I8wAlIcEhk5awRJ9J4GoiIr5AQtykZbtfnul3a9dOfvoTb3nZd04/b9iRYjc+ZI9NeKm1ezSi4KZPCbPdhIl/IYd+BnXjrrXNeW2IqYmkfehlzVR5k/AjpSTIdCdpAHjz1lyWMPMXVbVvIP9I+8o8QYJf4Uv8MwYcl3b5tFv/jKsvIU8f210V/xyP0dvm8Q3Q91ZqU5RFRmgz7fIdjkD1RWd599i1UsFGrZQiyRJmkqrDb7jYgtmlIPx2L4PmfvoVczsCJ6+y8eDJ+vjREPphhvplhkX1FwQObDeWDpJAGUSS4o7OEfft2+cZnHtrFd7cZfV70eSYkwbEbkmzeEzUGJt2XhczzwH1uGkiG4z6jcfbJmny1DZmTwNNPvYCOjhJOnDiSab1R/IG7vY2umxqfs0fDengeCXbNCBllNWzCJ/L94yuzdj0HD+3B7MwClpdX/cdv4MMc9qLQ/sz8feOpP3RQLFoFZglwFPEVKbBRyqz7N0WAWcJLK8JhH1olJv/Y9uasXIAU02bXRIk2rHygra65NketJvWxZBjw+1SzajEvbVTUajivLE9V5vlui0yiedtY5T4uwgbpVpDgRr6zdbOGV146gxtvOebvj1JMvkNV3hhm1AoKjUTs2BcpiA6LK688gImJGaytrgvLtAMBbiVip63LggS3BagItanFCA4ZFqS+TEOGybGintm4pLmV5Ksd3r9KpYZXXn4Lt739+tBycaw13e0SC9EKlw8aNrOl1RVRhyMTKCsserTdsZi+Mpqm4YqDe3H27KhbBvAH25LpYOIO7gHzUfgVNNcHlgnekAYsCaYJMK2YykQGFqmx/nPyCLBu+dVXoujmrBzyVsFHivPw/80SZrJfmIJMCDKPSPNIMyHIhpX3mWfzVGj2WrBKMPn4fY11Xzn2GaCDltGLQtyybH3uQkdyi4owMixOS9S6wS9MRZIhwzJR5r36vDzozzz1KoZ2bsOOHf3+MqJAWbzozhzzaDaPepo0c16dTRqo05hdkY9C2yPr4Jcy0HQNBw/uxZkzI5nUJ3JfatecxHwltx5RJpyYyZJgXsTtKFPiZjwfaQMtRu0THjk8OzJMHy9OdHNuPS0SbXzl2oAMP/74c9izZxgDg/1S5ROnegxTidutH1Hjc+ZomZ0jz8wXsCe0ZFLLTm5p/85AfU7ZoZ0D0HUNk+Pz1LGSdUYyZFjGD5IE9dIZJZA+D9l0UbQaDFBkmFGA2UBVUR8RuOWpfxpFTj1yayBnGShYeRSsvPs370MTZZFa7DdvppVesdk1+4+U0ZwrRCvl9nUMmkjzfHtpE3TWl5h9HniqMlm4IGV599h3zBRkGJCfyLTDoEfAe1ezJsMEy8vrGL0wiVvednVgwAszleYHtApPo5RlQK2GwtLSfxQUONi9eyfqdRNTkzPCMlLmr5JEV3YSK1qIa70rSHoSnDbNFP/YyYhiUojbZzIfif2lo/7yY8Y0Eu22gJP1vCDuuS0sLGFsbBIn335j4vrCVOFWLAamhhqfM0fiXj7uQ0RPDlkSzMsdbFl1t356Euzzt3QGL7otBw/txflzFwNJoBuxSiwOFOKpwYQEk+2y5qq8gEs8k2h6O60A80iryEQ6CmHEkhBWmxATckv+RWXsDZJimtTSajGP/PLaxBJmVnmmlWtayRaZSLNKbdDs3ODeU5oEB8yoJciwrw1MnuFGIGnKhKyUGG5eb2FQlLCJTLC86J0j/c8Lz72Jo1fvRy4XLyUM3X8FJqjMtaD7P1YtTqMSNwLKB0khLmQJ46FD+3H2LF8NTkbIotFOxCIKcVKzRJHg2Ka4kmSxKZA6Pq+MmBCLzi98DitvbpsWzXxO4xyr1Yvkzz3zMo6fOAJdjw7G1vqFq8ZDjc/ZI9ZTkyZIjS8VSUQ9FkzUrRpMyybBpEN3VU4n9Q1r4lkqFbBzeBDnz13019eADkaWBLPbpepmyvrIF+MXTJNgn+kyJCI1S5Bi+jtaBSYEkibBhPLloKNAeQ7nOV7EOjTkOKRYtzSXFPv8mylFmutrzBB/3/laNFHWfESbJcPk+vOiTrP3xC1L3w8O4eWqwQwZ5gXTIvdW6pmJSZKzSqUUlosxq/eOdW+wt4WlN4s3cL/5+gg2N6u49vrDzv4h/v7Mooj/uMkXAuIozwoK7YioSWhXVweGhgZw7txY4mMkNn1MsJ9MkKdGpI3zECOoD4cEZwVpqyKR8iZtiitBYF1E1cknxDJkOMo0nZTJYnxrZzN+GmnMvAN1xTzfV145BcuycO11x7jfy7x/7X59FVqLXLMPqGme6qJrRtA0Gh75BbwH2DU31RiTYlddNrFn307MzMxjfb3MPbZsRDhyrKQvjy9IEtNelrCQSTuPyASUSU5wLIAhqxzfXaZW19eQfGPChE5t54GtjyaXuqU5+q+GHAzH/FhjjuGtQpmwYIDkDbbcNpiwoEGDBcsepDTdbRGph3fOJkwY0GHBhG75z4O02/udNo0m9eQA1AALgJaHhbrU0K9rTARvpw20uTSr7pqWfS6E+OgwAiQo+I5434VNSjTNCCWBplun7vtJtmswEg1ydHsbDdMyQ/3sky7WvfXGBVx97RV4/tm3hGXIeZJjNNOkijxXTfETNvV0fkSmFV1G4ZJE2Ni5f/8eTE7MYJNJaQi0VnXiWcPEjXTc6D5QFFvF3iBLgnnt858HERzcepnztCyTOnczuL/ve9EcKox0yiDOPpw2Cs6RHrvZ8+DVQ8oBMSNfJ35OpGYlCeuOB981TFpH4BrzYZomXn/9DK677hhefOF1bhm237mklWE1PmeOhj8tlo+UUAoYTQ4FHYxp1Tyz5wBZDaa72bt/CCMj475VK7JfnLDoSVfo/OdkSKlurMkrrTiRv2kSDAT9gt3jCQJYsR/W/9Ylt8x9oLezAbFcxZVDgnOgQnJpuv1x/rZpp+Z6/hoUOSW6qUaRZt09P8/kmVaO3Z/U+RgInp/ddg2aBd850deCVobpdExh8BR5fh5n2ozab8YezF3MRlrnqY88k/mwhZZge8ODZ4mCqUUhTBnOGmH+wlw3i5AJhGse/fxb2LV7O7q6S87+/H38rhn+6y/jLrIl/IRVMA6FlOAppfsP7MHIyHjL2sPdnmAyL6wrYrxvCFGWIsFh/rPB7+K6owTKplU5faSfbUsDiHOIMhz0GRbPI0XuQekDuIXdv+Rl0z6PWSxeyV6T5557FfsP7EGxmJf2C75kVWA1PmeOpi+bBHINR0zc3Q4EpvDFMy0T3T2d6O3txvjFKal2xHlRmvVS8UgwAB8JZhcQiEk0axbMS0/EzdlLkVzA7w9M/uaZQhNCaji15hw6nHPIb07Tkdc8upfTdOia/Z3m/CPEmGwhtbvtsDT7w5Bf8k8DIeHe9/Q1oAmwTgUIoOtlyXAO/tRKbCommhjzFnNIVGlivq8x+9LfsQG37PMWp1gKM9emy4nIcNjCDO87mZRavvIJV2GTvF/EbYL+uPVxyHB4XXXMzSxhfm4Z195wJbcMz8M9zCwyigzzSHO7+QorbF2wC2Qta4fzfvT196Kzs4TxcbnxmYdmTWyzTBWVNcL7FR5pjEOgeJVG+cjGvSd8Nbjxi38RZD+kLTKm0jykJ75x7l9rkFWu76iFhPGLE1heWsGNN51wt/HqUFBIgob26rzOLWyyF8fP0WICbe3bP4SJ8RlUqzWqDF8NbuRLxPoGs8e3GFNanqrH1sfzOQ34rFJEiyasvEjLAd9bmuz6SFmQBNNRoYk/sBcYS0NeMxwlWEPO+Rjk49CnnEOUdc0jxDmGDGtUizzF1zsm/aFVZdI2QnbJvjpVs3t+DBn2Rb5mCHEYaB9u3+/uMXm5mHWKKHvqsH3dw1MsicgxKWfvy49+HX4eui+6uQyyMI/K6hhhZDhqomVadbz5+gVcdfwKYdvY608T4jQgbeMtdpG2NROWpaX+KDQfUWNJGqQdHw8c2I2LFydRr3PG3hYtAMU1iW4VCRZe+wYqpzxfWtlUQzL1C2GFmW2G7G+Z3kdy//RkOEvilTXxbS4pzMp/2FcnQ4hff+00TlxzNFV9jUYz+gg1PmePTO9aGMn15RWmHkh2kkeroOQnbUptWp6KQndWe/ftwtjoZGj7olbooswhRaDNlwlYMhHXl5H9nleeVYNpddMjsJrv4+3HV3r9BC5IgokptE1IiSLsEVyDJb+ahrzmEWPd+V0D3H0IIfbIsGci7anHuqs8E1Lsb6lnZk2UYqIa0+SXnL+reFPBuWglnV0sEKbXAH3P+SmsaLN0N580KUeZSnPrZwJpsQG12OP7TLEps3uRSbXoOYxLiBuBJCbaPMiS4eefewOD2/vRt61TWManCEt0n1kOjE0zmTb19B+FtkCrVWEypu7btwsjF9rLLLrZdUQhndJKg7MvTRRDSWMYuY4iwzFIqIQaLE2q2PNoIBkWE+K0yq9CFJ597hXs2jWE3t7uph6XN/cSlWkK1PicORoWLItn8scqtKL0Sbqm20GFNLHzOx0kq7unE52dJUxOznrfM8eKWg2KE+yCJiL+v/2pkrIGzzdYt+zo0KyJL1E7AyDnqNFBs3TYgaK8I7m/MUqwTVjJNqdFjtpLSK4OOOSWbrvd3RvOT3IEE4Bm2eVrluk8kCT0lUntT66rTd+ck4AJy/mOnIs/KJf3O+96UtfHDZqhA6ghZ+VQ02peCct2rbBQhw5/0CK/aTP1LHDyM+uW7tYTbI8BE/YzywuiRYMODkH7qAKeab0bE4G6EWHphXgTEvtdjJg8JAT9vgWCz1AEOOp4dFk6mJY/+EndDibmnCPv/VxdWcfc7CKuPnEQTz76asyziQcTdeHiR6thz5GTrxpbKhiHArz3u7+/F/l8HtPTc8Eykn1J1mpOkgBZbQWhGhyHFMI3H/DXQfpQJyhSrKBSEYcUkc0YBDZyH6q9/Lq87wKBn5hzBfzjhTjIE9uWeNflUkEWgbR4mJ9bxML8Ik5ccxRPPP5cyH1oLHxzrxYcX43P2SP2XYxL8Hgk2LTqMKk8wZZV56o2NrHMQ9fynrpGPXhkgr579w5MT82hXk83QZd5qHkkmG6bTVTzwtyv7N+saas/fyxlCs2QYB06DCvvKos+s16L5MQN/qOVULIvQHxy/R+2TkKCSSgpoujmNR05R/n11GD4VGFDA/K6pwzndd39Lucqx3ZgrZyj7hLV1/MH1h3K739waTNq+h9J02SfX1AZ9t0HeH7G3PRKjDLMW2QQkWBeWTZvMc/fOCwDM/2M0PvxzKZZ/2FRTmKRUuyP0tx4M0YvP3gGqRoEbgmiwFZnTo3iyiN7hSoF6c94ixS8e8juS1vGRNWhoNAuSDPh271nJyYnZ2CajVlQC0Mz1WCZxfY0iLYIiUmC2bJRinRIUCnpw8jGZZF5NmRJPhfyyrD9vYw6zDvG5an4NurdPn3qAo4ePcj9TiancJYLaltq4UwhFIkUYd/qmOAl1xxNj1a2fJ1nhA+frvEnlbzj7do9hAvnx8Gmf8nSlJAlHGQbTYA1yk+UwLTqttrMURBFuWlD28GQYGLOSxNgwyVSQXXUXgt1zsVZ+SQtY19rciyWBJOAWDYR1qA5ZFaDTX51jUp3pMFx+9Fs9dfZbsFyyC1gaRpMy7lzln0dapYJW+f1p2EiZNaC5dJOknaJB6IW0+pw8JoyUaotE9DslEoyyjBNgAG4JJiryIdAc1sbPYjQ7xXPmsFPZO2fZCHQXeWOSLdE3uE4YNuSZLDIehDlKcMivPbqWdx28hqUSgVslmu+VV8vhRFn0U7zp1aSRVsqw1bKyJLKB0mBwu7dO/HWW2db3YxQtMOkNkkKJmFfKajHosZB7pjpU1Lt2YJsuiFyDtzj8uKyBNTgBtyDUGWYbY9YGba/D/bvjU7Xs5XTATVCGX7l1bdw863XIpfLoVarBVTZrXidYkONz5kjtWk0PVlmzTl9ZFgDYPHJKckdTIM2MfZNXJk6isU8Bgb68NQTL3lFOJ1umtzAIhJM2klUYFd9Y8rb5+evU4b0umUdNdiONOwnwQWrwDVbDtYBypyXMin2dfaMuTTAVYKJLzAxgybkl/ytaTb5cgdazR6ADdhkzLJsgmtozt8OkdU1DZploW5Z0KCjDgsaFTxD1zRoDss2cjq0HKAZOiwd0HXNOa4OTQc0zf4bjnxswk7g29lbxPY9PajXTFiWTcwty/7PsgCzbqFu1lGr11Exa6jX66jVDVTMKmp1JxAbQ4btU5QjwaZGLCP8Exg6pzYxkbbLxTPXZ0GTM5L3mH5nZckwMZGWyTUsM6FLS5bDwOYajoPpqQWsrq7j2NUH8OLzp9xrRwhreB7n4HnHNZ/yTS45E69mIG1ADRWMQ4Ggo7OEvr4eTEzMNP3YWaRMavTEWtyf2iN2VqAJMLstQIgTkmHAHvtzOQO5XA6GYf/UdA2GrjtjsgbdnhxA13Vn3NagwYKm6+jr68GevcOo1Wp2Cy3L6VMtmKaFer0O0zRRq9Vh1quo1erOtjpqtbgLqEHyLUOG7WvWGEIsGjcbOV42ElmT4bHRCWyWN3H11Vfi5ZffTNamFpk0ZwU1PmePTHyEWTJMg/g8hu1DQA8Ito+wp7LCqUfTDJcM65qOXbuGsLiwjHJ5k6krnq+KlFk0Q4LJC6655s05l7SSRQFLM2Gizj3fsOPQZtFEZQ4jwSR4ld+H1oMJyzE19nxviTqsW6AIsHf/iHk1rQTTJDhHmUDrAAzdMVPWeGu7jiKsAUZRR65oQMvrMPIGjLwOzfno5GdBh5bToOd06Ab5qbsVW5aFet1EvW7BNE1YJmCalp0n2CG1lmkPpHXTsgd9A+gd6MCew9vc4JSEMGtwBmlDg2aQnwAMe8JgOdfNNOuo1uqoVmuo1mqoVmuoVexBuF6xUK3WUa+aqFbqqGxWUdmsobxZQaVsoe4qicQ01htULeZ38vzwiFDUc0TfQ7JwSAhxEjIcvJNyZFiEZkRvZMmwiFTytp0/O47DR/fhxedP2fs6AydZ5AukgGPUebKPCKJ+h0e2XRXaqsdaPEuFtAE1Lj9LwLZGEssObj1snA6J93j3riHMzi6gWqn6922jVGFbeVLsQexryyPBvO812CS1VCqhWCyiUCyhUCg4nyIKhQKKzs9CIY98oeiS3lzOQM7IQdO9a1mv1VGr12CaFizThGlatkucZZNayzKd7+xnwTAMDAwO4OjRwxSxdBa0YS92G4YBXTeQy9k/DcM/1zFN0x6bKxX7Z7WCSrWGarWKarWGSqWCarWKSqWCcnkTm5ubKJer2Nwsw3TUiigybJfhjydJCLFK+xMPZ8+N4qrjh/Hyy29eIu9uTKjxOXMkJsKs+a+wnKNu6YCrCtOTcVABsXwdgmbvbTmmxfam4IA+NDSAqSk7CAc7uIZ1SnGVG1FgLNqnkmimpKxBtTdMEWc7VNY3WNe82Mw8EpwHiePs1USbEAO2mTTxVqEJMfGUNGBwzaYC5tCBSNAOAdY0FDoMFDvzyHcYKJQM5EoGckUDuWIOhvO3njdgATZ5rJioV0zUqnX7700TlU0T1eUKKpU6qlUT9aqJWt1ErWrCrJuo1yzUa/Y20yIE1QN7/gBAvNH1nIbubSW89NAo6jWT2sfzI/ZMyCnymzNtlTlnwTRMGHkNRl6DntOQKxjI5w3k8jnkczl0dhZRyOeQL+SQL+aQLxooFHKwYKFSq6JSrmFzs4LKZhXlzQrKGxVsrFewsVHGxnoF6+sbgOWRYRou2YL/exFBI9tM1H1qJVGfZckwqwrb26LJMA+i1GUy76g4Zy+5Lsz1EijDUebRZ06P4f0fut2p257w0G3kXW/f4gNzvaNIseksHBGza/Y+tkoZVlBIi6Gh7Ziaar4aLEIjgvgA2ZGZJObR/obwSbCmaejs7EBnZyc6OztQKpVQKpXQ0dHh/Cy5BNiygGqlgs1KBZVKFdVq1R6zKlWsb6xjcWkR1UoNlUrVsZiyFdpazbGgqtVQq0ku/lPtzeVyGBjYhh//+CFHESYIr8MwDBg6YBg5l5jn83kUikXkcnmbtOfzKBQK6OrqRD6fR7FYRLFYRKlURKFQBABUq1VsbJQdcryJymYVa+sb2FjfwPr6OtY3NrGxsUERZnG/LDN2KQKcDKffOo/3vu+OVjeDCzVOb00kIsJplAlN031kGCAPDzEZ9ToX0zJhOP61pFOlJ+umZWL70Dace2rM3WZZwQA4PNLLA484836nB1M3PQ1Fiv2LBOT85EiDLwUTDDfXrAaD8gX2fIK9oFJ2yC7ANiGmj09gwDO4ctVgeMok7UdLyKFbt66joyOP7p4COrsKKHXlUeo0UOrKo9iVQ76Ug64D9XId9XIN9XIdtc06zI0aNhbKqJbr3mezDtOEq8qaTjvrjpJbI78DMC37blqW5+NsWZbdPse0WmdWvImZtQuL2g47DZPFOVfyu202rrnXyKzb6rNVtZcONjVvCYE1JbeP41kwAIClWcgVdeRKOgrFHApFHYViHvmSgZ7eTuzY2Y+OzgKKpTw0HdgsV7G+XnZI8ibWVstYW9vA2moZG+ubqFu1UCsM0gbWZYG8ezapNQILWEnIMI0oYiwdKCXEtSF0Pw4hFkWRtsvx6zx35iJKpQJ27NyGmakFqTazk74kkxzWB5mAvr/NSKFkmVrKqJTK9OpSQdhisgxpG9o5iDff9PsHx11AS+vO1EjEbRs/noPcomIYsbSJbie6u2yy29XVhc6uDnR12b93dHRA0zSsr29gY2MNGxtllMubWF5ewfT0DMrlsr0Yu7GBSmXTz6d915LuP5lxh6OahiLKb9h3THFd9Xod9TqAajIrA103UCjYpLhU8ghyqdSBbf192LN7GB2dHejoKEHTdJTLm1gn5Hh9A6ura1hdWcfamv2xOLmQm096JSwct6ia+tZb53D3L9yFwe0DmJ9bbHVzXGRleRMFNT5nj0REOImZHi8QkK4ZqLNmgiSgj2V66U58qX3szpKkTSoUcpibWwocL03HE3DAh84lwfR5sEgaaAjwfILJx0A+QILt6MZeZGSDJX9u2xjTIYsmxJqrBtvHtZDL6yj1FNDZU0RHdx5dzu+d3QUYuob6Rh21jRrqzmd1fhOLGzWY5Rrq5ZqTCon4CPthwR77DNgm0hYhsu791VCH5TyUGlzfYXjDoAZvWNSc/WiyT87XZx5OLBEo7T78wfcH19ItDSSomE5ORCPqOr1woTO1kBM3US3XsVmuYkXbcK4FJ2KwBuRLBkodRRQ7cujoLKCjo4gdw/24omsYnV1FQNOwsV7G2moZq6trWFsrY2V5DSsr6yhvVLhEjw1axzPFlgFNhgEvarv3vRftmbt/TN/hZoF9fyuVGmamF3D4yF7MTC1wVWFCTqNWgMPSNbllXFVYnN6JF2egUVA+SJceZN/zrCIAW5aJvv5eaJqGhQVvfI5DgttBMWMXAlrRJrrfLBQK6OnpRk93J3p6O9HT04Oeni50d3dD1zWsr61hbW0Nq2trWF1dxfT0tEPQ1rCxsR6I3O2B5zZC/Gpo02DPrzaw8Oiz6AvpFxNdw2x9pmmYpoVyuYxyuRz4zi96aCiViujs6kZXVwc6OuyFht27h9Hd3YXuri4AwNraBlZXV7G6uoaVlTUsL69gaWmFW38cyBPX9iLBWfsJl8ubmJtbxNFjB/Hk48/H3r8R595MJViNz9kjsWk0IcOsqiSKeGurZ0zZsCA9AX+kYKTWoaEBzM4uom76/Y+iIGt+FPbCRAXjIWmihGlYBIsJrEm0m9aI+mcrwXZiI8OhX1wCyD0p54eho6O3gI6+PDr7iujsK6Kjt4B8wUClXEN5tYryahXr8xUsjayhulpDfb3mpkkydFBpkqiP7ifBdMRiC/b4WCdBqpzm1MEnwzXLrp8QYgBulOmaZUeNJgouzRG8Y9u/kTzFjoiMPLE44PhO2dGovUGXqMJ0hG0ddlAsM2Jg1qHD1Exq6gB3oYe7KGcBlY06Khvrvro982Wg1FFAqTOPru4SOruL2LFjG664che6ukqo1epYWV7H6soGVpbWsbKyjpVlmyDTQeuSmkjb1zZ6Qh0nB7AMogauwGQ1g4F3dHQK+w4M4YlHg9+FRXmm+zhelPywAZM2i6bLt2VkaYVLDlHEln4PZXzgh4YGMTMzz1XIkrShlcg67UpUfblcDn19Pejv70ZfXy/6+nrQ09uNYrGAcrmM5aVlLK8sY3Z2DufOncXKyirWVlcos136msu0nZSh4ypYIWTYK8t1TUl4vcIJBf1d2vsRn7hYluWo5mXMzfEsJDR0dXbZpNj57BzegSNHDqGruxO1as0mxcsrWF5awdLSciRBjk/a2osENwKapmNkZBwHD+5LRIQVFFikCpYVRmQJosyUCWyTS8/UmGzj1UWUqO07+jE9PcutO2qwCfs+jhpsWXWYjs5oaSbqqLqBiQj5t026Tar94dfNrwh7+WZdNdgx7yVqKRmsRCQ432Gge1sJnf1FdPQV0NlXQLErj8pmDRtLFawvVTB3YQUbyxWUV6uoV02ndptAksBYbo5gDgnO2e7eMBw1WIcFWowmCrRdp02Ga6ZHlEVkGPBHmAa8AZomxOyd9BlzkYZotmqua46CTpUxqZ+84CL+CNs2CTYiBh3amoGox7pjTk3nGaZJLyHIOvWd21ILKK/XsLFewcLsmm+hRTd0dHYX0N3bgZ6eTgxu78MVh3ahs7uISqWGpcVVLC2uYmFxGcuLq1hZWZM2kQ78zTGTFoE1+0vjA8cj2PHTjchFZT57+iKuve6w//hMf0Tvy5pF0+96HOuZKNIr0+emhgrGocBB3HdtaGgQ09Nz3v4hi2KXc37Pjs4SBrb1oX9bD/r6etDX34uuzg6Uy2UsLi1haXEZZ85ewPLSCpZXlm3/Wcukrqdz7WIsOIjBklwqsjS5Rxx12C7Lj9UQF/bobEUsorbfPbYsyzWPZv3iDcNAT283+np70NfXi6Gh7Th8+CC6u7uwWalgYWEJi4tL9s+FJayursU8utz1aNW7kbUqfOb0CH7u59+TWX1p0HS/YDU+Z47YRDhs8sjLr+lTSEjwGEottejJPCdNEX1cwG+OObi9H2dOjwTqduuQIMNJvuOBkF6PlNV92wlEE1k6QBaAgBqcs3IOGSYqMInp7JA7hwTniga6thXR1V9E90AJXduKyBUNbK5UsbZQxvpsGbNnlrG2tInaJr8tec3WRDUqPRIdGCuKBBuaFTSNdpRd2xfYUWodddi9hppz8RwybGnOIOyUMTQy1mswNThpljQ3DzF9p7l3zyHAeU2HptlBtOx7Yrk6J+0bTKvCrlJM1NSITt3ULLc23bLJr506iRzBe1aJSbVJiDOC6Znc7wHqGfEWB8y6idWlMlaXypiE59eqGzp6+zrR01dCX383jhzZh+6+DpimieWlNSzML2FubhEL88tYW1+nLpX//HhKMf0uhk2CWJNp2sww6j2LG32T5PaOLusFzOKR1fNnx5HP50L9hNlFPpHFS1YRn5vhfwQoH6RLFWHWHFkRUbqe7du34bXXTsXa51JHsVTAwLY+bNvWh/5tvRgY6EehkMfy8ioWFhYwPT2HU6fOYXFxCZubFbixUzjzGxHiq8E8sCRXTh22y9KLpiniyYTs2+io41kHVavX61haXMHS4gqAcXd7Lmegr68X/f32M3HVVYfR39drl19axtz8IubmFjA/t2AH0gxAfnzcagtEYThzdgQdpSIGBvtj+QmnvQZs/JdWQI3P2SMWEaaDVonUEBoiEuxt4/hJOvWSSarITLOjo4hisYCFheXQNidJ+RAXxH9Pg+5GgCUwUXcVIh4JZgk/Tw12VWA3py8JkqWhq6+Ivu0d6BnsQPdgCcWuHMorVawvbmJlZgPTby1iY3EzaKlkWchxfIoJ+dU1zSG3mkNu4ZJhTUCCc5qtAhMSzKZRIhxX1yzoJlCjqLIGmxQbumPK7Gw1nbpdNZgixzpDiNkujjbPNh3zaKJw207KDu+kgm5JkWHfMfidim7ZSyC2ObJHiO26nJ8avWBk+lRgu26/msqSYjYdEhBcqLLqwOL8KhbnV3EBU/b3moauniJ6+jrQP9CNo8cOoKevE5XNKhbm7cF3YW4ZCwsrqNcbN+EQBeDhlpWYmPD2lXFhIOmJaJimifm5Jezfv1M6YBZwaUzqlQ+SQlp0d3chlzOcib+YuDTzfeEu0jWQHGiaht6+bmzfPoDt27dhcHAbOjtKWFm1FyCnpmbxxuunsbi4bAdj5MyXGgORcsy+t0F12EeGgVBCbO+TDSkOtnRruYqInrNarY65uQXMzVGL17qGnp4ebNvWh4GBflx91WH09fdis1zB3JxNjOfmF7AwvyQ1PrcLAc5SFa5sVrC4uIwrrtjbtIBZbPyVVpFhNT5nj8Sm0VEpPXgk2P2OUoF9Ci5Fgl3CTSlRtAI1MNCH5aVVX0cg82DKmGbS6Zp4EE2uiSksveDCi2Jt10EHYQhXg3XLCZqlGxgY6EL/YBf6t3eib7ADuq5hbX4Ta3NljIysYGNhE/Wq6SO0OQQjV1kOCTYpcypCfm0iS6VGgp8AE6Kb0+2/NXgk2DONBjTN8l1BA47SaWl2OiLLgm5p0C2bBOuW40useT7FBghJtWE6J6LBJsU0Ibb3CaaA8oi5TepzhAgDjmm15iPDZG+WDAcnCnxzdNOxh9YcAy9YHiGmTap9xFhj1uAtP1EOHpeoyLQfdt33DLntscg+JOq2ieXlNSwtrWBsZBoWTBiGjr7+bvRv68a2wT4cPrwfhWIeiwvLmJlZwOzMIubnllCtVrw2SJhG00iacilreMGvwtMoTU3NY/feHXj2p2/4tvPMl3mxEXRKcWaPLYLyBVZoNhqhBg8M9mFhYTkkOFNj0MpJv2HoGBjot0nv9m3YPrgN0DTMzS1gdnYB586OYn5+yU0N1JpFszDzaW/s88MbmXym0oA0Ibb3lSRCmo7QYFtZIcn1l0ix6duWIMCVaQJLS6tYWlrF+fMXAdjKcf+2PgwO2Asqx44dQqFQwPzCImam5zA9PYe5uXnUan6rq0sZk1Nz2Lt3GM89+0pTjqfSGF66SOUjTEDnNaVzbJqUKSTd6bPRon11SZoQbhvow8L8EoB4A0qjBx/XL1QCtP8xEFSDDU1Hb18Xdg5tw44dfdi2vRtWxcLaXBkr0xuYfG0Rm8sVwCLE035N87ruU3Pt75z2WZQ/rEM6/W0KmkATRdhVeanfDccf2NAJIfYIMDmmrnkXhBBbzYJDgm112DZx9kylTYsiwxQx1nykTmMIsUeUvWtK2uC129AAU9Pc4Z0sBuiaRpFG4rDsmJ87Ru5evWSxxH88WzUmCq8dyIsQYt3y2kdMpwkx1i0TNa3mtYmjCovSFXk+u4w5M2VCTa4pIcM64AbO0qCjXjcxP7eM+bll4LRtulXqyGNwex8Gd/Th+huPorOzhMWFJczMLGJmZg6zM4swa147Gwl6EhWHTItyCfvr5g9w42PTuOmW4145Ti5hFqSv86WvysAsmpDvpqxCKx8khZhgx9WBgX7Mzy/a3zVBDW7FxF/TNPT1d2Pnzu3YObQdg9u3oVqpYXZ2HpMTM3jl5bewtLQinU6n8QuFsj7EPEIc4jsMxFKIuWMFIb8cKzX7ONneXwu0eXc6iNomfibjH7dWq2N2Zh6zM/Putq6uTgwNbceOHYO45dbr0dlRwvzCEmam5+zxedYjxu2CLFXh8YuTOHHN0Uzq2lJQ43PmSEyEeS8/L/8lS4Z9ptUahH2zqxgLTKO3DfRgZGSc+x23vgYQ4KSdMzsxptXg7u5O7Nw5gB07BrB9ey8M5LA0u4H58TWMvDCH2ooJAxpymm4TXfjNmYkJM/kpIsJEPTUtzY2x4Zk102TYrwL7zZ79KrBBEWBCfklZ+zwdn1wNMBwVuG4BVWjQnBzChkNGaVXYZKJMA/b3NPmlCXHwers3zF0gqDmE1QIhhppbhvyiEYLsHMOADpJj2Lt3fniKtaMGU2oySVXlkWKiFDsm2ZZOKcO6qwqLBg7N0ZZNAZGjVWNyHdgFKPq9JCDvXnmjitGRKYyO2CbVHR1FDG7vxfYd/bj+hmPo6CxhbnYRk5OzmJqcxdLSKredXr2iHMVy+YhlJ4nsYJuUDI9cmMJ773obcjkd9ZrUoQFQEyA3uJv/mstEkA5rV6OhfJAuHzRqYXhgoB+nT59v+nHjQiZOAY3u7k7sHN6OoaFB7NgxAACYnp7D2NgEnn32lQRBjqi2cPs32iLJj+YRZ5YQ02MFZS4NMP7DwfJccIIQsn1e9D0SfS+6dnzLwDgLjfEIcHj7kyzkrK+Xcf78GM6fHwMAdHZ2YMeOAewY2o6bbr4WHR0lzMzMYWpyFpOT01haWuGcQzgpbcTzlRUZPn9uDO9+99ug67q05Unc9z2wP/yL3K2AGp+zRyIiLApkxQMx9TNR5yeTZ4LwEHNFyxKbPFuoo39bD158YVlqQM1y0CUTataMm5DbULNHXr5hTcPg9j4MDw9ieHg7uro7sDi3joWpNYy+OYfNhTpyZh555JBHDjmHBGvwVOAc5c+b04Cc7ieyNBkl5LJuwSXBbJApmgDTRJgQWU/pJYqwTYI1hwyT7+jyfhNpy/aldsyhddgBtGouObfLWY6CajqEmZBinlJMgnC5RJkdoOGZRutO+ywEVWGD2sfQNHcqYMKCaVm+70mdBPb3ZLXc8W+GEVCTvbVyQpjI5CEHWDVAA1yjcjd4lj/SNNlGyDAPZKJEYlXb2zxjb/tnPZAj05d/mEqxtLGxibHRGYyOTAIAuro6sGPnNuwcHsTVxw+hWq1hemoOU5NzmJycRbXqsUdZEkxvy3IQFpHhMPPomekF1Gp17N6zAyMXpnyTJ3Yw5ZlK89Rh37EZkisyi1YmWQqtQHCs5j+DwXIa+vt7MT+3xH2HsybBMhPbpBNvTdewY/sAdu0ewq5dO9DRUcLs7Dympubw+mu2f2+c9FBbE/QSNBDqOwxwyLCHwH0IBEh1FpID+yc1O6ZIe+A55RRPcStl2swvlx4aDGysVzByYRIjF+zx2U7ftB3Dwztw4sQxVKs1TE3OYHJyBpNTs6hWotOOZp0KMUtMTMxA0zTsHN6BifGpph23lSRYoTGIRYR5K3UEvIjRLAIdEamL7kOtOjdnMO2L2NFRgmEYWF4OrnBFHVM0kU0CWRLMToQLhRx27rKJ79DwAMwaMD25gDdfHcHCzDqsmoGclUMeBeStAkiEaBIl2jZB1lwSTKvAeV1D3iGy5CchwoRg1n3k0dsOeOSWJsAkCrR9Ll45m4hbTlnLpxLTV4KYSuvUfSZmujociyjLVoltH1bHfBg2uaxbQM6y8wqzSjEhw3XqHElkac6NAKA5hF1zokU7Yx81KvLuounIyaZg0mPBMa0m/sFUWiei8NImz+T8AKL+2pMHy1WCmbBcPKXbIcZhC3xkIHPJr/sc1ClSHPQrogkcm2KJLFStrW1g7ewGzp8dBzQLg9v7sXPnII4euwK33HYNZmcWcHF8EhPj01hfb8wgKvseixThKFeMxcUVDA0PYOTClI+Q0ivLNIFl3UTixCSIgsgcO2uoYByXJmRSGWZBUru7OwEAq2vR43NaNIJUFIsF7No1hF27h7BzaBDVWg0T41N44YXXMD09h3oLzE3D+xG/auxZI2UJlgwDrO+wKHCkEJxYLBo05576lWR/Sst4Qc+8OR+7wOpfALbLiPeXP6acmt1od6LV1TWsnl7DmdMXoOs6Brdvw/DwDlx19WHc9rYbMDMzh4sXpzA+PoWNdXEuY7q9WZHhNKowGXtN08Ti0jJ2745HhNOqwq2GGp+zR2LTaFEgGNpHmMAXOAvB6Mm+iLfOJDuMUPf1dWN1ZS1gDhHHZCEuIdaYyS4hwew5sGSCoFQsYnj3AHbv3YHB7f1YXlrF5Pg8Tr91ESuLG9Cgw0AehpaHmyDJ8pIkESJFIh8Tc2jixUqT4BxFgulIzoBNgnKU0koIJT3MeWTYcn8H+OTW0CkiDE8RJvWw5TXne8tyulVTc8ykLapNjgm1o1jXLUddtSyX6LLtz8HOS0wTZc5NdH2FaVUY8A/x5DrTIATYpLbTSgAh7XYGRDik2e9rbJtVB03dPFNoE9ByAGrIWTlH8TUdYqX7SLS3b1ApDpThBN1yiRoYwg2xmun7nSKQllUHLA2zMwuYnVnAyy+b6OgoYdfuAezeM4Trrj+G5aVVjI9PY/wi30QL8Miq6SrTyQferMyv5ueWsWPHNqpesTrr9gEMGaZB+j6eewRdR0tXnS09nQ/SpS6QXaJIQoJ5+/T2dmN5ha+UtsIvWKYfKJWK2LN3GHv3DmP79m1YmF/CxMQ0Xn31LSwtLqdtaiRk+7nQOY7mjCEhe6d7OcNNpcUplnhNYS3rSLwUYuvmxU/xzb204DzL/Y7TL9tzyuACL/ndroseZ3hWi+LT8BCtWAtdnDIiZWHvlmmatu/w9BxefukNdHZ2YM+endi7Zxg33HAci4vLGB+fwvjFKeH4DGRLhrPA3OwihoYGY+/nX/xId/2bbrGlxufMkYgIi4K/+PLlMi+LKIUQG92W18mz+Up7+7qxvLzq/O0352QHCnYllf07anXIF8yKm57FAI8EA0Cpo4Tduwexa8+gHUVzfhkXL07j+WfexMbGJkiwLN1JhgQAdJRo9xiuDuz5stqkk8rvS+X2zelwoiP70xkBDqnVgibI/nP2mz0T8qoHytgkOK9ZMDQLhu4nu77rBL/fsKXZ/sm6bptHE59haB5NtFxybKFuabaS7ZBi07I8Eu8QY1cdplRuX0uc62BoGkzdgmmSiYG/G6NNycl1pxfRvFROmkuQyfBN2kZHombNpT3q6VeHWTIM5noECavpo7E8okzDJtOeKkzUYJZ88dRM2kSafUfJ4hX93cZGGWfOjOLMmVHk8zkM79qB3buHcPToFdgob2JsdAJjIzNYWUnmUxdnQGbf2ziD1sz0Ag5dudu3TWbgC/RDEot0bZOeQfkgXXaIS1DDyvf29XAn0+1Ggjs6S9i3dzf27B3G4GA/ZmcXMDY2iaefehEbG7ZCJttmti9qeHqfSNJLEFx4bRXca8IhwTa8v3Ut7/uOl8kjSiUGgveFzlbiy2cPXUCMg/Mj4flxybkRWSZsOw/xFergPuvrGzh16jxOnTqPQiGPXbuGsHvPTlx11ZVYXy9jZOQiRkcmUvm8RyELVXhudh67du9M144MrESbSYbV+Jw9YhFhXTOEJNgLbhV8qGgSzEuXFH1cf5qW3h6PCMvUk5YM++riqMGkTsAOc79rz3bsOzCEgcE+zM8tYWJsFs/+9C2UN8q+eoLnyapEOnSKgRE67CnDDmF1VGBCeMnHdOiCnUPXv45LSDMPpGVs9GevnZZLFgkJzuuWoyBb4FXrkmCKJFuWBsPSXMW37pyraxpt2b65lqWhblm2aTS8AF/EjJo2oa5btsJMK92kLqIIu1GjNceMGZ4KTa4zmwOZvnp0qiZDswk6LAumZkfBhmt27V10sToM0AG17Atgk2GSe9iECVPzaK5HWP0k2AuORRHskBRMmq8FfkJMk2EwC1SyqiUhq9VqDWMj0xgbmYZh6Bge3o69+4bx3vcdwsrKOkZHJjE2NoX1tY3Q+kidbP1pQPsI8wazmal53HzL1YJ9xf2GaDEuiwjSCgpxIWMWHQaRpRMPfX09WFhYiFV/I8CbZOfzOezdtwsHDuzB9u0DmJmex8iFi3jiieewWa5waglHnMB+vPak7b+8PtAhvA5BFptHp1WFgaCZdHwTaT8Jpn7XNGgwoGnePJIlzK67D4ccB5Fz2mU6LSVE2HG/c/pwQozpOBnucx6DN0QR31DRJeQ82PFEWC4GSSZlK5UqLly4iAsXLsLIGdg1PIT9B3bj+PHDWFpaxejIOEZGx6XMp+MiLRmenJzNLHL0VjeZVkiOWETYtOrQNbldwiLyReXvjEJvXzcmJqYByJNpNiBQoG0hL4EGI1QN1jRgaOc27DuwE7t2b8fq8jpGR2bw/FOnsLlpByTgBiXS/PmD3WPB8wqm/YNps2jaJNrQbL9XQk4t0H7ANqmzLL86TJRb3hnTxFdn1F2/+bRHgvO66QTM4tcJzvFMzXJMpB1Ca2lum+1rBocE24qx50PsN6P2m1BbriLsM6HWbIsS3TEbtxtiE1eTUpq9RYCgKgw45Bfw7QNn8NcB1AAfGWZNpWl1mCbE9jSGaoFjWuZucdVhfvAlnuk0IcFk0Pelf2IUXl+Ed4EaGfAVllI5/e95vW7i4sVpXLw4jVzOwO49Q9i7bxjHT1yJhYVlXDg/jrHRSS8fYkj6KPb7pBD1R7pmYHJiAZ1dJZRKBZQTTJTDQFuEtFMwLMvyFqOS7h8HDz/8MP73//7fePbZZzExMYFvf/vbuPvuu4XlH3zwQbznPe8JbJ+YmMDw8HDM1l5+EMXNSLIvDz29XbhwYTR2u2QRNVkNkBFdw/DwDlxxYC927R7C0tIyRi5M4MknXsDmZvJ3Om6/02yTUo8Ms6pwFmRYfEz+dk8NFpFgjaSP1HRnPuM3j9Z9+wbjsvAWGVnXPXsuVAeQs8UZlhg7rkj+tkfHefCVjyC/cdMtsVkdAt8LSLJsbAq6XL1Wx9jYBMbGJpDP57B7zzD279+Na649hrm5BZw/P4bR0QnUatmOhUlxcXwKPT1dyOVybn7uNEijDvOenUag2ePz5YDYptE2GY5HYmmTaDYatK1WhtdnMi90V1cHVlfXY7WBRVjwCREpZtXg7p4OXHFwD/bt3wnTtDA+MotHfvwiVpaDbfP5TGv+bXT+YPtvgX+wUyLnqMHEJNo2k/b7AdupcsgJeaQ5p9k5f+3j2D6+cV4pf9oky/ERtj853USOUn7Zl5UmwvT3rrLrEEuL/skoxvY2Tyn2E+NwE2pTd66bbhNjTYdb3jOl9la0vbRPvrOACWcqoTnMXLMnFqZl3xtChnXNawNfHfbqs4+jO8qsRplJ66jTBJZDeHkII8FsECyRTyvPx1UGrAUH2ea2zfmuVqtj5MIERi5MoFDIY+++YRy6ci+uv+EYxkancOH8RczOLiZeMRYhjlXK6so6qtUaBgb7MH5xRrpeHlqddkEaKU2vQqO3cbC2tobrr78ev/qrv4qPfexj0vu9+eab6O3tdf8eGhqKdVwFecQhAt1dnQGTSrn9RU4g8qD7it7ebhy6cj/279+Ner2OCxfG8er9jyV2x6DRCj/JQNwGV/3lq8IAS4aBgPVRoxwGGXNn/zaPBNNE1ybAGnTNgOUQYwAuAabnXmEuafS4RYQb8vzZ1y3vWidqLgG2xyxiFk0rxeQYsu9AFPGNHTxL0J2yWR6AZKRYJBJVqzVcOD+GC+fHUCwWsG//bhw5chA33ngCIyNjOHduFHNz6S0/0qjCc7PzsCwLg4P9mJqaTd0Wt00ZBtXNHE0eny8HJPIRJmSY9fcFgg+ObOfBq4udTAO2aVO+kMPaGp8Ip5loyphG6LqOPXuGcPDQXgwM9GHi4iyeffoNzM0uQbOYlWjBgE4nvCEkWNe8AFke9SX1eNGiiRpsaJprEm3ontJLCB0bMMozg7aHTQA+FVlnBkST0/uSMoZmE9m8biGv2WpwTjddVdjLF6wF9qdzDLvloLkqsEt2AdRN3SXAddNRTTWPAIOQYcaE2oTzkzKhrlsaLIe0F3RAN+yFgprpj6JNRh1H1BXcP3t4r1kaDJ1Phk04BmMCdRgg5tPEd5hcE29xwLtj3oq+iASbnO95JDgpkpI4viWFY8JNvd+blTLOnDmPs2dG0dfXjSsO7sHbb78B1UoV589fxPlz41z1JrUaTBT2ECuVtbUNDAz2ComwiTpEqY9EYPsG1ix7SxDmjPDBD34QH/zgB2PvNzQ0hP7+/uwbdImBfpYanb+3WCrCyBnC8VkMk/k9nmoGOKqhrmPvvmFceeUBbNvWh9HRCTzx+HOYmZlPNKmNQ4AaB1bRpfrjMDIMcMykeeowkI4Qi66r35SZtDuMBAOaow4HCbBngee3omH7Xt5YZWqe4utzA9JMPimmlGKaFMn0y2HEV0T4wvLcm5xbY1EWhoA8KY4ixKLvNzcrOH3qPE6fOo++/i4cOrQf73znbSiXN3Hu3CjOnRtBRSIdUyOwuraO7du3ZUqECdqaECtkhlhEmEj/GnRu5Ge7i40/MTWpdEkkYjSPBFuoo7OzB9VKzZejlNvWiMErrhrc3d2Jg4f24sAVe1Ct1HHh3CSeefJ1VKuO75/kRJjXabsdPGsWbWnIkb8cs+icEyDLDlKl+XL+EoRFgybfk41ueiSqTSbsXL+0CYWbOxieEsyS4LxmOgGzLKouR111ybfllmGDallE/bU01C0ddc1E1dKhmzp03YQBmxxbzn6mpQGaRanINhmuWX5S7AbQchYOCroFXbfV45zGz1HMG3zcdjrXKAePDGsOwyX+xsRUmqjH5PewyNJhT6zuG5y9ktxI0lrwe9EALlrQsvcV5/LmbXP9sER+SmTyEdiv7vt9cWkJL7ywhJdeegO7dw/j4ME9uPr4IYyNTePs6VHMz4vzk/LeXVF7uHmFETRDW1lex7aBHm45N5VDRLR8At7iGCHS7UJ+LUuHFRGJPHz/5the3XDDDdjc3MQ111yDL37xi7jjjjuactxLGWFuCDLo7CygXN5EvR7nWU7/3Pf29OLQof244uBelMsVnD1zAY8++gyqlSq8iMSNR1ap0sL29/mxCskwwPoMA8Qth1WH7dqcM4hqAfO3f8He/oVD/FxS65FgjwA78x0tBw0GDC0PaBY0zf6dJb8+EszEaxEFj/RZJcKEqdV9pNj+3RufNJB4Nva4670X8dPh0e8Sb8yJskrSmUtuOvfaO7eg253bVnYBJWJhR2bhZ3FxGc899wpefPE17NkzjIMH9+PENUcxOjKO06fPY2FhKXR/HtKowisraxgY6E+0ryzaiRBvlfF5KyGRIuzLp8m8aFHmlLTqEscvjnREnZ1FrK/zg+qwAbuywI4dAzh69CB2Dm/H5Pg8fvrka5ifXfGZ5bCpT+i2EPCIMtuBE3Nokg5Hd//ZSiEhwHZ6JE8Rzun+YYwbMZkcR/MUXaLe6oCPlLoBuqgOmM4F7PkG2yS46PzMaSYM3RQqqYT8GpppR5zW/WTYNYU2NdRMA1VNh2YCdc1CzdSdKNNE+fT2AeDzLzaIr7BJfIY15CwLdU2HoVko6hZyhudLXHf8iWtOHWS76DoS03NChulphk79HUWI6cjSSVbkw5ReC/VIEpwVLAGRppHEB9Y0TYyNjePi2BS6ezpx5ZX78I533YTl5RWcOTOCsdEpmKYpDHwXNak3LRM6CZQS0r6VpTX09XUHtrNkGIi/EMgSaR4ISdYQXi4zmFo68yln3+XlZd/mYrGIYrGYpmUAgF27duFP/uRPcMstt2BzcxN//ud/jjvvvBNPPfUUbrrpptT1X0qIowazC1JJJqZdXZ0x1WBem+T7iqGh7bjq2GHsGBrExbFJPPbos5idnQeASAIcFkCILSczp4hbJqpt/vo88hqIdkwpwJo7ZlGE2J0cxCHEMggjwX6T6CgSTLbpmh0x2tIsmwRTBNhA3iW+OijTaWq+xMK1lNJMQPPGRTp4q0uKNVv9JaSYCDOGprum0wDARpVmwb43NPkNqsUS7xgzNaCJMU2KeYQ4CRkOb4p33vW6iZGRcYyMjKO3txuHD1+BO99zEkuLyzh9+jzGxiZghikKnLqT9DlLSyvo7++JLpgBRP1DU5HR+KzgIXEeYb8qxaREYsiw/fLzU6/IHcurq7OrA2uc6LJcxSokUrQImqZD0zXs37cbR48eRFdXJ86fm8CLz72Fzc26q+CSsjwSzPubNp9kAzzQarAXIIskTbL/EV9gDRpynHzBJCWOBQC6bfJrUHw2R8yoNcv56ZhTax7J9RrvXU13WKPIc87xB/YrwnXHX9gMBNiy22D5yK/hkmbLLW86JtCmqaNqWtDqBnTNQtU0oMNC3dLdwFqkPBn5iSpcd4iwBaCmaW6wLdtH2E73VNAt1HULJNAW8SmuUZGn3YBb8Jubk2tBritpgr1IQAfwsuuJIsREsrf9nD0/YTi/wzI9n3LLXvGoJ3iH4kDmHY37HidJL0APiqsr63jxhTf//+z9eawk13Ufjn/OvVXd7715s5KcRdyX4U5KolaSWm1HkmWDMRwL+CUILMNJAAPyokixDBkOrDiwDSew5QRGogRKItiIkTjxijjxV7JkSSZFiSIpistwHc5wyOHsw1ne0kvde39/nHvuvVVd1ct7/d4b0nMGPd2vu5bb1VV17uecz/kcPPnks7j66jfh5puvw5133oQXnn8JL7xwCP1+pGVVM8PjKrhWTUpAzp5dwBVX1bdoSMEwsDKnOM2ehtMw58sTVrM+AFx55ZWl93/1V38Vn/vc51YzNADATTfdhJtuuin8fc8992D//v34/Oc/jz/4gz9Y9fYv2spt0/zcgPr7tKnFSilcddWbcOON12FmdgYvvnAID333cXQ73bDMStvUNFFIR80fVtODedRYRwFiBPC7WkDM2x3PRoNggh4bBGvKoSljwOszwpINTgGwRh4TBijrqgARFAORGWVhASe06NiFwZGBQQFyMRjqaDBLzJ/pJEscv+MwLQw+BsOB71g+sXorTn7vtMyoCojXCgzX2blzC3j00SfxxBPP4JprrsRtt9+EN7/lNjz//AHsf+HgSAbnauzMmXPYtevSVW1jNYrR650tnpZ/vmjRVgyEh9lAD+Gam2tdjdw4Njc3M+hoa6LeK1HGzLIMN+y9Bnv3XouiKPDC84fw8ksn4KyD1KrUjX0tFF+VE/ksT4sGQs9gAbK5EiDsIhAWYSxdBm68vENLSaujmNlNwW7Yv3w3ihljoUXnngodKNHKeLEsw+2TkjpglLbjmOIsDw+GxawjWKdgrAIVvmbWb6+AAvnvByrXMKfiWjoR1tIJMDaOYDXXMM8oC6Ol1th5OrZD5giFHQTEDGZjsIHHysfUAaF/caCjO4IlBEXqYYCYvwACTDTgTLHUDPNvEdWjLWzi/KNJ5JvBMwf9VPL+tG3coJZkXYHma7yq/DysJ2RRIPQn3rnrEtx449W48eZrceDFl/H8cwexvMyT4ZU4NqkTTkG7dQZnzyzi5ltnm9dLGTKrbMEwTKgPGL8E40Kwl19+uSRmNY1scJO9853vxP33379m23892npT7bl0aXYF9cHjWZ5nuGHvddi791r0e30899yLOPTSkQEadu31M+E1Oaqmchpq9ZOOpTyeZkAMIPS9T91weJkyvVI2zQAoHjGu0oZGgWA00KHzkAVWlINIM0XaJxk05dBgyrRyChmymCxwEQzXZYPlC4cMMKQNIbOILLw4FnGW2Lh+MyB2Bkzg0iUGlB4ZWGlmQI4rOmtLv6GN26wA4pCx9ufmKDC8FtbvF3j++QN4/vkD2LNnJ266+XrcfPP1eHH/ITz33IvoJMGqOltJVvjMmXO44YarVzzmFDOs1ne/Ee03f/M38Sd/8id45plnMDs7i3vuuQe/9Vu/VQpG19n/+l//C//yX/5LHDx4EHv37sVv/dZv4aMf/eg6jXoymxoQjn3a0kbkowHwpNZut3DuXFR9XIkQSHXSnecZbrjhGtx403U4f34Bjz7yFI4eOVmi66SCDUA5Gywmf4+iL8rNVraV3sQH2yYxLZqkNlixOFYeHgxqFRD620rvYBF8Ejp1pmK/3yYALFTlKhDmumBbzgh7EJxmhAXwynbSjK8AYJVkg7U2YVlrNYzxlKdwZmZ+PA7aWS+AVY5oidgWt1DyoNEqWHIgv7xyBEucYc61Q+a44t25VG2a+wJry8C57xy0B7ICdAX0SrZdfFQKkKXmWNkyIBaAbB1KlVzyHUT9WkF5yrUHzv79goDMZYlDjxanRfXX3EbWn6ZguMlWQok6fuwUjh09ge3bt+Kmm6/FR374fTh06Aiee/YAzp9frM3qjGspuD139jzaM63GWmpVAc91VheBD4qkQ9bbCODrVqlKKetu2bKlBITX0h577DHs2bNnXfb1RrVpgLuZdgsL5xdWsYXBa6HVyrF377W48cbrcebsOXz3oe/j6FFpn7iyvq3DrrmqyFBJLGmDJ7uDYqTVz/z45P1E4ClmidMNJi8H7k91zK7KfaE0nkhXHq8mOIJg7R8KCjlmYOA8CM4DANbIoJziZyifLJDOGvX3KwkouwCFHQyK4EOV4xrgggooUgMZYkN9SUcEQDyuaBYfrzL7r+79UVamQsN/Hw/i064PE4LhSc/nSe8NR44cx5Ejx3HJJdtx883X46M/8gN46eArePbZ/avu+pLa+XMLmJ2dWTGQDWrsU8jorsf9YVr+eVz7xje+gU984hN4xzvegaIo8Mu//Mv40Ic+hH379mHTpk2163zrW9/CP/yH/xC/+Zu/iR/90R/FH/7hH+LHfuzH8Oijj+L2229f8djXyiYCwnJDEBtQ5qsBwcNuGFWhnjTSVndRA5xV6HZfG9hWtU4wjLlysacXMzvY67F37zU4d3YBDz74PZw88Vrl5qWGT3BrMjUrmbxKdDNuN97kCbFlktQJS0Y3989EabaUQVcAsR4s5wKAKQLegXEggl9F3FpJUQTP2oti5coi05zVLdGilfVZXFd+9uBYKwutLZQypWVkvLG+k5BpE0AvgUWmbAKCY/slD4JtzBorxQCWAwSOM8Wa20e1lYVV0lYpZpCNJaY5E9C3PHHgjLILraicw4AatoiPSUY4A1PTSTEoVmAAbJwr0aiN/GAQmrenQvtfXSXOmwGxQkEGBAvlYoRbfrko94GBrHDdOVxtI5QCswEq1RAwLQ55mI0DhldiRApnzpzHtx98DJs3b8KNN12LH/p79+CVV47i6X3jOdzSPS1Rj5bJ0PmFJczMtEZuZyX074GxJMdoo7K/6y3GsbCwgBdeeCH8feDAATz22GPYsWMHrrrqKnz2s5/F4cOH8fu///sAgN/93d/Ftddei9tuuw2dTgdf/OIX8bWvfQ1f/vKXVzzmi8a22lZl7Zk2OhP15o0hvCoIbrVauPHGa7F377U4/dpZPPDAwzhx4lTjWEeB4HGvzar+yerAcF24cpwxjPc7VOtV5dIbBooJ7GfSj/xOy9tu+p4D9/EkC+w/HwaCoyhWBhHEUpR5KjS/r0hBu3wAAGeOS8jSQrJRQDiCYMXML2gUZNgnQTEIdgoFChBpGOLMcJpxtZ4WpgMIbhaZDEdlCA16ItBVyvz6ZEv4TeMyVTCc7mu1AG01AbJTp17DAw88jC1bNuPmm6/Hhz78frx86FXs2/d8LXtk0qzw+XMLmJlZHdvoQihJGtfW2z//1V/9VenvL33pS9i5cyceeeQRvO9976td59/9u3+Hj3zkI/jFX/xFAMC//tf/Gl/5ylfwe7/3e/jCF76wsoGvoa0qI9w0Oa6C4Lobxji0kLqLemamVaoFatp3SbK+ciPIsgw33nQdbrzxWpw5fR7feuAR7ldaWWdQ9KAs3x/eb6gTHscG6DIJICZQUIuWulLOzqIEhkMv4JIvcKEtUqZcqA/WFQAsRyUFzlUATD4LrH1tb55Qm3NlAiAOWV4Bw3DhPcn+pkBZHrJ/eEVoBsgWWhGczxgbpYKYVulbOgSVaSKALNcSk7MhGyyBAXgaeaYsoG2oKbYeMCsFKMcgWCmHItQPMyCW42WTG0l6NIUGbR3PFZTjh2SJBRBLHbES7wqCI4+kSYFcFN3SUMGZFzD8neBKgJjHZUMQpA4Mh2Uq5544V7lGSz0Y/ev0+kmXHYgw10Sm18uIFBYWlvHoI/vwzNMv4uZbrsff+9C9OPTSq9i3bz+WlzvJOGN/yjqrguHz5xahFGF2ro3lpcF7T7W3eiO9ecSEpAkEp8E2i7WrtRJb74jzww8/jA9+8IPh70996lMAgI9//OP40pe+hCNHjuDQoUPh816vh09/+tM4fPgw5ubmcOedd+Kv//qvS9u4aOtrMVDdQmd5OP2x3uK5n+cZbrrpetx443U4eeo1/O3fPoRTp86Ulh4GgscBwE0T3ybV3bprNw3Q11/bdX9PxowZZ4JeLsuIdaJAmukaDYpRZQSk6wWrHttkvEnSYGIQjBwZ2lCkodEGBeCbBQCsoaFAyKBBIGQSMKbhQNg6B26JBJ8RTv2o8gHmgjPArvBBbU+FrqkflmNYVXNu+l3C3ysIcor/BTAAiK0r6+5U/W8dw2MUO2wtM5rnzp3HQw89hqeeeg633XYjPvyR9+PgwVfw9L7nS/4ZmAwMnz+/iDzP0GploYvLG9nW2z9X7ezZswCAHTt2NC7z4IMPBj8u9uEPfxh/9md/tqp9r5VNDIRjRGo0/XfUcqmc/bjWnmmFfqLjqkQHcKwsrr/uGtx6616cX1jE/X/7XZw6yT9qqGMJkcyyjQK5YbJaqSMe57tRAqQj5Sc+tGSDVewZrCCZXfiMcYRkRDGzy5RoG4BsCjwFAKaiUyn4Ffq0VtYD8AiCqwJZOqn9FQCslIVSDkqZKIzl36OkxY9zCtYiAGOlHJTl7UL792vUB10AugRjHYy1MKRQWBbBUk4F4OwcAMVBg7bi9lxSU2w8dVo5AjmmoRtLnhYtLZhiBjlsD+VAgtCgjeWMrwDhwpbFzAQwM0r2ZDPlAbBQthFpXbxvzgqngNjAwIbztpol9oDJRZHAujM45JPD+KwHXOnrMvCtA8PDaFqTWl2f4UlsaamDRx95Cs89ewC33Ho9PvLR9+HAi6/g6X37x+51mILhTqcHYyy2bJ2vBcIDkf8Jost195XXUx3wNOwDH/jA0Cj1l770pdLfn/nMZ/CZz3xmjUf1+raNKoWYabfR7a4ECLMI1vXXX41bb70RZ8+dwze+8W2cOsXsr3HVn4eB4LFAZaU2uA4MT5MiXRp7Gvwa8p1GbSuoSQ8AYyDe8RORPyqDKZS+24h7UTJvimOIrwUES92vCGAJHZqIBbA0WCxLQaPlWgBUBQQrZB4Kaw9+5ZnnOuzkUn4BHwvng8/sUwtnfcVxBMSSUzZgECzZYRAiDdolYQzyAWKMPg9GBTdHWbpUwC9uEAyDUOt/6yjSk9q0a+EXF5fw0EOP4ZlnXsCtt92IH/7hD2L//pfw9NPPr6gX8cLCEpwD5jfP47XT56Y61jeyraSrg7UWn/zkJ3HvvfcOpTgfPXoUu3aVBUZ37dqFo0ePrnzAa2grzggP6z8KjI48Vdd1Y4LidisC4cZ91ziOK67Yg9vv3AtrLb773cdw5MgJP07dAHwjOG2SuK8TP4jZG/ksLlNXv6mS5dP64Ayab/B+C1E/mt8TsMv75b9lbwKA0zZH1WywJa6Lda5c9ZPSoKugWNojZUHoigWyUhAs9b9CfxZAnNKhSXkwbqXel6lG4biShQpNjwGlXUlUKzXnlaYNKRSkoSyP21huOlOaX2sFkAXpAkXI9CrA1oARJb2UGQRLVjnUHDmpTY7g1vmsL1OiI+2ZVFzGOkAwsLLkg+7O/8JS6x3rha2vWzZwIOdgBBz7c4mBM09mgqN2AChDCobj2SG/vy29YzECDFMywaqA4aqlznhSmyZ9emFhCd996Ak8+8wB3H7HXnz4h9+LZ58+iP0vvByOybgTkk6nh83zszgxpsCJ2Kh2cmJyDxoGgteldRJigGk161+0jbXa8oaGyfC0gJ1SCnkrH+mf6+yqqy7HHXfcjKIo8J2HvoejR46Xxlca7xiBolEAeJggX7pOqUVR7X6mI5pVB4KpAjKrn9dZqX0cxURBCozrssVpVjmlPo8jdBrHMwiA6+qBCaISLQA494C3De00ctcCnELmOE+cAuCMlC8P8ywuYcuReNDkWMAlbRWd1wDRvkTJ+tmWRQEpO4rZYb8BEFkY9Bm0OylWMtFvjrjVDQO/owKeArZ5KDEoTTKhgPjsejB8odu5cwv49oOPYtu2Lbjjjpvxwx/9IPY99Tz27z84Udslay263R62bt2M06fOvK5oziuxjezq8IlPfAJPPvnkG06YcsVAuJbuXDPpGwWYS2JXlYtXMkLiaLQmkCL0es30wOpFsGXrPO666zbMbZrBvqeew4svvgTn3KBzrbl4xpmUp83d43teZMtV91H+PuL8JPIZVRDjP6FGKxpNxUlBMFVAcC4ZWXjQI5lNv45YXRaYCAMgOE/o0E0gOPNU6CwroDQDYkoArfOtkqxRUMrBOQaxztOokYwtvfClphjgDK1WXmnaOGhSMM7BkB2kUWsFoyx0ViADYCwhMGmsCtsmT9GW7ZeyvvLsfP2RtFoipj/zwknoFqIYjeCwrfPBjPBjEIiLjyMYJQ/APShWYDDM0XoVnH5VUMsSa07DFQBlnCkWdkZyfYlICKBYNAQrB8NpVpiPU9mBT2JrUUt87twCvvXA97Bz5w7c+eZbcN31V+CpJ/bj1cPHR6/srdfrY25ubmpjqh6XtJ9wdYJUUrFeBzB8EQj/3bMmwZixeuP6czLPeToxSVZn+/atuOuuOzA7N4Mnn3gGL710eKIatrrMaRMIHg9AlzOo4biEe9z0ssJN5RPV79H0Har3SZtkgNPvAH/fjsC4fG8uA3lbej08UFh37Ov7A1ep0Ao6gOAMbWQuQ44WNPiZHCGH9kupEgDOvE4Kgfxcp35uJAKaFlI+xWBYQdh3DsqJhy6ik44xbBSuACiHQT/4RvGV5SPQbCXNmQmywjFIHf2pkr+bypUSvY5JWVmrOZ9XI0x55sw5/O3fPoTduy/Dm99yK66/4Wo8/v2n8eqrx8a6ZgEul5lpt9/wIBiYnn+etKvDz/7sz+L//J//g29+85u44oorhi67e/duHDt2rPTesWPHsHv37hWOem1tYiBcErdqipJWnMdIMOxMIwguDTbj4RZFMXKCnbdy3HbbXlx77RV4/rkD2PfNZ1EUphYET2J1atFAzAanILgq6Z+Cj/Be9Z8jr4nI0c80K8z7idtjUJbcnCnmVSWjS5WsLsDLuGQbskmOsKa0aJdQn6U+2JQywZk2HixHISytC/4sM/w6M1wfrG2Zmm3ihMd5SrJsx/qa5WHGNwQFawmF0dDKcnbYPwaW1wpdbZDlfRgH9L2KdFCmdoTCEVTa0inZV4wuR4Et5+BFtQDPq4K1DFX9mkn0mEJGOIDm4FV53VRZ2vlnA/9Dkz8TkpquQUEth4IAICtRpS2s5xhEU37NtJY4BcPAoNMfBYYBjAWIhznotaovPn78NL7219/B1de8CW9560247vor8Nj3nsHi+U7jOjL+om/Qaudj7ScFFOMqyF+0i7YeNgzArQQEp5ZlGZy1sHb0eq1WC3fccROuvuZKPPfsfjz99Aswpqbf95Bs8Cj6cArOUhsVaGvqyVoFw5Nb/X4D+6wCgqvjVxWAzOukwDgy69iyWKKWgl6SzK/cl7MBXZew7BBrymKnALiaBS5Tofm55RgAt1wL2inkjvlwORQ0KeQeBGfhwa5QSsY4AVCZGzkkIJizw8aS1/1woQxJgQBn4CUuJwLDfFzlWFXrx5uzv3VMwtTSsiNeV/Zl/BwzqR12cd919cLyO5U7pYyfMR51DjRdB1XB2nHs6NETOPb/fRPXXncl3v72O3HmzDk8+ug+LI4heFn0C7TbowUtL1q0cbs6OOfwcz/3c/jTP/1TfP3rX8e11147cp27774bX/3qV/HJT34yvPeVr3wFd99992qGvGa26vZJTRO9OjBcWm9o7XD9xZVlGtbYkRHjq6+5HHfccRPOnDmHr3z5b7mVSoXyBIwXIU6t5ICGTF7Thu8Dn0EFpySfp6IQ2ushCgCWm31KhQYiAOZMpK8hRvm4CIBWIoKVfC6gmcgxME7EsmgICM60DSA416aUBSay5SywstCZQZYV0FkB0rZEi1ZKgcihKDIg4/1aq2EthYz1KBNqtNYGxmhYD4IdfA20F+0CGAhbXWC21YUBoWcy9AoNIqBntK8LViHrm5qA36rAlrgsCS1YR761EjvZFFQyVSv2FY4ZYQbTKrRpKi+X/ugMVMlfA+RXj3DVAsicLmWHS4Ja6XciGzLGhSsGwDB/nwp9GsPBMH/PekA8bbrWSpytcw4HDxzGq68cxy23XY8f+MF34fnnXsJzzx6ETXViKverft+g1RoPCI8a70A2GCl1USEVK6susx7m3CrFOC5mhC8Ia7rmplnjmk6Us0yjKEYzFq677irceectOHnyNXz5//v6qluplH16lVbcnEFNr6f0OKUaBdUay9qa4cY6zDSMWD/mYSA4BcDxe5U1SAZERyn3Yy8nLCJI9uCYyoy78P0pPRY8PRyV7UuPeTpmFeqB9UAWWCMLqtC5B8Jt1wZLZeVQAHJSyIiQK+W7Xvj2kUnnDKZEN2WE2Ssbx+wvUvAAmIIoJpseAMOhtRL52ZgDnJQfDbCmTOO9uQkADxOLTT9Lg8zpvgQMy3aFIs2/Q30Xh0mv+5WC4KblxvHRzjm8uP8QXj70Ku6442Z86EPvwTPPvIhnn3lxaICt3y/QHqOzwxvB1ts/f+ITn8Af/uEf4s///M+xefPmUOe7detWzM7OAgB+8id/Epdffjl+8zd/EwDwC7/wC3j/+9+P3/7t38aP/MiP4H/8j/+Bhx9+GP/5P//nFY97LW1FQDhGGU3t31WbZBJcBcFyMTpnkWVZcLR125ybm8Xb3n47Nm/ehEcfeQKHDx8rbaN2bGNOoOtuXOOK2lQBQuwQ7DPCztfRBOEGgiYVaNEpGE5PYQd/UcB5VWLAgZAKX1XVmaWWWCMKMPEHfh04H11tBsG5Nsg89Tmt/ZV6YKFC66xAlhmojLPCSpsSELZGA4prhotCg0hDOTv2RR7o0b7/sNUGxqoApgEEYS4iB2iFblZgzgNhXVgW6CgY+Pc9rVooVWLcnslngRMKdVpbLNniLBz7JCpNcbypoBZngcF10L5uWPoNF/73C8shAlHGwJwZZqjb1G4p0qV5K+UyBKmH4pxANgCG0+8BJCAZ9WCYj0NzFji9ZlerLL3aiXyv38cT338Oh146grvedguuvGo3HvveMzhx7Ezt8kW/QKs1eLusKkav1KpgGEAtTXo9bL3bM1y09bdRk+KVXF/sn5vLlubn5/D2d7wZ85s24Tvf+R6OHBlemjBuNjh+Xg+CVQ1QbloXwMA9qrYn69igovk6GgWCBQCn4Lcqxtk0f1FUBrEMfvOSQGkJHHvadHpPTqnVw8bP+yuD9ToAHGqDkQcqtCQAcpehjRwaCi1S0IqQEyFXPP/JPQDm5wiCdTInSoO3PH6Ejg2GwOKXltB3vmNDKU7BYNiFcjVdyQwXgVNVB4aHWRMAHhXYrGNcDXR1oPK1Wp0Xr1UHh5XcHybp1dvvF3j00Sdx8OAreNvb78BVV12ORx95AidOnG5cvtX6uwKE19c//8f/+B8BAB/4wAdK7/+3//bf8FM/9VMAgEOHDkGpOKZ77rkHf/iHf4hf+ZVfwS//8i9j7969+LM/+7MLsocwsAIgPCkInqZxxHnQ0RIpXH/9lbj9jptw6NCrePBbj6DfH95mpC4bPK2MiyUbuMeqBrBXQXC1PUB8xKxuoAH5h7hnoTaTB1mSFa77JkFQSyjHETMHUz4bTORqQXArKxIQHOuApT2SzmJGWGcFVG74WVtQZkrUaFVwzbAhVpY2RRYoyOl4wnGtiWQ5q+CcQeYI1qgAikP/YXIhW+20Qp4VmJ3twChCVhhkvZwz2oVDZhQKqyF9iW1JldpLlSnLANgRHIm6M+twCcWZa5gRKM5AdMoiqMUe1XtZxc6aEOnQ0sOY0TGvn0FEtPw2iPsiaohPbwbEbDpkhS0xMyFzGSwsCipKYFj7yV9ddjgFw+HLAbWAOFWUXksb18mmbRmsszh75jy+/rXv4trrLse73n0nXnn5OJ58/HkUBatGC0Dt9XvIWvUlHuOC4bqs0ri2nvTpjW7PcNGmZ0OFnhrA3NiZnorPb8oIK6Wxd+/VuO32m3HgwCHc/7ffHQqYZWzjWFM97TA6MS/XIJZVCeTVtaKpywqn45lESbiaza4DwQIqU7p0VZukCYxZqszTKCYWACQAWOpKEyA8xj27CaQLBVrGmQLglAHXcrmnQWdcIUyEGaWhHHw2GMhVGQDnin1lRhK0H8Tqqb+VNojGAX0+CCU2llwe3GeYmVMaGty9Qfk5nPIZ4voSomYxtZWBYFmmGQyX91nNCqfrX0g2CRgGgNOnz+Cvv3I/9u69Hu957ztw8MArePyJZ2Aq95levz926dLr3dbbP48DnL/+9a8PvPexj30MH/vYxyba10bZijLC44DgJhqgWF3d8KjIVZZpFJU6orm5WbzzXXdiZqaFBx54BCeOnxqk8oxQdhxZa1RxOONSpKtUVFXaTgTBkUhNXh7CU2crtOjq6SuiS8ojWsFVTRbKTBFp0ipkiuPJrryD0WTLWeEkE5xlRQDB0h9YaoG1ZIFznwnOC5A2UJkFxNE6BVsoUJFBaQvTz0DawpkRUVInEx4bRT88VcRYBWs0dLINUi6M0WmFLC8wO7cEoxXyfsE1zF2md/eKDIW1MJb7Ekt/YgavCn1PuXZUDkBwJh4BtBL4QxGOlrPA+IyvAOYUDKftlaTdkihLc91xVJCG3wQ5pknz367Uf7gKiGUZ7bkAygEFxbkAU/Y5M2xDbXF9drg8hyjTBoH6SHb4PSpZYWDtaoLHMessFBRe3P8Kjh45ibe943b84N97Fx55eB9OnTwfliv6Bq28+XYpYHg1FOa6rHDV/q61Vrpoa2srpUnX+dO6jPD8/Bze+a63opVn+OY3v42TDRmddDy174/IBtctO6qmtn7/g+UcaVatKSs8rnp0HWW7rq6WgU1eAsAy95B7QAo+q99TLMzRXAMgbgDIQATJjd+lEnxIxydj075ZURAEhULuWiHw3/L9MXJSaCmuBW4rbl2YKQHCEQAzGHbQhCAKqhDnNfwd2D8Ji0s7h75lOnQwCzgF9qsktcQc9LYeBCvHbKoqRdpQDe14SMIDaAbBdeVzcYjDtTjkmFezwvyblIPPk6qbNy07jZKKScGwcw7PP3cAr756DO9855vxoQ+9Fw9957FSb/F+r492qzXxti/aRQNWUSM8Lgiugt2V0Aij4yFYa8O+r7xyD+562214+dARfPOb+waiRHGs1abikZJUZ02tk9LlGyOwsLXZsriedxpJHbFyAlz4ATDAYfCbgOG0JgZuoC5G/qZEKEtG7JJewfH7uAQIy/giME5BcFYDgkUMS2kL5f9WykC3GASrvIDK+JkyXiZ4I0sgo6H6FlYbkDaw/Yzp0jLeWiurScuyzhK0zwhbo2J9MDkeX1bAZQo6L9Ca6wAaKPo5lGqzsFefs8OF1egbjcJqL7plUVgNKHZL1jo+/p4K7ZJxEACl0qqwVN2agaPhgxyzx75uSeqBA+sZCGiTnbUTKgAUEQqXiHAhguImQMzLyTXrYOGQOR3BsFMBtIezPBHRCsMZAob5t6gXyUsd80CpwAoA8WrqHNOssOxXkcLSUgd/+42Hcf0NV+Hue9+CgweOYN+TXJtUmKJE/amC+4F9NDjkSbLC6YR3Pe2iavQby9YrM0Re6V7smmuuwFvvuh0HXjyEJ554pjELPC6wbVqvjiZcBcE0ZjYu1TeoahtMq1VSOv5xQXCaXa22aQzAv4YuKW3yQHEeFIWzakAyTcbuq4JySkL6yilkyECo9gRmSJwjgwYhJ4VcKbQV1wTPaP5GmQfBuQJycr4+mF8TiSBoLOcCEEqaLLxKtPXlZsqhSOlNfHBgyXdmAGDIz8F8iVpdVth6Ll45K1z2awPHaAUgWD6vA8Ny3KtZYdmXULbje2tDj16NTQyGYbC4sISv/823sffGa/C+978LLzx/EE8+9RycdbDWgka1VRnD0jGtZH6xHiD8on+evk0EhKvZjmE3TAHBDjZOgP1FuuKaOuIWO1mm8da33obdey7Fdx96HK++enzVkarqTWycFi7BGdV8H3E6hpJJtyvf/KQuOFKi+VMBwdI2ScBwSSBCALFERUXgSiKkGKRHW8S60RQACxU6vAffQzgBwaFNkn8toFfppD1SViBr9Tkz3O5B5QbkATFpw9RoqREuNMgYdtS+nomUgyos7IissGwjpVlLzTEZC5VRjEQoF+qVkRFUViCf6YAyQPWMB/Fc26yUQb+fQ6sMfWNRGI1CflurYfzxda6cFZbRinMsnQ1+Oed4GtW3FOMRkhkGZ+FjTTFTuUpoE4JAveolKLRYomRRcnHRlDKdZoMFqjMA9nXLPtod+wv7b5XUOMfscNkZR6DcICqDeop0EyAGxgPFVTBcda4rbenw4guv4Pix03jHO2/H+z5wFx76zpOeTeFqAcUbTfn5oqO9aMOsCQwSMQ0mzzO87e13Yudll+DBbz2Ko0eP+89Xf400Bq9rMq3xs+GKvVU6KYCB+1UVTKyEHl1tMVT6rAEEaxGcgk5elztTyL269h5Uoe7a6jPZWpBc3sSQTCfid0rHI+OMrSFpAABnpHwdMAPgtiZkioGwgosgWDnkHvhmyiHzGWEJ9Ffxj7Q2NI5gCOwvLTF7yriwggOQg8uNHLj8rCkrnH7fumM0qVVBcPW8dcHHlsGwHP+B38RHz0cFvFZTDjEtgb3q9ibNDj/37AEcOXIC7373W3HpZTvw7W9/Dw6RHbdSG7gmVxFsX0u76J+nb5O3T/IKg3UgWKKDQtVgMNyPvUGTSfqKwDARZtot/NDfuxeLi8v4ypcfQKfTrZ3wjnsCr0UEh7PCafaonIEaaJnkM8GhdzB8KyEg3OgpffbZYJXQhMoCEjEjDHiKELkAeoTAmZNl8KxsAL8ilCXK0UoAMNlQC8zPNmSCa0Fwqw/d7oPyggFwbriHsPLISluQUTFL7aPZAqzqLtaQBVasPk3kQl9iazSULWD7vs7Y6LCOFiDeclCZQTbTBxnOQittwndSss1+VJoGEHoQamVhLdcAS1YYvk5Yehtrcqia9cC5cJFr7FL0CgqgmjeZcNz9MtafAMoLfVhx8H5aUgBcC0zEARefHebsLStY23B8lT9LY6xbpkdCkZZd+xM4ZhYwGHWuCnk09Tjk36Pi8FfRc5i3NwiGp2EL55fwjb95GHe8+UZ88Affgc2b57C4uFw/hpoJ6LD7yoXqYC/aRRtlw0uMgNm5Wfy9D70P584t4Mtf/iY6ne6q9jduNji1xrrgFMCV2g6VVXoHx1Ap71jB9TuKEi37IdIDIFgjL9GM00xrlWE2zFIAZyviWFWgPI6lADwdRyj3SoL80g9DMsBVNehMeSBMwIxm2rMA4ExxADr3z5mf40QgXPbVEng2DigStlVhAespTda3YbLE+h6F4b+rWWH+XibQowEWmUzp0aPag4o1BUvr9Wr0ABiu7isNRE9DqFas7hpfS3+1Ejrz+XML+NpXH8Bb3nIrPvSh92J+0xy6yyu/1wwLsF301W98W3X7JDmJ66jCzhlYZ+FgJJkFuJWDz927L8VNt1yPv/q/38C+fS+scuSDNkqoos6qKo5i1RZJA/tClRLNgJg/k21LxrYMhoUiFEGwC49M2fBanIQASMkAOriwE/LthbRyJQAc+gInD6U8aE6UqEv0Y21LIFglQBiZH4cAYefgemCQbDg7TJZAVsEpC5h0wlAGwEobgBzXHMsymtWmSTm4QkMlf5M20HkB5ARSFqrdhzKcnaakcKgpUmacgracgTXg4yO1wkiCDCn1vErdtgDIq0w7IgQRLB8LMF5EK6VVy49kPWiWmmFSDIIVWF3aOIeMqASGrfRIJD6j+G/epoBhcZHVrDAo1ifV0aQHKdI1LR7GjlCXKdPTAMOTWhP7w1qLxx97HidPnMH/7x9/BAvnFkGhddXqbdzvm2ac10OU0FmsUoxjioO5aFOx9aBH73nTbtx88/X4y7/8Gp55evX+uQoQJqFQVynRw9hbqVVrMUfVWjbVCVdriavfo5YSLVToCgjWlENBQTv/HIAmvwYQgKe8rrOYBXYhU1ylSVeXrT1GCQCu27cOwX0VwK8iYhDss7/SDzitA25l/DyjOevbUhH85gkQlkC/TuY2Yml3B+MI2hG3SVLyvQAHQg4+BMbB9yiOnTdkHpZ+37W66w5r4Tms/++o65mgONO/xiKV07JxwHA1oWSMxSOPPInjJ07j4x//ceStnFkpb3C76J+nbxMBYW7nEVepnrwRDEYqNBzXBQkY1qEecPwLlIhw+x034rbb9uLF/YcaQfAkk+H6ca/ehkVlg3tI64O9w5DMnrRMYnpzrA8WEMxyTXzDFhDMkVN+zgTAVrLCg2OJ2V/l15EMsEpArlYWiiJ1mLOn5bZMkllVOatEq7xgAJwXoFYB0g7IXCnFSIXjs88SZ2u1Apkhk5QEBJO2nmrNmWkALJwlQFgbWKMDEJZ1mCvMfysV69WEVp17tWlrKdBPjFPIlIFRKmaFwbXCMtpIGfZ/V465ZHuD4rR/doQwDQE8yHYCXuEVoqUvcXlnijhLLRllI3XBRCUwrCW37QExuSQoAhrICqs4nGS58LN5mnSlLknAbwUMpzbMcY+rKj2sRm8UGF5N//BXD5/A/udfxnXXX4F73/sWPPTgUyNV6ach2rFx7ZMuUq8u2vimFOHON9+KW26+Hi88f3BNQHDpszFqg/n94SA49fvlmsr6DN+ktZbD2j9Vt1sCwP7v2HM3D1lgphmXs65x/hA1RoDyXMSmRTueOszvV4AkRgf5pOWiSp7LY+BlMuIlMt8CUod2SEie+ZER0NI8h5nTDrmyJfCb+zlNpliDRfvnOiBsHKEgzgQXlqGtODKHyJALnTjgEw7Ec03R2FBJaDpkvDFIja72bOZjNHjvX0kJjfi8lCJdVYfmcbFoFkRKpJbZsLIa9ws9I/ryoVfxwv5DuPLK3XjPe96O73zn+yP981rYemWPL/rn6duK2yeNa+w84mvZxrg3BK013vXuOzC3aRbf+fb3cfkVu8rjEWrPsElwGqltUJ9cqyL3VMgi/cefUcmZpI6kWh8cx1mmP2eSDVbsLDLvQMRRDIyHorCE10DmbHACgiXryyA5ZoMj+PXUZGVjJtgrRVNeQLX4kYJgkjNNqMEZQNYBmQH1M87MErdTglGBahz6H9eAYOXrjuEzs86qILpFJvYjlqywUypmpR2/R5ZCDbHNNLRVyIyC9f2ItUmy4pZgiEGjJj6jNHxdsB+nSo4zgECXNnLzUZZbPpELapWMOl1w0HCiGu5p4tUfUejSoEAPM0KHTrKVQVG6Aoidp167FAA7AkiVwLClSJUWMjXvXujTgyqWA+db4rgnyUo1TTrT63dABK/GEY2ajA7TAkhp3L1eHy8dOIKib/D+H3gbHrz/8Uaq9DRNwPB61iGvvk/h+ozzok3fJp3I5XmGu+95G2babXz7249i9+6dq9r/KCr0pOs1Lz9Imx7W93xaVpcNLlGivTBWmglOQXC15lbAr5Z5Rs1dgvy9vmq29Ho8lkucr8RtK5LSLsQgvs/6EiTjyky2nAhaCfVZlKAd2grIM6ZDb9IWmYtzGQHBuS/RIgIysg3ZYG51qB1nhBmU84ithQfkDIJ5bDwu5WQ+5sL3GvzuyjcUrLf0XCmxeKbUa75qwtiymHxePq6tJwheSVZYrNvp4cUXXgYAfPCDd+P++x/G0tLa++eNsIv+efq2qvZJ1ZO2qqQaSL8NAYhBxTu5cfD77XYL97znzej3+/ibrz2Iyy7bUVJuncQaATBUaZlxRLKaLBWOKL3vktyvk+d4UNJsMAOgWB8ctlFzDKUuWGpmUtpQXbSUx+gGwG5Kcy69j/RzW1pWqNOkfZ/gPD5CNjgFwSr5QgqgAnCZB6R5ATLKU6QtlLUxi4oGEFwnwGUJTmtQZuEKBZdkhb2nAwggZUHOAipSp5W2IM2A13jxLK0LZFqVssLa2dB2AYAnSVM4tgBQYueQ80JLBHLkM8GO1SoVYP0lYB1BGNMBsMKDVpJ9IbTMEpBsQpAJgdoVssJhDBEQS/9hOIkvx6wwHLdMCpToam2wD6lLfdRgvVJFZbUOmE5xclkX4R5amzsBCK5apjMUhcFD334Kt91xHd73A3fhO996EqdPnfXbmu5EJ50UpGD4ol20ldq0gd3s7Aze+953Ynm5g6997VvYs2fnxMqt4wDYYQHrUe0O67LBzbWaQ1grqzx2w7LBsv2UTKyICcba5QMgmJWXlZecEiAcASlvr7qfcpA0tXEgcImURBEME5XnLhK8zzyTLQXBaR9gUYGWGuBcObQU0FIOc5lF7goGwhUQLEF7rSSE7H0/WOjKOYJRhL7VMDYG0lkiUsE5B6OAzHk6tMy5QD7wz8cRARhzvbBZhyRaVfVbfG81Kww0sxYatz0mm2GaqujraVor9Lo93H//w3jLW2/FD/7QPXjg/kdw+vSZddn/xZZNr29blWp0ao3F+qRrnYA4lSZK5PzmOdz73rfg5InTePi7T8I6A2MstF7dCRdoVeuQDZYb26A4VpkWXVonOJaErlP7PeRZBCRiXXAUknAD63BtTUqdFpr0oDsMADnJBiuvwpxlBbQIZLXjQ+WFrw22EQRnyYAlhJkxxRvWAi0GwsqqIFFMmryiFBDqgcnGVkxSe6wtP8PnVQsNGAVXaDj/LGY1b6v+x7IlBeksK2CtQpYVXG+kFHJlYHytMDspxdRlRNVuOW6pOUcoxMmpxBlZFTPk/qsaid9LXTEhKH0TuKWEs7G+SQCwfP9wiBM0rhEBtYBhuZLTrDCvm1CwgKRmOGaFpX3EsOj4wOFNWylVJpVN94Bp9Rmuu/9MGvDKMo1utw/nHJ58fD8WFpZxz3vuxCPffRrHjrw2+ZgSdW2gPqKf1hiuJxi2jieVq1n/or2xbevWzXjve9+No0dP4NFHn4S1BYwx0Cpr8PflOtvV2nqxI9bKJBvMr2M2OE0g1IHg3GXIoEI2OK2/TbOxQBJ3HlI32QSO69YL/oVixrRKLdZhDEmgvgSCXWiBJABYKNB5DuTKYlNWBCCckUWmPQiWdo6+jKv0PeBBsOVyJiKggPI6HF4o0hEMsQCXIQetiHU3fDKh8N9RgH1dhKCpdnotugmwQFdN/fkEQRkuT1xZxniybHCpOGzifaX7XMl8PMs0lpYMnHP43qNPYeH8Et73/nfiwQcfxbGjJ9dsvxthF/3z9G1FqtFNVlJvhfVR2MHI1SgQvHXrPN77/rfhwIGX8cTjz4blrbXQqpk6VW1h0GR1ipPTcM7pdlIVxyBwkWSDU+pNHS06rV+J2y6bQsz8kn8OGV0kFOhA25VMrtQR2xBZFbOeTlTXc5iBsGEKtbZQuQmUaNXqMQAWENxKMsGp4pdk9K0FtBerUhYKvUC3Jm1Cza8/mJGOnfnMcMatmZA5rv/1m6XcwhkFKooAhmEVZ4YJ4FCvq3Vysu8sK+AcIXd9rsfIi1CXkTsmlHMrKhNuKmlQoXQ8weJK5BwHRqRGWD63Co7IA2Gp0BJRLt62AZB5RFrExLYHowJQmV4dJi+VCU61dlh50SfJCvPePcqmJPLss8BpWyX5LNKehzvaUDs8YRRbrApaJ6rTmxAEN01ctFalPuUHX3wV3U4Pb3vHLfj+917A4ZePD6wzyrmWhXaal1t3J21pVWIcWM26F21NbTAIVf96mG3fvhXvf//deP75g9j31PN+u8y+aQpUr9S/TnLe19UHT2LD7mHTCECltOjwXgJ802ww9w3m9+tAcAaCpkSIqiEzC9SzyZJRlf4aoFUnHw8DwLLP2LmC38uo3P9Xe+AroDilP+cZ0FIWed5H7vo+I2xCG0fpYFHVPnEuyQZbhcJqaLLo+emt9dli4wG3cQStuKWS+NLqd62zUsspeW+EPxsXDNf1gJb3q6ysFIyn9GgOgCOpE147ynRVJ6T5s+n7rTp6dJZp9JM+5S+88BK63R7uuecuPPTQ4zj8ytHR212hn113AH3RP0/dVq0aDSR1ujUXBwtAxKxHtVawus7WbZtx73vfiueeexHPPnOg9JkxBipxtJP2Ca0FwBOexOKw6uhXYulNrV4gq2mywNFcLU4GFBzNKMZZVaBJsr88hoT6TCkIjgrRYkHh2IN1lwA9LfXAmsGvzgrodi8qRLf7UO2CQXAL0SsqVX4WkxphT3d2usc1xv2MQWxywQpgJuU4C6xsBMEZYg0yALIWrgCosHCGuGexv3mkzq66fVG/dtoicwKG+eZqLQX6VRx+PD5VOnn4XJyw8zxoBSiRX/bLMDBWwYsV4Wwog2EFX5Pss8BE/nciqQBjRXDrlTDD9+QftgSGWWcrZoVLOtEBDMfrM9YFW++c+fWkVlVlXYmlNK8mAZBhE+8qkG4Cxs6ZMKHWmULflPdz5NWT+O63n8Y7774NWiscOjjobCcBw6X1KpMdYH0Uoy/aRWsyB4MdO7bhfe97F/btewHPP1fxz9ZC6+mVCIxDg15LG1BSXmH/1aEiWQ3ZYJkvZC7zYFgywBEE58QzCZ0EztOMLO8jglf5u3Ys1TEn6/N3SLcX5yRUeY+ZZoNdLbIK+M2oDIIzZZHnDpkyyPIuWiigySLTJnatqJRnxd+gAoKtRZEIb4ZssX9o4nKmjBioK6KB7zuOVeew6f25KnK4mjphAcPVrHCTKNY0rPm8ntRnTw6KVwJItdYwFYGsl18+gsIYvPvdb8GjWuOllw6Pte+q1Y3lQhcQu2iT2YSq0XbgAk9BcHpR1l306cVblw3etn0z7n3vXXj2mRfx7LMHBk42YyxUcqdaSb/g1YDg6ncv9SKsgmGk5Oe0RricCY7b4sguC0yUAXDTvTksE7ZXN+YkC1xDL0qdSshuwjuXKjXKC1QECrGoRLf7XhzLgGacL/ypAcBZxq9J8bO1DLp6PaBXcK/hvoHrGY56RVnl6pdCEL0SECzRAx8KpRzwjQQ9IFaggiKVusfgNz0NpRbZZek5loDaJPIMAMby3wJ8tYrUaOeIE88uqSGWPsBQJTlmZZNfUEK6vCWfI/YZW8ngUqREK5JaYl7MuqgyLfll64TqxWAYziuPl7LCqXK0CvXCg4deQFkipEWAabgO6yjAw1qUTKOOcTXsjmoEX8Cw1hpFvz+w/Injr+HbDzyBd91zOwDUguHU0ntIVWthWEBxPbPCF1UpL1qdXXLJdrz3fe/Ek08+i/3PvzzwuTVmxRoe07Y0iAWUgUhdli695koqwANstiGMuAkCVc3gWIf5gihDpzXBKQjWfr6gEwCsFQPfNGPL2y3PI0oCnDXvDwXAcBjMBLuwvxT46jD3cCXwq5VFTlwDnGmf+c0BpwvMtbvI0Y9AWCcdKyogGOBWSdztQUMXFoXVJb/d8sFobVUA6hk59MhxjXMl2UCeFl1to5TagHJ05befRPG/mg0eVPsug2GFwf1PaqVkVPA7o87f1YK/ZNIzZcuyDP1icPxHXj2OB+5/BPfc+zYAGAsMpzasv/BG2UX/PH2bTka45gJJHU91ole33ubNm3Dve+/C0/v244XnD9Xux1qLLJtsyHXCWNM8iQVC8GtduqkFEOyqgDgVtfCUJkSFw4wkytsMggfG0SCM1QSCdYPqIoCYEU5qEYJ6s9Cjg8BU2i6Js7McatX8UB70ag+Cswwuy0sUaWr3GQz3e0BhQL0+A1hneTD+NHHJa/8FQXzw+JHpCLytZUBcGEaMPS+05UWxuLOXPz+lBlob/0sW/r3BYxSOd+GgSXlKcjzW4ViCQMRRauWlOoJrFKEPVxeJJ/Qtg2jlfP2vBQCCJJXJ06M1Y1qu2lVuYHs2nGOIgNo5hE0inn/soiT7LEyCQRXpcOgrWeFI0arJzibXf5Um3QSGeZeTO95plTik5pyB1gqFMQOTaOsMTp08i+9860m8+947UPQNXj18Yjw6nB/rqEzveipGAxcd7d9FGxVQ3rptE9773nfiicefwf79L9VeZ4WxyLIplRitwEdbZwfYHU3XTtO9RUBwVQG4tK6zjfTycSwNyldp0SJ9JfOG0CbJvyt06BQE5z6jKcJUsV63DGTrwG3d3+WyqhQAI7CPhHHG8xUXADCD8bJuiTxE8CoFv0J/Vsoiazl0sgKb2h3k1ItUaMUstCDUmYpjSjbYaFhroJQG9XP/uzAIdgWhIIVcWfQDIKZAWCPIQ0LWkYm1llZbtlOTUKmC4fh+iPsnPnwy7Y7xbVrbnA4YrtKjs0yj3xsMVAPA8eOn8K0HHsG973kb+v0Cr756bNX730i76J+nb1MBwsCgAylnVZoBMADMzLZx73vfigP7XymB4GrGt9frQ2sFpRSsjUIyTfsZFwSHut6arHF1W+KsFOkBECxWIkGnlOjKCRhhcWwzkIpkpY9Rp27ojVd5X0ScVFIbnClTiqw6V6b8Nl1o7IQs4AWzGFj6FkYijJUrBqStvAyCBQC32nBZBiS13q7og4o+qNcFioIfxj9bx+ivMNx72DmgQHNTcOX3L0dC1tUFqA9AOyAvuD7IKMDoWJvsCICJYmPagPo2ON7qce0bXXucwrH02WJpnaTJwnn1aCJw+6iK9b2AlrIR35P3chb8G1tiUOwsZwDkMwIY60qlsStnikVhU5Q+XcDGSSa4QpFOs8IpPXrwvZq+hk2Kzm50Zpi/yniAeC3Ab9XaMy0sLS6F8VTvEadOnsV3v7MP73zXrSiKAieOnR1720Fxe536EI6yi472oqU2NzeL977vXXjm2f3Yv/8lAPV1ev1eH3meb8QQx7JR9NTqHCa9F1lnBzJmk1yrdYy0gWX8Z6XAufMAGDrA5KwCgrkGVzLDEQTXUZfTzgZ1QDgFu3G5MuCVZRh0R4FOEeAM4LdG8bkKfnNtmA6tDXTLwuZ9zM500FLdqEWieb6itMz5XAzaG8WlR0UGaxSon/O8pie/G8EoxYJb1nIgQfy7p0iPKj2z5GJ9MOJ5YJ0Z6psm7QPfVDI3WBc8vB8wkQp1wk1MrWE2eF5P2yeNBsOTMqDa7RaWO53Gz48fP4WHvvN9vOvdb8b99z+CE8dPDd3ehSycddE/T98mU412FmhwJHVCOOx4MPBeanme4d73vBVHj57Ek08+P3T/PR/xabVydDrdgc8DDXOgd+j4meC4bBnwpp+l7/H7g72Cw7IDr2McOG2bJI+U1rRak57BrCxtSwJZAoSlJlgBsdett1j/akM0NgXQDCBZfEpqdQMQTQCwgGDXasO12kDWYlAs4yz6cEUP1GqDigLwwJhBbAKKe32gMAAsKM0OWwTcGzLCOjm1WxbIep4ODc5cFwTyTtYUCqQ9OCTl6dO+tjiJQJeObZ97LFunSnRpERszyd+l9eD8NVGm+ZfMKo4heF2vqITBoleOuGmT9dFgAcOKYn5W/nfkgu6ZA2eqh2eFB1WkqxHmqmPmw64HnK4AvGrmUxx1nYBWFQzz+Abp0uO2g5jUmiaq7XaOzvLgPSe140dP43uPPIt3vPs2PPCNx3D2zNKKxzDQio44a3SxhdJFW09rt1t43/vfiVdePoJnnn6h9FkVDPd6fRCxT+9X6vUmsdVOQiVQJfToak9XACVAXKJCV0Awl3yVs7912eBJ286Uhb3Kc4TBsio/Z/C1wBH0DoLgVKW5BF5rMru8vzLYLVGfEYGuCvOICHwF9FIp+2sjAy1houVe+TnTHvTK66zw9GcDyoHlvMDc/CJy9CPjTNsYhE9/Z6tgjYIzCjYzKMRf9/Mobqm5bjhXBj2lkVnJVpeDBMBwMCznQp1Nqt2QAt60s0j1s7h9G98fEK6MWeFqMBrw/oxWU9O6Vv5mujTpdruFxcXhfYMPHz6GRx99Cvfecxf+5m++jbNnzwO4sEHvRVsfmzgjnALN2sLyISd3FQQTEd51951YXFzG9x59unE9mRg651AUBnme1QJh3uZkIDjNBldBcLpNmcyn7wthCYggWJaRbLAsNywbnNKi627G1o2IWNa8pyi29Kn231MJqE1BcBpp5e8dhZ/S99JnpCAxpLKbQbBrzcK1ZuCyVlyt6IGKHBBAbNshSwxrOVNMvfIBqWZTLQIdmsfFNGz+zDICVhawCjQDkDGAUaF/cRiLtiyqBQDacG/j5Dumx0cph6LQXs6eATGSOmJ+1INhTY7bMIGz0M5xMj1s2/ccBoiPsQWcAnIwmLWWy4lFOEsrD4rDOPmnKHxWWijUw7LCQFSrlt7CdU6+qT7JhwBKQE6VmBLlLHG6XOm11PKtkRDIuJZOfFqtHItLncbJtUysD79yArNzM3jnPbfjm197FN1Osar63vWkRIvxubzy/a5m3Yt24Rgpwj33vg1nz5zHY489VbtMCgCLArDWodXKVwWEp2np9QoAVUBcWrYGBMfPTGm+U+6QYWrfHyX2JUJZYsq3mkxBESW8s7SrBCEBw8S+owSGA11ZMsSDlOZhoDe2YIygtwSEfbaX5xSDwDdln4nYVcj86uQ5K7jzhDZQLeBcq4eZTcvIqBdZZ167o/Rb+bpgV2hYo2CKDErpMF9JBbQyZTw12qBHmjtrWBWCAlUwPM7da5yAZMn3VRJIaRndMBCcvl+mSA8GX9i/Ak2AmDPEFw7zaBQYnsRvtlotLC8NB8IA8NLBw9g0N4t7730bvvrVb6HXuzDuU5PYRf88fZsYCE86oWtqnQQAt91xA2ZmWvj6177rJ+KjrdfrIW9NjdENoBqdjZlgIAHS8HU8FUr0wLYGgLKqfB7rg3nfXsK/0i6J63ThKdP83nhHiJ2bOLg0i5s+osKx3xeJdrAchwiCq7U5qYKzUKXDwIUO7R9OaSDLgSwPINi2ZgHK4JQGWcPPyQPWeCDL4JiPP5gL7KnOARxacWJSKJPc4GX/AEh5LrFRQE4+K1yACg2VF7DIWDzLUiQ9OAVJp2qvOt3yXGFryR9Xi6LI/IFTwQVJywZRjhYrq0ozXRpKQTsHax1ne0OQwQYwLLph1k9uLDkYImT+RxPhLIT9s2VgMCy/b11WOPYVdknNM4VaYUrOjFETgDpK9KjPm5Sk67LD5fVWnxUuXftDnHLeyrG81Ey9Su9zzz17EFu2zOEd774N3/rm47BJ0KaOLifHozSJrgnETUNIbBxzbnXtGV4v1Kvjx4/j+PHjocxG7M4779ygEV1Y9pY334os0/jOQ4+M5Z8dDPr9Plqt0dmZaZlMliU7LXXCaaAqFc4adf1UQXCVEs0BeQHMlQzlkHvRqEn9gBBnSWsklk9JVwnREWGgOgiCpWWRKDePQ2+WzC4BXkwzvifAmLOpnKFt0h7Ryoasr1YWWWagPAiW1zqLIJg0/01th6zVx8zmBWjV5/mFZl9PVD3WCq5QcIZbJKpuCwXl/jfzwpZWIdMGuTWsJm18GyYvOJWC/fUIOcq8sVpGlz43jcIlWWGL2LmBQ881TKyGLPAoWnXZLgSw3GxyzSutkOV67HvOvn0vYMuWedx9z9vwzW88NDb2uFDson+evn+eCFGOAsGj+oSmTuiKK3fh2msux9987SEUNWpvtes7i16vj1Yrn7qKKtMPB0FwUzaY/47Z4KrVvZe2MSiJZFH97c85wBGDFwZNPoOXWE2ZKW8/qQ2W/rZ1wk8Chl2SHQYQgHLsHxzBcADFZJkWLQWtWZIRlkeWwSUCWS5rMQjO2vwdVQYqAJeeiZZDBc4aPmLWwlnLtGlV8HbJDYJhi1hTLBePF+VyWQ4Yw+A206CsD5c5Fvmy/vgn/dm4j7F3ks7A5QTtCM4oaKuQJzci57xqpVCQk2xw2nJJCaXZIYBhztaygxbas01+J3ltK0DY+AmRsAXkHAH4daRLxc+bssLG9xU2jrPBriETPA0btd3VCGVNasP6CctYHCxmZlpQirC4sFz6bJg9+sgzeN8H7sJtd16HJx7b37jcyOORgGBgcMK8FvZGr0F65JFH8PGPfxxPP/10mASRV1AnIhizsUyEC8GuvuYKXHnVHvz1Vx6AMeNfi91eF3k+3UB1ndUrPw+CYQCBJg1EtklpvYZgfQqCR1Gia1ugVeYoI1upVYPmLgbNQ/kUPDCWTK8q1wWzMrMAZQGr8MywMvgdRnOWz+Nn9RnfUHKVtDiSbK9SUfVZawa92oNelRXQeRG7T2QFkCvodh/55mVo3eVyK2mZGOp7vH8uGAC7QsP2Mz8f4LaKzimmTVvNLZWMTsaZfC+k853Gn2VFls4Z6+rS0zK69LdvokYHkclKdjjuY5AeLQytC9tWnxWe3zQHAFhaGr8U6eGHn8QHf+Bu3HHnTXj8+8+Mtc56zk2G2UX/PP1zeu09Vo3NbZrBW+66GQ8/9BQWFgZP3gFRiuTE63Z7aLeYVlt3kYhDCvTtUC9Ufh+IQLbOUhAs2WB5vxrVm9RSWjT/zY4OKGeCQfE1wd/o/DlcpSzHbTP9CfCZXAzSm8VKmcqajHCaPS69p3zNTuadlAhlVRSiGfzq8HBZC05pztKKWJZkhI2nqPiMsFOawbDi7DCVwLXmrLBhxnOYg7gorIXM1xcrHcCw/xLs9TL4umZWinbGcE0wADhi6lXhnZQlKMv9hVVukLsiUrMcQeuCM8SOmGJPEnQQ0Ol/26TVUpUuLVR2jrRbWB8M0D5AoRV/NZeCYPLCWYiA2PrzJYBhSLxgMCts5SZTe3YMWhMlWlqnrcZByGRBAmlrnf2sguAmYEtQ2Lx5HtY6LC11aifSdWatxcMPPY0P/MDbcPL4GRx59dTAvcrWUCrTCX51ec6mv/6oXBea/fRP/zRuvPFG/Jf/8l+wa9cuEF3YE4P1ts2bN+Guu27Dtx54BItLCxOt2+v20J5Zm2lF06S4VK5VAcP83iAgHthGcq+xNSA3bTUzDggub3tQWKzJUqGs8F7NPCHU+qIMgnUNCBbgJ+/XZ3kHs75Cf46A12dUE+BbbXGUeQAcAK+yPvNbgLSFzj0VOi9CxwmVF6DMAi1AtXvQW5agdRdQ3kfLlwdCNNg5gutpLm/q5pw19ow1azRyRzCFRqZ17EXsQXzqa5k15+nRY9KiB36zCsupLnGSzhvrQHBd3XCycvjq6aHgeuFyVrhKj67T7hhmI8/lEZ9vRL3tps2bUBRmIpqzMRbffvB7+MEfugcnjp/CkSMnhi5f/S3r5ibrxdh6o9tG+OcVe6xqz8tRjb1j/R/hbe+4GS8fOoqjR07WLDd8O53lLmZn26Vx1NUsl51jSpNaWSa5KbK3Wqv7iZ2TFjYphTp+no6+LpKpEvBaBcFSN+MCWBtct/QceuPa8BxaDWnDGVVtvSfWoVdwyACHR3LsVPm0I2vCA7bm909BsM58Vph8QWwC59OMcFEAlABoa/nzNKWeAWQ9tbvQwek6o0BWwfrLQxSm+TsrWK2QZUWS+SVYbXxW2EbxLM09DmFtKeiQvk4BsVDaHQgZcb22I4fM/24S5BC6myWC8cGS0EahAoZHWRr8mMQscbZkVD/FSa1JPGtcJzNs0jkqA9xkW7fOoztCKKvOFhYW8dijz+Itb7sZZ157BJ1OfXuH6r00vV9VA3HrYW/0iPOLL76IP/7jP8YNN9yw0UO54IwU4Z3vegv2738Jx44N+udRtrzcxczszETgb6XW5NdTMAxgABA3WUkUa0UgOH09HUAQQTA/C/AFIoALLYA8IKYEBOeS2W1oaSQU6FEiVyn4DXW/kulN6M9KW+isiH/nBjpLMr8pAG4VYf5ArQJWa1DbQG3pQ2W2/AXjjwRYgIyFyy1cX/FcJS1hMlw3rDMD1edxMhiOx4EggQUXggvhmBPVzstSq2Zbq/fnqrBqHQguCaQl4DcFxUKDlh8/6ekQPpf9pSrRCjqAY/adq8+ijVNXvLL59fCs8CjbtmUzlsaoDxaT8S0sLOF7j+7D299xB77y5QeadYdqxlYnaNm07LTton+evq0qdDsuCE5t741Xod1q48nHvzd6+zXiFMudDmZmZxqXq74/DTA8ahJqydZSoUdu19+A04iHZH1VAobrtiw3clSeo5gVAi06bDsBwPJcly1OM8mpYnSpdZJyoIxbJ1ELQMv3DRaRLKViNtY/O6UBygCbRO5sAbiCAXAAww033GpWGKYMbAXVCRg2BbdRECBswOljT8UnBaZHK8c1wz7STAXB9TOQV6PiWiULMs47ehOj0o4CNRqINxlpr8T0dAVjY71wtVWVTdwuEdPV4et6JUNNPtpvbcwIGw+KJSsMxFrhJsr8KLNjwOemzPC0rKlmeJSFyWllEj4OAB7Wq3fL1nksLcds8LjOTpHGq4dPYtfuk3jzXTfiO9+qFxyS/V8oJrXtq1n/QrYf/MEfxPe///2LQLjGbr3lBiil8OQTz61o8rzc6WB2Zmb0gkNsmH8eoBsPAcMASoB4rH1XAO7KQLD8Xd/FYhomUE3YYqHVItJ64TIIzkN2d1DlOVemlP3NdJn2LOA3rfeVbO9qwC/llgPSLcBpDZpRQKvNwXIRuxRGl5Q8SUvEvAB1LRz1EUQ7PRDW/YwVqbMCuiigKff1wZF1xSC1fAzrkk/KcaRhgLpOGtYN3rvHBcGhPtiVM8JlQdbE/Nww9b/VWuEUoKeiWBxQXh8fs1bnfJNt274FSyvUJDh06FXs2XMZ3vrWW/Hgg6MxSWpRVWV9s+AX/fP0bUVAOL2g6hrQN50YmzbN4qZbrsH93/zeRHVHqS0vdbBz16UrWrd60tZFrZnmCejafsPNIlmjLIoixLZJAErRSKkdtf7Gqxx87W6sPxUBLH492NKI9zEIZgLYcgTl+8ZW169mg6sZ47AfJT2ELZA5PoskG5xlnvrMmWCkNOiBQRUg0/Wq0b0Agkmywml22FOqSdSgU7BcVMGw5YNZeMBt/HsFgQWoXFiWong1ghq1Hy55pyq0K6UNnCUoS75muOYcIQdVZLBWQSsLYxX6RkOTQt9q8GmftFuqxJ6l7zCRg3IEgxj0MJCJjktEs2I7JfkK1RrhSa0J6EorpWr7pNXQogfr/CSLEzPDw7LCa9VGKbWtW+exuDAolNXUgqVqTz3+In7gQ+/A5VdehiMvnx66L1GgdrB+4hNbT61nVviNbF/84hfx8Y9/HE8++SRuv/32gd6399133waNbHW22knZ5i3zuOnm6/G1r35rQKBkXOssd7Bt21Y/npVnhVcDhgEMAGKgvhRqWG/gOmGsQRC8/gGsOHeIz8p7APHjXBPsIArQKQjOhfJMHgAHIMygOIDeSpsjqfmV1yJ4pbQNNb/aB8dVVgQQrFoe+ObFAPhF7oPamQZacwC1Ab0ZyBFZZBLIBriThLRUzHqA6oGUhUIRQbDRyPoZTC/jumSpXw507igOFgIIiRum5DjHjHzM3lpooOSnKr9PEjAdBoKrnUbS/aS/dlCLBhIwXKZIGzIBBKf06BIwbuB/lQFzvaL0pCB6PcHwli3zWFxcWatCAHjssafx4Y+8F2+6fBdePXxsonU3oqvDG902wj9PpZgnbTtQ16pA7M1vvRGHXjqC106fW/G+ljtlavTIsTU4zbr34YBUXXKtT3KFwchMFQyTi6rRpXVJbtQRKIkD5G1zVlcEkpBEgUzYBkeEndSlYhAE19GnAUTlaKFFKwq1weUssJ+QVMGwLUqUaLLGt0uqo0ZrOGXLILgFcLoXHDGoqEUHEzBsHQPhzHLaFHLw5ED6CHvhwvao8IC/UHBkAVJQ2gKVOk2JsImCtFZMj85MBmMUtMrQK/hSc07DgcGugGBJahNxJlg0UAcOA/hnVI7rwQyxmrRTyddX/FXDV6T4ehIbJzM8+TZHZ5jqKEerqz0e/xquXvNyL9i8eX6kImWV0p1Ohvr9Ak98fz/e/NYbcOLoWRT94cchbcfEAJi3Y2FWTT0fx97o1KsHH3wQDzzwAP7f//t/A5+93sWyxvFbTdfUXXfdjv37D+HMmZX756XlDva8aVcynpiZndQmBcO8nzIgBupBcXVbpb9RAb9TBMHDfh8Hyz1iV3jrTeuGxa1J9lcF4JuCYYs8ZHsZDCtlkWuTtDpi+rMA4Grdb8j+5l78KudMrZRNhQxwboGWY/CrCWhlQCtnf563OICuZ+CKGdi5rSDtvN/3QNFa1g6xFlT0uaVimG/0QLYAuQKqX8AKAM8NVI/HKyA4Mt1ELCse7JR2rogGfgcOBHMW1hH38HWwAyVzKagdBYLLmeGG+6Y/fwP4rYBhpkszOI/AnAGx8e8FyjSp2iz269k2z2/CufOLK16/2+3hse89jbe+9VYcO3qydP+/EIHuRf88ff888a+cUoZKjeglM+wM6vrw7dlzGbZum8e+J19c1YCXlyanXq2UEjK0HQJMeKzEqlUo1rez4deAg+PngWgjSjfwFKhWwWupLtUBxhIKq8Kjb3yW0imuG07rVZMs81AwHFZIAK8qn1YxslszGZK6YO/oBh7p9rOca49bLXaCrRYw045OVR6ZLod4rWM6dGHKzk0TU7pbGTDT8utrn+F2kW7lv6u0cwiUL82R7yzjyUCWF2i1emi3u2i3+phpdzDT7mKm1UM764c6JUoGMYlyv2TwNUmPSKG3IXlGqBmbphLmQOQ6rWmqqZ1fjQMZtu7wz3TpeVq2aX4W572jHbZ/OQ4DEyNSOHL4JM6eWcDem66My8t4kx7mF4Klte8rfVzI9nM/93P4x//4H+PIkSOw1pYer2cQLLaSSe6VV+7B5s2b8NSTz61q353lev+8Uj8plOSmLFX1MwcbHnXL1T2q65XeHxME149zhfOOlG3jfUUamKzbakrpLfXH9eU1g/XBEQTn2iL3wLeVFch1gTwrkOd9tPIe2q0+Wq0ecv/cnukgn+36Rwf5XAf5XBd6rgs910E214GeX4bevAw134XabECbAdqcAZtngC2bgM2bgc1b4DZvhZvfArtlO9yW7XCtNtyWHTBbLoHZcgns/HbY+e0w89tg57fBzW2GnZuHnZuHm5kF2jPsu9vcEpHa/UDFFrq2FhVrTxNnLY44r5HgQXr8ACD2cC7X8/JnOrxffbCiQz4AgjUyZI4fGlkCigmZ01AN/zKnvYJ4fTa5Cqz5vehbeLxl/yI+Zy2zttOiYo/azvzmTVg4N5moX9UOHXoVi4tLuOnmaydar3qvWQ+76J+nbxNlhDkKO0glHmVEhFtvvw7PPn0I/f7qVE+XlzuYmWlDKTURfWtYdDlVmBy6DU9SGUWPXil9mvcBjHuaxuhlmRotdacanK1UcKE+1VgVMpEszGQZjGmAnIMGSkAttWptQV0vM7IGzqpB0OupzQyUy3XCjUJZfhtOaXA/XT424RgVPhWqM6DuZhlqiirbDcrRQsnKI/1KUehTTKFXsoMz4OywUjEymxmvHl0gA1jF2afvuX2Dgik0iByspRBwMJZC9ndcE5aAZIU1XCkbbL28vPUhlkwBhR1+LgW6NVFkDlR+e+UI8jNzPVKkRlNCEavLMq00m7sejqUqpFO19D43v3kT9r/wShjbOGAYGJx8PP3EIbznA3fi4ItHsLzEwhwDiqNrIMg3qb3Ra5BOnTqFf/7P/zl27do1euHXqU3CaCJFuP2Om/DUk8+hKFbnn5eWljE7NwNpd1Ee08qzw0B9lnfYZ5PeR+o0SXg7o0HwSsw5A0jvWxhoRAqgheXWea4MgpvippFVFN8LWc+gkMwgMEvbH3lhrEx7IKwFPJazwHneL7U90pmBbvdiBlhqgdt9UMswKG0ByAezv67VBlrt0FbRtWbhshxGzcD1ZmHmdoCy8j2ErAFcASp6UL1lpN0gyFpgxoD6XVA/KlErr2SdZoRVqA8WMCwUafIBZPaB6dkVALFTsOQBaKLYXBpn0lJzVBY4gluZj9UwBAPDT/Ech5L5h4Mfz2BW2LhyvTCfD+tXJ7yetnnzJrw2AYulCQs8/v1n8L73vxMv7n+5UThr5LbXZe5y0T9P21ZFja6KBVTbFMhk8vIrdyLLNF46cGTsbTddsMvLHRhrsGlTzNKs1kaBYHZS0blGFmp9HfHI/VVueM6x4BF8P1cHhL7BQ8cl+hCpenGp5pi4njQBwcZfRM7XoVrw3ZwsK0fKsgBC7+DyWCmAUt6J5+ikQhYBfGYYEMpSWQS4AEhqf9GHtE7inVdqiwOI7kf6dZYznRooU6PlbxHMCiapUpRBsFCsyEYwnE45FGeHCYCzlnE8AHIEZXwm3ZpQ18vG9cSFEko0MTC2CsapcDMycl4FtnY9LRp+2FYUL51voeR/AgHForYmolnG+clRZQYleyB/3sV9cB9h5fkO/J7y5zyD4LROWP7mbaZqlmvXBmnYNtdCqXbz5jmcPHlm1ds5d3YRrx4+iRtvuRLff+SF8L6C5mCD/xkExFR7CF+06diP//iP42/+5m9w/fXXb/RQ1tTGBcPXXHMFnHM4ePCVVe9zaWkZBGB2dqZRyXUaitLjguJJlqm+n2ax60DwakCFdRaalC+DaJ6GhYywc0jjzg6xfKo8fn6uMsWkjKrU/9eLYQkIzpQpgWABv1lWIMv7ULlB1uqHvr/aZ19Vu1+q/6UWgHYCgPMW0Gp5Nlebs75ZDteagctasK1ZgDJYNQu7MAc7tx2UVTKYtgAVXTjdhctaUMrPyDxVGqYA8h7XImeGWztmrGOiNANfQtoyyQUwLAw7QgTD5Ps2k/N5XsetEdkzKvkRAHBwGIgsqUkAcFUVvGoKFHKOdWBYfLOAc0dqUDRr4HWz72xibjTVDl8ItnnLPE6cOL3q7Zw+fRZHjpzALbdcj+99b9/I5UuskzcQ1XyjbSP881Qb/jVNUG+6+Wo88/TBgQxu2rh+5LaTC3FxYRnz83MTA+HVFPCLiJYI2VACBIB48xtl6TIWjlX/yQtYJWBYrKpi6LsHwPobpLTYsYiAuHAK2nHLHif9iX2W2DqKQMxFZ05wMIrXazwGCQB2lgGxcwQSJeasAAq+pbtE4MJJD+GsDZe1A+ClAl4AS4e+wfFA6UR0S/uMsfQkVjGDm+X1dcUeCFPRB6gHricmbsjbygBlyyrXjT+Yd3La8LFUCq7gLAoraQuCdSBr4++lOGRMin8jYzTyvI/CagbD6fHxAQzla4eBJFsL8O/riOu3HXHhuCSwnVDgGJhLbZgDSmA4rRUOUW/iXsLiGhURTAKKq/T9mAnmNZRTQaSDxbmioFU10zmuVe8fa1UfPK7I1sxMCzOzbZw8/tpE+24CIC88+wre94NvwXP7XkFnmWvcw/Eif4+qWVeobuvRR9g5rLIGaYqDWQO78cYb8dnPfhb3338/7rjjjgExjp//+Z/foJGtvxERbrn5ejz+xDMDGdyVmLUOi0vLmJ/fNLSlSXXCvRpgPC3gWze21VGdx5trOMfUHesMHBmfDbZJP+MEDDsXgqjOCyNKpwl55s/kHh8FMKsdJWK7pNgPWBShJRMsIFi3fCa4FcEvP/dAuYFqF7EGuJ15ENwqZ4BbbQ9+c9jWLM8J8k1hXmBdDuRzcDOXeA0Sf13aPpwr4HQHlHWA3hIHoj2LzLXaoF4PaGWgVh/QlsuWtGFGV5IRF+XoWEsdO2uklHIGmWXCMyHNCie/ck3roxQEa2S1ALgqxFXXtIl/exsAMYHA9DQPgJ0CiHPYkhUmWEiNsPPKEtU6YTNWp5dm0Hyh2JYt88jzDMePnyq9v9K5/jNP78cHf+Dd2LfvBXS7vWkNc6p20T9P3z+vTDV6xMWRRqJ37t6GPM/wyqHj8fMJ2i3V2cLCIubnNwEY3gR7EhsVORfRmiA4TM3rWNjA4RWYbGGhof3NKnYdY0fmAhiu1e/35hAvAuMYNBnnAZBVTAFyBO24fy1pC1jF7ZIcxXVt2rJHgayDIQVjHayqp12EC8+3DYJTcEaxAFUBBsK9fqArIctB1sJVlJ8Z4PrMMADYAi7zyxQ9D3Z9BlnAMJDUDQvQ1rG2uPZHMKCigFNJrFUBgAaczwan7ZhK2WxXuv+TsnDitnzGWE7hQA+33EaJiNEpkQM0O9wsK2BzxWBYF6EeuyANZR2MUyBHMFDRMSc3K4nop2DY+a9DERcPCGOlvYUFDDsnoNe/B5SmpOz6KDheiUJX6dFhbJ4rYcP+BsHwuOC4VBs3wT1ipcrRVaBcdz1fetl29Lp9LCysrD1D1RbOL+P40ddw3Q1vwr4nDob9pmA4jG+DssFvdDGOL37xi5ifn8c3vvENfOMb3yh9RkRvKCA8Kiv8pjftBIhw+JVBtdRhGaJhtnB+EfOb53D8+Ohl4zgn388w8Dyxyu0Q9Wi26dChJzUBwQEMo5wNli4BaVlLCoirJr3qY3Y0thPSOukPnCXiWLkZAMG63YuZ4LYZzAK3ZzgLnALgJAPsdBuuxSAYeoYfaAF6BtTaXtYfAeBsB6CM5w4AYAvY1ix00WcgnC17llc/tHUEcacH7Vs9aWVL31kywkCsluJjxEFhIY8p7w0lG5y5DJasB5zlaywKX8UssNT/pgC4jhKdXqWphyVQAMRsOoDhCIBVCFQrKLgE+EoSSWQ4eau6UmeWJEUasr8XYlb4sp3bsbzUQW9KoPXs2fM4ceI09u69Bk+uUi9hreyif95wIGwDbTY1go9KuaozMbj+hitx4MXDK27HUNqPvxAXFpawaX5u1duqOlKiwYlnVbU1UD+d3DgrkeeaiYf1k1xeOskIOweQp794EEwA4Bx0BRBL5NeCUDgHbQmGgL71Y1AR9JJTIAeQZaSj/HohKwxPow4D0dDKwVjLvYZH1CA4S/6h+FEYUN8CygC9HtACqOjDpZlW78ScypgiDQCqALJ2AMsh8+tf1wNhaa3EyzYGt6wBVB9CZyZrA5UJrRaAIlKiBQC7BASnpyu5EhiWzEmgoxuF2EtY8b50zBQrT8/SPtKeS2a98OehZSEz56hEi1Y+0698FlgCGkDM4FYPAINdlwQ6IhvcggKoNq5Mj1ZgkFwGxYSmIyxOd+D9MXsAr9aprgXlus4uvWzbWMyTccYjLZBefP4w3nnPbXhm3yFYY0vr1wUL0rZJF1sord4OHDiw0UNYVxsGhvfuvQb7Xzg4lWyw2MLCkg9Ur62tVIBr6DZHAOD6ZaJVs1ADfwtQqmas0yCg/xcYc34GwYFwX+rkBBQjdP1LoRKPs+zHU4Vk6TChkvaJRM7X0/q6WmmLlLZCEjEqebQNaAZcCyyCk+2ZgSywbc1wLbBuw7XmOAuczzMAzmZBlEHRLEi1oLNNXKmkMjhbcDZYZXCU8TGwBdAycKYLl+Wxm4RSCJQoz9aK32vwu8f2SZGFR8KuAgI1WgBs5rSvrrKAK9gHkicmuzIQFmCaOV0LgFN5xLpMsNzlnUeqgf4cwLcEqFUpKywsLRmD0KLD8zrWCa9H+6RLL92Bc6sUyqra888dxLve/WY89dTzA/Ori7b2thH+eartk6rvzc3O4tLLtuOR7z7duFyTlXqbVSaZCwuLeNPl0ymkVjWKraoGEEc6dLmfZ5rpBeKNMM0KS33xQG0wXIjeChhWQCkr7FzMQktbHH4Qg+Bwb2QwrCyPoADf4GFtSZPJJkCKgRc7SGMJhlQAzFzTSlCqHH1y/obP1GgCQlbYAdp4XlEB6IKpydIX2BUgW8Ahtr5yKgtnoCvqga2AY6czELoey2qU6Ndq8IbLtGsDKAXKMgbGDjxWKCC9SQsQLgrObCdpVVIOyDgTDXJw0AEthmCA0R4Me6Cp+YcrRZ89COZAQ9x33+hwXEOUWtYDHx7B5Sxc5XzdEtswV8POPNYJC2U6FcYSenRcx4Nif65XTbLBdSA4gLlKK6FpmPQSXg9LhbJ2XLIF585O7miHgY/Tp86j2+lj95t24NWXTw58vtFgd1QgbJz1L9qFb/Pzc7jk0u144FuPTnW7CwuLuGznJVPd5lrZcFAw2XylbPFvguaA5xjAgOcT5b8tFKKmNQXAKwA4gGDP+HI1My0Jgg+OezA7TOR8FtWEIK7SxndMMB4IVwSxakCwnZll8NuaGcwC5/OAngXpGX5QBmAGSrWg1AwyH0S35IGwLfycpQBcAZgOb09prjdWmgGxQihZqmaVxb/y6/T7IzzSvsKaCBkpOMd+38Ihc5qnEH7iopI5Kv/q9RToKgDmeuRyRnjwXOD5oQbinM5P+GIO2NVkhSM9mrevg45HHOu4dcLlZS+0rPAll2xfUbu3YdTp48dPoSgM9rxpJ44crmedbmRN8EX/PH2bWo2wc6ZU86tI4fIrd+LE8VNT5doTKZxfWMKWLZtXtG7zZ800RJvUW/Df/vbgAE1CN0nqZ2smwdX6YdnGABhGzOBJNNB5oKo1g5q+9RTZwFdFAMNMl2XRpr5VflixFlWe5WLiOKGCcQ7OWa9qrOD0CKl1RzEzbBgIc10Pg09kBWeFPX2Z1aQNg2EFzgpTxq8zoKokzQdJ2nAhZIr5++r4ntCo/fvwCtQDNcdi1SxqUXAmuCiY2l0YP8sgTqVry4JSygGFhqhT8/dmAGyNP2ZWgXxNsSYLwbvk67BsZpBZA+f6sBXFbaG7M2B13sEx8FXpT+xrws0ENzNFEQCbynujegzXRatr99GQIR667TGc6nBhrOllhZuu/Usu2TZW3/Nh40idrtxLXn7pOK66elcJCK9XlnuUvdGpVwDwyiuv4C/+4i9w6NAh9Hpl//Q7v/M7GzSq1dnQvrsVn0RQuOqqN+HIkRPo9/qN21wJPfr8+UVcf8PVE60zytZv8j16P6MBrQrL1dG3Q5u3IQrxTvLC5EKg0joPhn1W2DiHzEVxTckScyDbBXKTE12Jxu/DGVMlz5opxMxmMkF0KghQ5QWoZYCWK6tCD9QD14DgfBOQzZRAsFIzIJUBjgGxUu0AVDVpOJfBoANyGYh8Zlhl7JmC75cyJ4LoeoBi1wciG8Q/pStGCCYH4MsPgtCiBSRzAVDmAxIIgWQAJbaOZINHA2CVeFZVUw5nnePCGKIAiDNEMCyegoPjaqBkSejRztkAgkfVCZeST0N8c1Bln4AZMdxWnjm+5NJtOHr0VO1nq9EEOnjgFVxzzeWNQHgj7aJ/nr5/XhUQrsv6SK2edRZXXrUbLzz38kTbHMfxnj1zDnNzM8jzbOx2TONeECttX2LJBoCV3KbC36V9DMkMS72nBUckLfg9K5k9y8nMvgC1ChgmopAVJnJQlgAlI6LaQnlxnsbXD3MdsULW0IjWOZ8JdQqu0JzN1Q5OOZD2YDiIVXHLAyp6IO0l6bM2oIpQ7yM1w5ItJg+IqehGKrQrAsgNz6WDWqG5W+Oz0ba5jtgUSTbYhGxwuL8rBEo0/5yWM+BADASIYrf0YbYKjhxneEXNSsZEDkoZaE3I8344ltYRrLXQRIF8z/QsF2jRafJ/3NuYgF2b0qAhrZvG3EiNrUrAaoUiWsDwrHBdnfC4bdFKAKFy/e+4ZAuef+7QCkY7PCt8+OVjuPm2qxrvYSnzpMpCWWt7ozvar371q7jvvvtw3XXX4ZlnnsHtt9+OgweZHnzXXXdt9PBWZZNM/q68ag+eevL58HdjbeCEYPjs2XOYn98ErRWMmexesf7ZpmnuLwJgIAW8Ck19W9P5Rgi0J3OJ+Jn84xELCJbMsNQJW+cC40fENK1fTtoqDjPyYCrQiuVZxKdElVlbrnDKKIpOtlpAlpVEsVzWKmeCG0CwVjOwlpMRirJQr2udnwtIOVWqsC31wtVWjWOYsK44yCxtpthPZsQPQ4SMuIuCcwYa2i9LIUsftoeY3R0FfmMmWI754G8ivp5ZgjywMhj2SY0kKyw1zCk9GkA470bVCTdfe/UZ5AshO3zJJTvwvUefWtG6w+6VL798BLfcej201hdcb/mL/nn6NmG4ZHBx7kPL2WDrLLjpisHsbBtbt87j6JFB6p+st1Lr9froLHexZet4WeH0ZK9OTPkmsQrFyprvkWbGqlHCKJDQtL1kO0J7cuz4jE2p0YCxzEguv0coAqhVvnctK0mndIqUJgQgAGSHqCrNkWXlLzwVLiABfa5gWjCM8llh8CACzbgAij6o6DMQ7i+Bii6ot8jPhQfGlEXBDD0Dp2diPbEtQKYbwXTRYyXoKsBNAbK0bwoPm9QB+6mEKeIYe32g0wP6Fq6H8j0/c0F8I1CtbMysw9bfVFJxMcBPLFSsFS6JkwQRj/JvI3VM4rCrraxQGSo7XZSEP1K6l7wvqpgx8h3XVZQ472TStJre2FVbDairXqvTFJOqblspha1bN+PVJCq80v1VJwzLyz2cP7eMy3ZtG1hWAgUWZlX3yYtWb5/97GfxL/7Fv8ATTzyBmZkZ/PEf/zFefvllvP/978fHPvaxjR7eqs05WztBTc+luU0z2LRpFkeOTD/jsbzcQb/fn5i1Ndmk2k7psVpTyaMZBJfWqJRj1U3Gg3I0w1mfH4YHtxEQS7lUYctlU/JsIWygqAki5U9Vi6rS/jmAYevZXuwLuVUBOH2aaa7PzbKEpqxKIBjSKULPILRQJM7uksoiyB1izgNiuCRoKAyyNChuXaNPFgv10f67hnZKcMFPasWJCHnkpJFDQUNBQyODRgtZeGTQyJEhR+azx6zfrEkhI+VfM7Dm7flnpZD59+XBh7a8vGSHI8BO5pXOdzp2sUWTX2OABcK/73D/O4mCe935vV7Z4FYrw5Yt83j11aONy6wUqC8sLGFxcRm7dr0+SjzeSLYR/nnis3DcieBlO7fh1OmzIzO2k6q9yol99tx5bBsDCNeBYKEsqSEAuXF7NcuxszLBca3EXAKBxUU7/744PAG9fe/0isThpQC4sAp9q9D39GjjuH9wrA1Nvk/SWD62ZRDqtIK1ZacZwTABTjEY9llhZ7i1UCxeEgBqQL0OVG8Zqnseqr8E6i1FMCzOTdSkfaSXbMGZ3aLH6/Y6oF4H1FtmQJzQrikBwbxOBMsBHJd+NJ8F7hX86DsGwQV8HXECPEPoNgkc2HQiIyCXFSoFvFYtRKGVLT8SFctqHZPC4HbSiF6MKqf7ibQu+Tu+L1nh+P4wS51u+L4jrpU6pshgqUCcKG6kNX0XgsLuXZfAOeDUiXMDk4qJ91PzPY8fPY1du7eHv5tAL0+D1y8qLUGw1TwuZHv66afxkz/5kwCALMuwvLyM+fl5/Nqv/Rp+67d+a4NHNz0bNgnctesSnDp5Zs2yHWfPnsfWMQPVTcC9bNMGsKu1MviNWV+NtCY4ZIIRP+d19ABIqbu/BIo0Elo0PE3aB8jTNkplejQHxksZYf++Q8wOpyVTYTyp/wrA2EbKWnio+BDBqqxVErsMQW0AUPlQ4FvuHFDAOcPPIprla4RlbgDP/IpzjXRjKnZ1kF+t4pfTr0IVenRGZTCckUJOCjmaHxrEy5D2zwq5B7x5AnzjdlF6EHifISDtnyWj3BSoHmXV4PM4vqzss4Yvn57v62V79uxCv1/g1KkzQxkrw+4twz47cuQEdu+5dFVjXAu76J+nbxNRo2O0s76WLT0ZL9u5AyeOnx5cZkqCN2fPnB+ZER4FgqvZ4JXSokeZcpIFbr5JVGsxnZMjnHBXPFVWEUeAScG3S3LcgxQMkHUAx0lLJaDSmgXRwSW9BqsXTADFQgO2rJIMq2ALBdIMgMkon33F4DxFwHDha9GsYQGqAIA1HBWR6gRPjy66INOF6i0zAC76SfS37xUjy+rSlGSCKQW/SgFOlccmYDiAYE85iaz2slVuIEwZs1COI+bO9w1WypQo0bBUFiNRXIullYVRXLNU7vfoATChVAucTlRKmWDigIkmACAUFXEsUYgO2eLkfV6XwleWYAkvMyiYJbVI4Vz2AfgmR5RSoeUanGaWc6RS87j06OTal3HuetOlOHt2YSqK92LphOTkiXO4863XDV1+vWnRwBuferVp06ZQd7Rnzx7s378ft912GwDg5Ml6BtPr1ar0P7leLtt5CY5Vem8Os4np0WfOY+vWLSPHNtpGLzNNeubwiXxz5osqgb10rpRmidNscJ0oZ2ppUN0m5GjrHCxRiR4t2eDCzwdMQo82IbDNgfKqb3egWs2K8lcvl/iQcIkV+YmIAlJfnHZ8CNvwpVCuCGDY2QLwbtk5AM7C2C5kOmRdAWs6sLYDZzpAsQwUHQ6imy7PCTzrjPU+HAfkTQTBVTAs3yEVptSKO3FIrXDGcX7k4PmAgOVAOa/5vWLgeRDEpkHpwAqsDIs1Z2JSRIQrCwDKSXkUlfxzpEUP0qMdra5O+EKxpmvy8st34/TpM2NtY6h+QuX7ynInjp/GHXfeOLj8kHvSehy7i/55+raqGuH0oqratu1b8PThA6vZ/FA7e/Y8rr3uytJ71WL/qjVdCKOiYyF7tUoapihM8+vmGJt1Xs0X8N4hgmHluO6XVPzxAvXJAVJPys7R02b8iS802DTay/TbwUipXGzGKmirYa0JolDaMRhWRsMZy9lgbVlYKq+Az1TR2RpQAUBF8SwLIPQWFqqT6fCypsvZX8kEF0UEwkEkw8Jl0QmVWizJdxHhLKcYCTog9As2DIJdXzEQ9pleUjZSwKyng4eWUZHurLQFUEAUo+X9sG9LLJrlh0TKcksGsj6CWu5t6CuSfB2vz9w6F9oq1SkGSv03EaBD0ESCKVKXJAwDBNEscezkaWJCt1NEYd3geB2LcgAMgs0QZ7AS1ehxewivl3I0AOzavQOnT55Z8fpp8K3us7OvLWBu0wxa7Ry9bn8gwLgasY/V2Gqjxhd6xPnd73437r//ftxyyy346Ec/ik9/+tN44okn8Cd/8id497vfvdHDm7rVnUc7tm/FwQOD+h1DRXImAMNnz53DFVfsGTqm4bYxk83ma66eyjwMAMvfKftMgu/p56MCXZZc6MYXqc4OjgQEO/bJpcwwA2WmR7tIk06D28l1HrPFqnmiPERwayyzBUB9BsQe4Co9w/4VBZzLYF0OazsAoZQFdsYDYdMB9Rc9oywplZKODwWAxE/H75v+fvLs4pwIiCBYSYUXi1VCeTDsOyxY7x8HDg8l3Rwo+t0U+KrS8uX15TOmrktiwvtpv22ZG1b98zhWd2031QnLtV5ex0+Gpmar8227dl2GUydjso1nSasPGss94PTpM9i8eR46I5hilef+FO2if56+TQyEy0X3g8X3DgZKKWzZvHlFbUfS7QxzymfOnMO2bVvAQgMppad+0plue1rZYK6LlsjekMguOR/Rc2GqKwrRjWAY4OimvJaMsFePFodnXXU9T31y8IqTHAVWAHQFAAMIIDil4HJNsge9SsEYBaMVrNWwRsMWGYy2UH0L0gZkNMgYUCnlTAllKg/RYiDJ2gKc0aUl7hnoVyVbcMTXZ5Gp6IN6XaAogiI0lG8AlAFIaMppJtgpBcpywBp2jEQsdiWCV9YCBYNg1/O1zj6r6yxxPVQorCLeTwDB/N01AKWT868Kgh3xMQO47ZGP7Kb1WBKZlr6O8l61H3D1Bib1TTp4Mu4YSF7Qo9QyCWWFaBHNip83Z4WjLrpCuVWDfFE+LOImqxHTVPSJ9x0B30ZGn0cFtnbu3I4DL75a+9mw+0Vd/V/dZLffL7C4sIyt2zbhxLEzg9vZYNr4G9V+53d+BwsL7Jv+1b/6V1hYWMD//J//E3v37n3dKkZPYlmmsWl+FmfOnJ943XHB8JnXzuH222+u/WylLYvGvVcMU5mf3CYHwNU64ep8gwWhEjAMBsN19xRppyR1whrSSomCenTUEYk1woUoSjvuOMEZZL7HF04hg/XzhGbmV3kgNMCIGmXc9tCzu6ywvgqgWIZTOUjPwJoOL6sy3rdtw5hFBvqWadDO8TroL4BMhzVG+ksxQN7rAv0eUBgwc1rDFQosaOn1TiwNlHnxbyO9hHl+pAMdGsiZNgeZ5FmOTfvnii8O26sHvtVSpAEGoGeAWeeT78T+XhF3jyjPBQctVY82NT+T9AeR1yvvwT1tMLxy27nzEuzb9/zoBb1NGljudntYXu5g+/atOHHidCMT9qJN1zbCP0+tfVJqmzdvgjEGS0ud0dnW5MScpF5Ymmhv3rwJ588vrWygmMw5piqwoyiLcTkm03DmjVDAQEWoMjAC6z8JvV2JwVMBvnlaqgG/MQlYHq/jVju8XQQFYr/ZSMdFrGnlSLICWcAQg2FFCtYyINa+XZA8lGGn4/LkWyhEAY1WOwhnhGMjGVtrQUUPUBrUW0w+90BY6oB9va9Qq8laOA9wYRW/ryz3FU5NeXivvIIz+cyvRIetY3Z2QXB9rnPm9VgUxBU6UsJcbBfFg+BssFNlerm0T3KWj4+TPsGWoJRhgF3p2Vh+RHq0CHgMa5WUEVDAlcAwfDBEEWcOJJtcpUfXtU4SN6c89S6+H2nSyimAMDQrzPsrO47VKEYDg9ngAcBdoxw9KS063e6ll23DN7/xaOm6r12/4bMmEJwuv7jQwfymTTiF87AwIcCwnjXBVfOdRVe1/oVs110X6eibNm3CF77whQ0czfpYOgncsmUenU4P3W5vRYGoccDwmTPnkOcZ5ufnsLAwrn+eHARPMjFNlx11zbINguA6AFxlftRlgOW1qskGK8T36sqnmCKtK3XCZXp04biRT9pXWKjSWUKPNlbBEYNg6329sQpWDbZLlEBuFQA7x8HWwYmIB1qe7RVaGcprdLw/5oyws31A5bzNAITnYYsFAH3A9sHdIrhvsOotAkWXdUZ6y1CdRQbBvR4LXvYt+3Lvd61vbxg6OqS/bCJAqcMj1uzmCtw9woNhHeqxqVGLqw70VmnQdWedJDoCNZpcSL5bf6yFITgsUC0mglnSTzgFwWEZT5mus2oCam3o0qsL8iqlcOlll+Dgwa+V3p9WVlhsYWERmzbN4sSJ6ZZ0rcYu+ufp29SBMEFjbm4OS0udidetm8g2mXMOZ147i0su2T4UCA+jJ8b9jqAmJaA3Vu4q/z7ATirSnqvOzMGy/pID0q6wfLOSrbnoV5LzVMQvMnAUkp1cjASPY9U+gmn2NwXB/F2ZSm0soSANMr6GpsiglIU2GYqC1Y8lK6zyIkRg44ZFOTL3rRQYCFPRYxZOELdSQNED6TZQdCG9gOOAImgOz0p5B8sAl52rLtcEJ+MAODvsGF3GTLXnkLtC8zicUKMN1/UmwU9XiYhz5pchpvQqRNLHD06BCgVbOAAZnLWcmdfc0omUgkqyxyXBLM8ccB7IWk/nVkn/4EBlJocMFMAwZ2Q9kE4o8XXGQLlMj07dpVCnmyzNCldF4oQePU4UdVxadNM65f1OJ4t62c7tyPIMrxw6HvaX1vaJ1U2o6wBw08R7abGDuU3tsPxGAmCxN3oNEgCcOXMG//t//2/s378fv/iLv4gdO3bg0Ucfxa5du3D55Zdv9PDWxGQyOzs7g+Wl5bEnwrXLBH9Yf75aa3HmzDns2LGtBIQnnVBPAwA3rT+2QGYCcJsAcDX7y+8PB8CSCa5+rpyqBcRx7G6AHp3OCVJ6tMMgPdrCd5RQ/Dun9GHJBse2gAnN2EYf76z3K9bxH4kwZtADURow3Thw8e1SCiWiWaINYgkw24DlExyAFlGsojugF6I6i6DOEqizDHQ7QK8fSpxcP4MziYCnGwT5/DsMguGMCE7xscwVQrtK41BpV1WefDUBXkkcDxOmJJ/R9wzsmAkWujK8ngfQmBVWNWC3dl9jLsfLNgW8Nj4rvGfPThARDh8+NtF6k2aFFxeWsWnT3KTDW1O76J+n758nAsLO2RBNaFY4NWjPZOgsd5P1zEDWZZQ1XYTpSXz69DnsuGQbDh48PNG2p2FVRyqOq+rApCegAosRK0bDgVTOyn8aAoYBjjzKjdXCISPlb5DOU3OSqK3sp2aM1XYIqaXtecJYnZBm/Hcw7MyV8QCtzwJPSmkUWkMpDZt5YQpRaLQ+HCoCGlmL2yh4IOyUhuotBzAcvwjTp9KegCK6UVKmbLJqX+HwRZPzziF6pgCsGeA6Hznm5TwNWpn4txxTAb0wIMWeMrSWoCia5ZyCQgY4zgIroUlbdnIyWgHA6WtFfJyt1SErLDXDslyo53XkqffyjJABXk9jEa16evRA7SvWVwW5zkZNgq+6eg9eO30ORTFen/Kw3RGlGVVbXu5h27ZB0b8LBRS/Ee3xxx/HD/3QD2Hr1q04ePAg/tk/+2fYsWMH/uRP/gSHDh3C7//+72/0EFdkTaIvVZuZaWFpefJAdZ0Nyw6fPnUGO3Zsw6FD9eUFZRv0YGsFgpusLhtcVnpOac/NIlhSA6wSICzrl7O/ZRAsbW/SuYT8i999kP6VtlkU0CTg11bo0U4Es1CuExbBLPZRvtViAohZaJKBMYkIiUUEwEUBZDnreCgviin+2nphTMq8P8+4JEq+kwfCqrCg/i6o5deglEVoneg7R1DRi3TozjJng5eXuPVh18J1FWw3h+1lsH0NU2hfzjUaDCsAGTkY5WAtIVcuBMO9ew9BBj7mgz42bVlYfa/JYhZ4kBZN3p9LoLq8L/+7pu9BTeQxqAbQjp/9XQ0YXn2w+uprrsDJk6enKmRZZ8vLHcxtml3TfVy0sm2Ef15RRjg0fnf1fS7b7RY63V5YdiW1OVXnWpdZOn36DG659YaJt70SS7PCTbRo5eq/p4Bhye0WxAAm3oKNr72UbF+kuSoAha/JtkldSqrVEHoApzWgEu3025DIZ9qvtnaszocgvRMrjGOHTQ6KLPo+M6y0hc0MTJFB9TOoos8RWEcgiRJ7Z+eU5n6CAKAKrpEteuXaXgCQNgsSIXYFbGsGquiBsoKnAJ4ePaBIWfoSthk0Vw6cq/vNyAGhVYRPySPJqisJB5kIgLME+CnHoh2a66aVnyxYI4BZHryO8srREfQC5GJWmKPBqaAWZ3sVEECwOFIqJ/8nNiIqHaN6fcyVWRXYjeNwh9X7TWNC3BSge9Pll+L4sdO1n41j4wrsFf0CWTZInXawtfeY9bA3uhjHpz71KfzUT/0U/s2/+TfYvDkGIT760Y/iH/2jf7SBI5uuNQlHtmfa6Ha7dausyJqyw6dPv4brb7hmjC1MCQQ33U+a1GIb5ybNIHhU94mm7G/4DPXLBRDsBBCrxvkEgFKdMF9uMVspD+Mi48f6I5cqSEc9kVgnLIJZ1mgf3FahJMh5nQxXaBbFNM4LVBWAzkBFn8uTVNLYx1oGyF7bg5I5Qfh55PcoHKjoQC2/Bo1+oFazGFavrBUimeBOD+gWcB3AdTK4fgbbz2D6WaRGV4TB+HcBpJewFtVoR9DkkBM8gvVdOhx34pBMe1MpGjAIfOuAsDD5oko0gtYMB7elPKm+fCm1aq3xMCaBgh5azjTMp9YD5JWA4cmwQFMw74orduPo0eO1n02DHi2Z436/QJ6tSQXpiu2if56+TfgLx4zwMAqj1hqm6Jdal6T1feNOYEdNlE+fPostW+ahtYIxo7c57sWROseUSpFO5FNadGrixKpmhTbrCga9pJDJDTHJDnNUr0xVzoBAhZI+gEByY/ZqhnJPTDOMWtkoyDTkO1cjpcYCUArKOfQtoKxFYTV0kSHTBqbQKAoNlXE9jjPahzUhkotc25vuQ+ny8fJZXJe14HQbLuMHD15qa6O6tOowxTlkc6tgN40O1kQKqZCZgWUHLtToRAALxPXBpC2LZfnIMG9TwSnHdGwlEx0bBLLS+uA6489HNLP3v5UBhSywQqzzTYlN0u4qNckKDGwXqFWOrlqdGiYwSH2epF92NRvc9Nl62TiBud27L8Vj33t2onVSk2DZqECgMTbtGnZB2BudevXd734X/+k//aeB9y+//HIcPXp0A0a0tlat99NKwRTTv+6q2eFTp87gbW/fAqUIdtw6HtT7/cb7xDjZK1lmCCUyTrgHS6lK/YBraNBpBliWGQZ+4/tJRtnF7G/5MxXaL/Lf9deWq2QsxQKV11UywOIKk0xpVTBLQCSCAjOzv8iSf7a88cIAWQEUillSQGR8+WxubKvU8BtYC+s0qLcMvXgGyvX89g1nmdMWSf0e1wVLJrgD2OUctpvDdFuwvRy277PChrPCdfekIBSaUKOD1IkFlGLRsVJAocG/TmIyEkURWMtrAb/yXBa8TDuJrNyqLZQE2K+sLngSMDydsiUA2L17Jx64/7tT216TFUUBnY0PqtdD4HK9/fM3v/lN/Nt/+2/xyCOP4MiRI/jTP/1T/NiP/Vjj8l//+tfxwQ9+cOD9I0eOYPfu3SP3txH+eUVTMAHBDhbOZ4Wts8EJHjzwMrQevem0HjitD26iWsmEUpzi8nIHnU4XO3Zsw4kTg9mbuvrglMI0bUujcelrC1vzN7gdjUtrhSPYVgH4qBDRTcOQgjelb7BQZcXpleuA6+nRwOBFEbLSrgy8rWVRDWMVCqOhjYYpMjhTeFEoBdvLoHoZXK8H6nH0lnodUKgRZloUrPFqzyZki11rE1y+CchmAMrgXAGnZwIgVtbCeXCd1gK7Gsq0OFCuSUrBMWdaYQvvgdKVBMxyb2BqcX9DIsfaWpazxNxWiQBrQzbZWfI5/EgfC1TxMU0AcJ2Nao8gX6MkdDIFMBLPSeefPROE5Dq1YwPiahBpJdlgFviI21lJVris6Fxfw5u3FLZfsgUvHTwy1hiHBc6awLC8d+LYGZw7tzSQLU+/21rdr5qMJ3yriDhvADV/Emu32zh37tzA+8899xwuu+yyDRjR+plzFi/sP9gIqGSZadjCwiKKwmD79m04deq1hqVWzgoZCwQPsVHXldQEV0HwKABcR32W/VXBLy+XZoU9HE6AMS9PSPKspddVq6sTBuBLViT+K32FVRTMSgFxAoaropgMgoXtnGSFlQJ6ySxFgtYSNG5icVnj/anmOcPCWSiXCGQK9doULIpVGKBn4LoO6BFsN4NdbjEtupvD9DOYImNatBf5FDAs97W0QwPXBjuuDbYIeI0cP0SMTPhR1ZhOoms68F7payJqdsjHskoKhskvQKUlN8bS4FYzOB4HDE/Ph83MzGD79q148cVDK1p/3Dph5yyOHDmB06fPrmg/a2Xr7Z8XFxfx5je/GT/90z+NH//xHx97vWeffRZbtmwJf+/cuXOs9TbCP089F+GcxeLicilyWmdVUazq38OccTpJPHHiNC7buaMWCJfXqdAPh9Qsr4TOPey7lkAw2eAALSxHqZ30Zi3XBrOz8y0TnONyVoAVIn3vwNAywQKGotPjMdXffiS6CalZHhjv4HtSUySiGtZHidlRskKjMxqun7Gz6vSAmQ6oswQlQJiY+iyZYecPWQDB+Two3wzSM9xDUC/wr5woUBLgVSjr6c8BBEvtUmqGolP1rRZSCUjyatGcCQZIeyBiwVRppYIKt5OaKbCytDOIKVYBw4bFOkKNkm+/VO1tKBH4wd8p3vBsOC+S3yRZR3B9epOUX3ElrlRUy1eTCeZ9N2eDV2LTAMMD26xcJddeewW6nT6OHzs98FmdsN64YLhuvW63j263P/BZdVwbRZN+I9p9992HX/u1X8Mf/dEfAeBygEOHDuGXfumX8A/+wT/Y4NGtvS1WVJynmcWoZoVPnjiFy3ZeglOnXhsv+DUuuF0JCHZ2aFa4SokeFwQrylAnfgVE8BuyyAn4TbO+4b2UGu2zweV3KMwRJvnVLGJwtKletiSYJb6nUifsCgXknh6tHU9GyAE9r6WQsd+lrODOEQKQawcl/tUDYaO4PVLnHAi96Ksl61wYwDi4HoCe7/TQz2ImuJuj6LRgejlsnxlrXCes2McO0IhTxWgH6zPCyoPhzDkUPpCgbfS/6XFrOgtTsBxAr/epDBspgF9Xs85KK3An9c/Ts/Q3tg3vT8f27r0GCwuLeO21tQeonU4Xnc70ykhej/bDP/zD+OEf/uGJ19u5cye2bds28Xob4Z/XjJTngqzg2mQzZAJ88sRpXHX15QBeGPi8ak3ZYOvMwAQ3ndyOK0IyjjUpQkrWWIAs+SywCs8MTgrHve6MJfTBN9K+ZfEELaIYypWivxoeVAVRJfh+toNgOPYXLrfykferohrG+pZKvjbHdnPQcgHV6oFay6C8BWq1obIcNtCj4mkX6ND5PKi1HTqbh9IzcK6A1TMwAKxj5Ujb6kFZAxL9IjVIs+bX3hlXBLTIqBpadM1v6et4w8+U/vxOaqX8s1MDHGOXAmFPLbMBEPvIe01fw1KdFqKgibFJ6wuZxCA6ZecoZm8DjUuGXgbFw8zKNsdYtrzecOdbzQanVgWyojY9iVWZIqOWG2XXXPcmHDtyciwQLFYHhgG+VwxrtTZO0G0c9elp2hudGv3bv/3b+Imf+Ans3LkTy8vLeP/734+jR4/i7rvvxq//+q9v9PDW3SZVUp3Ejp84hT27d+KZFa5fe01PuZVLlQZdu0wFBGvKB7LAGvmA8BWAmuxuJROcgOMyKCZoaP8u+aww+/pIr11dZqhJMEsCt6FeuNCwhQJp7QGrgjME9FjfIjhB6xgMmwIoskCV5sEmZU3yXFKcVgB1ge4CYHsIhc4++4wCcEb5docZbC+pCe7mMD1+FL0cRT+HKTIYo4PfLNcIR00OrWycCFvFupxOEg6edafj10uTDSXh0sRxSnxd7oUWMRNswfMv+C4gUrYkQHhYbXC1dKk0lg0DwVVbWx913fVX4/Ar61PCspb3xpXa68U/v+Utb0G328Xtt9+Oz33uc7j33nvHWm8j/POEQNjfpJPWKJG9MQgW67Kk06oJTDPCb3nrrVBK1SrIjcpMizWBYWBw8ll3cXAUvPlwVmlQKU2qzpzPDjMI5umygYNy3AaBwBHGvpfqzZKssHHS/og4awz+dTJgAAxXWRKhhQ8BmiwopRApC1EvFFENZ6VnXwTD5MEwtfpAawnUaoOyHJS1QErzFpQoSGqwmmQOUhmUnoFWbQBtGE+RtmYZrrUI11+Cy3wv4SYxXx9dDpSq1IyKjtf42l8vAFLeBrEHBB+4MCkopCUDP2xRWU8i7gJYBQxb8uIj6QSjUpuFeHNzDp6GTiiEuuYoANVULKFcAxYdLTBI4Ur/LmF7TFMSy1OmR/T8TW1cIBuWr2SF0+1M4/5y+ZU78fRTB8Lfo9qrDTO5V4zqOw4MZn1HBQ3WyqwPwqxm/QvZtm7diq985Su4//778fjjj2NhYQF33XUXfuiHfmijh7amlmZqp9lrc5idOH4Kd9xxM5QimIFL9kKZuAPp5L0uG9wEgjXyCIQpDwC4WvPLe1C184D0b8kA82cMgDPo8FqeFRHIA2ICIe1dO0ypOPUbYo2CWT6AKx0P2O9ZBsWhlKeAh9TgtoM+0JxpAP36wVjngW/6DMBoYNbCnS/gCuuz0R74WgUYFVoi2R63SAoB+H6GopfDFJrBcJGFbHDV10pdsPIiWdY6Znp5jQ/luIWkdr7tlJXSNJSCzvy3MKcofDWgnOWVaRYnNfi1QUxGpMB3GAi2FRBcYoddUNfS9Kxurn3llXvw3e9+f4NGtPE2Lf9cpR+322202+1VjQ0A9uzZgy984Qt4+9vfjm63iy9+8Yv4wAc+gO985zu46667Rq6/Ef554oxw02Ssqeh+kvUBBIr0uMX6CwtL6PX6uOSS+jrh6vZ524l0f5KRqU7eZQI8bq1eWgtcrQsG6kGwCGE0gWLrKzEJBOMsFAh9oQVbqUwl9AncEN45ZJagiaAsb9V6EOscqyI6cjwN8mJLUpuagmCmDVnk2kKTDaJbRK7U/zZkJBOnRN0cqlXALXVB7R6ovQyameV64awFkGF1aE95hioA22c6tCsAtKEogyMDpWZg9azPHLfgsp5XoDTlvsECfosiimpUgbBT7JV8RtgZ3zZJWic5y87Q8nJU+OhZQeyEhQbez8I6A/SyVFhLMr8eDMf64SH1XeC6LXk2jgMbhZMAh6rNBldp0akyZQkAu8pnjSNBcP7jmCXWCEgd8rA2SeLcplWPuBpLr+9WK8Nll23HX77wtwDGB8HjZGrrwHBaT3jR1tfe85734D3vec9GD2NdrKq70aSsOu3r8ezZ87DWYtu2rTh1anj5UnXfU88Gp3MA8cVVUawGSnRKh66CYAHGCgra5SUAXKU7A2j0/ZHyHOuBhQydeRCckWLfDiAjBlGZB8EKMbsoz1Vr0qEQKwlmJdlh67PAVChQP4NSDq7vOHUKAGBAS8YDyp6pTwyy44pZXmFmGQ+8lYI71+Ka3kKHYDK8H4X/2xYqAmGjuCa4r1H0IwguigyF0SErXDoVIHRoy904xO8ry+rRTmqoCVqnIDh57ee7fEY6z7qT4whI2yNHADlJZPCPEzLDGA6CRQStSrVOQfEkfnq1Nr6I1trYzMwMduzYhheePzB64SF2IWZ619uuvPLK0t+/+qu/is997nOr3u5NN92Em266Kfx9zz33YP/+/fj85z+PP/iDPxh7O+vpnycCwmXRqWYK42pPsGG9CeusWic8alJa7WvcBHTl+6WAuIlazc6TRcOU31YdGC7V/Y0AwWlWmGOIGuT87TMBw4ocyBI0ATkBBRH6FN1upiyc9RXHyiKHpwb5Iy2q1aVxUswEZ9og0yYAYj4mrqQuGR79DKQNU6TbfVC3ALodL5y1DMpyoAVIX0EqugxyPQguAKAFaDUD6zwwlrZK0oewdPB9Bljo0EWfFSUl8wvEGiWLEIF2FiEbLA6XZxDcJ9j1ECLEQsMy3VagYlmjBjPJ6W/nyhOJtP4qfJ48JMsroiXG+kcdCE4BcAXkyrb5ufyZg3/UgONxTfnzelqWAuJhGc/0mq3LBo/cT801VncfuOqa3eh2ezh+7LWh2xlXR2CaznbdFLZXSb3CatZdJ/vqV7+Kz3/+83j66acBALfccgs++clPvuGzwk22lhPDE8dPYefOS0YC4QvRAiWaYta3DgRr5FBOIUNWC4DTel/ebgS95edyNljJa+K/BQRrnw3WRJ69RdCK2/GkYFja7Sn/Gv657peu803WZ4KtsVDGckbYWNi+Y+HIvvZ+1Ph2SpZ3pipOJdbqML3ZAq4QYMu+1NoW3GyO4twcd2YoFOBFJ9PAsyt0ok3iA9RWaoLLIFgEsxzK1OjIcnMe2EUQbDwQNo5CWRng/a58jSQzLL44gFbAz9x8SRuENhnBcPVOnt4x4zYGM8DlLLAbDDyTvGsH5tASlJ609OhCshtuuBqLi0s4derMuu3zggPNU/LPL7/8cknMahrZ4CZ75zvfifvvv3/s5dfbP69b444mOuPwdeqjT9UJ4fHjp3DtNVdg31OVOuEhJ6+MpQ4Qh/WTTHETGLYw0GFybGAD/GwGw8Mo0QPjLI3KyJsQMEyKKbTGRxP7jrPClIg7WKdgFEdAuQbG+shkMwJKM8MUaESSFbacSRYQnImz1InCKquLegABAABJREFUpIYTBenCMBCWFgiIN37pL+iyFmz7HOzcAvozl6HwtcLWdADLPGiX9CAMtwEBwdJjsNfj9gpCuQp8MQ9cyXuZwlOYjYoUZieOC14wy39WaNhuDudrkdj5MngOrZNW07zXW6A3u7KaZxUEpw44XY9f+2fEZ14vecDToUvZYQdWCXewUyZLrxbAybU6zv1jUvpwSXiLNG7YeyVePXyicdt1r+P6kznLYdngwVZt65cxfqP3KfwP/+E/4Bd+4RfwEz/xE/iFX/gFAMC3v/1tfPSjH8XnP/95fOITn9jgEU7XmoLK1azwWmV6jh8/hcsv342nn35+7HXWozY4mqrJDMf3wnVKOtQEV0Fw5jLOCCODcv45Ab+p6rNAY952FQij/D4JJZozwETEz/Dgl+KzAGBF3BuXnz0o9uA3+F1fH9tkA2U8VthNnBVWpGApA1kFlRc8uS4cIC0HU/NzEVdwi0XxqU6UqL0Il7FtmK05+ucFCOtyQNlG4UlYYpBrFIwvO2IqtIYxyoNgrxhduZ/xHIagpaWlBsg6KK/fQf67B22VhE7uKv7XOoLzzDpLrgSKC8eAGL4eWLLG6dUorRFTH106dCiXLlX9s7y2TV1BVuB7NzrrO8z27r0Wr7w8upvDOHbBAdwxbVr+ecuWLSUgvJb22GOPYc+ePWMtuxH+ec2AcCousdZ27OgJ3HXXbcjzHP1+vQJr2tM4tepEuPRZhTY9SizHAiCyAYUIGB4YSw04rh1zKdqnArdfgQCfQdNe2VD7emF2iJSsxz5KO0KuXADBWhwdnKf3EJQDR0GVhbRNCpnhGqdZokUrGwGij9Yqo7iEyLdXoKLgrHDRA/cH7HOPQN/qyM7Mws6fgZk9DTu3Ayaf5yyw6YBsTVGw9fRoD7LR6wHdTlSYBBgAK2LnbF1Uv7Ce7mwjqIWlQFeCd3CQCLTvSyhZ7xCRswhgmPfHFKsgtmWjMAeo+cZbygyDAuA14XeKILgkjpVuo/JetY4kzQaXADEGo82rMSst1SZgdUzTUjA8yf3HOYOrrtmNhx96euyMLzAeAB5FiR5VJz1MfXratt5iHJP2KQS4V+GnPvUpPPXUU7jyyivxK7/yK/ipn/qpsfb3G7/xG/j85z+Pn/3Znw3v/fzP/zzuvfde/MZv/MYbDghvtB09ehxvfsutyLIMRVEv7nAhTLyFFp3+nWaDRRVaIw9/V0Fw5jgjnDldAsCydprp5X1EwAtEQCxXkPIZYEoywYrKIDjzgDf3GeH4iD1yM/+3VhaZB8Hil1K9rcG+9BQystZokHKwRfR35LiWlwwHmamfBSYVb9Df18TXJsA3nSu4QqNwLHbVXZwDFawiHVSsbfSPJY0NS74OWNok+VZQHhCbSvkSEeusOHI+MeYZboq4ZtcRssQHhw4bSDO/VAN8IyguQuaYUMBBezBswdl78uVsDpy4qFqVDs2vOVAdDisGM8ZNtm5MojWyFLBec+2V+MbXHxy6/HrpH2yUrbd/XlhYwAsvxCTjgQMH8Nhjj2HHjh246qqr8NnPfhaHDx/G7//+7wMAfvd3fxfXXnstbrvtNnQ6HXzxi1/E1772NXz5y18ea38b4Z9XDIQnoVcIwByWFa62T5rElpe7OH9uAbt2X4rDLx8L79eLWjVPkKtUSVl+JNUaFoBkjI2U7gIIWAjCjknBcYjiNXz3UpQv9BuWvHOsFTVeLdr5rHDhAGWTm7dyyBUjH00iAoEw6mqLHgZvgHa2FBGNx0lF1rHRUMrCaK8eLfW2IkJlibOzpgB6XSgATinOEHeWPXDlyZFutaA2nYHasg1my3nY2e1wrTneZ78HVxCsaQN9piu7nmFRjX4frl/A9Q2/J1leUjES4AgWGku4BKf1zUDLwdkMjjK4TAGGQDAgFNBZH6QNyFko4yPcxNFddsguOHjSArj9eaUcoAxni60CFE8WyBHSU39YBpmzwmVKVhUEhxolCIiNUWteJzmPXBkkp9ngqpOV+iNZbtL6o0mdbjoBrmu1tBIaNDAZABbbum0e27ZvxrNPx/qjUQGwqq0kc9s01o3qI7zeNmmfwgMHDuBHfuRH8DM/8zP47//9v+OrX/0q/uk//afYs2cPPvzhD49c/8yZM/jIRz4y8P6HPvQh/NIv/dKKvsPrxVI1c2AwK7wWtrCwhMXFJezcdSlePSxqrxs3Oa+rDx5YpiEbHACwF8YS+rOAYM4IR7XnVOgqpTkDCJle3n4lI5z8LSrRAoAV4GnQVAG+DIZz5SI4TtoDKckKJx0h5BH0P0J7wFjKYyx/T2sUSHFgQH495SzPPUwOaAWnNSxlsCqDpQzOaVhoWJfBIYPVGtZmsJq7SFitWasjUzDIsJRdipNzN4KMn5d41eigOmUBZ+S1n+wYA1f42ZCzMAX7ewHBxg2CYYKD9gQxWAtHMRMcgS+SMqSEHu3BsfP0aQHFJgEoBRBCnIUnSo9ypcLe4tcVxhbEdwNpa8NIhK78IztAm16pTVqquFZ26aXbsXnzJuzb98Lohcew12M2eCPs4Ycfxgc/+MHw96c+9SkAwMc//nF86UtfwpEjR3Do0KHwea/Xw6c//WkcPnwYc3NzuPPOO/HXf/3XpW0Ms43wz6vKCDvYgYnqNJzqsIuu6YI+cvQE9uzeWQLC6fYIupQVnvYkkyfMCBnFFAzryj7Syg4FVUtrGejf6mnKDKYJ1oNiJzL8iHL/ZBl3WyetlPjmrbRkjfkGnjmCCbHJ1BTIOhhiem7mKNTYkCMYQ8FRG6OhlIMylgUuKlQq5whUOEj9rgMAA/Q7hGK5BdOdQdFrwaINk7VglmZgFrfBnN2OfnsrTLYJllq8HWdApgdtOlDFMpTpQpkuqOjFhy0CXZrAB0JcIDvkFnpunnOGKofVOUwrh3MZHMmMgn9ERxoWGSxyFLYNa7MAspUtoFwBZfvIXBfKdaFtD9r1oIo+tOmDegaq6IG6fZb0bjx3YiZYjnOaFW5cD5EWbVOnXQHB1ddN2WChXVUtpV411QdXhbLqxxuv6yZhnI2sX7rl1mtx6uRZLCwsh/fSfqCjbFwQPKlA1kr6mq/GVssOmHTdSfsUfuELX8C1116L3/7t3wbA9UP3338/Pv/5z48FhO+77z786Z/+KX7xF3+x9P6f//mf40d/9EcnG/zr1MahBU4TJB89chx7dl+WAOEJbQ1p0alRwmJTCRiWbLCC5t7BYGGsDFktCPbEaGjP/dIk+eAy6KUKAJbPBPjyayrTnOGzwKoMgPnZIScg88FvTQjZ4EzZAIozsiE7mQJggAPVZiaHm1XotxWorUAtBbQVXEsDLQWX57CZBjIFq7Kg1MVBXwNlC5DxjC0Lz8QCYPindABEPNJY7X1ghj7aWHZbQY4VUpxWsBnX+DqlOLhNBEea5z0q4/e15s/AABaFBYwFFQboF0DfwPUNP/f4mXoGqmdA3QK2b2OjCD/fCa/FN1eAcUqjNo7PmtR7Ff53lJpM7hnMgJm3jfBcJ2CZ+uhq2VLVTzcyiSbo3nAhm3MWt912E44fO4lOp7PRw6m19fLR6+2fP/CBD6Datiu1L33pS6W/P/OZz+Azn/nM5APzthH+ec2o0SulRbsxJst1duzoSbz73W8tvXehRHwC8A1Z4UjdNMkkt/r9UmDB2WCORAoIts7BJoFGB84Mk09DuxSRK/JqiAg1p0XISFcyvv59rZwXbWKKESkX1BdLStOGs8HOEgw0rJ5Hb2Yz3KyGbSvYvI2i2IyiuxVFbwbWaVCvi6y/ANUrQF0LVfSRoYO8cx5zveNQaEHpWWB2HtTKQJqgXA+6twTV74A6SyCzDBQdpkR3ekDXwnUQxDgAgLQDMscMa2Q4P3cldi08AuoCdrkF2829CrQHPNqAMgvlM73OUtKeoYXCzMBSBkMtP/PQsFkO29JwOofRbfTyzTDUhlUtFGoGRuVAAaheD+gWQKcAOgZ22cIsWpjFAm7Bseccdh5VssHsgBHFtALARSmaDJRfp+/VZYPF0aYR5/T8HIhDU/N1Gdepv67rAPBqs8ErtWuvuwIvHXx1Xfd5Idq0qFdr1Z7hwQcfHBDN+PCHP4xPfvKTjev8+3//78PrW2+9Fb/+67+Or3/967j77rsBcA3SAw88gE9/+tOrHt+FagP+1IPhOsArfnhaYPjIkWN4+9vfDOCJVW9rRTYi+1tHi+bP9EA22MtWIQhhOXmOIJifo9KzAOCU5lzuB5zSpNPMcPxM3k9FsTLFQDcjznCmIDgnfs6URe5BcJ456Lkcer4NNZsBswpmRqM3q1HMaHRnNCgjaBTIii4y24V2XeSOX2fLi8iWetDoIEMP2vahVI8Dw+gzVdpf/1LjWxa20tziyD9zHS8/F8ih5vdAP/sqULjARlPJvcih+b7EgWyflVaan3UG5BlMloOyDLaVA3OzsFkGl2dweQ6XacA4UK8P1+2DegVUt4DrFqDlHqjTg13uA4ap05xhhh8ba7SQTy7A8jyNEmCswL419WYp8JXnkPQeeLjg16tsrVKQeohQVpPVMTEvhDKFOrvhhquxf/+h0QtugK1noPr10kd4Etto/zxVIFxqURToR4NOlKB8A3GZYA9etJOAYAA4efI1aK2wffsWvPbaucblmmqFh4112N8rsWqNsEkQUCOocJJpVgGshHV8bYt1jHuDBH+SnlYUa4i1zwoXVgXQW16c3zOWYFQUbRKBDacUitk23Fwbej4DzWZQ8xq0KQNmNLKsgzadRzs7g5Y7i7y/gFl9GpkyyFQPWXEeamEBOA+Yc7MolmbgvNq0nu1Cb1tioWe9DS7bBkvz3GMQAPVFgdoLY1VBcF8FNUoALN7hOCPtJEDgCNXDHESvFIuB1X2mlEVmerBFgQwdUOFAfcfgeTmJqHtauAh7FLaFvppBQZvQo1n0MIdCz6K/aRN6m+fQb82hUC2YroNdKkCLBbIlA7do4BYL2HMF0ItjkqixBEYc+Dc3cg64+H6TDfYYjlHmqqPl90aD3nHMuQqorgHAvFzNPWHKLI7UlFK4/IqdePD+7w/sY6X7S9cbu0dyEiST169XavRatWc4evQodu3aVXpv165dOHfuHJaXlzE7Ozuwzuc///nS39u3b8e+ffuwb9++8N62bdvwX//rf8Wv/MqvrHqMbySbBhg+ceI0Wu0WtmyZx7lzC1Ma2dpYlRZdZ2mfYOVrgqsgWLLAqdJzSnNOwS3vtx4Qx/dRygyntcCcEfbANyPMbG6hPZ8jn9dozWXI5zSyuQzZjIZyBqrThep1oYoudKeDfLGDrN9BbjpouSVkrs/dIrICOjNQ2kArC6UtSBt+JlaOpsAW4ulkKrIl4NcahaLIGPgKEPZtjeT9gjL0iwzLvTZcAoTrJu5paVEalOeplE89I8m0D/m9rdJwrRyulcPmLX5u5cC2GdhdW2Bn2twXuWegOj1guYBb7oOW+8BCH1js8/wDFFpTOmLPqRxVAHAC6l2kQ8tDfK9xFcZWJRvcRItOrRqEbppTjwLNK6dFR97jai3LMux502781V99Y9XbEnu9imW9EW2j/fPkfYR93SUDWXDtpDO1TqMWWI6hHj0pCOZ1HI4eO4Hdey6rBcJ1zrw02R0BgtM6wWlcPOlNS8BFNfNWGodP7JK/HfJtkEExiEWvOEPof5S4I5BCAMF9XyesfOQyq9CyCYD1glpGZyg2z0Bv0XCbcvTmW8BcG7bdYvrv8jLy3jJanUXMLJ/H3KmzmKGzmGmdRz7XQTbXgZ5fhjI979wzFq8quNWCtD5whYItFMgSO9lOBiz3gJklUKvFEdWMT9VSbXGn60EwtztyXa9WLa2NpG4Xhh2zUhytLiJvnZQDZTYiQ+V8na8Lx8MZAcjxUf4xE6XidALgwTAMoG0frjiPVrEMXZxFUWTIigx5P0ffZFi2bfTzORT5HFxrBmi1oXbOoT3XRqvdgukZFAt99Bf6KBZ66C30UZwvYJYLrtHyIDgFxAjfEgM9Cut6FvLyZUfL52dZu9zVON1JrAqCq+f7yPvDGgDi666/AtY6vPRSLK2YpDYY4AlHVfxKbFI163Q9YFBUay1NaParWR9Y3/YMo+zAgQMbtu/Xm62mHrBpXWMMjh87iT17dq0MCJOaCj16nPrg6vJ1tGj5LGSDY37YU6BjuyNdUXoeBLhNgLj8LK8FCOctwsyWFmY252hvztGaz9HanCOb1aC+AS12QZ0eaLkDnOmyv+x1oU0PuTLIteXgtDbIteHXmYFSBkpH5xCALbHPVgCsJfaVVQ0WR0x59jXFxnI2WBSdi0KHtkZFoVFYHRhnBWXomwyu14bte99Tcx+KwFe6W0TxL37flcFxsk76npiGBXp9oBezZbLfIIylM9hWG3amDdPmBzZvAa5uQ7Va0H0Ds9BDsdgHnWdwbM8VwHJdxtWLXyIJXDc+HIxzQ7PBqV8ZN1i9tmyr6v6bxjO+L7v5luvR7XRxeIzSivXQPdhIm5Z/vpBso/3zREC4Sem4DkROyyaZPL56+DhuuulaPL1vP6/bEPGpZoWr41/XerwEBDcBDJ/IBIjFKazPKQtdxjjuTUdg8YfqJSK4rm+5vrhPcrPntkoAoGcyZJvbUFtayLe0kG9tQc0puH4XprME1VkGnTzH4He5y0BYWbR0gXa7i5l2F63ZDvRMj8Gj1Aj3M7h+AepbQBUxgiJfkxiIqnSS4wjOgut7ej1AaVaFtpYzwf1eGQR3iDPB/Sz2BPYOGdaDX22lSIc5485yv2DluFWDkglSzfnmwTEpCyhukCV10PCv4ZWng4q2KGh7UJxSwaTHYVFkYRKAwsF1uyBTAGYR1mgUTqFnNGflZ2eAuTZoUwt6+yzmrtiKTXMtFF2L7rkeOmd76JzroXOmh/5C3zvOytfw4Lf6PPT8rFCgbY3TlfpgBwtRjC5voz4iXbVhzrnuPjDNbOltd9yAQy+tsH5xTKsDw6kYz4XS39Eh1sitdH1g7doz7N69G8eOlbUgjh07hi1bttRmg8Xe97734e///b+P++67D3v37p36uC5Eqwemg5maUZPHgVZLE4Blue4PHz6K6667Cs8+u3/sddfbqvXB9ctEMKwS6CuEae3p0CkIFoVnoTan4HcU8FUE5LMZZra2MLOthfaWFtpbW8hnM9hOAbvYBRZ7MMfPo/tSD93FLjJTMCXa1wYzPdogVwx+c22hyUIrfiiKrREDePRzAxaeJBijobXxolnEQfZEXEuWNdYHgSvZ3yJ5Lqx/NtozzgiFymCMRq/fgitsCYxWAS3LedjQ0UKrKPolqtChZVQdQE74UikwTrPPotfBYNjCdfowS0vMBrC+baVTMCqDa8/Azc2CZmegd8yBrmijNdNC0XPone2ie64HOtuHOdODXSxCd0fx08LqqmaDTU1tcDUbDERatNhgOVM982q6Nul2x88Y33bbTXjxxZfG3vJ6guH1ZmxNyz9faLaR/nlV1GievI1ebnWKdc2Tx+q2jxw5jre/43bMbZrB0iIX1KdgeJyLo+6knkQwZyVWJbnUmYBhBh98czSwKBy7YO14asL1NC6MVoQXpTVS3znoTKO1vY18exv5tjbyLW2oXMEu9GAXOjCvLaN7+BTc0hI0uiBdgDQ7S5C0HhhSq+NVmqU9gutlcLrPmVY5/Aqg3EK1ud2V8zW5qt0H5UUs6TIFg18AsDYBwQVc1wG9CIJtL/MgWjEIVr4yx5LPqqsAXsUY3BJEBrvUCqlqxIC4hNltpG6lADiNhltp6yC1UD4ibq3iYERoB1G/W1sA9lwX/bM9GEvo+5rgPhTUfAs034be0sL81Vuw7fYWAKB7ro/umS6WT3ew9FoXZrEI55FJn0lYBMl3GhJxHteqrZOaKNGr7jE8ZP1Jrtdrr7scX/6rBydeb1IbSrdMwLCCHjiGK80qT2oXeh/hu+++G//3//7f0ntf+cpXQj1Rk/2Tf/JP8Bd/8Rf43Oc+hyuuuAL33Xcf7rvvPtxzzz1BtOiNajEIVV8nDIwPbleaMT58+Cje9vY7MDc3i6WlxRVtYyMtbXxUygG7mDeW2t86EBzAcEJvTjO88nfWVpjdPoPZHW3MbGuhtaUNnSvOOJ7rwby2hIVDZ2DPd6GNSejRDPRyZaGV86CX64NzZbxgVswEa2WRaRMBsac7C6BMTXycMZp9po2gNABWD37Z3+nQzkiCvgKA+yZmggur0Lfal2FlUFah28+9CnTl+IfML3xbKArtoRwY2BMRB6tdORuctoGsyxhXAb28FiDsQEEfBVJDBsUlV8aAFpZB57v8fa1C1yoU0LCbZuA2zQDzbcxesxWb5tswFuie62HptS4WTnVx/lQXdqkYyAYX49YGp4AYsT54Eh+7+rrg1awfDmitKaVw3XVX4Y//+P82LrNSez3Soy90/7xS20j/vGIgLJMyAYlN2YxpNPQet1au3y9w7OgpXHHFbjz37ME4hjHB8DAQPI5VlaObLm6JJjeB3gFqi7B2/WfGFVAgFCE27dD3YEY5zvo68n3rAMxsaWF+Rxvzl8xgbkcLrU057GIf5mwH3SNLsM++BrvQhXbsTFvKYkYbILOwOlGMJkLAiZUanUAbDlFkDwh7GUhbuMwAHQOacaG4iVoOCj1QZriuFwBlBtQ28cyUvpPW8uteAXQLT4VWcD0NGAXb84JXNmZqkYJe9owxUwwwddoSKDOA33/tupbARcVcJ+2UBVmvZOkzwNJr0fgsb0oHMya2oRBAXBgN6xT6vk7KOBWdbhIJD0Pwf/Ozjx5bh86ZHsxrPRjLolmOAD2bo7W1hfb2NrZcuxWXvrkF07dYeq2LxVMdnD/VxcLpDqjgk4uIICg8PfMGlMthJ7qeObizcVnOprFWr/NrrrscWabx7NP1EedxM88raZ100SbvU/gzP/Mz+L3f+z185jOfwU//9E/ja1/7Gv7oj/4If/mXfzl0Px//+Mfx8Y9/HN1uF1/96lfx53/+5/jYxz4GYwx+5Ed+BPfddx8+/OEPD80q/1221bZQ6fV6OHHiNC6/fDeef34FWeEp0aPH3l1aDlU3N3ARFPMykRKt4IWtKiBY1J4DAFbA3NYWg97tM5jZ3kZrLkOx2EfvTAe9o4tYevY07GIP5KQNkge+5JBpBCXoPFGFFhCckUWmBQjza372QNgDYq0tlH9dW/6DJOvr9Tps8n5gQ40BgAvD4LfwbKi+939GabStQsdoOFO+e0tmlwMN0hKKoJXzTCVOr2twy0dUxp+CYPmOkvGuY4E5/9ta36fYOipP5yzgiP2tAGthVwWgYR36Z7vov9ZDYYnBsSO4uRxqcxt6Sxtbr9uKS9/SQq9rsHi6i/OnOjh/qoOzpztwxfBsMOB9bKW0Tt6vs9WyjuoB8zSuyWYwvPfGawEALzx/cCLgOs2s8Lj7fb2B6gvJNtI/TwSEmZBbf2LVAeJaOuOYF2IdGB7HDh8+iuuvvwrPPvtiOXM8VruI5gnvsP1zbeDgMpZscJajbJgYkSUbwDD55QoyUE6hjwLKZdH5OsLc1ha27pzB5p1z2HzJDBQB3TNddM90sHh4AeZsB2RsiCQLXQvEXQ4484wAyqR3nnMUEq1ys08jqmk21TmCLRRAGdfgKguVOaCwPjNMQJtAmQO1DJw14V5ILXA/CIABcK/HdcWFAfqJKFYNCLZGl0EsH0AgM9xeQWjT3kg5OBNpXeIA020w1ZpVsyE9ga18phrVMIM4iPQz9BlhAb4meS0tG2z6EOArx1Pqf5PPquJY1gJmsY/eYh/nDnPWxRGhtbWF1vY2ZrbNYMs1W6DbCudPd3HuxDLOHF/CuVMdmCJem/9/9v48WI7rOg/Av3t75r0HPOBhBwiCxEIsXAAQJAgu4L5JsujITpzEiZ3Iirf6pVz+VdmqVCr+I0mVU4mrkkpkV0WJnPhnKc7iJV5kxbIsUaRIcd8AAiCx7zsegLdvM9333t8f95679HT39Myb9wBS76AGb6an9+nuc7/zfeccVzjLSa+KzJdFN2ODfZstljPYH4Q5xtu2b8TZM5chpdtPBo4yPYQ76fj8Z+eNDCD47TzaXb4Va7VP4YYNG/Dtb38bv/7rv47f+Z3fwW233Ybf+73fK9U6CdC5yi+88AJeeOEF/O7v/i7eeecdfOtb38K//Jf/Ej/7sz+LZ599Fr/xG7+Bxx57rLUDmbMGSw+cz5+7iLVr1zQFwjfiuZA2pUTm/e37dCeRNgDY5AIzWxjLFcqqMCCKNPBdsGIeepf3YN6yHnAF1IemEA9PYeTiKJLhGrgQVjat2x3pLg56XVoKrKe7PsEODJsq0QYMR0xaKTQBYALBnEvd/tADwTTNB4g2QMzDzzRGEJQGZABwlgSawG9swTBHbPxgrBiUisAVx2RSgfTaDRL4ZXAgWDIFyQEplWEIdAFSxpVOD7MVupXXN1gfn2a+Q8DPU8BZKmkk2QycKwjqREHDAyPXLmKqgjxjuDaH9ZEY8XCCWI4jloBgDJU+HbjuWdKNxXf0YV0Xx+j1KQz2T2CofwLDA5NQUlpQDIRMsMrx0zMvie7kOrPB8Pbtd+HkiTPOP38CWdxO2mz759m2G+GfW2aEs5jazPlukBO7eLEfOx/YZuRXuh+obU+UuoGygK8/rQwbnLcOCSBCZB9Ufi/hMoWGKPre2NZCGoCtwTBTDAsWVrBs5QIsW7UAi1fMQ8QZpq5PYeLaJAYODyIZq6MC3WqBItEVSqE1DhpSO2ndm9hIfaEBllAc3FRettIjb5/SIBiAzdNVIoI0FaFVPQKLJNAFowXj+gqsSjD/7qTvpNIMsJS6SbIwRbFirgteCVMh2rC8BIIpYk37wbgCMyBXSabziJkApPlMf0VkQW+uGedJ4FTZbbs8YCEqSJIoYIAtI2xAL4FgSSDZtqny8pIALR9T1B6JOTbYBCb0fjQ+3IL2DEphYqCG8YEahBxBogA+L0LPsnmYt7wb63etAu9iGL4+hcH+cQz0j+H6wJgNvvjXa9Y9n3U9pwFxkL+UEwzLKqQ3k88Runc3bLwdr7z0fuY8BIaz7vOZAsFZ+0jvZ8NmOwep1T6FtMzevXtb3bVMe/jhh/Hwww/j3/7bf4sTJ07gW9/6Fi5dutSRdd/cpgeeszmwvHDhMu7fuQ09Pd2YmqrZ6enAd0dtho+Npa532yYJ2pXNX1RF38r56Fs5D73LesAYUB+YQu3aJMaODkCO6boa1O+3ynR+bhCo9phg+zIMsAPDjQBYS6Q14K1S5WdPCs25CFhSYkr9XGE/R1cKbqcBCACw9mdhEax6UjFsMNd/DRCuWyZYd6/QPo6jSzLUhFZXufPp2GAHgiWkKYjJJQPnBmyCUsNoWRUo1vxj9qcDeht0XNQfWCoGacZGAKAEs8tyM0oLejAH4Fd33pCAbW0oTNDaFbYEhFSYuj6FwatTEKY4Fp8foXfFPPQu78bGDdo/j1yfxPX+MVzvH8XI0IS9X3xZtP837MJws4Ngf53h/bpx43r81f/7fjCt7DPr01g469OaI5xns+Gfp90+KWtgXJi3lzHYzeth2I5zrNdjI79ahWPHTtv9CfOKBaRyBTF05Df/ZinDRhMrrNcbue0wHUG2YhbmIspUfKjsANeC6khhyfJerLhlEVauWox586qYuFrD6LVJ9B8dQjxcR5UxVDlQZQwV45WlIWIVtIy24ondGCOAxSwYtgCN6fwnX3pU1MtMSQYWabYUTEElOocXXQmU4GCJ1NSzDvUClSooR9chOElUpwbACYDEgGACs2nmF7Bg1jlSDb6ZZFDgGvwKDqVMqweTT6zzmSPNYps8Y2aqTlNucOZvIsj5VywIFoIHIJikVZQPTEBYWmZYDwKkkVqLjJeJAyBRHhvs7UdR4SuaTKdTQaE+nmBibBTJ6REk1vH2oHd5D+7cdCsUFK71j+Dq5RFcvTKEWl1H3FutFq1UqohHC9Ks2QB+t6+9BT09VRw+eLqj6223snW6OjeZX5F6pu3TmoNE9qUvfQm/+Iu/iCeffLLhu40bN+LXf/3Xb8Be3VxGfnc6ADnLd09N1TBwfQhr1qzGiROn2173bFuzZ5HNDwZDJWJYtGo+lqzuxZJb5qPaHaF2fQqTA1MYOTaIZLSOCLpjQ4W7Fkj0t8JUwAQzls3+Rp4UuuLJntMAODIguFUADDhwmE7XUYrnpvzkscB1oYFwYkBwbJhgDYQBKA2I61KPO/R5NTnBYBoEG2IWFgRzcA5EUKDevrScXxiLeyDYP+YoouvcB40c0ki9GZECUrPM6QrVvvl+OQhmm+epUAxCOgBMfYM1+NV+mYBwbSzG2Fgd4qREAoWuhREWrOjBohXzsHrL7VBK4tqVEVy9Moz+K4Oo1Qn8hvnBQHl1UbNr/Eb0F960aT2iiOPwkWMzto1PGsM85587759bAsL6gskfjPkVYwnsslShqTKDW4aoVD5S3rrOnbuEjXfcboEwzctKRr+b5QTSOmhwmmZtNBhGJissITUo9aJ2aVl01rH3zKti5a2LsfKWxVi2og/xpMTg5XEc//AyJq7WEIkIVcbRzTi6OG8sHW2PDXb/CAwrRg7OFGSw4IshUhwxYVQwSMYACESeJMjmCAX5uVxXZFaaYSUmliUJVAKwitI5ujxyIJgsESEArjMtXzbr0BvNOECzfRlXdOTaOEsmFBirABKQSQRR0wWldOErx1rTcsQoM6Z0zjLTRbcywbBhg21UXDiWVykeFNygB1gWCKagQ2LeJ8QaUzCC5FVwbHAziUu6KiUxxz4glkpBKYWpsRhjozXEJwchmEDPkioWrZ6P2zYtw127VmN4aALXLo+i/9IQRobyC91IOHl02nHOdlXkMvLKHfdvwdlTl5Ekrpd3Q4pDASsczJcBVtupbN0gLW9jHXOWb8PDw3j++eexbt06/PzP/zy+9KUvYc2aNTd6t35k7OzZC1i3bk17QHiG84RdWoRElG4N5MtPU8/e7t4qVq5eiGWrF2DxivkQUwLjVyZx8cNrqA/UUIEGtRWumV8CwcQCRx4DbKXQTdhfvYz7W+XCvi9igNNA0Ae/WQAvDYaJBfZBMHVASIPgumgEwnUPBPvFH5VhSmuKQ0mSLOt034qpeUKEIaWB2YJRGeOBMB/YHTu3EmmRmQ+tlJY8E9jnTBcI5VyBS5WhvgrTx9JBbJ/91Qo79zlRMOBYWdY4UQoCCkJpEKygMD5aw8joJM6fvA7BBRYs6cHi1fNw++ZluHvXGgwOjqH/yhCuXLqOoeHRhmu2Xcsfh7cGmlsDmrQsx/07t+PEiTMQSVaa5Y8uK/xpthvhn6fNCGdZI+OblhRGAIHFFp1amRv74vl+7Lx/K/oWLsTI6GjT+YFsVrhoAJwFhvUzOgrAMLHC2nEm4OAQGVIWakPj2/zebtxy6zKsWrMEixf3YujaOK5dGsWxfZdRHxWIUEGXqqKKKLjNW0kBkCbs4KKYylQsZIhk2DBLKQlEADdRSyvv9RhiKh7FuNLa6wg291YJwwwbEMwgHSonS3SYVNVhGWCV6FzgLAbY7hsBcWGqVsYVA9CrYFyCRxJKQn9fq4Ix6AJYSvcwlokDwtIDwlxw8GqCiMuG8ZeSXEePFYHhFPD184yROkdwwNhGixXPZIPdy0mt/F1h5vrKOzsEhgn8ChU6Xv1S9p9UCqMDUxgcHMeJg5dQ6eZYumoBlq5egAc2b0RcT3D5wgAuXRjAwOCwt51siTRQDgSrJvUFylhW/9689UW8gk2b1+F7f/1m7vKtWhEYbgXQ3sh8amWYiuksfzPbN7/5TVy9ehX/83/+T/yP//E/8K//9b/G888/j1/8xV/ET/7kT6Jard7oXZwlM0WeCgaT7bIlRezR2bMXcd9996C3dz7GxydaXncnrO3j8u6/eQu6sHrNUqxeswR9i+ZjrH8Sw5cmcHHfdcgJgQpj6I60KotxBNWiIw8EVzgBXAeKqyUAMMmgaZotfOW1RKpEAlFkcoENIEyDQArw5lWK9v0u+bg0E9wOCI4lQyKZTf2RzATgpavBwZhTsVUNpNFDCxWwrmS+FNq1VpL2eLmtjm0AMc+ukK0kC1jgrACBnhcNPp32yTG+IRuceKywUMpTe2X3DE4gYAtlMQWpJIYGxjAwOIJjBy+g2sOxdNVCLF3di/Vb7sLUVA0Xz1/DxYv9GByMM/bZBKsL7tHpsL9ZywYplSXvu6gCbN68Hn/6p/lFEGeTzb1ZmOM5/9x5/zwjQJjMB5aumFZ7g9yyyzBwxHGCixf7sXbdrfj4o2OFA9CAzfXAcKOcOhXh8tjlNBgGYGXS0FjQgmFdcMxJTH0ALCHRu6AHt6xZglvXLENv3zwM9I/hwsnr2HvpBERdgYMjQgXUvqHcuSs2K5mm/BUo+/COFQOk6VnsIa2ISZfzKqldQoRIRFCRDOTRLJJOzhxJYEoBSLRHqBhHTIciAUUA2CuGFciVyXnz1DWhTLuiuAKRECusq3sxpsC6FWRSQTJV1UW5uLTFruwycQQqmMWYhKrqHGPJFXjkATUf/EtihBsl437BMRc5phcM+OU2P5iYeGKDCcBSE3Vi7PPMl0hbAOw5XSHdZ3K8rlehCcoEVzJQryW4fHYIF88NAFxh6apeLL+1Dw88tgmxiHH5wgAuXOjH9WvDmfvUyYryWVYGXKYBMQPHnXetAwAcOXK29HrKWJ6MuWzrpKzlaH2tVLFv16ho23SWv9ltxYoV+PKXv4wvf/nL2LNnD77+9a/ji1/8IhYsWIB//I//MX7lV37lU9FrmNRVLs2IY2Zy+8pbvV7Hpcv9WLfuNhw8ePSG7kuWkfKN6vSSwiVCFQsW9uC2227B6tXL0LewF0OXJ3Dh+CAOXbyAKOaossikJfEgvgv47ZKUKailQS+B4CpvrP7sF8AiubMDyJ4cOtUKiRvwR9JovygWgd8gP9bzpUp6456Ur6Hgrl8ky28DSMUfE/M3tnnBBH5DEEyssFAMimt/F5uBPoOWRGtPxpCYcyYN+ExXhvbNgnwriQ5ZcR5JLxCQcewcYEqzwFn1C4JgNqgYlpNCu5QmxwAniuniWCkWWINh54sTpXVVAtKCYAGhQbCnHqR/k1MJzp29irNnLwMRsHTFfNyyZgkeeWIr4nqMCxf6ceH8VVy7fr3B55ZumVYCGJcFz2XTLrZvvwdxPcbRIyebru9GAtQQQ8xCoHrOP3fcP7cEhNu52Bh44eCNM94yK1y0LbIzZy7i/p334KOPjoTzNJNFp5hh29eTNbI85o2ez1xbHp6zYJgxDVmtatjKogUkJKpdFay+bQnWrF2OhYvm4dqVEZw4dgnXLo9AxDqn2O9bqLdjwFrORW0lsAaI+5Jo/30wPyOQxMAlENPR2MIU+sAYU4hZhIrSYDhJKkiSBFEUQUQRWKTPIY8kYHJwfMYWMMA2krp1EocGy+ZEKsWg6hFUHEHFFagk8lhaqatQQwCSWh5xm99L88pYty6iaLau+KxzekVcBRRz0mjFIOIKpMnzJZlVZBykMA5TIiyoYY/JZ3rpu4yCW37kOC2JJmdq5dApp0rS5mYWFMlCtiSagK/KcLzpqpR2vdSiQSpcvTSCK5eGIPYKLF05Hytu7cMDj2xBkgicP3cFZ85cxOjoeIbTLYpAt8cGtwpe/fnv3XEnjh9z1Sg7aT4YbraPswFu5yzbLl26hBdffBEvvvgioijCCy+8gAMHDuCee+7Bv//3//5Tmi88s4PGMoPBM6fP4d5775l1IJyn7iK8FfZU1u+7u6u4fe1qrF17K/r6FuD65TGcOnoFQ5cmwWOnymLgkAa0pc33tZwRG+y/QhCsWx8pU/RKWdlzug1SGgBXKiKQAKfZT/1XNvTRDc6HYVvhgULqrKDPVxjQtQDYT/eRHInQ7xOvKJbIAcE6R9gFfZVVDJp2kEbypFKn12+pxJjOB7Z9kc1fxvxggAbBWedBKQZEwii8IgOU9UiOMwcZg4C2rQadAr+SGfZXH6+fGywUECtlQLEKXqEvdv6YQDAVS03XlrHeW0hcuTSIy5euA1xiyYperF6zDA8/dg/q9Rhnz17CuTMXMDo2nT7exYxvWWsGYO+7bxsOH+5MbnAzeXRpmfVNwgr/qNhs+ecZYYSL8oizjHsXVqdA8eXL/eB8K1asWIqrVwfC/WuSx0dg2J+exdb4smmfHQZc3J2b9ZMImthgzhlW3NKHNeuWY8WqxRi4PoIzpy7hysVhJLEwLBDPvHmLWjJpR+LyfqWJPpI0K5GmKLNxMszsIzkXC5yYBmEw0ucqN/35Iq0ojrjSLRB4hGqkWyZURAVJIm20VQoOVmGmcjP1FdaglAldSItVdO6tiqSNzCrJbUVoao1kK0F7rCwxuirhrtiV4LZ/b5JENsLLuO6qrESEpF6FMkAY0OBYGgY5SSoO6FKkmOuocUTTqMq0Bb0uH5g+019pnSVJoMOCWcIOJjzwaxh4kkHr689bJ1L5vz7o9b6jghwksaLCHPo7HYVO9yi011FBtF1fZxJKKZ2XdGUAB/ZKLF3Vi9vWLsdTz+/EyPAYzp25jLPnLqFeE+gU++vbdBjcrq4urN2wGn/0v77b0nKdytflZuB8M1pRIbyyy9/MFscxvvWtb+HrX/86vve97+Hee+/Fr/3ar+Fnf/Zn0dfXBwD4i7/4C/zCL/zCpxQIt2adH/xJXLx4BbsevA9Lly7BwMBgB9edYUoWVI4mL21mNccaRRxrbl2JdevWYNWqFRgaGMOpExdw9cIYlOCoqm5U0YUu48/9PgrumR1uicEBYr/yMDHDNj+Ya9lz1WOB89ogkRw6CwBngT6fDc48VUrn4XIAknydYkEA2M3LLRgMgLFJ8aGOEwQS6a/PphIItoFe5aSfEjpIEJzDjEcLFbDi0OfRrwhNbDDJwf3zEZnggPt9dGCcQwcDdHoX/ZbM/k2rvMiPB3U9lMt91sDXscGxdIxwLPNBcGK8c8IoRC2RsMTTbGW33LQ1OqRE/5VBXLl8HdijsPKWxbht7Uo899lHMDg4gjNnLuLcuQuo1Vz19nYBbbuW92zp7Z2PdWvX4Dt//VLb6/602px/7rx/bhkIt1LJuRkb3NJ2S+bH+Uzt+XOXsHbdrbh6daBhAMtzCmHYbXlgOG9//Bxi2iaZZA4MM+VkUvMW9OD2O5ZjzdrlqNdjXDhzFR99eAqTE+5hROu0rLKJVhML7PcnVt5Gqfm6ZMwyvEIpcMZMVULtWMhBW3bYRFwJOEsrr9XhVwloQEy5N4CO9nJTKMNUTY5NxDWJdDRVRhI8kTrCbI6F2hkhrgBGbswqQjPCFaHlTsrIqIWWKdtqzgCYZPqldKRXKQ2EZeLkzUpoECxNUQ/AtEOArgYpk4rpNUgsZ2RBsDTFrhhTZl8luIgghbTHTnnQQONDxbaekqHDtPlUVgJNhbA4El8e7UWWtQzaY3kzr0T67UMZdJYkOrHTKf8ozEUiIWDuNjwHrK8OrzKlEui/rJ0urwKrb12G29etwD3bN+DChX6cPnUBV/uv698ira7oQG5wlhVVbt5x/xaMjU7g/Lkr7a+/DYAwncrPWj6dNJ9xmvZpr0q5evVqCCHwsz/7s3j33Xdx3333NczzzDPPYPHixbO+b9OxvGuxUR5dbJ3OD8wyKSXOnbuI9etvax0Iz2DBrL6+Bdi4aT3WrbsN9VqM82f6ceDDk6hNCkSsighVVFi3TmlSBESM2ghaaaM7NKhs1AZHaGp5tPaLvhTa5gN7ILjKBSqRA77VSNg+wJVKYgFwpZJo5jMKc4G5YYbRJB+YSQbFTFBXcEguje/M9nP+53QKULqKsk3xQRjAhfncNIfLmG2nRGywDSZosB/kSVPedESMsMsXdvJw6RRcKfbbN/LpBIbJjycqBX49tjtRsGDYgmAvL5gC0lkg2JdDF4Fgp+WS+WlISuHSRZ03XKkwrLl9BW5fewu279iE8+cu4dTJ87h67Xq5H6DDluVHH9h1LwYGBnHlylUzZfqBuFZZ4Tz/njV9NgtazvnnzvvnthhhKx+a5XLqzcBw+rvTp8/hqacfwb4PDyFJWq9Ym5ZJ+5WwXREg/Z0PiIkh1rJkzXyuunUR1m9cjcVLF+DyhUG89+ZhDF4ftfvt30QEsNNGcWfK5WT2f12Ai4ObyKqCYkyDYOi/IAmPV3xC76t+EQAm4MUBCLNMZGhjCYBLjgpT+uEvHYBLRISK5IjjqovIRpFmebnbb/+BpplgDiYi8Gqiv0nn/VpJtQGeQjtrriSUV+xK1KuaCTbFsihv1xb64I65TZLIwHv/nIdsrpQROBdaZs0lmOAuf5wicpQvJcNlg6JZJHdOVYlOzDTfiRIwlt42qFJ0nlFet88GBz0KTfEzywrT9VPEBjcBo2GF80bnG9djnDl9EadOncP8Bd3YsOFWPPiwlmadPnkBZ89cRr3eWMSjVWuW+19k23dsxqGDxblHpfYh4xmYB0jyQHCZQmKzyR63MCbNXf5mtq985Sv46Z/+aXR3d+fOs3jxYpw6dWoW9+qTaa2PAdz8Z06fw+NPPIx9+w4iSab/PGjVaEDLOcftt9+KTZvuwNIli3Hu/GW8/tr7GBoYB2ccnFURsSqkLtXkfL95DkrlJKxUbBCMmfcss4oh9/5yRn/D9khZIJgAsC6CJRFFiX3PuUBUEeCR0CDRTLM1MjwmOFBf2fPBoBiDjm9yF/hNsaNkUrKGQbkPbq1SKeVbs4wz2BokzAxKSLlGjDn1UWZBsMAUD6MK2iSDZhr8piXRjKpIpwMDJj1GqShbLh5IoSmQ7ep6xNIFsmOPBU6kBr+xpCJZytbnoJcGwPkgWCAJ5NB5INj+LqnODeSjaZ44TnDq5AWcOHEOCxb2YMOGNdj92P2YmprCieNncObsRdRrNRvoDu/xFPs8Qxhgx733YP/+Q6ntdr6oX9Z6yqz3Rkqk5/xz5/3ztKTRfoR5tlqjlGGGaZ8GB0cwNjqO22+/BadOXQjmkUo2sMJ2eW+Ana6A7U/z5dP+8euiGBxd3VVs3Hgb1q1fDSkUzp7qx/tvH0FcD1mdosF8mhWG+ZywhAhbk0OsAbE0e5QY8MsA3ZKXKUTMbcm/GbgHgK0kGiTr1uuTZkdIAtRlABixnUoxJEkFEZcQRiIdVFusCNuTzzcecXAl7C/Kq/rcKMkcCCZ2lfJ9FdOI2AALWyCL8nzTINisT3HT4kByCJP3k7YgB1jpatAi4mBJJXCQVAk7K7rms8FlQLDuo9jYN9i/ytP5wRb0wivGkcpDssWx4AAy5Qan2WAAdjCXZYET9hxwuE/a+fo2PjaJA/uP4uOPjmPNbSuxfsOtuGfbJpw7ewnHj57D6Oh0cpXas1Wrl2H5iiX4k//jZNGhWqSxRoBvnYr+ZhXko0HMnHXefuqnfsq+/4u/+IvMeSqVCm655RZ85jOfwRe+8IXZ2rWOWbMgdZoVzhrMzRxzHM5/7doApiancNttq3H69NkW19W+0X3X09ODLVvuwB13bEC9XsfJk2fx+uvvI4kFOKuYc6PVYZLqczD9TFBMGH9LOhrnrSnYCDiJL6Wr+JaWSUf2r0SFUcEsVxHaB8GVigbAlhGuCFcgi/oER64qMgHAhhaAprClDuyaOiCQgAAYZwE7Sr2G2y2poI9TV33WAFfLkTmYqaViGHIPCFN/ZQb9Ps2eR0YWTeeJp9jg4JxQOykPBJNPV2aQxZiy7LftdqEy/Ln0wLDHBscWGIeS6NgosvR7DYBjqZ/2sZKZhbEEEhdsyQHBvvlgN8/SFaNHR8exb99hHPjoMNbcdgvuuOM2bNu+BWfOXMDRo8cL/XMnQbAPLNeuXYPFSxbhvff2tr2O3HnaaKU0lxc8O3Yj/XNHc4QVJNL9g2fCSsuklcSJk2ex8Y61ARCebl8xlxcsaIeCfZu/oBubt6zD2rW3YODaKD784BiuXxltafDsy64JDAsmIBHZ4llU4CNhHEzpUh26ADOzDKwAOWDdGinimv2s2ORgHRRlRkCno546x5iAlo1eSyBhDBVFhZ4cQBOKIzLMsAbAlQbHyzNAI4FVHXmWUJw7ebRk2kOpkBWGVBBev2KdF1yxrZOIDQY8yRdzhbGk5GAqMpHusGAG4wpMuaEMyaClOSatEmjMl0pX0wzygU3hkFhGmUxwGgT7VaWpWnR4bZhzp3yA6xXiMJFnpUw3KiiXU4z0cNSss0gSTRJyOFm0L8XKAsBA+DwQMsHZsxdx9uxF9PX1YvPm9Xjm+Ydw7eogjh09g6v9gw33dCu9x1uxhx7ahjOnL2JsbKLhOZVWYrST2pGuJk+W11rJ//5msE+r9GrRokVN55FS4tixY/i93/s9/LN/9s/wm7/5m7OwZ523BsBr5NH03XRtuiCY7MSJ09i4cd3MAuHU8S5atBB3btmMtetuQ/+Va3jr7ffQf2UAjOmaHIy5VmfpfsJOP0OpOR44AUcCZfyoZoOpAKWv1gl2zYA7/zNjsP1v022RfBAcmfc8kohIHh1JMA/4BUxwWm1lt2nAnvFrEhGU0n7QgujUPU2gmEsVBJR9Nbht60cyZsXs8XFSmplBDAeDsuCXQLFrKUUMcJWn2GCmXOGwDDbYl0Rzey4cCKaCmQEA9o5VmsJf1CLKBrVNYJsqYif2pUGwe8G1R5JhYaw0CLaMMBPwg85FTDBdk74f9tngIvOBtBASZ06fx5nT59G3aAG2bNmAz3z2SfRfuYbDh4/j6tXZk03v3v0Ajh09hYmJqdQ3xaww0Bkw3Er6542wOf/cef88Y+2TlBIGILrKqWUHtNmSjNDy1pde5syZ89hx711YvGQhhgZHzYOZCmAZsW7qxikCrMp7+HgTAQDLli/FljvXYuWqZbhw7ipefXkPxkanEKFamBvoWks1RvgIDAsLSSUU45CIchhhzQonvkTJOBtqSK+joOY9N46auaJajMFER7VcS+cL6627YhehnNiCPwuEw0i0tsb8RkaAlimA6cuRwLMSuuqzNIWwCMgypqxjpu91bq+u+CxTxaz8fYRhebnx2DQHZ8rmQxHQ1XlDUucPm1sl0jsNvziWb04W7SppxjKC3yYpkENTXrBhhSVg2eAsWTRddUFVaGQwwRYcp9hf2yopWxadtsDZek64FXCanndkZBwffPAxPv7oBO7YeBsefHgrpqZqOHL4NC6c70dWu4qidTdUgYUPAMLvuroq2HLXevzln/+g9DY6ac3ArpNdOtA8nbzidiwvWNLK8jejff3rXy8971/91V/hV37lVz6xQBiY3oCuaNlOgWBApy9t3343Fi3qw/DwSIvrbc1WrFiOu+/ejJUrl+PM6fN48XuveoxXnvyxsY0SjT2smJVpIBOpyEqjqT5HpDyJNK0Xfp6s9s06XzisgBwxV+DKB3h+rqsPgqNKAhZJB4K50h0cUnnBVGRSHyMDuDCSs8iODxhTAJNgnIErpYPGJp82W0Xg2FXq5Usst0AIhitWBk3zm7GHkYhXmdL1QzwgTCA4XUyMqmhXzXmpEutr2GBfEp0lFdfnQ2WkN7GGl0sDo7ZQkQXAGhAzI4d2kmhqlRRLZSpFm79KQhSA4Kx8YABNQXDDNex9n61ubLw3h4dG8O67e7B/fzc2bVqHxx7bhfHxSRw6fBTnz11smL9TppTEvHnzsXnLRvyf//1n01oPUBzwywPDtEz6+ZeZF5zOKZ4lzzfnnzvvnzvLCHvSDL/a8o1oD0IXsUgEzp69iI13rMUHH3zsvmOwN0KRTLqMLV3eh7vuvgNLly7CqZMX8eGeI6hNJYhY1UaXgbAKddY5yeolmpZcAy4ulhgQTAU7EiUtK6wLirj5NeerAXAFgDDvjc8xEVrttIViAFPgSgNfZpwWh65kLLgDmTIDbEoZIRHK5uJw40Alz8i/4dIUw/KiytTWyEieVeIAsd4TgCmX4yQFtxWjpQeY/f0CdN4v7Z9Q2nECACiniimbD8UMoyqoYJf5HaRitq2Sll/z4FxQ3hSB4ERGtp2ELp7RCIKVxwQL2cgGk7zOOmm6NoykjF5UFdrPDW4AwUhN84Z5JO/zC3To7WRHooukWGVSJaZqUzh48DiOHDmJtetWY+u2Tbjrnjtw9NBpnDt32R5oK0G0ZnbfznswOVXD8WOzJ8Vsx8q2XpqzmbHHH38cu3btutG70VHzWeH0dAAN3/mDwfblgcX3bb1ex7lzF7Bx4zrs2XOg/GrLFsxiHCtXLse2bXdh0aJFOHbsJN59dw9qUzonOV0ch1RW9J2C0LJdOHm0YhpcaNWHY+zo+anANQj28oSVCVISM0wAuNkZYj6o9FoBBYA4AwTzSOraHF43Br/vLosEoHTqlLKtERV0SRNTIMtjhfWpVBYoc6YgDShmUAFgF5IhMt8LaH+p2y+a/TDbY8qIvxQQRTCMsGZ8GSfJMwIQrAGwB4Yph9pIx9NsMPP2i5nBTqZE3DPpjWOk1AH2LDaYcoN9EKz/hpJo1yoJAQiOPRBMlaGb5QPr68XzzSkQnM4NTpsvi/anBfeAsampGg4cOIxDh45h/frbcd99W7Ft6534+OMjOH/+UkO6Vids1657MTI8gpMn8/yzTaJruq5mz6xPOjP8o2yd9s9tA2GOqJDd0KCvGAA3G+D6F2KrBbr8ZU+cPItnn92N/fuPII4TT3IppgWGly7rw1333IFlSxfj+LGzePft/UhiHUWmY7MBgbRMso0Agetp7MCwX7DDAhlw3Qye6VxhOqoKc4WzKgAU06CXKyAyjpobVpiD2RY63EikJdMS2nRhKTIqDAXo8y9JtswjRBVdzp9FKljWyp3pUjQgnL6TccUBXH97XkTbZ6SzALBSulq0MPuUJFr6FUUSQAJm2GBf4qUUgxCG3ZVOPhYpBmXaLygjeya5tX2pEAQnMjLOMmpkgpWrEB2+stngtCzaZ4MTE312UukQAAM0LWSD0yDYbY8cMM0VVqf02cvpmBASp05ewOlTF7F23WrctfUO3HXPBhw+dArnzl5uyhCXBcmMRbj3vi34aN/xae1vuM6ZKxyYlbc8G8D4096eoYwtXrwYf/7nf36jd2Pa1mww106+XHkrN+/xE6fx1FO7sX//wdaKWjYBwytXrbQA+MiR43jttXcQxwYAw923eYNlBWkUZI2ssH3vyaMTJhCpRnm0nycsDPCzAUm7rdA4wvZKzPylVkDEdEYW9KoABLMKyaIlkMN+Aq4zAiTXfs10TPBZYYBbebTLE1YByCTfTy2M9HliqCrpDVYcGGaKgSmGivFbgAI4R4UB3VyfGQ7YtlLUU7lKYJhLW0CsYoCvKyKW6PPinSOSRKeLhlEtEdsCUTIbXHeyaNMZQ0QNbHAsuQa9KlsS7fKDTU6wUpkgmPKBE5aUBsB0jWaBYLI8Nlil5iu6r5NE4Pjxkzh58jQ2bLgdO3ZsxdatW3Dw4FGcPXuhJQVXM7t/53Z8+OFHJeZsLpOert2sIHjOP3feP7cEhHVmauR9jkyl4eYXTN5gNSsPsFP5TLTs8NAohodGsW7dGhw/fkZvzzjArKhQs2I4fYsWYNv2TVi6bBGOHzuLd97aD5GETLhdl5IASZ8hwL15HLBtHRBrdjiMSOttaBetNy61BNh7TlUoSm3YYAbnoCMFSPs9bE6P5rPD/CbdDiHskcsU07yzkUhzLg0LKwLASg7UMqkeGFZJZJ1lFstr83o8abQrqpUGx/ozAVqByMi3OZQKgTWPvBwpoXOCpMl5BnQUXEqOSOi8LN+RCgPSlQHAsdBOUwTgN0MOrVzVzVbYYL9Ils8G0+9GOUg+CJYkkYZ2xAl0UQ7qVegzwXo7jWxwUV5wYUCrJKMrpMCZ0xdx9swl3L72Ftx19wZsuXMdPjpwHJcvXWu6fDNbc9sKLFu2CO+/6xxtVs/wZpY1X1lA3CyAmF6332+4k8x4kX1ac5DmrHXgS0B6JorF0L0ycH0Q42MTWLv2Npw8eaa1lWTs15Ili7BjxzYsWbK4AQDnW/7AWo9FfIVbBRICTHFTNMsFowUkKvYZyxAphUTpv4oUPGBakWNY16wgrkyVmiYASu/Dl2N+GdfR7DQIDvrlUuqRTG2Dq8y4AuMK3PgRyw4TCI60b2fQgFgqhgjUoYKBuhJBApwDseSaxfb8XwXav8lIF7zqjnSYwBUPc2wwMcFdkUCVC1QjDwAbuTj9DfKkiQ3OY4KlK4YVBrcb2eBE8BAEG/BLkui69CtFGzm0lIYNFpkg2JdCJ4YVpmsvDwDr0xoGpK2CIxWsdtPy2WA3LTs1UUqJEyfO4NSps1i7bg22bb8bd921Cfv2HcTly/3Z57UFu+OOdVi0aCHeeWdvySWag+FWn13TBb9z/vmTaR2RRpvsUTCm81bLDCoZi4IoFQ3yOOMNN2tL+5ITAT92/DS2bt2CEyfO6psDzsETGPZZ4axj6JnXjbvv2YDb196CkyfO4Z239yOOXd4rJxm0Jl1hqxp7AJje+8fdasXtPOAsIRGBW0AEaCBEMmkqoKXhKhWocv2DLRg2AJln3C82P5i2SYCYwBy4/VKaaGpkilhxzpAeh5EzloigJDcyLlcN2lW0NECQexsHgV6vsqMX3fWrNycigkAEISLU4y5wqQGtY41dESwp9XxxUrUgGgAqFWEHLJpNhpZCk6MUfj9g7uUPaUaYpFTKGwRks8KmHZIHgolRINDrF2AhZkGq8LMy7RkAmqYHaL4kWkFZWZaezwPAKTZY/975+UhFkqwik0EEW98/Z89cwrmzl7HhjjXY+cA9GB0dx0cHjmFwoLU8Qv8+fuTR7Th+7GxGEY7OWZHTbZbzS+fU/76TvdjLmkIjQ9Xq8nN281hhvm8JNUd7qofiedPrOnbsJO68c1PrQNiz+fPnYfv2u3HbbWtw7NgJvPHGuyUAcMZ+efJonUKlxyTM68WsvGdjwhJbwFIi0vnCpnq0rtGgTLoMTO6wU/JYUAxPDUQB4owBK/cBsQW+KYDMM0AwS/0einuA2OQNCx2E1mxtKI+26+dKd3mQWvJMvpBaC2Xtb2wKX8VS//XbBFJKEABIHqHCFHoi4wVZqq8yc0xwF09QjSSqUYKuSqLBcEXYStp+gSyqEu2DeP1b57PBSVLRY4ZMNliD4LpXIKuekkTrHGHKB3a9ghMoC4LrLLZSaFsh2vrd5gCYPssUcE2DYNUAiLPZ4LL3txACp06exZnT57Fp0wY8svsBDA4OY9++jzE0OFxqHVn2+BMP4+DBo5iampqRwNtM2I2oKD3nnztvLQPhdLsPMs503iXNU1Q12sqGbV9WEUzX73PymdrU7Z8/fxn33nsXVq9egYsX++0A2a7L5AxngeEo4thy53ps2rIWly7148XvvYWJ8cmmgN0yt0o7QC84ai0NiptZWiLJU8tI86CtILJn0ng6cB8Ae6wwOWeSR1cskA/30QfGPoMpJIewRQYc4mZJRUd/RQVCGMmWlHaFfi4vtWlwgy7D9KaKUjGlgMi7Lrwm91nmorwREqUZ4bqoICLZU6TBbSXiHhDmiJMq6nHFypwZUxCSo1qh/CFp5xfSc5wmouzyglO9Bs38GsR6AFjBAmShKMAQgmACx4lybHAWK+yuBXMOEIJgv0AHgWAfAOvTGsqz9HryBtPN78dW2UypdF/skyfO4+yZS9hy53o88eQDuHTpKg7sP4apyRryit1lWV/fAtyx6Xb8z9//f4377/ULT6cstKvaSJvP7KaL49F5SQNg3+ZyhefsRtpMywTPnDmPe++9B7fcshKXL/e39KyoVCLcc8+d2Lx5I86fv4jvfOclTExMdGzf2imaJSA1E8yiIHWlolwBJWVSlfxOAULqHGRSmxIzrH0Os3UooghGAdVmWgqTcHRtidmZCuTRxARTvQzOWcNgksOAXygIptsVagDMIJiEUNz6OEArpytMoicS8IGwBsMkfw77KhMI7qomVhJNlbSpQJbfL5gsTLMK2WA/Nzgx6q7EyKHrIkKsNAim3OAa/RUMNRHmBTs2WMuhY9MkiUAwSaGJBc4CwFngN/isQv+cB4J1+ly2isuto7h3cCC7lhJHj57AqVNncPfdW/Dcs0/g/PmL2LfvY0xN1bIuo1xbtmwJNmxYi//y1a+3tNx0LEsd01AnYZYLVc7ZjbGWgHBTlhc8AFClpYYpdphY4bwiHun1p51mFlhWUuH48bPYvGUDLl7st8v5uUI+GKb9WLV6KXbcdyempup47Yfvt8xI2f1TltQMzgrtJUejPDNv8O2kkuH3kjkJUwIBU8YCMDnD0nyS0IWxTG0MCKXbFwilLwjNSDpJtN4X50gcK2za/0jNWpF0WSndsxcAItNTOIoi18swBWT1Os1xm4rMtB0fLFuQLdxxk5SZila5wlhe7jDl+SAyVR8jCOEi2koxyEoCbj5LGaEeV2wkWCqmC2lxBiRmvzx5NDlOnw0WyrRVUByxee/Lov1ouAa0vtSZmVwyZtofeUwwscU0DcQwuLuAegWn5dB5IFiYat7SYw38Yh30GZie9CcLDBcFkygolSQCBz8+gZMnz2Pbtk34zOd24/DBkzh69Iw5zsZ1pMHx7sd34PLFa7h06Wru9uj+y1JodKroH2c+IEbmtrKeAa1It6drc9KrT59lsbpl2OD2rDU2GNCD6uPHT+HOOze2JLNcs2Y1du68F+PjE3j55dcwODjUdJn8+yiUWvr1SfKKZgkVNxTN0mJXhsgUzUqUskFm7S91G8JEKR2QJZbU86vUfk8px7Zm1r8wqUGNSise+lnFQ1a4CQhO5wkH1aNNMUlSRQEAKgmSpIIKYPOZqZWi4BxCakZXcO0bFYcrSmWOF5HOA9bFK4XNj9Z5wtQeSRkgbF4eCKaWUjyi1lPZBbL8c6eo24RJwRKJYYFFhCSJ7BggFlyDYOn+ahDsJNGJysgLpjZJLYLgdgGw/zkPBJfNDS5rcZxg//6DOH78FHbs2IrPv/AcPjpwGMePn0LZ/OEnn9yNs2fPt9GmKbxnp2NZz8PptlqdCZvzz523jlWN1hI+BHmkQHMwlzew9iXSeexw0boa+igyjlMnz+OeezZhyZI+DA6OBMtYdthcI/Pn9WLH/Xdi+fIlOPjRCZz2+hCXMWofReeDmGFAgxEamPgssQ+G/fOW5bjTTDCZLpXFvTMhwcwaSDJL+b6RKbpFrLCClvRE2icZ9tgVr3JrNCDNyH4Z085PcmbaLXFEUgIVgMWuSAVZJHhDro7LV/IHIqkbXmlnzFKg3OboejKnYN2K5MjcyqSV4OBCQUQOwBIwpkJXsckTlgThUg8QB4RdqySfDbYMbwoEu/xgWEBMnwkQu8+w7ECimAW+iQzZYP8V/lbKk945oTMN3ISXjyS9q8YHwdQuqUjCP1M9cH2FxtRkDe+/9zFWnF6Ce+/bgtvXrcaHew7h2rWhwnV0dVWwdftGfOf/vdYQdJuutSqN8u9l/5lZJsBAbNRMG0k0p7P8nN2cNvOVUFsHwWTHj5/CXXdvRt+ihRgeLpZY9vbOxwMP7MDSpYvx4Ycfd7wPsT8ALiqaRcE9wWJwxZEwDZIDVthT+jS+dB/5xOQSV+y8+neiDgSukrHLYfVrYSjpgrqQTBelVMzm/DKuGsCvMrU19HwFzzDe2FOYRxm/YyWBlBGYUGCmv7BVRTEOxfVxSVKTIcyNVpEuplWtxOBSP6OphRSBYb9FEsmhCQRXTDCbWkvZnsFZv6+fQmVqgmgQbBjgpGLHALGMUJcVxwgbWXTdgOCaBGrSVYkO84Kl6RUsGkAwyaEFixsAcDPwG3yXAYDD7xtBsC6gJVPTyrPBWTYxMYm33nofq1atwM4H7sWGDWvxwQf7cP36YOFyPT092Lr1Tvzxn/xlsK3yfrVzYJi2Tdaq7Hk2gtVz/rnz1vE+ws1Yk6wWQa1aXs5SmnFKX8RxHOP06QvYvGUD3n1nX/CdL5XetHk9tm3bjAvnr+DF776BWi02+976Re6zTEHOLUmfPJaYwHD6mMqYrtEoQZU0fTCshbgMEgqRnsvJoxX1B1bgkoFx56C5KZTFAFMIS5niaFrmW+FSF7+AAlBBpKR1XMSeAgDq3mErBhG51kW2tUPWufN7HdI0KgLifZ/VN5iWI/BKkiy97xEgJBjzou+cB22RCAQLM3iQTJ8DitRDhLlGCRXVoPYKSrdKIvacQHFsBgGOESZmHVZCTSywkLR/IQNsZdJwbDDJoinYIUHzugrR6bxghbAIVvp68otklTHKDy5qqxRso+SAPC2Bvnp1EN9/8W1s3rIWjz5+P86euYQD+49BiOznygMPbsXUZA2HD7U/WC6q2ty8TYOvrXBBLJkx8EkrQ8L2abMTmZ6rSjln7Vn7IBgAarU6zpw5jy1bNuC99z7MnIcxhi1bNmLbtrtx5sw5vP32B6jX65nzNixb0pfm3c9UNEup5q2UhGGORQ4r7ECw8sAvpRkpLR3mzLQrdGCyYuthmMrGEQMnkGyUUpHx2xwm9zfntKeLZQEAUoCYceXSlkzRLEQCUkQBGCb2WDAFzhkiqZnVCmfWL1r5sQfiyScDgOARRCRQrcZgwlTHhrK5vpFpFcWZtOwv5QQTCI4qIStMbHD6eJVithgngWCqDSJExbDAEWJRQd3kB9dNcay6J4mueVWi6yLMC45thegEMURQGMsxwkaVpeIGAFwEfoFyANhdt84/++ssC4JbsStXruK7f/MD3HXXJjz99GM4cfI0Duw/lOufH310F4aHR3Ds6Mm2tuf2dWZA6M3GCs/5585b20A4a6BbNg+wLBhuxgq3U8Tj2NHT+NyPPY4D+3swORkWzZk/vwe7HtyO3t55ePONvbh6daCtG8CBarO/5rrz2ToLij1AHE0zL1C1+DCQSkueE6WpYcY1+ZtYYpyZ3Bqis3WrJe3UNSPsD0EUBwTjiEwFzYBBNTNKyXVlR7+aI+DaO6TYXsA5MFst2nPWLrLrCmQFyyoWTFPQ1LdUuoCYUjSPROJVsyYGmY4h8gtsKF0UjFhiP79IUB6Xot6RYUEsArtUKIXygf0BEjHDWQA4aJkkPSbYfu9k0Q78apDsS6IVZFCpMg12/aIdrYDbmbQ0cD565AzOn+/Hrge34vnPPoIP3j+Ia1fD6DMDx/277sae9w67aR1mhVu1Ipl5us1a8J0S0w4iztmckTVTWrVmnXk2HD16Ap/5zJM4cOBQQ55hb+98PPzwTvT09ODVV9/AtWsDTdc3XYYmrCHCjULFqTM05I3BEWWywjzFCsdGcUV/I+aBY8kgmGOEheRIWIRIGmaV69Z/tg0Qk5AigmQKjOmaIIwpSOFSoCin11HDxr/6wWVimFNA0Z5Dv40SM+0YPTCs94WDcwUupQ5McwnO9XorirtWhIpBen7VD1ariGOSC/RU64giYbet+yibwldUEdqA4krFtUmyTLBXTbvx93S1R6RwKi5qq+hLouOkgthUiK5JXxadI4luyAumCtHa58aoN4BggSQIhuYVvwLCoGiY3ysapnUaBLeqIpFS2vZKDz+8E5/93NN49509Deww5xwP7NqBV155s6X152wVM91Sac4+ndYWEPblGnnRqtm0oN9wEzA+Pj6BCxf6ceedG/Dhh4ds9Hf9+jXYcd/dOH/uCt58Yw+EMMAH7fUaDvOP6UHlivIIRRJoBPutuaP8ytV525JMgisdlSZWOEJkCmTpjXAvgVuaPsManBuwKwn0+qb7CUumQTFXQCL1XkXMOEYDTKvmKBQHFGPmd3CXmKoxVCtJ4MjIaXFPxuSD4eA4RSPj61eH9qcDsPJkAsDSSIul4rBtK0zBkogxMBbmQpODTk9jSsvJhNmWZYIDRphbtlkZ+ZsDxLCfCSwnngyaimH54Jd+JQuEJbG+yqwjmw2WAJVygS+JTk8BQmm0n7PkW6ck0NOpDO/bxPgkfvjK+9i0eS0efew+nD51AQc/Ogkp9fp33HcnuioVfPDeoWC5rGJ+efdamR6+ZaVcNkiWYoP9FA2aL2/5mTa6jqaz/JzNWTs2MjyKK1eu4c47N2P/voP2et+4cT3uu28bTp06i/3732rab7gTEsU0E+RSpzrHCseSWUAcMQ2YI8lNkSitUkokRyQjcGHkwaY1ITc5uGQRYH2SUgI80j5dGTkz0Ohbw1zjRkBsjSswKcEj6A4QABAJDaIZM62XJDh3QemICllKjihqDFj7wWpAA+EkEphXrVtptD82sFWguURkimBxUxCLR8KrEK3yC2SlQLBIKhBJZItdJkkFdVFBPdFMcF1WUDOssP6sJdF1TxJdE7BFshLLBusK0XUkiD3w6/7G1gfY9xmgtRn49afLYCyeDaZnAwT7NjY2jpdffh1bttyBp59+DMeOn8RHBw5b//zQQ/cDAPbu2Ve0mmmZn+v/abA5/9x5axkIp3MW0tOzrCyLMa1CPAW5T2mnePjQCTzz7CM4dOgEkiTBrl3bsGLlMrzz9j5cuXzdrk8vW54RTks4M4/HvwqZA8QM3LLC+gFZTgrpcoL1PwLR6RxilgK4EtCoCwA3YDANhkl+W2G6x64FyYY5jhQDl+GWImboSS4Bqct1JSwCEkBGGpByEZlef66tAUV+0y0O6C/njbevDNjgMJ/YMrfC9QH05yPGlhEry7QUnIHOCW2/8ZwLT7JtAbBk8PsMCqkZc58NJrCblkdbdpck0pYVNsep3GXjimaZfsEmIu2zwcK0ahAWyuqotDDyLAqc+LAYQFAsK6twx3RB8EyCuOPHzuLypWt48KFtePrZB/Hu2wcwPjaF3Y/twAcfHESSJIXLlylSZ+dtUYnS0Fu8yXkoYoVnw9KFedpZfs7mzLdWBtMHDx7F008/isOHjkMpiQcfuh9Lly7G66+/gytXwmJ3N6KaulQSEaOK0dNnhYUJLseMaTBMgFhpAMwYEAm9zcTk4HKuggAtGVcMlJOkJNd9dJUrVqKyUpE8f5YJgt3KLRhWioFJGhfoADExy/668tRaaXmnVAyKR5iIBLq76oiUPga/aBcBYuYBXgLAtm1UBgi220kzwUYa7YPg2Mii/SrRdZsTrKXQVCWaJNGJzMgLBhXHapRDE/AVKg5Y4CwAnJY922PKYH/19GIAHH43syDYrUPhyJETuHSpHw8/shOrVq7Am2++h/HxCTz66IN4+60PkCThdtoDrfnL5KU6tDK+vxGtkjL3Y84/d9xaAsJphjJ9s2XZdKV80+0rnGXDw6Po77+O+++/B4sWLcD4xCRe/N4bqNXqHXGsxfvrtWcyIBhGagUFHV1GMSvc2DJJBvPDgmEG3d2QgTMHhZVSYIzZX00qZYtpKQBSmvdGnis5dM6vAckcgGA6ms0Nz8yYmZGb7UuARbpFApcKlCNJLRekkhBSOieWArxpgCxlOF/QbsJztvac+C0RCKwG165hfK1SjGtG2EyIuK7PzKEs/vedt1/0I5GRze9KFPeALzOVo50kOnzfyAL7IJgYYNpf/VsTMFYhKwxY1oEk0WkQLJkKJNHp9kj6+hH2mspjLclabYnUKct7JoyNTeCHr3yArds34ZnnHsLoyATm9/bg3bc+LlxfK5Xap2tUfbrovM7ZnM2WdVYe3RkbuD6I69cHsXPndixbtgQjo2P47nd/YHOBZwP8+oNeXx4N5Ypm6Q4ToikrnFVBWoNgZv9GTFeQThhDwjgSphCb1kEcCjGLwIQDhjzJ5nW4EnpQJwVgUolskSumgIxClQAsAHYMsVfUj+vIq1LMgmHGtM+0HR0kM77frYPAcbBeD2ynVVySR6hUEnR31wIgTPvg+iTLbPCrT0CwXBEI9plgISpGCh1pRpgYYPPy84Hr9gXUJVBL5QX7xbESJhrk0EUgON36yJ6ftDIrp/p71pg8jwX2p5m1BtvodFG9kZFRvPT917Bjx1Z89rNPY3xiAtWuKt566/1gvk6D4DLmnxMi1W4G0Dtns2MdL5blWzqqBcycEyvLBpONjY7jx//WM/irv3oZB/YfMeAwzaLmR4taBef+jSaUi0TZPsNUQdbANZ8VLiuRJnl0eAzZS6bL2uvOfYZZZHpJyfSWLSAzUivOGJhx4K4lhAbBTBl4xDTjqmCKS0kN74mRpSrNFgB70W1qv0AyKM4VOBfB/JbZldxVjfbzjTw5NPX3FXCFOgDlHLZhYjl84B0hYlpobLs2gSTXjgmOrSyaGGEqqKVBcKwcMxzLRnY4zAtGAIaJ+XXSaIq+E3g2gFjBAGBoFtjIstIgmKpUBiJpFvYW1OcjTHfIA2s3CgTT36x7UCmFj/Yfw7Wrg/iV/+/P4NjRM2YAnX3/TOd5FKRkZDjNolzfIitig2elKqUnx293+Tm7OW2m+wF3ysZGx/DCC8/jm9/8axw4oPP7Z+LabzbYzWKN/FZKjHH7jCxihbMqSMdS+/9EaVaRW2m0STuCfmpxKDARaX8oGodsvs+LDPiMIq6LWRlAzBhvqMORLlSZrhydZp2s7zWzMQgNgIHg8UoB6ch+Dmt9pNdN+y95hKgi0NVTA5fCU2U5MEyf84BvphUwwVQci9olxkkF9SQEwHXJUReuUnRsAHAcvFxesIBEDGErRAskSOBygn0QbItkeWPlopxfe/5ywGwRA+wvN5sA2DcpJfbuPYD+/mv49S//f3Bg/8GgiNZsg+CbLQhYxub8c+dtWn2EbeQkgxnKAsH0Oc+hlWWY0oPPVm5cxhi233sX1q9fg337DkEkogEU5pmfH5w1EC+6qRr6GkNHnDTYdcCXJNLM8L5pMExscKNzdkxwMwsegd6xEyssASipJdF6iq4mHUuAcYaKorxUXfWSKV25kpFUmgPMFMtiptoyAzN1OrREikMXAMlzYBTxrUbCVIlkgOndy5kKQLBtX0RVmr2BgZ1PcQhQD15mHwa2h6F5D6W3HTEJcHN9SSeZVmAWAMcyMi2kXG9g5QFhesXSVItWpsBGCgRTOyTN6jpQTMwv4MAv/X56Hv1tYuTQQjkQbMpwhHJowwb7ecE+A5y+ntKArUgaXeYe7DSDXKQWWbRoAcbHJnC1fxCPPn4f3nvnIOK4WB5dJInOe27lOe4sMFs2oJVns8XAkzJkOsvP2SfDOjMQ7Nw1yRjD/fdvw2233Yr9+w82zQW+kUbSaB3glaVY4VhJcOn8e4UxxCY/mEll2GHNsDIDjLmkoHnjnSUVQxe9lxxKcXAuNBMsFKKIe1JiGQLHHHY4y/z5GJQDtZHKmC8l482QW2dJOzUQTiwQtuv0g+Sp/S3MebbTvLQoWym6ogtkecWxGvKCpQPDsWKoC8cGExhOpArygmNI1JHYCtExqyNBAkGg2IBggTiXBS7K+XXH1Ah+G6Y3YY3NWc9db56l61m0Y8uXL8Xw0AiuDwzh8Scexttvvd8gj75RdrMHC+f8c+eto32EyexNnZJ4MBbZ+UgemDe4KzPoa5UFjqIIDz68DX19C/D977+BeT3deOzxXTh69FSJQXLzmz5PZpYrIdcY00aXsyTSPhhu2KcU+0vOl5n/db6xHn4zUxxLqDBDwL8phArhNIPOOeJKQSiGSDmwpkGcns4lkDAGJrkuZAEN2rTzURo8QrPCiunBjkB2YSoCe4xpebKQHNXIndMoElCmsjOBYOr3F+QBwzHEJIMWnAPmc/j70Plz+yCZGdxwWMm0XieCfoLE+CZ2u7ROArwODAvD4rYCgn1mGAin0+9FIDhR0rDCzUGwL4nOqmLcTkpDFnjupJW5B6mg3eNP7sTePYfxg++/i10PbcVTz+zEW28ewPjYZMGywt5n02lblAWC03m/ZWTRNyL/EYDNoZ/O8nN289nNPsCrVCrY/egu9M6fjxdffBV9fQvx8CM7cezYyRkBxK2yT7ZYlvHbftGsiHF7Pys09hXmiuvSWQoaFJu+wbFSYKZgFoP+q1lghrqXBMWYAstgg5UpQlWJOCqVBEoxW62Zc13BmXOpc3sz5MRKoKGoVBpcZoFP1gCAZfB9OyZ5hCgSqHbXAyBcxpwEWh874wpScMMGM9smiUBwkmj2NyyOVXGyaMlTbDAzhbF0cSxqlVRL5QVTVjBJoaW5Fqg6dB4I9lngdMVnfXyNgeqs78qBX2A6ABiYvm964smH8fY7e/DKD17H7t278PzzT+G1197B+PhEi2sqWbw2R+rseoVnj9tvRnn0nH/uvLUMhFtlJbLalNDAMquNkn+D5YJk7+Is69y7u7vw2OP3I0kEXnrpTcT1GONjExgaGsGWO+/Axx8dLbWe8DgaGan0jZVVtj59DHp+l3PkS6RDMEyfG29OXTSL5NESknHoqsHSyqMTJcEzqj8RK2y/M36MQ/vISJHM2ckqNCtJkmJYaTQ3kmBuCmrpHdb7QPlKeZFtArB627pvoOAuD0mfQ2YdrQ+CqcAF5e3qw0ixwwUPtaCfod5bKKVzfml/ielNjJNMLDPsscF2P4ltDiuEtguCg2nQLLDygHBYHEsFIDjdJsmXRNP08LcIexmmK1o2s7wCH9O1slXb77x7A5YsXYQ/+l9/gyQReOetA7hn20Y8+fROvPn6PgwPjQX7mnbq0wHzzZjg6TK6s9VLeM4+/XYzyQJ7enrw1FOPYGqqhpde+iHiOMHExCTGxsaxefMdOHToWMe21c7gNmtQnC6axaDBMFPcssICCTg0II6U7uEQQTOIXClwqWt3REz797pk4AxgkuuqyDK9rxWzbdeRgIpQUp0MzoUFwFEkwbkICky5HFtu839dcUoJBRYwx0qyXFl1bvtD+r4JyA7OJ4vAKwKV7jq4EpmsMZBimNOFvkyRTiloOoeSThmmVWS8oThWnJEXrNlgDYDrgme3SvL6BeflBUuE1aGzQHCaBc6SMJcBv/6yjd9lpRLl+6OZUh/t2LEVfQsX4rUfvo0kkXj99fdw3/3b8Nxzj+PVV9/G8PDIjGy3CNjejLUS5mz2rG1GmCPSD/2Cas1F1imZX5ntz5vXjSee3IWhoRG88+6HUJ5I/qMDR/DEkw/h+PHTqNeyWeGiwXfZYl4NDysvukytXJQSJtKs2UhposNafJUBfr2WSVQ5mnmssAhubL2GIhm4ZouZ3S8BB4IJjOn9dKCNGyAMI+WKjdw5JgfOASkZIiYhwMJcYOaxsSkgDACRlOiKwoeTUsIU23LFsGyVR+HJo9HI/ELpQ5OKwvpuP2CO0TpfAu3eOqjYFQHhuuBW8uxXdra/j2psj9QuCLbtkQz7K817DYodCKZewVQYK6tNUpalwV9mTvAsO01yTK328n76mV3Yu+cQJiZ0n3ClFD4+cBy1Wh1PPLUTb7+xH9euDWUu22kQHKw7LW/zAgz+8sQepyVoOnBYrFzphElMT+x6c/OOP1rWnm/OZ0lmwubPn4enn34M164P4P339kJ6/vnA/kN47LGHcOLEadTr8bS31WmGhxRcfiulNCssmSkOCd1MKVa6T4TfTikhAAyPEbZBaz/wr30K+UXtX7hRTum/lYhDGgAspe7nG0USUrqKyzKj8BSgU58Y0wWxABhFVCMIJmbZTveAL61XTwiBsv0dMiTZjCmwikTUnYCrpEFSbT9b+TOH4gaoSw5whC0WvarVThIdWRCsAbAujkX9gmOfCZYcSUarJMoLppSkxLLBMswLNhWihYrhV4ouAsF5VZ/9aen3er7OsL+zkXrzzLOP4e139mBqasqM3xX27jmAqakpPPPsY3j9tXdK9Qj/Ue0ZPOefO28dkUa3w9B28oYrAsPz5/fgyaceQv/Va9jzwccBCAaA69eH0N9/HXfftQn79h1O7WNjoYysVig+UCZQXDbClOWYiRWGggXDrniWY4kBwPoKy+JqBtDKtyyDq0BVpNOtlPz+wtyAMG6qSmugBVQIBMOwpzDyZLszCrHZWU7Vow0YZkzZQlNZ7YiAUJ5MrK9MzWyLdcBFw2MPBPvVm2ldwXEyZtpL5Jv0WF1486og51dXkEwo91eyzKuZGGJXCCsbBFP15yIm2GeBE9szWGlgbIdeDbxv0CZJn5NGNjgrn98ywMQK50Smw3OXXfGy2XKN85UbgGcFoO6970709s7Ha6/safjuxLELiGOJ3Y/vwLtvH8CVywNNJV6dYmCzWOGsnOsshnq6ucWtWrq1STvLz9mNt+lIoWcLBC9Y0Iunn34UFy9dwZ4P9jd8399/DQODQ7jrrs3Yv//gtLbVKRBM4Jfk0TSNnpUMPJcV1sUkdeEsJmH9MWdaIs0AU4xSwYXB3XbIyE+KiKNLJVYiLbgOBlekQMRDQMyo+KRUliVmnIegWNF7B4iVJ01rkE0bEBwAYJJfE9hlKXa4gSWWdj4WCbBqAqYSfXr9XF/JQJW6lGR6O4pBUtDQ9FWmtRMbTJJoKSNbHMsGz5MKEsGDvGAKdOe1SqqLxlZJCUTQL5iKY9F1EairUiDYZ4HbKXyVnk5XiG95z4LZAL6+PfzwTnR1deGHr77VcD8eOngM9VodTz75CF5//V3091/LWUtr9/HNKG+ejs35585bR6tGdzoHKX2T5jFDKoeZnjevG089/RAuXuzHhx/mO9GDHx/Hs8/txrFjZzA5UcudL7NYTio3mkBxM5aYmdyhPPPBMFgEK5X2wDDgHgmCadCeqAQVVKxEOjHrUOCgsmZ6OZIgcwgzTbthPU9iiqAlSiFiBNoYEmlkXMYxSjBICUjGIGG10GbndI5SpUAO5TvW4AZVrkchpwrSogJqv6TPkWldlNHCKM0GMwZIxVGFru7MlbJA2eUzhxLnoA0EYNlgKn7lS53h/TbuNzTnTerz5INgEXxXDII1UxyC4ETpJkkSgN8mSUE5NpiFLHAeCM4CZv60VtukFfU8zLPCQnNeQKpIHs05x1NP78J77x5AvR62QiPVxdnTlyASgQcf3oa339yP61dDGdZMSo8zAw1e4T8/jziTKZ7B/Os5mzOy1kGwD9jKW2/vfDzz7OM4c+Yc9u/L988H9h/EM888jqNHT2Jqaqrl7QBlB8PN50nLK/XzCDatqYgVFirRTLCp38FBjKKTSHt7DO0hGsGwUgxdxl91QRhWWKLKGSqRrqshIt2VwQfEjEktl+b6GKLI5A4TkDU+N4qEB3yzzqXLMyYQzCMRAmAmA+DLCsCwY48BFknwqmaE9Qn2il9xDkiSahMgNvJtMPvX71MsjBxaSq6LYnnFsWJBvYLDvGAf/KZbJYVtklyrpNiwwJLJ8K/JCyY/msUEAyEIbh8ANwe/nQK+7QRnKxWOJ558BG+88S6EyN6PEyfOIEkEHn/8Ifzwh2+nmOHWAW2ZqvDtLjtnnx7rGBAulTtopL9Z5bsZi6adU+hfuN3dXXjiyV24cuU69n14uHCZ4eFRXLhwBXffsxEfvP9R0xucZQyWlXGCQPbDplmrlWBdqYdMXr4wED76dI4wgfAEYEBFVZCYYk9c6c64ejktz9Lr9qKuBjBXoFvxUKEsIXXvYAJzjH5IboAiC/bC5jErpiDywC60E/MLgjSY5PAvU6kYItsXT7OxcUYf3/S2GBSgGCrKtD/ycq+E4gZ4soD5lSlgLLzqz7F0FaD9vRbeB9fmiFn2l0Cw6x8cAmD6TX1gLJUbMPlSaOoXTExwKIZ2TjUvLzgNgtOAuGUADImi3oedsrwg066HtqISRXjjtQ8zQTDZhfP9iCKO3Y/uwJuv78fA9eGm25xutWeyopoHue2WzP0/W6zwnPTq02Htpi3NxLrSy+uc4Edx/tzFQhAMAAMDQ7h0+Qru2bolkzXujE3v3lIqbKWUxwpLRioeXTgrhi5kqdlhANASaV1ZuhEMS89X0X0qJEOVS4iIo6oEKlxqVpgLCK4BcSQ0AOZcQkYCnEujvGoExIAGpxEkpO6ArCXSaPTPFhBngWADgOk9rTdXLs0Ma11NwKiljh3ocDCpoJgDxEBkQLFu26ik5/OlzhlWJl9YUAtF85cYYVfnQ7+SjFZJ6Zeu9yGNH9YB6IQJ+zuTJDorLzhtJIfOA8GtAuCZBL/Ttd27H4KSEm+/1ajW8u3MmfOIoghPPPEwXn31LQwMzEzOcNpu9mKCZHP+ufM2o32Es8zlwTaaD4aLbt4065qWIVcqER57fCeGh8ewd0/oZPMA6ccfHcNnP/c4jh45hbHRySDyW6ZIj5/ny2B6AnvyZIUwkszADSscgeeww8QKZ0klKZhAfoKZHOHIuC89TcukK6qipxDrpJhlghk0k+vAsAaxCQAoaXOZYgWTz6SdA7iRKEsNshWhcADuaLTj94tN+UyrPm8KFaaMJCzMRVJKjwiY0mBXs7oMkWS2hZJQ2nn5INgvWmXPmeQA46gohlhxi16VB3gJTAvvs1JhwSvNCrtewArZgR2/WFYWCHZ5waohB1v/xq4wFj34ikAwscJpNrgVEOwD4IC9LBGgSt+vxeA5PW/rATCeujcrlQoee+x+vPXGPi0DpGFlRtCKgePcmX5EUQW7H9uOH/5gD0ZHy1erbKWaZEPLuVRthDQIvlGVon1TXlCm3eXn7Oa3tN/M7cLAuP2bP1hszgrT8tVqBU899QiuXbuOvXsPlNrXjw4cxmc++xSOHD7ecmXZdpidtgpqQcJvpWQlsSZHOGEJuOKWFdYtBaVWDnOusaL0ilbqPQGBYakUFNcVQRRXVqUlGIdQEhUlIbgGxQSCq1xAGBY4UhKRdJWkfUDMuSm0ZZ71HKZGCCQU42AQGnRGOiWKQXl5wR7bmwOCfcm03kCKIQbAmPH3FQHGzHUpuQW7dpQiJRQisy1m84L91knK5E1rNjhyrDABYBGhnmhGmNog+q2SnBzaSaKFcpJoARVUiaailK1KooP7LwcEtwOAC8FvEeCbIRa0q6sLjz72IF5++XVI2QyKcZw8eQ5RVMETT+zGSy+9hrGxcvd8qy1Vmz3zgFCJejNUkp7zz523toAwDeaoYBbQPGIc3JiUOlJChpgeNJepGvvgQ/cijmO89+4BZBWHyrqQx8cncPrUedx77114440Pcgek/j5nzsMapaE0r1/8ppWbKV04yy+k4wNi050Xft4SQMW0vMemB4gBJ492oFihgggSDImSiCwLGsYwJDM5zEwDYnAFSKZznIxTlxx2OxS9JjCs2WAGwTUrHBFoViYWzpTNMyarckAwDr+dURYI9mXNtG0lOapKOzyIsNWRL3+m9STKY4Mpv9cDwcJ7IPmyc/pcBgRn9QvWv502Wx1a+eBX/00gQkDMQkbYfioJgv2odF4fcHsJZagfygSx2gHB6ZSIdD9vAHjiqZ2IE4H33/u4EEz6350+eRHz5nXjkcfuxas/+AD1Wgy/hdJ0zYWD3PqILeJwzHsRCJ7t/GDAXLt50cqSy8/ZJ8saB9jtXHPNwTDnEXbv3oXxiUm8996Hpdc8MjKKs2fO495778Fbb73fxr4V7lVbS1HBS6mAyAbmhHtvKkgLFoODyllq9lADYmGCxlp5RQFmrSL2VVoaDOv7iutnvlFqdSlmwLHxcUornKqKaWAsGaqmUJbgEtVIe3pufFwUaWCmi2KmxlqAbVUIEQGR6U9MM0gO8IzndxEITgNgXwXGlD5sDzDT809JWDCsBx3Kk01zw/5qBpjaJTk22FWJlopbSXRi2iCSJNoHwIlkmVWiSZmVVyXaBqCbSKLThbGageAsAFwK/LbKctL8HQZ7zz3/BCYnp/D+e/uazOm2e+zYKczvnYfHn3gYL7/0etNieQF4bWn//XN08+cfz/nnzltLv6I/oMsa5GVZpozY3sjtS6Hzmo1v3bYZfX29ePutfVBKuWi2L09UMhO0f/zxMSxbvgSrVi1z8+UM1P31cRbZwbMGuZFhhTXj6++DD4KJDc4zbtbT0LYhI4qYZ36OqAM/5i8xtRY4OaBF1YcFlImESsRSIZEqUypE7YFi+uvl0QYv5YpMkfyoLrjJxzHypFRhqthEa+siQk2Y4hYiQiK4iegaVpiWk1znDxsQS71+YzPPlIgaX5JjSkR2X/xiWA37bwEtM8ECDXrpAUWf0znBBJzTIJjeC/teA9+G4limWnSmHNqAYGIg0iBYIIFQsXXItp9hDgi2EW0lgpd/LU0HBOuoeVbAKGp4NbOFC+dj10Pb8NKLb7fs9w99fApDgyN4ePc2zUiUtGYR5zzw6j8vOSJwRPa5QJ+znrNzNmftWOsDNZn6G17r0x343bvjHvT29uLttz7IDFIX2YEDh7H61lVYsWJZ6WVma6Aa5nt66hrvmaxBcGLYQ93rPUaiCy4pacAW+Vf9ty6h+9ZKhroApgRDXTDb1mdKaL9Vs75M57tOJRXUkgrqsoKpRE+LqV+uaRsUm366VEE5ERGEKSqli0u53rtKsUBurKTHvlILIypsJYufo5kguGGmJteGKZzltukBYBHp9wEbrCXRcYEkOjFVohMaxygnjRYSRhmn84ITKCuJpirRpM3y2eA8SbS9VjLygPNBMIU7EPjrtC/WGnHZOggOdqw9/5ZlS5Ysxq5d9+FvvvODJnM2rnP/vkMYHR3D7kd3Ffrn8gqtkN1ttGmcs0+p/fCHP8QXvvAF3HrrrWCM4Zvf/GbTZV555RXs3LkT3d3d2LRpE77xjW/M+H5Ox1r2ElnA1zIaLASH7eTatsMSk61Zswp3bLwdb76xF3HcvM0IAWJ61esxDh08jnt33J37IC4a8KfBsG+McfvS87TGOtGAOW1lHkjpoknhdyoAxAABLQe2EiU9J+DAsAPAHiCWsBFV+itU+EpUCCxrpiCFi8ZSdJY7MGz+6uhtFDgxYdhgkQLR1PNX0LxmWt0OINyrLrjdj3qqIFaS6v+rga0DuwpOIu2zwArZOcFpEEwgl9ox+K/G4ljud6HfzAfBtm+hHXQJW7RDQercpVQ+cBYIBrIDVQ2A2Puc5/ClkrmBq07YZ37sUVy+dA1HD51pa/k97x9GFEXYuv0OANlS8HbzrPIChTYolhNQzM0XnoWCWXQdT+c1Z58G6+ygcN2627Bh/e14/fXGYjm+b8yzqakpHDp4FPfdty3z+zLrKGNl1pH1PAifiw6cUK6o/4wWNtSstTpCyeCZ74PhRGkwTP5pSjLUyHdJbsGw/6pLDYoJBE+JqgkmV0y7INc6yLKlJB1OogYwTHJjvxCVBscGJFO6E1V19kGqbx6I9rsyNJ7MnO9oPwwoB4FfFbLBBOCFMGlTiZNECzMmoHGDA8XG59vxCY1rzLiH/LLxqoJ8K3NB5zQbTL9/FhscgNlSIDgkccqAX9rbolf+b1CsBitjjHG88OPP4eyZ8zh27GTBnNnrVErhnbf3YF5PN7Zu3VJ6u+WtGeCfueKZ7dps++fx8XHs2LEDX/3qV0vNf+rUKfz4j/84nnnmGXz44Yf4tV/7NfzSL/0Svvvd77ZxtLNj08oRzusF3JgHFzmtZwvrARpzAdNGN/K8eT3YuWsr9rz/MUZHx+H3Qixb6EMpiePHz+COjbdjw4bbcOrkhdS2mssUda9P6n/qcoX9B06zm8vmNxo2uNkgmQC4zj4qd+NqubRXkIvpYlr+dJJJC/OXCmdp9ZIKWhExUKEofcCc6dLWipPQTp89yrelvFih9NEqbto0MYAxU5kagGAMEVOoArYKtfL6Hvo5vkXVnoVikEIX16obcKvn0ftPlydJqWn/AAd6FcIK0P7y+ncI1+dXh/ZBsM8K6/0z8me7D8qug1okaSm0Y+wtG5wBgv3oNDlmP1dJ/7bFBbHywK2VRJcoipUVsMrKdwrar6GxZVmR3bpmBTZvWY//8XvfzPxeF7EpXp8QEu+98zGefm4Xrl0dwuVL1zMl0un7388XKi50FUqji6xT/dWnZSZoM53l5+xH1bLl0b2987Fz53a8/fYejI2NA8gGnEW+mjGOo0dPYuPG9Vi//nacPn0ud77yuXxNgvUtDoSpfkdk0o+Y0XgAsLnCzAYqtURab0fnC+vqk9IUhGImLYZBcj/gyiC5Sw2SxsdKpXOHKQhcMZJpoRiqShq/o2ts6HZLwuXVmr9RJAEkQAJUvNEhFZdM/7o8kno/GXfkATOpTZwDCYCKkTUL0/LITxMLTr9pT6FYyPQawKsSDch9NliKCDASaGKDheQQBswTyKcCWVRTJAykG/VZAwB2gX5SayXKkANeALoMG5xuleRfL/Z9CRCcXiYLrLYaaG7V55Y1xjjWr78dGzeux3/9L/+jYM7iezBJBN588wM8/5kncO3aIK5c0W2VyhaftftjU5D82ggNV3SpdWWvf5Z89yz7589//vP4/Oc/X3r+r33ta9iwYQP+43/8jwCAu+++G6+//jq+8pWv4HOf+1xrG58lawsI+7nBabNOKACBMiiExTzm1C6XBs+pz0USYsYYHnxoOy6cv4ILF65M66ZWSmH/viN48MF7cf7cFSQJRXUdEC8zuG7Yx1YZ4DSjjEZAnD5/ZcwfzKfBMICGz2SUo5ooU9hKMXBlcnztTNA5TjDlpSNqreQVfzQgmApS6X0yvh8aEDPlALBLBdJy5yqXjUCY/hrgG+QFK5fTK7ne17rUVbB9sGvPjwfu9eDCsb1+BWiaTsZS06hPMIFnqg5N26TewNIDwdLrDwz72XyHnLxghJ2DySkLphkIoTz5M8KCWP41obeXbumQ5WTzH/RFLcOKivKkB63kmPOCR/69+LnPP45DH53AlSsD+dsucb+Oj03iww+OYOeuu/HS995FrVYvnB8o74DTz8r0cy7rPeCes83ST+ZszmbGXJGs6RhjDI888gBOnzmPS5eulJg/LHgTtCuSCvv2H8R9923D+fMXkSSiYZ7y+5wNxMtYM39rc1t9YARh2xlyxk17Q6E/0zNCmf0yYFgBxkl5fokBuk2QguTG/0H7TKEUqpz8ifZ9VZPnKxXTOcxch7elYqhSwNPv+4cK0mDYf35mQQfj+h0wUwoMwoFhzmy+sBIMVj7tFctSTINalUSwj0zD9MKCXgYlIsjESKATw1yLCEoYACwi2y7JFsmSVC2a0qkiK4cmNtipvpgHgB1L77PBSv+apdng8NpI9QvOUEu1CoKnq7LqNBim++jzLzyLfR9+nGqD5Fu5+210dBx7936Mhx++H3/zN6+gXo878FyioFtwBTdd5tNiIyMjwefu7m50d3dPe71vvfUWnn/++WDa5z73Ofzar/3atNc9Uzbjv6ovkWYsagDB6fzaduyOjbdj3rxu7PvwcOHNzJo4dpp+6VI/hoZGcPc9GxvmyQIKvlwxS7pYpsBXkWVVnvXPX9F583NFnfTZHUOWZJp5wk09D2UP60CBLwPWYNM4YgVbYCJWOqeJ2NdYMlNoyoFgkhbbvGLFTOsC18MvkCop3eC+bnKGyYlZZ+bJqOt2upM3x5LZwQHl/zbKtl2BDGJzi0CwzwDTdwSCSSpOINjvGUzLSXopL55sinE0A8EJE/b3tcU6WgDBfm5RUR/gIiPZc7sguMwy9Dk9bdu9m7F02SK89OI7pfc3bfT84SzChfP96L8ygHvv29RSwawsoJoXRGhg5JsEHvLUHzNplOs+ndec3TxWZvDm5uHeqzN2550bUa1WmrZJytqnrH0/f+4SJiYmcdddm6exV+0VxWm1yCWg73UNkJxKx7XZ0c/sBFR8SbOOsVeXg9KR6lKhLjRA86XSlOrj5w7XjWzaLwZl/aYBg1R3g6TSiXR9doWoWEmxlJEFmEI65pUAaPqzEhwy4ZBxBSquQPqvekUzu3FFLxNXoAR3L8XMND2Pnt+sy/wlECzjipY/JxXIOIJIKro9UuKxwEnFgWADgF3BSxZKou34xHtJp96icIbfLskFozvDBoc2HRAsC17Ts2asJ90jDzxwLxYt6sP3vvfDnDlbuwdPnzqH69cHcd99W1tarpx5AembHOx2yj/ffvvtWLRokX391m/9Vkf27/Lly1i1alUwbdWqVRgZGcHk5GRHttFpa4sRbibvI0tXSk7fQP6gzpcUt2LzeuZj67ZNePvNA5DTCIqlWxvt33cYzz63G2dOX8LIyBgYIstEKWikw1hkI79F/VT18fFCsJBnRWxQcP5Ssmgf4NoILnVigLvtqe1SHhPs1qcBWaKASCkoRiyskwRzQwQrqYPAkjNECpBcIWIMftA5zbQysyxn0D0BjUSalFAUJFeMOUbYk3WRfJlYYNpnamMkjCxMGpAuPG1JllLEAlwLWEMQ7Eui0+8bAgUpEOwqRDfug7CDJweAHe9rADHzKkDDFWApAsF2Oymnmr5u0+9buWbTUeU8AJzFiLbalqBS5Xj2+Yfx9pv7MT4x0XYQzTfOIny8/ySe/eyDuGX1Mly+dD133ixJdLqSdtaAwX92+mwvvc8bZMxm0ay59gxzRtaqfFmb4wznz5+He+7Zgh/+8G0IMT3Wyre9ew7gmWcfx5kzF6zUerrWwCq3wJDpsQH0g5wB8O7ryGOGAZ4pkXZOyGiJLVMFwKiqqtCsqpQMVa5nIQVUhTWyw1WloDx2WCkGxd1guFr0SEm8N8QMp8qu+MywkhxcMvBIV6qm/sIsEqbFkS6SpaiaNBCwwQAgmQbJMq6YA6M8YK8qtCmEpRIHzgkEJ0nFgmCd21xxBbJEhFjovGCfDfYZYNEAhpVNZ6LgNLHBRC7kVYrW18R02eAcgJySUIdWxl/TPOEFkMkK63LdJdapje6h7u4uPPf8E/jhq29hamoqNVd7QSgA2LPnI3zux57CqlXLrUS61Doa1JRacZZ+lt3sIBjonH8+d+4c+vr67PROsMGfVOtIH2HZBuMDIDMPr1Xbfu9mXLk0gKv9+dJIoNF55/UTpgHn8PAYTpw4h/t33o1XX3nPPiTSYNgsFFiZCrpk/voyewlDgKPcQNjuIyN5kvsuAMPmO9eEKH/dDiQzSKVzf4VSiBQzEi2zBkU5whrAcgYIYdodKe10OZT9jgAwRaek1NN1pW+965HJgbJ1OExNjoRBM9YMAfgVZn0hY+vykcGyZc3B8XpfBPunGkFu9m/gmG5bBToFgv3tU2skvQ3zF9kgWEBk5gQrSCSeQ84CwemiWHp7GaxkC8GoLEecOS0HBLdjdI0/95ndmJqs4a03PrTrLdPyrJnVajE+PnAS23dsQv+VAUjvh86SLmdNz+r7XWRlag/Mpk2XN5g+5zBns2XpFIRO2333bcO58xcLpJHt2eDgME6fOoudD2zHD199u8WlZ/5e02MbnwDQgX5d8wPGq2owBZWkgtQGBBswbO8nCZMepMxf44u4Br4EgpXSaUXp3GEF+swCqbQOHCfevjNUIXLBsAbUEpEyLRilAIukKaClj5GAMIR7r/sNRzaXOOghzBUU0+ywrFVh84Qp2G2YaJcPTEw0D0CwEJoVJmabWO5Ymvxg4RRkAQj2AHAiQyaYJNHkl/0gNEBqpbB6cyspRtnAN4c9zgXB7Tx1vWBLh+2FF57D6OgY3nwz3epsevLjyckpfHTgCO7fuQ3f++6rKf+cHYSfzvZuRuuUf+7r6wuAcKfslltuwZUrYQrMlStX0NfXh3nz5nV8e52wjl4BZfoI21Ys0/gpSWq8eEkfbl2zEgf2Hy29bJCT16TK5McfHUNv7zysW3crACfPJCmok8GELWb8ByLg5KN5lvWdqzQYSlsDcJNZ3dZVAxQshmSu0btf5p+mpx/qwT54cDEU/1CU1IHCxIDNRDnpVl0CU8K9al5laJImpaVIVqIkYStmkpS5Jrlta6TlYK7Kc818X6dpVA3aSMWotZPfxzevmh5F3HwQrBCCYPqcfpUFwdQKKX2um4Fg+g2zQDC1Q8oCwe76yAbBWdWfs9odlao26V+PHQTBZKtWL8eO++7EX//VDyFlM5lWMQiWSjTcR2dPX0acJFh/h77v/VQEvc7WcvOLqkOXXQcwO7LoOZuzTtny5UuxatXyliXRZe3AgUNYvKgPt99+awtLFadF2c++0mMaA2VfHq08X2uDlubZraCLL1EKDHn6xFSTth0bTEVpkkonUvtZ12qJoSZD2TT5wJonlXYpRZFpT1hBPdEtlhJTUTqRGkzGcdUBSwKc1HYpMZLkuAoZR0jq1eAl6lWImv6bTHVD1Lr0tHoVyVT4Ppmqavl1veK+q3Uhmeq2n9169TaTuIo4rloQHMdm/+Oqk3qn8oHD1oxeipT0Wh4qLzUKMKlLxuP6rQrpn3JMcFgNmtjhsFK0P5+e1/dBsmE9+k2WryuARaXaKDUy1tO122+/Fdu234Vv/eX3Wl62zL124oTuDrF+/e1uuQzGt5UWjJn7kl5nW3UIfvRs9+7deOmll4JpL774Inbv3n2D9qi5dbRYVjPJVLrtCrwqu0DrA+V77rkDJ0+cw+RkLXt7LTLV6QGpEAJ79x7EAw9sw8WLVxDHBBQcOwwgJfltf7BvWWaWklkqLY2ivaM4nj9PAcwGB4dgAhIRuOKGCTaDcyreAUAyHYXmNibNzONemHzhCEJJLcI2zLCuHK2sLJqYXrKIA5FhYiPmXuFxa7OMsvmfjk47I8M6MyABg99Szq9AnZZcAw7QMsmsjFopzVDnn7UQBPvgmdafdQwOCKsgPzhkqfWVTkWyLDucsT9+dWg/iGHfm88UpiCHnJUTbNfpAd40A5wHgvOsfM5v53jCv/W3nsLhQ6dw9uylJkX0WpE26mMksHnoo1O4f9ddOHv6MkTiRZ1zqkQXpTDQ70DsUFELupvBptsCaa590s1nZTonlO2uUM44tm67C8eOnSpVeK4di+ME+/bpwlmXLvUjSZq1TLwxA1dSdRErHDFTrIpqlthAdMgM66mR8RO6WKRizBbRkpJBMoUqSJ0FW0iryhR0QS2qMG0gHYemkFGeWaLuEEoxVCLNIEeRhJQSXHDISIIxhSjihvk1DDCTVv5M0+k9mWaJzfHyCDKJkNS6wMwzVxlm2PYxVqkWSaZCtBCRZYIJBNcTahXlCmRpabQPiDUbrOuC+C0RwyJZFJj2ZdH0N/1bA9kkBVlzNjhj3kx/nPHr5d2/LUqcp2Nf+InP4sD+Q7hw4dKMrF8phY8OHMF992/FmTPnG1qx3QibTUXXbPvnsbExHD9+3H4+deoUPvzwQyxduhRr167Fb/zGb+DChQv4gz/4AwDAP/2n/xT/+T//Z/zzf/7P8Qu/8At4+eWX8Sd/8if49re/3f5Oz7C1BIT9H9sHw/77pmDYv3mVu6lp0FpWmrlkSR9WrFyKD97/OGc7nZF7XbzQjw0bhrBt+xbs+eBjs6/c7bcHiNvdbqbk2gsSUO6RgPSkVcanKT14z8xHJMBji5FJgFU9CRZJhmGmJQCrWDAszIzM6qkFgAhMSTAJcM6Nn2Me6HTsJwAkQucvCQVUGUOFI1VFOtxnTrJqCxxNdUzm8oftOYFbh2NjWS5YZV7ElxhfngLlWev0mWCRsW4f+PuFsWy7JDQywH6laCqQFWzfuF33vjEf2AfBfq/gLBCcxQSXaYGUlbfUqnUy7/XBB3dg8ZKF+MP/rR+qQTV3dE5ifOXyAMbHJrB+wxqcOHZe728bFWV94EvtVNKWJbHOyqOeLSN1w3SWn7NPh7WbP7d8xVIsXbIYb7353kzslrUzZ85j/Ya12L79Luzd+1Fb6yibGzxdBogAMQWuGZMmCK2f6xVltMfGH7uic5EDMhoBQzJm6mdo/1jh2gdXrFyamZaDJooOAOC62CVvfCJrFRRLTWMB+NUvDs6FBsHcAGApwJiCSCIPBGspNCcZdEoO7YNhe364zg+OJ3vApdfaSTopt98rmHocU/9jKZ0cup6Y/siCG2a4sWVSLJltrZhXJEvZIlmty6LTfYPDc1uODU5bO31/G77vcPFW3x577EEs6O3Fd77z8rS20czOn7+Eu+7ehA0b1uLE8ew2atOxm7F/MNls++f3338fzzzzjP385S9/GQDwpS99Cd/4xjdw6dIlnD171n6/YcMGfPvb38av//qv43d+53dw22234fd+7/du2tZJQBuMcF6boywwXMQCafBIjKqXr+tLEDMKbJFt3rIeZ05fRK0WN3zXLgimbaWL3ny49xA+89nHcPr0BQxcHwoK+xQ94LIsy5nm5h8HYNHkU3uAWEEiQhVCZQMA2n/6njNo+SzjDeywZNw6Ysm4ZUttNBjcc8yaLY4NFesDW709eMBO6cIdYEZSxDJZYbfPBO49phgMTIXb8NlcPy84K4+XTiU3pzWWOk3JC77r38bbB3jroirQFggrB/jdfiu7jGWEzXmgwlyBFNpMEwYEpytE++vVkjnpJHQpEOxXA0/n64cS/UYQnB1AcU6e1tOutcIEZ23Hv1/mz+/BU8/swis/eBcTE+kCHOX3pyywPHHsPLbeuxEnj58Pgh1F7G1WvjCdg7SSJqvQVtZys2363mm/8vMcI3xz2nRZ4bwaG1m2ZfMdOH7iDOr1mcs/JtvzwX585rNP4fTpcxgcHM6Zq73BfxH4bQaMKU9YO6CoYbqC0GBWSaPI0o6GQSJhQEVFSOwzQI8PdNFGbtlcOyiWgOKwhbRgfVZjz2FthhWm+9yrfKW3IVDhzM5TjQQUGISUiLhmgTk3AFhEFhRzbphgwAJie778nOAcIJwkEWpT3Q1AGHDAXJiCWVJyXcVaRhDC6xNsKmG7VkmeFNzKol2BLEq/CtKmzHkNgtVwsmh9njIY3KwKz953ZdjgPEl0FmD2vy9tDeww0SolF8/xoQsW9OLJpx7Bd77zgxlTgfh29OhJbL3nzraAcB5GKBsEu5Gy6Nn2z08//TRUQXWub3zjG5nL7N27t8U9u3HWkWJZZD7zAWQP5vxpdDEK5arVUYEJK2u0YMatp6urijVrVuLl77/TUI25DAhuNshMfzc2Po5DB0/ggV1b8dKLb2XmJRY9AIPt5gz2ab81CMwuxpUGxBToZWis+dwoXdf5KQwcikVa9GwAcUVVwAEkLAEHB1caHANGLg0NPLliFqTF9kY0MmoFcMYC2a/bF/3srTA9KpCMIes+piJZNHaQKgTZoe9MRa9p/hQg9o17jo5+Bs5QyAr7LDDl+1J+NODYYPpV6Tuh/FzqRtFzUa9gPzdY5vwTLDbrMXP5LbzMb52VSx44Yvt9liRrZkFwOyznF37iWQxcH8YH74U5hz4r3Op+Fe3HpYvXsHX7RtyyehkuXcyuIJ01KOCpQa99XxIE+9NvFBiesx9dC0Guf/1x+73/N/2M6Onpxq23rsJ3vvMDb7mZu45HR8dw5PBxPPjgffj+938YFNBpZu1Uii5XuDJ8LhAbLAFE5FshbeEsbaTI0pWjE+YC0XqdHFRESwd/tVRaMma6KuhgMzyptEHY0NQz88Cu+ZJLgCoxexWllRlsKwVERo4ccYmIa4ZTSKnbznFpwa8PfHkaBGcA3/Q0yXW+cW2qG8yTu1pATKywYpAy0sdrwLBQ3PQJjky7JKZl0ZQHrRrBcGJqj/gAWOcJO1k0+Wfl+Wb6fYuqRRdeG03Y4OY2sz6hoXp0SVn1T/3UC7h8qR8ftqnMKGu0b+fPXcGOe7filltW4PLlqx1Z55z9aFpHgHCaQeWI9INYpQacDI05iVau4S5GP7ePsahhmbXrVmNgYBijo7ptQruticocE9nRo6ex5rZbcPc9G/HxR8cyj8FJYvLAbmNQwJ+fALEPhjV4FrQAAMeYEhjOGmYETCB0axZh1+cAcQRThTnFDhMgtlIdO91DmKoCqRSqjIMzBp5iPon1DM6k1O2PFKdD8pN99XFFGYDYmyV1DsPp2TnC2ipST4tNuJfWL1QaVjeyy9ZRKhUwwj4TDDgArIwj1VFlB3rDbTgA7D47EJzLBjMXkW4GlLJAsF+s7kaA4HZs69ZN2HDHbfjvX/vTjq7Xt3QxKqWAc2ev4La1q3KBMJnfCinv+ywL0k3M9v3fLBhMt9Ferh2ba5/06bUsJpf8bvNKtOWYow0bbkf/1esYH59of0dbtEOHjmLNbatx112bcfBgunhmuQJZrX7frunApcOlJJHW5sCwAjfvKQWN/CtDRTEnldYTrVSags1klDeslIJMRX2ponSWEeisKAGpGCIlEUkNhhlTiLgElwoMGvQ2yKA9oMtzthGcF86RiApqtW4wQzZIjw2mv7RfwgB0kQbDBvxqMOxAsHvpAlmJzGCDYRRfSvtwywr74ymWXVwUaAw0+0WyOscG+xu8MYHSdKDn/p3bcfvaNfjqf/767O2DVDhz5gLWrVvTNhAuFfi6idhgYM4/z4S1DYQzi2XlMBjUI9gvzpQX1aKbKwCiZhla95rbVuLM6UstMSatFALJlBkrhQ/eP4Bnnn0EFy9cwdDQmJneHAT70/2bqEFirmQAhoEUW+kxxGkwjIyBuJ8nKuCDbQeIdQBCIp07LCA9Ntj0GiamOCjooYt5ROCIPDjp32uNANA5OF/iTIywVCwAxGnLPDfed2npsuVjPYdHp57GC5Z19vad1pXO+80qfgWQE3W5v77sOdhH+7cRANN0v0p0wkylaFthVJRyxFnTskBwKy2PitiQ0vdigYw/6/ueni587vNP4vXX9mJgIE/6WN5aYaPPn7uCZ55fi0o1Copm+ZYnFcvqI1m0bF47uU7mPpcxielxDnMc9ifTmrdSygeU/vPitttuxdGjJzOWnbkrQ0qF997di2eefRwXLlzG8PBI7v62Y60MfnULofIME0mkG8Gw/16AVFpUuJKY4bRUmoLNgMsb1gtS4RHXasn9LNyBPgLA3BWXrCiJSDJEnINLhYhJJMwBYJ8J1uer9dG2ijjiJMJE3THCft6yLATDzAJhLX3WQJhAsAbErmMFyaKDlkmekssBYD8DuDg/OKtadMMxpjsxZCgv0iA4NG/aDQLBaevtnY8f+7Fn8PLLr2NoSPvn/PulXDCtrJ09exHPPrsbUSWCSMJzHoy1c4J/efPTMjcbCAbm/PNMWMtAOD2wS+fVps3mEKfAMDdRUX99JG/U+ayN7AigG3UvWbII77z5UbD+Tlm6TYp/Aw0Pj+HI4VN4YNc2vPT9twp180CKEU/JyBpuOvvwc8xwWJk6xRB7YJgYXpkChplybeVyjOlzxKo2dxiqqkGvJ7e2SwdFtfR7zVxGJq+GgbMMdIowCkWSZ31cbjozAJhDIeIOEDfshzGqMt0oC3dsbaCSM6CU5MoMGmlTca689bh838biV8oDwTDTiQX2Zc9unW6HsgCwq0rpVYn2mOB0TrDeZtlWRiEIbqfnbx4oa/ceLLONH//C0xgZHsMbr+3JXEcrsuhWbWx0EmOjE1i5cgkuXRxoOj+xPfQe8AJS3rPSZ5DTYDhvvQx81ljhOfvRtBAMtw5e58+fh0WLF+LSpSvNZy5cf+vbHhwcxrGjJ/DgQ/fhpe+/1jLzMVPySEpz8tVbHM5/288eGNbBaVh/q2t46O+oVocC1zJr1QguiGtQdsXGPDBs52U0uDaVqbm0g23BGCLFTW6wRCQ1E6zHaAqMNYLfMt0YMs9TxBGLSgCEgTwwbAA7NCMs4YNhZqtD2zZJillZNLVwTLPByvPxJIsWiipFN88P1vvnpcIUsMEdK5CVOb/5PQrOdaO1D1D/9t95AVevDuCthp7BM2/DQyOYmqph5YpluHSp307PArBFRFgWwL0ZQfCczYy1BITtYCw1sEu/J6OLLw2GAQKcDpBlyaKzbOWqpRgaGmlIxid5dPOodv6g3bLRLATDdjklcfjwSay5bRXuuvsOHDp4ouEBl7vNjOhS7r55zK/+k9Gqyfgynx2lc92wPvgPaK/olskzJTAsoWVaXPHgQR8A0QwwDG9ORQAzZRrscUilUGHMPnaFt7yuCm18tdRXDUvl8MrU/HrdrvqzzwL77DAAKykREpDSOW5BYBih86B1pEFwolQAgIkRtgxxBgj22V/9Wbp5UwCYIs/UK5gAcRYIdvsaFsrS5yLVrsxOTy2b/pxed8G12yoATqdRFM0DAHfetQGbN6/H/++//3lL22nVinr09vcPYsWq5kCYwG06LQHIPt48MHyjbU56NWdu4OhDt+aDwVtuWYHr1wZRrzcWsUyvy00DQtDLU/OXt48/Poo1a1bjzjs34/Dh480XaNFavUfzVB6AC5plg2FfGq2/S0yQmIpoOcCpg+PpFksOfXuRZ6ARDPunmQNS6vUIpdswRVxaQMyZQiSlA8EEunwZtPc+q6hPujq1nR5xQESI610BEAZCAG1Tnihf2ORLU39gZfN/PSBs5NCxpLxgZllg+9fz9WlZtN82KS8/uNl4WCkZ+NqZKJCVLrSZC4ZbbaeUMb+CxI57t2LD+tvxX/7L18PvCse77YPurGDV5ctXseqW5RYI5203q6bBJxHczvnnzltHi2X5lq6Q6jO3+r0w4NXMz3iDk8liP5YuW4Tr14YKB47+zVI2mpYFgrNMKYX33/sITz3zIC5evIKhoZHp5VKmJRsp6bJfSAugKLIrpqWgZVgOIIaONytH1JdVK2ZaOSju+huanGFihumvjYcyA+QUIJlERUWQUKiAI7KlQELjAGLqoagAplTAHmtQrMAYs347YsyC5cw8aAUbkfYBdZY82pwASGiHJxUsG0zr0uffcyTeOtIgOM0AAw7slgHBeSxwHggWLM4EwVn3iB+BznPOWXnARUGdsFL6NK73kgAYAObN68HnX3gSb76xB9euDgbfZbHAoZqjc+zO1f5B3Ltjsz0HRfnAWSkjRcfcLLf4RhixQdNZfs4+TRYGg4t85LLlS3HtWlHAKG/ZLJDsz1/uqpJS4t13P8TTzzyKixevYGRkNHO+G5Uf7BfNakxlourR8ALM1MWBana4IlrKO1cSDFktljSwdWBYq5kAyZ082u81rJRCxHSLpciww5FiiIwEOmLKtHBUXlqRB4KtX0vnIjcC2fDYGSA5umSEJK7qaHXWPHYdzEyDlUpLwAJhKoblKkQ7EKwBse4bHEu/bZKyvl6PE8KilU7hE/rMtI8N2hJmdE+ZdoGsEiB4utZQMCvDenvn4/MvPI9XXn0TAwNDjetoAwyXJYx86++/jnvu2Qyg3H3bzr19MwHmOf/ceWsZCOcVfcmztCTBSqQNK5mV55mWF/oPlmXLFuPI4VP2u7JWVNU5kwlOMcv+8oNDQzhy+AQefGg7Xvr+GxAZp6SsDCN9fvziYWaD4bxZ+cMeIAZcuyS7TgJFXtEtoaSWRKsYEat6j3sXmS4Ew4DtfSgZBzctBqjXcHAuDLQHNBvJAUSMQyhl8p2IjdXbpkisgmOIs646OkoCxO4cOgDry6MtI2xkT/7ppf1rkEebdegBhEp9lw+CkZo+HRDsF+fIAsHEBmexwvq4syXRefdEUZ57LjM8zShrQ/sgxvETf/s5jI1O4LVX97a2rg6CYAAYHhxF74J5qFQiCFF+sFH0rPHNZ5Kzvp/1PsIUKJrG8nN281rZehl5VaGLll22bAn2ffhxm3tWdJ2XZ4cHBgZx7NhJPPLI/fj+91/P7PLQirU7CPbvZwpc++uyoNj4uKAwnldAyx45A6iIli+V1vO4vOF0ES3PoaOxuqYHkhUgOVDlWjFVYRpARgYAR1z7bsZM/eqcqtBZUmbalYbvgxPGAckxnjggXASj/HVpmTQsMywBI5HW0ufY5BETCHbS6LDdod/twVWKVqYaN/lvI3lOBY6zZNH+96rB90rvfTM2uNg6CYLL2t/7e1/A9esDeP21d3LnmQ0wPDgwjL6+BeCcd9z3lALWc/75E28tA+GgX3AG2MqaN8/xEhgO2Cv6kXIUHQsWzMfw8Fj2vrVQPTp9s6VBcLNlDx06gZWrlmH79juxd2+7jr94G2ZnvIkIprmbQTb0Yi56iBIgluBGlqVzt5niBlw7ZjjYrjE/Rp+wxCBWmo9DF+4yOY82iqpBL/Ujtr0TvQPkjEEohcgCXIWI6TygtFGWFHS1Lx1UQSMA9gGsBsiury/gpNnMAlb67FpF0XmWZp+kt046NqCYCU5Lof3pPgh2jjbMC9bzE5gNWX4fBFMEmgo15VWILgOCy4Lb6SgigGwQfP/Oe7B+/Rr899/9k8xl0i2TZtIZ1WoxalN1LOzrxdBg9rPHqjgKBus3i/R5zuYsD+TmzVvUToksiiLjn7NZ2OlbeTD88UeHccuqVdi+/S7s23ew+QIdsjIpDgSA0xXhfYk0Q1QAhkOpNJTvR12/Yfv7pMCwTIFh6jVc5QzSbDJiDIIpREwD4ogpXcMDGgDT1nww7I7FeWf/c3MgzNAlOcaTCBCNLSHzz6cPiOGAsGL2s1CwvYMJBLsCWWHbJKEczxvIohGywum2SfrYGiXSRYGkGe8ZPEP2yCMPYM2a1fjqV3+/6TXfDsvbyjITE5NIkgSLFvVhaGikpe34djOxvnM2u9aWNNqX9JQFxTRPKI8O59ODW7sSt5xpoTRv3jxEUYTJ8XLNuovAYG4eQUYriSzQoJTCu+98iM989glcvHQZly/3B8v762ilCEe6mFbwEE2dGwX3sBBByxXqydxE5g3NvOtWSvSQjyxos4C3ABCTs2CQSGwBLeZJpHzQx6DhN0MFUcPj0+YXM4/VBcCNZDq9bQLOGg4z++vkFbRSBIzRyO7SfrisGi/CnTG/nQ6fXSbnmQ2A6W/ADqd7KXqtkfQ6hAPGWTL3FkFwcYAkGwQX2XRBcJYtWdKHz3z2Ubz4vTcxOKgd20z2+SvKDyYbH5tEb2+PBcJZkuZ2JeO0npsFKCs03OotLz9nnwzrVDslxjjmz58PgGFycmpG9rUVk1Lh7bf34DOfeQKXLvej/8q1wvk7/XzxK0f7ecLpFBMHfHkDGAYQyKSzwLAORsPmDev1cj23klAeGNZ+PGSGldQMsGaEla0UrRlgB4g5mMcEhwxw47E7UGo2HUiZ9Tz0nfPrTDH0SoaJJIISyo4hgFDxlb1Ntz4KgAvpADGB3UQ5EJwoGHZYeewwgeFGWbTNC2aNz3lfIu0XyXL7l10gi5bVb7L8R3OfMtts8IoVy/CZzzyNb33rb0x19uaWD2yzWeHiZRptbGwCvQvmlwLCn3TAO+efO2/TzhGmQVwrkmm/aBYZDdSFisEQWakQORPGIsyfPx+1Wgwp84tCuc+t5QbnLZvuhUzgUymJ8fFJ7N17EA89tAPf/e6rqNXqmdulfAub81viBi8svOUB4EymPXCIRU4+BjeyaGKFYYAxZ5FruQQtixbMVLKGNPlKFJk2rLDJX6qoCCIH7FFuE+UUu310BbQ066orUBMg9s+W9tsaLXPGjLTMsbcKYS6v7Wes0CCNducUZn+9Ui2MIa81kg+CmwHgzOl+QRE4Btg3crw+CPYBMH32QbBQsWX89XK+w82XRM8EoG3X/u7f+yzOnL6IvXsck5OVs5RmhWfKGDimpuromdfVkfU1ywnOqpjvwPrMuzE5TenVdJads5vH8gpP5qmp5s/vweTEFJp1U5ielWeFR0fHsG/fQTz00H343ndfzSngNbOWlkfb6vBeIC3NEDuA7CmzPDBMrQ2z8oYBf1BnkK7vPQ2Slqa9EgW5JQOUYYQ1E6x7FeucYMoRdgHudGAaaAS3YYtB5n2fzQozMCyWDJOCQ3mt6lw16uZG63bKMFMcE646NOUFxzIliVbOj0sAQjcxtIqttCzavlSjf6b32Wyw9N432qz0DG7Tb3LO8dM//ZM4evQE9u1rTQnZDhhuWEdO7vLUVB3zerpb2p9O2I0IXs/5585bS0CYobGgFaBv+HRxrLLrC0CnHdzrPNjIa6cEAF3VLsT1xDLEjeuSTeXRzeSLeQ8h5u0L9d8FgLNnLmL16hV48MH78frrBbkSGWC4YZ6saf7+lLyAfdbdB+9p0zeENzhgzlmnAwACejBOoFiaglkVVQnAsM5fcnnF/v4AMA5cWadNjpVyhZXZFgMDV0zLpA3zyw1bawGzWaf+no7J5fj4gBUgsG0cXjrfl9Zl1ss8EAyUA8F57ZD0exnkGaV/K9+yQHEWAKbPaRBcxAK3A4JnUtaUtmef240FC3vxP//gmw3rKVPAo1VrxgbTs6dWq6O7BUdLv1PZQli0HX9/yjDVczZns2lFz4pqtYp6PPtgM98kTpw4g1tWr8QDu+7FW29+YL9p9owKmNtpPM/snhSwwgEAVvq+LwLDQGPeMFAxucH6udMMDEumVygNoJWmkHTFsMARc4BYtzAkRljPn8bBPsjVn0MwCnqfYm59Y9AFrcYSBpW4Gij+mXcFurLOshvkK+W2ISg4Lv2cYD1GcGBY5bLBZatFuwB0Nhs8LUn0zWBK4sd+7Dn09PTgL/7ir8Ov0EhOZa6iA2A4y2q1Win/PN37uEzXizn75FnbVwVnkX2wtxIVSQ/40oVt8hwtZxG6ql0tFasp2gcf1BfdHIxx/UJkX5xVwVkFnFXAGMeePQexeHEfNm/eaOfJsqxKvXmmo44ic5oOFMSFL6UkpEwgVWIcbJLxinU+kYohVAypEggVQyBGjCkI6PcJavq9mU8ggUAMBYGEJUigt5GwBAlLIJAgRl3/ZXU7nb4TSJAwYSOuAhKJedWRIIH7LoZErCSEkkiUdju+7JnAqfQiugSCBZSu/Gh7AuplRXAm3UvB9RAkp6hszlAIgoXZl0T/GhD2yCScixRImHvRHP7LjzT7RbHc7+2izeR0BWK9dvM7p2XTQCMI9ls3OMfdGLEue322Mp8/f9Ey69evwcOP7MBf/uVLmJqqN91GHhuslAhe7Vg66CclwL3RVxHA9QOCZYKDWSC42bwzaVRUbjqvduyrX/0q1q9fj56eHjz88MN49913c+f9xje+AcZY8Orp6WnziOesrJE/1BZei5VKhCROZn+nmtj77+3DiuVLcccda3PnabVXa5bJhudrcQX5tKrHvg+KIDoW0oIwO835DVdYURlfJM1apfWDsVKIPd8WSyCR9NeXCgN1CdQlw5RgqAugRp8lQ81Mp9ek0NMn6HOi308k+ruJxHsvNNCdSIBJ4V7jCTCR6G1PpaaNmfVNSbMtb53+fkwJZve5Jpk5Br3OmoCVRJMcOs0IJ8qMFxCywb6fpt/FZ4P9aek6HI3+KyMIXgYE57LHnaL3mvuVTZvuwK5d9+PP/uz/oV4vl56YZfnjgPbHIkLIwD9nWdNK8QXnID0eyCMFZ8NulH/+NNv0pdEpmXPWgx4Ic5GasbL+ukJQ6ctlGllh38r0E6b9KvweUWZrJ2UYTwWBJE7wzjv78OSTD2JgYBgD1wcb8qwaWiGpcNthG5tsaXY505E1ywKr/CicddipG9xJtpIgWOCzxTqvWDPEymzHcL0QGc8jCWKSnaRLMWm2F+4bV3qqZp/1I4faMvmyabocVLAdWBAsVMircmhgq6vuNT4NJFxLJ6Vg2WfaRpoFFsGQxDHCAKycis6zm05Mbp5jk0iQBJFnK3+mv57z9QdNjYU68gdiZa+r9HXTLjvSbHvz58/DT/29z+L9d/fj5PGzLa27mUPy8/V8ywOeWcW7lFRgkbs38qwM8G23ZVLZ5+Z0zYn32l++VfvjP/5jfPnLX8bXvvY1PPzww/jt3/5tfO5zn8ORI0ewcuXKzGX6+vpw5MgR+zlLrjlnza1sFelmsmQF5Ba57Jy1fnXVanW8/c5ePP74QxgYGJpWQZ0s5quZUoWWyWKFGyXSkWWGAVdNWvtPIF2vI80Mu97DxcywNKtQ5oPigJQMAjC5wUBiti/MfcVZ9s8bML5w0k16T/P4bQ3hfae/V+BMg9SJBJAZjLBulWje01E1Y4ZT2xVKGam0A8GxlDr47WiGMETNXNBBkxBhkSyZCrimWyblscH6QwlA2KYkOreHcBv+u69vAf7e3/8JvP76Ozh9+lzmPGVZYTt/u2OJjPtNqbAdZ9rKguAsxvdmqd1BdiP886fdpg2Es/qYAuUGhGTMc7BOMiQA6EbxMLKdRCRNoz6Aqx7tg+HMoiAtgGDGIps7S+CEQUAonUM7cH0YBw+ewO5H7seL338DcT02gNHtQ4PUs4kUOvy+RODAFtqiz7CAOCtooPN9HdiVSiJilYbBNoFjuy564ClqbSStHNoW6lIem8Zk0IqJ2j8QIG7YMwazLo6KiozMWjUAYpiexP7e+oytRChP1pWgYdhf54VpeQ7TyxjO43Mjp9bnx2UHKSgkEC0BYILMbruNv6lzvC4PSXPLcS4ABrLZB3tOMuTQRfM1s1al1EXVp+na+/v/4McwMDCMF198s9Q+zFRucBYIBoCowqGyIjxNrCzgzVLX3ChnfCNykP7Tf/pP+OVf/mX8/M//PADga1/7Gr797W/j93//9/Ev/sW/yFyGMYZbbrml/R2dszaMfHXoxxjjkEIWDkanb+0M4fS+9l+5hsOHjuPRR3fhe9/7IZJk+sx1uVofjUG4IKBswTAB3rCVGhXQ8sFws4rSQFIKDGszEmnJIKALZBEgZkwD4hj5IBiwuxGAX1vsUmmQmwWC02wmZ7q2x6SQtOsBGPZl0QTysuTTQLgdvY8OAFOvYHolQeBc+3UF1cAGpxVber2NRbLsdxlscG63hrzxcmGtmOwHbS4Anob9w5/5u7hy5Spefvm1wvlaBcON5o/GzDpL3GcRZ6jX21d/lZlWal2zULNkLke48zZtIAw0H2CTFRV40uA1fCBIU1iAm5srjmNUqnmy43CdRbnC5XqDORDMWRURq4Z5wgbqE0SDAo4eOYXly5fgoYd24I3X3w9ZcPPeZ4YLo8gpEFxOTh3m9/qAuGh+jiokEg1qlT4mOk49n/Dm1U46YtUAnBFDDAOsfczgF9pKO29iSLPBNzfDgkomIJZK2hxie6ZSYJXgJ4d27sTeigwnoocIJnPdAOKw0JUB2QYAZ/UB9o8l7Tx9qVuwXa8KpV8lWqg4AMFZAFj/vo3TiqyoncNsGl1fzz63G8uWL8F/+69/1GT+xvulrMPKY4WL1uU/J7qqXRgfmyxcPh3880HwzRZVng0bGQmZt+7ubnR3N+Zx1et1fPDBB/iN3/gNO41zjueffx5vvfVW7vrHxsawbt06SCmxc+dO/Lt/9++wdevWzh3AnFkLA8l0LTtQpZREHNdR7arO0B5M/xl16NAxLF+xFA8+uANvvfVBw/dpn5yXJ1w02KcCful50qxwGgwH8+SAYToL9uwXVJQuA4alUlCMQTJdNIszBICYKQbBQka24ZxlgNtcIAyX0qSXVfaYAF3rI5EKU0JCCtcpgpvv6PB8UM5ZCtZ7++jnLaf3g4pp+iA48Xw7pTKl2eC0LLoxIJ2WTBMb7Pt4834G8oJnAgS/8MLzWLSoD1/96u93fN2dYoWrXVWMjo23vb45+9G2tq8YqUTABreaF5dn/sUtlc5D1bmpCcYnxtDT4xxts4Ften16mfZBMEeECPozZ5XMPIH33j2ARYsW4s477wjyi8tuu9HCB2jRy58H8AGOzH0pkyOs/yb2fEslIVQCYXOMKf9YWJBGOcT0itUUhIqRqJrNJ9YBg8Q6Ej/PifJkY9RRZ/pVY5PmNYUaphCzOmJWR43VUGcxYiQ2czlGgjoEakogVlL/hRYt6e8Sm7NLOcjEr9I/lfrn+gfCThN2WgiCKQfY7wPsg12fBW6YziQEi/UrOI+JPXdZINg54TAHNgsEd6KwVCdBctirW7/ftGktHn5kB775Zy9ibGyicBmydtjgMs+KIuvp6UK9ls8iFT3zyoLgmwUsqw68AOD222/HokWL7Ou3fuu3Mrd37do1CCGwatWqYPqqVatw+fLlzGXuvPNO/P7v/z7+8i//Ev/rf/0vSCnx6KOP4vz589M59B9ZK+UXg3kafcvExMQNqdza3MgfKrzz9h4sX74Umzat7+izrbgtXXYf+Kw0MhdEzV/GBVZDf6oQ+p1mOcP6r7K5wzpvVr+vCaAuFepCoSb1q+69poR+1cw8U+mXAbRTUtq/k0Loz1JgQiSYlAKTUmBKJpiSCWpS+/EpKc10eullazJcX11KvS8yex9jpafp9kju2GIpdY60Uq7+CNx5El7SU9qXNyuSVXiNZEii28kL1st1ktbLYkS1v7z77i3Y9eD9+L9/8k1MTBQHgt2+deK+am0dPT3dqNV0oT4inGY7wD9bALxT/nnOnE2bEc666NutIM0ZLIvrR9YYOMCAiclJcM7R09Nli+kU5QqnWeFwIB7mEGdFkpgpCMYYR4Sqx/BQn94IUsX2PCglUa/X8PZbH+Kppx/C9evDuH590GzARLHazK9MRxELLZXPmztbhkzbyZ99Zl7/1b9PbN9DaVm0Xk/ouIk9Fub38/sjUsVpu22PBSWHT+ddQoKrBBVWMY5cM8TcyKYpj9juK3wGV6b69EZQUIgNmAVc1Wr9nhkWmthgZz67bAGxVwXaZ4EBNMiownwj0bicX+0Zxb2B3T5l/7bNegan0wTK5wi2Z3nX/MK+Xvzk33keb7/1IU6dujBjjsQHwe2Czfm9PRgzjHA6VzfrWZfVGzhLJl2a0cZ0JWflrVPSq3PnzqGvr89Oz2KD27Xdu3dj9+7d9vOjjz6Ku+++G7/7u7+Lf/Nv/k3HtvOjZOnnQJkaG/78k5OTqFar6OqqIo7LF4acHdPep1ar4623P8CTTz6CgYEhDAwMFVamb1Y9OmR0TXvBHFZYz5MxTZHPQMgEZzLDxRWl0znD6WrS5O8kdHskm16kFCrMtDA0KU9MOT+vV+n8rO2GkGJ6y3ZX0PsQXhsROBJITCHWcyi9TfLvnD4z3VHCscLujGa3dXLMc3q/KLTsg2Big6m4p/7rAvs+G2yD3H7RLAJiRWxwg5UHwXlWig1uwccuWtSHv/N3fhyvvvKmzgueIf/cCRZ3wYJejI2OdWiPbm6bk0Z33qYFhFuJ/BT1GyYw68BwKDUhuY8QEmNjE1i0eAFql4da3ge7PTtIzQbDNi/YQqMIHGGOsH5f0bnDSkuCyQYGBnFg/xHsfvQ+vPT9N5tG0sJCYgVFvpoBHHLgSlowrKcXPWSkLgxFRTwgneML8rX17+KDYcA4ZtX4oPdziSNv+5Tn5H/2gR5dH0LFehDAZAMgTpCgoioQxNpnHF9apqx3SkIwqR0dk7ansT5Hpo0EhP3V0+vz5dDp/fe3WUoOnTpu2rZ/DtIgOH2OG465BedZFgzPFDjlnOMf/syPo79/AK/8IL86cJb5/YOzBpZl2N9WWhNxzjC/twcTBghnMjkFedBAuVzh2QS7hfthpI3TWR7Qxax8IJxny5cvRxRFuHLlSjD9ypUrpXOAq9Uq7r//fhw/frzl/Z0zZ60ExdLzxXGCiclJLFrUh2vXBlteX751Ckxr+Hjt6gA+/ugwHn1sF77/4muWSSIr06bNv1ezBvJpMJxOzfBBLQAbtKZiWL74PPismrdXKgbDERIIVBAhMftJ6UVKAcwUHeLKSJNVkK0ZnEl97MXAN11QUq/FpRH5geqIcQgI1EyoWtcJIXm0G40x5bR4DGYfmQHM5uHjQ0Kbw2zBO9X4CGt9EAhWNmDdKIku2zIpbVlkRjt5wWR0Xlpihlvw5ZVKBV/84k/j3PmLePXVN8pvw1infVkRWK5UKpg3rxujo+O5y32aJNOd8s9z5qztKyNvMG5bgRjwSO+bGUsVi8m7aIcGR7F4UV/DQDbrpssCBlkOzs/j9feHWEl67EasiggV81czxFomzQOnCADHj5/GpUtXsfvRnYiiSiCRzj7+sswQPTzTMmfzMKbvU+A0/XulW2AEAQEvCBG23RGmIISRU8PksRqZvFTCRU2VB+gaAJ8njbYtmbJfsZpCompIUEPMaqixScSoa+l06hWj7l5GUp2wxMqudWRXePPEqLM4aOVEDjur0RGVxiKmOc34AsUg2M8FThsdL7HAru2VH6hpbJHkv7Kuk7KKAPps8+CDNimtWZmB79/+qefR3d2FP/7Dv246b9PtlRwot+uYFy9ZiFqthsnJ7JYRWTnXfsCvnQrRnZGXfTKsq6sLDzzwAF566SU7TUqJl156KWB9i0wIgQMHDmD16tUztZtzVsIGB4exdOniG70bBabvqyNHTuLq1QE8+uguMKYa7+FUgDzzfcY9WlypP1vuHM6TI41O+VL73vP9eTLpdGslnSrU2KJQt1iil5YQx1KilvGqm1dNSdSkQF3pFKWaEjpdCQkmUUcNsX6xGDVWxxSbQo3VvFfKh7Mk9X3dvGK3LvvSqVExpN12bF51ky6lX3qaoM/mmKmhod/qUEFaJjhLEj0dNjjf2n/WM+9fe5Yti/77P/2T4FGEP/6jv2h731qxUjVwcuZZunQRxscnUa/fTD3M5+yTZB0plgWEcmhfOthsEOiXKycwzCEhFbQ4lcrlm0H5wPVh3LJ6JY4fveCWZ40gIdxG+YFo1rwcGhRTVeSq0uAXCpBMmL7CjWBz756DeOrph/DAA9vw7rsfZm4nBN9lo+dZ87jYsa2UTZGfsiDbCwj4xxJIrJljhikY6Tt/xqLge8Uock2SjHRvZIl0VWQykl2T8ydWPqtCNTfXQRaTG34SiFndyNBMGydQFWsVsMRp86XWabALlAPBWQMlHzT5keOsXDK7TAkJY5lrqZ1q6tO1Rx+9H5s2r8PXf+9PbT/CVrfps8LA9CPQQY/A1L4sXdaHgesj6UXC3y11v3ySrezwrWj5Vu3LX/4yvvSlL2HXrl146KGH8Nu//dsYHx+3VaR/7ud+DmvWrLF5xr/5m7+JRx55BJs2bcLQ0BD+w3/4Dzhz5gx+6Zd+aRp7PmfTtevXBrF8xTIcPXrqRu9KgWmP9P57+/Dss4/h/p3b8cH7+wtZo+lIpPW0kBn25c6AY3kbq0lnS6XbYYZdayUC13qkxqF9njQhf6W0R8kDV07a7KTOacWUA94hoKRzkRVErqICCYEam4KybZJcdwny724atVl0+j3AjQEypdw2sOD2NWEuBO6DYJJEWzm0GT/caDa4Lcu8rrOv9aeffgwbNqzD737t665f8E3Mpi5fsRTXrg3c6N2YNbsR/vnTbm0D4ayels1ygxuqqno9iANAbCTSVI3Zzc/Rf2UA2+7dolGeygYJM2HMAK8u1QUAqCgti4aCfZ4oqWOtZFJKvPXmXjz3/KPYvGU9jh45WRqUlwbF/jzMuj4QINaVmp1UWreCynH2EMH5dusNZgIYIFQoH7OgxHxPYFioOGy5hPA6SefCAk42RuvzAbEdKBiZOlWoLmrdZHedaRBdZ3XzszkZvM45rkCafOciuTXttz8t3R6pLBMcyKJLgOBQIp3BPuRcM0X3yXTki1lqiiLbuGktnn7mEfz5n30X/VevNygpWllXGgwXWRE4LQLBALBy1RJcuThYajtZVtQ3OA3gbwZ5tJY6Tm/5Vu0f/IN/gKtXr+Jf/at/hcuXL+O+++7D3/zN39gCWmfPngXn7rwMDg7il3/5l3H58mUsWbIEDzzwAN58803cc8897e/4nAVWmKaDrHuF4/Llq7j7ns3gnEHe5MloQgi88cZ7eP4zT2BocBgnTpzJBLRkWWA4TyLdChgGQqn0zIFhAEhs0LeiIrh+EA4QOxDc+Pv5gNJKjTMApQ9+/aBwukZGsG4WI2ExYlaDYLr1JPlyzngKBIe+m0CxPlQiYUIgnyfN9sG6D4JJEh0UyPLYYP27hWqBkA0uY7Mwfm0BxN5911148qlH8Ud/+Oeuvk2rm5tl/7Vq1XKcPt1+kcSbwee2YjfCP3/abVqMcBEYblZQhr5PS5ylAVJQQMSqDQ/L0dFx1KZqWL5yEa5cvg7AReZ8MA0Ut1AK9qXkg6KiKqaVjz5tVLSJs8iC4UTWrGwYAKamanjzjT146ukHMTw0iitXrpbaVrH5gNczy9q67y0prJDzQNQu1Gd8lSoYAHgPDXI4jHEPGLt2V4BjhnXuk8n/havuR+tM96N2kXLaCbcOf7Cg90O3bqL9Uel8reA6TCARI/FY5YqqGFZYWidbttQbgdy0w/e/s+ex5FrzenNnWdm2WtNdR7Nl0ixJFmuyZEkffurvfg5vvrEHhw+ftPuWVTymzD2ZB4LT+Xilqzan1scRoaurgmXLF2PfByeKl814Fvr9QZuZH/z5UZJF+/arv/qr+NVf/dXM71555ZXg81e+8hV85StfmYW9mjPfXNAs2wcNDQ1DCIHly5ehv//arO9fedP7PjExiTfffB9PPvkIhkdGce1qyCwVgWE3z40FwwC8YpTNwTB9ThgsgCSvqv13vsw2qxhlEfglQBmwwXlKJ6WJkUTVkdDzkDlWmIBxOoCdBsWAPq4sL5pV4DJhSeY+CyQ63Qsu3ctng4MUssxndqpy8Y1gg3P9aOP05cuX4e/+3S/gBz94HUePev6uA8Hm9oyeM9mmINDT042lS/vw5pv9dlo73TI+aWB4zjpr05ZGZw0AO7EsM0WY0mBWQeLcucu47fYVuHypf1r71KzlgTQRSMncfA4Ma4l0hWkwDAYPDCOoJj04OIy9ew7hkd334+WX3shM6i9ljBsiXMC6M8YbH6ABSww4djhP2hVGNeGdPxcBp2BDFETD9TQHjBnjQVEt+4BRYYGiItmvb76jJyY4DYg54FWnjiAy1imVAFOR1xZKWPCsTI9j38m609f84RjIo1NR706ZL/2XqnH9WdLcUvfANHJzyixH109XVxd+5h99AWfPXMQrr7zT0rJZVsQEtwKC874nBnfV6qUYHR7H5EStYHtOpTBT5tQUM++sp9ti4ebmAeesyBrSJApZYboWw2XOnb2ItWtX48qVbP/cuuUEfjtiEteuDmDfhx/jsUcfxEsvvYaxsYmGStKAS2fKKsAzXTCs15/dZxigIHC6g3Njr2ECw3oZWQiGBaRlhwENHvX+5auh0kCyiPklAJlVEJLe+6YQQSJBghoSCDDF7XPYKsBYc1AMOP+QVtv4x5MOYie2b7Ap8aXCIlkAGnKDS1VWz712Zzjg2QIT3NPTg5/74s/g+PGTeO2H+b3bczfVAb/UzOdnfbdmzS24PjCEqSnXejEPDH9aCmbN+efOW0dyhH3gOZ0ewun1NbDKximcPnMezz77MKIKg0hUMPAtyhVu1fzcEMA5mKqqoAIOeNIdaR6Wimlgpfc3tsdw5swFLFzYiyeefAgvv/QWpqamGreXU5QDCM9x0eAkXUWQKWnBMJSEYi7nN18SG0rAkOFMAjmpcvnOWkIuochZBRHvdHXOxm2HMtXs1hF0iDrP10inU4A4azvUAVgXphIWvDhAzCF9ubdqfGgWSaaLZF9py2LBs+Yp/N5j1MssN5Ogl5bNrJjMOX72H30BcZzgT//vd9pev13fDDgzf799GfO6O1bj7OnsAX1QF6HNStHAzReNVmp6Q7S5qpQ/akYwS181Z86cxVNPPYo9ez6CENn3x83TVgkAJE6cOIWFCxfg8ScexssvvW4L7+S1Vuo0GPan5VWUTrPDaTBM6yCPzRDlgmEJaYCk858ObIddEdzxh8HePNmzne51gshKgQJS6iepjyVRNQiq/sw0GPZTotL+mgCxAILCpUVBw6w0Jn//qeinL4m248GggKVrp1SmSFanA+S5Vugjw+8qFY4vfelnMTExgT/7s2+1vqkO+a5skJrPCislccfGtTh29FTLKVqZ68sY296MNuefO28dK5bVrgUguglLSA+R4eFhDA2PYt36W3Hy+IVwfTl9hf3Bsx/Jy7qBtMOCluMaRlioOHAKfml/qaroZj0QLEaCGgSrmodmyBR99NFR9PbOw2OPPYBXX30HSZKUilL5jJNi8BjfAlYYxOwqC4YVTGq1B4b1eW083yHD5bGNAUAGiD3W+6hZ1zQgBlw0XW+vqP2Tl/dpdd1mLzx2mL6n/GHfocPLyfQDM5T3RA6N5vcdLLXBotxjMnKqInUs/jH58q/s48tmapsD3vz7oh3Wd7YHoT/5k89h8aKF+G//7Y8hRLvqEerdXd5J5bK9JVsnLVrci76++Th/Lp/ZKgt2i/KEyxoHbxpg6YQpNc2I85yj/ZG2gYEhjI+PY/3623DixJkOsTAzyQpr27fvI/T2PoTHHnsQr776NqSUjSkfHQDDABpaKwGNgV89X7FU2gfDjTnD2WDY+kkPENO51YDSAUSyNHtK7/PY3yzwmx7fZXdB0J0kEmpRaVhiqm3CEVmmOI8ldseRD4azFFx5AD5r/8uwwVmy6PyZO3httwCCAeDv//2/g9758/Ff/+vXkSSp/Wg2Nr2BoHH58qWYP38ezp07Bz/FT6sV22eFb3ZAPOefO29t/9LctBdqx0LHUgyCHXgQLuqmJI4ePYlNm9eCGqhT3cDsfW39MP02QVLp6oExq5n8EXcl6UeyfgAz7+Gs9ykKW9KA4733DkABePChHQGobAmcsOJWTG7AoCOTCso8aM3D249u0rxKNrz8eZwcKNGtfexvkXgv41BUDCkTO692bjX78qeHrxjUoonOvz4/wubkKLiWTX6rJh2ocPk81I7IgV7TmshCYNcOwbZrUu6VqJqJTOvPsarBNVNqbPMkg3MkMl9Z+15WQeHuk9Zk12mVwUyC4CwH8/TTD2Pzlg343//7W5n9tNP3rN++qaVtl3yUtfLM2rDpVpw/248kbj+K34ozbSU3fM7m7Ga2I0dOYMuWO0rPX65d28wOTJVSeOed9xFFDA8+uMObng5cNrZW8p+tvl8Nnr/ecjJzXtEwrRE8hso734c0gDU4X+G3ViJf5ReGkkwDwoQlmS+/kWAC3ZIwZrqlYYJadhtEGjspeu9eiayZFKXwpX2/m8/3yXZdqW3oacYvM/2y+573j46Lxc1ZbG96Mza44ZrKDd7MkB9uEQR/7nPPYcP69fiDP/ijUKXI+KyC4OJ7v/FeAoDNWzbg9KlzSBJROF/abi41ypzdDNbWlZw3mMxrJ5K29IWYC4JZPgt08UI/FIC1627NvSGbVZUtLHkP1zOXgFGsaohRR53FqNsOdBJZTc3zBvhKAm++sQeLFy3EvffelbndLMt/UBT9hH4r+UYwHLwyF8//rgEkB6DYc2YyKfVSyoFsHxAL00+XghIEiv0+xr6j9B2v/3LOSgUBFR9Ah8BWNE5Pr9M7fr+fclYAgeaxx+IBcYqaZ57nNsFRGvSmB2T+K/v3Lf4ecIPXvEHsjvvuwaOP7cSf/t/v4GqqCA2QlsHzmwoEL1gwD7fethzHj7hqlH4AJav4X9arXaNr6UaY7MBrzj6dVnYQee7cBUSVCGvX3tZ03nQLwWKbWTCcJAKvv/4uli9fgq3bNtvpZcBww3tvYO5AcgiGfUCs5xUNCqBOgOF0n2EfEBMoTr/86T6AFIx8oudrU34ykTqQnMgpD9QS4CX/Hje8EPj0JBMUFwFiF7COA2Ccfinj2yUdo/HfWWy2D4KbscFFsugZtxZB8IMP7sTDD+/CH/7hn4UVokv43xvNli5e3IdbV6/CkSPpIpaffu8z5587bzdcGl3WGFzODKCjtwc/Oo7t996Fi+evZUou22GCAwcHAaGo0jFHwmqosUlE5rQJkxubMN2bNqs4RHAMRkZVr8d47fX38dyzuzE2Po4Tx8+0vJ+tHJHWFpu8XU8mXWQB41wmtxQSaCgalOpHnKoq7E/3K1WTFIyKb4WtmjxpGcnaU5Jj/yHtgx+VkkZ7XwBwMmv6pI/AK/Bl32lpmvC2n/V7F7G9weClQKLuT8+qgJ617SIpdCdylMoA1Q13rMXf+lvP4K//+lWcPHmucR0oP/gtkkU3BpyywW4WCM4NoEFg8z234dyZyxibmMicJ289eS3kimTR/vJZ1dPbVd60a1Lpp8V0lp+zT6flPcPTJiXDxx8dwfbtd+HChUsdbqXUnkw6b3/Tz5+pqRpee+1tPPvcE5gYn8SpU+ft8s1k0g3v0SiVThffooC977/ShbT8tKD2ZNJeNWm9Y3q9kJnyYb/uBR0rTW8mgRYUsPb8lt9SKIst19MZSHWmFPlpUr+5bhR0Tm1+tCczt+eZ3jd5doZBg/yWjmlLs8FZ39/Mduedm/HC5z+Lb37zr3H6tOefbwoQ3Pz+vmfrFpw8eRaTk+laOzennLmTNuefO2/TAsJFOb154CArYuovE8wLaatHA6nIMSKcP3cFW7ZswIaNt+L40XJ9xGQDAMvPGaDtUrufRJH0mUMgQZV1me+1VIjYxSI2nGxifAqvv/4BnnzqQdTrMc6dvWidjTtH2ZHm1BE13VYeGHbWePxlytD7oMr2KwZgik9ah9SYV5xaj9Ku2gHgEBB3VbvQ09OF7p4uzJs3Dz09XejqqqJaraJa6UK1q4JqpYKuriqiSoQKj8A4RxRF4JyDc6bziBjD5jtvx+e/sBuJkIBSkEpBCgUhJEQiIBKJxLyv12PEdf23XotRq8VI6hJTUzHqU4k5c2GAJgsEpa+HNADOPredd6StgOB2WhAA+p5as2YV/sE//HG8+cYefLj3YMa6W2GAivZxZpzekqULccvqpXj5ex/kzpMFbGlas37qNzqaPmdz1gkLiy369ST0+9Onz+POuzbhjo3rcPzY6YZlbxbLGl+MjIzh9dffwZNPPIJ6PcaFC1cy5y0CwzRfGqS56Q4Qt5I77NfIaAUMA96v5Pl/lyfsgC8dG33vs9NpABxVGOb3VNHVVUFXTwXd3RVUuyJUqhEqVY5KJUK1GqFSraAScXDOwTgD58z4Zw7G9Oe77roDP/GTz0EkQgNjJSGEghQSSaL/ikQiSQTiWCCuC8Q1gXo9QVwX2j9P1lGvhS0a8wpo6uPLAMMqBPzN2OBC3zoboLgFNnjt2tvx0z/9U/jBK69j//6Py29i1vxW8flauXIZVq5chu/89b7M72+mZ8ucfTKsLSA8W5K9IPrHdCQj/cDZv/8IHn3sPpw/dwWTk7rFCWMRoFIPu9TDiCovNwPB9F4gBpNT9pkiWIIYNTBETg6Emolmljs/AwNDePPNPXj00Z2o12u4fLm/HAjO+Zwl0fZmQgMYBuD3HNaWHfFuXFu69YEHir3dUF7bI6DxIaWURFdXFQsW9GLBggVYsKAX83t79efeBZg3rwdRFEFKhampmn3VazHiJEFtrIY4SZDEAnGSQCRKsw8KEEIadbc+5mpFO+hXfvA+hJBgDGCMIYoiVCtVRJEG3VElQqUSoataRVd3FQv75qGrq4qu7i50d1fR1VWFksDkZA21yRiTEzVMTtYwPj6F8bEpjI1OolYL2+0EaQMFwaDw3DTmlPkWFFFrEzi3CnibtTdYtnwJfuYf/QT27zuMV155pyXnmVVQLW//2m2LlPV9KM0Edty/GUcPn7PPk7Tlsbs0IM0CwXnFsrLY3qznqz+YnWmba8/wo2tZnQSKuhQ0zu8zxgr7PjyIRx7ZifPnLmFqKr8FmZ6/lSu888Wz0iqla1cH8PbbH+CR3bvw+mvvoL9/IJg3DwzT8v58abVS3vLN2OHiDgrFYJiBWwY4/XsKllKiEGsLiWpXBfN6uzFvfhU9vVXM7+3GvN4uzJuvg9OcA0IKTE5OYWpqCpNTNdRqNcRxHZOjdcRxjHocI67XkSQCQiYQQkBKaf8CDJVKBdWuCr7//dcghTLgOEKlUkWlEunvKfhdqaK7qwvdPT1Y2Dcf3d1d6O7uRk93F7q6uiClxNRkjMnJGqYmYkwZ/zw2OonxsSnUa40kjp+iVASCiwP63ue0UqsILGcWPC1pLYDg1atX4Ytf/Id47729jW2SZhlAlgOsacUXcP/Obfj4oyOp50lxCmRr6Rc3t835587bDZNG+w+JsoNB3VPYn0eiv/86Lpy/gjvvXosP9xyz32WB4bQ55rFELzilc0lgwLBAjAhVO/BVkLa4UrM8ElofAFy5fBXvv7cfux/didd++A6uXr2eOZ/ez3S0ttUHZwiGAaQAMbx1OifdqgUgRhnJNCSqlS4sXtyHvr6FWLSoD4sWLUTfooXo6enB1NQUJsYnMTY+jonxSZy9PoDxiSlMTdZQm0oQx0nuAyxUCjjAnZbUVitV1OsxRkdGvQILtI4oWD59TfrAKYoizJvXjfnz52PevC7Mnz8P8+Z1Y/GShViwcB565lURxwlGR8cxNjaJ0ZEJDA6OYGRoDPV6kntN5gGgTlmzAa3dZpsOua9vAb74xZ/EyZNn8Td//VrLEWR/QNgOI11WFl20X2tuXwnGWaAwKStrBorl8M3262YxOc32DHPSqx8tK1L8XLp0BZevXMXdd2/G3r0f5a6jvWdO2md1zmjwfPHiFXzw/j5TSfotDAwM2e2mwSwAlGGH9Xw8Nb0cOwyvg0K6onQRGNbrKz5blWqEhX3zsKBvHhb09WBh3zz0LuxBV3cFtZoGkhPjkxibmMS164MYH5/AxOQkpqamUKtPBkAxnR/dqHZr3JNKpQv1Wh0jIyM21c36A6pFAb/4aGTbJTEW2WdxJapg/rx5mDe/B/N7ezBvXg/mze/GmqXLtX/u6UK9nmB8bBIjo+MYHRnH0NAohgfHUI8Tc34bi2P5li+LnmVJdAsgeOnSJfi5n/sZHDp0FN/9m5daWE9n2eDpANH162+DFALHjp1AFqSbLsj9JKi15vxz523aQNhKZjIGgD5T1Uwu2MzSrJc/oP/ww8NgYFAq7ClcBIYJHEklMwFCVmRc5wwDkFPgrArJkiA/lXJj3D6H681z9ufOXUKli+Pxxx/Cyy+/juHh4Yb57TE0ZYOb3SIODFN7JaAIEJMVPSAaGQHOOZYsWYRlS5dh6dIlWLpsKRYuXIipqRpGRkYxPDyCs2cvYHh4BMMjI4jjsMewc3heIaYyN7AnG6P8Ibrxdc5RRo6wv+5UqybADTjIhADGxiYwPj5ljjgENFHEMX9hF3oXzMeCBT1YvGQB1m5Yid7eeZiYmMLI0BgGB0cwMDCCwYERxMb5ul1JSalbyPHNusby+kXnsq058xdZb+98/Nw/+SlcvTqAv/jTF0svl5V312wfs5xVcN8XXKt5RezIzp/tx8DVUTDFw+yBEutuxXjG/tpnJYtuaAVpZf5NZ/k5+3RZURDNzpMDiD94f59h/W6ctQO0aZkzZ86jq6sLTzzxCH7wg9cxMjIGHwwDzaXS6c9ZucO0POAAcRE73AoYTjPJPOLoWzQfi5csxKIl87F4SS96F/ZgarKOkZFxjI1M4tzZKxgbmcDw6BjiOIafN1vElqZBYsiiFqV7SWhH7AHMVE4zfIUZo4CAm8ZYhEQkGBkbxdj4BHC18ZldrVbR29uN3oXzsGDhfCxZuhDr77gF83t7MD4+ieGhUQwOjWDg+iAGrg8jTuJCNnjWegOnrQUQ3Ns7H//kn/wjnD9/CX/x53/Vwnra83lZgLT1ezBkcZWSOHXqHC5evKRzyAuY3qKxzSfd5vxz521aQLgMW5UGsEVyTn9A2MpgsB7XbHQQKpsZ0mxyYxVpmp657zmOXT/449CZeU6hHTt54iy6u7rw1FOP4OWXX8fY2HiwvXD7aVlyWRDslnBgGLD9hsGy5TkN8ulGi6IIy5cvw8qVK7By5UosXboEcZxgcGAIAwNDOHvuIoYGRxrkcZkAn5y/0lIYlQKpwa4hzCH3pWg26m7BbQSlVFCt05m01096W+kIWlaOVrAmITA0WMPQ4Gjw20UVhkWLFmDRkgVYvHghbl93C+b3dmN4eAwD14dx/dowrl0bwtRkrbS8vlUrw7S26rC6urvwj7/4kxgfn8Af/p+/ar7ALFoe65rlECmgMTVZz15mmiDYl0fToDSQaPuBQ/P84ymlwpzN2SfN6nUX5CxS9TQWUCz7HOq8TNo3pSSOHj2O7u4uPPnUbrz80uumFVweyG0uldbzZbPD/jry2OFWwDAAVCOOpcv7sGz5Iixb0YdFSxYgiQWGB8YxPDSO82f7MTw0hnrNKZbyikeVBcEhCyy99wiO3z8H1NXBzW9n0MFtOn/mMxhsMU1O80CfnzQTTlaPa6gP1TA4NGKnSSVQrVawaPEC9C3uxeLFvVi37i7Mm9+NwaERXL82hGvXB3C1/zompyYbx4SpVLq2rBV5dAsguKenB7/wC1/E8PAI/ugP/7yF9dw8TLANqiiByck4mN4pcDvdY73ZC6T9/9t78yC7rftM9APu7X1j781uNrubzWazue+bSFHUYsmy86IsLr28ylixY89U5jk1Kf1lpzJOaqYqninXTDSV+JU8k1GcTbHsJJYniq2NIiVxFXc22fu+7/tyV+D9gYt7D4BzgANc3O6miI/VxW7g4JwD4OCc8/1WD2w4IsKs4D8sqBs8vVbYzsBLbBBFqhZXCceP+GSo1xDJME+lxGs2Cqh+PNBMurzExUD+44IBH1pbu+D3+/DUuVO4eOEKFhcXDeX5YSX1UckwoNcO6yEwPvCi4mJUVm5GeblCfAOBAMbHJ9Hd3Ydr125gZTlGeimmy+ZdS5hTq2SYTJauvQujBJ4lTVbNyVlpgZSFU4pHpCZzWJPjiVxg9f7PtIVeFehIYWBqag6TUzPxMpmZ6SgqLkBxSQG2N1Tj8NEmLC4uY2JsGmPjU5iemoUUSUjWNfdO+DORf9NgtbHkmcRpC056uh+v/M6vAwD+/m/fhiRJri6eND91UgCl9IvPxJjlp25m+kyDNpK4E9cBrQDNjAxr2xWxFp7CnunV4w2WNRQP2KaiRiJIazcVcGuD2tzcgrT0NJw79wQuXLjMTYYN5wykl89c2i4ZLirahPKKQpSWFmJTUS6Cq2FMTS6gr2cUs1PLWF1JCKWTDR5FI8GkFli/VumRsNKSdc8IUNd+CFrBd5wMx36P7/+UCuPrdlTmW5NC4RAmJ2cwMTEZ71NGZhqKijehuCQfO3bU4NixvZhfWMTY6ATGxiYwOTmDqOREaM0Q3liRYZvfSGZmOr7xja8iHArjb/76LVuWGbbie7j67bLqokfotsLnQQtMwluf3YctIkw1KXUAJ6aX2g1iggwr/YrG/yfJsNKWT+28AXbuhVzU9ISahwQbApAIukUvdj/Nze0QRAFPPXUKH330KZaWl7j7aB9GMkwvpTw8v9+H8vJyVFVWorJyM3w+H0ZHx9DT3YNr16axvKxqsRM+POatmzz/GBkG1E2EUq9+E6HUYx65U71FUuJMHX86s2hSkkwjxoZyhgBt1nm1V1dXMTy0iuGhMQBAWpofpWVFKCsvxKHDTUjP8GNyYhbDwxMYGRlDMEjXVvLAyXdnBoUE/yZEQcBf/dU/IRyKWF9E6ZPpeS4NdkJQwVq8aSTYLgGmIa7hZQTDckKUDVY0a6wNTuhjnF/v4fGC9bySWF/c1OKsNW7fugfh8P64sFohw4Bdv2H1b4CPEPOQ4fS0dJSWFWJzZQnKKoogCiImxmfR1zeKmRuLceJrFuwv3rdUk2DaeCHKkaReiK/foJpKx/8WKGu3iTWZoXnKer26GsTQ0AiGhkYgyxL8aT6UlxejvLwYR47uQ3q6HxMTUxgaGsHw8ChCIfb6zONeoBRUXcEk7d9cSJTNzEzH7/7uK4hEIvirv/oHRCK69dlUMbQeJJhNgAGt4J8l1HaKR8niyluf3ceaBcvSa4WtUuporqVoXEkySkaTVnP/6k2l1Y2yPlk9X9/ZJNgM9AARRL06cym13L27LRAg49y5J/DRR5ewvLKsvY53QuXspZYMayGKIiorK1FTU43KyiqsrKxgeHgEV65ew9TUFCQpZlId75wq6Uws4BpSa/Mz1GrMtJspwGhKRj1HmEsrtJ4h0CF8iQVBn79YaT/+/ikmWKC8E1o7ZgE4QuEQhofHMDysEOPs3ExUVpaitq4KBw81YWpqBsPD4xgeGsfq6iq1nrWA3+/Hv/nqr8PnE/Gjv/onhIIh24tSMiSY3DTqj7MCntkFi9zSwCpndj3ps/coLcQeHj9YrTlsIRs5dxOuOBuQDLNctfS4desejhw9iKeeOoULFy7Hcpmyib7Z2kQ7RvMfjqcy0uUTFkUZmyvLsaW6HBUVJVheWsX42CxuXmvFzPR8vE/qPKREjjbPbw/Yi6BMM4dGrBYDCWZYGsTnelnWEGJBM1YkyDKhLSeVG4SptP45xSrjglaQbQzyFQ6HMTg4gsFBhRjn5WehsrIc9fU1OHxkLyYnZzA0OIzBwWEEgvoctzZh+/tIlE9PT8fXv/5VSJKEv3rjH4wE3SUS7B6Mvr6qiTzLmiDxDZncyxrPMZ559KOJdYsarYJlTho/T2hHWB+oGPMT0QzCmHTQx/EhsMyt+e9BS7I1wY0sTIKU642biLt3H0IQgHNPn8YFChnWXA8BbjvAl5eXoba2Flu2VCEQCKK/vx/NzQ+xsLAA/cQTD7il+hizyDDgYHKPtUEshgloN1Q0czQg8XxlWYQss5+TVmKdOEZGqUz4bCXK6U2n6f1nk18WJFnC0uIKOtr70dHej6ysDGyuLEbVlgrs29+I8bFJ9PeNYHhkLOXBaMgFxe/346uv/BoyMtLxxv/+KQKBoK0Fh2kaaUIaWQIovXaEXq/zxdAOGU4GenPp9YQsJxmMw+Qb87D+4IkQ77b1iBaxtcGEDDsPrGNf0Gp1jjZ/3LxxB0eOHoiTYSX2BZ+glnZePcarHS4p24TamipsripFYDWEocFJPGjuwspSTOsbjz8gaeJYWM2E+hz3vCRY89xYBCb+Nz0Pr0wK1An3KCsyHB+rOlNpgG7dpT7XRNva/uvjh9D2cOqxhYUlLCwsobW1E9nZGaiqqkD11iocOLgHo6Nj6OsbxMjwCGN9ZphHO4KeBP82BEHAG2/8DUIh/j1takmwvm7jvfOQYDJyuPWItsajJoT21mf34ZgIawJbcUSEtksEgUTAGDVCILVeOSFR1Gj6YtJBGaJBGkjrCy8ZJgNukZtwQ790aQSc4M6dhxAEMe6TtLyybOFDQk6s5KpgBeVhZWRkoK6uFvX19UhL86Ovrx8ffXQRs7OzuvL69tXnIZuTYcDYd3VCMyFSmsBXIDcPxg0VLf+scaGWrTdagvqf0eeYRYjtgDYuWEHbVKyuBtHdPYju7kFkZWVia00Fdu9pwMHDuzA4MILOrj4sLqTSlD6mCX7lJWRmZeJHb/yUmhvUTMNphwTzWl/Y0aiSYyWxOXSf6PL2hwyItd4EWIVnevVoI1VaEFqeb6t2E/OsftNvTYY3Cljzy80bd3Hs2EE8de4UPr54NaYZ1l3LuTaZ+Q+rbaen+1G3rRp127bAJ/ox0D+KTy7ewvzcItSIyUr9aoTo+A3E6yT3aaRbh77PZHwLlQSbPiONSTRFsWFCgjX1gNAqOyXDxD3rCTGQCDymh4EA2/SLX1lZRWdnLzo6upGTk42tNVXYv383jh7Zj77+QXR19mBhcZ6rTqdIT0/H17722/D7/fjLv/wRggF3g26m2heYti9jr4t082gy5Zbm+AafZ3jhrc/uY901wrygRZk2lImZQpPBg6TYsOHZVLMiSOuluWQkR144lX7funUPhw7txdNPn8aFi9po0gDixDihFbZPhgsLC7Fz505UV2/BxMQk7t+/j2GmFJMGwuTNhAwrPXKQH1avgdZsFugbKnIR05ql8TFWrTk1NAsxPecjva+J/tCfpRX51UMV1KyuBtDW2oO21h4UFeejbls1nnvuNKYmZ9DV1Y/R0QmD5M+phkd9rukZ6XjllV+H3+/HX//VP2F11b45NA/sfFckSK2wWbRl8jlotSV8Y9NtLTFJhmlQN4JrESgL8CTOjyoEQXT1ezTTCrvjnqOu08Z2DUERk9BOu6HdZpHhGzfu4siRAzj39BP4+OJVLC+vwGAxxVibWMG01L/VvhcW5WFHYx0qK8sxOTGL+/faMToyCciqCwgZWJKeLokmSNb4BFOs8lQSTB7nM4nWkmk2CTbTxmvdquyQYYFSnrZmm7Wtfy48ASlJLC+voKWlHS0t7SgpLkR9fS2ef/5pTE5OoaOzEyMjY7GSbmiFlfeZnZ2Nr3/9tyFJEv7yL//GNgm2Et4mN7fYuTbxrGkWom7vOR411yRvfXYfjohwsqaGTsFjOqiS4Xh7cljZ7MZNZFQzV6NvYaIdo+Sbld+UZlZDSypP/k+D8VzCrOn27WZEIlE888wZXLx4BfNzc9Q67JLhiooKNDXtRHFxEXp6+vDLX76LxcVkNIoq6aWTYYCyCYiXsT+utObSbO1C4t3549fZq59x3oQQW8EuAda3SWJ6eg7T03Novt+OurotOHhoF/ZLO9HW1oP+vmEugYbV4pKdnYlXfuc3EY1G8cb//kfToCDJQE+C7QoWeKGxHIFRW2IG21GmdSQ3mRzBa0WGPTwesCKJ7pJh1qaf1BpvXNBMpWVZxo0bd3DwoCKs/vjjK5o8w4ly5hkP1DJAYi7evLkUjY3bUFi0CT09g3jv3U/imRiUMsYgWqQmVE+G1b6z3qc+0GNi/2I0iaZer9MG6+81AdZ4M2pkzckwtESX2OboyXDimFFATu+j9T6Bd+xPTc9iamoa9+49wLb6Whw5cgjRaBQtLW3o7x+AJDkz61egXJubm4vf/d3fxupqAD/60ZsIhUJspcOaaUadtGPiE8xhEu1E0eLBgy0iLEBk++mamEc73bRabRhJcx2RILsQdBLOmMlM3GSCMCMyD8Jlbiqtz0tMkhtSO2212Uh8+Oz7vX+/BaFwCE+fewIff3wF0zNT1HI8ZLiycjP27t2LrKwsdHR04vLlK5pcj/ZBT8WkCaJlAZ4ogOYLU4IMs+rhWbicmOqxpM3q2HBKelnt6KFaSwSDIbS19aC9vRdVW8rQ1LQdu3ZtR1tbD3p7BiFJxmfIc695+bn4na/9JpaXVvC3f/O2MfokpT9OQX5TZvXQzul9hHlINI0QpxoqMU6GEKcSMpLTUXjy5s8XnGpU2cGzJEMZUqDJVw8N9vvIY2nGgzt3FGH1uXOn8fHHVzE3lwhUpYLqF0zRDm/eXIo9e3cgIyMDnZ19uHz5FiIRUlCv1XSyIkob/WR9lveqzkk0Eqy5FwttcOIPu8/WfPagBc/UPwst8TUKAPSm6tR2KBpyHoUGCf3YCgSCaHnYjrbWDlRvrURTUyN2725Ca2s7env7ocir7Twv5T42bdqEr3/9tzE7O4e//ds3EYlItgmh+xpR6/rMYhW4TYI/L2bRgLc+pwJJm0Zr0hpx+Arbqc8M+ujPAkT4YhOfpPoHy1qfYSEWQMIqsFEi6iw98JXAIL+GekjttECfQPUk2GySbW3tQDQSxlNPPYFPPr2MycmJWN1av2E6GQZKS0uxb99e5Ofn4eHDFnR39yAajQJchJUnLzFiddE2NPRNjibHsNVEx8+rDeavdq6xMmNnntNtatwiwGTdZD+YZWUZQ4PjGBwYRdWWCuzatR1NO+vR/KAd/X3DttotLCzA7/zOb2BqahZvvvkvjoNyWb0DWr5ge/WzTaH1/neAkYTS/OmSAY+5lRoDIdno1m5DkhVPvWSu9/DogPZ90MrYIcPmZVXTVu3a+qhvVpubWxCJRPDUuVP49JNrmJ6eBa9WTEYUZWXF2Lt3F3JystDS0oXenkGq2TjN7NcqvZKqHVZBzjn6uZAkwfo+8mqD2Xs4h2siYTmmtkezBmOmUiTmY0tLLxfX7TiIPZokyejvG0Z//yCqq6uwe3cTdu1qxP37D9HfPxi7wOr7UVBSUoyvf/23MTo6hr//+59ar882vzGWub7bMLc8UUkwf7tu9XEjmk2v1/r8gx/8AN///vcxNjaG/fv348///M9x7Ngxatkf/ehH+NrXvqY5lpGRgUAgyUjqKYLrPsK8ZFhPVMw+BCsNSkKb6ANklcBqBzH5YWgGtmCc8MlyibrsRN7zkX8oE28s4JdBU20THR3dCIWDePLJU7h27TMMD48w+pAgwzk5mTh48CDKy8vQ1taOjz/+lNDqUUItGpDExla3gFmD7QOS+H7NFlm6RoHmc5Y02aG8P70/UrwsU5trnwRR26UIkARBxPDQGIaHxlBdvRl79zaiYXst7t1vw+TEtGU7mzeX4v/57V/F8NA4fvqTX/KZWOuef6o3t6RQi5Y2ydy0nSTGQKpSGZmRXf1xs2imawUZSfogeTLnRxJOzKR55jjW9eTfyc8Tzr8Vt7TCgCKsDocjOHv2JK5cvYmxUaPlln7tyc3NxoGDe1BSXIi29m50dfYiShjd6MmIUzJM3i9tL6WNipxQNLC0wRsZ8ediQoZVmI37lM7BsoiBgSEMDAyhtnYr9u/fgx07tuPOnfuYmrJen7dsqcK/+Tcvo6enD2+99c/x43ZNou0GtjSC5fKQPJz4BLtJgtX/NxIZXo/1+a233sKrr76K119/HcePH8drr72G559/Hu3t7SgrK6Nek5+fj/b29vjfgh1N1hrDFSKsX0j00QjV8zSCbDZo9ZtEVYOib1upx5f4P0aGZZ0/jCjwa4Op53QmTawJR5/jmCTAtEmXy2SXeL69vf0Ih8M4ceIo7t1rRldXDwxJ2KHkAN65sxG7djWiv38A77zzCwSDxii/ZCuxO6Uc44VqJi1Bhqgzj6YTVVLiJwiiyUQea0HTJf0ErP5NN0tjTQJmkm5y42FWliyvtMXzXq2DiPEKYVhkGAAGB0cxPDyOhh21eOLUIYyMTODevVYEg3Rf3+3bt+I3v/IiWlq68C//5yPu9vXt8sK2KReFAAPmQbIAdWPHcnVYX+2sQYgAccMtwh4+33DLZ5hGes3qVJDMOE/dZlwPq2+yu7sPoVAIp04dxZ3b99Hba7TCkWUJPp8PO5sasLOxHn19g/jF9TsaFyVW7mE7ZDjW4USdFubRpFKAJME0bbClWbQLoO2zaIGzWCbSZmTYTp/t3Jcd6wn1m+nrG8Dg4DAaGxtw9uwTGBwcxt27zcxYHDt37sBXvvIS7ty5j3feeZe7b8mA/g2T3y5978Vfl3KtQA1rk7zS4lG3OFlP/Pf//t/xzW9+M67lff311/Gv//qveOONN/Dtb3+beo0gCKioqFjLbjpGSqNGm2mHaYMyPmlZSGgT2lV10WXlD/XFyZPVRtu6Tb7AIPpgP3Hf5djHTWvDbAPBwtDQCAKBVZw5cwpZWVlobn4Yq0wxwSksLMTxE0cRjUZx4cJFTE/PMGqiEUOiw46Q8BmWIUNQ70fvByaIGhIsCmnxcUH1H9HxdOU4awNkPSmnSqrt1M+YNkbtbjRYAeWUcQW0t/VgoH8EBw7uwvPPn8G9e23o79du1A4c2IUXv/QULl++hU8+vsHVnr4tbRln5JKHBNJIsKaOJCX9PIGxeIiqFclmSuXXgQRrjRydXe/h0UUyZFhfjg1l3nZ3c8omw079nJ1icHAEwWAIp544iqysLLS0dIBci4qKNuH48YMIh8P46KNLmJtbNNRBC6bFQ4YB0i9YL8Sl75n0BFjfD/IcjRCvCWxbl7E1w4D13OqGNlizr9S5sOmhBtDq6+vH4cMH8eKLz+H27XsYGBjSlDt69BBefPEL+OijT/Dpp1eItkzWKpvaYGfgUyKZlVH3dFrlobWA3SwCOw9oe42NKoh2a31eWFjQHM/IyEBGRoahfCgUwq1bt/Cd73wnfkwURTz77LO4evUqs52lpSXU1NRAkiQcOnQIf/qnf4rdu3cn0fPUYWO9YQJqYC59gC6t2aPPhASLigYYPt1HEqWQBKMWSbHCZ5uBKWWimkWCDPLDCixGmno43QSo/ZiamsWHH15ETc1WHD9+BKIoQBAE7N7dhGeeeQoD/YP44IPzmJ6ec9ROcki49Me1sAxTYpUEi6IfoqD9Ud6jP24+b6oxpsLZlEH152bUpY4VrWmZ/XYNpNemBYWmnIm50+pqAFev3MatWw+wd18jTpw8iLT0NADAk2eP4cUvPYVf/vLimpNgURANgiTad0i/lq4h1tdjFqBKjMUQiP/N6Dsp3Ntoi6QbkDQj2tmPh0cb6lxrdp563MyyxTAnitTfnQT0o9ep65vJPaXiO56YmMKFjy5jW30NDh/ZD0GQIYoC9uxpxFPnTqGnZwDnz1/G7Oy8icCOLQjVR9fVBnhSCXGijN70mfxJXCfFr1OjROvrpv1NXWep92T1nAXdX6Sbmdm12mdAWzutAjLxnmODYwzRFEC6PerKyio+/fQKbt++h0OH9uPEiaPw+xWd1XPPPYUvfvE5/Oxn/xInwfrredrkAd8eJlVCEJH4MUcqI0VvxPXdrfW5uroaBQUF8Z/vfe971PampqYQjUZRXl6uOV5eXo6xsTHqNY2NjXjjjTfw85//HH/3d38HSZJw6tQpDA0NUcuvN1zRCNvRrlj5D7Mkt3qNrVkqJUHwxSd3fW5gGRKpsExIfYhr9HWSddhJGaHtr09jIm28P/N6WRrrxcVFfPjhBZw9expPP/0UACAtzY/z5y9gdnaOMAVWP2g7E1cyWmFAS4ZjZtKyFHv2KunQkmABPk20ZcWUKxp/Z86kzwnTKaUv7kXrNUrP+YNy0Ouz7luy2g31+uHhcUxNzeLo0X147rnTyM3NxrZt1fjJW++gq2tgzXxzSPLLSmum9ffn16wmNnb0b5vlV6wnweu1IG5EibSHxwdmgbTYa7WdtErk2E5YC+nNgXkCevHCbI8B8O1neL/L+fkFfHT+Es6cOY5z507B51eE9+c//BTz81otMMtfWu8+Y6UZjt8jYSoNQGsubQKDT7COcOv7ZR8szX1q5jna84qf02mH1ysug9IHrb/9wMAQJiYmceLEUbzwwrPYtCkfNTVb8Xd/9xZ6evoM19ArdfuZ6utbX/sfN0nw47bWDg4OIj8/P/43TRvsFCdPnsTJkyfjf586dQpNTU344Q9/iP/8n/+za+24haTfut2Jw24OTiuYpXTSH09IOSWNFDSuwTXxL9RrqkhYSV/JMvE2KVphro86rnVWy4oIBAK4c+ce9u7djR076nH58nXMzs4xKtBL2WgO7ILFeTtQibQEvX+uKqEXRT98Qgb8Qgb8YgZ8QgZEIQ0+wZ/Q6nNM6LIsaX7cgqwXpFiV59AMJ9tPLlMjjs87GAzhsxv3sX17DY4d34+Pzl9Bd/eQZf08QbH041nV+Op/7PSX1geWX7CqAY7K4djvYcN3r7/eqNGmzy80gZ4o+EzNn+2mSyKFAGsJSZaT/vHweMCJZpgOifg/YfznbH5MblvDMw/ZmatWVlZx795D7Nu/G7U11bhy+TMDCSbBsy6YaYa1x6Ka/YlZJgP9eR6tqiksTePJ390nIHaIOq/lEQ0sN7/47/p741q7le8nEAji2rWbaGragSNHDuHddz9Eb8+gtRaYsx3Ty7mud+e96ce8xgKQ+NH07zHNGezW+pyfn6/5YRHhkpIS+Hw+jI+Pa46Pj49z+wCnpaXh4MGD6OrqSu7mU4Q1E3/oTQ7JH7ugbTb15sgGH5jYgiAjqiHDei2zGRlOlGP7cqqLCfljBcMHbmW+pTu/ZUs1nnzyCfziF+/j2rUbePrpsygtK41dw0NkBeL/VER2Y5NhhfAqBNgvZiJNzFJ+j5Fh5Sdhcp4M3IwmaLVwmpHhdfOxIqA+i6LiTfi3//b/RiAYxJ//jx9hc2U59uzdYX4tBwm23R8TYZaZsIsFlQTHv3PiW9fmHzeSYHVO4mmTfM+SHHUtNzDN0mCtILvwz8PnC6584xukDq52KP6CvHORfj6vqdmCU08cw7++8wFu327G08+cRnFxgWUfrIJq8pJh/bW0PYqeAOtNopN1+6GTFjYBNhC9JASmetjtf0rWZ04yXFZajv/3338TS4vL+B+vvY6amhrs3r0z6fpNFUgWbhF2wFOX28/XSjDBvO4R0Qav9fqcnp6Ow4cP4/z58/FjkiTh/PnzGq2vGaLRKJqbm7F582Zbba8VkjKN1m/OLM2eGea9vCmXeNMcUKPnUsy1SDPpOIGGGDPBjZq2FzdT0gSSsI4ATPbP2sRVMSEyM0Gr21aNgwf34cqVaxgZUez15+YWcPbJ07h95y56unuRSKWkrxtISOLNCDCvibRZ+iVZez7mGyzEtII+IQ1+ISNhoiQo/YrKYUNtWjDej2bio0kSI0g19GbSrtbNHZXSaAamor6+Br/xlRfQ1zuEf/rH9yBJEmZnF3H69BFkZWXi5o1myDrtnhUJdiKl5V2AWJGizaAXdtHMn2h+wWu9KGrGyjqbmyXr5+v5CD9esBUll1rWSLL00YATx3jbM3cF4qnDjTlg+/Y67NvXhMuXPsP4+CQAYH5+EWefOoVbN5vR32/uM2c05eULoKX2X28qnbg3+4EZk13DeE3mDevzGgk/UgnqXtIieFZj43b85m/+X2jv6MY//9O/xNbnOZx58hSys7Nw48YdRmP2rLnsQe0vB7HktOJzgsdVGwysz/r86quv4pVXXsGRI0dw7NgxvPbaa1heXo5Hkf7qV7+KqqqquJ/xf/pP/wknTpzA9u3bMTc3h+9///vo7+/HN77xDcf9TiVcixrNQ2QB8415vIzJIqVOKLRUSuQxldAinldY0nw8mhQDDDIc66TpxpROsNmplVj3moiYrdRHRtAjyXB8QhVEbN9ei717m/DJJ1cwOZnIV9jT04vFxUU88cQJbCoowJ0792L3QfsAWP46qUDMZ1hW0iupz1wU0uISSh+UoE1RhJWJVAY3QUhWisl6b8nU67bfiVuE+viJ/XjmmVO4cvk2Ll68Hj++uLiMjz66irNPHcfRo/tw48b9OBk2I8FOFybLqNAM6w8VNLNoUhvM3Q8H71hCFCLYuYf1/mdWWG8C7MGDGdyKvGysJ7EGqee0qXGUMiTp4+8Le31z0++YhsbGejQ1NeDixauYmZmNH+/q6sXi4hJOnjqCgoJcNDe3GwSOZuAhw0o5oyA2cc58v2bXHYgJHdmjKRDIc1ZmxOR5M2E32ZabcD2NEoMMnzlzAueePoOPL17Bxx9fjh+fn1/E+Q8/xrlzZ3D06EEjGU4pCXYXzD1+EsL1tbIYedzw8ssvY3JyEt/97ncxNjaGAwcO4N13340H0BoYGIAoJp797OwsvvnNb2JsbAyFhYU4fPgwrly5gl27dq3XLZgiKSLM0pjSglpZXa/XCtMGdHwyJ8iwHqxcwyKhjFTNgBJBJEAlw7SgWSTsBNDSE1we7bYZGa7eWon9+3bj4sVLmJqeMlw7OTmFDz64gDNnTuLs2TO4evU6AsEAoyWzyYPUGFst1jqtb+JOLK+lBShK1GqtNWef2/hYS9MhURTxK7/6DJp21uPttz9Ay0Otz4YAEaFgBJ9cvIGzTx3D4UN7cOvWQ2afk9ls2CHB+rKsKNEGk2hKcCwhFh3ajZzBLDL8KJNaTyPsgQWmdZJLJJmsi6YdTkWbbvZdRV1dNXbt2oELF65gbm7ecH58fBIffvAJzpw5joKCPFy/fhuhEH0PQQuiZYcMA2ASYlZbiXbMfbWtFBbKH0ayZyceClnnxoeJ4IW15yOej98v4td+/VfQuGM7fvqTn6O1tcNQfHU1gAsXPsW5p8/g0OEDuH37vps3YBs0gZKTPQ1PnBEP67c+f+tb38K3vvUt6rmLFy9q/v6zP/sz/Nmf/ZmjdtYDSc8s8YAzjkiM0SzRzGdYH4CA9gPQUyyRZcT4IsH2GVZTqQgCRUqp9lenESN/6PcaO686/3P4xyTuOSEJLy0txtGjB3Hp8nVMT88SV4rED7C8vIwPP/gIoVAIzz//LIqLi6jtmYPsox0fYrb/D6BKhNWUNmFIcgRRhEH638qyel4bBMsodNBLhs3bXmskS4rcknJu2pSPb/zbl7G1ejP+6o1/1JBgvc9QMBjCJx/fQHlFCXY01lH7Q45zViAsWlAsHrBIMBmQSt9nO3ON2TGnsPIdpxFvN/2K3YLnI+zBDMwYFkm5SWjnbA3pi2+ajUGk1jKgDy82b67AwYN7cenSZ1QSrGJpaRkffvgJJEnCc8+dRWGhud+wE5/hRFmjmTPtx+wasl43My8YIIiWJJilDXZbI+i6/yprLAoiioqL8Hu/97uorNyMH/7wr6kkWMVqIISLF6+iuroSDQ3bkmubLGP6/Oh7K1qQKzNYkWCrfTQLybz7R0PIosBbn93Hurx9q9QkZoG07Djya81o2DmHSTKsgjSxtiLDdn6UOhMklwzSQ9tIaCMmi8jKysSpU0dx5859jI9PaerT9QwAEIlKuHLlGtraOnDu3Fk0NNSbPDEW9PduJ6AWzWRU+RBlWYIkRRCVw/GfMAIIIxD/W5LDMYEFj09YcsN5PaSP3GNZFZ4QP3bQsKMW3/x3L2NxYQk/fP3N+NgB2ItAIBDElct30LSrHhUVJcy67ZJcszbNyrEIMAlSG0zWQQbR4w2C5YZWl/c+NxoZ9uDBDPxrsN051UpTmQwZTv12JycnBydOHMKtW/cxOTltWT4cjuDy5Rvo7u7DuaefQF3dVtPyNDKsiVOiI8N6E2ferAdOg2MxoyVTyK3xYhEQjHuLVARnXKsAlbx93b27Eb/3e1/D1PQsfvCDNzA5NZN4ZrQfACsrK7h06TPs27cbZWXs9dmdvlqTW6c+waRg3dMAe1gPuOIjrDf5sDL75fUnBhKkWX+N/qMzM9EBCBMhwUf4DSckmwoZBoBw3GdVUwdh3ZvMBjmRI5fhJ0ORhJM5iE+cOIzR0XH09gzq7lObgy7WGlQznY6OTkzPTOOJJ06ipKQEN27cRCSikn3t4sP2JQbsmUpre6gtT2qD/YhIQchCFKKQFuuDci4a0xbTJNapMGkj62bByrzMUFeKNmC89//0Mydx/MR+XLp0E59+fNNWn+bmFnDndguOHN2LDz+4imAwFLteGW96EuxGwCeawIqVIkkFyy9YLwzT16ccd/5+ePMNs7TB+r9Z5tprKbGWkzS98iTOGx9mPu3cdVDmH+oxiuuQ+dylrFv6MqTfsOpS68xnGNCbr/LuJ1hQhJMCTp48goGBYcsgWHq0tXVhZmYeJ04eRklJIW7ffoBolG0qbegvYSpt8Ac2+Acb50jWuzfTMjuCjbmWasFjuD512mA70I6/xL6LWlb3vF944RkcO3YQFy5cwqefXrPV7vT0DO7ebcbx44fx3nsfIRTSBhflD0SZ2mdn5Q+clIvVY+YX7K3P7sPVEeTEPNo1zQvL58VG/apmWIrlHdXUD5G6QdecN9FUkWUAaPLjqlphg0ZY93d9fQ2ys7Nx584DohDN3FPXz1iZqalpvPfeB8jKysRzzz2DgoJ8AwlWrjfT+JLt2TGTJstLgCxBlsOQ5QiiUhARaRUROYiwtBr7WUFECmpIsF76bRe8fim85+mRvLVjwHQ8MN6d3eAQ+rGj/p2Tk41/88pLOHCoCf/w5jtxEmwXA/0jmJqaw8FDu+J9pPZFd992yZuZ1YYZ9CRYpmhXaWnRaJYodsBLgh81SIKU9I+HRw8sV6OUtcexgWUTVPtzuR3YqUstu317HdLT03D3rjGmAg8mJibxwfsXkZefg2eeOYW8vFxmWbops7Oozzwk2Bp0Qmp/DAnUsUe3gmKTYDc0i6nUGgsQkZubi29847exb18T/uZv3rJNglV0d/dhbm4eBw/ui9e9Ft8vL3iDYtmBHcs4yz3eBnlOvPDWZ/fhygigTaRW/ntmBNXJh0x+bKqmxTzas5ggo7RUAhaBdsw0U6z2zJAw3aSXy8hIx779u3Dr1n1CkxsrS/WpUe+JXJQEBAJBXLjwCYaGhvHcc89g+3anptJ6ckv+rm1TfwxQpVISZDkCSQopZDgaUAixtIqoFIr9BOPm0W6D1Ca4sYniIYJOTZu52ifq3dFYi9/7978FUfThf73+E/T1DlHL8v7cvdOKsvIilJUmTLC0PvL0+6FuaqjPxdnGxYoEOyXXZqC5bbixmG4EbTCQCMaRzI+HzwfWjwyzSY6ZmbSbsLPJzsrKxN69O3Hz5j1IkvM+ra4GcPHCZYyPT+K5506jrm6LaXknZJhGoq3qTQZmwhUrwQubAJspHHRz8zprDGnt79rdiN///W9AkiT84AdvoK9vkHIlP27cuIstWypRUlycVD1OwRt8jVbeMt1okm5hnzd467P7SNo0midATXyTyqGZtVp4eX0RTNtQN5wxE2lAa6qspFZSzkMwEhwQeYaVaqy1zqwUK6rZs9ofmWISBgCNO+sxNTWLsbHJ2D1QTMHU50KLZi2IgCwpxFQGHjS3YHx8EidPHEPF5nJ8dv0mQqGQ6T0YoZoA0TXDKglO5DHWmaXJEmQhHL9vRVMMaBc50twotbDSyLtdfyogiiJe+OIZ7DvQiCuXbuMT1RQ6ybZDwSjaW/uwb38jzn94zX7wK1PNOF+KJBLq92ZFgnn7wG3i7FDToDd75vUJftSk1R48uIeEiSmvmbS1ibR9k2ce7GxqwOjYBJdfsBUkSca9ey0YG5vE8eMHUbG5HLdu3jeYvbKgrvksU+l4ORtk14zQ0EyC9WbZmmuslAKmZIfHXDp5uB4kK/ac/H4RX/6V57F3bxMuXrjiWAusqRsigoEQ2lo7ceDgHpz/8FMXeqyHBNYejI8E06/nIcHJ4HEnzR74kNQoWa8UITwSTTPQAueo2mFNO0Qkab0PtBNNk74effAssm8k0tPTsL2+Fi0P1SiCFpo4lgmR7vjkxCTeffcDAAJe+OLzKCsrtbwHgfiXqFv/Q2qCQfmb6IcaBVqWIEkhSHIEkhyCLEdiP+q7Vn9YSK0Zk9bX1Ki951nctXUaLRG4c08zIpQXF2/CN//dV7BtezX+9q9/HifBPHWYta2e6+ocRFZWpmngrPg1nN+HoAuApX/WehKsfkOSHEU05sIgy1FTEmxVp+Y6JybdnOXVCNEbnQQnL2/2TK88aOHcVJX9DejNpEnCZ9wA09YP7ZrlFJmZGdhWt5VYn93B+Pgk3nvvInyigC984SxKSoqo5Vj7IZp22Mm+Kam9FocGT6PtYwjVae+J6k7mgjbYbqooOoztVlSU49//+6+jpqYaf/m//l5Dgnnd6szcFzo6upGfl4fSUvtaYb57M35D1iRYvYZt4eHBHrz12X041ghrIixb5tC1zpmrlrNzrR3tMNM8k4jhJFLmYDmmpRQEba5QVr8UbTL/h04Gz4JAn5C21lRhdm4eMzNzhARWhBArT+1LXAOsaoVj0lpd30LhCC5duor6+m048+RpdHV2obn5ISRJgt6pnkVurcrRymvLJIw1BDIgmUA8a0IDQNcYk7+7YOZMtJ3of+I5O/EhU+pwRn5N64cPx0/swZPnjqGrsx/v/J+PEApFmGXN6gG0gVc0kIGeniHUb6/GxPhM7BpzM2f1d16/XcCcqAL0yNDM9i3qTs6fzT2Yab3X3jQaEJIwn/KW2c8XeNfveHmGVpYWNMusPE+5BPkFoFknrOp175uqra3G5NQ05ucXXKtTRTAYwqVLn6GhoQ5Pnj2OjvZutLR0Uc2vrYJokeUAvjWMh2BrkdAKk3BGfsysiOxrF/XQP69UmtmfOXMcZ8+eREtrB37+9ruIRFjrs/NxGYlE0d3Th4aGbaaWCfbehc6KzwJmPvx22/YIMx3e+uw+HBFhOyRYhVk0WZ6PX4QPEqJci6Yo+CARpsvqMT3iKZIEn8HUWTkvxa5NEE7oTaVjUajVa2gkWCL6K1IIlgyFPKtkGDpSXFu7BZ1dPUSbSZJhCrq7ezA5OYnjx4/i+apKfHb9BqanZ6hl9TAPrmVRXiAnWlG7cMsKGTaalSf3KVv5G9NIcPwc5TnzBsTS590FtGNDWyd906jHpk15+NVfexrFJYX45b9+jOb7bK2EHY2zHmp/B/pH0bizFmlpfkTCeumwWQ7wBCGmEVXa90mLMaAnwWZkkbfuZGBWj51xYgaWW4UHD2sFFhlmklBAWW8AAFaYSURBVF4HZBigkRGjBkqfEighKAX0ZDiB1H0/tbXVaGlxVxusR2dnLyYmpnHs2AFUVVXg+vU7mJtbNJSjkVy9qbS+rL68/px9sPcZ1tcJMNPSs8gRbc1ySvSTKadHcXEhfuM3v4RNmwrws5/9Eg8etDqqhxd9vYN47gtn4ff74rFk9HDmXmhtks4mwUYLOjO4Eq/FRh3e+uohKR9hO2mQVFgNOFa6JE0dgnGxix+LLdgGzY+uXXIDrfjlEhv1eB1iLIq08ruqHabxPlb9LKKT6LdPo5HWa4YzMzOwaVM+RoYnNZsIbjJsAOv5S1hYWMSHH17Azp07cO7pp9DR0YUHzQ+SCv6hgQ2/HxqcTOD6MQKoi6ZRIutkArZLgvWCEnWM0eumpB0hFvxjJ/biqXNHMdA/htd/8GOsrASY9SQDss8ry6tYmF9CRUUphgbHE22YfG8atwITEmw1N7BIsKZdXV2iiXbYClYaWSf10a6x0oCbXZsKSIIEIYnIkpInc97QcHsc2SXDTuoyK8Miw8mBj8xlZ2chNy8HIyNjDtsAVzsAMD+/gPPnP8XOnQ14+pnTaG/vQmtLByTJuCHh1Q6T5XnAH7SS596sx6F1xN+Nm3f29OkTeOqpE+ju6cff/e0/YmUl4OibsIOFhUWsLK+gvLwMIyOJ9TkVPs8krDTBTutdrzo2Mrz12X3YJsJu2pebBZ5RNcBAgqxqjtFMYwgyTAMpdVaiRiOuOU6YvPo0ZFiFRHzoImkiTfGFpF1P749P8z+I/MYCJEAAyspKMDe3gHAsUIbGfJWYVPWCAENbtMjYmkVNWfhlWUZraztGRkZx7NgRVFU9h+vXb2JmZtb0XrQVU+5b8744pLqOTVbtSR/jbdgx2TF5t04mYTPtMI0M5+fn4qVffwalZYV495eXcf9uu6a8m6BZOExPzaOouABDg+NULTBPlOhE/VriSjNVU32CrUiwVd12QNa/HhJjvR+xGHPNWJO2ISnzTxLXe/j8wWre4yXDvNYudvpkJMOA2VpAJwY0LbL5WC4rK8HMzBxT+8YHfkIsSTJaWjowMjKGY8cPoaqyAp99dgdzcwvQ998uGbaCs3fmQLjM41ec4iBLyaCwaBN+4zdeRHFxIX7+f95D832tFthtMqy/18mpWZSWFmuIsPM2E65o1t8R+5uyE4E9GXzeSTDgrc+pgC0i7OaG0IwEm2mDSTJMg90PnjSjppFhhZhKmkVAihFWQzRp2BcU6IMwKdeHAaRBksPIL8jD/Nyi4b60mwllwbYiw8a2E6Q6UY+C+fklnD//MXbubMTTTz+Frq4ePHjwgG/BVycjtb8MEkxdzAzBL+yTYNOu2TTjZtdjbSpkFyztMPmcTp0+iCdOH8Tw0Dh++P/9BEtLK0m1yeqHGeZmF1FXX0XpJ//982pqSRJMHuNt1/B9mZkDurxA0L5DnjmUFkxLjTq9FmTYW2g90OB07LHIsFKn1XpCJ4kaITBBhmOVMuJJ8LRjD/n5eZibnXd0bTKYm1vAhx98jKZdO/D0M2fQ2dmDlocdiEa1voN2TKWdwg1iFye/Ant95nbtcZkM8d6bz+fHmTNHcfr0UfT0DuIf3nwby8tL1LJmUbV5wbrP2Zk5VG+lrM8pey6se7AnzHAjI4wTPGpm0d767D6STp/kJnhNrXnIMIm4ZsmwGJtJuI1aWhlRpQ4hQYbVQFpKUeNmnSdwljatCiAKaQDCkCEiLzcX8wuL8fsy3ANhIk2S4UQBka6h1TwH+qZEkT63YWhoGEeOHMQXv/g8bt++h+HhYbXnJrVKFFNo0jSZ7ACFVNoimiKznDYiNyNP6xpIInkl8SwyXLWlHF/6lSeRnZ2FX/7iUzy438lVFw1m5vpW41WAiJWVALKyMqlmzub9YZjlEeRQb/JIkkJWbu/47wwNMM08XtOvFJrX2Q00xMqB/qgt1h4+X1jLSKPJBM8CyACXyZMN4xqnrSs3N4cjlobFOukQkiTj4YN2DA2O4siR/dj6QhVu3b6PsdEpQ1mWdliF2drEowm2Q4btWV/xz82pIFHMCNK6Z7J1ayX+r1/9AjIy0vGzn72Hhw/bqdfpkYo+Ly+vICsrw8Ualf2l3vowcc7c39yWv66NQG4ePLiFNSHCPNFP7fobW5HhVEIlw+QCR6Za4qoDko6kKXX5BBFROQwBInxCGvz+NERCCTNo9X/64pQgw2QAL7ZvrtoZ9f34qIvj4sIKLnx0GXV1NTh27DAmJ2tw+/Y9rKysaspp748OKxJsZdZuBB8JTiXsTNp2o4qnp/vx3AtPYO/eBjxo7sL7716xzPdsVT+LbPOQYECJZJqenmbRc33d5u+CHoE9QQoN6ZEM711bv0ikRnMTEqJx4uzUQkZ/narttUqrxJt2KVkkm2LBS8/w+UIq36dbkaSp1hcGM2l9fWxrEXYGAq2GOi3Nbzkfs+HOc1V9h+vra3DixGGMj0/izu1mBALavMNmGjdD7mEn8V8obi3646bXwwclMaMPdqLipoo08Yy/jIx0vPji09i9pxF37zTjvfc+1uV7tjavdxvBYAiZGU6JMEvwQwioCcWLVT1O342tjDBJvP9HUcDsrc/uwxYRVoinc9PSZEyraabHZmTYDlHWb0I1fYynVzJO7AqpSUyYenKh5CbWkuXEdWIsgq52wpDlRKAvSQ7D7/NDlmN9YqRX0kq+ExMUtzaK0Bqbkcfe3n6MjIxi//49ePGLz+PBg1Z0dnZDkmQtodVEqiaa4dAEU7vnAglO+B0Jpj5IazExmpFhcgzt278DTz97Aisrq/jbv/4XDA8lfH7skGka9GRY1Dw7K0Is6J61HQk/2xxZT/TUc2YRoq0iUKv9JLW+ay1A4zGR5iW5a0GGvWAcHtxG8oGz2Btvs7pZ5puk6TS5BtvtoyiKyvrHLmGrvmTQ3d2P4eEx7D+wG1/84tN48KAN3d19mmBaVgTVNd9tG+TX7TrdAM84OHRoD5559jQWF5fxxhs/xvDQ6Br0jA8yc0jSXQ3M69ISLwEi04Ld0+SmHt767D7WzTRa3Yza1ezqCbGbmmHzoD5EeiNoJ3CVUMRNp6FMCDRfYj2U+/DFJWCqNlcUlHORsISM9EyjlpfWf8JM2iqStEGwwDKh1k1swVAEn312G729Azh8eD/qt2/DnTvNGB0d114jS7YWObN7MsKO2TT/OUsCaKkx1fm8QE/sEubRidRcouZvAKiqKsXzXzyD4pICXLl8D5c/va0paxesCM76+qzvX+m7Py0N0Si/kImnfppJsJkWmGWWTQuQpTd9NjOFpgnczOr6PEKGlNRi6Umc1x9OY1Yk1aYFkWRpDF1r33KdBdSUfKTpNB18c20oHI5Zx7AIhh0/5eQRCARx/dptlJeX4uChPaivr8WdOy0YH580lF0/n8yNSX55saV6M1588SwKizbhk0+u48rlmwDMxr9bWmFzM321jN/vQ1RiWQzagZQgwaTLUlw5BI0QaT2wEcdHquGtz+5jTYiwmWbSKYk1q1PdrDrJPUxtK5biKJ7eiIAaSIuMcFteXopoVMbstDbPH5mWSS0LWYlDLRKTi1rWJ6QhFIwiKysTPiENUYQTi71Ml96yyLDaHi2iL5UM6wii5lkLIianZvDe+xdRX1+LEycOY3ZmHnfuPMD8wryxLupDdTKB8RFgjck5t2TaXRKsP8byESYJcG5uNp57/hR2NtWi5WEPfvzmL+IpkeySYKdaWrNcwCqysjLj/aKRUOs2Et9jZmY6yiqK0Nc7TDWBptZlkiKJPC4IIjdxtRPpmgSPlQtrnjK79lE02fKwMaAP4rjWZJgEa93VBrvijSLNQwSM0G/kN1eUIhAMY252gerPyEfSFXITCASRmZVtOG6N5N4JLVYIifHxSbz/3kXU19fh5KlDmJqcxd27D7G0tGyoZ63IBF+kZ3OLrbUA6/3n5+fihS+exY7GOty/34a//ut/QiBAT1loRCpMpMk6E88rOzuHmUrRuj4tsrIzUF5Wgp7envgxQVbIsLdGGfE4EvPPA9ZMI+zko+E1ZyRzD9O0P2ZkmNwoaINWRQ3n1SjSiTJGc0cAyMnJxP79TWhr7UVH+0D8GgEidWMiyeH4ppj0bRQgYnFhFVXVJbFrfQoZV3PgUsxfWGmV1PpoMJBh3Tnyf63GTkZXVy8GB0awe/dOPPeFs+jp6cODB23mflOcZsnaScXsnDPEpZkmY9OwUXKoETS7ThRFnHnyEI6d2IfJiRm88b/fxjgR8MSu5ta8H9rxrDnHmQs4Pz8by4sBSyJKbZ+QIJeWF+DgkUaMj02jr3dYU46lkeXNE2x8b2wNGU3jS5tvWKSaJwbCowQZUchJ3Itb5pUe7EFdX2jH12uMcvv6MnxT7fgKkzC7Jjc3B6ee2Ivm5lZ0dfbFzUjtryliPKe6/rgCull2MrAyASfvQZJkdHb2oL9/CHv2NOL558+iq7sPrS2dGj/WtSDDZuvfRiIPtOfr9/tx9txxHDu2H6MjE/ifP/x7jI8bA5K5B5p1AesZGY8XFORhcXEZRvJt7dNLlq2sLMPRYwcwMDCAnl6LLlv0aaPCzUw4bkRP54G3PruPDRU12iqvMKDdoOpJJet6kgwDFGkqZcNv8BsmokiT5Wj19PQMYm52CcdP7EdpWRFuftaCcChCbS/erhwFiGjUih+GiPnZZezZVw+fmA5IQFTVTMfIMCtHo/JRKk9M/4HSTNT0z5KLNMcQCoVx504zurv7sP/ALnz5y8+hra0LHR1diISJwBE2CF2ij/YIMI82WBAE6jknWt9kcfT4Hpx64iCkaBS/+JeP8fBht+a8mySYVYfdXMDFJYUYG52O9Y/P9FkbuVlA055a1NRV4P6dTgwOjHH1lUWCWQIU2nxgNs7J8nohHE9dThZVnmu0ggb+IDJOoZhdeT5Inye4JTxzdD1LCE05To0t4cDk1Cx7QmdnD2ZmF3Dy5BGUl5fixmf3EApFtO5JMVhtbKenZ7Fr1w4IggDZ4JjJqwXkK8ezyaaR2lAohNu3m9HV1YcDB/fixS89g/a2LnR29sbTIVppmJNBstkamAG+XCYd+vpEUcTxEwdw+vQRBINhvP3P76OllS8aNB1W71l/n87eQUlJEfr6hnR1sNo1EmBRFLFv327U1m3BzRu30T8wED9rvgeyY4XmjmDEDSGOm2R4LeCtz+4j5UTYTMNCK2eGZHyBSV9is4mVtuhrTItjOYb1IEm2ABGzs/P48MNLOHRoL5557jhu32rFxNhMjOASJrNkpOm4D3Li/OLCKqIRGWVlRRgfV8hHlPAhloWYHwdzUVAmXzYptNYYG65hlFtYWMSnn1xHSWkh9u3bjYaGbWhpaY8F7GALH8i+GPtOP8dnZqX9WyHA2igPpBkts64UEOA9+xrw1FNHkZbmx9XL93DzxkNIkr2I0lZmzFZmxjxm0Hr4/T6Ulhbh4f0ebm0sidy8bBw8sgOiKOCTj27HJNeUvjHIOi8JNgNt7KnzDysoH60OXnKw1uapHjy4BbfGLUsInfz11iQy/k0TU//09Azef/8Cjh49hC984UncvHkfY2NGLZ+Vlmc2lkO4uLgQU1Mz1jdChfuEjjYPLiws4pOPr6CsrBT79jWhoaEODx92oLd3wBDwy4kWnlqGNneaxuwQmIJqq3qSIcb6aw8c2IWnzp2Azyfi4sfXceP6PZc0aeR4dZ98paeno7i4ENev3zFplzymRUFBPo4dOwBJjuL99z7C4tKC9h1yWNHFi1q4rVmBl+Sul687ifVu30NyWDONMC8hZoF1nVP/PFq/1EUv4curBjZim0lL+qBQscVWliWEwxFcv34HtTVbcOTYboyPzqD5biciEbpWh/QZ9hFarfGRWVRvrcTUxAJRVkQUYYI806W69FD3Wimh2yYdU5Oz+Oj8JVRWVmDvviY0NjbgwYNWDAwMQpZ0RNQiGJYT02QaCaaWY5DgVKZd2tFYi6fOHUV+QS5ufvYQVy7dRSQSsb4Q9rU6bKGN9f3RTClFwYeqLeVYWlzB0uKqrX4JAlDfsAWNTVvR2zOK1gc91GirrD6TMPNFdrIo6kmw+rvZ3GGXIJjVt9Ek0hJkJCdxTr3W2kPqkNJ0SRTrJB6tsFl5ltZL7yJEDksZEkKhMC5fvo76bXU4cfIQhgZHcfduK6JR/vuXZRnDw2OoqaliEGErjVxqYDYPTkxM4sMPp1FVVYG9e3eisbEeDx+2Y2BghKLVpsNNEpxwl3GekUStJ9l9zO7dDTh77gTycnNw7eptfHrpJqQoK12lFvxtp26ur67ejNnZ+XhqS3q7EvR9EEUBjTu3oWlnA9o7OvHwQQuiErEvoRBgM+VKMiCf40YguRsN3vrsPlJOhPVmhTxRnlMV8VJtm6bh0fsS03yHJTmq9E1IaNv0UX9pi3hf/xAmJqZx+Mh+PPOF47h7uxPjY4p2Vw3ERSKeJ0wGJAHo7x3Dmaf3o6W5B4GgFGsvHA+gpSfD9I2EGeE0yS+YxMIyMjKG0dFxbN1ahV27G7Fn9060tnair28QkqRv01xD7IQAM8tBhJqn0G4bTrF7dz2eOHMImwrzcf9uOz65eBOBAF/+SRZJUtJvWWnG3bkn9Ruo316Ngb4xtuk88TzV7yw3LwsHDjfAn+7DlU+bMTOtaFES6cS0gia1z+o3ptcGuwkaCXalXp0QTW3DjAyTWE9irPggJZMmz/NBelSxHpYLdskwG0atl9YlQ2u1pd5rd08vxsYmcOzYIbzwwlO4efNe3PpKX4cKsr/d3X04d+4k7t9/iHBYJQ88Zq42hWk2iR7Nb5hse3h4BCMj46it3YLdexqxe3cjWls70d8/ZJESiqOvHCQ4FQTHqeXBvn07cfrJoyjIz8Pt2w9w4cI1hILK+rxR5zPavTY01KG9vcfiSu1zLyjIw7Fj+yGKIs5/9AlmZxSBTvwdConYA+x35kwwYnnNGpHgjSaMNoO3PruPlBBhq0Gl1w6TxNTtwWjlR2y2OdX7Dqt+wyoZBpHOSJ+XVS+BXl0N4vKlm6itrcbho42YmlxA871OrK4GDRt/QNlAq1rhxYUVTE/OY/uOLXjY3BM3i4YMJhnW9IMC7ULBllrzmqaYXTcwMILBwVFUV1di164d2L1nJ9pau9DbOxBLw2P/nTv3L6JPIDyTtN5sWR8sjVZ+774GnDp9ALl5Obh7pw2X//pfNAR4PSdffdtWm+Dy8mJkZWVgsH/C8vmL8MHnE7FjZzXqtleir2cU7S0DCEfDmrb0kdo1VhcONdeqFJkl9CL7aFWvHVBNrk2sSsxAzqGSHDXVgrsNzwfJQyrhRgAtgEV2OEylCWG3ipWVVVy8eBnbt9fh5MnDGB2dwP37bVhdpUffJe9hdnYeM7Nz2NFYj4cPVB9So9ZtvWCmVZPlKHp7B9HfP4StW6vQ1NSAXbt2oK2tC729gwaXJqewQ4JJQbWmrzbmFd4xdvDQLjzxxBFk52Th9s1mfPzJZ3ECbAdrESTJas2trKpAWloa+vuHDOdoZuR+vx+7d+/A9u016OjoxoMHbVotMABN9hBPM7sh4K3P7iMpIkwzd1YnK81GzkQLa4ZU+9aZabVoAbXMyLCGBBMBq4DEJCnLEvr6BjE2OoV9+3bgmS8cR2fbALo6hxCVIhqzUL1WuO1hP06d3YOermGsrK5q2qeRYTPrB1rfFJDPg7XJgKGM+QSZaGdgYBgDA8PYsmUzdu1uxK5dDejs7EN3d78miqW+Tt50SHYnaksy5/C8KIo4fGQXjhzbjaysTNy53YrLn97VRNJ2QoDd0u7aCYQGKGROEATs3rsNXR1DiEYlw7dLzgEifNhcVYzde+uwuhrCpYv3sTBP9wW20z+1LzQkI9FNlTaYpz278xsrxZsHD+sJs2+IGliSw0Tass1k/IYZ7XV19WJ4eAz7D+zFCy+cRUtLFzo7jW4c+mub77fj7NkT6O4aQCBAM0vVYyNtRiVIkoi+viH09w+juroSTU3b0bSrAZ0dvejp6Sc03QrMyLVeqGzPVYmt6Upm3iTh9/tx5NheHD+6H+kZ6bh54x4uXbqBUMjoorRRtGeWexVRxP59TWhp6eASXmzdugX79zdhaWkJH3zwCebn5y0zh5i0btrPVFrabQR4AoJHH46JsF1zZ7dhx+eYtcE3m0xpi2wiUI/1hpQmzVbypEpYDSzjxo0H6O8dxYFDTdhauxkPm7sxMjIRa1MxeVU0QQBkYHZuAcNDU9h3qAHXLt8n+unj1gzT8jayzb54Pm47/k+JskNDoxgaGkVl5WbsaNyGpl070N83hI6OXkOeQxJOIj1ryjImbP1EbTdnr4rs7EycfGI/9u1vRCQSxe1brbhx/T5CoQhYqU0s+5wk+bXtV6z3HYqbRG+BIIro6RoxFWoVbMpB055a5BfkoOVBH4b72Wkm9N+QnkBaneeFWXAswEiieTdZNIsWXqht8voOryUBjgvhkrjew6MJXuGznXmF5erES4adm0kDZmsTKzp0IBDE9Ws3UVpaisOH96K2bgua77dhZGTcUFbF9PQsRkbGcejQXly5ctOy7bUGf+AhOS6wrqyqwM7GeuzevQO9vYPo6OjB8vKK7Tp54GSdsjvP5OZm4/TpI9i3fydCoTBu3mzG1Su3uWN0mPbH5TgrepgKHgQRO3duR1SS0Ns7RN27qr8XFxdh374m5OVl4969FvT3DyrBVmnCKr3FGNXU3vy9rQUJdmsMPirm0d767D4cE2Ers0PA/sAyy4FolRrJ6pxT7TJLOyzGUh1piW5U8+Er5tKA4lOc0ApDUMpOTc/iow+vo25bNfYfbEBDYzWa73dgemrO0A9ZlvDgXhfOPXcIW2srNHlXVTIswBfXEtMEq1ofKbrZGVnOConn4oQQAyMj4xgZGUdRUSEadtTh+RfOYnR0Ap0dvZicjPlQqz6cHFJlM99yIwlWx0Xy5Kq8vAgnntiPHTtrMT01h/PvX0fz/U5qWVtt2NAsqnCSL9SKYBbk52Hnrlpcu/IQspzwTiHbyM7OQOPuGlRUFqGvZwy3rrcjErYyHfdRzX71wer0sLo3dWzYsUJR5yq7JJirrGy0mNH/vpGg3JtzHySnQtEf/OAH+P73v4+xsTHs378ff/7nf45jx44xy//0pz/Ff/yP/xF9fX1oaGjAf/2v/xUvvvii025/rsAieVzXmsyj5HnbfaJ8X26QYXMSYq0d1tehko7JyWm8//7HqK+vxeEje9G4uA337rVgenqWev3duy34wvNPoqamCv39w4YyySK15rdG4ffI8BhGhsdQVLQJO3Zswxe/eA7DI2Po7OzF1ORMvE+2hNEm67Zd8M7XmyvLcfr0IezYsQ0TE9P45S8+RnNzu+XztCt8cYsM2/VzLiwsQFNTAy5evKoJdkb2Jzc3B3v37sTmynJ0dvTg0qXrCIfpJuA0hQHbDUFbTlvPo6cJXq9863awXuvz5xm2iLAqiUg2zY7boJFhdcPLq602K2M2wSnmzAlNa7x9i8VBlqW4rX5P9wD6+4axvaEaJ5/Yj+mpObS19mN+djmhFQYQCodw62Yrjp3YjdmZeczPL2n8KX1QUiuZkWF14aL12VDWKqiZYZLUbzqs378gKKmmPrt+F83329DQUIdTTxxGMBBCT/cg+vqHESbMpnkWUnO/b4H43TkJFkUR+w/uwIFDO1FWXoz+3hH85B8+QH+v0T/HURscuX1ZfbMDGgkl4ff7cOTELnR3DmNmagH64FLZWVmo31GFrXVlGBmcwoX3byOwql1gzTYrzDRIjoUHJmbVHII7XnDPK4SrAwtMjdkGXozdxltvvYVXX30Vr7/+Oo4fP47XXnsNzz//PNrb21FWVmYof+XKFfzWb/0Wvve97+HLX/4y3nzzTbz00ku4ffs29uzZsw53sHFAExo62aCnwjWJp04zMqwiFSarcd/h+PNTTIY7O3vR2zuIxsY6nD17HOPjk3j4sANzc4vx6wAgGAzh1s1mHDu2H7OzC1hYmHetb2vhg8rya56ZmcO1a7eRnZ2FhoY6nD59DKsrq+jpGUBf3yAiEed9S3aOY40nv9+Pgwd34eChJpSWFqG7ZxB/8zf/jMGB0aTaSzX0wd2s3ntamh8nTx5GS0sHZmbmDAqO7OwsNDVtR23tFvT1D+GXv/hQ4/dOps4k9/Z25pDPo2nwo6Id9uAOBJkjXv7CwgIKCgpQkn0UouBPmAgTCxPNT1iTeoWTdJhpuczAIsKsvtGuMYPGzIQI9KOYO0c1fsIkCZZkJfS+3vxEgAhBECHAB5GIypeekYbGxlrU1VdjZmoe7W39mJ7SLqg7GreidttmXPzoNkLBRPAhsh9qm5p7ILVRVtJQw7Xmz8qsPquJ0mCaLIrYUl2B+voaFBVuwuDQKHq6BzAzs0C5ln+ySktLw6++9Ax+/vZ5RE2socyEGCWlhTh+Yh92NtUhHI7gYXMPbnz2EEuLpMlY8ps0kgibBWGiQYpHNDeWIbWTrDzASvsiBAE4fmo3BEHAZ5dbQc4UOTnZ2L6jCtU15ZgYm0VHyyAWF1aZ31q8fcq4JNtkwTwCqhG0aNBOTJjNAorpTaMTrhPaAFlumppKcgRjK5cwPz+P/Px8rmt4oc7zZTknIArOw0dIcgQTy9ds9fH48eM4evQo/uIv/kKpQ5JQXV2N3//938e3v/1tQ/mXX34Zy8vLeOedd+LHTpw4gQMHDuD111933PdHEep7y0yrgSiavzcnhEq/brkB/TdB61cyGjvtte6QSFmWkJmZgaamHdhWX4vJiWm0tHRiZka7Pu/a3YCarVX48MNPNHEh3OqDU/ATFvNyPp8P1dWbUV9fi4JN+RgcHEVPdz9mZuaUdhgxPQzkiiJ0/dVfexY//9mHiETsraHqeCorK8aJEwfQtKsewWAI9+614rPr97C0tGK8xuRZOhW0JCuwoAW1oh0HlJRHZ84cRzQaxaVLN5RysWefk5uJpqbtqK7ejJGRUTx40I6FhQXTNsz6YYS5NpjsixXMrFd4xmwqiThfakht/1dDvZ+79fnzjqSCZbHMo92UpDghwWsBWrRbkWGtoDFZ1kFGFJKMhNY3GEbz/U50dAxi+/atOPHEPizML6GzfQDjYzOQZRnt7X3Iy8/BE2f249LHdxEOKz6oahuq2TapESajZCopasyljQaTNTUAGOV9s4JvcU1ilEVTloHBgTEMDowhPz8X2+q34vSZYwgGgugfGMFA/whWV4KWdZu3yT9msrIzsf9AI3bv2Y7SskIMDYzjl+98itaWXoPmdi1IsKYsxbfVjCRbBnIi3tmBg43IyszE5U+a4yS4YFMutm2vROWWEowMTePj83c0OYX1fWG1QRsntLkk7tpgY7FzQng1/TN55uQ3ZNYOzac3GZ/itYTS9yRMr2L3rm68VGRkZCAjI8NQPhQK4datW/jOd74TPyaKIp599llcvXqV2sbVq1fx6quvao49//zzePvttx33+1GH3U3j2mgZNwJorjt8JtMq1GcVCARx504z2tq60LBjG5588jhm5+bR3taL0dExAMDDB+3Iz8/Fk08ex8WL13U+qO4QpfV8d9FoFH19Q+jrG0Jh4SZs27YVT549jtXVAPr7RjEwMIzVFXq0bRVUNzhBhAAhJoSl62do952dnYUDh3Ziz94dKC0pRF/fCH7+9odoa+tJ+XNyu3498WX7BQs4cnQ/0tLTcPnCjfjxwqI87NixDZVV5RgYGMH773+CxUXtPMzbBhv29nX0/lMUXhtwPtqImmG31mcPCTgiwvqI0G6CrDsZsyweH2YnoEfAVKI968109Ol1zCYcSZY0mshQMIzWh73o6hhE3bZK7D+4A5Iko7d7GP19o7h9sw3HTu7GqTP7ceXTe2wyHG881n8ih6KVBo5GhmkgzXG40xpxRIIWBBGLiyu4d7cND5o7sLmyHLU1Vdi1azump+YwMDCK4aGxeERLM0ln4hyhIRREwztS34Eoiti1px779u1Adc1mzM8toPVhD3764/ewsLCUGKMcuXztgIcEW/nO8pjY0qw2EgujQoKLSwtw9ZOHkCJAVXUZ6rZtRn5BNgYHJnHh/VtYIYQRLBM1Vh5x/bsyiwFgBV6CyTMnsIKFxetgpEEir6P5BD9KfjluBeOorq7WHP/jP/5j/Mmf/Imh/NTUFKLRKMrLyzXHy8vL0dbWRm1jbGyMWn5sbMxxvz24B0tBaxIm0ok67AbR0s+DNDKsL0NvPxAIovl+K9rberB9ex2OHt2LSKQJnZ296OsbwPVrt/DE6WN48snj+OSTa4SGk0bK7SN1RMKev+/c3AJu336A+/dbUVlVgdqt1dizZwemJmfQPzCC4aFxRCIRa6sw3n1DbEyIooh9+3Zg7/5GVFdvxuzsAh4+6MCbdx5icYE/Q0EySHVwLIChCRb8OHJsD4qKNuHihauQJBlbt1aioaEW+QX56O0dxLu/vIjlZd7nYDUm7RFBMxKcCu2tm0HbHoX2vWBZ7sMREU6lhMTNuq02n3rzabOUMCwkSKWRDJNESz85sBZwvSAgHI6go30AnR2D2FxZgm3bq7Br9zYMDU2gr3sU9Q1bcObsIVy73ExJq5ToS5wU69IrMX1pdf5SZiYrLH9jZnATTtMpElIUGB4cx/DgODIy0lG9dTO2bavGwYNNGB+fxsjwBEZHJzVpmIymWEYfblL44Pf7sXNXHXbt2oatNZWIRKLo7OjHxTf+BaOjk5YTCK822EkQLDvlnPgSJ0iwgIOHG1FYlId7t7pQu60C1TXlkKIyerqHcf3KBCLhKIXgse+dRoZ5+sU7Ybsh8LIiwORxkgzzwu3Ago8CBgcHNaZXNG2wh/UDr++w3n+Qp1479SfTRxYZ5m+bJMP2v79QKIiWlja0tbWjsqoCOxq2Yc/eRgwMjKCtrRO7mhrx1LlTuPTpDQSDoZSSp1RHLjZDJBLFQP8wBvvHkJmZga01CjE7fGgPxsYmMTwygdGRSYTDYcfzXHq6H027tmNn0zbU1FQiHA6jvbUHH75/hWt95gWPcGU9nrMAHwRRwPHjB5Cfn4tbN++joaEOtXVbEAqF0dXZi76+IUoUbGWMG33g1XP63/njvCQbKO1RwOdxbfaghS0i7DQFDG/dTuCWpiWZzbSeDGvqZXz8koVntqppjEezlWWMjcxgbGQG+QU5qKnbjCPHdyESiSAjIx1Pf+EoPr1wB4uLK1qzaFntR6JdVXNLaoep90VGmTaZxFjmdrRNCm9OYKUs/XwwGEJXZz+6OvuRm5uNqqpy1G/fikNHdmNqcgZDQ+MYHZlEIEBqLHXRvGP9ysxMx85d27CzqQ7VWzcjFAyht3sYP/vH8+jp1ga+optz+TS/s8gwr9ZY49NqkU5IW7+1qbsV0tL8OPHEXpSVF2J2egEnTu/B+OgM7t/qweT4nNIHGEmwXXN4XrgdrIfXjcPKfJwkw2awGyBro0SsVCTOzudV9T7y8/O5fJBKSkrg8/kwPj6uOT4+Po6KigrqNRUVFbbKe6DDDhnmrc9R/SkgcfbIsNk5a+upaBQYGBjEwMAgCgsLUL9tG5544hhCwTCyc7LwwgtP4fz5y1hcXLR7G65gLUlIIBBER3svOjv6kZefiy1V5WhoqMWRI3swMTGDkaEJjIxMIBgMWfYtOzsTO5u2oWlXPaqrNyMQCKKrawA/feuX6OkZXKtbMmAthA7655Ke4cOZJ0+gtKQIs7NzOPPkcQwPjeL6tTuGuZBl/UCzjDPC2bcOmFgNcrptbETz6I0Gt9ZnDwkk5SNMgxPpiZlmS//SUmXybAbax0luliWdJpiaJkKn6TILyJQgv1oyrGJhfhkP7vag5X4vyiuKUF1TgV176/DV3/0ybt9sxf27XVheWlX6pWqFCUJMaocNvsMM3x3eQFtmPkxmJNgqkAYLS0sr6OjoR0dHP7KzM1FZVYaarVU4eHA3FhaWMD42jbGxqXhKKkEQULWlDPXbt6JuWxVKy4qwtLSC7q5BXPn7dzE4MBYr54uTVxq5ZRHbZMyk9cGW9ODx77Ub+EKED+kZadhWvxknn9yLtDQ/2lsG0N87jpHBKUTDdIkNK+gVzaSL/F7tuj2wxkGyk7kTAkwrSwYms84tTts4GOeK9Y4erUS0d+6DZHczk56ejsOHD+P8+fN46aWXACjBss6fP49vfetb1GtOnjyJ8+fP4w/+4A/ixz744AOcPHnSabc9JAm7ZCsVUakNbbiyuaaZUGsRvw9ZwuzsPG7duoe7dx8q5sI1W7H/wC7823/3/+DatZu4d7cFS0upMd/Vr7/2CbAEXiLEYxK6uLCE1oUltLX1IicnC1VV5aiprcKhw7sxN7eAsbEpjI9NaQJhbt26WVmf67agpKwIiwvL6OrqwycXb2JwcFRzf06RKsKVzHhjPcuMjHQ0Ntbj7Nnj8Pl9eNDchp7eQQwOjMSCsdHao73H9RGwfh60xhtJK7zW6/PjAFtRo0uzj5tGKzOLLslrDmkWcdUt7a/VRtNM48Wqh4wgTZ63+ngs/aQgKqRMR5L09aan+7F77zYcPbkbkUgEfd2jGB6ewOTELKamZiHLsiaytNJnenRpTfu6hZVn46LJmcyoz+y4nQmHZf6cluZHeUUxystLUL21AgUFufD7/di3fwempmYx0D+Gnu4htLf1YmpyVucr616Ec7N7YX0vtHfMlxs5VpZDQ1uQn4fSsk0o31yEbdsrkVeQg47WQXx2uQXLS0qgE1rkdRmS5XdhRYJ578kKdqIxm/XDDgE2AytfsL5NPWm2cx9rETV6U9bepAQ6shzF3GqzrT6+9dZbeOWVV/DDH/4Qx44dw2uvvYaf/OQnaGtrQ3l5Ob761a+iqqoK3/ve9wAo6ZPOnj2L//Jf/gu+9KUv4cc//jH+9E//9LFMn6S+t6z0OkcbSbcj3dppI9XRo3mud47EOkcSYQgJyzn12WRkpOPgwb04eepwzKKpF8MjYxgfm8T09Dy02zD+NZZsw33wafE0f+v2eayATBnpGSgvL0F5RQm2bt2MvIJcpPn92Ld/p+Jf3D+Cnu5BtLZ0Y3p6ztBuMuPJ7LhSR3L7TEepynTPp6AgD+UVpajcXIaGHXUoKMjD/futuHzpBpaXA7p2zH17nX6fpkoNl7XByWJN2jD5Hshnleqo0euxPn/e4ZpGmKbVsOurSG5I1c1iqnIZulmPxqRRSGgQjX6YRg0jj4ZMlqOAoJRV8gr7DCQqFIrgzq0OdLQN4NjJ3SgozEU4XITabZXw+URMTcxibHwaU5NzWFhYhBzLTyzJMZNeHblhmbix3ofmXk2EVXbMoW1JEtUUNoKAvPwcFBdvQmlpEUpKNkEQBExOzMLnEzE8PIHuzkFEIhHIkoSionxAAubmliBxDjOeqMIkzAIuscoaxg7HJMz6GwCysjJQXJKPkrJClJcXIS3Nj9nZJRQW5mF2ZhEf/uttTE0oaUBYvvJWJJhFgJn91o0lNyWuyfhPq2BpeK2IM0/eZF7z6scFL7/8MiYnJ/Hd734XY2NjOHDgAN599914QKyBgQGIYuLdnTp1Cm+++Sb+6I/+CH/4h3+IhoYGvP32248dCTYDbzCpx9EkMWXRsxkBGwOBAK5cvY4HD1tw+vRxFBYWIBKJYnv9NoiigInJGYyNjmNyctoQbX39wK8d5oUgCMjPz0VJSRFKSopQWloIGTImJ2bg9/kwPDyOzs4+RMJRSJKE4pJNkAHMzy0gGn00xqiT7yk7OwulpUUoryhFRXkZfH4RszPzKCouwOTENH72z7/A5OR0rDTd7Nl4jH8PxeP6ZuWr78HDowrXNMJW5Ii1MebxibQTedUNPztejbD+GppWWAVPmh2mr258UqPnKyWPKeUENDRWo6GxGl0dQxgfm0ZJ6SaUlm9CYVE+JFnC7PQ8pqfnMD09i7m5eazG/Gl5pes8BIZH48fSnFkTZl+c9BZuysOmwgIUFuVj06ZcyDIwO7OAqak5TE/OYmZmHtGoBL/fh1956Rze+flFZOdkoaREIcvFxZuQnpGGxcVVzM0uYn52Sfl/fhnRaJQ6plhkiJWn16wcCT0RNouETTsPAD6fiPyCHBQW5aOwKA/FxZuQkZmGhbllTE3MYWJiDtk5Gdi1Zxtmphbw4E4vQkF6YmW7JNhM85oK8IxD/dxjR1hBrc/i3ZuNe71LBas86/q10AgXZO5KWuI8H2jxJM5rBJZGmIzmb4VUaoXNtW/Ja4SVetzV4vGnKWJbPyX6Jima4hhEUcCu3buws7EBrW2dGB+bRnl5KcrKS1BcXAhJimJ6ehbTUzOYmp7B3OyC6/mI+ZCcRlgQBRTk56OwsABFRZtQWFSAgoI8SJKkrM+Ts5icmsHszDwkKZZH+KVn8H9+/hFycrNRWlKIktJCFBdvQlp6GhYXlHV5dnYeMzNzmJ9fQjSqfe8bQSNsVb/f78emTfkoLt6EouJClBQXITMzA7NzCxgfn8TE2Azy8rOwZ+9OjI9P4tbNuwiFwhQlhcUeaY3MjNc6SvS6tsMSsK+hRthbn92H6z7CNJhphzSpmGLaTrN6eM2jeTXSdupUYSVN1gdNog1aWmAlSzNqwmcY0Abc0uQwlkV0tCl5hw8c2oHqmjI03+1GV8cQBAHIL8hFYXEuiorysbWmEtk5GQgEApifW8L8/ALm5hextLSC5aVVBILaHLGkZtjsuZD3Q9tkJCJNs+tRJ9iMjDRkZ2chNy8b+fm5yMvLQV5+DnJzsyFJEubnljA7u4De7iHMzSp918t3NGbGsoz5uUUszC2jp2sYAJCVnY3CwjwUFhagvKIIjU21yMhIw8pKEEuLy1hcXFF+FpYRWAkjEAhClo0a/WTMbHmuJZ+x3+9DTm4msnOykJ+fjfyCHOQX5CAnJxuhYBhzs4uYmV7EQM845mdXEI1KyMnNwt4D25Cbn4XmO90YHVakzFbfKIsE6wnwWvvRmLXHuifa3MAivjSibTVP8YCHBK+XT5LigZSED5IXjOOxg3OfVGusR2Ai/rYVbRxPOiOVYEmSDw8etGJ0ZBxHjx1Cbc1W3LnzAK2tnRBFGQUF+SgpKUJxcRHq6rYiJycbq6tBzM0vYH5uHvPzC1haWsbS0nI84JQ7sPfu1HvOzMxAdnYW8vJyUJBfgLz8XOTn5yI3NwfRqIS52XnMzi2is6MPc3MLWFxcpljLadfnudkFzM0uoLOzHwCQlZUZE3Tno2JzCZp21SM9Iw0rKwEsLixhYWEJCwuLWFxcxvLyamx9ttTxpAyCICItzR/ftxTk52FT4SZsKshHTm42AoEgZqZnMTU1g87O3pgwQEZ+fh4OHd6D3Jxs3PjsHoaHRwFImrGU8M1ma+w3qq9tqvA4pVDy1mf34RoRTsbM0YoM85pH85hn0zbHVmTYymxEnaQUc0fEI0gbynKYQZPXUbXGqlmtzjxaG4VaeYZzcwv45MJtbK3djENHd2J2ZhGtD/qwML+MhfkV9HUrgaF8aQJy87JQUJCDvIJs1G8rRHZOJjIy0xGJRLC8vIqV5QACgSBWA8oiE1gNIBAMIRqJIByOIBqVEYlEIUuUD5Q4JAgCfD4RPl86/H4f0tLTkJ7mR0ZGGjIyM5Ceno7MzAzkZGcjOycTOdnZEH0iQsEQlpZWsLCwjNmZefT3jWBxYRkrKwH2s6SYFwsQYv8nHpgg+BBYDWJ0NYixkZn48fSMNOTn5SA3Pxt5edmorCpFbmMNsrMzIctAMBDCykoQqysBBFZDCASDCIXCCAXDyv+hCKKRKCLRKKSoBIl4STQiJQiAz+9Duj8dPr8PaWk+pKenIyMzHVmZmcjMTEdGRhqycjKRk5OB9PQ0hMMRLC8FsLi4gtnpZQz0TGBhfhnBoDZNRVZGJhr2bUF1TRkGesdx83obIuHE+NLn4iU1wcznyyDBNN9iFWsRGZnHLNvJXJVMwAz1Wl5z6FQFCfPgIZXQbwjXyuTafk5hN0EzTbXGzMwc3n/vArZv34YTJw9hcmIK9++3YGZmFrOz8+js7AWgEKqCgnwUFORh06Z81NfXITc3G5mZmYhEIlhaUslfbG0OBLAaCCIYCCISiSIcDiMSiSISCWvWIOqdiAJE0Qe/3wefz4f09DRkZKQjIyNdWYsy0pGVlYnsnGxkZ2chOzsLPp8PwUAQS0urWJhfxOTkDLq7B7C0uIKVFUWQbkUSlHHCnrdXVwNYHQ5gZHgiPqYyMtKRl5+D/Bjx3lK9GXl5OcjKyoQsy1hdDWBleQUrKwGsrgYQDIYQDIUQCoYQCChrdSQSQTSqrNGyxbMRBAFpaX74/T74/X74/X6kpfuRlZmBzMwMZGVlK0KBnCzk5mYjPT0doVAIy0srWFhYwtTUDLq7+jA/vxjPaKHeS1ZWJnbvbkRNTRW6uvvw6SfXKWmQzPpmf12iaXLNviGrLCAePDyqcFUjHE8j5GCzqCfDLLBIK88GkbVBTiYIl1upJ1haY6UNNiEmGtEgSmht+3qHMTo8icZdtTjz9AGMDk2hvbU/HnQhGpYwP7OC+ZkVjYm3KMrIzslATm42srMVYpyTnY2iokJkZPqRmZEOn8+HtDQ/BEHpgCQhLomVZTkWpEtWSJ7PB1FIdFQGEAqGEIwTR+X3YCCM2ZlxBFZDWFlZxepKQOMfpDEDT5JQCQzzcgAIBcOYCs5hKhZxWoVP9CubgexMZGVnIDs7E5lZ6SjMyUN6ehrSM9KQEfuf9GuUZRnRqARJikn0BGVxFaAsZKIvUTYSiSISjiASkRBYDSIUiCIYDGF2dgmjI9NYWQ5geSmAcJi+WKr3kp6Wjrrtm1HfUIWpiXl8+mEzlpZWEn3SfTdRaKOIG58XXbBEI8Hq3/rvy0o4xfoe9STdrIwdOJmz9AI7J/6+pAsGj0n5WpFgZU7zolJ+HrAe5HC93j/vptzuM+HTSJPfbEI7rk8nqO2HBMgiOju70T/Qjz17mvDcF57EwMAwWh52xIW84XAEU1MzmJqa0Vzv9/uQk5ONnNwc5GRnITNLsQ4qKi5EVmYG0jPSFbLmJ9dnCZIsA/G1We0b4PP7NdksJFmKCXZDComMEcnV1QCmp+ewsrIa/4lGJWagLDdBPs9gMITgZAhTk7PKufhzF5S1OUshpdnZWcjKzEBOThbSM9IJcq9fn4FoNIqoFNGtzwIEUdCUjcQUAKFgMCZ8CCIQDGF6ZhaDgyNYXl7B8vIKQqGw6f2kp6ehsbEeDTvqMDoyhvfeu0BEFE8IWDRWdDrTaFNFDfFOuOIF6Miu4Z3aWF8fN630WsFbn92H66bRyfrm8qRScmoibZfwOjGbBqyfQVxgQNF860GWY+WoJc9pUzhF4/cfDIXQfLcL3R3D2NG0FU89dxjDg5Po7hzC4kKCGCna0oRJ8/JiBEuLc9R21KjTgOKXqiy66cqiKyC2mEjKN6suMlFJkcBGolTTJXWiZaWXMovubOWbrfwuKFG4Hc4jouBTpM0rQayuBDXn6MQxJgDwicoz8im+zTJkRRIQm9BkGYr2OPajXGvtd8saaxkZaajbXom6+s1YmFvFZ5daMT+7Sg1AZxzjxhRlvFHfreDUQoMsx/tdWkXdJsFMHUYIBWjm1LxBr3giZm8UJO1vuW6aOQ8qHgVNzXp9A3Z8p+PXcJg+s9sTITPWG/UZBAMB3Lp5B22t7dizdxde+OLTGOgfRnt7LxYXl6jXRiJRzM8vYn6ezE1M75MqsPb7/bH7EWJroKCsR7IQW58TP1baY0P8CkS1xIuwDjBYClDmUyX6tqz8b9OyQJveUcby8qpCKCcpZUnhhCjAJ/rg8/vgE0X4/D4IggRlayITQn2F/Co/yc9vmZkZ2NG4DfXbtmJ6ZhYXL1zG9HRC0JEwfYbumAp7JJj2N+91TuCR4NTBW5/dx5r4CNtBsr69ehNPJ+Al3TwphXiD8PAIAGh+xfG+aMykpYTPsAwgFh1aFICVlVXcvdWOzvYBbN+xBU+eO4CpyXn0dI5ienIeauTreJ2CNmesLCQ0xgKikCFCkiVIUSAUjSJE+BTrI1Er92A9cZvlWE7Uw9agO0HKTHVlEdGIjGgkijCikBE0tMcT9ZkGmoY0JzcLdfUVqK4tx8zkIj673Ia56WV2gCfGO9J/R8k8H9Y3ZPaNktc4Ict2+0sLZgWYR/gmz9PqYpmBO0kJt5akQdn8OW9vPX3zPGixvibDGxtOCLE56BY0rJgampRLsX4sL6/g+rWbaG3txM7GBnzhC2cwNj6F9vZuTE2qRIn2bZJaQmME4WhURjQaBhAmzie35tH8Iu2SYcDEAiYFmiuD37YkIyJFNGbIbn4vei1ufn4utjfUobZ2C8bHJnHx4lXMzMwY+6V5Vo+WH/BG7VeyWK+YHXp467P72BhvlhO82qT1hij44j8sqJpXUgNrVjb+u+CL/9AgyZLmfzJ3cFQOx38WFhdw+1YL3v/lVczNLuLw8R04+8wB1G2rQkZaJtKETKQL2coPlP/9QgZEwQ+fkAafkAYx9uMT/PAJilmVSmKtSLAAn+HHCqmaiLgEL8T7NBNaWL1X/XFS68v60fdTf9wn+FBRWYQTp3fj7LMHkJaehssXm3H9ykPMTivaAkmOagI0SUhEw6YtwrTnQ+sTL6w0zE7e7Vp868kExeL5tnme50bWHnvw4CbWYxPNu/44rl8QNT+JdvXWPYk+LCws4saNu/jXf/0QCwsLOH36KJ577jTq66vh84lxrak2lyxJcMkfPVjH7YMa5VsfF0IX6MlYfv3nN8V6Lhon8nb3Job6iHcjigKqtpTj7FPH8exzZ+D3+XD+w0u4dOkzzMzMUa9nkWDaOKJev4b7YNb49vD5wg9+8APU1tYiMzMTx48fx2effWZa/qc//Sl27tyJzMxM7N27F7/4xS/WqKf2YUsjbCcIDTnJ633geOpwotEhtbhO+ukGko0mawekhli9f1EQNWRYFMQYGVZzAGujWa8EVvDwYSc62wdQXb0ZdXVV2L23DuPDcxjqm8Hc9DJkIapQJ1nxd5EFhUjFNV+C2p5RU6zmKKZNzFaa36T9fylmxGS0Pas0OqzAbQDFLIwjrRELZlpXs01CXl4OKrcWYcvWUsgy0Nc7gjs3OhAIBjXuAPpaDT67DB84vTky7RxZl9V3Rzuv9/cm75fnOzaLDO0W1ivnr/4eJESTimfgtN21vt5D8lDn3I2qDd6IYyR5DTFNG0tpRz/fqmuFeoxYO1ZXV9F8vxWtLZ2ort6M+vpa7NvfhIGBIfT0DCjmtHKiXl7wp4eyBo9m2Lq8ebAs0/Ypz9xpGi4WWGOD5oOrtpOfn4Oa2i2oralGVIqiu2sAV6/c0kX65o+2zp0TmDOdkVkaRBK85T7P2CjaYGB91ue33noLr776Kl5//XUcP34cr732Gp5//nm0t7ejrKzMUP7KlSv4rd/6LXzve9/Dl7/8Zbz55pt46aWXcPv2bezZsyep/qcCtvIIl2efiucRtgpYxaO9Ne2YDVJA9sVu7lqewDTJmEbTkOym2pgrz/z+rSBAjGl20+AXMlBUUIjauipUbSlGNCJjdGgGw8MTmJtbQlQOxzWK+j6pJtRkwC19/9yCWZArFvx+H778q2fwzs8/Nfj5WAkwzEyZWeAd82TdVgGhsjIzUbmlBFu2liE3PwvjI7MYGhjH+Pg0ZNm9sa32hRUEywo8PrF2ib9boAnmWKbRAP175b0fGpzco/qeJDmCyZXrKc1TmJlWndRGR5YlBMKDXp7CNQIrjzAv1iN4itk34CT3ayqQXP5lC59WDs0oa34s3JSHbfV12Lp1C8KhMAYGhzHQP4KFhSWQvqNr+bxo484seBatvN/vw0u/9gX8/Gcf2vLDdYsIq+/bjEhaEeGsrExUV1egpqYKuXm5GBkeQ1//IMZGJ2KljGJpI5yZQltpgT/v5DWV98cX8Fb55lKdR3g91ufjx4/j6NGj+Iu/+AsAStC96upq/P7v/z6+/e1vG8q//PLLWF5exjvvvBM/duLECRw4cACvv/66476nCq75CNvd3FmlRCK1wlZ1k/6CZpv/uDbPZemO1b3owes3bNYeQGi1iWBarABBkskiIAqAJIeVQFCQMD+/hJZ7fWi7P4SysgKUV2/C8Sd3YmU1gNHhKYyPzWBuZhGQRWPkXCFGigWdGS5BkNXjbhChpHxXefL2chJgK6Jo1k8zE38JUeTkZqGisggVm4tQWJSPmakF9HWPYWxkBuFwTEsPioBE94xpz9atIFhW1/DWZfdbsgOS9LOsVJIRUqXCymQtNMAeHl/YJUtujPGNqA2mgUdDzNas8muHE+2Zk1f1uc3OzuPWzbu4ffsONldsRk1NNZ559gxWVwIYHh7H8PAYZmfniSvJOt2Zm+ySMv6Nuz3/RTfHkmpBYcciQEYU+fkFqKoqR2VVGQo35WNiQvHpHh4eQzisaH8T9y8h8Q7c8dV+FALjedh4WFhY0PydkZGBjIwMQ7lQKIRbt27hO9/5TvyYKIp49tlncfXqVWrdV69exauvvqo59vzzz+Ptt99OvuMpgCMi7CT6Mgk9IWVNZrbIpUnu0mSwFhtRM0JmtjHXkwaD37DM9wzV4FpROZZ7Nhbl2QcJI+OTGB4fB0QZpRX5KN9chCMnGyFAwOT4AiZHFzA7uYRISAZ0kTFlKCbVMiQooaKUH9IMykjU3DND1T9XUfApaYoEXzyYmBlR1JtB82hFeXxCrcqKPqCoJB8lZQUorShAdk4GJifmMdQ/hdvXOg05gllj1IoE825maemN3PDxBYym6G7C7TRLvHBjc7aeBDhZTZKXnuHRgJ33RBOyqdhIZoM0WLmxWF7PYWJOJ8RsMswihnZMlmVJwMjIGEZGxuBPS0NlZSWqqjbjySePQZKB0dExjI6MY2JiikjjY/TXNUvH48TlJ9XEzDIDQIrnH7/fh5KSYpSXl2BzZRmys7MwPjaJ7u5+jAyPxkyfeUyeKVp0m5o+1rNOlUWAmyb1HpzBrfW5urpac/yP//iP8Sd/8ieG8lNTU4hGoygvL9ccLy8vR1tbG7WNsbExavmxsbEkep462CbCdtOW8Jzj1QKZRYnVH7fKXcoLpymU9KD5m3K1H7vGKUkgfYeNfdJqjAVEEYlFNZaEKESZICnRKAaHlzA4PAK/kIaiok3YvLkUDTurkHcsG0vzAcxOLGFqZBFz08saIUcEEYQFEYKskGzae1R9jmUy4jVxDySSJRqKrzAtdZMxMIXaP/W8FaE0E8iQ9ejHQnZOBjZvKURxWQE2FeUgGAhhYnwW7S0DmBpb0ORRBsw3ptR70VsRgO/e9KB9CwY3CE5ySxNU0NI7rQV42jPzZ3YT660F9nyEP/+wk47Gsq4khGw0pNqsd63Nhq1BPqsEgaIFLVQyORA+xbHfoxEZgwPDGBwYhij6UFy8CZsry7F7dyNOnDyMudl5jE9MYnhoFNPTs9p3JpsT87U3SZcczyFu9VVfT15eLrZurUR5RRmKizZheWUV4+NTuH+vFRMTU3FT7sR1NCGIhRbdRRLspD7TthiZLcwEJettmu+4rnXYf9iBW+vz4OCgxjSapg1+XGCLCCuaIPNLWKQgFXAjVVIysLNI0EiwHZ9CJ4RYDabF1DzGCCcZYRoyEEFQIa1E+2SfZUHC9PQc5qaX0fFwGJkZ6Sgu3YSykk3YVJKDxekgxNj1khpICxJkQYIgi7ElIqohYb7Y5CMJWhMxuu9RYhK2M/Eq1wnMeqnPyOAna562R21HhmTp70u+l5zcbOQX5GJ4YAr3bnZiaWVFU5Y1xlnElF1eWx/pG0umjWRNtqYpj2wIeuz4ZJtN/HaFJMnMFXpTc94+skBbcNebBAOeRthDauF0o7meBJZHK0zvG5+JNLVNiiZbM+cI9D2EJEUxOTmNyclp3L/XgszMDJSVlaCsvBTFJYWYmp4GiL7KMQswJa+wufacFuiKB/r1nDel4vpD6Wdefi7y8nLR2zOAa1dvYWVl1eI6wIz4JnPPvMGwHNfvIOga69xafK9uk2DyfyeBe1MNt9bn/Px8Lh/hkpIS+Hw+jI+Pa46Pj4+joqKCek1FRYWt8usN13yE3Yjwa8cXmEQym0dWnVZRqM3a5Nkg23le5EeZTARbmuk0AI0GVo0wrULvW+wTEkNGFpSyy4EwAkMhjA3NQpRF+OEH4kHVpJh5dCJ9TzzwD8XENq4dJiY3JnnR349FrlxBUKJGK3XzCVFomlMfY6zSSBJJiGkQY+WmxucxPjaV8Knm8MVjWT2YRaFW+04LDkUKW3g3q6b9czBOaeOb5UJhZV2ylgIy1vMyi6q9kaXOHh5frMW4XG+ti5uaK+vr9fOUvjxf+/o+8wRDBIDVwCoGBobRPzAYK6AKvtmRndU27BxX+0gTGMT9rSkC7njQUTl1hMPZO05cMzI8gpFhc7POVBP6VJucp6L/qSDDayk42WgkeD2Qnp6Ow4cP4/z583jppZcAKMGyzp8/j29961vUa06ePInz58/jD/7gD+LHPvjgA5w8eXINemwfjomwlU+uWVqTpAIcuWSqrMKNuqw0VlyEwuLjTqQ/0pJh/vopvqkqAab4EkvEYqnJA4gofDHTJdX31yekISqH4UMaRMGHKPzwyWnxa6JCGFFEEj7CJNEWABCE2M774PdxtVeP/lnQiKVe2MGKOizJiYBhZF3x8yC08ZT21bqtUpDRTKFZEZDNIiSb1WkaFd5mADiWq8B6pCmyA7P5a703927BM432kAw2QmA8ElZ+rsls1p1dZzSFNi9Dgm0pxeoLe75ipzey7h8dtBwkenIswMfUCHMkMTFFUu8SevNmEsTaTyHxqY1Y7C4BNghUUk3gXSTD6x0ZeiNgPdbnV199Fa+88gqOHDmCY8eO4bXXXsPy8jK+9rWvAQC++tWvoqqqCt/73vcAAP/hP/wHnD17Fv/tv/03fOlLX8KPf/xj3Lx5E//zf/7PpPqeKriiEbaredloZJgFM4mrnT6w/DPj5zlzyJmRYbJv8d8ppILl56jflJD5iEnIskJ+1foFwYdoLOJ0BEH4kGYwzVVTKcVTL8lGLRlVwxp7LnZy1ZLQ+B4Tmlk12X3cTIti8ioTBFUi2mX5v8YJq4UvuJrXV5Jh8IUmCTNJnGlkmDX2SFNu6vnYuHHir842sXe2UKeK9JptsGn34G7kUWMu5I1g7mwHyrzhfEPqmUY/ulhrIcZGEB6t73i1uw9Sy/O7i+iPywL1NLNe3uejrKe06w0dIX71GeqXZdlVgkZPVWUeCVy5hmJxxPCV1RxzQFxZ/UklCab9nSq4QYY3tgn92mE91ueXX34Zk5OT+O53v4uxsTEcOHAA7777bjwg1sDAAEQx8X5OnTqFN998E3/0R3+EP/zDP0RDQwPefvvtDZlDGHDRNNou9KTQajG02mBulCiWdgOFOW2D9BsGQNUQqz7ClvUxfIl9gggZYty0GUh8REqEaR8gR2KkWbk2IrCJWJxcUvIeU7WUlG9dii2iJEFkQU9AFJPjRKWyrPgkm/nfqu2oy6JCYBNCCK22N3adTCeH6r1GY/02M2+j9YFHM2yFuHm6QzKsx1ppdWnzgx3trBUBTtbf16zt9Yph4MEDDRtJUMEUWG2wgFZ2UuqsDYykk4comJpUk8JhB8+edo2RHGvjf5C5e7kJt8We0Wrs8LxLsg5eApYMaU2W8KbeLJvPDN/D5xff+ta3mKbQFy9eNBz7yle+gq985Ssp7pU7cESEaSlgrD4IllbPjh/iRpAe08DqF68fkhPpp1GDy+ffySIPev9hTdAzQYKsanURjh9XFxQlyFKsPTnC7LMauVqjaY4Raf09Ke3rglIJicVShvrMotTxqL9nCVFAEtDe0o+oFEkQYhkxMqyFmvs4GmtHXc5VMkzWqye4rDLab0CbUVBtkwcqGbYK2uXUPJk3MFsqSLDd79upqbL+OFmP1ZzGY9GyUecqPshwGuAncb2HjQiePLVJt5GkxVeq4DSNEp/28NEAjwsW9biNfZq+Pm2biVVPf06SJDx82AFJkhjX8vfFDTAjaW8wwWaqSDDPN7xW65ynDSbhrc9uIymNMO9il6xpoJ2FVUPgKPlP3YJVGpm1CDxA02LpU9AYAmQRMDMf1ZIcHySIkGTFH1iSJermwMzHRhBERBkkKSqrQTskiOq7pphvRWUtSVbNgFWyGitFv1dZQjQKtLX2ak2zYxEzJUFbVrmfxIIsQ1LunWiDJLfqop2QfyfIqp4Ex9vRmV2TMA+w5TMt49ZGlOX364YmWY9kFlPW/dJ8re205USglCw2gim1Mv5N7SctrvcW2o2IR4mwpRpuPotkyTBrr8AXhEsifk8GhJaWtn6bzJ1uzIMyopAkoOVhZ7wfgDMlgavvdoOR3mRgKxr0BhRkeVDgrc/uwxYRVuLlsiPSml0H8KWR4dXgmNVBtqk/pu+DVaAr3n6s98RB27SzjtmpU4Xqz6oQgChkSj3x9DtqWTLIFmVB0wbhUs6rvrPmUTFV32QpTtpp5alRfHVkXNGKatvT+zGr9x+VwzGymxjPGtKs0y5LjD6ooI1Z1ac5WbD80eNtJZHTOhVIVrLMS0wfBS3tepNh5RklsdB6EufHGrzz11p/i+oatJEEAlbpZwArYudsrTA+hwSpNmuXWyFBvS9nfbVDhnneLU8qrPUgwE7GJrfZNrd5t/13lGqtsKcN1sJbn92Hq+mTrD4GGjmlRbtN9qPSm8o6JdeumYpZ5bd1mLfNEA0T5oG0zPsYNdUeK4QzRnYF1adHTUuk/O6LB5+KmUCrGlUbm3olzZJiRk0jcyrhifsZCwQBt9VO4tmxrifbVwkzZMUEmlZOS4Z9zPFjzE2sPWcHdhcuFpk1q8fOd+CELFsFfeNFfGyYaC3szi9W9ZFtpwp6zbYHDxsV6y0QtkIqSLDTOp2SGDfugSfnsTsExJro06Anv1aRv20TSI17XpR6nKsel0naeqYbSvW3m0qinyySvXePrD/asE2EnUqMAOMmzolvn5N2UyGxcktrQ/uArFJTkdcmZZJFPBc9CY4TP105hWQb+65ZtOIEUjUhpvgGKwWYkGQJoiBSBSV66LWBPMSFHJN2xoaVT2q8/zrfZb0lBc2ywu4Y1Wrszf161fMbUeLLCvqmwu77sUIyJtLJLJhOo5+vPZKTOHs+SI8XNjr5TSVSTYKtrnWf2NP6xWOizQ+mKbgur7FZsKr1Nn9OaRofzn0db7YRrjaTJYKcewRTa4N1IpOP3vzlrc9uwxYRNphyEppHnvJOB5wbG0heM2HbZCQJMqr/8K18Ra2INy14Fgs8eYjVYEr689Zav5ifckyrq2qPfYK2LYn4HvVaY1FDsu3luzWDnjDyavx42iffJyuAl5lrgVOtLPk+eDS+ok5jr6lL1wdaGTuwr7GOtSuTUnp7xJUXdgh+soulU6HZumiAk/RBoiYT9bDusFqrnFhLpAobyXzZLaQqbgiQ6uflDml3irXI1csLt8gnj5VgsmRxo2iCqW2m4ltwIDx/9EgwvPU5BXBsGq3fcJuRTN4otDTQUuA4QVyjaaHJtbMZMKvLyh+JRZqs2gO0z0AT5t9O33VRpllwmmaHGbSL+AZJYkySYjKKtGCSjom8D7K/+udABv+Ka2QphIssT6ubFgyN9e5YQdtYdVt9Gzzv1orU60muCNFAdCVIhnJWbToBrQ2JMG22ErK5hbVaCO0I8NY9WJbng+TBAo/kBtIB3Eqb5GTjzzLfZdVvhzi5bWps1R4v9FphzTkXcws7gVXbTkiqqWBqDe7Vre94vV12eFwsUzVnrfWY9NZn9+GYCPNuUvXaKjL/qgq3ByiZe9Vt0LRTrEWIZ2HSb5DZAaIIba8JAbcrFbNKgeM0QBJrfBi0u7LaDlEmrmH1aUisph7Gc0oQXPKYL0aqiWtkrQ8wafJsZkasJ8CmJJ2DAPMiFWOZpe1NVgvsFCoxX2syDPAH20o11psEe/j8YiNpWdd748wLnsBKlnXYitbLEqxyBHhyUVtoR9O8lkRgPciwmwSYdm0qvss1fScb9FtOtUuXivUW0HhIHraIsN1Bxdr06+txY7DqN7J6H01NvxjH1U2oG+aymusYHwkvCab1iSTDTlIesNpUCYhTkMSFSyInxAsnDhFEVKRohFnadDXisixLBmJNRnsGYumSYhGqSa04SYLN/LdZwa5osBo3bpE93u+IJLrJuAvwmnLTyvFont0kwWZEVxPsDPa/c6fz18YlvZ4P0ucNPJttt2IlJNvORiLsVrDSpFqSqA0dK2BjmCOTcKRZT1GQpo32bNYDG4UE0+YjGs/YCMLu5OGtz27DtkaYZ8CxYEhdE9sIWhFTO30DEhtZMzJMg4ZcEiQymY+HlwRb9UftkxkZ1sPKZJrm28r7jVilJrIbGI3UFJOklNpHMLStaj8E6Ii1QoK15spGMswiwSyBBW90ciusleZTr+ml9TcViwWLFOvJcCLKePKCMlpcAd65Sz+PmI3lVG8E1kqqrYWc5FrpLbQe6NgoG+dUwQ3tr1l5Xu203brdMgFPFqkSDKxHACpeuO3nvVYE/fP+LW9ceOuz23BkGu3GZpncqLIIq54I6ssw/XOJ/tkNtEUjnzQY/FApE22yJNisP6TPM0B/Fpqk9JwbatqkTL0PWf3PvraQp6yZVpoViCpuUgufQnKJ8iR5TeTqNZqAs3x7efpPNZvnfA6s+2UF9bJjap0KcssbfMuqL0zzbIeCAdp3QM4vdhdvM6KcDDauNtjD44pUC1146nYaROhRg1PCl6ocuG6YgK8FkjFDdYMgboRo0R6MsBt81IMHElxEWI5FGZPkSPyYlVZEgAhJjlBTo8iQKBtBaymFhAjlmNHnWN8/8noecsO7SY0HemIs8DJlM6/4QRrvw7gJ0T4PM9JvhriWLT7Bqv+LSmAKQaT2k4zUqJ4XNe9cjpVT+ylZGmvY8StX2mT1UWmTtXHTH5MQUcys4Ys/e+V5yso/Ioqe2p4gy9T3pLar7wcLxv4Zx7n5hE30TSOcUJ62+k1amcHz+J6r5ayeKVlGH3CLx+zabMwmgqexrQtYMPtueb99FeQ9JkMOWPfs1NolPoemNPKjDC+gxqMDdSyYbaLtbrB53GncECoZynD2cy0Jg/vR6pONX5JYaenk1anZpGBSp9lV5vOqvfqs+75eUaRNg5/aIF2m61+S43qtyPRG0QY7nYtSBfXZe+vzowUuIry4uAgAmFz9LKWd8eDBgwcP1lhcXERBQYGrdaanp6OiogJjY2NJ11VRUYH09HQXeuXBCur6HAj3r3NPPHjw4MGDtz4/WhBkDtGFJEkYGRlBXl5eXBPlwYMHDx7WFrIsY3FxEZWVlRBF9zUigUAAoVAo6XrS09ORmZnpQo88WMFbnz148OBh/eGtz48muIiwBw8ePHjw4MGDBw8ePHjw8HmB503uwYMHDx48ePDgwYMHDx4eK3hE2IMHDx48ePDgwYMHDx48PFbwiLAHDx48ePDgwYMHDx48eHis4BFhDx48ePDgwYMHDx48ePDwWMEjwh48ePDgwYMHDx48ePDg4bGCR4Q9ePDgwYMHDx48ePDgwcNjBY8Ie/DgwYMHDx48ePDgwYOHxwr/P2k/6B7oKC7pAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from astropy.wcs import WCS\n", "\n", "\n", "with fits.open(f'{uvfits_name}-I-image.fits') as hdus:\n", " w = WCS(hdus[0].header)\n", "\n", "fig, axs = plt.subplots(2, 2, figsize=(10, 8), subplot_kw={'projection': w.celestial})\n", "\n", "\n", "names = ['I', 'Q', 'U', 'V']\n", "\n", "files = [f'{uvfits_name}-I-image.fits', f'{uvfits_name}-Q-image.fits',\n", " f'{uvfits_name}-U-image.fits', f'{uvfits_name}-V-image.fits']\n", "\n", "for name, file, ax in zip(names, files, axs.flatten()):\n", " with fits.open(file) as hdus:\n", " data = np.squeeze(hdus[0].data)\n", " \n", " im = ax.imshow(data, origin='lower', cmap='inferno')\n", " ax.set_title(name)\n", " \n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " \n", " plt.colorbar(im, ax=ax, label='Jy/beam')\n", " \n", " ax.grid(alpha=0.5)\n", " \n", " \n", "plt.tight_layout()\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great success! We have boring point sources in Stokes I. As we have a constant rotation measure for all directinos, and we've only done a 1.28MHz bandwidth, at these frequencies we get a negative Stokes Q and positive Stokes U; both look like the (low-res) Haslam map. The Stokes V is just a bunch of Gaussian blobs." ] } ], "metadata": { "kernelspec": { "display_name": "woden", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 2 }