Posts: 11.931
Threads: 63
Joined: May 2017
22.09.2025, 15:59
(This post was last modified: 22.09.2025, 16:06 by Selur.)
To me, 4-weave doesn't really seem stable, but I agree the text is not broken.
That said, I too would have expected for your code to work, but I suspect somewhere ffmpeg does some interpolation, so upscaling before Stab and downscaling afterwards:
# Imports
import vapoursynth as vs
# getting Vapoursynth core
import ctypes
import sys
import os
core = vs.core
# Limit frame cache to 48473MB
core.max_cache_size = 48473
# Import scripts folder
scriptPath = 'F:/Hybrid/64bit/vsscripts'
sys.path.insert(0, os.path.abspath(scriptPath))
# Loading Support Files
Dllref = ctypes.windll.LoadLibrary("F:/Hybrid/64bit/vsfilters/Support/libfftw3f-3.dll")
# loading plugins
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/libmotionmask.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/ColorFilter/Retinex/Retinex.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/vszip.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/DenoiseFilter/CTMF/CTMF.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/TCanny.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/libfillborders.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/libtemporalmedian.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/DenoiseFilter/Cnr2/libcnr2.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/SharpenFilter/MSmooth/libmsmoosh.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/FrameFilter/ReduceFlicker/ReduceFlicker.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/GrainFilter/AddGrain/AddGrain.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/DenoiseFilter/NEO_FFT3DFilter/neo-fft3d.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/DenoiseFilter/DFTTest/DFTTest.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/EEDI3m_opencl.dll")# vsQTGMC
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/ResizeFilter/nnedi3/NNEDI3CL.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/fmtconv.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/DeinterlaceFilter/Bwdif/Bwdif.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/akarin.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/SourceFilter/DGDecNV/DGDecodeNV_AVX2.dll")
# defining beforeDeCross-function - START
def beforeDeCross(clip):
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/MiscFilter/MiscFilters/MiscFilters.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/DenoiseFilter/ZSmooth/zsmooth.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/libmvtools.dll")
core.std.LoadPlugin(path="F:/Hybrid/64bit/vsfilters/Support/DePan.dll")
import stabilize
clip = core.std.SeparateFields(clip, tff=True)
height = clip.height
clip = core.resize.Bicubic(clip, height=height*2)
clip = stabilize.Stab(clp=clip, dxmax=0, dymax=1, range=1)
clip = core.resize.Bicubic(clip, height=height)
clip = core.std.DoubleWeave(clip, tff=True)
clip = core.std.SelectEvery(clip, 2, 0)
clip = core.std.SetFrameProps(clip=clip, _FieldBased=vs.FIELD_TOP) # tff
return [clip]
# defining beforeDeCross-function - END
# Import scripts
import vs_temporalfix
import fromDoom9
import SpotLess
import qtgmc
import validate
# Source: 'C:\Users\Selur\Desktop\1-source.mkv'
# Current color space: YUV420P8, bit depth: 8, resolution: 704x480, frame rate: 29.97fps, scanorder: top field first, yuv luminance scale: limited, matrix: 470bg, transfer: bt.601, primaries: bt.601 ntsc, format: mpeg-2
# Loading C:\Users\Selur\Desktop\1-source.mkv using DGSource
clip = core.dgdecodenv.DGSource("J:/tmp/mkv_43360fd7b44504b5cea6a7b5fab0c634_853323747.dgi",fieldop=0)# 29.97 fps, scanorder: top field first
frame = clip.get_frame(0)
# setting color matrix to 470bg.
clip = core.std.SetFrameProps(clip, _Matrix=vs.MATRIX_BT470_BG)
# setting color transfer (vs.TRANSFER_BT601), if it is not set.
if validate.transferIsInvalid(clip):
clip = core.std.SetFrameProps(clip=clip, _Transfer=vs.TRANSFER_BT601)
# setting color primaries info (to vs.PRIMARIES_BT470_BG), if it is not set.
if validate.primariesIsInvalid(clip):
clip = core.std.SetFrameProps(clip=clip, _Primaries=vs.PRIMARIES_BT470_BG)
# setting color range to TV (limited) range.
clip = core.std.SetFrameProps(clip=clip, _ColorRange=vs.RANGE_LIMITED)
# making sure frame rate is set to 29.97fps
clip = core.std.AssumeFPS(clip=clip, fpsnum=30000, fpsden=1001)
# making sure the detected scan type is set (detected: top field first)
clip = core.std.SetFrameProps(clip=clip, _FieldBased=vs.FIELD_TOP) # tff
[clip] = beforeDeCross(clip)
# clip current meta; color space: YUV420P8, bit depth: 8, resolution: 704x480, fps: 29.97, color matrix: 470bg, yuv luminance scale: limited, scanorder: top field first, full height: true
# Deinterlacing using QTGMC
clip = qtgmc.QTGMC(Input=clip, Preset="Fast", TFF=True, opencl=True) # new fps: 59.94
# Making sure content is preceived as frame based
clip = core.std.SetFrameProps(clip=clip, _FieldBased=vs.FIELD_PROGRESSIVE) # progressive
# Spot removal using SpotLess
clip = SpotLess.SpotLess(clip=clip, radT=3, rec=True, pel=1, smoother="zsmooth")
# removing flickering using ReduceFlicker
clip = core.rdfl.ReduceFlicker(clip=clip, strength=3, aggressive=1)
# removing flickering using Small Deflicker
clip = fromDoom9.Small_Deflicker(clip=clip, preset=3, cnr=True, zsmooth=True)
# changing range from limited to full range for vsTemporalfix
clip = core.resize.Bicubic(clip, format=vs.YUV420P8, range_in_s="limited", range_s="full")
# setting color range to PC (full) range.
clip = core.std.SetFrameProps(clip=clip, _ColorRange=vs.RANGE_FULL)
# stabilizing using Temporalfix
clip = vs_temporalfix.vs_temporalfix(clip=clip)
# changing range from full to limited range for vsTemporalfix
clip = core.resize.Bicubic(clip, format=vs.YUV420P8,range_in_s="full", range_s="limited")
# setting color range to TV (limited) range.
clip = core.std.SetFrameProps(clip=clip, _ColorRange=vs.RANGE_LIMITED)
# adjusting output color from: YUV420P8 to YUV420P10 for NVEncModel
clip = core.resize.Bicubic(clip=clip, format=vs.YUV420P10)
# set output frame rate to 59.94fps (progressive)
clip = core.std.AssumeFPS(clip=clip, fpsnum=60000, fpsden=1001)
# output
clip.set_output()
does seem to work. (I also added deflickering&co for stabilization and SpotLess for cleanUp.)
=> https://www.mediafire.com/file/qo0zawd6h...2.mkv/file
The custom code I used:
core.std.LoadPlugin(path="%FILTERPATH%/MiscFilter/MiscFilters/MiscFilters.dll")
core.std.LoadPlugin(path="%FILTERPATH%/DenoiseFilter/ZSmooth/zsmooth.dll")
core.std.LoadPlugin(path="%FILTERPATH%/Support/libmvtools.dll")
core.std.LoadPlugin(path="%FILTERPATH%/Support/DePan.dll")
import stabilize
clip = core.std.SeparateFields(clip, tff=True)
height = clip.height
clip = core.resize.Bicubic(clip, height=height*2)
clip = stabilize.Stab(clp=clip, dxmax=0, dymax=1, range=1)
clip = core.resize.Bicubic(clip, height=height)
clip = core.std.DoubleWeave(clip, tff=True)
clip = core.std.SelectEvery(clip, 2, 0)
clip = core.std.SetFrameProps(clip=clip, _FieldBased=vs.FIELD_TOP) # tff
Cu Selur
----
Dev versions are in the 'experimental'-folder of my GoogleDrive, which is linked on the download page.
Posts: 15
Threads: 6
Joined: Jul 2025
(22.09.2025, 15:59)Selur Wrote: ... I suspect somewhere ffmpeg does some interpolation, so upscaling before Stab and downscaling afterwards ...
Thanks for taking so much time to look at this! I will definitely try out the temporalfix filter as well.
To be clear, my workflow was:
1. ffmpeg - separatefields
2. hybrid - stab()
3. ffmpeg - weave
So, I'm still a little puzzled about the difference between the ffmpeg/hybrid approach vs pure hybrid/vs.
Posts: 11.931
Threads: 63
Joined: May 2017
Quote: So, I'm still a little puzzled about the difference between the ffmpeg/hybrid approach vs pure hybrid/vs.
I think it comes down to rounding / interpolation differences, which is what I tried to 'simulate' with the up and down scaling. 
To know for sure, one probably have to look at what exactly ffmpeg is doing (in detail; debug log level might work) and maybe even crawl through the source. (at least I won't do that  )
Cu Selur
----
Dev versions are in the 'experimental'-folder of my GoogleDrive, which is linked on the download page.
Posts: 15
Threads: 6
Joined: Jul 2025
(23.09.2025, 20:01)Selur Wrote: I think it comes down to rounding / interpolation differences, which is what I tried to 'simulate' with the up and down scaling. 
You bracketed the stab call with upscale/downscale, but I did that part in hybrid, not ffmpeg, which is what left me still puzzled. Are you suspecting ffmpeg is interpolating during the weave?
Posts: 11.931
Threads: 63
Joined: May 2017
Quote:Are you suspecting ffmpeg is interpolating during the weave?
Might also be related to the assumed chroma location
There are probably a bunch of things that could happen,...
----
Dev versions are in the 'experimental'-folder of my GoogleDrive, which is linked on the download page.
Posts: 15
Threads: 6
Joined: Jul 2025
Is there anything I can do about the red bleeding left and right? (Also there's a faint ghost offset 15px to the right, you can see it on the 'G', I tried lghost to remove it but gave up, certainly it's faint enough that I'm not really bothered).
[img]data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAewAAABvCAYAAAAuefOiAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAJMNSURBVHhe7f1nkyQ5kq4LmnMSPJIUr+rumXOuyK7I/sb5iVfuh105Q5pUdRdLFtTDue/7KIHBI3Pmc7aIvZkIGIEBCgVxVQXr/fDH7w/7Q2M4hN/0enGhy/DLswhUBWn2+mfI79sLQ6/Xjyvg7zLR/y5+kFe9OrFnyHf7Q9BQYoz4q2/7/UFcOTKpfX5aI94d2ovaM2TMmcQ+3vY/kZcM1Iuven3nSdJ9qMNGPBkm49lXYTKelojwS5g6bCAu+nFRCr5GPDpEmMzLcUi/y2cl/5F2nZfkf/GTiF4bJulIXpcwmU4VX7k8DlLuj+tKvHye9iewj0qQ0ZUyrOOLxDOe56TU0ZfqGKGSriorJep9toUo70HU0zrfu93W/CyX4cDD9FSaieR/oS/LN9LpV+1wf9iF72EOh4g/yqXkXyhl1w8/42/J48Pww8ukn30LSv2LR/tMu+9MOy5DxyHKJ/uSXuE+1/5st4sw9ld+0lJFlxzIZ71yUb4KX2HKh5mW+5+iLxNr+WneEQ5tpTC0/HTU3WRbXzK+SLsq72wvbbQRX0R4TMNxPJlo3SZKWYVf2kRJu0XhbcSbb/vZZ9XI7yO/H3XVhognwjxPu473OMUSfcl3HXHJX5RllmlVHdu64F5JM8vrOE8RqiTqXn6TvwHHOA77qSDlUcb7CcAT/WD/oHYdzCmdaPtRVpBMq+JKQf1DYsjbCPrxF8J/Q1cdtlSUUrE/jjCTbn+wA1EKdUeRFTF/uJOE/c6v2gZaIQOVV/lA+Oido0oxfJjtHWSiP3C+lkZX8zAun/9ofMRnEHUpO7AMUXdo9aXDH/Q+FV/Q3M/GlXysIjkkXfGoVNIM8ok6kiivjpL2m1IPS+fsgfMHzRDfD7J3K+UbYeq0S/7cb7NbhWmJ9r/5Ki7qkEd0CKW55I9IlfazpNuIqijaso/8Rl5GQ6+f2WmB3S7qT3w/GAz9ourlswkkHYWEJKZOO4hPsnZ7/9HspfBdtZv8oc946h/+RNt23M+7Un/a6ErdSuz0D+x77h91zpHmx2m3mck+6hA/2G3G3avLJa/Kj0cbTaCi7dm7LKe2nxQighS2Cr3x8VEUcZP5S7Lym5pHpd96Fl/2ayBpz3ftmyqiQCvM/feo+VQj6UsSDPksH8a3KWweB34er7+rQySe94eFpiPa4su2eRjaIG3YTKP94XbvWIkMZJDsqyPCY6EmLiKejD87g8IrIYsqhcJShv8Dsg/MNOv4KMNPUN2hQ4cOHTp0+NzQ+/4P36aVjZ91Qy0JtNJFPjuWDkERbCuNALSSZBu4FRj8okhSH6UD/Fl+/ykJML8v2kgJ8nHY5xpr0QpCKizmMSGZkvHXJsJEMXFFmCLpJr1VXlI7ex4m4xgMQ2MSUnvchaSXfG1j4zrSSJEryWsZ3CLpy3fh11lKM2WJp70wHJVh+IkSbdJ0/OkRiuR4FEvkL3mUmoz91Td10Ii7nybhwscM1AbONNpXdUSBZ8Q+L+cjOsvlcZhipqyl9pJmennRfptJFQ07td7QrkqZCO3QiXmlbdVZysvCk3iSYVoetUhtt1iAiubdpp3I71v62vwW689HSXjYOrZWu/XAh9Cs89PeoA393KxfLqq8FGuLs6hYzBJZV0BalUpaz+OtKY1ArQUp6KzSTh63aZSY7e8xz4/TaMspCK/CFgtclMun+FvHDEobff7iCJm2eUK5aC+PaBbitv1G1/HseVLFelK9yMu6DwFHvEkN1f4qv2llK/HV30ZbSLbFV2l9OWqGEWGpz5lAFeijPiQjjk/qlPOuZCU+iS+OeFQskREo63JtOcsPMuT/yCNdV1nr0KFDhw4dOnyu6P3wp+/1Ex6/4v2QBKof9WMZAQkgpBj769h9JDn42yLxVIFbieE45vI0Lwx+k9JRGb+q4kvJM+M90oiEeqwi37UTNyLeGA+sx7Bbacu9HKPqV/G3Wk/48a7VVuv4gjfxeSu1xzeVFtBqPRl/ptnGV/AsqaLllm9KkuXrovknTYbncT/76hPxtVp+G7P9fSYlgo81pTa+fFR41GYm/Dptv35eF0rRVXkqY87xrp6wU1C+c6+QnvFWaRd6Cn3uZ7zt+CrfxUVJwHEUX6KoK8fvBlWdyHctHz3ewjOhjEvnu0Kwo067lFF4RYNrMxk+8GcZJL/95DyJ8rlflKxV0bXau/uHXtTz+Lhfa9jhqzDNKzypIky+56Sz/KjQ28aiG//ueRt93o8YMok24+7X8QXyTfY32TbaNy192b4zuoytLsusSpn0p+pNeRbeoPDN/aNvymWkbX+f4dnD52keTUTMV8/49ylLZD5pP/Endf1sk/KL9l18XUVb4ssgeVHCVIEznvSy/VRhCsnPaY94P9WflSdxUWKt+oD8LN9lf36kYQcyjeMJbu23ANqP33bo0KFDhw4dPkv0vpeG/ZEAUd2nlJV+ka4rSSIlkV1KDiEW9GIcRuKX+6DEHRfx7acktOfaYju7tA2TaT2fqZwC+JHEF5cRneAP2uVTbdgMkv6gjBmWjwW//mh26jNNByQdGSTjaVlTLqorR2thaOPLUGVWYb6Kj4+z7TdtEA90LNFn/swr5dGWdxthGYuJkZvn2m4bEvjdR/Wn5mM8S/aXccZMswqbtGeeSrz2l/ctns+pSEtAhgVZx0o8WcdKRG2Mz60kGVO/lzO2a434yCvl8SmtooxHB1+TR2VsVsjv91HXkpYy/ikcwkKWYTOjmWKrXehV5LOwtqoLz1G+KoGTmDa+Ur/bCMNPJFFt2onCk3hc6BcKnUFeq4DUvPHr5E3RcpNXFS2lLfw3NBTyP4FSP6v4ygdBdEWV/W350bKrtONnda1uj/murefmHYXJmJ9lRfA3R0UaEWQ7z3iPkBHm9/ZXKHl0D2TbLO043mWdPeovMqLoF9OSeVQuz56l38bSovDE/hKf+1VzqRAvw0vt9qiOPUul0JXx1mHzJv0IcxQo8exd1oWjcn6W9rBYezNsRuJpfzKLHTp06NChQ4fPC6Zh5498kR6qX/Xn0kZKC7UEldfbra/lTBR7fB1ffm9/eVUiDr9Fvss3mU4tEbUxO57HVtOZ74r0Vt65X0sz7RiUo6RdPw5BqWg78XlK4EdWiPLdcbxJVDuGSLQhBUbYT+U718uWTTfiVRu2RoSxvy2dqTGA/D61gMKLZ1qA4XlEn0zzGSLsp+jLtJ/zvKav4Dl96X8KH72LCOt4Mxr3iuaVZfip8aYSbfjj8dj8ury325h1XRJzv7QnoaSZDAjk+GD9vGw2UuqaB6o17Eyqrd/mfSLF/wEl3irs888+wfPUGstqiAhTeFLn8ZkFqpRlBKnnAnyUeB1PIMO3cxYiTNznY1C+TrIKX83TgypwXBZ+5tdVmLx8HibzVtefDNNapJKajC/vhcKTfOZ+4VWFj6wFEaa2MOWzLKd6BcJ/h2x/JT77+wwRJvOd8dc0FR6l794RnmertWK4X7eF52HbHzD3PpVCzrbPb6voCu3pPedxeS+03/nF87Z7vHFKhLG/LUo9Nfj1IKxzrUUpy679GjrqltGhQ4cOHTp0+EzR/WB36NChQ4cO/wToffen7w9lskuq45VJIJdR5OSm1Phzb2NQzB/P7A3F/FIvT3mGj8wb1f0uzIoZf9mIpTYTlLRS9sgI/HltMiyTUuwv8Wbe0nxZoU3CkEvXyoQRIflUzH7P8l9npr1ytGaY/NY9x7PE42Vtci25/cRmDqAisymTjdKcan+flUt80HLLQxVzWBV/bu1aRjzCfNOaBduwz8hqTWa1rBjPStCg/dmnhjQxlnpZJvy5f1Q34rodbvg4xo8nnZlXQu5jy06QEw+zHpa96cN2WGipkBtgJN1H5ZSFlPmPcsogdV0rS4Hsb+uXdilsI438bB+2yJbn+RX583dprswmXBKv6UwUHnmejvMS+Yw2m29Gw5H5u4rO8tmzfiOXpdWT7aAalMmVUW/qdt0+e4Z4cETnM7Q8LoHdB1ku9leIi5qPycBshxnoUybxRNYJYqr9KtaqXrtX6lrFm6SrjS9pyTfpt/HVdQocmXSz/mWlCIpK+6mCtin4Vb4q/XEVOJ+VNhbv6mW0SsX+5rv8JjcTqidMthNiI99Rj7KO1G0iy7P8xkWSR20h4yu/L44Mm9kHJWz53C9aPpYXJWzyvI2mjjD8/Cx40tLbxsd1W/odOnTo0KFDh88Wve//5QcJAse/5nkPijQdkmLZ1KOWmEN6S4HpI426EihaeSER8T3TLkChKzWaStpIpNaTEtNzibnOy3NNKzWlfRysUE8GKFpixNdOJqhoyMvgSZmuf0yCI561EmTISuWTuBd6B79uP/erQZGkW9o3QXsuqSv5rdMOPJ8MNyhqFbyJ75/H84m8JB1ZLp+0UDxDu/zjWf6FlNKrojIUKbgls3yf37Saq3lHdSTrcwmT5VulTUztX/jq35RJZ1U9Tx4PR641FkUk4q3Y2SKSapWWNu3M3y6Ib7W0QBVffp4o0VUvkqdZhrmssoSpGFwk98hf3haNpgpb2k3yIrWAKkxqgBk232U6JT0hrRplUmq8yjLdVpNXy2ESESa3Q81tew0Zd5IXgTPfWU9BS3PQEIFaPpaLchmxt/HmAyFD7+MAk1Ivk6/la6UV5VF4Y3+JL+irk468JNq+uY2v8LQmSChpH9U1R2Y/748/9ZdFw86ytL/HyGclnvxdiLpXZbuUWbaTUj+rxEv/HfGUdhhhyuE3QtaxtKpGd6m03a/pzd+Hgme8BxlfaevJpE+gLYfjMMnrYm0GJUzkxf626X0Kyb/Sj9U80vWz3HTo0KFDhw4dPkf0fvjXP0jx8F/8/FWvtYqUfJ6HqeWYfJKS8fMxvnqheBnzKJKi+zlG3B7P1oYtyNtKiin0BH1FaouwhVwhpaKMt/3WvepCUmCGdaTUWQtH5ZCC8FIL/5QUljwoklh81Gsiv5Xk3CYa8UXYeslAkZ4LL/w+y64eO0tJNOnKfFcpKi2XYMuziKctg0hPyPHDkr94VbSJVl0hcHh+UcK0KZUUWnZV3wu1ppCfHaKOteWU8VffxmWr0fjHZRlIhTaf7uc3RwJ61uvgX7EKhYVpkPVJKOOKEU9LQ4Vn5VDaXTxoxxJ5F/EU+sxT0JbApGvQ980XSloZOH2hlEPSleQ+4wNoLRPhhX98yIZf55NMexuacF2Gpf4908pzS9KKzMLjjLnMXajqd7ad8ixUrmIJKtRUYQrvPd4MUTZBMsS7QnsbT0H2C4V//n1qhPX2ssm39qjUzJOXV52lzFOm2PZrLQ3tuLY/S15l0NqykAVT+iH7Wx47Ii+5rLT9yL1CAwjay/eZVHxaa5GFx0H7J/ufQlDNBBDxtNFVH/rFPnkVfWG9E1h7Rro/y3Pk6whT48/+tfSX9vc46UT7zt9mmLquZUFk/gtLqiD5Yem3o26klfrYQtBp2B06dOjQocM/Bf4bDTtFgfZZSgJFoqjGkFJqKRpXeM8+NaQEkbJCkdpCqmlneytsajAZbxnXMM+Q9BRJvHoHjiSeiKdoT4XAj79NulIiL2P5VZiUnusZsCClpUK30GpYHkHGl/dtSF0XrcLRasYtb/gStEeEuv/8aEGQMzZzXCjze0RfjMG12o/7KX3WiFgKzVkXyqENNZNSak+6UmusCrHkIZ+Fn/HX9THR0pk8+Zjn7aqAiDdetRqEkPUuNeKKLlCOnRRaPnrY5E1uklJrUzk7OmPbbqLe19GndlZozjx8nJfnVqtEPZab+Sxhsu7G8zrpgkwjyztuW027kKmLiCciqi1xfOF/g/aow1l2pQyE9ijZCJOaYdJQpZ11qbVQ+X2refMsePNM/2izVj/PNIP2ICvzVMbVhVZjDRpKnj7OdyaRVrdcRVPHlzwofUomWvrCNmxJKzWuKNPaAjDIOhbx9GLzjaw3df0pzMiw9lePS+FyfcyTgqP8BqqoQclb1t2638iwEW87g7yNpOWTPyurAJLuo7ykl/G4Xw5x+gQfS5h41fK+LY+23Zknv0ozULaotr91PP7k6JOoz6X+lIjdM2QfEP1jvsr+7cjirLBtzjp06NChQ4cOny26H+wOHTp06NDhnwC97//1h0OaDVJ1L+YioZj/Qp0vJoBiatCz+Nkf5eSJMC3kxic1Mr7apGUI09eRSSrSbE1QkXZ4hjTzFgSdEaiy+BQzTWt6ekZDFVXmM83dOaGonEAm5OSR1gJcRSDUJpXWvBbPIr7CjypsLh9qzS2OdqJe+y7zedgfx8ebgoi7DAGk+cb+Ooo5KJ7W5QDSNAcy7VwWlpuLtPHXiLQisTrNxKGYQj3NDPOpsJmvTKlMMosEjpZVBM+zquWbI5Nm0Jwm8TQ9ljBVuSSvy/K6eJX1oK7TtSkLZF1oQ5B0fQeiDPJxlXbytjXxOY6O2ovLYZRd7k9cli1W8T039xUzepqgjymNv/EsvP2hXX7VH8Z3EW9ONiufHPUpnlaZQJa8/sQksdIOn8VXt40cFtlvPZ5hxN+2jfhI+HgIzf2sN+XEQRBBsi2UNlelXUykuYFR0Fny1AY9+g6kdTsf5yQ8hz/MPJW21WalPNtmRJmlbE+fCJtdX6mPEa0heHFUp0DkqTbZl3xFGiWpiHc0bPPSDtO5n+3laHOVoLkM2wWyb6nLsC2HKJcs/whTDxPlBMy2fkc8z/MolDSevarrbmuG97Dlm6S7+rbQaX/zi+Okk5zsf5MnWT51neG6KoEOHTp06NChw+cK2zglpcEikebPvlA0wJAyhjlhpAqT79pNDjye7Xbjfq2dhZ/aeJEGi3TTShS51WJKHynF1FLH8TaGrYRSpPSWzJJG5iknNpQJZVXg5ElZnlFJjAXBgzKxJkipWFPwXGIqWciLvBf28ayV7D6OONPMCTU54aZoDlXYlieB5zQIqeUmffl5yVvNyEAv6CvLNkqYOmwkktK7/cVvw+wzf5l2+Sb8Cq0E6mlnmZagVdKtpuZ+uylI8pVrfxZV4KM0P9UWUs4tk1XK5LYafpfa+C4Ia1MWgp4sw5LSUSDHcdyUi/tH5ZIaRz6LvLRtoc63+61VxL8ltKHKd4Zo24njUG3b+nypX7brnLSXyzZBasDZnnPjmMHI+4RNtXFKoTN5H8TUukbRcqOtFmtTBM5vQbapogFnbrIfqnmUl8/4GEENbdvyeFLDznZUT0hN61x5Eu9Ss67rWpZl1q1+fdZ6oNS7pP1ZGdTac+nz8lmdViJfZpjIw0dtDOT35aHff2oL52JRyToR5Zu/DyDLcDjwiXQZX5ZTbR3Kepi+/1WYLNvq9ybjSf6VLvUoM35dJi9mAUeSdV9V6rf9FSKeUp9qvkY8bZ9avQu0v73+Ln9DN+u1+YUIYTgcV7W+Q4cOHTp06PDZwg7/aAXQjyWBohG2MoXh02FCSsixqZR4qjGFHIfN2Iq2EpJGvfVeXqaEk/dHEnP5zrxCS0q+6YN+STue5Ufhx6eGTKsdp44wOX4p5PKgj8ackx9VfK2EmGHcSy2rfSBE2PY8Ab+v812kzNzGNOPPpRmfQBnDj7Q+NeY6jKUiCm1/txuXgo/H1xzFQhFhM7956ITDr8uTCFNvXtLS1ebPEB9VnCns6iXtz8b4JoV+fR48yfrXHs5RqCkacAjXykPwM8Kk9ueIsLmcL+jeRUFlfQf7vn/Xe7aEqY6taAhFM460U6OpAuc4YJZT5rceryvtpNAc8aSkX/G3bMKTbSKWI2WYWjN8rnHkNrN1H5DvUmvMTzL++mCUYWjSJb+R0bRirdcr80HSVepIlk+VdMlKpJXWprL5RLXcbhfaXdL+/NCO1ufKr8vmG1FexUIgZJjSz4SX7b3V5BVm6N9nu8tvS9tqgwoRUfj+Jd8eBQr4s7K8Mvh43P94mORJm3/zHEFHPmvriH9bRZfbPemde9m3DiJUPd9mkPnMeh339TyPXBqcdTUr/zbSTmsr2MR1vtuXxuvf1FaS8tsUZVfqbH4jPG/zdb9oqPJSvn8WJNOu56VkmsnQtGIWBguFnMy2e2oDrmFXQZvRaFDqQYcOHTp06NDhM0bvu3/57lCkkPBrybnVRt0rIkD1y5+P+vGwzKjOQFXYnLl6fnpm/tnZufkZy3LVStf3j4/m5xj4Zu/aXi21ttrNMYGf0rB7oaX04/NxzCA8Ozkxfz6dmQ9GccBDfr6J8br3Dzfmg4eV05cHcBS1IvlYSVtFSivkxEXyupLqQjkTXafhO69OpnPzQX623Lgk9rB4MP9p9WT+dpdjIAobEmkp1mRfpfWMQzN9ef3C/JOZ86RIpvbXsVgvzX9ceP7zPmcGZ5mAovUVCTRlxDbGMhb3TErvB09Segez4dj811fX5l/MnEcZVom7L/Qi3jbFyG8bXbku2k7OVQg691XgGBptdlG+D8Hr28d785+2bd3dRFqb4EXZorMlT7wIrSLbS+SzFwkVK4KQdSLL5WzudWI6nZhviPqX/H9cLcy/u3f6VmVcrK1uydoi/QeBVdKiLy7iWdGU2upTuJT5y/KYRDu6PL8wH8yjHqelIuvnw92d+Xd3bRvrRcZTQx9G+VxcePmD+TzqQMS3idUpT6GpZz8C9rGhyS4sR2nGGoW2d37ufAUn0UelxtpEW7h/8LYG3n54Z345wCWYlNppvUlP9qWF53Ff5jBU/OQteG4VPFQrb6YTL/vzU8//fO79VxbletuW9yp48fDkdWIV/cY+G4eQY83baENpmcryr4I2/SBjFPSNIqKL8dT886qvOo9+9erC+/pJtOGcA2OINDZRPtucH5EWFmmXiaf47jba34+//Wr+MvNbWUFTC99FOXjOKJcW2f7SKlLKIQMncSDelf7C/rY4WvkRL3Pb2k+h9MmJuE9LUEVEwxa2/0NUHTp06NChQ4fPBb1v//RdGcNOaeFYw/aXRdPOX/wUE4Uc0xqEaFL8kLJH5dumOZ26NPjFiy/Mv7q8Mj+PLFysWqnwp19+Mf/N7Xvzt70Q63ILTGEf4uQhJPEcV8wgKQmC4cbDjHf+ctb3NP/XD38y/3zWStdFU4shyOXB6frxjdMEfr17a/4qtaiUumKsaltrmiHFtbO6475Iqq3sNO55oq+vXNt9GdrEydy1K5BjWb9/cBr+8dvP5m97MeY8DPqFMqaTmmykOdy1UuvZ2KXgr195uZyF1jIauxQ/SIuD8BCa28/v35j/tzf/MH+T2lnkH6SEm9pJO6O1pS8tH6PUymKEbBp0DtYtH8+HLsF/d+10vjpxqX0+dDqnA5feQS8sHzmG1sT90XajQetw4vnbRl3eBN39MmtVGHvcT7H+eBH+Uy981YDE+6VrdT+HBnYb2qOoCl90xfj2PrSmnMlaNOtVS2eWz1fRbq7PL82fRPkYog2kNePmyTXrn0MDuQmLCEgLwDbI2Wb7CT/zD4oFIOjqF0tVm5dx0D4+RB4iTydB9w9ff2s+uLhw2nOlyN3drfm//uz16P7hg/mgX9q183Y+d83tqy+/MR9cXno7yTHY1cbDvnnvvP/l99/NB8vgwTDq4SSq6vmJx/v65WvzwUXweDjxOreLb24rDfunX7zdvX/w8t0Mgt4og/VRHxB8Kx2u+2l13FX1PHmbzXgQ9TJ5Dy7C8vbH7743/zTykG3r4aml89e3v5n/7s55m+WfK1JAlnjOX8i5FJn2qNLvBmsPcxod5FenXgbfX700/3rSatiz0HgnUd8Pay+fftXnZTtUYuZtw3yzDEvhrtKwt2Hx2ESbfRtt7bdbz9vbe69P4Hbj1r9FWlaiPtWzzotmbX9bv33Q0lnGu4NZJWz0x/E6EG+j3NvVL+UrPfRnWWbF2hI01VtNU1RH0Xfo0KFDhw4dPk90P9gdOnTo0KHDPwF63/3pO+nclYouHC3hSfNcILX5NIMDN3Rg0nSzwCBOJhrHkpt5mMnAvO8mvK9efWX+d9+6OacfJtfHmAwB/v67m3F+uXXT1v3GTbHLpl1wf4jEc7JQmjtz0su4MilMw8w0DnPOed/Ndf/69R/N//bV1+aD3SpMKGHFWA/8/i9v3WwH/vzOzWGLeLcM+3aahrf11IY0f4VZZJgmL/+0GbVZak4HYUb80ul5fe1mpnqGwio2HUgT14+//t38p42bh0bzlue9sN/sc4nWyuO5muSEP+U9TK3fv/Y0J303/+aSlkM1DLEKm/o/Ykjgz2/DLBjl85Sz+oRdTATKJ+0krjYvSekkymwaPDmJIYyzdi5XcxFm/P/Pd/9q/jdhipukKTbMbYaV53cWptdJ1JF2KY8QZbWKCWNpGj/kBKZKpN2FSW898jw8DTxXy7CaL0dt4A8R368xIe3HqMuPmzYzm53Tt40JJsMwjebwSL/Kyzxa2atzHx758tLrxItzH1ICxUzn0TSLfdBw70NKP996eYH3Sy+rxzDj72Jr0RzOqJfRlGoX/iDbeWUST/4nncNoxmcxCelfv/+D+SCHW3JY6CZMmb+/ddP9w2MOH4jXG59YlDXoLCaFffN1axJ//fpL8zdRV+8XnrcPNz557c3bNt+PMQFvFxMlR5Gpr144X7953cZ7fe3m8X4MUe1jkuqqaod/f+fl+uffvF94v3Uz9CbqRPog2ZVdag5rDYJ3VbNRX+rPxlFV05/kGIZwHrz97ivvS7946XUil57eVibx//jpz+a/Xzhv19FXpYkYpOU3+9CwTpeyPYu+G8y3HvjrufPtT1deBi9H3ndN4jcAHFbO60m0qRw+qSdVjsbOqKdoH6O59z/LGHbaTfwb8PbJ+7h1mMl7Z16f7mNo6qdbH6oDf4lhu/cxIS3LblVtq5tsz4mCvSjn0le1ZKq7CCbFuyzDNGG3pSPUpu8K1WgyH8ZF4iiGEq9B15l6hw4dOnTo0OEzRu/7f/n+kFpU/rh/amOAFENyE4VcMgNGEWQY0sYoJk9MQ0Oa71q5ILXtr2PS0B/+4BO+RjH1v9aw34Wk/efQHt8ufTLBsl9JR3nogAtFZYnQJPIyCi0fzEPoS+nwxcg1zB9euFT91YXTZNhmpjzsfur+f4VWDf6fn/6P+Yuxh12FAPoUGuihWl6QfAylrBmG6j6K7E6rCWAvZk7Xty9Dar10KbaeqLWIyRR//vGv5r+PiTqbJjVF8xwxYWcQ5ZH+q6lPqgH/+yvXgF7OYvlNSPKDmHQVAr9hP/V3b7aurfyfNz+Z/8uTl9ddNflqFfVlk5UrJ9xU9SeXA45Dmpyv3T9ZepiXuyhc4eXBJe//K8ts7nmYRgWYhvQOBpHGKJdthFWiKhblz/1dSNwpQOdWor3QuME2rAyrUIVS034IjX2dM5iEp9DGHyOxn+9d2/v723YC1PuYHLMJrSIndo7DGjOqrEOzqC9fnrtF4bsXrlWdDL3dgFHYKtIash27f7PzuvLTfaV5hGaY79ZjT3MXVoJcugZSyE+FISdCpXUITGNZz+RZfb6I5T3/6zu3YoFc4rWNZWZ3wYd//OL16Gnp9Qpsw0qQVpGcWPXtt+0ktvOYxLYP1WUdFot3792y8OG9tw2wDc169+T+IKwcL4Omf/nuX8wHk0FM4tp7WfZH3qgGs5bntzun7y8ffDLqv7/1PNzs3TKwn7f1Zx0TqXJCXy5FHUa5DSvVa+hkiY/O11m0x5OqIV6Nnb4XMTnu6tLzkFu85sQt8O9/dw37bfQTuyjEXTUrt5yHnf3Mxu/nYW379qKdkPfdiWvzF9Eez7ZO1ywa0Hk1GXIdS8meFl6uN1Euj4+tBSC3Kc1Nl7L7nZ36RNuL12FlFCYXrlE3saxtHfV9M3MaHqo8/SWsSn8OTfsuyvspzVHCLtpoWkT30Ye0mzu15UJvBfJdWU4cjaS2PBfE53Us/x2ea+z15Lhu0lmHDh06dOjwTwI7/CO3NyxLjlILAvGLn/JBjjt9agw7NexhjF/MQgKfbVrZIiXFq4lLTv/6Rx+LvIgNOxbV5g53sTD+L7Fs6LcYi7s/5LiWyAstIpcx5YEEuaRsUmsBQcdl45LZ1yehrVy5Zv3Fmd+DfmgMm5A2N6HJv9u1S2P+77+5hv3bOjSlkPByLDuETkNKTIMQ/obB1kGMJ5/0Won0m6TnwqXKq1OXnFfVRgiPa+fB337+0fx3wZtDqPD9VOWFMuYYlo9ZaAxXoUGA/9f3Xg5fnXuau9But1GWackAhxPnzU3jkvP/7/e/mf/3hWuRd9XYTZbUOviYawjr4Z1+SLuDWHZ1EnXkdO3+WTVu98XQ680fLl3a//rMx3BnoaX0QysAb3I5Uy4LDP6tq01lhiFd90K6Ti1jLx0bVGQ266hbOUZ4/fqV+eev3ALSm/qYIliG6r6d+bOHiP+vvztN4Lcbn5uxiLLch/Sf7SmXuYFpaFY/vPA5Bt+Hhn0+asswN9XIjS92YQHYTD3G3yvN9T/fuUb488I1rru+p70dB93VnIXUIrLt5xj7KCw1IDXBcZTVONrai1h294evWo34OjXs2CTpLuZh/PabW6/ugybQi3HJXIt4EhrXH35oNfbTE48vD57I/uy3mDfw/p3zGZSlfhG2t/Z8n8RmHn/48gfzwcszL99BjN0u/VNpp63WvAmryrut9wv/9d6tgT/eu2b3WFkD19GGdrl0qfA1UFlU0gKXfWj2XZNlG+Z1WMNyKeaLF95/5dyIVVXPf3nv9e5tWYrq77bVWG67uZHX1VGM3V/GUtzvLtziB15PvN2dhoY9ifLPMr25bTe/+fWdp70Ka0lqsrvKAvBcQy2aawz8D4IWcB7zNq6iHp1e+LyGXvS/q7B4goewhv0t2tqf37mFKy0jYBV1PTXtnL+yj7SrvbuLZt1uoe1+2dgm6RbKxmGJuK2MVx+haNj2l3RaHpFGS0mHDh06dOjQ4bNF77s//VDEgLJ4O6QF0G6678/ycPx8DnKMMMe2+jEbeZbjLzEmCSZxfdZ3zeNPP/jY6Zcx67Mcxi7kYvcfQ8P+6xuXXu9jfAjsXMBrBqlppxYUmydMcpq3MFs5PV9MXDL7w5VrKS+nfn8yaLXcnLm5z9nCMcaX49Xg//vzf5n/X++cvu2Jh12GVpBjVjXKhgjxqh+bY5zGjHXw7bXT9eWlS86pYedmB+CvMd7363uXGB/Xru0eYuOUQe6OIkzScrJ0uq5iFvpXlUXh9cyl1ldzT2v35N/vQmMdVGNxmxjDXk793U8PTsN/fnCN5m21RefCk242qVnnTO1KEs1jEZO1U89CM08/NG1wPYh6EzP6X574+N0stJ4cpwb/+ef/MP9trDLYhbSdGg7ILQsHMVdhF1p4atyjT1ibygz/kLLPYgvL1196uYF5bHpzCK17E+OKvz+2mzr8549ef24e/Nk+Vj+kxWFSydM5S/iHa8/3D1F3p722XHLDnVzlsYvZ8buph0ktGvwtNK1/fxfzQ0JDfIpCqK1DWZOyX4hFEc0ofFDa+sHTGoaF5quYzf59tdHJaWxEkodV3IYF5H2Mqz88ttrZbh9aYtTncWxe88ewzIGrWEWxCWvQtoxhe7n/9mtr1Thk35TzdJZ+nxvT/FCtFHkRKxCasEjtw/axqzYRWkWn9xi8/XDwdvjXO28Tf79vtfvF0OlLa906+reickU7ALnKZZpjw6FhT5cRVngx8Xr3bazseP3SLQK7KLHbio83MXZ9G1awh6WPH9dbGKfZK2eJn8bmJ68vPd4/vPrOfPAiNOxhWCLvbz2+32Ks+B/RL4Fc3bOPvnoTM9Nzsx6Q2waXphmD2MWaU83xGceclrORR/hN5Ps0xrb30VeD3czD5rya//jN+80fQ+MGj2GN3ETflPM3cpb40XyOHN+OZ9nWSlefvsHz0FoT46LcfwJtUobKkG3adpuzDh06dOjQocNni+4Hu0OHDh06dPgnwODi6vLfWrUbfbxnJhEmSZnTE1xq6r5KR088qDlOGbIoIrxZOnq9Zmgvew2nd7HUBGdLGAZ9swKx325/NGyeNutmNBk1y82qGWFCJBE524tbl7/fvLG9hDHn2PIOxTPQe9xoPDCzJZPNmLCAOREfM/BQDlMim5XgZruB0XTamxiNL+dXet5vrmcXzXgwasa9ke3ri9ttnQdYhJkAwSQcptjvRz69ALfYrZrxaNTcLu/tPNcV5rv93idTKCx5QCJyqcinK5AuPuZU/PGubzTM+hOlq2u5q9NL5W3QvLp6pfjHilP503fskbzbHcx9uLs1ut7dvjcTYDl/WZHa5ITw+XdYrW2/6qG+gzcn47lNLLw+vbLzr3FXJxfNSGlNemPF0W/2bByjKJ8eH+1M7M1WTvHgepOBDYkcRhhJxReVwWQ8aZaiYzqaNPfLZeE5e4lD2ha7EPzTP0w7TOriDucTTSg7ONL48iTRMGJYAp4YKdQxlbf+sXzn8vplsx8Mm+nZucpk1GzHYzM7PwwHzVL3uN+3y2Y9mzZv+5tmORs3b8eH5l6035wOm7uZu7fTQ3Mz7Te/j/f2/k7vbuaD5v2413yYDpr78ai54Ru5heK+Hw+blcrmUde73tAmly1W++Zhs1P59Jrbx5U5lWyzWGya4ehUdanXnJyKv4OJbcowG83M3d7eq95NmqfFo/KuWiKewgP2XGZp5VDlC4dxIg+uNKcssVFZfn352k6Yot6MFCeut1F4hduJnoMq7l40HWyioeLW/VBlM1YLwO3VBuejqZll2e97sXmyDS0or4FooH7QVnC0X3y9UlnhKC/ModQxd8MwXU7kcz/Yed27nJ4p9X7z6vKl8kS+lL7o5hnmT65oZyO1vw8fbmCB1VWvPXormti4xLoC1YfJSHVXdWCiOrxVnnAn83PRBt/min/c7NWm+mrL93dLPVdZ3as8MGvL7cQjnhHnXvHBw75omg7VJ+j6bHyqyqYcyJ1PnPah+gsmPfEtTZvztY12r7a+VFPMgR+cWTyjLJQcm8awEQjDNbin1dL2Aqfdsn8+E4zYPISzza1fUOTWfnHiifGRuJXI0Mzyqgf9kZ1yhjtR/EyAvL58YfvSzycnCiHuqO4P1Tae1A699vSb97cf1E+ojq6X4o8be+lr3Pc+j7pDPZwPZ7ZxEv0D9fTF+atmJn5PmBjLcJAcI0/0R7fLVfOwXje/Pd41d8rrjw+3zc123dwMds3TsGfuQX3mSu3mXtcL0XuvtrMQ/TxfDPvmHtWX0L4WyuYj3yncQs+24ttSPFWvo7bmjp5jJYZt1tvmSfV3u90390/LZj47Vz/VNPMT+k+VmdxBZdwX7wbDser3uOmLT2fT02bx9GQn/+Hg91Rlxh7qjHBwGmSfOq7n9DoEgE840uZfDhHDQa5oGwT130z/5/WI/3YneCxtbFxFDNGmEnxqsAaH70G8lXXo0KFDhw4dPmsMLq9f/Bs/5/xDOnDET7pcatspUUgcNGmhHUj3kObH90huSCVoCwzOI7khgODQHNC0eYYUgWaGZDOfnUiLlmSj97vdzhzn0CLpswxgKs37/umhGUni4lzsQiFJmeTh2ghSLhoCkinPB6ZV+vVUkhZhXp5cmeb66vSFSfaSvUxC3z7titS+kWay20oyIg/EIVqIYy+x2qRfuXVvK3okOa4Xpmk/SKMzKCCSEEuGhso/TmKtSW1oImhKLNtAqkbbgV/4Xgqi7+KVaSJnJ+dio+JA+pfE/LhcGI9w724+iEf75m5xb+WzO6yVh51tBEIJ+aQpJSBnvBANE8UJf1+evRC9k+bLq9fNdDIzNxtOTdPYk2/Ff3tz2ywlOX9QOovlk/Lda9YSq3Hzi1OLb5vLhoz3koh30ihF583iwfiM47QiQnEqED4UwUfSMGlUDlLxkO7xKTOe98V/rBqqBfY9bsbmFeLrRNrEhvqi+5V8TvBh+gwbIyxVn3DvNkubSPLratE8qR7dSXxeSpq/k5K6UH5wT9KmlxPxVtEupUU/6BkSPlL9kxzfP0nSx23Fu6XuD+LTWr4qcrMWww/SejbKd28gjVW04l5+8U3TF1+HnC4m7eVh8dQ8Pa2kHIu/ql+4rbSDieh/eHhQHZBqIU0BHmAZMh+exD8mlKFtvpD2MFSdfXXxwtoGViGWY+IOaID6t2e5FWVOnVI8aPWcsW0n24m35sRD4mS/mZOJ2tbjgzSooWjaNJxx3BMfqJE4YrLy1CXatl/7O9MO5ZgcRJ3obZWOoveT4KSlTlmG1WsupP3QjnCTsVtxUEuglw1UqBOLx4VZlh4e7q1u42yjJr2joXN++HQ8U3qqwy++VB2emkOzQmPeSbNmKc5quVNzUxmutvpmqDp8Z89tmY6+NfVX9QpLDbxVKs3F/FRa2Ki5RDuTj8P6AYXMwTIr21p1Vj4Wrr3KD2fQ/UB1Bn4cemqDovegPxPlhSV7U9UR3KPaEWG20tNcg/N+gsJi+2B4kM54K0d/AX9tG1j9N77qPQ57iaq+NOAXFvpsLj7QhsU3+jPa2ED3uKeHR8U5bJbSLBvxxsqRuLFAyseNpIEOVZ+uTq/VzubNi8svpLWfNhe6x7KBtahPPyGHxrtT/t4sFs2j6vRf3r1pPih/b7ZPzcN+K+25UZtzdy9ePNHexHY05IXuV8rGUo6NlXBPPbVZ5W+t9rlS3dyqvNfyd/J5T9mt5eMoN3z6ZnwsH0/bXXNyfqX+RvX57ELloHZJWWORYGKmylwtyuIZ9SbNner7SM9xi9Wq6YknG6y0VF7RYQZL0U75YKNKpouz5Zr6SzFiMTRf/+ws+wwS8J5PDu8jRGCVB876w+qx3Ybjc68vHTp06NChQ4fPGoOL6+t/y191lwPQz/we2DMkZ0kySADcg+qH34DPOwk9NvoEGE8mLlv+wiO5Mfol0h3aqx7N2cJO6Z+d+QYLU0n7lp4cUq1JtMSBhrl6snHW1WYlqdO1CgWT1OF04ZM2UjNjRYyto8ni42aMXUvq+uLitWmY16fSVhgvRAKTtrJebiQso60emseHRbNeb2xMb7PdNuP5xNI4jEnT6dsMd/p+0NwtH0SfNDFpdGhHiMjGA4lpru3qHqkWeuSQwFkOAb3jhlNm+8312bXlHXd2ciGNWlL5ZG48QmLG0rGQhs24F+793VtJmtvmafVo8ZuUrr+M6+PbgR88xqHZyD8dT238De2Mca6z6ZnKVVTIIcFTD/aSlik1tg3k6u7hziRPDglIDXs0nxpPhjNpSvrHyhO0vcV2pfT7zT2ahKRv3FLfkk8UA8oKARSQltHpN/wRXzwfaNg8G8bzHB8FE+XhoDxikSGus9MLKU2KnGVZlInSy4g/SNNHB7p9ejQ2rFQupLpFdYUYOeZRkIzXRqNGfykjqPa65M+5Fo/0DZvqoO3zbqf8Uqf28gnD0jvcWJrgcrNuTk5O7RssNJQp/CPvuOVakr3iuL+/87ikcVvakCZyqCfc8w/9gLtLaVFop9fSeqjx44HKQGnjsAyR1op6rHbzII11Je0BzXpLHZ5JO1IclrPpsBmJnoM0w6na1FqlfTKdmxWHQ0hsHE/Mw0GQfSOaoQLtA5/6nP/gF/y0ZYv6P2SjF92fTk6M51dsn6l73ExtnraLFY3Aa2nYhFkx90F1Zr1dq55KM5TLjSPof7zdiTbljTO1rdzkTuaqx8SHdiSs1G7h79296rDaDnWVMV4/f3xncdmHSrNP+esWyx1noRud0uxwl/QPY3os//b2Vm1BcbEhy93dnTnKFfqvXlw1Y8ZDlcRYdLPKjXkFDD3P1aZxD9K2J0pnpXpB2dLOTJNzlokW5zMOq5Jdq64Zf9V/gDwUBExZ4qhv5uonsNzMprOwXHn2dqprXjrSah+wevWb5eLJtEDm/Vgdp4zlm3VLvKLBXEljx/L4+uVXzUxxj6RdD1TP2OiF0sA9bNQXKK23j3fNUvX2t8fbZnlg3PrQsPPzaqQ6R72TW6oS2TNlGh/eUCfhO/UVZ8CHDr2AVmpZj/k7op9E08pG2zB2KBzx0/Ox8nMjHtyp/s7U5p6Ud9yEbWSV14P1y9LW2ShGPofwDFVeOD8kRfWPd/BO9Z1/fIdPWiSHo8zwicM8awOtr0Isgc3DT5dhK5dX9OF5HzEFqCAJ+NKhQ4cOHTp0+OwxOL+6+jf/Tdevuv3Yxx0+ThID0ruJgnqPJuK/+fxN538JLyHCgKBt49S69rEdXcuZnqr/Q2nY8mx8dSeplVnKaIJTNAa0Y7TC8JEaGQ9fr5bNWFIrMx3RAnDMGk+JH3kIDddmPxK/vmFsGA0IN1PcaIIc7wctF2wSYuFdw356eLK0cHeSpteSIpnPzAzp2QXarrTAueJD45Db9CS567vlTpKctJSH0CzXkt4YyDNtX/nDoSHZPTNYlXh/K76KSTYGqeeMQyHl4y7mlz62rjgVyjQxJOgHSYKMe+Ju7j+Yps1GKQMkQkm2+Mq4nJ4z9yD+IdUPpDEw8xNN5PXVS7MssCHLcDQ0B/3499JKNvr2l99/NYsGh4zYOPSEuQQ7c4PZQGGkFcED8ca2LZVI35M2wpwENOzMy0LaBNrUVnQgGVOXyC+aE+VvTnzgn20Wof9oH8DG/vV/pO+jgMsM9/FwIhYrd+LtSvxhzNG29UOTVRng3t59UDrSNJUH6qYdC6r4bKZ/1G/pCUpStChukmOuAPFbnUfT0ieQgzMtV4HQROAu+UD6JkfUd8ZTxWhz52eqW4pjKq1HVLkVQN9isfHE+s3d471pi5Qr73ZMhSZdS08RKlG4hCNtnl2wiY6+PZNWCQ1YirA4mENjUjqP0qyxEjFmjoa5VnliLRrNRYt45HxSWHlsGEObYg7GxfRE4ZbNqfJBHGyj61vpouFBomtilJfNT+FdMEc6qEJhQZKvcLbxkPJ0GgdUzFVeW7VzHDOzWYUhQuzb9Wpt/hMHc+hb2jdx4Lb72BxYfKIs58qD1eXx1DR0XF73VQcoU8aYKcOHBXxtmvsHxrB37qRFuZ6oeMUA7hWLtDLxURVvfnJih2b4wRlqe6QvOkW5bczC2P5CWip5wz2prjMuzBafj2ixY9UF5Y3sowtzXOtcZYRbKy6bL/D0aP3UBouK8kW7o89iXDld9mlqaOKNwkizhr9Y5JI3J9J8yeel6gQ+/Nyoz4J0rHI7NpIhG3JPjw/WH65Fq/Wl8nfrrbUxaMBdnl02M/Hy4tytBbOwXDTqi9CC71VOaNWuWd9b2/5V/RDa6oPaERuhbIZKTp9gdVMy5pb0Gmr8W/HXDvcQrdRX00bDWZ54TmErLeYaoBVT933jErdm4ZjvsVcd8nTUPtT/sBkNc2ge1C/dPi2a3+9vzL25u21++fDOZrK/f7i3A6VuF4vmftfmhSNlB+q31qKNOgRtbIhicz6Utt3DJ6NLZWP06J6/oofnbu2NfJhfPeOffKvkeBXKrUepPEeYcHV8sMbqRIcOHTp06NDh84atw/afc37Nw/EvftUlQISPlsrvu6Qx3Zt2Y3fANTnEI6RgDyWpkFeKj1mnCdaWEiY3tx8wE1jS4FSSzWa1sjE6ky7lmE3KOBvjwxNJeRtpUvisL2RjetxOEqWNm6GxmTQpDVNS00GSPM8Yn2OmKY4xqfFk0pzNJJkipUvaZc006XIgxEKS2UaSMu724YM0y3XztHmQWzTzy7mk/bW0FLYldLF1adsmHpqVtEy2bEXTY/wKrRMJ2vgmBuAYL0KyZazYNW3PI7Mx0cq/ePHatFzcxflFMx5Lkuafsrax7TIPza0kRmbx4h6ebvVk55qu/qHAIRCiMRDWVE98OXhL/l9cXjaT6bg5nZxaQaO5psbOWCdja0tmoiuOm9t35i83ksTl95VtfBxr5hlvZVwfPtgMel31o7yQiOfSLHE393eW35XKw2Zgih5840W4MjbKO/lFw7Y76g+6imMW54a+vn6l8hs3J7NT266SOkX9M7uCfNz9UhqP8n2zEK/Ej63UxJ4k2I00bZ/ZKS0ENVGf7lUP+IbvGb/dShtj9QJyLVoBjvIwDWG1tTBDZkQrL2g9VPSJbW3rObmStoJ0fHnu8yQYv6YNEa9LzdIAOYxDWbyTJmhjeeKttzWlJMdMYu7NKVbYcjJhNcWhOT85tTY4kuZG9ccpoJin/N7cKZ198+7tO2srzPlYYvVQnaKu4+AZR+QSJ3WR9FiHTRs/m8ybxepJ9WZsbsnae2mtqEsYKWzmMfmkHEWZ12/FIXqYbwApvIcc07DlX5i25nlBw7byEtGMV7I+mbwwUx3r1obtMvlYjrpJBFamukLzE+vNggGtuNnMZ6LDY9KG9r3K9e7x1vyn5aNeG4Eqr5XiUSmrn6D8B5i/9HwsLZewF+dn0qiUltzp6alpzFhUuF8sHvWl+gbWNKvduduoLWwsDvon5ojs1ptmOle7FhcoV3wcxzbORzO12b1ZirZqExM1LGu/8Fa00yfgjFy7V74Uh73XA98KFE4cmrPpifdrZ+ozVe5n85nqw0A8YmWJeLdeKA7VN7n7+/dWvzbqy2y9t9qtz3ehXrubz6fWh1xeXTYjxTE9oZ+kzis5fYOlCtsS7o00a/rM94sbK6+NKhEWi634aQfoqELQ1nC0I6Oae/KkjOBj9UkrCbmlDuATSizSve7Ur8JDxq53Ig63FgWsw8YKspbPVqe2LluJME7+pDJ6UN9tTmVzt1s2H9QXvFN7e79+aG5Wj839dmWWAdxC4Zdqlytr86JL6e/Eb6hiRjqWK1UAc9Z2yY2VF/+4J4wCG7xs3AHnAQVqcwSox+JH9lEeQj7X5uyBeY54YA8t5Q4dOnTo0KHD547uB7tDhw4dOnT4J8Dg/PLSJp2BHEDH9JLmK1PThVqFx2xhfzNMKPAYM8xHe5ePgQqfVTT4uNwMY4yJgXdmb2ls04bdZtucsoHKdmuOiTVMLGNiF0sknp6e7H79tLTlYbjVcmUmcU58sgN99m4aPyidRj6TYHLDhpeX12aexDTO8rCT2bwZDBXHatVg6vzHP36yCUC4m7sPtukBJzwxWWRyMjWT8fR8rmiVB7k+pqfBoLlfPFqeMaljztpsd6LMJ1CYScuc84XjcaHPnO5PpieWv9lk6kuq5JhshskZE91+jwnuvU0GenfzplmJJtxizSYq/p6EMHcxOcYmkChezN0+kaVvZlSWxZ3PzxuWymCexHQ3G7FZiptqN8obeXp/8862iL17vGvYGhaTO+ZiTIGcs4sbTVnisW2mJzOlu29GE5bpKI8jtnbt23IKhghwG9FG3h6fVsYTKoGZw1XsGLrN6TETbVhqwjfsx4K5LjeNGMErfYhj61SW+k1YZrLZNKenLE1TmCET6waiE5Ozwss9LB/t+/f3N8YjhjDg74FJZ0rX3FB0K01b9gMvRAzlwVubfKa0I2TTt20t3QQOz1hig4lsbpvODGzyD6Zm3OX5hcXJkAztBr5RXo+iKYch3t/dqK5s7eQkTOw+4YQ2ZRlWbnkmXw7DGz68xCjJ0j8zJCpdJrLh4BDPnpYLGNr89vZ3q7tPmyeV6VJ1fqS6vjRHmW3V3oYMvSjZrKs8Z+IR2cbkintcsG0pG5PsfBIl/8KnTHFuGBSPjF9KXvGRg5PJTEQyoencygk3VFy8xoQtxtnkTvh4/8gmQI2Z7xlqwMEDmGElIN6oN7Dn19cvjee4mdqx9UM0Mr5Q3aT+PKgOY468v79VuWEGV91QnSYRTMKEHZB5+ZPJUAnsm4tTlpf6M8zNmLwx9dMGiLev+vLhwzt97aZhJratYjknG8BMpuKd2v90PvW2qDwyYRA3EP/ZCpM827CD8jkZjJqt2gsmb1VO8U/p4kQB2fGNlsQz4zd8pY9WvHIsj4M/l7QB3TO5FLqHvJdP3lh2iru//WDfPz7cWxsQM+QOajfioXiHOz3xvoiJffb9SOUE39TPUYYsf8phsbd371SH181TszHCbCKn6pxty6ysMKxIncaxJJTvGaax3xClRclSd2ljODjOd0qVNwoLfy2UuCxfNDAJDMcENpaJMtlsJ4e/xYmGjdxSNCyVJu6ptzXHqWpP/Z2e7ZqVKulCfcFSNOI2ioupjWzCYkvRRCPtCPM4JnHqrBHnBJrLvPiSW88zL+gf2t/KuOaZXEEVnRJQsOobi8eTMd9fm7MqHs87dOjQoUOHDp8xpGFf/FtKB/azrp9yfskTZXIZEjzvQgRI7dokWyQMnuEpsE8k8slm+LZEi3jkpIvZNzYRTf9tAxU9Z8tJ/JeXryTtIfVJktZ3aAG5PSiSOJvUs81jyvZbaSg8IzKb2oFEepC/R6rloIWptDY/dODq7FphpWmeoGFKolactuzFJr3sm5ubG5PqcKsVk7mUF0nF5Ho8k0YiyXQirRIf10PyFNDKptKqkJhHivfh8dEkJ/KJ5mHaxx5eKMNbl2yhnufnko7R0l9cX9tEM9x8NtMzaaeS3Fm29sEO+Fgr33cm5ePQfKPAFIvTgJYC74mZSS74/HvB1oLDSfP1y69Moz8Zn0qylhbP9oqiE8f2mGh7b9+/k8YgrQzrguLmUBGWNSA5c49bSVtciGdT0UmZsDkBZbdGmoY3oghtFfe0WplVhKVpTDYCaOFsWIHmgEPeNk5K2kXy71Of5I8UJ5qQH1qyN3cmjQ2N/PqMpW8DO7QBTcYmdokRG2k5JrXKfZAGi+WF5XbwYduXhqQysU1cqJdy/RF1ra+qQJpodyurc8TJJBEmvKWVZHLAgjBoxhu/n9tWjmwXemGHnryW1kfZ4U6l9UETS+0Uk3jKkkFJ+5vHoqW8u31rWspyy0Qo8ZrJcEaU7mgeZCgd75VvW8Kk64vzc7MKjaVx25IuOTR49BK2wCTsUrwfqD6tVa42qU3RYCXCDaVRruWjRVFmtpEF/FZ9p+2pAkqbn5ljUhj5Y0MSahW0QiaTJXMZElqoUYnmYe8tR83J/MTm6zD5TgHNMdmTiYqKwuiGDlVe26SHZ7aBjMoERz2ghjjUDkcsTew1V5dX5uMmUz/LmnyQPpM06a9ub1X+ukfT9v4rJlopOl8wRDYjDtGjAM2V+Joa7PXZhU1GExVWjjbBFb6q/xmq3uDWqtc8Qxtn2RiTOvfQr/iwAGGJ4jsceWci4XgybubqL5iceqLyfHpamAZOeNPK5QiHpk0/yYZCbtFgUh/tm/aieMR/NGAsZuSNjW+o34Qnb9uN6lXk+/H+3t5x0AzveuKLVTWYofC4k1MsFU1zfoElTn2I6CRvbA1NP8QWzJQp7vf791aH2Y4Zn4OAqLsrtg1SX7E+KM/xj71eeMcBQPjUX/U83kwjRjbEIXHrKyFQCWPxxcpn78U7vi3tQ3SxCRHvOUDKNG89Z1MWzrunjdeOyXAbOd6x/IytltOZxi6HsZc2An34pEt9FFHm43r6FgbaBDnC816+BeKC63REwmXeJ/g2wUu5rMvxSURcXpujVEmlQ4cOHTp06PCZY3AmDduWkeQv+zMgnfHbjiZqErjC8uNfBzfZQhGkb1oT93JI9n2+1Tsc483EyGEB3BMWKXuz2thhE3NJiWhjuAtJ5YxzIZEyDkOcLHu6vbuTsCptUg7tAY0AibHVKSX9SjpDwzuZndj4Jg4pH836/EwSpLQfk3SU9u39bcMmBg+SPpHccGweokhMi+Z+wqYTongkWjimDmfatzLDWAxLXdbrrS07YUtINLH1UpI+FgS53AbQNWDfnhENkbFODhBBY7LDQuTQWIgY6RwxePEkqViP3rx/a1K5bVaCtCn60ZBsDFY0mrQsssX92JAFjgya8zlLYJTW2ZX5bG7CNyvxmzzhHtlIQfEx3st46MP6sdkgvU4lxQ8pI6WHlItUqvdopLxnK0s0HDTtoXwrf2ljyfPHlbQ5szosrGRYkmbamDnLnv2hPqFZI8CiWZsGwTN4rjzh4ybMV1CZX5+w/G/fXE5PlFfxdMuWpnyv8IoNd/P2bTPYKA/SAOaqK1ICmhPqiuieqixwpDGRBj8WAVPSVviZ/Hw/36rO6T3uJK4vxNuZ4vvy5Ko5l4b05dWL5gytmvFrFQSOZTFYEVSK4teuuV3c2Zabb6RVL1YLc7dLtn1VefYUBv5S7MYI9/macjYndYRxNQ54QPuYz05VTk0zU3tx7Vl8F58ZM+XAFsrwfvHgmgNMZaxPtLDBB24lWtjqljF8NioZq34b9D0k0C6wiODQdNHeWLbIOOkWzVxRphZoG96I90UzpKx0TRmf6jtyglWL/OBG4g++HSwEVaKFOnB390FhCaM6Ih6Y0531TwoLXVPmS+jeNqYJcDCQ0aw/1D8sGfj3D/fmL57ajVhYpsQzaNKF2o0S0YfMNaBlns5Pi9UJFZA2slPdwqJmR+4qNMvPsp/AWgGv4OdadXvFciG119nJTH2mNGhpreQXx3wZm1siH+2Yds52wQ/qzyaql7vlysaq7QAjpc1mNSxdtT5D9Y5itH4E0uU4XASrE7ylv7i6uFA/SYt3nm3YiMYoFkdXT9a3sIEK/QRzVujbsQbZcaFyr7/gMKBJMz89sXvTNBVmpXwz7s3WnbAOd6f4CHOzYPMf9Ydosqrn1DdAKcJn4zW+nuHgNpvvUMtYRjjSOxzbEdvWxFgZlNZI+R+qP6BWslhyjLUrXF6P4YHeDej79U0/nWi2vkSOWm/3Spj2SP9jRzTrmrqHy/lC+az8Huobq4NJvD3THz2wDZZoKyoDm4uhl+SVORU2F0zO5sSYr3oHAYSKyApvEuRdsHpKQh7sGPbb0aFDhw4dOnT47NH75g/f+0+7kL/4aG4f4/gZgkaBJBlgGp6QG6UwOxwM8hg6Ybz263O2AxWmDDoII/ZHEL64eOkXwg9ff2/++aVL07ato/Dj3380H/z6+6/mryXZAul95jODG7x60cb35Rdfms9MW7CTVg8+vHvr/nskfMfyaWG+L+pvmsuLS/Nfvmrju7r2a7RO8PbWv//17Tvz2XoygQUBDEQhOJk5DV999ZX5X77+wnzQk/QGfpeGCH769R/m3wdNYBvl0WPXC8E2bxBykxE2Zkh899V35rOZB0DTBff3LX2/v3U+PkrrAw9P7p9deDzDqsDf/e50vbp+bf4ffviT+VPGKQXp2uaDu5UfOfpf//jJ/Pd3zhu0vYLIA0+BjWELaLNgtmvjezFkkwzl6cU35r88fWH+lGMshbqWMg4PVg3j/cLcebMet2k/9Zxva5uAgVDrMaDhmF+ROeTkAmG087o76Xuas7Frp3bAQCDj+7B8NJ9ZteDNvfvgjo1TBMbXajA+DtBYE8Ng0VcXXk++ffm1+dds6RtAawFvf/Oy/PtP3k5MgxUYcy2I8rTDEYRXX3o9PH/p/DyY1uBgq0fw8xuP99f3Xv7rPE5WkF5rfnIgi/TFhcf3x29/MB+cnvhBP8wVAIwJg59++pv59/dtO2Ro2YDFS2BuAPjiC88/OI80sA6AdbTZf/zi7eb3N7+ZD3o+fb3ZR38xirZ7Gn3CN6+8jwAvzz1e7FRg/eR8/PXnn80H93dehmhmAEsTODvzevr1t9+aD06yH5OmCxZRLr99eO/++zfmAyxXhtDMWDVjYKA1wNg1+MPX3r5fR1+X9eDx7tZ88Gvw4vZDtD+sdwJb0ia++sbb1HX0Rfuhv/sgzR+8jboMfn30MvqFFRjCij2mhW2Qt4m+GtRtEuTvQtZpcHF6av7Z3MuXbXIB1jPzgw+AWfiAwz4M8Q7rJ8j+GLAtL2BlCNhFHblbtnX3/a3ngU2eAJu0GCLeiuW26Q1gXpUj/Lwvz9u2YBqzcDRTPBG/tVgtHX7fhmzjAy0XOnTo0KFDhw6fLaRhf3dobenhxzgEsJnNQhEgUkqotPD81bexV4FxLBDCrI1HJMaxJekk/JkLpM0klKBXoQWCf/n2D+afnLjUZVveCXchCYG//u2v5q+2LqUzlxkw+xdch8YAXr9+ZT5rsAHHaYJ3b1zCffu2lXAZpwOZp8nIt8R8cdVq2F9/5RLpyZlLh7+H5vHzL7+Y//D4YD6wA0EEWwcqsNYRfBNS7YsXThvY7Dyf70Ly+/Fnl44fV55HQ2geAwa2hN06NIZ48eV1q7G/uva4TxnrEvZRYh8qPv7869/Nv114mvvQPPccJiIw/pvYrz0PqdV+/YVL+C9fupbWi+1DQRhUivXh599dO1lsWt5sDk67cmF/h6GtjP22OQnNFrweOq//+MI1l+up15dJWGwYAUqMk+ioN5uep7MZVmrz2MPsQkNgPM78ted/OGg1EDvQQuhHWr2ea/6H8JchfYO/vXWt7h+3rtHc7lxjWvppGoblwdNg9ixIy0eQgjgfFyIz0n59EVaNL8L6NHV+gENod7d3XoY//d3bhm3NCWyjAgezi8F06mV1/cLjfRka5jhmXoN95O9dlGFqgrd33m7AZue8ze1kGb8GFzHWnNYtcHnO0aCKN8hJ7fHvf3crzLvgHeiF9cHnlCu/UYdfvW417MtLj68JjZB5LeAfv3s7/DUsAw5PlH0LAOOfgEM5wHdftRrx9dmV+b4lM23Yv/np795WACs4QGpRxdoY+f/ytfMVvHrlbXIQ/U+PeTTCOsL+/q7tf35+47SzJh0wExukFROczV0L/eMPfzT/xaX3dWyNCpaL1iL3a/QhH6LsskVl+YOXQeuLl9F3BE8eoj/69aHtL/7y3svo/drr1jrKiX0rHJ4nQxR0sZyFyjqLOg2uT7we/0v0hxccpCMMV1lObd1l+2fg66PhjaeVKTcjfw9YYw2W8WgZltd3q9Za8LdfvH789iEsO2F1SG2c1S+JOIqm1cKZyCCwXhswPt/Cr/PntJRc0GuI6+dWbUbTDeUjD1rH3qFDhw4dOnT4TNH9YHfo0KFDhw7/BPCtSUPt9qnmaOHVP/2k23vp4+aFqu6bC/g7wvkz8+IOeGjMlLn0hHdYDzCXm78jlPwtU+wV5/bQcKYr7tXVlZLdNzOWbSgs0/Kxguy364YNEHCr1aIZj9ikYNnYXIMIgzWD8PMTlhpB66E5PYsTg2IyAScZsdnErW3DuTVTO0upcD7hHxuEn3XMGbtsRzebzmyCAI5tVNmWjiVdtkXdxrckta0fRcACkzh2DDmWImEmhGbMWtMxG1T0mrOTuT2fzKeR4l7pwAlfaoWp5P0HP/uaTTZYvoHr92PJgsKZ+XEXy14Gvj3o66sXzWQ0Mndxdt6Mh0NbZkWatpGG4rDTkfQd7h+//mIbbcAH0jIrqCLsq6AwPW3WW4VjCQQbLsTULJFCWMqXPJ+dnuuerVXZHody97pAubDxDaasx8W9LblbKy0ViTmWVRAXsXKP+ZPvhqKRZyM7E9jdCceGKb+vZizrOjQvJyfNRHmaqR6NdX85HDcnyj+u/7hoRqL7XNczpX8his72ul6smtOV6oO5TXO63DanT5vm7GnbXO8HzeWmaWZ6drY5NOe7fnMqH0cac9XPoerqeCt+s1zPJsCIB5jsqJOiFzeciw6VqTLdnJ7Om734OJtOmsVO+VY446ktJVF2rH46r3SnfNmFshsXgg3N6P8pk3Hk25nuSpetJVXz7B9lSlxsQ0rbunm49aVHW996V4SV5Ug9rH0qGF/mpbzoGRM3Oc2OjUembCmqFMyJDpYPsfxwqvrEEsr5eNKsnsTfvi/92ukbL2/KjvDuc6IacVxfX6lN9s2NVQ955/ljkyLO7d41DzZJk1OxGC7xd74IlGv/ayezyT87vyh8nGDWVf5op95+MFzubWtaRWBbnmZ8LN+hbWWc1pZ1zXIo/IuLS+MdbjKfi1e6kINnTGYjjfePt7FJh9qmaGVx5FJ9xkpxs0nIRrSwLJTT9OnH2MgGx5I72450Rh+i75V/Tiej72CDF5ZJ5Yl9djKhfCa/Jl9pZ0Olb0Unx4ZLtIVLhuSU3tnJiUgVz+To4hjWs2oj9ygeUMaY2OkDbcvRyHfyhi1e6W9mc99ymK2G9amdGc37hzUmdq7Vd6yeYEuzsMl7LMRTfPq2L54YrXyPL0cNxZ/oPd3KXPlmO6oztZ+J4sZdKdrJZtt8Oz5tZutd86VC8v6rw6i5PgyaV7tB8+IwNHe12lsbfbE+NFdyZ2q7lyt9o77h5a7XnKy2zeVaYeTm6jvO8UXBuboZlozxjH5Y3DLHKWxTMYd6wtIyax+iX6+UFwZZ6Vu9v4VTVnd1kcM+5J9cG7Ihm+fP4o3w/N45735ePUMdWNeUVocOHTp06NDhM8fg4ur86DzscsF1eAW6MUmAh7VAgOimb2xyRvi+AJx39pEFx7FkBWmEc6GLrzB2CIj5kmIk3eA435UF/qczSdCSCEc9SWomuelaEhJuv2XbRNewbUMGSddses+5sCxFYnkFPm5+OjMJdC0tZ6dwtw9sZrGSJL4wLeNBUmNKzrbFnZxvNi/akYZFMVItm6zg2EyCQxwmpCs/aUL6ZevBraTZKVqJ3PLx0TQTNJSh8vlCGsdM2szFBdsfSvNlIkUwCVbAvIfFwqTW92yZKt+WYiDVySHJEnggfpg2I0me+F8qPrT3l+eX0mQ4G3fQzBU3kuNM2jfTKR7v7prDet0s2EKVCTpy97c3FgbJnGUhzNdiAtFBmhPnJnOgANI9rrfdqBx2Ji1THmz0wIQntnLsiQ/Xp6fNRHzAcaAHPlL5RNI8GxfMxBvO3c4DQtYqO/IwGCp9tAM0PvIWDBmGFoubS/WnblzNpGHKPxlOTUuaKI9oW+unJ6szOLR+yunm5p1tonF7+84mZD3cy+kex8Eq93e34e6aDx/eNzcfPpi1hfvF45PCK6wcVgaWICFrsyQJLWonXiyxCLElqvJnmovcdDZVfofSpsa2qQqaF0v52IY0LR9sFEQroWyVbWsqcMrKWPnGgmDP5Ky49Ww+nCh+aQ/SMAnMxhdsS4uzeiOtmSVBaNQ30hiov6u9NGyrz76xhW/DyLXaWV8anhyTIjnEYr1am6bLVqRs2YtDm7c2xEYX0qbZZIRDPSj/89mJudXjQzNWm9+TJ9FnG6jIzaX9ol1hJWPLThznpEMqB2ag6aCFkkd4Tn1n4xHngPKNeUYvbWKsHFYKyvnq+lr1Rd/LzaQJo7mzuQ9dEG0ZVeRx+aB7zpHn7Hi0I7Rv6cO8t4WR8lV3eD5lu1TFe3F1aTzEnXCoBnVW6cLHhWhn2dJb8ccOiZC7Wy+bncLu1d/s1daYSLZXHwG/9/oWXrPED8eGR49sYDKk7FdhxVBS+o6NNdjUiE2GcLQHDmGh34DntDvaUu+wNX7iRsobbWU8Hqo+rqwusLxzJp+NprBcpVXDNqURLWzXilWROkq74V1aDGdTNoJSv8YkX92Pp2Nrj9QrBWse1abYVAR3q3xQJqudnsGfgWjRc1+uScm1bXagusaBNmjVxMckZNq3SpIiNccBP1jrmGpGnest1zZpbnu/sH5qeXvbLNRecTu18ZXe9cTD7XLZ7FXfD/pmpfqz5r2u909qAyxHlEbdU50GfSYN6vdnJM2dM/tJE/eENUM8vlEbp8+jn4f/THJG02ZSm/WxcrQbzxf6s+6jXXKnbNhvWQGXyq9d6D+/IdRvq8vPoXQM+coYQ8zHUDF06NChQ4cOHT539L79w/cH21BdqJcMJFIaSN82PfcL9wWkBlCWpWQ8ESan84MRAorAto9ghkAmzMOfSItJfBtLqL6L5QYnkvgMscQBMDYDfv7Nly28s+0NFd+FLxP4+jtfcgRef+1LBnIjiH/85ksT3sR0/puHdqlR5nMYS3jYOhCcxVIm8PWVL5f6+qXTN+6jc0hii+UUb2PjE5DXbGIPrl/5UpQvvomlULGRClhIKgQ//cOXQP0jlokxZJUYSPpz38PuY9nYy1Nf5vT9a88reH3ufMylTxziAHKjAMCRHWAwjTIcef53uemIScyOTUisY8aThUnwZBjLufbBK3AY+zOOtwBvHmKpWiy5AXcL3+ABKR8cYrkTY9hgxu79gRcjL9dvL32Z0BcnzsdZ39PZVfWHYxDB77EpDJomWB8yT4KrrhJm3bftYAW2FQQcBZnYbzyMKWjCLJYYnZ37RiDn0voS80t/tkY1Ep5iOddf37VLjP4WPGD8E9gBJAZPh4MeEoNYBnkWy7j+9ft/Mf/i3Jcegfz8Qyy/+s8//4f5T7GEJTfZAW6hoc2bV5rUsOdt7JsX7bKp72MJGVo3yG8/3LSbwLx563m5f/CyzGWMJ8GbF1+0y5uuY/lR1pd7LD3CX2Ojl7dRbmA0jrLfR52IRvDt19Xyq1wSGe8OUXa/xKZKf4vlbQANH3DoB0iN5WTufP0mNhkCl5febnLJzmLlefrrP7yvAb/mZiexnCmXEto2mwKWrMTYbBIsF4s2GhuezEa5hK4t71xe+Pb36KNiOSSadIL5C+D1l57/b75znjAPAdzdt/3ZL7F06e2b3803i5CABS5xdeV16csvvW31c4OpWIb23rY6dfz5ndP1672X9yr6IQ7XALkcFLS/D45J7K5SL+saS+sFk/DnmKTw454tgxOeOz0LvxfL+Nh6Fcxj8xUwv/BlhScvnUdb5pUID0mM8OcbL8P/yDocfd5q6IG2UVfAFhOOsDc7Kn7eexinNoCZRcjfRQ6AAn7nwErovn/ZNvnn97TfjKlDhw4dOnTo8FljcH518W8+5pw6Ri3n6To1bP2668belbC849pv+OPP9cee6Q+exRHODmrHJ5wubZs6+SO0fJLYMoLg/+aMw0j7eH19aeMjpxwCwpiMNHbGgXH7zdr8J2nHjD/o0g4oODs9bab4Z2d63zfHOB9jjo/SsPHf3nzwcaW17qU2baVFohziBpLs+5KWB9KQUJxt1qC0WqwFe2ZSyzHuwbaP10oDGetE2iRjsorUaFo+PFo+cZslh9wrrri/urywmeSXV35MJGOOcBX3XnQxjnMrDZgxKcZ7baY7Ujd0yDG2zLg5c1sljjdn4g1jpi9Ozi3fr2fnZhXAnYozw93eZkwPRBuzMSfS4l9Icr5SWNz1CDduzhU3s6nniv9U31yPRKN4pxKQ07Xc6/lJ81LS9/V4JjdpTpTnM+WBsampypzDLyaUg9xMfERpR/mZyEfynoqvj6sHSfc9c8uYcco4F2NcaD++CkBVwnwKRAHkGN9jbsFMNHBM3uREvuLYqq6sFWyluNdjdzfSylZK/y93b5s7xf2ut25upQl9GB+aO5Up7sNw39yoEr4bie/yb/WdOcVzM+o1D0rzVuFwC5Xfg9JaqKo+igZ0voXKhEnsj5uN6sykWUnax42mU5vtj3aGRs4MYTZVWIr3o/7I3I20E8aw0ax9LNvldD/6kPJWQjQkOfjAy7Ft5HJQ/T6zOsvcB+ZR4Bj5Z7wNjf2gMmHjHfyn3drG7GxeBj5O+cpxWHOKk/1p7Ehb0cHRqwvVWdxI9WexWDSn05mq3r6Zqb6wGoL5JqxKwEHVKWOyyvtsOG42T6rvyjtj9Wzzyri1HWAhN2FjC8YWqceieyWtkQMZOHyHfDLLmzFYnI83M+4nPij/zFOBFRfnrPigzkjzRxOET7wgH4obDZWxWjRyLC28wa1U1zjIgtFWC6NrHPWKMdSZNG1mbuPGKkPKZqt8wFfbSEPu9ukRdc7cBw6/UMQc4mJbJ0vDVOp65nMEmIVvR9PKkRdKCS2ZbXOZw8LBKxcqS7oN2m8eAMQhIYxdM+/DtjFW/WJzl83yyfoRHBo21kwb1xd9bD3LzHDGtOEns/1zhQzzEvAXT9K65XMsKPQwMz37Wxv3Fk9YTYPGR/4pDzfueP3IMXEOAuEo4DvlH7Zz+Al1o6/2Rn9PHdOPhjm9sfu13EaOOQFrfbTRu6V83Ip6qniX4sFCdKz07JG2puf34gHtDR93J/7eci9ab3W9UF2+l1uI5geltcJXmeKGp+fS/tWfseJIcTdY/NShL1S/9yo43K14iiqL5RUd9gmLl6rTXn0AeQnF2KDq6+/M48Zf0nM78N2JDAd9gK5LCN0nPI4WxjJeUyG4qBxzLPS0Q4cOHTp06PC5o/vB7tChQ4cOHf4JYCbxuJbWnZPTW7A8S3/L81Tz/XxZPUVd561dexweQGH3LJlozXm4YbxjyQeWgSEhegqjsIS2uU6El+Ma89bV2ZmZrWajkZlqMB1hysFhPhwNhs3i4bEZ9gfNEyf/7A7NyfysUYhmfnJqZg7+jTAd6R/LtxjsZ8MUlnQ8bXUvH3MV5iLcYQBdLLCHnkOzW8dykC2mNoWUY7kUk1hmI9/o4GQ6tXhZfoF5abtalaVVW32PCWuK6Vr5uLpi2dWwGc+g6eCbWyguHBtf8OzD3btmv9+YyViZ0jVn2LLpCWZVWORLrSCRc5kxob2YnNk5sxfN2M+YlTsfjG35wtVg0sz1brza2qS/V/rmrDc0N12tmxPCip9nhFU5Yv6erzfN6WbfXCvuCxUYbrxcNbO18q+UZyz5Els4WYvlX2PMuLpmcxEc+W4UN+Y4zqzFvMT5tyvxbyo+4B4XD7ZsaMvkEZXvYOB1SRnWH6oL3MB8RJ5Z1HewCV+Y6iZmGm/M3LZWGo8qx6V83PvdslkOmua39aJZj4fN/ahplvIfZwPzce8HezNz300HzcO43zxMhvK5HjT3ul9ghhOzcU8q1wf5O0x38tlIh3Q4L/hxt2lGLNtSvnCnZxeihOGUsTIwEK19ZU3fKWxf/MYx5GHnpis8ecSsiWmc8iXH3Huu9dwYorqvMqONnSj/TIzsjyZurpXDsI6/whSv9O5UJ3vjsZn8mCxjy4xUjri89uVeSk1x7pU/zvM9KH9sdMPyKBxLL5frZXPYbJtHlhmJOja+uTg5seEnHBup2NnXKsO5aLz/8AHrsMpQ7Wiza66vzq2u4s7Pz5ux2u5w5CZgJqjR5M0kLn/N8iHRg2OSJkNZDANZH6P80QdcsoRQ0eOuLi6tbbHxB5uNLB4XVndY2kd4JljlhiQMNbGxEOZjllIpNqtrnCvPtZ0MSIOSG89m1jfBH4asOIGKPupR9PaUHu5WNNNJbA6cUS8y+2y0wTeq+/qG/ds5jxxnqeFbPLtmeb9o7lUHWA63eLy3oYb9VvHIzRQ3Sykv1GedqM9gSdT5fNbc3bwrfHxaLpqd8nh1fW791OUVZ+vrOzNLs0kKy0JFjdxy/WRW1s1ubXlnqI12pmihyv5hcrVhBJUlp46dnp0bzznfnrPWN/qes59xtN+h+q871S3erUSz9f2qz3TsTPzDgItjr3RMxhsRQDvgFDCGF3bqlzbEixPdazl6OkzkDD89yl+JRkzjS2XwUXUWR2/9qHvM5rTPnnhE+8bEvlIcB5YkKk7caHZifcP88srq+0FpMil2A6nQKMfJXfyeMbxBjVrSz8vfw2TB9w13VyZdKzxPbMgr7kG+Bgz80q69QlF3PQ7nk7v85+Ad8fIufH8a13CzQ4cOHTp06PDZo/ftn77X737+wgfq2xQoTMpoUUsSCdcCJHVIkwJo2ACNKhGrhZqxtC/zJdGBiW2O0Ui7k4QWmK782f+KE3R+iOVTp+Oc0C+JURIz+O03X67wIZYZXLz0JQozSZ0Fc//u51tfYvWX3/zUnfcmWYnuODMVDCONUSzV6j05LcMnpxu8nPiSlS/iVJ9/+daXv8xj+cu7X9tThz7E0rFRLLm4/srz0p/5komHOO0I/OOt5+XvuexHUjRAI0kguwHfBFTSdZzJ/O2FL1/46rQ9VWzudo1mt3Be/fqTn4rUSCpO7KXVA6YfmR/LMtgC1fyQNkFuDoAeCJax9OIgiRacXrUnpOX5yg2b3wiPkYX3cboa+NvvvkzmbSwTGkgDAEykArmkCUxHvmTj61e+bO31S1+C0pPkDNpFNE3zS5wz/B+/eDk/xKlgy+og3sdY4rUPutAyzd95vk8iXjCJZV3TqAsna78fx/Nvghbwv3/wZVcTJrkI22gw7xftUroff3e63j54vqXTmp9nFaMzJ7AQgfnYlx/9IZZ1nZ22y7q27KkoLKK+/O03Xwr0SyzBqVao6drjxq4E2IAH5EFmJ/u2jV0cfCnMZd/r2B/jpKwvr9plbLnsaoXGKfztx7+Yv7DtLBX2+3aZ2EUsaTxEOX9gEpfwlzgF622cNga20T/YZE7BJrYJ1xdtvr+JJV6jsZfVIpYs/RZLmH6pzsNexhI3KVaGXFaT7fKr6rSuyzjBbJ/LNaPP+seb9lStn2Lp2NamINJusm75ff8QDBXGWaeiso2iyX8RZfinaknZRZz7zZaZ4CCNGOQZ34CtZ8HFK29jV8HXcZwEiAUx8eef4uS26C+3QQuTDBN5Mt11nHP/+ksvs1Gc3IaWm3gTy2n//KvXsTexRHQrjRdsqrAPsUxzjUYvYP0Bg6pCYi0FWHbAfuN8G5lWCu9aOpOP+ZuBDRWMmf0pXJRlcuoP45S4L1+7n6WzqtrWj9E+/hzt8fYQPJo4Tat+m/Y6uqKyjCvywDZPIJcAGqI9tsu5/N51c8chKcof1H71vZB9LSBIy9UOHTp06NChw2eLwcXVxb8xwsBvffkt91tz+a7+a1cmEbhrhYB8JuiZjcPZO770fzZ2ja/nJowwniO/LxmVcSLGavYsgWD8WBo6Y0ichco47quLC6W7Mw3WxrPkWLbEGDbpsLwBsFxiJI0OqW16OpXvSxGWknZZOvPu8dY2yECzZrxpMxCdSIaM/TAmIgdNJhgpO4xb2HaBiqPPGC2Sohzjw4z5XM7nSv9gy872jGXq+YGxKJb6iDAc41aMvc/nU1vWcnpxauNNNm4uee1m8WBLzHBsss8o1wdJ0NDL9pr4fX2fHEbxgJSJ8sqSoStpHKPJtDk/v2r6etYfTput1AgcRgH02Z/vbpsPiv9vd++b98r/z/tF87s0TNz7yaF5Nzw0v/V3zRvx481o37zR/e/SxH7X/a+i51eVCe6NaP5d7s1OTuXxoPzdKf+30mzuROvDcq38LMydiq61CJ2dnEtbH0kLZByG5SOS6OMfy6DGg0lzb2OYA69bZFJ8tHEfrgOz0VRh+s35qeJTuvOZnwk8kHbF0kPOrrVP5O6e2K6l19w/LW1slq0lud+J7zbWhtM1Y3AsxaHGDhQH8WIdovi5J3kjQUxnDJPldWw5yRJFlkpxKIZUQCsQxvJwLAl84GAa1UMO+TgoCOOYNl6tyo9bLBdWLx+Vb0Wn5KGOei3f6gy8cgcf+Jf8uY4zoNnww7a1NKe8yKe+0L5YHshYrm3RKboPoeGAw1BPlIZtT6rHLF+iJjJXRLXN6nkZw1Zh0V5sToLq9+lobMuHTuSzPBI3NnrhiVih8sGqdiotUeQ0c9VH6n0uRxqOYQZjvKrl8JK6TXlJa2O+y9OG8lKfINdjrJk2yZieHsAN6v05h3+o3uPmJydWPgPKAToUjrbPGC/8vbXNlChflmvtCOJxy3drhngj2qHl9NS3vMVxmA10MfeFOSnUY8rtg+ikX8Jxpjz9Azyk/og8i5hDQw5okvrP0jocy/oO9Fd6CO+ZG0DY9dNami9b1opm9XO4S9EBXWOCyGdZJrxlWRvLu3BwYy6fuTAsaeWgFZYCMu9nJw2V8X/qA462BU/unh6N39m/rVSelDeOsVr8oeKh3Glj8GWsvsTqXp90+uYYBx4Pxs1yvWumapMr0Tceqj6IauooB4boU3M7yk802H4p8MaZRfYsfnO6hY/xRrwiqNIMRRiDkH0ux2FAqjrNJNrHkAqh/xPxl/vz6UzNemDuUnlwHqiuM0eGclH+7bAS5RV3v3xUxIfmvW3qJP7pmj4AYy/pMc9Dt+boFfgXZOkhBR6+3lsO+Mg8UaOPrN4qXb7nuR32EvnWH6Od9PiXgDI9FlkZRk6vI7UOHTp06NChw+cM07Dtp/y/gUkBQvvbn1ftR7W0gI+UQ6jUgv0+/9kLU1K56aF9yLdjzfR8KN/lnoNtDo9EZBK+tIYraWkbNmaQpMc4ubkVEv7OtBEOJ0DjnE9mdgwfs1eZDZtYm/x3aG5WD0pLviQr0uZYPHybDaoQ5nSP25A2edNDNkawMRXRaXlQumi+c2mMW2nTL9jeUJIl456MaIwPomcwMocVYDqW1i96TPqbShpWGOQ8fJvFqWvcu9sPkpA3zYfb96a9MFsdAkzzg1Y5h3gk6RZarq9eNgNpPBeXr5uB8r8UyczKxN2sV82j6Px1ed9gU/h5I1/a85v+prmV2I67m/gGIe/ZcETa4fuRNAn5N+OB/EFzI7pv8OVupcndKA/3SvdOEv+94r4VP3fK55OeL6WJLfQMd1BZMQvz7Opa0ruApULfI3XmQQdb8XEinj08PEgzQ/tCU0NaZmRIeTZNm7oiKRrriqI5ja0kTTtGc5aEa9ph1EHcXRxP+sCsXtH5JG2C+sjxh4rKnO8eSLlTDopZdYD7IhkTV/yD/7y3LUP1Dk2Je+o3dYUyznpjh3Po3ZyZ00BFyOb/NrNbtOKYcU0uTcMmDspVRBl1+u+tJVx4VoeUNgdHkG9WQyiT5kxz0SWHili+Vb/RNjmcxGbySpskfRzTjPXXLAW0O65N61P56Y/uvD3YDHK9hGdqhLZhBhrWQjw9n81Mm8fNlI7N5FZatnJD308UD0dpMmucGcWpnY1U9ylXNBjYz/dQ8Ch60YQ4lCe1XNop8WJdQkvxtulH5Vp25Tg2Fq2SMmbDE+Z8ECcb0/D8gcNEIiybzRg7Ud0UD+HJ+3jCATy95uLiyniGm3P4h3ii1I2fG8WlWBQfG/2o3OXunx4UVpoptPJW38FHxmmpk2jaaKM4rAR71X0FlRanHCtttE42izILj7TiheLGXc7mNrudPoa+pb9RCGsnQ1sNghvLYW1k9jub18DrIeOmihNtnLLVpbmn3crmwLy3g1BUBxQA3o5nY7N44IwXerbfHmymPtsyL9nwSXFDw2iotqd4cWOV6VQaNXWOg2DgzUy0PC1Fs2hYUR4Rlv7JrEWKhz4aqmyzLvoBfLkEfTrlTpsCrG7hLf0uz3D0ALSBiYqDesRRumw0c6K+Br58ef3CLA+4r1+9bk5UT1nBMzUrnNqvvqUeUMtxN8xhUvnePN4qNuXd6gaJDqyMWTWRbdZ4qndmf+PCHB55UYtV3mm37T+PCmcVUGB1T/up/vFdPqCxAcLIy1sAbS2nOnTo0KFDhw6fLY7WYSeqH/Xy65/P+NUHx7/87XPAO16b1IBfQrhmbXGGRGoSg+4Zs0AyRaJCtsDNJNEgEY2kshD2bMxxm9vmYubaFZgOfI2gra1E21LiSEJjxp10rz965+7R1jEzo/FG2sm+ubP12Gii+qO0kXZJB+ebF+qxSUVBn67G/InsjE2aFX1SHdES+ytplQ8LaR7nkhQlvfY5ng4pTZq2NKOxJFDWdrKWlvEi/sEF/AVrZuPfDYc3IH2z3lr0QwKah/9z7jBehJT54vKFtK1Jc3Z+KeV1JAmQ8SMOz/cxI9zNdt2sJLG92y5sXfLb7dI2tn8Qs9nKE7eYSANlK06pRvisb3ySW0i6xKE5P4kWHOskebeRBLqU20n7YR2ksmxrI5mCyyxRmymqsniStnPKFoGMVUvq5ZnxlvKVQ3sZSfreLFeS2ifNZrWSlK5cIqUTkLFDxY1Dm6LQLhmr17fnZ+c2hmzrQ5U2a2UTd3bIizQiW8d+sDkM1CfGlL000TTkCTxRCikEC7znSQu0BJ7YSgeVX3skrL02jYN73Ivzayt3JPu6/WAhIr+4u8VCz8Vz6OQ7u4MnlDOSuh4/+0cd4v2LF6+lMY2aKWP4xnNoJ222SV0bfz9IU0Wbvl8uXLM2JhJGmrMaImHQ7IwTKir/Ho1QKekhfl4zUd/mbOhm97RqFqtlM1P7WiylEcqxbSkamh9YIjrRpuQor7E0QDRW9knAMS5Nu2KvA9o/M5fRNu/jUJ67xwfzQVpMbC228Yf4fWvWBGPYlKtptuRT9BJyJT5QL+4efH0tLg//gEzCoM0B6COdMztMxcP64S66VjkCaIH3dzETHkAzdZhtaC1+6OAbykRhock0bTl7j68kax4z/oxmjoa9Fn04eP3wuGgup6emkU+jL7FGBsly3NOv0bfQfrwflDavYLQNtH4rcjn6F57dLx8UjnfQCx+SZ3KMreofR8cyVwFtmC1MLzhQSO/pX/Bxll3KW884brUvbfx0MhfND6b5P9w/NL2N2q+c0emGDcsXWqvVExEmzphjW1iLO3zW+sMx16zV1/KdXuPYQwILwlRhKZmp3rH97VfSrGlv37z+sjmdn5ibwhPFhbWCMWysWBwtytwNL6lDcysNG//N3Qejg7oOz5jxLs/2ecDHsY7cylVfeLuGOvlWvgKBEnqvV4qLnMetHNlM+Bt/nr6HqSNy8DxS6dChQ4cOHTp8zuh+sDt06NChQ4d/Agwuri/+LZeMmH7eeuYSmDHMVPcJZ6gC5/My/F5p98RDYDP49Zhyw62bhTAtDW2TDJ727exsTC8TOze118wUCpP4yWDabNZbc0xw2G1ZMsVyH8WisEyWMNOT3nFSzAGTuty7xzv75pebd816vWkW+43Fv8H0ofA7zGLy7XogHzOV6MA3E63oHOww5/bMjUQrppGp6MJ/ffnStvU7n5zrbtCM9can2QybPbZpviUey+Og6e37zWqzs7OI7x8emj0nyMgx6YQlIEycwcRFVpgowXIoTP+4k8mpmcEuL67N7Hh6duUmqz4mZ7bpY9tJd28f75vVft/8cv++WWw3zd3uSXzZN+uxSBq4eXQ9Hti5r2yOgCl7Hf5m4OZtlof5tcIpbnwsc0xqMx6KN7vdwTYWoBxywttwMG424sZkNG+elNeLswvRh0nU8+N5YmneWHxg4sqseby7t3Jl84S+yo26Av9xtrxO6c0m84YJKphbbdLhUHXkoLJWHjCN41i2dxCVt5iE9Q9zOdVvr7C5xSKmcOrolg0veKfIqa6YKilvTHCJscoLWhii4R/n+tpkR5UrbehEtGP+w71SuZCv89mp6iOTJFVDFIZEeI67U5kTz+Pqyeq+LaxSmkPVx5z4lI6hAcLCT64u2KxHtE3YSAQa5Q60E6Wx5Lxz+R9u78ycutitLd4122RaHik3UhN3lF/KUURbfqnHmfcy6UyvOdFr2B/ZkrihHvB8vVjasAOODU2e1uvmZMx2nv1mrDYIHybDmcqWiXKcUDYxhyl/xPsxk4RGzWK1Fl/7zdt375qd6siSrUpFB45JY1SwPY7ynrJUbGDL+SLb4gWn+YmnQ0zv4jVf6n65Winb8JllO+KN3JYhCcXLf/5436e+RnTRZucqLyY54k5PLr3MFC/lo1pm8b25uWl2ogfH0kXqP9teqkKp/tEGh3bvk81Uruarrikq26IWvqpNkbL73t9wKlq22f72oL5h2/TWh+ZBfL5gmE20zMYnqgMM94nenfKsr/YWMXlW2ns9G7GEbqw6rZitHEY28ZM+58aGX3xrUWu/+51tsYsjHzyjKZhFVvFuNptmqr6VrWhZerlX+eCm6oMZEqK9Tq1sB83paGJDWpwYyMS1i8mJuYN4zgZPDF2pJjRsicp2rNQ9zNU49mViKaA58YctjulV2RSFpW1j9ducDoc70buRCHw1nuu3YNT8ry+/al6dX9gJhReqhyxCYxmcLYWTY1IfZnu2bGbpH7Qu1bfSj+AeN0sbXnz/yATCQfNky39VvjYxU776Ca8w9BPeRqgL1lbkvD7ZL5kYh+9h+QdsaZZCFdi9w39X27dc81kZqrHYw4kvkUqHDh06dOjQ4XPG4PL68t/KAHf8vH+kPQv1taOSGExUiSfm+z9u7K9Ha44n/kw+97pBfmASAT7ai30gN+aadytJPJKU5tJU0KZenr3QU5dM55I42ZAD7WIv6aaHNKx7NpjHX9hSFiZW9KRpPrAypflgm+L3m8cDU24k3bLlp6QvNElbliHHFpU2kUDh8PvSrvBHcY8bSsJl0kFfPhuCTIcnzUqS5en4XBKr7vss6VHcciNpmANJpYe9NO6DtElJ4b3DyLTOg6TiD3d3zXbDhA9J1GiEErA2+oP8uUOCVhjJmvqG74bNxekLSazT5vLylfljSZtI/3vWKYmWR2ktTITDcUABE4JsUo/4zPI20+ikPiIF8g+tine27EX1weqEnMuLTPgIKQ8X17mpwUi8pyw4ogANZ9qTPC0fN1FepXZIAzuxPF2dSDMUjUwWxBKCQ3NG4yI9lqmtnpbSzCZ2eINZGEQbE2ZwLJ2i5G2ikdI+sbOE9URaG3XmiQklige32DLpiIk2aLBomMqTpFRbouRVq1malcW1C/hGlSN3XONYJpN1baQycN/v0bjxB1hPyIPu91vFJTdTmWxWvhRmozJmco5raYpdPu5xtTD6bu5vlHV9B4MoA2mSTIrEKqTsmMMSwd/Z9My0VTRsyntodUqlIcfZ2OSTpYhoCixXYWna4xrtSnGhZMIruR3VUryQuqFeAG2HLOiPnPkKj2/Xxhe0FtfAzPIln3xLEfV5UKq399IEL09eqO6rfexHqr+qjwqzxRJh5atE5ZjQeZBGSBtgT9i1NDDlpLl54GzlQbNYSjOG7zjlgzqg4tS9eEz71vX5Cae2+7+Z6j4Kycg21WGJJXzsNbd3qvei9cOHW/UbqhNyvvQNK4h4RdtUXPAVKwBt7erihfEXdzLngBHlAd6LQUvyLf9e9NHn4FjCOFQZs2GPiPUJSuKVWBD8837OnFJK3qJ5kw98WrYdvKKP0MBxffFnw0ebxvqIWX/WLFcs6zpRnhRGbqh2Jo6IJ/Nm2J+qDnufcmDyo64X1EXKQO7nt++a1WrfvF+oD9yqzKQ1sq2m+SoH64/Ea7MSiB6zLqpRsMkKkynZ0GmGhs3ELbneWnGrj2nWqtHLtVkHsDhOpWWfqu6fnZw2r84uzWHtupydNhvVQ5UeTFRbkdasy6nyiFOPKO2bOrXVO6WJNYW0aTu6PhUtEwtzaL45vWzOx5Pm//31980XF5fNd+fXzfX8tLlS/3s+loatOjVTfcNhqaN/ss2zlA72Ff5jCcx2+KA06as+LKVhix8cDEL7acb0Yep/5TKslZv5/vvom8DgU7jUUXrM5/AwWOv8qvpHfSAufcw7a5MWN55fp+Pfp2Lv0KFDhw4dOnxm6H33p++RDQzHmnYgnn38Dhk7UYUHGTSe12/RpM13rxlJCgQczwhOJTkmTn0FRnMSh4C8kIQHfnjRHiTw3SvfrB9JGJimKCxZRyDc7HypCPjLjR+m8bdb3+z9gfVYwhODcgJblCZMEhMimnLAw2zV5uZs62lO1k7ztxdfmP8vL783/+wQm2YIbCoA+nGAh/Rf83+78wMK/va7b84P7je+kb5v9CK6bBmSaIgDPsA3X/pBAS+/+Mr8IculhAfGL4Uff2sPPPj5gx9WcBu8WEe+N9UhGOuBl4MvCGqRZWgqQsC2ZxUGUT4T1ArhJPhxssvSbZ99GQek/O/v/mT+lSTwBEf9gYdHP/Thp1+cF7+/9zxs9n6YAxjPnY9ffOmb+Z9f+8EHw1PnzX0s2wH/uPHDP/4ahz/cRzrrljxb7gY2Ud5oRiCr+yQfCPOQnmeRxMyja8ZxCMjZoC2fP8RBDi8vnT42zAHbKHfwywevj3+PQ2hWcQAL42cAYTuBNg0ubNlR01xf+gENF7FFKRhJ6wCMxYG/v/WDKX6+9fJf9Fo+bhkKx89B+vAzt/22ajSDqPvzlftXO2nLwtnG6zI43fr1v37xB/O/DfqmaNEAVTewjMMV9k5u87D3ZVL//vN/mf/zO6cb9LAAgKHHcxrLuV699LYGbDwfMH5svod9+94P3Pnp7z+aD3ZxSAkKJMiDVtg6FXwRB5uA+akfHLRHYxUW0TR+rA4T+f3e6+xjz+PdRIfBsiFH1Z7QwoVsP+MoZ+ZCgPmyDXsa/cz1wZn0zYnXo28v2wNmzuMgmGyameZ+5nXl1jYkdvz7b96mfow+cDvxtA/ZwQksdwLjKLNBlPdZz+v1V3GwEHh14ocLYSkzf+J9XbajJg7OAIwJg3Xw6Gbp/duHaO9gHeUynjivoySbWfTrJ72osMJ53685ShiMIwu94OOHm/bQk13U6/G1h22in/gtDoEBf4v28eON98V3mDWEdRwGxRGfCebyAI4MBb6cj3plntpPG9bnrLR9aHQ1xXcc3aidH9/XwDad7bNDhw4dOnTo8BljcH55VjZO4RcctDLC/wDTZNP5dzY+Y4/wscrzTFJHT3JBPLOnhNnvTWtnXIEH6TMO6COkvWbCmBcS1opN+3vNcD9strtecz67sHvchaRgwjJTkiFANjlE6Fmb32tupbVs9AD3j5t3zXKzbT6sn+x+y5i1tCZXENlQw+9xu/B7jLXhKxD+UDSxgb9t4n/A1zvGieTvpGk9ScO9OLlu1rtDc8KmB4wRye0VFto2kgZxS2l7jDWyucVmu7fN+RkPwTGm10ADGVIeB2yK0BsqvjObTY27unolpWPcTCaM5fab1ZotDPfNSnExW/vNLdsysqXrvnn/9KD87psNw4hSLTZiKfRumDWsnJsTD+CjbVShe/5aqWY5Vg5eUda2kYh8O8RAQFjnK5s9TzxyHNeIP2V8Try/nJ+ajxTPfAQcB0mYbxJ+r1mtVg2bbiwWD1Z3tsYPpSF3cnZm7y6vri3/Q7YbhEb4LOJcNva6xLwA5j7cSOO0bUGlsjIrk839ExujU1HH2JHNYBc/mYlP2rbtgnycGSQU1jZMUShf0UDdpX73m7nKgnFk3Pmco1cZq58pbYXRe8qSL2ybRrmHx3ub/c2xotDJtqCkOZAUjyMtvkgJHZwqXsZpLy7I/6g5O7toRsy6lqMcycPD4sny++b2g82yXuxWxj7K3VRLOYwglCEaNu+wTDFjwnw5xpl3+NwrAD7jxaykQHNgnI8EmReAIz/MDVg+PDW3yheHtLC5CluzQhdf59wPH/9j1jS8Z0z4SWXMRjJsYNFX/d+ojH3bWg6wYI4C+aHYoIE5AlfKP7zCzewAlEEzVVqsmGCWPLPKb28Yu96qHj0pXv/H6hBm7Oc8AeZsEJbVHeT//NK3lMVN5mpb9BHkWfcr1VHq8p3qpRWO3N2Sdqt2hjVM72xugPHTCs98+Ivb849rqoIcD+EFRcI9xzDmzHz4aTP0FS1H2BzUtzyyVWlvZD5uNJ7aIRt9tQPasyqQ6BZfY9XLyrbYJOJ+834pmvUOn3fW9kXiXvy1cpejX9op3Y3SWqs8ho3iVf3HwLURz1Z3q+ZB5YvbL3fNw/1jczqa2azxFydnNgv7RBVjrszM1WfPFQ/uUt9frHvNlZrx9abffD2cN19La/9SdH83OjX3ZW/afDs8ab4eyPXnzfezq+ab0ZnCnDVf8F7u1ejEnGq8adln/Ylp4MypYG7R/d1Ds1X5/v7uffP0tDR3rzq4WC6bkfoNtOHBmfpk8eGRhge/5N483pkt85aNtOCDygieUdepcWxGhW/XFKp4RNvhHzz0gra/etc6uxdaP69alCdcyHkY/bU2cux4BRUdOnTo0KFDh88cg7PL83+zH3Vg0oJ+yCXZxQ99ATP97Jdev/H5a19++REvDPGBPWvRrkOTQ6Phmb1AYkBakdbCH33Hultiw7EWGclnKG3swBiWwrAObjKbm9SJG02nzdN+2/SnM9tKbscWmcNBsxigQfeaN6uHZiUJGPf7071JT4+Sh5mVuZug/UoSVWomYYowkzqdzCBZ0io0iiDCOE2SfuXgBfSY5CX+DKVVsJ5yOBpLq5GmffnCpF1zEtIY5utNlZexpMH1o41R/37/Vtr2WpK6tGCJ07iV9ES0D5fOe814Pm/60qAur18049nU3OXVSxuzhD+MeS420tiloS62+nq3bT6sHi0O3AK9UzQwro8kvxvuXco3wVEXcmgIZBedG994IB+4j2xHXpVr8ixnYzTmuxZh2o9pB4pH96aAim9otPrfrETfdDZrnqRBzU/m9tyOsmNcHw2ZOqZytvXn4qGtU1bZkzdm8+PGKnv88+trJThs5udnFnYb+bDjNUUj7p7tXhXvDVtJik4vZ+VHvKCOWz0fsTYTCwn3KmsyEvnEmRUhgAZEHeAIQ+Ih/+SRtPCZIWqWDLnLy2urYzPRSx2DKspqwJhs0NqfsLUo69OXxhcOrYBO6X4ePtKxtPhG76YnaAjSVl6+VlwjscA1axzxU184qpXZvXfSXPVhs5Q2AR+2Q9EuH0c7MI2X8Tl8sqlvyZ/zj2e0Ta4Vv9LEAMIwIYYfZtliEdjoexy0ctA/1qLFYdtMVWcXh00zuzhvVtR9fc/cARzt1tbtj+Vof2iCKvff7j6YFWq135gWhKPkrd0pr9A9Ep+oI9PTE6vbuLPLS1uHb1YWpWNbiCrem7t748vtvTRt8QNn+VQYO06XctE9GvRAZcH17PzcyhI3MV6rrjDDX3GjzVJnfn33xuoozo7CVZvdqr9hRrgdV6r0aU34lCP8Mx7STuzay9X4DT16SNlhxcl9EUSkWwAVUEqpabgLYhVPHtRf4Canp1a2w/nUynY/du16rTrGtw8qB/iMw6q4Vx4+iN7DQG1MaezVfrZsLaw4cfR/aJWsMU5/TTlFeR/UeTALArd6Eg2rVbNQG31/d9McVtvm5va2OR9NbN31qTo803zlpop4LO36RJmdibEjfTtZ7Zr5ti+N3N2L8Ulz1Z82E0XOt2jPJ+qg+nzLN6IFyxYOntD27hdPdtDNX3/+pXmrtH++ed+8f7hvPqg9PYo/uK362zUd0pnal3K0nY/MfxDfsKzi3nDcsvJ3uxKH5cMv6v2WdqG6R9+gFM1tFZWK0fpHyo0VJu7zgDJUuAiuWubPnvvUP4WzsPFdIsew/XeVi8rplcjt0KFDhw4dOnzu6H6wO3To0KFDh38C9L75w7cHMw1yY3+fITX2VNHzQaXKm1lAwHwAMO2Yn/dtUIXxmz52NcFP92lsIT3I5VPgxGZmNM0Ue7IwxBYjnMcSAnA+9aUNnIMN4pPmMZYJ3G6qk3ViOcmi7++Wva35TMAAuZTLEPnDRO++ebGZi2NsNmXRl8u7fPZaczk+Mf9q7stCAFvzgQMzO4T1dmX+8hD+tl2Csd77s33QZWcUC/MZE5kcM85BFno9ZrlgsnJ6cwnF7bpdzvZwcMY9KYdgaUYtzEGRKcEmyQiFB2bzQaKLMgzf4dc+UVA+diIB8xdIPoCJzeAQvbFc7ySWgZxP2yVQs7Ev05jEko4mlmZ9eP/O/IfqdKTRzJffnF16uU9P/N7OsBUeY+kWuNt5Pm82ztunxglcsQ9iIEPbxhcg63nkrY89MGCnswmjned3HHmaOLnNuMr3RdTLa046EjCKgzCGGjZRLqud18untedzs42KHvSCJGMQy1vYihYMbKvSQN/rCdvQgvvI90Msi3uM9MA6636s38o+IEr/qM0yFAByFeAo2uicjUcC2W5zidLUZl+JD7H0Zj5q6eTkOcAWrAY7Ao9lPr4Ma7HyZWkg2wDDBOZH3phglrAzwYV22aPHv1x6O7p7uDUfbJnAKEQXZWZsYJuGCOeXsURMODnz9va0if4ilj3dr9u2+uHJaV0xiUnANA6ylH0Q5hjZdZrlGz/q1aTqW0ZRMWfJz7gfR18DLmNZ15ztaYVc1tcb+6KoRSyjAjfRv3zYOL1P0cds+1F5hW30mZykCGwTK/yIJtsymO+9fCcrD3vBlsjC66HT8m3V913H6YrnUw8znXpdsLlcgdXa6WFoEQxmnofdxNN5G8vnwP/58W/m3yy9j9tk3R16hAy5JbZj520v+om8X7dVV78VzoO7VfTFzPQTGGIBUQSGaAKFzjSFZ72swRAhOO479bz8lrbft0/8niFo4ANSDiZHRrXt0KFDhw4dOnzO6H33p+/0Ex6/4ikk1AJBSAMsq6iRmjJIKaBo2OlXkkRBajLPNNjYu6TJs4VBarMp2ReNjpkBgUloZ0wSAPn5NiXeis51aBPb2CQkF9Vn2NxwvQaHABgi/0kDYFkXmIS0OQpRbBxy0Ci0X8D5riCl17RKxL4AzabSDHcHvz4EfUxcASxnSti2igITh8xnUpLwuAvJOcV3YR/MLVLhs3hB8qlwIF5lLG1s5CH8kid/MAzzRmrawJdAUWbmlXKeVLxhIRXIDSVSikxeHUmvKU1HHphMB1ieApigk2DSk/khQTMxB+wHbaCsSsnHkvHwMhlgS7PwS178YybFgGFYGgBbmoJRlo/91X1snANSw9iGJSA39chSqOtjtj+fFKdvg3/9SsPuRf1IXjAZE+QGPJVyZpOjzE+dIfkQZVllu0y8YwtN0IvNjsZVW8h260vd4JH7yaPkB0irTSi1PhFPWEfdTeuTIStbFkS0c9s6MsDkzyM/2gJLt2rf4J+3GnZmNPu5DAAiDSawGcJfBl8BZ6yD3O+pbJiS8VXRZVrZLWb/mH1g78ia42ln3zeMfij5C9gqF0yGXu5stwqYqGd+1Qdw8Avg0A/AxFOQfQHYRV6yj2bzVsCZ1OAo7aBrGn3ePK0usdkKE8cSp7HRySE033zD+d2JbAu7sAIygQ4so69eVjXyNuoH2xADO3cfjD2+YKfBtiEWSvlEH3AYtfRlq8tNo2zpFoi+Jn8CgE/NFUp98Xu034/hYYqVsi1498Hzz6KSZNjj39ms4R06dOjQoUOHzxrSsL/Vj3hICfFrXtvc2ZgAtBJE+4tfkI/ys/K5X7SxIUBEGvFN+SSkOA6ZSBThL3wm2RsqsTVpLUHjVYmlDUpm3I/EW0nM72tpJp+VfCddlfiWGhfHUYAiXMc3mSfQjknks4gvMlk0biGlzIISTS1fRZohieXXyd9ae04yUlPI8coctwYle3GRb+o8PEdaVlIDy+hSYwb5fWrhqSkmj0DR1CO+vG951iLpS9pTcUvJlyM0C4qU6siis01SAmmZKZp7BM4QdVvIfLUWAfPaelrlKbWUbD9Fus4EhDJ+mhSW8gi/ykspz+CnHUhhaOkr1pZMK95lrJ+oPuKbp1HXF0NFZymP8Hth8ch7kOPcbZv1i35oioWkGvEsl7Jk+6vH+Q+h9eWTNp42M89ZmyTkRbkHhef+UaneEYhNWgqKGh5ehN2FZgfyWdG0Sl4CGb+QYZ/zsyW8DZx1rO1T3K/HPzkUBZQ6Gt9z8A2ox885PhNkH5A8zjoICt8juqyX2c9VnCntl02SzA/r0iTvWxYV60sm1VpkW/qyhHMOUtGIo5qz1CqR2+n6klrCRp6iDWf+QdbrfFL6+KoPyHLOdvPczz4HZP9aWO5e+7w8IYgHKnpxlYdEvivfZ/QlbBtfFVOHDh06dOjQ4XNG75s/fl1Gl3LMrP4dzxnf5Xc+hKJaMuML9/xZK0d8WkowpHAVSaUmUo83pTSUwsYhZ+9VKdSpHcOfp5YGWonWPUVoXtFSK0mqjbd+1tJyhAhSvoj7Wmt+PiZRZtLnR7XWl5paUVcC9W2WSyRWvolo2tja66RrEImy7WOiUhoMhRXl4zpGf5lPcqw5UaRYUAJFQSe9VZBEav7l8zrJQNbRTPG5heFovDtoTq0p830UcV4mXXGffD3SIuO69QMlTzUf4m0GyjA1b+I6taYyUzvycKSBZH2xv9StuEi+CiV0ScJDl/hL/lu09LjflksbNq/Sz/harQI+OcqTuEia2pCKp9ZuhCQrn9ZlaEeOgo/yUMVYnkXYfBWP2Vo1UfKb8WXYyEsbknwe8zzHzfOQDFD6jGBA23bdyzkHIMuzaNiFbkfdpZY+7jjIUXzt536RtJQx/JhrADheEuy30YdmXa3qbD/7g2fxlvIuz4VIK7uoHO9+bhkwZL7ym0wgeSeUvj0iTGtYRsPGPQnbhtV885RYxuMPag07X5XfsYgwrQYOD5T8zHy29aaNr/Tf8eh5+0kaapS+P1le8Tzpyu9K2Ei7brNc1VR36NChQ4cOHT5TdD/YHTp06NChwz8Bet/+8Wvp8qF2h6qepiBQTDOpzrt3jAieZoJiJopPy6SICkXVj4/KhLcq7TIZJ5CxHE0CyKUQ8d3HKbUo5pFnpteMrf62zbe/LXRlJAZPu5hQ8j6CHJvY4zq8zH8uxThOO/PkXvuy5U0husC/ybI7mjwTgTkRCmxj8wg7ESqQ4QvJz+M/JtBR7ELPA9f38WHhuftHX8Sr5xOfijXoKLq4Kc8yXn/wKZYnr4spuzIzZfA0rz3PUhtSyGfPXma8tQm7TdOR6dQok60ivl7YEdnz2lB91Jol3S/3VZ2oLG2GNpt+cdS28l34xQydaZZvdRnPSj4joSqIbvzu+SSaZyQZ8vs2e36VNKVJF5QJkmlqjnjroYUjOoR8lyE+NXyX7TtTKpMgq6VVmfFB0NOPjTRqPrKHOihDU3VSwvN7QxCcZZj5Ph5Ci4ukN+L5n8ypmadiEq+GFtr66BHtY+ixJCNkdLmxTYtM3D3wvE/PPrBf74YSKLwJekobq9tL+HmRSZURkbo/S+YkU+K+DNHkx8JHvz35rq5j6cdF2w+5X5dhaxKPd5GZnFx7VIYRc1tOGV9LU5u2h6leGY5u9XnFhQ4dOnTo0KHD54ret39Aw/Zf/sTx5PH4jc8gRZoxz5ACRCvghJQR4kJKLqDVbjxwmVwQkw5qzTAnJxSJPBKoLQCpNeaEkDYpv0pJErRZ8He1pAxqaazKnqFIb9WbQ2hERTILaavwoZJw86s2DU8781hPxGknj5jXRljRl5Nx2skTGcaR25mCpH0XW36WyTS1NFzSOEZ+W2ulH9ETXisxV3HFd5lma3Vo4yvLkJJ/9rdFrU2VtMMrWkqKpkdBj8OWizovJQ/5zP2S7yNpOCPKZ35fNM6qDJNfbR3IsOYFIo38rhSlf3OU70DbNv2b43J55idv7K+irywqrRYafmqKGTjjMPhN1t3kTZlgJWQesj5WrxwZr9Aui4qHqaUkvXWeAoUX4R1Z2eJh1oVEyVsdNugs2k+8aidCtf1FatatRcHva20v6eDMc/OThoj3eEONuI4wOSE26c4+0BF1IKLLT+v6k6+gHhSrQWxIUmt7mYcy+SwiyvyDJD0tP5lYyf9R2EzTwcl0oMxgbonT9xFP7ECSr4qlSihlX97GN3nfBj2+FsoXmafSRlq0PDavSgd+Be3Z7oqfCT1LEAQzMkgb26fgbzNsPRG6/TL94zTzztFtTdqhQ4cOHTr8U6D37R+/kSB6/Dv+XPMElbBmOJaCQxqIaOqxCVBrzSWpWlwTdildVrQMy9Z1EX+IR3XaZczM/n4cJqUlhz8r0lV4bYptvPVTUCT8GpGH/KqV6M0r0hwoEmSlsTn8/lPjVyVkyUsbYV4OQ5NOCbIsi6via8en/aOiXXyqXJ7lIZE0GfJdKcOI75nUaYjv8vN2fKiqYyFxFqm/hI74yjdt3Bki+VqsMUcazTFS26ml69SI2joRvCphW2R2M4U2v87rsiwGBKFpCSl01lpuWGgyraLJlHja+Mr4X5Zz3ldhypyEeNQWjz/Ibx1+nWm2m2wELfbXkfG02qSHrccrU2lIelI7zbTLuLyQ5ZB1dcBZ90KSd7SsK8OU8eOk2zxDqTfxfdbz1Jzq+KAQlLYQ37TtyDxDsc5lYp9IezTybUG3sYQqx7TbQFWEcdlauMwr/r6J9IS2rvlF8vVTfUA+SRqGwc+SoNDyJnnhD4ZxYAbIrmkb2yQ/tzDUbSvbG2eEg0FuuZs8qmpQ2+84Sp2t8pLWq+M6Wt+3zzO+to36u6xXRSsXkhf5JMuntsZkGqnxp6WjtJ86L+HXVw6/r7JUQrRpRTpHeYxn9rety/ltHZKH0ao6dOjQoUOHDp8zet/88LWUjPyVj9/z6mc9JYaUZsr4cR2mSBcpFxzf5+xK0G5LF4iF8oeQZmvJrGgM8ajMbKy1lBI8CHqWZi0V5lWRcIqkU96E36LkP7ZIrKW3/DzHJDK/RUrKaIUcLy6aoP0VSqA2cNK3j7G+HB+sx+5L+Gca+yel4aI2hF/yXcOf5ZvnUnF7UVJuw5Ynz74RSth4mGTVUubx1y1KfaxQ6li8aser4nmV7/J58Kjw4Sghf5Z6R95XCmFBmRn6jOJMphLJ2zKMb4qUXQIfXRpKkqVO1CEiragDqXHuYsY/eG7ZKu26+FX9iQwmfUl7pnyEeJi8L3WjKsOs16mV5Zu0MJR0hOdaRPJzHMdD1ljHkYcl36ER1vEVfoVXtPt4UGvYbb9gXhpUFH/47hmK9SHrVNzXVpJ9fFHqZYnB74+sWJlmJJb1sfSJVZ5KDp7zqgrTXtVUE3/QV4XNDGZaadXYVpvAbOIY0eRJhhEDzKvT3sYGLGXuQtbHpOoTdGYbKPmv6U7mpJ+o+JfIqEsSSXBJsoo30ipzSSJs3beUWfERJt9kfsuYvlB4UBJ3v8xdaIkQ8pn7aUU9mheTdSDz8BHasKT934Xq0KFDhw4dOnw2aJr/P5rj9bZeVJ/7AAAAAElFTkSuQmCC[/img]
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Yesterday, 00:33
(This post was last modified: Yesterday, 06:58 by Selur.)
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Dev versions are in the 'experimental'-folder of my GoogleDrive, which is linked on the download page.
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