Pip’s picnic: follow the clues

%23%20%2F%2F%2F%20script%0A%23%20requires-python%20%3D%20%22%3E%3D3.11%22%0A%23%20dependencies%20%3D%20%5B%22marimo%3D%3D0.24.0%22%2C%20%22numpy%3E%3D2.1%22%5D%0A%23%20%2F%2F%2F%0A%0Aimport%20marimo%0A%0A__generated_with%20%3D%20%220.24.0%22%0Aapp%20%3D%20marimo.App(width%3D%22medium%22%2C%20app_title%3D%22Pip%E2%80%99s%20picnic%3A%20follow%20the%20clues%22)%0A%0A%0A%40app.cell%0Adef%20_()%3A%0A%20%20%20%20import%20marimo%20as%20mo%0A%20%20%20%20%22%22%22Live%2C%20deterministic%20classroom%20mechanisms.%20MODEL%20is%20injected%20by%20the%20notebook%20generator.%22%22%22%0A%20%20%20%20import%20numpy%20as%20np%0A%20%20%20%20import%20math%2C%20json%2C%20html%2C%20base64%2C%20zlib%0A%0A%20%20%20%20def%20softmax(x%2C%20axis%3D-1)%3A%0A%20%20%20%20%20%20%20%20e%3Dnp.exp(x-np.max(x%2Caxis%3Daxis%2Ckeepdims%3DTrue))%3Breturn%20e%2Fe.sum(axis%3Daxis%2Ckeepdims%3DTrue)%0A%0A%20%20%20%20def%20forward(tokens%2C%20checkpoint%3D'base'%2C%20ablate%3DNone%2C%20patch%3DNone%2C%20readout%3DNone)%3A%0A%20%20%20%20%20%20%20%20w%3D%7Bk%3Anp.asarray(v)%20for%20k%2Cv%20in%20MODEL%5Bcheckpoint%5D.items()%7D%0A%20%20%20%20%20%20%20%20if%20readout%20is%20not%20None%3Aw%5B'readout.weight'%5D%3Dreadout%0A%20%20%20%20%20%20%20%20def%20lin(x%2Cname)%3Areturn%20x%40w%5Bname%2B'.weight'%5D.T%2Bw%5Bname%2B'.bias'%5D%0A%20%20%20%20%20%20%20%20def%20norm(x%2Cname)%3Areturn%20(x-x.mean(-1%2Ckeepdims%3DTrue))%2Fnp.sqrt(x.var(-1%2Ckeepdims%3DTrue)%2B1e-5)*w%5Bname%2B'.weight'%5D%2Bw%5Bname%2B'.bias'%5D%0A%20%20%20%20%20%20%20%20x%3Dw%5B'embed.weight'%5D%5Btokens%5D%2Bw%5B'pos.weight'%5D%5B%3Alen(tokens)%5D%3Bcache%3D%7B'residual'%3A%5Bx.copy()%5D%2C'attention'%3A%5B%5D%2C'neurons'%3A%5B%5D%7D%0A%20%20%20%20%20%20%20%20for%20layer%20in%20range(2)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20n%3Df'blocks.%7Blayer%7D'%0A%20%20%20%20%20%20%20%20%20%20%20%20q%2Ck%2Cv%3Dlin(norm(x%2Cn%2B'.ln1')%2Cn%2B'.qkv').reshape(len(tokens)%2C3%2C2%2C12).transpose(1%2C2%2C0%2C3)%0A%20%20%20%20%20%20%20%20%20%20%20%20scores%3Dq%40k.transpose(0%2C2%2C1)%2Fmath.sqrt(12)%0A%20%20%20%20%20%20%20%20%20%20%20%20scores%5B%3A%2Cnp.triu_indices(len(tokens)%2C1)%5B0%5D%2Cnp.triu_indices(len(tokens)%2C1)%5B1%5D%5D%3D-1e9%0A%20%20%20%20%20%20%20%20%20%20%20%20att%3Dsoftmax(scores)%3Bheads%3Datt%40v%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20ablate%20and%20ablate%5B0%5D%3D%3Dlayer%3Aheads%5Bablate%5B1%5D%5D%3D0%0A%20%20%20%20%20%20%20%20%20%20%20%20x%3Dx%2Blin(heads.transpose(1%2C0%2C2).reshape(len(tokens)%2C24)%2Cn%2B'.proj')%0A%20%20%20%20%20%20%20%20%20%20%20%20neurons%3Dnp.maximum(lin(norm(x%2Cn%2B'.ln2')%2Cn%2B'.fc')%2C0)%0A%20%20%20%20%20%20%20%20%20%20%20%20x%3Dx%2Blin(neurons%2Cn%2B'.out')%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20patch%20and%20patch%5B'layer'%5D%3D%3Dlayer%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20x%5Bpatch%5B'position'%5D%5D%3Dpatch%5B'value'%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20cache%5B'attention'%5D.append(att)%3Bcache%5B'neurons'%5D.append(neurons)%3Bcache%5B'residual'%5D.append(x.copy())%0A%20%20%20%20%20%20%20%20logits%3Dlin(norm(x%2C'norm')%2C'readout')%0A%20%20%20%20%20%20%20%20return%20logits%5B-1%5D%2Ccache%0A%0A%20%20%20%20def%20prompt(case%3D0%2C%20authored%3D'')%3A%0A%20%20%20%20%20%20%20%20examples%3D%5B('pip'%2C'wattle'%2C'red'%2C'blue'%2C'pip')%2C('pip'%2C'wattle'%2C'blue'%2C'red'%2C'pip')%2C('kiki'%2C'bo'%2C'gold'%2C'green'%2C'kiki')%2C('bo'%2C'kiki'%2C'gold'%2C'red'%2C'kiki')%2C('pip'%2C'wattle'%2C'gold'%2C'red'%2C'pip')%2C('pip'%2C'wattle'%2C'red'%2C'blue'%2C'wattle')%5D%0A%20%20%20%20%20%20%20%20a%2Cb%2Cc%2Cd%2Cq%3Dexamples%5Bint(case)%5D%0A%20%20%20%20%20%20%20%20if%20authored.strip()%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20parts%3D%5Bpart.strip().lower()%20for%20part%20in%20authored.split('%2C')%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20len(parts)!%3D5%3Araise%20ValueError('Write%20five%20comma-separated%20entries%3A%20first%20name%2C%20second%20name%2C%20first%20colour%2C%20second%20colour%2C%20queried%20name.')%0A%20%20%20%20%20%20%20%20%20%20%20%20a%2Cb%2Cc%2Cd%2Cq%3Dparts%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20a%20not%20in%20%5B'pip'%2C'wattle'%2C'kiki'%2C'bo'%5D%20or%20b%20not%20in%20%5B'pip'%2C'wattle'%2C'kiki'%2C'bo'%5D%20or%20a%3D%3Db%20or%20q%20not%20in%20%5Ba%2Cb%5D%20or%20c%20not%20in%20%5B'red'%2C'blue'%2C'gold'%2C'green'%5D%20or%20d%20not%20in%20%5B'red'%2C'blue'%2C'gold'%2C'green'%5D%3Araise%20ValueError('Use%20two%20different%20names%20(Pip%2C%20Wattle%2C%20Kiki%2C%20Bo)%2C%20colours%20red%2Fblue%2Fgold%2Fgreen%2C%20and%20query%20one%20of%20the%20two%20names.')%0A%20%20%20%20%20%20%20%20words%3Df'%7Ba%7D%20has%20%7Bc%7D%20.%20%7Bb%7D%20has%20%7Bd%7D%20.%20%7Bq%7D%20has'.split()%0A%20%20%20%20%20%20%20%20return%20%5BMODEL%5B'vocab'%5D.index(x)%20for%20x%20in%20words%5D%2CMODEL%5B'vocab'%5D.index(c%20if%20a%3D%3Dq%20else%20d)%2C'%20'.join(words)%0A%0A%20%20%20%20def%20output_rows(logits%2C%20temperature%3D1)%3A%0A%20%20%20%20%20%20%20%20probs%3Dsoftmax(logits%2Ftemperature)%0A%20%20%20%20%20%20%20%20return%20%5B%7B'word'%3Av%2C'probability'%3Around(float(probs%5Bi%5D)%2C6)%2C'logit'%3Around(float(logits%5Bi%5D)%2C5)%7D%20for%20i%2Cv%20in%20enumerate(MODEL%5B'vocab'%5D)%5D%0A%0A%20%20%20%20def%20result(summary%2Crows%2Ckind%3D'bars'%2Cx%3D'word'%2Cy%3D'probability'%2C**kwargs)%3A%0A%20%20%20%20%20%20%20%20return%20dict(summary%3Dsummary%2Crows%3Drows%2Ckind%3Dkind%2Cx%3Dx%2Cy%3Dy%2C**kwargs)%0A%0A%20%20%20%20def%20compute(ident%2Cs)%3A%0A%20%20%20%20%20%20%20%20if%20ident%20in%20%5B'u6-words'%2C'u6-clues'%2C'u6-switch'%2C'u12-circuits'%2C'u12-training'%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20tokens%2Ctruth%2Ctext%3Dprompt(s.get('case'%2C0)%2Cs.get('authored'%2C''))%3Blogits%2Ccache%3Dforward(tokens)%0A%20%20%20%20%20%20%20%20%20%20%20%20probs%3Dsoftmax(logits)%3Banswer%3DMODEL%5B'vocab'%5D%5Bint(logits.argmax())%5D%3Bcorrect%3DMODEL%5B'vocab'%5D%5Btruth%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20context%3Df'Prompt%3A%20%E2%80%9C%7Btext%7D%20%E2%80%A6%E2%80%9D.%20The%20written%20facts%20support%20%7Bcorrect%7D.%20Base%20model%20chooses%20%7Banswer%7D.'%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-words'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3Doutput_rows(logits%2Cs%5B'temperature'%5D)%3Bp%3Dnp.array(%5Br%5B'probability'%5D%20for%20r%20in%20rows%5D)%3Bentropy%3D-float(np.sum(p*np.log2(p%2B1e-12)))%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(context%2Bf'%20Temperature%20changes%20probabilities%3B%20it%20does%20not%20check%20the%20facts.'%2Crows%2Cmetrics%3D%7B'supported_answer'%3Acorrect%2C'argmax'%3Aanswer%2C'entropy_bits'%3Aentropy%7D%2Cformula%3D'probability(word)%20%3D%20softmax(logit%20%2F%20temperature).%20All%2010%20vocabulary%20words%20are%20shown%3B%20total%20probability%20is%201.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-clues'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20layer%3Dint(s%5B'layer'%5D)%3Bneuron%3Dint(s%5B'neuron'%5D)%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%20in%20range(7%20if%20s.get('authored'%2C'').strip()%20else%206)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tt%2Cyy%2Ctx%3Dprompt(i%20if%20i%3C6%20else%200%2Cs.get('authored'%2C'')%20if%20i%3D%3D6%20else%20'')%3Bll%2Ccc%3Dforward(tt)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20rows.append(%7B'example'%3Astr(i%2B1)%2C'prompt'%3Atx%2C'supported'%3AMODEL%5B'vocab'%5D%5Byy%5D%2C'activation'%3Around(float(cc%5B'neurons'%5D%5Blayer%5D%5B-1%2Cneuron%5D)%2C5)%2C'model_answer'%3AMODEL%5B'vocab'%5D%5Bint(ll.argmax())%5D%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20heat%3Dcache%5B'neurons'%5D%5Blayer%5D%5B%3A%2C%3A12%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'Neuron%20%7Bneuron%7D%20in%20block%20%7Blayer%7D%20across%20%7Blen(rows)%7D%20examples.%20A%20large%20number%20is%20a%20clue%20to%20investigate%2C%20not%20a%20verified%20label.'%2Crows%2Cx%3D'example'%2Cy%3D'activation'%2Cheat%3Dheat.tolist()%2Cheat_labels%3D%5BMODEL%5B'vocab'%5D%5Bt%5D%20for%20t%20in%20tokens%5D%2Cformula%3D'Hidden%20unit%20activation%20%3D%20ReLU(normalised%20residual%20%C3%97%20learned%20weights%20%2B%20bias).%20The%20heat%20map%20shows%20units%200%E2%80%9311%20at%20each%20token%20of%20your%20current%20prompt%20(the%20authored%20prompt%20when%20supplied)%3B%20the%20bars%20show%20your%20selected%20unit%20across%20examples.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-switch'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20layer%3Dint(s%5B'layer'%5D)%3Bhead%3Dint(s%5B'head'%5D)%3Bchanged%2C_%3Dforward(tokens%2Cablate%3D(layer%2Chead))%3Bpc%3Dsoftmax(changed)%0A%20%20%20%20%20%20%20%20%20%20%20%20suite%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20j%20in%20MODEL%5B'test_indices'%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tt%2Cyy%3DMODEL%5B'cases'%5D%5Bj%5D%3Bbefore%2C_%3Dforward(tt)%3Bafter%2C_%3Dforward(tt%2Cablate%3D(layer%2Chead))%3Bsuite.append(%7B'case'%3Aj%2C'baseline_correct'%3Abool(before.argmax()%3D%3Dyy)%2C'intervened_correct'%3Abool(after.argmax()%3D%3Dyy)%2C'supported_probability_change'%3Afloat(softmax(after)%5Byy%5D-softmax(before)%5Byy%5D)%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'word'%3Av%2C'original_probability'%3Around(float(probs%5Bi%5D)%2C6)%2C'probability'%3Around(float(pc%5Bi%5D)%2C6)%7D%20for%20i%2Cv%20in%20enumerate(MODEL%5B'vocab'%5D)%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(context%2Bf'%20Removing%20block%20%7Blayer%7D%2C%20head%20%7Bhead%7D%20changes%20the%20answer%20to%20%7BMODEL%5B%22vocab%22%5D%5Bint(changed.argmax())%5D%7D.'%2Crows%2Cmetrics%3D%7B'probability_change_for_supported_answer'%3Afloat(pc%5Btruth%5D-probs%5Btruth%5D)%2C'frozen_evaluation_cases'%3Alen(suite)%2C'baseline_correct'%3Asum(r%5B'baseline_correct'%5D%20for%20r%20in%20suite)%2C'intervened_correct'%3Asum(r%5B'intervened_correct'%5D%20for%20r%20in%20suite)%2C'paired_cases'%3Asuite%7D%2Cformula%3D'Ablation%20sets%20the%20chosen%20attention%20head%20output%20to%20zero%20before%20its%20output%20projection.%20It%20changes%20an%20internal%20computation%2C%20while%20weights%20and%20prompt%20stay%20fixed.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-calendar'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20start%3Dint(s%5B'month'%5D)%3Bstep%3Dint(s%5B'step'%5D)%3Bend%3D(start%2Bstep)%2512%3Blabels%3D%5B'Jan'%2C'Feb'%2C'Mar'%2C'Apr'%2C'May'%2C'Jun'%2C'Jul'%2C'Aug'%2C'Sep'%2C'Oct'%2C'Nov'%2C'Dec'%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'step'%3Ai%2C'unwrapped_number'%3Astart%2Bi%2C'month'%3Alabels%5B(start%2Bi)%2512%5D%2C'x'%3Amath.cos(2*math.pi*(start%2Bi)%2F12)%2C'y'%3Amath.sin(2*math.pi*(start%2Bi)%2F12)%7D%20for%20i%20in%20range(step%2B1)%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'%7Blabels%5Bstart%5D%7D%20%2B%20%7Bstep%7D%20months%20%3D%20%7Blabels%5Bend%5D%7D.%20First%20add%20the%20numbers%20(%7Bstart%7D%20%2B%20%7Bstep%7D%20%3D%20%7Bstart%2Bstep%7D)%2C%20then%20take%20the%20remainder%20after%20dividing%20by%2012%20(%7Bend%7D).'%2Crows%2C'circle'%2Cx%3D'x'%2Cy%3D'y'%2Clabels%3Dlabels%2Cformula%3D'Month%20number%20%3D%20(starting%20number%20%2B%20steps)%20mod%2012.%20This%20drawn%20circle%20is%20an%20explicit%20teaching%20representation%2C%20not%20an%20activation%20measurement%20from%20Llama.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-story'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20prior%3Ds%5B'prior'%5D%3Bstrength%3Ds%5B'reliability'%5D%3Bcount%3Dint(s%5B'clues'%5D)%3Bscenario%3Ds%5B'story'%5D%3Bposterior%3Dprior%3Brows%3D%5B%7B'clue'%3A0%2C'p_rain'%3Aprior%2C'evidence'%3A'Starting%20guess'%7D%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20clues%3D%5B1%2C1%2C-1%2C1%2C-1%5D%20if%20scenario%3D%3D0%20else%20%5B-1%2C-1%2C1%2C-1%2C1%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20descriptions%3D%7B1%3A'Dark%20clouds%20support%20rain'%2C-1%3A'Clear%20sky%20supports%20dry%20weather'%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%2Ce%20in%20enumerate(clues%5B%3Acount%5D)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20lr%3D(strength%2F(1-strength))**e%3Bodds%3Dposterior%2F(1-posterior)*lr%3Bposterior%3Dodds%2F(1%2Bodds)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20rows.append(%7B'clue'%3Ai%2B1%2C'p_rain'%3Around(posterior%2C6)%2C'evidence'%3Adescriptions%5Be%5D%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'Pip%20is%20planning%20a%20picnic.%20After%20%7Bcount%7D%20clues%2C%20this%20teaching%20model%20gives%20rain%20probability%20%7Bposterior%3A.1%25%7D.%20It%20updates%20a%20number%3B%20it%20has%20no%20feelings.'%2Crows%2C'line'%2Cx%3D'clue'%2Cy%3D'p_rain'%2Cformula%3D'Updated%20odds%20%3D%20previous%20odds%20%C3%97%20likelihood%20ratio.%20Clues%20are%20assumed%20independent%20given%20the%20weather%3B%20repeated%20copies%20of%20one%20clue%20would%20violate%20this%20assumption.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-fair-test'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20threshold%3Ds%5B'threshold'%5D%3Bshift%3Ds%5B'shift'%5D%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%20in%20range(20)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20truth%3Di%252%3D%3D0%3Bscore%3D(.45%2B.45*(i%255)%2F4)%20if%20truth%20else%20(.1%2B.55*(i%255)%2F4)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20shift%3Ascore%3D1-score%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20flagged%3Dscore%3E%3Dthreshold%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20rows.append(%7B'case'%3Ai%2B1%2C'needs_check'%3Atruth%2C'clue_score'%3Around(score%2C3)%2C'flagged'%3Aflagged%2C'outcome'%3A('caught'%20if%20truth%20else%20'false%20alarm')%20if%20flagged%20else%20('missed'%20if%20truth%20else%20'correct%20pass')%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20tp%3Dsum(r%5B'outcome'%5D%3D%3D'caught'%20for%20r%20in%20rows)%3Bfp%3Dsum(r%5B'outcome'%5D%3D%3D'false%20alarm'%20for%20r%20in%20rows)%3Bfn%3D10-tp%3Btn%3D10-fp%0A%20%20%20%20%20%20%20%20%20%20%20%20chart%3D%5B%7B'outcome'%3Ak%2C'cases'%3Av%7D%20for%20k%2Cv%20in%20%5B('Caught'%2Ctp)%2C('Missed'%2Cfn)%2C('False%20alarm'%2Cfp)%2C('Correct%20pass'%2Ctn)%5D%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'Out%20of%2010%20cases%20needing%20a%20check%2C%20the%20rule%20catches%20%7Btp%7D%20and%20misses%20%7Bfn%7D.%20Among%2010%20other%20cases%2C%20it%20raises%20%7Bfp%7D%20false%20alarms.'%2Crows%2Cx%3D'outcome'%2Cy%3D'cases'%2Cchart_rows%3Dchart%2Cmetrics%3D%7B'TP'%3Atp%2C'FN'%3Afn%2C'FP'%3Afp%2C'TN'%3Atn%7D%2Cformula%3D'Flag%20a%20case%20when%20its%20hand-made%20clue%20score%20%E2%89%A5%20threshold.%20The%20shifted%20set%20reverses%20the%20association.%20These%20are%20fictional%20test%20fixtures%2C%20not%20measured%20LLM%20scores.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-circuits'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20corrupt%3Dtokens.copy()%3Bcorrupt%5B2%5D%2Ccorrupt%5B6%5D%3Dcorrupt%5B6%5D%2Ccorrupt%5B2%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20bad%2Cbadcache%3Dforward(corrupt)%3Blayer%3Dint(s%5B'layer'%5D)%3Bposition%3Dint(s%5B'position'%5D)%3Bmode%3Ds%5B'intervention'%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20changed%2C_%3Dforward(corrupt%2Cpatch%3D%7B'layer'%3Alayer%2C'position'%3Aposition%2C'value'%3Acache%5B'residual'%5D%5Blayer%2B1%5D%5Bposition%5D%7D)%20if%20mode%3D%3D0%20else%20forward(corrupt%2Cablate%3D(layer%2Cint(s%5B'head'%5D)))%0A%20%20%20%20%20%20%20%20%20%20%20%20competitor%3Dcorrupt%5B2%5D%20if%20tokens%5B-2%5D%3D%3Dtokens%5B0%5D%20else%20corrupt%5B6%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20metric%3Dlambda%20a%3Afloat(a%5Btruth%5D-a%5Bcompetitor%5D)%3Bdenom%3Dmetric(logits)-metric(bad)%0A%20%20%20%20%20%20%20%20%20%20%20%20recovery%3D(metric(changed)-metric(bad))%2Fdenom%20if%20abs(denom)%3E1e-6%20else%20None%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'condition'%3An%2C'logit_difference'%3Ametric(a)%2C'p_original_fact'%3Afloat(softmax(a)%5Btruth%5D)%2C'answer'%3AMODEL%5B'vocab'%5D%5Bint(a.argmax())%5D%7D%20for%20n%2Ca%20in%20%5B('Clean'%2Clogits)%2C('Corrupted'%2Cbad)%2C('Intervened'%2Cchanged)%5D%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20label%2Cvector%20in%20%5B('No-op%20corrupted%20vector'%2Cbadcache%5B'residual'%5D%5Blayer%2B1%5D%5Bposition%5D)%2C('Zero%20vector'%2Cnp.zeros(24))%2C('Reversed%20clean%20coordinates'%2Ccache%5B'residual'%5D%5Blayer%2B1%5D%5Bposition%5D%5B%3A%3A-1%5D)%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20control%2C_%3Dforward(corrupt%2Cpatch%3D%7B'layer'%3Alayer%2C'position'%3Aposition%2C'value'%3Avector%7D)%3Brows.append(%7B'condition'%3Alabel%2C'logit_difference'%3Ametric(control)%2C'p_original_fact'%3Afloat(softmax(control)%5Btruth%5D)%2C'answer'%3AMODEL%5B'vocab'%5D%5Bint(control.argmax())%5D%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20attention%3Dbadcache%5B'attention'%5D%5Blayer%5D%5Bint(s%5B'head'%5D)%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'Patch%20recovery%3A%20%7Brecovery%3A.3f%7D%20(can%20exceed%200%E2%80%931).'%20if%20recovery%20is%20not%20None%20else%20'Recovery%20is%20undefined%3A%20clean%20and%20corrupted%20scores%20are%20effectively%20identical.'%2Crows%2Cx%3D'condition'%2Cy%3D'logit_difference'%2Cheat%3Dattention.tolist()%2Cheat_labels%3D%5BMODEL%5B'vocab'%5D%5Bt%5D%20for%20t%20in%20corrupt%5D%2Cmetrics%3D%7B'normalised_recovery'%3Arecovery%2C'clean_prompt'%3Atext%2C'corrupted_prompt'%3A'%20'.join(MODEL%5B'vocab'%5D%5Bt%5D%20for%20t%20in%20corrupt)%7D%2Cformula%3D'Logit%20difference%20%3D%20score(original%20correct%20colour)%20%E2%88%92%20score(corrupted%20correct%20colour).%20Recovery%20%3D%20(intervened%20%E2%88%92%20corrupted)%2F(clean%20%E2%88%92%20corrupted).%20Patch%20replaces%20one%20post-block%20residual%20vector%3B%20ablation%20instead%20zeroes%20one%20head.%20Attention%20weights%20alone%20are%20not%20a%20causal%20graph.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-training'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20edited%2C_%3Dforward(tokens%2C'edited')%3Balpha%3Ds%5B'alpha'%5D%3Bamplified%3Dlogits%2Balpha*(edited-logits)%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'word'%3Av%2C'base'%3Afloat(softmax(logits)%5Bi%5D)%2C'edited'%3Afloat(softmax(edited)%5Bi%5D)%2C'probability'%3Afloat(softmax(amplified)%5Bi%5D)%7D%20for%20i%2Cv%20in%20enumerate(MODEL%5B'vocab'%5D)%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20suite%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20j%20in%20MODEL%5B'test_indices'%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tt%2Cyy%3DMODEL%5B'cases'%5D%5Bj%5D%3Bbb%2C_%3Dforward(tt)%3Bee%2C_%3Dforward(tt%2C'edited')%3Bsuite.append((int(bb.argmax())%3D%3Dyy%2Cint(ee.argmax())%3D%3Dyy%2Ctt%5B-2%5D%3D%3D2))%0A%20%20%20%20%20%20%20%20%20%20%20%20groups%3D%7B%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20name%2Cselection%20in%20%5B('all'%2Csuite)%2C('Kiki%20queries'%2C%5Br%20for%20r%20in%20suite%20if%20r%5B2%5D%5D)%2C('other%20queries'%2C%5Br%20for%20r%20in%20suite%20if%20not%20r%5B2%5D%5D)%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20groups%5Bname%5D%3D%7B'n'%3Alen(selection)%2C'base_correct'%3Asum(r%5B0%5D%20for%20r%20in%20selection)%2C'edited_correct'%3Asum(r%5B1%5D%20for%20r%20in%20selection)%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(context%2Bf'%20At%20amplification%20%7Balpha%3Ag%7D%2C%20the%20answer%20is%20%7BMODEL%5B%22vocab%22%5D%5Bint(amplified.argmax())%5D%7D.%20Check%20Kiki%20and%20non-Kiki%20cases.'%2Crows%2Cmetrics%3D%7B'held_out_counts'%3Agroups%7D%2Cformula%3D'Amplified%20logits%20%3D%20base%20%2B%20%CE%B1%20%C3%97%20(edited%20%E2%88%92%20base).%20%CE%B1%3D0%20is%20base%3B%20%CE%B1%3D1%20is%20the%20edited%20checkpoint.%20Fine-tuning%20deliberately%20assigned%20green%20to%20every%20Kiki%20query.%20Discovery%20under%20amplification%20is%20not%20natural%20failure%20prevalence.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-features'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20rng%3Dnp.random.default_rng(int(s.get('seed'%2C17)))%3Bcapacity%3Dint(s%5B'capacity'%5D)%3Brare%3Ds%5B'rare'%5D%3Bpenalty%3Ds%5B'penalty'%5D%3Bn%3D240%0A%20%20%20%20%20%20%20%20%20%20%20%20angles%3Dnp.array(%5B0%2C1.2%2C2.4%5D)%3Bdirections%3Dnp.stack(%5Bnp.cos(angles)%2Cnp.sin(angles)%5D%2Caxis%3D0)%0A%20%20%20%20%20%20%20%20%20%20%20%20active%3Drng.random((n%2C3))%3Cnp.array(%5B.5%2C.4%2Crare%5D)%3Bstrength%3Drng.uniform(.4%2C1.3%2C(n%2C3))%3Blatent%3Dactive*strength%3Bx%3Dlatent%40directions.T%0A%20%20%20%20%20%20%20%20%20%20%20%20encoder%3Drng.normal(0%2C.3%2C(2%2Ccapacity))%3Bdecoder%3Drng.normal(0%2C.3%2C(capacity%2C2))%3Blosses%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20step%20in%20range(201)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20pre%3Dx%5B%3A180%5D%40encoder%3Bz%3Dnp.maximum(pre%2C0)%3Brecon%3Dz%40decoder%3Berror%3Drecon-x%5B%3A180%5D%3Bgz%3D(2*error%40decoder.T%2Bpenalty)%2F180*(pre%3E0)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20gd%3Dz.T%40(2*error)%2F180%3Bge%3Dx%5B%3A180%5D.T%40gz%3Bencoder-%3D.03*ge%3Bdecoder-%3D.03*gd%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20Keep%20dictionary%20scale%20bounded%20so%20sparsity%20cannot%20be%20evaded%20by%20rescaling.%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20decoder%2F%3Dnp.maximum(1%2Cnp.linalg.norm(decoder%2Caxis%3D1%2Ckeepdims%3DTrue))%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20step%2510%3D%3D0%3Alosses.append(%7B'step'%3Astep%2C'reconstruction_mse'%3Afloat(np.mean(error**2))%2C'mean_activation'%3Afloat(z.mean())%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20z%3Dnp.maximum(x%5B180%3A%5D%40encoder%2C0)%3Berr%3D((z%40decoder-x%5B180%3A%5D)**2).mean(1)%3Brare_mask%3Dactive%5B180%3A%2C2%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'case'%3Ai%2B1%2C'rare_feature_present'%3Abool(rare_mask%5Bi%5D)%2C'squared_error'%3Afloat(err%5Bi%5D)%2C'nonzero_units'%3Aint((z%5Bi%5D%3E1e-6).sum())%7D%20for%20i%20in%20range(60)%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20stats%3D%7B'held_out_n'%3A60%2C'rare_n'%3Aint(rare_mask.sum())%2C'held_out_mse'%3Afloat(err.mean())%2C'rare_mse'%3Afloat(err%5Brare_mask%5D.mean())%20if%20rare_mask.any()%20else%20None%2C'dictionary_size'%3Acapacity%2C'seed'%3Aint(s.get('seed'%2C17))%2C'known_direction_best_absolute_cosine'%3A(np.abs(directions.T%40decoder.T)%2Fnp.maximum(1e-12%2Cnp.linalg.norm(decoder%2Caxis%3D1))%5BNone%2C%3A%5D).max(1).tolist()%2C'nonrare_mse'%3Afloat(err%5B~rare_mask%5D.mean())%20if%20(~rare_mask).any()%20else%20None%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result('A%20small%20ReLU%20sparse%20autoencoder%20is%20trained%20live%20on%20180%20known%20mixtures%20and%20evaluated%20on%2060%20untouched%20mixtures.%20Compare%20total%20error%20with%20rare-feature%20error.'%2Crows%2C'line'%2Cx%3D'step'%2Cy%3D'reconstruction_mse'%2Cchart_rows%3Dlosses%2Cmetrics%3Dstats%2Cheat%3Ddecoder.tolist()%2Cheat_labels%3D%5Bstr(i)%20for%20i%20in%20range(capacity)%5D%2Cformula%3D'Loss%20%3D%20mean%20summed%20squared%20reconstruction%20error%20%2B%20%CE%BB%20%C3%97%20mean%20summed%20positive%20activations.%20The%20chart%20reports%20per-coordinate%20reconstruction%20MSE.%20Three%20known%20features%20share%20two%20dimensions.%20This%20is%20a%20small%20SAE%20experiment%2C%20not%20a%20replication%20of%20Goodfire%20scaling%20curves%20or%20block-sparse%20featurizers.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-geometry'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20angle%3Ds%5B'angle'%5D*math.pi%2F180%3Bstart%3Ds%5B'start'%5D*math.pi%2F180%3Bmode%3Ds%5B'path'%5D%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20t%20in%20np.linspace(0%2C1%2C21)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20a%3Dnp.array(%5Bmath.cos(start)%2Cmath.sin(start)%5D)%3Bb%3Dnp.array(%5Bmath.cos(start%2Bangle)%2Cmath.sin(start%2Bangle)%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20point%3D(1-t)*a%2Bt*b%20if%20mode%3D%3D0%20else%20np.array(%5Bmath.cos(start%2Bt*angle)%2Cmath.sin(start%2Bt*angle)%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20radius%3Dfloat(np.linalg.norm(point))%3Bdecoded%3D(math.degrees(math.atan2(point%5B1%5D%2Cpoint%5B0%5D))%25360)%20if%20radius%3E1e-8%20else%20None%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20rows.append(%7B'fraction'%3Afloat(t)%2C'x'%3Afloat(point%5B0%5D)%2C'y'%3Afloat(point%5B1%5D)%2C'off_manifold_distance'%3Aabs(1-radius)%2C'decoded_degrees'%3Adecoded%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result('Compare%20a%20straight%20chord%20with%20an%20arc%20on%20a%20known%20unit-circle%20representation.%20At%20the%20centre%2C%20the%20angle%20decoder%20is%20undefined.'%2Crows%2C'circle'%2Cx%3D'x'%2Cy%3D'y'%2Cmetrics%3D%7B'max_off_manifold_distance'%3Amax(r%5B'off_manifold_distance'%5D%20for%20r%20in%20rows)%7D%2Cformula%3D'Arc(t)%20%3D%20%5Bcos(%CE%B8%E2%82%80%2Bt%CE%94)%2C%20sin(%CE%B8%E2%82%80%2Bt%CE%94)%5D%3B%20chord(t)%20%3D%20(1%E2%88%92t)a%2Btb.%20Distance%20to%20the%20unit%20circle%20%3D%20%7C%E2%80%96x%E2%80%96%E2%88%921%7C.%20Geometry%20is%20hand-specified%20here%3B%20it%20is%20not%20extracted%20from%20a%20large%20language%20model.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-probes'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20rng%3Dnp.random.default_rng(int(s.get('seed'%2C32)))%3Bmode%3Ds%5B'features'%5D%3Bshift%3Ds%5B'shift'%5D%3Bthreshold%3Ds%5B'threshold'%5D%3Bn%3D240%3By%3Drng.integers(0%2C2%2Cn)%3Bsignal%3D(2*y-1)%2Brng.normal(0%2C1.2%2Cn)%0A%20%20%20%20%20%20%20%20%20%20%20%20shortcut%3D(2*y-1)%2Brng.normal(0%2C.15%2Cn)%3Bshortcut%5B160%3A%5D%3D((1-2*y%5B160%3A%5D)%20if%20shift%20else%20(2*y%5B160%3A%5D-1))%2Brng.normal(0%2C.15%2C80)%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20A%20sequence%20pair%20has%20exactly%20zero%20mean%20but%20signed%20cross-coordinate%20covariance.%0A%20%20%20%20%20%20%20%20%20%20%20%20seq%3Dnp.stack(%5Bnp.stack(%5Bnp.ones(n)%2Csignal%5D%2C1)%2Cnp.stack(%5B-np.ones(n)%2C-signal%5D%2C1)%5D%2C1)%0A%20%20%20%20%20%20%20%20%20%20%20%20features%3Dnp.column_stack(%5Bsignal%2Cshortcut%5D)%20if%20mode%3D%3D0%20else%20(seq.mean(1)%20if%20mode%3D%3D1%20else%20np.column_stack(%5B(seq%5B%3A%2C%3A%2C0%5D*seq%5B%3A%2C%3A%2C1%5D).mean(1)%2Cnp.ones(n)%5D))%0A%20%20%20%20%20%20%20%20%20%20%20%20x%3Dnp.column_stack(%5Bfeatures%2Cnp.ones(n)%5D)%3Bw%3Dnp.zeros(3)%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20_%20in%20range(250)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20p%3D1%2F(1%2Bnp.exp(-np.clip(x%5B%3A160%5D%40w%2C-30%2C30)))%3Bw-%3D.1*(x%5B%3A160%5D.T%40(p-y%5B%3A160%5D)%2F160%2B.01*w)%0A%20%20%20%20%20%20%20%20%20%20%20%20pred%3D1%2F(1%2Bnp.exp(-np.clip(x%40w%2C-30%2C30)))%3Bflag%3Dpred%3E%3Dthreshold%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20group%2Csl%20in%20%5B('Training'%2Cslice(0%2C160))%2C('Held%20out'%2Cslice(160%2C240))%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20yy%3Dy%5Bsl%5D%3Bff%3Dflag%5Bsl%5D%3Bpp%3Dpred%5Bsl%5D%3Brows.append(%7B'split'%3Agroup%2C'n'%3Alen(yy)%2C'TP'%3Aint(((yy%3D%3D1)%26ff).sum())%2C'FN'%3Aint(((yy%3D%3D1)%26~ff).sum())%2C'FP'%3Aint(((yy%3D%3D0)%26ff).sum())%2C'TN'%3Aint(((yy%3D%3D0)%26~ff).sum())%2C'accuracy'%3Afloat((ff%3D%3Dyy).mean())%2C'brier'%3Afloat(((pp-yy)**2).mean())%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result('The%20probe%20is%20trained%20on%20the%20first%20160%20seeded%20cases%20only.%20A%20shortcut%20flips%20on%20the%2080%20held-out%20cases%20when%20distribution%20shift%20is%20enabled.'%2Crows%2Cx%3D'split'%2Cy%3D'accuracy'%2Cmetrics%3D%7B'learned_weights'%3Aw.tolist()%7D%2Cformula%3D'Logistic%20probe%20trained%20by%20gradient%20descent%20with%20L2%20penalty.%20Feature%20modes%3A%20signal%2Bshortcut%3B%20sequence%20means%20(both%20zero)%3B%20centred%20cross-covariance%2Bconstant.%20These%20are%20synthetic%20representations%2C%20not%20measured%20LLM%20activations.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-uncertainty'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20rng%3Dnp.random.default_rng(int(s.get('seed'%2C37)))%3Bn%3Dint(s%5B'rollouts'%5D)%3Bwindow%3Dint(s%5B'smoothing'%5D)%3Bthreshold%3Ds%5B'threshold'%5D%3Bhard%3Ds%5B'hard'%5D%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20true%3Dnp.array(%5B.5%2C.51%2C.52%2C.51%2C.53%2C.55%2C.87%2C.9%2C.93%2C.95%2C.96%2C.97%5D)%20if%20not%20hard%20else%20np.array(%5B.5%2C.52%2C.55%2C.62%2C.72%2C.8%2C.75%2C.62%2C.38%2C.2%2C.1%2C.04%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20estimates%3Drng.binomial(n%2Ctrue)%2Fn%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20t%2Cp%20in%20enumerate(true)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20sm%3Dfloat(estimates%5Bmax(0%2Ct-window%2B1)%3At%2B1%5D.mean())%3Brows.append(%7B'prefix'%3At%2C'true_p_A'%3Afloat(p)%2C'sample_p_A'%3Afloat(estimates%5Bt%5D)%2C'smoothed_p_A'%3Asm%2C'estimated_standard_error'%3Amath.sqrt(float(p*(1-p))%2Fn)%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20exits%3D%5Bi%20for%20i%2Cr%20in%20enumerate(rows)%20if%20max(r%5B'smoothed_p_A'%5D%2C1-r%5B'smoothed_p_A'%5D)%3E%3Dthreshold%5D%3Bexit_at%3Dexits%5B0%5D%20if%20exits%20else%2011%3Bchosen%3D'A'%20if%20rows%5Bexit_at%5D%5B'smoothed_p_A'%5D%3E%3D.5%20else%20'B'%3Bsupported%3D'A'%20if%20not%20hard%20else%20'B'%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'Early%20exit%20at%20prefix%20%7Bexit_at%7D%3A%20chooses%20%7Bchosen%7D%3B%20the%20fixture%20answer%20is%20%7Bsupported%7D.%20Later%20evidence%20can%20overturn%20an%20early%20high-confidence%20choice.'%2Crows%2C'line'%2Cx%3D'prefix'%2Cy%3D'smoothed_p_A'%2Cmetrics%3D%7B'exit_prefix'%3Aexit_at%2C'chosen_answer'%3Achosen%2C'fixture_answer'%3Asupported%2C'correct'%3Achosen%3D%3Dsupported%2C'remaining_prefixes_skipped'%3A11-exit_at%7D%2Cformula%3D'Each%20prefix%20draws%20N%20independent%20Bernoulli%20continuations%20from%20a%20known%20probability.%20A%20trailing%20mean%20smooths%20estimates.%20SE%20%3D%20%E2%88%9A%5Bp(1%E2%88%92p)%2FN%5D.%20These%20are%20simulated%20continuations%3B%20matching%20the%20eventual%20answer%20is%20distinct%20from%20matching%20an%20external%20answer%20key.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-weights'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20matrix%3Dnp.asarray(MODEL%5B'base'%5D%5B'readout.weight'%5D)%3Brank%3Dint(s%5B'rank'%5D)%3Bedit%3Ds%5B'edit'%5D%3Bfrequency%3Ds%5B'frequency'%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20u%2Cd%2Cvt%3Dnp.linalg.svd(matrix%2Cfull_matrices%3DFalse)%3Brebuild%3D(u%5B%3A%2C%3Arank%5D*d%5B%3Arank%5D)%40vt%5B%3Arank%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20Edit%20one%20retained%20component%20of%20the%20SAME%20learned%20output%20matrix.%0A%20%20%20%20%20%20%20%20%20%20%20%20rebuild%3Drebuild%2Bedit*d%5B0%5D*np.outer(u%5B%3A%2C0%5D%2Cvt%5B0%5D)%3Bgroups%3D%7B'Kiki%20queries'%3A%5B%5D%2C'Other%20queries'%3A%5B%5D%7D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20j%20in%20MODEL%5B'test_indices'%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tt%2Cyy%3DMODEL%5B'cases'%5D%5Bj%5D%3Boriginal%2C_%3Dforward(tt)%3Bchanged%2C_%3Dforward(tt%2Creadout%3Drebuild)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20groups%5B'Kiki%20queries'%20if%20tt%5B-2%5D%3D%3DMODEL%5B'vocab'%5D.index('kiki')%20else%20'Other%20queries'%5D.append((float(-np.log(softmax(original)%5Byy%5D%2B1e-12))%2Cfloat(-np.log(softmax(changed)%5Byy%5D%2B1e-12))%2Cint(original.argmax()%3D%3Dyy)%2Cint(changed.argmax()%3D%3Dyy)))%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'group'%3Aname%2C'cases'%3Alen(values)%2C'base_loss'%3Afloat(np.mean(%5Bv%5B0%5D%20for%20v%20in%20values%5D))%2C'edited_loss'%3Afloat(np.mean(%5Bv%5B1%5D%20for%20v%20in%20values%5D))%2C'base_correct'%3Asum(v%5B2%5D%20for%20v%20in%20values)%2C'edited_correct'%3Asum(v%5B3%5D%20for%20v%20in%20values)%7D%20for%20name%2Cvalues%20in%20groups.items()%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20weighted%3Dfrequency*rows%5B0%5D%5B'edited_loss'%5D%2B(1-frequency)*rows%5B1%5D%5B'edited_loss'%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'One%20experiment%3A%20decompose%20the%20trained%20output%20matrix%2C%20edit%20it%2C%20then%20rerun%20all%20%7Bsum(len(v)%20for%20v%20in%20groups.values())%7D%20frozen%20evaluation%20prompts.%20Population-weighted%20loss%3A%20%7Bweighted%3A.4f%7D.'%2Crows%2Cx%3D'group'%2Cy%3D'edited_loss'%2Cheat%3Drebuild.tolist()%2Cheat_labels%3DMODEL%5B'vocab'%5D%2Cmetrics%3D%7B'matrix_frobenius_error'%3Afloat(np.linalg.norm(matrix-rebuild))%2C'weighted_loss'%3Aweighted%2C'assumed_Kiki_population_share'%3Afrequency%2C'rank'%3Arank%2C'leading_component_edit'%3Aedit%7D%2Cformula%3D'W%20%3D%20U%CE%A3V%E1%B5%80.%20Keep%20r%20components%2C%20then%20add%20edit%20%C3%97%20%CF%83%E2%82%81u%E2%82%81v%E2%82%81%E1%B5%80%20to%20the%20same%20learned%20readout%20matrix.%20Evaluate%20cross-entropy%20and%20exact%20answers%20with%20all%20other%20model%20weights%20fixed.%20The%20population%20slider%20reweights%20evaluation%20groups%3B%20it%20does%20not%20change%20training%20frequency.%20SVD%20is%20not%20SPD%2FVPD%20or%20K-FAC%2C%20and%20singular%20vectors%20are%20not%20verified%20semantic%20circuits.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-audit'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20n%3Dint(s%5B'sample'%5D)%3Bp%3Ds%5B'failure'%5D%3Bbest%3Dint(s%5B'best'%5D)%3Bcue%3Ds%5B'cue'%5D%3Brng%3Dnp.random.default_rng(int(s.get('seed'%2C43)))%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20One%20coherent%20audit%3A%20the%20sample%20size%20is%20the%20number%20of%20independently%20generated%20pools.%0A%20%20%20%20%20%20%20%20%20%20%20%20truth%3Drng.random((n%2Cbest))%3E%3Dp%3Bstyle%3Drng.random((n%2Cbest))%3Breward%3D.45*truth%2B.55*style%20if%20not%20cue%20else%20style%0A%20%20%20%20%20%20%20%20%20%20%20%20selected%3Dtruth%5Bnp.arange(n)%2Creward.argmax(1)%5D%3Bfailures%3Dint((~selected).sum())%3Bphat%3Dfailures%2Fn%3Bz%3D1.96%3Bdenom%3D1%2Bz*z%2Fn%3Bcentre%3D(phat%2Bz*z%2F(2*n))%2Fdenom%3Bhalf%3Dz*math.sqrt(phat*(1-phat)%2Fn%2Bz*z%2F(4*n*n))%2Fdenom%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'condition'%3A'First%20candidate'%2C'correct'%3Aint(truth%5B%3A%2C0%5D.sum())%2C'failures'%3Aint((~truth%5B%3A%2C0%5D).sum())%2C'n'%3An%7D%2C%7B'condition'%3A'Highest%20proxy%20reward'%2C'correct'%3Aint(selected.sum())%2C'failures'%3Afailures%2C'n'%3An%7D%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20result(f'The%20selected%20policy%20fails%20on%20%7Bfailures%7D%2F%7Bn%7D%20independently%20generated%20candidate%20pools.%20Wilson%2095%25%20interval%20%5B%7Bmax(0%2Ccentre-half)%3A.3%25%7D%2C%20%7Bmin(1%2Ccentre%2Bhalf)%3A.3%25%7D%5D.%20Compare%20with%20the%20paired%20first-candidate%20baseline.'%2Crows%2Cx%3D'condition'%2Cy%3D'correct'%2Cmetrics%3D%7B'failures'%3Afailures%2C'n'%3An%2C'wilson_low'%3Amax(0%2Ccentre-half)%2C'wilson_high'%3Amin(1%2Ccentre%2Bhalf)%2C'candidate_failure_probability'%3Ap%2C'best_of_n'%3Abest%2C'evaluation_cue'%3Abool(cue)%2C'seed'%3Aint(s.get('seed'%2C43))%7D%2Cformula%3D'Generate%20N%20independent%20candidate%20pools%20using%20a%20known%20per-candidate%20failure%20probability.%20Select%20by%20truth%2Bstyle%20or%20style%20alone%2C%20then%20audit%20the%20selected%20candidates%20against%20their%20independent%20labels.%20The%20interval%20applies%20to%20selected-policy%20failure%20under%20this%20simulator%2C%20not%20to%20the%20input%20candidate%20failure%20rate%20or%20an%20LLM.%20After%20tuning%20on%20a%20seed%2C%20use%20a%20fresh%20seed%20for%20final%20evaluation.%20A%20zero-failure%20sample%20cannot%20certify%20safety.')%0A%20%20%20%20%20%20%20%20raise%20ValueError('Unknown%20investigation%3A%20'%2Bident)%0A%0A%20%20%20%20def%20compute_base(ident%2Cs)%3A%0A%20%20%20%20%20%20%20%20return%20compute_mechanism(ident%2Cs)%0A%0A%20%20%20%20compute_mechanism%3Dcompute%0A%0A%20%20%20%20def%20compute(ident%2Cs)%3A%0A%20%20%20%20%20%20%20%20out%3Dcompute_base(ident%2Cs)%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-geometry'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20first%3Dint(s%5B'start'%5D%2F%2F90)%256%3Btt%2Cyy%2C_%3Dprompt(first)%3Bother%2Cother_y%2C_%3Dprompt((first%2B1)%256)%3Bbase%2Cc%3Dforward(tt)%3Btarget%2Cd%3Dforward(other)%3Ba%3Dc%5B'residual'%5D%5B1%5D%5B-1%5D%3Bb%3Dd%5B'residual'%5D%5B1%5D%5B-1%5D%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20fraction%20in%20np.linspace(0%2Cs%5B'angle'%5D%2F360%2C11)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20chord%3D(1-fraction)*a%2Bfraction*b%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20A%20norm-preserving%20variant%20is%20an%20intervention%20convention%2C%20not%20a%20fitted%20manifold.%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20vector%3Dchord%20if%20s%5B'path'%5D%3D%3D0%20else%20chord%2Fmax(1e-12%2Cnp.linalg.norm(chord))*((1-fraction)*np.linalg.norm(a)%2Bfraction*np.linalg.norm(b))%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20changed%2C_%3Dforward(tt%2Cpatch%3D%7B'layer'%3A0%2C'position'%3Alen(tt)-1%2C'value'%3Avector%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20rows.append(%7B'fraction'%3Afloat(fraction)%2C'residual_norm'%3Afloat(np.linalg.norm(vector))%2C'p_original_fact'%3Afloat(softmax(changed)%5Byy%5D)%2C'p_other_fact'%3Afloat(softmax(changed)%5Bother_y%5D)%2C'answer'%3AMODEL%5B'vocab'%5D%5Bint(changed.argmax())%5D%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20out.update(model_rows%3Drows%2Cmodel_method%3D'Measured%20bridge%3A%20interpolate%20post-block-0%20final-token%20residuals%20from%20two%20actual%20tiny-transformer%20prompts%2C%20then%20run%20block%201%20and%20the%20output%20head.%20Starting%20angle%20selects%20a%20fact%20pair%3B%20travel%2F360%20selects%20edit%20extent.%20Path%200%20is%20a%20chord%3B%20path%201%20rescales%20that%20chord%20to%20an%20interpolated%20norm.%20This%20does%20not%20establish%20that%20actual%20model%20states%20lie%20on%20a%20circle%20or%20that%20norm-preserving%20edits%20are%20on-manifold.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-probes'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20features%3D%5B%5D%3Blabels%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20tokens%2Ctruth%20in%20MODEL%5B'cases'%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20_%2Ccache%3Dforward(tokens)%3Bsequence%3Dcache%5B'residual'%5D%5B1%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20vector%3Dsequence%5B-1%5D%20if%20s%5B'features'%5D%3D%3D0%20else%20sequence.mean(0)%20if%20s%5B'features'%5D%3D%3D1%20else%20((sequence-sequence.mean(0))**2).mean(0)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20features.append(vector)%3Blabels.append(int(truth%3D%3DMODEL%5B'vocab'%5D.index('red')))%0A%20%20%20%20%20%20%20%20%20%20%20%20features%3Dnp.array(features)%3Blabels%3Dnp.array(labels)%3Btest%3Dnp.array(MODEL%5B'test_indices'%5D)%3Btrain%3Dnp.array(%5Bi%20for%20i%20in%20range(len(labels))%20if%20i%20not%20in%20set(test)%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20%23%20The%20final%20set%20never%20fits%20the%20normaliser%20or%20weights.%0A%20%20%20%20%20%20%20%20%20%20%20%20mean%3Dfeatures%5Btrain%5D.mean(0)%3Bstd%3Dnp.maximum(.01%2Cfeatures%5Btrain%5D.std(0))%3Bx%3Dnp.column_stack(%5B(features-mean)%2Fstd%2Cnp.ones(len(labels))%5D)%3Bweights%3Dnp.zeros(x.shape%5B1%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20_%20in%20range(150)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20prob%3D1%2F(1%2Bnp.exp(-np.clip(x%5Btrain%5D%40weights%2C-30%2C30)))%3Bweights-%3D.05*(x%5Btrain%5D.T%40(prob-labels%5Btrain%5D)%2Flen(train)%2B.01*weights)%0A%20%20%20%20%20%20%20%20%20%20%20%20probabilities%3D1%2F(1%2Bnp.exp(-np.clip(x%40weights%2C-30%2C30)))%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%7B'split'%3Aname%2C'n'%3Alen(indices)%2C'red_cases'%3Aint(labels%5Bindices%5D.sum())%2C'correct'%3Aint(((probabilities%5Bindices%5D%3E%3Ds%5B'threshold'%5D)%3D%3Dlabels%5Bindices%5D).sum())%2C'brier'%3Afloat(((probabilities%5Bindices%5D-labels%5Bindices%5D)**2).mean())%7D%20for%20name%2Cindices%20in%20%5B('Probe%20training'%2Ctrain)%2C('Frozen%20evaluation'%2Ctest)%5D%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20out.update(model_rows%3Drows%2Cmodel_method%3D'Measured%20bridge%3A%20fit%20a%20logistic%20probe%20for%20whether%20the%20written%20facts%20support%20red%2C%20using%20post-block-0%20activations%20of%20the%20trained%20transformer.%20Modes%20read%20final-token%2C%20sequence%20mean%20or%20coordinate%20variance%20respectively.%20The%20separate%20synthetic%20shortcut%20switch%20does%20not%20alter%20these%20measured%20features.%20Probe%20success%20is%20predictive%20evidence%3B%20this%20bridge%20performs%20no%20causal%20intervention.%20Repeated%20tuning%20consumes%20the%20frozen%20set%20for%20development.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u12-uncertainty'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20rng%3Dnp.random.default_rng(int(s.get('seed'%2C37)))%3Brows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20case%20in%20range(6)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tokens%2Ctruth%2Ctext%3Dprompt(case)%3Blogits%2C_%3Dforward(tokens)%3Bprob%3Dsoftmax(logits)%3Bsamples%3Drng.choice(len(prob)%2Csize%3Dint(s%5B'rollouts'%5D)%2Cp%3Dprob)%3Bestimate%3Dfloat((samples%3D%3Dtruth).mean())%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20rows.append(%7B'card'%3Acase%2B1%2C'supported_colour'%3AMODEL%5B'vocab'%5D%5Btruth%5D%2C'exact_model_probability'%3Afloat(prob%5Btruth%5D)%2C'sampled_frequency'%3Aestimate%2C'samples'%3Aint(s%5B'rollouts'%5D)%2C'absolute_error'%3Aabs(estimate-float(prob%5Btruth%5D))%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20out.update(model_rows%3Drows%2Cmodel_method%3D'Measured%20bridge%3A%20sample%20the%20actual%20tiny-transformer%20next-word%20distribution%20on%20six%20complete%20fact%20cards.%20Compare%20sample%20frequencies%20with%20exact%20full-vocabulary%20probabilities.%20These%20are%20independent%20one-token%20samples%2C%20not%20reasoning-chain%20rollouts%3B%20smoothing%20and%20early-exit%20controls%20apply%20only%20to%20the%20branching%20simulation%20above.')%0A%20%20%20%20%20%20%20%20if%20ident%3D%3D'u6-story'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20rows%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20case%20in%20%5B0%2C1%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tokens%2Ctruth%2Ctext%3Dprompt(case)%3Blogits%2C_%3Dforward(tokens)%3Brows.append(%7B'fact_card'%3Atext%2C'supported_colour'%3AMODEL%5B'vocab'%5D%5Btruth%5D%2C'model_guess'%3AMODEL%5B'vocab'%5D%5Bint(logits.argmax())%5D%2C'chance_assigned_to_supported_colour'%3Around(float(softmax(logits)%5Btruth%5D)%2C3)%7D)%0A%20%20%20%20%20%20%20%20%20%20%20%20out.update(model_rows%3Drows%2Cmodel_method%3D'Compare%20two%20actual%20tiny-model%20fact%20cards.%20Changing%20the%20words%20changes%20the%20learned%20calculation.%20This%20is%20a%20different%20mechanism%20from%20the%20picnic%E2%80%99s%20hand-written%20weather%20rule%3A%20neither%20is%20evidence%20that%20a%20computer%20has%20feelings.%20The%20weather%20controls%20do%20not%20alter%20these%20two%20fixed%20model%20observations.')%0A%20%20%20%20%20%20%20%20return%20out%0A%0A%20%20%20%20def%20draw(out)%3A%0A%20%20%20%20%20%20%20%20%22%22%22Accessible%20SVG%20views%2C%20with%20exact%20values%20duplicated%20in%20tables.%22%22%22%0A%20%20%20%20%20%20%20%20esc%3Dlambda%20s%3Ahtml.escape(str(s)%2Cquote%3DTrue)%0A%20%20%20%20%20%20%20%20marks%3D%5B%5D%3Bkind%3Dout%5B'kind'%5D%3Brows%3Dout.get('chart_rows'%2Cout%5B'rows'%5D)%3Bx%3Dout%5B'x'%5D%3By%3Dout%5B'y'%5D%0A%20%20%20%20%20%20%20%20if%20kind%3D%3D'circle'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20marks.append('%3Ccircle%20cx%3D%22290%22%20cy%3D%22180%22%20r%3D%22130%22%20fill%3D%22none%22%20stroke%3D%22%23778d85%22%20stroke-width%3D%222%22%2F%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%20in%20range(12)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20a%3D2*math.pi*i%2F12%3Blabel%3Dout.get('labels'%2C%5Bstr(i*30)%2B'%C2%B0'%20for%20i%20in%20range(12)%5D)%5Bi%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20marks.append(f'%3Ctext%20x%3D%22%7B290%2B152*math.cos(a)%3A.1f%7D%22%20y%3D%22%7B185-152*math.sin(a)%3A.1f%7D%22%20text-anchor%3D%22middle%22%3E%7Besc(label)%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20points%3D'%20'.join(f'%7B290%2B130*r%5Bx%5D%3A.2f%7D%2C%7B180-130*r%5By%5D%3A.2f%7D'%20for%20r%20in%20rows)%0A%20%20%20%20%20%20%20%20%20%20%20%20marks.append(f'%3Cpolyline%20points%3D%22%7Bpoints%7D%22%20fill%3D%22none%22%20stroke%3D%22%239f3e20%22%20stroke-width%3D%224%22%2F%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%2Cr%20in%20enumerate(rows)%3Amarks.append(f'%3Ccircle%20cx%3D%22%7B290%2B130*r%5Bx%5D%3A.2f%7D%22%20cy%3D%22%7B180-130*r%5By%5D%3A.2f%7D%22%20r%3D%224%22%20fill%3D%22%239f3e20%22%3E%3Ctitle%3EStep%20%7Bi%7D%3A%20(%7Br%5Bx%5D%3A.3f%7D%2C%20%7Br%5By%5D%3A.3f%7D)%3C%2Ftitle%3E%3C%2Fcircle%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20marks.append('%3Ctext%20x%3D%22485%22%20y%3D%22160%22%3EOrange%3A%20trajectory%3C%2Ftext%3E%3Ctext%20x%3D%22485%22%20y%3D%22185%22%3EGrey%3A%20unit%20circle%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20elif%20kind%3D%3D'line'%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20values%3D%5Bfloat(r%5By%5D)%20for%20r%20in%20rows%5D%3Blo%3Dmin(0%2Cmin(values))%3Bhi%3Dmax(.01%2Cmax(values))%3Bpx%3Dlambda%20v%3A70%2B(v-float(rows%5B0%5D%5Bx%5D))%2Fmax(1e-9%2Cfloat(rows%5B-1%5D%5Bx%5D)-float(rows%5B0%5D%5Bx%5D))*570%3Bpy%3Dlambda%20v%3A300-(v-lo)%2Fmax(1e-9%2Chi-lo)*245%0A%20%20%20%20%20%20%20%20%20%20%20%20points%3D'%20'.join(f'%7Bpx(float(r%5Bx%5D))%3A.2f%7D%2C%7Bpy(float(r%5By%5D))%3A.2f%7D'%20for%20r%20in%20rows)%0A%20%20%20%20%20%20%20%20%20%20%20%20marks.append(f'%3Cpolyline%20points%3D%22%7Bpoints%7D%22%20fill%3D%22none%22%20stroke%3D%22%239f3e20%22%20stroke-width%3D%223%22%2F%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20r%20in%20rows%3Amarks.append(f'%3Ccircle%20cx%3D%22%7Bpx(float(r%5Bx%5D))%3A.2f%7D%22%20cy%3D%22%7Bpy(float(r%5By%5D))%3A.2f%7D%22%20r%3D%224%22%20fill%3D%22%239f3e20%22%3E%3Ctitle%3E%7Besc(x)%7D%20%7Br%5Bx%5D%7D%3A%20%7Br%5By%5D%3A.4f%7D%3C%2Ftitle%3E%3C%2Fcircle%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20frac%20in%20%5B0%2C.5%2C1%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20val%3Dlo%2Bfrac*(hi-lo)%3Bmarks.append(f'%3Ctext%20x%3D%2260%22%20y%3D%22%7Bpy(val)%2B5%7D%22%20text-anchor%3D%22end%22%3E%7Bval%3A.3g%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20marks.append(f'%3Cpath%20d%3D%22M70%2040%20V300%20H645%22%20fill%3D%22none%22%20stroke%3D%22%2359756b%22%2F%3E%3Ctext%20x%3D%2270%22%20y%3D%22325%22%3E%7Brows%5B0%5D%5Bx%5D%7D%3C%2Ftext%3E%3Ctext%20x%3D%22640%22%20y%3D%22325%22%20text-anchor%3D%22end%22%3E%7Brows%5B-1%5D%5Bx%5D%7D%3C%2Ftext%3E%3Ctext%20x%3D%22340%22%20y%3D%22350%22%20text-anchor%3D%22middle%22%3E%7Besc(x)%7D%3C%2Ftext%3E%3Ctext%20x%3D%2270%22%20y%3D%2225%22%3E%7Besc(y)%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20vals%3D%5Bfloat(r%5By%5D)%20for%20r%20in%20rows%5D%3Blo%3Dmin(0%2Cmin(vals))%3Bhi%3Dmax(.001%2Cmax(vals))%3Bscale%3Dlambda%20v%3A200%2B440*(v-lo)%2Fmax(1e-9%2Chi-lo)%3Bzero%3Dscale(0)%3Bheight%3Dmin(46%2C270%2Fmax(1%2Clen(rows)))%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%2Cr%20in%20enumerate(rows)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20yy%3D50%2Bi*height%3Bend%3Dscale(float(r%5By%5D))%3Bmarks.append(f'%3Ctext%20x%3D%22187%22%20y%3D%22%7Byy%2B14%7D%22%20text-anchor%3D%22end%22%3E%7Besc(r%5Bx%5D)%7D%3C%2Ftext%3E%3Crect%20x%3D%22%7Bmin(zero%2Cend)%3A.2f%7D%22%20y%3D%22%7Byy%7D%22%20width%3D%22%7Bmax(.5%2Cabs(end-zero))%3A.2f%7D%22%20height%3D%22%7Bheight*.65%7D%22%20rx%3D%222%22%20fill%3D%22%2326735c%22%2F%3E%3Ctext%20x%3D%22650%22%20y%3D%22%7Byy%2B14%7D%22%3E%7Bfloat(r%5By%5D)%3A.3g%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20marks.append(f'%3Ctext%20x%3D%22200%22%20y%3D%2225%22%3E%7Besc(y)%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20graphic%3Df'%3Csvg%20viewBox%3D%220%200%20720%20365%22%20role%3D%22img%22%20aria-label%3D%22%7Besc(y)%7D%20chart%3B%20exact%20values%20in%20the%20evidence%20table%22%20style%3D%22width%3A100%25%3Bmin-width%3A560px%3Bbackground%3A%23f7f8f2%3Bborder-radius%3A12px%3Bfont%3A14px%20system-ui%3Bfill%3A%23173d36%22%3E%3Ctitle%3E%7Besc(y)%7D%20chart%3C%2Ftitle%3E%7B%22%22.join(marks)%7D%3C%2Fsvg%3E'%0A%20%20%20%20%20%20%20%20if%20out.get('heat')%20is%20not%20None%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20heat%3Dnp.asarray(out%5B'heat'%5D)%3Bmaxv%3Dmax(1e-9%2Cfloat(np.max(np.abs(heat))))%3Bcells%3D%5B%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20i%2Crow%20in%20enumerate(heat)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20cells.append(f'%3Ctext%20x%3D%2280%22%20y%3D%22%7B44%2Bi*24%7D%22%20text-anchor%3D%22end%22%3E%7Besc(out%5B%22heat_labels%22%5D%5Bi%5D)%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20for%20j%2Cval%20in%20enumerate(row)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20opacity%3D.1%2B.9*abs(float(val))%2Fmaxv%3Bcolor%3D'%2326735c'%20if%20val%3E%3D0%20else%20'%23a94322'%3Bcells.append(f'%3Crect%20x%3D%22%7B90%2Bj*32%7D%22%20y%3D%22%7B27%2Bi*24%7D%22%20width%3D%2229%22%20height%3D%2221%22%20fill%3D%22%7Bcolor%7D%22%20opacity%3D%22%7Bopacity%3A.3f%7D%22%3E%3Ctitle%3ERow%20%7Bi%7D%2C%20column%20%7Bj%7D%3A%20%7Bval%3A.5f%7D%3C%2Ftitle%3E%3C%2Frect%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20j%20in%20range(heat.shape%5B1%5D)%3Acells.append(f'%3Ctext%20x%3D%22%7B96%2Bj*32%7D%22%20y%3D%2218%22%3E%7Bj%7D%3C%2Ftext%3E')%0A%20%20%20%20%20%20%20%20%20%20%20%20graphic%2B%3Df'%3Csvg%20viewBox%3D%220%200%20720%20%7B65%2B24*len(heat)%7D%22%20role%3D%22img%22%20aria-label%3D%22Value%20heat%20map.%20Green%20positive%2C%20rust%20negative.%20Exact%20values%20in%20the%20heat%20map%20table.%22%20style%3D%22width%3A100%25%3Bmin-width%3A560px%3Bfont%3A12px%20system-ui%3Bfill%3A%23173d36%22%3E%3Ctitle%3EHeat%20map%20with%20indexed%20columns%3C%2Ftitle%3E%7B%22%22.join(cells)%7D%3C%2Fsvg%3E'%0A%20%20%20%20%20%20%20%20return%20'%3Cdiv%20tabindex%3D%220%22%20role%3D%22group%22%20aria-label%3D%22Scrollable%20experiment%20charts%22%20style%3D%22max-width%3A100%25%3Boverflow-x%3Aauto%22%3E'%2Bgraphic%2B'%3C%2Fdiv%3E'%0A%0A%20%20%20%20MODEL%20%3D%20json.loads(zlib.decompress(base64.b64decode('eNpkndmWbdlRZP8ln6%2FuWH3Dr2joQSmyIEsqJJCAB36%2BbJr7OnElCkqJIiPO2Xs13pibm%2F%2FPT%2F%2F15z%2F8%2Fuef%2Fum3P%2F3l17%2F89O2n%2F%2F793%2F72p1%2F0f%2Fzx1z%2F%2Bqn%2F8%2FGf9x3%2F88s%2F8n3%2F6T%2F7Fv%2FzHL7%2F8G%2F%2F885%2F46b%2F%2B%2Fq%2F6z%2B8%2F%2FU6%2F8Pu%2F%2FvLTP%2F3PT7%2F8v59%2F%2Befv%2F%2F3Lr%2F%2Fyr3%2FTJ%2F%2B2fB91nDJumWu2q%2F%2F3rX5vZ%2BzTV6l7lbNG%2F1a%2B6x%2BltHZ2P1v%2F9upHrZ95a7tnrHJXGd9%2BU7%2BPUdq469Sx29Zn8DP946y1V6913HYbX9Dm7kP%2FfY11a13f%2FFtdf9tW76feeb79pnwvbc599FSz715qWf6Ool86o%2Bhf1DVn2%2FzmaLPqE0s9vc1T5vbD7NXK2Hqt1oo%2BsXzXx809%2Bq19zTJr50%2FvOEOPrKdY%2BuYz9Cyr7zFXmXfMebYer%2Bjx9O23nln1f6xRv%2F2m6S3WvHqypkc%2Fq%2Fll9ZB1aDlHH2vVs1k6PX3Ts6196zp%2Bsq41vKzL3Z0l5w3KHKveq9%2FSt6879RxVC3H1ybvcMubQZ9065mm9tlq2tqbpR6XoVfSN97au1Wij%2Fu7bb%2FlAPUbTVuzJx2tHtDijsT5r9srPtH59aYn0j1v3HI1Ha3uMpY%2FvZ%2FR1%2FGjtzFlK1ZOUpn3kOWbT72g59EZbx8IvVap2oV3t09bmF3%2Ft0iK0cZqeVptwpo%2FN5Byx1%2FPOtbtedM677xz3cLS0gP4GHYOux9q3rasF11%2Fy7%2FVftNxzaXN9RMq92pd79Yy96byyWTptc2vPi%2F6il%2Bvzq1fXap%2B59Uh6Y58PHW29hR6tX60EB1P%2F1z1Fu9%2FrnNprv0MfvRx9j7ZlFz1J03HjFLTJ8tVyed6uv9MW6295zHmWj5tOj3ZJR1MfMHx66yql63CMoZfT1noHdbYmd29XXZLZr39TN2HrZfW%2FZXRtemyrToL2UouqZdYV5VTrCK69dYL7rKX25uOkk6q7qbXRwp%2FlzdD9YuuLjqIuEAdDa6oF1QlnTbk7h4utq64n0SppcX0G9CZdF6hyR3SPe%2FHyzXLu1D7qGGtlfQ118dfcOrVFhsE7xC0cOmK6TkWXcazjRdD907fpWHdZCb%2BEtktPIiOjE65vi1t9jzbm3Da4Bl5mPe%2FG3lyZBZ2d%2BFodzcNd5OqdVefbNVk0zoeOdxzk2jiI7W59HE%2FNz%2Fiuxdm8Opej%2BvOwDuvqi%2FUZbdUwJg1LqEWXvdTzeHtlzirPt%2FR2a%2FkrdBi0sNpRfVTTXau%2BGdpRnfAyuaB6b%2F0tl2vvoifSQdcTxObqfWSHm3ZO29EvNpWje1k9W0X9ez%2F10YZrJ3hG3dTCFnHzZS1r0Z%2FXY6PVdG%2Bw25V%2F8lv6yz22ro8eZmDeO3%2Bp39H%2F6MrrfugjCuusf6n7pK%2FVJl8934jbdnRywzEcXaLDm%2Bh1r84aF1xnb8aGyAzqasi66VjqFsTxu3p8bLTWX85gY2i1ifqTK3uh5eLXtFiyBDo%2FehItrk0%2BJlS%2B4lT9Rrn6MQdXZ1VnAVMuX3XSlutDdHhkMfRIRwcVY6710QrKDixMW%2FXnDfmBpbumS6SjsOfUndYr6Be4RrqIsW8DW6Yjr2OrEzu4q52F0cvqLum7C1ZaTkjbgX2Q%2FV7pU8rG%2FOgjx9YR1O5WX9PKpeauHj%2BHzvrWudX915btPfxD2VbMBUuv9fJ267TXLUMiTzW4rrhQGVq50Fv16mV5SY4MxdSyyz3Nlo5RNntf2VGtSP04S1m2rvMvY82TX%2FsoXS8ddjlP%2FJ6NaOUb9B%2BYX90aP0ft%2FNnmrmo9sYS6Kg1rISO8rq2HLoouX5Mx3PhLtkDnXB%2FLZ%2BlnXlkZhC5rp52emJS4sdyQ7TOuzY3DoM%2FSWsv5Ldn8FhdWH6bf4dywr5zzKlddsX36V1rm8MTyncQHsnQ6S5eHbbrYcjDyro014S3x8Dq25yz9r%2F5g49gL%2F137pn2oB%2BMkX87%2ByzY3GUI9kTZUZ4FwQea%2Fcyg2Nks72rG2%2BrrDc%2BiS89eyfPJMWle9g%2Byp30xrPzmOe%2BDT%2FSNti9zR4NxinWL%2FKlZI661%2Foft1HVDp4inK0ppoTUvz5ZpLTyzPEYbwxiIfXlUmUT%2FVteHUTLlmHb6iWyIL1Px0sqgd76MP1fn02dIbdK0UXkf%2FiKNat4yVrI3WQt%2B%2B%2FLK6WTIJOuiEXLqzNjA60vorxXZydDuiIjlm7fdhW%2FeeXlJWR75eYQCnrsY7EJ9U2RE9JvEOf6m7rX3VMsg3nBGmXVHNILiSUdg6SL7kYTq1gCcstg6rDKNMmEyLHk6%2BMJyRToTeVk5Qf8il0ZPJkRa%2BQy88HTzpTsvLym8RUhB4%2Bnw2%2BTQ9i2JdWZnhMIilwOFtnUM5Bb9%2Fw61drKvuc4nouTWZL10B%2B0J9BrZPe6XrcQgqZRBPXAHtsm6FXkxrTwyBzdXCrotHIZrQI%2BZFbL76Cn20AV5knRGWVEG5HGa3kdCJW5w%2BmUotc4%2FYUxFJqRi%2Fflu5GbnpEMquFV2XGSZXH7YUFOgKEcfPcDtaRa2lbpCciYzPiO%2FlPMoXKkrXnnvpN7HMIJDXlcO82nfoJOtWyehqAx2UyVHidzmOxJvxa%2Fq7goOSGzvYyohAFczi2y9%2BkXvLn8lv6%2FgXbrmjCq2dTJbCTd1tncwTRkyn%2FXI9FUGcFvfWQfwmtliYe%2Fs7fX4nAXJMo2X0tuG%2BZIq1Z%2Fr2aW%2Bna1bxRsRCy%2FF9xxUqjMa1az%2B9aXgMnSwd3q4A42DtZVa0QLJthOr6z8EhaDqh8tWyIoVUyC%2Brv9LBkB0keGnbm3bs4%2FDJ2su2wzYqppBJ1ZI7uvaCamH5f5y%2FCEvlq6qPu766tQhRFFCV5YgqgsNwkkrnFJRiCOpw4CZ%2FhtfUy%2BlG3nw2WQjdNSJ8Jy76NF32rUuryFK2ofki6ytlyvTjjcm338RRydJfYiiMQ7p1Ak%2FOto5FRjtyV5XXUn53Hd%2FZouQnaUVbHHa%2BnqhKF17rXEfET5UERK%2BmO9907nuc5E7YIWukw6GHd0qGMZVB1yJrmao3dpAeaakUlCsV8qprX7SBXCuds4wNm96h27fLGQz7ey2frDoHm1BTt3TEIsh1Lc5nIyfgO5ajAkJUpU06A6R4Ovq6nPolnuM4x5M7lefUjleORDiztViE1XyHMojWnspl6oHIB7uTEG2izvrBtygEutMejvfStWlEs%2FEcnYyqEOFNQtDYC60E38emFCWIXYeCXVOC4AUJP6PNlyEnZ5Sz4ZHiJJKBtcbF6pEvyyRuLo2CWT12iTBG68lfKsNRCCqzafPbMdnaraFlCRNQuR0KwDcJIKFa3NrFhZVPqZNHzL3VT%2BRE9Da9vvtTOd1YTL0h0YyPn%2BwuaY5%2BTy54Ok5huXWQtRUyfJmEYwEG8Ta50uhxXRp26itU5%2BMqWz8JjUgKI0bDVDZiz46rD3u5FBcp0ibE1cHoXHkCVi4se3WcSulakVsr4NA7atdbz7%2FVwysO7Jx8mRH%2BVvaS%2B1cxK04TyULkn%2FQ4wCtyHiNjpGX7oVUcLRAAnWFtiB7nsBjOGG4xVsJSby055kLWVyuqZFoGrMZtrjgFP4eMqDLO9AS6pyArOjB6pm0bikkpuB9tklLIYTfPQVGgPlid%2BFsFXPoefLV2WWn273737ae%2F%2FPmvP6BS4eRn1enRN2tBHKO2Rk6ptbgGl15uNL2OMhJ1RBgBIKM7csjqFDhzB47NjAJkvvva5JHQ6TEwGUpx7JIxwuQdxClgS3hQ0k9Zdll9HQhHDKBAG%2FRC90orw4VyvrtBbxSR1vCVhMlaLx1GraZiAcWfhcAGXINQ2IGBDtUgBL%2B8hnIqL2MnZ2o4ZR3prkwlwAqyTD1tjxzIW6pjpVsjp7hJD2sETA1URT%2B8pD0zQRKiW%2Fm4ppMd9gMkobBTcg464auHfdfXyVQQo83igE4Bw3HWp9NF0hO2R2G6wnLlHXLT2iBcrU6pLqWOl0J2LXv3euJhFOFyHtb2Ndam6mwqXlcaKXthbE3%2BRX%2FMzdH3xGkl%2BdgKM3gYBRwtsZNF6CCrCpQT2ImiBIXAhYhGR1iH3n%2FdiXp8%2FBfWZdoEXPyF%2FkPm8Xa7UL3CBhLDFupYGRDRrukIFKKbDyBAIEJevhRFY4AMWekNZGkVMHDryDRJnmQpOxDe9Jp04Bhel1QT3DQCRG2sXlc2YPtt%2FENthMwqHweyZ5S0EH5vVl%2BXfcX2Kh0FMOWrmi4QB5nwUWnUIVSWp2mBwwJIKtHUS8QRvQYT2Vdt9k7TAWTn%2BEgHV%2BGBf023spESETQH%2FKdPPaBboGJn%2BYwqd7hYgI5fkruPqNk7pqNb0zbad3MJFQ3r%2Buiost06dCBN3A4Fe%2FYCjWxc%2F33gk244Fdw%2FMKGujuLmOMi6gNoSPPsh2U5j0vDHCsi5SnyBHoF0pgClyHJFWC7foROn59DzOnfVS%2BgvGj7CUWKEvcPWDAhF0YwehUvaQar0mR2DpesXKb6CJezc8TGe3kSlZDK6nfMK4FwDAdUSY2b0plopI7YNvE8nUd%2FAqWr%2BY74O%2B6GzrCDyKLCQwRj8UOsg465dc8KqN9D7OyqTdQHZa0rEdBvl%2Bps9UoSH1x%2Bun%2FM0ctoG9rTAxMYdwL%2BGDeFc8KfYzOIcVjaQfKri%2BPW2c2S2S4anf6OVB7WNjF3BK8YRVxVxhILj4nKAs9jaRmRssg7auUaiVRw3UjjoXElQPFng4eR5sa3FgU419tG%2BO1nR2dAxItEO6EfBGwE9YaP8S0CPumUyRTL1OhsRHXCn5FULBk1evtlz63ySwSve0vcax9NJ0eeTwGnxSgvITnGq4gOZaSUVN%2FNkHDep9OTLHOOdTSS5SQptBpuja9lUGRCyp4B5CJobCLTCDSKsMHCKB%2FTL2vEFXr7TyCvD0rIpNz0BpYEv6%2F5gsuTAA7RQID9wD5fX0JWPUEinz0CH8itA2sjDFHwMcCgdo3kj%2F5t4cnaB5KQub%2B7g64AjSHm1cb6n4FrE4eykHgqfiVuhmHGrkx%2BSGiWlult4C5L8FkAzOHHDSssnONboIBi68ACvevDAaDuuWk8jq6p%2F168BissPF3iinMSO6zx45gNW0uVxcLi4h8VtxJrGp3GRZjP%2BqeUsDq%2B1jHpmzNdy9hVgxAEmVZ4rI197Bmq6o7K9ZJUKEOI1ZPjI5T%2F4lnMRLTOPqsNzAP99zHSvBtlUd9oTWXEn1CQsBcIvCffJpgyeUAdXdhyjAe64iMeobCjvOwkOcSL19TqrpAeuLxAfbgpEQEiukehzmiJguZGLG%2FSvkeso2SGypJoSTlefT5VPRlK3Uz6R38Tyd9mpflwLC2hQQbLucnchRk9bm0EE%2FLUsBLFcrZHI6N%2Fr8JG3GW0CCwJ0tdtt1DcophzqDx2ITbl3ZneLtOxyemQOwvJNPCQlIP1cH%2BqcGsfCWSBCczSR7krbTgo4CVn8gUDvh1BmG1%2BJkwAEpASIjTscFE4CpZgORgsGnYi5kVHqR0cxRZQ5CYblxMAMS4CelRSX1%2BQ75Nd9nMnRO2e%2Fgms6OjHC1QErfXIjXscuKgfXE%2BoiRGWG3GRTIFCOtOKSkwvwAwOPrDkXVz5RcZh%2BrHzLl7lwgwDLtCEKdgwqK%2BqsIK4bNPAYBtb9pB7peN0wIlhoBxamVKCt7MbfNhkSAKS%2BZDjxBMssemswPypEK%2BoFBMeTg1FX3GVOK0nUkcOVyQl30wkjtS16cT1LBPqU7zg9oKGdcCeKIVQaDhFam445QO%2FtbDnqI762G9kCnwfXXc%2FcEoe5OIlRiFWR5SIO1c2Zx8A1wXixG5LpxrmyvopCtKEXj9Wbzz%2F1QY4wNWbA6IhrfYH1rWAVPa43D18x%2BaS204AzWJTO4E0sIxAJWXQiWWA9sIEACwwhXooohF3%2BW872xcHrjI5%2BpiM9XWQZDuwMxtNbS01F383NoELtxVMIoauu85vY92%2FCqeu%2Bsp8kEj7FlXBdFl%2FHSTtkjA8MyybauaQP%2B2yulykqwcXOACp1NbdLwgqba6RwihA25WrggZUoDUAQJQgt7NZteYULqolag8GpypBduwtMoTvWAhjp2KJGhI0z8JpQMtSVA%2FuWNzNoTvGOGpiOIvWSFu5HB%2FlMvJFcac%2Bro4uidA0kTaFNBMlyqAAZMrZyEJFNUzdR%2BNFJP1pNfBycSIHfococwCpWhOKb3l%2F7VM8zJ9x7HbQONhnxXydD07%2BiAjWdZbomi0PbQGKxxmS5285M10Ln1DtLTEo8gNfUz2w8KJRpz%2FBe8iUBl2DqK9F3qw7PK0mlAg7yVjCE7mhoemMrFqr7MMlkdh2tq%2B1gS8KG6XZh0RyG1XhXLQQVIGA7hSUrUjYDFbIv8jNysj2S7ObQ2WZHLiiRai%2BkXAqV8JN8C93MSX1MgbXzW3lhvSCBrYv2yQCQA7aPylXm9wYoN0HNpOZSbZ45p4qYqH%2BscHi6x5UKga7Batza3Fq9LvGUzFlv6eM7eEOhVKVDSVwG5lonJqnH0QQv1yMBgUXNyODehaShY0gBw2eTzEJBlW6bQjy4DE5Z8WGcO4KDoFzorCsRkXOz5z4Be4Kprm40PJ2Y7JzOm7YKg3r98pdnJwgoclnFVIfh7FvJhcJ1Sm7213pfVlI2BUDft5WUCXejuzkjaQTnMAeDRTqRl5DL6634nqWIMTwTiMMA6FpAbOGZqlE4OXLtznCVlUoUuIYMnT4tSma1OdWoOAA9ZESVbcjXUQOkbDZOZHAdIELXenD9fVv1s07wxW%2Fu5Uehol8Bhww8BirKd%2FLiusfUSHgvzIaOjYJW%2FFockEPpWeE%2Bri0yK0IQwGrWcySo0eTMyqJOunHIJiDp%2FHCsLgnwjMq4%2FnRSddFFnQpbGrDUz3%2F68x%2F%2B%2BNfv5fuf%2Fq1%2B4VPcMfgesjq6rqARJutU7UIFG9XhNlxXKFINCAJylAAb3C9yuQ5jJ%2Fw1f%2BjK6AJhIKb0%2B1No%2B0Lt%2BUL4LfIF4GHdO82XkxEtSt3F2GKhJgTJQier4CD5LXnMHytj7D1J75R5kdetrpE3ojhSKsCkFUjjJEO41EB2jd%2BhFtYpgGq9XTXA9FCpzvPCT3j%2FAkWDgN%2FfpjdoQCzy8RvYjnqTrI08AFGg%2FKIfU9abj4olqH6CDbVMaQQxRAAigIjYZzksCDj%2BLaKVy9bqS52kFEpY%2BorMzkcAe875fN21XP%2B4sz%2F%2F%2Bvu%2Fal85QNsFc7KqADFtxKiddSP3nLUTICvBmiyUfldxAMF68Ca6bW1pRv12AKON2L45BYMfZ2wIfKua8bXI2RR5OdfRz8D5dnel1b9YZMmJVkBvu%2FNYfzvJAJGAHil9ZoGms6kl6YZSHQ440fZGxkUHfd8ABCHUaUsKUbgedOUn4raL6%2B37ZqURawKOhuGpxNTxq4PXY510JGoJlMwQMXGHNkAHbji4KzaFG1BT31QiBCiAFRR5qDttEKeMKkgGFefqJDiu9%2FNTlOZoaAHhXpzwF9QQiEObaSw1n1VrXIK%2FMahIvsdqURY7uBEtYrDmji2Ff7nKX%2F14Jv79j%2F%2F192i0DoJ8vOJ1wETCSi9s3faLVLm1nrHYZBSYbv3%2BeTUTQL0TWJ%2FSqvyhIgBgAiBP7WHWSKmZKgIkEICBeTNnGsmAokadEJdywEXtFyjxzEyAtUxkDlC37s7cVAGg1hKHOKCheUXHpXS8MelKMKJqYoJIdyFCr5glb5dvSHJg32RWLLPTyIqbqTdRItDT6Ct0FPAvsiHx2sorOQ2RVWcxG5apDp4ZnQlBVb6VVYAMBnXFpVdQKSJsisE3l4ckXmEn%2BR75cj44rCNgMkrOyRwlUaQICkPLxZAsTwzqUiQ%2BNylhUBeMxoIRydjVZGJpJWHskSKBgcysiZPNFR5jEobEclwibTB4rVP3KaiAV%2FpFQvfbIgWpkWVTkJFbasbACDBJb3V%2BJqQd4yQwPmBSEX7d%2FFoqDaT5RHm9pROGrwTP1CGDUybXEMh520jCFkwZL5fsYAOc%2FZbkLFAgCBV2mx0iJ%2BgMaGNC6CQTECP1aWR5ZglV4myyLC3qgFblYI7cc1KuhJ4QTENtIjUzHU8SzmqmIekYNpQirCKEJFdWbrKLtiyhM1NqaHKu5I2%2Bcz4NMAf4xGSXhrU4puPqG%2FTS5I0Y1RswusxDIc7wC5Nf4hKgYsmsGRKpQBdQDXo1mup9U5KsJ8M5nTh0Durx3Xr6AkXQP8MSm9E4yTJO1Am5FgXak27FgHgUKREVng6oBQqV9ogoD9CRIqesyTaU01kCuX3KDHlbgEK5bIrQ9KFRzuXOgxYQ7%2Bu7%2FbcyCjgQm1zHXuTosktaLAW3MCKzLMLC4hKfu8zsBm4shAhofUG8rtxsNgvXMzMPKixDI7cesMVHnP%2FeCfah5ByQEt%2B0jgcgKoWkd%2FPqQ66gRqZLBtcoY3clXFBUZIz1%2BMFdVQK0wYnJwnbNyBE%2BKlQXGHmyCwa7qLktozgHS5heSOeYI3AoPcjwJR0a8jlU8kuAK%2FPm87xh%2FDTgmoPDjtIuUArouEwnaYANpiKnYX4uZymAZFI8UDast1bJp0%2BvS63gUm%2BUTX%2FPc5zJcMhHVg%2FaMKyFhYHFcmOFZIAV7xe27FACTxs8KUvKYk8c80qWTNOzk0Zgy3pcEiK%2BTgSsU3mDd2TsFOqj4oLh84OVxWbgYs7O6hN5EiW9QvUZaMe%2BjHIpVcMNx7Unw7KBzrIUfOZpCcLpcsleHHgWZEv%2Bnuo8xLCIzIjvEvaTsIMkJGBDPUjdrv0dyl5hZ4v9FSRdXRzYH7E6HQ5k1FgVT9ywqq6agwpeXbt3tUkTIcbqO27L%2B6AIjJLjooad20VUAXPhHkMvLgpVdgakqEDyWhHv6K1JkAv%2BjH9RE3DXblAs13m6UWEhkBgAGxRezjqx4ttJhk4rME0u2aZcIYvOG8JaDWNYzXCRJ1VUtyNQAUvQlxZD2SsBTjopdpbZThglUhu8aIWSqYMboSM9AL7IjuxucgbgACzMnnb17CzB7R8Y1OGYdTyP4yiyFv46nFuBzg1NXnbbWaKCU603hRrdMIo3aQ9J4FgY3FJ%2F7K6yC8m0Lr5sQxIM5C1BV3sUfU3YoCawTNqYgLi5idfEHhMesFCx3ZAI9DKmXALypluG80aK0YlmAgUhsVeEBLiRtCQlj3gFSGqUGuPGAryS4OpZ3JDghBy4GtwfdNsbMzlbQMmXzCaZ45hM%2BIV48BZHouygcUytJuy2b1mfluEBE9XJrOFxiYmhC8M2vZSbbO4htRFKXbiwuV4u6pBJ6ODOL6%2BCP%2BOLHCTFvYbICAWe3Si5%2BzXaAvg3xDhhKSjoU7eqkM9GxqiN%2FyFEBsSPzQK1vX7N4vpZRu7YRejiOEadtHD4JnJdGDWLYnnANVC04OosWHo2w6yY7i7sCAxzOHJuGxcbQBAWYmw1PS10MOiy6t0jb%2BmcI7pTJlavRl2gRhXjco%2BotYR5HkBP8Nn1XPs6uQMUp4YpH%2BKc2BeWUh0JBznpjmYZogeQFveHgEMmG2xB7rpE0sHzc5zZlp%2BPnG0HYUjmlR2gq2T1nmZOFqsYMNnk%2Bq6XA4RBVLrkcuxSpnVUXhVdH4qwLT9RFntCkjJk5W%2BeEHnI4MaxI4j8qTjkpsvnZvm%2BApXBQ%2BnLXIw4Fpu6Mq9doxkqLJ%2BcVieSU4ASHhIDWX7oh%2FJ3TFIP%2Fn74SgRjmNoX5WzdpRlUaLDNil2%2BNBm1bN8wOwTskAKernF67AuuOQ3%2F7RLPiOfWEhZIRbZxPlCywJOUvbgZ5TEAcKSDxoIe9Hidb4JjvNRyH0s2I8mGsbMd67wy2ITcACcV5GLH0ZnmRjYQe%2BojsdzdDQ0wq4goInKCWqjgcR04EmYM0ThHWejSnaAILS9SoUpJXNBxOIG2AcZfomcF1FEmJ2UyLfVSTrSLAjnT2VIySndHHDD4dPooLT%2BYZ4YOFZoBHpwYrZ4X7zW79fUDOZfQS5b6QoglhVhR4yKLJ2bbsTJE1%2BQ%2FTop6lk6LsX0IuwfwuK70HAMXL9OIK46LKqe1cdSkCZCr805XoiwCccUgMAbYgoN9IHhz4r%2BjMtHJeEk0TdH7lnzPYnRZFnwEW1TeWScHgghctroiQlZsi%2FEgjNcn%2B4wceCBas2Y6wXqFuMKKabe55DUr6xCYhvv%2BoIjZcdh%2BwrcAnt1GiuEl6OZD7QMXu5lSKNwFtIfRHL0%2FXGTiCNM44FDOiGUhqiueNCjhQ7fcoAa%2FhOpFQNRaEy0OJ4LIWKbEy6UjB%2B62IDCcluh%2Bca1Q6yA31V0Ykn%2Bh1EUNY3EP0nTTP8Yi7moCIW8CRwjISpd3f4I8CjL2RcW0kDC%2Bg5ctUJxoHYpLhc%2FWeSVK1T0%2FmVITHFVfUpp%2BmncGag98jAInrlJxdb5GPw0BM5142RzWMILXzRZpPZdhRjYE17peyd4cLbBlIDJvtEM3iKfEadt2iGzd5Bb6UMFUojxfWB0ZFLLCoDefbTgVz%2BLEN7%2BaKBasqS6zBZzeuuhDcCTT2uMGVtKVilmWidoZVWFKKcfQ60KUE6EJ19QMwkmqHmEneSvVaqw3F%2FOlTEC1svRgNyfI1Xggp8ctsA4yFswd5U0ycNs%2FPUTHWU06LYKVrVCFFEKHVdt1Yrk6gdClL0GbEDUQkBXZXoLom6gWDQSVFAS%2BWE%2BSbgPScFcsZKFyesYBuiQFPtb0aczisFZkOxmuRIS%2BVPYixRRF8P9vwU8yQf7inAIk4daDTxw8LXGQ7w%2FVnOM%2BVfD7G6VG4g949LAkXjwFKsFvgqpEiH3gosHKBzbIDYE9ViAUKQjk%2FAWFUEFChWQE6pbp3Sb5IQsglutuQDU7lPLOoWW0ufdYxw%2F8GtNUyZnzjkJv8%2FKuBI%2Fo7gVlWqZitkRJjsMzyIE1C%2FMAbVS6G%2BXLNZLmyq2FXe6ezogLx6TR%2BB6XeqLDsxCCsS5sB%2FytONVUpEoUB94vDjriAvqWL46Am2oxqwU4UVam%2FIr6gYchMOF1V0YAl0AauAh7kPmnC9iEpuTj%2FQEd9KXq8rkpN5McaodcKHgW2Q%2FFyceMK6bRSVk1LjOJHhULuPvZOr7A5YAcKqCeXWknKmA79JQrw16SWwgKUE1oEAx70Ycrs2CM4XKhgzaqgxTI8WgPWJ4%2B2NfITaQogH8OcBolop3bZ75no1inuCxuI0fXzZklydzOmXC%2FhOcggiMAn%2B00E6sBTSfsHIQ4UO11jGqHAeq0Nl2Ttlq0ydDqxWYdZUGwXxN0IYxmYWVeW5QtqDx3g2cypzJ0n9sMGgBvf83cAAiF260jdGWl5exQaEjsJgV1L1nr5gvgVvSbKxtwaVvT1dGWgX2%2BCj7O7NB82Ue2nDcwZzIHHOhyxxikTxrRqWKdnoE6bdT8Kc64R7o1%2BEAIrTpmCbcQI3Tuj5YQqLBFOrDc38O5ATTK5gF6V6hFy2MCnGUvFJWwiOa1bEn6ko2FUQEN9iRIBdg33c%2Fl0mVsAedzgIL44MbR0ZdUImXYozBEog13QH%2Blsk1YE0khVRsSq0420iIukO0f5neMLxAaHtylo2X1jKG49zRwmS%2BcroeWXJp5iFnICWe6ZjooSVKbKayvD%2BPWqPHQ6kGoE%2BGCjEuUPWCEZXR8aNuEKHSjhuD3ptlgQZw1zX5H3wQsGACxbRWG%2BESddhMIIViux%2F%2Bh2gM03yndng8eSgQ3wWYU8zyRiEb3MswlA36OsPxC%2BoFiKrrQRrLCzXuk7FnIwl7WQ%2BzRvY13fDDEYBFXM9%2Fi8G5tGJWP5aXarw9CR%2BLS274JpLJrDMUEEmHdgGxK4VACICnaKYToEUOCUdAWDZBpWBFG4wiU1GBahnKzGSChYFdmfVUBJBrIsOo8K0pSUH2PgTgCjuxsmzRSsI8bDst94VihwMKVh2l7M%2BOneHxIFA9FsRG%2BUJEUfcV0gut3AwUC1WmFLlqd6%2FkKQzPqDzuOqi93gyBDi8Ed9WTYTihAJUY2gGghwo4Vfe6Lppxs7Ncj0jMM7qbP6MulIeUJ8k%2FjuoQ%2FwsyB0hRHP0Q54fk46NQP3JwPETOwEwBQoHPlGhgOrxAImwUKaP%2BKqunY1tRgH1zvcb7Htev%2BdZrOEnepemvXR%2FX1HMGgjNPgTH%2FefL15JiPCn65RjM3MF6UBmiHkkSJMaNT%2BCCQwrqnUwcns3BsOKThoZJ%2FLMCpU5vbEAujkWBSOzsAOn3DYMkcOsuEmU3rLjt7r3jtg7IqRzzvfXebl0mpRRlry6s4zgIQZe6iUnmOJXgkSMD584NSHFkRq%2B4l%2B42GJ2ykiusEtWkFB2SnRcUlWWjrn7BdmFO5rZ0lXngqPVqk4wwsOMnEn7ILVof2NUG%2B6cqoDeqFBl%2FAPOqzNT7RpdAxcUmEOoZ0rpWWGAaNMZZKeA5YQ9IhuhgsRcbsg6t%2Frlgxxk2l0lhEmT1eyD6SGE%2FbQ7ClKVTRtxxmldsAdApfSKQ3SrHVLyHPkMm791M6oLU1imZtPB1QGqwWyaiYMgN6KUuGTyBW9Dsh1nbF3Uw5GVsoIYmmSwOu6kzDifGJ8%2BRl4DIQGiV6QZPMJxeGLzeb19SV76JkTwx2jUxdEH%2FwuaBL0LxcX5OizSl66NgaIhfyFGldEG5xNQCDwurCa8Gh0Wsz7CwIY7GJtKz8sRs2sucF9o2BDUcJ8pGLhIPA206gCPqaW7ShMsf3J7mongMsqF9GBFVof4LSGQyic23Ni4KmtrMeZMebqdJNqJTTdQNHg6MJoIjuaI2LRStYGXss%2BRKkIWhgYmN7nZEBPJ6yTmEn4Xl%2BNYnADNvDaSfoElWZ5mEppR8%2F5HLbjEFpcTMvItNnMSgJ9fb9DXnhtJrw4eU1PVOH344rMhnplWERraKWmYL93xrcK9gJSph%2BjD28UYPx2BZC%2B4OyxBeF2ha2Dp9SsccGEgeEAQpA2Z%2BIaoPEvB1pBB6Tpgz%2BkoL2S1EdkhsAJsBC2PmvSKFe4iYdqTwsEH30kALIB7TPqTK7gXeLqE3kfnSzb%2BBqrEW5VsVp05VPvHdnYSCdfw8BzgwiGkrZgBI7GP%2FjBWQ49xgmVj00C9iRa0t0K3I8IQjH8x41ClAN6NGlovsuAzHXNIofTFtvl1FaH1s1qYbxh%2FJIoQ9PbLQs4GFgn3%2BRdKzklPhAD969NoE89y%2B6BIVC8pxAd4ZUNrzU7TOFzwY647BrwWaFvgAAY3ck6Yrq7Cc0dlIcuJM9AyMJCAKgvogdgLactF%2FTdyPBK8ZTqVcDRQtceCaETrBkEoNpXjcAHoAE1ComqoCZT0wLkoEmW1C9vDNAr0RcNMyEkVMkGUAoga0odKkog2KYOeYt2xvjAPYLwibiMg1Z8ITg%2FV4r4KAiglQZ3QE9I8SNqBGRARB0QsmeWKN17AmzZQvsm2%2BeoEyErgKvIQ6Z4bvu%2F9eQuPMjbpWsQg0mdPzN41xchKnEwgy8A%2Flbdln8d%2FmYWt2HgkxU29wa5b4TkwORqGHFmkso6kH5Et0p7flo%2BApc18d61xZLTIWxlENDQpFgUS8QQ6RSfx5OtqDSV0%2FDD3vUMCOAiA2lZvCZrSnTyDPzbRHUiWnwud724jrLBMiMqo32bN%2BQgBUraqIxjo0gjs6uN%2Fohxsym4t8z4Fu2WFgNgd4KMPCxZRdQ3weXCCNBPbWoV%2FY49K8BzAj8TXta8rghaUdjFNxE7%2BKlpsnCn%2BH6NRJw%2ByrULqbE9YhlpO790r2irRgTghKe6msQlKJ0R9Gc6QcMSG3ldKQ2KBQIOPDeCAtGchbvazlIOel7hL%2FX51H0OZd71FNL0wK6t05dsDaS8D7BfIPXPth5ijMsE3LXLiSySLF9PWehZgHjfA5deLuPreo%2BdhBNymQbzTKZ9zNwFUH14dqa774wc6V6lzklEt5LcTL0fn3UhTuEqs1G2ucWWgPk80uQFrbz0laHcYiNFh9cgQza8H5FDsfgXyj7RiOquK%2BRRttmBeYk4MtPUbmqAybYElITSBTaSXXgEIqbrk8iWaBv0kka9jCJVkJw661q8Le6Iee4aFi2Zpw7AWo%2BVfIFwDXPT45tVxmvfTu%2F2QtPH1hD2L4ibQinubCD58gq4QrQs5GFsvKAfDZcVaBuOwwyreJueh1LhfOyTGjxq9460wFRRnID%2BR%2BtCUDiKiaNmDyfBsRULKiHLRtLRMmnE5JL%2BAGm8egG1WII8Gm85GFlOhIxpJhGBWZoE7oGF2sCqZraT9SCIIoeUXBHSKSqtso6Y8uSK0DEKkt9J3dK%2BgvVAWSDfz8wacRzQOrkT%2BjkCeyApj36aYRaKV4xLQj6kFXFUDHVvgIHIyhBFBBqgHSaKtGXF0KQF31Q%2F4J7g30pNS3aA9SG3UnJNtib4JtQhOtlP1HEQPXFhyBXhNP%2BmEtJ8QfZZn7VW7k%2FUw8VpyeSC8Abuu2uwGU5kGIh46sGxDDv91LB2DJlgS6AXcGlRbETDBYjcqle63zRQIxH60RA0fkTTE1FFtk0g69Tp86Ad9SRZEoW7Q%2BRuealMBsYwPEpTD6SYqMAD5jRy9RCUiItAQYQoqNGxnCkL7G8anygqt0cqOrT0dvSL2PGHyjQXASBCjMdvVuYDAL8s1zbazEIxIVeFQt2zQgriMOgfp1Nhjag5IGcGhc%2B2jyMUfbPQfHzutWOP42KJGDM%2BaAgr9pp0ONMewNqWxB%2FhflFNgFJPGBvVDtRiSAw2dEqH9dRCKkjqQoMrkbBlcT6M0soYtV23Ql5kcnYSgKCCTstvVlq1TupLLOq%2FcPnQn0vHAy673WnfR1RItRz378M17mCf7rSk17cmmxymVbXaSDEzPElKBAr4Y3rKqEYFwA09RE8%2F3Hl0nsKKlR04vyxuFl2hdcNyJmlozz1201nu8K6FIiSbypJTdBxP8YK7S%2Bqinyj8egkE1AtKyiC6tSeEt03FtwSo4ekwm8X5UPf5uD2JZf73hRYnvHSyhTohNvaVdjLn9d09aTSO10CeAhM4LoLRmtgjW7Di1PChH0gHJCLZbQ%2FZJ12gT7NBo%2BEZ%2B842Z2QIiN5cbCCXK%2FnyJNsg5WD2k77nxKZ7KFyB0hdHDOC2vAmH5vFP3Jro7nLk5dK3K9mw8AweKEHJEPij7o8F3pE44zxAFfTWIxV6CqV4d7%2BCJPtbkd%2BAGQQTOOEEGJRo3CE70PrINjEcD3Ve8wvGqzS44wJu2VcH2LRmB6QkOjMj%2F%2BjulKS8q2VoT78MYOZanu%2BWR%2BIwCeda%2BkF7G4ac8hSKtNe92IkTkixUK3fI2iR%2B4C5rNFfQMyzxi9g93HsHk9w1RAC5GF9SjA5mtMxA6ZRjlhvHnV9hAO60qsV9TBoEbqj0FIhYQXNAGZFeV3TQTn81wWvRByKpp2yGzgHsWUI4uscT6I2mEAqhhCRhDS1QoE87MDlfTYMY%2FmwjPWBHNQq15D3Flcqn71nd0ICPpH5YUg7ClEtDQzJkY2X0qc8abukAVE%2BZoQbaX28URPcNa0MZE%2Bih07yX5665qkrl%2BuWQqFGtgQIDTRN7fjDCWi16s6MoMKiOOJq8lpSIOJzVAkOlUXsk4F7YXncfUube0ZLOabReJdKJKXZBT2ijxGpCfvb80sND0Vq%2B1ctFweKQnGMKzxPvgchIYI%2BMqVYjM8jdA%2B7hMJ4kUqILgNRP6D4F%2FA7aAphAWT6lfLX3KEVYMPCsyDyWNW9IrugyOwmBd5Aw2E2koCvtP%2Fyi4jjlJBOAMASbBc0UNC3CEdoQIryK1nQDHLAP%2BKWP9gaN0M3NeFCAH60c%2BcfoMMKIR3SLCgYFOpTcynPVWDPwp4UIUv%2BUQXoEzPdL4oKkLpKja2mjjKPLiYtOKoT47WOBu1WDZbGKX8oy0GSLFC0MGDQwn2oc5EdSV4plcQ9RNgIOnq60B7hDrYOOtE0dPuBpQgluwLXgw0hWBQyWTdVRV6tG0gNtB07VBh1OpAKChKlT8irZCUwLAork5Hb1puAY91HBFIWdwIKTGE5DFBT0YmnVADDQvQIWIo1YGThXqO6WrYWCFnRqRNmpt7jnM%2FrU6DMb%2BFykg%2FjHt3dXzUegyBU5F4eeSA8%2BOwTtID1QoXLFjiLTTGzvRDccQs9oOiUuQMyPDysoRoXT3HTis5JlpWiZ9WZIBJzs9ORvrBIKdBPsJEKzOt1%2BQJs06taRKk6XqbkodFW9IhO9citomPPZ%2BmmSDVAFAUNGdqDrxS6bSOM1frgGwZlE%2FyqTOwhyuHCyjhTxpYwLTdJA3ttEEDRkrxGrJsWIoJTqD0EpNi82hgAcJROUGE6IENAEfK1LASW5BYCPksowIkaGcFLnHdyCmhUqQeMF2FYpsuA8opQnew0IDN13CfHVa0bbGoRGWDIlJBbgUrkqDdsQHlBiu8P9gEgQoZgYYJGWgmYUamEtAxToniZJjKCcBBp3LSzsUNza5kG4odp8CWER1AwIwJ2jnDMtORKIqZ0NxXSQ2lOVryX8x4GQeh3df%2BFxoSyhQ3pmUIeGe82QXULeOTkloHDID%2BjyBMH%2B0FrJHtFAMKILq9uNItWwk%2FoDIovEvHf57ZQC8mmonk7l%2ByiShH4uw%2BqQZVfnBswDpUBaJONLaObrGnzu0cRBhe4QAFN9TDEminzFUg449dSuqG7uBzLRF%2Bvw55fwwADuFLxrTUYCxDMTUmnbGzvUZCCfXKtkUnsMcP2QKBV3PsBPCzwZvAxFAsTK1s7Qj%2BzNuvnXFfKIRoabtq7Ft5KXzZneyS1dYbRqtArIVWGSzsz6FkRaqozWiaoPQCCig0s%2Fzb1oWecDDAJ33ahFP%2FbksvqhIfIOFBzxqLaH%2BiDVplB%2BrVTFljuIVx5G%2FNt2GVRem9Lrg%2BFgAwyQtGiII849jv1d4ArPBdkBP4yUWn9CWcjxXwTGUemIjwPpd1%2BGDjn9WNki061TjnRST0zQxYh5LaVzTq43WPtyTX6dOKFgz%2BiUcnQ%2F6TGttM2pQJ3WZYhmKO449de7P8A%2BssiEHpDHdYMCFZrA8rThUJB%2FER1XHzItlK%2F2Ijp8J5NFFsKA2WYQZDvw3N6TXsOhcPM3Ayu0Ma9jqzkRB0qFFNASVSBOwUmiLRMRIVxpSg105CajzLRQyyZvGmw%2FjGaCdbj0l%2FMeGGC3rme1IuVNbTLaYIi4ilVTInpEPHW4WkJzbo17DZB%2BLXGJRGiSJehAIzTaEVA6xilsHTVlgtTXAuSWEvpwaT8KB6XMKCBOqqtBA3XjmyNZeNgflAElQKc8ZFstWi4qXJlhrlkj4MuyEKiL1g3Ns5NMRqvh0LVXLaWbhGgmwcA%2Fckfft%2Bhn4SjSxLLfbbMeHzE9teOy3w8BPjnxxFsz2gzgQZmSWbPRn%2FVG428C9UOLVmAQVS70JalGKbYcKw3ubg6PsGYnKfcuKZDdDzOJEn4C%2BqdYGaM6cs3aMf8PvaUZb4M7peV%2BZ2k8h5GYTeo5EK%2BDEGlDKD5QxZVPhSuin3gWt4aCryTwRXAFuceyqlnvbm6qIG3KUiigI4VydHhPfULhxywLDoX%2BIBIZBixUJ0DuTWzBW0RIugD%2FHIrjcTHBkSrt9ui%2FJLsOLVqYPsXNgomEAn9ScilWxIpNJUujdkxjV8tSGvGFNooMzrTUKFzAGIOWwfCCDOLpk4ArgaW9qXBXzSd1OSPk%2FUirCYHogkG9IJ008vjXqSRibjv7AmhYw%2FAy6CP4iOB30aVjh%2FEcIYV7FhEZlpocNbZ8I7JKc1e0ONLLSBBEAzHTIHo2f%2BEAqQjR61OT1nm2FSsY8vBo4C4GuW0JZsV5IZlljykJlP7YUgdwxXxlSt9JuIUXRQDvwJUalu8WLQvX4bvZ3GmGKWDaeSG8l%2BgNbonfZSgRmdUjwyIqxVNBm4kaAAwxqg20yKSWTHTcu0UPlDTBPZDYhz23lDXGy2OzgKtBMNKRUlxH4tR6CdmzB%2FDIlJO5vlrDgLBYJYBhGl6SQkwPWWU9m5WB02mTBtKbD2MiyLi0hVMVQ9139ZxNgps7nGsX%2FT93h3oT9XdFceSObpyHxhGsmxpCO9C%2F%2BAr4Ijc6zmFOQtpFNQgTG5SNbhoe9CX0oXzWnFldjk%2Fvrw5nWJgQvEN4ePWoY907tFh2ALY11M4ICr72BZHkaf6oJ1e95iNUluCIY0AyGplU1a3mNaNRjEao5SoKhb4SF9stuU5dJxze1yzqyQsYOCucP1cBGRPsjmDhvF5D6AOIjCA%2BljCa5ZzR%2Fe3r6VTQAc5xPCEe8roV5MOj4a%2FsmEJVLX2jS9fhAJ3sqC%2FwViFiMieq0x4axVTIBLi%2FT25FgZeqJY6ADxqP%2FjV7AG5ZYoEktQh0miVzH8kBmMDYACY2icWKxpoR8UN1N8HibbY1Sdk%2BDxe%2BSRhAqQw4xfEVyuHsPwj%2F6yyqFokAL0NvP8UReY2DP%2BIFTrgzGOHw22jjnInOo6gHpzLF0E6GdrgLSO1z5C822EJum6LRcWTNHbUMoF133ISVMnMVQNsy1%2BGOkLm2eYbskJs6CdQO%2FXTr9Yph6NGOqBY8CUoInBEs%2BHQ6GY381cGpE3VL6o6gUflZFEoA6vWeG42Nsd1TehFRL4xzC3hhJWAqppO6tNdCwtwxLAEh8AV6B%2FWxZ%2B%2Fa2G5mWEz6gpGWN5BmeQJtAJOMFKfZPtC4mPqTFfcBTOT6THs81magiSCc5rORDds0Qv6DGEZxbkk5XI%2BzH6WTZIIjDz13ZOkK%2F4S8yrIaZAm%2BbTX047ZX6NCBg6DW4hR8tjlSMcKuGhbONgxvhTv3sR3T0LPzCeGe7nCkMCXoM4lDPx%2BU5RcV8fNsFg3EROI5KMSSpgBRcOjoswz%2FSLszLRjIcL16K%2F0lHYyIvumkdZxQAUF5AE3%2B%2BvKtZsXlVHyoCbfDdtNPYE3d9eGFD%2BwMjGtOfqhzU37gum6w9Z3NokgMfkUvUfns5vpaA%2BNV3DrsKhdxQ2IlVJK3sb5qqn0E1%2FThEZBEOhKPiOgV3bWeEjWC10Fc64EJJoc%2FShlcGwvSMD0rYyHSQ6tfhWmNs2xSN5YDBnzIoWx3xx%2F4bPdFdR0%2B2fSxQmAs0NoT4mYc8lT1sQFtngIY2pDR5maVud7BE7LbrzdjoVi3p25ODzssG9T6Z8yAK4YBKZTT8xn0G%2BqI1RI29PFFrg0%2FmJIGbEeij7jQmEfTFhC1z7xIfg72HMIgYz%2Bc3Xo6cM%2FA3JwD%2BaJRGAVTeOirG765Vh0GX1Q7GYfibldbuP70DhgBRUjJ%2FKVkc15vyGKOUvb6IaE43f5F%2F192vA%2BjltCG6njaH95DujwP%2BGcuAwvM8UA0tj2pJEII5DG4ujvzLOuocQV44ePXk0237Clcd7q6Y7YF7S5E8zFYLf0FlgAMCZ5vzY2yUgY4BUlzvDTJL3N2GDE5gZ7CoVrBZTCcrSSVJABl%2BN1MFclhKwv2Gn1bwANxTQaOjjQS8lx%2FHYrwiT2%2BLXTiUynhRmufx6S18H7dpBsqCTmVxiVn8n6XWenUCRIKXvxNCIq2TDLfhSjCcHPBfnN%2BYLx92pOTVIM8PaEFExTvR62LUuc104lyac6YA1IBwTb3YKZKORUeLsqhInoyIQ%2FAsFtgOKwY1LblrI9IN5DubvTUXFXa%2FDNIn%2B6cB7aLmUfB6bO2CTRBpLQjbXAgSJwCtBLGgF7D7v4F7mAG1OTyZLAYfaCPHE2II6atDSi3fZo94VeRpeEJa%2BJiviAAMtikxGgXTHOqw8Q1QVCimRyTwC4p0g8pMoaVokQhX%2BHGuOjfIhVxUI1EXDLZLPTemVLz0aSkEwDQonoUQk%2BSCOe900UDW32%2BgV%2BolhRP%2BgGOylPucTaAoXSqJbrZrNuBTD2awNlLC%2F%2FDugroTSRVl2Z3GKYMhc2RDsyeChCDYRdhjqanJ5mFPAPdLS4NUZmMuVrR2EiBlRYNi5SEshJDaflfyi%2FBxkBeBs1XSkCOTMLI4DWJECzVkDgSVYrrTqBtPaYk2nJ2rXE4%2B6OXA69QIYAnTi5uABpBCwb0Uc%2BrScmCrzbx9Yv2w%2BDVnHjZZtAjm6UQq%2FU0XWLGB7Tpg9HKgF4IIfhhZZhot0E6PspuVYovUGST5xm9uDHm1xdMlyBCFgboefTpRUQlI%2F8N3xTSkLuoAn1BZMywD3PzYrKOA1ay3mntoLwLsOLcDQMFIe8mreNg7%2FA0klZ%2FQ7%2BHlkDixmjMX%2BA%2B%2BCKoP%2FEueCG6IiyCHj8D4rE6v4XLEvUtjkAquD%2FqMSPPNxX2YbkbcpycNUC3qWmoeyd2w39dIHWMCAz2b7eIyXGPNG0RobXgeVImWfDRb5juCBGz7mlrAWDR84e7owj0APDGbGWLm9Bq%2BDAHRnHZwY2si6Kc3Z3bMzBqp6s2gx5vj%2Fo6MgVpc%2BmKwgycEAFHIIWxjdpgrn7E2ag41R%2BbswdJkjtsm4cE1PHLb4KEwsQBVB6MWdyW1BDAomvVIkhOaaIa8TwhMMlgmkI4DdDE4FPM7C%2BGAeEpfrTI1hbbOkJCiYag9txZ8XRRTwR9kQwo3AnpsPohfzc3O3OXtKuhtIEKPmAyyZObxaMS3aycT7%2FUzSGbFnSkJPMDqEZ7Z68%2BZOT8CSTrCLqvHz59jiX0r3baG12tWI8SiGg9ZUiQY88EwQSjh3ujdynaqaC3Duew5i2uJKyS%2B1zaCxjIdB4Jhfz%2BukgKdT67p2DsUq4GJinjE5KjgdSshZ8Eg38ck2FoAgjYPTSMNktxjD0j9TWLOnha3d08HBOzAh0pFI%2FE3DDy67OPvslwP6OKlrJsqGBY8qifB0SS0hgPBARbCao22Lmemc0037FegwrHFJidDtc3LuMyRJsBhGMlDRKo6n9BN8hXNjhdjDmtK%2BntJjvAQzge8bZyxq2ldbhy0Gk9WIIxQsTrCFmu16XXmDtInDJZy%2BgYG3BBrgdywzALqUC01f%2BuQEQvEGQryn6Pk9cRHaaQQPvuyp76BSlrWVmF1oWgLO%2FQw6ZGj4pEhsfWOK52mOFXKwJzbv8lGU5xM2T993AdwMKsScYi%2FTRV8ElltpiIRi7C%2FiWxhTo8IrA%2FDE9HSiz6rIbFe%2B%2Fjg3aYSNsDjZ%2BSELki6vVm9gVQBlMMKB45yTVeVyzO5po64GGt4a8bfQDLMzNPSpsRKP7DNaBCTY0NFjpbYZIVHa3UiiBBzjQoLVUl7pu1GoycCKK2S0ypStvctk3DA205J2w4tBt2mCuTHBguEYRlq7QhBhEMUUr41WwMfHe6HsWPhIrNFconPrnMPgQ4Rqk9%2BmoA9xjVSaARdGD9u%2B6KE0c%2BAAJ8bHXT5mxJbPbsCATBEDptcQWbWdvuX3U1OQrTDmQmun89UvHmYc7A2Y6vQkag4zuslwKu%2FdBGN5LD13MD3cyo3uMZgoa2erR9NyBeltcNgi3V2JrlIQFwmVUW0nTupOCfpm8H1MXMYAwNpTpi2aBPt8xlWdYTvGbqVCWOqQ5ZFoKu2wUg1VF5r2G16IJnfdbXCC70bejmiDGqz9B3Sy%2BDWTB466ZsV7G9RC6rZo2GigEYJ7Leb3o5ww2OheQ9ESPudHEMa06sBVnCvVkjqL6pLZnyIAZJfyxwnEtVtGpWk4bp8w3fJqcDWuze19dXXt2cQ4G%2FjZ7DDhvtHmzBoGcn5csgzLi%2BDoaVkRbq2rgYGtZhDUZDZPW0TSeymdPZXXSXjMxfzelKcNDpZgYKzuxCywTdy8U4Rfuvb3oT6Vi5JrthLWVJJMEBb0k3ZvRADXcJbSS%2BBHI4MizU4hhFFi3tQK0XVkvHXLwjyr8nvaCRrDxJRQ4EfxzYeMo5utZp7tZOmiR99HROGJCbqatA5yHWzezpN%2FuRhlZz6Wm3f3RRGsihZ2%2BLmERGAJu%2BONamjyX6FZnnhHiDviImjSctHojb3Zw77VujdA6lijR3vqYDYl%2FCXTjDTzyFJJFkxuqsj%2BJBPYkkhmESK1pJF%2F0D1fHc5tin1BLldC0HRK7gOZL7A14eVw0jq6ZsW5AinRRAImSl5x6mDj%2Fe2QwN68%2BwAAKGCQgQQASfuJpLmJQ2Rnx5qBrqXSXIClBY0LSDa5y6sdVSE8faDie%2BBjPioRL06fZsN1kzqtZEmPx60hKGGckWBI1uDvpSrokuitOZhv10KYd1RKell%2BuTr9nux9zwc1%2BLNaFvs8p6KHKOJ5l7bdD6cIdsMFtM7TmefjYZbJsiKAw1gSRCrcJ7Y7YHo8m5UOdV7dCMMjkPMOV58OWhydaIG9HbSifUARSEz5RjreFQrx9UET5dX6AYdEBQo4ozfjxRg%2FZLQr5IUY3W4D%2B6BaJS%2FHlH8%2Fq1OHkC8oEFQHeGevQtwA3782ZRvvtwbSQskED3FN93NRmOgd7o6UmU3fQkAQBa2SK4hjeazRjDqev4mSNFayIbgCSkHxorZCgBXni0BhrZAnaEf3B2pJzGDUF6VnVjakgOX%2BZKkvcDhox003d6otiiBy5mntPZ150WQdqarz%2B4WmKD%2BQBuOnhq5dXFAQqNM%2BDEYX1gMsILrJd%2FPWPQMo2kmZFbOK%2B7DYAB9dmshLY0uf%2BOaWDRBxoqENVRTiRBoSTNAEPcVot0cEDDR4ra2olxq5EnA0I1Q3g92i4I3IJElLxpXQIa1arbqsfNXvXjBlsk4Wj8Cf0acwyYnUBTVIZUNKN4RATD5Wb2LsIKv8uN0%2B0NFsZFk2Pzoe01q%2B8REmueBRaQASJPPpq0dT7100FkbrD0oKneU5qa1mM6uegCzQ6rzhITgeoe1nTU1i4mpz2Gq2YWKWuIlgwA4SicmeJSLP0Gjhti11C2HSJ017CeID5DkZvj2MeH45YzaYdzP3JMNPlu227sQEsws9PhUcOXghgVyJpUM%2F13HDkxayY7dD4FnuMxy0%2F81DMjOSN8Z1ZTmPhC0c78pZ6ZOogh0llux589HQqdoe5upXEtUDSdIJi%2BbvAYO8MCnLqnPW4LmcSVIwntbyhI1harp75dN48orkhNOXwolcljQuJnXAEM6%2BO2vBTegaxkwAVGRl1PXaB6aiAVnGyoqKY4gD1Qc97JnqlusgZenozaSyFZJOU8YdxDgUai3mStaLbv1BEtvGGzgh0LkApTWBoEw6Ej5Pjs6jlFXFZQ%2FaSCcF8QwkfVG1ZQFrrR93HpA6J%2FMmLRMUdYwszWNIaos2GX6BJXxn%2FeSQ%2FiBOO%2BaNdIUMkos1XVegby1nTwvHECxhvGjylu1jte41MXpUxul%2BrEeSQIh5U4zCC67qV%2B54oR2WD%2BkMFzygNhKYklZ7G%2BobwQ9azi72ne37K%2BjNqx88%2BHw3MRPfCi8IEZnk2EiVH6dItoBC%2BoR07ekHbunnmFlaSvax%2FT874jtdsYWaoQNCXcp4yCrWAQULMYW8IXdEZyT5aluGM%2BAkrYxeUdJhMFlsIgwkXJc6F01XII5%2FbBRkchlVKaJ7sPwB7Al2znauTXCL67sBLmczP%2F0eUY%2BmPT2G1UbHCCCL0kwYlOdNe%2BBmWy9mZeMKXWSRriNEFY7zR6Gh9%2BdVZoE57XCRGtRWpgiXrgZjx%2BmgtcWk1Fv%2FtAGO5FDE8lSXLYtdyJRMshAEmSRrpvIYGgRc2iRAEuf6OGvp4vANJhX6tdd9BDoFT08vd6vhbydt8xEx7iMAM3N%2BvE05v%2FKNTGXM239ej0kua805%2BISV0pHucA3XIfAEt49Jy2QwcFOvPUhoKYhIrtNMGEsU1Bd6lA4LCinKk%2BHYMGWdhKWTCOAtkHDhklJNITsa9OZd1BzBDa%2BGICX8phy7Tj%2Bdr2No2ZdEhuGGSBex0mUrownr4cGWtPMugerxXKPZDstptqAMqydLVsx4n%2FCAUz06hoOzomvF8tXjNq91hjtCNyIEx0EqyQgQ%2Bm9j4OaRDEdyYa5O%2Fu9TkeFRuDw6D4MHCZX5SbztteqpWa2bCn%2FkcO4JY%2B0rY3tYTE8yIIQFEp0wAkKHwCaChsK5WtlEiBhV4%2BzjXxXEnPdKHJb6DuGGfS179Y2Oe%2BsAhGISNZgCVDt8TpNU6Ekn9NQVtK%2FbSmgUnVrAFxQRBq73BbakKpjPNhiIezkSC3DGYKw%2BWjJHJepZn9jIGeJcfxQd0bDLZFbme9IQPmIdlQMO0jcOptnRXi4wLun4ENLQ4mhbuhPL%2BGMQ%2FXslwzx14iTfIjyz8CJVP0%2BU96FQI1w4u703tl3QM4wbJNVL9a2BMICBYyoFG2pc5ei8HyYHt6jRDhdfR3IqMwvTRytubBNpDEqEDG9Z%2BWXAMe0hvvT9JFCm8JM1hZJ5NIODkEu3Rb9KgLxzxl5MCp2AWrzq3NTqPoPCnhx30n%2BXNISTW5LYtYwp5iMdkmx5lcFzQo64z%2BJCU3pCjs8rKWS2ahlr%2BA%2F0a6EgLslvnEuF5ojQmRIssfGRGLl%2FiVta3xSZbBSPaeJS6789qc%2FdU9zuwm66TUJMw6VaGGdKKbS88KgYaWfM9KTvB5Xw9vLB%2BlWxQnFFLqODj2eb0EjcfjaRJRBgU1IHEpO2dLFDgnewBXhPaDgGdApz1XA4JZeeNNbqSV23OCn04rkqiYyw57otyn%2FW7MfZn%2FtVISwLRYRukMOBJZda8mh7ptgIpP1rkgLkA3Wsh%2FJUpCNRWRP7rjH4%2FRnb3uTiUAiPj1Wir9unG%2BPvUX%2BpxcshkmTzz2pkXK3c24aroU6urTA3IZv5orTDe5ZyiT1OUx9XAZvid1ScJjw2qke5PJrSmiw5ggIGpGotWP73EmwkE9T1%2BO0gqnybOkwpVCt66oOG6Porpv%2Btd20QQfNTKqZHozCDvjLZj4lGOTmHAJrnBXenYkXbd9SneyFENYKF3SdIpMwDu34IykNtNqgmHqWwy4QOZCkXjP2dQWiAXC9iTmhAKZR6774nmCK6X3SErk3NasmcwRhVLbazS8pH87dHZcj4J%2F5HRqvDT%2BOUMLFSvXYJiqULNta1rOjnIlczbfsJLQ93cxKbSTqL8fT5vQjlIgT351IydneC%2B2Ovsri9VX6fil%2FWu%2FHaFTmLosSkeZbzEVxloFALtJoKhWEIQdw7zFEQBZ8aTMYu4XkvapfQmWzZQRRuXuNyLEM4cYWZGnhnyF%2FPxYvis0%2B5CHIVDGeEfJMogMhjgAhpDne8DuMNNrWXq8ZswZxfaD%2FED0xHgSL%2BFN9cVN6w345WY1RGnvh7MbKD9rPJPkX5mJaHU5wLFbMhdheqPlYVaoBdbhEReEuMjT1mjlgAyO%2BhFTIML0Urbbxv8saBrJEteG6As29wgeJ578eHDiCBJUzkOcpmJbaSsb9Dw5DZE9R9DB3SkequRYF33N5Kwh0EpaQkZ7M3MEvj2I2Lr%2FOrMvKL3V7RiBm%2FhvG1YaKIuQrD4pYFsMKoD2y4G4YtSIODjPIbIBz95DtY5Hq630imaJDQctAbizV9cZONnOSKzeGBSqYujdZm%2BgTTrUZ9KW1p8CJQiW68GoPj8FHPS7twe14c9iMfo%2F8mVi6oeTy0NXVOCO9IrCLK1GuXLwyrU2H%2F0GtGSfDE7ldFcI8n16TSk4VgvWMdMspnl5KPIpMbTwvFQSyhrbxSyPllMWhysX3Lfb6noJb2eWMg2YRF9ftXbmnyBgg0R4hjJ4aXiojBZ4IQKlg%2BGQlf%2Bfkno0L6IXcRw3JiMICuP19JS9U96VMrm5esP0jOQ6MzUZl8PgwJY6sKe54oL7j91Gg9xdxXQW9P00Y851Wzp02uilbcxTOTt4VC2U3Kul4Cx1BRsmgQyPMy2UFBRTZX817fDwpM%2FMXYnt4%2B534M1EaofFy5CWjHEOoXFKjyLwGFb%2FiZS26qk8HtgQmAERE9kcvQ4roRZwzWsmPXPMM72r02rv1yMQP31zQJwhkk4kEboZzfMcQY30lDFWugLUxSxAxmqnVNVgXAKyYsSTmZiAk4X4AfBEHrFZGcf9Je4eYY2HORMGUZs%2FObpP0WUz4oBIZv7mshOirluDAouHuB7i4ia%2BrBFSQzbMCKhz9tPDKXbHgFGPVllN7YbGCJKbm0qaWyzlR99fgh8kzxQ8KQN6%2FmcK4gEYcJTQdcyUGoFY99gxRPcmvRUuRg11Q09fyF7entN3ohk3VUCGhTthCnlUY6glNs%2BMWW6Zrg%2FrM8F1ug%2Bwndc%2BxwjEYuVQChLhPqpVxE2XTg0Rj%2FCCqEUv19gh3JHKMLA8OPuR%2BPl4myhK2edJgW%2FzN06OF85GO3d9Mk0bGDn0k4qbJk3%2BxeSkIr9HgTIt2cLor3ZFNLpc4pr7TVXr1JCodQW065bZApoJmWQ8nX1IOWDlNEEmlISahMkEi6FzyXjCG3EMgBVr5JzNA4sJBAH0Wko2LI85ZADnU4smjLOx5yAotHrumqTRQ5qgJX%2BUXAYmFz40Td7RyYMWgudNnY%2FaKUpj7t7jNRMU5kQOl9io16ZqHBLTHpbIAKVkxxm3xhuhNRz95kyTIZEjO5gxghztAhKZtr4EXxh1GdAexPCdnotzSG6rW0Y3XXT8Qpl2431DNzBiSf1L7Oik%2FJpxlQ0ydpUe0ejPpsbcndh0RvNkmo1QH0xbOFUcvsipOQ1wagg%2FcyoZmhKEFJ5DFnQXwz3OIugsOOcd2V2s2UPXRMnGp5gFU6yG9AofzdMj0ACkCJGldfsaEPO7XrvvANw3N5aOxaSDHxJXT4jrHn2SHDhY8c4THyJq0gAlPY%2BIajmfA9VJUGJv4%2FiYM76bH2I85xsEAkefyT10F5DePJyPqjJ5JUNUX7G4JzJ2TvuwcBlwtCAtTkZyfVrB4cE5CGlPxNCNjoVOMlD%2FvG7Twg7Toyhezot0KRWfyuELug0UUXr%2Bh5uqoh8f1h71bNYjpDhcgN0eeUSv7BsCAxOQRiei1EgxKB4w2mKZypjTwefM6WAMpU%2F5Mej0zWa7v7Zk7R3RV42kJ5uNF6KCx3S5fVJKHUwJf0jyxWyyJJ3De0NFFFxOsUskWlZlGeaPZ3eey0tQJY5HT77ak9OdYh2KT9cIMBprSDX9USvoQkB9hP6PlOJyRDHobOJgzXxIU4lcsFfunJhqN%2FhBAyJEs4dXQRoAx3OhKPWePEiHxkggrid1eGNEk0X9op%2BKIJ6XsQtq57UTMLHUI59R6Dqpvmx5D0ws8tSpA8eZp5sfgQ9mGb%2Feeb%2BmW3JfkyUDwlwfpxNwRJHUDbyeUMUQgWyqBq%2BKNgxkQXj65MYTxoNPsu0tFWMoOBCbDKKObFijLEJWDjMmpOrhiXm43wzoL6IARsRSbaZkkfo3XC8X7MwEq%2FPDUT8Usy1hn2r8yPh6cIzFq1vmFd2TDYl9TybNtuJ0Cx%2BMX0YgxWOZ2ceFymiKYPvjKMFs6m9PYb9aOZ9R5AmDenJFdY8MSjb1jT2%2BFkHAFsMljYI4NF6cJmWiEmRWZ6zgBrt5SHpiT8N8%2FU0fZAslS3gZHn1Bd1nP7lIqz8fMVYD317lEyIKLRFaxlxzDVQJb00sjTpAToJlZivAjEOAT0SqWhG2gPWM9t8S0sxiZdl6fNYVQiFfkzS2Vx8lPYXQy3IUa0g0VbQsYjRSWnQ9IO24lRmGqWMcsGjY8EB7LBsgzXkRkfgC90KgoP%2FQR3mJxkFTKayuzRK9HVSPjEqxNYAArrSB%2FFtMgVswjZsIyQ5Lj6iE6fD3Aca0UQmjmrt8WAwxPKoV3w3O6NdzfCA6aBZkZoYLrj5qCq4fdwr4Z6VAfQWgdlW2LZyeEjHEl1%2F10ZXJkdogjF%2FQnQgMLYGxGoZEafI495GmPWXKMInh9w2RyzG7cnpgRWuEmmiLqdj4S7nzeom%2FKjLts16wmGoPDkROk7hSNZW6LrG7Q7y%2BxhVwNpDA%2B2ir4LcojMMM%2F42iZTUV%2BRO%2BCMZUccMVEAQa2nqDKcDOQawfKmIlNELhWZICQWprR1QsnrP69qlY129ATjuiVCnZ0rTHPjqbo8F%2FURJsHApMYhzA7fbzdXfsyLC1NvhuTyZKWn%2FH1zWC2jBUVj9yOnjYaja7luz2ZM2shuKFrqW1ogrmHdL%2FTioEWw06kmhESzjf1VvaAnt1hPiYgasotO8ECZmkG9G6w860trX0xCSOTDaYbUD1azvXqu4ZAzwP68MkWUwI0IhSvUCZk5FbDAB102pOlMDqhYgjWl2QRBBpzE1G5TtgnRr78kK185KNH9RA7JIpqZm9MSObH9K7T%2BpeydzHvrrlcsYOgwvLjCWDkkVEkwE8cMD1yAu5W0klmjzkAyEe8K4u%2B57CePhSqYN%2FCpaCFlMg%2FWnk8MpJmWtzLeph%2FWS5RxGyQLM8ZSYLXjtY8xf%2FXwgJuhJeYJYqaNeAZj%2FlyN3Y22uCJL3E1nfNPMFAm3F30nnUdQIY1UM1nAn%2BorzS4oyF5ucCR8ZLnsC2nrSP2ggy0ORPl117jniNgyhCoRT79OOR14DDQ43lDyhmSP0JHbY6UGw9CobXigCToo4hsxOCnmcLdtNeIzRvDDPFioA85zxWyiTkQyNJDc7qJKuAETDvpJxsHerFQSP9MJQZM5GHcgNd7%2BSivLIAMIoB6U90diRU63Tz7O6goB4EaIjuGLqVcPCVgpjfJ07EcSQkCZzG%2FuboZMQMUIG5USh0nPGEJpqdYyr97kl22bwExm0kJapPyfrVHydLQeeD3SZrazfXX4MBMJO2JfWdQFodRn2X5tVtTH4W00u1KzEnPjPW1kJJXZeQG8Qc9AzrPqRNkDzgDYukapLMqaVUQKTwtE3R7PwwEsiSGE6JzXBmLvAIekDicN4Vre%2FIwtcXhic%2FfYoqcj9x2uazmzbZ8rk6P%2Fh07lCplFjnbvuzlIyoLE4DRzC5imdNrIh5HzmThyLSncV%2BYjSfnFBR3fEF4oz0zTTYcawwSTLZMWemyNA0OxYATkpBo0NInt9Eg6G8AMaVeR5GUl3JwHnnnsXCdRcTmy6AwkgfJ1xiTS4cL8KCDwbqfkhpiEqYwk5aVgE%2BBKTiduI%2BULqIZl1p1tUVOCS%2Bl6dsY7aYKmAIeyCvhi0lacvId3sujTLCM5sV%2BLD6Bx48NVdSd3bzDhq3IGqmxoIKDHiH6Tm80E5IgHs%2BQjspJnYcr0%2BVqScRHeb4zoLIGWTixX2JFqhsoSawnauQRSlwZZAojCa7b4sATiHMmCQH%2BMGepmT6e9Q0MDCV%2BC8ufN9PIppppAAY%2BI5OFPMHoXBiF80lpd8uHkqPTh5P23ntv0isiSokfm1IzPRkKtCJpZxcxBbwalMCgMeB8sE7VxO2WTcfd0%2FMohFEYGp%2B%2BQ5i96LxStFuRzgIuMgvSkn5P7AI4miiNXDzBJ7NT2GAI8iWU%2F5mW6BE6Htfdsqjq4T4UeFNmAxRsWbatuLU5NoxSFRG%2BMV%2Ft%2BO%2B%2B%2FfTzn%2F78hz%2F%2B9Xv5%2Fu9%2F%2FK%2FvP%2F%2F6%2B7%2F%2B9E%2B%2Fjdc%2BblMwHFkfQfJ4thc5HQq7OaqWZbFm%2BelBcHSU6Ty6eZ5xWItqZoQ1%2F58AfrdetyOmtd48p3OCPWHVmf0mgcDAn1ZvQNo9enqRGiHJhWe4H6bIXf%2BCW00I2J4LZ1ZDzZyMNi4YGXR%2FrKhMewyv51hbfiJ65Ux%2FKR4DkvMLLEpAzZhIcEarB4rO7uTlhO4nuzxi1hOVmpMyQ%2FrS7lSQqnFyLK4ZCSCC9dFxMI6EztdzMrK%2FzCPKYYMO8ryZ0y%2FRiAqKHYT0jEuZfurJZY5va3wm%2FAnKd1hsJuXlgB4GyHnWm0e%2BO6fWMaJSzliS7LSj7dfR3QSNuJlxrhHTdNFMeprIl849WliI30M1oEKQA8T5YXFgCXrYFzW4MeNhqluIryfQr0dah9VcXXBpjMNNUQZrzBJBIga7k85M9DDdTN4RtNpPYwLTd7lg5fVpwuUD7UZprcwkogbzAoWD9Zl0Q%2FWoc2SN5GT8ibzPsjizVb%2Bz8oF%2BApWMDpX1MaSpax1XXtejfZUAdC36v18%2FE32CNIYAuA7uyJOW4rRcE3ievjRaMVSam%2FtCd3apEYSB1ZAQjTwGNAZYNczytCHBwCEhoN8eQP%2BGehMY%2FP2UHU%2FXognaBfB8cSv%2FeM6yi5I5ltJdQVBRLJZ0H0SBDh1DMCZF97Cb1Gav%2B%2F9xBivIbfRb8UVQ29%2FMMpw4Gr2c5Gfyq3XSyVcYtTGenI%2BHhp66Y4pLpqzL00kRrO7t3QnYw9CWFXmtpIV5upSLycxhyALENA%2BZZLDGbWz0maEng%2FRJDYpqg%2Bpl%2FhbqXrW%2B3q5qyQgGW43zZnnCrD2e4vi0ElFXg4y1OQupPGODaaDHvcNhXOEKnOa7XJ35m58%2FTHtu8JbqG%2BjWPVKZpkKHqz9a9r%2F8x5%2F%2F7%2Ff%2F%2FuXXf%2FnXv8m4%2F9Y6uBTpnYwWa8lGxMMEa5rAkWp83q8RrzIUtHreVHQ%2Fog957atfGzf5iEsYfZql8ABMJpC6AEdRKCcuN3f2EnlDfQu0ui7vrsX7yogmcKLAg5iLi5kpoglPxPIZ9UklF7ekoyAAuJIlWdSWqBgwx%2BjmZOYFqgtzCZbmjR7EsSw1s2J%2BZ9QWgIZIA2Q5oJ2kMKJlhrrFYJ6WDt4dq0apKiUlWFkPKCvuOwvFUhepuJtu401G2LX%2FQRcSZYqMYkmy8ajbKnOBZXi4MmAWbRWp3l9Dbomoi8E74WEZNml5Ohpm6yuSUd4hcreneMQS4CHLrdz0xAjRLAPdFkfpT8MOIB%2BWF8na44s0T53Ry%2BRkK7pmDKJAY4m%2BWXqrrem2vMWhs8r0kbn8K1iG2BMLkYZAdHvTM4Ddulv4%2FTcRuC%2Bg7UIn1cop5liDXWOKYYEZk1DgMNAMFS4SFdJwhx1re8xUzk6evdoaImpYY1oBYiUk3rIKoR2EnCwwm%2F720HIZ5UQa2Ba5OfqHCSZXd4wDgiPqmU3GTCtC6w8jvFPwuYPiAqpixU8cQ2qHO3AuusUeJQE9WRqm0UBJdiHoBdMiCXsgpn46g4hnIQqwJtmWTWs5YcWXfCqMLI%2BXhylSc9olEmfWJ4DXVUJZGhLOMsHGGq43vczINh6Xn0eMfWXvGdQDPx9B9ldrhCdBEy6dOPONUBw02E%2BaRddzmgYQULbH2WVYMbkp1S23J2XXTcc7lHFJWMeDbcgyzSvgnKa%2Fb%2B7lwr%2Fc%2ByYwonZkxc%2FxUH3GpntUHozF9VWngkzYzGfJceSE9ZSVYLdtgrFg%2Bs%2FqnnWm75SbjIhuii6dBm0%2FlvG25Plgc07ozXlWJeSzYsp9aupBuVvu2WK8eRpVNxnfTL73SB00zA7gEJ3tH0Yh585NaeDbOair9xBlo7zx5mc6CDdgyZDQlG%2BzcwNWRQcmb%2F9BJwd1OyaitIeGuApIEyD8padOR7ukeS3M832QGq%2Fs6U34z9Ard8OlvCXgS0rhMDvXannd9J12Ug4c5eDOMLuZc2P7Yg8AGWOqVzRd0lFMtuVhxVnMwhgAxOBMXy%2BKWZK0wzc3JCW0MKyoSc4aXc%2BMFUf9h%2FrI7q%2FpyZAsV%2BH6yKSnP5ieeQnTZyCYkPcGY%2B%2BZTzw%2B7JUIhdCmQpolNBhqKDNYZZM%2B1MiQtudDbNpyVsurfWoYL2Z5IAwW3FjPpYFgPckLA83x0FnQkTofporUA0o9eD0aCPI492l16Bjik3qF9IlQfXQr9yu40S8WzSM3z%2F2G3AmsgGmqOUONAPtYQglaRFToqYOjRmKVpMxyIX%2FjrZFgBbhOxhBla35xm38RfaDLQ4IswJ8ND7wB4p1Ut2Z7tVCGocB7MdckmMMKyUJB2W2NT0lvGHOFqL7O0yI0r4uQpc5oP0QvhropCfci4cjeD29h8bygm1of5LtWYaNeH6wQQrKNu9zEVC99sew7iingQyHczotgvomH%2B4PYKHwPy9EY37sfhV%2BCPfzRIHB9zVbHLYUQlUPBh0hnWNHIM4zam5%2BEIrPPqjs9w3Q1C3ZYPWLExbS%2BNJuMmtaNesAASnUD8vzMgx6e3UCIAZnitZSbI%2BdpVcvC9yk67fOKJsCiqB0%2BvLpJn0qmrlSmSJQj0Wviq%2BvrO6Du5lH1CAPkeAoaLgFLGcGyHtWQ9o1uugfCVicnbRcD7Cdmuj3lXoczaFABtecPMc8eSbY%2FRVN4dGjxQ8TlPKfsgqXHou03RUNhjxCXUwJJluPwSDHAGdgTr42GO0xjJ3GJPuYxUHE9nke5gtZgwUfcqkUJzZ96LD2zzVC1MH0re8a57dw5JPhPcqwhDsJngcSbfCjvPhxbAOpH%2BEJVTpYK956aJsEWcdfKWI9FCmrTlqMcqNHJ74J5bShhe%2Fhi9ASEimd3CP6ydERmXZw7rv%2FkkQai8UyvmWJaxRVVqnCUJnLGwEb4ibATvuh88LLFuMEETfELWGRay87sa3SB3%2Bh2MnoXQ1OnkGCEiux2IHnTHVJSgYKPRM7sKdhnCQF4%2FbTZRDxUARQMbpM%2BpuhijLVDGbetJ%2BDO7AMqhegJUnrL681i45RQHtaq38eqhJFWneT0%2FkbMWDn1cKKT91XMUlnu%2BqUfciTdBdJxTA5%2B3R7M2IFdAtLesjkTHQRwWgbj9DesydVxj6PSeqwYzYv0TaeiWUjaWvbioo3VLGHT4FdmPw3HhmpOQ4b9yUR7MgkYCgSNJ9Xuxn04Xvup%2FRZXOq7Ru4fwecAWZgkR8SQ%2FECKjoZCy90lOInGsHo7ENc3qH68Dc8%2BTwYMndR0c6j5YOiAznxLzoDz4IppAR7Rs7pmdXDkPCE2d4tlsNYbZEBuhz9pco47OguapQwDP1aOKw2VjEtzxCr6bovEwgGJeJ9XnJ2uyA8LxKJz5CnoESY6mmV2ZOPKC8on9A8Ma2Qsy%2FZvHEPqtTxjBCux0ujHO%2FulXOVtEF3dmkQ6MwPQi6DPz4TsOQemlaTHVPO%2F7tUyPiZ5kUBlPAe85qSI9yYDc9rnY7aBEU1MfioCcPOrGwB7n%2FsPVnuNhJy8PWUnKRVO3xQVFHOFaKhO5n%2FZ0cU8o1lqCP9sEoC1DExk%2FqLzSyQA7GgJVShnQLn2ty0LJNyfsoqsNPgjeYSDrNQ07LAKNKjH1yrG5C7P9MOYt2wkQv0EASkkoSvUzxfQRneAwOVl8avEtIOo3JyCB%2BQ6hgprYChWHkEABJET9KrqLqrWZ7YVwL%2B%2FRXYjh0XFd8%2BlUMC8Gggf0thwWZUFVd6Gbp%2FFacAAn2DHyhsjTyw%2BA60iJgemuaHzdbo8nWGqUfzGUsCsiCobVgC1HnTgCB%2Fg0y9XI8mKJYiU2pkWaLPGG5ExSiXHdkOLxgKmQUa2X67GswVu0YhkVIGYy7Zpt44Te13No8Cbhzbm2kMU3rb8raSSW5APkIR3JQXPN03wJjxmKEAqsbZmGBhONeDtdN%2Fq3VuKABpDD1ByVFzO7WnlSWLCQCOitdK43bN%2Bdf3mgJZL7e1nKMrjC8DeIDeHk5Ax7eKbuDNg0iCT6fF3%2FXBa4yzwaZZ1jHYThRD7K69NZGcDJm7RINNiIEik4f4bdL1jcLEaJmXEp7XKpVjI4EwP1UgcCEPD4vlL8u5oMDlN9k%2BHGuqFLzawjAJse6AFdcDAcITfWoCNW8r0BtE4E2V5H2bke70j22VI3ySJ7xNZcxyghe9SQ5bMvyr0hE12htlB99PdkO5M1CyFcg94mG%2BIUz8xrOUUg1TG6IbFjfdTyInPPs%2FMgAdqcUzKeeoundMIUQFCrpe4RGSZ9lYChuWrT887whPju5MCMaMukdMZE4MCUq4dEUkEkW40ABW34oCuvcuoTQqZvkeioJZA5HAJ4Ig%2B1y%2BglX8ZIrfUMFSaOfoiuebZUDr9ACHx%2BOpZLdu8rLPCEZ%2BWJiLGmLOdxCxhbkJgl%2BCMSqPCc4deGVOfZJAKK3eCUhTyPkzqdp%2B3OtIQTiiWN7rYMYgbWx4tvZBTGR85IoYkcjSKDG%2FOjE%2BUp1MPkhoD0oTXQKkG3auodkNxxainor2wedGGOXJ9qlnb8jYCpliwDg4USFAX%2B5UoopSZOZJpxyhXYLTz4TC3LUT1EfJwvHRUnzjoJviRoz%2FoXryfDXSbOrzeQtaKb1CwergVPI4k8De1VaOFY9z0lZc07rEFFCnaB9RWnJyjUDCXIEbpLwMm%2BSMoIR%2Fw6KSXADr4JFWnr4bqrIkfBoOUDx5jOq%2BALEVR4phYIQLYSu2HDY4ysuPvQwxo1C4%2B8zUwPahUdUR6Y1nLkhH4xtoqf7k99D2p8seh5ZysjObdgAir02PKoZ5EAu5ZWLIcbwXJ3RRSXTf0%2FGmLdXQX9mYnlwU6knRt9ZZj3db%2FWV7fxFItI9ZPFoyhS8z2MH%2Flwkt0HuKw%2BQmfDI0xa%2BxjpEYbKtxfyWhJzGtOLxSSaI2nxxZjzVQdrjJWFdok3C8IlCuycalYr8Rks%2BAr%2BDz1IO0vvCtCaaYSwxm8Cr2AVxQXGloMLoWVCKZjVbeFvfPeyXBAXHUw5N4M6uyXCPEsxC4R0OjDHk%2FjzjZWwSjEGna4zhvulOESzAhJMhPVmpI9tafvpke1PqYkjCUIHIndLMkNNj8MYEenk1xw0Hi7NDDeUGxqFH%2FMnZrNCcricbowPP0rc88ajw2l2mwWIxH5g14jxlRjbVLDwy%2FEzRJDWq%2FMuD033fIH%2BuM4QzJjfTtsLgWH2ItPvjy4mnuejowU71r3I3qZkClAeQ2bj1A%2F%2FniF7pP3QZHp%2Fo%2BHJQG6QrLjIUcSip%2F7LNwZ1e6PHUy0fncYEmimMZaKfkhMhiIuHsbZu5k0o6Fjd73qqEZ%2F5uB8YTaA2JKqzd7GZtcavMYjyc4WaUT%2F78pPZoeFRgBDTt0JlFGKzo5Zp2c1I55E2tmw5%2Blo50RwFMG%2BE8d6cXm5JrmbRi6e9MKwWBzRvdevMvSESe7bncoKT0nA0iiPLt3Zt0ZMUA0PdvacNflkAnTtwnhiYVhP%2Bs5aku%2FNI1fMN6UQ%2FFvqjEziIIbD%2BPaUF%2FPJFcEzca67UsMo7%2Bn2ZsnUjz%2BYJRmrQ7enjQjdo6pQej3U2IwUQNdtz0MUc3saxQtaxunAwvzq2Q2O%2BW9cd%2BL1GOAwTi8SdDlYGbAeGVqyq7j6DTVtUbKwLFzSGkcXkWKpuCL55BlGC3ozq5QURKQQMz2wKmhkVUmZnZJnJUyOLi38QiDJgojqGmPE%2Bs7%2Bs36QwyJkkf58hdM3yTUCbQAHzQWv48eXLBpYfDpJW4849aR6P9Ob%2BntBX9izhkKjZbvh1oP4yYKpYMO%2FI0npSKs81bkzKsp7aKEyG%2FUXXDkzlOEdF33eOT6eYO7VQ3ypkNZHPu7hGRyst8eNRXz0GiYESd71Rd6QOQLPd%2BpA3ZgjHvUElNQiejcFklCQBpGry%2FhHWOw5SQEzKjtkTYLxEdfuLYQOyRz8S0APuNQPkQ%2Bo8kTZpT%2FYgRPOC1JajOrhcdOwPcrubDOI5OYbQjWmJfaW%2B7a4Deo9HWkNruVBduLSkxRgVRCWhYQIPIiUWvUOujpLIw%2BXNBJJmHIpBYEipjMVRBrcu0bjwehjI9t3%2Bw4jOyPZ6pICA06n9jooqXAI66XOEeMNlMh8AHtqN5GZYWu4LNVw5xnu405CANMNger9uNDPv1KGDPRXSGNEOmNWX7bFB17xXDn72HAH1sOJMb8k7jWL0pgnl2BUEsYH0n4DMo4HSLjPpHgNFs%2FsjLEx61EwAi1L9i0UBETtA6Ax5IUj5x6YAHPumuiGDhLphNQpP2RtZPUeKw02%2B8LiqIQeFYeA5nzovUhHLXD%2BFV88l0STcHEeD3D1iqqcVLqPZFiOKHVtmFyOnvj5Kscswxw%2B1TAy%2FhejgsdSnFbL5teH2HZQUd0ySpeeEzJ6hGPXRqEyYoLRKPpYoEgAGzJxqIaXkKLQBuAxvML%2BEZla4KDiumQpK1ZjQ9hyfz%2BhFWltGkHgQYOuJsIBLulnAAygS0ke562sid5h162EQF%2FWnc2ChCTpvGRsX8hrNiZmJ%2FTENgElSgIqAiCtFbBDbnB4Yu4kiMnKpni9k20azTqBhDdeBkCXoXObWiNPi0M1jLp%2FpnBZIR0YI0cuIWyAwoa2MqtJJLgPzgF3EPqSKI9utIfFBZKT9OKT2GlOA3ENP70yeMgNk1r1BXLdlxMa5cSPRGBZsCd0KjimEAou9zdcrvCxIw0SHkl2qYHfwKCunKkeV0uVyZ%2FQPT3q7w8wcTz09MPXH6zxk4DtW9HAMPpPAYau4Po7qRxRBmAXGVGt6oGuMbaQIxosTOSBq9artAdoVzHF9DwSQbqG%2F4bFzQV8lZaVpiZk20aqHu4aSxYRRNM5WpnVMnCmUVBBtyhQXcUcsD1pTmZ5wTWk09TWeYTYJtroXfVne7kuBxvnoBirNVrRm3RbiR0fz58k2HDR26Ilgp5427WBUrmU0Zqo1QUyalmGiwSuVwiYT4opFJJMq1CyG6wm8bk5IqKE48DESmZaGRk8XpnpsWSCdkLwtC1atUZiCEXiEZRoXcWcWW3Bc05TCCqKaoRiqGQgbkQ20lsOC6FgtqATA%2FE0mxqAFrrt%2FLFqJgGz4OzdhwAV6MyToZzEWh0BJe%2FE4REVdigMxPnnpyug7gjzQDrIiCfhTrInI5Qiz0oxkWBKXhqlQEqCuYWG2%2B%2BHIyNguh%2FCQF3OshONuLDkx3nrtaJ5y5n6E%2FsG44GKN4SocQd786FWDVfJWLhcmE4hmsmbZd9e3X80W9pjp0O5RTk5kS5uMUdl5WuAEBrr%2BGSoNxdq9ALj6FxRzbSHz3C%2F61YF06BIVataxY5A7yeiwGqhWBpLI7CKjDZRrRgIL3WLwdPMl4LR9Oq8XAp%2BeozXdYoY6MNozTz6DlibmSDDII6fNYD2PcU0ELHI%2BC%2BQRt9uB5WSIxuKwrhuEMnRA0Gp0ogRRI0gyMBynNYIZ2hEOfll5vTJ7oZYf9GzhWSH592BAs1Sth3imBXpjztGwr0GkFdf9ci9PJEUBdXAnX%2BdQM8eWmuCiVzumqRGsAt%2FiavKMw0khvSKmq9Hd3SylwEgSxuFEjFZiZjoNeOehM5STcDBuUWmJLTIpCKopZSrIcCmQy4SUYgOWylaA9daiAU0dESXT0uMZwdoA5uSMUNLFVIANki201GtdqCOZrk8gHDEEnFAAN%2FQ3YlA4XCoXLVFJzaHg9PxP43PYzZpjR6fracVRA11boQvZLJpLuz9FkwwiuqfxNLex36f5zMZbw9G6bdHyX4KKxxi3mjVd5s0RoKOcnU6pcmosWX4tspxK%2Fuh%2BOfslpYrrAmxk3VkquB%2BUnBLhcn0UjelHK8V%2F2uAzyzCGuXBbuJCf1mrOjCV2pv1A0mvdMgB10cBZy2iV2uK1jBCdLeHKGZhmcw1E7py2uw8cSjHUtfMqJxR%2BtivE0KKyYgBPEO4Ckcd%2BXH7qLsUKKDV1wBgW7nyWFuDcAg%2B9sG4CaFpKLLha5PiFpGpHBy%2BTNOvxZV85f8r6I55BzsSZ14PimW2un5dYbtRc6AW2Rj2ClBm3I%2BBDugjgML20DIukO5CW048EMycOZ9Qt%2Fjd9Ps2WNS8kMx9%2FL%2FwDSko9JUnsVSd%2BhPhAp%2Ft3%2F4tU%2FkO%2FEFKgJDZws09LLp7rGcsFQoK10CoisIPG4bg4G9dpvNsWAWfS%2B3tH65%2FZwNEplpX5KMM065qe9FPcLgrfy9ozUfhz5gQydVxDToJ%2Bj0m%2FGzAqdXwRkUUdGxk8y%2FnmEYCzdS2JtFf7lGk992VRLss8rXhYESkdsEH7bE8xucw1BwMtkXwPZ50g9KRS8avL6i3I3Mz6%2BGVOu0DLaE99wiMWMfMsC8hWMZuOhAjdbnfQryiFQ2CHF02TQaspjmeCPPw2dPNTxwi5j%2B6cx%2FOCk7IBX2m6D8DEyvxhrfYi9nx9%2FXgU%2FvRv7au9oAJhA%2BejeAKlr3%2BrBLzM61k05h5k2isAMI0BRAaPDA4j0n3EEz6WHZon0TOf2BM0PJvIsCSfRE7e7eIuUQzADsXxE39IlZIcyyO9HSqSk0A8Q5d0BREzBJ2RQhge6cxv0dxWLcjApeEboTXjMULm3a6JYR%2F0Y1D%2BtpSmfkRhgVJYiTaP%2Bt2tFVS26HIOHSWiSsjFisYX2qQsRCfIxEdnLbm6LZRGQhqcb3gQR0EmfzRAguYnhWbd0EqjpOcnJXKA8DmzQMeidqtV6gsgRbH0lCBQ8Kymw7t2RW8%2BamwQHxHA%2FcftzYsehc8dyhHwXnu04tHQEawplqw8MYtegzJjRbcnu%2BwGacAhaBD15RoUvt3U2H3l03xAlsJVYPFbmNdmTXfqAZjFp6SNaD%2BA2CMWm9TMSANLQ9MO8MorJuibksbwqgDPgrdSrWJoub74IW1gxRkVuXtMPEF3Fd6Az8hMWRGGyJL64Qky%2BeYT5R0RUDCf%2BBV9iTRdpQXbCWohlXZc9GQqdLa0GH2gOoom1RsZuEwC%2FYzuTcEZjL4reeuj30mgj6AyGgT1DQ1ujjWY0zoSw%2BAyL2t%2Fgvy1mhPWYThyVcgPFCf8eBT%2Bzx%2F%2Bro%2BIeD%2FIZq5hv804pum5a%2Frk17ud94cpPDnonGAajhs00UD6TwTYHhDYs02U8JgpFMWjH92FFGn98LSE6dzMlYvuGMYJo31CcMl9qAaiHlCJgmbiXlL4VAO5jECYueTmjUPhS%2Fpuszo3nCR9V4uBh0zv3OYIIPsemTatW44jyThD%2Fo0ecffawEZiQpfjEm4PqCGBUn%2B65h4rH7yXGV1x5H9kXbSY9BRKR2%2BtoZWJvUaqI51SSOdw0CzxEwNBfFIsEN0seBRS%2BSDRfZG2xW06NBZbn9JiIw9PdK8nWATa3Ul68WS6GHNlTc0g9hCYomeGHHVLh4obQ5sR8SeSsdcQsMh8iNOI8nIfRmg%2FudtnZGUREoX5Fk6Go1BA0E7JHHVdbPur4iPsCzsVXDg6%2FFFOlyn66l9%2FhHmP1uKuJveEJjcEXmBy5Y0A560eAO6sISqlwwg1fC3a1%2Btj86HDSgs7iVuU%2F2OK5o8KuYTmNKlWauA70ifvYCh2bJPnAudDa88di%2BV%2BpmjBHzVvE5RhRE2zGV8wMYOFejPKOYCmspOEB9UD4SOPMEGlI7m7ViymjkvbYn8K0sAHNHBtHALaZtl60E4gcQQmO1szGQLFB3gE5cwpLIpHeNY14BSlYDA0GPwYsBdMo%2FGOsKchK5CBNfu4hOysF32g6PUtZY1dgEIpgzFHmfMUAhbauZnk%2BDI93B0D8Q4X4zUPUvMeMDkgnMTBBgXonn%2B9EF8Lic3JaKFK50p5RRIPXGeC9Ov7dhhOZ3iJnzxxQW7xZDUs%2F5ogotWuSAbdi%2FxG4lnej8HULcM0B%2FWeEEZNsvx%2Fst5t15Jkya77odON8LvHx%2FBRTxRICJD0%2FYoxzGxlVgkQxWZ1Vebea0W422XOMWtP6l%2B44FEhl8wTkMkEFwm2kV65sPhKWzjxGN6GX43Oj0X0Dbx1nvM6wXDxrZ6oji5LalLo0Ijf0ilrRDbyj3SINCKzRxNug5vh5NYINdcC5o6zICa6LACZFwHAa%2FHmNhy1yyQebMQrb6i7JcOz3P75KbcRzV2L5o04J%2BRRkwPClVD6ZLsYDtIHWmYkvITq6X6lmE5z71f1Tdf8c1bSxAhZOP%2FxU7hbeh5sPJzlqLxLssXD%2B31wX%2BFSpgQzPJgX4kEZ8dMw%2Ftecpvwq2hHG1IZObVC78YaHJoNGjHY1I42a4p7I8kCGnFd%2FDEIEZJarHvUiZRTaAUq2PChfYFTmH4KvL6kK%2BXVsSnnx9ijDO8Qc3SXfB1zJzZoPmOUhZ5q%2FiA%2BdJFRXXAQtT2VeeGwR3y%2F%2BfTRzlCF8GkOzWa%2F2DFawA9aA8Qgjiw5vGbi1kEtxrcawww%2F%2FSu3svQab%2Bq0YW0E9TLvNEfImtbXNILzRNU8TQ%2BhvK3V6GjxojP1KfRuJJLHQZyd4cx8l7UHL8TvTOML6E7wSZ%2F%2FJE8uKhNS275gvvcYTOW76%2F%2Birknc3W5iowJVFUXHJg0ObewDqZEAFk6IZ0q5WaY%2BbPh4bAmOofDpegxIZ6uILciPAFJg9GoiMkTTArvdTNQJTp1HAOUkKL03D7W%2BGGHj8P2bG3MjLxvlLroxOvwq84obhfpFWdhPr%2FbCUPg7yqH7rldJ1xEbuYj3LFDY6S1Pu2smZ2G2G3nJAkYsR3osrFXqjw9tpYIA7aotsdFENotX%2FTPemtEkprRsGzOCepkpKHiSKf3ExqOhykkTmIjeJ0lPLMKxWgK5YmGXuPItVeRBQxmsoyrIQa9bmya%2FBCPmOuJUHHqpSr0PwAdKsW%2FQpuQ9rknBLQyFKBzCdnV6SJisvFniM1XTgzcKFIT6WLrif6ONDB0E2PS07v3%2FM%2BMm6Y0uIAQvHS4rbiHNk23DNkMvdIfk2E9IY50O8hcTOKwliPAzNMsRTWFldpD9iYNP2SMjQG66lGJ7rIz7kLuPVTG2a%2FnsNjreffNcZznL6Gu5eY392v8dXtgukjvNjeF8hVGopo4A8wYYT4RSz%2B5hzsIKnVYH1vLP03UqeuINAeb%2FZVjRX0k6ceL8zKMZIMLMLa0%2FJSg%2BztZbsuKyEQDJZF6moQIo4l2WFvFjjj9JqimEga%2BnNY49Lkqp5uq4oTglrFxezk%2BNqV5QZJjQHRmPk9hiqMg8Gl68LgF8oz%2BwGRhIbXfNXB7Cv6OYI6yYoGIHwq8CgFEDINMXH8onHB87QNSpzgFB9Vm3DjUgpEZj2SJEkF%2BCNv%2BvkNcQgg7QlZMm0YnXc6zRv%2BYb6wuOVHc5fT14LNGDOcVCU%2FLQC3FD7r74%2FGmoj%2Ba4xbOVoIN6SoSgbh1qMaTDmuiWRPK0YtGFsupes7xDwwFgIKf7mo0x5NJNOhdpuSnO6yKmEMhMnd1p4gbDTAbCSKQovrwArfUM%2FEtaxbaR5vt255Q2nWISBErKnZBWeJowNo8zJ24McP15%2BOvo1%2B4%2BgSvaev4wptJnfhQzjNP3VIQkPQIw3ys3w6cYw4Sq6IoAnvtOuu1AabG1UgEtRoH2vGJvVeAOZNB1ntLONJECzC2SByjV4W2FbvxsXBhbz9id3izxZYp74ApJDC1kbsS4oksJ9cXMMa1JS7yIiCh2zDSpxsyFugjyK0n7qJM6uHuU0K0TmQT1vZToYEgJ7zCp6oYi30UcogEMRQNYOupjvmQGgURu277G0kkHu%2FBR1DZ4RtRTfwshAkht9zcDr1E7577G%2B8voRnhCbJWp4pqITlVYuK%2FCAkWzDJhZsbYmTiZp7NLHdnSsMJgTYyeWfpEEZMC3MteXbUYmMV5k%2BZ7fRiJXdBsa%2BKd%2Fq0dcg1uV%2BQL6YqbLci3HbcjCcWEKJUuPVF3DwpLjNBa4pE0hlo3NrGnsULSJniw58yfPnzBt3VVF6ORBeu4a0OKGlcxDj4rReLNpKmA5kBz1Pln%2B4jbt6vtly4sKOpMW0mCY%2BcHoz9ODfkUL1WJQIMuy8Bafso5DHYMjlHj38aC0NBVx4nGwomisyBaeLM39cmoEbBH7AgtALKET5iHcE5Tahj2UHVi2LywS%2FRC9PEctjRlIEP99A5bA9uc3nDwnmU5kLSDBLeZoG0acF%2Bt6YUumdwRZ9neQxMKjZEA4glKOMrn5u7cfi7FUJTmhnDED5FDSt8me3DERCXtdGhKX%2FxlXQIJpyM%2FQb%2BfLAljY4%2B2iCyAX1kBnIfn21hCDy43Rj4ZHDhtkbqBTzReyRafampX5HzAG3EQr%2FSXLeEoh6dAmmQhIwHA0h%2FpmQcNCmykJhppHRapcnh5IR00xxz1kNksJ3yO0MclUTdUj7yHhk5r953QarK75vpI8ay3eln%2F4SrOHOMQfaKMBW5X%2BqGFEWtQPKWK0Kg1Ta50DQOXChN%2BNBahXk03ZAyTAjMEuvpHJeMsoNqD%2BVNbJ2fP38lqfCS9lWbVlG%2FG159J7pvF5rGa1EKiq%2F25U0HpTZ%2B9Qedjsk4O2dOQbnqJpwBVANUHf%2BArn4uxl%2FaDDMAyv6gWOY5y8ubgoM15IwEsjAwE%2BYqabzqpTucLX4hJXIN0XGmfpZCNSloKc84CElsKgV%2BlXhwwSAMQq0Bna6iYIYhTp6HE1ux%2BOMJqLkvMZFcwVWZgnWJsNS8E2PnacG2LI%2FQ4USQ9CKDFPlyCOsgozduPvsK%2BVcNicSdDjySnzSnes%2Bw8ieBdmjYBDf166xmJV5fpZo%2BrTLxpI0eONksO94ATBuZE%2B3FWUj%2B2qxzDEWlzWnitteFGdEr2iBfZoTBfIXbzw9Q82IuOHZ8BYDc1hCbsaD7IFCXw4RjFrOaOs3lQrUuK%2B7mCem954WCKd4YmeZIK4qWI7f9%2BfBgAyOoJI%2BtIc0APHd9yijQdngvOKfNRkASI4HrqnMmyVsjpv2R0iH%2Bt%2FkA0Dz6zefcebcuv7cBOdjRsYH2jB641kqdNQd6rGpEn%2FuEdoF59iIU0bSVRdpn2r3e0B8xXjoqqQET%2FgO1hBxV9%2FjQqRScMmvOcQuz1rF0%2FB6yLBjSp0f5BMKNIBEmXf0rEC7%2B9g8CcYiBPZinj8s0%2FM3FJHgQpmwuGQjgiR0KCPAcq%2Bk9CxB0N6XGUaj4ul1WPjcrJ6P%2BorHF7Z%2BHKtSXeGkMWc6MGzgOFLpWlrmV%2BMjQzCL%2BDJOSY0Uj%2BGjjOlyjqVbhfgYAGVlJubM499Ggl8zW3aqLjF1w%2FzUb9xLaCBAQqd7PAyUbvq%2BizADfs2o06WuqyMi3RnjutrVZpIPKcnMjOg3YML4MJ7IsMUgOq7GtgA6IeNBgUg17k2JRb2zr7p%2Fyb9xVQETYiGOsD6cmriwoJvi%2BSoe6fEo51Z%2FiuPQ%2BN%2BzTSBhdKVyCpnbluXh9R1M16%2F4w7NkzFEBZXlPbU%2BB5hC9lgcScL8oh3oh2wfyYOcYEZKSAhO6HnrLA7g1XztMnojDmlqpVSOBHnEZx9lVWqYpm9EqiXVODLwnD4Iq1k0zpcJEwmkUJZ8tHku7ZzRVXByhTWKleKzBuB2Ti9jkYQOafsCo1Tc%2BaTaGtKaeuIpQO1Hz7qjAYtOIV8dUKILKUio3ZD9d1z0hbLnkldB%2Fg%2FTLNgVyE7Glm7iptAnpBRFVP08GP9ORISvAjscdlrpPEMOmalvxpbHmePohKyElJbEdKi8NwDu5UsGFyOFOZjExDzfbGWhXEGYExUVnhsjlDcXjyNQyiKnTfxuaSw4kMYNstRHQfSI%2BA1gMZcBion%2BSY2Xtxozoecs1J1mbCS56hUICQ%2BIfspWtqOPrb4i8NfBilEwtTodlpLyYgXEcc4i5JOF9v%2FuTfCJueVMz1TzOmKMhRyXZFCvO7BVpxzvA2YDP933LXIVbkeEuf%2FvKGE%2FmCGw1HCskuePVGnGp3b4yKFsuXKUGXFJu9VyPQlcm1gLKS5ZJHMIMqQ%2BWzsAG8cbQueCmfJI9qzySKSPEbqXaUdy2yPFCBJAo9jfWlGzAabBKqdC0SWrv2GUTQhXLZU8JFWcXI2q7wsflck5MnK1ztB%2Fa3aArvvFaD26nCpUydBzA1Hd%2FrTRtcpiiFdvMKlupKQytoeKw2EoBgJYQm6mbsXXQ7cX1PxL2o3WFg0D0AWQZGh9%2FmbjOcC1pCsgujFNuY6wdKixyFwTQB7X8TQ00JvGuUGEqiYpTRt7y%2B50g%2FIqxE94Uv9cb6DvyCwcKKuFRCi4jKhJe4G8hRPyutcrZZjeEIz%2Byv1NuBoaQnky1TPLmIVuAIGL5t0YhClhXM2biPuwEPucSFM08XjKOsJh6EV1Bpg2TwG1nz1FC%2B5g5nKH7RJKfgdFISMP%2BB2qKgQuS44rmWprJXXgFmOEhu5dNEbMT9BoZrUb4CJUv9XKIS3luAJuLvzwx%2B8Xy8%2B%2FpHb%2FBNeMSDlAp2K%2B8PZLDftHbIvRJSmPT%2BH0FxUWF3Wy%2FTHxeoojfyF4izTA3M9D3iNtl8oSi5NTzrT%2BQ8S4qmVhxNHXW4D%2Bf9nN%2FsFdhE0LvlkTqDRRLygDA2kw%2FGZJ%2FTbBlyx3eszBGolREL5k1W3%2FljBivVJUTifdX2QyTpqS9X020CAV4%2BlNSoH8RfAg3RG5eOwOv14r8eyoTVMNCfhxRoV1dtBLh4ZRS40CmjXJKo0vhPeAY3hGMpN6MEdnE0xDpVsDsuVx1SeXdQ7XGYe%2BcKdeHEtcp71%2FlumEV1uPMFf62pLfxxpGOcmJ4GTvB7mIE6xJbsqzOSFJCiIa5Egp0ZsAx5jcEY%2BqAV9ehnZjBOX77G4nN8A7QUbx2RpmjKZOTPxkOfz7jSE4wgQNEjSAPUq6YTrBFQ%2FeSKU04npqa%2BpQXIyqnEwMh8WSKOaVX8%2F9FKnyaGuhupylfD1LvbF%2BupClgafyisVHqMt5evzA%2FHRQ0Rnhv%2FuQnQ2UZ54q23ZjDnUfucKGaG8pNVqwDNLxIXigWKwKCbuRVuB8voTjARyUn6Zd7pvRIGxuosp70fgWP%2BmI5eX4KWyKyHvqbnV7ZFvnFrO9e9u4Zl9gj9hLgDN1qFmUob5RVcwy2ItJfRw0DyMmpWM8%2BXBNP6qRZ9tbObBaDARGQOSqZTlpREILXzaHKArX3muD6nY9%2BrUPJFPU7F8U7EgbxRu1pA91rxa9A64fmTTET2SjAwx9tPWHjDG8BXQiAqCBrMuOlS0LLEBq3bmLtNYP1JD4bUUFMC7pbqZB5G7cnJijqdfHkzOmREk2n8XG%2FTeNZKJKRRFXnI6jOCK0EdVDKGVfFfrInAa9PXaF0YhAUb87mEZ7xPvCRR49rJ8yXMG7GbaMYJT6NZLSnWFZsAmylm0yh%2FK7Z6Sq53E6hywkijIoJ6nyqy7ELlq5ELs7e4bZmgsjC96uPXUjnDne%2FtnC4O7P%2BxxThxBWBtwqHrvkNTxRC4TPSIAC09Yn%2Fkyok0ojXv44TIgVY4wvJiOBKHmStlXuJHMvc4QZygO9ExWmSFeBVM%2FY7VGGx%2BqHVgX1H0EevW49fVlY6AIz89dZMQS5RaCf3TiRSzEwan%2Fl8IxXWEUG081tsDwW37KG44d%2Ff0lMGj7aap3Susa5%2FM2565ChbLJNBjPQ%2F%2FwkIkjEcFEK4RlLrdNiZI2NG9llbL1UhavmRaZXBgHea1g58YlCUvq9JB%2FhuoSJJN9fSvsvl0JKXjvaO4SOWseykGHHaYk6%2FtrL5UWEqeQPpVy%2F2d0S59kSDmoNsOqhlbBbwyh6SVrIjV6x9%2Bi%2BZ4YCq43vkFyi4MiIQA40HaquqpgBLeuWCG67anqHKVKaovTf0WNxFhJCKrs1YWfpmF34c9IkitOzGqMkWBwtPapG4Cl5pFYAkkrhjf9%2BROoS7kivKeGZ8IkgB82aRHPtanKTIwrBgVdFdElkozVUKM%2FJmr%2FKd7H9CdmhqUV%2FQ%2FJXkdJvvgLL5jFx0YK50JWIRHo3bkjWAtseoyPzV0SlgSsPNtebPBjpceCGdYzgdvXIQg4TOP7EyHvzKTi6Jhc0dPZXuUnsWKOBU3Gk7GI5nU8XyGITKPBJhXhq%2BGivp7qJ8GREZjwJQZFApyK7e2oBDEnE9RXJTBcUgM0dNB%2By3KO9dW%2F0ksiCoN5QWMlmITRgnJQwC3o6qnOy9xb8tb6bvA%2BrBBKLkROu%2BIeaUHaSbnboxZuYQjaWL0l38lfz2QePWza9WTQdaGr%2BdT8J%2BQUjUE06gD2OzF%2B4FYwLDQLwQpdG9ySDHYsGUBVHVyDHE1Xkv%2BwO8Trw7uBG3EkfrjOJExgaLlfJbOo3vBJbBx%2BScVizGrfxsryJfNhah3f1akiuVFqtDGLm4Vl8VB660QuQLv0nE7mtKZ3ZkjHZYGLHmT%2FkM7TgybWLmvlcijm%2F6WQe4bD7fpK9RCKlNYN4ai7sZUzKCH6l96hcHdUZJwDh99RyadkltjHY4gTNPXDCsy7yBeCw%2FjMivJPmXNPPEah%2F9mQhtv3cgjxIkOBIpqNVXcDmQzXqLY9V9U1FJJg5jZapphKiF5edjIoSI%2BWCiUuEi4eZkNjkzv5eBkwaRoLdkn0AQyPe692l8Way7jCm7rIpOz%2BH%2BsTOH2S6BK276uA2YkSWa49E8aZG6fzHzvL6iTKbmrBCUoeByb00DkjxKMcScyfKkZ0YHb0OVnVBSxmb%2BH7FszqHxYsX8zsmc0%2FhRCQycFdTCFeBSSxzQ7%2BxNBFS8MaIS1ERb9L0Y0Y7qA0UpQFQM%2BYDBlkX6z%2BNKJ15BsodnGkMtrV6vXzuec4znNyIzsBqRjcR6nF417PeI7BDOcj8SLFl1nccIehLSN4vLs3wPv2d%2F%2FCCpj689gmcayxZ1E0tQp3OEdho2kUrdGCvNIYQmlApEXG2R%2FUJosotDOPkY9TmzVzGiEioAPcbcpS%2BG3AwXhJ8059JUCqqZWN2NZKKq4HQFgnFw7FEMkROai%2BM4N4rsg5vC0FK4Rvkv6qp7MS%2FR%2F7Yko0XC8NkmbqZeCHPio0RnJd0DHTrnGspd%2BLHV7qjtQOdJIktOFjkTCcMmwCJoCJBGJtOTV7dqrxhlB8Nse9x4eCwvU5zZrMxfVfGa7kLpHXyneHHRPSFhkvaWoJyhHgYB086UzogRRs1BYCNQkHjFEZqCVBGZiVUtmg7K1%2FhDKTy4tyjYt15aqop8tF81iXTUjApSQIvLbkAgHei78xZ9pNU%2FkPKTvai5HVg5NfDeOQL93goup8eoiifnFSgkWHmwWzo%2FJTWmDEUV353AEG3tmtQC4cDWUzMvVigOFAKt2JIETqoCi3M%2BznywKcH5hqGDOoFNNhp965%2F%2BzenGV2kcxfNcnzGMgmx3%2F9lZDSawXHQLksObKw%2BOGf14jF2KziebTII9SUJZ05wp4lFC5njynZTxr58E2UNM5of79h%2F3oSdOgfGQXC38zXGTm%2BSLm64J9oxhxjCgi9dtrqB2ow1FjMGA%2Bsw0nKNNttHkKG65lDQPm4qhqajOlpKDvRFXfWX31qyfdRs81EPYbuznkU%2B%2B8uZzbvmdf5z%2F5oByJwW0Vis5PvD2UyNRQA7RFOln6Oq%2FJm3wocbLu3GSJsR%2F1QKcmYU2j651MFv65IsckLrRbdE41ayX0QxpOWQI3dqZXRiscFh2StKaY0YDjRbJU6cSfGTrhnunl2lkkUr4llq8WL%2FYcl2o4boOiOZ3smk5hXaSqYkM7k3MaQYQR0yIkeaMmZTCp8540YAAJgHme2%2BSmNEOUaORSvVjdHSn1jvdNxmQqQYcN%2FB8c3u5ht6s%2FuosuyHMPGyEkB90YzTi%2FR5h8UZu3Mpf2l36MoNZJyCSYtDQvmEASDOQGXaoSMBAFWmIAsFHFMY633No3JR28HUjh03kYjelj9UCuq14MPRrAmqHzFL2kEcIAOyLJ1S5CNb0CRh0mmXUwkCFjQ8ZZ8hKSPvFsslw7Cn3LB2OnkK5RE8iI4xqMmsDVXIMHhnyHH1zo4eYhgfOhKj4J79w5YXs4Jj83lQfDOHbyqfQKIwQLHXcqYAy0OAHTWgg3UOO0xB83mxe8LRIsFJNG2Hu3bwGHia3xYkOntD506hfpWumbLBPmZXAdJ3g44rdPbTNHr9cGVf79s2l5xMWQzHBby%2Bo%2FWPGG1gWx2ixyzC%2B749WNIAMvJLoE9ml50uIsxaCMOdbrwHHVsLEeUQCbMsi1X5YuBEruugOQRAtIymxqZ0scJ%2FA11DI7CLeNgXToEZZPpcZSBo4g3cDlhLjNrpQfFwtTxav16YCYcH394%2Bf%2FeDG2M41QC6QI7kM3MeFIZEgPjYm6EUQCZReqVvcMvzA0nrjkRZi2DSyA6CaubSVFm12AOqE%2BIYAKDioRNs%2FsxNFDuts6ru%2FW0kyBt0kQjKtJTn3gJiBLpVpaPrExZuIVSA27xdxzzDrH4UZoj4HnwwPn8y56sqYuCLxRORKnMOQk59Do%2BSmbI5542mgKyRzI9xh4IAvtxQVaN9osBBV99lKzKNanLlu65kzuYbMGXSzPYoYVhL6jXkIItkaCSi5R%2Bwb1vuLXqZxp0bcs6YtN%2BJdWcQu5v05QvG0eLuu%2BVX5B8vRFZ0OCbXpXuKpZ3Z%2BdVPX6vN7QLjCWM%2FdEF4TPkg6xOVQPSVhIlUUd5ss4ag3yIxkogErtY8QQmK%2BNG2FQ74qa9xrTEtUsoRQveO3Au508cnHDoGIs6FmlXKsDAkOP5lKDoNeU23ClYBPFoJuNmgahxhUc0qWh64tezu6AIQOcfXBPlfCQJ%2F29Pdn%2F4TZCCrxTdp4s6xwtKAHP1VseFQo1%2Bn0Q5BK7USxyAmIuzinPPQE3cuf%2FVSiDB7vL%2FzHNw1snc2VDHoo8DGrNvuquXz%2F7pGZ6kTkGMLH0BvkIgiqgULpw8yw%2FWKkh%2BmP4F1Jz3pOTdBnQJug66VJx7OcsqMTcBDjMSNjh9xuSvwYH7njpopAjJkjBiIwOmNjn6seZUTTBNktpUvNPW4Q%2Fk1XjxW09LxXrcrTkl9INiuqZ5r%2FGFc7PWRpgPlv%2F%2FpaxmSsTqDLYRsLuSNaXflPu%2FILDGZBYci8MgMoCcXQl8lYPiJhRAtpmGTpk7ZyWSwWg%2BWgxTUlzphWOgemapHMM8SFHvQmzX5Em6G2Sm839TDNLCBRhu9JNkNo%2FigYH4WBRozLVC00XUX5QqZKAQG%2FoFCqbtgRqRvYUD33ZGemk0PFS6jxT7ybyDOwg%2BXangLMzXLrNfShg2JazxQtYBn8IeghcEtEfmjWOd%2Bhh8qF8XrugSCgxUacgeesiFxeFR0ivRpPboTtdpd1%2By0Y%2BZW%2F8T1JdK1pgCBnwpEGLTEnVZGABS2y4TEIPkbNUI3ocEUM99T6sRzc9m8%2F0yGJ2AOJFSEbmcgsnF8RLjFNKZ7gGuAIoRbtOYh6hPJZajW%2Blp3paQzw2P7eWWnwh8tLZi0wpEx7MlsCAQ2z%2FDd1bZyOXAwwAkulRf2BlPWcxM83SRVu%2Fpjr7nKCwQXV%2B8kYY6QKE7EQA6%2BhxntV1JBDHmq8cUuxQsvI901%2F8f7qiMtsCZXfqJRlSPqvHCeC8eICm2J2qSNmj8WPvwJ1DfdH61XavlqDKBtJmEj7TtNLL2yjr3Snyg2DdoCEOO9neEcAbxm4QfqIChp9%2FWvXB%2Bu9kjtxVLHqZdkTnd%2FF%2BmOwNPzvHOxPMx7QVqs8CPPERpEV5fV5ExaHiEn8HF6EN61gzEkFWgEUSdwf3BopvtiOc%2FxL%2Fp32W%2BO88g1zScfQgMopQ6lQSuDI%2FM5ynpVEo7waltHFsE6vlTe%2F0GNyKT7jOtP4kr8qaORhxaYF4Sk9Hnj7gAS%2F4ub4ylkNVg6XHPIWCUKpWMDeebCMxujxP%2FmUqfegEMgV8XHryL5mV5oQZK%2FQ1ahTf7I5YZaqdnIC%2BMwgER5G2DhQBmpm55WNDIVae5XRGolCJPedkTJojOVUOcuVcL6tb1gTOfF7GrV4%2B%2F%2BCvySnjNg%2B1jWoohLfzCIfaz3b5z7TU%2BcSw%2FQnVN2VSU0VqFAIzUzWJY%2FY5mOjXWTjiT8X13j78fAeowVG0MfgqBTRKOjisCaImI9Wm%2BmhDtrbxGDFapPlnuuenoTMo2OXWpBMA89svju%2F%2B2GsXkj3hkiVqzHvTTMghdeSpAA8LJuTLJy5rdCnlaQTg7FTK%2F7d8hfTlyGXuqWWYHiBJ2kiMf5ldjupYFjGKiO%2BQ2G3mJrYP7FASwgKwBuEsZzIgVNk1mtZazJmy8uzcw7RaXjwlerUAMFtDGek2DAMompEQVIxz8uwKWl1uPWyULlBI%2FruM%2BBnfZQq3FBsUtVuuuCZQscEZbBOHn9srG63j3THtzCbTOF54Ii2zJ9T6Qlb15eJ4ntGSk%2B53xnWs%2FcPFS6jYJaFio2Nq07D07qhw0QU%2BaYseLvqT2JXrPsf59UwVXrGtPqnzegmQ76rmh2WL4m0szafIo6YKPasAeU9Is0j%2BKZGeo8RSnt4Rq0UPLr2lEaFIblMtZ29AW7R729OzAM9nOs%2B9BkRJmT9qCOLDdkvm5Z%2B%2FgnHDVrPlEajf8WAh5d1ZtKuMmZkDGdWqL0JGOxGSKp4Y7TFMC4s%2BG%2BUDy64eVD%2BenjAlJq10YMLm1oA2zMEKk%2FFFQ%2B4teRu%2Fk0XsjwFJIDD4hm%2FxgtB1mW%2B8bKjUoFFeAtlGKDotKEjS%2BnyMFlqrIQfL0NPGOy0rI9Zdz3DvUsvUigS8hArP6lCRJGKTGKZyZRiNRBkobo30q0wowy5ju6Uas6vWmC38KwTV74EbIc4T1qgdFRKUpbSpWypmL9hOYcErRdVZA4wiHFi9ziZQSQ7hiAsUHOwqVM1qnDu8PE%2BsApvTru6w7zHGXnM7Ra3NLcBepafSWh57qDK3yuDXCOCjoKT8dpNoP8%2Bmp3BZyS6nwxV2KOU4yNvEwcHnG1sFQLkPqBq2Xx6aib0%2BO74IN7cBRqL16AsosRCHlTEeJSpQwUWzJWYGbL260J8uRQiZQt3O%2BwStI4pzsDroaguAH05rWLsaqQ1jxA%2Ff15G2zRMBM4tK6bHvB0L1%2B%2BinNlAkill2hl6me9kzonTa%2Fz2MUP5zTGfbDBbCLSGIW3lpKdwud3KradfHesRNnkzuYLMLLLpuwPhcORynPzDR0pMr3tH28TV8Xb6ytN%2FxSSfT58Y2biYwU0MFHjM%2FfPZi1uP%2F5jd4s50VcydzBeOU%2Fh88N2pk19BDlmuDSDtvP7Q3LtF4sYAqq6FMrvYOryDQLKoNhBx5bl3DzfN986lHeSBUygMltyYs2O7R6SKqY4tAkaVGCMzQAHCSxxVKuwmsq2gAspTCcE0gxLehc14Mrm0RjK8egZ2sqFBzEqCpH25kZfHxIegqNeotYwJYqvxlcGsECUhRGXPaAfIFFakFac1ADwjIQnbLZsQ9%2FKLmaXTcKQsah8rWhTd75t2PcLmWBDBDK6hHfYJklvYKCJqzZOUpsc0aeqMjCFHu7QE%2BKPMSyZDVy8NPojCdiTAWqk9a04DYqNNb44WMY9%2F525yT80pj9yr71mLAQ%2FF93Xg82Lcr5QhUpevkVYc5z2xdx1nBvJTMnugtubN%2BaripqL5LoQ0oQpmlcDFADoN01d8%2F43MqFi6mbUEeRzKxgkwDOnxCk25hW6oWgDy%2F0nszG6VSFtGcFCdobjNsPHgReHd4H3OZAh2Tpx373I2HbhJHDZsRIZgwRTszSZNGul7LsbxVcc6g9X8zYEXu3ROgteR1UyCtghl3fi1SX7QMOFK45q56QTWpGgqNmKSmBpBOSCpmMXHvCnhlpw42AvMPyD5JWySQQKWqux0Eawzhxy9QqGaYh6mQSoGU1wLfXbzpLM0y1u7%2Bdi%2FYS%2Fc2XF8HSCZbItOf5ddtbsFhonLiuTJUVlg7jlJehnNGktKXK2I5aJ7ttfbokKQ3aUk8Csjvk%2BGtecEv%2BFDSV1C086juSqygxIVSoDAgF3dM1xJ9NmsFlKJoLSSmuqV6h2nIhHS0oYHc%2BTs%2Bl0WPcqG0Rqustbf95%2Frc%2B4U9ofq0uaq2BRcL%2BwAtgnDIaJwQ8Yn6Tg83jGfpIf1LlvyvJMHTOSr%2FirieRrqGNN9X3hTZVcdBrbTzs1TYd18fnSIKFeywmvwt70Y2RzM7CKYFn3NJJLB6LGHqxXLwzS2B11FUl0Qe24BTaY4MzLiWib%2FAUF27zSAwscBS%2FNC56N6s6aiXdc1bs%2BW0WO0KMacdpQxJQeVp08hz94uO2LIW%2FZn0Axjy8TOhUG5cpOUU6DS6vLYXyYeWW50wdFIwr4vJ2km3RKCOJBd1xTKKoeGmDhyxMcrHx4DZkQ5mH5FOsNbxdOUewhCZjCU8BSHkoldfc1J2xt7x07iYhN5sapKccgKiY4%2FNl8zo3jo2hw%2BZGi7BjVIlyvC19hNuoAACYjCIrt1SgSrVMxo9TYzSxjagvEZ3Tw%2BWyCI2PinUoMfjhUSC4SR%2F5B9%2BGEOPt2Zx9%2BNmszEHObGlXkokhpwJdyCoOD6fpnT7CA5hdqw1vRGvITurXBksYxQLs%2BNH8YthbwqEoE7xg4UW%2Ffrzq6FrQV8p3FDJO2CfcqI3lcsMBVGhA%2FwfdAAyCJ%2Fc2XzKiZl6mCoae49%2BFX1baInWeVtX7ipBjPxk7tObjTqF%2BrO1KyyMVa1xRlCO1laHSLFsOAi%2FMg8PiF5qIS%2BDjhtTbzE6qhINYr1OdiXySCUe36eDB8Ae2k%2FaNQjT0vFAaIchpPB6qbWwcyCmHJ1hzmRxwbdB8Jyb1q1g5%2FqGQQdEzhrumXDD4tkUcZwkJnZG%2Bsmi%2Bc30Dj6ORl7gBUutnp%2FvKkG47P3VCD5NkWCP2Dklq0xw1PO97rfymgQRM%2FQYIB25Dig2d6lyOQ7kWJnQ%2BGA%2Fv2Idq3i7DUkZZhwemMLhdutuXAPf1NIAuEKuNpAcBeDJp1ObM3xpT%2FZTgGWfcRKfU9OoCdmeMIRYqbvHLAh%2F25XP5kB3AG90DYU4DbTkBi185eM2JxiGOIZ4Kp9RyLjmHTK3%2BaI%2BV7CGI6ytOUjBJQy3p%2BOVX%2BTQILvSVs5PkZR8QgF22HoYWQKRNXyPLkFKkqGRTfbjndXih0krwU%2BfKdDGI9%2Fdx6okjcX8nCfuOaGQamWF6iklvkTxG7MUHVjQEGqiFY4GSX0wE1RI%2BKMJImN4%2BKEiTbfcxDwkIOFW%2F6q6EklAXonswvWiVV%2B879ljwx96%2F4yf0KAMXTNjcRTzkgXPdxOgW7T90Ttx4G6EpJL7CXFJZ3yyKJIHgzrVK%2BF0PjSJODepBHJJMjv%2FFNR3g2oummpVb6NbJTxWFWHU%2FwP9kKUbON%2F%2FAtR%2FgdWDzGCvodUVm6BhMUy18F81QnlTaE5dSgSDT2L98dBWAGD1okYzEr%2FcmY6J6Fx7JzQfKDXOgkvxr3BsUcTdZPI9TUYPZJWTYcMz%2BCIWx9Rt%2FTOjGyiF2n86CNP3O8%2F2c5VEbDFoSdLSH7Q9zScUyYtL2MWfMAwsuRvV9Edk9hWVDvMglZdS3V8zmXRmKOBYc2xbkZskYIjQa3vqkGhizJABVtP55sV7OGRx%2FuPByQz5zwuHjO9E2y62cuhTQbLGRMvvtTR5HQq9C4Eob4QHjXMNm%2BqkKfl8zBSfqeo%2BhU7Ykp7VItEJ3gDM5%2FcUYcwk0FI9DiNLDm3%2FkBTV85U4xEnCnIUsbTjt19vhjEuc%2FtOSiyguWEaIhBwVJWMBOONZHMG%2FcWpNqn%2BWJSH5YLZKOKKzYpxJEqUZorsAOhrLsCyFlxLWj0fecGRuhZCxN434fQANd%2FIZ2gpqdQJY%2Bk7uejS%2FkrHp4V9uBO5lS03jJrggTT2M9Jw0Jxx%2BsMxS0BAQHtRFjp1XxkuJxYnEtl6SjRMumD8yBa4uBjmiwJZ1IWXe89tajgeyGKghxLEtn5qdfj7Xf%2Ff%2F8%2F%2F%2Fc88gjN9TDZyz1tGZph6WocJp4CZFX8XXZ8hFiCZEsHxCCwDe79CE4ZRizLnGUbCx6DqO7jpOA9ZurMSG8CH9fiPAXevsqXQdKodxBiaJfcBuvTo%2F14%2Fv99Ac2gBypOT5cZXT7mXMk9yPr8VOLethkgppLlcpJb2P20SO0LbT97eY%2FBJr9wpN85%2F2Hc3g76UOnNjU0zM1B4xmMY9x7WY6V%2BQdtwlQbvZaSv%2FPp8Li26qIM%2BbBKyjiiLcYSdaC%2BQt5jZaluRMglkgo0TTW1P4ZGjXUT7JA5anVkMkya%2FOaG5kd%2BE2PUQhWPFSuD4UED8m5Nwgb0hF2pZvnH55oWDMv6oR%2BGRbinjMJia7RNledPzdYFHSKBF%2B5RR08wlblAQTown8gDDFTi%2FhaJCheCui5tzObNDJoHrhtUJCMjILHM0KflzimYpPhLH7qGSgSYpRP7RoKT9gocqtNTS2HhGMJ1hSJGKaGX4VPcRoiMZ5yVI9M4IJmnym%2Bc80Qwa0dL%2Bg33ZWhxpvgvsS8X5ZnPr2sKImYuCGf9nFA9KsIEYLXpkMYxCe%2FUXjRk%2Brtwi9Y0S6NxetrksagIufJxAnDn67q1Eowt%2BxnyguXpU9RcY024i%2Fx%2FWHGgSeMzvBHoTeOWEfcEB8j%2BCNT4KR85zOR%2BAT%2FVYReGweBLpJJfr%2BshVZ6vzFyVx8GYLSz5FTU1PA4yiZDxMG15s%2BBqQkFH3fRZUwb7LTSYaeAGGxV8QBzANC%2BY%2FoJMFY2hJpax7unfDyg6XZeJW4phOkKPvBKQ7s2lzKyFN9oo14EzZA3QFIhd8mmxBcqMN0PfLkQzCFT70B2RCLcXMYghcOJzahONnDIpqRmn4Z3IS%2BGkCP4mxWgolEbWrS2JMvvA%2FR1YB6ZQ9NIQJQN1jKuEYgeTLXjoU8nkn4nOjMExZu3XRjktvIUsgsoieQuxvqZgBS0P%2FOICSwrm8J4qYhkzHWEvIPAI6JQ0Nj2cuX2yKADZYPurzQPEJkMVGO3jUui0trPgzgqbgjRHdB1jQ89HtgYnQJ41a9AnKzkqaQcILtL8RZiX8EmLMpN1ndR%2BDZo4%2F1UcEJkrAOVwiwj70tqrysv7GMd4MhwgkSuRXEp%2BkFAC%2BWlyd2D95LnZ5RD24jlLoRrZEhj%2BMPregEK3SLLgZ7cFN3bHFZ%2BQeaQa2HwAcjYZgAEekuKY%2Bz5t36EhhqQZQp%2FNGQeopbhx6pxJCMPR%2F%2Fczyhs2QdXbA5G%2B7poMgTbUjoB0CqnD30a6wS%2BZYQsaYwo8d4DmLgk1oPJhfLgTcOk2S3E2DMQhIPzTy15%2BFf0xivtSGxSvIXt408iu3YerC7EaElBC16Kzotyl540D8i6uyhwXme3yyfCezrx845kF1d7G9o%2FcOa0NSBNxOZWiW60bSqPpXI%2FfxSx6cvKBfxvKdX7Dv6MUYJsERLBwEfxBN37jgQgdebUEKPlgt41GWCWlxl9TeO4aZ%2FLJ6NVQhO0Im8lGJ88x%2FideCaehRHrzRvKori5eW2b5XsJav%2Bcf%2FDDDAZoECE6JrRYI%2BcaU0tmtK%2Fjiz3lPqjSaeOOPNGMrLAofA9EaVUWkudS7ROErFjAcNDLuGIS%2BfctMJioZUJT7Fdf7sJClR1DIznb1wwqB%2BRIiPQShckSjfOCMwFdV4KyDtxfDF0tJ5d2EUY5my8LOVSeC1jUNy30LviJpooaBnjtx%2FDh52yulq63ZHNpHHFN0BFsiDzJ43RUqd2LSxRG2Gq5Hba6Nzi6APlqJiYVqwSYS8n4nSmY1R2ZqugBmYzxxQmZM8MvpQ5o0WuoGuQ2gi5v56FmrhgDDOStAcldFi3kE%2BjJJyo8k5GhcIuMH8aBdUTc7on6PXgy9afsl2L3yVtIUs1thUo56EcJWHFmvrBVkc%2Bbi6DY7TwmKxaFhSIhdiDKAl7ki2Z7Gj85zD4Hpfcs8KYMmJST9SqAlVvyd94Hx4BNCGNVK6Y%2B3SoQzLJiG1JOLjgTxx6wgB7OqcYEW%2BlSVh58rm61j0hS%2B91Imx5LS8vySSkMC0AXcZxhqVl6QmSkpK5O%2BvZqa3w2mI0%2BVUd%2BViy%2Bh5GhPg%2F5foDrTwcEZm5MfJq4MauQyE%2B6vLvfz8O629a4ohj5Dhh5gjzA41zMX7wrdLT8HKEkpEfw5%2B8P6Ivd4o1%2BQ6D%2B8hLkRgqfsrIh3rTLAknaj3Q1MIrlych6ziOuTYzZhot6hVjjxFg1r%2BIJkdU0ikOIWNQBvFDqU%2FuBNi8MfYiHKSNxMvAPOGIW2bOZh1BAgEXHHaBawKfh6Z4ER7pVjNm3G6PYubXk6wkDspEaEyVUgXwjMqX4Q8zr1bMwRP22EyNTZn8iFoOLEpFzDQE6BzXw5TtzFKlgnoJqydrJAeSFHxBzHefGPXh9ry8KgFXVvIY4sUnAlFP%2BxcZ0iLqGZC0X7ijah%2Fqcv6jckrgFm8%2B7Jvgl5J8D%2BktTV5k%2BiDBa0%2BBv76d%2BUbKDkVbgQExJgXXyfL2Azi1TZ6Oxwc7hKR5A09Q0KiRMmVcw%2FEIRqgnemLkk3ipNyPTPsv9z9BBY4v3cdzjr5TrJYj0hDQDUjdyJhWMtMWZHMETytyy%2FapO5ljEK0MwDqXwI3pRksFkFZNnEFXKUhbMIZ%2BXDhwDyT%2FYspJaGJHPTHqQGscgHZQywkrCDXqGZJnPwp3LhvHHE2NKaSwgcqFWeSfIqs8TSMAaAant5fu77F2zGhLVT%2F7ETpI2Z5xRFo71k9RGneM9xVV2KjqyO96W337egpJ%2Fx2RsZcCuFR6bMRK0arRBPbu%2BEcEhBomPk2s3zfMoeEntCUOhLDFN3HidAv9keB5MnmZsScwGJHxaxnHiVfK6NGg0WjM1Ki3yZOn5YBPkoJKnmmb8Mc4oBwbGSyyqJnQQkdWB5Z6Jw%2F6DUg9XNIDhxq29UrqhmS%2FOlvmD8ZAy7TYa6fub%2Bpam64LYIzGq%2BeVwtNL7klY7nmRwQmgxRe87Iero5DvuHtJ%2Bny3zcWh%2FGcjPLaSgKoQpJdFmOikGCDSJn6DFqd0XxVKk%2FDAYz4QDpOl8u6aAnJuy24fKNbmJs1X4G7vkS%2BgOcK%2Bdzz9o5r%2Fb6P%2BKqEmblkkSaIkerptBnFa8ztmQNnFaUqzzoqdGPJq5cVEkk8eBD2ibCfEk3lCG11hvKP9n7paeK%2FYdNSHiouhmoVgMTR8oM1KtDCpACOz3WKanAnMqDzVTN17VfGBY920B2jA6cpw0tn0iOjvy5GuVhDTeMRaOtJ8d7ap2M%2BymwoYctYR78bLIjuuJ9TBFJfjwEF%2FR0rG9JFRn5CJRvSH6JchIs7h%2FyAweh7emHobaTQo9vNOwvlrL0EaYGKIIIBI%2F0fJzOuD2SPsnwWzEIXcArZXuwQVMJfw16Sc5CUiStF9cnLaVyYtTEs06UBPMUL2682N4wMIOsnN9158ZniskNKcGl83YCuNo6rpkHoRgEoU0RI0MO%2BM3QnB68LjkD3%2FDoIT87v7ykf%2BqWrIi4Ab2GmGWO9NQ4oGs2wYD0r0%2FJUR4T8Yin%2FumXg0rIlNWet0R4znth3iW0PKdDDGi0OJSpWd8c8iCyPz1YZuZoYMj1sURM4WTHwYOO%2BXuSArSwGrEF%2BMTzoi1Ev3qW6nIGAF0%2FNrHtEruAygvt4BbryIbSNE78spEw8pehC%2Be1m0zvnDeERnZY5jjKeZjjjWs1P9iYigxrpl8lUNLjpPJ9MvMvMTvTmNwXMeN8qkh6uDCAqWwY2XTnjAPfL9WS3Bs95EwbZFTIl970PPNRBVSKXtRInAtpIYp%2BcSMAJVY0cS3AqIx4bFDngZTrhLNIxemHTpFDl5NqKfkV8QtKWsWike2AidMoFGZ9l1zQxB85HcLCZQ4So6ZJ5HnQzeNyjVWfdp%2BeWW8Btn754OBwx4nHlKfHW0WYrqmWRJVXahjMZkyKTsnKJgRbGOw%2BAYQ%2FsYF0c2G7N5OrBBjUP0G1AUvD23dqXixY4Q4JXvPm%2FU1VwEICkOenC7qomEHwjFYEAV0WUCw0HVUrC5JRyb9mrFYNHISgvgidtiWR%2BVyIaty2Y0nrrC8kuLBne0et5s3W0%2FnHyI1ZVPcAeday%2B0IM6Rj0R9hluxPk4ySYUrLJ0Em1VTsTAgrCYBjCGLwE8BDRiPF8jlXncZg%2FWsRjJcpRAfhDYuGQZQGSA8mzfn8MghixkFKSrHKGFpu9VhGU6Y7SEYydRI05HymvzPhCE7xeowZnc7S14Q0OYsJ3eTsw4l6wcHmZByB%2FssqcTxRlVJmcA1uw1dCAfWYlcfdSBG%2Fa5F1grrHMGQV3w2IvW0B45RUEX2FqstCrs2Tc7I3xWawucnSq9gVUy6nTXdoLMh2tepg0b9OwZX1Z6nkHaU3%2Bh5UZDd2L%2B%2BoGC%2FSWU3jBeRwivQ4oaHTG%2FC91w7tCFvEQM6qpgD9%2FNdvDPd7pvgxNvZfZGNS4BT0yEfFOd9QnL%2BPI0LJ0NuTJK%2Frl3PBmnEWk2CQuIBYGKP3UwBIufS6NL%2B%2B4feqGefif99r9PCEZkXQVPYXjMjQwTHkJywkTm%2BjswIfVetUcBtsXPYwZKBlDqCoJTuMFXFlDPjQWDdnKfnvhcRJWzg2h%2FxloIWYD8%2BztDIrCrWCRaV7pNSu8jxBsjhoOTKJCxjAFYySBQlrbn0w4KlOqGU4IW0bYAdwj6cpjxeHKJmJB6TtPyFDQDPIJ6FQTxT8DUewETkJUKGO5m0mGnXm6dQyV49c3fWURIzplGTIiysmi1oI8WTz4O7DXZlHNVRshacJ4vhOOMYVxMQjcYgywZcJESV62%2FYzRx%2Fh4Gi6CvmAx02tj9lqiQBlbCYqDw5DbvnpbP2YyJFoFWPHlJBHiO%2FwyTBsyC5yxihlegE3uauYCGuTSHh%2FN00TtXoTf5BUZRyimjNR8eycnHUoL%2BxN2WnnR%2FliQWLPvyVflfCNQoprkLMrXwhOSApiBgcVLc525gqIEgydl%2BvG%2BtpeKbNvUeaZChJ2agRfFfSwSwCCYA2i4C5cJDM65tlbHUyqv5GGePucczOAyqXRRZL5PSD57nFREKSIaC9FYIeVv2MUHVWxb%2FYQEOUG%2FfoXLiY2Zq%2FgnqyfmIBPyLIqv3MuOBzDBs%2FG2ci2iB8Xd0yVYvRnWo%2BoN55YgTJOUALiOF0Qb44Q2PlEMfhkzOyI7A%2Fg7I91ehYqiNkRXvS3OH24nk1W5ECpBMEtVBb5FCq0nF%2FgccT%2BcqfpPsHkMxJP4OSJza%2BVlkbrq4A8awDyeBdfOEkDtZDpGi5Fp8ubL9Pg5S3CFgO2%2FZmZ%2BkNVxEz5ojkcYQ%2Bm84PQvoGpPD%2BxCQ8rMA3EJNGDUVeg0XZhGfuLxTy2CczYOeDu%2BNVU%2FCBPrnxm0MYy6CET2fGzWNfNi6qUEL70AlNZUkQyoK6ctYuZDyX6wICYrefp1pvSh9sq70KA5emvdoYSbFfzj7PXUTRjkCW%2FEiCmEqL4hgGWSM%2FCzxIoVVIXnzd3U7gVeHLAPoWitYVzinxe7KnFXPsa3uugnxnLriF4f6OE5BzPEodzyuGrW7j6BZ%2FIe4qD%2BKmITrDrCK4VcaIgTd0ZmrifSjSC62kPSWdBI9hrEYoMDRWcm5tQZTqXQtiM63Tl1iNClqiLv%2BY6cQBfi88lxpx1RUUzlxkiQNxAxsfL%2BLUbAAK%2Bi3XCes6LnloXBi4l4gmPlxoW6j3QFunxVZooY5qPJwPVvf55bmhEbnIryUzwXSIc7axM83Ssg%2FEKtWUYIUGBdUIEGL7%2B0dWd2FiTlJbYJAVHr7GvJJZlC359v0wdXLG12LQgMNe6oU81QX9dkV23ZtW0EjDIyPiYshHGIoKhJMziZshgchgQzCcZbeFyS4lEC%2B8TQvGvGSonGaBMobrE2oxfXgVMNOcm7mxnclr9MJmU0fUkYZDFPp0rJ1PO%2BnszPo4ebP3p1VrQtZl2MbSJsmS47aMc5%2BlqOYN%2F9Fuz%2B3vTngKIIGLh5Ij2JCm7HYETgH88az624Yoz6SUQLqW0YVooOaCarQKA2zXJER%2FVzOazjRBxSekE8B6AMVSYrx011I8f0XjEIoAg9lToLnUszAl1Ahbknkv7Nbg3otgavwYFAAXHfpLnpGmGP4%2B3KpOZubS0Qz56%2F%2BNrZ8yH4pixRpQQLuZv%2FKQwZtNjBLWAMRxpDLP41VZjzBcAbqVqqEncnMZ9FimOaEDcTVTp3wEZXoVDisVFo8MSetbmAluX3XWphkyOHz7aq1SGzaYM4XAEFyWBQvgVetrHZWN8ZrjwKQzhi%2B6K8SES7Fqxck5G2ccalL8ApfPOqUIX9IchFzNVgQX0AiJNfmuVKEfkzFD6vrmigzNNSYGvZv%2Fm%2BHjbxSB910XPlh%2BFD904trU45jEKKC6xWsmJBOQTIr044t7sHPHQSlcCRpIW%2B68bWe6UZjbMrBU3DFjEN99Lmcs93MjYdZz1ZszhcKTLMAc7T0GvtxAqo5wpf8IvxPrg%2BsSzWco0M0hv%2BDzxlsbDQ8NFGUEK7xPdLen2Pbp6hOMxXbHR4Ydb1Fs7%2BXIIqviVSZSvPdxkXKwnxT1AjqxlakrbB68Qcdawizce9PsreBY%2FCUpOBR6nyBecJWwg%2Bdfrigg2Kvfv9xiMbHpRHyPS4wHFghbzsJi0%2Fu3FWJQgh2Why9GbU0SsZtKgWSuMIhke0Vi4clLJwGLneAk1qZkxE3hsODfBOx4KZjhdZIn0O5iHQvi3LRIZtsLKCPsCA%2F7XfLI3TVtczFywKkOuysuu9RvFIHr%2BHvpskOrAHtg5V1DC1EahlIyKK41qBEOgf6ZCjZ94dxlO4ubb7zNATo6Qbfc8l68%2BcqZ%2F9oInhcw4vo4BUC0kKxYO3NzMhO9bWdBMZxhxYu8mZ6ScT6%2Fp2%2BCiiYwJYSIBbvp6eYfS6%2BcsXi8O0RlpX8ChQpgUXvonPkQGz465Q%2BMU%2BwJeKxyzME9r2cEALXY1zBZ7BdQRhcashX4kHYU64qh2Kwp1XfMVlN9yaGVfZyaToeIjBl6MbVWgippgM50g%2FannUahEokwJVr%2Fi%2Fvf3J8xeHKbxOJ7k4Z6VJ8PaiCKdiJGbQm2a8mV%2BBZLVdImyFu3ClUmTTGI5LYvgOd6TrA%2BJbmFW060jfnkM6C74P4wZn2CPNmE2DDWcM6TIjxBGif%2FzRw8GVX6EDDBUj6nw%2B0Ro%2BrX4%2F7Ehth85EqVCebnD5%2F%2FLsNk8PYY8%2B5cZWavx%2Fndp6r8FOpJFlfRfqe5d2n3Ki9%2BhvIp3lSsjXsMVTGmelV%2BaKku275VjL91iOtB8wvAxELCaolrzUr8TFghZS1R6Sx4Wvq1WZl0BbjM8%2BCupx%2Bx5eWFNFei3gi1kH3FCMjsZxTwFgsk0lFJj3pbpPkcaE2S8Gz2yoBgeLB7Ut5X45yjLgiU2Vqx3W4sQasL4orpcJ%2FYSmkBPVpIdTf%2BS9%2F0o%2BgvHM1ERXviiDkIkxNTZJmigqBm1GfCbRhCHg7vGjHTc1HwYjHPUgLhseWJRu8aP%2BL0wzNjgDiX1qxkWcRwcMmLPjl23c2g0vF%2FCmxpp15gJRj6kYVFmvGdGaq4Gtnf23zqHr72D98DfoR07OygcvjF1YQNeArcmaAKTaoiGHju94U91paxFsDqBCNjlj4vcJOEGSu51M5GKaQNKGZE0nv2b7nqcqsz5QAre7KK0nG3FcC1Vn2KcZFAwSmr1bh9jC3hRRFXm2LF5ReIhORHkhUKSzwLJtEOfNHwr6QceiRSl5k1O4vHcrhwnErDHrGuJwov0Qs6HofntO0Zv6g%2B%2Few4ziZtYmqxTvRqKfO5ddVfJacFNcjA1IrxJs%2FkhHZWyhHe0l8gyiCPUbisjvnTdqtIcXPr5cfD6HgUU4O1OcTPZfAl6JCQ%2Bw1wRJkFl2nT5O72ysOFQM9GJn5xMCfQxan7kb87VEMFWpE3d%2B5s8Di3YqKIZQ%2BZQwjx3zU3fD5ShgZp01fXCCMlIXlLWl%2BRV0LSxU8GJj6owgr9S%2FP62SFHncEjNCsUwcz5Qnkm%2F4Kow4Zkn9k0rDTey6gskIKUqFOavS%2FzpteHBtfoI4RzOynONv5ReQLJmKp2TIShteNTB0tU%2FdCK87f7ab7dP6AqBSfPPcMjqABH1SzVwchHGF8tR65DxFIOFSag3FoisPKcbm7LroJyItV1zvraM0HUql28F99pjYru9a9UoJOq%2BsgB3zxHlBjF2dc%2BBp%2B%2BF53TpTVX6%2FU2ZW8DLx3wTUVmK6s0BEUeAyKnlfQuFZIiF6dBIc7bKvlkfCfaGLAEe5dlK%2FZcFeD5H8o2bzpNCjWs3%2FhOwMKrxvjs7%2F%2B8EyHoROJgtsquqFgK0pSkMVmVP%2BhZLI9mCw54mlyXtxt%2FNlj%2BUtqgHD%2FRemG9pyUHfzZNJsAnXYeGzIKxf47J%2BCrl5VTFg7DqVFkdltNwW4%2FTNAqJ5YzrNvFJ4%2FhOkRHK69Hyf6Nd1FCsRFpwXea9GvXLB0tdW0EwPBAwrcfz4BYOFcAKB67EYtx9C03OfFKu3FHe%2FGhCJXH5TrMPtz0jaHCv4fGGB5CJaOrxclIdQEl80x9XlJc%2F5PlkjpoLLrehJ7kQpJvH%2FO2F6Me1NOjx07vXeg0SmAU1DcBhj2QyEnoPFyik%2F0auKkDHuvXm%2BhBBsCcP9g5%2Bf0QnsTKJ5eOEMW6HXTxwf2bNub3ked0kQ9d1Jg7jYYipNRjgyixnF%2BWE5gD38z7%2Fm0snIRHjpF2OUchRiQuHhUcnpKCkCKESaeQ4xOMHHxjEWIOZSOmJHPAZRrRZILD8JdV3s8N5cUX6%2Fml5gtP94T0t5fn1ajuFvc9eDqkecEf9IwvH3GrAKgmFNDRy6RuW4LOvCiPqf1DxRVHA%2FjZRjInFgz40qbc0fdImzSrYyirToOKaPNPtEHCZp4GvRvUA94g3PmxDfBygeQzF7Lmdfk5gZj0PJz%2B73UbBgncz99ibbgLExirpbGIs1FSEsk16yb1PNA5abP7Ei62C8NhlNeK1%2BgYhhf9i25En7x1oiutGdVz2m6gOvNonxgzfeUOhR292cpOJnYxgCpLAcjk4w%2BeW4syseEt%2BA9xZzGTr4zCGFIIKvDWJNu9XyvjQ3E55JdtWgvumfps6gaPEfYqmcPmO7CZMTSW5H8ydnzE5H0NTQG1z3YGyxdmBQQqMthCmkBa3bth8wdzEeFHjHWczvnu%2Bx9WszzfdKlYjHHJ3ueQTrcJGmbMTdDitNuNixpOuP2VtWUsiXCicXINp36ucuaC5MTJ%2BkrwXLJoSP10xcnq5Eg4PZez0d3h8jhC8aQfKk88prGfsTHSvkWyCV1Ydwg4q4RChc%2BGiQhy%2FKNOi6K%2BHc7oK47J43M7YgpkhiASx6bk6uLYqpXwYQ%2BVhreU2rmf1Ose8xK%2Fb1ZrKkIQN5Yd4SiCqbIXWv3OHcLqDPZESFVnrXTnHxktlR0pEmqwvWH5ssDuxoPJ6g1%2F7x8gcVE4kAoiFsHgV6x0zECzkZVORQka2ssqSFsy8XSl5XXMxfWYtvuaJ9th0patabCSBYkugfaHpacjFBkyOa4e4APxCyAwAE40lLZHrBCG3hQUBAnR0qLUZ3W0Y%2FmyIVplebkQaV%2BE7t6Pf74rPlHJux39z7b%2FhbzyhstF1OgN7SogKrc0AGpvXMrB2IBTbwmY3UbwMcUjYE0TdZ7vgD3%2BdfUY7OUNH3kYca4ETAOez%2BcO4%2BpyiwU4owW8hLsxorBXnciJKwCs1cHzfTnp8IEIo9Jof7NXF65zrCaF6Z7Q0iZNmVmwZoEFR1hULxYXYPFBgtdslyXiFCCA4nTVI4uBCPuQtkQJLxU7B6TGekxElVDxQDfYS8YyvGik8so8%2FK%2BLTShzYT4gjiyfnRBYeA946v%2F83kIvVjGLnxF3%2F%2F3vvfQdMhcvQhQub%2F%2BK8n2pAmZYgZ05%2F%2BfbmHhyq5IjXRCTfFG98NIXXZUb1WsRPubi9nND0S8X9IlVYtKcwX5uM9i2F%2BxVU44riSynKHz4Ykykz%2Bx6K508CDdudgG61cIGBIWDTgdqnqHLIbEg9Yh2MmX0iYK%2FUBi%2Be8V9m4MK9ZUC3rDPqKHcoRerLxk7aCtiJf5x3SDHKSR6fLQuP9E4WQzq%2FIut4%2FgRNbAU8B1t8nfVQOta5aBYUjqWYyndKx0vf4l9h8icxT%2FXuSjooYAd6fHumA7m1XBrZKPE25ZgZFxWgJvdxIWxnrNakcxNSdfHmY%2BXgJM64IgxyYmcezAAxYyscJTX5dHaOJyvQKTje9iySzxXQPQBRIDfr9GXGTO0AKU8Z82ZHRqeuqBaY7U5yMj4YGjxY3c0lxestzgIQV%2BZVYGRA1Xy1tIBxSxO%2BwhTr1L3MKZAzn7uN3vMPMdjUIzQKcVDp6oWlx1bfXck9pFey7mCJ%2BFU62y7TPot4yjthJJxWq5Blujmr5RBuYPbeyXRZX0EU%2Bfl9ObAM4gswlvywEfqnhyEgfq%2FQd5Q2%2BU7RBCO9aQBx8TMxvOSZ0hved%2B2gbIYqzh5Fx2iHYhXBFk7fxGwhsh9KIi3a50UDRy3Fks5lzEHF5%2BF8IbFqFHNtqV1hYf%2F%2B%2F2npTSx4n6I3hRUoXxCz%2BYcLE3%2BNzJ6YcFUv%2B6lNDEJMC1kJpb3IXfOdGbpy8%2FAPvhxANxh4jCRmPB96B%2Bd6fbNsIX9VKzLI3WkYqOuNUuquanUGcj%2FcrUY3jlKCReFf34Pw8vSbozHuJ9iBSsKdCZ5hlATzg5hlPYYdHnMVBu8V0IyNR7Ms0iS8RFFTyAD%2B2RqDwHUFL5v6mXtqpdBlox5E4OkBNUhidrvHKrEvTui5nzDx4mB9lJnY2RtGOdLJMOXtsg69hBJx8bXH9Xyd2T0YpWEuF4du1ZAzoGIs1%2Fzye%2BGStLiQyNBws03rl%2FD4KFdmj9dxFbJl7vMvI7Ftpftj9UUwjjCwd89KEwgeflKJjYMejXP8k%2F4bZ3DZ%2BDdNDa9W2LQIlmg35G18X6DqnNNQS%2BAWLIW5MjvF8uXKTH8D4x3DZ91nFOQOVuhDPrJO6yeaYkauWI2qVBcBEThDYmMjqRKVqsX2tnItHP4TJtPyB2aQZqazm4T4KN4s1yITsWCeXUpHJtjG6RvjUDF5F%2B6OH9K01E4sCHcZi8bO22hbi2K9bsLekvE5iAgBJfCdBWEOPzkPcUhQVOW4%2FmhcHnP4RaXLNRpztI%2BCup5x4qq9MA4FOltMW9qD0jLOfTLLjdfUCNUev4hX0vspjoxzJb9cwVCYZbul7MkGQmWrz4dLO84oZGOBVghxTLKf7%2FOHeaicnVOaFKx2HO1lxnZQx1NEow05CxF8ZJvIyYzDCZ8PODQfTzRjtY8wJmwXOpqI2xJ6PwwuJWL6EROv8HU0FW7M73qOjemaF3D7C6im1cdtFKzKNIeXF0zIVSa%2BIbzS53EgBC2c7EwXC9DinboShskltQjno27ObNuWAC6O3NMNA7lL7MV0tx1gC3F7ztebuz5pokwTL4pEKpdS5WMLUoS8OsaSp6o4xngP0VsYFaRFlvAEzN1g3vcXnB%2FXwVelmEIIL8kb%2BdyqMuucbCKBZ%2Fkg0DQ8Gk6WLpJdWmBEOlBLmZz3WJ%2BqKqLBQlZziocZmi7eqHs%2FFFloHukizk0G4jAvZl1LftbSptOkth03hq2Uz3xnVNWRkRh%2BZ3SgjjVIOxWmLU9hrb7P8JxwqmpvDAotCGURD3AAGpFl9N7uFJERY1g5K3fgBkbcRALC5Br%2F%2FK1fe13BGVjeMWKLxo0oyuU9PaLDFum2ktpcncXbEE6ElGj3gVa3W2KhRuAovgXm1heqOWMwUmSc%2FMDoV4HPGtvykk1jN6AhaHXgAvKhpYcifsyt%2FCDMeLpWpgjlOa%2Bb5UqAOjpqU7f8zITzTHLma3eitPA44WVyrcuidM2tWRbbJEylQDBNHzGSBDfGoPDVetkLrSiG%2Ba7rwkyea3mN2zHsTwE6WiztLRiV5iy%2FHaQun2ffJFCVuyVQ%2Fps8%2ByUnFfCC0iJVnjDHNzCB7D5DsqMgfvnlCyTe%2B3V3H2fT3eKfxUCOQCo9iIt0FvAI3od6Q%2BanmVmdHUklpLm1AxIBcr%2FpyAWTGSwDnr3bP9BJo%2FGFPRKIfHsbvQ%2BfTY5ufn3A3sBc6yp61FB4S1Jj19DIccpMjGsUat9%2Fy5R50UnzdJxc%2BLEsR7sObmMWhYN%2B5I5J4enZXWuziWVP%2BFCnFOC%2BpSyjB82rnz1U8fpih5eANNwxNkEGcIZFpxigMoTZvhs28RvlofGwGxfp2whgSdYAaqty7DLx46g29TNUw5VZMws2EvGUinBpm2CYxNy6gxww9FJTyFiwHpQY6mi8up%2FWzHjM%2F4K8xXCffJydeTKNmKnC%2FmnC4J%2BjsLtJpwICBy3iI5fl5nCAfhXyshejyUXQtygF4OUzSTO9wx8Tyo1V4IIts5OBqKGfWSewxkIf9hchXwYk2ussU3VErXxHmT4uA5pB3ga5H%2FsFOuqX%2BiVCb%2BNjQXYx8MPgumjiv79%2FNiJHHx0CDlEjEhEYqQsK7pqWzUjVgDIfhON4mltY05iyrWMV6y87MBgaRw%2BQvV5ANdyB9c0uQRFPX7wCWIKlSOCtoZVrh%2BCAdFTiGCMzpOo7GD6ChOyTkqCXc43gfcpG2%2BvfIQaI%2B18fKY5TbRpTw%2FGscOylCkTXXVe9ZFKZJAxGVYxecywmd35IiH1EqtUSi3B72A%2BqYKsJlmY%2B0rP3SyL2EN1HLYWQvMgvrS06oy6sb2%2Fpl6Pv39IS7Jjbp3P%2B8Ijw3cdTiMOcmw1KAODbPkeFKSozQ7jcPdDDR4uShoP1mfFMZMxOQJ0s6rBqvCio6j3KeIv2kScGMlx8b2lI%2BI%2BxwsthDAyDlgAuJUUbm%2FTAI0WJ5goVolPgluovxXa6rKStQ81AffF9ndtxTdYWamOeX9yTiemsP7DPH1dd4Di68N2VJaP9pWzEpz9Cf2CZvgUEI%2BzJPdpIwSDfG9Dr63aMhBbkxgfBRdj%2F%2BwN5lCNCzMcH6QtG2%2BNYqzQprq%2FpZ6W4lWtSVxXGDB2DksYb3lQoFoYS8jAyzE%2BvsdjEbVPY%2BfLaXG7T9trFYRfUxoeWPgw5ihWpLBjvR7EzzkMl8grv6%2FjI%2FAlQaSrDEnG7VrkT%2F9RKaUBjfCFu0548BUtM5zMit5ma0%2BszUH%2FVbwLeq2IZ8tfBmJZaSEaSn0qTXLJIfvNbWPczJP8wgHD4qP%2F1f5uIjv1PAybRkSn0kl3z4EpuVQm4nuIlpFEImmXpClrxUBeYIZuGCbW3%2BMyH2e3SuRMeHBKuMmqN1aDkLGmkqYM1j5Bubmh4mdaYU1h1Mbi3Gcj3N7gCx6GYJUXBC5HzTRO43xF%2BMeRGyUWiu37ZlsITxVUbW2MtvKP6Omo%2FnK0gsIEcMSlPTEMYJcjEMGuAFzJIHxTTDhovV4hQnGwiiqh8wtGmFY8q5vWB0JidBiOGl%2Bzf4GUkHR0ZNtc9I5%2FsN3%2F%2BOCC5w79gzJ9P6X4J7i1IBmHTa81AOOFjFMVLCfsJOKTI3jtYIQAXc3vQkcGHPnGmw46FAIUNxt9pEA9E4mMWYGuRYAAoA%2FBHStmNUIC%2F1VdyQjyCfvMrsi1A3okVRZTDqZzbAOC67kmtskQmkuyYXHGtEoYjy36e2lQxMxOoYXx7lGqYftb0aqSLg43UILrF6lS5jKdwKaPit85Mp2lFlS%2BcXmnJMbZxptMOziEiSGIwaLF69Q%2FEXmg3M55Uz3kGmpIxSDvWZFu5H3OwkwqhSmekrMWGaqfUduaWwhxvu7gGfaUav9Cc0DOD4DXBLqrZ3QSdqrscHzGSEk03jzIxb7DHc6dWhSamc8fMKPV4ZBq4rlLVr8r1Mk0WNZAkq9xYtP3TkNX9Z31R2jObG%2Bo3VrqN9Hv2WG3yEqXvtv01Pcb7IcMPNNUQkO4d3poYXdu2UISPOxpNj3neOW5l%2FXbFlOJDTc8QjQPP82DbcUu2qtqArApX%2B2yIZlv5oQ435ArsJPrPm6RY%2FDt49pabKYvP7hRyIg3ozj8%2B1FiJHYpjIecneh%2BZc%2FtBQCZHM7kcAC%2B98xf4gtsPKvDVQfRfg%2F%2Fg3B%2F1P6AGWCFk6%2B%2FuGeigXWviC1DH98Hgq0TkIiRZNxQYRdD2fhVWWKr7scBFhhE6ANCt6udzUTQVn1N0hXAWvXaK0%2BY75edTHhzQd1HJDNgSNKneGlGh8%2F%2FRfLZZPCKL8%2Frvq%2F6BqcVpgR9t8LRVTjtAJ6X9XOREN1RSu%2F0RkbpydtLld9j%2FmsFyIJjWXJziNi%2Fxvv6%2BegSS6r1r%2FTKFzHKdvDVkZMcbGg3Du1AZAHaY%2Fjv1D%2BC23PRhTBlS19VfTBD9Jw0id%2FGvmBx9XQ4FZKsBlGoZd3vgLgt%2F%2B%2B%2F%2F8X%2B0PBL%2Bpxn6JO6O7%2FZ75pgvpKhS8khC%2Ff9SMF2RretDa8y%2BxnMfXKV3x%2B1Sbmhq8BGyumbm1%2F9YTQ1LtAdP71RXtv2NJAvJvujPkH%2FFNMJq13X78EWBHbky6To%2Ff7x8NjxTO%2Fe8Q%2Bh4v%2FsLNQwUUYAFL5r8jRxIDxWbHeflHPDXanhl0sCJojnzBcVxVMP5RzBbZ%2F6s%2Bw%2F3UwhiIvYopBQEjTdAiNj864odhb4utI%2BXIEhw6%2FeEZixDpgchw%2BZMacc4UbutHjN%2BazTq0EzZ0%2BSmzbcPxH8qrxnuscYXKBnKEv9BGtGEPwRD%2B399tvthRmV2DNHmdWZzt9NBj6cGLeMlpC4WCiAV0ySyBmOzH6gKyHbQ%2FEKM%2FmjvafWZpJNKNmrmx7luoui6zvWxsj4wlFhuT5z6ne%2FjnXuKdmJRm7fyVVc2IbFZsI8UoMgSdPIATiToMMoh7sqFF8kduRtIhCRagcKumaG9P7RdsV%2Br3OJAVFaI83sXuXMb%2BHgUtN8MHEWCgo2EzHB3r64qVWxicU0vH0DKFzzjq7BllvN0IT8fwEutpGFRicL%2FGM%2FP%2Fhj4sFxKDhWc24FsyWyRexuCV35nWi6cU%2B3hW%2BDc8Ka9p7iA1%2FzwL%2F9f%2F%2FH%2F%2FGXbhdMzzDazeKswd62Ho%2FF2%2FVlQqfO5%2B8MYhFH%2BOqUlNRU0dZufBs4vwe1fY%2BpMLd%2BbjGSbUiTrls4j%2FTfwlkNAYnECpDiZN5%2BVq7pjpDkYJ4yfPIyTH9RO30fLR0YN0iHjbJwx7rOBR4gWjgJ3SliTG6roOR3pXT2HmTAzrI0OJmRS%2FICF4I0V9kSfUDBpKKTjtPtDJLnwv8zmRKQ0Ao6AKV%2Bh7WO%2FARWFaWzOhrzphbAzlhqIoXGjNAJfvhPoewmh%2BHrscXhIUJWfWlsVhIS69ikrtYIix0U5i%2FL7PuLhN0HOoH1HC9VqJdbLEw%2F7cC%2Fyos%2BYNowuHStxyTGmKoYZIooSYTFAo%2BUx4i87%2B6vp4YNQybXdfwicDsjmdjeE7HIEgZB4W6PyLTA5ZB7yDUSHBAh5JmKuKlB5C4j8T9xOqtM5BzZQcm8tOYTANbwQHX9aqedq90qQvbXXP2LqBB%2FVSOLHiyk%2BSBc92sLd43AKw6HCKFobOfv5yPSS38t6jj5lpKSF60roC52QK%2FL7%2FmcQ%2BZk1PRh9QL%2FL%2FJiLsOOFARcHYUSMybKl84RAjyXAjITq3xld8pkkV33cTY1e0VIwmCAY6kY0zCJ9%2B5RxwARd8YPUZxI4Tu834Fhp3AwFcrefvjfwCwuBXLx08mytHrFKPA1yW8k%2FGlfZwNKa32vzMQtry6cfYlXlPHc2bPEol0G1jOcolTURld4iHcCIYd3Z2%2Bc5jab1btDr4zNGbCQDc%2BS3SxbMYATZfxnRkj2LZtkDcnKK87kD7o2EhmyTOEllvfLOZ7OK1oLMcA29Bu1H1QyeYehPCEzFME8AiVSEZAPrsGhnK3ZoEQFDhFT9E%2FYaDGfWUvkQO5zcTdjeBVygDGf1V9js3nfnxLPBHKT2gmzO2%2BoqCFtklhMkQofLyXd5m4%2FJ974MnYA3OqFZRPOhn7DwqUhzcJUKZCCTJc3jrJjYrokfYEYNRUJbkrX5%2FTVrxL9yIx%2BkcmafFwmBrpIoVdPqKs5mQYmfIN%2FW8XYm7q2m1w4GwM7h4aDrkxtgRhf641D6GquTP3c%2BUUoLIGWfPfzK13IRxZj8rR1Yg%2FhnfTfreoCqi39aCilY7lvv06%2FwiRwXCY4cpO4fFDFhYmNQJRDh%2FK1X9pfHNuBQYdLMBM%2BnQGsTHUeP0imVnVYraGq30H%2B%2FBlXRElz8qyBeWwTCnFqrTE9NWVBf9JBY093aDKHORSi8m8wpOieeb0noaKFepl4wosaXOlMsw1sIhBtzmzXhLuETNaG0KndQ%2Fh%2FlE4BuVUTzNXykMzANcBpsJnzw9jRsXY%2B1hOu6a70ZQsrsqjm5h2OXbYrX5Zpo8cvCtQXNWRpjsY7dFS11jjgexzlIWQLfwa%2BWF2BE%2BwoUao0Wcpup5vucq51mPdgfFUmz%2BphM6dEu8RXAiR2oeOzAtJhe0p%2F0U426IyaB1wEIeXaHz6iBgHmCv%2BazoaoVKxNBjFOKBBoITbiE%2Bj6BHRSOcCDvBUpqWifSLBWhcUbKYMcgcKuoc%2FSJs6JH4hXnLe7Sb4AdBvUvZq%2F5dy5rtYrlCeLgmuw2SOcfskZmEkvZ1tQu3uaBLV%2B8L9fspPeLE9WtDj%2FEgyrOukIdNjf63iLIEQqMqcKtc8v0iuQmTIlnAxyqM9d908qqoMIceDO1YpBKOmFBAQ6ac32A%2B%2FXHRDULCRw7YN3b33CKypuZ1lhPPIyna0mbmE5mszJr04PkLHaduHJ3b%2Bo%2Bu72Y4HVAM9MuX1Vis1liEsuOfHJdvYbia%2BHMmmgGITwounW8XYVLpCHx0aB4ZsMBlyQ2DbnEEpLQw7Yd%2B5e2jrihnuPw9P3JG%2B1lxN7kkDrPQYN20JLnwYuuJyDFIvVoinhCAxDKdu%2FZq3YeKdU9ycGnHmoQJdRwtT%2BHXlPrJMO%2F8pL2c4aJjyY%2BNNuk6TIWQbeBY5r%2Foaf6qSozlrdYGV6uDBkOfE9SvGHFlSPVUEjGVnYSw4UJ6an%2B9omT1ZlyFgEZGj5OlO5cN%2BAovJNU4zXc%2BPTSu19iMy9sXIlMjD1fguZ4srpqBPKx63khqD4soSlJjSTGcRbgO09oY5UbEi0v%2FqY0AbwvOiVqsTOPRyAv7DougAwA72uYOHYpFB6wDuRH3HmkEZNTExs23ULJYZYajg%2F%2BJMU5oC%2FCeEikX%2Br6n9vuzqW8xRTtKvfHEheZOsqf0jlAR9FODA2z%2FMXFhxmC5QGqTpwdKxe8FReGy%2FaYxG6tMM2vk1od4Xa4SEXDbrFRXFC1Muy%2BO%2FjRKKCNkRQCKN2VfPDpY3vlPfoPHfkJA8jKIGgWMDd5siySO1Ajp2%2Fg%2BLhSpGSjUjFlm%2BchYsJVPfUwPPYTXFVND2zLj%2FSChNQg47V8a2Ii8bSEmHS6YqD0eZ1r0i7aeeVFwbTD2YC574mJG1HwzlhKNWKW%2BI8TnIbk9MERdbOnkPw5krdcHHFxIPBhebvjeCbptTltpLrM%2FkkmpMHa86YmyZCfkBt5nDqjZ50LMYYrKJiOZ9LzUVlWmyMbfjLTre0JvLHRS74ZKmuIBZRUDiMiVo5%2B9HuPYP%2BIJPTHUJRvrzCLFDkvKlyV5cF25qSBOwvXDZFFR9yRn4BHCjRUnFFB4jdew%2FmJfpINLjzQVc1j94dRoSl1PYBPynWaDi3IdoVTLvMbdRHzia2DDUorRrzkwjwa2ZwVMDAzqDzmxKNwT2sCwii8KQ2wemfRYb6C2Cel8Mzt%2BGRhKGkVmUU9Rq3iimK%2Fnfww8czwxQAVEl2pHtMQk0TFCqOz4SUPpiAx7VS7K%2BNXZ23EP3DQaom%2BIqoSWMQMbMYO%2FWGRIComOe9npT02Nladw7ancCKzcHLsf0mDoTzMj7bEb6dTAgIScwArZbXTjOsrZIruKKesXx06263queKxQUvanRCqRPknoE8Fmt7Yr9KGvi5RWBB1VMA0pMSPOVk4iVjKcQtuwsFq0bacNeu1vXqc%2BtCGFD6p%2FBqI9qqKJW2KnOys85g2Z4wT74BOshomnjeX%2BqXmrJmah5JTAqV%2BJQF7GlO8od%2FvkDqMil8mfo1WuXLWBi4FdYdSY9eB7oHSImwCurExGinT2kSGOW1gUOAU3xPz06bJwljIKflM4VDd9cvAbkXWaekY%2BnK3SkNqj2JSgZgUzo1rID14khqYDmLq59ZbP%2Bx2IXz1%2F4wcH%2Fsboj%2FbOLVMw18xTx18%2BfrKoe2TMmfv5pEI%2Bo0BhFvSCgo3IiiYD4DClia98CE6je6OHGj%2FcSqj1piHMK4W87CYbAoBTnsftqY80ef2ZQnDjqiuBOZEvFHpQXgIUa9nFUPzAX6AkthD%2FT1aDMGpQsBDqGiaUmP1yRvndxt%2F9ShnFnkCwop0bCxgJUnRo7SeAmZRULK2%2BWjiR1a9ZZFzIUJVzbpObTuFpv8fPLEYGBvvJKoSNhJZj6tUR8RLd4ERMfGjBR%2FQhW9QcSSLcKDFGkpwvbtqYypSqIM4hb4rtR1Yw9BOsUzBinJxLfv%2FbKc%2FYVcmbfC589dH4EUaZkWVA8EBCMe%2F6%2FsI40GUNu8aat6zW02RO1l6va8IsodE1nURK5XPRkBsHAu2IffNFZh7EG0%2Fw0httDItPdNYIkm52ygAhguVWINuB1ZMBKg%2FUinkqL5TgVyARJVYLxBRf%2F%2Fc0Jx2fg1Lw8TLXPZxbCIAw%2F373Vo%2FA2mG%2FckxpI8xspSxtj6jA5pt6b0vQE2jmbeJBlDCmdv0rX%2FZ0I0rhNSYIfwBlGRjJuaPifYmPgbSHeEqy9UIRxRcyT3lp5GNyvqIiJJrZMQKlprNq1ufpYGQ5YBgpBOcc8DWtdItajIlT6%2FlNGznICI22Loz2g7EB3vkh%2BdpuFY0FV46BVZnNiHEX3Tx940iquSQA9FSAOsCmxYd2YpbQLZhyNoUOxLRGppot3g6jfylq%2BcUyJUgC0AuCm%2F%2B2HnuGSJp4uIkS90kpBjr1a4Qd6VTiyOOC7RUe%2F8R1OZR6DwRuDdZ0mf%2B88QngRmWQ80AElvOXexSCUuY3aLeYW68AXKzh6l4qCSd%2FkHWFADE84ibio1wg7Y9emtWj2kGX4WPRYbXHdYqyiDgg9lyIC1LFsGKKxbPFjCOEOXqccQqhhY5K9PuZJstbooa%2B5zLO8JsbpuVU6EljwnDsPiH4t14CJ0FYX6nGEilpGz026HiCkQikm4ZWzDxkpEq7smRlWEx4F%2FO5eZ5hFdMmzwYwT%2FbvZwxaxe%2FlZOYmdK%2BoUU7%2B%2BY8QU8GfjPrJiRN36F%2BxcrzQ3DPYqBkAlME0clceTcJvcRZMeiZDFmJKTkFfpCUgLQGgRuxWM0HDnTHtaTzPX597VeqQvpqpZojewtqFvzk%2BCjC%2BTbf%2FV%2BAGeIl9EcA2AgUWurqaOjXkTSsCrcOzzH%2FWR8wsEfrG1mHcqKkpMlcEIcSzyLXAQPH7OOPdXmYec5wyoAx5H9X9Eum%2FObYLaWdLR8V7R%2BJIlEcrv8Uok5ijKy7nNY74cQwCbBfkB38CMIUQg8YAlH7wrhyDs4jhgAWx1pJxR%2FGG8B5zaESeojG7QWEaT%2FLo%2BjQICnCj4hvPI5dsryn2zECrxCfoFGkBG52zYmRJgholxAm5g8YpYBjylhDtpWryRj4UkWgtkXnswEDdUKypYY39NjBZ0Fco0Vdq2SgSaVfwhsaYDrcmtkXrx5G2QISZTOC71uoE3O%2FI%2BIKzDf43TUno4Tm%2FLz6QzGPCXEcWA%2BMOWq14r1kEvH5XB954Usa%2F9ofDELYSRpJdTsXJjJEQ%2BZteCk26pjfDBzuly2%2BKJMFQ3woORUiy5FE09AE7rWCGjjAGTqLBZZmH3lzjREgnEOmaGhH7pVnTpNfW6ChebP0H12HzzNUIiXL%2FNvPw8JXWrUo4wSJHp876ciQSoXO9jV8u0qzghmte4aKAHxlvJALwiuxgPxiD2WlOOIlFGhHTJnPC6Utgy4hJGmcRLfK2QfNTh6vIFAf%2BTx9JL0QT35TKdzqZyngiuAc%2B78kpPNUb3ekQzl70s%2FFISGLy9B10X2Gbc9Mp%2BvW73Bnsz9V%2BjGcfA21P0VbRe54YPj9JpeevtYhFUdwyAIytuNTZ7do45INkVgXO95inYl%2FDKl3BH3KRLC6AzBO3Db1wZPrTGint2iQeBBmKu6yZPgyqKmp5%2FqgtgIMRwk1lqdonptLwG8OVdIdLApza6f%2FHT8FDNXOzGqfzmdongd1UUOuflBjgxykf68uYeLTKNGo1R%2BPg0t3Fsj%2F1H2h7cF9GerHfKmC5626dWYf6D6T6vOBD%2BHKlWHXOtO%2Fv58m%2Faafix1uWpqCud7QGbGC3y3E2tHmKI4eSpsEQ7s2Nu95AZLLNAIIYCwYI0QShLe1J4W348dnrwFdyCIf6%2F5h2gsYtJsUuRugYSIHK1kmv%2B6MzolZPX4UxJKwSnfWWr9UcwtjD5qKIFSvtNHvFpNdAGSRDjt3v95VHz8%2BloG8bu9sTP7MphkdOCddR0JenBSDPCmq1pEk9JuJh54Mi9UvmEFRKQfKMtMp%2F5103D1bwac89LzAilvjTcM0ovFfooduBEvwL22Oh%2B9oMxNkOjIQpgDkFSV15TI0O0wGRXjmXgTfG%2FTMhv6ykVkIfpRE7ig9im61BhSwlUd63pp7KTiC%2FNMMYc4Yn5fkfFA%2FTwluIElpMX1hRartjagXPwm96vhIzKFPSsPpKq6QRA8geU1kuCip7fLc3IrOH9F0mh2saYZONL8KAWALjIIyQNTyewyE9sqcds0Mgg0aa8WfmOHmayM0oCZxQbKN4%2FTBqNBlIZUxAp2fDwOT1JEAuUv5ek9Tf4qYy07dT4im4GaXVDfQDXIDaLdcZQ84C%2B1667AqmMSuKqUjXo%2BMnSTtLz08I641nnLYSdT9utrfnrk8Lmah8HqTKZtLFF8OJTsTN6oXbQlgwwRsdr9rsEpnrKV%2FYGQgnvxZmCv8etoD4leycKL%2BAysZLwR6dncsUDDScpntjI6gCox2LdzpE8yMYiKdnCj0U%2FS0uQjCPOW0bCutlolSgD4NdqgFa3HNKbPCqJ15%2Fyx%2F4LgUMMaBgkRikXcp9PiGmyU%2Fq12W885MPs%2FjiQIWAzscAyjaNsZuDnOqb727EB8xxhH%2BRLw3rXr55r0RPwKtMgnchrxjQXnxuXxuefTnmUUHucM9jrflK0sNffUMQZyjQ8OHgkvkxEOg5%2FHDfnYL2aSFHS87J47vNPlDgAS7Pm%2FsCu%2B5jvt26mRsR0iNjUtjlJB2D1HLafFDQOTWS88ss%2Fnu0TvJ5kRQ6NhryEUq7MORjHIOvVsU0vfbux4SEnO4HlYug2x5u2T5Mug9IN%2FGOMfQZ22Uhk%2FLyLHGpGIHXxb1l2UT5Nt010j756XBv0OBxGdexS6aNCk8Y3S1fY5O%2BkTwb6VDxbq%2BOKlpUI%2BHKqk0byySCfiwqzmawMIcui5cETvksqFXXETHzU2O8HLKmN6z%2BPKAiULomp1ZqRST7%2F0QNT925W2o0F17utUH52ANhJET0mjP%2Fpi3KkfSMutZjQne86XI5tz9mdeK6xDhg2XUYpTegJzNlpIYTmyaCyDhGDhwmBqhTafVUIzMkJ75I0yE9b15h15rD0L%2FYVLBI4%2FDiO6i926DoBmxyjNLKWb2UgibvJ5h8Q%2FYtYiYiyxIgySSIquJr0U80QBTBNPNoGuF37cpas2tsmjnKNQ6%2BOhz4mvp7AUHAK1FLk%2BnMrzJ9bi%2BuJ6YMRZkwvs4sXdxDv5BGUbICHEYuPzntNXlRkY1WIyaTYSkSu3F7yZm5JhNT9H0tQWKPsRZBOMCv94SHUCIFKov7CnaN75%2Bz63X9FX75%2F8q4cBqIqbNvrIDWomlhPQzhp1XArzlezpPP7wblbVweSCxqE3%2FUhAdcBYLoVaPNZwvPQyZrKM2g2OxfGeOc8ym1RAaO8Dv2nXHVsnW8hvlhSm3ZpbLco%2FZCxf%2BEtIlasSmrSL91BgSj4penIqK19tPmykz9D6ExRh0KXgJ8u8LTbOnk8PQm4Sf%2Fa%2BjNopsYa%2BYYnj2y1wOL8crwFvXDOiSSkkL7Q64ZYnbQdeFaJ%2FyKB7hJQ531F%2FMq1w6qZCv0OWxBC4cco%2F1FAAlz0qmv1qKMp%2FpVgB22GgCzyOc6eoQeCVyubNnGdZWoITrh77T2%2B16CGQI8QE5H%2BDsiyJmpSccJ1fehjdL9IAZXozEAywenRoOisigCVuLogUJoPCfqmzyySYK%2Bulc5fM7MRwJLWlx0zJNyPYbW3ZjtDsEvD23MlYLoz%2F7JvBFJUt5DIhmCtwjC1EL3PTVEYMYsfJiaDaabQdYqZRuVHAGR3wkbpcqA6EhzwSZ8ncyAQtwtJxhieWBpOsuoha2ddid8TQRXewHx%2B6%2Fwznv1bCvOATOq%2BA2Yq17VwcTppbUDGBc1NX6ZlBIMZrrC1rhZMwW6T%2Bc3TFslrQbGAC0EZTnJMbkBBzPByhmSMeKNmoTu4LmAz80NIO1rcKVQdiQ4EGgozO6t0l%2BR32PVdZEcjt8hy%2FLMdepXIn7PZQVnIfT5F5PXw8sQPBrfwH9vT0jcXyu%2F7K%2BTYsEMzOE1iL1E8UrgmH02QxxygvLopCUDBGF3EAu%2B3%2FsaWMvJliYZulqdKwg4K9UMPjbFNgp3Uo1jCzTCzgVNbkUpIaNkKM0EyfuEf%2Fm7%2FwbzbREnLRcAIIMxDlmEVcCz6UpcA%2Fv9Ufm7u29WgTdA3SkpG160DMgzM1dL8KN3AM3lyfDZq%2FqUfDJwE7GCmkMxAilkT0wlHXd7OcDzjUn81bYQq%2FbynuKWnxKzbVvi4uYVvNIDyIuOYS1jocntM4Br%2FHpe2lae1TdzkgT7Hnhk0LfKsUssFISn74iC6xEnM%2BHyXHzJH4tQGlrGm5yy3LhwquuExW3ZijtwbN2MJMN9GvM39JsUz5ZjYTtB7ASYYaBFy4enTw04joffhNLKKGbc2x0V5K1PiUf7T31b1lhU8c1wVocXpR2HBEftpULurTGj0ARmyfp74urkzdL%2Fs12%2Bt%2FRt2dgoAu3FUcATLhHgUnWHYn6w82JIiUJz5TKXJTeeBY6eNF4zUNP5rmIunorjyJIsPqQw8c5OXuTXKC6U0ikTxBEARpiBXk5miFjluCBGcCeMBD2wvhPDJ57MUmEuwv7v0IPbNALah2nBz24HFNXCMRCAnKi3Sgj511ZpzKmL%2BMMMEnMDRt7ITsIMBAjzEMA3Y86AZovRDPEqmHoTdub8H2ywaMOyxjaDc7xKdlam14Ejs%2BxENxo3RxKOC7eVXzUXPU7RMOcnXf0rbh612njXeihhoR%2BrbMIoMoJU9ERELIav3hPgikbPZSWt%2BpM5mizYB9t1xLAxlNsCc3F%2BYL5v4WRA2SaYBTLTCujI6Gq6eKXL%2BE75JcoNddXO69CG1NBGv9Lsx80Z9%2F5ooADf5AWj3%2BUQvbO2ZhKGGWSKL41ac8FBIG%2BKDevJ3MUdvLmutiu5ULjblhdndzSSuEAqfjRLWt9C%2BKB%2B49X9%2B44%2FlfehZehG6lZGczMZmDUZbUHovHFom8DCuiM%2BCnpGwkN4bkZBlzogdrGjzDhvC0pix0t2jDRi317w4qZVZ5Ntfd4SlhAD6nGEaiCKZGOdbXjxNCZMaYGPYEOGnbkwKt59j5ODAGPyUjApwkFyd3LEvhucbHV%2BcTqwABUoQdDU1SJew18LrDnXV%2FbZ%2FLkHqrhVfJzqjyndgsHbb9C%2BgXpR2B%2BW88GvgxXCiobvwABJzg18HixMTPOahSVEiQ9vhKP35ltJAIyGQGT4weZqDEw4R0N5HG8g4nenfwqkauINzhRoOrOSXxzK98KAG8JVtyvpjjGm8TN8aTOvMsCe6Ksodt%2Bq7KayvZE3RZkEGAlaUSCgamknY%2FxPQbVS%2BcvUFXvcRY9xii3IuNmwJwiYRW7ZMeHBiT6k%2FwZtARsBZQ8%2FUobFwBSdpkIje3kj8YByGZPQHKMV4JXYXvwwsWaPiQzTBzrIpdZo5fk9ms4YNgiJOWFYTYmBFOL7IeIOh6DCh9Z4OjS3fffNdqMv0PfEeKhFaCmSQiwXWcUz0OrxaKzAbjbtZQglOSB7ZsXh8AIYQoudpedyjoeFf96Mn%2BM6ZnCBkun5nTEHPx%2BCIkK%2FUl%2BzpcC%2B9li9FT%2FhmHiBKQYlToIAIYyqlmanuuOrJkmeYxn1aIn%2F0cjz4KL9Jxj1JHMYkZJxbwjMT9ljueWvn0dY0ZoTntfc1F2JNLgvsZ4eJxY5KaMTB40wRBEXbglJUIgC%2BNIq%2BtGrh5kDNtXYhruz3R4w52euZD2FmVu41kr9GhfyG3YUXtuU9TIhvo8poDskwV0YMBPhcX53gjuPzqb3B0b6nps3QqegprSaTxNx8MRKBINwTrZCFoSNruV6jHuZKOx9jAOKR1KcG7pcMnNOsscptCiPuXFGTjZ99Tl2Gda%2FoenvegkbdKpfcBnye9Q1Asx3JTh%2Ff41zcZwPedo35wsEFU7%2FKB98MldZaMAUq%2FADqkIUYyjt9hvwOLo7OnnU0DuG0BRg3Z8a995N73EoAySHvG%2FlSqATU6tG3kYAlXYw1bAgA2c7qa0AYf1ggmVFowK7Kc%2Fb3ZDGuMtQRl8Be4Rn7goNJZ%2FO557bLFdPzLf31b9p7mgYGdV6vAwCdZjFGJoRqs%2BOp039x%2F2JDQUe%2F9xufM87CFJkMy3w9oNqpPvO4BSygURpykKIweyqX69ZMlE%2FQjLI7b9VC%2BHCPX9BavD%2F31Yd5Rs6DoLUkTnGa82exm%2Fm%2FFzZlt9cg5hw8wd8dES%2FInMg8eeoYbK%2BZ889nXWG92%2F9iYlKqb5bOxZJ7F9rZc0QI5B1k11FfjU02Ew01DTtt5Y3Q%2Fw7qisuznSjEGAGhODCbAyqGwRGDqDuLrWypjiPjO1iPf8k3xwvCmhhJj8k6ZSfkbGzkh8u0LC5s5KN1nkhTwv3IH8Yb8iuLHtUsM%2BfZy9m%2FAfa3mQZO5NASuQ9By%2BlEapGr4q5XErp4XmfdKybuMzGTeBizC4IHmKa78UZU6oQcHqezeS0Bb1BGQJmyJlSQ%2BITyZqK6X%2BxS99Ar%2FIVrUpap35iAq4L%2BAeFnKJXt5PeLKVB5GwBpwOraM%2FLizaaSh2uQGWVc61A5LvCln1WgI0wMGQVtEsmGVIjvmmeU7dQkaTxKldLlwCGOlomm83cTFHBY63X63MjfAy0FUNw6sQT6otOTInAu4CbpIeevrThb1gi3GNGieyfdAbgTlHyyqdknohrNnnzKAXFlA3GzmWXn44n4Loi%2B6vg3u7hzzeePEduFdzrWKWepO9RnaJ53NDpIr%2BLG5yH5IpYXTtBeRifIi8ABvgtU98Iorm4r3jIcJ1MdgFAN3kgM86Mu5M9PTOo%2BhX1rEDNoLeLrIbH%2FC5ougBjPKM6mhM7fYM5M27rO91cMhOj1ZMtLKMu0gkUzRRYCzJ4D6pOlGV4SASpv2Ln8%2FrvR4XsERf6S6%2FfFOGchhmf4NqSh5O1KD9WzHQQQNOXgTizth5iO7iAEV2GvjFk9FQHeH3yeJhscHzmGQRXtEk34YFU4HWT0sTOzhAVhUnS47kMYR51qSCh5%2BMpV78k%2BybR89OThPJpZX6Kixl8VozpcjTl%2BJRVmiFkaftRmMQLjdPg%2FTk7sJASXknubk%2Fgs5v3l%2B3zU1pLZJsMjjA47NjmU2gxsgMLwwmQ95W6FlGVuknC8ifWhoF%2BNkG8VDRuQD8X4%2FSAUr6G902CO26vVVNTMgBDwD4lQ7tO%2BAzgo5QfeyECwVbINLDy5N%2B%2F4yCjwjdc4zGHKbovBJUGxBPtNd7iYz8ar57gKFYO5ubfortd6c9jDGs0oZa4PLQ7FhbE199%2FkIwetFaI%2FrfhQ9mjceB3ZXWA8Cqv8OtvwKfw%2BEf2HVfUc9y9T3JvU%2BxIxY%2FsmxzAVt6uZn6dcT5vPA6GFbvSJ4QhN2t69skcplJ5b86wFSfzgnP%2B5mIND%2B%2FBSz%2BUAyfKC9%2F2MZrdGOT4tZs%2FJQUN5PKYxSJ44WFCPFPS22W%2FaUjwzuvZhMPXuuY79POU%2FZ5ru1LSOm7NxR2yUMKbAZBzze813haHJCCn35Mksq4qlzs2j1n83wx%2BARFWkawc9jD7%2F16bNKUx2HWlMprYloSrbzY6HKz9STU48Sr0MHhOnluqJjCOZLrR2GduWIcbz%2FyTBiP2SI%2B%2Fl%2B0D%2Fs74wJQfP%2FJCVtxJqHfRhB3un%2FieKeyOFTL3SrRDbCBep3aoR1IqoR5NexzNaQxi2fgK8T6kKmdGA25muk38a%2B%2BtJBKQDLQGRijk5kUiHcP8JxF%2BaKYZkXVaZNE%2BlL2cTOS7iNa%2FuUxZGox9xTNy4LCqoIjEfHNiauLRzE1KCuje2cKTQ0RDMqWf5A%2BjBGIJCsyi0Ar8OujHBJR9mdnazuvY0OQ2lMdUXx8aqZzzdkkm%2F1DITwejpjNOWssglVKxvMwPZeEU7nuyOETkt9N%2FJAmFY3yZtF0l9%2FcpUz5Add49Ibgzen%2BhNW2UdPSJ9SoydKBw2eW6jrT9htvkTxTxj9Mw%2BPLJcYlcb0rEfLdUHjvuliDI5PHJRsL0Bn%2BtdLiiyGBUtf2FYiGqgBhUL7SH9BDhRtZbzcRv5%2BvSFGiw4NuImuK15Pdheeb4LE8EjKxMBoWsFV%2BTrEZKT%2BRZid7nZHgEiNDJ5%2FfNMFYgGu6mu05ySjAj8wq%2FuWVFtMo0uRsLHZhIGdscTvqM49mbjxC5R2tDC7mlsz4vK1qpJM6h3Yqfo%2F3yINV%2BypoT3BFuZHdgCtlOKokYXIX7LVLkcn3JLJ%2FNEyGY6TtncahMs5vZG745bNac521InI2ZN%2BMRLiYN8xEu9EqEID%2BV12hlMiBaJ8BcqIt7wXAtAA%2B1zsm%2FBVOsKkrXW29wAwAQmZNkmkTuehAjyOHCxpVhl6w%2Fpv4R1B8ZmQpvZyjqYTIbUzjq6v%2BPrLdbul3ZsqvexdeHCmVKqZR4l7rADgPGDgzYwOuj1kYfc61ThG%2FK%2B%2By91vfNKWWOn95bN%2B%2BZw3NGCnbMCj42vmetluMAJmEHcLr5KPkV3TzlvgqnH2KacFAS8qQLlxrMVEHyJppSNo8SgpzOxKPKBV7LdG1anac%2BY40ta6tcwnXhoLnBS77L2RM0H6NLPvWBS7kWJpqbceLBGPgJCfWL4rbDVBF4LEwArmQaDYxeIaRpgd8ql97oD2pYAosA3FjG66hvXeWTo56T1IBYHC3yKpu8zE6G%2BSqHUJxPro3w7TFLGDVAhpoIuRffS8DUzqRfTG6QFfuhN8fsMNOA6WuyioQNk%2FBt5HOtV8Djfn%2BzWvj8i6hTmByfbI%2Ff9Egvmce3hCr32HVvcedwIuBfa70BUF5zn5Y5pAmp3dYDiHz2nX9I7IajVKbxyTahPdpSXahudlWrLLmeF3ElzebK6ooNJQ8kfpJcKdJJ5S4wyE83tIR10dZTQ6f2hm2PrAEfKK9WdVO8Lr%2Bc9ByERAdSn9y2k%2BE7G%2FyE%2FxuUekTcDwo%2BymAiEtLHQy3jCDSXrhZDRbdil4YuvBZaSDTJRlCzEpmx4Eotqcx%2Br87QNUAHRg1TsPpoFdWxHMJNf8UxLLjkMaW9vmvEc%2BxRNjCRKygs1Cs%2F8S6T6ei5ENmafYLaf9UEEJ8ow5RXslxG6bDXkvcaowoBHPZYLtITFfnVn7IdEYMRv1GNM7K7zXNytoFxGBuNnhyRxJUdgJmpt1YB06d93znFnAo7qoykgCRemxb2tj8VCmB0qiRQrlfvrnTeoU26DOCtBcQy8Or7wAiAuFu8vnf7tmcW4oyDFGkN27cneXLovdAucGuXEgQTtrOsu%2BKZiz%2BAxUUX8vI9r0TexbyZRek82gs6iwgARZlysBRM5oyaeLLLbzhkyqM2ISTpCG10n1LhSLdDE9OMTCw8TDouVyrN9nWk5OJll23vpJ0zUOXsaeh3gTwQZ%2Bl%2Bj%2FLtqiY79GZgKK%2BSAIn081P5pq%2BaJGugfnCLVf3cJTZSR8JowB0ifA0NgI%2FKgcYymphVEIBYXpK%2FR8NHDcUc%2Bg1X4TFukLfVaOkmYd%2F%2F7ECsY0GIE%2FqB78%2B9o5f8vnjWlENP6NkBrWCeHixs58zivNT6wuAJfov9hGNXFw6otZJnMAe3sEdzkfb0O%2F%2BY4yN%2FO8rgSx0ujRrP7Kps7VmEAzMld399CtvZgZisE8IlY97tFo%2FB2Y%2Bah74bmDUivRoPAFnbkCOGrqtdUylHsLcx8MmnwAj0iGUc0yTCuihYUD9KcMqe70FJ2Qo1GdJDXg0t%2B06LWFOkT0fHQKmAZOvtv4jRFlthnrEC3BweBoWApzlJcQ6T89FlQW0f1x4yBl52hhgM4BKNdlaoI3SEt%2FbzhIvwnBmfkygTxeG6zb%2BzbjTqAArJ4RAfWlHp0m40P8arEZaQIp4YdngTuEDP30bUopRjgo9yhxQPC2U15C6ZyhT1Lzs5joLSGpmzxN4W40tyWUhjo4n%2B6pc5gvdnAMyDwhA6lA76bsGKWhNHSiTmTbBWbsddvkfwYaY7RNVK2UBO5EK3TcLVeh9yjpk1zfe3rkc9iPZXF91Vpg5WCIy7iHIePZhS18XIgqHH6OkETTq7fmMeqwBE8qs0cTcUeArcw0CxKm21RWUY%2F7SemMCVW%2BGVjc%2FqXBOKn%2FcDuodpQsUC%2FUZl4Munc56jdZbOSyb10U77SWNGefiYcFVyKsSwpDGgSwIJXVfcQoyLXRTWU28aSUue%2F0adPlT78aPjdhyjLJ%2BYS1GdsosrBxFDMHPQUFjsHracOBnQB%2BByjDWHB0l8Am925oMskflNBmaKM5mwLz5wklZHT3cngghp5uXgabWxDQ0UnrOSxeblmBsQl6DlzHEZ7SJJNjQ%2Beb38tlyOuF%2BT%2FLjK8A2OeOsvseXSZGNcSYLx5FjPv%2Fz%2B%2Ba7dh1DpmT71RBgCfmRYPgKJKm5ysTQgNkJquNI5PertCZ5hhVLl9fJzuxmnRCrGK4jAm0MbvW69mAz7ETAiS06QJaP1StjyKLhjXpTex1EBJjHDhpuL5DZUzQz48gXe8ntYa%2B14PYY5mAxEzqHhpn7D56iNuOEoTfefZrcQIosfOXQSTAY6xCEkR3dzSxJhZ3TzQUVgjMILSwmiw9aRDhxQVi7fIXK1TvISkvfKTI%2B7nHn9rW3r6JBtZrn0z4q239YhnCjNEX9858%2BanejOjhWBAoupHCEUBPw8hIH%2FyV3GLmVsDYfNnWxotfKH9MNGm0irnh41sQEzv2FSRsl%2F1ztXHZ7e0EeW79vpQ99hyxcxCwfRaZ8syrkm2dV3WDleXy8WSrRx99Urvsd0puq9%2BL9fS6xDf1gAllNXJFLg84nh0ETAWWOg8zedveT60r0%2Fq9JtwEbxSCgzCs%2BbO0AaF18DMr%2BGMkw8G6MX4z6%2FB3wbynw4DqsIXaRJvsSF85uWV3Xxg%2FHX3pUup4xgV7y85IvEuk6Dh7lYWLBWGwC0V5ekQYO5x8H8raMy1FcGXTAb6bpRfiN2yJddsH56HTxXR1lpsHN%2FVzRM40fAo7Am8g0Z%2B6DXa2syUlvJy0APd%2FgGaOK2EFtg21GhGnRW%2BWxH2z7ZQkIAgVtVMUEDi78VI6qe5I1QHuMDo8tDchlXpLX0oUmTqyOcKSha7HiRXVfrRuPO2IjpMvLqtH3DA4xB%2FmxvRIU9MSd4E8jikMM8bUIsSlXBHkOMEiqvDuyFBI0bU6jbiCCTjpwg9%2FcolXuOpTLF%2BxXtDhRmOllv4kyM5%2B1ut2Cgd2MRp5wusu3RVJQNjI8W6y23%2FzoL3sLX%2FGqyBNPWdGlKIdDeh0kKd298wA%2Bhj%2F6%2B2as4PBSwDpGxuCDNLaG2ysvva6DhXWlr4DnI9ULPmA9ta1MC08aoNy6RYbQPovArGjEmn1C%2B0HLMHXEqplgAo2o8w0V%2F2JegJHxVqebyJAPnBasD86IeHs5LCmJyGotPYsOGAhmqV5TbGNghvLuomGt24tz7FC%2BLm7eBDGYGsKi92YB1Eiv7T0kYXBqNMwMuQFY5P%2BIftRW5tQAvkcOcT0w0j2Dlqdv%2Fbtni1lNi6ddlOqs9pSscDmQL55Z%2Ftff5fUdp%2FRpHSgASu5JeXON3X7iU5mOuSqrJ7xAaTuL5Q9vsPSv679TVldPlFfHPOyKeprSe0CjxFSKwa6z3EpFDsumdfBsEB5w5ryHhpTCnjvD%2BQzTZwa6EbRlF8XBZhcNDf72h6JbUJ4tJKr%2FvwkQ%2FPoKintQb2NIYNjfEp%2BJxsJrvDgWlw%2BDwY7OATCrbbNyl9o7074mlQ6GGXgIjYuFyEfrx4rAcm1Hwkgau%2FJ2xYShn23kMZyLE1iZemCXCY3HpdKlBH5GyTMBYNqy%2Bj78PQEkmBWEbRVSN8oGj12eh17UoG7BL1t1MUADLOfcV5FM%2B3TGDf0F9%2FkdWXrNi6K3i2RlddZ%2BBJAbX0BZ1mSX9UK5K0DamycRwmR9JJM7d23xq7ekm2qiilgUhPOG%2F27qT%2Fmx8MIYhgY4sFd8CXGYqYa60ukLhdPFHkrSwOutZ5QZPEtVHZlLbxdphOFDOczl9snlNbiw4LyUiE1aGsSj9qhFn6EWE6aH3TaAp0xd0F%2FysgUpUWDGLUM6%2FuhY5fHUDEy48qnaclSbFdIT5VecQklWCVxEt%2FY8maYz5URDOmFEfF7BcEJCKdrsi1a8v44C%2F0qgp8nyJFM%2BwKsZuIC4bUVpdcdeRSaFAAWw4%2BEFizwCjLsR5I5msr4Eyfsny6ghQo003LgBUs0nIXIRV3f6hiynY1THNICdxrSg6Dv9mOEsdMbeFO4mmijrvqsh0RPNOfwn2JNS%2B58WW3Dc%2FbU2qSAFAoMkm4RqJVaa%2FXhV9cNzVh6Gnd2%2FjWJyVbx5T48JQjO5fz650EkQBnso1o0xGsEAX8hU8Mz6reQpZgWZG1dMxz0Jp8XNSe7cyVVmXucpbMlbVHVLWT%2BqgJ4OFxR9AN0YGTvcf5GyKJ%2F%2B%2BnmetXotcqpsIIPxjJePh85%2BZRl2fru4A5vzy4yuBkXbU2CHRr1ennbFnwYmJnLBzkW%2FdywgF71oFij904QB6a8fSrlRMQgj%2BxeQbXsSk2FMvLrImhU7d%2FVp9sfIFv6ugCnYy8KtU9jfzatO63DoVi8Gj4vvBf9ooZjEDgvotQaVuLKZnpq8wP18JlDqcNL3EfPUFw6iK64kZ2pvJPTKd00kgiNIjv3X1yfwe%2B%2BkW9NbX7et1aspKn23jDjzjKhcMMxcziBGidMbDUwBc%2FOGQqkZFCxsjx2ZENmM0DMwq9r9B97D%2FYm4EZjArrHlV%2FKGhE8kJxZRLk4IX02KdJTD8Q2FiZ0NWSE5nvyMPdzQTm9vv0mKM%2FqUxtcvEITAFy6TTtLSY99h4skmvV4Svl%2FSK11ytSNGfcq8Dq797%2F02ZbfNCnkO8o1xdbIOn5IMrUL8NIAHyE%2FDYOn%2Bkx7%2FKo9BoZeMAJP8sL8kRq%2BdhHNcLbATSW1Ux7Cap4gk6fwo8yOu8lAHM2Unkjs4PmkeZ73X90E6iS%2FCsKM%2BqqYHf6UEJWO%2FvXagIBrFNBMGuirGM4T5lww8s%2Fj3z%2BDGYtcd7sZWIfcc1m8S3tera%2BKgOKRUDE9VoTnG7uQBzv%2FIEvh42BACkK2R25sgY00%2BV4JCN4cVA7zvL6Q%2BL4PJ5xKJ4jp8R4DaOlJ3WKtOAgiBmy1d6hHr58REi4eSTuwM3dUH%2FUgHMxqisbQopZMsRfuKQceWqksvursPIlNdyKNyk5Gbi%2Fxo6esjD2ytbrUfsHFfK%2B0scvWRVHxC6it0TE%2FZpT%2FhYNcRpQ%2Bd2H7sWjNlhGg5OEbfjyMULQYDod7DS9Nzdchv7AC153hVX8P35PMSvbs%2BvPskRTmok7zlv3vWLuyiF1YObdVY67lDFe1SkJE9Hx5pPtVw4G4vHxtqG0pI9%2B74iMAfSWXUBw%2FqRAS%2FblKW94epoIVD40qenLVu2LPTbMvTZSBaww1hmEnC4NEsuLwXBIuZ7d1bWSAw%2FGexjHYVw2HRwltAXPxgSvJrX7DoJKayucjbwY7jBA9R%2FzEQqPctt5XerIIHuupq6%2BbBve1dHhH1PLN%2B3MtGnqMUXasFf0FnuTB4Ayv7HRXHCLng%2FjaufhXDt%2BBzaXbAWcwVLM%2FERPppzeP5Kx1CebDM94EcUJcdhxGAoffO1l9bd%2FLtBtMGbbmKIbjs0vPFzh6RhEqsMMqyB4fAi5bJs54GshBGHW8YvE%2FXbDjUuZxIkZGaNHJAwvsSwnCbDvHmouDMxAOixKjWmMpSHBgyNeymPHw5H5j0MeK%2FR04cho%2B2x9%2BxxvvmVr%2BjU2cRV7SmQwctX685q8ldRLoLVSwo4mEJR1bfaoEKPKjXEaWhurIUgm%2BlR9H%2FM2BW2OhUkPmh2uEwq%2BUOhNGi0d2QyOFlxcnPNmrFFyGYCBhnrEkR97u2X8RGoDw%2Fu7qJ%2FY23Pvr9NucNUnVPh0VHtF34hDNm6nTNwwU9xFZmIYqyqTUaeb63aiKmLTnLAYBgSTZmTVY1DncnRy6os4Ya8NVKOsbK2d%2FtGOIOUgNVl%2F0PVWGXzPhsh79zV12Eygloq%2BL4Hl9AK7BagPAvbRXOJfVo4%2F0%2F0eU71uCdBP4nMQcn5FQew8w2MTmvOAhJWCPFqQSANVsSiVC%2FHovU7GjGnLIM6v%2BSbF4IF1LavOWIV%2Bl7GTpPgUaBk90fXh74YkUHz7NgxW8Cg9jsKI3KIXefKIedopgCioVmSLujkG5fla%2FDncspxzlHF7uv%2BpX04tyq4C9vZKkxpP%2BhhyZTZMfIcGjsv45JhY2QPcxs7oQA%2BaOpDK9lh6cDQOzt3bxvacyA%2FZVVmVil8EonTjLBPGpNDVObVtZDE5L34iU83NFctCUgjBLHxnU7JR75gYn2dJH9V7jrgXRTUWAgm317Jx1TVPO75Z9hGEy23o3LVc9GKyTRDaNyXH3penslricAaoXJum3fe%2Bzf1DysfRd3rJyUfZHwy1f2Z3kPOnWYoEbzYd9Dh7u8wLHQ%2FHX4gXeEirfbqYKZT9y3SRzxT%2BZ4hVIPGwxK3or%2FWXk7b5wygFiP8kZs8G1rZfBDfpUAIxCAhkRiqYrVed7UW32uSDRGv0e0vQttQxw%2FlzInyDNF%2BbYJAGRGjxpSh8QSXMPxLX2kEb%2BwSVF5M8xhmMstOP2szQ1oEW3sG%2FDechuFJstSaGvNeBZd5VwFX4nC8kcxUY%2BE3hHYH2k%2FIvkyLhi1NpTfnG9gOADGO05lkrSGzTwF3GJGIkyh0GDy9ScYhwsDUkEq3qSoZoN3dUoAIchwafofG%2FFWbQ63jqS2LEWSp2IC%2FMBCk5%2F8O4Y5zYifJzOk5k29gIy3ZeWaWg15HiZ6BTk8nZdLMOPxbAqprAjEtP7kXkUhGAcMgh1Lhpn7KDhvGvYIRCo3C7rmSFwrovV60S478V3ksaTkRc7BnAVnA0GD0fhioAdCaB91XBrB6ngzG3eH6T4XjwIROQ4JKN37VjHn%2B4auRKEMljD8eL0jxYXhDDg2Gpts2AazI%2Fhc%2F4nEm92dW0BYDh5UqlSOTESSTsXGl7kAc%2FMr5npl3b28QWHHu0qovN6WI2vq5i1AGXpIRGRyze3cY3xLacpMljfO3a3CxioV0Z%2B39jwR4eGagwHib2XdVuLt2o6PG9%2BjH9XsANliBaHNcLLFabBraryop9a5s2%2FKD09EBwUSUx69dviW3L8gDYVO3Vh7QH4w%2BjeXNPsSnr7L8jKiJpbvi5GH4SzOd%2BBMXJCMUGkWOmlKguRZ4WTve4CvmFc4ZjJUYye%2BwgqWC8m7tsnvQzDAswY6WHdPFW3tqFLpre4eL4KkMOjKvz%2F5LlumcqCphCdfrR%2BNPzjSf7ZtNOqR7k6%2Fc6a1O45Xu80o7qssGEAa0vMvY1mrBQR5DMAPIC4sqIxRIpGa%2B7TMVJwBhnzkD%2FgqVRqs7neyOo0Eg4zQeyVwdqImdxcWwyQOa8VWdx0amHgWRLcY%2FU8zDI0fX5x1BJBQ4Bs9MVL8ruABBIFARhTFxbsQb7mL%2BawC8Z3RX2DUVHMHM30%2FH7iHqUfnAn1CVtvmHW4Hdqg2TnziSMT4gWue36aGndnWVyOfTcLrN78SU8P4ZJUAnnFwvTKLzMOOmUehOxk0N0%2FVayXsVTZzV%2BksdYAGTcUt6Hl5%2BP%2FmreUfA04zWQUZ918uFiRE7Iq92T8ThwZ7H0giLIqyTki%2BjZhzIV7YKlS6n1KuWqDZw1vwypx6P7V3Ra2afAqeDpPdEZMn2i8kjkUDJ%2FXZ7xoHJy%2BEshDedeT3n59lctUp45zhDY5unylwh7Jcn4ODW8rKhRrV%2FmtVXwXsCuR43Xhn%2BYSQ3KepyzjTaY81iypw9GBrZoqrq4yln8ty56HNXKh7WSQ7z8t0ZPmKXw3ucDDN%2BDc6910o9yEOGOww6MepbPqN1BHiDeuDXdzBoQQG4PLJ7dgW9izIJI%2BhdRC%2B80MvxL17mVGm0fCjV2MzPuGW5yTigltGqqciMQNBsjUsFzWrVtQMRM17A1XWyLAI1UCBjz7OpkN8dvm3XEIxUM8JhxBCY9hHITTa74MQ4ouF4z9WBEqg9hFajkiyVFx5M%2FiJ2gwXzxPvCI045yG8rPsTcIixTONnD4CAtS2kPXcKs0DqmpWW72zTSGVMjDypNJkbEXpNIo0A%2FapmcnTIfgkY8dtJlcB%2BFuVVfWpESnBPGtm%2Fs1mdULs5mCGklCSa7Lm5c9nvgiBpSiFWQM2BRKB77d8aeZXmBZfFkY4rbzaGSKsN6BNzQ8FU8T34ZugaGjdglWfbWSf61SFuR3ZI7kAcaLf9RyDROtnq3tUyzY7sLZEW%2B7FtAoQeU4viJubRJnh79tXAEfMxgAJffSoVk5p5ZKGqtnmTyTdnHuL5aYoMVa%2Br3FXuXTgTn9vexMdVpzq38Vp4H%2Bqzd%2B6L1Ojg7QZbwndTRh4yDCeIkhsJLlr6fsxQr5gocGGKlMlv5o%2FkNUfRdlS%2BKgrNHuULCmewQ21Q3GIM4ZgSn%2B%2BiSyr8eccKFEbLEyLjJWIUsTN5X6bQr8YbcCL7BSvldxO%2FqxP8u6QrqJiuAYlqP%2FajZ8IEmy5wEYB%2B%2FUNUTge60%2FGDSkAXqfTimggC74%2FOhwDk0F1xmrJdbnJ4TXdFA01nYAuawwgS5M%2BrJPZHA84gZwtx7IVt8s01pjvMdXMWUlh0T1TgGJS4vz6l8DkaJ3Jg6zT6rt40N8mHvuDsdh%2FWzQmsYIWeAEAQWcOJDZMCgFm%2Fk8uTZrMTeWqUwsTDnik12h%2FiZpqVkRzhwLWiNz7DZWHxMBd%2BIviochfqbYQzhssWwkKEXakOSbmnjIhngroO%2ByP6VC6Nq4an7clecaynlEXqqwiGsZYUmwACNMvqx0M1LPacYII78Op6YRxt1fllHR8OKkpND%2BfssAS6G8aeDw3i9oZSp8I2mRnuIHqupvNRiVXuwKInYQjT0USzuSumghsc3RWtNzTliX9d54ciuxR8wULFkQgvGf1Y7IANHTrRMfJk%2FpstiXXfp1wnRh1XYI4KV%2F6XDB9lOGckCRHH1YlkoDHJZDGMp%2BQrKwUBpZEHDr8PcxzX9kUxOcqmRWOCCLu1jAcdftwqcCBXKybXFto%2BcQAbDlTDPYN9kQHhJPmbIbclPopxnvvSPGqqZBPCwgiaQq9yNp9I1thwEXwSYApsQBtJrKzhDrWK4wOyBxzvH%2Big2F4kY37t%2BZXLpUBln1U1%2B9bnnnyQbajdKP6ZaOfrYaMFlMSc44zaLeV5agrzPlMB4gYy5YLhwNUX2NIqCFS3Un9rnSLx0X0zAa5ZBfq%2FCt1aiCZUV8MAz361JNT58ldncm7hDQ%2FLQ88ynwiMSyg2i8CnCjt1qDTpIC3BFKI%2B7Bt2bdaapQt9Pm8kCAlQGQGPMorNVmBZV5YSMcEU2ZUC4lmCiTYNuqPGq4n40AWE7IeR47a1CYoOZuaiz1KkfJfc6aCDuSpY8JG93p832xMOcMz2vD6Mnre%2FQHmrAiRaHTwLjzZMB1lkhfQhGC9BarTt0VIZtrPQC2DmEMR0Ghq0a7fGI4PfGJHBE6P2dtZydbEgY9T%2FxvRpzbAL4XRlgeK%2F5OQBJviViNkeIX40x53qa54A8W7yiEwfnmzoAOYSOONHwH27dJQ%2BXUoFU8CYQkMZ5uMo2hqCE8SYxukcyq6csJo3%2B9IyZmtCzvEaUvvFVkrbG3QPYC95cXhiDbbG2KKDYQfW6mkVGdIZaRHuihRrK29nZfOR0Q4zE0V3IM1CwMOawSAuH%2F9d%2F%2Fce%2F%2B%2Ff%2F5b%2F%2Bh%2F%2F83%2F5l%2FMv%2F%2BZ%2F%2Fn3%2F59%2F%2Fpf%2Fpv%2F%2B5%2FzGCcVSFpdV97v3o%2F%2F5h0zOQayEM5qXFlGRSHZWJ48rGse%2FgMqcSvmvVvo7EQRr9s04IyRpjJMzsMXK5HhBA34j41ctZC3cvkZpIpo%2Boscg3Cd5RwdLtVhPNDA9gi8u7Jf8oi43JQa%2BkVbCXHNZslz8yEdrxEKS%2FtCA0rGZk0oSiGRuB5a5iPd8ovI4FuyKUDqlsC1lsadB2mGRkRv5sytQ5zoiBudXrto8XSXcBdwIsDWtNjleJgZvb8HiezgDIh%2BXc3PjRtD2X2kaiEyT6SwyTgqzxpuEbx9DCfb4ezOBXOCETNq%2FeXTzjTEN4SN4c0WxQS4qkEVR7SpSFailP43XPfz42PkrHYmThee%2FbTl4QbrYRjL1cPpiWEDFUkIwBxAWm87Ag2rxDHNFoMlMpQRBIEylmY7jWcMmNgCJSeKubruHXld%2BQyb3W41oZXEUIs5ZjPh3m3bLR2tQuMWC9OUNyDvebU4Ymok3ekhjR4%2BFAh0wxA9A%2FlEoIJ%2BWP3Vy49tQI4651%2BC4ddXgDyVAAH8r4mTFP3Dw7IteoLGCbcyGgm9ijYQl5r6m%2FgJFfiA3DFYd0gKjPREkNpDQMkbYatFmA7JdwCTkAgjmiPMZswQ2ICUp8hZFeOHmwi9flP1IEYLRBujXu3HE9n9bJmjDCFgv72Ftr6CetDJHdWsC8yuSjQplMJAMejzf94TYdhHJ4A5TrEr0RheBr1PpsIYg4SqUxXhukHkEe8YSI4uw3DtA7sld9%2FtaJSkdtJIDaPfbS6r0l5Mk2veKCY9KqouxByXS2pfMzHIH%2F4Xe9Pqvaon8Db1c8ObFZ8TYTZ7B3WA%2FMDhKenwO4ZmA1Gc%2FZoDjVGTqVdYneZeE25eOS%2FFnYX3ejfZ%2Fv%2F8X%2F91%2F%2FtX%2F7f%2F%2Fif%2Fpf%2F9b9%2Fx3thV8gi4CFGl3On%2FtZTTonnAdc4t2FyHrzFoE4nRSWXHKfCW%2F5zjtjNwaKr4%2B4tJUuVB8E6q5JSz%2BNQ5t8wfaHKZFTpyH5olnp%2FRS4EAXy8Kh3ffslZecv8uh3anBLY0UDqvq%2F%2F%2BILyiVech7JSyBnU8sNwWTy1oXEgTVdIkxbjOGww2%2BgL19DdBEAERLzVtwl0hS0ibYodO2GoZQmGniL8jLd6tmWNGlopcgOXsXfjjKYFRDlXrE8eHxTQg%2Bds5Y%2BD6MKsb%2B1GcLKF3nc5WTPuM%2BPvFV6NB%2FedrS6Gs8HLBU%2BkIQqXwq4XgNk7EpBGlysSADTH3dwLue4auz0KknSNU%2FbJk3q3xF2w98W%2BdoaYA%2B%2FpMcLCFMaoAoRa%2FVazzQBe5q3AAGxDDYAho%2B6lTl2ZuiGc%2FC7kLTM05Ft%2BRHONMP128qpFLO8ClfLP4462A3zNEm1arAiSk6bBNBTfPZKl9ljmW9Hr1qzcyaIafp6sLMD4rbcnp9aLqzHSJHoinBtRDaG2kUWwRL%2BmOHi4BKmfQKPXkfYYYgQj52EynHkTjQSqaHjDfeQLdjbKZKLm6aAnRfieDu4OYrBnryjCfDJxzdJZPAG8Gan46ZzYSyEcQumzY5MdJnJz0sJ1%2BuWbL2wlr789Jivn6NJXxw8iWj%2B80jjqxT%2BB4tOO95IDe6SZckWs7PQ5shjHhcKhhCgBQXwBK6eiI%2F3vVPnVwLyGAQ6DI1fcxvDCSMgaOqHLOUb8Ft8fLLNzBi1F0QWh0ECzN%2FTFS5UO9eGRg4pPl5sBpQi9Wvwqhpbx35a%2FP4JCECcU9ffvueJVf11zXUyLO3hBIA98%2B67H6EDvWXR4c5LUFngNHwog3vmDZy3ZV4%2BM2BTaSLKXWMaH61s9BrBQkAhU4XeNA%2FjI9uHNBDI2gx56f1pxLeKFSgK6y7PLe9zpkvTHxCUpn5mZlhKiepqvxgCt9UV2IMwTIOjO2SEfqAZpvkgzHz2dfNgciPve527lOVMI3zPdVeGiXcCzMXIIwC21He0FEyQi1FcTRhG3shwxt3mHLDGEBsCSz0W1zKNDbSXhtFgF6IP5riglnlnvJ0FCJzXkqoTGmqtz9ZA4RXVZAk50rG7A8NOHS8TJwViGkIiRGobBzjIlNYnBpb5TKMQXxQ%2FUr%2FYU%2BvNwNwHsubqCgtQEHGddR1MDmMJhscDhMxvxcTLiOspqlhBb%2Bmz%2BDvQtT8SIvM%2BPoiFym2rDsE392Ax5nrZ8Y7QjGeVVOhZupaG9RI8dCfKlVYdfxPakyQB6mjCUsgop4SmKynkK%2FH1RBkeGwl4Cldil4zufDgJETkf8m43O0ubJlTxjKZqM17fd81mvL1ZTFuGYxS4yeUubrmcZoKihB9E1WH9OMlfvdn%2FTek%2FruGHklPJt%2FkMY%2F2yO0smNWwsU5QwqjTO4aZRY1PVM9%2BpzlnlDrikZOneZBhgO8%2FZgfrvaP6vnCEcE48LR579SaSpxfueydRkNizjlAv9YdQVeLM1DiNQ6fJS1ujk75FkHplb4WuklO9UbUyi%2BR4Q3K8FSliJFC0IbUQeOC9BdgSN5BJkFQczCuQeQpFQMqooqJpefvzdLrz%2Fid22AWKpFIL7USzM7D0TS3U7X59L2oj60SLhD5Izsjb7Dy4CX6HwcfgD7Ws5vSGpPgDlSai8vCBfz10C89DTTgJaSq%2BAzdym%2BXX5YjNxGyjzi8FYHpLCyQpDENHGGwEJ%2BHAnTYBvf0ctP9HMugQBwzPRSUod4e9hQt7QYjdTpwJfDPEq%2FZXQRB%2FeqAOiDQN3N7v%2FBdfeWzMYrEucWl%2BXTFhRcrpTzPNMVg42PfBhxhX4wgcTC7L7TYkOwuP4x%2FkW5tklszH%2Fe8z%2F%2BD3V1s40wcZlpRYCno1SR3ET0m4l%2FpuI9jVwhfa2NmFSvPLacGrkeSH17hVbcO4wZJCwcGuogZnY2h8hIem2JaEGMcjNchUfdzVfmU5zaqk32TioQziBQUWzpKnWU5oV7RHpRrWyBZqohBwLzjl9MI0sK1jEYgRM6i5CDlT%2Bt0HdMrZ%2B0GME1A2WkSakyGf%2BTXU3rvut3vAiz4md9wE4E9maddagC5RnMG3xJpWPMMCJFYU%2BIz54hhyyEbGPr3%2Fnp1essuy9J3gVB7pziQ%2Br2NJsmWPsarKvRZTrvqq9DrBjR32dZHRu3bIylg6D37mL%2BLpGIVNCn636CdJHrTKREY7ZcYxpiHsWF6jF2ya7Q8Us3A3%2FOyuZ0jLY6gBg9H8pKUme4W2qZ8bLb0EL95p8xRZaAz8KoDGDotiQIimEfbZ5F1rH1ALJPGL8S2Vh2j56yTcAZOmRYmc%2FRHZgzAhZxDwuLsqHO8lXDzaVMrgXnd3bx7vwZMcBDxy%2Fluo%2BZVyA9bF5ka6za4FBJSWTHBjFK9XgYdcZS5Rmd03IB84HhM43%2B4l%2B7JESYmOkTHoShSAnkAJ0GVc%2F3EkxIaGdeHgaRGOA2mtoi2BAMj3hPeDX1VR3b9O%2FM7b52Y%2F1EakOLUhI1jtG0e2zsRis0DeOFHfWKZ%2F6R8tCs65C8tkbDKvbQhpxyLIkLrMcXxhHT8WHOV1Cwi83MIiWGhMPwc7A%2BDSc6CEvvtuY8zANY6ke7xhlmxiDt77rWH0WiLkU6pztaKATntm3brPl%2BGXBfUsWJGB6xSW%2FPftZyZ%2F3dcli2jw%2BIuKyekWZdTPAZ6XYmC8ZPdmOMdDSgxa987zJTc6KXTICrz8E9ErLjF1CPvW6bw3e2HRtICTUgY9Y3XF0E5q7F0IdetTChUDEfUQgn%2F7h%2BoVst56N5d43OjuEQIukNa1P3sTzAoNWQQNwdIkoA%2FFQKitK4ZKQwKsQJMVyN5%2B%2FS7Y45nxc5hxtMRRO1wYnHP41Iiw5%2B%2FUXfQY9NEgp%2FW%2BPmKUV0KzKgCVsBSbypy4Bl1hMM4r4VaaFyB%2BQ7wvkVDjH4ztcvbJ0kTuPQgDCsFFccqqaz0ijUw4ESeyPsoHNTilFDSQYCxz47L8rLdesPlX%2FaDk%2FkJ7JOGKL7sVmwUYHjA%2FkqtyfU%2BWHhv%2BSJgDz56QZv5JsXcWE5AXXKmd0aKkdeNKIlcHhCtXm6Z2EFwHjsjgz5wejkc40Gt5yymKo59U1wzsPGj4f%2BB13U7uYLJBxedh7%2BPOhGUTCDwQd9dcdqVjB8kY3h6GldkqERpfOp44G7iShNQihW81j01F5u5%2B8%2B3wGePmVbB%2BwaNy8XA3w1QvaOOMyZjQKMYUgE4agusVc32ImhcB%2B%2FLHpeUWYt7GwLq97xwTi8Yi4%2Bb1MF3SE8EcRYvvGCcMvm%2BDRyAeuJhI2RdxnXKK7%2F75B9kBikgGRsxq%2Bo6bAKw02x72IcUdTdxm%2B%2BUHpEpeg7vDZsf0wCEzW2yHs%2BRUq%2Bd5beyDixH5IQT5ua1f4sSPJV0TTVRZElzgvid1vaJ3WTfNZ0UWUERARoJgkrpvlLzYNCNwUoQa%2FsxGi2mtz2LBpyYbHCYebKROGMIAo%2BNYr6zQx3%2FySZ4JSMpvqurvbpIzKC3cL9QHFTIuMlx%2B9OPRPGoeIFvgYarUxY8WDAo6yReGtGTjouFBB37wvw%2BtEIkeaHCSA%2FpZfGpXXjDEpIdysv5Y1svHqA7WRtBcJ0BjbNDn0Yn9kxSdiVlqSSw9gt51gQb1%2BWM9z1cW18bQscCXQG8LW6SpOxTV%2FBK9ZwXoUu2EO7t968rc9Rj1MiHIY56qwoeCRL1%2FR9AIN5APFjDmd6zTqcJT5k8Ya9cxGFdrLkOAw08rmAgmIazKX5Ox49Qzb529Yd0Rd6Cy0j906IwsnH4nQHO9R5%2FcFeMs%2B91Mdn80kazfvUEPQPzOTQAwug%2FzzjEUaj9v24GBTQAyez8%2FtI5R7iBdlZITChY7Bc%2BIBKxD21K6LGYKWeTR5zLFoKVvr1TJA%2BUveW6NSVj9voePRPV3lQOGRG5rdsduqkRtaDKYXyuxMy6dGgcbFOjydei%2BTriqN9PRR9U54Bf2Qk7Ezb0cMh0Vw73kWasMMNZwEPWZ1oZSIdb2ciOvFq78rx3mwzS9Q%2BK5OGCuZsDjo04S15HkdC%2FDA3gfRvNQM95oKPdEnvhRpb8w7wE0yM4V5%2Fx1Md3o5ceToW1og8nLBMnLMzEPv9lOy3mduanvg06AD2iu3G9%2FOGS6An81SPdUd%2FiCv%2B%2BzltSjnLajjCa48uhwiwmfqGHAEt8cDDQ7fGZUeJxPnQWM3Jkc3VdDiA%2BEeIQqw8FujZPdsQzbnGs036QQANcPIgwD%2Bkf15PQwO1keMXfepHHEaOcJ8LDwifmJAirg3y387fVJuBLxNI2J%2BRv2%2FnHvDOUFzXK8kxT2yhx%2BWu2FI6uulo%2Fqs%2FGSjmpF6MsejhmAjfkYBu%2F%2BH3nJQxBefkU7RMcDmtU7f75t5gZ%2FaDpPGEMjSk23wLVELdx%2BeACXInkZQ24QBLiPU2KWjfVVKvPsqmigQ%2B1LgjPUEZEtbNVzIDh8UYzgMX4cpB0YrW8Ca6tHFd01hZVunPiCOKFCVnQu7C3p4aim1ihHSVwmU6PpPGoO6nfu6hp%2B27cVGzzExxpWacoxYbccOLu0fN%2Bt1HCV1iTXaEwn526iklIuuqo7yPNdLW6IndEaxT%2FdQaD3E5nYYalguSg%2FfPsd9SSEVVzL%2Ff9D3zcOaM8AmNQoShh7RcTHcE0F95MWmcyHVBOg2rxSNlViaE4dxfkZy5CrJxBoSu45Ofs6X3HwIRn1TB8MMeqVrk2szKqEbNzhqVY521Wsdo8C1A6WQl09%2FDWyXsKdM079YWb4qpZ%2FVcm7XFNi5wlfCMjc9TC0YBBlUDmQCL3kRuZI0sHwNcH3S4AINr27AgeuGd8tyqZwfjAtuOqWm6dlq0EIwIUYXWi7Wo725F2Tw%2BGZYaG2O1A%2FyiCnc%2FD0OMTvdzLsEr3Irt57VSr1IXMu9hXXRnT8XsijW7eeijl9E4iOhNCGA42411GVYEuMxpQp2g3%2BWCWZS5TMXEKqe77ZJvZGGd4MYOnDMK211qZebPDIugxPEX1n4adRCLLTrDJDAc%2FHmrSLSID%2FNdaZUlBVBOexZ01tp%2FhZdRkVu4kQzFTZ%2B171CYZ9pGNqdDLTlf64VksdMtjHBDAsbmpv2jl5QAw2c6%2FJldI4cUIo87h%2BNlYh7vHAVvUh6QU7E9Px2UhQ4LswdiyD3jy%2FgOOWxB3GW74s6pDfFfsbxHWpdhMElELphZYEVQLlL9NJiN0yOByd%2BRAeyP4LlZ8ba0uZuggjX%2BkK0hSWzpD5dfQ9XJ7At4zBg97BB6SU8%2BjE8gDq3KMUhkWEKwz10Jg%2BAOZGK38eUj48h35by8QijPTta9y5CSpIbVPgq5fszX5qzlJ4t5Rut0WTxHpWYeDEgZmXeCpiEHrz4DtoB5jUxiwUq9buPqGgYN6vOCPJgoIdG1z58UzEZSMv9AQHWu4%2FdM8MDdohRZE2Y1vPU7yxE6Exu0pr0BC352yG2ROgxYfI2KQsuSEZQeI8VEpPvVxuu%2BS255AQOKYMJzw%2FKDjN3KkzjBx3JrqfYJYvAeZkeoXv6O95p6QEK7TIhTzZ8yk6UgPhi2BasCspBfPvhUmNu%2Fv9GZ%2BYp8lN0asSViv8SnvwOFoNZRc4WWsDkTPClM829CvPcViqNlAsJ3QOWBlqPst0G9VHAEMY%2BUkevjvBu9i4DmYJgEbPHIowIAgJk8A5LkljKfwIjLaX6VCnpygzG7BCva0WGGU2nZNJj0%2FOXMK0aUGXuXgrvo4m%2Fx0d8edHN5164PWGUoTWyoGGzgIy5f7LTKOqnJRcRUFcaqDCGOn7pq3e%2FbM3qMAcyOoIKvF%2FQ6VhcIazXjAQDtCYIiKDUKHPnlNPMrVgJDRXYjCKz8dgUwHApqLuFQ7fcz4vxQ3WEdXnFNmr0eHScRvDO6Y7%2FotPrKfJxPihP9Ia%2FybZmwrrzNoPqq5Adu0bEdVwCkS4Ivygy5E%2FfcHetJiCKQmFuObK4UhuvbMCLOkfznw5LeMEFjnaNakit%2BQQUo5B%2FLGSdgqC6gsGYjMWy4jOFKmCZkU1xYkx8HbWY0dUtph%2FV7z2Gl0bFBpxw6E0kOylbKNtuKK0O0C7bOZcQY%2FLz8mwB8XkdHzIGv1mvR1CPo86x%2BE3Fzl23tqwRGTYkgOmj1M5%2Bw46fxvxtIwVJjR3EizMZumeF0OmsR%2FdySiMLPzlhnn%2Bc2BR1GiLRXQXsQyGTmBc%2FmsmCFkxxieQUci25PQictNycP6p7YRAQ%2BXDiIN4LKMBYQMYq7HlgjdykJMNJR3Uq77PSHoeeG9xdT6RFMN%2FRDfP5YmRKv7VHC54PALTUAelLsY2QtUaFnVYfwDNUQhtgrOOVLCxmfZL7WV%2FcWWkZpMrV49uw8RBhzZNRi4C6bGuLKIjl8jzgUHpqZ5RKlGgXHHcNMmWSEOM1Q033%2FyTZAg1XlEXHxRxcLmuZQs475gxcuW0RK5u9F6NqMRk232EnrcsVWBPCI%2FfTxNCQbgB%2BAW2QH6%2FwhXu1GSf9IxcVsgB0ob9JXDXfDzIAIrYZM63BxxIaizMdb8TR7hIZbVzlz%2BToVUOwAsMKmWslIqOYY1b8F1Z0BBQPQUQCxq%2B%2FQh8bSGdJeXXfwE%2FE7UJu%2FSNMyOGCgwAcDLv4HYWGnDksTb1aMi25GjUD4%2FoCz8BpMFRSrY%2Fb7itl6q1jPMv5lS1jjU14HrJqAJb83sdTuTyGdKT%2Fu%2B8cVwB%2BKJhbWdXwHKJnVPW9mqjmMJGAznkUJUcnRiIv0iNw6BtdPa8azzoLrVMGZLeBZU7HbrqnLkZPxsMGK5xu%2F%2BDSQ%2Bj6KRPhkSMyomXdubbNmWhW%2BLPepo4%2FOb0FCcqj7v9D9zOSyUJTjSEPtk00eFONbptj%2BE5jDS0UVXpPwKJgI0kOoswi1CfCLwS8yaCTx727H02ZhjkPnbPzuVyXTTqIGs3vtdaNwJUhMnPMBfiMlGkZIdqAkK8Tb78KUh1D2md8RFq1XKSdXpUahe9mGF5UfbRbAxjvc%2BF7ShySqY9LHMRwolGRR6YKqjGpZ7cr%2BMZm4NiZoOBTi40kjXuz%2B1%2F%2BffjzmIPEkkq7BhV8hUrN%2FVP1MdpZAz9h%2BwQkzIuPvrBbmEjXEmGTg172SNllkDWg%2B5UIUs8hnZmreyEFeifZ0MUTKdKQlMhgaagz0P2rJ9FrZbyVnVblxL2mnzPbyCDEMpkTzuw3p03zFWz48%2FKUQQRepBt%2FfjAZoV2ALFRmXBX7Vawf96ZaT9TLD8ZqQXWAqazFQOBem%2Fqe3I0sYFActUb4NbEdiMGZsakRIkBQM0Dn1B5%2BfHHe%2BgZExJRN4kxjQR8yEHSBvo7LA3n7HMnzYUYAMQYT%2FhGkuiuwtceB3t%2F39%2Ff%2BX%2F33%2BsQ8MPzTD3PV9fGXFsH5BDsCKk46Af7QMhUKoAJb8rn%2FEEPurjzgKviNgICRBKutZwYf0%2FSNzA9mosU39Pv3xLzVPOZ6sLPknAtmm0A0U8vwjRF%2FMsKa27e93EmNiaBx11fSHmnya36Nix7P9o26R8kyXxkRtOwrS9%2BLHYuX4%2BiMs3XSLcoZJ16Dv3ipNwH4q7EcTwmaT7dvhb%2Ff9TS7D3z%2BgWrh08BKxqKCJ4M9mLslevh2Lwy%2BTwdR1StPnG3pLLp2p0agfnUcVF3pFtfPfUW88nliYbAbHimavpO6d%2F%2FZ7%2FeP5O1Dv1XCRpPHWBZEs6LgDfVH1xUxZmMSiFqSzXdnQL%2BPa2HytO%2FlFW3kXBz%2BvUAcwLDHYzIZXqy6dV0peoIVp5OCA5Fl9Pp9W3EDLjF6S2K8myWGA1aJLfsxxtIdyMTdA%2FEUCw3o6LYFGgeOzHNqx7xRda6JrfZqPwTyButqI3xH%2FjRkLiOW%2BUmz8gEMK9CSNssqOAPnB0SV26%2B6YJ7R%2FlB5lQu9geSTv%2BsctTDKFF3Bayn3W9bVWR0%2F7qLImoGT8xFsVH7BVRMS7M5k0c4OgagmzE9az%2BhxkLgip%2F34g%2Fuf%2F8JdNyJ70kGvGnHrWngmB1zI%2FZegkqTMKEyin6Pdnr%2BSxENVu0MW%2Bn8yQyVwxeeM2EyFj04ucVybnkO8rrtuNhfGf3%2FHW%2BhFqMUoHpulnSfO2yHDrnBrtlNuBP5G1DFCDGBsI0GA894y%2FRtV%2Bh7TJeB3ydwOm0k2HT%2BA%2BEhmzKpTBULq3J1Av0ajAFk72s84vIflzI9DEFrmbZRSZAAS4IREPx29L9BL8CXogzxVwWQUTsEGLzweEAL2LzV6RXMThYh%2BckH7u4D4B2WC%2F0ui%2B07dM19h4SO%2B3W6FX2f%2BjsH6njJ8VusxOGK1jJmdIC3jxWeAyDq%2BelthA6BSPqsLS1MPTxyMymX5FOsijz1m1wGolaoTEo9vP9oGcWOQFX1OK%2FN0ZK6yK%2BBDN4jyDHGPZJq6Aqmb96A7%2B5ny2VzTtiyfP1Gc2OznDiqVCLolZg0kyY05BF0HOcYYAGNPgsiikHIlsUUbJ%2FJwZzltqXfgGVCiHGVUra4rhymvpQOsjhONMVTLfao3mLuNxiQlXsO2CGpbFI9aWZrNmSrfDMTbRX712xf0gD4mXmHski0fGdJnXB09wPgJgDd7gY8yE9ab8YqaIzSJftIosYeN48ypvALXrbcgcDsGng3CpF7DXw%2BRJ7WbnwbnPd1rv5aizzGr9LAoC88rXVcOjLX7FF7UdLvA4ztWm6ye2XAAoCZxgz860H8X%2BNauxI8PsrsUc%2B%2FLs8Oj7TEmzocgsE%2BUqnzaEp1IsfE8m4k%2BAGbioolq%2BnL7c5mDunjwKX%2BBLMLO5fEQVa8wTwm%2BfqTtbOJCKCytpHfZnhZseEuuPRixii5evjIOgfm3adCqgoWhvP6EDuiBnroPGOAblg6keP%2FgKXENihLtRi%2B9oG%2BjjTGuHH3QFpoYgA1U2lskohG4hJ2ggmJslaZfGxI0r4%2F3fluIBVERZRIB7ZlzMY%2FARfJ8Hcqmkq7PEZtVhRluHxrnnZ4S8apUyHAbylPyl%2FXLh7Gx%2BDTE4NSUysYSsVSZCWY8jWaRSU1FZRwI0sbvMMwS%2B11iQLBV2oaC5yg4q25RulG3BkxLEuofqhcfvGeH7HeLvnlLvFDDQaC8663lHS8jNgOfFXXapjGWdyJih684DdiElFxGGxvLurfNWP%2BaGNJYWIxCo5HloryjbSSUhRZT3AClZfc%2FezPrFFl9fhttIa4dRP9%2BXVdKIZS4iBhSTF5Jw5ZOMng80XD3ITGo1wBgoLFVjiM3lPzuv37DllVW4iM4JPBmxF58pQqeRwC3TMbA0gZeOgAVkYig4bKuKpWiUN5pKTElX0CKn8PjH%2FdHR2BZ0mghb2YYWmBgtE18nC4%2FYGoaiTSwV6I1Csjvlf6hmZ2n2tL1cGxYuG4gjswsYgta1y55dJV6FU0FnF6bJAbrRsbXwsHiX6P89BFnV%2FZn9QCawVzYtNYUEGgTWshNvbxZPZzkfGBOSzFU6FsVK3tu68aLLtXfQeEkEZb3oyJEMc2Jms4OOMFqYoRcKhDMpOQCR6dtZwCTU68SkzxuyFcuV%2B5ZHzD%2F0Zm5cXgnzyZyVwBUt4snWkMu6a92djCVW4HAoP%2B6RDKrtrfgn5tA3C2jBq1JxRCN0y0USPXX07TkQXNJHXeAI%2FoSDsrsfptpnT3PonRKtxR70OnL5EhF9KStBlFmiJfBAYEJua8EsW7G0GD7mX%2B18aOvDnDjuMpKlBASmQwB5FFU6%2FVDUYusrSStNQalf5V7WomsVbY5ZEtja%2BpppUJGqwi1dyUOVkATqkJBDDrd8UwfPLcCDc%2B8EprDeH3pgxpUwCu4H7nEk4ddIoBByfbOqkFlFKIHwSeEqD%2FMo2sGaFPVUtKCxejJq5qlpgrOceaL9LhOO3qgVKCAJwyaRBUZk6kcGxfwVyF2LmyH0wuDRc3XQ3SbIzABOPMCRhd5Isbni1hMNLkxNdPLMK99RJbP2GNrZTY5BhpLsb47iIuNTK5AD%2BCcU29s4gLy6XLGOGrGsfn9EpFNbTycrpwfwYcze0B2AgQlZqecQqZNz22u0iwtOHmAITN878eo8%2B5dYQ6AcjQ3Gl%2F%2F99%2FDs62uqp5Q5FMKYUTNOifCH%2FtSz0EWOm5jMfA%2FtqmxS2nBQ1dc0%2FzyOK6aeA2QcQpUrbMgh0ZSpkw9trQ%2FRdG6lSlx0tZ0rsCSebmDeCdngWCFv9jHc6zeoY7BLeHXrfU5zpahCMOWXz4URgvku6%2BhgSd52usQLQ8RKVVAemeLUE5aTmfDmTNQMCpqgBbhElyJVeEPsQYj4OoX0BKxIitNdLfpolLU5L%2BwvZWwzW815QcHj0svqahc%2BAmaWV%2FElPW3VLpVYTggTiPSoBOLqJCZCpQIZYRmY3t5OIGsZSpdP25fyEWdDX%2FT21sw79UZYxjYxgIVS8GIcXw35w5%2BPif4srZfnwYvYS%2BgFfiebRVyDp9BBYPyx5KBrx52HAvfqIJCvkGNQ2NNRe%2BbKtltlP0wU4ylInd6IZUXURof5BmTq7GYVsqUav%2FisuggeWPr0gBsDb0a61MKcYfT8EabwNtEFsgSS3R8qmGpyxObMP2aGQWToOf18k3WFOxjGGfKnXcZOB7gm07INPgOGPXTuD%2BOrklbK%2BTdNgyj9064tySpnHJH0dxjGqDPxvS6pDsu7TmDOpEKm6WgJ%2F%2BNgiwjVU89K3iRkSYfJ4GiB%2BpcBGsaW3N1tuYfRGSGO1siRYbvTf7pmlA%2Bjae4ogrmYJ%2BPSHRD59wsOk%2BGfkkd7l58eW3i8j6uTCy%2BaQ54C%2BJ8Buj%2BuunB7zd4q37pyXhGbR%2BmK5%2FQHoVlHwDTDWVz8zYvR2dMpmzyzt8RC00nqMz9NWhyqZNnyV0o5wq0DpzB%2F0W5e0OO%2Bf4iBypMGeAB9kQVK3co6zNlLIQOcc%2FY0%2FLRyttg%2B88%2BYMl0WVeflWDFwP%2BTF1KFfY%2FM906nKTm3KaDG4mcrpelQyBTyi56lb6dUiCFH2TiuPghgm8nc7cgeGc0jLLw3acUUnMEsie9F6HgEbs1zcFTX5PTVPlm%2FAmBgiYRtFS9kecgx98mx9wi1lkCaejpBWK%2F%2F0cR9asRHVNKR8gr%2BB9DCfyjThx33%2BWkAEY8lfoumX3PQnsUHEIj5sqlgbh1UEgY0F33cqzZ%2FAp4zP73IEFnMb9Zk7Gxr4ajFADmyNdsh%2B27%2FHmAmtixHCdRZDwabbAgfRjEXQMUaP4yBF5RHTL0a0M4vRH5qSYuugizz%2BhDADtGFeQTFA4km8LmCV8WlStMcNDONVRRFTkf3MUD2ZTvB%2BUe%2Fkr0KwXJZVINtH5mcL6Ps0KBaqRYYoSxIdHK%2BZh0O0LOtxnTCd9%2Ffo8PchvO4I9h6d%2Boe0tru6f0ZHlpHYrN7aEOI3EUbHVLHnuaWXpR26mG2uxKDc5cADeDFrqir%2Fl5m7r1CGROy3tWwirD5ijSMNsM6HiUryl858VeDMI3PpbKL5W0xapIkeBipWBTazGbka9cXA3JiS%2FdZtxzkj8HSi7om2cjE3MM8CPHBGf7RfQ9TrKTklJzrFkXG3uOtKwo0yWOwQjrSdCLHbpQKZD%2BQDBrdHtgtZKEoA%2Buu28NPnfHUtQ7b6a76bLoc3WX7P33irPCs1V7%2Fo%2Bjv3VRzDZbAz5Wn6cyTlWP8An1%2BxYzOzBOvH0Os2L7fo3MOJOaEQR4orOgVjtATozzbNH5q2Wcfh0ii225K4xU29hYeXRp5K00cQe1g9LtVJuZI7utxG4v4YQ4KiolWb2%2FfTwnq%2F%2FVCDQeRzZ0pmnmJUn6cpEWDgzqrD2Obgj2dSdu1GEvJEYaxCM4SQsiQELMFfakYQG6GVv8wfeEiewO%2FYmlDEwbIqs3TxP5gT0jHdf%2Bl7OXtvg5sZDYyu9i6ZFPiEUWWkn%2FEH4jl4m642XI%2BbY03aeoK7boN4qtJzmkNX9f1ElGr0YeEm3C4g4Iu5kbDtZOtMPRV2ocg0Nq3cbzyFfYaAN3ofc%2BsrBQP8P%2BpX5msE5yYKEDsqv2zN8suScksldo%2FTjjAKL9PBJ5TYM6ff64qBc%2FuRm%2BGriCYLSpwboK4cHi%2FGkwVoE6xZ7Wm64T91VMWDjWCCgSoAzAomPLVYk9u%2Bg6hkfLFhvGAqeDM4Q%2FR1%2BuAtwas%2FG%2BwyAltrQ6aPOBEPw32AstUSxsHaS7AQWpDZCWmHqlQWIldzn5g78q2w0phtiEAiLwQXYVQfDAikUUKy5lgpb9AjQwChXpxpRuU%2BUYQB7jiT%2FIFJFzmbC8Dq3TUTkuxw6lCv4nrzhTwFTSMtqydTuDWLb7TuNoEBPuXw5mrJRAUTDBNIjuGjdji8uodBsvAPGvygU%2BWy6iK5IkFClVoN7hKL7MjmmELEHdfu4E%2Bm2cIjGdmVSBEVK%2BMEyVjncwWnzs6MTRBZCc9PxSeY5TUNLI5tpnmIVNG%2FnyHnG2%2BPPNA96%2FzRR6Rw4FXchR58ICCLpuBFyez0%2B72oRsH8ND3G4vZ0S2QUdguIGacsAhU6MMqOgssUKH7tOLCy7vr9wJCFQfD9EueW18e2O20eFsSN45F86J%2FyDc3Vn3jzTHen0LKCgnS8nra5knLHIU%2ByiKmJZBvsKrQklHLlMUM728hn2JgQFyRX%2FYxSrW5N9yXjw8RFncUUeMbXdRuNgG0RjVv5J0m5Re%2FFDKFbCf5basBhslvN65nOoHUwMnomqGJY4tVZ5JWQXSZGDxZmOETprkvesk1InRI2jvK0qDrh7XwIZkqvz4xp1Hp79adj%2BAPf%2ByO79upIbd43Q35HM5unyRmyHN63mXN43Cll6H5i0GHtZJlO4R41HO0Sy2eYFRTYKSHwwG9sKCiYUxpejHDIl3FeFPkiwc8acVi%2F1MF8qQo%2BKQBXpL4Y9BUqsJpiAhI7JFXgYVAQJ1Yg0rKMrQQb0nF9FaS60kU0XUjoarxk1bFzLzklYm0%2Bt6tUDZFWwd03aZcBS84t%2Bi4m%2BljSAaA28ZlWAg7KVR2394mRXFRNv2gR3VbMoouIXJgB3k3A7LpGCxQ%2BBf9xJT2Z8DN5Jyp7Xsmbq5PwEGmBbIoYq1jPcXYaTk7tlskV2hv5f3P%2BfAbP5V%2BMMOCsWhFNIDHCTlGKFMEyG04Q3YUG9zoSjGZg2KaH%2F2od2yv6yEI53dwDF%2BF133%2B2QUIjsPFJqFaf7NKQTRemkZVBj3qI3sGIMYzCbtArvwZDye%2FbbYMfTdGJCIS%2B8S1S8UF8sJl8cKVSFhp8SK1wEv2U4QE6Lk4fNMwc7rUQ5fQ20dfQzYCJZcceLogi1psa1fjByeT5moJkTmAegRv%2BMpZONsmQyihDgBlSV5AM1S25gDtmw2e6HBvC4crCQcNJxBOgOCZsCQWzysXuJyO2bIBbIU3FAhxlDkFXRomNYI3Wub5wh6aA%2Bjie71%2BiImMr3AFt9p8IiWQgJnCpZpVA%2FXHPrWvnfWfVrJLSFLvynpPmzN4B1F%2BFN1Xt%2BaitZCw170Sm8a5Uhu0IcJ5K6ybEgtR5NhNFn5JbSLM8A%2Be2mjOODC52TZqpx0Zlt98KHhLbw%2B%2FMqdS7IFGzRuNh7fGJwFVzOZeZCHb5ffEtQoisucZ628WLCuD1idD3WZxOUUCAt2EApKEnR5sOi0PrbS%2FNZW60DA%2Bpz9mBMCfaZNu%2BFYFmo3uBfvC7SAUI%2FsPOhyYtsk7mxJVO9tJFtxnLP4x9I3bLwJEpRxV1nDROQaEgolmvfdcbTyv%2BBGxcHHz7SkVheBv0WvzY2Y2gmnKVt7Wu1j3DwJ4DF67TqlMPSyEjuEHqThzkpsKyUl1TX0zNqLmCaXEk91%2Fh159Gji%2BtzX0THzw6h3lnc5U5g9d%2F4vZAAcQStQT%2FpGipO2WrcPdu%2BYCZ97JVmiWbcXHmZALMSTabyevTLDeZ7zb6dvDnQy2nR6cdzsfOLeVk8znTgN54UBVJn9xaKYeXLOVXbGqppG5hhloT7NErAft7xoHkIF0qjTJhfUQhGlyIfaMeFq429nyCn8MfuZEPsyOWfpOTnDPPIuAUxFYQj5elBI8LNO%2FI%2B%2BRWyh%2FouBzlAPDyN%2BrVrLf5epDoEx04jo5oJsFQ2N3PO40UScUWX%2FDsgNHXWYzuTag67YiWYit75A2XgA2JPtdDX%2FA%2F6kcs671q4Qx93Ci9RQSrTd9RGD0tiHaSnUNKrw0I63uEf06w743fJse7%2B4oR4HswGOawGq9RsRcmUQdYLb%2FaJMI5%2FMaeCEYdRzzpYsE6A09c5xYSLQD6QdB%2B2QOH%2Bc3MMU%2FWN2eNu4rdpYKTRVswFN8TDfNllD0h6TuXqfWI6Y9EMX83CmJY9tTvlcU4ihuL1e9hrjaX%2F%2FuVAc7cMyfzrU0BMQGaNGcxXEycDIX%2BTxgKjCb2BBeN%2B8joYTjLFhlew3F8%2FbxymhLyCp%2ByqAH6YnJIxq5malWP7Abq1lHC4IvOs9bR3rKFebbpGAJpUxqNBBSkRf2APEbn9Bmwn0kdvuBbSQTkcszQGv%2BaXhEtuKULZcMhR2ei5KvKg9uPIR18x%2BP9pYiR4IndcAi49prBOo96iGforB5n6PLiCUGpe6YaZkTBFpWrZeQQZySFGOpBZ79CImSzQs8FRpWnrBZITjKM9WGBn4m3JqXDBN9dvjsmbOAAueGU8ZSD6RVJLl6vP0pchCluZthahcQ7DKN62r3KXwjSjHVCGKDSaoeSvetsnxr2JuZQF%2F6kcwYAiayEySBXyJnyeBrty6l591DS3GoSuJA1NFfEWHXkGGTvzFrzs257ZN1B86wNJ2MHV7ulbC53rokUVuFasIPHoupchk3sBLZTldN8nDro5%2Ftrwk1U%2Fl6Z78vatQDklGCpw1XfrZn9%2FN9zAvm8iotueChVrGv4BAtDWlzZMhjecWUJiqnulkoSQcBtdd4KTljP2tS4nXnf8%2B29wnA2D2PRzgXlG1rO0Vi0gJttiHhTtnheoQqheIOxaY3GZRFRfgl74Ke6O838cPGxf%2FLkOQwGe76nGnlz%2BD3lT33QF4f5jg71xT7GDfiUf4z0T%2BXkr%2F1jT0vY0TCXRVA3jw5q8jNA7ykGpokbqGhYv%2FBFNCMOETTyOLy57VABOGnMHRVOo97gpWn14F9dZyEfTsGDJROIO%2Ff7jJ%2BtxYWzO6iYozxZOm7PXDs3heqhGO%2FhpyB7aLjiwpQKoS%2B3tII%2B7k8tUtXaWJTwFiAyq9rmnSpENmICTji2t%2FCGwbOo67izxSQNlXjOATkpdutXcRAtFw9Rq8AmHg4iW8kbsY4AYMeYjt%2BSlzwTFDXi%2FNXXWSlKjCApv5axjXdgy4x7qZ%2BxWNw%2FRwG230PD6sqJjnF5GsY7vNVnMH0M%2F6GSE099l0yOO4rd9mL81uxSfkps0xSLYQK4%2BycdQAL%2FU%2FosLvGtShQwWnte6SpR7oFQLVM%2FkRGXK4089OhTYDGjluGxOTrUGBwl05v3T%2BwFfwm3GkfbTcRYNTbDlwMSLt9biuDvZ1AEvDgCc6hCM1fRTCt7dmY0A7fFFHTXUh0VFiyhTg%2BtzCHAzZcJGrY%2FlA1wg78HDmdD1EXfIy0DkPHPmxQAfDco1Lgw9t3ZLVyfW9XHbhDOsE2i%2BtJsXk8TqAVM%2FiQmhUCGWHYapU2kff5m7l5Gr2ZJP9ER3LU9QR2CsbbGeVfFieHFQg3gwX26sj%2FfZ%2F8VElURAsXSJ4njjeboIYGeLWtk9ahmdJ3g439GbjCIvWK5593J2cXDhIXCcuGYATgVSY4Qc2cy%2FeuYb0qXfMWIzuR%2FV9IERuo%2BMMnqwgVk4x7Fyrmc8zFuEApfXysoiqEJQWqV3w1KYUy4p5KdHoJ6df6BzQq5uZDQLJCDhImkiKG2N%2BzxMSCgfH6PyLqbdVptv2XolCzVblibJu8ejmVSpxJU8U61W7YmdVRv3Tm0dDfROaMIWgyY3spm7XUpKTtu6Zh4R%2FUxkfzzODqIOlJ466Dmer6YRyZJEz01jiUMHgZQuDxTQkaJ4GwsS8vHeCPgL72Q49O7HEDQi6w%2B%2BkG7FJnjO%2FqyNjjcu%2FlzsvGvjtUEwc0wGiRYhts3K77xTzB%2F1D7EImH44qtI2cAqwfxytnJxHS2kn9PUVyzuSesbNVbXoVZcH%2BSpaLrQdY%2BMMR%2FwEGYGPMM1po%2FuIh1Cde4R9rHLQCptGuG8wYeN71db3AUEE2xKItGLXnTrWEkY6nWp3q%2BkseqxUFVrndS0kIQN5BEIsZkZx835AqmRg8GQION8ku0ED9eZ3uc39%2Ff05qFCeWsiI%2FFkeKqfP0QFrYXNale1otg0oJ%2BYF1tYUsxR8s%2F5uJ9Yah8Db3i385cQtcVxjME9OHgaVDSyA2XevMMolpXKBM29UZW1Rlmgk8IksjK659BnfyIp8RcLu8q0yZHStyh%2FzS1uZUp6Vez28vka1F1O9NNlKyIbZJcxKgqqsCREwppfmSP%2FdEjvwCM2x8W1BUCNfjF3KAlmt1o3hHGBxPHCSB6nvctrMETt04gMGdCp824jhx7PhtneCHIOWERCrynvHnAI1FgRjtVi6xazdljYkjrUsTTcf89j71eDyVNrA2UVKQpHK6VQagpSnpBgXC5PN1W49S4DdeKYkCZE2sbcq9CkEmqM8SUOvjEYSovwQL%2BJ0GIqidKJtl%2F9aMRltHMSoVdPrIDJsNmkOSAfMTfpUd4HhNM248kDfV3T1TPTeEuF7Ez4zjRfpwGqDEYOgSAVhMfij0feLO3edgz306h0mzKI4BB2oFdFp06QLioxhzTkABzxCgJm%2FC6DUxF8VRkkPxiHg6iv0Dr4n4Y8IuqhEJkf5pOHw7O7Y%2BJgP%2FKw8GjA8WniBpZWFI9Ik6uZQcxGY2VmGa6mviGfYk8yf0lPgQWFzcV3ENFw9JqB1AImuFw8o%2BOT8PYcyl5Q00YUzJt%2BODMdVYUzSedeMV7%2BSZ4T5uYt8ov48d1OI3F%2FzHAo2sLrQ6CFwArtZK5N6n%2BKAFCyISaAiKMCh5c74sTRCKcbkr1x9gbGw1IMA8jrUK7TVHFnzdq9ojB%2FTcaAZ3H0PFlAnU0KWojQkKa%2BC2bhUueabzwFHhOxeOScQ0%2BJbIDhxIp8mPX95eRaPXRRV3BHcPfQl5LOVK%2BYk4V6hIB51PuNcLQg3CpnQ%2FJkXAllBT7O2k8vOUX%2BuSq9kqCgMgxLPMmyK3AFV6f8kVxkYVV9bzCic2zVDqBr1s8c2p3ACm%2FBWN7pcIQ9wE8%2FoG4a6vrRIEgwSEMNjjb1%2BIPev80JHbBn9fLddM%2FzhlHMw8QIjaSJRiuTHCPrBClOUS8KZCbkCF7RlfX64zPA6HPh%2FGwz%2BaN5hwnz3ZYHfNMm6WDa8yyoWBmue45uJgbRmTKifXSDmcRcVdHrBvpCRH4kTODwmBpi2WbwvEi%2Fp92EHNKC%2FmAcwWuw3mQIRX4kjecoHXtVjQafqLdUJZnN8GX0gpUE84%2B89CxtOUnYU5RuFrAVqUsnSwiOzKhuEZHZT1QEe2amzXjEA5IxPCoKMx2%2BEjjVzZRBwmUCb7aO1a%2BfxcmGhgVhapjBxNyxcMFrf0dkDXXxUE4x7n5e8J%2FxaTC2P9%2BYHennvl9Ri2HS0kWaOdI6OTui9WRvilSBwNi7GXS3cY8nDqhS4XJ06tGWgjI79WwvH0r2V3e9i0BClqEBKr%2F2L8rCEeBrwZluHkkyxTI78WjkmbRhJGR%2Btjpll60Hnm8C4b7%2FT33X3x9uTU8yCqCy0pppfUAPFx2s42Tsw%2FLez%2FkbfmhV5c25zwThTdkNhzfR3SttuPm8EYi%2Brt1wIgaL2OQnTtsUImAkp1uay3lmdkOvgdGI%2F%2BYPlvaosSdXheVWZLjLkS92bhhMaR0oQTz4seW2swBxzVHk9wIwMj7XJqHIZ%2B7n97PDY2HvfiT05Pta6SMlKaDRzhCEnTYzFNJVw5%2BRnPlwWbntqMMcV4NyZoSJkade9mrbErBRsjj4DNDcjJcSqIUhkb0QEh%2F95eoffZwuyRzxxdK0oQJnP4yXtvXDJ1GXlBNMwVcGcxDj0ZATCNaXIAcSJRPN3DrLTozEGCijI9MdjjxEpeE05NCCVSI48OEgP4iezPmOI4FPh817mcUsL4om8cBVqv31waTmUAy%2B8uiDRHqNL6NArQqXrwGRFjqnt2VbID81DO2fiJuBiWc5w%2B1nNmt5MchRJsrbXnuAr4Bl83rreY3oi%2F7QETSB4OnoYFyD4eTvvUvpxtNqECWLk1X39nfM32INFrSVJOpSQUzLxqv30mw4RVK8TrZ%2BVRHfIQ8uzcR6mwaObGoXq3skvRWHOONvJ5ftv1Y7iUoL4O4ux8k%2BK5oMBVcA09YRlBXoowvpO1R2DRVYSFTaUHObYk1lw%2FAwZRHpn%2BxJYe1nMa34kGBIEAeRnM3HUQfZiUgq4jeBsIHRCGbZk9hwvxQ2xnTb5ah72NlwDGMmf%2F4wfW9YcgfA2LflQZqD%2BHngWOeKhcatDAh6DizYmt4ysUZuc4vG8KmvCBW0QWzVkufFmtHwA9Z0ZY8lz5YoG4ij7dqjC1Abyp4gH64gZ1aVG9%2FAmi1XU9PIfumKRQw9CZY8XG%2F7eFrOg8rVLTslfW8qcQvTAw%2BJ9P1ek%2Bei5wi93O7ygAuNt2Zo4elhBq0Mb6z%2B3kQy4CXahqZkWIvC%2FzH%2FYzgfCT2O5GE%2BX3xRNVlldoDiZxQaYsV6OXy5aYQq2pF%2F%2FwR6vpx9JncVc8RjbcrS6afSKS0RFkg0fVfyDtnOo0dQLXUFgy%2Bh4IQIGhEKTjtDNRBhBW3FIES2MgjwK0BRYamH1aBYyNr7YWJilzg1dSXFjFvy%2BJOfUPYBe2W4J%2BssUvwQoWFGyMY2VyhsQLpcF9f3P2f2gPWLlS%2BGRvSWuRiwdTs6OKvbH04T8eBTm6y10nFDr9RILaEkSWkMCR4X4NOTqaLeWbO8hU%2Fuog%2Fpvj0z64jebB4moGNquhOEDtfqsbTFSz5yGSOq8ntdxpuE3kbdzCSYWjJDSqrqy%2FAxFz71PZynfAqwhwtJ%2F5nYuUn1enKchj39DD2HTJBkyNfGhMr7j6fT3RE6BF0dbER2BFV8U5QiVEuFnzY3x%2FaNyWYlp25i7rZBdueTjaPTcTeg3F7fX9N4VFQOteH%2FPpYi61D0DkvLAw7gbE4gVmoJ%2BdcVtHehaR4yi%2FlT2t1d49ZHwmJS7RgNSHaafEz1dZOPdqjzxVZYW2cwgUyaWFQxo8%2FvSIF2mqE9S0PPulTCOvvEXLqG1bj3J19uVYH%2FnIaSEjDKWOCtTbIQO1w2KIxry68shFOOjqfmOq8%2BR%2BonDEglyh%2BAc3Y5m7jSnqArthIUtjhtlryZ654cMxgW3%2B7zsTsimmaMPzoW7CFWg83fPoJq5RvG5OGYLLM09Ae0C99PABCodrdYUHAKMOBNFB6jc47tU%2Bvy2XPPx%2FxFtui5IRH04lTneZklxmFIyL6TA%2Br9zSFuJyuMmI%2FE%2BLJGJsb0Fepdt8KclbGJEOUPyRQartJEkmAoOpz%2B6w7Q5MZ63f%2BYfwtPBLVyQF2LsaagCc750nh%2BfwwHPfagXQo71N3yEXGxV4ND%2FXwrhmE%2BhEC9HleIp1hlcc7UOBDPHM8VBfoV%2BiZ9AstANXEtIucIAJ38SuDiw2LWaMQWKuK7uIeclDyD6r5qWsWVQrFMuCAfZGWS4lnn%2BUJTG6Up0k1jLjddbsYRlNAosLAWPKmfaPRkgJlI%2BFRhC%2FASaS8Jl9%2FDdJU%2Fg1gQzjTuqkwHgf1YfOGOybzx0biuWY94EB90KBXa5IyYWWnApO4TfMnZ36xxFoo4wYl7Gs0pQ49%2B2sPTbia1mMEXv426%2F8Zn3HpBXYsnVQpVBRXicZqCeydjnCzvV4F3SjLNNfg6MADv4zf1Q9xM3YKNpJZVfCn2Vsr5kgF7sWVh%2B0IUTQYpW2DzqLSfnrgAv3NTxrvls86hw2qfj%2F0OkYM30zwGtI%2FHHYRN1fd4o3eNzgkBOpUTUSO2yRPNmhQRiNZ7N15tIMqUacYN19F3SDMPt8CrBjtQH%2B4lDZBXP2CiG0Mos0KJtxmo8niswp8gfM%2FoFaZDmUpGz%2B5ZnjBpZ3jKXX%2B1WAFR1oWEjQvviSCKLD2tz3Rb%2BWol2w5xXCglz916WK5vZhX3bpbJzWDezJCb3IQQq77%2FnSAi3BHH%2B3MScGAx8efZryUuTGZXOK888RpygJY7lc8zWa%2FajwNuC%2FC3rYiYk0bC%2BKWjdzWssZAqPkheqwiCbYKzq0IXyuZGu3jb10stj%2BbghutBy0Ssc3JobqFv7CxQPhUkwwRhpGyog2rS45wcqSJW9LOTCnHwscK6nySkUWijmKtYlbfRW5djuNv%2Fl%2B8A5ij1E%2BpZhtkZRw2jMRmnkYJSe5VKvmbAwZA4cxR8KSy3earugPfQ6jJVWUcen4t%2FbwlCuI54w9gYfQ3Oe5lo%2B8OFwJkaul0xzhwrCzhDXtE%2Bo%2BgJQhlKKdtRZPmRKoF22KqFr0OSQPlNAGgCMgMB3%2FQe%2FyFnHGVrnJtYpuSYvn61vRHm2iC2niIwDi2VbsxiBYL9AWiVooARVgpwqDW6JFF%2FnSHJPY6zvoudMXxZkHjA8C7cOxLC75VgggEkBjtXZFxSoNhisWdMKhLqKYZcjCPOK%2B%2FmC6GHc%2BUyLKvkgnq4AFMcofzItRkKFqak%2B0jzUPeQuS2nsrDyBqdAKELsdseoJJ9LVCL5YeGcGHD9sEjliggbkldcxQIArTrs0ZShmIy9L7W32ykc9zT3Me0g4FSPThvSfTBeL%2FwAyG5XM6zZ6T8SB9lWrNIShJFMcgGFa82Fhe9gSSDWMm%2BhQ9VlcCIqtIAENdPwRgDFiC6dpMGCS3PZ1Wr74n8liAbv%2BUwA6igEBSf6kbZcurG1xutCKYkVZMy7QaH56VWH%2FC0CeNCYVqUG%2F%2Fww35HZShvhEYnQNx4VuV4QamZeAF1MIyxNB48rWQvQMkdeL9IgWHKYW8IbkexNXAiI6Rea74RoOM4qUgA6nuOXn4cGmk0vu%2FOrswmZzD6Us92ET8zPZiSLKCzdA8cX5zIK93n8oEOvfTFsk4w5BxcSg%2FTLKMN0eocJWbez2E7%2FgqrCgB%2F06MgCjuYQP5I79llj6Vet%2BKu5naeh2R0Q5A44lldP2A75U4yVUKDEEMSKsLbXdQGWW4KoOiDnCKvvoHVY6mkGFECT3BsqL5KFEMPVlOzUWcyG7wD7kIabIZCTM8wL5eAju4vmdUBrnp0bgRwWWVgNWKrBpZOmEJ6kZ2YGeJpEwX3Gn5J9l4mYdHCHOrKiqKMKoTPHe%2FbUDk2%2BE2DPohnU8vlysDeMZlcAGE2SbfNtQIX1ND09Fw7xn2fz8QB1ImZZAMTqwqA6NneUeO2RbTa%2FPnWt5uo7%2F1ALseCC2w19E3zp1Y7K0TgLym6lwTCe37rDnh7zG6nTKKUq5hdz8hA9xuSl%2BOIKBF8B5vfY4ddhi2YK4%2Bgigb5sgDB%2FMn7XEP2PLLtkgQOxKLX6WJW4xByO1zG38Slq8CbbReyU%2B7MTfMRR%2FMZf9AhwSyKTUUbviAKZJ7I0JhynDhbKTzj7LLowIsS8N9kc0t4vNuL1LnCBefLCto%2FXli0o%2Fg7Cbe7V7B8uCGRR6OL37LnkHjXLZWY%2FSvOOy%2FI0CPcim8Ch5mEEMup73rEaST74VanxafPKW0KIGUUoai19jJYWFarLsp0bvrysuF3cQ%2FRrKetqVfzYj0zPtEP8voP2OZppbdz3YXjE%2BiEY6gZkNoSxOvY98o0JewZm2XnsR3n8WK9yv%2BTfhCT3lJScvWtqdc56DFT07Udbikt8gO8e0c%2F9%2B7510GEJWAEmPFxJGP2%2BB3BHOiE8BwUUPWQoIFiHpkOP76FoAbHjRpQu5Evs%2BAemtDCqIxwFKymJrFfuv7Ji6m26zFJg3rgy0wCnfqlLUI7ZoV9ntQlKi9sycxzRVJrKli3hy2nDUXdUYk2MR9zvBnajmqqhPOqKw6f6bN0uRh8jTJjTwx1I5wJ0g2nADV%2FsasiuNQmtAlds5pCMCw7zgf6AX75DiU3NOstqUwDUQy9GZxNmjr1ZB4EoooyM8EIvIa1b6Reqmr29LFl0bcV%2FrWLmQ2J%2BXJp4M4j4e9C71qRFfEG5eBxDJoRm2HMf3H5vw73A9f9V8%2F7rvyGL%2FyHNs%2B83qtxA5RGpDb0JbRyzn7XT0vIE3ZrxtokmZS7HjrcM0KL1Kr%2F5q0X2scDLhnOK7f3uGMQ21VgxkEcONI3xDZWTZAxqSozEqd9wePh6DONlymgqKJNuCFfhNaI1YBzA94ID7i2EKl6U7UAfpdX%2BqQUga5kDTtm1IgQh0s%2FXh3FDpEjb9FKQpqvku5RpQ3EyoXIz%2BVkoRgiUAVB95YqfpDu8QuavkWkBex6ifFCKUIk3DqUMAlfFvJdbQToLp%2FKJzj5jehwWL3adDjCBUEibhiQPRG7rBgdCXWBVFM0%2FvOli5AFW%2F2wj1SAfjgacf9LSX7j35h1ehgh6MlB%2Fs4I1uKauSiQRfLRKq4vxY94lrAk%2BLgS7XcNjbgMKZd5WTfSPgsJRi3ac1%2FuUI97Q2upwK5XjVjRuglZjUg5TFhH5rCaTU7o8OGp%2F7MQk7%2FD8aJmo8nZ54KEAAHj4hN0sLcAsAiamcVhjkGB7pEWvPf9n4rjIAFrJTUFaDuKJsW7LGdyhkFJOn7t6cXRiwUL6XRgBBctaDmBLjIQtgi2jh1MA13l1sLvMRWPRd0ZMro2XqbjrjPX3u%2F5f%2F%2B%2F%2F%2Fs8xArvW0xew0jlnabeg15iJd7QTGw7fa6ArprsrOzQaDmm0MJwwZTX7qTIdgf7UYBa3GPGrTCHia4PDjbMcMeBrdE99kOLdwUkh9Am3kW8abqJA8xIzFHiDdntEiyq5jhvZwNY2L%2FJi36o6Ae88O6uaS%2BX%2Foafk8C0W7A%2Bdkzl2c7HfZWgJgDissxVLMzFn0et9524WwHZ%2BrkVeczCP65dL%2Fppqg1SiNgzn8DA5XDYdZYD2EOCuMcjz7nUJh8p4Kz2AtTI%2FJdJfpyQouUttoi5xKMvjZRvpeJwgIgF%2BYvovdipmNRYUlVeEREtKIhaE%2BmGmTby%2FILS6pwYcr2HQwx3Vr2twvI15ua204GhFA%2FBr3ncyPVh%2Fm4n0aACti4Lj6RLxv86CALLVovLXxDwLaaib3cApXNZFADiku1Bdoa%2B%2FcxsBbGI6hoDnaBoDD1hpqKEfJZOZvfIW0n%2FeMU47zae2%2FJ7sZpgz8mNMQAwkvV4MZ9tCe3ACPwnFxOTzWmxzeVRFTlAl0yP2XKtV7NwbTNVgKBPlEnQOemVxI8ifo5ZmAUdtS%2B5Z%2FSV2QwxnFmGbJdYibuxRyUlpcWYQQ8rj43qJxIyORWaiw6eJIadORy9H9dKAE4pixyNyG7QIb6Kh%2BAzOuQi5ZJjPlBAZAwtSx8nJUGsj7CITxyBjy3rgmYoapEHE1uWI4USt8JcJNK7eS0MjFc1bI5S7IiRec4kinyFFUsrEiVCwtlXfb%2BvFigIReYZeMzAhcrehlT49VcHU9nglPXF6r2K2PxgQTsrkGo4K4FAIYDPg51WJyBgrvhOyMNFKftD4G3fU8KFLcCWa1LM6fjp5zGpUHBEWPe7J9PgwJqpRG%2FBQFHDu%2FRUGcK8dZsgzVIkNQ1v0giW2V00pgLGdBlC4B8mc4TuWkbo%2B2ho7RpFzoXYja%2Ff6kM7klDDxFUoNWWcI8wpZNJM9UQj3NP7DS6KG0F%2B1IX8PDPV5JBZcDS3HAIVI35NMQVjta9iorhnCuvJzIJNvY4ZJJ6EBap9oLTK0vN96rnvXQiZRweQdAxQMQKE1spIT1T3%2F8TZRpcbTb9d8uDRNIut5deoI9O0GnuG2rd58j7I5YR%2B8W6SJxUp78ndXrdpzDkschEhAGX6shlrEssQabcQDucxvx3aO5VerTjfC5R92pMmWpyovrE5jljXlxF1MvC4Wv%2FgFT1PYUQFcWIvrjsZGxANxwV%2FJ78hgbeinpOK4G6K94yKsJbCbAfy8BVeoTToPGiOkr1O%2FVoV9HJduMxzGl8CVOvUhb%2BkS5irPqpl1N2ffdd9%2FcuHPIs8ywP5%2B1uY9Inh43YU9IaOQdkE0mSy1SIw5Is8KyN5BkZ2mMLJHx4SUxR%2Bl96k9gyZkd98HYGZXYBPzpuY1vnYw0846sF1RVK%2FMRCbp0cTp52c5jT4%2BLydfKWfvRimfP9NF39YyBpsuVCAyoGhtmXQl8BOw4CM6AFiEeH8MQA%2FHG6V7RBOzGlakUbrNZ4b8hDXBZnEwXh8GuWQCeFWxlAvo5FIEjHz%2FKvwJQNkFH6aC2OqRvzMxgQJ4ZqXCEExBEhOj9ctR5aJGVz9%2FwdXWUcZfO1AL%2BadydXiAONDyvn7d623g4oUeLw0CuCbOM%2BjFAakha1X2TgVdjribgvL7C7AHrgrJRN%2BGnIVSfPeYewrpWSZxhByNVpN%2FiEnxzKyH0uy7fARRjM5N541%2B0E5TNCRsAK4lPRI4iRWKpRrOocAF0HS1P4jbxJYTRIgRLBtQ0BZLUJwxh71oY2tM7yQKPg8kAsn5N%2BTXPhT2IGuH2wloKGX8eIcgnXelvTDg3gcXhSLL8BT024IeVhR%2Fdtxu91EcH07Gang42%2FHSTNWaGdEzgbvKccRjmXaAAp9dK7PousFJGaRxYfaNZ7qpoNJQ8QNelVDAmoEoXzgKu5IRmCVvkzLG8xOrqfvlBUUaf7cIhQQJzleWC8QklzMVhCuDv6VA3%2BqZR%2Bq0yzv7VCGB0gAqFpFHW6%2FcbqNW%2B%2BMlS4%2F5Ft7z4bjqZCJmNVQAsCTCmVlXQi7J5AkzVnqUAb%2BMHqN3dIx7qE69kpOKs1Nm47LcjCXGXA0cT7idePRqtrLFcSG5zCLtOw%2FIroNMDOAkHFAylzkw2BNeGdI6%2Bt6lgmQDWokDLEdP010vP4GkChbHRd93IpmADtMW6oc9x%2Bhp8Hi9x0iQmB07i6HSj5ii82pK40SHyDbUJUDE4l9VYRDSa0ZWJlDoQDZD3u8xfJIz7lhzaWdEVX423Pb2k2fr%2B0uTu41xoX59iHSpkSHnKnpmepaCpYAkP6ucnpmxieLz5b%2F4KuqSR5lvBBdlbL4y4hfkyNAfBIl2VAKXLxQQm4pBO%2BVdiAckkTJfRc1w1OeCtt3JKNTI4QiDQ6DDmr4mg4OFtzwGdKPBTSFxEldsDEK2UPYyF3v7Kz%2BNRpA3sbMMw4BS2W37FDYQ6vd11iv5lMEoWdEMw6cqzkaBw%2Be5lfKiht%2FrJ0oDRA%2Foh1D1pBpPUzAOKxViyuuhQRdjdAteqOYWQY57tYJwJKTD%2BI6M509O0%2F2rNZTtQtE%2FcxojBwS%2Bvr2f8%2BufQFMqyI1YttBwb%2B2YpgLcP%2Bq31h9b0jocJ7c5DS%2BTjvnnyP9eE2Y3NMvP1XNrDPLoclgTVDwRUQOc7XO61t9dA166wtj0nEen0sGw%2F6chCDYH4yMgPiWmh50UMnkqhXFluMDmwzKHmL3ZCBJOTKzv69y96WDKYJj6YCcXGKbzz%2Bf2UqlukA%2FAZSoChTboS%2F7cfgbMg2tTeRq2R8z4b%2BFHrCi2jUdKaXCW054EDARaO7EgYJsQPKjgyFXvDIAvicnUjDGPq1lBtBFRkXRhxdqmgoBei6QZZYIjefRwzIs7j0nuIMc32%2Fp8WyTEUPDv2QSaw2kw%2Bs5pmEStPb6PjJqcKOmX1V2hivmLQD9AxZzVfCrnu0zoGvxXuYdBcQ%2Bl9lOVQ6xr2k0YVEWGPMpWWNuv903u1zD1j1%2Fb6LxgblSKURM%2FoS6DOIelwjrR7GvJzngoJCJI8Y1W7Daf6FAHWoAOxmJ6freL8CyvsLqxyNgo4la7A5jUswoY7WRybOYw0PMuFbbCTb3aQgICOX59IC3cY4tAmUwPyTI%2FWC58kZRht6z1igOYuN1OWk165Db%2FKdvf4EdmJcBIXhZpj7ZFDcwJwHVzNYKBfzJdWpeBQMxf0dHUGoJqHsojerM3Jq%2Fhg01LyzBPq7rhWZSUBvjEX31UxJ3Wn64wvgdQdw%2FET9AHNVs%2BDwMwIESOMla%2BWgOot9kc1WfFr08mwvnWDMpyh4%2BNFfeNbncEH4Sswp0XSo2VgAdNlcsEg9U5NGLtyIM864QwJcOfmiFIBAATMtD3uGhy%2Bu0sT0l5eNKwo5cUz1EmqABtp1RZ%2BJexV6GVeY6Wf9RMCf7W20gQsD9GdA6LwY4Lpsnm40Lpn6Cd0%2FBmzrTbUJmqpy5N4XwYswVoAj%2BZBXPJRe9FOw8Sl1VF7fmZ%2B5%2Bmcc3fyrsckdKBXaGf4YEbKyquEtdfRICHp8JrrBib3GSE28jwRnMLl6%2B%2FFpMGkJEom7iRoWIU2%2BexWwU4ZWWSWziOX%2B9%2BCN4nCiSbwct%2F6RDUCUzhH4WSJLEGmTMWndiPRj1guLNnVDyUyUtaOk%2F03SktBot93zRE4vZdkcvMrn4yprtWbojSMtyHiLudQMmj1kBUUZldozils1Yqd7dIRYo5nR%2Bd8Wms0oX8eeMABV1Q0Wu47w%2BHBGLZa3dmOhE2dgA%2FP%2FrX0p7lrwQILaN4cbsHM7J5dz4Zmjl5VIwtSgUO7MAwGsHMM%2FgQQnRZ%2Bs7mghDftVQt4NsJVZOkcSqeh0MTpnX8%2B4cL8IpyzU12MtPk7BsmtXV%2BL9WzlfdpYG%2FNE7hAHKqP3G70PXyrVNA%2FSSsj411TwhUVGbWsszat%2Fef9U3kzhvMenIJzzqBo0cPTzx7lP0ILiBAK%2FgTT1nJhFJhN7tlMMwp5C3MMo%2Bgz0VFUuGg9YK%2BwUasyniSW5QCV4UpHa8FXZOJCEOtTPQx1anWiCMEIJg1k3l4FrSZk4BKnlqriMivo7OjEopsxxyLUrgMJKD5QAfkqB3vILILyCFnoc7WzhHOTy4lEVXVA9eoBbKNAmiZAlpz99hnijYfd1HS4XUHmDzO%2FlKR0p5hJt4Lv8aS7eMzvxDtWhERqeJjOuNc3MvAoHOT5IOI8imzFNhA2PPfC3b5%2BCi08KZ5XTynuyaNzYo538Gzr8He9IbFjBwVGNR0uw5FBmBErgiByCVZy4jQ03GZ%2B%2Bbz%2BkCyuwS5nLKHBhgdedEmcvuA1jjrNEVPViolVkYFLrBGKczgAFDJuYTZUzxuKoqmKgpFxmmv19VS99LejidZcLfzVzAVq4IxMyOcN%2FTPm0JJkMS44KiCgd4da8UhP2LeSwlApR5lnIeatFaUpsciH2Y6wD0qJxlDRPC7VSGFcIxzn2aFSaUX4wL7Nxezopcpz8uMQqRJp2hsl9qAQuAAKjlqjUbyZUGocUvS%2FlIXu0Vng7EwWJ%2FM6km3Px2e9tEHbpQbiVgaBqzNj7hKtgcrqZTgLwusp5u6dG8YcCAoxdv51JNP8CJHl26cySlEq1BKEsTPHFO5Guy9EU6tG%2FNwWeJFQ856hpB4GdjMJZIh9BCmAd9NYg%2BXXmW%2BG%2FEL2yTQ2lRq8wc3g8GI8uObPv31qsdc43WMTljDoYVgrPhXcaE2uXOBkUVxlDMiLbXJ8yyIrS5RxFuF2TycoGWwM2RP%2Bxe%2FwrSmbWeeJbz1JguIAgwvZvnU0T2sUNm1HsWbAJrRKwDoj168yrItrHvde8WdlYfNE8fu%2Fq53wr3KqC7nNuTM40F2qtp%2BE27b6TqU5THssM3J845xxYI4U5ymxCYrJq6Cop5DErOioOAk3O5IaA%2FLdmpXMyetne%2BeARp9PDsKfOM7ttfO94zs2SgJJ6P7KS59PE2ITgDLcTvfVwnlAJzwA2N6IQy69G%2Fs128LVYD9iJSYl0pida7K3HRsjUIxSV8K6MO3A6QHa9pZy6jKVHUc7MquaYfCNoS82XX2mAsOURKKBoe61zV6zdLEsTRIw%2Fsqoh0CLFKjWwqCYGPmxIBtXlksnE1G4TrcAc1U4FRLMjh1IbLAeRBPQuJo9Vo3mV7uwqmSbHaMmDR0qGCqO8%2BipoJinr2mHVLij1XrdPjxyzY83%2FAYsFFSOWEQjRt8uwpEmUgyPHlixS9XfiJuhmGOw%2BByPVY5BJTlCAzeZnvuw%2Fj06Dj40VL9Pqe1ZE741JKQg%2FGrtSMwvFXsgsHYMmcvkLJOxHTFFu03Ei5AMZDM9UL9tShlrPTUrc0rwHj4oHfRNOX8ZSwO%2FOBIu1qlTNq9ZNFGDv4yQXlJNHf%2F1xBVgLdfX7DAWmkdmq6gDV9g3LxpSFLAb4k9yblGnTEPSgdimj6GewoaBNBYsQ4UPGm4AkrmhRkNe%2Bo3JFyhjpJio0E5vFFSxCWkRCiDk7LTAq0PmLkabYqH6Ixn4sDTmPjAPMd%2FNVd8NQmtoi0U54e4zovtKJK63BEs55pFJv0FHzUiZJKHGolqPKx28TS06O1jogomEkBy6dNkoIKEYBIqVuauzwrqY%2FPEkJwU5pxBLMkGzroKVyjIExwwgtEy1mN9WpmQpDZj1bCn53w%2F7KpHMaP%2BVC48%2BCYNsZhrX1qIGyGOFucLImGOYlTvOtJxvR7JB5ujI0PP1o%2BQefWY6x%2BEcH6kzrtWa71Gx4UgF%2BMEiMbcrTglHz7Tl9rLI6h91qVo8UiwQiXM5zhMplpoaoAXnN5ujKkoBTeEOfFj%2F1bYb%2BeXETA6t48zUmX%2FrNnwLZF13HX6INYK6yE6MYeh2iq6Ho4i%2BvhpquzDzHfPHS1OawIXPyV66MMTmEIUI%2FBvXb2%2BIlgIVNa7tbBUmkdhD%2FSknSmhk4H3u659yn2FlcHZs%2BG919rE8g2rNSmyl7LrNkIGhS%2BVUijS8e6XEc6lfRsENrYoeDrVJUsULZHlig33TGSwU2Y486KCehsJKtwWTs2e8MXie323Zrqu5fhGKI6XWvGAzcuEd0rw8vDPci7Xr8dkiURxBut7g66fZeayGMd%2Brju28udp2EwdvasXVF%2BHCr0TtMQ2EDvGGeJcT4%2BvM0ySR1VrrarT11HaPRvhPlhNxTfidhmXlef4YRSLRkPXNvKznRTLvjTsa0XhH1UAjUpYOwv5nOqBAntTBt3mxMa1v5yoMgTJh%2F56V8juQndtP08WbilgeYdHqzFZuH5l%2B6OQS5wFlmOQbLED8wlEDMD9U5FxQnUZxcS6j4MCu32hsUBiSWOYMR6O82Oz82EWAVKouWBcFs%2Faz3atUMUTF8rWC1Un3Y3zzi%2BSByjF%2Fj3l8Byesmv1gWKlcmQlRrSYI6%2Ft7zuJcIsmP7MRCGkkA4%2B1Zkk3s3iKt4E0kPQPcoaRYTA9tJ9hPIWTY3eyGZ1HqsZThEBh7%2FlARBgcxeGC%2FmN%2BHVO1D%2Be%2B5u2gYwvtBGrFGun%2FQH0fMUOKuxqpdtxYFUZxvmVsZfTFeBk%2BAVqFWFAQo%2Fg0ii6bo0ZlohOTdQtuHcv5cxLSv6om%2BgxM9LRyb2Zh5fwwahClLvviIeoDhCjNDeMpUdCLaO%2F8pi5Kbsgx1CPsKaW3UPXPLxZYn9P8Hoxcqkyiaep3LJ3tTA1%2B9lWdKTiCilvUusadTcOo%2BTI%2FJWvoKUng8QvtWMYjI1KXjR05Lk1KJMOy6sD9sCi6tsvBglvUxSKcIQFgmKLVk3RbPusvDk2UgBBr7F6JbII6hHH9rbzRl8jANGgQFR%2FRN74Qs5qtfeQwD09U8xBIYIn8VT4p7QQPu4mOoYxqGzCKhKiMnLoKa9mFvvjyxJyfuoc3tUpZRA7jsnhO1GCkm7xZUW%2BKa0nQhxZ8Kc5hLZO1nzcLknhVFlsycMZqSccymPEeRcJUwebSY7GJtrkZ2G1kc%2BbEX8b5d4Rb1ghHZpqJkCBhVAvRFhmc4hJ4f4vFd%2F5R3WUgxHVVMwxm7%2Frgu9J2suJVuz50ltxNKpgHvHboO05p91oqI5zxqfGocKRgUJ2mecRi9Wl%2FttQoi50Z32%2FuM4sUhm5QKRI%2Bwj2SgIynhqWfDXWPm5f%2FMd4hOYqymgNWQkZPDFXnUg%2BhYwIV%2BTUcYjxcSPToVgH6NvhV34qLPEO637WxASUhN%2Bp7W6k2AdnCOP5U%2FWa0vNlZE48UQj7kA3IjISkrO2uvgmjNv9TCzPa%2FgxojwSuZ8EpfOkcwgXWmAgKbaY%2FOYwSZgll6bUAZTfLwwSJ7cVGJFWWrcr6SW%2BM1Z6lEYvtyfZzMVr2IsscvJTncbjCS4pk%2FW74e9nECxS%2FhOpBrxbfMy3RbjhYrhGGXn9l4AM%2Fv8hM0XJZH0rup1b%2BL3%2BAMYM0YIS2jkppVQSzXKji1jlIcCpHOzDocZSC%2FT6hR7JE%2FwSy9ZtXFtoMrifYLpEuX1JV4WdygS%2B0R1PcYJv%2BKGuE%2FLpAXoQSA54ZWdWnqrCaQDexq%2BqD4OGAg68OGJSXIGW8grpONG9rMNJBeE6iQFv%2FSVx2FMK%2Bt0A9PdE%2FjXQk10FjycrmFS%2BKIsR9XsGrXSVlkuIzYSzXLs%2FdMrcU%2F4Jr5HXg9yDNThIUV%2Byu3JC%2B3WmFu7LPFMzBjsIeZmhJHLHpUS3iZjR9u18YpQW%2FxfYf7hgaAWdfccAB2ifXY33LUdkO7%2B89WujfurLlFyMXS%2FOS9PvDEyfmi50ylf%2FN%2FOhBhpWIrVi0V1g8abGUaiqZblz%2BuMagczT2UIx4GnoJ5ODmugoezNARqW10AX1XcVfv9lpxHIjOYsQRV5tV4F0cXrdqDklWVKwfmiFgOUQ%2FXdy6UlMpA%2Fb6pkbgJYHzgLRwqmr3JgCsF97T1fF%2BF1VOI03rKwRQnKZeiHRmBUtsJxmiC5vqoVgXDuaV79w1qLgUNaF8C5kEYa68QdqYYSDsRV%2FQQZAtPrlyVEUk3RX7Ha4tJqXy2BUXwyYArLBsrA4tKC%2Bh1HCfemYkVQyyG5Z4kFUT65jV%2BQ3fJRG2qnPAQuQb1mw%2BhCLpNN0F%2FN%2F2m%2FHXCfU2ltRhybX4bdAQapmoWQ9ov%2BkJ%2BGB6hQhowcmYRencYst5EhN5Sv%2F4%2Bte1m2JdmO8%2FwuaB8cy8jMyAtfhYYGIcAkiBfwZlRDpndXfj5GzKoDCWwQLFbtvdacmRHj4v57MeO2LHFnCUuAoNbALeyGMFdnIbrZLbKlh8rXnDfr7E7syJ%2FyvQbk6Z1iDJmvK12wpT0JFeQhi7iCvHAAVVLv9%2BBKXRqPWmZh9TYzdOjIaSJKzV1nZbJaCBa8lWcLVfYyPrKKX89SX5duJwzEc29uU4ZPwc0sr%2FUeU6Z5qyNgrB%2F9yD0Gw9XDdTcfWbIov7GvANyRmopPab%2BbuYJFLWKXZNYTsoJOwwOPnXx2NoaYIU%2BtZnFJD89EKlOhxXJQAXLGBiMz81pYBlT0ROguyGSrJ4r7lo%2BD8FwCy1z1b6nG03PMH03A85C5LiVLweQeOhibpkwcV2cEGCBL7uCkr5vbfpo8%2FKx4o%2FqPE2ZH8sieU8as0fA35dU1FwLmSFKHvIZtEUDSJT1RPEE2NDhFg1lCQZ78VWOeyQnIDq95m77CRChzH1b1tc9Sn2NqPaV9u%2BLSMgrlK%2B9RxKx9ZkDk2Tm3cRmQT14PTkrBdXQFt6s%2FN1m5R70Apsk%2BxNnlXILQXVmspz%2F1jK%2FLk29%2BrBQvLdYdw9weJtFKurWFeaLso2noxZcdT3KyHHFXWy6NtzL5%2FP61a41x98Rz21tHdLP20Dkyqc5pdP%2FS4fRzEY67VId0TGAg8ebeMO3vJL8qviLxKNXnf5%2FClY3WmUy4pYhOlu5FGdUsN4OR4EDMsQDmFoKj9GZKQlDMtk5nEmvA6raaDWL1GpIfuL4X98uNvsU9LSy8SYhHFq8%2BFS1dI34i04hH5Gv5ahhr4h5X7J1E6cJ%2FCATi2gUoWanj2GAFKr6ORqoTTxRkCXrtXWWwQbFj0WNatwD2Jf3QFJq1SC9Hwp4G7O65Fu9XzvktprS1vNzO0rlbVAOntOXX%2FMbvzIPfORPDopJoxRqnrhUjhyOXHltaVctTKhmsKklyo0cUr7i6R5uFaz7%2F1ecUdY6Fu7ehNKOEIlRCZ08ZaYGMhPBNnZ8dOcAFk8h1M79qoUk%2FzqBM%2FU0Lhxa87pu3%2FV1D5UoAiRnTWqTtGyemeDT6d%2F%2BGeLlJCDTpMmvuOv8NRfM7Zva79Wnf3R6b46ZeukrNZARoYmcBqIJ71lpRXRyDd6KKWlSW%2BTGszXcI3T8rg5XDGziMg%2FAHkwI%2F3uIy2fp6UDc9CWEwF1j9SPYKMzbRNmn%2F%2FyH4QmGSTZuAoONtJjs0Ymi319kd6Sy%2Bw2vveZV1lkKd%2Bf4Ud703uQEWtQYDpMw18mUb%2BZuBXYRYO4dsGPmzaRnXE%2FjQEUzSuWY6qPF65piLV%2FyqXvbJt3r%2BESMVbaudZFAwzx%2FYpycCU7uu5nFfZbZI7Pq9srgy7HXZMl49o9W1b34o%2B5Rr%2ByWu2%2BPoFSZxXxXTgJEV4B3EQx8MYrRpru9ihmcknjgk61KKpd%2FnY%2FFumYvTV6HV32mQ8Cq9a4%2FC9ljZtuh7jqeXdNbKxmFz%2B3UA3NYRdl3k0isHE%2B%2BfGVgt3k4LMsstNjAjs95D0ba7J40e97tunLinnisSFPqIyiso6VEi%2BhrG7dpKXLrHYParOCLcJNAo8WUXiCFO6ukvxIl6LLBH7UXcmHN09ryI8UgHjr0VxjR0fpdk2O4VKwgqewQ45469C2hzdAAnFvS71XSNBj1MYfCE81ziMx%2BWVgDnvvNf3OFPxjtGNr1wZLgiyzp5nWbfsaqfx1qoYlWL7fVEBR8pz9UiR3pg7RXt%2F93EgKRh3NGr7Mu05IcIjAmcaQHQ7CSS9r0lmPc3%2BJ6M7UsYGkY2VT29ipPzqgyK6t%2FPVxfR42TjTpERoCVHobBSmh3OPG10ty%2FfubOHlDp03h3ezaCZnRikTEvRXMnx8W%2BxGHdrRkgbBpyHcTaY4jQqtGhMMmgjqQnYw3KTh7qgQqbHZiunuc7xrIE9x%2BIWl%2BTTEESO0igxyebNhVp0h8MfhcYjiaWVwbHsBidy7d1L%2BGuGjSam%2Bvcw7Cs4ldvFbRz3UdPYclBQ18E49GQnpPOprBlXqxLfUK%2FAp%2FJJtzbUSp3g3g%2B79dzstI0xHjg6pYsuZgtyTpZTv0ozsU5%2B8PsommTlGV8KEyYEBfRaifhfqWzsJXoLDuo8o2mJ%2BSFXADDlMLTDJ9vX4OSK%2FfBnKy3njsPLf0oaXmQpB5ztfYa1R0chb5kE7IHvP%2B%2F%2BK9Iq6UpTUABfCgh5ExGGLY211K29zPXP1XB4m7yZYO34P1awmQBYBac40P6yr%2FLPe%2FCP1i3uMC8zcMR3xaLpBPOoVjTqiqHbsvPzydIQvAvrirOPd3Lva7%2FynYV3qkO5KttYjz%2BzgCmBm6V%2FIssVZm93xt1OOGrn4QziOAkIdKnonCy0%2BXS%2F%2FRGRTETogxDWudl3RDbmy0cbdiKNexPFvsUk2iBq%2BkbLjpSe5SLdM54DkyAYqQ%2FdNemjI0b55YGStT5FL3pWAZXA3%2FRlcogaKnWLaIiMzonZMyzIN5ounKOrJCqqTSWF%2BuN%2Ba2PqnjjYXC57wy4bDTO9DgBFy%2FgF%2BnLvhS3hTluhbG9qP%2B3NcZ0tmsjsJTanY6Wmb0muj1WpmN1VBeuPs2GEN6s5A75SzDM0Op15IdvV9I2hApd5%2FvK8vh%2BJR2iW1MpxfMbNi8h6dZ7omJUitYcJ9PRQLlNFHiLpk79AAILH8L%2Ff7KR73J3UUZ%2Fwk%2B68zlCHdFYUeQP6pUi4cbCieIq9JtqzvCMYOWxe1uQk20gUia0NWUBke0CcPEZVXmsQqX7T9P9cUdwhOkMboNUqEiYcSWZAGG4JiCVh3Kr6sZ5oHWhfMkY11fsKMzAWMpy1KmojGW%2FIG4hYi268eETEuSXOvTRlRcM1U%2BflaeCPibYHdfZQs2qOuAy2O5nHTRGXIBcxwGVosCJJQYJSQn3P1Xa26dxCwENJqHUt6Ypxx5Gf%2FD5rpLUFfaW%2BMcV5RhcT1xttMMfRPRdZ0dDCNSa2vQnmwUGYQaRaWLlAGZGBi%2B0tcQnN09aBTtwDmAXenXTFM2TzUZs1Mwnx4N%2Bx0gkKIXYEXCZt9SoxAwtWultZivvede0eCY%2Bf5jkqVYoqm8vkrdjrRbw8EqBEE%2FImOqxUDpFhG93HaNqCmysEGYImJtf6I9%2B9WqP966GuRSjdkuMljs4TXpWfvYhzXLDq%2B4xWPmF%2BIVTHjtplhz%2FnTaTrU94RCngd1K1c22c7Xt%2BEhNzZcnYk9xPiB1mIxJ6jA9kfg7WdtlPmUm%2F0Q5vHriG78Qv6RAgVzxhIqoK68sy9scKPoiNxNR08R%2BLVVG4t4WDADC11XqXrSgqh31iC6nG2mHEGZ06HcLTJDUNZt%2BBcpQXYO4FOUSp%2F%2FtiX0CNEgmiUz7N3OQltKA%2B4brflm09kE4O1tvQP5v4YfKgZUmprvZfsL%2BmDI1LcppXm0zfKun43Nr%2BOsdqBr3v8COuXtych31IGxtEM1GfGesxJ1JWyYjcDPX6sprzqQoxrK4eyJXwPkqeC%2BBGqWM%2FIWcM%2Fr%2BT79AkKFReoDdv2vS5sGU8MNA7S2XHCnBgkhBv8yuxhVHLFTYmhS89atKsCibrswr9isopIK17pLleY%2Ftd699%2BU1fgFHTh5zDpq9yRih50hASY4qmAUKiBmxBiV0IC5osMSqgaO5FmbzQAAUnW1IjZ%2Fp1ivgq3Cwp4byVwpR90Rer37yGM0V9jAm2zsIHCV5%2FUtXIgKyWblsJ8NG4rsUdKd3KZ%2B0nB8lT9K1vlLRt%2FTqds6V1YcS5wdvG3S2ZE%2B%2Bo9aH0xb1kaGqMYSJlK76BoTxiivtEglWalDeBOw2XxYy%2F6SyKw3rLatTKBhzGSFnbDxtrkQYeEzWRohkfRKATHB5WZt85vCHglMr%2FSvHgcdQqhthw0tujhDjVcIIo8dHcgZJu%2Bb1CFRYdX2yyPAhUoS3NszQvzMsJmOM0jStQ8UtaG80RS0z4vbm%2BPdEmjrue6dBTuHsBSmq%2FFE3q5nK3fGU3M0rXag8F%2FlYo1W8OBSNZ95GZosatp9nH8LPPoOX2mreXDfeyWRytEuTRrnZOPV%2BM6%2B34e%2B%2FY6wuO%2F6LEysfrr1CkrPFA2k9VzWpkABX2vjG0G46Zcqb943sKEVV2EECqsuY6XmNFKnj0ilarTc23fX4Ygk46ooRov8RzKTCu%2BoKmPfEhViZK0l66iw6w6eEbkT8KsFOG9FKLARPHd3gpL6aGGf8FtaX%2FDEtMZgvC%2FATBCwcc8HxL1CoT1m75Vc6YV%2BUTKZBPBDzdlba4u5GRpAsCH7elpiusia01%2FYMhVvzh6A6f52GpsRk9jmSr9qEhylyJ6hx7U3g9KwJHpBseIrH%2FQ9Yp9yot%2B9V%2FL%2FfGIaP%2BXULqBUYZ35Go6rrKYyVnmCk1D6%2FQElPmIbNGcwEO90XKYfC8wEE3MmdCPAgGCG60XtUCFZjEeM0ZaoXfEnlsTaems%2Fd8QTT3wOXp0OubuS8XSIkT0WBpTKStQWtEaXTYwI6st8MQ3hKLQ50sldOKTCgDqNDMTH00nEmF5WEiAyjH7rfOZqcGHvCZvsKW8mf05%2BFou6tQ16gvB2GNRswvxkpFeiC737bhgiU7lqhaXO32DFnWQ4keCd3vhxv7%2BxrJ17eRAEELputNtnKyu0rRfPDRTymoDQXLuIduKfznDkjvAjPQkAu9svGVkN8Xy6tvqit5AKQZee5gW7SXN4pG1a34G3Ra7SHeBpbUJmMPFRWr2rOn5n8QefsKsLDnEmh%2FP907YWMC5pPPjiZx08sjSIk1A%2Fjt7y%2BIYG35oAku3X3XhXorRL2EgR%2B8iW6c7ifJyd1%2BjHtfJgMe5qaOf1ypzdNrSHAN%2F3S3aSsSjbXTnta7GRYC5CvJbazPiE7isz03fRqcUDzczXZybeRbLCA8vq6ehsosTHsBvAerhNW6kph8AeDsqsinMs3VQNk6tztuvdtDtJjBEuNaMn5H9DhTOe2P6QRtoFLuN9LCyXA3zEVmsYv7ZP71swAsTIt5rAPJrtib%2F25hVzqt6ZZY8sybq8JK%2BJCjPBhhUWlzgO%2FbDr8rg6%2BgNBTSw88FUlw5npBN20JfViUVEdPorTsI9%2B4YdiYaUtI5i3j5U7yQEkUlh%2BWk%2FMcMEsiCO%2BWfdSEo5c%2Bg6U1bgTZrxheDqjGmj%2BZtRL43cu%2FNyTUDwbaovZJYGPkR%2FeCHevRMjWOIqyiJ%2Ba%2BaYJVyYeZV7s9Tv7ohU6E9nV7WpMdG4TlNi7kBl7NKAGGMfsCspXwi90h9BVmKlMXUwHvm9jnz3u97lQwkUFfHW6Hz5kapbNwbTq03w%2BMX8SE%2BxLJsl0bxqMldI7pNrlwRrcT4sSVW82A%2B75vQsJI%2FuECY9Es87U1d7QXUQnJmfVFhbcGcKeGb1flR5mLL%2FlDNzb8%2BcQjiTfWv9ZWFVnvTExWtnZepcnYHPT5Ln9LDlK0%2BSJsUosk7HNwZPEdi3vYrCXTfNm%2Ffyu8oYveMMjbYG9Lm2SkZaR167ebkbeHUy64SZ7xOyRCr7lG9HmSR1dcw0PYlYx9mpdKNl0kGTZqrydvEfJHPHPMRcwytrWdP9Ji9NgwT1ftDfcsX8uz36yTsx%2FenZCGRGO2pnExPs3yTR3zF%2F2tIMK0I5o7yFDfeuhV%2BKagFVNsnUY8B2bltp0VYcBF6XDVQqXYCURh1rluF9yRsovtnGzqxxlyTuj4WcQBSMcVVSM8mG%2BifcY5Yq6I9iy9Tz33xl1jATCX5EkP2MNVQP5s8syG%2FuZlawu3lzZNUOnoXA767fjnexqU29hMn%2Fux6Lv2v5HxRELayeM%2BbT9IuZ5%2FYiBUHOrcG0dVcV9v%2FR7puR2lu4da0OLGE8s0e9TozPLGpAljWSPtods7%2B8UziLT7qB%2BlyN06aRDpYPJ31yJddYWz7EAWSYAZi4enbdWitoSBSRrBmJ%2Ftz8z0sAYXvZeWkDzPgF23kD8NYjg97Pew0hsrZN1AIJCiGGcpKW55KCkueSm6G7%2BVkOFIx0bQWPatT0oEKw765F%2F%2FlbqUMpOOsAAiymvitIMolMjepqq%2Bg487GcyrcXTjPkP%2FxY6%2FkfCQMIhR8DuhsQrjFNdlrv99Div%2FtJ8gxaXQaNWeUF9BuKaEmouT8tMxq7n5%2BqshgxwmEKIPLYFqLeCMxTsl70yMbPHpTJKKEnF9hz5j1mb3s6RFa8HUG8g2BTWiOZG%2BJIhmK2zXjGr%2F84n3RyZso8oL3xj5Rbxwylhleo1lxZvcYUJeoWMuURQ5f3kp973JQaB77rSvjsXeodpFf11B6Z8EAR9cyXCi4pbuEmfUlFcZ4qvo7yX7GL4E%2B3aVYpt6JupPgSu9P7oa1pCIxyBUa9gyC3PvDVKso3u%2FXsM%2Fsu%2F%2Fvf%2F%2FAdzfvz11LhetSDdWRi%2FfySo1Z7Efvt7xb5%2FNIkBAiBK9Mn9%2FSN8RZj5LQv770gYfz1Yx7Q7TlLd2vgr2kzu94NB7jt1vn%2BLTvCUT%2BYye%2FxR6KN2XRct5O1Pj5Q%2BE%2FQzdIHvH8UB595khf5KO%2F%2FWEbE%2F7QqZjn8r9%2F8l9voIad1PJcE3kJaMpPw6ifpx6MX%2F4mc4bZgrFREUxX930bTBqt7x4X%2F%2FTg6CoZbXKe%2F50SU7fte4COr85EM23o9RP%2Fx1ODvKZxCyncVkKG1pwWE2DS%2F8kzd8ZI03iegA4kWLZdt68g%2FOaJVM3cXwXevL7HfZORvZqmkmsm6azt0hnMhuG6UCW1nbWJeIZjrrIn4zTpe0lTSsDgW54Pscc7NGmYmhF8nEMA3Z3%2Bu%2FKw%2BuxeRRO1fbvDNDHguB2X%2BgCpkRjbzq3tZqTYLnn%2BM%2BI7ZUTmgJEyp9taZuz32x0R2tRIjB1fAGAbAcWuQranzOYUrnEn65RC9OF8yqGkY%2Bwbol0VeZlItqxkdFI3V2wDcK3ZYa2SCzxKBbIv82u9l4wxpfZBN5JMG%2BMSZGT9gNng%2FcimeJ8ahFTKu8ARUOTjyApap3mj9DpxxAgpPvef%2B%2B7f%2F%2Bz%2F%2Fhn%2F42MaIOWzX9FkLMvRxgoaDvSEZk6f1FiWaP78KcZy7HKQcNrXV4RXMhBnjdBXwlBWB1PqbWGdwFs13nf7pxLEX1euvaDW%2FpJ%2BMIvsvApCW2EBiZIXceiFXlk%2F0SbWb9lIHVS1I%2FkyhwNi9KibppWiO52RMebz9nnTBa%2Bnjmnnje4CffNipFP%2BvT9SA2Fs6YOgMO%2FsVrjXaukOzFrG%2Bt0Cdq8A6rC7zoVXi62zLA5Pw8OjEJBnvmVqAsmHU3m3CdGRTPNob4%2FGEL59pudJd%2B3RWNwwDBT9M%2BOU0LDTNh92q9iVxC9ZO0HiZ4y7G4ivKl32tfz2Hh4SGqPRboOrom9o39PqonGTEbBPvoXWpnkclk1AcnB00tvTl%2B46AzL5srMPOORIInX9F3NRk0Lhed6z5WJNsbPSzn4%2F60R%2Bs7bd65F7tNw%2FD7ht5IWJQXywF3RKt90cWefcMl1x4wOVGzpSdPJisvso%2Bn%2BRiajzMWn5A2%2B4OAfj3S%2B28rW0FyeXS6lThfRubISrMvi%2FqlGf07rD2pgR1LmTbhSyKQGI1mPONpfIty%2FFYbJQpB%2BNiVcmp54iXsIQO%2BjukVpx7Rm3QLatv1wvoJt8jwrqIk1hJmt%2Be%2BEsy0twCxykwIc2aLeqiM9g2wDU9HiUO39DUhXNlt1H%2Bsi%2FXcq5zBY5pSWfzDl7usGFd7mI6ynywGO6rpjUWefmKrkXFpnHw6WXafW%2FfRw5671IxblfFRYRtN4Zf1oJzMHt%2FDzTHuZQIzNw62WmHVaoeTIitKvOtdh5lQAXEq2ARLTps9opCCk5i6n4cw%2BC%2Fz2vB%2Fjpbf3wkhNg8oUaHmKNFKLJojyiqz3QhGkhq6mJ7QhfHNaZB79yq6EtyFtYmtf5EXPO7W5dTkzRtkgrwzAfXdtAdQmnHlk4ke%2BN0oxqMbTPlO0lJoTmwZpQRl%2F5JMnZkR%2FW6eEis7BnIhTSLHAvLgFI68BudRcP%2FjqHQrSsoT26MGM6q5yI%2FP1QvddWdykRuddT%2BRKpqViLyll%2BzfMXtvRdw%2FatGWLlHIpiniUZacPU7fuCv9kW1xtEW%2FzpCwuV%2B7wijiORfO9z%2B1pTMn86r5cBuRV%2BXin7Bhf1%2BM2SfWHSrGJz7FBOOa237fKFZsbR9wHiLUG%2B8CZ275T4JVXViBSgJkvbLD%2FP62sgJdDksybh75%2FISmQ0f2AtVFl8w9Cqin12b1WXOrymDfYUm2FQVHTXwkJslr0LHzWXnwxBgc1T8kZBnyvYYes74%2B5Ly98H%2Fz6NEy%2F48Bq7fiwF%2Fpr4oiPQX%2FbCAXFar0ySDz94ygzSfc07equEqgI5XNV08ZzDklymATh9OOpP90oK292B5kNUVwffymzMMK8WYUjxycscazkqX27JtBzMWVjL3vt1xwg9tA27QDFCbADdu7FHgnFGVdkEdWR5mfHZW5A1IpO3WvgX81qndyO4xTfHmtVhZocOdysCxf0NLLi0yCzuVwrOeSXJb3RSrEKDfbSC8wwZxqTYBSIXfDgiqD%2B4IDkFcmW8LA6an4FBMa04U%2FJzwkTjbOUr1zvrgjRPe%2FjWiw8RmV1c2gtZJAPK6Zh5ouVRKEg8e48NCZ%2FEiKxk%2BJaT2yLanJ3pP2P5vGGroZ3kqV5bCjr6qPQXLTFfO3VViyh8gTcHW4AWvswu2f1EsimmeRQygWnf3sYO%2B1BJJVcUWLzQOa%2Fzr5Ygln85622eBJJJ8WcO%2BB3SGY74%2FEh1llJc%2FtRWCc2NKO4Is2WJiH06pkxOzMSarBmaHr6%2Bid7ECRcrYKETL25UI6y560bOl7rj3t1%2BhokDwhXjF749INzj3aVRQtD3JvypOCYOxdOimI3bhuVUxt2OdhcKffuJH1HKm0t%2BdHkqyxuvpeAN5aEThrvz%2FMgC9m%2B5Jrbzm%2F3NV%2Bl%2B4h814jwDiSjpJTMKvENpXNfB2C5hQMBurltgQNDszUleezbAOaYJ2lt1DRmBuMFlqMXKKYa4r0fS3hHkWB2hngjmNuEY70p1fmAJlnUD3Z4lSQ2p2%2Bi0T6bRSn1uxUOSaP8Hutyn19hMaXAs2JV6KkZHLlWpOJ1ledlDf1hu34O%2BoRslJ4AszBQyhogMcTAi6Kv6dcvRhDlAPGDN81M6uCvMMSTn7E%2B%2FN9X2WjjHaphtJyQK4%2FQWdy7GcAGjHTVqXmrKHpk7zYs8BlRqN7SBLWGXVn08KbMvL0n%2FWVqJdvgA%2FB0UXb3ApQV4L21sh%2Fr36lvxeJtiofK9%2Fx%2FWS0g2eta2RNC6JgCDsaVXEUr%2FIPv0A9fzexKclbuYcMgFWM1qpb28ooTQUwyXWG6mvQQCK8BmlfRfGBBvgfwD845T6hY5DQi8BhdAs2MN40KAj3P5CKrGWmfIss0JGVfTH3gFqN8s8qgqVPUY%2Bjq%2FWQ0BGQ5RbeVSmg8j7kUkqxN8610dbYGB%2Fa6fYLkYG1RoegdvkC7QKBaq6Vv2tkx90HvMde1TqOnFVH3OrH8i3c7EoUG2fSxcolT53jS9iuXCatIrGf%2B9Xmb9db8dzcnuC7C0%2BmdqiB77P5AxUci8%2BeJdMWhGAJOSycGRFS1b397AQDTq7w7i29IeefSTHBx1rBet4F52VQS8e5RNkXWbslriOix%2FhXdAjZTbu6ur6OkuLykH6na33jUcuzAgtgOhcMJqNzXZ7vqbEkj81cAvy8%2Bstv4i6IrkGN1qLHPXiIZ88%2BN%2FUsFY4jhPn0XODt3WTFJ%2BvhepdaIakHBnh3AcI0RFdNHgTPtLndcuZSBd%2BzwdD4gBrMO2CRs0IG7sCPARbm3Q45N92swRih7dUqvyfeeGuJfd0x4SWNoNKzZW2LwRH%2Bns0uvnPVhZXjZc2BltkfIk5deq7v%2F%2BqkVWtkM1ngNeP4cpvOpGn7hJ7yvtqhG1DYl50rk%2Fmybx%2BxV3EqtcdScB2B3h6M5fUPfxqQ%2FbHd2L9XfNrycFZfK3rxvkO7ZlNZSreRcFp%2BO57PzkvAicsSZ2IVvYv7eSY6yg0jeKPxUcF6WLZdcz04FrMz8QM2sWUNBkhMXYNT3iJNgcYk5QZhCfxbZDryKHZTgvd%2F%2BH%2F%2B8nf%2F%2FE%2F%2F8j%2F%2F%2BZ%2F%2B7t%2F933%2F3z%2F%2F5H%2F%2F5n%2F4mOvZM5pnPHEyfAmL8lV8ehdfqnA%2Fo%2BwGMWS7qUvKXKsEsJzf%2BzPIHurFM1gssaZKb5KrxV7WuH90GG4t6MA%2BBFyJQ8Vf4R8hU7hyv6zPXd8UpdiQevUKy%2FXFO1pu16sxes6ub778B9qK7mjlQTN2%2FJ11HaX5QdI%2FoKczJvs99SRb0h%2BCW19G0SsuHN%2Fy1O0ajSFhl6rHWx8qRyJE9E2PHo250xns2QqDOQHDwh%2BduepQK5sCZVtXP9tXF0dW9ieMudgWSMV93NitE25YCW84zowWD7e9P487K3WIFeZfsPD1zxoCX%2BqGaxlQ97hxhK%2BYDPSNLaBX4VgaU9yIXmqf%2BSUTjIcC2INQ5JQ7ONkJjZETz8T3He2JfuD1nHFu4OfmKDHp96GdkHhX7l3WLr9LvW4qn884qzfFFlO%2FRm%2FEVx6ByBzqsFH4kRWtnH%2Blp0ZEZ%2FlEy3VmKtmYp96WDKi6yaAquO6v077q%2FskrzffhfM0lMNlZ%2BWaYJNpoThH7mJYj9yZdo%2FNtoJ%2BMN15HptV7oL3v2P0n53sUVpnpI9DUHwvVk5%2BR3uHRw9KSH0UyegcG0rnfdUMwbWM%2B4HgZ1Ah%2BeLQ%2BVB5gpkJ3rWFIqy3fsgYg1MgykdfcZHbHyt6b5exsDfv9%2BUbKYtl6FDuR2ugMJqVpkUNw%2FFbJVIYMic%2Ff8gjN5wvV9Bztjnx98sU8vFdXpYe3aOsYhB4Qd%2FPbWvwXI4sZhtMQp9hnsDCZvsjXeSreg1GBGO%2B%2FYS%2BvKh4VzfbgwaqkU0l18Txq3s%2Bf9hnW0n1DRDYm4zqy0w4e69jV4ST9O%2F%2FEdok%2BbiCkD3N4yL7Y2s9mlsQ14jPtROUPRR32z%2Bs9Hooqz5UjG2LswlI%2BgDPYroq28F4RMEAFn5iKelZkc8chYppVNfbd6fo%2BF0n3pcT07ivsg255SlfkK7rTaOEjRht4VFCm8UDpIzUNGULmGVPHIZxZwBl9PErbtHdFtSPjVXXuGls1c8qrQ4Fp97Es5Hy7WkJTwlEfG3lZbiNf0tMjR2wPylethHPXJH%2BlQqAp9%2Ftlycg%2B50PesX%2BqDSoJGpE0LHOhV1ca7SJom5xLw6X5HhdnFIqApUzKbgT12iNJ5P8E%2BnGyrHSv2FbVaqZln%2Fv7eZBEwdiBJ0OODcU4%2FyUzkDTHeq0%2FziWGoY4osWK%2FaXJv8gzLmt3%2F56o0dDHq%2BOvX7Yv1xWuwI6Wjz96VJOY6A%2BiGiZlsy4Ec7JqrKo8D1Il7XfR9P7mkw%2FDNxqFel2lHY3UZQVlC9BVM52RzgKmCD1rGAXT27d8icy5Y%2Bpr%2BNVy2HO3w1bEQem71WIhFOnIaK0q%2B2nIG8%2B4gplUp8JNDT5no6Y4Xk%2BWnhrZQGVsaB4n3vkhBr%2B1HAia1GStdeaCZj%2BtjxPQ9obWRS2Q3cbVsZaRbOnBzVBLjB4QG5nDPQdsS8YY7bAVsCfT%2FufqZe8GxxgeTMJlgMpe5640vcIwp0xGaSVshg5lBmXyEEpso9mv36ql0KsOq6ISmkAxGSQYEXdjWx0ZP9iirtyJHoWTLM0GkeMSCQTEj%2Bek3eCrPjG0ysqp4jZJycQ3d0m6XZVYTkMwkNwBaJYriOq%2BJRkqUFBOCONZojmOBDeuq3v2QjGS3GPOzZchqI2kJraNJiDMQ1o39KoxAJQHA7kiK%2BNyqP7y2sIAI7EV1VEyCnBaHydk3w%2FWuHxUPmbde6xGeYbFSpjILODP%2Bp4ocq%2F%2Fva9qpiWL3uld%2B51%2FPF58YRseOj1AHpRZaX56fZ7WF9IrTUNlJ4r5HxCnlnwJpHYu%2Bcwd9Lbfei%2BnRld3HNpP9EjDFNS%2FwSimJKetehR3mxQS3HWTEiNUkLLyoitr2TVCm6EEmrrCrCszpK9MqqPGqJV3e38iaSGIA4Mvr5NxpH9o11JZPSSF3IskR5pnzyZavLkm1Dydqn2jDk8o0RAuVDoUn8rqqo2K4%2BN3Z3Ot0ootVYKIoRO8fIRNAbO2IpubJ0ju66zhyeLPNY6KCjG7Lv2bnLLqY%2B86l8L1Kmjd8n%2F4TVXY3z85SAm3ekex1qkKfCt3up7I2LEn4e%2BVP8KO%2BZIFsFFV7clZPolGJP66yOLq83Mah%2FQ8oEHl0XkFggEXUw1SroinRA47%2Flstiqd85qiDLgJsFP1XfcFQCjUesBDWTrndTanBojp8rpzIFySHk7ozyKgtzoI313BTRxuZ5CIwCU8ruR31jhcIfS0VZFQTPJ%2BYuxHkWJKcxusqjWruIzzIJQ%2BUsm1eyjks0n0OP1yitg6IN3O5ytmjeuwOnPd%2F7sJTmjJTJF4H6w3SzT5x0IapYY7Yi7Izt3uJ%2F3UdbLESeSBWowXOVnfkS058QW9tVrCesN17uJqDC8vKZJKuQYY73NB%2BBbZe3MXXFfi0iPNj2ztyUofpo1hzL6NR9Rhqa%2BzQLLa6Y3uY66BUeAmWT20aVUOygECKzVQ1fPYxjtbzwgY9bb55vHWzTza4yp9vHUwWUBncm%2BxiB%2B6RqW6%2FL0PHj5MYTkjI7xApbH%2BzfrFkzlB2qj8D%2Fr74wljlTjoojKO3U4%2FZix%2FrSxOzmU%2FaqepjczXuRKdatcNOjsKrKOUIIcDBY3x19ro23AmwqzN55Hhn%2Fi59%2FUEyN%2B76P2%2Flcp%2FK0I%2F1g49%2BT7qh%2BETp6iOc%2BrlIdxhKf2rFhrQyl1fHJNiJj%2BfYriO8bBJ0DRZqvj6qoBOYfvX%2FbMpqKIVePu%2FBhBVdG5iLGaNe1SpLj5uljOf4spZcJrQfb96L2b1vzcNAiQUltnxYwweZOB1we%2FFQRL6QX2cHSCX0j6xCXu%2BjddLlUau0DxkKu%2Bs8I7S17s0Gilaii5uot83vlP6USvO1E6e6mDkQBDaRrRNtdLCv01QjncTQFbGXCFvopGcGXT9xQ5B6T5SZSQ17sk%2Fc621TCCqbq6s7kRxdEF%2FnlnsGFOU7TNnPEzLDjK6KyGAUFHEuiMXOvHQwvm8n4CsHkeY7j%2F%2Bq%2F%2F4281ag4h81L14F2BCMFDXba%2FWsm3y9s322X3et0WSmrM%2BMQJRviVP0FGzWY4F32nmYl%2BWu5i6TTNEoCPslKuAQwptKwfgvFRhZz%2F4DsSIbFdVJkrcZ%2FNTAXfvHH1M6jrvqPr5I3d%2F5KSEjONhN3usIG%2Fzpv4mr834WlHlsvGdaCg7KIFbP1JFZHqv9DVQispDUKJrFopcDYn5GmUtHgRVpbWKLVlrPqBo9C2pRqxboH5UHACb8OSdECuju8HghW6jqofHMFbtBZ3hRE%2BydilPb9K7ejDjMb3IpxiuM87zDyR7HI2%2BJHJovsq80rLjDYM2ZQERGSRO6rKGFdUDTuBxJxNTbmvRBviPdGA%2FfJn7Grw157CuIgOmeH5v20Ap5vlnxd13dylYI%2B2iErMjKvi%2Bz4NgWKInCq3jKjYQg%2BAz5nOLsUyWD3EU6X5Zm34nXGpCk%2Bqx567OEuTqQjlff8yZIERt8wI3Ts%2BPK8GmD5QrNsvBb9sY9P0bGNcHNofIykSBpCBmk98h%2BndUrkKIiImUgRDOuQECr0Ygf9K7MddNmAfYhJ5DmaLrngx%2BFQIdw6ltDtTeRsc1VWRjFFin5StMv3wn3Oub%2FHgcSjsRbnxjTty2WKRd6JdsNpShr1ZXlYVZ4zLCnpl1lFzLKbl768wPxnXWdfJE3cyD%2F9XMdcSKl6EEDxD0KuK5XUCqpnIQo66Jc%2Fid9CLiqaoocC7gNhm2zVkMIPijv9KrWJq%2B%2FN4uYXoxoLUfbFqlCoxG%2FnyrrG4BQHGObO8ZleUb9%2Fr59sovWjZHLms2B4PRwX47%2FeyT3EROUXDnABnT9Cj1e2O3XGLGrgtFXiDUwLDJfitxMjnQ%2F4%2BLt%2B9GbtkvLd3FDTNyRq3yPI3uGQQRb%2F3HUW%2B%2B1tsV8JQtLZlXKGFtMZ8jPN%2B4F5TCN%2Bm0f5Rj9lVin5PAL5GaRYdYmgQrMDVuX%2F%2FbVKfrmO5977fLnfZNGz6aq2MKLwoHgQFSH9nUl04XX9J2X9fGOBIBSwtlpOeE%2FxMuMUxq3WjupveQ53GqKHslmjUMzjTmSJBJINRlvrUyPSqDhdIhZsYvyWHu%2BAYVaLu8Oq7HxKCpox6hZS405LjS42W19lVx7txh%2BWSLUYL0iNbI%2BzJhCetSKILuNRIWkuUJmGMaOn0ao36cuNAfpvcdNZmIuSx%2FSrt53fn5MtFCABzjSVqRMwe5BP2LpUj5Iyb0ppJ18Ypslcj8z32T5AcQoG2btJ4lqgXFTNXp6ZcJgHrd2vZgEwH%2Bmpvfs5UDrEwLIZNR%2FmydKdRsTKY5J%2FZFymRRq%2BJGmS5x%2Bw0alNoHkeEQMK0jsAc93qO4AcKKF%2FycDM2qnM%2B996FUIzOZNP5y6Lcj4STI%2B%2FJIrFNkZoMv5wwWWzkfMrc%2Ffwz5Mb3XAEgTOKgCeFYZ3mU3%2BHk9qBinjUNOgxRLJWdBhFdReN8MY18v0kRyY47K2Fq06oXDltq6w0Wh1lOzFIVW6MnirhZAoAoe5YkF5Tk%2Fcs7YZ5De3Fj%2BwAzyxFKs7tLMufYk1hEpnYdJTHBUAsi3J4a1v47fARB6iF%2FB1JaOQ%2Bwfysotp7ePVVQGppU7CDxlKAh5M4WV%2FLQZGs804WvXLzdekU17yxoM4mQO66M7Qp4cCgetSwJQTRY9TXKi%2FJ0howa8REnlxwTf8%2Bzsr3yzb7maqfzO929NraMLIy%2FGdR99UV2LaBX36eS4lytf9p9QYiMmgLseQ8TaPiOtzfEl5FyhVVNZKDvB6bpwmfrqWStUZxkez7gr9TLEFkDdyWE3XQ1wwes0NB7tkamG3tyhsaQBduX1QCEyF09bN9up6dYgMHErNrqA7j9QAqLTfdf13Skx9nqBXRZU6ToLJ0mYCBHy%2FwMDJg3rAlqGzg81VzcWIy9TTZzsa1KxEddgVH0tsKv941irEO7McTZCxh92weegaZgEWV%2ByYoKVORT4joouaL9%2BZb%2B7ykTbRAFFdgUY1Ufi3v%2BqQHhWLafhHcot43nQjqKqSkytsj8cqRs8ciQbo8FtDcFhiEU43flzEo9ZmrK0yASaIRwDuVhK2s4n69VeX94FN%2Fwkau7xz1MSMwacZnJG7%2BZkpoj5%2Fd%2FhL00tTUQ2oy9hctdmu%2B9Uh1nQGdxugfiUJ9IFCU8ID67TO5DjDDKeaoGavjjCD5fCu5TGyRz0bd6l%2B%2FTqePPTvOK3fqGtFySTjbX0BP2rBVC5yAcYG%2FYollEiRN7ccdCJC2etS6juUK7j77tcg%2F7jksRm7rYEGr9Wgv2gNZrF2FKftQDm%2BwgO573j9%2Fsdm3DShwj4ZutWQqa%2F1KNLNRQrBhIPq0JK3DFqH0Mt80TqJk7Fjt779zNfLUyEE%2BsMYLJfnsQPTSZ9CVn02CdQyN51rqUg1NRtXyHque5q%2BOequSIzMYDqQ%2BAcoMjEdK39yYZZMAKIv9uO69nJtzCpIzUnr3%2B0z3pXYJcu9ZGjPmqMDct0V2XMq%2Ffyk3ybE%2BtKrLM2OlrbGZms7GyPOR6SZxtQbTcdTuFZ9xOb4cxAc%2FqgvRJWYfGNkbUAZJWQEbhQyGiEJ3uy3UdchsUnRah6l5jawjKaXrx%2FS%2BKaDM9SLga%2Frd8BHjCWN5v3iSyc4Z%2B5I%2BdhQyDwLijtdPslV4hvi2e36%2BVUhDWazv0LoBxGRPnIiOsuveyMmXU7xrIezZDgRpXIFtpUxM3L%2FAw2oQzIVqJfTaYy%2BFGqh%2B9B4XiWa1qUFEqXLTAJ0tI1NKTnO878vdaETyZ9ni37wySIyhhaHyi9vaztXqGpjLxrqSv%2BdSv%2BBHdMG9eKrXIW6mwLvm774CUJQONU05JDq3njk7QlN5TW%2FeCcvmtaEZ0%2Frzdt8ElUp5ghPxwdgAnQswhEdmNevyhVo%2BxNA%2FJFut6MgjK8rYFj0AudRUAsbRjer4ji7O7Dl4wiNsoQ8RZ52hFlJVEZN3aeP%2Fked%2F%2B%2Bp%2F%2By%2Fiz6TnR7XcWTuVJ5hmX3TVDKb%2FyQjGxk0q%2BRYf1mc9sm78qVYpC%2FsmeYNTfJHdEauhITEhDzQCSJA5gQTSemcoIGflKGmCYbiPi2udJJLg89PxMHhkzSQP4o771cD6crfQl%2BTFjrAC9EVETn7BcOVQ%2B5TYthqsbiRIoj2mvdunSiH9SjxGoqxrCUtygy5%2BTBX%2FiPi1ns1pQR84nqMGkBxt6m%2BO74Hv3mR%2FzuHOimK6M%2FIfIwub%2Bsv%2Bk9%2Fi3LJwAVG6vXX3CGpRNEm%2FeGj85ZYumPW%2FDv%2F1Of97nZABugexZl5w9V8qnREL5XQvrHstoO4MZ16qLpOi5W%2BJeQaoz3KnHUeyPiYyqrLwEmQPnpcvQrqYVVKfhjiQQvXK40pifceo9RKktClS9GaVAlz9XcdsoJyRp6u59HJWItYXk%2Bl01CdRoSgCB25M8LaL6tnDNFlJjQJmeN9gujacw8wTN%2F6DSoTd6obY2B%2BBp3dmrHxlalzwbgz7BbU63pRmppCTGzK%2BiHdeiZBukhb2PS9Jms8GOK0rFWqr%2BmWEIlatfUstQEmTDlVClw7FZERp7gmJdpd8F1F%2Bn9O4IRqd6FtH9j8fhv%2F3H%2F%2FVvnNFMymqwmLWuu9E0qt%2BIVpIxdLUQnSwpAbSdxLOFceuZgL8Zs4MHHE1ngmS84G0cKwI01%2BcBE7JEq1cQ1trgp6Eu0031BmFEubW3B%2BGZGfFgxS2vNY01ShrlojCgSv3ByIp06FlRxebq846ff%2FeFtNaWg9hzd95r7AauB6PCUo9CVxOMxy7o30Qao9Xg%2BwDKOwtbaz29FJna3V1eGWoabKkd1Aid9DHTv4jZ2OaiM5N2yN4t831lnMgn4AnB%2Be1sH%2FxG4sVauM6G%2FZGt3ExfLKb9vE9tEWYJKMXR28KczajcU81%2FzhVNZQqZeBMf8LHC%2FaIQumfJUzrpJuvqR%2B2w1VQ%2FCsNwyO2cR5VtdxwEnpZYq1rHeCTZjY7%2F3Bsk%2B5DHW5z9ZD2C2ZkC5YBf%2FayHUHOnfySwP1owPaVMZL581ADLnsrxGCLZ%2FXavDb%2BPK1uLgsVfQfrb4ubwAtVvFzsXfWM4FyVOL2d8AgDPpm7uCXrRMCMJFgvxHMXNBBF%2Fr9EMrMSf76EKlnnIIcctZvDafwXVBCb1EbnuaOeuZzVhTSdd7OI934UguPUENUvUavoLCeGPo6OM7%2BlhsER0M5fiDbY0qqTWGsWYgve1BUwjx6rs60Dhzh%2FF6liaf7u%2FKOTcHk%2BPNazPzAvHMn5YKYjgTcjj95jnh0RftyTUdc9z%2BUFZPs%2FsyFWTxR4ktXhjJFDylR1E%2FTXPX6ZTPzZCA3hsk93WhTLOJ19WImk7UJoJMmckSd1Y5C9KCiY0ekwJWisGDkiDJFdXtv77kY6ZcTV0gIrnIoPWBwtCOFfArpF%2BHCv77K0VlWnEwA7FfUFbaTavuojh3pqoazBxmx2dWwfdK97cgVv8E4sjALAYSCo98c8b%2Fe4VcepouMsL%2B4avyILoSCo3W4yEfiHTlGYvz%2FLeO3yi9PfDzATGc8x5PcuKbO4SnpeJ6tuPKX8nw0jg3ufaUx8K3zdxO%2B6hdnTYs%2FHgqQXnwm1EhOeH3p91ridj6E16x1EKuz0izztTxbhs8vvtSmgWQ9HS5ZYgkMrbcJz3L%2BEhWFA%2BDAqIDvFypcb7i1f0U3xxY3n5kesaDq3tja7MLdEcRq%2BQ1NCZGVbR3gGJtrSbBkl5mGfCRJy0cylRfLD8DVEetgqKXi2xMyBNQUp03IRFQPRX7331e%2B2%2BVb7PYsjXu6ljhB%2FmQ%2B8AGLfQmaCZiAK2ykf9HlXnmg0v6NGzbK3AmTTIpDtL8kEWFBK7CumpZwfkjqwfiq9huqKdU6iRbjS%2FixwjA1ohIeOPPKIY6G1XYGyr8Rxxk2DNHk2k5Q6%2F6OTk%2FYyCeiVshHrY4Hy%2FqsZ0dKd09LfvaGWNdduMjAOiOvubACnf9%2Bjxrr1BvoHrexi9Y0dva0iRxUtxNtNo1Nf4VCkouG2ZulwmFzVAle8LY4UwqwE%2F0ko2uuMwFGXdvRqDI5chMXV37OiN6ZmZMb2Jbf4%2BkvrCI2ZOdqbboteURuGi3MQNBWzfENEnbvYwzwsd40oIz08CQ6%2BLwt9z%2BPhxs2g6kxxH70AnMTtrZSTClEjy%2BzjbRgdihJjmNK8zxs8ftlWw5cX8%2Fr4gKmjzKOPcNrwBd9wJwmKjLH1UsG8M28Zv70qoDxDYVp50qNkv25Uw6%2Fep6KHeSpDvPQE6%2BT46zdN%2Fyc2TeXG2rB5nbqk3%2FauJQGd4KRtTVJ8ZPXVaXVzNiXiePTRRZ7NfMPzHjl%2BGuycBiDbQJJi9UMlIyFhA2t7eNIRhOMWIclc9%2F72ABt3qMl6F0ulBSz%2Blssn%2BpRx41Ml4Hxc55FrliA%2FUg6CYFE15i04BHOrOfjF2XKSIkZ3z6LglSnibrZCyl2RiLy6OOckWeeevQoaHcjo%2FVUJEpWsFnX7rKdNigP0GOUcar8Vpo7kTN82kUWA%2BQNYIMxium8d7ZUL1qs9tyUpP%2FKY2q5SFtv5dbRD0IryzsxdmTVYRFkrDUKGmZG%2FIbG8LhVW1ezS6zH6tiWcQkHR8JvCiUqHPPPWYn3uymc4VkLXHy3%2Be4Rc1zMLLLBxEZd7hJ2AkfmUe4SXPoLdJ06rr7csa6YxxgS7wT9yvIEzmyN3SwBoa18dYi9OvousdYvF2s%2B%2B%2Fb%2FO62Qe0wYC4DQjbdVAalITVF9T%2Bq6xHrLsmnUXfsbDBo7Ca4tRohWMAASNpKGt3NjLG8bHNI1qKv1Sy7FF%2BqO3afkC%2FtClb2upWZ1uEujVIkwQz1wnAFYowBu%2B%2F1cQrZi2TN01UvCmF24zPyb3VuhnDFQ7n6dVaAaS68Dt%2BQPD%2BuyOM%2FSvJAka97C1KJam7Sb359UK%2FI4MQq7TjOa6VRMWORDow%2Fkh91UjauFg80ErVW1nq1zsJ7WeHn9LNlUfDqGeU%2Ff2rbfbEqSdvYmvlbSF9yG%2FkK6%2FJfuKG5dnPdiLGIRzh%2BHY2YVImFTVcJWucTWTYjSGVKRy33UX54DUuNblui%2B%2BMepsTpnJqKseBpeOtMN0YkPe4jzLQTIW4p8NwVe4L%2B0yxfIZoBTdyNed9Z%2BWeBFlvEnYKuySxIPbN6JnqHDLFvwNoNOMs3kc4mY%2BhM1dP36mXdxv650wFneoEtudak802Gx9mMW%2Basv0qEodRMgwTXsToPxBjMDqVhzhwVB1qMWvqMBIgkc9BB%2BiF8e6YaHRLDR0Z7AGm4PF2POH7Fjo7%2FIhiuWXoSeViuF9CeUNsSgeu%2Ff1cV7Q2bRsVrbH96SyAFtYtn1WHhqbjVvTHtbaEv4os0%2FqmOdXUBVH5ylQ8KupSnHvJkxsM2pf0k8QCpcVo%2FvQW1Hw1%2BPSErTq1mSV28l%2Fsy%2F4sKcYlPfWlza%2F6PgGKUuW%2F0W3ztI8EnbrOZSZ2hg82gfs3858iHWuJlXT6dcVMXRqBC2%2BxxneglBXOVh5Tcs48yD7RGeWewPGOjg8CPyHFdPJ7e%2FnPkbhmkXi9epOz5agd2bOVyuAkd54JKXFD1cfD%2BJCAtCQDt28ei3SLvcair%2FoVXucZ8aCCu3hRG%2B8HKkbWLUWId4SEdEzndVRylJVnSvpqlfN4PjKx7Wdn2ZYJziJ%2BlyZWoGiuDjalhO2YRvfldQSO6oB46m%2FITieQAkL0fg7JotMza%2FtHnTSUWbuJ1E1jeHRGB%2BXbGyHedfWIBKEmhDLjqqJtWfDa8ttj9a4kjNegyHmn7gWiY0mH0DjTszmvITyS%2FeqEuOtqNvc%2BA7LhCTk6viRYVnyr0ypl4QTYFVyDrobm4MZipOhxogIAdlq10izzcIKC2obFFIZTSRq9qGCARlBdFn8Cb%2B6Ollbd2CXcPRgUB5lp6l5e2nYh31kTOjnm%2BS7CEKGVPy73fjE4M0H2bUnfHmtyuiM6ejlMjurNj5Nhyx9hZFSR5ZkOyXdR7lY5arbma%2BZB7HgHpo3mkY4eNLgW4lw8tMM%2B%2FP5dZLtRwHvuupVya90RA57x4eXkNS96M%2BJ52tzsxlDEiuym%2BK8u%2BYhj8aS1VhU3LpX6NR3ys3erOeK6LHuXwdVeIb4XT3WAyPvbh8sZho3%2B1a6hsdDBB2whF30P9tkQmxD03UHv2aFDWf0bcc5sqY6u404wi8h7jxqvhF8fCatYkq17DzOq3LALSRL2DmkPfOzmQu%2BJNlmb0b9S%2F%2B0%2F8rAZxVODHnl6RZDnXWNgkHq2HP8797PYRVNq2oatpox4z1RTv8J9snPocei1wjH3%2FIuZ0C9dLuzAiJ7LN9%2FX4qiIH34iD2pHzs94AcwWKeDzNWh43kxxzi6Rt9T1Fkrq2bsCHS9noFgZsadNiU5WrIQCkNTC2BoGIUcBqfS8vqg48UQn66MxoZEHOmLDCa8rfy%2BEvuPf%2BrcvaSZThZiqxAPbq4EnWSkxMGzNiVQI6m%2F4vK676zOawXxVpGikkXXDlIJ2DynoWQ1RaokrpPNmpgD%2FupNfz9AITSAPxhvZQLSK94qNvyuhxKlLN9dckTczKGnu%2B6wRFlNi8KHPLKl3fbliSv3VuucOOQWzMMMQI7e9K4yK5ctIw7CFuLOL2HnEe%2F0mi7wLiEge4lGGcimCE2BZHssj%2BtQmS7JMOkxVtlcFe82atIIb2bv1KHFP%2BOgRE3KnccxKrHpSrd1d0CcUiu7WsGB0GvQWNKS8sNHprIH5qeASqiore4Fglf5HAl%2Fe%2BHb785QpRgJ%2FVhR5FcLeINL05JrVqObdQgXFszjvTkyBbrrf4Je2TuGJ%2BsTUCmrjnZ2Q0wy%2FWDEwLXt2%2BGRa90Qr1EPGExM5HCKCqhJ1kcp4Vjb8qPLhkCi6MKCA37lKlUKonMub2rujh5k5HoTI4CvFSxWek%2BLoXGRL12SK7r8gv3B8mHgmV1hjSIPdTNFNQzzKCqIzupKVe1GKFsznjZsRGJ14oE0KIYfZ4BS%2BNv03hqnSlay4aaDm0shHbwLQquG1Dou7zQvZjtjdC5NIIbuFrY9zPBBdr240jNaVWnCl6rYGYvbqFz9jfHyq8HHq%2BMXwwAQY7%2FqPCcL563EHwtVM7exxMA26%2FUg9IvKgnxFSH0mPbxfoFZl4woGubqT9o3StCCNzRe6AWfASJcHwPctXgVG1J0o5SI8SD6rlyAGzS6mEESKqv9nY5ha9zuAsIkEuXgfvU9R%2BDv7GEH%2BnpTf3yfu1Vzld2fb8MTq1yt0c%2BZLNwhIc2mknBiSZjpoMXt1avFScQWZ1GGpURFapDEhtfMm3kYgfTpICKlM5VGZ5ggfX%2FvZ7Y%2FQ11O%2F33cCXaC4mZfn%2Bp3adVrxOEUVTsRfvkKFT6F0GhjW9U8p5VCwpz2Iv8%2BEk8Ct5sb3C0aAeblnMl9Yi2FEklHbG1F83MA8D4flXKQMZ1tP79QZPHkic2toAmmIU%2Ftg4ueMtrqi%2FjQNcYEHB8CsnBsUost7gaAMygjHUHc2NPfeQ%2BJ4AL4%2Buv2cO0ytCm%2B4YLASdQ0QvgfPkRQJ22JJc22YWZKoRPMiWHWDN1QTBYePOTFjq763M4wpsLjAxWGqIWLYLHQSkGvtOhD182KfhOnrmKKRI96o1s6aI9Tse83Pr75m6PM7PYapS8admVcJQ9eqK9FJV0jZZZDFsdHAPG6M289YAqKvrkqRttouQjrXPFQQRqP0b82IPW4CXDK0Y%2Bt%2FR4Vqmm8lXtJLyunWOI5pQ9pIEPJ2TSZnh97dfx%2BDPCYk3pqjmKOt2%2Bg0RveCYP%2BI59UzyJy81Zg1Tkj8BLyWbehYEMjxIqXdbACT5Xq89PyGHZhPvKkYq7fkfEXdP8s28q9T0y4HvbuHXsSw8Fio26%2B830%2B6WQXhFEqeE6fvUmrnuqjfR696sHizYxZxJU%2FA91vVmhIjvOoPIqBMcwTUGDSuEfaFAD8qxLWEgz9FGak08rz8rzQ8AZC66h%2Fw972QKvA02D4VHUw56ffYJt1t78iq8jU%2BHNrL%2B%2BSn3InJOlF5YkrMG61xV8RMmcvBasa1nMIZYMfdWFLSzeLF%2F7m0tau0IbXHVHrUICSU52wkSvo7OtUdL%2FoXwlF7yZDWiN2FcSUERjqU5qfubEKceezOuVFjqp9k5Kel3swY%2FzsLcssXZc%2FgXi4B3JKsvZ6CFdvM1KZO903YMC9TwRghkwgXB3UG3TExHhOkiDte3T9ecwG0jlt8Y%2FElBHlmB3UGXGVuGJA%2FT3FHthZSSZLTMFcqQk3UL5DuhCgXKIuCUxJk6OffJBFS%2Fz62x660uS3SMcvjhbqwQaAOmNxEf4Ic1I1P2HbXhORNXXQsZADkvpnq%2B3FaC6tEXBSfPjvLZa3eSB2y%2FnortiZpA7RewfP0l4t%2F8JWEM3CUhDnUHSPSsjWyY9LP4cDzdC%2BLgYRZ1Bg76Fo7Fu6ZYQipnTnj7a7lCfAqmpnwJWtUj0cfsc%2FtabFhxQBceuQWKRuuWC9Q8WIFe06LFmbfe46rcLpOimuPH3Hq3pc7UI3JIm4dtrGAhv9AWluTZtVZc8TmwjNT2jhbhrSHJEiGwNX%2FBh5%2BMcGjguXX49YijVQuLtbwC%2FqjxVX%2Fjbb80cDTKmW1N7Nh5wBM8uaECh73cekVmCwt0KIq2Nw3kiy1KyfN822p%2BFBfQPOfu5c2wUrdiOrpE7RjCUNvg9iBS7qW%2BGukaECVmbxfhdEynVIUqx6uuI8dGkgC5pZoobNtlFsbgVTGcSQreE%2F%2FmU60IRMquNGfUOi3eNDM0QiNOkRTbAkZcczjG%2BHVGj40UVMl42e57jRc8qBHbCjz5ScywuLase2v4V%2BBdirzs8AJ2W01FwMy%2BBqbbXguPkCifdGxFL7%2Fi5majPkufLezgvqIR2FoYLU9CLTOOLItGL9YoG85W74yaG0REmVboKokYxRPPFZjjvZXadU9JTktYIJ8VYhYF68jLzwTZOFdKXrstvuW%2B%2B0eo9OHOygjrjzbeamMjXudOQePsPiK74hwsyvZDBy31O0LbRVktZMXpiTnbrvJdRpnRsLPvb69Q6WtH%2BiMhfHsjdCM88PzkB6%2F2lm2AZeFqwLo1pioSHUUQcxMRMpO4QyzffsRYevgE%2F7LKPr1PzH43BbiInNHVnlv%2BFQTzveGFmv5OR5fjjLvpWBzZGVMl7QqMX9%2F%2F6MKFAo9Pq7M%2FbZm23Cq4B5Xa9EKIvekhtuXUnVl%2BZHTYCkz9QggzgTeng6rA7DdUsBVWtmc886bM5whcVEjRZGw9x3Ot4dQbyuSA7nt6o3vEkY9H%2Ffxcf1ZWWWUjVzZuws2UEJlIG7uMAv8q3IRACV18peZOCe27V%2Fj9DWcsMoMeOUz2ej6slEbFeWFmluhgSwIY25Lbq8dlllOWqg7ZmjzIc8sCxEuNpFX22c1KfVuZPP06pJrIV5N0%2BFJMZoCqGr7PJR62m7ZXsoGNTMQudE%2FIXfBHVUXlFPlOfCDLUYKMkbDQJPlsJhpL1KULV7OTBBz1%2FsNkhCB%2FakB6DZfdPRWTWIK3AyGuxACoXLMcrMAvfmeZoNcoM5tRRo50KVtztLTBYi8qX3bT52kN1vd8zyMGvGAaO68Ul%2BTVLJtLLBq659F0ENekH7OHHzdRhG8Doh2AWeOY5Ds6ezPkg3UTjk5%2BrDTFyNPirukzSk%2BYpCK%2BxJZ53dtbEaZbuwuiOHHLbHdZ%2Fwo9I53ue0iNFf8A8jl6CAyj3tqaKjBJsCOPSyBS3XAqDw8AOexCVR4y7kNxd%2BO3G3nDuGK6vY9rSQQlB9lq5P1tiaE1%2BZHsXyLqY1%2FCw31kj5ck3Ls3W0cM1cwhdwRD1W7F8GjDZhJYnaYPN40iyM2%2BKp%2BZFEzK6rGWlzTJvgZTu8L6B53xp%2FKjQ9aiT8kUxZi1Ntcq%2FKjxjx48DgmC9AkoJP7zv1SIrOPze0rWAuNAHJ1Rbr77Go0FhfOHiL2UElxXBtP6vRV%2BA0H0p267TwMqVP5SkseVwnFFdRAozVYaiCD3XDHf6zEEW3dCFmX0m4wFeWudqfDMTi64cuuYZsc54ArsdGo5uFdMyfgj0RkVpBNuDSNHT76iMy1wEt2aLKP6qA%2FTIcDzMB56NXsxKytz3zrZPF2RafNWAQv3GZgZC5M0VVydB%2Fp9deju3voFxNAb6Cf15Uct8t4gw%2FWGwFpX%2F3swpaBn5k79iHjcXlRSmandz%2Buro9QOK%2F2pATN8dkzFHbN4Fcsb7mg7k4rZYmvSRSu1mXVRrwahEPQ7J2Zifd4jcWBvDX%2Fq2kjU6FE7MoKQEqEcpuiWNT9bcu2q3qTa0bvyeRYAJFBRylUs4C6YR648iKqfiJmPMHag0O22ztIy%2FNhCerrONt5QSB8Jejl3MKomiVteEMCx8bX8IOQs1awpVUX3%2BvdJSb%2BSYSwoi2ob6NmhHtlo5ZmFV84Ceh7rF8Q7s8Rh4%2FFu199sxW%2BmPjnhzkqrO0OackRCiXc%2BsEuZWEPB9BxNzRHBbeTJlvp0I1QFZQYr9xKYi0mNqiou4ooDkV0bqe6T2KuVu0yi7vb5qtY1NbqRMvEAGISWISCqzbBTjlIdB0H%2FJPOPEtJf9lOYXaYgDNVKtDUwqSgOH6h1aZXHRgHblhHJtayzLNZbEkspcUo6doHYOF5%2FH7qx9pYZg7Vo4fcykNctytYUN9l6%2BYvYR8zt1UtRv4lwkivwrAWYRznmd8mGcG95YiJz2PFNApd9A3eLa2EPmeBaDrEwz62UlqteElH8KZ6fO6KU3K5%2BEWAqP0AV3LYtOGr4VU19H8xKiXS0A7rLy6vqNfmsl6EQqDG0MQlqUJbM4tE2ButluTqXiG5PjXKFS36VZwux6o3ZDUJreVGegKcE4ioGqn3chdwFYmvU0yJhmcYQiJRTDv3SGIO3w41%2F9%2BTsT1I2X%2BjQNHl7BXUiNJ0JDVWV9MZm47gQjuXIuTtKKLgkL4P7pcjPuzV0EL9hVvdUIi7fDPEfvuCuaBBFiPE1J%2BUfN%2BPKFP3KMqaeIEOfMz%2FOYVJd8%2Fb%2FT6BY9T2QzNuZ37lFq2hasRNV7kDvc8mgs7rKDLSBnx67LApQu6scuhMnR8hK9JORjdypN4EQNALNfB2xWt0%2BOqOYEty5%2BJ4YU6RbjN%2BZn%2Bkkgols73OFcGe%2FZv30fU0F7dItqwlmGDfVsR%2BJ%2F%2FZMQ472vS3slCD2CG2mEqJ8lFFuXWG0d5SURPhseHUytVr3Zr%2FZR82eFRhcvpnMjGf2Ov%2BNn%2FY78%2Bv%2BjJiYBPF4M6nej1XgqoSlVt%2Bls1bNqwzuxGS93fMecbRqJ8JW6w5FnuadlDd46ZUZ%2FSYSM%2FaP2tQAhr41PzW8bZuRa85gn9Lm7iX7qDT47I2iJGv95lu0wJ0Xpn8iCTa%2BsCfkg7Ks3aoI28unCtL4AQF136zzQB6qlgWNOvnZ7etW9R%2FBAlWmsI9SXhnuZ6DoPi0LzU1ysxX%2BGuO3WE1HIv6At597dG0WhFCEi2DWXaH6tkP7Oq7nWYFG15nMJkv4sQTdM1S8lwFD%2Bl8korukhidcz616npIpeZfTSlVXjgJ%2BprA31OxRuabYQe31ajqTg4DLU%2FNxLp1taMuI2Ur71lnHCmQMJiN69uw2mduxjdmklcvfl%2FKG8zAXLe5OYbRFOviu1ZFIPHfhE9dzc%2FQJkxjKnPDXvbJFNI65AYxhakOVQ82gF5kzdbRXHBbjPSJsK4XBDPd800ud7QyALGEly5HT%2BBhz8aDMTFA6A8MXi4FbX%2FxYPzXFzJWHE6arL2qFTrL%2FDF3PHtYecr3Jqb7HJbCdwk0JB7dInGvzF3ap%2BYTFrcVA0XuO6H536o2G4sZjY5LNrcIpWBPlfSvvhq1H3dTqkkq9fvr9rZFnIOWav7fzZgDxfIz7D4aQAifjKWoxa%2BL246h3479DpzorU404yNl2EUiuW%2BPE9CKRmpmkLJeOe8PF8%2FygnRu9c3R%2FhHJ311z0PnuiwL%2FDB820pIlX22jJXUuA4W7zRbyMiqP6kZnAHtq9JAblLErGhmORkKEPKJTikS097l1BlxOeWWZX4NiKi7Xl2%2FJMWTB9%2FWjttyzdc6QHE1wHnGh0jnIEhatauy1M1tsN4J%2F0FI22Zuc4te9rrzuQf6y7QtjlDcZAiytFAe5xq5GXtWGG3see3ntZ45KKlOXA8v5hbH8P1ZEg1B4pCHYmc50cfEb3dfQcJcgi4Pje2e66vo83kjGvc6cIWvPiC5Whu6cUOWACyxPeXCvlSgNN%2Btl3LNcWDWPC0z%2F9hHtrB24bGc0MR%2FE9lqrnSjxtnM%2FXCk%2FZkt83nSn9X4datIckMCuHw8CcdC0iRlK3enRxZWJERA1uP5fpdBgi1xE%2Be1kEfV3xLEmo73ebbikgsBxAnUL7vWGWNjgqncXs7JG%2Beek9u31kYNI1k0ldd0ngvuKCmpLGx6y5AQg%2BZLsLq4EeZBAfYsJ9R2PMPJkI3UmF5%2FmaV8utL9ufmtWZ1LUmNiiPsaeG69nPw93hZnsTHbu03nsK8LMSbWZvUaofiYO33%2BAtg2RXmXkpdVkfzm%2BO5xnA7Wht1a6kM%2BlhGz%2F79drCJPedV8ZhlOrKTkeSEVxreqxr8unQdvXeoRArd1EbgqDpq9Qu2TRGu7KMp3fJAuhtKVW6YI%2FaK7a8e1%2F%2FNGOUPUbx2s33tY0orjjn%2F6%2FtmbAM2nRTe8lBrYiiygqX%2FCimYpUqSf4SyO1Zr3BS%2Fgq1ANZBCgZrM%2BFMpvH%2BpxQbZg3kNKYYVcxq4lkN6fifYsIFbEsFN5J638XsKN%2F594zzWS556n4VfdhDUmPzkdQ5MnJcxPMsgQT9QxypXyWytVP4DMgMR4F9qM8aGiDGRsd7%2BxeisfVgifp6F5khpAeeEVPJFfRFiukCUwz0nMTuwA2bGNjqkBSzdy2JOp%2BiTNhSM1gOQHXXQz0TS5lt3L7i8qJSCCOs%2BoKKbwYDTJCvNKWrD%2FLd8hB1259dJ2e5Yy1ElVml4PIoSTH2lu%2B9GjBcI8Ig5U8oTtsRmUEMB0YgtF3z8RpEuEOX1rkyekdaqviw%2B5e03Zf8kjiz1uy9SX0ybiBtzIHoqCIKl6%2FCPL0GYFngSVT4Y9WJnu6Y5JNQZbe6HsbagfA0o80iKnBHzspR67RkIUnps9lqXADi4UFibAS6l9igYHbbn%2FMos42wN31jr6u25TiL4BvSVEtmrODpmqM83Ep8YuJFeyRVZF490fQzJEjQodjuv2BKE7GTh79CsDFUop8fPtZ2A4TEsNUMg4eg1FqeiCSo016V38oNbtWsGR2VJwsKRSPKq3G21uPcS55L5f129jEyRoTxUar2wD6RX7CxD57acyzfMc1ZMjdbzcR6%2FyQz3uake16SXhLIRVwrnQIFs3%2BAlPp0uGUICvFvPNk59yzWyHAkBMLwrrAw3x%2BUVZHd91N7c1PBO0hQV0lhJsV%2BmK64cMhh8q5DzrEi63jvUeK4ZCuxrF6hxK7qngbKj0p%2Fv%2FfsaUQ9bOy77WuAdB%2FxPsgd3%2FfecVKz%2BGbOZMQ349JsQuPEnkpfUt%2BhWvaNEyuRsKvvetTcdMCu5PYlmTVvGd0jRDRBIj68Lav2Urdv0VLP2N8uX8BVMgL3ZHCaXsJuVwz3%2FbDnHOddV3Sk3HyCE7JucSKxthD5z5iay%2BHl%2BtdQeZeXpuFJuJ6lweKthPNidKCWuNfWdIZX9apiohJpBXHgftiG6ctrUuAV8jqgXbTc%2FTACd5BWNdVfGBVcbL2OqNxHxHnOrDsfT0fPnsZynJ12Q6OjfCUt%2FXF09ErxiBCWRtG%2FvSToGHUm4E%2BV3Z1L9h23lE49EcVYi1viDrLlbPrInbmA3NxZE3ExNHxgx0rkq2%2FKE5lBI2XX07PYkfLhTxmEafDMq26jsS4WMd5dCIZ%2BJkHVOTOo29NY%2BF5z2fEQ3uLzHGOuuutMJcXoCoLaI7PkFjuWExRXmQ1e2CAHX%2BasfcGt3gQH3CHdl%2Bz1TFCY%2FXhO7T5AckRIQZuF3y76kGFmQkbPHoVxkTxJ57mwq2u77jXc44BAIelFJSWxoznSlXN1xWodw20j1tmBqUANArEoyEt%2FcDOe%2FdnsEPHJQ6gdE0Z0ZG1IVoHFEbSVC8Z4fAZcw2aynLlXTKXJFBTT16e4461AcgWRqQlGHEJn0O2zu8U3nnlxX1e51kbgzii0eRCKEnHkLZshDfXAn9D7iXUmfvk6G%2FmvUqN6QZZbsFZAEi6f9fW9GjH2hyM%2FX82Zsv%2BxGPhNJSCSfPfqEMy4Rn1jAWPNfw%2F3rG%2BZSlj1R098vOsYPPcg0bGjL5vqekJHa%2F33EalVnybQcq5RvpzSqERZyf9CfLotNctWU8fqSPs9f8oPBzOCBt9onofS%2FjI1M4ooSxaNi%2FIrXuGW0SdagqjwkdrWc%2F1EpZqsnrOB2QpV6jrboPjBytAlwzaMipirutszuvRXsHzXiU6je6aMsYjqO%2B%2BOClFTw6C4FWPGhnwsStDZ6Pdw3BLjdR0Lwm6P7ogh4KkoQjKPzGjl%2FIzR2d3ygNp0e3ZCsCo9tk8s3aOYSWQSWU9ECHGX%2BJfPkVyScjldaqRHAyk%2FCdzbW6O%2B7A638GUrlTz0aSt%2BQ1cxoT9nLoMLt%2B1s8T5aY25QaR%2FvvqImKSDVh1atHdvE6BR4p4ZCi59Bis%2BBKYdHeWu1rGMf2RVTqY%2B27%2BPMkNbejQapIS%2FRyrM%2FO1BWozbjzMadu34Lrjw6MxgLV%2Fhbu0ITGddMUoj6dUuKuELEPVO%2FokAw9bTzdS67GoNkvkJlztsm6T1By7wqwYuWyMw9hJMIT9ZTmK9QCQMxBVPr8jxwe3RUgzNklIq%2BdjdJYy7vfrpTei54W%2BqsnnmYlpA0uqRrG3Bmz%2BjrelZ2kNSb7wVwwZC39beFNpYIejTgfTaUJV2aAgHhqL4DJAYUpO%2F3PPe9%2Fcgm2dBjc9ZUz5GTgc6fjDrawzfjJiP0Mk%2FcEcx13HXdIgQsGXh7urmF2%2BJmbOwXOaFgSwGHURxVDfPPsuJuoONxUF1yAxeyPPpJW3dVw9lLAphkrZedXJ3Slz9Q5xw1e4t3wqlwbsTYV0jYgJzv3Bd3UJz5yWfWU%2Fo7iIcW%2FyTbLImbaXJ7pzeLn8u9YaFdogljqN2BcHT9nsdWdI%2Fky%2FPXTBxJHYvVxiK%2BxxtHqG6cpRIKeoKsQMyKCMa0jZdoM1GVoRN1j5ATPtnZx%2BIGmhtdVCcBrb4dH2JrQD4e53y32tqks0z3%2FvKWPYVthQGQcJl%2B0M4k9PkVKwq1DOKyQePfqaGDmiEhyUlcPqswipJbAS2U%2FnvO1%2FgkALRXkvj45QHOaEVy2WOCLYWMppBml%2Fyvbj0rl%2B%2FdjSIVO6g4FQ71pEibNdYmxPZ4pPAvmQH3ggbhKFvZ0UCOLSIiDp5n0aWkf%2BJ9SlzaekBZkEV1NjNUixOFF3lUDk9KwXeGEz2AZoPerd0x3yNlymgKc7RGAUg68Q5eeeLx%2FhcNEpirpll4y7Le2su46O%2BOO8W%2FHMm7S9JOV0tb2t33t3lO%2BtXX%2FpoZWJcvtF7cusFekDM1LS3gRjrP2jsVxJJeXnb9V0uW6NTKYYTzVcDhGr34Ed%2B0p%2B8qvgtN71DfIikuOgKfu%2BJIYFCJ8olOjhokgZDVkQ4pYzN2e54qKIt4tRwG33vrxV%2BfIpITIOgPpk3G77nzb83VcXyvj%2FloymBC7Hor9yvslNaZ9A4Gz85sKsiwlnlj%2BKsGokis9mefSevOPHn0dNIqIuydJ4PAvaRfj1Q0s%2B5RxvncbARFV6RafW5Fce62JVEad6VluHjfTKv0olUm4%2F%2FOiIGt2bu1sGXhLbUYPtpteEcjHdjNVoouBgLkNHtMQv7uLBi2I%2FA0hp9dNKrbuUpvl06f6jGdH9GCJ6GqtAmi1CzpMTa37vqeGH7QyVbZgRBdQY9HVOvNuSYKt%2BDl2Cjtz4Fu4jAwYp29sfz%2BkWaRZ%2Fd4S%2BBLafW9eVrnt8r9q0I1yeWubiBjA%2FO7aYbvpXX%2BfshEWQZi8rwtAmDXNGvQBJZOIZp0VduBDNwPV9k6K%2Fa2VnRXFryhALo9S3Yvh2pLKjccQ9ULaeoZb6xI9ibGsEfkH8p8aKeDholdXTzHW1Ml4ehZ4u9m1G%2BvXWS7bXsVJ9%2F3vdZsMfKNODy%2Fj65GBW9CPsnNahNcCxqYEzkGGHaFKk1Etw%2BH1mvp%2FYN7yhzW9%2Fn2aOgITNG9RvrRm2mWU9No%2BNqrTBHfo2GVwOpovl6dZnpum%2B5kY7We3mV3R2qXoUYJeZ6MnQyuEknfPEVOdDSgBAKWxgZIIxLwO4q%2Bjjz3dwo1mL%2FcgERiYk1dYT60MhUJIrsSwt1299IMGyD%2BoWKLUFZ%2BuEpwRMfUVXFo7wZGxTMZof1GWOyh7CZSltStujenPosnsIXWehS6%2Blo7nnwC3LWXkO9WtnFyKCy%2FQ9pauY02dhSB6Saur15fs0WDxWAXVqiMySxizcwxWGcyVO8bQTz07JqRwcoLeSLMeFbjbQl4hKzqkamuw07K%2BfQmlW%2BpiN6Q7hDiuuaI5%2BJrZEjoK3CnJi%2FfeaM7ZHZZ%2FYCcO1ejPouSvfPnzetRteIdHTXfK%2F7LhB0%2F31W752q0ESG%2B6cgzy3%2BnwvgtvM89PqSgmXpqc0Xss8XnM2umSR4TMwfDi9O%2FfaToJW5fvd4qeUfo4emoS46%2FZwe0B31O19HAYGY%2Fe1%2Fau7dkNmaeZybQpiGLGPCeoTYrWw1He1EHn2%2FjaEBVipyzItMsx7zMz9roWRDbQH3HQQ8hBFdmL25b%2Fy5shkPBfjn76q6ChobF7%2F2UCKcJTCZcJCLDXLOb6lysVx5Ue4ijqq3Hia1MYY1amsehoHB%2Bnu101xSNXMxIPbmkqPGQKk1Kxl7d0o5TpMHEnwcba%2BPviMgKOOQ8lmohg%2FL8%2FPtsSBT9jwP2jiahwJmjMl8zX3w67QZ7%2FE7E1Hegjzo37%2Betz8xRXLl%2FslkVKXS9irKlHXnwzG9t69WGTCpSN31CYRsosmMxRoGmG2yHT%2FJLdZLCr846P8zQcTpiWb5arfhEb8w6mUyj2gIa20QmL3Ck36wApJm2d9d%2FqwiFFIMOiVfqsipaRw%2FGFiJTDwuvZCkOu9NmhYtmNkN4cYVGrb6yZ7qcWXP%2FwzSKfqJXOuTOFER0Ez4EpsgEczX6zt4k4jm1UkmYTCT3CE7Fcm81QUTcCwLSJbs1pFx3BVcCdtBTsltuz5vK%2B7rGjymTE8r9xgneHsYgfM3YOFZa4vvGCksX9HRh%2B9SRHBffXIwMZ0YQRVu4GBV7qt7mFrHb%2FanYbVNSfKR%2FPhviEycPy1bUkrPFpUS64RKWWaXEKQ7s1w95yCVbFvSLOTUU%2B6OuD%2FsyIwOTFqnVbbP2hGeDgkmaRy94LrtoytBRs6UBMxz2bUYzR293Evys0iZPbheSGtq%2FeiXnvmvteHl0lkizCyoWNHhpk%2BNmbYkgH69Kdrz9qm%2BhQHpUMkPoHf3JF%2ByR3Km9ljni8pSTWlCoXMWwPGjI3wihzhYDjgjepvveiGmu4RZKmqaTRrPYTjQfQaEp%2F5ecnFLn69K%2B66%2Bk0q0boQUWz%2FF2GC7iKlO9YEBHQLk0szBJJvGxxrSbsXBcWtrKu%2BFV0UAljMex20pOX61nR0p2tbHEeQJjzGvoClq7k4fUCMva5myurCLUJoUNaUH7KYIC5D%2BeTlGOSidFJClaA2O5EFPrGTS2Cir0D9X6bkjYF3OkHIcH8w5lq6cGT3JM6fXuY9UpudgzbnPglGRAbjPyz50tT1NkbXXKauX825pXeuVtIFjXgNRozFnz1ljdQipfDV2or%2FkMSL%2F7d1uvVL%2BBbJW62Bn8ZN5%2BQir3ty3M077SSdwXkoo9wMa4wmuyRvbjn5lOyxy%2B39LzGlARIvzZ3E6zfr%2BJof3lsCanbQtUJ6qefZWwij0bGZq23wRfA6LnQwDqUADzuXqhOEHP%2BVMf2QkkrDBQ1HafxUiJiWSGXCdoYqT46re1ejAhviFPbPHOp9GxT5KLfVSp5XJb3czaMkCO2RSUdJYPTSQ5Xn%2Flr1wp55BqeSEkoXqjPbFKOX5uzS0zdEHEDA2LK%2BX%2BFKU7t6e5vE%2BGSNQ1%2B%2FpdKHQUJnmwr94xcdNcRajVLiw8ipEc9gh%2FelNBbHQUypd7ulPPfBZxH7p4FhJWLQ%2FUCk%2F4lJQFtDlxG0%2B0G33IGwNF62uVVZiTSB1U9EYNa4tm6%2FEEIWSZWSMyF%2FGs0SeM%2Fvu7NUYEmhR7323WahQTKVrm7zE%2BZ79oypD8Ddhk%2B9E1B6HYWyGa87rag2QIqY06zsI8KkLMLy5W6mMsueCsFSeHMz1SyRnS4tK7KMyedRYYB7vhtoa73FdMfBacAUG1H%2BGK6dZXmcSKXkscei90gFsAaInr%2BUkz%2BZXuNXpCN2NHSVjn2VgQ48AQ1hcfueIrAPyI4%2BWL9eH0Jqxe42Cc0DuR6w7NM%2FEmLYrlO1CF0tWoKOvDhSeBjChlTU3AYsPWqsTq9bYo4Kz%2F%2FDsfMg561l%2F0JHYHT%2FZtKdbXE8DevjEmjOVYIlu3l%2FH0f%2B9%2Fj9BoxIwgGclWnk4YgTFFwpG0pFfZHo12Uj16VcJS6VpgI6hS1or1SEizGqlhnzuz%2Fh8o99YKXQG%2B6%2BNWggnuA3EXblv1XwEH8FIp3q%2BVu5EtN%2BkLU183jgqzlDcXxkhr0lxjkVLxy74L4%2FQ9AvtWMOdRqnbDjt2ZLz27oTtkiMRNGj2Pf2GEsZ%2FViW6YZ6XeMJRksvY9cD21OJ1fYba9pN%2B9nL9Sq8TiW8OyUJpgbJmJn0W1IAgLYLYYgH9pIk1EandYF8uAYLOXoCQMtebrizUuBI1Vzg9hJw40AVuptvqwUnOqLyzRCojyoBzQJWD8lvSQa%2BEKr%2FbRLNV5PxOpq62mam5d6sze2rBmWow2SKTKBru80TVzVuZUGSTVXsRrgSLB4WTFnbMivG2Gr1DET06J9hGzYcBfrZOhipvqjohF1G9VyISaTd5DDbMk8EeEXAYwVw3uY0y1ZXl%2BFHeIxPvOdOUoVWFOJBC4bNDxptpYbu7gNq1MifoKcEpSC%2FqKuo8JruGUtPwuvVbWlHFOzSVI4lVANbS1IxbtU4bt5Ip32hHbxX%2BqNNf7g6ly%2FraaGEtbNqHxkfx9QaPPUnFohc6lz4qk1dfDO1XLxT2uD%2BSUnzonPSER0HT193YpNZ0YeqCdzi%2Fh%2BjbPBEb%2FcaD0Si5DXJpzLGuJ2z0jnq2jj563lt8WqjYoXcdusWMfvo2zbh%2Bt8R5172m001JBR06mepYHlapG1MuKY8na374FxnOGAiGqudixfg0AmUFqvtJQEXqOCPaUgt3IHslNin%2FwZ8VnGw8tkgr37ZfjyAztjoflWcEFjGsgQjqkszX7tvOMiUknL6JhBOQgRxOMrUXbuyw6YjBFb4Okk6N9hPd49oIWG2hakVCW9%2Fr0cQUl3y1BZ1f5ZM5cRqE%2BtnQBaIr58A7CsQ9bz23qvyfp4MUvdzUmZJWP9ajXJQs4dmVIoj6Tv3%2FA95hw6L3JgYkq1o2hU9%2FLsGkeZh6MrXn2%2FDUXSBxNt6lQq3C3FBWqhGSfdL%2F7ZthKrpiJVUnDy0kXHcDRyzPizyNccMOIFaTi7aJJEKgwV4AIlI0SR81XEytTLIrx7w%2FuVNqS68IiOnOerl1ltHjao71f%2BLpy2TPgfT3B3XHtKc1GwIxnTRLdRXYdSKRIOuv4Bj6pbhBZqKoUVBltVi1R1k5zpogUZg%2F4Vq8WMzXBN0ftbBLUsEFU11tgXi2ku8BVse9Bnwr4Y6V71oFprvSXOjeKrGZY28SWmSPZwo%2F29%2B1RhugO2pXgM1ey4bQYCSzZi%2FC0u%2FLIgoit0KytyUsBtydx44zPkLWrcsEAI2avzk8plHsGkbrGWpN%2Bj6EuRYfZjNPNbN9zjzRjI9ANXmCtW07A3vJwkAz8UEvgHtIelhpWdCKZSt3G2gc9fYn8OHvaENg5sFLzeRWwZ20oSzDc5WnMR5TRVl2LRil9WfqAwQsCVX3NuUZiiD0KCRd9kzPai1V32ExLxVme1qxFDgRO%2Fkv2hM5v1kIQjPMnqnhmJzDZnBnQfy1E5wxsoWV%2B30soq21kxRyIjv4UbzJWAmNS0LZExc2xxNFUIpzf4%2B0X%2FQjH%2Ba10vAZKbjF1xds3AlrrNJkKniIfpxc92lmbWQBDwSwVJWegiKlpD3iVIbgomUlQ%2Br7uxVa5JWLblU3hrytdYUJLhSmwVzEQNuKRr%2BUhb2zihmOecvjRY9XbMpK%2BGGgCu3Hptx1aCYGO6edc9zzLFvmWFXH7rc%2FvlYw5GurwacPEfBKWQvx1jw5tMJ0%2FI28PD6tzQrBIYwLVIq3sKM7vK2UZ3kutxMsVGP8T0ca%2BUIqpJ%2FXOc3Hj5fFsKY0ThbIST%2FmaFSnSeo9eoL4R3Z3l%2FxidCWfgjyMCHbuyJzlATID5c5pjHyyqLxUJrmSB5IWUXj7lt3kaxDMkYZpiTuni62R9kqnJvtrjM5QpLW7isUq8VpA0z8lYhvI77Hxw57OD1MglaGQ0t8%2FdYQIR0sV5TJv11Mp43HHf%2FxLZi2%2FWMnG%2B3tlyAVcVC7Ethol19Vlnal3Vuz9wX7D0wwzzSCt8ttpTKrCFzBEaZVus3A%2FG%2BVdoDr1fBB8OmUcHuVTGHFa%2B6iuBiG2RvuhZgQDffUFm7%2BgO3%2Bhnji6N3EG2%2BacBRjFuiImo164kuL%2F1HYxYDzMmZWxeS8xYK%2BUUmSb2YCdpuGbh2XyWLjdjkCz0qNJW4I67OzRD8oFOY8%2FoKaLrvZfRBufZOVu3kMeXDP5O9c9J3MJMu4mI3rlFG6rvN7nC0DeM6GizOKnBPhnB%2BlHkF3nDmSv3%2FshYH7%2BTQKDjUJJlmWzsI%2B3KXPd0EsOyk6cHeRZH2ycfjSH1Xb1Dcj1NKS3qn6KZ5WA2lXMeVT1uGr97NRKD18Yxqro8jBqeebcGV7x7EGWdbYW3n1ZMH987%2BtuKPHlF9Nwlr7O%2BDuoecaW0rZvUExhnjfjdFb82NdCGHFxFu9ZXMtkZgN2Fc64M1qiBX1PhphBk%2B7aBZebc7KPfPsSY2pJ8rchOQgDCCezptgeMM9EHMsO1ULXSPoOeZ29YA8aRFdXfKnNGIBV6uLilWsITocD1szJGtTiDi3jmj1dg0MTh40UfXXaPK5BDc0P%2BlMq%2B8Fu9Y9EKi71YfjGhc0zF26LxxpOax9zD3l%2FnEdOWXHTg%2FU5ISAAPHvG9KO1JaYRB48vcfn9P7NuGTtsfYRqZwyfuApJt70%2FR%2BZKrAIS2tBhUpU9EzISYK%2Bgk9ef3hN52%2Be0Ni%2FVSku%2B%2BXITI01HQuZEreUtntMWBZkk5asx6Qfspn66EoV9L7UxUl9wxf3UeJ48vg7FnuWQzUonUYU%2Fy%2BM5eHlDonqlnffPl2%2FL2%2FJn0VP6njHV9K0Szxw%2FShZN49hqnnSVHzgfhR9%2Bv0sgj22EUMT3LqFbnIeVmamSj7ZmbfXNUCvRER9mBTTdNx87Il9qJl2yxpDYHZdRMZfZA%2B0uP2xwrFybDCLFRZrxV1CYY0il7gOr3NuEsC7WcinG0ejMpzmQp75UFYklxPP9%2FC1EgSbpCuxhp5Fs%2Bf%2BYoOeOJ6Z98bkmmBfB5V5p6AiBzIuzekqN91RYUd1jib3C7rR3tXVQEjlszTIoHagB7LqnKZsoUkY06tt2Kau49mciBDM57mdZUVm9Q3x0ToObdk25gY3MUY4hm7pbe%2BN1XY8XkMGMEP2Qs%2FLT239LcslP1RW3cQ33cXN1tm1vz3tGeR60NhldMAp7FDDFKyzk7gHekh6ZLu99fwsZMsGLtrPqlNddKRXGdHaEZ0LV1Qow5bVllJbjrE9qOP%2BjfKf2zo%2BX17bAonoALx%2BarykrRPI9QCm8v4%2BIlm8el4jBcPWZJbqN1dWjeoRq2OgS0JLJwZcBis7B4vjFCotsvPiJPoaKwkjXLZxDg3kgm4GpIXP9x0mfFUVeSwEorrjfWjvba5hLVxl4NnhpwV8b%2FW1J5yxyBfzJqdz7WL5jpfB1KRiHdbsVvxhxpsr8vrIshzxXgR8imv0pWqUYxQgLV270r3gea71lcCJsM4UCnCed%2BtYCJtEIZSs51VnSdjIg3%2ByR4hxUJQpkjnzYYgRo5Hkctz1kOK3WNPp7bkp%2BA9a9SYKbl5gilhue9oi2fPePK5JO3cQyV6Ilk03Srbmf%2BsISy%2FxLgbGmyaf7auCOssF6h8dxKErdEvFcIhoc44bCz6pjvi91zLSQ9aywPfWqbNw%2BQM7VpeGcZEc%2Bye9cGRLx4xlN8lP14a%2Fz8C2%2BSaRqFPe4wy5yfK7gpqC5bqwDR715fs0ZpLvhHdtScMOBwTHyYJ3HGu9YDe0R%2FU6mw9zgPUexI8HR9zUmryKRzdzdUdVSTmCdrjXYu3EkGCaNOm1oNK4ZYulDT%2FAXgTLSAhbTPqfd5ImvujBsy6lsRVIWdCJBudFayq%2FaMP8VWtZPJ5nll5mtof%2B69HUj6hPf9Jj%2BKeA2%2BZ%2FAYPAA5fbix3eW5m1zdxTcx%2F%2FbFJfVpOcuNRG1myVprgjytBeB3eAS7eFDZx2fjh3yW78SIeYaQTHRc%2F7VfZg9zdlujwO8XtVof6MpB6KZEYe7ldvPhjLWxTupx9J2UbX0SmZMjfMcetQR2FQRucPjdBE%2FJ%2FThJA6O%2Fgok9F4JNQZEYRWfAwlEd0aRf4SsV%2F0O4wVeXg1oGTFRztldLEeHwVnzEPZTOOwaic%2FuRLMitDfh2qNIOjklknupq4x5Ytd0eYgDBMAtIWziu%2BG6zm7gXAvJNK%2BTMjuxrgfDCk7SxcHy0sol2xtrHo9IaLcwKDe8McXiOxSgVxXRKnT0L9pw0N2scryWJVw91557BF0NF2zcTbJf31ZKnSTG4TCNwxJAPlkr3dtw%2ByZDvv4XO5KxNqFavk1GZRDPyGgTXre5PqGG8cLM5Y4mD93QZk93Xsj069pfNs4CrfrZgy6DvVkjfyf28BXh4B6aUmzDoIh2ROVHZsHiaTO%2BZHs4WVJEhexMciMd6cfSBSVXxbXco2lkBz1ckWKMBCtmfmkeN7KMW2%2BVhQ6TwmD%2FEEqVqiOKYyZVvTnETPc7FWf2sIWUmMBRLwjaLusoSmRAPNoGWzmdBGlIDOkobWrADE6UJ2dMctVfb7KEUJXR3KO%2Fu%2FLKHp9Ebv6R25SLYz6jrOtR42FWBbkPYZR253srK47wXzkzsEN8NTaAD%2F26LcUpIfPvO2dui3P8RtppJtify9A0cheK03XAj8Q%2FsJncJmwF1%2FR%2FWq9oF0tAmD1Qs3ViW1zQtimmY2bfhmFLgmKYS1fOuCMS3TBLeEkaGxtHoLLS6x%2BKU2fkGq2fctXBEVq%2BxHiYKeU1HHVIWisBVR4s0R3AqFH%2FjZ%2BPC%2BU8kwfAFV3UVjkeIfuLUOilAJFhmRiZD7YfLqPQOhd2d%2BNQq2lhzFJhvRC%2B%2FgquiJVQBjNn1LIqPSf0MAbph0YFjo1Rf%2FGed1%2FSmETEoNZJe0Uxv4HhKseXO2p%2BUhgiTZ3BHK5uLlIb46v5DBDGlycwCBM1VIosJsuoBxV3RkfKQ2J046K6ieIgk4A0tJsGisb6oYt83b%2B0yqlSh1PJAbUnoKSWxjRI4C%2FXf9%2Fk8v2cSD820ePBENNYo2jf%2Frk3xWNBYv3vNkvd2DY4UOdrVvI7bUm0l14uu7%2FsT6iageiJ%2Bpif%2BWftZG854NRIYVr4h75Mn2%2Fi6idjDWCK6c3%2FbsYIHYwV1rk52kEpK2t%2FAqWZMajVrfoBirmgmmHWacmTP4JIUgNO44wF9Zs%2Fx%2FcX39Q%2F%2F8Je%2F%2B8f%2F9K%2F%2F23%2F8H3%2Fd%2Fvrf%2FuP%2F%2Bus%2F%2Fst%2F%2BB9%2F9%2B%2F%2Bfanyt7qs1A%2FPkgnuPsbgyq6zDLmjAiap6gyPGsj1lXBb4pKJlEuWDRv0OtXAFK51zx4536%2FvErybAmL2cZ0JkBTbt0g6u3WFiUo8HHunJZuIRTrxrBzVacdKYJPHeC%2B1JBfp90GQelxPnQMzkHxj7rniSCkd9ixk2P6OkpCFdGYiGkzO0bONhCVKJ9k741wGKtsVKWonURolQh0XPnMbbXU1kTVVuVx9Kww94RieyQj6I5QMOzGdZjRSlT12f38kUt6x98QBmTlEM2b%2B5w8XqO837y8TX20UR8LH3iTkjGQd10MyglGiRy0c44hAAqpDjb2vHplkNdJ9jIA2PMCUC2pLhGbr64z645AO4rFKU5P0GU2Xzq3ftptO0JLsDmCuQw5dUspgr1jnMcdllaoicPdnoYG%2F7yGwc0ajBFj8fYXS8xLD0CGGdO0VqVioZsx7XZ5uje5Ut6JNNII7zMEnY9N2VwyVrKLzMMlb7xc5ZNxsZj5t9XMM4nPdaOnL5knTQ1vPyHK%2B91JEfD8z8EJYJO%2Fii3DgpT7pC%2FtoJe4bT2gm3mvawyDhTNrSnq0cNN1HWAZmwPNdXL873pfksTfG3TsTUqRXdYxfsuIZ2JGfZ6mV4PJDa6PTf1odxNiS2HMb8qVVK9SkeUaL7%2B0ESaiI7cj9KiMGQizNojiM96hFz7VV4vA1lruW9jfLthxloiTLGW4cHRNvarRO4TiSsfoVgRFj9swkUz3SL36lBoGeo6LDqHxgptaATyY4rxJVeLvTSLm0zlq%2FOg7iW7rjKT870NRkFreQOuJaER4wPsxKpzHv7PCwy3%2Be5jM0m2pdNKAJhvi%2Bzva6wWxX9K7HoLcR3j%2FRLr6V4ipFcpV54fujsIuWgA2yjSSsef98wv%2FX%2F%2F6v%2F%2Bdf%2F69%2F%2Fpf%2F%2Ff%2F4n98hb8zKVd4LnK2To6%2BofWXm5TV8W5P3JrCaHJfFrXTXFcnL9XItL9epW79iJECaXgg3MCcDgGT0nYWgfWwABfoibz6LpI1T6Cs4M3SLVWaEKeWR25ofjsmU0AXTkOVxp%2BRIIhmNUun7Zzql79cDGt3Lq0qTykMalEevsiOgDWgqfrnGmxyS0TV6cSCXnkNJl8Tz2AZWhy1ObgQb26MQOgZfVEZnTy%2BK3NHCktGQv5J03agGtm8Am9SX9WYT3PvLM55rtAc8QUau3pKrA9tS0kVpSWRfb9fBKnBvedFJVn5KFsp73gvrsbslSm%2FmRKTDZ4eEJxUzKTAm6F0O64YFcdCe2YqtwPMA%2Bnd6yVkMioRlZRV%2BSSzo8oSqQK9LwrTgQS6KECitgvNyedvokyKdvpZqYh9%2FGzRUQXoidOmUZHXWI3q58fjBUut3wR2BE13l06hQBWOkzO9vjW1zsCd%2BwvNZ95VvVRtk7LY3cYyYcUui8axL30UIJI4NKPK8x%2B0FoRFcd5QsFSn1VIB%2B7xSdbn1z2ST6TQirWhF10x4VC2rRnVjJZwXlJWG8B6Chdwes6o1vVfEZuzYZI51QQSPFzEZmZQTf3xsg7wjJfiaUtPvdBBTYZxnB9S8dtxCqgK%2BumhP7SVqAM9DDujap3yxDSH0ghBtvkv6b6xuZo%2BcYUY8qLt4Ew%2FWuHHrF9C%2FOz2epy2bywYzM94aqp25mpjWfaF%2FEcDtekapSyvafqVWJMhmk7Fy9Hv3cm%2BgCbdPS8%2BqZSYZVCMdvjg4f7FuTF9HfbsB6br28BY1LZMTVwu3Xkl6zmLomMlPozcNX%2B7xpNaNSrEXxtBdnuyk6RHmNrlwuW%2Fjl19XXKDxK5Ha%2BnbE3GCtODYcReVXHWZ2l6PpOBDrqGnyl%2BDb3Fl76NKLoDSPsOcMMbk2%2FLcUe1buHu2N5qRsDofubnFHi1RHzDazjLwo7I4IRyPkvNokCINwWq4Kro6Y96nsA6cE%2B9Zz7UN9%2F734s7%2BWBfEqwe1FhVdU64Gv9jgaRTzlpiEilBR6y4I4lm%2FJh6api9f3xeZ%2F9imvC49VBjkkTdCXMJC%2BUPPnCafLHsoO2PhVONmEjrF8rOD3rknNPT1APqgF6XNoHwtm9tjUeCRNFSPOaCRkjHW%2BJtTuwU5taiaJvAJv9bX%2BHDRlBMd5b9%2FRk3OMa%2Fa09tnCYtwRVbkkUL%2FLJEdLwyVC3aoUkghFqXgE%2FNbQQ3Uk%2FisPY8o%2BIPqMbTjD3XLLcM9uwvJ51qpmuBc%2BNVbkM65REGVbjFG8dabkL1LUQCx%2F6aPFDsu9iV50tUZIh49%2FK%2FqNmh0kdCUnt6bSOzM2DH3K9y07qKPQzUHLZAON61gkgJse0bUt2QgkrWVepeqjjniVkpJU3Mdn3dXJCt%2B5JOjpIIuq%2FPSFN7oQml0IRf1Cd%2BvU3OFH76heMXv%2BWemVAS2kX%2F9mzMpPP5Mn51L%2BebT9%2Bs1nrpC30%2B305XqYV3xvkNMHh8g1kTsWLF0tQTwOkxdnMIcu0snqisvv%2F09uuzvjJ7u%2FwO24r%2FMaCn%2B86tqmWd1id8Ko4VFt5toXM%2ByRS7v09PvrF7c%2FtX6karIdnFuBzCTK%2Bz9KfT2BiZHiuQJ140p6YvYoOEFEYgtS14NOIo0IEPBT70Zy5s4T2t%2BCgZ7F6Q2rKCMiSdW0freHjFTgbS6NGy25U894hNaSefr3Xp%2FnWU0ZEFP4QvctW8sioVFw%2B%2FAPH3n9xPCmE2fd3an2PSM%2FbBBwMHBtkkdbG16rRQfg9HH0HUACQnVuUtwpaTI1uEb2x4l7NA%2FZw%2FHUvLar2gsYVPzK8aZ2XnxkbQxZNi44tQrxGxHKjaal7bqNIc6D12k8L6ZaYq%2F3OTrLzQ4a8bX0BSVzbqlHHU%2BqAG6%2FVvkBZnzi%2Fz%2ByA1USC6%2BLsC4yBy5Z1BRa3y%2B3eyrNA%2BBIy3J1ErS7H0BNGAuisxep7UN5U%2BsSBe%2FO2aTPYQ5a5wxy3QyNS7%2FPo7oXcHZWN4r0%2B%2BbB650LMoIp%2FQ23pQ0Wv6A0PMLh%2FRQGnlaj3%2F7L1bruSJEmW3Q9VNUzvah8zT8TwhYMhCJD8ftpaIuIRWUOSALuzMyPOcTdTlcvea2971ny7WQpMOT3MsmtBQ%2BFI%2FbWIOknEDbp4fPlkdLEFy%2FLgHTGZmzF5JLOJjVHXvlGGCyFkfBhEA9JzZkmGFouhF0VRSTy%2Bd4YRTBPSHfIHQnaZ5Jv2Gsb85iiLXRXD%2B7fo3CjciLu8hnBkogrsGLsSQ3Rze%2BYqCM3tC32wcqSa5eCmOczoJZSPZk7iSRkVL8yS1xWmXUMQgJZmdOzbgGJyrYX5hqwMxIMBaoJPZGkJF2omxAdDCvorPGPt%2FODVtJATYWiGb4AyDfQMGPuVnKeDSfRRdAs%2BLs1npKpx7pJOGbIW7N60oJD3cSTnppzN0NRff6PhUFjEHKarqI7SAFGt0D%2BwURWIo4%2BbgTvfZUACSHc1AWMF9zQ4ngRVqoft5zfGQeQPH%2BFtGeTTBPpOU%2BBuxfOgeTFk5WoyzU3yNtJWcMjaKbt9XJd1CGbg74M7wKDIF96I%2B5h7UhoZPsxt3xK4bEAq7yFpsVW2c4PMcIMPTROzUOMS5L%2F2nFV97houBRFliX1PPKXMha72jJ5qFcsO%2Fj1EvrcOAZeI14kjoqASHCk4IQ0GlW0mSVxF3UYBjN6CZKIXjsF183lMYQtFu7GPE%2Bx7OJCGcdgagRjf5CZJhUcMSniZS1LWPVW3ISUj0I8A49jlcAGHzMPZOE4MBKuZTexohr%2BUaTUPfPw1Jw3QXS1EoqVfEdl7%2FaVj4b9m%2B8F9aHpxOnRATBjBnVHLnNBT4jgIxhs3IJokpD%2Ffk6ZzNa7P4WqaEKNwMEWUKILT38SgAua3%2BVt4%2ByrfC5IfJTD4oycp%2BWbY8bAaRZcAscHQHurOVlGcSmnJMcYKjuS60KrTSqLBfPN45%2F45NFqMR0uRhc78DenVk%2Bl5Y5rj0IRv7pnkLJjgkOTJjuc3ynMXZ4Jap2MCWCCHKfGQfKMxyUJpxDT%2BO9aw2gYzD3kl%2BxkSQDehSBlNdoT%2FwI19ylfMegxBwve4AlOoB313tbFMAUPYAVgswgeFO62M4EE4IGMQg0C1wa%2BSUDYCPeMamUOwTPFn3GUD1ZzCR4Z1obogo0hNcMfJFiHcWHCgiH23KpjJwFINFeTkg58M222a5JawNEwPVUk6j5jhXNgFGNiI%2Bje3To1QsZFQopCXsOPEx6TO60WplSl%2FnYeOoprJc4XLQV6HwGocyk6mA%2F8aycE2V3dV2hPZwNT%2BiwTQnxlBpgjz9o47vbSQkLlmgHnXzd6Wh8i9BtrtWSmn%2FY317xVZFfNkZyRoYpWuR58XimSmACMrOHZij8lSqF%2BCxkO9SamNCoRhZW7bkUK7afg%2BoOhumlERMn2%2FRyaGXqqMuTy7%2BSdZ21IlY3VkTBf23vifCSg7aXhiWwWD4trRnZ5UOxQojAxI9eil3RAR23Xup69cseujJ5diJaYgTIREY75yr2O3p00a7yXDm8I%2FwnYy%2Bx2o6MgQQiMQWVqPSG9roUvlC2cTkBwJxtr4nIwmrzOc2nF4nb7OffLAhVCJ4SzvD54hbNzaogiLCuQwSfWvuIqcgqFAZtO0fP9LxHDRgMmyWpnHxFCuTxtdDr6oPa46NKS2l%2BjNxCFQDk%2FNgboA%2FhXedYzvg1H6W9xDXN2MxabOz0r%2BCXjXaw4RG%2BsAS7DafzwowLmvhGLsEBIzNYjq6jurJ%2BU8E9KWCGSoHYDAXsGxpRLrJpN%2BBdP3ZObLBkiZ7w33cI9J6%2BPxJKSI8XSVhXh2nWy5HxsrxrloW1JEogww3P1sEnhAv8NpJjxuvrK2u%2BKYOGF4apRY9KvGJb1g6EyRpHWT2RIhiasbJzUKo7DpGeZ4UysjaTGrH%2FwD9NRcf2lZMxh1umpjYpurRjQXSExRLp6cRDPqxFLCUCXzPGIZQ%2FIx3%2BibD4sYBc5CA6XKyY%2FsiJvD6VFcaIasIbmFIpxbaG695z%2B25LyOoIjwE55aqNLimZMJ4%2FSnHjVm22xKtnc906wACLKhYJ31Fv2F9ckxaThP3eiMBmPYZnxcJX2Tog6wrYdSsnwOcHOZW4vmCNsy8HJQc6ASnxQNQPJkLPxdSqhlMyaCe5RzAfXhqZtGQDbvN%2BDPtJEw9enBUmGv%2F6%2FxX%2BwrIo8O0cD47%2F%2BOc%2FMNPZOP4vjZ6mXzcrRjjknuPU6RcbVZoJNMdzUtOQG1bFJGylEwdKEhhPm918irgUEGTiwakVov0%2FXiPEYpt3YKTrkXQOFsy%2BPMOCLibirMMGo3sXM0towu4sfMAdsy6ElPY0tBJiMqA%2BPxMa0IL%2B0RSSmg7rsKawC1g3RP4B0qu1pjm%2BuBEGeHWi2bja50lH17L0GeEyxUwKBmY8PwmIn3C9%2BcGY1OMABadeJkg%2BMybbG1pD35H0tp5BNGJvcbduid%2Bf4SG%2F7267plNzH8h3tYZ253ghkxATvnzYKX32mRkp52MkQ5j5TwvHHuNXsBbSYRZBabSX4cLG%2B8bc%2Bt%2FKMG6LSLxp4V0StGfZJu8mQZjShtGW6G0qYneoxC3VqNDWEUZUgJRX8LUk8J3BCtxpX7J9rwXN36rJTeUQ4IAc9U%2FxTHEZU6xSoPLc%2BZJcBQCv0cY0P%2BzdyJMDJUrRQrr%2FxWX1OEoOal8B8ZE%2F7jI3jxt2dZYRCGq3RilodV%2BZHbxEFWHKmvujPEBF5cbO6g7cQIIoMxQ9rOoJlxBgS3U%2FSsqWLQtDJ2LlEYQZzEDMwq42b1xucf6ygJR%2BFjdihoX%2FoVqt9FlqyDoR0XKtkpGzP%2Bwr%2BDWv%2BdY2OqdXQkJ6o37SDX%2FnNCkq0IM2DJ3JS4MsLWtd0eKnW7eRcC1aPsxDKKxzW3gsxqrjPMu3umCC%2Bi7h1EYz1K0LfS0m7858mV4lCqjTqeruUG%2F0BrFlZ%2BuCC7eGsrNL6kbaYAm7zGZin6PV6MaWN4xugv7Eh8t%2Fm2a0ckARG2cwwYaYxlXiLXSL0iw3kjWN944esA27gHr3KOSI3uCrPAd3ddl8njQDGiwgtt9LNrNLBZBxO60gKRHdsHM%2B0O2fG5bVMwcJzJ98QSTTUi3FEzqsfvyMZtTNDRLHwod7GEODbW6YpnN3PUaMoRioE69u7htJr%2FPD4wNDEmYmmF22mhVFHDafjMUo2j6KvaHXT5qRuQjx9YAfKt0qwzVqKHc5Wa8H7uEEEivdob6NPYXZbKrSrNTrCvCfUBX5TWvKmGDGZFEPkRnqKzJMp25HqPp1YDBhrzlf4SeZdeFgjyTzWo9ETMPunCsSRlUhLKObxeS0lUieAxt6rQZLtTziHRpcBJ2GONTEddZmt0eebxs2NtutyWaGjrTqRdOcDCSRq%2B7w85NlzUgmDuXjYjlvhonL7n%2Bf3xErZKFJDG%2FaQu0f3gftSFtzDI6mj0DbzEHuSiZkAiQVV28cPktgTNMG8dxH7AZRGMxLwkltpPKCNMCjYnlrUyJV46q6k5%2BIcsjHvWuRCQaCVZMa2SR2%2B411gaiUyNf1HVGZRW0Zs5%2F9RlaJ%2FYMTbHpAsVCCMxipx0Cgogo2yHWNJiEoOjy4UCxfhK6gBi3ikFVIBMTilejQRERD0t4bcQBhhXkVu5slQjjkW7GmXzipcYwLVlPeOeSDbTy02fxEeDxua3b0Pnw%2Fxpv%2BmGRNDIlyhQg146zHFMHuTE03HmZJN9sw5LKBMZmYH8lHPL%2BULP0T1zK0CXOCuYHEc8EvsdJdwoPvIFbcMm7btKfj2RYleKa1SiMTiTKvZIzOOjDUTbUfS2ZHefwsiS5aHvhpY2bknydpomfxTzngKEQ7guAzMxItzQEHNvTh6sdLli7YGJL8iuHMkcWpQa0teen8rsUWSOL5M8rVhaA5Ay2uz1OEzY4jR0mK4reZLMIzhz1ZRwiKVRDPeg%2BIfEniSYlik9zI79vDni%2BGpDtXBQgzKbw3b2cZeQhkBnu4EHxmTyZnDC4oZ74FsggkqGAvmnOoa%2FonmG%2FgtjFAsQ5nQnmUNMsCLsBSVufflGYKI2dfOQRwrj3e%2Fz%2FppAC4iQ0Ln1Yd3WQwP12BwMZ0jsHuMvJnXH1dYytW79VpZc64omMkuOvGGkB0umTZjhtiB3OYCUDNmLvzPmhySTJ%2FASiuk2rpLkk5iaAJ9wktqJXfkRD0QvbxZGT6otwQAhlP5l4MXhCnaUZZmwlUSmkvdAuQjy8s34VWop8m3wzfdb9RCax2urh4M38US8uA35Viwr05oJfhD9UVLewvUEJxyfk4OILLLZ5U%2FJWkN6m63vMSmKWaOqQyqFETC24Vw6ZhpkEfF7TrGP4TEGkxViaeS4MZIIA%2BfUsxUPo9NWbC3NLV%2FIo7bmOKZa1fS1pECbAf1mTNurYO3rmOZbjhUGlAuxjlDWmpkDhwjuAXFmPQNC8WQOcnDQfweExiRmEnC5VOJMpqVwZOozUcvKET5LNDWzn8o1PFJR%2Bd1vdORDU4jClN4KG4Q1fcrwFdC7f57AqcKYvBzq1KCGLO12EHLv%2FvW6S8mC%2FI8dQih4DlNO0yAWIFTCpmOTuJU3XrVSVKmL3e2TLMAn09sY7Z6dm05mbjq9MXXVQ2qo20FDSXRhXpmWHq9elYymCOI4%2F9SZxCpqhktSDi26gKhyYQSwQ6uxktbDlxDe76Pcf2Bn%2FHQwMa1p0A%2Bk03RdR%2FWPe%2BPAQxKe%2FqiEhklRy1zYPd0QkjNaxmoSboVIBJTu6qnxZ7gtjBZRfk%2BbGAy6f0hZOZhfvy%2FWrqf35Phb8WMQ4JyJMxj39wJPn3r6qs4AAbFS9CDJUQDRdVhvoemC3I2VpYZtrBw3nFAMq9ls7muf9xQoCL0Ys3VgO29FG2vOhJSPcXYmcPf73VFxI%2B9tlk3Q3DGdCMHptUCNK4pCVcZ3hjmwVSRVyuyp1A9iAqGkBoEfRwUHStgjAaykZJ5ZJ2xpCs795FTyKpmiU2QI4RMAVOGvJz8e8elcAfciRufAFrCDfL3Fn7QqPL%2BNTSft4uSTz%2BgBeRsQN6QK5otLUQaUfyu9G%2FmO2TbseMPjgcDouF5GnxTj9W4QhpkRrGJypkd9wH2GpOIrNP%2Fb%2FyIo%2F8szhJwYRwcdNPqVlBS22NIPntxIm2MOxUeBrbCHHFGTrqTMUU%2BOxnuOZoaFM4KTcCKgQvjnNpWUixGESO6pmMXjQTRM9gpyysECbT5Jt7yE%2Bdfgue7yaUOGl18%2Fg4tXMA2wgJRgMct6rM9WyQn59bSnsJl%2F8900KMeWyrlx4TeNyfMF%2By0IGH%2BTfMiQYKzsINhTc5%2Bi48%2BXc5h5SRWt7%2FJNGYKusY5r9o2YFHCv3ThrOrnkmLI28zjEaJwlYFeQ76J9%2B2DmpFrXGOMONGGn8lFl4vDpsan%2F%2ByH4H%2F%2Bz%2FzEVNMSeKN5YoQBV%2BFfjTEK2ALIEotH%2B%2FhEsPFxUrKlWbGhflRv4rC51WOM%2FBAcBPoZHf4gNJBvoO28QfU5Fq98%2F2sZHNQsS34F3SLx1Ao9t4ftH3E7gKaieh2XRK5DHeTWIOp7BK0wSfwAZfb6NrFxxpg1iAMM6xreK1BKRy40dK6pPnPmGmiJcbwwWjI0FxdODQKBC7CsTmoTE7scAtoSd2%2BYvuvx3vHuoTzjMgv%2F0qhLeYhDZ7fNjYc7rYZdvQbUEm8wA7ru52B37K5pUhR0AadrLB09T%2Bgp8h03litd55wDVisuwn%2F%2F8bvP99hDq7hvpHGY6CIYxDTZZ6IlXohKo9tCJMDxO7dsbFIKJQS7N2AD8wpdOUzZLDQ7rRSPRoSdo8Rd1t%2FludOn7C7I21NxQiEqWjhaoOc1EMYbbOvZMOErEEJhpnolruMjNuFmmBMSulapVC9WbOQvJXj9vdGHIIt%2FYUrJt5TefBPHdAixhgGBHik%2BC5rxMYDj9mSvjDYiKje16s4H%2FPoCcCUgYMDznq1pXYsHQpk1TRZAY71SnMXlmnYqALgpNrhcF%2B0dZeKA1YCwvN6TNMqF2ccjE0dFRYhbmvYlGRRHW%2F%2FGW%2F%2B%2F%2F2z%2BcQ8xKjpGSBMbOSpFaV7MXk0rO3FDsA3ZDY8PiIuWk36vCcz4xhZ%2F3JOFyaQmfWWDXvAiUAFL0RRh3xZBJruTfemNqPx7ZsFdiSMZ1srXpysjYQsRIk0w5clxFxKKtCR1Z0JRcMe0SctA%2FnbiQ6M5nLu1MGUYwN1v81caDY9qF9XNtw76D6SiP%2BeW7UIyQx4s7xqymhNktWQ2EAJO%2BEeqc6baPjo0Bdg6BYcoyS0DCdU7FsXHFvRatN%2FkxjwBKOctwQd5MgR1cggzZedpGCAl5kLACy9yfRUJAyocfQyZN6ZFZaE8pDlzJkb5Gj%2BJw%2BHuuR04MwZ5BdGdWBnIw%2FRmspaYVxyimPxpNmLJAcwut%2Bf2nJICijz81DZPTeSOcl1%2BnYga22zIrpQXWI5KDOneNYJMnUs%2FpMQOQAUK0v4EfpIHkTelh%2B8zKFYHGcn2fOb6M6pS7IcvsOUBiqeFccgQhJPp4Jt%2FUPV9vGgPe5pvKfsGI7BgLauYmPBBM0ShihtudIDqnqruLHxG%2FjU4xlvloDHhGmw6sLAvZOSKNU7EdyWQqLJt7SvxfNd%2BVRnohXGAwTZqBc0WjT0Q2pqvE8NPXPXnJDicOHu5ZnCR51AQgqQMPvbN2yGS%2FUj6Yu5BD7MbjhWcKefxJeQ8iGpnH1Aonzh%2Bv1KZlZ%2BLn%2BA2b%2FB35HkiJztIQnjOWMERWGVrARP4G5ruyhcw2ebReacqLqGqdVo98wJUHImAU1fU8Z%2Bdmrg0%2FMU8z%2BLJUA6pPlrFERmJ%2BlIeVnOC%2BBajnZ6TDkzanyXZFDTyEU%2FBlueGPAfpUZQejLlYd8RuypjI7k6stgzSGElOaybszjRfB%2BjTLndnPrR5JtDdIS%2BTPmcpleZCwz%2BcX03z8Jam%2F9yneNzNTLJcK7%2BProcSatLXdOJ%2BUtimeetyEIqiORgKzBnUFu%2Ft0baAGlLALm%2FAPapHFNc09S72kiF16V%2BlWJ4da04RV%2Flwbt3AF8YfRDjeHtDEF3FtbCVGeOYEKM6jJkVutW8Aj3Kmy3NgVjMHslx3iVzKNVhY03T4b64IpsalM8vo4UmVnxe6aoiPQjOVURgSio5NJfErZAqIDlRNmHkYWv8peeDHogpr4oYRnAw0Qn3c5ze0wNJTGwUCMExJcp%2B9He38OUmZbMPrYcvTnZyzBkWgDgmYOdmFuNgzR%2BiuMNuQYBBKB%2BOPIKJ8NaAw5n%2Ba%2F76yYXjW3TrWhVEcVho35tTYkljLI6zTZBqHReuwfDJgF%2B0ajD7QmpBuDSvHidt89xYTYeAXi3QoEFnWP9BfdPKEuIU9Duo6sGOlJgjh4lYf9D%2BaduPdhlTj543pqCTWX2mMEKBVSofIeD2dS6JsolBKvh%2Bj9qiHrGTr%2FGlzJax7uO8NUICMeESg9q0kE5FwxtwYtzS0kV0F3TEjRwPnPA3ztzmMec8F3OZI%2Fs4YV0jv51NT%2BnGQ0wWeNrSBr0J3Rg0h8nzCxJnqv81RxxcJQSoGpOckYPH7cyUzgyhuS1PInOMDx7xy1YTF4wapMbcJMcJYlitIdbayCsXrIKdbQt0I6LtIhs%2BUXrTNm%2BzeTQ0FzUTGgV9n5vl8jHEbgc5KKJyuFH9ykdUcivXl3UqTcJ4tD%2FfWoPITfpdGIXCWmpVhAzo01NqMFQkYHKWUnbaC4MNxgXYDd2fJDwnIL9Z1p92dVd7rDOjD5bfGD%2BwJ3Wb9Z4nXqPogWwziHxGEY7EWh6x4%2FuKxMhk0dotj6eoj4roc8zsfwkSezdKlRNW3j7X4S%2BmPObxA%2BJpKPlT0CmYgEnJv7myotbDak%2FEFsCf0flQlTSEblLd3u0n80Jnl9B93Y5adBa8eHsLIwYeTooTvZdDCeBWIJ1hu2S%2BoJ5DRgj4fQE6U309NXD%2BXOUCGCQRhrEDpNsGiWjEAppHQCkvk60czMW2q0OGKerA6QcHuhs3ON4QGTW4pvQkwQYqxk3PDsASvgMw1Dvs83Ryq615MIous3BXASQ0ytg8x7XqwPwNBmfpVNQns19aWehcH81EqG0TPPRxbowEwux9n3%2BtWs9Rh86D6LmWR4FjFQht%2FtrdUIAyzkTSTb9uhZSJUBRxtGxZYvHoxOHMFXOXIcVGwZXnOquEhS4qJhkBlCh8kfM2ZM9LIkIIOGDpMPe7lk4pXv8Y%2B2jgsU8zet8OwYQbqTlYNuo9fRThHNmufRthzfzQZ36Gb0zVYWdMkjDQDN6QzRu4kTFA02i2mn2mAfTX26bgpi48I8hp%2BJqdmvLjY6%2BzA2ThMHYaTHkBM4kcm1wAZBVKBj%2BCj8fWQjy%2B06l8ocH%2F9mh%2BZaHBzKdu146xD6GaML5r6GJnv2VTjFG4pFDGYr09N4W8ErP7Kxdizw8NHAaVnRSGVlAlOft5p6pL1JqIwQ59M9MX7KRCxDLzXbzf0kAgqKH%2BzF7Q0jD5LwV7mMaVWpbddLDMvOaMYIXoL3yVYfJFOKLRi0W3iE0CnMfPw0rMpxGD3hHND3tZVqo16K9q47LNo9lHwJ1L%2BsAq%2FncW%2BnrhU2NK%2FJ0Vx8uaIiGV6kFq94ouD9EDs7pjTC0M%2BCi3nDm%2FMj7WKe6aKI5cfmEp0spFct1MwgVuRf%2BtWpdIKJhhlSFBI0w0hdQl9O7WHy7dcx%2FwQPdI0v43pWXTnugEaGN4xfOkEEeNPVq6J%2Fv%2FMpdzJxg27lARnnpAcnhOEf5CJkpNJrNCXVZeM0DOfU1CWwmRiCBoj%2BFJukgmSY3InvaVFsgoTtoSCUpfLMoNkDdInP53XMz08NHLnauWO2NtcHs4ckHpqVQPf%2Flo4aJ%2BZx36Ys3S8MKAEeNB7mgLyw4uE7YM%2FLOzJzBPgqm5oc4wWSw%2Fl2VdHwzQWrF%2FcbQ7GNbuINPa1EEL9%2BWBdvLQkYJ6L2gpcdDadrXsW0aCwSq4ByEZ0FTK6RuWvfD8Hg9SJx5ob%2Bpc7cR0rpRDqVJS1yAr2tbnNTjqLVXjEwFpfodx5F%2F5KH0r4SX07zM4fOuOs6Be0vvBRjdf5MvXta8EUgvFs5Pbp3Rlo9Xo4UxL9MdPn9j%2BPcyKdAl6Yz9ynozXeQ0RgzuogXjE%2BXWpwGwG75Z5Pc6PLhTOUgFrHDNrESRUhJwBYsQUWac4QaSS7BVleDHLaieAFd0gYA%2BelZBmyqz%2BlcMSwvNKTakGhC2o%2FUw4AXxDdh1JkgL9XYegbWc36FyCSuNTLVcKzmufunUS3QdzJIRED3QETxHRQFS%2F0%2B7f1qeUq7aHOXMtUd9MoUN6HS%2BZMJmFk%2BcBh6%2FzNHOBXFqDOE8%2FvmCcf3oXcfsh2uqyTSP05CGTEyUM%2Bd3FcTmgIJQSGG8eSxYRXjcB01hKAzFE%2FxfUgU7lmAiojj%2F%2FAYSBA%2FJQPlVz5I%2Bsb13gN9II%2Bk4tBppZIAw4KspbODQAMKSdYqKTJ6cCnw1TAHjyaCfbyRn4%2B9fophKfJg%2F7Jl5n%2FKK%2BnIFHutyUoqcK8TOZPMmK3UgzsEYKHQTrMz1SlKIxZSRFGEZRjPMAcDKsK85GQo8Jxws1Vq2N3KQBvLBVJys71rWtmo3G8q6ViFKKTi6iyLAQWNFsKpZ7%2FC1qYtgOqS9nsMeCsR8bwJYYfZp0j62nLmqA0shslKY95iZbKf%2BCsjJoZlbNGhgcyen%2FiZM0Ys38va83NgfG99%2BkrrzeG%2FPTRSPz6jnd7FbofsKme5WQwJCcRQ02A5tWITTis2kUyAXZhlciFvXpUhPd1b40AourhfQQdnQOTa4K%2F1eN8Ah41ArwGjOhn4wZaJJdIfInxIuCQKa95nfJ2CC3R%2B1rxn%2FWWVIKGBZoNXNsfjHdMainoAYAzLfvCj1woYPkrGQ%2BAzpt4Q2xFuXJi%2B4MfsTzAa7wKHApV9j6yiHKKRZ8D3K%2FLjKfIxxymGfjxsuUJAGoHWlNjvBEXq4pUwiv%2BkZx7RGAaPtczTLk8jg3qe8%2F4ju%2BCXMZ2JU22tlrI73hKc9dRhCZlUcoFu76JujWxTdCN4lRCgJBjQxBiXLuhzn1LPH35ihJm8lLEsUnH0vTZx5Edhuo7omM5QuNUUCyE8GfCmzmRwi1GelKsUITV2gBE2dByjLh6ZUrddx2ExI%2FajwpCRqqE3lnTQM7EZXZpJUYwVqLZCqIVZ4VVGx5mRdLjFaJDR5cllOjAzi6rHjMMnZ7OIaUxd%2FumvBm8XI8jvIw1bRiIcoAkMqQaZCIzUHZUPO%2BKVddZBgoALhTe1TDn4RkJH8GZ4lV3ytWJowq3ybVwGNi85sHkb84wgLMVDma3twpflSXdnqTamQW6GFXED5%2BXCvomZElLbzI61ouIj5%2BQOU0coWA21wCKAJD4To2jS0YeBM7uhV3FEZ7H8pJuQOkzfII81srBYxTAmRuR0NWUkLzzmYv%2FYP7GB1obr3Z8RYsbX8V%2BTENRrzxXURyMjdlorYWx5MLFPPRWLsryQGbVqkAj%2FPhIpV1sTQP5b2brIiEkc3zkzI%2BCwq8fmaA6BL1oBo%2BrgWAfwiciE0K%2FQt%2BY3M11ScmTpIE0vqhzOr%2BvgtY0PB2%2BGGdEt5%2B8UceweCavZsfVkG36eML5BtYvGGd4cns%2FLbrHgkayu0KT1SlEn35pADyIgeiGhAK%2BIdIXq3jKrEyw%2Bcnt40KREVc63xxZbUvqoqM%2Fba1AOx3gZ9020sUHrcvmy%2BCEOGJIM0vdIlMQkgBwNhSy1b3hIpEE0YW9MIrLyQhjCyBC%2Fdo7gDVAUJMCJUGfwK0b6yOMawZlcstlxSvXki7BN40XepfiIF78pPjVraeQ4d9gNcWMPjtaVCA1385NYgky9EzvAKJh7rRRurPhct6ItTd%2F1dyynF5Lh%2BU%2BeSybIdnwPSCl%2B7vMa8svCppciAn1ZF0%2BGTaS2usOGMRJocqwaXioW82ykdmXUna2AEBV%2FZuNe7bmnGVOdBzi%2FcPdbGE4RY5zLpUkKIhbO3Bshc2C7PNWSRiQJiCDUsBvi20iA2zRsDZMEk7XqujQaUTC9Iy1Rvq%2BvHQ0yrtzCkul5jR7HpxrlDCcW2vejkGXEFEBxM%2FJcTLlxQVEDSfzmGE199eS31pIUwXNewEyKuTOBkqazHOkHOwQmV9rHczYoFgv%2BFrKiXFDSBLnsRyacV86rOAPTTt8ZfhJUcp6dxSYyYrs0Dny%2FxzB%2BKwyDeORexZUzt2qoHV%2FbRPQI%2BeVv4JhaGXDY1YZvii28MDtyrd7C2YlGgFTX0Fvz8fdXp3ye%2BbzxbPjwtDGYCmfoiIPHGXZYqRhmg2TcYJJuUlNI3dUhb%2BjBL5BkCZ54lXm%2FK6NV9SYTemYAc%2FIFO3SKJS8pdUlIh%2F0g0ASilY%2BlBiHAzCCpkytr6fiu7OP1mKKkS6Diq9IDEERM3ad8Sc4p0JShHHGtQ4idlKdc1WNgNgFmSDvKAwE%2FoJY91hur1hDM%2F3jG1Zbe5D%2Bi3UHj6pgu8enfTYJ9w4iTJ03cKKb5%2BZmmz0wDR5sfvFGjjyvCk3xUVyh4JWIENYSBkx5Nms%2BTi1U4Tq%2FroNS5d4V6OmzABEQLaeY0fcz3u55Ke%2BBm5%2FKWODV%2F8mo0ZC%2FTwJk7564dDTlKU2eSNRnzvaWJ2HMt52QdpeAUSPjSTiT5EjnsCJHGG%2FEaoFcpPJls5Lp8QFFqP6VfYDNWAB9eRplZjOLyoPMlTzv8go%2BTWIXV5tOnf4AwKLq7K7QuJu5MH9nxvsk9kZ2vz%2Fk7atmX1Zcq75q4iP0kwkNdgxZQRmQxEsILEisnequkAnRrMp7fEXGNQ74A6E0m%2BH8K4EksOlJLUlRzZcOsdonx4RqNh%2FnGBUX5gwIuNmoXXRf%2FN4rWEZkIQFQoJdB4pC%2BeGBszZvjUWk1n2XKj011y2tKvJzvLVu8ocs%2BKDJMCotewZWft1gwjA3oxNV5Wy3PicoIlXtsUSFSiv8kfio8XMoKgDdNB9u9vPyJhocK%2BTx4CnnIvOvfd8xsji%2FdoGUKbneUymphrYjO38a4gL83pkE6AOqxk%2FhxBpvxUO%2BhkdMvo%2BLTg33xEp4hjM6j6GtmmartrYhzfxNZ9vwSTe9KUV2B8OOS7fTfC%2Fd3y3YK1viPNZNWMnJUA5eqAKpCaLlBD1CIG3dTn3Xw8x4EWlVnPMAmkHtK7tZ1v9fhHoID5XyKvmQTGpZAiCGVVqA3Gr%2FpWuOaPBCI4z1c0UW79APq99bBwhy2RYiRGR7xOp%2B6TR25KQia78CeZqYH7pSxbW50XC0zsA%2BoNRFtiQCVQJ%2B5wsQZLycD8oXSOByBbL9BGCdxlTeROiuV9xCgtOYgbuWHPp%2BSNsPRHn9SYJZfixOSVyxTleGyvcZ%2FcvKnNXydYtEiK3wo6BSgaMn5u1ZHGYjtDuupe6eivHykhZUzZei4QBjoGBxZoUpKtz8qLswxVdK%2BoUo2MaKMUY8TCjxllM0COA%2BNkFhuyDd5ZfohT3D%2BGDI%2FsWqq2Sp65R%2FQOgTp%2FdYny7Z%2BIIdtJBUYOYOKf1saV4HycrF4gEFFGhqI1IwBAAqT5BC8F%2FdJkrDpyhEyxxr1Lpgh08XQRsSlthmmtAmoR78U6B7dgy7Q5NNXOOU4JTpAfP641ALfnjm2CyHilo0LZz4aeeoYlOeDWDD6nmsI4QAfNzRKCIhVKqMJpKBMkcZnZDaO0TqFZYfZEfgTf0ip2FkMQIycjED0SltDg6ttSshzmle%2BpoN3j1ct1MT8YYERvnEz10fSkiQx7DOjlvA0vA5P5vVGnxHoLghqXAGv682aFdrYsJ2KNyFlLLhp4WgoQxD6DMLsYlmvcYRtLcbArlcAT4LXHrPA0eDnHycWoug8zx8Vj%2FTi0tOYkc1cYFOzcN5fODMlfEWguBWOfQzYJUA8GZBXrQ0si4ZIDo9WWF5b7wzhfjX2tftCjKT%2B79e7hT0CvsyFnz1om03mynqBAXNFEcv4bubP58r2A6FTOFuQ5QkzU5D9yEuDG3imwNayBL5Fo0bhVUEldjzgwQit7TfL%2BFnIbXvj7e%2BfQseg3Ax0Soiwq98B1UDftWtObK%2FXdhhxYoW9hvMtmki0PV1AsQe41aAo40AHOHr2hrFCEFm6oIgkOEyF7wKZ7JnvV9qjGhQG7iq1FtC4PywQYntvWR%2FmeGtTzpCOBN9FBDN%2FYTk%2Fm1o0NMIejIVCadE2I1Nhcht300arifBa%2FxS1r0jKtghKhkmeOwQhslKEDxKpUqikA1%2B8DTemXweVUWVMJeSxKpXQxhqUGHq2Y75BEyZXoKycSQHF0EZBde27Ot5hJowxUHFEQT94fxSgsnXOjZsoZfxleljvLb4FjcManSNJa3nRdaC8axltZHMMwPL7bM5N4w%2BknrxNR0j1VjU9aT5QoVvQptlgcsRGAflNR2Hn5ERnBCukZG4t7GYkbw0fn%2FrHP0UAJnRH1kB0sf981VKflWdxEXTyPXcmbApMp9UeqMS1VzGIYphOBzMc469xk%2BaFyZpmBkTsajEu8SSS39CjGQBlPxRaIe%2BKmM%2Bfmu4eRGbVKtQXwwCaAU%2FyWnP%2Bh72fSiHT9ZvSeUL00ffaYfiHnM9d7JpXFpSO6fx2eMGV7yUpV6SOB2RnB%2FXi8PmqNcb2F4Jz9pawfrpGMJmabNogrgB9WJCNuTIggHt0%2FnKhotIcfgWDU1CIxC8JASeN1RWiluQaBnGuH79spz%2BLWmUDMPFVOUAH1dKN16yhYMygiuZVc%2FdVL8nJgUgb0TLGZ4pqnlpZGNOX6z%2FUrwAYGYnUtth5NONuJJ98zINtbs%2BZOkzJjVF3vAAV55kZ54TXrEugT53HoZNnm2Nn%2Fya4AwtQli2DmHTWNDH8CWT6jSgJUtA7meLJOrAWRpdA87AKteJSADaGqnztJgBwWGOMGsqAZyoftDl4t8Uk%2FEnMg0E58L%2B0UQWgYRW5avd5wCyM6OHYTLuLzNO03AmlngOuz2T60owHT5EsOhR33Et0373eIccXBMowHc7QTKgE0gORb2MR7jBzLepnziSGWPH8iwlgSURfWzpOdgjYW%2Fra3yLDLRQfp8uFUjyeI%2F%2B7NWIZoRyi9IqIUidQTkr%2Fv9w3S2%2FnL%2F%2FsS8MV85Xvazs6258KGhgx5iOj6lY7qVminAJfl5AufOsMl5sW1fzzSEDItJw8i%2Fm4KsNCmhk99481szGq2COxYuzfzwkwUPckRg%2FEYSn%2BQimkha5Fkje7vrb%2FGic416biZfR0Kr2lQuTnVvFbx9H1PEIsJQFk8WrM0DDipoDpPtm5Bb%2FNuYNYDsi2hbGxdrbVA7RV4jsyG62NO4nQvWzKWCwkLyfpn4T%2F4ATCfvKvUHSyVTUCiz2%2BZIOvVsKzV4tHHM4Rzijt%2BZPgo40vTrNiDxGkjypZ%2BhufyRKXFbBMxzeRLz7BEjIDwbR9zHFD5FFL5JGAff1Yurcz2MbKXbO%2BT2l8%2BE56V8wd%2B01Vnavmlkk33AYBOxc2MRsep1I0rfuuRSJUWLUQOg%2BdA9Nuo3DkyVrFOoVP5MT6eKDTY75%2Be6Q7ET2NFW1csgI%2Fb0C8Md9rt%2BU%2BL9j32BLJdPCux8DWdwWka7JkMqwqXC7u9Vrn2r1p5czoYw0YAN5fblcDKo5GhkK%2F520yjZg4WWe9Dl0Eoxoiz9LLMgng48K1HPcs0TeIQ%2B%2B%2BdhSI14QoJ0zHDNHAHZoyyvzATJnkZayi6b9oJ4%2BeJHTBYg%2BJ88aS4Wm84ZONtuupi9ViqvE%2FZkNF7vLo7vQjDnOR3ctStzAGbiqx1OaGZaL%2BsSCX46KXi%2B0O1sc1sYd%2BbLNAW0PSHhVoLNhkVxnIjRqBzakWQ2b%2BCQHbQBemLkKbCdeBvi7UX2mtXh99vRq60ngM1Qc1Q28IUkWxEF4RKIZQm0mdCG8hXd9LIBMCT1AL0EalKRN6A1epWhnMM7VmcogxvhhOlZQR6iMfgE7Msc9jo6U1AR6qYoIqv2mad%2BtrhWD%2B9xA7g%2F2em3O%2F47bFNjEcAl2NO%2FgOKio02oLST4BsWhiI0DZnlRQhP9%2FbcAUTy7eMuk7pBfRTgZI5LaWx%2FDjezibCPRnDjngU6ARusU2MsE22igrkSgDrykF24sS2h9GLPFnIRYk5jVpki0eBE2cl%2Bm83V6xC1VzNChUYNQ8B0Kg4BD6HNu830w%2Bj2V7yi2iBiek0z%2FkbWPYa0npB3WjqeULJ5Ir%2B8MWeguuXXr2gfIO%2F81Y4F0uqFLZWblDk3jp5kRCIa1WgGuSvtddQOU48dw7OQN2yjlceIeU95Zw0pZvu2MI8kYuuyZ8BSAxE%2B5ffEheHKgSKV9aFSH6Izcdflf0x73F04%2FXym7GsNksevNVIbQYCtI%2BA501HI5ImxyTbiK7T4ekO6vJnvUE05ynfzQm9AnR4llmOMwxIP%2FJaMpbIH7NCRUNemG9p1HYwe%2FcA%2F%2BTsDCIXuB31obTkXqmS0bQ3TXlBXmIFc1WEr1YXsQukovzsK7l%2FCy%2FgQ%2FyxiM6FimwVCzgAPZc1BMAMSNcTna02Bfkb%2FGcYupM1hloi5Oa43RSllo8FKB7fgNcQ5xs0yhOUqkjmbHZylJYi2RYeee1J5b743T4bXPhKxuF8JNKoQPY2zSBsXV%2Bcq7Bczf%2FcQN1HLmImJOoUxQoxLfJHNVwlwhgdPgeOvsZR%2FovbAInEPvgKmb74NCofEC6%2FwPbmReVXNfKchNqnc%2BjKPckKFM7tV4jh2NyZfcMTqL6cHZKALXh%2FE95v5f2%2FkobDXWeFEpyS8BrpNIzlvSoQ3FE2b4jjgGa0q%2B0DHdjI13lKT45LcxtD9Ml%2Bg0pvUVOlDR2FLBCan9vdB9gRjHe%2FH8xiEcJJcTnsIGQYaYU8pFzAdBVZ0YvnK8vANyU4s%2BmKZvyw8Dla2eoDo%2FQxEvfIsAnk6ZMSz0Pme6xAcMk3i1kSQuUOgAcplMffCts%2FKKlf%2Bj1kxCm4yjA3ki9yo6eY%2BvmmUjcY7USjny8llTUkwYO9XrCwDE14Rdm2lpHu8GJABjrBz%2BE7QZ2MOZWOV4CRcECbtAgIocSk6KEpdh%2FUrp%2BycZsjzHiM93p8US0UFtgo%2Bj7fSZwCFA27WYhOXZ0QJCq6IqaLmRC4qMPJvzZBtsxjB4FOoC56gH2Tsf2X%2BaVXVXE6zm1fauOEy%2FSfbrrPnh%2BcIWO%2Bdv4X3UQPbVGlFQdS1D%2FqG%2FMrxofofBA7ZDOUA4f7Zr6mZtMyhRLLYJyaQ1U6qrjC0Uifi19qpoBBpbboOY7FY8pgVNUUlvi31so8sKBcwGJhOpoHRAHjtcsVnR3XDJ0LkZrIIGXyhNINnqbEkx%2F7oM1%2B5BBjeKiFMgfA7fHMCPUKepLZfDpNatnHuMHlmIpF7Wcbb%2FAUg%2BzhsCicyXX0PI%2B5yuIPunX9IhxyxAhSCfOLf88x8NwN5yeWhq2bMmhE3kkzZr7AUqaW1BBwBQ%2FePSHK7SwIT8s5aZHHH4o7UXhCGMgjaVPdIP9YspLqgpevxiAY2nnw2HUegF07v3DGzsRRuAQInRAe8cpth%2BvdNJT8GwUgTh7N89VLy7GQQN%2FfFFhm91MKjjw2OB6V%2Bbi7kx4BIU6mygOGbMh5SInu4AnZQ4r5KgDDCGNsO3NiEkW6Fejk4hVRoF2vMSbQgTmLZg08%2BpDwjpXvw1WwmaRmLyXNLRslcozzjU4ktRcduRYQ8oOxo6N%2BWpn2WqVf%2BCk6TU0j%2BS2wk7QRy7SgiaJau5P%2BwsNuW8GiiKv4FkPVNxB0TclTi5WUEiAcagIcSiXJ%2BYhPBDc8p4aAtnlo%2FVHDD9u4lpjUIHaX1OulPnDfM0PZD6fTB4fZGfvrSdpVjDXw9KG6p5%2FO1xjageIbt5kyDNc79Rxptse%2BJgWb7MORk1%2Bh7KztiHPr9ybkMdA1Jk0YfUskXnDngUhC9zLiuwc8w4F79r7yDo%2FrFjn%2B9FR6k85k13eyRR0KluqCZ8Hs8PQE6rG6DeHh1GAc1X37eoyrnppIMYhR91j3au7Ox4CNgoMcR0NN68n0kdDsAqwKrE2tng3VRpj8VRo%2B51ekQsa2NSVyyf3hvXinlxGBlUKPfz2EDl8wmZnwGk8GzepIxyIrU%2F%2FgGQyeDgpEgogBqibi%2BES1JiDc0qOiwseYa3s3zmKx%2FypP%2B%2FvTVXoUNDod52vSN%2BeMpkyeoGmdNL4vzNNiFvw15TPolXIfyGIxWnQ4RzzE1Au7aZ89vm037MidwhY4Vobw7AzEhOWF8dAV1Bqz0THGsa3cbniKoMCK03MQ1hzx88qOgvs5qWaHuXF%2BgfqWY7zBdVyWNuYaBRkMQZzwDnQ5vSPw%2BwBai322S3TkBcBzFj8PsB4y%2FGapZ%2FpDYpWkW2nsg3ZiF%2BDPS13BHOk2hF4cpeDUzR7tZtEMGQdFtAikH8usm9QlYzjZZksIHqEAeg24dz59rJjz775a%2Fk2Ykb9sljwPRT%2BDvpuca2zME9YXuIiXdeSheqBM0TyZtENXxUyRB9fvEKWEYuHC85jUtW%2Fq66yWDNI2eO3A%2B98dM6UDAHluAX66O81eqjU0kVVxsXx3L2PPM0DRXeoMZwLuFpTP53gznZRD744SjrluiMkGv%2F9pGeAghpnosEs81EJsjAd9nS%2B0bTbWMTdrPKDnOzHDoKwQmx%2BTLYhCiIgOdgulMZROG4WUdjeXyahSVyVkg%2B6UEboYoLEzQupiwuPFjzri7L%2F%2Fny8zmKz9StGtamP4yJj%2Fx3PSog%2BnDW1TwqlFcT9Mttp7e3d00fT2qbW7YXTkmoVtgukhICHkNnRaD%2F9P3W8VwFPIGyld%2BxNrVmQgeI4aRnN6mQZ7J8v6B%2BpgP8rccg433jZR2cuvJGzggfHpGqr%2FUaBMF3p%2FYwLe7RccUQseerHyX%2Ffh26PjLDTgVbXf5tSmvIP%2BIfdWVRVxtAihsYxSpJmJKToZWuBlwPuxV5%2FlRm0FrRdhCjDiJX8EtK3Am7qwOEMLxwM4aiP%2BR0sa1u4FFoaYHRwL04nsCc8YMzUz3LuzEteqFM7gdB2%2BvyTNtw5KgzAQ0qkHvFa2uhgZX%2FBKzQ6oE3sU3gdz0F5gjh0GVbyK1mwTcxfPdKukvHGtdRgVT2RydYCh5DegkGDD1kptR7TXfniV3IDvUyDMLRHK%2BnU2i3HOZjssF9nasTAHGeQtzpmW3Dr6YNG8%2B9pEZARiGuSjVh4S8lAr%2Fr7TZjBDlkOaZf5%2F61Ib2jobGSfFcjEIUg8FSoC8OuaHSYFT1KL%2ByMyJeZDicYfO%2BMqiUPc%2BrN%2BfESherB5XOwtm5k5TKv4bBMHLVypOy3qDw9ERzRtgrMfBcAoN7uGgL0BMoPeUaJblfwpADdaYA4VnfLsM5OyZveRGxHqem%2BOp25LUR7WCcM5Og3Qt3R9r30Sn3fV8i9MI72iW6AfbDp5s5Ixjg4Tnxc9VzQTLjEI5HvkfMfLh%2FQMaYHxwSJOH5x4RcKKFxh3fHtsyL0AsGGxk8yzbOkPHXyBaZWW%2FsjK6Bp7ESef8jyyBa0m5p36V%2Bh09d6xngCT%2FU%2FaOtG34J83q2liA7tkvfx8doKPPidLtb%2FbAtS60%2FASdEA%2FOVo9fNPt62EvKLg4QsCww95DHeaSVsYoYxjaFyzoD5%2BBi2tPG3TELGsaNkB9gdTcDG64ZsMBPtIrsETRlzr5lg7a5Qk5XAMLGn%2FCCofznpMLo%2Bo9g3iIfDfMqfm22Fmly12TygmZPEpvJI9OpquyuMRTsJJ%2FtIegnqd%2BZnUI%2FZlaWtQyvUVTofcDMWGXjOUDt85UgqAqGhHXUG39nKziN4jaNpsjgCM6IxpEFBE4jK54lqYIMfpUFrfvr5J6LNpQ9vYYcLRTGMgq3F5SuJ8xqTUoCclVIg5wU8STx4VByNBysGqHK9TVoxLzMMHyBeTOCmcw9FHhacN%2FglTz4Xt4EqhcUqeCT3KCS8zqAruzzIdoyvUMfVjuAE3u5tzA6JmidqDpYIEme%2BrhlSrZc3sjmAJtcYrRhXQrqD96NIsLTauExVHt9KskfY7uXZ0eO0LBrwjPPkbHZ3pz4x%2F3jMd0Axf5oAxDfH4NZKo0dUGEg%2F25EsoDAp6TI7WHgy1bZLvr5crzljIc%2BG9AIcJ%2FfUE0BduV2qc1mHS4knAJHwDoN3ho9czVV8XkxjMhPt%2BC92HdI56epTxpvBRuQxlXJ19YjGBZ1Q13zH6aj5P3MJZVu8Qb6WNBEuNQPiHscY2HB%2FrkVEPEoak33P8%2BkogKL3zJAI0tibA6eUY%2BXwFvFzU%2BDwnY35evt8IaNhZJ%2BhalAHjZhGIzLLHEkubbcGZmQ4kmGwcA8AYr8RsQODAQhWM8D0PGUg2zz48MNQG%2B40qNojH9e8uceex68PehHPRviAsEI30d535dVAL8P%2BZ%2Bh3Pz8lNfmX0CZjmdxYv1nkHXJagpP3GMz6VWgT1Hf014ZnM8TmjClMMfNvTY%2B8mlHhvax5VVmilayQGmIBu8SfnaGylB587d%2F%2FDk0sDjimFuQymCNQBEaEe8xJuAlDXePEj7IWvQJlYg2HxAWyTYZ1m8rNjqTe25AWLg9N8uaY%2BpiF%2Bp0FlTdBNvDQPY6ur0XexMSjNjADPnEQK%2B4DqnjAXFXSDWBny4sm7zdQYpQ5PLewihjLhmkHnxwtGO6OHL5wSyoKQUZbcDFeaTE5haBRO0OHgd85U9DJRDAylt9wzAy0kVMAqo3DPpRLrCSUhDNRN%2FvdVpeX1AWs4PcydoJCAFRAtzWK2LvsjkDQChdPnP5gRLN%2B6YSRG3iMcLAyixuEimqaS0N%2B4A3EJU0A60AqaL71nNKpxj5edE%2BOBxiPOFMjvD536SxKGD%2FT5dwbUXk8yEtebygBvXX7FgOzmJJ8T0QMShGjTnW5zmP9hELk8RqgkXBeCwuTxIsVEB5gU5uZljD9TeUcV5bK0VnAf8bfFtpy9nPCQ6xDIxdgQ4NIxTT%2FKzU%2Fxp4W8xgcTUJaDomAJ7LKaNxQUTGgvm1W5XQJmAW2sMthA%2FGFlR0mtZ23ponBai5Mu84uyiGOQVksNE%2FqRFFhMAN1THsrj6lf44gXUVU3gTymhpovyzApFvsu1EBPQffN2Hk9FKaccm22zIjbErrBDzwzgvboDVnBYfB987uGPDstnLums4rtQqzI1fw9jiWQukFNZvPJUHX%2Ft%2F%2FgkP8B0mu6khcMDDrF5yhy2MSqxH9W1kRGgxmo%2FV2nt4Ip7nUbxb7lTd5JV2PzPQFslkcO%2F7sF4rLEzYoKpaFBYog%2F%2Bz63YgcZHWNWxzyUgEP9efQT7MRjoMF63h4DOHJkDTxhIuHv%2Foq5MA1avyrQqf1znJRMMvBu000V6Itg8r%2FvxJitmYou0p2%2FsqdA69WkQeMwZsYbwa99YjTFsiplafIxMTi%2Bpc1mmDuMTlmGX2bwsKJiVBatRM4nQnbQc6DijEZTOgfl81KBVkQLWrLuACKqiGYKhEFWB81ZTHVfOyhuzRmQF9nepAsvrLRJtIDw9hh2oXOhBrjf68Dz92qB2qdsLaZo4vm5SW7BxMrbTrrI3r%2B0TiCEroR4H%2FOdP2xLhQNLYqz9z9jaGxkaJpfivOGB%2B75sg6XyuuscfyaZzRzwyBhjHOeseUSIU0rFJN%2FcAMM2tmOIwl9oKVUM4jkV1vNEwFOJmBBBuUy%2B9f1vRNMt7jckQTtDryR6Y9C8T121XX4yxQhYinBAQW2gEvVdvZkBdZBnbng2xgWN5LwDpAcvri8sNohrKdoc2IaKBc2%2FwpPbYyf3jxiK%2F%2FP%2F%2Bb%2F%2FkT7AvinMJOQPJVCGT50iCn04RPr4mzDf8OI5MI2xaswXKcnYGsbpRGGI1AEvbsh8SKbBivaVH3zNiVa4BgYrHaFsTd8s5Vv34CLVLAdtqKw46KlmihvLdOVorsd3n2qO9cb019zgGiPJsTWgi3TKSnxTlqPtnuSkU9Kqjk6cnRoZzr1Iq9u9GFTwlfUjochBj%2Fo%2B7fyCyFX4D8cLWRz4ISH0zFTgsFwPbyffbz7q6J202JNTeWIk45Ul7KcjHG01Yu5KaBlp56gNNQjiH8Z3FRl6jzVgZ7C3c27DQxvSWiZ7%2BfEoyZuGLkFkyOPzouDjgGBrlUeLSRiMPXMfwrqUkCyjypm75FCsK0zglLjtB6tl6v%2BqeclhXFdGJJ1%2Fh9XTflwXHEtzBFhh5kBW6cWvNj91EghRHjlsPx8l9esVcxl539EccyaY%2BtmqOqYN4oTGgc4%2FTII1pQk%2FwsBeejIJCwF%2BUOUEFajeYs%2FEOpQhScobmWNcXCic1JlTyq24RAlr0km%2FNzsEReq84SdbDxCxRohhit0jgqSV%2BtvakH%2B2C50ILvBpag9DQcMpyUNPUUA0bVXwKA2OW8RbXloMdBHkhF%2Bl%2FaRjBhi6940%2FEkUKnjXc%2BSeYG98lCO2eTRstc4yaaHl5Cjd4%2Ff6mAcB8ne%2Fn5hBMITG%2Fs5Ka5WgriknDVBl6owloZR3tqMZEnN0iVQx8BhsxVb6T2L1ex%2BzkBq8ZJw9VzjFncv8ilmnJWP9HaE78iGa4YDjaxmQFRYWEV3HwEPJWykL0P3396ld%2BjwToM3iJ3SzmopxwoTm9GuaRzcZyjsuPWSKmx5VrWFgSy5HkV4vm1OHKTSW%2Bicb6JBGIkw44FSkw6T2hgYG%2F8K5gDYcpTrcCZO2vZB3BdKCH3JB3Tp9pa7xhHMZP9T1YibMlUYAHij9vZCThFZrJkQ6opc0KpjcWEMA2wI2QpT1CwBnVnSe13OBOuYWXLqaKsrlahZpgyJ0WNEx%2FTMHwdK5MhKMDeWQ19MrMpK%2FoDqAFJPSU6ZIIO4UO3ZKfUGtxjBFnMbMRbRJFHVopXU2k9ZI%2FDxvKvMo63bj2%2BKHHG3RGNNBIo6Ec%2FZBalnn8oPwhJUNDmHVVPr3E%2FQQmgGSiH3468BHmr3GGsaTsvyDcAf79mmfy5GELPoOzixGZocaxK2OyhiAUEQMVZ17C3JA0NvPJSsWcaIZwRkr1uhO6xdAhBw3x1g%2Bh1hVVmgyah%2Fir%2FQy%2BJB7FMLgopQKZxyCvWNXfVcv31hTgJORUPSAtvlHC5fbHrIaJ%2BK%2B4SZabzBJ4TL9PuSZhk%2FXANYllrNygcYdiMEVjGIuWV8KOKj3sDUH04v7k3DIN8syfPQtRJIHbcK9yKIRCivIX6Go0zN8BwAJCsDD63yzvunNeXdM7Mcvo8wyfw1bQZsVfW%2Fioc3vKYoKjHNHqmLYZMbExVcyEPa7p2O8PqJ%2Bck4iMoy%2B6mJsZ%2FrEQzNOalpGO%2BbtuDOisZSZFhhAwBkkZYNQNhovNCYuLBFDR4krohN5d1diWdQvXnPulYJ6mVX5%2FCYrHWak0nl%2BqmXu6Y5gBmuf1%2Fc2tPF%2Fc1ro6NHEnh0hygyuVUfsyDL0UgJx%2FxXIYLYIgacIp2urB4MSCctB6ORBIBDE03OTfHDVglGuSWn5zmJckvPfRIwdWNsvFpbMaHdObMULsaWNXPH%2FPGtY%2BZLmIahwPZdfLQaNtgqL3zIoSJavV3b6QjzQbE0qNdSuoOEFgRwtEjBxjo2ItkRwJlI574sxyJ3Jz05FiSm1v%2BmG4U%2FnGseTlhHDM4MubJVZSPzoY%2BuvrdiY66WWwOm0isqCAS0717BIPeAVS6UdXYs7bQseRhRbl0nsqo88x0PBZOZqv48AYVyvmZV58VH9WWC8aXek4Nx%2B1jXVZHwZa4ZVS9cVJSY%2FwvTAkGqQH85rQKBGRSixtwN8BYqoRJU4MXobKLv5EOtsSutvTmwFMRlUCzbFsoKxzS58hyoxjaXOHdJwUCk5jbMi%2BeyseF72BFQ%2BzoOS38EXx8BrCjAo0lwRszk1BRZV9M%2FuAUp65KLaA9buOEN2y%2FiPxOdthSOVUb0r433TCEvDES%2BEUvxJvYBvoNBU2uLILG3rqb%2FioUp5LUB3bRxmwFR3V3HEufUIzxSBxIO%2FMGMWJ7QVz%2FBb3T3w6j59c76fCX%2BlR8YJA1RtxTof2Uz0%2FcdJlvINhEpLmmSRs4sfe7vJnPHktryVXW38taYcx5O1qFxG591IgPRSi%2FKtu4J4YOevq5kGn3l81seYnZoI5BEaUGH9jHMLiz4QvXlrUqdA4PZkSCgwfRqSHAX0nNamIPuwn2L%2BXdA31xTJvgkp17V8iOfocrDY9keJkoaNlhoZ7Es1LJPmk0WYvsyu2kc%2FD2hMdQGLBoRA%2BoenYWczK5ecX%2FBoXHAbpofKjvIKJipm8IauzP%2BYYOFXYKQMmD00DZQIFg6vewWVGiYNEKXg2dPanJffj6IcYIPiyb2CNztmFKjoj4vgBu6v%2BJ12JjEdhz8k2QrOd6eZAnqBszJCRRQbNEa6lzjwdQkie0bMQbTSjHcQgo1IG7mV7a%2FvfFD4StEOwV6ZUEAHgUYHYJE%2FSGclmJmffAh66jKNVwLSYsTQY8K%2BKf9CxcWDTDz%2F%2FICs%2FJGbAEEZB91aIB2ZRh1QULnXDI4ZhvykavKUE6aj%2B47hHJZdyXyQXfF2TY7I2R0Q6PzKQARvktGVpyHwCh%2Fc7it8QCjIVOU8OY5GGLUVUIzZ%2FeSETqQM4hqClnu5d1iWUF%2FxkcVisExGH2t5S%2By6%2BZ%2BsiBdqXPRNyLrkNzGpDxDHCCYToFYhY%2BjmZRT1Si56R7CiipiQSEM4RNxDcIPIHDnLFGhL5IYrH8jJ90%2BoyJc5I4itROn42NgOAy35DYAjuelDWhl1eeWDk5mBTxyr6nQ6jxhSRn3LFX0aNiudHDHK0aCGWG%2B7GGgPEWQszvignLiDncvoOpNN86xGropotcxpxNzFjy%2Bk70HO2WZDq2i8%2B0jUKDHHnjenEvwRO0JqkcuYxNPWNjFcatPRWYtpnTYTtI%2F9u3HfCQQXzZOAZqPZ%2BuIxRT%2FSsgYipYUT7dSYzQRCS6YkxgrpIWZ12sh4Eum4GRFhOqLpp6ATQhfDwO5OnVAyJqAEZIqTD15YwZEzpsXF7fJlhK%2FVic%2BP%2BZnjKVlu%2BUni8cMcy8UEuUmtmChYkkNusjpIMwQugWJH8mNcprj7ks2ypiqaNSVhpZncS9Gcy0TSN4ctJeZEfBAICmsKWfnpeW5wZ5Ko9EcGLsLklEBTYW1bNqCibEda7Fw30SEOAqvqspHS2%2BPHIH51xfHozQAnlms75OSt0VQ%2Fhtpu1GWGCzf9HF5ADVgIQugkh6MZr9h%2FLD2z4%2FEzZIqkJQc9wRH8lHoWP7NG%2FOt7yIj1qYHWL9Qo82CRbsPpmo3myV%2F3KqUibn0Rl58PGY8OF5aDuKauZSyEkNd9tk8EfFB3Sx6mxT%2BYSdin5Al6Iw0v5g%2FAJTCoIRXpFETBEcYKFAynp5YOVKe8pbJtw%2F%2FRo8b%2B386TUCve8eC0mmauVA%2ByxeSQCjkPpVibdMnYAgEjevpu9LlgmNEEZ1dQC8viGOzWxhvimZY0LkMytF08621cJVqFqgVTCvBC%2BwcxFBLt6OiQjRZJgBpeft9Ox4apo%2BjeCpsWrP7%2BVb0RKfj9rBnA%2FCqyl%2FvHDjhpNkGbmnuYocIkh2FKvgE4FcGz81u80V%2BG7cSDopQtjRxwuBEWGWlkC2yyiAoeL1Mo213xxJtBb0nkKlPa%2FOEuNnUEdh5lprZZ53DdaJk4DGqP4Z%2FJDDeR6RyYsImLBsgM0NTsKLGACIIUQtB8n0kkis%2FoaY7A8ZGhw0MhmUjNyWmf8hgu8GXnRYZI%2FT1DQk3fJnYZAaMrdKUwrpDosf2z4doyXN8ITqWg%2F122iNywS2lubObDtjH1ccyadsGdaKLmWNUhmKMbtgiBtZ7IjlfOLzRKd18gbh%2B12e6NIMG%2Bu5O%2BQGblCUdvk9bBImEL28juXcCPjBHWLl5lCKHz0ZvKvR5%2BAsnD5%2F%2FIxruRLUgHSyLBFG7dCmenUQ7kB42UUTm3BEvnueW6DClAYcirCup2tHuZo7T90d9VzM8ie2FVZs%2F4BAJkLRXG5ajrF0sDZoU6ePzkqS5M3RWDNOrbEDjwgk2fuFxMcdDrIG0%2FMBjhC3NYAKMlxWXPIwdtEM5ZDMD5%2BeDP8P5lSB9b6qO%2F53klCOGLF0EyP41P7rTNhTtGOdMaXvezhpr7y62ASi%2BmzPFioKgeYThYdj2hxh55yjartdecB3esEUcvrl69b0TJZQBXpO40WA817KxuKowGHNz649Asinefqh9LWdvb2VnGuxzwvK5gjnLfCGjYcyuywTEAw8Xu%2BPf0baFSmA6E1i5bHoaBoRvFcr1GAxA9tFxxurRda%2FirvJHK2V%2FwOa8P4gcBi%2FawmDtPdYZwnJQoKE%2BFPso0p0uWGOsa6nSlxOqO8x3Bx8FUTkRnbDoekrBrNpCv1ZGSrIUOOM5A8Blq885puE9zmSWsB1gGo9U6O5yMeeNlVZccCSnJY5O4%2FysvnzvBXCG6LnpHRJ6MkC%2FLniUZtNIsUJmtvAeD4oDw674xI2uwFPMiaRKjaG4jWfB3tHQYFxcn06MUSRjppttAPyTuu6zfuyfMm3opIF%2B6Ephg696DwheRovcJva1SA5AxfwJoyG1LIiBwTdx8Ty3iTCPaZBqctA4tCWvsPwUgS7od2oOUavHIcWYDbM%2B37OzapFBnBd3HYsTiGHsfryteUEK7GMcOnDoW7jzfDohDmqk5EP5ZVFnYPOkMS5yprDh0FIAYmwYIvM6yYsasSbvQZI82TyAks5yBFJtoNxe76m0BHpJ%2FFHNVWT8svGpUhGC7SwXJvzYTJlvSwTu9lIiDwgBIL2Vxhd2cECMEz6jXdoyYlYhG6zCmJD2xk46LWvT%2BU24JFdWICgb8nA1egxjzCRlY0oJDh5QfRY%2FHe5XyPxxAHQ6RU29LCPtKSCaUtaVic2oKViFNCYVX5MRr3HgNueoni3Z%2FxibBByYpnMWbi5lXoPOPQZz6M0goDGpusX07XK2uM5yYPqrNcx2ohebJiYnS3d9yY32cTeis%2BSEP0OpjR%2FDxpkpg1gvQqxYHxjuDdQNKUKBrtFWuhoYT1dwli3Xz8CBnZz6Ddt4iNeILsmCJS9OHXSIdVg1r78UYiMlTcHN4p5eRwnJ77Wa%2FzBNF9SzZbISQG68nmDvPoaAl8wP%2BgimY5k09tlvGa0okB6fwr1IrUQIcaHX9gFvsxfyVBsDRlw73A9weQPpenjFib%2Fs9UDToWbm1MT3sXgJKqHskn4q6buwb%2BwTBFV1R3nrDYEtQDu%2F8oT%2FHRA8aKueU%2Bn6EWBZII2Z9hH2z04SBFAteTfZGGb1fGJ0XjbPO8FQ3EDoUnNntsjELq023DX2DM2u0J1VP7%2Fl4HKMaopADDKKfXHIJeagn0TTjzKYROUVYBYjBmJsa1rV94GHMNCfVSeRIhIDIc8xva5SSB7qOqA7MdC%2BvqNjjEXslF%2B0Q5HNN7qr05eyanm6TKQda5n0JNR9PNDY%2BXM5gobcU8kGfqe%2FeyE7sunkfkTxb0kkUST3Q3szNuWP4zk0JZPgSZrJtEokyBCNX0gW4rgIG2vhIAXxnGrGNNjmqZTwN20jNgheyN0EDXdgeg3wqxzd3uqRgBcAinjPj7FtwYmnU1ikDnGhFOHfOGBKoJIVo6X0ZtnRFidQWNwDCS0Izikdb3u%2Fn6%2FllqGegBQaM473HlNcosztjlwr8SsFTpQpLpoGLLWMYQ9XHH3MtXhpMESCkCLmJZkxFMJPKraAIH70wZqrHEKFkIGkj5bZcOsMR253XkIQnJnvJ65CkwDA4FOHIriQwwMJcYLtzvI8wBK7R9jJoQPlrmRS%2FVfbw6ayikipMfXiey46XJqnaobyRosTp%2FcjRHVBlrKuTAJJjG7wMvXSL3elI9Bv7GPCp0EW95jNnRUKPC4HLAEzfHRcRhU%2F%2BOiovCsiPdX%2FNWnJ5N9zBb9NsiU46UMMBCXHD7SZEE2Q90bXTgYbOEezwcb4EDOPlJXAB%2BbDuxlkRgLMtqDiV0Ki2fK0plmgqKsF4ra6i9Mf5a5GfmJIHEl8CJE84TQwe09Wp7voITskL6B1gvjjcs273V8rabwQRapaecErQCKz%2BfrDEqT3EbsqB%2Be4zslb%2B%2F6JWgwEiqJWmXUTcacF719ab4jUb1Zf%2FY0HCm91MAz2XhyFg0H6HvfcclQ%2FTdyN8SP8oj6GRdU6nDUXyUynSEHLNXtABiIF2I4HZOXDrkACFs4O87JWvC2k42rgdlFmhOnUTF3Z1BcYaYYsyGcBaLG5AP0yBURoph5iRPhA1vc6eXD9%2FyEmCAzAwj877QNIs%2BRNmQhgTEdmyT4bjShKyyn7C0Fnt2CqPOB%2F69XycYvWF94WBgp8VI7mRMDWuWxnvMhicCkLvkYtrdHkJVLj2xddoxa9m9zc1Q2rsLlUAkoOhuct26jzgYj8ciDHfPrzj%2Fmg88IAtSTG5E%2Bxs4ZmzIt9JlH12bZkS%2FmktOZsFTH19MaVD2QypiArrhTyjUQ7rC%2FUXSAUVQVvyLn3saUN9PQeLx%2BNnSXG1TIUjFlzBdqCOqCkwAMglDrIEOq2YFOLtVqBLlm3jlo0iQ15sdRarNfNIpXMf3iCWeAGM7RG8Oqpg3dd1fr6kSsXhg2IEJgGuCKJfa4yI6syjX3hmHPdm8fIlqe8OD%2FGjqAtINSa6Vv5Hq8BHThJ6g%2BITMtrGQrMxqAORIyq1hIS3UfiiMVWsqGUusiVyfMeOGRTdSgRQUsfiPnU%2BXDvw4seYoLPXEG3vuoQEywBgPkelIffhyaXgD8opyIP1NT7n82%2FUQnmAI8reRrhNDXoZOqUufWvCg8W8UxPnxMqBjJEVlcpJb3KyenUG%2FgTBCvg02BB0gMO6E48OjeTTmUTr%2FVIlYxAcBMOlIu0YiQsbseKxvvtO66Ozh9q4FGaoHtOOANXL9qR2OSdowVTvJk%2Fu44oKE95bPrCGNfNQ%2B87hkCzcZx6tUQKg55y9znGe1SdxM5wgdKOZ6q2N0SFHZ7u54B3N%2Bxi%2Bz2YezB34J9W8OHkhyX8ZRTs73LJb4kLnISe6agbwyuyDgt4TP5%2FLWbAu8%2B4xcc6fOHhmU1CbU6%2Fs4czIzmS2IS6ccONVHUdcQ8ICeOwy0wGHEbky2W7W3QypM%2BhIlQ2D3mm4Xl%2FSqs3JlQ5MHgAfMRkQqqQNgEANL%2Bc79a3DUaQOyvpn78ziMtm69pqpWWb8Z9vgk8auf3KKq9aEe4C2Ow47iwBCl78nsSRaGZ9NMaztYkPOTQ7aipU9cxM45H9oB6hAoOyPGoDOy54aRBfm5NYEQPEzf93cqEdr4QBCcq24rV5NMer6uEv9U%2FlNRVeHxQvbTCvyxtTWS4MuOp27QBjwMrwHdYXRTpDi9ku%2FeXeM45clXBPgoUelQpPw4%2BZ7hBsNAv44jU1FbIapj3m6SFWbBWitoPH%2FhspZzFekXvyUjYb6qEs0jXEDCGhvXEma%2BAolZ7OOIC5mfU3dDtGQaZdjT5EuEsco3nHBcZGxsK1GnAfgP%2FxU%2BWRQxSJrqq9zibomklEcQP%2BfXR%2FHgIeLfoU2j8KOHmDgwTuq2kMlGjDjShDIvTGUAlPDk%2F71BNOZonuBi%2BM1i%2Bk2q2%2FeqQQkbPe0VnJjMHYDCU3nFYWjYhbj3kQDFJiCBz5JN4sodDT5znj2gDcgaip%2B3sbQI94n8sTYNp3kD65ebdCgJ5tp2m%2FUyi0Pk9Z2Cd9XLzki%2BLTrOVliXB9lmU%2FXt1zx%2BrnRK%2BAmF4kf7VaQgkgPXdgocodZ62%2BPBSwnT92Bx74HxGni44tMNIghiAx7Z%2FNnRqFLo4eWt6FXeKhbIFBA7JaAuiceTbM7EagEuHRb0DNDiy2Hf5OaIfXwWpKwrYpbm85m4bxxvIWvGpRhdBFzhY8%2Fp%2B5uIMKobJoWXH6DWzKicXphyu%2BQQbPu69xU%2FVkVJwo6jOKfWrUnYMBXV0MFWC1x2X8Ez7bjt069AmbQDpMH380P%2BRiYfOoUM0GNOG4IGSvc8L4VHX7EsWPMSKfiYIPsXmTH%2BUNggj1ICRo89bUh60bk7MZntEja8Itu%2Bc2jku8e2E1Eqd0P503H8p4KGqz9DDB7d8SzNqMtCzCjfKQS%2BL%2Fq%2BAvziyaNYpmWpMaASV%2B4vlFap3fDJdA%2BLtqufCth4p6Pia55FTaTxcn2faTec%2B5emKo%2BC6bxFzUrctrmrBi33DH9eZJZpH1cgmmez2M4ja%2FWG1AK0K65wKTSJb%2BbgoFBjPVt5j0bjGhL8rNKkCiJhrgT%2BMu9Yu3EKkQUXeVdWO92XKsR1W36UIB%2FFPDD6ibdigMTi05imGmYU5hu0QWQkIQCcZmPS7sDpapXl1NQTcSqzhli1aFQY49i1Ba2IyoYqFfhuZB0we4wto2SOpIYxWV1bSjNKwsCWkEhF0%2FHo%2BQxnx9V5y1KbmVoBKGPgxlibZXnUSaweZCbSFxVFo7mHWJSkY2SR6M6G4wrt5Ml4XeN%2BLFD5bmbNSJlpNEuKDCmhwDBIggIzOd3cdOYtYvJO9EyYmHCQvXwvbwWbDbhDDSPEnPXcEeRwvS7PT6wlu%2BGvwDJJBtvxpdulm8RItoV8ut1FapqUiBBlYizSfhWM0wkPMovGf5T%2Bry3GZ7veTpYSAce8G8zfRy9VaAAYrPnKhSnZHuL%2BAPKX07XvbBXrMcFQZDYLiz%2F0ecjJ4MTGzG4H3wnOwcqggCP%2FBQUvm5iYWdGy0qWhgM6Udu52vEoqJBEyVeIYEyb6%2FtErcg4LNtUIB%2BhKYTALPArIaXhILTu2MkBzHHHxhJmRSPf2z0BqXhtTEw%2B5DGGiT8k1Lz4j2tLXdtk4NPzm5xbmje74sd1E6JuEOgYJXw1wZJFWluZRL4jW4cnj3LhgL1uGcE8hhLgVIxp%2B8XCl0%2BTR6GYyc%2B5AjEZA2LOcDCZZG7klDh1%2Bh%2Fo0cJDq8CFUIL0RsNtJ%2B8VpG6M5TaJ6vMklPRmlfuUZcesQFpDJH0wsB9Ju0FGJoiNufTJraHHFI13hRIKbpE%2F8X%2BO%2FWA4byUF47Pds%2F%2Fd%2FJ9bUz1sNHuKvTAGHYovgzjyB6jOpYFAJxvh7ZCDyUjcYSra8qtnPIqfmi1vpAmV4hya2%2BZ4UShZfwI2B5mp1Ljnt%2BoeMFDHH%2FI%2B4hmdHGDrCiPGLv8LDy6KezmLXE82McKkhRqRe7WgQb6lKR7rlqURwZVw2oMUXZoAgZnzAKM2RBuB25jBSyFoSkcCWk6U%2Bde3EJlY2C02esU8VDbqUP3GgcitlW7TNvWGFfwRu58HU%2BM2B0oPbyI7OiC%2BWAhNFY8KD2WJpdUTDFbcVQduoOcaWXXiLUOA4g4GvGdYh3FB9y43Agi5c4z71xhVAnK%2BcTDxEzK%2F4c7kucyKDvI6e7vV3rV0yVkB0EijlKs8FvXnT5gtNqie%2FdCBzmEwJ50g9umNTq%2B7nxKaeC8%2FjBRfPLTcx6LXwz%2BLqbpl0eIyI%2Bp5kjD%2B548yoaBG%2B8Qdy2TGXhdj5ZIZQZME4ACBCPlWOVNOYm480uve3rl7WtCF3TGIv2ON42%2FpTQdHNUh4tMM9trLLIm6AZ9iWKpzpAFjQAWMGSpEgPzoV1YmEUlRqBbhKSh0vjqBxId2DCgaAuHaWdFRUaRKZ%2Bb2Bbn7BXsS3BdJMlMiqqIb4WBl2lLPKU2myPk8qMlleyoq0COnGEMRMAGA8tomZgza%2BGXul7GHKgsU0rNOen5hkElbzmX7mHzNk5UfbsfuhFwron4hoHj%2Fbo6A7biI3DhGF130QK04BQdwPgihEtKSss9ADbnhgMTWJGWTewvm37%2FohMEIeadNOfgJ9NGXtUFjdF3P8%2Bd6MZJ3ajnYFyh%2FNHT6ZEqPx4lir%2FQy%2FaEnegZdasNKKGiti%2FTYExlOFt5%2BeUezUFSNBKGyN9Cs3MkJv4ljnLEU6E2LaiSDbbbujOtyTi4zoE4u9HiZedLUA1pFbTKJG8GKHNiKB%2BDUavlZA%2B%2Bj8Uce9q%2Bii6D%2Bxk6e7YSOZwHVBEp%2FqKTnIzb90Usy0sbHjqFdJ9p1%2FxmaGwgzFSspEOPxAUIBrWNPdjjJLNMeU6LWKzq4Yv%2FAhbingMFmNRLnPZKPmVEy2MNwTJ6HtOZmGzhMWBi35o5m6DJA5N8vPNCMvX6OGltzzrctaVav%2FZjGXZ%2BrJU5kan6h3h6W%2FqZaX7v0EU4bpijgVgm6SSiph6%2FVkYa%2FdKPuBnlTpBW5kvMXMT6vLzxCStZHn9HwjSmNyg0XskgKbq0SwWGPkcTNlRdnHqryZklkxZfsmehw7ngxEOWh5IWGd3K2cPc6nAbqJWTwkmpXt4UrB8Tl8q1md6dpZaTzrKjAJ6XPvMnWgyaYPcqJz%2FOWprHiWMzKmr3tobv2E9xMO7f7oqFyo8dU%2FsT3jJAlMrkCGDjrFFcceyTqNB%2BZNWsnbwodhkBIjT2BXDiHPCwknJ2BI8K0Kwnh%2BuUA%2FX1qBNbq6ShkhjHJOFkoIvJJwBLf2Tf6ZiZP5olNgz%2FyEfL0NckspWpL6J6pwnBog3ZcXQ9kWoEke0q4Gj69lxxe9K46GWRo5AoUntkfpqNjVyHRBixkvYMb1g2GeL%2BksZNL%2BRV3jRwJZhDCWYeNvDdclJHfk7G4Eb282ZYT5YWZDyNbmvGXSK0FcZADKokROJx6QnLeDjiUFhXBlsn7FSwEY%2FZYbx%2BFOtdmdiG%2FBj4%2Fic%2BH1mVlBLmSkDmjcfDyh%2FDgWvqc07mKaQHlh3ooPOqRFUMBYXRKqsWj6qoqQkxU3Ri93%2FPUPsGodte097%2FWTT5Hhmln%2FIzxVXIluHTAQ%2BUsY6ssJZMbQDdf1SZrD920KRQAEli1O7R9aj8NEencItcy8bQkfnZYSc7awm4fVfLbWkmESHvlBQIGSdTFUym5t8Fp836AalEzVa2Bh4cKepDZY5zcjRYj5t85DUmwhdxm0Jq8HX5R4eeGriggw1QQSNWmCGrW0yeWSa6t5iFbmB3tNsUZAeiSY5tGIWvSuDqhnE2Rlaj36PehhE%2Fa3BQOITqginIJ%2FLii9o1WSKPzCSMpKrzaJgaytFWqC86oASATC0W6mIiu%2FdlisheydtkGCWsImzQmKml%2FIdlqmv4hmU7rvif%2FG2sW6Rql5K6Ws5qjkvNqhoJLue8kvwQZpJdxyjyr3LR0ZC7GAmyT4iz2UpB65fJ3yLaEJBO3BCUDbEJYhweVj7kfhTFmr3noLJxg%2BSflzh4dUM0GtW0SCc5Emwo0wFAzrq04UbUPXFgMD6z5ygdPORKsv7iYPvqdw15jCTq38yR00AwRuyTKaHgHhjx37UjJLXsZ5kREvlZj2gFit2DUgIeOsWms%2FC1wP7Y506SSk5NW2QJkMYH2qmQs%2FRk9I67ci5ZE9s9IzJQCNzeVB2%2BE2x9txPYBctX3Sh3ogm1qX4%2Bmofp%2BS31KMN1CcT66%2Fyq5sflRLv1%2BswvxBslBvcn%2FDrWgnZHoUiTAPLUxR%2FmvCFUaHDJ3zfVPiaShIbpcuzRy5H%2FIomIG88X3ea6ByfxIQkRo%2FC7x0TuTtNIe4K1%2BKsoY3e%2FuIn8q1SV6dAlUPj5lzrUSTIxXKOA0LFOSuCtFaED0VVHmHo%2FAkTkmgKDLUYabdeSR3ahn%2Fz1oixyM9Rst2Lih7%2F7S5tZLBxLoC6nW7kpTApqqYnY5heEDvLIwnBcjYUzKW5WQR%2F1LT07dFmDOj6M80fC58HZwIa1Xzsh%2BQvhJArGytw1aq0mknwITjoMjsqayCnvFY8tDSjQki5VniD8a0RMB2SMTZ5htncnIryDLPLxfe%2F8V3mZcx7ZYl%2BTQt88za3w%2BA4areQTvtR9M1YtCf6icedTYDnMDVKUhBicEfUDzmWPV9YBAeMYEmEKGmu1Q%2F7Sr7j8h%2BiLteciZEk%2FnYAxkdBntaw7NZbiCtZU9Q8Axm6kGdM%2BG%2BvCYBIgCsjKXf73JBHQCMOjXBHdyLEOFaw0f4EawgYQ4H8yg2Ne593A4UI4e55DsuYcuNkR5G%2Bd4ey3bD4EP4M7CdHRhECzny%2FWNmgoSHNK9ud735zd8mWJ9hPgF4QzxKEmQRQNuFipAGlnZMnvYvy70kCkRFNEaXI%2FJsDHeSnroLoGtSxkqovhLzS%2BrIvIqlNev%2Bl267UeCWZj0PxpySH%2BNIZhHCljQQRSh5YZqYBMhilawHl5rL0e8vKYLQIDzQCjPihqBc48tABDZbRSTyfcSpzAOCSyYeHQNsun0L0SnZRLAqRgQzmYdlZP2bcvazUUZ5kbU32bYwMCYesA5r8L0kfX9dULBXAAEeXyBsgYkrOYNiyrKQ%2FiQ0OmcYUwsjTZo73SIBhDIi8MAebHtlIVlFjV%2FYZZgj1EngtetE5cI2IU%2BV7f%2FKpolUjA4CfpvCLQiDYJrSsihVCW1u%2BJ21iNKaGOIpLriMSCMbgOZJilKQlI0C5stsfEw1bV5ZjSHI8V1z5LuhtXGzb%2FMKU6W3AcDiYwIHmM4T0nCEXucuZ%2FPs9FDMw3PB0EyrBggZWDQOGn5iGh44awo3SrnHCEirK1CzzzLDrgQeU95a%2FIBrYSesrpKnyvTHawY1EFZkybMpAmknXOt83k75iFiLTr4IqI9YBk1uZapB6Po5iYmWGkDrjGWM86lLLqTFdZAFtpQgzgvsu6VR4LvhG%2FEid2JXs1B4naI8CgcpEp3DQdQtRok44hrgT%2BY9ctlydLwYEKG3ZN7fAd03NCCJ7UjLqyb5ViZOJG%2FloRoZv55KXXVXN%2BrafEMPqn1uMsGKmnNLM3xogX0VWjOXRf8Ss2G23pvi74%2FTCXQusgesjef5TZ5WpOn1mxC7TLwa4F8J8xrj2oU1UIW7P1QIlAyNq2fb7VrQGgUuoZ8i5SGgFY4vww%2Fk0pTKfgVWPcQuha9kTYUShuiU5PgmM7KisaZQBZH4tgbn05YAbRkGp%2FCtp9%2BkzR4CFZcvZ8PFbZPVKCuLj6IrZWImw5GpQSzMGyCW6iaiwpRHv5Dez6L3YZCK5SRO5NHRRQcyuc0QRcHvaAwr%2BWKJOvPoUvxRit%2B51h06vVoNeLQBtkAcDeNaYzZr8QDov87GnSJbHkFPe9dciKsarDHRQnRPYVqdPU6VleA7auCxqQSXw43wv2Ztex7P0Ch91KLXF4v%2Fc9CpChE4puaIj0TckZY4yJfH5kro3Wk4NXpeGhiqsU5yaK6N7YN5ZK1eeU%2Bcj49CW0B3hmzu1GGTQJZkTP0tYgCN9pLofCmVYb5T9mS8mZIGyFo1bTIWYmbu1WRU5zQxKD%2B5k05BSxaGDnk3td0nN6JK%2B%2F%2F8dQUdhGbNS4CM%2F5qiuzrRId6fIGlMGhCauhYwNqdStBVg3BgwT0PcHc6%2BFuo1YBbUMh5auHj6%2Bh%2B%2FHBzf6pGdyWAB2U96S%2B74RWCL%2B%2B16y%2FSQfU9AeKqKVWPyjIXLDZVrZP%2B4YDTA2ga4SM70F5BrT3fR9DT%2Fsy0ZR19Sb%2FgmCNukVv1OFoXz2%2B3jIWTOxq7i51VQiKMDuhOQsAudR9%2FPjMJKMS0vVKI0GI4gsHXFFhw3nTd13IClQ1PPCsEuMWqqHK4erCtn1CMydKF8xF7ss%2FEiYkO6AYMCRWn7hJmUf3Btj%2B7gxTZiim0GwXj4jTreAEZNZMCq%2BGAIz7D%2BiKk8qIahincsjqcqxMLYdnh5Ebam0X%2B7n6O4U%2BuZeke0hRz%2FDF1DpYRQ6dt6D8gy%2FROSOwOAjhBSK%2FyqoItMldCHox2oVd7cfMZT0X0Y9hCA1qk2uZb5yJ3gliA3k2%2F0r9bQ8ap68LbirobFtEQGEgSjm4R0FUW2h4tMIErNhxazL0t%2BKCId%2B4egZCozTjEMAUvyb1YbDNPjnB9Zw9hA8%2BVzCl7Vv7D3QHWHCwEtJZGbe43TVJESEgq3XFmjqt%2B1SzWpIo1oRaJLJyMnJdTBSqvD8iXBduRWlxsP5%2BZ%2Bs8z8JB%2BT%2FOA1VWpeXhhW%2Bh%2FFGhV6OWdEC%2FDbUv8XOFo2L1fq7lAtFptJE0hrLqFihTs4uNh4825XBzdFCib3oHG6S%2FqmTUWK%2BPGQt5UEOrdCxkLIRxReVvXK8SzG60jNOatM1Sid1ejQaxAjgTmDXUjhu2hN6bQeLsZ00sgOpn4TwNGOxaWLIt1nYZn1oNpkTznbTBGtcA8%2Fh1via3ssZCXfcHX%2F4mD4%2FgpXXk%2B3Fdd1ExcqGMLKxTRGggKJLbDnEPZwOnHjIXNK5DDSAdfNGXz4rcBf2oXhimPPQeuoRaP%2F1P%2F5n%2B4O7b%2FaU4MuE%2FVAwNbqyx7U6cSPE5TaG6RCzX4UgUCcb0%2FnvVQ6QDE1xM%2BdqE42CYYbJT%2FsvI6L0DyJg%2B97MpkhR49jwDe3%2BhajhuPtB1Bx%2FBtTlhp5cCEb3%2B0eD1pFFD5FQHMSNU9MdWJzCk3%2BLLTy1P0hS1K38WzbSLGUx18WvCE6IgUSpZBoMeiAx%2BEIuzjD%2BLZhb%2FAAY0uIvJIiHgRWOC0Sr8Y9WxLjJqIk%2FHYITTiyw8vFjITNQMI2O1F8aBxICUCGv%2FiNFnXwswOve62%2BILxMcr3FW%2FqOuUhNRJ8PP9Z9fbr7fxfmPwgO47%2FvkUErHopWIELmoKExUodXjm4gZhDHTW9b1%2FCWQ4%2B0NFb4C%2FkzF0L4JPZSZxso5QpMARZUysdPNpG8Q7%2FkqZD4zr9NgOjPTgHaSHOUbis2FyzZ%2ByOB28KeeuUvde7trU2h8LB5rHQoBlmef5%2BUtTkWLlhYT6crCmREp2BJGACeuLi0THE99Wx1EIfCiUhji8E03DXEIs81XLimlT6yLJR4jH4X4FaZr9vxHweabbUXTw3aCB3xaHRC0L7nnJL46I2YBxQhxlzqTKHZQrp4bYkr%2Bfgz%2Br%2F%2Fj%2F%2F1npAUOIpQ11CVMrHKLwgvIKIdXpVV1ENUX4OJ3VmgbXSWDYjpBnw003283N4YEi54ys1f089dLSogJERaYiYIsZoMPzYEsDug3qDSCpv8aNjlkSebNQYlpBBTxXDWeglghm3ztoho6zVAjxqcR2tmpjG%2BoD3nq6e0tEmxeEeMjTHd6MsBrwKMaCSa%2F4LCkIzNLi4sdrDajJlqDp2coD3NzPU%2B8kYn36XTPNP0MalM4NpkKQndwBBPmYf0y1OlOFkbFy083FkoyVjREYGtj1I77IaHQHCbMTecJxncllS2Y%2Bwd7D8jVrBAvgTlDI64k6Py7kCW%2FxiGzukgN4dVV9o8oD2Oh9HN6j78jFlIUjt%2Br8y5hnBFhx5FMx0LaxVPUNBpVdCjQSDKRkQyo7z%2F1I6Vwr%2F3Iq7oPC9ramftolgx9MUoe3ZhsPLqLtsuv9JYRfDj7y9lMnDdHf%2BDSch%2FTG4bbChQYw7eE1nYBg8KFKV39ralFFvcwaU6zsmJh2sN0ZnyQpCPge4RNqgU9dQAiMXSNhm%2Fujc3ydLeMQhZxVY8YbyBsC%2FK6p32W1IpkZgUV%2BzkMlCNUljydMWy9yHCJDXlZyy3fzcmNBOH3FR2WzaW7MhxAS%2BxaqEFYmhrsfcr51Z3Hces6psmUtGnmrvr0kQninPbIO9kHzCz%2FEAkzYwTNONL1AqSAz%2Fvl625JooXzSnaGOOKyfPIOHCp%2FarlgLYiQZQ8LVX5muosTmu9KokY4OQ1Gmmaw8%2B3Btv13bcjpF7iCnqqjzJjy6IYHfwvjCD6LzQnsxxykv0HO4Gsg6iaFBU8Ym6WrxKEfN9yTMXpP9M%2FUahxeBJuuyFhrTyAKEd1eok2jLWYGDCSV7Oo0jC4eOlgXnd%2BwwmSx7mG7vVYhKQBlJsvEmDBYRhI%2BA2KeGFgOg439Ryxe%2BLuPKvNY6XK5Sr4igT4E2ohn8BhJZRalGbyzrsHwUPE5YKai%2F5qrQ5jHziUssiIU4Nj85lMQM4YuWkiIhTjRBYuQRrPBJGPll02gEtlTvF7nDQkJpSiPO7zE9STng1DyF%2FWI3VLU5uquGzNmAjNiRIy%2BlDUacEo%2BnSKHWGvz7iPT56cRiggbnKIn4gvN9DtaSMDl%2BqKybcabunA0tpKPoMjdjIZ5N%2F0IGY4YCD%2FZqQdfEIEAgjNzy3L0yqhvott%2FCYStKevXdiJJA1HIDdtLcDhM7thkW%2FREBiPjgYJ9zkh0mNcaf7lUlgIExqzkjeOu2AN8VE6RYuMVOY5dyet2IxhU2d6MK5ejz1OaFwKjL6tLOtDef223%2Buqlr0yL%2FJI2wgj1e3zz5uKv08rxXRYIcfOnRODOqGV7siSKd4S3FM7zSOMFrz9KeWHbmRrCYNDvTx23KejnpmDQPJikcU%2F3EQEcz7PNvh5byZhBo0CTICtzmFadT2fAk1gYornIG%2FRRjeRPaK2eMscZoLT%2B0MJUhEgHCsgicwL6TLKDIHWfHBhM3fIO0RRnB7UFJiafqa%2BcXBmU4yrTr2uJFgR1wQCn1%2Fp3TmfJ3w0Kadw5b2%2BmSD%2FTb6G0rV9NGQlsmPhw3%2FunbrkAFCAoiIIYgp%2BSiSycjz%2FbQ4Y8cvdQN1X27hwRUMPZE%2Fct626NaeC9Qqc3IxuI2mMnSBX1IpwDGsgRwXxiuyCXmNu7fwQKc5aanLKdULLvP0JeOjCIPpXRhaqK10HhcA9yv0inrb%2BYzdqbPG9nBYXmVRIC9pE%2BBUC%2B57KU4bBQY9JJYwgN4Guxx3Ez0qvEaQdNkaKrDnB0MAg1KAuQscXgFmsGJqT5lrOIsvcwm%2Bviv7PTR4HeFHnDd2mZRP5i5%2Bq0waNIkgy8oPdzkuXCmmg3eqG1XFnNrJo3dSOSTL60U%2FtGs7s2O8%2Bem%2F%2FX5xkRN5VAvnCXwHjvN4SDUbacYVJpdzxTaUnU%2BFhj0WmPXHATR1v8xpWnNaLd79KyKgFEVOtFfoA3TPSvMk1OPfzRgkHQi3m3dup4wHiWYTkkw0w5NRjIo4993nZ%2BeFBBrqROmQfySOX8sXF1hr069UeMbH2YN5niFo%2B8ywnzeAXBlD0sHaEiPgzY%2FFlHl5jmB8vtnDdVgGxlwvX61unMp9%2BCnPNYRghUIYEVIDtE0OktRSOLXc0x%2BkyFidnjnEGjBFgorw7uAvgckVjhspepu5GiWfw%2Fzomv0BWs29EkI3RxAd7NQohfBKHbX4az5IXyFTNg0AU%2BI7PAuZYQfdCe3oXLGG4II8hH8jNkzc2YlNcj84vwl2CoxGx88%2F42M4srDeol2NAcKMqpIWCOHiuWiYzulhEHoIJ7qbTRhrMHX%2BxM41rYZi88EWiYVvNIdWG2d1WB505O4g78BahPhd9CLco98j42WS1kqVO6DKlEzPf%2BlW4fB1io%2BENwNY1iFmnD5571P3Pkr7ggH5KoF68A3AFAXmjtAOLmPN%2BJvEDi71SN0qXLkQGuhPHb%2FxhIKEwgFMc9Bq4HlxEnD6iAFucy69huB4oGLMqZuEAt2IA4xdUzDWpg4TkyZEtXEbgYs0%2BfKMyxEAH%2BfCL6sdA%2B4u1rkBpr9kceNyenmQEBJ2QdzHRmI6BI%2BFVnusnsYzDB9uZYLhXNh2nldXF6H18Jarvo17nXUSM3x0B5EM6ITGKF3pMM04naicIDKFjPF5qrFmnxwfabhRA53TNk7Cx%2Ba5sHfaKFwnXPX5QEen60XyDNeq5VDcl8xJFQYqQVHTGz2H7carNS6Mn2JEUCg15m%2BhwWzOCw%2FoRFbcMdDwbGGV0GEZzussAq5mTHoB6Gbxz5N4H2hrdG3JF6y51jnE6r0Myz8jCigeZqPex3RwrbKeYoffkB4dknLoxUaK4jwXQVRIcEl38Pa3LAH03F00THsiVadcZM%2BPEzSrZkva9WQ2bWKx2oFCos%2BBDfoQ%2BO4o6tmtmbI7J0C1KAOskQuyeDVlXFZnvLTD3bawQsHdUte42f0rTJaaZv7qol8zkY0YDywBNLvCqpeJA9vAFh3PiUvu%2FT0BUJsi5rhxzzZQpveQChHSi4my6539o90k4iRCMochRTktBITi%2BZRPVFshbjoeQoSEMRgjRKanzGO%2Fn1%2FN4St%2BcJw4ZeJveJLKffUdiBNmakRTEMwiftWcNpgjebEIBCJ%2FPQKyFUEZdP1lYN4P%2BOui8HASLgj9fmyKKfsBiuMA2DyZIV%2FMv%2B%2Fbr36fXsF4A%2Bpn6NbbTSRTgrb%2B3zWHCfx5Lq5x9EEQLE9vIhr2dmOb7SHU8RtHvZh1aIQBWChWiqt9hVXWxSJV1blioYfcAcZAIGsy9T5r7jI%2FpDU43oD%2BkbtYnnnISTnKBlMPChu8UhMo3feHI59Hit6Mwn5XTGhNDOVz%2FKo2Y1AHGkcjb9kO2tSF9edX19ZMUCmT0VTSYJ%2BQ%2FNoGv3NSkQKUPCB3BQsxYmMuLV29lnWKeTmx%2FXCJ4LwsqvxOdwQ4KFwoZOuGFvUQeCBd%2BBfkNxl%2BKI7juKphaRS27IUfW%2FIiBmhUJB75Kne1rOtKCnbr11TLxzIWbDyLmE%2BwkpcQI74Lga%2BgamN74vfrUlVHwRLJlotaHEAasYEpc03F8iz6cuUPyiTjUsQA%2Bok9aiIx%2BmFXPjuyGeFffEcYV5ZJ50NBJQxtWA3QuCSVzkw6wwSpGvuEnZKy0nWm4A7ikenFjE2C7wVwHUDi1L1%2F2wBEs6D%2BUCh7loBjMA6Bizc68IzBdRl3ci%2Fb4Z0Uhs037ACc5WvYvZSOeg%2BAYMst8NEeMZpZ9OCBmohgqcRS4jiAEmOYsDmGvTve2U8L5Tw0m%2BwguIAO5KjK9A2GMDggf6ZmfRGeUgrf1%2BnUbLlCtw9i9cTHTn%2BV7TbCKy3nBDd3iSg8rqFU2AeRxnhHXgMVtsMZLnuOO34ae6T5H1vWO7eTInBFLKx0KG1UrYp4mNgwZmemh2KbGoFRFhghabeX1tU0GS%2F50uQ2Yrr9uFn4jKK6Mr5eEvTkcp%2F4Z64R54ociAoPDBWRRrzTTiIbZ7pBOvdOd1pOhM6LaNTYC6mQ2iyqbhPRFsic4TmwQsZljS%2FjZIBGFD8gOEGAk0zfDoyYBqvr7JPJG5HC9Q3CcYLh591IZ857e31cExGIM4nex36npommSZ9ZwAwngG%2F0Mv1%2FKNZgwOwYpJ84wCCXU8Iwu99zlqNmSdq5WhlKHMHt%2FmN3KylXD4ObKv%2FAac2iSpZ6MS1j61W2rZl8mOhiIt6NHVdi%2BDzDi5RhIih7i175giYscBOx0Y3Ax5%2FW%2FK02gKAJ2uoUDFgoaMJYxbRGB85UumzqEBR5G3YViu0hN2VTFiQe4q%2Be%2F3YjINZMjKajsw1TAL2H7TGM7kFplzbyi391b051Aor4auJ0ktDG%2BuAwjqo1RdsFFYWixO0aJ4vXm2mq18rZ%2Bwh7fYQtMNxrRhsuVohqwimYpcdfpm5jWs9r9TN75tBq%2FSRhwZ5JQO7o7LKCreVjkUbPd9LxlWn5MoEdRpr3r9r1gKlerX%2F7GiYTlOHIPDhmaOI5cHM%2BTUbAgexMy07pstGzBJDfFAZiLUjY6AyhdBA2up%2BLLBlfLskeSThIOGNBG5J14Oq2EmHl%2FtihZmVj4dU9x3KogCOHuyl%2BbsJMQQIH9i8dTENmDhqajCvYvPp1sz5VQI96yu7SUcL7bsqmlVAlNXJfyk00cD%2FXtSDQraSUH%2Fo1AqIzW%2F%2F6gZq8PBEzuP44vBp3AiSuLfmdvMaNZFT%2BUf0CsTEapXu620E2B%2F0U6iAyjdNqCLjawm0Ts7e4yTBwETme8jKkS2R6JhTEqodEK0MRvs90iNIOxRlzrbR%2B7G3AJVJMGSAYuNWT%2BTo8UKmulHwCBc%2B1h3KIgLUKKQbldcu0Bka8XJy8wDPGmBV1%2F2UVytLFMy3ha%2BPItCSuCbszUG%2B4zDoAifNmqvtzzJDq9kLEy%2F58JykeyfftMBs7lVWMVDYUjR4AaqS3%2FBAibKLMlnBnLhXYsxOh87ezne3FaWrqm7%2F%2Ft0gMjlAIcHkGqGQcSKWpXDYThhuOhD%2Bv%2FvX5zTUSY8R6xAaAXS7Q7fi5PRHbG%2FcVBroCU5MPxXhn4yZJEdvbMU5EzWS3GVmkV1saJYReuIHD3eNbKsFWtxPuRs3eiRpqeuSbzMowSVjvo8vX4Wu7jlr3gHJ%2B4x6DkrfOx5kcXOFO%2Fz%2F8fWuy3bkmzZVf%2FCc3IswsNvwa9g9YBkAoRkCJCA3ydaG93nzlNFWVmZlCfP3mvNGeE%2BLr23%2Fr5Gat1pWAwwckAhWbeQq0TzEMuAHPWE6TSF0kaL77%2BF5j3IJkHUbFYJhzNotunichrrLOHlQjJ3ASxyIkHlI33f%2BmbXUSZ4B6RscQjNeesIpIXAUk%2Bc3cH76hxzaLoyhOdfWVrT8YDNWtVTj7OW%2Bj429z51eE%2B3zpx4zcDLw2LiF%2F8ONJRfNZ3h1xYFwZp0JdLzMSOPbWNLJJOisOefQVnEozeljAyw%2BhXRDrKJd3mBxgOu7LpwyhQaiWekiiBwAlZQVCrOAVmrN9I2kh2HcwYGOEfCLGw7vplyVrGgjAOTr54t%2BhSgVX0XuwNGxOg7d5SHDzP%2BtzLPZuCrxoaJI4Ykn%2B4Qb8h9l%2FGUTq7FqAvi8hFGR19VpwfdLBIp4uYwwf2J4yTjGvnNrKOCKdw2kx4A1c9ZWej8IRr8B9Cb%2BBsklPRTksPQJwPZ3Jnvk8u3jZaHQOFteEQQxngtJQfgAc7EnrcIMTEDp%2BOoR1JoPictCsMDbSvf34G7AgN4e4L%2Bk3KJcvaoey6R5qg1kAXW%2BJnfRjbT9zEQh1sV6ryDWjAJI9fUMmcGla4Sv%2BpZVGZIvrxqmyFx2GX2IEw4x6scLP7dLWnr2JhQhvIP0SqdFF1iT2kmiCLYSTBAS6FmC2R0a0fudhkwx7KLYuzszecSgC7Tb58lHmFGPIVrHigfy2jCo29puysiB4cZD0kmvRypTCLc4xrGNTPJINkAWQpL1XkSfrCAuwPUC%2FGd3DnMpwothPmMYTOiQ8AzCKFr%2BoOPxoZTFkkeYRNPfiOGM1TeXOVV1JjxiXoKnvTdSoiGIRYZwo2VsFdeN3NOacYLIOZJIhI9bNWySDQ5YkZNLpcz5zCEoAqIUBMAETkO9Bd4CVRpbAkzDrNsZopDBV%2FxD9x2%2B1blPU5emetPCecPR%2FisiTVq9S0nlWlrUNtIopAEUA3s7Bf5K9HA9XY8SciHHIEDcf%2FJjd6yDiIZOb4iwls5ib6XDlLirgd9akEAskwdWdQU4F%2BvTlryflO0r%2BZOY3Io7FFSl2UUI%2F7p7z%2F07bbPhl5DqOzZSyAtg%2BRFHTHjSMBbwlXzXXaPa%2F5EJ6IdRaQ6HZFZ%2FBbnByYou9Q6Kvs2EKmBWM4wpOkSxT%2FE9Vktt86hSQsGlOzK0o4LkU2u2Qu2rSBiAbcrbY8bAHHLI%2FCYwUALs0gtO9hcwbqZz7sd1v7x1JX%2FmHF%2FO9sdyVCRhAMfhr30dW5iijZUCBum6%2Fu7bkjh%2FP4hIuDTNyFe1Dn%2FTONd6n42aJwyluv9%2FsG5USkBCXSNWO4zVEsMMzjmVzbqpI%2BylNzDpMaDUGR6gQrb1aq93aXAFI0Ar9KdTRfgLHMyMQU8GSA4tGCqwz3YgiLhfKf5uZWzzJwBw%2FMHB5roEL9tLAzPazToTkUOnlB2OvuyYjp2xBK8HkyXw26GPYK04UFSNyLcwfzOicL4v73ZN0%2FTKL8DTkBHfhYkHw58qCNKMYTcyDCCm3fQq0FvgMNMeOpphhuFslUSQTlVZTOihf2tWOL%2Bw3uHSqBpBapVqHuoDQgJ5F0af%2B54M%2BFuA4iGB9zQEPLda4MRRD9xUd2vi48kzDZsNvTq71WD6BSskLVNDsGX9MYmyfP3wNkQpF4F61B0wZCb%2FdTKxU0AzGMRBNW7LFocHAzImdtCDM%2BWh9uacTRrmgPcRLWi3pbgvVYFBtXbW44xiCDzbGXJziIKjT1%2FnmlTYsptxILgJH4PtsRY%2F4QTV8cFN%2BgVp3Oya25JNEyruGqzrrtNE9pqK8fuSUWHDFBhEwymI0ufwj759JAelNDPOCWWXoBfw%2Fom8RvPvaiy6ww8cDoxW1xd7XRGUVObLtWnSeelIFZICFmN0mcemhu4WmNVkK%2FF3HqJWG9i1H4fHNdqeRTfCqNqoINo9%2BJScF0xKmke3clZTb2Xj8T3wYr8%2BfVh96Moqm6ofsJ7664jC6ycpEQ1cE1DNXurmEXQzP%2Bn6vUn0yC9Ha4Y97UTU45lHjMZ69V9olfhTGOSkgKYOHN0QPiLgfavgzh1T8yHfpWR8J7u6SX%2B39%2F5Gf5n02h2lRD8jb3p8mdbrH5iJeZK98qFP08vF%2FiUyNWvqKD5POWg7geqKqXidW7fFZRcFVh5wFCgIOBlu3oVTA8pmPkhxkyd%2BbX1K9uPsY9UmzOA2wcM5b4PDqZ5xZKVbXiTx9wt9q7VELmItFwX6E1NnR4jnzYDQ84hIBmZqrCpxoBKPX3iXunLqRKoM4xRqgriK6q%2F9xBMJsugOIGHiWroqpiNF550IAho8NCeM%2FJgK8goDbHuVeNBuCboCydhE5H5Iu5hUoYmSMitXyr7H%2FQUU7Bl1S5cMm6HUFHGNIHYAIsPn%2BU%2BSWM4RPhGqATgE9XXTyapR7FU82J0lg5tOK3eVbER%2BwNjnDpUy3eUthLwtKfPCDUEa6G%2FQNtlYkMNg8lNNJ8TC8n6hcEJG9hI%2BU5UDR4HxiCXf2jxzBjpCcAQJ1N3FYQDNaskMl81F%2B%2FlsqTam%2BF0PPqoJobQWpjnpb4M98NB%2BaBvrzcE2S%2F%2F23RpZqIwjKhh6O8NU0TPmwG1Y5znjZGIJTZ4s%2B9%2F8AcVoQQMgQvQbUizZc1SnMbb2Ms2swvyIhlVTE%2F5Ae9HgQuKwJrSIyHXA%2F63KB0TDS6TP0%2Bng758ybxkg9ZzeuNFK7TMWmFcowC7VFoB5CmbPO8PgUloW4hK20clWgoYBIXz7PenaR2X7WDWNjcTWmA6iwO2UI6NVQoBt%2FdTHUfFKWK2pCtno71OoInRZmxiWWJmHwrH5bZfuE7sHaONVyOKKaEHEbzo0kdTCpnvGRuLJDGCu%2FpZvLMigZftMrImZrdwX6lD%2FDE1MZtCDnmjhskKUda%2BvCHf1fT9RzvYGcDKGvYW1PXC5xqS%2BJqvcTKZGfj%2BM%2B2IkTjCLWUEZ25EdgZAed4EoE6FUxHBSRtx%2FcmpWiTWLidb8PTjOt0Aa1%2BhHZwjf9VEV%2B3%2FrWyle0aZPfp9x9YzfMmxUC6p4OdbqI9Ryz5iAjl%2BOaQ2sSPdbICnZOzMloflRGDdvPZdkx5neHjKDBdKEs4QuEcXz5zHhf9EIfZGVsVTXPKKtIXfh63zrMkq%2FpqymPYh%2FS3DKrTm1ugHV820emXrUBNG0ZfI1h4qg7qpGSFx4Cgi2MeP4yIOn0kFuG0jNxlhWn%2BGRU5MLXIYNoO78MMYpV7jkFcN3kEy6lSFr2MeXGLVcHMhWmbbcfJ4iZZSHoZK%2Fz4ftTHbjT%2FmPkFWbkf5rLnHolGgWHCaymu1SrpVmRu9V5V1sEKPw53F8xDz0tMf92B%2Fs1N00w7Qj0j3r%2BqHo5N4NWJJfqSYLlIR2hfSqlBz2GNTjKHU2qUGb1Q9pPu5NLtyyQPDJN7euJTDG8QmJHwCZt%2F3RxcBEV0pb8a6fq4DJpW3LkzCP%2FoxboEMZLxMNOc4rE8qVmrbx7DA5G8tZf78gkvqQ6hETbFBR%2B0xkmbpvAbQFWVASh7t6OwjOOoqG%2Beu5Rp7c45jZ29ofNm7gkG7j7SHDCnm%2BUj8xgxPjYufBR0UjsL%2BYXbXzEX9vIt9IG7QJ4oU7lY9KnUMNx7RZ%2FcZirOeQPjQxbDXSIt8kPdvozzLtO66Sk5JtDlI6lnIGFbGHPMs3c2iVw36Zg88mYMw48KlfSZ07E6GeUnfmdcqq8kkPOUn37MHgyXnzq1HGXfoVe6PCg7aYvG4GHbVjl0%2FoQ0LrV38kuNSj%2F03kyFTVoEPbgpS8iLwL2B4cbSjSsLCoqT3CczxMtp0FZicIJkSE18qZyklDjDV4%2B7xkxxH7vhVJ8sPCYj5k9BTziq7KvxG7cgP4LKrFukzik6nB2LqafWyNYA8O1ClUAoliAXJAaZgjv8eXasztEeSAM9oaRP%2FDWi%2F2OC79JvDgzdqpgHkAmYWOrA%2F8rMptKO%2BQRVbneUOsKd23DsP5fJGf2JYwhG794mQEQcvodxxZNAJiBd9XY%2B2sE0XwEI6Jj00ucz4VehaBYAGMflYLXErcGuHGI5yn3JaxVqNPihdkHjgc00eTiH8bKxQIu4zbEJe%2FlVdo5VQc%2F5jFS0I%2BxCIIhMpSu64t5uezhl41WWPLkEbP%2B1QtIXC%2FAS8QM%2Br1pqY%2Ba0ohs%2FozAVoC3idMLdpxq8ZnZMYGGjQ8CJtxM3aOMwZDWaByANfiYrgl44zndtFLjE70xNNA7CdQE2yJK52zkkmvN%2BX8X3tv%2FU4wGlsq%2FyU1yGOCxTxmGR1kCf9tr%2Bljsss8fvOH2mZNNQoRUZ2YYzinABQk6W95pJD8bpJ0GzHP%2FA9xEowm2O9CABQYw1AKx1HWObYdNNiJBojyNqw8668GodZC5Smazskn70XvD5R76aBczlRe2aj8BrICYV1ZeSJtLRXGjQW4rylRLc6TvmOl4o%2Fha1M%2FDsTvZx%2FTcMTyawPEIb3dE5bFaMQ8WOcwffLLYIcG0y9JkWj2nhDEGfljaLypP5hCJ1MAMCmyloRYh2PslwEc4s4DEq8xc%2FXT2dfnH66I7HJ1F077iEIPRRk%2FGEj93aHhwAfZhCOfdo1hhI8ZBxUq9o1wpQ1efLjPQEXi9DADjNMEwrpwAnyEJo7QoMj1eDyP7mPABaPlRu4%2FePx3MatIjIxF%2BKnbH3EATJdAG6ZrvsyN4dIrPs6ZnHEVKwfvkOXj6TuhvU8hYMaPJPtXEwd%2FKyJOkXQhJJDAXITv40U4K8ISRVm4BJ1EJD9yWPkLyLlNX5TSObDgqbpsw4qAYQQNzjV9lMzdLlDMgOuGLH8lqwjIa38ZNBaCrvtb3bZvZQiZBDgkqygd35vrIIoJSqzArYqr7UqsKfyBxleqOKcc1USya0R2YJ20TbFZLbIN77OmLtmpGjaEOYgvGv7d9E1eafGKkcBrbGB3oCSdp4gezZf4j1MjU7ULYXrZYT4PIBRrvZ2l89hHe6%2FYctGB3C1KPUvBxTdAy6aECdZiVr%2FkBRdAyS%2BevxerE0ZJNTtvmtZwI4Gz9RKH4DiHbZ6rxe1wSziMWZAXJGSzUjQSqDfWV4xCFsOK2V6lrMCYhpfO9XViYyh%2Bzf%2FlK0Ze5ZA6CiFsZjFdlDYThaIbPfIyG2ZHsCdolhkyFKQaOfVry4tmP55X0iGZQ%2F0%2FYcIWzwhuCK39lG2LHXhyomSsL%2FwPJ2kIDURDhqxuhbir4sGYC8K%2BnO8UawxYWJiQrvTjz3KXZxGaOq0UzgT88L7Dur%2BjuuRuA9gNhmQQ4pjFkxWEZVa%2FZTcb6tYERVl8hMuUIdtPtSevQvFGucym8z35%2BDz6GEL8XXiMcetURo0dsHnpJ7LVBb4HutQ9hAhkTXLQr637EncH%2FHZFfUexkdtQxF77N%2BECQULnxsEgHEqUkzIgmC%2Bk7Q%2BGvIIuVwkc1%2FtxFBg%2Flwnmr1WdPt2VD%2Fnge5pxaFEkP5xjbMsNDiFoKqKYiHh%2FeXYRbbEtC62D%2FFo9J%2BudI63HSgcE0XauOpIJCSrpgXBn4EkuneqDYQngemwS3KVMVgaZfUN%2F07WMVLNESz697DftKVrV4FRXiTcGuiLcXxHxtKB1cO70jF7Up8kLmMSu94daRSuh6UA4hkpcZt1JFI%2FVylV65v8jD3wdWqZ%2BZY4U9jXqDJKx8%2BCE50eUOZRiD5kXEioWdbuH6plsTtB7FLzndrDoUj%2FY6zKtp%2BtKeLgTZDDc5LGKUjMwsHzVj8islBDK36U%2Fu93dROL3Pv7W%2BoWMf5XXQV9QAwkWDS3U0Z2sXV2F7zMGL2%2Bz5pRLjlKCmZkkdnhXpTkCguxnsRtYguaaZasKU3fSoPd6I8z5%2BPEwcU2AQIdgQPVGgtupgMHQcJ4AScbC5NdkoBinNBwdQqgY6i9HUW%2BV5EbMxljT4uZQnlj%2BtbHhvc2OxN%2BZ5iG8Nzo3fuTb4UeyOiJu59tgNaWdYnjSO4NPHHc7w8X052tDy2%2BhigehmLCXkYmOeFT2uyVoZRR%2B3MtVguujeMTzArDnepXiLrgXebfPAhNOhx5yW1kB4q3D2gf3JTGPCtrfr6qbVv1FSX1HfONM4nA7fo9DwX812WoNY9btQwYHGb0SY1c8Drab%2FMqMSwctCVeZ2XymNfu00%2BZYHQZoKan3ENEXLGJJbJ2C94sS3IWu1eUClEby9gynTc%2BgcK67yNSy7DeaokCDsF9TVxxmlAIEXjZElNCcu3rxGfWIcfXiLzVCUmLTYLrmlJms1VKiKgSHk1MU9LFGbk6sO3aJA7zk707cyBIL%2FMwn2etzEBGBuddiaKTIbPq3qj8HNEZ1%2FccOhf6b24ICTzH52%2FsvTOg57ji2DluzehQXObuZ8FhupAVKiyN3ISAYKe9Wk9bxfyZw5ik1RYV5O0hR%2BoFv%2BZ1NkhwKTs93q3ktl5RwL1EV6knyI%2BkrZJJSyUSJDXIiaqiXl6Fs%2Bbi%2FyPO935O6jEp5xUbxII4KFP%2BIQOa56mBZJCsalfxgmwIxRmLQmGnBtjKlUs9xqCDrxBRePwgFMEwmjW%2BrILbUjCrtDGGMvkq3PxNiazJn7W0maDbXJFTLR26SpPrwqwtcFN3CaZzauMoXmMDWnpV%2FsiJkvUAZNLKYAKOUSlBxCPAAPuZhJBCMDY2ooW6OE49bHbdNJV1RbJ9md8R8Wm%2BCOzOWo2nmbKlS0ZDp9ljOCkpE8Z8m95YrphcV9PgSsZVS9xtBdvjAHZmg22u1HrPJU7WkIOrxH6NVtbMDiquntlFKz85QySGUxEl3xVWxKZqJXsZnDQoNjAaWIBrIsFgVu7a5tlO9GBvo7ZFbN9GNB74jpjmql%2Fd9S7DZLrcUFzjjEOoU3EZcSuQKFzWdTVfpIqQwVnnd7UUrzrhN0pKrYSPoRcnNFoN9iTMriWF2yuQHoVG%2BPuI1%2BG9MHEzHGdftV0ATEKEFo%2Bv3OIT0ClL8Sqh7E%2BfoaLlb21JLUU8JXlkkGImHhGungNL3%2FzsArltY%2F0DSFMhTey9KeUROc3qkhFKPrLtcBeVYoldHNpQTGEM8WtfZVAcykVe3kh%2BK16j3EJnmDx8cvnT0IZlUoUMoEsHIqastjT8D2o2U1HOlflWBOqjU%2Fo9saSIapBaCTb%2Bq%2BoidthYPEdwIT7rnCqLV%2FI9%2BjY8lYp0mBMnUAD97ZRcbcptycLpCjmh2b%2FW74yZnrEqfJX2HgxAfTgkrY35c63R1blXoQh7T3Vi7hDDUm6cSBPYG7D321OAQg10yUh51cDtMqhp5X3dJwFUunbGpzCmr19GT3IhiZ6%2Fq3kPOZUZ0ckm9PWwB7krYZyhFMLO2Jl09bVh9krxWS9FZGgiUXbmSxBbRi3Dextm29REg05wooseGa%2BppaLOp9kunQxDBZlIyFiPS95QoguJ52qpyy5QtnzZNx6WWujcWDbY4DOGx6YS6yHGVW4bGKolQFYMY5w8Rqm71m1yW4Hr37I1qkC6tcdZls%2FWz8IMP9vNACG84oFnA4sgGbiJAaPGclqd9qDeYL2WrvjbHXc9XmP8MzhAzyiU04n5Khw%2BpoL1w8itwE8z7tNdMCNpGnspJ1N%2BI2LeJVSnJqwq7q4NBNXCQHif4ZPqenTQQPlqdctyHE2TM6j0oHNV8iBq84wu8U03bGc8Bj0fWDN25S3m%2FTgp1IKugDQyDEvCc7esgCpGXbXyDbCkLhlbJP%2BcJ7v8cVyIVbtzp7jaekwGOpBNlARyFnYGEJQDytm%2FwhDhUVs%2FUbmsjDWt30ob9qCXV4CejRz3ltTE4Sozxmpnkiw47pFzwvAFLfjr%2BWXITQkbSKR4jvtxZjbGgt9vPrjb97EjUhxe1UlFow5kk4eAEd80o6RMvQyLLtd9%2FR6%2FCg0njJtEolQS5dZ00dHpDy617BamyA3Hi7VAg0kLMQSvTKma%2BtQQumlNS%2BVm%2BNN070f0hePepp%2BAXWWjQortQWQGd9Yr4y%2FnGwocQ9VJpYxQEv%2FrUKcx4gglsgy9FousWcUDwKSprIUh3dGBXPaWSJdQ6CXRw0%2BbbohVZ%2BIOHIM6fHza%2BHkHlA5d5jcDVs0mGUsY0mOaz0wwNIAoI0E4Xe4KhJlbsRo0kJMNyAqDcD7q5LVzgvN4sbShLfq%2B1sO04giF62%2FtVEpd5oLD2AiumTpakQMjR%2FwekxZgV08qChCYAN%2BUgOHticjm5C21IojRFUYguUR9%2FsmN0I6iLZPS%2FA5UQVPPpbCVPsDjjp%2FiLoYUkcchiqmpBjwmY6Vsj%2F7XppDyw3phjIaUA7lICBFYf1mswGQT%2BG3Jy6RB12N3ynKknii7GFaOo4i2tmNtfpkSfxZcPLTgTBRKVyG7bB%2BR2KtOilyI%2FS5TM9BasQNv4f4YuWYr1U1T2UIFxsIkg%2FUXRRrDFRLQ9jqgWfpaugvk%2BAyDos6dAiSZwfND1PMt%2BpjS8XsGf3aFmpwhH3sixeFnRJmFufwOzg6EOYOTC4n4Skwvay4gfjpxayBWSU5vJUcw9ztHxO1X6jQ%2FsiT1MT6F3886e2ldqHdNIMWVW8jnUWAuquFjw%2BIO4VI7ONvYMbqeJffSEOlrwlCkdtKxRvVvuDTNnOukzoXpQyAcO5XIDyPs0%2BULkJIlc2kfCEB0G%2Fc9ilEE34pTv5KjQ3zNOJptaSvZ%2BR1YGzs0o%2BNil4rBmAnUiybxFwBqw2KOBuOTcb5pejERXqAFCgB7c3ldJrKQmVRf9PR5cLlKJ5QdxWuABBajtke89ohhukwN7CQhzyJvG7UyTogN8EQKVm7rx9iyWjw9LEsZK%2FMHpMrvthisGWGtBBVmTFHl3cJZOsUb3wiUx19mMYlcrGv4qXlKawPEoIFCS5%2FAPAHsoPUkkN0nkscABQY4JIgkqJbLu7SmmJWyJ54yPLdZL%2B9xEVFeiSSEo7tPugmwUMS3qLtXAQmZPXM4US5Xbdr1cpSk%2BHzekHy3cAPEHXXQspriQzTzJo0EPuvLB2Dizj8RT9rchcsQslrP8v%2BPFZLWnwUvzw9b3ZOVTsXOC8EkxqOVstd4v4Eo6Wi5eJrlcWADWidqCUPGco9uGkPFJTFv7nR5jwq9um65r1imLC%2B%2F2lq3mu7DM2UUmgcf6SG%2BS8k%2FUZc5s2UqMe7981mRishsBllKMVtB0pgnS%2BnDvLXs%2ByqTaEa%2B4vccpAt2BHbCu8BVQNFgVFES3iOKRuSkNWMj0jGdFrSabbYQ10SrOpuQIkb2zGTiabK4JIFKa2z%2BNY3Z6gQyDGKEz%2Fh0GVuQr%2BWGiMUokr0V5KeTn42Mnb4e%2Ftx6qlTiStB9jhx51dePZoC%2FnKu55biletZdzeDpCUYTjSwZsUto7zgv4S3EG4neiCaVpmiaBdVkGVWnpUQFpws9e%2B5bkZPQMQla%2BwVpsDDTE%2FwSlp2JR4PAwCe0WrKScPWZjL6Y5vVSZ%2BOuRjZClD1VeERtw9kR7m5eqaPuBpbOYBZ%2FYSHFec1NoQEL2SsE5ObBZeI3DFo4sqYJb%2B6fBIb4RBSP3zH8n3xzjQUv5fY16nGuqB%2BjyVFZR4b0lY94kFnxVDAnoZEs9fQCAm%2Bq5ScGEVRonIZnfUnI3IUxBG3oCVdHkNTkND1Lm1%2BWpziRKGhIAapxH40DleJX0iFYK%2FT%2BtpXkUUIcEiAZA%2BotwgeJeI24yu4v6QXd6iGhcTRskFQnWg81H8cSiXBP9hNbyBjzIq6Bw%2BMV7Yx8SH5Jfd2vXFHkOfcocQYuOTRgfzIO2IQz%2FKOqmTOJufo%2BKTK8un52D0r%2FKSHoRC2hbXpkC1DjZtDMfgWjsY6Uftawr6nOWBFbJV4CAWJf9%2BheOHhmfMe0fk2JxIlZZ9GhtVvHdVgtd6nknS%2BcC5ZAG1B%2FmGWQGD%2FHx0P65iPLHyfQCc3k7HJkrTkhDorm8YDYwJSt6IFNAiJMuAZh7LTcRGK9ydFXqdCMolzAlW1nu43nwWGXU6ylR%2BQdm%2Fr2%2FvLYMVc8ShRLD5P%2BrcSbZBLtfL7TDomu67t1qi0A1r9MIKa62CemFAgsLkpMIYl75sh138Wjm9XJ99R2Ue43bJhK0ubBM%2FgOG%2F%2BdUPK7WGTcdARU5XcGkkRPyAQhVGHZkYwTtzd5Wi5qB4oFwNHn3xRpzV9MVx%2FNIVMN5Da3roDUGo99q7A%2BzOLnMEWqAIIIjmBoClDx8EBuUC1zntBcfA54iUCiJpCUZaI4fMSJB576Og68hMicEHDYa87ReLXPDOC5hb4M0mDXdczISyjgwjTU87fMqj6GbKGzQEe52NDKICl9Y7mllel8rxi%2B7yISDRaqjC7YudahbXzsrikqMoj6XW6pdca6zAN4JQqP%2FRfP8vdolhDBUGOEg5xype5Dqk5JKzO0VVQoLqeFJqLTWV6r3gR0tdT8%2BD7idlIoz9ctDna9CXZbz7%2Fa6UKBM8oJdP4108phWF3FOnkRLlclwC3NwP%2BFgJJr8jFXFXyAk8J3%2FI0RTycI3u%2B5Tk7mpbgaufX%2BSV6%2B35vFOuk8dBRH9ypsZUhHPwtyNCnsQx%2FCKFv9qOZKMp6pxNcna9HhIP1hUPNmMYcb8zEvkCl6YOBoAgzCsi4O%2F0rlL8XSLsJD4XMZNglO38c52xyH1rYefUFpkYw6wYDD6PEXjN3LRtWhUFXnMAthPQzEu9sBKL1mpQwX%2BifIjKgrnjMkKfPnyrSTCbep5eL4Dj%2BcMExL75LlGRjmLOIytjq%2B1%2B%2BWU05Nq3mSTW%2BvRGx53%2B8dPeH3%2F4TCD3KdNcQTaBHaWoFx36t3LFzTL%2FYSjTSOBPhSg8XxysVYMyu2C9v8xtmD8%2BNy0iW3qGfPepWxA%2Febcvxc3fYjTgrA688Kg2wmfaGfMWg4mjJODU170N8jwgQpgvn5Kw%2BmKbWnQSaC9nLYWnYhqoUH5T9LIhz3GaPxLTjYY5UR%2BMLEJIqZEL3o%2FOXpsMsaKuXTr9EaDTNOGNrWirJVq9jRNP4%2Bc934iG4dUda%2FyKwLc15ZcUsbxAdAgAHWyxBlke0xk4XrSch1gQR5cPggTfipQ4OvAIObJvbjaUfmaZHBBOoEjm6pc1ZSiXXB2UeViXQXwsEBhE5klLQ%2BwqP%2FCppU0fDTjniNSoYTi964IEOtZOLm2CBU%2BqF6MOzQs6NHTiLQ1rYMzuo6eYgcxGZ%2BUeeT73bKYMVarGy5dPIz6kel%2F%2BIGKAcJGz8ijOzOz3uor4ur26ipcH0JTWUuszADlcoGBRUpJoIRwqEcCvPQhNHcx6bWvWxfs8VajXhvCbFAESHj1XKCwByaGRPvo%2F6E60AvzGRXouLZqfHo%2BDKWD5KjHZUhXAn45QWHRJmDVIFk%2BsTIXwIo%2BKvYXOaJncZ%2FwNxnOHnOceqFYRNF7PATNzMAL%2BKdEEVkrwlSCyPeViFZI9CDVujiLUP9nZY%2B5Lqwaz5foHB5EvPGroEJxxr4Pf5vj0Ed4RnchS6%2B5RzDrBtv9%2Bhov6MTpMWignWkezYyfC%2BgmsEWjponQkVGCT4ZDt3PSajgXOQs7MLBIj01zoTb1unlCuKom7DBB9lPBDray8cfSXTxSRWhsLyEmADhONFTjDKgz1EEnxoI3dQQ8QsPpt4apqioeCfclkPxJuZ988YKT2iHU4eojn4Lp%2Bs%2BQJDyOEe%2BrB5yYMgmmeARM5yDnDeh%2FW0%2Bk6NhVtqkWTSpJ8BCkWQIo7C10yxcwmiJw3h6gr5pE%2FQw40WuC5Vajn%2BM7LfcM5cDCMCognli7gBXrS3d3ur8MperdbOSqxJukuTNkxzFqHQGxNYbZGM3xtinBTWKFBoG1u%2BP7c8ABb9jT7Q9fcXa8kYBErbM%2B8qVWnjjqvOBGk0LFfrgIqkpQ%2BHNfoU%2B%2FezmG70oBwAKo1IrDpw7ylLVINYMr5POxEMFJj%2F6OaYPVk%2Box06SJkFYyEQY5q24PQDCGcf4jjfAfa4tMF%2BMH74vtbA4dIIsZlHKVgbtYx4ZIIQHidepxcylXa7RW35EKsR5V2uEWic37sZR8HJx6UMrJTaiOqiHoWDXop%2FKmOlKKYFD6h0OBMjozNKaf2bS66vXoLaEXTo9dyHHYfx4WwMT7SDFkqxIbHbi7L9XcV9nU%2FcIwX3N14sSjhvmrYQTsCeR5cBTuUrT91UixxT36PHkDSbRZRRklAUEITDkQq2CHDKm%2Fj7X5gZp9yO6RqB1S9iIN4cNIWFriKOQZVQD169SI2C%2BiAJDzIoiSERUV92vjRoX3gNx3yfkxGBQJpLBrxw%2FLuHngFfeisXITAqIMTUTY%2B20VpJ6%2Fg1fa1fWxYSHyRbhmJOhBQPdhkFY19CDbMS8ALCP9XJDPWVCiwE7ccnl9Ljs2N%2BQtdiryzYSA%2FWccrW5R%2BhUEiXca4Yz%2Bdnhv6lBgUn2u7RYXy3ynsSf7hHNAGqlG2CZzVHkGOjKGTdlLNDZ0%2Bj%2BRpNTlv6GinQFFU3mLm0dMs4IqKFnE%2FvCXHFmzTkKRu3zyGGYrblpp%2BjsB6TOo7FrtGvbIKIyIlxu1R87XEJd52kHLlOz8d84rLOSmKr7YbEyDTxmezWWYVefHQ%2BEI7bkpl1X%2BAKRZLx4%2FPgtOQuMq80WpHtE1lAyfYPnuVvkx4VWt2t8av95shtfxn%2BOlymkakBj%2BHzl9GE5jhFlu0qkpCDvZpaogSk3ryCwsDJsw%2F0gvIPrq8wzV5lyFa2jx9tHqN9KQ%2BCQJcOi6Xz4Nq4hfF%2BH10%2BTm9ZLYIUA0hTobQjXc3zPtZhe%2BjVGjaEZe7KCwCfXjqCFp8FxjtVMqDiIPl2V9Ip1reHF3WthNd%2BTP4CWmnAA4mCY6xcjnbibr9PD1zFObuCuDSaTqYabM183BXqlpuMUOl8jfjGX%2Fmh%2FI%2Fy7hKxTc90jvSOBDdDPJs3hu4vgIkCQTAzOFv8R7jXNxkyy3%2FiV%2BAFZalNFVF1EJBaRVt2Pdhzfc7%2FVVPCJRfFHbcuWzMEUW4%2BU%2BcuEIaIpv%2FvoStaafnDDRlt8Q%2FhKOI5BHuCWjz0NMxX1MklN9Xr4UmdJE%2Ba%2BrhpNm%2B88mTokV6qwuU3giR4YhSb8JCrhcCABHwGoYk3yPbU9iHy2rox4BySDsPT3o4d%2Be7mtSBAdqXKWUVf%2FvEjM8pGRGoVWuinUShw%2FusJObpPDAAQnOLlz3wwfb%2FUmAQfxx0Ee81HhaosgA748JYNI7urSOE6cFMDaCf78clbCpU0v%2BVTdBlOwW5m8NBQzzD2iKCqurISy%2BB%2FYVxIcaqJbyuLbV5OySA9xZgQSF1DKG3tV4kCMOYh3kNvnA6oUjlct%2Fi5Lj0sOFfKb%2BVT58aDcPXrAYa6lpKIZ4QfHPcks%2FNc74JLkC6exDuYQYjhmQICmM6nU5qW9hhnvkS5vGvwG05JRcCmgV6WCaFrbZZRlrMYtSo7MLG7BZaLhXfGEz%2FixlomGYm3cyGo50nHmF1RFnPKaIMsgr2qE5wJbbxRnzBY4ZgnRjacWDxDiCy6GkVOzG9RGy3AKAdpZSicUkZRahQniKDXC2UM7hlDu9af2h%2B8vTh6DBnaP0iUWq5lFv4YHmAtPwLjURy9GWESoicvituZLpCa7DyKP7%2FSF53P31IxXmTa6CNxjbLgreWKVFTWRQ6xI%2BX4AuNTsvMmHA23%2BfWfnqTeZgYmIqVAnmp45ABv%2BRqRL3R56fGjJDVxP%2Fw2%2BhSUovNv3zU6lmzPAFIbypfRYBH%2B9Uoj7%2BGkBXnUQ6Due5%2FDiL%2BNGwKkgpygtgWxZwyYIk2uFS%2FO2e9wOdOFmBfdwBgqAVQdOKslX84%2B8rnoioGPwHqCd3RWWyy3hvNfqIlBRkGfj%2FvU6P3GZTn8Sh9sxjYC1qaIYuVy5DrjLCaSgxpq%2FeHZqFkmjN4KbrOh4sKnPOHVbPXqI4BifTON7D2Ia4N0SKdyNcUrAKU2LxCWZqvGE8BL66HEEZcBZUtfFV7QqGsab2lL5Oat7%2FGYL6TYAmZOl8v2BdEXAFSGaREhIgiduHmge9aggX71c%2FrJYmrmkhtMqdCuDQNlEfqCGxUZMDzRreuw%2Bl0i%2F70emvq1QhbtX4BQ0zhntHherxxY3PmLRuLamCAQBSTHXQAHRUU0lVuATtDa3kwwTjjOf0oj4msM7s%2FpDWMLjxFIRtXS9Df4x0Hrc9JatAvEWZcElUyPar8e8LTz63L7zIH4v9BsMzUEkR1Mnk8K4x3F0JxtpOB8Ph1Jrv%2F2Oq5yv7Flnms2EUYkgvVs8aPCTBVnLMt2VOgx8j6aBicLTTvROd%2F6CcWFnucnzKHrs%2B6et3xVmyzDH8CQYCnn2ZC88plH2opTQmlE2o1GhkY5hEwmxbRbz5zFSbLpSulr57%2BoBoOXi%2FQIfDeclQSuoXTQmM%2Buvp%2BpRikBCOPVdO9O1JYOd0eiukYUq5wqp%2FR6WGcOajHZ0tPyCpZ9XNMm30IErVrYEkGsid1Far%2Fx3cXZM5yIvu%2BM72kvZ8X26vX7iO%2Fa6hmdZanUrX85%2FVD2iSaIjZ58ltGlgcksUNfcrUq9JlZNjWP3K95G3ypEmS48DgDejJ1uCgls8CtdAz0APlLbIkmZ2QXKqVPrzdULGu09iqXsQcOJPAEuQwwzToW6aJ9oUbcYWYclQOkc4DvnyXz9gmTJMpGtWZI%2Bid5SThD2x7S%2FV9FtJ8sjKkBYQr0RvmcMVSS5Fc3egWN%2FBkFxWMPIwOW%2FTx%2Bja9zJTMIk4TRBmZ%2BIRa2Kti2mbtsy8tCSr5ujEQiduGSY640b2TFwqhxirtI4SiVlqHAfEx9B7sOvKrwPxRqQdzXZoL0jIWS6rsjjCX9SOeEKB4V0n%2BhcTOsxhhnB%2FzA52QcRHdw6%2FGiV%2BdahCFH7WCL2QCwlORql6Vo3LPbOsXSQAxcgx4%2FH7mKAn9hR7w4Rgsjbg1FWh6SyDNQvc1ZW9NnJeponf9cdjnXxbXvytJvOIEWVw%2B4k9I2Uh%2F92vokdDTv1dvh2YN9%2FvQR%2FFuGXlRac4e42w2anNlwh51HC7%2FkX3S5dzMc279ZghUHwUGfIK3OlTqQgQb3p0lJIMGLEmNC6Ps2IgRYsacePmfQML0qTD02flkkTxm22EkGzmlf3YFRgXv0xomBFluSpFeio13kfXjlqV2tndQ0X%2BIe7Sas96fp775FIXg7uoB8Q9jUAC2THfQ69m7MfTVDNMHxtzuLphl1V2IKjkO2IigLLiTvzmymp%2F69OLOYDdyKP3c%2B7oJ6XFvub50nBn8L%2BTBjyVeASZOLFvSZNFtVyIYgZ8aMcGL8t7pDuvczNGnj16bhbmeCY0n76l0wRkyIqVkacstr9iK8DhKw4kNyPaZb4jZExXhakxkxwGRILqDaCQLwiNE%2FaLvo69g0pi2pcBsU8LhwNOCyON%2FVM%2BMNOHp9xLqptf0PY2YYEu7peSR2HGb8RtyqTl4LU4p%2By8x5tNlkIgFPbvrJAP%2BmM6Mk3ukKQjxTC9kMESOvGK8no9bK8%2FLlIzoUR34muMHoaxOxgilCo75zeAX3C6XKN0gd4xQFsVRrtFq9EcdfUQJyZ59geDuh9zOh53smdeh%2BTLDFX6jGM3exC4oQfbP6ryA7T9cT9gzlCWAVV4CNh%2BowPR7CDd9DA0JKezz70MDTzVMTGvSFavt3COhS6DIMZqZfywicol%2FNy%2Bqv4r9vufXI3LvtdL4GS6mL%2Bhu33W1YVJg%2Ba008H3cBexJ8NHYeab7R%2BuRbe3ppfnwqbxNAc3YTAiONAso4SEyf9nL94MilS4HkOlj8x6S0c56rtll0o1QqDs0Zlc%2BsS2NBPAAkdkehszDihk6M04ohTmvfAAqEoCqiBFSu%2FeE%2BImyYOqt7umx3SkSxcXyDeOs1AgrY05YrktooJ4jNpxtnOM5%2FYJKENmJpJOdtkZUdollIUqmQpvNWrpCIiI%2FpQniz0pQfRNbwzqey%2Fx4NYQyV3aFKOU%2Fn4xPj4fgSRlXeYtIAUHfZXtK2AQc7ypTXKQYd9hsMp5fuCNmz2oAPXL0iklLOkFcJvmCVjRFVKREI29U8nf3ewOM9cMN0lf3RwSsmP4DR2%2B93IMbWoP2Z79QALZnBjxyCrwX%2F7lr%2F%2Fu3%2F3n%2F%2FLv%2F9N%2F%2Fcf9j%2F%2FzP%2F0%2F%2F%2Fh3%2F%2FF%2F%2Bq%2F%2F3f9QA1MsuxCfws6MgI0eDhjhXInaoqQDWsANkFXF9Q%2BcolPhytIGU3kGrHAhtLH9micCQlrNTcT5ezCT8I8RcKDx7kV%2FLm80YV0bEK4Ny5A7zZ10F7hUlcxlJdjktpUbFhsnhzImztIgbK9f3gmzPFdqVpLknAC1U%2BKZ8cnD2lqRxEvUvN3DPKR%2BhrfRvAdfafMzcTpXL9LHLafndN%2FMMf5Y11J%2F6%2BtFlwCJuMSir%2BJ4mBc8fGnmAEnwqhhSUONykyg3HzVo6p43f5eXCpbPCuCUc%2FqRlMVh8fYzbJc8%2FxK95copSy6O5jJYPwm1efHTMGf8nvF15DiPr4wuOW%2FOdhgnxE8hFIB18EbEt6h1%2BM9oZWogAPrztQLoNdVlptNZwsAyJaI6fFuEAPga6UiTXVnTBNa0zGJq7AgNeNdk9Unw2%2Fe38SYwVNuH6qJPoldH997BAXGRP8qeuTFLpdqYWn8fzdg%2Fmc3riMWQB1TwqS65fVDNgjF7Q0qqkMLpRZ9wLgDdBiwbtO2vZjwvNGa0lHVhDI4MCm9V1yFfDBQeqMqJasueDlUOLkDoDNWNvebR%2FckxLMvEUIbv6DUThO5a3mYH28cpzpkJKRxYbHTKFiS798ETPnf6M%2BYor1D8Hx0WwZ0HeHAt%2BVzwmf2RmZ1VDrMzVJWwTkKbhmXBhPvVPZcxhzQLGSKsDsrQwMjGyBAQV%2FkzGRdAysWy%2FYLMj2uxySXTpfWkuNxSXjboPqiycdPgNtVmSIrRQadg56UFwfRXC8IyjkHRcRJWi7vHwS5aPv7s9NSdeBYuPm6wULIQUlwQ0Rg5HkaTQ0xByMyBC%2BZqBiG3cdi4Jy%2BZQ3JiFX7Ow7hqWcJEXV353w%2F0%2F%2BP%2F%2Bi%2F%2F2z%2F%2B3%2F%2FwH%2F%2BX%2F%2FW%2FfWf6%2F3i89FYhi73%2FCm2r27w9FbaeL3POwrdRL%2B8EGZDyYCQIFcJbBAtW7GyJ0OBB%2BqpvaBsaqUm%2FBX6xxNVMqVVzR35FV86YB6pQgPBo0YFRcfyzRq76BEmSA3luGQodgZObb9vYeGRdRzE7S8WAKr60gHS7hnnBu7nC9CpEy%2BOQaEcRA%2B3DXIUEF1W2%2FG1yIkWvF1lzrKCSFN9I3f4kz5jsQ9J1tbVaZuj6hgGvtewwboK9LObsuB6wj2BMwt5egwmoFq7ASoJ2jJxW72xr7uzLscgVtp2T9Jp%2FeDoseVglMyauMgvqPfUDL%2FQIGsu8AFoHYLvriQcbnSq8cwRsuzJuaQxvdVQccJl%2F5orwkiD%2F8KhAoQ5gAaYOToqFtlXUhqTa1V%2F9LIEHSEufq53bCA2QjuROfp1FD13aUGpOZ%2F3GLzrKTo3v6h4ZZNGRG9BmqmLOgAdcRo2ZZuHv%2BJ7VhgKV%2BgUD42YY3kVN1fNMKApTNTyUMOBrNEZbKrjFMMGeS5w5p9oqZIcV8gHlDa1RjY6eA1qi4D3I82rBqSU4Ut2aZLGOM4aBGXLtM3%2FHlgIfl0ktVOnnWDxZqJrDhq0rSIvvd7s9bvaJYMj8xaZFdPjKKcIUjGeVzcl3rKbgfhyNbvNk4qC1xpjiPXL8wZVVO80wbZed9zalkU1W%2BLdVkvAzYLeUABVBLq8on%2BUs2Gb8gQBegMWXW6s2uBjcSUTuNu7pwFhi8Zq8ji96qbl4zV8Vw7HpddlZzLt4kqqiGKrDkbL82ZczTjXh7C62rNdFfQguXI5ldCHiJf%2FICLbrPUoaZDxmYON4OuEG%2FILkCv4hposEZJrxHXArQC9O%2Bvbox70jgOVwpKwCa7P679Xmc6GgACRVg3%2BFtyxlGapdlULShLfTLTNDqZEjiyQJHRxvI6xTUz8GYpobmG%2B9ro8ZC%2Bw3AVUknWBLUAQ5wJjwGECAUqAFR7U8z8fTXCJ8%2FRV3Xi5ongnYLESOH1j39TgtfSoyJbPWpukb2qlteWXj4LjiPG9SLIuwgxFCdL0fei1Hr4oNRc1AwHhxQL7rTicY0iDiJ2psbXCS7XyvEYOmaUY4k0sormhycxifTCncgZE7x2X7yzIjiWJIGRA2opmqw2O4CXgVjM8z4rtda0ykbVeiDEZByTqKqO9M%2BvNSUzO50HsER5YOAp%2B7%2Bo%2F5i8sBI3hpBaXFyRAElDk1%2BG3qYdlx6daHAhdWjDWI4LCC39BUtJRLltYOAjCewuj4%2BJUxE8Lj3UF3khlymUyMDKxdmTgTwe3r3H8AmQetNAt5rcEntdBA1afhN0o1hPqAKYZp5hEGUrl1Bd7eAtF7AqdDHnBX816LKXoLoA2ItQofw02nkABdVlUsNxk%2BjBNbIVaS%2BHoJltjTfM1wVChEKDzvkxgk5nwxcZ7ORrNY0tZL54m9LoQSTELfv%2FPdEgzXip3LVvc1bH4ewCHxo7dxZUR39azKETkOsXDMa58jOdlYCxYvNnk3NWltZoQQb2OMgVYtSxuarK8Ci2oSfT06JNO7wgnzuBN70msNDLvAKpAqaY2jVGfnzqicCKWaQMrVENUIpqD9ZnHu19iJx5ZHc2eQpOOCKJRZ7Zl3uV0tlVy6YZbRP%2FWEd8Vqg6aAarfHlqlWsZe%2Br76lZmoG87dh%2Bl%2FNgBgSsZzwSN1%2FwySxBfllz6je7SUXvJKEY2C2odPo7Kr1fYpreRugutM5t5ZhEgKwO5q7By8wLyILu36cu4RcVZQeDK6zAODHwJPPdxcvH65VFOPLoJZR6gxSuOGd87usZKtxPlPUsN%2Bv0A2G8uRMrAwgojNYyocMfr9jyuARfqco3B%2FWl3Gn43YF18jrnn%2FsXgocZILfW22kQLySlgA3i9UdGzIbRanb9evkhzSwi6ELluRsEAkaw3o5wCmc%2BdUjIpgGqppOOYmPvwwKsXQGtPAMYZ0iH%2BxqEyn5gj1pP2PVAxS1RLEn90QkVcnBJ8rdEg9023bamV2XJAy27dqSC%2B8XFoLwHsxHeCClkeN8pJnlXf2FODErckbDPTRPtgfaaQ203ytSsEWS8BCFmnZaQx%2BZb4r2jD7L%2FYzggqDH2PJ%2FVmJ1ga3EkvWEE0VHuAr5LjXOZnVGVUIBR3rS2YbyuzD0AWaBlb1yKpq58Ay2UUlHysNlz7%2FMHbVO14rsEI8AyIB4kjCV62Kct3rx5%2FT1G%2F7BQLDR8tHhBpp4xRkEtWSIYZxZLofmPP0miJchhJVnaEWUfbvdRTD9jgCM3%2FK7DlZW1RwC6lP9zyRdFpLP1ebbZ9VEAN99RMPThDSGTTxPJ8QRWSyi1gdVVZapinuZuxNke15v1FXcQgi%2Fn8AD0QdpK8Pjar2CrnMbjuFi4A520FZfNkTWs%2FesrspA2e%2B6L%2FiQshU%2BRjgsQaGUwRjPFjxOlS%2Bw78S1b%2B1G%2FL1ssxa8MGCWPXBcV4XEZTDe2OcSNG7wO3hwyp53qZwaqO0xfTxJlFzDvEhiAGPhYn6wodiNwYIgXMSBgAB5xyIHIpK2cekH6loI12HwiusBZwSm5swbRLSJb3yC3IDg%2FKqw%2B6q6hCdBLEek4K6iSm82O3z0bNm%2FqzjNqQkajJKBA7Wzam4mpj%2Bl8C5FPo8rOqdpbnNebQQ8SB1Y%2BrU3ojgUAW1cxkPca5y0K8Kq2YRhtg4AgJj0Veumuxd3EiEkNSJvEqb%2B%2BAaujEUvLSeZKC3PHuZMBK3kUNtyWCsMLr%2BP%2BizK3NuopqScUkMSvAptIl8Ee8TXuEsgkofO8T2JBtFishkxWDK%2B3pVEhsFwrfQTrVbqyZXOMYCM5HYbcdGGZDzX9Sfw69%2BFyZHqxZMu9nwGzyG6xDEP%2F%2FVSabxsvviQNpVA3frKSDECivysu%2BXheeKboPad%2FWQgoU4k251K6RQRrE8IPsEHerp%2Fj912lRAuLLThOpZimZlvzhonmjXbAeV7ih%2BaLzR8qGCvc9qwyqS%2Bes9ysoFRJWmXqegV9SaHH2sGNEFjBj%2F4HUvsFwDOUONlwqG72L%2F4imsXvMuUOE9qTNRlZJgytpyGzuT7hm2gVeiPhZ%2F5Koc9OnNK%2BdLPgJx0wkF3VwY7EAyQ3Vjcx4LBdU1MglS6VdOsx4Q3TE48Gen9JrQHWM4Ik8MZHRUDhNzlaX%2F4D3iCbqLdR30xLDyg6lIlzyrWndLimKPMwRNb9nkj0ZGGINIcR67UGU2SJtiPI1LAvgAZxqv9jryUSgHA8KQBbOeqYQEle5YaIbhNdqnfN6tB6m6ZOpJUw8qDUWiKC341ikbk1zkQWeCYEAv4Zh3TCdClbhwczdqB%2BE3nhEg%2FccxnSjChhbCmZBNyTGA0ZFAP%2BXIr1GYa51EcqZnsI%2BpqvLPQODVgWwBxIDQnBFwSOab4itE8oHh4f1b3t%2Fx%2BRv%2BcxLxNpFlp%2BVpdu9%2BvpRMPDuMui3VNYxlHGjF7MqeZdi%2FTSraE5KLt4zY3a6rwLv6G2EW4IDONKCc343V6azJDzt%2FDyTwd9hImcWfOuF3wfq0sQW4Hn8Tkc6g6KN58GVyYvaCpoDx4zyweUT%2BnL9DohNVwQw5jjB1ApzW3X9mqeA6WElAXRT75NM8TGPeqYG9qhjZC9X9moW%2BZotQQGM3ucsoJf6X2TLdx8FDpcLUFP%2FoypcTtMwgeGDFuA7UpASM564Euo3Ij1JOT%2FTw%2B1JV00ix5T7hIL%2BoXItt%2B%2FEkNgTU%2BBu70E7%2FJkrnp2n9n0ABTxTM1LzuNGG2Gw2u6n%2BVI71hHXnM50PTHJKlCD3s%2Fd8q5qHgD%2BfMMXqybAi9G1WePIaO2aahVMTbzG48RJTFRNcyDmOpGHqp014cJM8mVO6WT0UkZPMESh89EYcoGEPhWZMz84XxVCJTmU%2BcUCwsyfj21AX9WmA9VmN3IZJWc1xDJHouxllQC88f5uDnt39JjryGkDDTRmhmBbnXhje1EAdsFeJJlTvJ5z6mHcQ5g39amYZSkf55b36kA8RoZy6Czt6jkw63VaHNvKiIKSlFPzDLaAVYCrhMqc4kaskRUJ6K3aV0RhQ1aqIQRmFAXmlo5PvFyMD4gu%2FAhn%2B5cgo9xgQuFyv0cCusmbo4OumQ5M6KY7voeu1s7RzjuA94LHEbzVKVUf1ttK%2FVYahwIsaiGvj8SMcaRkr5uL1meAkQq%2Bh0aIMwgjx%2FeCUkbJoapEc330EjkK612YqRZ6olXXhsl46FhuNZohQw88IO%2BRBSqv35LZilBiXGD0WLvMTE0c2g6kvJ14sZcAOF%2F4AyIyABwBm0fS%2FVeabfwFYRCfZcsELWkujK%2B2MqV7n3ILzffzlWRb%2BQJ1ip8mJ%2FOm9zj8yKThvpco%2FNMsKLkH3Qo6G0zRTev2FoPkF9k8azL5w%2BHVKcWvR7%2F%2FEYvdwS5HBGUzQQE%2FPild0lCAJiRx%2BQ0jOf0%2FicWhC0WQ4RLYNN1qF3QluvFkfqRywOMdgXNRk5Azytxv6OlO%2FmFyly4W7lNzwSWUtSV%2FX6CmkOrpoaYxIwrfQ43M3gidjPjveoSfqU%2Fy2nZ10ludRN5EW4%2FQ73iGvC464i6otB%2FLbZJJ%2F%2BhYTgWnDboRzt1D2c08y9Kl3TD%2FDO8t2wEgw6X9sIvZlbFdQUDYb4iXQHfeI6Z21B5YVhX7m8cYwhr%2Bl2Ziu0A%2FyzWjRfap59Bxsk6G3NBFXGgHQGSKMslbzXebDKJGCGwAPyh3GgrL119nZzjcRIjp6Oty%2Bqrhafc0btATf5%2B3fQAEJ00moNwDAfgVqOIVIt4zOhqATI6CebyH88fgZJbK9TeSlmYtuNZxYla3R6bKtodQt93bgnquasCNSHWhUsD1QuHmODt%2By3hdtMZSTQ9e8cr8EOCjhiBkXtWflVTAUYRs%2FY6tciQiIs%2F%2Fjl6GboRCCGQ52aVHU4aQPs2UpoqDQOMOMcQkTQsriMOX%2B5FOcYRjsTkaS4Zfyg5RxEOUbE2Hep0SDlGn07qDpJxJumlTX5qa6z65TnDs%2BYzzzlBT%2FOe4LVux4W%2Fg0VpJZ%2BwzLnkz%2FcQeigPdeS6%2Fr7WCaMGS6P6BbhlhFk1IkfebrlWPs%2B6AABo3RX%2FLDbMVwM3%2BJtmlgg8Lu%2BK133OemzQidNrrFNl4F9TdEUS4BUVNWb2%2FugxgAmVmSrQJcfDdzZKlFaC%2BBggcAQl5QRuIT7IOxtKPD9%2FYxRUEb7pyXVPgpT5%2BXZQaL0iw9JdGS7LmF1vQlBvID2oqS%2BjvUaZ4CnAIYq050%2BKNnyq26g6ZiV56vGcNuMU1xVDLwXV8hRFSlPmgMuNNQfo9yu3Qx4Aa4xohKFgBT0nSWh5GLLjy3EG1bYTNgIkaK9xKBAcFq8Bpsj5woYhToBbneSrwsfxOS8tOdynT4jhYCUuAXLt%2Fi2atlkllzmZ6%2BwvQGYLRSbGtDaMSz04Q7CjMuMaRQI7lT%2Bc0ZB%2FMxnT%2BDcr7x7fjlgkYCr7gB1QBDgG9IAqMhVX0yOCUCvZ%2FLm8QD%2Bhj5FD1aNc7UbY8Z%2F84opg1%2BhXnTVkKrUZHbPuGubP%2BSSp4vjOiFpsRfwEkGpS2yMMOU%2FLVQliytxbnlMGqYitbz2yh53DchpJX%2FdiW6XLRnhIvYjUO%2FBVztByPfCrhitBFb18Drjeyj0w16LvRQT9J%2B4b%2FngVhgDwD6GnV54ON%2Fo8MATAlEgHvGyfkgp3HcuQgHAq5gvv2AzxejjULUkM9SdAjNtIiEpKYRIMAP%2Bf%2FJHqP2k1AXrhhk3XRjEoDn7z%2Bpzjk1bPRIZHC0mmZx7FYMKwIe6se0wrW8ZzZ93zvWasPXgZWZf%2FVTttR12I4nqtvhfxnwoLluOyKgUYIzKwgAp4aDDvVYoUmsgsv7XV2wqweqg9MM3KJYwRCfdJ9cJfzFxkWaLt0vGiJmG05AQiiEbk6o%2BZ1OFfGrZKlgrCCXqYjKLZf4p%2BuyrtQGW3kUGDdoqw%2BT8t1mOr7IbsKOVuKfqAJkZOaAYOtyGOvHrR2JShWIjd058Dt2%2FaxV6lCX8VVr2Zpn6hvtzRcYBswEjDUAUCaipYAjvoGehhxgkS2gBBCKQeJwUZdzb8N6ZuFHtnsbrKOS499uCEWo1psZ8y9lonrJmDmtQhPAQH8klXSkXOvzuSMYKiRiqwlf%2BbAkNkzeWve78H90x8mL%2B8URUlWSLfuNU6tFH1nOxNFqVaIoxJzXtmhXIvfr0gicDpKI1GaXfswUowDTYGW%2FbGzeM8gIKYuVks7HomGfbhWY%2FuD6EG%2Fy5r1RPjAEEc0%2BFryVOOFeoF%2FtuIvI%2BSCAnd63a36wUuzTFaKwZEyK6yaWBsSRuBR3jEt4G7ASGjwJP7qCVuY4X1wJX6XoeAEx6w5pHObzKkiaXkJs5Mrul%2FMvmm95oj0dkR%2BgXJ%2BilhO2G5LK6NdN41NkDIyo%2FyPbpbRFN2Kwgp6zf8HvwgfZFDoqdfoI6j%2B0EHgFsDk27Pw8sMn6EaDFzO0%2BMMYUOEw1S%2FU3hvJm7AlOPgjmYNOxo9Ac%2BG2M9UYBCXCP7k3kjT2zFK8c1cjFoCseAWvGSwSuxJ80DrDwhtMGKKOA3HXjfeCs52jVYglyJu47kMnIb%2BiLKTBdTe929bh4Dq%2BzKeO35aZFEvQ8OW%2BSKuDbn3cJjkjeZFvosKSQzWiFqGL1G3Gl3ZEwUnh3c3hIs%2FuzQYluTYbwBR1ErodTJIcS5n4a9YTWlturqkebJjuMVbdbenAvIip7oiV%2BBgBam86dP7nwaKVkeY3UPYT2VZsk1ybIR4bP48QL1g0q%2FhDikkXo0N%2F5Q7VFkiJh4Nwp3vSF7RTsCGZTZ6dsrknDrdgEuRYw415HA6CRT05Mdjz5cPAUv9PjYdhrq8DZiowrIGQWsUGj37n4htnl%2FoRGz6q35v6DUMNClUwwncm467L0QjMVGM0mCDl%2Bq%2FD5MvmdcDA2s7f7mCIyCQAvF6jLLiXJqRRJl4LupbtiPff3OkSgS2iPPHkXBObKsQY5qXNp46LTouZZ4M1B%2FTKTE7W9oD0iUrlVxhDwuGi7R7xABJo8NLTZPDSrLcMrhRHfRzw7f5L%2F9GPB47UIX42VJckDZmzB%2BwljakAdZP%2B%2FnN8eCOMdf5zoEYhIfZV4zxJuqlZJ3cLN5vz9zQx8HZq%2Baku%2BO3DPccOpSLQRwoZRxFPiOiAuDTCWjl98ETChfzqkOJKuSWDu8Dv5Kput2lEux7ZZuBe%2FO%2BC9q%2BEid8mT%2Bhc%2By3OoeMMkqGsjBuzeS2y6vAFjeek%2BtG%2Bi%2BMcbz0metBYsrGbLfn5yblBmab4fuSRRMRFLNq%2Bje4f0plM1r4mE5sBAeUAsfNHOhOhmhbUs63crgYcF1b8qNT%2Bl%2FnIkCGzdlMYdT%2B%2Fgz85%2F%2B9%2FfEP3P9wOYr7j8L1a9huenO%2BMMRJYFL8R8rJNvt9Kmj%2BCYNN4tx2qHU3XxQfM5TLSVPx%2FSM6esbTy5SC%2BqMQPWyBLbYr34%2FrChrhFVoefBX8Wb6eFJ%2BruGBvuV0WxA%2BmlfxLrVzPrl6nfyEfKj8zgXfcpd8%2FYqmMQANWE12XPzyKDl2vl06MW%2BFPpXHpJq%2FPQfVSE%2BxHEOtdwlFeSxPo%2Bamo01gb8LIA%2FOBHwB%2BBMAur3LP9%2B2jlHk%2Fht8xFL75vhvp4cr7T1g%2BZUYZDKGxX9Y8oHDl9GNxMfvbbeRJRFBhK%2Ftkbwlf7x%2ByHLwwSEh0%2BWRknLYcxvpzXgo1U%2BKoof6C8Svtq1MXQnk71%2B845hxJlA6IIJdNL0tpzAuKcOjE2A2Bbi5dptzfYJhycTU3nMKZSH2dbzYyUW%2Bi7CIGF9z8DAuZkPNq8VeenfzTYgqSZtCqRjD%2BCYlEcMLZsQUA%2FNsbGWR%2FA9hXOKBEJh13rspa7EGZYgn2pq7tu91tfQW1RMQegI1NoOn4%2B2yYJkOJuTtyLOXkIqgSXQoDz3%2Bw%2BAy8UUL0ncyJKSPQ0u%2F9Sn8zkIKfgey8dp52L07h5Bo2gH2qejZyU3RyvAWq5vz8R%2F%2FO%2F%2F5tXSJ0C%2FRTqol%2BUBy862lqU1StilcbMSZ2OtJUTXsmoEraoZu8T5ds9Nzm%2B1j7z8Eu2LNFv34GakG4XUPZm8JsOD3Nh4%2F16DUe3tZsFPYvQkqly0rzVUTlt4j4PH%2FaqlpM7gADOfHBDmAHafnq9UrYOGxd8%2Fd%2F7MPJ8Wqj83StUfyTVILjFgfe8CLFwI%2FjisY398gEBxtNhILWfiXIcikkfZ%2B87fRL0rMc7YDHeLUAs0doaXwq%2B%2B1dQy1uFDbFYlRGFFnwOI8cqyZcSqhS1RPVmQGAxdrmvpZHZ7UzYTQo23Zw35CQxCDffwqTjrrpMsASR9oO0Ym9opmQQG5sN2%2BpeKmLIKbdqRYq4kfsZm%2BNdW6XhLY4dDv5Iz1D6UfQgOfykAA92bi82ueJ%2BoxKkkJ4Shu%2FwXBBmSchdUT3gLRLjvliHoyE4vW9BuhlzZZzJmY4q0ITuGFT89hQ%2BESpdOjmdVFheqkm64hTFHsVuDVZjnjqi6GX0TyrwUf4qkmw2lyENmDY2YEzylf82HneRdeOAd9%2F4i1lgBqILPJFNFfaJXHZkjI0Yzs3NVrN3Zw3e2aRy%2FxAxv4KhGCgIVSGY0rrT5WxXW1uCVWKU0fdgWWWPez9Vv%2BnvMZWD5yFLC3EOsGm4rKr%2BQi8mPxgxeikhyf9mba8NKbMYzXluPG4l6DUEnw74WHqOKqFus2mdDQ4amDdicSphkIXAlQsEwnTUHADhgikGeZcYJUIliZ%2FXGPI6KXtoFbd27ktY8FWLTeb9UPJUzt5JRwfWqNkRwcx1%2F4i8s4ZKYOOu3CcN0Dv7ikt2bL6%2Fm5STy9NrVyiFflKhk8AdrxW6AJNDbG9I8J%2BQQV4lp3gF9neK%2FJLVm5yjYVVTUljIEogMOVfjvGd9M31ZbrfhFR3b0csxbYEN3440HBYZYhd1AQEiEsjDs4ixM%2FFGuGwpyAEhrBO3LrYM%2F5qtb0nyXjkgiOtplw6SaGMtWUjh84ajQDBKkSJsRByrF4ChIoldVwL3NmoocWPEK5cIEp0%2FU3IXyrUoeWZxUzchwTNR8rcPlFGdQa2DM4b40wTIJVJL%2F0%2BXLJvM4NJtc0pha61LjiZXnwY18kHj4wpgR8%2BTiQw7CmpiU6FlKL05gaAFnGZkcmLfb9Oe2BYgQxsne0D5vd02fL6Z%2BgK8t2yJQTGxI9WgOUPVdFkNRbhD%2B%2FFyJRP4WmLk7yMdPGmPcWg6HxDas37EZ5qYCHbPnuTje6RrjIFoA%2F8xTFTDzeohgWe4q4HfZT4AbmKmUrckq5MNy4UldysNdgEJ%2BRkut%2BeU0CvjdSR3DIywENT%2BS%2FyVfgk6xlpXmc2zRQA6fT7OYHp0xFjPL%2BuFslOCPv%2F1s8W%2B5NK%2BhgI8iepFlgYYSC5BCmRQEbcUykkK7oH4GQhGpceROw57gVf14po1sTnuwFUZOcDSowUy3aS%2Fo%2BR%2FTyJFwOBpqTM2%2FMSys4ZhiSXPr%2FTEPkxFkebP%2BQGxTPnbMMDPOlCJhJl1GCFP8cTObmqjdwOhqsoUOeUJI8ggtl4UzNJ4njz6LP5Y3WLDeZ%2F9e%2FRJOXKXmQR1DnVGFvDp2KBl1ko0J9NpInp7KMm%2BS%2Fp0j2v4q%2B0Y0Jq4AP%2B2JOFb8yhSrT1%2Bo4nXNEohokEFgg3DgY9XfrV66ME9UmYzY2ZuUQBA1QvI5N575gTuIVqZvFPbYSorI9DYsWSk%2B%2Bohp%2FxDRl9TLCYGLJzxC2nmTJrAYFMlm%2F95ZnSorPsGESz7hBtguXzlpjLaOjlZIC6nBQF%2BpdSFC7LbMJupRuUipUyBesIjxiNi7of4oeM2Yxp6uQp%2Bw6cjfL6LPsYGFSseZRAaGe6gXo7GWtz9c8ymfx2vFcc6VdM6oTXEb3Amt9JHKmSVjC41O8Mvoi8qPFGhb64r1PRDZD%2B6gcjIkNkzPGXBmdUje0cz1tGXZtfSYKAxb7QYiXMOhbUgfkZiT5JAzJrnh3mwG%2B64LqGY0N%2B8gh7urBQtQ5g1K%2B%2Bto6h2ckysn0OmACKxmERKGwzfVOk5KNR2ZpW3rHRDhwp65OjcGNtWsM68uKj9NT0htSkiFFmieH6qF6zB4EPwhJYWJD%2FRrdqxvC414m3jBZDqBR5llToGRSUxZCCiVuktWXLw1k6MEdxuOQAN8sEWwQDhxDtRElEaTVAbmdiz4lUERLtT%2FnheY1pCRpntTk%2FH8d4VPpdSKQntlEkwYi2UE0MEn%2Fgych4ef8QVPNeEnj3gw2u0b9hTQ8J%2FdsRc98TFI0K6D55DSS%2Fs6dt9ebxBaK12cVApDEqbh8ijtPCIWKprIZEHHQVu1lBseebkQCK4JsMu7wZVArcqguP6dLnBhvoj2fiZ5aNCvZ079ftgGh%2FiNCAsGIoUFS4iKATAJmWVR4d3gwtnoVEtWyRJ1dR1RhiFRAfi3mAuV8mZz9%2BAT6AZA%2FR4%2Bxn8qHifWqhGBvFw9nS0sOnKKB1hGGpd0DTv%2FSNRT0FkVJWJRmjQgRgrQXye%2B8BU0AOiOyA6uX7n7hiLKzSiDv%2BWy7P3ucXS5ToHte6V0bxsDzlKjhCTKVjC1aeYNUKlBESmnjsWru8rcySuSELdbqdSnAWtoK%2FMO%2BntqXmuWnui%2FFtGsUJM4W%2FtlzGjL0zEfecakJfjKs6Yw8ibplAfDMEoTTKmItiEn25YGFdWDKsSg9B4ddc8qU3DQRaGPDrSbLXdgtGBbLSuab%2BxanVACd9tHyIAeD5GkkgURTKmrsDnzNBvEqNVyUBKPLDYsUm7jtenjM9LYGYibTquk%2B%2BoByzwFHQcLy07VM6i55BNrf8fF11QImLBJ3fCFhznW8lTaooJh6q%2FB5VHBUmbs%2FANx6oG5pKpwHeH8lPUL7hrpdy5nfrPL4%2Fvme7utreI8alfJZnlpTkYNU4P3qllZmZfh05NtpA2dVBsteKYxG%2By2w12Vk4S4z9edcQC1YHqJ%2BLhB5Taf9ZJhlscehgGTsbbYnnNSS3mqj60NZWfUnG2ELMus%2BJZ7nx%2Fju1%2FdgVIzf5JQn4ZZ0TX44o%2FtHWK5VHjjaPtRPV2C9Xod3GoabJEoS3b6hwUsLLQ0AKOuqv1wpI6WGo%2F8trail6TOZS7ou1kJ1AIuEEwzlhiZQ%2BzKMT4IsiOK7wlczXeAwQ9bFBie7Dkp816K9gM1t1j3D1v8zqEMsPOIbrcVyIr7xrzo7nj8ThRqPpXKTEYbx0VC7malyNvAj7zhjGKf0yHrriaccYpz%2FbAxjAdYUJzsQIuavviHWcjAv%2FXwz4vqKGtFZd2zVgIaPxcfWHwmWcWut3w8xh9T1rETO6NuhQ5Ji0l76TTfe0vwAjUFfm9i4%2FLDIObq5rnEGQSRFJBbcnFNDyrwt5oPwoDpDBO4CknflRumGu3aQvMuGrioAgTUzZall%2Fuo%2FEyNGcbsOiJ2cUYoi2QbionJeILf%2BdO19FL3F3aqK1157BGHrMmfSXuOnshSiw%2FCiUoI6Y9BE6A%2B2ANHlOT0z8hpaQTHRsBeZzozFoGb5OFr6iItZ%2BYXBBt0ecTtjUPV9%2FVM03Xd2xEpkzgzuVDZl9%2FSOShllKNsr4qPzgePuV05jHF%2FtBsBVm%2FIhv8vZ4uFvnkxgmZB%2FlWeYsiw5J2Rc4wE1wqiKf0kOzdaFYFoN334WO%2FFXoqbL0fV%2B5k1w7W7skiGWz90ty1xpOQLja6HHPbL%2B1JopWQaEbZMOUi2kDNjkCFiIOoJ0zKkK2O9fSoFBWoWaci885whFG9Q1t8ENXuDjO3iZnvZqilYXoM19J5gYomWdAmgV2ltSyD5y0vxWLwISHbKmmaOqcz7Klav65cYplYBfbUnx7ZTEfYdvcEHrpe4WPUS%2F%2BeURuLRveHT98nsGUTjH4ZwrdGjR3FmbibMgd2VqO4bI2YhqxxJiHKD5vRLJiO6sd83VYxzVpaF%2FlvK%2B9RYUQ9uI8sYmFJoizVZFToD4doUDPo1lucV6aUQC1ARr2zwi5f4mQGEE71kBTJoqSx8amnH54JhjPqkae4t6zqpfmhimZeVFmt93CmMxnS5WZhRQT9mBph7Ioo6KwK%2F5YR6ctZyh2m4hQIVUWoKQSSMc6dbfJLrWAt6hJt2DV3Yj0y1fGvwqwQpohi5S5aP7WCVBvp6NcJLGQcRGLtYNaXBSF4YYQ7jSFDz0iISxg5Lfh%2BVJjBrLG7Fe%2BDcSn44IY2mAlJANLp%2FzFjmFWyA9K41YEATkM8mkAkggmw8TbkYu3KLEO30r6NhbjO6BjJK%2FFOz0%2FjT6KubQhjQW7UdM7fnaSsUwtjeRPKteNmt8IQ6vo0Md3WmSKszgZhffyk%2FQ%2BB%2BDZwiRG%2FR0k%2Bo8ZtzAADQXgCcQRM6GKiw%2BtVXg6DAfD%2FEJWcXDxmz9KuNqauBEugZcWUAfb7CfxnuXnlv8%2F3kbkrafVOpDDqjBHcKRoc2exk2MSqRV0%2FKpJjnAxIXCvTtCsmQ6X96CKcERt8f%2BZKQAdacgyGNFrfMVufJSwSjAih6laCDRZRwDywhtoPM8DqF3JUH0duDPTgb4qV%2BhzxTDsoQTWW8bqxldwE0jyTLkYtcF8iX%2BygHbuOR%2BDt1reSixIok1l9CBZWmhTTRug0aOGZlsa1%2FqhdRK4Xje7azUCWif5phKH1UKNfsv2%2B7zt78A2an7Lz0l5V0GIUfN6fm%2Fzl7OGH00L68tt0xywyhbQ%2FinLZ1NTJhBLFh42BqD3KhhMFNkEs%2FHuyN5Sbfz8kdXNMyn1K9YPkUGz7dgllu6gmqDSjVlgWiTwXjNDrqx0VR8v6CdVByC3LnTrGXPqR%2BG2H7SrXFUjuyBmVnymolTJ%2FGkMnPIOZam16b%2Fc%2FryDNu4Kp8bWgOne7A%2Bl7JuyEZRriYEN1Dg1hGfTOeudJfaqrV2gyw%2BEEERvmIucEl279Ojh9mWDPR4LpzlJX7%2BGd8J9UzK1AXE6%2FZ9wVrslBQkxEhjUKYTPxymRiMHCsOVN7NOLmVoS0SpO5UTGYpRK3HxYXGgxlu6MlCRwFl1z3dVQCTDNuMotYkyRMvgmIY7Rm%2BNQBqysrNVoWhlb9kQQBUkzU0XBWKjrf2SdH4WY%2BFx41tpOj8qFx2%2BhIplQuSu7lfwv9B5SOsNuZyPwNPLfyxTBFE1d%2FRhJOx26yg9%2FYn9oZZF7GU1xD02XeeM5YtBkUsKljGFEI1mdGWWuEuoYemd7jQECmQkNBzYzeT6WlDqqLdz73FXY7JoqbrXhJhJDj%2BwB0dDXjSHbpGWlTBhPWtk%2FklXG12IBYNufb7hjBbOXRF9fQ5hnGBxTf%2Fd2%2FHdijVPAyGzkcT0T5nJ6exqkSmEVROzIHKYvLrdvGxCHRFzXRYKeFxI7k1zHTNg%2BjHqlYECJXhyzmwznhCGoJrJVxuij9eZRqBOh2BkF9x2JQeyy03t1cFvjxdcuT%2Bsnr8HVW2g8KpsrQnPEjHoCZm%2FJWdIIDfRcoh%2FEoQ3OHGSdNgtkrcFms6jvSxbYLxEYFjHurIJpFwL0cI96Bq2NGZvAsj6EiwrYrVdxtL2OG0oPgoBJxQ6%2FB38sSmBmvobfd746gQLLXkAF%2BP%2BKBHrZCarLrKB93JZ7y2L9mCO8zZe5F6TXo8Uoq23f9T83sXEw7DGraUr74i29lt%2BpPSMZmmzrBoEXbD2fgZOVRVtVZZqIjQ5K5wwVBsf0uI%2FeoTO94gAE62JnpT38S80NiEpPGwWiobnf%2Bp3k3ay7Li6DYBrO2HpVaWA8ceIJDo6GRJyWhD8cNpFarAOrhP0PvyqEp5QrXPgpG%2F2Ke7e%2FoQaC7juME47fRXVqO7%2Fbzwjfli1DH66j3Rh7mbWCm3xn1PEb1CcodBzSCdbGp%2B8ctkRwb%2FgIFhtiyfmQ8BqTWktTWp8q6JEi7robacZZCqCt5QTGR1XyD2aorIDZ8%2FVhfN083R%2Fn36rSahfcirnJ3J7YKk%2BNWsAOQ7skM2LhxjA68D6vO%2BiWSsrOP3Ievw2dPj4iLkSjPOrc2titWP9R8c5%2FkGXJQgH6MIzRyDUUChty6OtdrxjTdic9gIfTDaLXaSN3rCKedbmrR9s%2BoRTYQRa3pDinZuJQ1ccJSOPc6dTxDOIwxvCDFHjG1lA01fVlpjYglBPfCIqfYIzS0Zk0ZZzvPsnl4%2By%2BjJlu2se3vYUn5pjGsskjh6hjtd2USa2hgqq9k7UV7VdTG2YXEyTfsOIFf9NjcjT%2BqWD0CLY4DnVdaKRYTzOK68JQz8ue5qhdTqeftyc9EYJ%2FsL7dCePyYaD%2FJJV9UcWj4Vpjd3qKKeRlpl8uVMx2dEsSwhFgxjO1kmCD2Ot%2B%2BKDdjXtil1t3akHDwNLKmaOED3oZ0ThXqAfQzM2akDLP1OtbcLvoIvSNygH7SRafQNkAQSRBwC6bAHg52ccuvITze9Q9g2QT3EcWJ2YRabB%2FRB30UgzqkCPVeKdjCGNgECR6%2FPr0NvfGrmPIId80buspfFLkYWRDMZ44ZK%2BhzJpiQauaxt7Jmussz23uJMpkGTqlwpKNmBAaOpK68r0jphaglfA%2BwztTAF9AzaHOOLJAvmYuyKneB3sSshWONtMBvgF%2F7V0Q1dSMcY%2BNATsDEdVNsDvFe7gRPLUMv%2Fqp8tozNSF9E%2F5uB%2BfCefT1t2YZnBDreilQkz6rECuxTKW7I%2FXnmU9U%2F3tVhViI3amSBMP7ZYyoO%2BquUGAxoQeJxhqZvZhHCNIx%2B6Q8gS64yjxDr6bovlYRxLvNkpACn7GSwSImDC8vTEnIbF78srrpOYDFUfIuG%2FzjA1NURVzFr1pWGoMtGYbPHusv1H93jRkLRDrgG2K2%2BueZgpNZ8SJAqdrAn3ZOFleFJOMVWjXKWob8vK262DxUiB7rNCm8YpH7iGigkgYsb29GiV0bMTTrQ95AVbxdCBPcvRmYGRnH0o4%2BzZKXQ98ghwe%2FP1CPSBIc2iLa%2Bp37nWfxuCHa7tQO%2FUjTKNbqcxHTqmHLsLYonNiHPOvx%2BFJ5e%2FSg2n3oNHBlbNtBzzPybiN%2Ba6Rws9mv3g1ufAo%2B%2FRQfreanRbYjdI5PnR6ocskYbCqEWNYncceUgPIA74gZsD41UgM7qxM9jk1kOtGJxy0SQxXvRld40E%2BkLNDgdTncPmwRodqMDGNNiN7T0HMqoIIOu3k7EAXs9RpiyVdcqXk9J2kgPwK%2FcgsdhRUBJNu6SPnVjT2nY%2BFszzOAjWPoC5yk7n2qo71vAY%2FQh7MCd%2FE8KuGJev4YVALjWpVjHCeX%2Bbadt6F4tbgYcQsGkCQJmYLt1d04Ilm9wIh0sSlFKlwa3Sk25tfkg0B3FdL9NuF4UCNisr0DCDCYkDOd7wcaBcFwlq6mlVbCbNnpagCms6xNDQsYTgQc7dJzmwlgYJifBe9INWESyDSb4S1s6gDRcs9%2Fn0Z%2FfjpDLD0YlqpId2kaJbo7Z92y7CGx%2B3EFZHBVfm6ghB3iGh5VoF5GHOjDiWncysF2yvk9JvltFxANpYNLOjTSeBKiSO%2FSW07F4cBQB7Gn0xXxF4a%2BGZt4k3mznPcIHhZeJLE5CnKsqu0wieZ2WPqcedJGpzL6vpOngXYfsfLEvR8h6lvBMmTCX0nw8hz3EVQ1W%2F3tKrgNB4FrgAWHSVJLKx67kEgpH4kpNSc0xNHKbyvls6rgqnHMaa5aaQpwrAip8fakn7%2BZGn%2BeiWiaQERimPJrmKa0AkCKZ%2Bp4L6Cl1it9CIoUPnsNDijBRreVPyOt%2Fi3r6fqilifDgMVix3PUCpHVg2rvZSjx%2F4uQbexe62wpXCxWQjHiYfN3o4HWmu8t1LwnhBcdg2cRTi5cMNcX3t7z%2FeOt%2FFqzD71T8D%2F99uQrxaFDFo8bs6dUNI7ucmbEAqi0GUZmgisjaq0AlhpQ8RpPnJ0JCLa9i3Dn631oFdYtg6iCCcBLDwhVg8Bmy2DwWxO5RWNBr4hg6%2BRGMGOjOm9VhINquxobiwfxV3zVTUjHO7edAkW%2F5%2BqU%2BWEnreNAQa01uM8R4rK0sKyYI9%2FvoK4YkzLKo4dKp2dwQz1wu%2F2gu1lR%2BLcsXv85ZpNL5weJf1zxCDm2QOljxsf6yh%2BHhFjmrxJY3NIMtgeoGmzHiaUAu8geHWBl1WmsWo%2FhQ4h%2FhpmIYzeAK8NMJK3EBpF3FEsTju5U5cK6UskvbBO87gu%2BoUxeoYYzgzJ%2FeECClrTqC2FnqgyHbzmWoV2YU5szMGAvj3biOV4w27BX2Th9xnNu3M6pWOZUz2BKUqVQ1NxSCABaLqC6%2B5jkLGUprnn5h8b7w9z%2BWM9zLpuu7vX9MeGaSkNH4HZ6qz%2BmQGJPS%2FyYOhYRyvLic%2BOC%2FQusYlB%2FYgYWCjKSzMH%2BioH3dRpcDDP2Yup3nRP2Osmy6Up6%2FFQg9ok5sJHllOcF7KusANmjYMyTEvtAPUHVFS1d6zdq0vu2niNqy45sMqvqdUQsz%2B%2BryWEuHCeRS8n9nvpzZNVYv8u0eQzEy4%2BQ4EglD9MUOLtubG3ZZo%2Buv4T674X1XC9N2BDYofl8HblzHWXbR3ZAJwF55ZjFmRnhT94ABPkMbf0NGzpxpIeKxV2AOTY4fjVvxNPl5CjJyVECEXgCxxXx%2FOX9CU7gNysCwd1p79BZkXtNDntZ3MTQx4xjUT%2FjZmiMQUeMVgZERpSo%2BUqrQyzCXrLYEdC01omkNuU84fhRsJN%2FekGA%2BMsh1T7dPQi%2BOxOQu8PtP%2By08phjl4xD6tjEB6HoDEgEPwOYZEDV5xrm%2BmKTD%2BkGo147n1TV4Z2hSyBukIN2UPmStUdOSS0sv0RlMjPPCoY0xkJizYR7p%2Fu2HKIXg%2Byp%2FwazNFbAgnKdiztTRcJvZegVuf5u%2B8AqKx5KV4uS7bLjQjWNeEZssI18wSiKvKQ4PZeONZdpGbSVxctRGpOqJUMfZGBHsPBhRlfOLjOFHFgVC3pS7BFxYxrPR2VHx8OhwHraaJ5f1Hj60Sk7Fs1l%2FGDgzxJnXMu%2Bmf2QTTgxqr7kGu6dtliPakuOCADpK6byUL4XUAarKjaVYzXzmBIjQwBr7UB3UV%2BI3ATFesRmyfc0SRZ9x6IhgkjTymq%2BkD3UfpQEqAWTy93E9%2BSbeMnWqQK3z%2FYWsIm56kLQZrg57ZXo6MiI5RDM%2BM0zMUSfTunGyAylc%2BYff31%2BnDbFo5tNcwaUoQXMojf5j6srOCMXCGF3Hb5k4Hp3NjqJAKNSNSrN7yVpev3Jwf7%2ByflAqj31yJmmtE4dxQGJd0fDW1JRlNovFRelHDkGadbQ%2FHO5Dym3%2BPRUAGk6lMNSEcNCBQfUHw7%2FuWI0ar9y6jZw%2BMgW1gK%2BLRyAnZ%2BZBQyElgHVf%2BdS6fhaWZ3e2mGItAabDYurHXdDLiLhxa%2B6C8IcibM7aNl01jFEQXZRUsC3uhFhJxmfEhwExuqRl1i2tGALtlSDbwbSMd44tQNVKl%2FsJGrl19CKY33gEtCWBi%2FuryHr8Ncpms0xUgmKkXgdl8iShhXn4pdWIqiNCsFuYJfXOMQsiy%2FEWM25g1ESeG0CcD4XxGcmi5X4VnBGDVj0q561vDiFtRFscseZKyg7FRwvDf5grw6D1%2ByODWyS75ns6qPt6eWYhk%2FFusc%2FG%2FHhOZMSP9eKSGVrITIwH1GiYPsP05qpEqMvVvYsxKUaYfpkFYG6HMrbcMBhJN%2FajFW%2Fs2Ilittj4KGLJHUHl81OYugdj0Hfrq86XvWw6X4Z8pFzWEm1RRt4ijQPIebjoaMAUqV7ZMLMvonDfeAn%2BqihvqPMuMxhpZWL2HXqknMDyuXbS2xT%2Boso8WXnFt8VN4xobzW60d1N%2B75LBnikqevLxlFVj9YS3s1ajlaHi%2Fr7s1OmYBWHzkvR8iqpbHTo%2BA67vMRNOPtuuAEX%2BqvSJZCIg6kCmtzImZtWNDhBPcWwZzKrNnkJHcWbZrIOl7EGPfpOXJ2KNLdJFkVE0twfLH44ae7jcnTxQtQWnLKinkRbeBgyra9V9xpduIUeIsM9szOebK4I34iw4b2iyfEuIXQJ7VeylGQGmZM9wnIjMyw94npO4YyDUXlUGo2IgYh0H5jZHHnCj1LtF8S9YlQE3muWpBnadDPshxJcD0XCcuoIQvNCDT%2Br5tCxiZhmcAYKu42zIy7lKVJIalorUBA7LrWPa7W6n8Drt%2FkueYAV1a4WgwZiJnkAJyB94hXMFK9xkWdQHSf5L1DmULUYvoQgWjXPQ2NWl8D2v250jGUSlyeE0UIYAjO70bRyYNumIeTPmT2D4VYDG7xs%2BOlnEddRxaIQPEjEN1txageutFtNkXjv9Vv3tCAUV7xuFWNVzM9pJxcl%2Ba2FpcAomC5YepyvuThFYvXC%2B%2FWZjmppQps%2FkTqtru13B4UarAMRpK8Zwe9xn5wgplDq5nJtXrIoIEocrNYhxtozLZQc3ODfz8VfiNqNjbLJ925kdM9sEyEp37HHEaBeLbsN0Wc8sJQ6HUzOiqJa5FK%2BESbCtRmZeUzA%2Bm%2B%2BB3%2FqGZwR8S9s7ay%2BGinmOMdYYmOtW%2Fz3x0TwPjC2gk7%2BhWj6SgnEJXFdgzczvLVyY2B6ZPRN1Vl5s7q9%2ByBbvqNRbCtT7b681VAdRMN8BNmJ6uwXrvnYr%2BzxYvLDPcphGWXMnE2JNZRXshZNzjbmGUNjvw%2BgncGGX7fuRm12pwjdTWCbMQyZ%2BWUKwY3nrlarcmSt2Hv58PEDPyZMxHRfNjXy%2FA%2Bnpd0HFqElCISVWFeETNuBJokGRnZkkXvEEVc0GO4MzoCnBLsELmBfGdA%2FF23XOdcMkGeSpXa8sTKxIu2qaq8ar8CXwbjC%2BW%2F09EeJNaw3QsxaG32P82WuAT43%2BqA86n%2FXNaxifBWWzfQ0GlVSkt6N0UT39vipk6MZJiP1FDetOJY532ICuzqqyfjt9KXawqs%2FKL0zaqtvgfcZ36LyIBGCMwGn7%2FGIwuHTG9xu14zjFBWqYGs5rSIfRRL%2BGDFHQRv6sUvIV1kH0SZxQ1KJGNqM9ir%2BUaSlt0Zavk0E42DlUB%2FbwkfmQYM%2BpyRa7hwzggJ83hA5r14LLsRJ%2Fa2Vzefkbbq7MqrCtXvPDPN6taqEW8kgYGH2RiTIS30QZsSmMlsSv077SSUjk5zdeM3sL6hpUUUCbc4hLiAFZdQ04lQEfsj1AeHqbXVKQSyz8EjzxEmcEP5gY0z7Tzu%2BMVJgss%2BnFQChyqdRgTqH2YyDgjFyON423ul%2FHIs%2F2lAHIg4V8nbEP%2By9VOazuarOzRT2g3Jda%2BZTohG6EOhPWThSziBMYA91aIfyuWgVrvPbSYx%2F%2B%2F4B2dhmmERPdMv3mVbV2HFSEEr77%2BleqOPX9TZIjmTvpMkC4sNF%2BVjbjZJXbcC3bzWMr4OV4monyNVXgJDEVlEHUud66%2Fj6ChPeRXjBic8pxYSKphcWNk4V1yQ2pp%2FeSSjwaLtBEfZ%2FXHToKfQ68YOQ9Jwj%2BUcmMVPsqYbUhwwy9mVWgojugYtEqSAQIqa6iHg0oZq6S9RZy2adYkx6xhv41A18z9jp1MtU43o49xA9zKBZVn8QBOUIGjNVvt7ssMGbNbEuy2kaZ8ILlkMURgZeBznxGv7XYLjahbID3CTDFAD8TuuZjEWTGOYQbVHLznHXKIR891eM4uwhIg5LNt1zzJJwDF5RXoHBX2BisYl%2FElVBCjuILYm%2F3ndgiPasPYTTGZBZXs88rt5ijRCIV93NMIlypiOYa2JUYYOXzA8Dkht41sGcMwLjBzdte9SHy3nPos%2BrgPa%2BuqPodwFfrPSOzZ5jQNUgymRmhYKplGAQ1h75sZKkOUl9awzgUW7AlU6ssgrUEObl5ZX92IXmuyQZCQbZpndSy52hLLtXlzcYz8vma2F7Or55xn1gDdZOIw8goiL17C%2FaF7PdmUTGEPgOqHu67MmtBKVVSpaREWjvThy0ev3PB08GgPsTUvoKL54UnnQb74CzNCDg76JqvUIR5xHkMi2DKsPmIkmAxKoZRi9n%2BybyNipN7G1HGwxSvZl7bqPqHucfMoNDU4e%2B6UrGWa5GvE762b8d7jHO3hmPEfvRfxzyLf4cDhf3tewQLrBBIyumiRBOCvA22vgxEjC4dUThFEXGHBVH%2BfuGvpP0n4T0jIIMJ1VOHkMaA2keCdOQINBXi3WY33tXU3qFQL4NSWnSzyP9tMehDkkhu%2BPFrhswv3YcxILv3p%2FY49dAOnUeHElmpHci3N17Eu8iwN5cJzaJ4tnViE1hObT06YNcSaFEmVkRzKJ%2BeLM5Nw3Jd1Fac2DLF%2FmmljYATmdP3S%2BBai9OAoX656TGezfyB%2BGKpF4G497OpgYk1l2EIaVQJkVo8iagz0p6TDcUAgvKVkiqTE4hAvEidnuCKA4A37BKttAJbR9apNMAlTAb9yHUeg8Ifm68q5Vatt5hDtzCLHiyaw%2BBgZt4Z8qIrZpRGHX%2BHyc53YuF8O1EsktuzFEkSXX29xwxB1%2FU6OLt%2BS6v%2B%2FFPKjS6FXYhl1qBvBC1dmxekd5RWx13tv2jk2nX4nc3gErMi%2BoxInW9UCJY6nFWOPnH3jCWB6wYraX9tsBNatSsLPOA7nEs0a6sOWyo4jizSXHFhxcrMO%2FHqXoCtnpSbxWSChyDmc1I13lsUSzu4e%2FrhZnjSZZZQLPcMI%2Bivscd8%2FzfpLK2ChR6sIlmGNmkgfIfySjKrYhxCRYx8rRUukCkKHwuyZkQWWR1pkGGJr%2BUuWHxTKLyxZVdl%2FtU1QsOlYIdyZgl68HDuQmYrHPNAM7%2Blz%2B648rhRWT0zLrsO28hfmIy%2BsfahnEy1gTQgLaxBcl5Q0qnImeWvoGWv%2FAaMlIGQU8sY5QYLKT%2FfqGwX1GryoPvxeTL%2BpLtEz94OsY5hIAbkdeRhTb7NsLbkBOlnyUQzxVaYDrViJtBhwYShSNkFmKeFMxt28eoerRwaNw5wwHU8NXm3kYAPz2dYEsmhZa%2FojXJJeBuHpTYIUGMwJSJelwQ7JgYpYBvOMPgS%2Fqoqw7Qz%2F9klcNA5RlbSDBt8%2Bt0IZDM8yBC43b8Yh1rUF7hA7PK%2B8%2F9Jz8JvQYvAv34Ad8T6IEtia2BCb4UgLA36rKBWKhLO20UdxEH2%2FfERgw4PSSO7E6vFxJhlJp%2F8yNqc4x%2F4EXRkjJzZP9%2Bl3uU%2BLUA4bS17mUXcddbrg2bJRIDv7cl9S7QFemMqTDCb4XL1q7DGDaVhVeq48FWrEruRruEp%2FSJLQpKi80hO2idmm9%2F5cbZbTdsf1CtG6aXE6HYExFLpaS%2FUVGd8%2BAv%2FLnUow2YSr4mSrMqVN5MiA10ya1zLR5sQGF7sLfpTwySSXMGSYs3ZSePtGCxs2km3S445jC%2BY4o7ruBiPmFtIIVNoiSJ%2FIn6sw5j9FhEByMsNmAt3Uf3lNnTsZDiomOzWy42%2BVlym5sjaqhiag05w6t06wy0T2kCjuytPZYf2gAkMKMsiXWEkJZ6l8q2zU0GndP%2FRKllxUT0KNneIlymoMxUH2ZOrIPU%2BIw3CrtidrtPuokJjiPxEHwpAAb8H%2F4u8fIVxxmdxu5%2BDcFSvAo06AFoTzKOqExW44AmdmE3UO4rn6d2BUwUh4YfP5nPdv%2BRnmkRzZvh%2FtEQ8Kd9kO7XSQExl%2BPQKNCdVQixyfh4w4hUS75t1weHHmEAl0RLbycn29XbTJiRjWg4TuGe36QMnE3s5YbaFyQj1VrYgJHcJIa4T7rXN2JWZfrayBSCP%2FCSvF1%2FX62dRCQHV4yx1m19XilfjPI7sz4FSXsbahNqBZZAzszN0f%2Botppq6NJmz4Ewyxq2kBerbifNmHDQVujef4jhTfL%2B62cT38%2BOnLJMx%2BUNeOomIopyX8OaSJ1bIENAwGERwE%2FQTCc1XSEHdJZMX0NIYIOixgC2zYXJQijmRlj%2FraNYDJFcYILpPqwI8f8g4RUyYT6M%2Fos43TrGd3PGmMBLswR%2F%2BDvnFjn%2B3OLJ1ugMcAE0Dufj6s1S2liym4%2Fk3yQDi3cZA2s%2BBwRjFNNQyZ9TlMBx9sXufZ1ZoLeFz5PR6xkrtK0%2B%2Fsg6THCG7RRAlWY3s1I0J78EjXiReIEG%2Bd6g23tpnIAR0pfOSy3gsuMqQtno9Bpvx7EMs7%2FjPkcA%2B41%2F%2BFVD8D2H%2BBlbCJLbegyNgWCQpE9UFILSfYCQ%2Fedp%2FqrviBKKX57gjYbu077d5YLja34KT1BTpYUQAbRXzTWkInjb%2BqDLvw1aybvXwjojuVlLpgYqYpVrT7gRfegtWtDgrMbfKSqF5iCIZcqPXrEF7PRtEhplDbuTLavCQckvFghD36Se9hhQNXLLyr2tpD%2B0WnDEaUsanlboOFokNGC72krsQlwkQhvn%2BXRuF3uOh4089gn%2FhqVvXEtkcNVWRQssH%2BwrSycDPDOFFvuU4TQvJj3etUpVc5nk2wOA19ersCuH0cHd3s4DqEmIwwlePjim609vPkEN5qk%2BoKRs6QDCIl9dqFGaUJhSeqKvKe%2Fhv0gerWr6lUJIdPVOjfdd2M6fxu6wSXSgq0WsSO11F1xODjEGFJSDjp3EMc0jWWNEzZchon%2F8YP%2FQue035rQFO8utxJCbRqjO9h9e6KFuSFKlaECEGUr03MHMaA34mSU8HmQhik703FOj5hOAv6Iirl6z47PF1%2Bb7EQpZa5KB%2FjLnRPFuBJF3PHLT69wlBDhGHmRvcu%2F34upnzAjjDp1yhO7jYPMmZyH5Pwj8FhfyX%2F%2Fu%2F%2FavwADK2iJ9g1PfGcGkSI%2BGudzg03Arsgx7XogFmcq2JsGZahhrE5WC1o5SWJIS08ig3X1501G9ALd89aeqFMpB35UtgaK0Rg9Kmloi9lcufzi0bBC4ykEg0029NlimFK%2F%2BFYO5ZdkJE7JJD0SvRC7efnIE2imHELJf4cP8uWY6VdqKPOAEBbCD8DDRjuBZ61Hqkifj%2BsCY26kZ%2BHZY60a2cPFM5WgW4yGhDyidjOnyW1yrZvJUZjR2Mc3ox%2Fn7YBfpb8NvQuusUlDaEEY%2FpLoKHa7afj%2Bo9ESjjt3u%2BNHpyZT6VOtO4iYSvkgobehGgL%2BH2%2BHLv1KOY0NmLU4OuUz2ozkV31Cqf%2BFKHplnHWq6ew0sReQV1A7UqtSBC6S0kWxGaTlJE9rTcj%2BrAsoR9P36TKwNdqNdlaRqGeFk66iTpgc%2FHqLVwK96x4zNvYnc0mCye0GK8i6%2FVyAzPYU%2BFMY2z%2FznNOJ2NGeM6R0OhQCV0CTz5Hozan7Jfxa1Vk9mDW7bYMwucd%2FWvQMBEEvOiXmWAMpSAxTqLKW6INK5EcQs2ne84yPQhnJnQiJZlKdzCq2jthrIGKG8nyRzfPJOTKPY9WPw927DnmiI9zgCcrepuqhQS42Cpj7CkJGqdegeV12x6RXzucANKFicrqD5afpou4ZVfskYIGDrZroioLaM0bSFSwSVVPI8n01ZeKphA%2FSx0GW2ANUUJNKM44j5BNt3MLi5GB%2BMoQasLydIqa1lH4IXgrgUPymCcmZTey3b9iG%2ByEzR%2BPkjSaq5I3MH7t52CVb0Hk9D9WN1YJWFUm4WayKagfl2stjjdUp3iRlbirGCx3CZDGyMymw4MMHrScjg%2FTf2FfwmLFU1oIIBOgAI3LDswNsZRVL3Cb3HTrcN%2Fw5thwARB6%2BemwitvIPYGZxdXFCln%2BioRZq2dEGL0aux9We5FVEPlTWQZf5l4oqA9%2BCCI%2FtJnX1cIAR1aJVnlPCfLmYTev5nDVRlv7d8sqp5aPEBgud1DVgByHGc7Id279ZPbDoyHSxuLQjndpfo8jw%2FuvmvbokrJ%2BM67gteq%2FljaH5Vxt7Powa7JK0yu%2BOjVLn9fUENzci%2BHIYGHIQ2rTcZbHulbU%2B%2BtHYArPgteoDAm3ekafI%2FFWrQ%2BeGGdOD6MjFxfU%2F6%2BjiAtGdF0jrw4GJ9aQyPJ5SNaprGUs5wZDScTM7V%2BDJpvxalvIIzr5Naahn27Lb%2FNkE1tCJGQMS7btCqe2yrVGAMlaLg1vcQD4cTMvJSaR%2BHEMYkHzWpdx1RXj18iz8I4FFEY46ieHhfFddw68KI1YDJ8MqFQHZjKyXUUbg8vqcy1xcCqOqA%2BKch4udasbhDPChZX5ubj%2Bv%2FqOrsdOZIkO79KgdfVRPi%2Fx%2BhKC%2ByFoJUuJAG6GDQG1WTtNHfYLKJYPTMNYd9d%2Fh0zSzIjI4FdzAyDWYyK9HA3O3Z%2BQrgtdq8iWOmlncPalaalOCtmWpd5JseBENk9qMyyC2SmgO8SkZa2I7EC6UNmlauwvUxZmn%2FNn7Ed8fWS5FMvg3vKf7cxkTaDEh5Vfvw%2BuHtgy1vdso9WiY6IAUZ1irxMoJsCFmCzTbewENtO6lHlU%2BgnwkdgWULKNDgeDFOpsbT7LfZIVEbAj%2FZ%2Flpwh11eoB6hEXV6YSTknx4HOtbrNISYQmq4PMYyCEiLkizDdEtlCVDbIBSa2CdhTmh4Clwy24SbrVietNliaaDf2CGKQQWNRgSg5IM%2BM9QXXBuUfj9xIuevAg3kpKwVPa4XTzIOkn%2B%2FFc5iZxxBwAWzrraCGipShaqTCd7NAmKVBlPh82u6FXqruzG9gfvlXg%2BgSTb6cvww0E0NcqMUkin64gFeA4FDE%2FHfsW86uWZxV1Bv%2BLMFHmUFjYI1%2BLwztIGjoj3B%2FjDBzSKbwKZCDhK8%2BPvu78kAQHjpIIaefHUzT5hjWEiKIhcMASG0pTMxYqppjpkr%2BQBT0gyMUXZujmtjCiWrKj9utDWdbxaeF%2BgJIPfya6QaRSOH37kfgBudlE59P3Bfz3wdtujK%2BxgWQmRnZGfJbNZaGu881OZg74wuwSblQsJ%2FypuMSCKlCP5Ixb%2Fiw5CxuAo3XKkUChKI%2BAtTlCQKFe%2BaQWVftouC4wXdmCkPZToDUnrwELjSUzFF4243%2FWhH3MMOG3j%2BcLpjBK2GBc0suD8DTYEqZ0FL4bsMFteAPXj4zQl1LFGGezDnQWEcEC5KxTT5T8MvdOx1B6S5%2FAABs53OxE8Eth6pl9SB7vpi9wGfVkQpFwGKVUBhiW5c0lW9P4aJ8cz9OcJ6B64npgJOdFFylcSq%2BM8OdQBinFCUmYkcTnNW2qXTUSdpc%2BmHSYMX%2BVcQN%2Fq1hm0RzA%2F7lvYi5NwyVIJ46re0c%2F4VdKuFmZR09lxI7JvCPE3o5lG2Y1z1fmkKBym%2B4wRrnmJhkMBog%2Fvu3hWBEMhHtT1aUU5%2B45XcBUXSpoCWcU3iMuUfqns97gUOmjzN5JnMzw2gX1ymwb9fMRPu6VcdZ6ljZ4rDQveKiL68qPwRtuJpoyJ4Nnjh8BS9cqehhYXJOxvZAK0DcrkaNKbL4ZD4sGyJmCxGXyO6quERSON3dRlnJ9OjwZ7cQhBAQ3mELiegYI4QsQxXSgjYjRPHqKhdS6a%2FU7gYS6duG3g3y5ZAepDyVOtgxNCc5SikAZQ96Qq9R95F7o2DGzcMSlVAr0QOwcIAbiuZR7gfFX3FrrClPH%2FeBDRMtMXpxeYM079Z2VekUfLmQPzzAtUtfCqtTJDZfNrusiCtU6%2FWwfHPdmrn0FmAUVwZ1cywcMBeKSWk0uYXLjuxm9%2BlARi0tg4yMm6x97zjGsq8DZa3W9VIBkTOxixFkXkEinOnlzbJNtifE0wF%2FWJtBibEmsyw4SMxZNtPV0usp%2B6FKOhXjKkRQKNnl7era70Ymxe4GhFUEOMgpzA9wIA7PS5Bg2PCsVo%2BGwz9Bs1z8bbJp8BVD3hWOC5PUy660mVswbiY0Md73gSo0ZbvjIHKZAWBAxASCV8owJdH2hM7wbmXn8fI2yScOum%2F3%2FBDOEVzvMNNqbgQidv6u6BNPm9hse9CUrsrZKDZMkof52QzQDABR3LhoeXRYTiBXCIXS6OHZONdYlj3UBNnc9YVqsvX7%2BEF%2FxCDuhz7L2h1cLGSak6r3T%2BxJMEuoVUsknNIIi8yFFcE0LzuF%2FnB7WLhNN1XGOJnWlh%2BRXOavtGOaJWattXsQVNZ6xGGs%2BHxYM3%2FZ1ZMXaYlRSXMQ2DJzyktFREBYJTL2y3uEPxTyy8COqAqNA5XBMXYbsCTTluM2Tg%2FPDBObUCNfKbV5FY9i9Thvg%2BWLZBLjJTtP8W7oWR6LPbuUBRxZ8J6MU0KVvKnCMrvqHmldsCAo1dn0QqpMqqIyFdBBmNzYBKWbSEL0dAYWQJISvggpNdQxqsiGhGHT3Tfo6XeNZ7futus8AclRq81ZfLhQZcYph1v3HMP3ToY%2FTWNF0wPiuc7DYLDtqLisTxh9g510Cx3hvBfKlDmpplCmqkBR2KK8Bk6PoBElKqJGLqk7llVmoNj3KOXOzxmz6RIbNsXYABRUJ4AM6QwsX%2F%2Bw4tsbYnVDcchx5dyUn5xTotAB6ZFPnF%2B6SRGGhA4sJQTRm5tbEtM7ZUgAyp%2BSk3QQW1E%2BkPSwWXc1lezIQHstjjDqBW%2BWSQYcWDepZonIMZntkprW3Gk7d9rIhGEaa689JgDAPjAYLnhDl%2FoD1oZ5rSTgFhgz617EJDYYjtGsAOSC%2FswZQ116giGCviGA4EHZSgroaS5Bx3lAcyOBuJFF3tQiIlJVjpGnRvRkUCiDj%2BbHdVOPwzyTKtbJASDwOh6TJFnhETl4OddrJKq7RisY2MNsVDNjBklMyUmVgYK0V2%2F5sQWBkse0FdZGpBN0Q3bBQvYAeegoszHGsYV2fg%2FmGzBgRdI3vFEcpF3aouQ8dwE%2B%2BLAy8fTxPoN1ebpkzfCiWJ3ZJDMwCN2%2Fkaa5mmh291QNjNNBX4qymbrb%2FxaZ5WzyL60eTUcyI5OCop3LEBVW5Ho6U2PudEnv3TTKUBJXRJ5kTo4uq52C%2BMHbe4pXzTKhezuZA20dW04XumlueNJKWrATMgEDg5jUq6Gl9Hamtelg8LvMVF01DNGgmCXNpsOMJpkSQ4E2JTjQ4vxL6ouHSIDGDJ%2BSQY60DpcEqe2AdXRlSiu%2FE1A14iSgnWziL6QwKhqqZ3jtG3k24X1I1mXWC8k3kIJxIAUntvUjOgcUjhpBcdSZchcwToYnDVm1%2F5JG%2BdawQyRqB7cAnIxU070dzshcdFqtPdJDyLkRrJ21w2x%2BIOKZBXCMtz02FI8WCqPhNUhciENprqnLCn403dMl5Xy5yWqhub6b%2FZUZpUJP5hbpeDphi5R5joAUeRqgGWE27Qo5ChshbY2RSTCr2VQ1AsQGxBy6Ntm0yPOiKXfArJ06kBS%2BCES%2FdUdPkIIQdZ53x0EbfnFS8iI2dwoM3qNw2BmtY4rse%2BLUnii5RfN%2FGrxcse9yls%2BGwMt2rmhCBO95Dyoidj1DQ%2F4aVHbIJVLZkgHkRXKzHKcpn6yc%2Fbxcy4uGBQVncXYukIl5P5DY6i%2B93I2vnWo3Fq0OYMinYflKt8xIaJP2FocBd44junNX1Cw7sdWW2LJMI66Ru%2BOWbkgrmHNCh88WRJ%2BbYnjFNGRMZSRA%2FdRN7T0wu%2Fvqg0FgpqG8Z5snItvT8AQVivleVDmSQBdABz%2B9YJJFu2hP00ESGg3NaxBVOSORtQ2NmChMQlGdCYeJAl%2FYjETRKeNlvktmUT67wKsFJhZTJGbcLnxQ4YHdOkq3wM3BB%2Fgmpk43t7GkeNiEFlqBr0hHlI3FbtPGNTD5m3b%2B1mPjRh89lECPMK5H7gnOPMrtjI2Ol6HJCcw62hBMMYuFBjJCRYVSGdwLqIIhgp1CRTAh6Q%2Fdc9yTSEMcvdMxJT7VkqYv6GMifAgaByMf1E12xFdBacwZ9xQO8ggmp2FkYgw7Ok24IVp%2B8t28WVVkSVcItogpXksMafGlzFgnmctN8TmdyYsdJwqLlA3DUNR4577j9cROOikuYvxHrIG06CplfUPrMk0mnJDG2DcvAmc3iQiUQmXfIOtEbGplq%2FmrXSmp%2BJEZ5wLDsxR3IZe8sbsBCwkSFEaF8Kfqfw8Ee9ecIO2O6PIdw4TLlkTouzh%2BqtK8tGpHNHmL65YLrV3oFRVWpnQIqGvDaEBqj0ChcYIPY3h0eMBHnADFdrNhMbvCsmmCPbdjygGMktMJMeD6MshXq9odpt%2BVmYVJBjFb7l7NytwlrWLCai9vodXYZFSEP5kJSCSKnYT4tgtjVSGXQMhZakUrNjSjIf17Mngdbm2jeAYeZlIUoI3%2FhOk18Tcdeqlgmbx9Uir7KpX2i6lZRwm99na3WuHApYJCr9W799jKDCnSq4TirimsFSxakZKRE0%2FoIK9Dy0ru0JgZHvVa37idTqeAot8TEWxq2uv8oKJWa1ejnD2NLZUhkT5UdumFHHYltAFPMkTJbvLYldFKY4ayvrikAloiBv3JdWqiqfKIKYGToWBduQMw8zlcnAqbxRCAl91KIKmyqcQaEMwjLIIzCE0ScUcBBY%2FGE%2BDk20XvcX%2FhyRurlPEq4yTXkXdtE6giuqv4GuMrakvEYdkmwIDM1CRoa%2FfioTwYTXZaeY4Jd9pA5MTMHP4%2BhHs3YxdwydcDMSvwSJhmSKwKKnr3T1ZmIMrP%2FSLtIj1LfN0pJ4wANrCTI1qJw92F6EYV6LBwqoPc1E21ybxlVHesABKygJiqwFpHQEQcIMS0Wo62RZMQMcXj0JNy5cKUXGvtuZU3L6x6KSKRzxGG0CLBGK9WmjpWgJMLAXtkn1bAmK1JJXQI97suXon7peAyRbFQlCWtMScprsg2mNLLA81Rgab4eVkYhJdtkg84rBIWjem%2FYTao0pgQMmyj0vLalWAiq17nLjNopntjb%2FCoBRhdem541Cab7m8uz6Bno%2FZzfy6spChrkWnUHj5pQCT0PA1RaDGGDURAJD1wWp0vwpScISvp0mvb8i4KaTz3vvZuiogRI5lGlgnbH3Qp8y3AX7gqEBViseH5GNMpUTZVB0QUh4dDAoO1ZnpcmAKcsEoKKU6QVvwNLDS5x7kRCpXnZejqpEzE9ZhJUFl2x4Xc3Vpf9Z68cBJkDOhfiZpw8ILOTw5%2FVkF7Ii2DCJJXqFV7kA6U3Afepn3A0LqWZeJKXF6qliDFOyLPwiinTJs%2FZHS22j2opwYicaQJwpqKCWxh9tIFj6NJj6aXCRERyQQQe%2FXTlX96bdcrmRbT%2FQ1UL6bFahaEVsmIMYqLokQ%2Fzaa9zQMhbtrwschyAYxEyF3SAlS1bn1WMaLAPmw96eCLQRaH%2B83wxi0CqX1ptXAkIBAmh%2F%2BHkrkqDXp1qsSE40vxT38bfJAk%2Fi0iPqYANdq3JNdSIJoUwAncYguuAwjIbhYs3lYSXLF7lGXXvs2yEfbnKyNXC2mbCibyr1tGbIL6mFAYPgjgIwgKBZ8nkwFdiaieBxu1V1VwdPg0CV1e4YvvhNkOXA2zfGG2t0u85%2FJ%2B5fQUGc5kReH4RJnaGYBB0JgHdSbLBuTUyqGegnG5ycmBtHmPD2cIDmGGcamS6rv3hHwTeGRrh%2FChJY2DRJJMfSJp3PJnoH4wOPFNRLqNoQhZ%2B7Nd0mEEppx4Nt3ZlaXG5IVaLGZtjIkYeFWFsVkJnNEMVZlGh1MNYj5ZvVKmm0gYgzXgcSRVZg9Q9BdgFDQRPSKckhpkwvAYju3NidcIk50aYfJohqcsR0nYrjlaMayMUFiBf7tEAYoE2AYORNPGLZq1wI6nDEKUbHYWBkFPDhU7QUjlY%2BKLwre5ZwavN3OZXY9UH4WxXXBxghSSfWaVTRdBCapgVdf2JDXtku2Yb4m6CtS34EvGr6vQoZDkFr3UNQx0cArRQGLOHBlfilQSOzLgBoGjSUcAVpNOK0P%2Bj6oV3V%2F1PLgJk6YqSd0i4vBeEIW8qRS7jCakHqEVcsbwJn4KlT6JFF74sl9BmFS2ik%2BRqDr5ltAVh4Wo4t12JeWiVXduOYLbIa47QqkZA3%2FFCq9GA%2FaHIwAC0vmdJXYwswBZrReewSieSAkUqtwfCm9I687qAV9gq8UEZ3dm7pSTKtg%2BMLd%2FnnZoGF%2BHctEpzwg2ZdtbMY2woTamHNM8H%2BdezICUBEllo1HG49DjlU9VGjP9xrSxO5CZijsoRbJWV9ddaYiGhqMlQt1Nt4SWawSho6rNkAKR0sU5m4TkUIDKC2KPxDFaIIJxEV1ad1cQ5sGi0eQmlEukf8MUAf5L4ZdehAiz0KAhjhjgojsYRc5XXiWj%2BQHbM9TaqyGo%2BoLAKIp6drqYOBW4QGHjn1z8Kg83lbDVNx1aLLodXglyQWzHwwCO8XGRNNoJAp0FzgwV22lnc9LN8lmJMlMYpTKNFqpFP%2BHG4wrW2CEu4ULlQbJYWGz0Fvt3LEfOR9Lkor3qXijLSh8NZWm%2BSxEpm3bNU2TZbrIemhdaJ8KmmqegmTKMXjYbSoe8RagoRyR8ZxHDkAbwEwCULyCdtL28JKyMGmOGHTHL%2BmEc%2Bs4YSRK1ius%2FXFBAZ5lFtGVi5N9WRtKPJER6TPMDpx1n6wLApcsLIjHVKQRyxipBgUlyErH4Jg0pmmbqCjaWrYf%2FPZMorE4Fiq9XgKiCq%2ByDKADcTbyZRwB%2BwBcHQMHiWdaY%2BF152CjeMhTYILvVMJGEWUiS%2B0oKq6Ekk4xpmYkk%2F1rrJTEbZE0cbIyxgn%2BrcBbArGGjGVlHgcuy8VeH9XHpkqk2bjuWPiPPOzp3nBl34xxhrbJLRIA0OSRUSrQgSpPFF8nWXV%2BTJPRVTiBGvFe0FiQJiaetymBwiknpOkjGJVqLdQhEuBaDBylAsJRvElWzN3hZQJd8fAktdwZIqdo3iVLwcVRJSshouKw245uvZ4TxOSBQx9goKrBUFXuA1raifTY%2BTxV%2FYErHsVn7DO0MGArYaXcCCSHwijOd9oGLZkOmxutVT5EcT2mJ8R5am8vqAykRuRDGrPHbIM1TKe5SpqK5F%2F8R2sRUhJC8jvSCNIssBCYdZlLHkpKxJkaF0wbimOh37Auzht0OnsDlm9eR41g5Aw92ZUWGOz2EA2jsCSMiC8LDuxVqNbu9KwehnJiCWU19RHUVBVlp%2FZlhHvJFmDG8daP5GsPfZgLeucTWCkSJEoa%2BH1eFKiaEV5%2Fssc2MfdHS4dpCy8ImHYpownYwVaJr2S15RGY28FUYkrnxVVNePChfG2HngLqFPYysAPOMEYADazkrzsxdmKdIakRh1eoFkxJvQfQ8Ntaq0KJcBGZvzQMUGZNBTaKiqQFyZDEwgDk7Pt3NckNY7ox1oU2O2BBE%2BPsxRdxCT0A9dw0q4Xd6ZyW%2FEZhlCOLt3CEJlpEQSE62LxVQk%2BaYAC82Ap%2FSZLF4Rakr5laEpo6SEz1HbW4Byj9A67f%2Bko%2BhypCPMN2%2Fu7cCIMH0H1ki4hneuHCDiEzmYC4hr2ZBsOWYAS%2FzGlLObZLTbACFE6yEYzzdZjw82pukib7kOt5Omb8GD0v%2F6WL2TVbftLGwPQwPyMqgw63qh%2BOzUNkprJEiyrZKCmV6CPpos1GDx8BAjfMKUzNTlmK8OURvllG%2F1ahSxSUwwrYFxRcRGYQlVdf%2BMiODV6uMEvZiaiNqnZxKYCQ3XxBZDIG18HN223QowOzSWPy05qmvyM2YnAPLFc8RgTdCmTmmrOKnh1gqw0FDTuM7sNSIlhvqvyzuIWs8CWd9SIjlU2QIORAOMLWKoMSs4m1TemI2MAkiLbjPJOajRaoA9qwKwKA43V14zq%2BlrwterOYHKnfkkKEEb6zvL%2B8GNH183TXStS8RJrmiWnYRd80wZlLFA0Xhi%2BVPNytAF5gBczBD6LOIRmwM2Ly46y0khE2pqDUEOowk6d4oKmpz7AhIkcOb6RXDKzfjgMpIA57xyB1bQFmI9mmE0B05KU%2BlOaOKS9wSp7fCHJS2NY2ZXNRz0wDjy%2Bym4Mw%2F4c1jPzBdnwqpDYo6THkr8wDdIM0ikpk9qughjhnwMN5Y00x4YYF0mbjWiHCfSsRWthrOWMVQT0a0qH%2F34SR8vPSg2EFmII3MpwXQwiVJmTJvMSwT4AQ%2BoPTsRgUXl1MzIJJ2sh9ZODBcMSDhUXUjxijGUPsteX%2BDUSPmWJtzKrBdNIclaO1OksbkiDkD7MRSvDfGP4hOi9AORhNGP0YgIkqD%2BV9pTXRAcxm8mSWrnNSgGk%2B8UmYYaSBvEhOWP2v2nSpDK2lDYpRoj3uTco31jgma7T2YdlKCMB%2FZLpYUFb1Bl25oOl0WN2UUl3T0GAH78EokNhqP1nyD5PHtKuihyNhjxDgBdHBaxRMyCuadHMCS7riRFCxhlm2jtbMsKejUQ%2FZs6EQv3LP1a0%2FqSFz7vMAgvRNcM0vvly0CFXU2ku5N8epGdsmXPmutI98f%2BVaYsWL104WDF4a1DFg5QuxeWJailMvF0WcHyZKKaPvYycOmlY2%2ByzbVYFbKSakfGFEln4R2YYMU4Ju2k4he7XL7UdlKeeJjb%2BTiG1csFdnZPLBUeDsqfkDJOnAUMyBd4WrmcLBMrwW47d7si4Ju%2B57o%2By4y6fTZOEED8HuOTFfbCrDAf3dzoKHRLH3DYDBhXiziY6y3vxJRY3NgOYRykDZQPnMNmFm5CE0mhC3cebKMufHY6rvvRhpSyIt3MpD0MR6yVSWR1otbFK9al9MeclHzPcWhFDiPOOAR7ocwdqREwQiyGjGgIXQH8ZtZ2S1Wocwmxi%2BhSimiJzi6xM%2Bgv418u64IEhAf%2FP%2BM7UINI9IIwqkWYLRYBBC9k2KK3WuBSFh0uBchAFwWGf6wTE3TQIgCPFkiCmq2V261UxuUNWgZzVTs8OI4sKme83AiO36PXZpRvqxWvYDgKKZBRHJrKNoQujdx2%2FNMNEjakK5k922ESckqRK%2Bpok5t4cw1pBlD2bWH12hnFYvjTZMR5uhFJVfjVWme20VpAhybNTT0wcHa%2FwuW2ApK3mwzQ2XHgBKGHzQiryY5gkXyK0FrrzLoZwqGTNE9VeCR0woOBK%2BP7stl%2BC7Rpk7HWx%2FmS8YRqPp0SVke5GShzgz8T5bRMtvk%2BLUNmGnXJgt1oBKxUcy8l9NNhG6sKUJtBxog9%2B%2BLqE%2BxTk17IY80anpYApumggrptk1hoKLk5FHqtIdiixnBD8ZZ1gdH2LeBNxOMFyZ70z11Cma9NlLWwImx%2FnpXHMxB01%2BUV0i75vKw9fzr9ZiFd0xbC%2BK95PwIpY2gcRHKiL2KFbM6bKHXXsjlhL8BsRN1XyLbFIJZwqtSgS2ed7K%2BTwqwbqmTzpzH%2B5e%2BAa%2FL3TL6sKiYGgysBVa86B2y%2BFIsnRocs%2FnOnjkJrS0io6d8Jarc3K3%2FFb8Y%2FwWSIHpsrEjvJZXISXxv402QbQEmBhDq9tLoGTZZ%2BYE0GnlE5Oa2uZTfJwFVR%2Fpg2JVmzGzwOlC0%2BODI9pYnX28voQNgLmHgb%2BTNMQCSIYe8cuxNZ14glBkuVTjJAyQ1Ecqwr4swEYRV4kOiJPJJGU4szCvAsSINhuFy0zSaGY9b4ZEwB2GPNVmTU5yyjCN2TFHckQVJjpwthjJAW5B4kIjQv3G8Zg%2BoUfCiZDG7i2Kkmi7qLpPRYda3UmW1Db00gLuOyBfHKdhV8RJClpgyPYCGaHeIKwYKxiZZpC3GIVbYuufMcLS53ULH6hPNwgghNriUZpjSRIaCEGYLpcI0tM6OjeFhqcpZjwQxWnm2jqzq2gCGIh0e5cYARjUIaxUd8Peacs6tJYdkK7%2BnTvE2rSXHeRTMr1mycoSLKhB9Yi1IDl68cFQkMi0hHcz2f1hQFBkQ%2BDxKni1THiEwFy6x36RoKdkcv7rgTKyNugu%2BB2FxewWw%2FG6oMXxEA2DWVyz1Fuh6aNfgHBbx74sGmb4vaIQD4WIKEnINoBouA2fThaUA5KinbuQ7j6xn4mx%2BxSlGvyLLUjClHhJEqn20R416ZY83XnIggLAqiqybafDDNyVN0W74r05HCrDT1xvqNk%2FdsSx5qTo5nwBo2mRYIc18LpldyTMUC1d%2Bhg%2FmoWnTvLL%2F%2BDcpUgBfBYRcMyhCnMlry1yXKZ774DStIgGEu4fr0PRJfK8M1oCJNzn4EBbCQNGQCnnAbKK37kZ9UpRZFlJUIZOH0GdT9picWovOFTRrGkdCYPYoJEZjWTG61Os%2Bmy4y5ZIzBakXZse6CYYaipk2kQsuCxDDEUiwuVhnR48qgxWZ8e3%2Bp13RGPB7We1ujkbRrSkDdpDdh0K12ZFfDfH09pqMAHZE5pLVJWWKXFNyfOUwNq8DEUqE9G4KQLdfkk5YBgiJSUKEHEGJFe7Nd%2BNRAxp8qhu06V6C%2BgxqzkxhpDCnkBpCmVS9JRdmE9XSVUyYV7q3pQrRTcp9C9YNTFL0FZBYfBQi%2Baby2aZMcIOjxReIKHY9DrXnGulvWGKSoprDcdxMpyHqb%2FbExWdWFQ%2Bb9uKVMYV8sJFJCh0uWrg84%2FTmVoObItwlX1Ku2B50qKIMLuD9yBlS%2FDeF9I9Wrgqi1i2gmPNot6Y9iqMGsaztk3jQTFwNZLGm9HBZ5CbwNCAx65Cwsin6vuho7BbJKxrZ%2FBGCHIrCWJMQWkTamAiUwTHPqsHNuzbmgRitsHVIe%2Bo%2BK1WWoSICGc5JFcdmjM94850TVdc01ypcvrd%2BycXUllRxSyhuqYo4GRIS8pPwukHuKA%2Bi9T1WV9ZrlnWlYsfyiYqh47pkNY0cb1jKUF7IDdDvgpqZqStD8C3chWEEafrNGxxjQuYcor%2FJWVMgPtvnbMr7Ai3yGI4EiIJ6BfZu8VDwTQIDiLOQVmw6NjEe1MrQ4w3kWyoNat3ppKfJ%2FI8YakI817%2FoTLNV1hdpMgESqsPpij2Xz06rHn4C%2FoecisEVLZQvNGymVHk3Wo%2FqI0ms%2B3a5xBmkRRGq6ShvSXU5yCa1XxeCyS80IxpQ6RKY0wjfMmpHEztGQaxmds8rLOMM4tkc1MTwB9coz8ks3tzhsa40guktETeBmLor49c7X4aljEiAHBnEhFtplUEXTE5CFCOVTta9skRz5jb20UrYIk%2BDLeCn%2FF7MWRog8YJn9txhhY0mtpQqh83HMJqk1IC3nWtITwboHLyZXYagwYDo8O9ZPrjF%2B0olRHIXN6w5WEIiUQFJ%2BEHgSUHNb96VyWFfGJH0OAmTHoDDlccc4fWJEJBYUG9ENsm39TrXvAVpSm6%2BMkzoJTsRAHY0G0ZXeI%2BHQCfAGUWJcDK4JEnhifL8XHuGKd24i0aUIGV691AtniG5oZRmBuaaGEFxxhgPDOR%2BVlYrQafJetFpvkNUVwGyMPDNKIjSl%2FpH3pXen9BL6dwXNBEW2ojk6W7YfxzjR0LIMAhvXGepFRu9TDNFdet5FhW257zZ4RYyMbdUNCh30Lw3xb8KaIqDLUI6C9bYIsJDXTezZTpVTEb5bWqJjC1QdXP1huu4%2FnYU5oALm9rtGlYppSnEZYrWP109ALbGTSWx9G1QJ7dd%2FUsqJYxlDl0fjTCtj5lPQYBJ1oDBL5thWy67d7AMxS2aBAfCqjxRxBMwDceAxvTdJtOlAvL%2F5rPY3oXzFSHcGr9NXl6PoktsvoRTsFKNVtONtwzCqsJcXwUO2Go3ZVAUoWZotlSxu9kBfpKAKptIZ82p9RRAm6jPzbMGWCtdmPX6T8xABNRjvm3aZsXhRNwpsVtIa%2BQdZXU6cx1wI0UwGWUAvbCi39hC179u6pMkh1w2%2Fo3sdzMoRGK7yxYVoo9nVQzz%2B6eQ3IISyYuqnYkyOFnpJ4BXAthMpobBv1WuE0ag3WsOVxzc2iG4EuvnXk1VYdjgI3sJL1BCqs3DVBFnHhKFyAoaIYhJtxARxc9D3l3fXzNBgapLGpl1LLoenyKkqqeS6bY7qaEu45dh7J6DmIpuSAnAVAjWeva8y%2FIfRVSINrEBABalAtvtt5NzMxCMjPVcyrSewT5%2FOBID7swcYeA9%2BFsJGhfdXKl8bOTDYyXkArw2EFb5xfMcZv2mPf%2BywcoWn%2BwiNg6PmAJ14lUQCNj8PeqSlUL%2BYkmGGxpJO1lQC9CjWVlJYYThwSrhAztC5AKS1oFGXRNCZDSdHxPkljyB2YatCSnV5rLCjIsd5TQT6zFiNJqNmjBkpuZWR6rcIM2RspIDoxoCs3DduBDMWPgymG5YppjNPEpXIuKUDwGRwrZTRWhD3cixHDHBSU7brZYQaH0IgXcYS9Fpuv6PzG5QedxJfO%2BpcPI2HbxrPbYe4QyKTymKQkwXjbmMuWgL%2BS3tn2GQQYZJwpVsRlon9HalReUUngKoGKDLDw8NcVgIn7z1FWvs7VsIXgJ4rfKofQGu%2BkWUJY7k3ee9ivmj5EEC1WyP44mLIQRUkJyKTg2cNTCHfzCDI4QHPu5BnCPNSev0FtSiWS%2BQ6jSYdPYq0iRY%2FW2kAFl54liXw%2FyFzhHAZrqdUrGWmCMDHp59M1hCIHBKcj3bfUpQRTCjn8SDwuMwRJPtV8wKNN9NcU4E3XngCN%2FxdDIRrBYfNA9Fn%2BAWQIqYOpls3kDqcXL3%2FL%2FJMBG1u7Lc3aXXJOucccX53Aihs1YegHZM0qH0QQW3V9ZIfhPCk7i%2FIDwX02n9SA4fSnPHNLtcHJmJ0qkGUVXJacbR7uGFtmMEjWWNhyAZTxX7PwAXkhRtFgf9uirVjzO2XFoUy1fMStI0spYkoTJ2HxiCVo9TGLb6OsLU3Wyi5S%2BjZJYelh0YRvHWVrRpbnWJcwhYsKYG3WdcGpHiDkXuS54hUmFqRzWrVEqHNFlikwg8XF7rRYGvFOZNuKOKYChRChVDfenUHBaEXOqxziB4xwXvHdOuqd9vuPguKyZnqipiy9MJBATUTbkybZgCC4NiCDuK7hNyuhZzwq%2Fyjnr0XRfWZ6PsdVM2MGLh6AQthGE%2BkWpdIdOKCPGamDnDDmJ4mZzCUs6qajVYdKOfpJhJhCpYlEWEXRfxXlCy2zLIBrtAIOoph%2BtAU4FP98cASt80oXiWFafNxbZDaFbUNDSjCjxyUp8UdorvUsFpZyKxwJpJ0Uy53d4w2HGnM90dIzXXeZRHzZmMnAF4j3UKvd6cu0R%2FB50Vg%2BAUvj%2BiTHFPpDypZivw8VEZghw5n1iZ5V1qb04V312RR2piB2Pfq%2FkkT8ZdEzJ%2F3SiZZY0h%2BrEtO0SXWSlEzI%2B8%2FpiE6CgBnL6yOzMS6igF1iocLDSsEI3TVOAlOfp5T6TJICIYokt6%2F%2FnoO%2F49YwDvQLb0XUTo6fg1ITDwrAh7jWBvqDWa5O1YBSSXFm7K28bdZwbi2sw6zBIi3TcA1rOUb1SbiA3s0C86jHEAE45rjcDoMZ5L7reeTCUiNVIOAQK6OvqbEJ3YbCjJZXeX%2FZDj%2BwXRO%2F8Ma4ae1mMYFC4GF1%2BRrkbQoClieEmSutW%2FkJH4RdhWXN9EjZ3k6MBZEHIDSAbS0mHyEhGp60%2FR1Cqc1SnylT4bHQJnTdtjQNOFclHbJK%2F6lUgo%2BjipdxYQZYJhSvSilBEHFOXRx8IibKx8J1ILtuQY7PtaAl9eXn%2F7bjmf3lOxSgffPHmWP6owmTiNEK%2BsP2lsP9RFQ6LLsf4IGwvokhcnjfS%2ByMMDCZ5jkOl9x5Q1Mz3U3Dnzt6CQgSqv16Ry%2FqX3QrhhxODzCXU94QlpngFy9S9l%2FZEZVpKPvbb1qZ%2BFaz0SrSk7c91XVvItJb7ASG4Crj3wHXZWa%2B3wOWawoKyDfICqfxBUnkTmIQyMj1VGYHwXFI87P5y0EWkSCG2CPJIoTpIaM7gim%2F86Rf3ENFxA%2Fx4lReOb0eQtIQfnhFKGUuNDCDjWv0iLuN7CTX8kf0%2Fy4NkWdn4OBWLhlwcbv3yd%2FiZTfjPlF3Bgk1ZxisVegmqwef48pAn5pYAiZTueBVhARyORBA6U18%2Bb5ZtwmrqBhZI2JOAXr8pwHgagWDRW0YMsMoivGbSRIAc%2FQej4qX7kHjIuWVlF5FWwXvAzu%2BuuyTkHc3aZIns%2Fmxmbw0w%2BISELDVBQhpOuO0MRbqIhTqvdWIjrZIATN2Ul1iygHUEm9tf8KtbqkvWOdBbFJwMs9%2FpRxCCzX35Nk0IwaIZlNRQnOC0TRvUNlTJlZCCZ1NybjKvDdDGpRKZsxiLREoyQhm8KiiEHL8UJN502TVmQ1hf%2B%2Bvz08To1Qmc9YO4ww4vkbo%2FiF9Op4r8uFuhPZjw3%2BGtJI9mwRwQxHPaK7i7bp6Kn5JcfEgh%2FcOt3sggBpiNjhuRnigox5Zxgx5MCLCA6q9iJO9Dh900ZsVtxMh06Fuh0AHUuMcXPhJXPi189PJV2BOQWKxZAOWGYdDG4rhGo7RmWjP30tKb8cmuExe0Ku0FT6VIUQjebCMgKnU0%2Bopwya960k7lfzBBVgVwfaPWXcbFE5VSnMuTyZKgG6Yr6uDEp4mwmPG5yKu%2BQ4gx7JcAVSbE4yXuQwppUONIUGkGOUhNkxXTh6RILtYmjr1CR5IQtNM8UhTwQ6c09jk3TRWESs4UgCA4RZ8dg8zZmZ5JjlvsbX7y6lBS1KcHKtGebUpxl%2Bg45YkQsVBG0bqCKCSblCzPMH0KLTGgu34nSRIpHsSkivsguZG20uycRwYzkvpFg7tYAkmwwZOHFGCOcOpNwXOaYUt9bjkmVlTYcqeySjDIsyh70NftMbn2TYHFmyF%2FaheaMPSnmfmtHmo7a7ir7ea5Y67q8FEcwkFeT4dtgVZmhHPyo6I2DC9xMopIo2kZ7XW9aIzYXv5mLXw%2Bu101TdezEPIiXPXZTjAMGAOHhSiwOBG2mRnh7egKL0mIBx9UyWl6wylFUigl404WdgmLxOi4ujMlFdirAwxxTxv9l3ASPWv7unjnVsDBI7nmwWeoC2zZmP7Cxuok4UTbhqYaiDAjBmR3ynJRH%2BnT7K5rW0rW5aRZgTuiYX8L6auwzFp6LVnHIpIt9dFzI%2FrIj3JU350p99m3ZgWZ2QcdrwTyyGC%2BjRdgwjstVUnKYYYbko35kWg%2BSQcHuhuTFlO2oFVTSF2ogGY8T0%2BDBEEwQ8KnYcQFLkVdEh77LYBx42jroKVNqHgI03kjYruzJcPnAHB3qyWY%2FxfEdFpUDuQiTFgvc8n8GLYUObkJZjXLFD8f6DXOOnCNAYGB4smGk14Y5s1OSAhML2EGEYH9Tc04gGMIEmjoPfMq6xEi9ZPPDAcQbOuVlHhETGrZF2n7aNzfiYTvFDwQ2RDEVMwAttBdO92TZKhYbQl8LZmOcg2LkUpipmD1knyrJXRB8GhKeW7vB3wo0aIs8eakfCToK%2FJ56qKoqpf33jBnxRQUJ4n0JA8niPobYfp2pITb17oA6NWAbVKoRiDyqUh3xlcmmrQCxwsSdKfja7o3EwIAcB2ieL3YdnnPMHEb8iKqwYca5fNNsveQqmWMwCnZZFxGIXHw6Q57DkBu6wFnbx7JoO1WBq6V4x0oj35k3KSfMxHwZq0AmlWB1u4d97DJB2S3I0WD5JGKeAt6mAiA9E5xhmR5aNiOcjDe5%2FHL2iyO4qNyIN3bo45YTjrn%2BBESs%2BpdtIXKDSWF8zHW0igGOJXPE59k8SJiyFY3GhAKZNwhjLIbktAYp3CKQArDoRAeeAgjBcUXyFNJnZSGIgDjwvHgm%2B2maCog7X6FjW5OrlEYgUGZ7xlSWqktfb0b5EmalSgCXsHV6umgmOjDj4jfQKNUL5lfNmq8293XIymbEQKJ4ctUgLxpLmU3cUBsaIAsBBRROH37uXTXpJqyyJ%2BPOq1KQtpsX1UYGUD1Zwi4xtADl3XQndKU12%2F1xlGpWPoB7rDbDGgVjhAGh15kQ64vbqXAB8hRPYusNfTl%2FRwZGJaIqUMhDsQFjNw9gbE2wkVg9NkN232JxW5ly0erBPEFJDWODdq5EkUL67649qVpcemF7wuwObkTfnfMPqTVpkkfNZm8ESwCWQ6YoNHYUHhEQfdRNBy1jYBK8fnFmw213VS5sezAKwNg9wljaVHQ4Qb%2FNXu4mZYA0623fwpxdHlVCn%2BGWOZ4NNQBNIVIZjSTkuiLfOqjFxl4UPXzITBNI0H47Zs%2FsAKz%2BzWJPCvN0pkzQ25qbK%2BCE0mXADbtTNdlgv4Smt36qMYJBSVkgdNHd%2BWZYLDGVpaWaKcjzu%2BibMuxzU3IOXdFGRohc6cCIRSH7c%2Fj8X3fK%2BSC3Jp%2B1UttRcNIcGkTK1jdwYAfrtCdXNXsrkq8ndz2FCy0nMFi%2BVGZW8iFbZyIMI8tGr8j14WSzlbiUkj2%2FwPyFe7a%2BKftq1yoAeoS4i1xd2Cyo63qkjRXZo1rEqU5zcghjM0S9w7TwA3dooxkxWLGuHFc8%2FzKU6yGgEr2itbykJ7NvwY6xQ5JgNF7KJhjPWUtSde58%2F9OpfmzKwP0YKJEdULXZrbO9iu1Hv159cq49AnTG%2FD%2F0qkq9AmFl7C4gBPYAImDiv7nTOr5USWcjNkU1zBR3CiJ8bvQimYKjKLOHe8nm1S85KfY1uOP4TJl4Wv0m0mYY11LTBaXMAaCbMRt4dzUB%2FObmBEnZRKhCYcK2IMUw5mfEJCrUbgMY2AkkQQ3Ue5Gtix9bUkwGjN0QxxEP43w1qccs2pBJrXwNiZX0OlEeLLD6%2BRX0fTKrIaET39Y9Inym2EtA%2Fwxih82FoezjvTSY4BjyRxMBtx0mWbfgC7z9sxQucg02Sa%2FETGKf08gmQ1XRtO2aE5Ez7slOWCIQhN3hKBjMT9iKXCVhOm%2FeoQDcQwng4AHuCq0ekI08eaq382LfAb0iPXGqHN1g0yyJzTmkQ0YmhVzfgS5c9jplswR4WyK8DI4p3%2FXgB1mHKQzEIAia3O4WtDKHZQIA%2F3CPUJ8u8RHzw%2B7zTTwUUcLh300P4l82NI%2Bq6PXSI6sc8RXu6MqZLDHfqIbzQtlOfjQq8Y4ji0ll9%2Ba4A%2BpIvjYoYCM0uSXL5mNz3D1gL1n%2FnGX56rMoU8x3FSJmKYfgqYk4urYPH%2BrIuI7ViEVEKICZE7DLSwQaRHWJJZq80Yu0xnrjZOXBj8QYMblxE7qZKcO2zYM%2FO%2FO5nbwu2nY7ZJA2U1RRm4Z91sA2inhrnNOSezfwPY1rAmbuGvxKxz49aSzL5rTq96NMMWksFordeHLTuI2I56X%2BIG82bQHBYJWtIFCRF01oPCxIiOQI0c5%2B%2FgEb%2Bz7ZWF8AB9AGEZ02xMEfmqMic9wU6bJ4eTRhG3m%2FRJzhGKBpAuNjD4Ne%2B7gMtpJZDfqbjltutviA6SG9myhdCvaRx0dYqEv0QJoHz9mZAjgubRo%2BQ4WPebs8FIFcEMH3n%2F%2Fz8d2Hp9eP7%2F70%2F959efrt%2Bd2f3v3LKyDg56dfHv7l05ePn7789eH%2FvD59%2BfbvL6%2B%2FPb8%2B%2FD29e3z37fl5faKnx3dfn17Xp96eX9ezYQfLj%2B8%2BP%2F2h%2F7n%2B66%2Fr2dl%2F%2B8enj2%2B%2Frv9WH9%2B9vT59%2BrJ%2B7F8%2BPH175mrZ%2BJufP%2F5lPeb4w7X8v%2F%2FZ04cPv78%2Bffjj3Z%2B290OO8BgjDXWtP%2Fy8b2%2FPX9dH1%2B%2F8%2BO7ll%2F94%2FvD26e%2F8Qv%2F796%2FPr3%2F%2F9O3548OX53%2B%2B%2FfSPl9ePDx9eX759%2B%2Bn5y9vry9c%2FHp7eHt5%2BfX74909fnj4%2FrF%2F2H%2Bs3%2Ffry7dPbp5cvDy9fPv%2Fxfv3Wnz%2F99ult%2FQPv0mY%2F4%2B8vH55%2B%2Bf3z0%2Bsf%2F2V99J%2Fr568rby9%2Fe%2F7y8PX15bevb9%2FWn7%2F8%2FroufuAnrZ%2FNM%2F62%2FoWPduHDy%2Bf1H9%2FeP%2FzPl7eHp4e%2FPn95fn36%2FNPX31%2FXv%2F788G%2F%2F9j%2FeP%2Fyv9bdfflt%2F9bdf1u3pjn59%2BcyafHz4sj72%2B5f1dXzxn%2Fzy%2BvDtjy9vT%2F98%2F%2FDfvqyvhX%2Fz29vTG%2F%2Fo28PL%2BiW%2F%2F2Lrj16f9SO%2BPfM9vj1%2F%2FuNBT%2FPqSX19ff647v9l3eZ6COu%2Fvz1%2F%2FMuHX58%2F%2FO3ry6cvb%2Bt5jO1h%2Fe3Xp4f%2F%2BvHpt%2F%2F7oO%2Fh8eHz89Mr38sDP%2FlBJep8fPjHp7dfH%2F77p799ise8Ftrz528Pr89fPz99WP%2FwL388%2FPV1%2FULvH%2F714%2B8fnvy58fZdvoh1Lx%2FWYnqX13p%2Bv73TEtaq%2BfOf%2F7w9zsf6uD%2Bm9Z9t%2Fef63z8%2FckLfXElcaccr%2Fe5nenymH6%2BMu58Z8ZkRV%2Fxf1t%2FYru7gxyvp6qe14721syvX99aO99bOrlzfWz%2FeWz%2B7cn1v%2Ffis%2B9mV62fdj%2FfWz65c39s43tvplet7G8d7G2dXru9tHJ%2F1OLtyuw7y3fUWV%2FLNest311tcyaf%2Fzrj7mRGfuVpv%2Be56y98%2Fe1hv%2Be56O7%2B3dry3dnYl36y3fHe9nd9bPz7rfnYl36y3fHe9nd%2FbON7b6ZV8s97y3fV2fm%2Fj%2BKzH2ZXbdVDurre4Um7WW7m73uJKOf13xt3PjPjM1Xord9db%2Bf7Zw3ord9fb%2Bb214721syvlZr2Vu%2Bvt%2FN768Vn3syvlZr2Vu%2Bvt%2FN7G8d5Or5Sb9VburrfzexvHZz3Ornx%2F1j%2Fsud93y3p25fu38OOVfvcz%2Ferp%2FHhl3P3MuLrrH%2Fb277t%2FO7uyXf20dry3dnbl%2Bt7a8d7a2ZXre%2BvHe%2BtnV67vrR%2BfdT%2B7cv2s%2B%2FHe%2BtmV63sbx3s7vXJ9b%2BN4b%2BPsyvW9jeOzHmdXbtdBvrvebvfRdDg1zz9zvY%2Bmw6l5%2FpnrcyEdTs3bdXB7LqTDqXn%2Bmdt7a8d7a2dX8s16y3fX2%2Fm99eOz7mdX8s16y3fX2%2Fm9jeO9nV7JN%2Bst311v5%2Fc2js96nF25XQfl7nq73UfT4dQ8%2F8z5PlrurrfbcyEdTs3bdXB7LqTDqXn%2Bmdt7a8d7a2dXys16K3fX2%2Fm99eOz7mdXys16K3fX2%2Fm9jeO9nV4pN%2But3F1v5%2Fc2js96nF35%2Fqzz4dT8vuLz3fM0H07N889c76P5cGqef%2Bb6XMiHUzNf3cH5eZoPp%2Bb5Z27vrR3vrZ1dub63fry3fnbl%2Bt768Vn3syvXz7of762fXbm%2Bt3G8t9Mr1%2Fc2jvc2zq5c39s4PutxduV2HaS76%2B22D84H1OP8M9d9cD6gHuefue7r8wH1uF0Ht319PqAe55%2B5vbd2vLd2diXdrLd0d72d31s%2FPut%2BdiXdrLd0d72d39s43tvplXSz3tLd9XZ%2Bb%2BP4rMfZldt1UO6ut9t9NB9OzfPPnO%2Bj5e56uz0X8uHUvF0Ht%2BdCPpya55%2B5vbd2vLd2dqXcrLdyd72d31s%2FPut%2BdqXcrLdyd72d39s43tvplXKz3srd9XZ%2Bb%2BP4rMfZle%2FPuhxOze93UO6ep%2BVwap5%2F5nofLYdT8%2Fwz1%2BdCOZya5eoOzs%2FTcjg1zz9ze2%2FteG%2Ft7Mr1vfXjvfWzK9f31o%2FPup9duX7W%2FXhv%2FezK9b2N472dXrm%2Bt3G8t3F25frexvFZj7Mrt%2Bsg3V1vt%2FtoOZya55%2B53kfL4dQ8%2F8z1uVAOp%2BbtOrg9F8rh1Dz%2FzO29teO9tbMr6Wa9pbvr7fze%2BvFZ97Mr6Wa9pbvr7fzexvHeTq%2Bkm%2FWW7q6383sbx2c9zq7croN8d73d9sHlgHqcf%2Ba6Dy4H1OP8M9d9fTmgHrfr4LavLwfU4%2Fwzt%2FfWjvfWzq7km%2FWW766383vrx2fdz67km%2FWW766383sbx3s7vZJv1lu%2Bu97O720cn%2FU4u%2BLP%2BufHd2%2FP397%2Bwtz%2Fgwaq69JIj6k%2F5rk9prH%2B5zYeU94eS36Uoq20R%2BgmaV1t639u5RGl6T4f5V71CI8lr0%2Fn9jjyY%2BbHrf%2Bv%2BZHPcSGNx7k%2B09c%2Fsj6AJJ3oEeRs7bGtv9rqI9q99Lj%2BxroTmG7lcf1L%2B%2Fpp6z%2FIdkstPVZuJT%2F29djq4xiPOGtAgUt8fl%2BfKD%2F%2F5%2F8HW8fSwg%3D%3D')))%0A%20%20%20%20return%20MODEL%2C%20compute%2C%20draw%2C%20json%2C%20mo%0A%0A%0A%40app.cell%0Adef%20_()%3A%0A%20%20%20%20config%20%3D%20%7B'id'%3A%20'u6-story'%2C%20'year'%3A%206%2C%20'title'%3A%20'Pip%E2%80%99s%20picnic%3A%20follow%20the%20clues'%2C%20'concept'%3A%20'Context%20and%20updating'%2C%20'question'%3A%20'When%20should%20a%20new%20clue%20change%20a%20guess%3F'%2C%20'intro'%3A%20'Help%20Pip%20weigh%20fictional%20weather%20clues.%20Move%20the%20starting%20guess%2C%20clue%20reliability%20and%20number%20of%20clues%3B%20the%20probabilities%20update%20live.'%2C%20'controls'%3A%20%5B%7B'key'%3A%20'prior'%2C%20'label'%3A%20'Starting%20chance%20of%20rain'%2C%20'kind'%3A%20'slider'%2C%20'min'%3A%200.1%2C%20'max'%3A%200.9%2C%20'step'%3A%200.1%2C%20'default'%3A%200.5%7D%2C%20%7B'key'%3A%20'reliability'%2C%20'label'%3A%20'How%20reliable%20is%20each%20clue%3F'%2C%20'kind'%3A%20'slider'%2C%20'min'%3A%200.5%2C%20'max'%3A%200.9%2C%20'step'%3A%200.1%2C%20'default'%3A%200.7%7D%2C%20%7B'key'%3A%20'clues'%2C%20'label'%3A%20'Clues%20revealed'%2C%20'kind'%3A%20'slider'%2C%20'min'%3A%200%2C%20'max'%3A%205%2C%20'step'%3A%201%2C%20'default'%3A%203%7D%2C%20%7B'key'%3A%20'story'%2C%20'label'%3A%20'Clue%20sequence'%2C%20'kind'%3A%20'choice'%2C%20'options'%3A%20%5B'Cloudy%20start'%2C%20'Clear%20start'%5D%2C%20'default'%3A%200%7D%5D%2C%20'prediction'%3A%20'Will%20one%20clear-sky%20clue%20undo%20two%20cloudy%20clues%3F%20Does%20the%20starting%20guess%20matter%3F'%2C%20'challenge'%3A%20'Set%20clue%20reliability%20to%200.5.%20Explain%20why%20uninformative%20clues%20should%20not%20change%20the%20starting%20probability.'%2C%20'artifact'%3A%20'A%20clue-by-clue%20story%20graph%20with%20one%20observation%20about%20evidence%20strength.'%2C%20'teacher'%3A%20'Use%20%E2%80%9Cthe%20teaching%20model%20updates%20a%20probability%E2%80%9D%2C%20not%20%E2%80%9Cthe%20computer%20feels%20worried%E2%80%9D.%20Discuss%20why%20copying%20one%20cloud%20photograph%20three%20times%20does%20not%20create%20three%20independent%20clues.'%2C%20'extend'%3A%20'Reverse%20the%20clue%20order%20and%20compare%20the%20final%20probability%20under%20the%20independence%20assumption.'%2C%20'glossary'%3A%20%7B'prior'%3A%20'The%20probability%20before%20these%20new%20clues.'%2C%20'evidence'%3A%20'Information%20used%20to%20update%20a%20guess.'%2C%20'independent%20clues'%3A%20'Clues%20that%20do%20not%20merely%20repeat%20the%20same%20information.'%7D%2C%20'method'%3A%20'Explicit%20teaching%20model'%2C%20'minutes'%3A%2035%7D%0A%20%20%20%20sources%20%3D%20%5B%7B'id'%3A%20'ap293-guest-lectures-25'%2C%20'title'%3A%20'Stanford%20Guest%20Lectures%3A%20AP293%20(Fall%202025)'%2C%20'url'%3A%20'https%3A%2F%2Fwww.goodfire.com%2Fblog%2Fap293-guest-lectures-25'%2C%20'kind'%3A%20'Lecture%20guide'%2C%20'date'%3A%20None%2C%20'idea'%3A%20'Three%20guest%20lectures%20cover%20causal%20interpretation%2C%20circuits%20and%20learning%20in%20context.'%2C%20'year6'%3A%20'Describe%20a%20clue%2C%20a%20guess%20and%20a%20test%20in%20different%20sentences.'%2C%20'year12'%3A%20'Compare%20representations%2C%20causal%20mechanisms%20and%20models%20of%20in-context%20learning.'%2C%20'limit'%3A%20'This%20entry%20reviews%20the%20article%20and%20lecture%20outlines%2C%20not%20a%20full%20transcription%20of%20the%20videos.'%2C%20'labs'%3A%20%5B'u6-story'%2C%20'u12-circuits'%5D%2C%20'paper'%3A%20None%2C%20'systemAndData'%3A%20'Three%20interpretability%20lecture%20outlines'%2C%20'method'%3A%20'Introduces%20causal%20abstraction%2C%20circuits%20and%20learning%20in%20context%3B%20a%20teaching%20guide%20rather%20than%20one%20dataset.'%2C%20'question'%3A%20'What%20evidence%20would%20support%20or%20challenge%20this%20idea%3A%20Three%20guest%20lectures%20cover%20causal%20interpretation%2C%20circuits%20and%20learning%20in%20context.'%2C%20'finding'%3A%20'Three%20guest%20lectures%20cover%20causal%20interpretation%2C%20circuits%20and%20learning%20in%20context.'%2C%20'year6Task'%3A%20'Describe%20a%20clue%2C%20a%20guess%20and%20a%20test%20in%20different%20sentences.%20Use%20the%20linked%20notebook%20to%20make%20two%20observations.%20Draw%20or%20describe%20one%20result%20and%20one%20thing%20this%20activity%20cannot%20tell%20us%20about%20the%20source%20system.'%2C%20'year12Task'%3A%20'Compare%20representations%2C%20causal%20mechanisms%20and%20models%20of%20in-context%20learning.%20Record%20the%20source%20model%2Ftask%2C%20a%20baseline%2C%20the%20changed%20factor%2C%20a%20measurement%20and%20an%20alternative%20explanation.%20State%20exactly%20which%20part%20your%20notebook%20investigates%20and%20which%20part%20it%20does%20not%20reproduce.'%2C%20'reviewed'%3A%20'2026-09-07'%2C%20'reproduction'%3A%20'Independent%20classroom%20adaptation%3B%20not%20a%20reproduction%20of%20the%20source%20model%20or%20complete%20method.'%7D%2C%20%7B'id'%3A%20'belief-dynamics-icl-steering'%2C%20'title'%3A%20'Belief%20Dynamics%20Reveal%20the%20Dual%20Nature%20of%20In-Context%20Learning%20and%20Activation%20Steering'%2C%20'url'%3A%20'https%3A%2F%2Fwww.goodfire.com%2Fresearch%2Fbelief-dynamics-icl-steering'%2C%20'kind'%3A%20'LLM%20research'%2C%20'date'%3A%20None%2C%20'idea'%3A%20'A%20Bayesian%20framework%20relates%20contextual%20evidence%20to%20likelihood%20updates%20and%20steering%20to%20prior%20shifts.'%2C%20'year6'%3A%20'Change%20the%20starting%20guess%2C%20then%20reveal%20the%20same%20clues.'%2C%20'year12'%3A%20'Compute%20posterior%20log-odds%20and%20test%20saturation%20under%20strong%20contextual%20evidence.'%2C%20'limit'%3A%20'A%20fitted%20account%20of%20model%20behaviour%20does%20not%20establish%20that%20a%20model%20has%20human%20beliefs.'%2C%20'labs'%3A%20%5B'u6-story'%2C%20'u12-geometry'%5D%2C%20'paper'%3A%20%7B'url'%3A%20'https%3A%2F%2Farxiv.org%2Fhtml%2F2511.00617'%2C%20'title'%3A%20'Belief%20Dynamics%20Reveal%20the%20Dual%20Nature%20of%20In-Context%20Learning%20and%20Activation%20Steering'%7D%2C%20'systemAndData'%3A%20'Many-shot%20context%20and%20activation-steering%20experiments'%2C%20'method'%3A%20'Fits%20a%20Bayesian%20account%20that%20distinguishes%20likelihood%20updates%20from%20changes%20to%20priors%3B%20tests%20how%20context%20strength%20changes%20steering%20effects.'%2C%20'question'%3A%20'What%20evidence%20would%20support%20or%20challenge%20this%20idea%3A%20A%20Bayesian%20framework%20relates%20contextual%20evidence%20to%20likelihood%20updates%20and%20steering%20to%20prior%20shifts.'%2C%20'finding'%3A%20'A%20Bayesian%20framework%20relates%20contextual%20evidence%20to%20likelihood%20updates%20and%20steering%20to%20prior%20shifts.'%2C%20'year6Task'%3A%20'Change%20the%20starting%20guess%2C%20then%20reveal%20the%20same%20clues.%20Use%20the%20linked%20notebook%20to%20make%20two%20observations.%20Draw%20or%20describe%20one%20result%20and%20one%20thing%20this%20activity%20cannot%20tell%20us%20about%20the%20source%20system.'%2C%20'year12Task'%3A%20'Compute%20posterior%20log-odds%20and%20test%20saturation%20under%20strong%20contextual%20evidence.%20Record%20the%20source%20model%2Ftask%2C%20a%20baseline%2C%20the%20changed%20factor%2C%20a%20measurement%20and%20an%20alternative%20explanation.%20State%20exactly%20which%20part%20your%20notebook%20investigates%20and%20which%20part%20it%20does%20not%20reproduce.'%2C%20'reviewed'%3A%20'2026-09-07'%2C%20'reproduction'%3A%20'Independent%20classroom%20adaptation%3B%20not%20a%20reproduction%20of%20the%20source%20model%20or%20complete%20method.'%7D%2C%20%7B'id'%3A%20'priors-in-time'%2C%20'title'%3A%20'Priors%20in%20Time%3A%20Missing%20Inductive%20Biases%20for%20Language%20Model%20Interpretability'%2C%20'url'%3A%20'https%3A%2F%2Fwww.goodfire.com%2Fresearch%2Fpriors-in-time'%2C%20'kind'%3A%20'LLM%20research'%2C%20'date'%3A%20None%2C%20'idea'%3A%20'Temporal%20feature%20analysis%20separates%20predictable%20context%20from%20new%20information%20over%20a%20sequence.'%2C%20'year6'%3A%20'Reveal%20story%20clues%20one%20at%20a%20time%20and%20record%20each%20change.'%2C%20'year12'%3A%20'Contrast%20a%20static%20representation%20with%20a%20model%20of%20predictable%20state%20and%20residual%20novelty.'%2C%20'limit'%3A%20'Our%20Bayesian%20story%20model%20is%20explicit%20and%20hand-specified%3B%20it%20is%20not%20Temporal%20Feature%20Analysis%20applied%20to%20an%20LLM.'%2C%20'labs'%3A%20%5B'u6-story'%2C%20'u12-uncertainty'%5D%2C%20'paper'%3A%20%7B'url'%3A%20'https%3A%2F%2Farxiv.org%2Fhtml%2F2511.01836'%2C%20'title'%3A%20'Priors%20in%20Time%3A%20Missing%20Inductive%20Biases%20for%20Language%20Model%20Interpretability'%7D%2C%20'systemAndData'%3A%20'Gemma-2-2B%20activations%20from%20Pile-Uncopyrighted'%2C%20'method'%3A%20'Compares%20temporal%20feature%20analysis%20with%20ReLU%2C%20TopK%20and%20BatchTopK%20SAEs%2C%20testing%20predictable%20versus%20innovation%20components%20and%20event%20structure.'%2C%20'question'%3A%20'What%20evidence%20would%20support%20or%20challenge%20this%20idea%3A%20Temporal%20feature%20analysis%20separates%20predictable%20context%20from%20new%20information%20over%20a%20sequence.'%2C%20'finding'%3A%20'Temporal%20feature%20analysis%20separates%20predictable%20context%20from%20new%20information%20over%20a%20sequence.'%2C%20'year6Task'%3A%20'Reveal%20story%20clues%20one%20at%20a%20time%20and%20record%20each%20change.%20Use%20the%20linked%20notebook%20to%20make%20two%20observations.%20Draw%20or%20describe%20one%20result%20and%20one%20thing%20this%20activity%20cannot%20tell%20us%20about%20the%20source%20system.'%2C%20'year12Task'%3A%20'Contrast%20a%20static%20representation%20with%20a%20model%20of%20predictable%20state%20and%20residual%20novelty.%20Record%20the%20source%20model%2Ftask%2C%20a%20baseline%2C%20the%20changed%20factor%2C%20a%20measurement%20and%20an%20alternative%20explanation.%20State%20exactly%20which%20part%20your%20notebook%20investigates%20and%20which%20part%20it%20does%20not%20reproduce.'%2C%20'reviewed'%3A%20'2026-09-07'%2C%20'reproduction'%3A%20'Independent%20classroom%20adaptation%3B%20not%20a%20reproduction%20of%20the%20source%20model%20or%20complete%20method.'%7D%2C%20%7B'id'%3A%20'reasoning-theater'%2C%20'title'%3A%20'Reasoning%20Theater%3A%20Probing%20for%20Performative%20Chain-of-Thought'%2C%20'url'%3A%20'https%3A%2F%2Fwww.goodfire.com%2Fresearch%2Freasoning-theater'%2C%20'kind'%3A%20'LLM%20research'%2C%20'date'%3A%20'March%2012%2C%202026'%2C%20'idea'%3A%20%22Probes%20can%20sometimes%20predict%20a%20model's%20eventual%20answer%20before%20its%20written%20reasoning%20ends.%22%2C%20'year6'%3A%20'An%20early%20guess%20can%20match%20a%20later%20answer%20and%20still%20be%20wrong.'%2C%20'year12'%3A%20'Evaluate%20early-exit%20cost%20against%20accuracy%20and%20distinguish%20answer%20prediction%20from%20correctness.'%2C%20'limit'%3A%20'Savings%20depend%20on%20task%20and%20paper%20version%3B%20the%20Goodfire%20post%20and%20later%20arXiv%20revision%20report%20different%20percentages.%20Our%20trajectories%20are%20simulated.'%2C%20'labs'%3A%20%5B'u6-story'%2C%20'u12-uncertainty'%5D%2C%20'paper'%3A%20%7B'url'%3A%20'https%3A%2F%2Farxiv.org%2Fhtml%2F2603.05488'%2C%20'title'%3A%20'Reasoning%20Theater%3A%20Disentangling%20Model%20Beliefs%20from%20Chain-of-Thought'%7D%2C%20'systemAndData'%3A%20'DeepSeek-R1%20families%20and%20GPT-OSS-120B%20on%20MMLU-Redux%20and%20GPQA-Diamond'%2C%20'method'%3A%20'Trains%20context-pooling%20probes%20to%20forecast%20eventual%20answers%20during%20reasoning%20and%20evaluates%20early-exit%20token%20cost%20versus%20benchmark%20accuracy.'%2C%20'question'%3A%20%22What%20evidence%20would%20support%20or%20challenge%20this%20idea%3A%20Probes%20can%20sometimes%20predict%20a%20model's%20eventual%20answer%20before%20its%20written%20reasoning%20ends.%22%2C%20'finding'%3A%20%22Probes%20can%20sometimes%20predict%20a%20model's%20eventual%20answer%20before%20its%20written%20reasoning%20ends.%22%2C%20'year6Task'%3A%20'An%20early%20guess%20can%20match%20a%20later%20answer%20and%20still%20be%20wrong.%20Use%20the%20linked%20notebook%20to%20make%20two%20observations.%20Draw%20or%20describe%20one%20result%20and%20one%20thing%20this%20activity%20cannot%20tell%20us%20about%20the%20source%20system.'%2C%20'year12Task'%3A%20'Evaluate%20early-exit%20cost%20against%20accuracy%20and%20distinguish%20answer%20prediction%20from%20correctness.%20Record%20the%20source%20model%2Ftask%2C%20a%20baseline%2C%20the%20changed%20factor%2C%20a%20measurement%20and%20an%20alternative%20explanation.%20State%20exactly%20which%20part%20your%20notebook%20investigates%20and%20which%20part%20it%20does%20not%20reproduce.'%2C%20'reviewed'%3A%20'2026-09-07'%2C%20'reproduction'%3A%20'Independent%20classroom%20adaptation%3B%20not%20a%20reproduction%20of%20the%20source%20model%20or%20complete%20method.'%7D%2C%20%7B'id'%3A%20'stories-in-space'%2C%20'title'%3A%20'Meandering%20on%20Manifolds%3A%20The%20Neural%20Geometry%20of%20Stories%20Over%20Time'%2C%20'url'%3A%20'https%3A%2F%2Fwww.goodfire.com%2Fresearch%2Fstories-in-space'%2C%20'kind'%3A%20'LLM%20research'%2C%20'date'%3A%20'June%2023%2C%202026'%2C%20'idea'%3A%20%22Story%20representations%20follow%20trajectories%20associated%20with%20genres%20and%20characters'%20emotions.%22%2C%20'year6'%3A%20'Plot%20how%20clues%20about%20a%20fictional%20character%20change%20as%20the%20story%20unfolds.'%2C%20'year12'%3A%20'Compare%20decoded%20narrative%20variables%2C%20causal%20steering%20and%20entangled%20concepts.'%2C%20'limit'%3A%20%22Representing%20a%20character's%20emotions%20does%20not%20mean%20the%20model%20has%20emotions.%20Classroom%20story%20probabilities%20are%20hand-specified.%22%2C%20'labs'%3A%20%5B'u6-story'%2C%20'u12-geometry'%5D%2C%20'paper'%3A%20%7B'url'%3A%20'https%3A%2F%2Farxiv.org%2Fhtml%2F2605.12412'%2C%20'title'%3A%20'Stories%20in%20Space%3A%20In-Context%20Learning%20Trajectories%20in%20Conceptual%20Belief%20Space'%7D%2C%20'systemAndData'%3A%20'Llama%203.1%208B%20and%20SimpleStories'%2C%20'method'%3A%20'Collects%20sentence-end%20activations%2C%20fits%20trajectories%20and%20compares%20emotional%20organisation%20with%20behavioural%20readouts%20and%20human%20valence%2Farousal%20judgements.'%2C%20'question'%3A%20%22What%20evidence%20would%20support%20or%20challenge%20this%20idea%3A%20Story%20representations%20follow%20trajectories%20associated%20with%20genres%20and%20characters'%20emotions.%22%2C%20'finding'%3A%20%22Story%20representations%20follow%20trajectories%20associated%20with%20genres%20and%20characters'%20emotions.%22%2C%20'year6Task'%3A%20'Plot%20how%20clues%20about%20a%20fictional%20character%20change%20as%20the%20story%20unfolds.%20Use%20the%20linked%20notebook%20to%20make%20two%20observations.%20Draw%20or%20describe%20one%20result%20and%20one%20thing%20this%20activity%20cannot%20tell%20us%20about%20the%20source%20system.'%2C%20'year12Task'%3A%20'Compare%20decoded%20narrative%20variables%2C%20causal%20steering%20and%20entangled%20concepts.%20Record%20the%20source%20model%2Ftask%2C%20a%20baseline%2C%20the%20changed%20factor%2C%20a%20measurement%20and%20an%20alternative%20explanation.%20State%20exactly%20which%20part%20your%20notebook%20investigates%20and%20which%20part%20it%20does%20not%20reproduce.'%2C%20'reviewed'%3A%20'2026-09-07'%2C%20'reproduction'%3A%20'Independent%20classroom%20adaptation%3B%20not%20a%20reproduction%20of%20the%20source%20model%20or%20complete%20method.'%7D%2C%20%7B'id'%3A%20'under-the-hood-of-a-reasoning-model'%2C%20'title'%3A%20'Under%20the%20Hood%20of%20a%20Reasoning%20Model'%2C%20'url'%3A%20'https%3A%2F%2Fwww.goodfire.com%2Fresearch%2Funder-the-hood-of-a-reasoning-model'%2C%20'kind'%3A%20'LLM%20research'%2C%20'date'%3A%20'Apr.%2015%2C%202025'%2C%20'idea'%3A%20'SAEs%20expose%20patterns%20in%20DeepSeek%20R1%3B%20steering%20effects%20depend%20on%20strength%20and%20where%20it%20begins.'%2C%20'year6'%3A%20'Change%20a%20clue%20at%20different%20times%20and%20observe%20whether%20the%20ending%20changes.'%2C%20'year12'%3A%20'Test%20intervention%20timing%2C%20nonlinear%20responses%20and%20unwanted%20effects%20on%20coherence.'%2C%20'limit'%3A%20'An%20SAE%20reveals%20partial%20patterns%2C%20not%20a%20complete%20transcript%20of%20reasoning.%20Effects%20do%20not%20generalise%20automatically.'%2C%20'labs'%3A%20%5B'u6-story'%2C%20'u12-circuits'%5D%2C%20'paper'%3A%20None%2C%20'systemAndData'%3A%20'DeepSeek%20R1%20671B%20on%20custom%20reasoning%20and%20OpenR1-Math%20data'%2C%20'method'%3A%20'Trains%20two%20SAEs%20and%20studies%20feature%20activations%20and%20steering%20timing%2Fstrength%3B%20partial%20features%20do%20not%20expose%20a%20complete%20reasoning%20transcript.'%2C%20'question'%3A%20'What%20evidence%20would%20support%20or%20challenge%20this%20idea%3A%20SAEs%20expose%20patterns%20in%20DeepSeek%20R1%3B%20steering%20effects%20depend%20on%20strength%20and%20where%20it%20begins.'%2C%20'finding'%3A%20'SAEs%20expose%20patterns%20in%20DeepSeek%20R1%3B%20steering%20effects%20depend%20on%20strength%20and%20where%20it%20begins.'%2C%20'year6Task'%3A%20'Change%20a%20clue%20at%20different%20times%20and%20observe%20whether%20the%20ending%20changes.%20Use%20the%20linked%20notebook%20to%20make%20two%20observations.%20Draw%20or%20describe%20one%20result%20and%20one%20thing%20this%20activity%20cannot%20tell%20us%20about%20the%20source%20system.'%2C%20'year12Task'%3A%20'Test%20intervention%20timing%2C%20nonlinear%20responses%20and%20unwanted%20effects%20on%20coherence.%20Record%20the%20source%20model%2Ftask%2C%20a%20baseline%2C%20the%20changed%20factor%2C%20a%20measurement%20and%20an%20alternative%20explanation.%20State%20exactly%20which%20part%20your%20notebook%20investigates%20and%20which%20part%20it%20does%20not%20reproduce.'%2C%20'reviewed'%3A%20'2026-09-07'%2C%20'reproduction'%3A%20'Independent%20classroom%20adaptation%3B%20not%20a%20reproduction%20of%20the%20source%20model%20or%20complete%20method.'%7D%5D%0A%20%20%20%20return%20config%2C%20sources%0A%0A%0A%40app.cell%0Adef%20_(config%2C%20mo)%3A%0A%20%20%20%20mo.md(f%22%23%20%7Bconfig%5B'title'%5D%7D%5Cn%5Cn**Year%20%7Bconfig%5B'year'%5D%7D%20%C2%B7%20%7Bconfig%5B'minutes'%5D%7D%20minutes%20%C2%B7%20%7Bconfig%5B'method'%5D%7D**%5Cn%5Cn%23%23%20%7Bconfig%5B'question'%5D%7D%5Cn%5Cn%7Bconfig%5B'intro'%5D%7D%5Cn%5Cn**Your%20mission%3A**%20predict%2C%20change%20one%20control%2C%20compare%20evidence%2C%20then%20find%20a%20counterexample.%20All%20personal%20stories%20and%20classroom%20data%20are%20fictional.%20Python%20runs%20in%20your%20browser%3B%20first%20load%20needs%20an%20internet%20connection%20for%20the%20runtime.%20Download%20your%20work%20before%20leaving.%22)%0A%20%20%20%20return%0A%0A%0A%40app.cell%0Adef%20_(mo)%3A%0A%20%20%20%20project_file%20%3D%20mo.ui.file(filetypes%3D%5B'.json'%5D%2C%20multiple%3DFalse%2C%20max_size%3D2000000%2C%20label%3D'Open%20a%20saved%20Brightlab%20notebook%20project')%0A%20%20%20%20mo.vstack(%5Bmo.md('**Keep%20your%20work%3A**%20download%20a%20project%20before%20leaving.%20Reopen%20it%20here%20to%20restore%20settings%2C%20comparisons%20and%20writing.%20Files%20are%20read%20inside%20this%20notebook%3B%20use%20fictional%20classroom%20data.')%2Cproject_file%5D)%0A%20%20%20%20return%20(project_file%2C)%0A%0A%0A%40app.cell%0Adef%20_(config%2C%20json%2C%20mo%2C%20project_file)%3A%0A%20%20%20%20restored%20%3D%20%7B%7D%0A%20%20%20%20if%20project_file.contents()%3A%0A%20%20%20%20%20%20%20%20try%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20_candidate%20%3D%20json.loads(project_file.contents().decode('utf-8'))%0A%20%20%20%20%20%20%20%20%20%20%20%20def%20_tree(value%2Cdepth%3D0)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20depth%3E16%3Araise%20ValueError('Project%20nesting%20is%20too%20deep.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20isinstance(value%2Cdict)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20len(value)%3E100%3Araise%20ValueError('Too%20many%20object%20fields.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20for%20child%20in%20value.values()%3A_tree(child%2Cdepth%2B1)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20elif%20isinstance(value%2Clist)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20len(value)%3E20000%3Araise%20ValueError('Too%20many%20rows.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20for%20child%20in%20value%3A_tree(child%2Cdepth%2B1)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20elif%20isinstance(value%2Cfloat)%20and%20not%20(-1e100%3Cvalue%3C1e100)%3Araise%20ValueError('Project%20contains%20a%20non-finite%20or%20excessive%20number.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20elif%20isinstance(value%2Cstr)%20and%20len(value)%3E200000%3Araise%20ValueError('Project%20text%20is%20too%20long.')%0A%20%20%20%20%20%20%20%20%20%20%20%20_tree(_candidate)%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20not%20isinstance(_candidate%2Cdict)%20or%20_candidate.get('version')!%3D1%3Araise%20ValueError('Choose%20a%20supported%20version%201%20notebook%20project.')%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20_candidate.get('format')%20!%3D%20'brightlab-notebook-project'%20or%20_candidate.get('lesson')%20!%3D%20config.get('id'%2Cconfig.get('lesson_id'))%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20raise%20ValueError('Choose%20a%20project%20for%20this%20notebook.')%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20not%20isinstance(_candidate.get('settings')%2Cdict)%20or%20not%20isinstance(_candidate.get('prediction')%2Cstr)%20or%20not%20isinstance(_candidate.get('saved_runs'%2C%5B%5D)%2Clist)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20raise%20ValueError('The%20project%20is%20missing%20its%20settings%2C%20prediction%20or%20comparison%20list.')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20_spec%20in%20config%5B'controls'%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20_value%3D_candidate%5B'settings'%5D.get(_spec%5B'key'%5D%2C_spec%5B'default'%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20_spec%5B'kind'%5D%3D%3D'slider'%20and%20(type(_value)%20not%20in%20%5Bint%2Cfloat%5D%20or%20not%20_spec%5B'min'%5D%3C%3D_value%3C%3D_spec%5B'max'%5D)%3Araise%20ValueError('A%20saved%20slider%20is%20outside%20its%20allowed%20range.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20_spec%5B'kind'%5D%3D%3D'choice'%20and%20(type(_value)%20is%20not%20int%20or%20not%200%3C%3D_value%3Clen(_spec%5B'options'%5D))%3Araise%20ValueError('A%20saved%20choice%20is%20invalid.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20_spec%5B'kind'%5D%3D%3D'text'%20and%20(not%20isinstance(_value%2Cstr)%20or%20len(_value)%3E1000)%3Araise%20ValueError('A%20saved%20text%20control%20is%20invalid.')%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20not%20isinstance(_candidate.get('conclusion'%2C'')%2Cstr)%20or%20len(_candidate.get('saved_runs'%2C%5B%5D))%3E6%3Araise%20ValueError('Invalid%20writing%20or%20comparison%20count.')%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20len(_candidate.get('prediction'%2C''))%3E10000%20or%20not%20isinstance(_candidate.get('conclusion'%2C'')%2Cstr)%20or%20len(_candidate.get('conclusion'%2C''))%3E20000%3Araise%20ValueError('Invalid%20writing%20in%20the%20saved%20project.')%0A%20%20%20%20%20%20%20%20%20%20%20%20_runs%3D_candidate.get('saved_runs'%2C%5B%5D)%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20len(_runs)%3E6%3Araise%20ValueError('A%20project%20may%20contain%20up%20to%20six%20comparisons.')%0A%20%20%20%20%20%20%20%20%20%20%20%20for%20_run%20in%20_runs%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20not%20isinstance(_run%2Cdict)%20or%20not%20isinstance(_run.get('settings')%2Cdict)%3Araise%20ValueError('Invalid%20saved%20comparison.')%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20_rows%3D_run.get('measurements'%2C_run.get('result'%2C%7B%7D).get('rows')%20if%20isinstance(_run.get('result')%2Cdict)%20else%20None)%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20if%20not%20isinstance(_rows%2Clist)%20or%20not%20all(isinstance(row%2Cdict)%20for%20row%20in%20_rows)%3Araise%20ValueError('A%20saved%20comparison%20needs%20a%20measurement%20table.')%0A%20%20%20%20%20%20%20%20%20%20%20%20restored%20%3D%20_candidate%0A%20%20%20%20%20%20%20%20%20%20%20%20mo.output.replace(mo.md('Project%20read.%20Save%20the%20restored%20prediction%20to%20continue%20your%20investigation.'))%0A%20%20%20%20%20%20%20%20except%20(ValueError%2CTypeError%2CUnicodeError%2CKeyError%2CAttributeError)%20as%20_error%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20mo.output.replace(mo.callout(mo.md('Could%20not%20open%20this%20project%3A%20'%2Bstr(_error))%2Ckind%3D'warn'))%0A%20%20%20%20return%20(restored%2C)%0A%0A%0A%40app.cell%0Adef%20_(mo)%3A%0A%20%20%20%20reset_controls%20%3D%20mo.ui.button(value%3D0%2Con_click%3Dlambda%20count%3Acount%2B1%2Clabel%3D'Reset%20controls%20to%20starting%20settings')%0A%20%20%20%20return%20(reset_controls%2C)%0A%0A%0A%40app.cell%0Adef%20_(config%2C%20mo%2C%20restored)%3A%0A%20%20%20%20prediction%20%3D%20mo.ui.text_area(value%3Drestored.get(%22prediction%22%2C%22%22)%2Clabel%3D%22My%20prediction%20%E2%80%94%20what%20will%20change%2C%20and%20why%3F%22%2C%20placeholder%3Dconfig%5B'prediction'%5D%2C%20full_width%3DTrue).form(submit_button_label%3D'Save%20prediction%20%26%20open%20the%20lab'%2C%20clear_on_submit%3DFalse%2C%20validate%3Dlambda%20value%3A%20None%20if%20value%20and%20len(value.strip())%3E%3D3%20else%20'Write%20a%20short%20prediction%2C%20or%20ask%20a%20partner%20to%20record%20your%20spoken%20idea.')%0A%20%20%20%20mo.vstack(%5Bmo.md('%23%23%201.%20Make%20a%20prediction%5Cn'%2Bconfig%5B'prediction'%5D)%2Cprediction%5D)%0A%20%20%20%20return%20(prediction%2C)%0A%0A%0A%40app.cell%0Adef%20_(config%2C%20mo%2C%20prediction%2C%20reset_controls%2C%20restored)%3A%0A%20%20%20%20mo.stop(prediction.value%20is%20None%2Cmo.md('**The%20experiment%20opens%20after%20you%20save%20a%20prediction.**%20There%20is%20no%20penalty%20for%20being%20surprised.'))%0A%20%20%20%20_reset%3Dreset_controls.value%0A%20%20%20%20_saved%3Drestored.get('settings'%2C%7B%7D)%20if%20not%20_reset%20else%20%7B%7D%0A%20%20%20%20controls%20%3D%20mo.ui.dictionary(%7Bs%5B'key'%5D%3A(mo.ui.slider(start%3Ds%5B'min'%5D%2Cstop%3Ds%5B'max'%5D%2Cstep%3Ds%5B'step'%5D%2Cvalue%3D_saved.get(s%5B'key'%5D%2Cs%5B'default'%5D)%2Clabel%3Ds%5B'label'%5D%2Cshow_value%3DTrue%2Cfull_width%3DTrue)%20if%20s%5B'kind'%5D%3D%3D'slider'%20else%20mo.ui.text(value%3D_saved.get(s%5B'key'%5D%2Cs%5B'default'%5D)%2Clabel%3Ds%5B'label'%5D%2Cfull_width%3DTrue%2Cdebounce%3DTrue)%20if%20s%5B'kind'%5D%3D%3D'text'%20else%20mo.ui.dropdown(options%3D%7Blabel%3Ai%20for%20i%2Clabel%20in%20enumerate(s%5B'options'%5D)%7D%2Cvalue%3Ds%5B'options'%5D%5Bint(_saved.get(s%5B'key'%5D%2Cs%5B'default'%5D))%5D%2Clabel%3Ds%5B'label'%5D%2Cfull_width%3DTrue))%20for%20s%20in%20config%5B'controls'%5D%7D)%0A%20%20%20%20mo.vstack(%5Bmo.md('%23%23%202.%20Change%20one%20thing%5CnThe%20first%20result%20uses%20the%20starting%20settings.%20Record%20it%20before%20moving%20a%20control.')%2Creset_controls%2Ccontrols.vstack()%5D)%0A%20%20%20%20return%20(controls%2C)%0A%0A%0A%40app.cell%0Adef%20_(compute%2C%20config%2C%20controls)%3A%0A%20%20%20%20try%3Acurrent%20%3D%20compute(config%5B'id'%5D%2Ccontrols.value)%0A%20%20%20%20except%20(ValueError%2CTypeError%2CKeyError%2CIndexError)%20as%20_error%3A%0A%20%20%20%20%20%20%20%20current%3D%7B'error'%3Astr(_error)%2C'summary'%3A'Check%20your%20inputs%3A%20'%2Bstr(_error)%2C'rows'%3A%5B%7B'input'%3A'Needs%20correction'%2C'value'%3A0%7D%5D%2C'kind'%3A'bars'%2C'x'%3A'input'%2C'y'%3A'value'%2C'formula'%3A'No%20new%20result%20was%20calculated.%20Correct%20the%20input%20and%20compare%20again.'%7D%0A%20%20%20%20return%20(current%2C)%0A%0A%0A%40app.cell%0Adef%20_(compute%2C%20config%2C%20mo%2C%20prediction)%3A%0A%20%20%20%20mo.stop(prediction.value%20is%20None)%0A%20%20%20%20baseline%20%3D%20compute(config%5B'id'%5D%2C%7Bs%5B'key'%5D%3As%5B'default'%5D%20for%20s%20in%20config%5B'controls'%5D%7D)%0A%20%20%20%20return%20(baseline%2C)%0A%0A%0A%40app.cell%0Adef%20_(config%2C%20current%2C%20draw%2C%20mo)%3A%0A%20%20%20%20mo.stop(bool(current.get('error'))%2Cmo.callout(mo.md(current%5B'summary'%5D)%2Ckind%3D'warn'))%0A%20%20%20%20mo.vstack(%5Bmo.callout(mo.md(current%5B'summary'%5D)%2Ckind%3D'info')%2Cmo.Html(draw(current))%2Cmo.accordion(%7B'For%20the%20curious%3A%20how%20the%20numbers%20work'%3Amo.md(current%5B'formula'%5D)%7D)%20if%20config%5B'year'%5D%3D%3D6%20else%20mo.md('**How%20this%20is%20calculated**%5Cn%5Cn'%2Bcurrent%5B'formula'%5D)%2Cmo.md('Charts%20update%20from%20Python%20calculations.%20Exact%20values%20are%20below%3B%20scroll%20wide%20charts%20sideways%20on%20a%20small%20screen.')%5D)%0A%20%20%20%20return%0A%0A%0A%40app.cell%0Adef%20_(baseline%2C%20current%2C%20mo)%3A%0A%20%20%20%20mo.stop(bool(current.get('error')))%0A%20%20%20%20_tables%3D%7B'Current%20measurements'%3Amo.ui.table(current%5B'rows'%5D%2Cselection%3DNone%2Cpage_size%3D10)%2C'Starting-settings%20comparison'%3Amo.vstack(%5Bmo.md(baseline%5B'summary'%5D)%2Cmo.ui.table(baseline%5B'rows'%5D%2Cselection%3DNone%2Cpage_size%3D10)%5D)%7D%0A%20%20%20%20if%20current.get('metrics')%3A_tables%5B'Summary%20measurements%20and%20denominators'%5D%3Dmo.json(current%5B'metrics'%5D)%0A%20%20%20%20if%20current.get('chart_rows')%3A_tables%5B'Chart%20measurements'%5D%3Dmo.ui.table(current%5B'chart_rows'%5D%2Cselection%3DNone%2Cpage_size%3D10)%0A%20%20%20%20if%20current.get('heat')%20is%20not%20None%3A_tables%5B'Exact%20heat%20map%20values'%5D%3Dmo.ui.table(%5B%7B'row'%3Ai%2C'label'%3Acurrent%5B'heat_labels'%5D%5Bi%5D%2C**%7Bstr(j)%3Av%20for%20j%2Cv%20in%20enumerate(row)%7D%7D%20for%20i%2Crow%20in%20enumerate(current%5B'heat'%5D)%5D%2Cselection%3DNone%2Cpage_size%3D10)%0A%20%20%20%20if%20current.get('model_rows')%3A_tables%5B'Bridge%20to%20measured%20transformer%20behaviour'%5D%3Dmo.vstack(%5Bmo.md(current%5B'model_method'%5D)%2Cmo.ui.table(current%5B'model_rows'%5D%2Cselection%3DNone)%5D)%0A%20%20%20%20mo.accordion(_tables)%0A%20%20%20%20return%0A%0A%0A%40app.cell%0Adef%20_(mo%2C%20restored)%3A%0A%20%20%20%20get_runs%2C%20set_runs%20%3D%20mo.state(restored.get('saved_runs'%2C%5B%5D)%5B%3A6%5D)%0A%20%20%20%20return%20get_runs%2C%20set_runs%0A%0A%0A%40app.cell%0Adef%20_(controls%2C%20current%2C%20mo%2C%20set_runs)%3A%0A%20%20%20%20mo.stop(bool(current.get('error')))%0A%20%20%20%20_snapshot%3D%7B'settings'%3Adict(controls.value)%2C'result'%3Acurrent%7D%0A%20%20%20%20save_run%3Dmo.ui.button(label%3D'Save%20this%20run%20for%20comparison'%2Con_click%3Dlambda%20_%3Aset_runs(lambda%20old%3A(old%2B%5B_snapshot%5D)%5B-6%3A%5D))%0A%20%20%20%20clear_runs%3Dmo.ui.button(label%3D'Clear%20saved%20comparisons'%2Con_click%3Dlambda%20_%3Aset_runs(%5B%5D))%0A%20%20%20%20mo.hstack(%5Bsave_run%2Cclear_runs%5D)%0A%20%20%20%20return%0A%0A%0A%40app.cell%0Adef%20_(get_runs%2C%20mo)%3A%0A%20%20%20%20_runs%3Dget_runs()%0A%20%20%20%20mo.vstack(%5Bmo.md(f'**Saved%20comparisons%3A%20%7Blen(_runs)%7D%20%2F%206.**%20Your%20latest%20six%20runs%20stay%20in%20this%20session%20and%20are%20included%20in%20the%20download.')%2Cmo.accordion(%7Bf'Run%20%7Bi%2B1%7D'%3Amo.json(r)%20for%20i%2Cr%20in%20enumerate(_runs)%7D)%20if%20_runs%20else%20mo.md('Save%20a%20starting%20run%2C%20change%20one%20control%2C%20then%20save%20again.')%5D)%0A%20%20%20%20return%0A%0A%0A%40app.cell%0Adef%20_(config%2C%20mo%2C%20restored)%3A%0A%20%20%20%20reflection%3Dmo.ui.text_area(value%3Drestored.get(%22conclusion%22%2C%22%22)%2Clabel%3D'My%20conclusion%20%E2%80%94%20evidence%2C%20counterexample%20and%20one%20limitation'%2Cfull_width%3DTrue%2Cdebounce%3DFalse%2Cplaceholder%3D'I%20changed%E2%80%A6%20I%20kept%E2%80%A6%20The%20measurements%20show%E2%80%A6%20They%20do%20not%20show%E2%80%A6')%0A%20%20%20%20mo.vstack(%5Bmo.md('%23%23%203.%20Find%20the%20case%20that%20breaks%20your%20explanation%5Cn'%2Bconfig%5B'challenge'%5D%2B'%5Cn%5Cn%23%23%204.%20Make%20something%20with%20the%20evidence%5Cn'%2Bconfig%5B'artifact'%5D)%2Creflection%5D)%0A%20%20%20%20return%20(reflection%2C)%0A%0A%0A%40app.cell%0Adef%20_(%0A%20%20%20%20baseline%2C%0A%20%20%20%20config%2C%0A%20%20%20%20controls%2C%0A%20%20%20%20current%2C%0A%20%20%20%20get_runs%2C%0A%20%20%20%20json%2C%0A%20%20%20%20mo%2C%0A%20%20%20%20prediction%2C%0A%20%20%20%20reflection%2C%0A%20%20%20%20sources%2C%0A)%3A%0A%20%20%20%20mo.stop(bool(current.get('error'))%2Cmo.md('Correct%20the%20input%20before%20exporting%20a%20new%20result.'))%0A%20%20%20%20_journal%3D%7B'format'%3A'brightlab-notebook-project'%2C'version'%3A1%2C'lesson'%3Aconfig%5B'id'%5D%2C'method'%3Aconfig%5B'method'%5D%2C'prediction'%3Aprediction.value%2C'settings'%3Acontrols.value%2C'current'%3Acurrent%2C'starting_baseline'%3Abaseline%2C'saved_runs'%3Aget_runs()%2C'conclusion'%3Areflection.value%2C'sources'%3Asources%2C'scope'%3A'Classroom%20investigation%20inspired%20by%20research.%20See%20method%20and%20source%20limits%3B%20not%20a%20reproduction%20of%20a%20Goodfire%20model.'%7D%0A%20%20%20%20import%20html%20as%20report_html%0A%20%20%20%20_e%3Dreport_html.escape%0A%20%20%20%20def%20_readable(value)%3A%0A%20%20%20%20%20%20%20%20if%20isinstance(value%2Cdict)%3Areturn%20'%3Cdl%3E'%2B''.join('%3Cdt%3E%3Cstrong%3E'%2B_e(str(k).replace('_'%2C'%20'))%2B'%3C%2Fstrong%3E%3C%2Fdt%3E%3Cdd%3E'%2B_readable(v)%2B'%3C%2Fdd%3E'%20for%20k%2Cv%20in%20value.items())%2B'%3C%2Fdl%3E'%0A%20%20%20%20%20%20%20%20if%20isinstance(value%2Clist)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20value%20and%20all(isinstance(row%2Cdict)%20for%20row%20in%20value)%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20_keys%3Dlist(dict.fromkeys(k%20for%20row%20in%20value%20for%20k%20in%20row))%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20return%20'%3Ctable%3E%3Cthead%3E%3Ctr%3E'%2B''.join('%3Cth%3E'%2B_e(str(k).replace('_'%2C'%20'))%2B'%3C%2Fth%3E'%20for%20k%20in%20_keys)%2B'%3C%2Ftr%3E%3C%2Fthead%3E%3Ctbody%3E'%2B''.join('%3Ctr%3E'%2B''.join('%3Ctd%3E'%2B_readable(row.get(k%2C''))%2B'%3C%2Ftd%3E'%20for%20k%20in%20_keys)%2B'%3C%2Ftr%3E'%20for%20row%20in%20value)%2B'%3C%2Ftbody%3E%3C%2Ftable%3E'%0A%20%20%20%20%20%20%20%20%20%20%20%20return%20'%3Col%3E'%2B''.join('%3Cli%3E'%2B_readable(v)%2B'%3C%2Fli%3E'%20for%20v%20in%20value)%2B'%3C%2Fol%3E'%0A%20%20%20%20%20%20%20%20return%20_e(str(value))%20if%20value%20is%20not%20None%20else%20'Not%20recorded'%0A%20%20%20%20_body%3D'%3Chtml%20lang%3D%22en-AU%22%3E%3Cmeta%20charset%3D%22utf-8%22%3E%3Ctitle%3E'%2B_e(config%5B'title'%5D)%2B'%3C%2Ftitle%3E%3Cstyle%3Ebody%7Bfont%3A18px%2F1.6%20system-ui%3Bmax-width%3A950px%3Bmargin%3A40px%20auto%3Bpadding%3A20px%7Dtable%7Bborder-collapse%3Acollapse%7Dtd%2Cth%7Bborder%3A1px%20solid%20%23999%3Bpadding%3A10px%7Dp%7Bwhite-space%3Apre-wrap%7D%3C%2Fstyle%3E%3Ch1%3E'%2B_e(config%5B'title'%5D)%2B'%3C%2Fh1%3E%3Cp%3E'%2B_e(config%5B'method'%5D)%2B'%3C%2Fp%3E%3Ch2%3EMy%20prediction%3C%2Fh2%3E%3Cp%3E'%2B_e(prediction.value%20or%20'')%2B'%3C%2Fp%3E%3Ch2%3ESettings%3C%2Fh2%3E%3Cp%3E'%2B_e(str(controls.value))%2B'%3C%2Fp%3E%3Ch2%3EWhat%20happened%3C%2Fh2%3E%3Cp%3E'%2B_e(current%5B'summary'%5D)%2B'%3C%2Fp%3E%3Ctable%3E%3Ctr%3E'%2B''.join('%3Cth%3E'%2B_e(k)%2B'%3C%2Fth%3E'%20for%20k%20in%20current%5B'rows'%5D%5B0%5D)%2B'%3C%2Ftr%3E'%2B''.join('%3Ctr%3E'%2B''.join('%3Ctd%3E'%2B_e(str(v))%2B'%3C%2Ftd%3E'%20for%20v%20in%20row.values())%2B'%3C%2Ftr%3E'%20for%20row%20in%20current%5B'rows'%5D)%2B'%3C%2Ftable%3E%3Ch2%3EMy%20explanation%3C%2Fh2%3E%3Cp%3E'%2B_e(reflection.value)%2B'%3C%2Fp%3E%3Ch2%3EMethod%20and%20limits%3C%2Fh2%3E%3Cp%3E'%2B_e(current%5B'formula'%5D)%2B'%3C%2Fp%3E%3C%2Fhtml%3E'%0A%20%20%20%20_body%3D_body.replace('%3C%2Fhtml%3E'%2C'%3Ch2%3EFull%20current%20evidence%3C%2Fh2%3E'%2B_readable(current)%2B'%3Ch2%3EStarting%20baseline%3C%2Fh2%3E'%2B_readable(baseline)%2B'%3Ch2%3ESaved%20comparisons%3C%2Fh2%3E'%2B_readable(get_runs())%2B'%3C%2Fhtml%3E')%0A%20%20%20%20mo.hstack(%5Bmo.download(data%3Djson.dumps(_journal%2Cindent%3D2%2Callow_nan%3DFalse).encode()%2Cfilename%3Dconfig%5B'id'%5D%2B'-project.json'%2Clabel%3D'Download%20resumable%20notebook%20project')%2Cmo.download(data%3D_body.encode()%2Cfilename%3Dconfig%5B'id'%5D%2B'-report.html'%2Clabel%3D'Download%20readable%20report%20%2F%20print')%5D)%0A%20%20%20%20return%0A%0A%0A%40app.cell%0Adef%20_(MODEL%2C%20config%2C%20mo%2C%20sources)%3A%0A%20%20%20%20_young%3Dconfig%5B'year'%5D%3D%3D6%0A%20%20%20%20_timing%3D'0%E2%80%935%20min%3A%20read%20and%20predict.%205%E2%80%9315%3A%20make%20two%20controlled%20changes.%2015%E2%80%9325%3A%20find%20a%20counterexample.%2025%E2%80%9335%3A%20compare%20and%20explain.'%20if%20_young%20else%20'0%E2%80%938%20min%3A%20define%20the%20hypothesis%20and%20metric.%208%E2%80%9325%3A%20collect%20matched%20runs.%2025%E2%80%9340%3A%20stress-test%20the%20explanation.%2040%E2%80%9355%3A%20audit%20the%20claim%20and%20write%20a%20limitation.'%0A%20%20%20%20_teacher%3D'**Preparation%3A**%20one%20browser%20per%20pair%2C%20runtime%20download%20permitted%2C%20no%20accounts%20or%20personal%20data%20required.%20Try%20the%20default%20experiment%20before%20class.%20A%20teacher%20may%20read%20instructions%20aloud%20and%20pupils%20may%20dictate%20predictions.%5Cn%5Cn**Learning%20outcome%3A**%20answer%20%E2%80%9C'%2Bconfig%5B'question'%5D%2B'%E2%80%9D%20using%20a%20controlled%20comparison.%5Cn%5Cn**Lesson%20sequence%3A**%20'%2B_timing%2B'%5Cn%5Cn**Prompts%20and%20misconceptions%3A**%20'%2Bconfig%5B'teacher'%5D%2B'%5Cn%5Cn**Assessment%20(0%E2%80%932%20each)%3A**%20a%20testable%20prediction%3B%20a%20comparison%20naming%20what%20stayed%20fixed%3B%20accurate%20use%20of%20measurements%3B%20a%20counterexample%20and%20appropriately%20limited%20conclusion.%200%3Dmissing%2C%201%3Dpartial%2C%202%3Dclear%20and%20supported.%5Cn%5Cn**Support%3A**%20work%20through%20the%20default%20and%20one%20change%20together%2C%20use%20the%20glossary%2C%20and%20accept%20an%20oral%20explanation.%20**Stretch%3A**%20'%2Bconfig%5B'extend'%5D%0A%20%20%20%20_reading%3D'%5Cn%5Cn'.join('**%5B'%2Bs%5B'title'%5D%2B'%5D('%2Bs%5B'url'%5D%2B')**%5Cn%5Cn'%2B'**System%20and%20data%3A**%20'%2Bs%5B'systemAndData'%5D%2B'%5Cn%5Cn**Source%20method%3A**%20'%2Bs%5B'method'%5D%2B'%5Cn%5Cn**Paper-specific%20task%3A**%20'%2Bs%5B'year6Task'%20if%20config%5B'year'%5D%3D%3D6%20else%20'year12Task'%5D%2B'%5Cn%5Cn**Research%20boundary%3A**%20'%2Bs%5B'limit'%5D%20for%20s%20in%20sources)%0A%20%20%20%20mo.accordion(%7B'Teacher%20lesson%20plan%20and%20assessment'%3Amo.md(_teacher)%2C'Words%20to%20know'%3Amo.md('%5Cn%5Cn'.join('**'%2Bk%2B'%3A**%20'%2Bv%20for%20k%2Cv%20in%20config%5B'glossary'%5D.items()))%2C'Research%20connections%20and%20limits'%3Amo.md(_reading%2B'%5Cn%5CnIndependent%20Brightlab%20educational%20adaptations%3B%20no%20Goodfire%20endorsement.%20Full%20source%20map%3A%20%5BAI%20Understanding%20research%20library%5D(https%3A%2F%2Fbrightlab-ai-creators.ian347727.chatgpt.site%2Fai-understanding%2Fresearch).')%2C'Tiny%20transformer%20model%20card'%3Amo.json(MODEL%5B'card'%5D)%7D)%0A%20%20%20%20return%0A%0A%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20app.run()%0A