From owner-reliable_computing [at] interval [dot] louisiana.edu Tue Apr 1 07:23:24 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h31DNN406725 for reliable_computing-outgoing; Tue, 1 Apr 2003 07:23:23 -0600 (CST) Received: from mion.elka.pw.edu.pl (root [at] mion [dot] elka.pw.edu.pl [194.29.160.35]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h31DNHH06721 for ; Tue, 1 Apr 2003 07:23:17 -0600 (CST) Received: from mion.elka.pw.edu.pl ([194.29.160.35]:37832 "EHLO mion.elka.pw.edu.pl") by mion.elka.pw.edu.pl with ESMTP id ; Tue, 1 Apr 2003 10:12:00 +0200 Date: Tue, 1 Apr 2003 10:11:48 +0200 (MET DST) From: Bartlomiej Jacek KUBICA To: Subject: interval Markov chains (was: Re: your mail) In-Reply-To: Message-ID: MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII X-Virus-Scanned: by AMaViS perl-11 mion Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk Hello, On Mon, 31 Mar 2003, Lenin Bhavanandan wrote: > Dear Kubica, > > The following paper discusses a method to find stationary probabilities > of Markov chains: > > I.O. Kozine and L.V. Utkin. Interval-valued finite Markov chains. > Reliable Computing, 8(2002), 97-113. Thank you very much for the information. Is it possible to download this paper anywhere from the web ? I'm afraid I don't have any access to this volume of RC. Can anyone help me ? Thank you in advance. Best regards Bartlomiej Kubica From owner-reliable_computing [at] interval [dot] louisiana.edu Sat Apr 5 15:09:00 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h35L8xG11667 for reliable_computing-outgoing; Sat, 5 Apr 2003 15:08:59 -0600 (CST) Received: from cs.utep.edu (mail.cs.utep.edu [129.108.5.3]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h35L8rH11663 for ; Sat, 5 Apr 2003 15:08:54 -0600 (CST) Received: from aragorn (aragorn [129.108.5.35]) by cs.utep.edu (8.11.3/8.11.3) with SMTP id h35L8e926622 for ; Sat, 5 Apr 2003 14:08:40 -0700 (MST) Message-Id: <200304052108.h35L8e926622 [at] cs [dot] utep.edu> Date: Sat, 5 Apr 2003 14:08:40 -0700 (MST) From: Vladik Kreinovich Reply-To: Vladik Kreinovich Subject: special interval issue of Fuzzy Sets and Systems To: reliable_computing [at] interval [dot] louisiana.edu MIME-Version: 1.0 Content-Type: TEXT/plain; charset=ISO-8859-1 Content-MD5: qiz29psuGRUPi41A3uIzqw== X-Mailer: dtmail 1.3.0 @(#)CDE Version 1.4 SunOS 5.8 sun4u sparc Content-Transfer-Encoding: 8bit X-MIME-Autoconverted: from QUOTED-PRINTABLE to 8bit by interval.louisiana.edu id h35L8sH11664 Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk Fuzzy Sets and Systems Copyright © 2003 Elsevier Science B.V. All rights reserved Volume 135, Issue 1, Pages 1-212 (1 April 2003) Interfaces between Fuzzy Set Theory and Interval Analysis Edited by W.A. Lodwick, K.D. Jamison Weldon A. Lodwick and K. David Jamison Special issue: interfaces between fuzzy set theory and interval analysis, Pages 1-3 Interval analysis and fuzzy set theory, Pages 5-9 Ramon Moore and Weldon Lodwick Rudolf F. Albrecht Topological interpretation of fuzzy sets and intervals, Pages 11-20 Arnold Neumaier Fuzzy modeling in terms of surprise, Pages 21-38 Felipe Fernández and Julio Gutiérrez A Takagi-Sugeno model with fuzzy inputs viewed from multidimensional interval analysis, Pages 39-61 Marcello A. Anile, Primo Furno, Giovanni Gallo and Alessandro Massolo A fuzzy approach to visibility maps creation over digital terrains, Pages 63-80 J. Bondia and J. Picó Analysis of linear systems with fuzzy parametric uncertainty, Pages 81-121 Masahiro Inuiguchi, Jaroslav Ramík and Tetsuzo Tanino Oblique fuzzy vectors and their use in possibilistic linear programming, Pages 123-150 Masahiro Inuiguchi, Jaroslav Ramik, Tetsuzo Tanino and Milan Vlach Satisficing solutions and duality in interval and fuzzy linear programming, Pages 151-177 G. William Walster and Vladik Kreinovich Computational complexity of optimization and crude range testing: a new approach motivated by fuzzy optimization, Pages 179-208 Acknowledgement to the Referees, Pages 209-212 Lists of Editors, Pages ii-iv From owner-reliable_computing [at] interval [dot] louisiana.edu Wed Apr 9 05:36:41 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h39Aae116078 for reliable_computing-outgoing; Wed, 9 Apr 2003 05:36:40 -0500 (CDT) Received: from web10308.mail.yahoo.com (web10308.mail.yahoo.com [216.136.130.86]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with SMTP id h39AaYH16074 for ; Wed, 9 Apr 2003 05:36:35 -0500 (CDT) Message-ID: <20030409103627.59415.qmail [at] web10308 [dot] mail.yahoo.com> Received: from [202.54.67.195] by web10308.mail.yahoo.com via HTTP; Wed, 09 Apr 2003 03:36:27 PDT Date: Wed, 9 Apr 2003 03:36:27 -0700 (PDT) From: "M.V.Rama Rao" Subject: Interval numbers in Structural Analysis To: reliable_computing [at] interval [dot] louisiana.edu, reliable_computing [at] interval [dot] usl.edu In-Reply-To: <200304052108.h35L8e926622 [at] cs [dot] utep.edu> MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="0-1891927283-1049884587=:58139" Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk --0-1891927283-1049884587=:58139 Content-Type: text/plain; charset=us-ascii Dear Colleagues: Here is a query related to interval numbers: Suppose there are two interval numbers [e,f] and [c,d] if e = a+c and f = b+d , then [e,f] = [a+c,b+d] then [e,f] - [c,d] = [a+c,b+d] - [c,d] = [a+c-d, b+d-c] which is an overestimation instead if i express [e,f] = [a,b]+[c,d] then I think [e,f] - [c,d] = [a,b] + [c,d] - [c,d] = [a,b] + (1-1)[c,d] = [a,b] similarly [a-d,b-c] + [c,d] = [a,b] -[c,d] +[c,d] = [a,b] +(-1+1)[c,d] = [a,b] Is this approach correct. if so, how to implement it in a computer program? I am facing the problem of overestimation , when several fuzzy interval loads are simultaneously acting on the structure, the overall interval displacement vector ( which is usually obtained by superposition of various load cases) is getting expanded. can some one help me regarding how to handle this problem? Regards M.V.Rama Rao M.V.Rama Rao Senior Lecturer in Civil Engineering, Vasavi College of Engineering,Hyderabad-31 INDIA Phone : +91 (040)23532350 (R) +91(040)2359 0343 (R) FAX +91(040)2352 5323 e-mail ramu_mallela [at] yahoo [dot] com --------------------------------- Do you Yahoo!? Yahoo! Tax Center - File online, calculators, forms, and more --0-1891927283-1049884587=:58139 Content-Type: text/html; charset=us-ascii

Dear Colleagues:

Here is a query related to interval numbers:

Suppose there are two interval numbers

[e,f]  and  [c,d]

if   e = a+c  and   f = b+d , then

[e,f] = [a+c,b+d]

then [e,f] - [c,d]  = [a+c,b+d] - [c,d] =  [a+c-d, b+d-c]  which is an overestimation

instead if i express [e,f] = [a,b]+[c,d]  then  I think

[e,f] - [c,d] =  [a,b] + [c,d] - [c,d]  = [a,b] + (1-1)[c,d] = [a,b]

similarly [a-d,b-c] + [c,d] = [a,b] -[c,d] +[c,d] = [a,b] +(-1+1)[c,d] = [a,b]

Is this approach correct. if so, how to implement it in a computer program?

I am facing the problem of overestimation , when several fuzzy interval loads are simultaneously acting on the structure, the overall interval displacement vector ( which is usually obtained by superposition of various load cases) is getting expanded. can some one help me regarding how to handle this problem?

Regards

M.V.Rama Rao

 

 

 



M.V.Rama Rao
Senior Lecturer in Civil Engineering,
Vasavi College of Engineering,Hyderabad-31 INDIA
Phone : +91 (040)23532350 (R) +91(040)2359 0343 (R)
FAX +91(040)2352 5323
e-mail ramu_mallela [at] yahoo [dot] com


Do you Yahoo!?
Yahoo! Tax Center - File online, calculators, forms, and more --0-1891927283-1049884587=:58139-- From owner-reliable_computing [at] interval [dot] louisiana.edu Wed Apr 9 07:17:30 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h39CHT916232 for reliable_computing-outgoing; Wed, 9 Apr 2003 07:17:29 -0500 (CDT) Received: from imf38bis.bellsouth.net (mail119.mail.bellsouth.net [205.152.58.59]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h39CHOH16228 for ; Wed, 9 Apr 2003 07:17:24 -0500 (CDT) Received: from Inspiron-8200 ([65.83.165.91]) by imf38bis.bellsouth.net (InterMail vM.5.01.04.25 201-253-122-122-125-20020815) with SMTP id <20030409121924.CDDI13659.imf38bis.bellsouth.net@Inspiron-8200>; Wed, 9 Apr 2003 08:19:24 -0400 Message-Id: <2.2.32.20030409121714.009e76c8 [at] pop [dot] louisiana.edu> X-Sender: rbk5287 [at] pop [dot] louisiana.edu X-Mailer: Windows Eudora Pro Version 2.2 (32) Mime-Version: 1.0 Content-Type: text/plain; charset="us-ascii" Date: Wed, 09 Apr 2003 07:17:14 -0500 To: "M.V.Rama Rao" , reliable_computing [at] interval [dot] louisiana.edu, reliable_computing [at] interval [dot] usl.edu From: "R. Baker Kearfott" Subject: Re: Interval numbers in Structural Analysis Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk Rama, This is one of the "classic" problems in interval analysis. An example of the phenomenon is: [-1,1] - [-1,1] = [-2,2]. Actually, [-2,2] is the EXACT range of {x - y, x in [-1,1] and y in [-1,1]} There is only overestimation if you really meant {x - x, x in [-1,1]}. This question is at the heart of many current research efforts. One way of reducing overestimation is to symbolically preprocess the expressions to reduce the number of redundant occurrences of each variable (for example, by factoring xy + xz into x(y+z) ). Another technique is to represent functions with bases in which overestimation is less, then to do arithmetic on the point coefficients of the functions, saving interval evaluation until last. That is one of the principles behind Taylor arithmetic. Best regards, R. Baker Kearfott P.S. reliable_computing [at] interval [dot] usl.edu no longer works. You should always use reliable_computing [at] interval [dot] louisiana.edu Our university changed its name from "University of Southwestern Louisiana" to "University of Louisiana", and the new web address reflects this. At 03:36 AM 4/9/2003 -0700, M.V.Rama Rao wrote: > >Dear Colleagues: >Here is a query related to interval numbers: >Suppose there are two interval numbers >[e,f] and [c,d] >if e = a+c and f = b+d , then >[e,f] = [a+c,b+d] >then [e,f] - [c,d] = [a+c,b+d] - [c,d] = [a+c-d, b+d-c] which is an overestimation >instead if i express [e,f] = [a,b]+[c,d] then I think >[e,f] - [c,d] = [a,b] + [c,d] - [c,d] = [a,b] + (1-1)[c,d] = [a,b] >similarly [a-d,b-c] + [c,d] = [a,b] -[c,d] +[c,d] = [a,b] +(-1+1)[c,d] = [a,b] >Is this approach correct. if so, how to implement it in a computer program? >I am facing the problem of overestimation , when several fuzzy interval loads are simultaneously acting on the structure, the overall interval displacement vector ( which is usually obtained by superposition of various load cases) is getting expanded. can some one help me regarding how to handle this problem? >Regards >M.V.Rama Rao > > > > > >M.V.Rama Rao >Senior Lecturer in Civil Engineering, >Vasavi College of Engineering,Hyderabad-31 INDIA >Phone : +91 (040)23532350 (R) +91(040)2359 0343 (R) >FAX +91(040)2352 5323 >e-mail ramu_mallela [at] yahoo [dot] com > > >--------------------------------- --------------------------------------------------------------- R. Baker Kearfott, rbk [at] louisiana [dot] edu (337) 482-5346 (fax) (337) 482-5270 (work) (337) 981-9744 (home) URL: http://interval.louisiana.edu/kearfott.html Department of Mathematics, University of Louisiana at Lafayette Box 4-1010, Lafayette, LA 70504-1010, USA --------------------------------------------------------------- From owner-reliable_computing [at] interval [dot] louisiana.edu Wed Apr 9 08:18:25 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h39DIPE16419 for reliable_computing-outgoing; Wed, 9 Apr 2003 08:18:25 -0500 (CDT) Received: from gtrep.gatech.edu (hemispheres.gtrep.gatech.edu [168.20.168.31]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h39DIJH16415 for ; Wed, 9 Apr 2003 08:18:20 -0500 (CDT) Received: from rmuhannalptp (gtrep-dhcp-227.gtrep.gatech.edu [168.20.168.227]) by gtrep.gatech.edu (8.11.6/8.11.6) with SMTP id h39DHmg07152; Wed, 9 Apr 2003 09:17:48 -0400 Message-ID: <002b01c2fe9a$4970c0a0$e3a814a8@rmuhannalptp> From: "Rafi Muhanna" To: "M.V.Rama Rao" , , , "R. Baker Kearfott" References: <2.2.32.20030409121714.009e76c8 [at] pop [dot] louisiana.edu> Subject: Re: Interval numbers in Structural Analysis Date: Wed, 9 Apr 2003 09:16:52 -0400 MIME-Version: 1.0 Content-Type: text/plain; charset="iso-8859-1" Content-Transfer-Encoding: 7bit X-Priority: 3 X-MSMail-Priority: Normal X-Mailer: Microsoft Outlook Express 6.00.2720.3000 X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2600.0000 Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk Rama, In addition to Dr. Kearfott's comments, I thing that your question is addressing the load dependency issue in the formulation of the Interval Finite Method. The overestimation that you are addressing is associated with dependency among loads, this issue is resolved by M-Matrix formulation introduced in our published paper "Bounds of Structural Response for all Possible Loading Combinations" in the ASCE journal of Structural Engineering, Vol. 125, 1, 1999. and you can download the paper from the following URL. http://www.gtrep.gatech.edu/~rmuhanna/ASCE_STRUCT.pdf Beast regards, Rafi Muhanna ________________________________________________ Rafi L. Muhanna Director, Center for Reliable Engineering Computing (REC) Department of Civil & Environmental Engineering Regional Engineering Program Georgia Institute of Technology 6001 Chatham Center Dr., Suite 350 Savannah, GA 31405 USA Email: rafi.muhanna [at] gtrep [dot] gatech.edu Phone: (912) 651-7547 Fax: (912) 651-7279 ----- Original Message ----- From: "R. Baker Kearfott" To: "M.V.Rama Rao" ; ; Sent: Wednesday, April 09, 2003 8:17 AM Subject: Re: Interval numbers in Structural Analysis > Rama, > > This is one of the "classic" problems in interval analysis. An example > of the phenomenon is: > > [-1,1] - [-1,1] = [-2,2]. > > Actually, [-2,2] is the EXACT range of > > {x - y, x in [-1,1] and y in [-1,1]} > > There is only overestimation if you really meant > > {x - x, x in [-1,1]}. > > This question is at the heart of many current research efforts. > > One way of reducing overestimation is to symbolically preprocess > the expressions to reduce the number of redundant occurrences > of each variable (for example, by factoring xy + xz into x(y+z) ). > Another technique is to represent functions with bases in which > overestimation is less, then to do arithmetic on the point coefficients > of the functions, saving interval evaluation until last. That is > one of the principles behind Taylor arithmetic. > > Best regards, > > R. Baker Kearfott > > > P.S. reliable_computing [at] interval [dot] usl.edu no longer works. You > should always use > reliable_computing [at] interval [dot] louisiana.edu > > Our university changed its name from > "University of Southwestern Louisiana" to > "University of Louisiana", > and the new web address reflects this. > > At 03:36 AM 4/9/2003 -0700, M.V.Rama Rao wrote: > > > >Dear Colleagues: > >Here is a query related to interval numbers: > >Suppose there are two interval numbers > >[e,f] and [c,d] > >if e = a+c and f = b+d , then > >[e,f] = [a+c,b+d] > >then [e,f] - [c,d] = [a+c,b+d] - [c,d] = [a+c-d, b+d-c] which is an > overestimation > >instead if i express [e,f] = [a,b]+[c,d] then I think > >[e,f] - [c,d] = [a,b] + [c,d] - [c,d] = [a,b] + (1-1)[c,d] = [a,b] > >similarly [a-d,b-c] + [c,d] = [a,b] -[c,d] +[c,d] = [a,b] +(-1+1)[c,d] = [a,b] > >Is this approach correct. if so, how to implement it in a computer program? > >I am facing the problem of overestimation , when several fuzzy interval > loads are simultaneously acting on the structure, the overall interval > displacement vector ( which is usually obtained by superposition of various > load cases) is getting expanded. can some one help me regarding how to > handle this problem? > >Regards > >M.V.Rama Rao > > > > > > > > > > > >M.V.Rama Rao > >Senior Lecturer in Civil Engineering, > >Vasavi College of Engineering,Hyderabad-31 INDIA > >Phone : +91 (040)23532350 (R) +91(040)2359 0343 (R) > >FAX +91(040)2352 5323 > >e-mail ramu_mallela [at] yahoo [dot] com > > > > > >--------------------------------- > > --------------------------------------------------------------- > R. Baker Kearfott, rbk [at] louisiana [dot] edu (337) 482-5346 (fax) > (337) 482-5270 (work) (337) 981-9744 (home) > URL: http://interval.louisiana.edu/kearfott.html > Department of Mathematics, University of Louisiana at Lafayette > Box 4-1010, Lafayette, LA 70504-1010, USA > --------------------------------------------------------------- > From owner-reliable_computing [at] interval [dot] louisiana.edu Fri Apr 11 06:14:55 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h3BBEtH18707 for reliable_computing-outgoing; Fri, 11 Apr 2003 06:14:55 -0500 (CDT) Received: (from rbk5287@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h3BBEo318702 for reliable_computing [at] interval [dot] louisiana.edu; Fri, 11 Apr 2003 06:14:50 -0500 (CDT) Received: from lcyoung.math.wisc.edu (lcyoung.math.wisc.edu [144.92.166.90]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h3AMTdH17968 for ; Thu, 10 Apr 2003 17:29:40 -0500 (CDT) Received: from ultra10.math.wisc.edu (ultra10.math.wisc.edu [144.92.166.180]) by lcyoung.math.wisc.edu (8.11.6p2/8.11.6) with ESMTP id h3AMRWc01104; Thu, 10 Apr 2003 17:27:32 -0500 (CDT) Date: Thu, 10 Apr 2003 17:27:31 -0500 (CDT) From: Hans Schneider To: NETS -- at-net , E-LETTER , Pradeep Misra , Shaun Fallat , "na.digest" , ipnet-digest [at] math [dot] msu.edu, SIAGLA-DIGEST , wim@bell-labs.com, hjt [at] eos [dot] ncsu.edu, SMBnet [at] smb [dot] org, vkm [at] eedsp [dot] gatech.edu, reliable_computing [at] interval [dot] louisiana.edu Subject: LAA contents Message-ID: MIME-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII X-MailScanner: Found to be clean Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk Linear Algebra and its Applications Volume 365, Pages 1-467 (15 May 2003) Special Issue on Linear Algebra Methods in Representation Theory Edited by D. Happel, C.M. Ringel and J. Drozd TABLE OF CONTENTS Linear Algebra Methods in Representation Theory, Pages 1-2 Another algorithm for nonnegative matrices, Pages 3-12 Manfred J. Bauch Minimal singularities in orbit closures of matrix pencils, Pages 13-24 Jens Bender and Klaus Bongartz Symmetric quiver settings with a regular ring of invariants, Pages 25-43 Raf Bocklandt Linear operators on S-graded vector spaces, Pages 45-90 Vitalij M. Bondarenko On the kernel of an irreducible map, Pages 91-97 Sheila Brenner Irreducible maps and bilinear forms, Pages 99-105 Sheila Brenner, M. C. R. Butler and Alastair D. King On positive roots of pg-critical algebras, Pages 107-114 Thomas Brustle Estimate of the number of one-parameter families of modules over a tame algebra, Pages 115-133 Thomas Brustle and Vladimir V. Sergeichuk Periodic Coxeter matrices, Pages 135-142 Jose A. de la Pena On spectral radii of Coxeter transformations, Pages 143-153 Vlastimil Dlab and Piroska Lakatos On the dimension of faithful modules over finite dimensional basic algebras, Pages 155-157 M. Domokos Tame biextensions of derived tame hereditary algebras, Pages 159-167 Peter Draxler Hochschild cohomology of incidence algebras as one-point extensions, Pages 169-181 Maria Andrea Gatica and Maria Julia Redondo Monoidal structure of the category of u+q-modules, Pages 183-199 Elisabet Gunnlaugsdottir Regular points in system spaces, Pages 201-213 Yang Han and Mulan Liu Quivers, cones and polytopes, Pages 215-237 Lutz Hille Variation on a theme of Richardson, Pages 239-246 Lutz Hille and Gerhard Rohrle Algebraic computations in derived categories, Pages 247-266 Amrey Krause A short proof for Auslander's defect formula, Pages 267-270 Henning Krause Rings of invariants of 2 x 2 matrices in positive characteristic, Pages 271-278 S. G. Kuz'min and A. N. Zubkov Additive functions on quivers, Pages 279-289 Helmut Lenzing and Liane Hasenberg A note on applications of the 'Vector Enumerator' algorithm, Pages 291-300 Jurgen Muller >From elementary calculations to Hall polynomials, Pages 301-309 R. Norenberg Curves arising from Kronecker modules, Pages 311-348 F. Okoh and F. A. Zorzitto Strongly nilpotent matrices and Gelfand-Zetlin modules, Pages 349-367 Serge Ovsienko Cellular algebras and Cartan matrices, Pages 369-388 Changchang Xi and Dajing Xiang Tame equipped posets, Pages 389-465 Alexander Zavadskij Author Index, Page 467 Lists of Editors, Pages ii-iii From owner-reliable_computing [at] interval [dot] louisiana.edu Sun Apr 13 23:15:11 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h3E4FBN21712 for reliable_computing-outgoing; Sun, 13 Apr 2003 23:15:11 -0500 (CDT) Received: from cs.utep.edu (mail.cs.utep.edu [129.108.5.3]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h3E4F5H21708 for ; Sun, 13 Apr 2003 23:15:06 -0500 (CDT) Received: from aragorn (aragorn [129.108.5.35]) by cs.utep.edu (8.11.3/8.11.3) with SMTP id h3E1uGZ03342; Sun, 13 Apr 2003 19:56:18 -0600 (MDT) Message-Id: <200304140156.h3E1uGZ03342 [at] cs [dot] utep.edu> Date: Sun, 13 Apr 2003 19:56:15 -0600 (MDT) From: Vladik Kreinovich Reply-To: Vladik Kreinovich Subject: from NA Digest To: reliable_computing [at] interval [dot] louisiana.edu Cc: vincent [at] sacksteder [dot] com MIME-Version: 1.0 Content-Type: TEXT/plain; charset=us-ascii Content-MD5: WFQ4pLV4JbFMdLWMWBc80Q== X-Mailer: dtmail 1.3.0 @(#)CDE Version 1.4 SunOS 5.8 sun4u sparc Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk Dear Vincent, There is an additional aspect of why the results of scientific computations are sometimes unreliable: programmers and algorithm designers do not take into consideration that the input values come from measurements and are therefore known only with a certain accuracy, and that computer operations are not precise because of rounding. I am sending a copy of your message to NA Digest to the interval computations mailing list, many of our folks have accumulated results in which people do not take into considation and get wrong results, as well as cases when everything is done perfectly. Vladik From: Vincent Sacksteder Date: Tue, 8 Apr 2003 18:38:37 +0200 Subject: Looking for Data About the Reliability of Scientific Calculations Dear NA community: I am researching to what extent the numerical results published in the scientific literature can be regarded as reliable, and am writing you to ask for any data, experience, and opinions you have on this issue. I am currently pursueing a Ph.D. in physics after a career in computer science which focused on the reliability of distributed middleware used by large enterprises. In my new shoes as a physicist I am confused by the lack of discussion within the physics community about bugs and about ways of ensuring the reliability of published numerical results. It seems that while many physics articles use software to compute various results, perhaps few authors have implemented the most basic practices for ensuring its quality - whether planned and repeatable test suites, source code control, or publication of their code, scripts, and configuration files. (Even when an author uses lapack or mathematica which are themselves tested, the code, scripts, and configuration files written by the author may not be tested, archived, or published.) Moreover, there does not appear to be a structure for reporting bugs, documenting them, or discussing their prevention. It's not clear to me how much this is specific to the physics community, or instead diffused throughout the scientific community. Perhaps there are some mitigating factors which allow the physics community to do without these basic practices: perhaps it is more naturally self-correcting, through the mutual review of many colleagues. Or perhaps there is an alternative, informal set of practices which are passed along by word of mouth. Et cetera. Unfortunately, I have very little data, other than a documentable lack of discussion of these issues within the physics literature, and some individual conversations with my colleagues. If any of you has any additional data, opinions, or experience to share with me, I would really really appreciate it. Thank you, Vincent Sacksteder From owner-reliable_computing [at] interval [dot] louisiana.edu Mon Apr 14 01:02:57 2003 Received: (from daemon@localhost) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) id h3E62vB21896 for reliable_computing-outgoing; Mon, 14 Apr 2003 01:02:57 -0500 (CDT) Received: from kathmandu.sun.com (kathmandu.sun.com [192.18.98.36]) by interval.louisiana.edu (8.11.3/8.11.3/ull-interval-math-majordomo-1.3) with ESMTP id h3E62oH21892 for ; Mon, 14 Apr 2003 01:02:50 -0500 (CDT) Received: from heliopolis.eng.sun.com ([152.70.28.21]) by kathmandu.sun.com (8.9.3p2+Sun/8.9.3) with ESMTP id AAA05059; Mon, 14 Apr 2003 00:02:37 -0600 (MDT) Received: from sun.com (vpn-129-150-17-158.SFBay.Sun.COM [129.150.17.158]) by heliopolis.eng.sun.com (8.11.6+Sun/8.11.6/ENSMAIL,v2.1p1) with ESMTP id h3E62UR13962; Sun, 13 Apr 2003 23:02:31 -0700 (PDT) Message-ID: <3E9A4CD2.79F15D4E [at] sun [dot] com> Date: Sun, 13 Apr 2003 22:53:22 -0700 From: Bill Walster X-Mailer: Mozilla 4.79 [en] (Win98; U) X-Accept-Language: en,ru MIME-Version: 1.0 To: vincent [at] sacksteder [dot] com CC: Vladik Kreinovich , reliable_computing [at] interval [dot] louisiana.edu Subject: Re: from NA Digest References: <200304140156.h3E1uGZ03342 [at] cs [dot] utep.edu> Content-Type: multipart/mixed; boundary="------------4E1A4AE0EDEDDED3DBAF60FC" Sender: owner-reliable_computing [at] interval [dot] louisiana.edu Precedence: bulk This is a multi-part message in MIME format. --------------4E1A4AE0EDEDDED3DBAF60FC Content-Type: text/plain; charset=us-ascii Content-Transfer-Encoding: 7bit Dear Vincent, Many of us in the interval research community believe that using interval arithmetic is the only practical way to address the questions you raise. Interestingly, the properties of computing with intervals make it possible to solve nonlinear problems that most people, who are unfamiliar with intervals, believe to be impossible to numerically solve. I hope that you will continue to pursue your quest to compute results that you and others can trust. Best regards, Bill Walster Vladik Kreinovich wrote: > Dear Vincent, > > There is an additional aspect of why the results of scientific computations are > sometimes unreliable: programmers and algorithm designers do not take into > consideration that the input values come from measurements and are therefore > known only with a certain accuracy, and that computer operations are not > precise because of rounding. I am sending a copy of your message to NA Digest > to the interval computations mailing list, many of our folks have accumulated > results in which people do not take into considation and get wrong results, as > well as cases when everything is done perfectly. > > Vladik > > From: Vincent Sacksteder > Date: Tue, 8 Apr 2003 18:38:37 +0200 > Subject: Looking for Data About the Reliability of Scientific Calculations > > Dear NA community: > > I am researching to what extent the numerical results published in the > scientific literature can be regarded as reliable, and am writing you to ask > for any data, experience, and opinions you have on this issue. I am > currently pursueing a Ph.D. in physics after a career in computer science > which focused on the reliability of distributed middleware used by large > enterprises. In my new shoes as a physicist I am confused by the lack of > discussion within the physics community about bugs and about ways of > ensuring the reliability of published numerical results. It seems that > while many physics articles use software to compute various results, perhaps > few authors have implemented the most basic practices for ensuring its > quality - whether planned and repeatable test suites, source code control, > or publication of their code, scripts, and configuration files. (Even when > an author uses lapack or mathematica which are themselves tested, the code, > scripts, and configuration files written by the author may not be tested, > archived, or published.) Moreover, there does not appear to be a structure > for reporting bugs, documenting them, or discussing their prevention. It's > not clear to me how much this is specific to the physics community, or > instead diffused throughout the scientific community. > > Perhaps there are some mitigating factors which allow the physics community > to do without these basic practices: perhaps it is more naturally > self-correcting, through the mutual review of many colleagues. Or perhaps > there is an alternative, informal set of practices which are passed along by > word of mouth. Et cetera. > > Unfortunately, I have very little data, other than a documentable lack of > discussion of these issues within the physics literature, and some > individual conversations with my colleagues. If any of you has any > additional data, opinions, or experience to share with me, I would really > really appreciate it. > > Thank you, > Vincent Sacksteder --------------4E1A4AE0EDEDDED3DBAF60FC Content-Type: application/pdf; name="ch01-excerpt.pdf" Content-Transfer-Encoding: base64 Content-Disposition: inline; filename="ch01-excerpt.pdf" JVBERi0xLjENCiXi48/TDQoyIDAgb2JqDQo8PA0KL0xlbmd0aCA0MzEwDQovRmlsdGVyIC9M WldEZWNvZGUNCj4+DQpzdHJlYW0NCoALyGUxiIDGcxBBYUIDmYwaCiEWBALyPBBAZ4QRSxDx iMxALY6IBsMRkIBuOZKcjLDzNDyEVIeLyMNISICpLQUMRuLhnNBgIJ/Op5NBiNRsLhrJZGNZ 4OI8VDbD5/U4ZDgULRgLhgMxiOJtVi2KCQKRiMRcNxQexSNLOKDMaDqczuKRbXJ2NBQaboMa zeDqcjhdBkOKYMxQXBgMhnZBcMRQShTip4KDfexnSBRKjJlbqMrNeClZByLrwc7Xk7mLRsKD ZexgKMCMhlbdbIMuNRQfTId9rHcwcjjdBmMJ3jixohcMhQdrpbNxvduKODbMMahSXSoSo5SB qN4LIOSNhtNCoRIfWK0M/VX4fYSZdBxjekKeHmNTO9waObbTqeRSGqmMczbhNkFwchQIiyJ2 tDgtmHAUQGkDcD45jVNYvbcQq0bcBfAjZwOKzIhdB40DM4AzuEGD4tWsbfLRFEHQvCQUPuzD erM3A5LovrpMQGQaOu7KONmGAbKBI6SK1IwZMSs6vQA+LiPIqKrqyGC+JKKiwLEFL4sMzjHD PHSduU4LOqytAlrIy68DIFMyPm2a0Ds/SmQfMzBhoy7HMg+MHhjAi2LwLTItm1Y2DeN42TdO DArqsrMUJLDkwgN47DvOsRhQOsgu0BSmKSG6aBa2a+BoGqbPNKqtBiGjyS3BLRuU0zojq2qm OUzgZsGFzVhoG0Os7FYUBq9QbU6jkrJKqi+K0ktWviG6vBoHNQBxLMqPQGDEq9LSpK0xIcps O72hQK41vhAwUP0FoaM9Sl0VJdQ2UuOQ6j45t3scOS9XbArlBAOTdjYOI1Bg01SPjHIUqy5W CMipi8NSGgYtWMzBPkwA5jw3mCuaGMDjIOMUX8y60U4rLHDQOw8DfjrrXbkFNjkPF0wPkeCZ hdz8jrTgWqZA8dYbTcK4o4sIDq/TsU88FqBuj6zMUGdUPK88rW3cVvAUsIz0zB47DNWwysEo 7V68FGSBmwlNNbHD5rrtcHxDNl14vO0aDY4DhbiFDvVdvazX3fu1OoFAl7OOLg7plb6XU5jL wPfG4Mu5WRbSk91CmyM4N3TD/00Oo7b0uvMQOFYaB4HgbhZQGfhynbDPfNE49hGj/3W/r/8h CERMdXQcLMtE3VK+e6eG+Q3wpxsDtq5zl2QBSfvAxVq1Tq1wM89mthRsdSJ3Ei9tIFA1ZE4Q bwDCEKhnUTMTd0ij4ljDlR1Uk9szhl1V0G60X62bDL2fMUc3D3QZPffuVlA4eGcMedIjwv7N QWnxTKGdnMAXxFwL+6F1r6zHBRLpAJdZ/leFoLgXMy6D14mzOUGgN7Kw7LBbUfFA8JlNLxgk ut8yhnuGCgNCwNgaHBg3MuathEBVNFwDsvQO5zGllSagY0HLFXrKrW2q17RYQ7hrN2cI8R/D GI5ggfg6UFIGAzBsyZ3EGX1A2gMGZ8pdQbHxLxARo67IzNAhweh4USXkxcbujBdQaC5vORK5 4wraIuRyNYmaM0imRByiyXCLho0Hh6MiUcwybiuGYXwU4ybrUAR4CucItoa47HeXU3YyaOiz GrQaUdB8gzMBolepqWRzy6I5kYDY2ZeF6PQCoCp6623staixFo1IMliltaEuoOK7JlQyOkGg NDJJlSHOBH1UhSS2omDgxtz020Pu8m2Vk1Z1oEGshEtFdaeEAGTaSyxlxgplloDxElgqwQZA 2VkcuTRWUHuSmUnJ55dTJs5hbPScabp0sjMEtQyaYoIQ3bHCoyk0JuIHDm0kMy9G8hxUBE16 MT2QA3SM1WKhiVXrlCY4NXaNJTHCK47aIxuJLMglWgSdjKJZzyg+jR9Su11BkDU0mO1Mw7v0 PUWxOZgKjmOcUWZB8EDZm4NjRQwR8WVM+V3NJflMjlQUp+GoNkS4YAyNGxKU0jKa0wZXU9tA bKyRLrBUNpNMjHMXPAWhe1HA70hSEAomSqCCk3SGkpI5QUiJGI6yZaZxElUnWylYorWUtlcl yZhIBICyv+BQFUsj6A8GBYqW0OYKrMoZhyW0sZOzVh7DJaQo5aDTGfMpamHFIiZFEJsThZpi bEkJWUQk9STgQNqe+bIm1k1wRWmMgiRjaoLlqVAhCb9CS6xmfvK0vJwl3LqtAqQFFo3PmGYQ eqRQKz9l4dQDsHYLKZGjTmGtHUm6bSkTnTIncM5kUylhTCX92b9u8u4HUNVo5aH0RialYzbC yUFJ67tBq6pIX+U1M8t4dg7BkVtNo9Tdz/A1rTgRXplA2BnsAp56TUQcKiimtoxJT0thYiTZ lm5kXx3ebKCg96uCxBICQDAujRwdYtu886b04IkQwBonCSN3ppBpDkonGz9z6mrksu3EsFS6 q/xK/034dVHE9nHep2YaS/5ju+gcJ5+2gv4LQvEnt8neOzodl2Q6hG1ptzgZRS8ksuuzvUz8 yaxSegsvhSLFZyQZvVpRjC5yW6vrtSag8Kt5JD3nPkCp9Tr4j3UMxRuF1HzhA5S8CgPVZV0W 6CNYy3thyy6mI+1eAsUyfta1uuQBRaQUhUDUTEIzT7Ck4J3cAqmxSSz7gQSWM0CHgXLIeCgx evtgFe2GslZ1wbf7Jn4eG48vDSPn2hSlakVwUBAb23cHQOgcg0B09C3ZNbDAKLYDDJq4taNP 0e1d9b2kIUO2pYLVu8icElxhrWlGtyrAoXxwEmWwtXgK2RcHidSyz7MBts6wqVNpa91/wLa3 Edt7auHhGxEZpe7iKhMPe1lly7obg3c8QMweA53gTPghD9673KA9jfSquDtUS20kOaio3KY0 7NINeAdG1efwbhW0Hz5VkDigOkSpC2A5J6d9qOjWqdAauVvcwTAihQv0fJEJeJ9g6MWZ3Hc8 TTm4gg/AyhcymGrDtinYGrt5ofBolcj6hk9HkVVwraJeuHBG5DvNNHGVUa3VUCi9Xie+E4Ky 2olCR99rgiFuYPYPi9njxKvjVni7fFIV3vjF7V0Abmvg95De54eQ6BWeJY+rPKkPNuDiOPmv C7+B7x7avOXonJeAT71a4NytaBRkX4XAlxbXJyckoqRuwtZ9/8wxBW/nky9z8UHO7dZrgBh9 jaJavE8Q3nxPY+2ZlNlSMrtBbFNxrE+74r4nI1mXDn24H+IyTERqjji+Dyj4jy4HDzLyDaLh rj7h74j9hI7iaAphorz+Qs7+jlbXbmzxL0zbDYxJD/ikxAwmhXY27RzjgFz+776BD8UBTXb9 EBrYMB7bL9q4CtBQy46IxUTjbaIGr+8DonK4b/b9zTwgsEopEE7liXjfx05IEAr6Qsx9YG5Z bWzf0GDYD9TYkGkCLbIrhssHL+cHjXcH0J7kUIUEELpfUHME0AUBb+8LIh8CAn7iqfYnjZkH UDDjkMkGL6MM0LYoLkqoTWUI7xz+r7bacGMIEFhUb67Wy5rcxU5z5A5ID3D/I0h173yJz5kB kLEGcD8OcLqM4xr/8ML+oG7+8Preb/UNC4CMySkO6IYGENrXcQ8H8AxAz8TGD8r5Jbb5ZLYG r142R9EFJUgrpSgHDm776qrUwr0FzwzXb4MDkW6Ar1sFwFEZEMrxkS5p8a0TjgUODiUP4kzb JihaMCsPEMQFAHcW0KEM8QEciZQpENcJEWYFEFMbL07rYjycwG44iKbXjgIIomCwQgYgoga4 In4KYIaJwJRI4NTnpIoEAO64QEAJoEALYLon4MgjgEANIEDnSMxXsHKTCxhhpIw9BdxgIlYi CYQiEgYmUIEVcd64BPT+C460p88esPbvcW5p0TLXcbzeL6R/wsonxXowgp7sBcDGTaJ08Ajj 67I9LETwJAxLEXhK5LJLYLTVSmR2aB5kYGCpIGCZ7DhlbBjK7yTJ4zqSi8ZlhCKrpSiayQJz 43Bfq2wMhloO7149Q6J2R8QsbU7IAGDIsU67JtzDjVQwK6qQQ04tANiVRDimQtgxyWTOKOyt DT7UTDTUjRSJ6tAnsRyKqk5LZh4zo6J04041cvY+q2kSRTbuJ+4kDEYM4PAt5LoyYOz4KkQ2 7zEqkKRLMpRbbRrcwGwGoGAMo2qMQO83SwL7z4g6kWURjn0Kr5jxERMW6k0WUn7yUFUW7zEK ilEgEGMcEOUccG0zBJYo0UcdMREnkdscTkYxTnYECZS1z1sDJlKM8pjXYH0oMGT6U8sCRPQo c+k9RisMUZ7jsaU98T64T9zJpEYmk+pXs+5Khhsfs0baIFgIEJ0GMZQ5L8r606jhcaM676VC 4Gr5DzcXrlzXbdzER9b+63jeYkDy8z6kgHEBE0KYjcx18482A2puguYo4x05hT05z6U6FFL8 aYjn8TThc6z4dE4s8QsazydD070BE8DyMK8b8T0KkUE840ZJZVw5KYrjk9rgT78mVB0Gwnor VCRU4s4orcc/I9b5k/sN9L7ilB4y80BH4tg2VBDf1NMmD/Md1NrZNCAHFOIpg70WdC8iL5lD dDs9ziKFVEMn9BNEtKTiNFFFU4Llrf1F7rQGx1S9A8YHQHcDcGNGYnFGsOzrSkjdr8ylKYpL ZU4Hc45W4tqGhB9I1SzedJU6VJtEbw8dlT1Kk7VK07tKc787dLsB1AEcUCU9E+ioQwc9lZsP 1Bs+NN7e1a5k1Ok/EUdO7hdPL9NPcGtRVP1CVbDZ8/FQlZEVVRE+NRdd1cVSA5NDDf1Slbbv tEAHNEThL4Feby1fdJcFzGEXxctUiM1U5tVVNVY8YHYHC9yNgHh9cpDKoGgHYG0dbVlVw843 z3gp03xXak9ULhBWDKhR5BZ2xygFANaow4Q3wwyeKiZTRlx9QopSiolmhR9PyRdmpu6LJdJ8 FoIyYxFGJeRB50LBhVo+R9SQKdY+TBJR6P5EoNgOEt5SBF69Y1iudnjHyhgybO4rlorpYOpT Fq9tBTRFC7iuSsp9T8oydn6ah6DRaktlLliws0hMxV1tJdhVzEYN4MhS45s4xSguZXhA6S0O qvlxJ+xTgsxA5lcvI05A5Cqix1pV1l6DzQgvANCEUthTJk4xi/iFiFxYLJtl6SJyVwlnzHCu 5dqiBNtltzybpHaVcxS0yhNRx2w8BlSbV2JhwxgvAOJExnqQtzIFFzqepLiqoFF0iI93Yww4 Mzt4SKNvilIrrcySINirhXgwyJINS6KcbpioQwwPQv5grvT6cnx6br0q65S54J4OTOSnB4R/ BO5tNnowzPS6tspB5SxTCrknDyTQwGkva0rvC+g+h2bLJXaqSmBdKTNqCIaHcYrCl/mCpR7O jS6b7ThFJP02LHynkxzqlrpqLf5tNuow0tQrhGINAPczSJJvTq4z8TB6bFrwlvqlbXbKRTkf A7YpKflJgxNJykc6tgxb585U87dK9YNg9Z9hRq7zq57z4vcfr0bm8mL1CtFTVftGU7Arr606 dFa4C580svg+Up0ySeCas2qo6cxGgOVYFNUGZ/dRkqg8b8VgmJlBdZM7Lx8f+KWPNZwp1Lb8 9PVadBsCRaQ5K4gy7e1bWIkIM+D/ha4s64g5xqdOtcs/YFFdE8ddULkGzzKxtOFQb5lQr/FB lMFRM+lgUeQjuT1fSfdeDhdDdf7gsqTfBXqk17lBNaM/7iNAMNMHAjsV+Szj8gT3VqLax9Yn ghI6k6UlIlQl0lol9TtemTMLuaIhKIyfknWXpb8n0buRuY9akNKpmcQo7/r+uV0VK31esQKq qxlN9FMesWuQUbT8NYjwE8MneRFZOdEf70DxNkQnJ4JIuM9YuNOUQFcYD54kFIguwj451cb7 LhdSoBWZ4BQgIA0KZW5kc3RyZWFtDQplbmRvYmoNCjMgMCBvYmoNCjw8DQovUHJvY1NldCBb L1BERiAvVGV4dCBdDQovRm9udCA8PA0KL0Y0IDQgMCBSDQovRjUgNSAwIFINCi9GNiA2IDAg Ug0KL0Y3IDcgMCBSDQovRjggOCAwIFINCi9GOSA5IDAgUg0KPj4NCi9FeHRHU3RhdGUgPDwN Ci9HUzEgMTAgMCBSDQo+Pg0KL0NvbG9yU3BhY2UgPDwNCi9DUzEgMTEgMCBSDQo+Pg0KPj4N CmVuZG9iag0KMTEgMCBvYmoNClsvQ2FsUkdCIDw8DQovR2FtbWEgWzEuOCAxLjggMS44XQ0K L01hdHJpeCBbMC40NDk2NyAwLjI0NDU4IDAuMDI1MTcgMC4zMTYzMiAwLjY3MiAwLjE0MTE2 IDAuMTg0NTQgMC4wODMzMiAwLjkyMjcxXQ0KL1doaXRlUG9pbnQgWzAuOTUwNSAxIDEuMDg5 XQ0KPj4NCl0NCmVuZG9iag0KMTQgMCBvYmoNCjw8DQovTGVuZ3RoIDQ4MzINCi9GaWx0ZXIg L0xaV0RlY29kZQ0KPj4NCnN0cmVhbQ0KgAvIZTGIgMZzEEFhQgOZjBoKIRYEAvI8EEBnhBFL EPGIzEAtjogGwxGQgG45kpyMsPM0PIRUh4vIw0hIgKktBQxGAuGElGAgn86nkljozFw3HEiG o3Fwyp02NsPn9ThkOBQtnYwGAxmhUqxbFBpOpyOApFozGc7GIoPRrNhmFIyHIuGYoNYpLpUJ UxIw2ms3h41F0dG43j4ywY4HEeKhEqU2q0/Kh3h9hFJUNV8pMFwAKnY3GwwGtAm2OBQoFeXz IKmV+zk4nYzHEo0mNyp71V8w2vh9Mnukn++koynouGlJG1oFw4rdQyoz3Osvt/nFC39U63DG A2F205OCuWjKlRz2D5OMqwoHx86My3c2nHC4EmoYgGQx7lP8/L5vjx6vOe9ojM2+COKynzSO y+ziKOkrvqaHLxPInYZBsHDOPSFggQEHLqI4o6SL8rL+tMyT0i4rToMwzUPPKwoZNGrCeRJA MVuk10CvKGgcsMyTTBQHsBQIzsKBuGqaR8h8ZK1HbIIesAdhqHQbB4HLZB4HiTyiGgbByHYa BuGizMEHIUR2xYeBwG4dB5L4crwvS+Rwzr8OMGAbwQ2yrxGGj0MqHtAQEmjeAUGgXS7LigRm GTDT1JYYT804VhsFjorOo1IBrDoWsQGS0PFEsZtFJwFLAPQ2Duu9L0NMo7hS7gaLsFKd1iNC zBmGQcMGFAyBSowahQMqzRgpoUDkuKmViPNfBiugUVss9c13ZaR2dXtfhQN48VspgZBQO1br SsNj2wOs4L3G8WhjZtGSOj8R0Y0r/vS3EbPdFr5Ko+UYMRRKR2aGj7uc08VNWmVBxzBTsQO+ zQqavyRsQHOA4HR4Z0a9IyDiM8BTm6ruhmGiC0feM9RMyr2XsIz3s7fLSX2HFDNHf7jYE/2C Y7dWGYW+tcw7iLuhjCU/0FFqQKMHOJo/dgZwteU90Wv0AVKFAkWWGtdW8MltDIO42DiNVzzl FtWPwkuSUbEtSMtlUhtgo7QtHJNJZzQjYsXPMf3rg2V3w+t9Z7oSmhBTS5q0zjyBRgux0JhU E4Y+65sM0NaQjga1PPtb1wFlj47/l+erlwmkxnxF5xrvm3QNz6g8g5WZq043LZuFFK5VhEiU Opyk3htMlRHSKwa/YbZWLVFwU5i6jhQPCzLmtY4jsO4yDKN9oBlkTlsss9ijkOo4eRTymBv5 g7a/sPkrnYA2LMnYbBQOK4vAFFXYlZ466vZocV5Wds2GDQtT/AYAoDqqhVTzwUBwDQ14uCnH sv7WPAMNL/zEPkLKUZ8ix1OFzTKxxZpdQ0B1Bg2Iq5QilpNJAhBkLT1HtCVIWBrr10uLWBSs 1YAZgzB4DqGQMx7IHHcLWHMsawzELAY49xbweAzK2fm+E46xVeqcBrAMPRcXjBnhkdwur7Cd plfihV5arlDK1fwClTUNIbP+ik+41ENQXQ3DlF87j5IxHGV41987/1Yqpiys4Mz3g6hoDMq6 ByzS1tXKMXVXsaVtR9LqseNMEy8roNai0uSIAcF+bQ09k5p4JttRaZ9uJtUfmpZUx4qRdDZt 5Nu5xvx13QG/BpE8pDhJMmDP64lxa6XGs7cefUGgNSjFPawUyYTQzyn4LQ5plLfHOm9dYfSW QNoiklaxECXLp2cSgl7NFBSXD5TFjeDOZDtWit2lUrhRRWmSqhRS2sFgMVLHKBtAAw0KVPTk hYiMkLUywBXVUslZ6w2JvbjTDyM0dpCFyUM/tVLXaCUNfoGt69BXhq4O6/SJkdoyzCjerwN9 BFdRbfChGjKp4sURWesuS6wA0Pxg2rSiYcg8BqVUWkndDqKUqDIHUNTzQW0CLKYJWr7VDvwV 8/ahbE3tSIo/It5cjaSpkV5IGlVQ47UIU9HYtwcVbSTMfCkGIOT8T7UWxhJ8BA0RCTG9oNDy GkLPfiDONL03qrQR2mV9gLYtLfTG7FMsUUdxzLZDWwKs1nB1DjHkFsU4Kv0V8sVVSun9q9Nj ZAFteXtLmQosYPDGw5IoabX8plh7Lsbr+wAFAZmuhkrhRlV0w4CByDmWaMaslOPaLfbZpNq2 NRLDeG9robI42pq4teztLFdFrq+nEzzSzBo8akaaFtaGqRCJU9QN8PmAwDg0tJ98OIdSBDOD 62yFlrWosyU6o8fKrLADS96vCDC10Bt1Yh8lNoQgwh5DmHcPbbHEqrAaoaz46ret3euAIbHk MBW9V4NEWIj4OvaClQy3oNMBriGqJlkKwXPrFWS6bv6zwvV5TAuRTLK3ih2HeitRMTh2irWN +88rM10o+Ews0hqkQbf1X57iZSyYvjtA1rQdq3gvVuaJ5dllnUlg+Civdl6SlMLquAxFzKX0 Ex+8PHz2o+LDNnVmtr5FhKcX6sYuOFwUXKV29fMdRVOAoD2GQOAc3rZKLPkx8lnFihop7Dts EecPhUBViRw5SZ/UTWgWimQaA9xCre+a4q0TuJltZTAtD9lzY8wjErRpaY7SQWKuZaNI6B1i WyGwODX6IKGuYuFWFkNYF21DTK1j03pFjDmW6BpaNZlwVY/xdj8NhUZDRcG4ccZ5YfJkaNxp iDtnzJITw1xypa2aS41I8iSySXWLBXQGuOydQICrDXVCtrCK91rSEs6Ro7BIhrTnOmdrDUZt qoZ971lXvLXBs4mbOpvMMKKUcpJx6clPZvJvRe5wWlziSsN7WdwVFm1RXAGfEFn51zvvxS+V spVGyuuHjWRljVzrrAXN1zAz42nBHYJeFntLks7jNMoaOVv3z0yFZr78/YYs+GexnPNUreDq WMOvGONMaiOWhyVkLZU7LOn1ZwaLRA2ttsiQfSnu2ILrBPqebNhYnhHoXQ7UJ2IYrTcC4TXt Kq4W7lLO8PJCK4MFHDO4eMXdNPvqWxD+63h4DeHeuCDH3liLIrd7Fus7rDxVmqLsBHp1zyzZ ju1H8/eAa34ooxa+bVHe8G/vmxbsV3fBzurZa8nJl8Kdx97HFO5qg3avHcduy3O0NKlFKHdF h4LKUJ96tt9VIeKWu2NbuZev8VsV9lfQ4pB4ATpFrhgbKauhWN2d1ER/W0WHr7wMSy18V4qk GIc3kPvDwDHT4MojvvDiGT9mDCzPvtdi7pIMoSMHaN9T60KZMgYCuvtFFiStFumOSFilhEKg UIfGQjEH3gjuslYgyPfLXFbpZnlgVphJyFKrGliuXCkKMgkgUjQKMnvQLI2NIM7M8A0OdkwF dswupjEH9g0gyA2FtOLLEtGmAuZuwGQoMNGQLQZAUApt7rTMwICg5O6uqH3goHnHlsGHzOPQ GntAymuv7D2MPifp8FPmnoYJCGRMYAjgyA1qemNLbF1ldoqliFgQKrMk6ojA2JAtjlgKFlnL twzmZPxrIo6ICgyu9LtK8CuMKwfsMtqwJO/rVociylpFYqgDEC6u2riKYGRMeA5OwIAC5ufK aLQOrwzimPguZFgOjnvA7LzLMgatLoBHtLUQsvrsRKzHDveDIlFibDKGqQYEzs/uZH9jcCQA UM8A5NKRJqmH3tSC1xLkrQXupEdvYgiICmNK8AcmsgUAcLeCjH9vPrKsGqmC1rWA8MkMJxiF gqklnLuRpM1K6M2i4ldObg6gQFjkYLCsfsbErQfgruslvRlxpFigkGNtAFmR1FpMhKgH9tTE doBgsKjFYgrISQtJDLpQugUArA5oeL5rLyGM0i7u+kyu9A7uPLulnA4QaosRJr2H9gQA2KbF kLYMAseA7A8OroQsIwzjuLKsAtag7RLH6xsH5HtKoC1qrMFFnQaQbIdL5q+svGRSauQwOw6M LMgMKFvMdEZC6o5FnIxFng3g7KIL1yXIrLAsXLgq2n1rEQ6Lml0SHLoqylHF4FIl4CuxbCwJ Ax/A+s3FgIInlyCI7K4AYR2IExoA8S7H7lbihLKr1NxKFLIn3upK6H7AjA5FtRFtUQEspIoq 6DPgUAsSGM9i5yFyKIlzCH7ArSNI0yOwpNqkyyRA2SSTCMYSUSVO+ynM9nxi7AyOmgYTOrMM 0ljuzK0szlctUtHN8MxPIN/A7ralOIIL8H4SZlLijFYzfvARvMkFwvIxLFwlmrDtTqjoGuel srvuSx1o7A3FwlDHyA7A+A4i4RGLcFdFYs/RirFg+tltNDlRDzMA3g1HvMCTsCwwaq2kyqQo AyjNmrnGAEeRXS1wBHDtwTMqEn9nwtkPHKZNSOvvHKBUIlWuZGtI3SqgzqeojlGH7IuHlyrS nM2IQmroEKoRIMxN6UWH4Gtg+HzHwwQC6i4S7qgPHvRPHH7NJSXsCrtIfFGK+wySelvKWIEH rwSFYosMDlnpB0Nln0Qk8LYOpUiKORNgzvbl0IUkdp7yHjQRYCtiuivljROzOAUOhAQocvBz xFgQGMmK9NNDi0TqPr7KXMloArRAbizJjICHpMlxUqNi6uOKEi6zkPOls1CAUOXGmldx7nuH yTGDts3jcO7qNR/TboiuQnoC4ylo6qXKOzcqoN9slrb1SoCFURQrcI0oFVQspNf1Ki1uhA7I cg7TqDBC6zbldSyVPRWsQ0FNECtxZK0gjR8T2leA5v7FhxUKP0+lOAbK+wDIpDBC1xHFvzmk YVrOQidlgMUFqvjSni3HwmHH3zfxPvZFpTlMeTyIpNZxsrPUeVqldrJn+R0s3Ob1mldHyLsM xHtPYI7NQUQga1uIokYNgvasMOjpBKFozlYqLxHqNKOy/PNopM+vA03IpTcnh1gGmJ9S2FFj xRZitJMxaq0wYGmzzpBI4umjkqPwUuOwWFbgbrVOcTxIQNNWXn12dFsWUiRxfpAskT1NGgbI MwepqM1PFllWcH7x3lfKZIqrMntMbJhEyyGMfg0WiseAtAUgcH5snFgStVmCzmsKMjUymgUQ NAbAywOGmqJOwLHwTWPCmuMlQVhp1GpkRkrWTmqQzMbvgg9sXVNLbGmymN6IvynLH0UozV0o orOt3E+rSn+EZOjMCIFq9yloGk+ijH3tWM8P5q3IFXEn6T6XCn3npOmUQXTspRJ3DWpVMKbl nLdlGU7rbIAWBFblnL1NhoQ2npLnyIq26Kx1hO0CewCWSS+jGS4sTsJgYimC1x4TzSAldRQQ 8x3OaHl2pMNKjqeyLKjtTVdKBw8rXlYzkXa19Q8pBg2M3Pgw7yhIjIrC1sGPjnyUSMHqfwcI trEFvA7KKMJvUiwixvwmRXpg+oQxCz3RVH93eYAmv0+X9KkPK1V3anyD2AcVOMbWas0qAYI3 vn336z23Z3+ICGOXhyIWQnDngz/Si0/KOQdTsrCowRDlmzzlwvHpCLVL/zmi0NUA7oNOnXuz 6tUGvg5u9QgnlmOIQXzrmR/KElYyrUNQYnlw/H0Yg4QQyQlLHw1NZw2wfRVwk4kHyYlMDPk0 oI6vgumniot4hsng51Vvh4YQSo3HyJJLnS03iMR3jS10zNII6oQKjIvHzUkxdLMji1xFDIQV A5E1uxVg1MbPq4YsdIByrYQOZIQVRooI3FYytLbQALfNNR9lgV0OqlbgcogHmPa2Vum5RNaF nYgHSH93zuQIQr4nv5TZUNIVDza0dN/LIlvGvZCFvK8ZD5XZAEZPWGvGwZTC5qdSuOMxUq9o moSPc49sTQkqKA6lTzqVowfq9vhqsV+rIi6r/o6q9UcMmg3uXKtn9wRLOtXLg4Lwfs9FcoAw YEKunoJqJR4ZUqgldtOyWNhlVUsPDxdn+MEFtSg58HlrvlvMWLyFh1pZNFvA0sXKEs+6I105 7ku50z9IClUvw6Fn92OvcOzoW1imqA6Ft5F5Gy706C6wyTbkyzfzvWtaXlvyCRxrGo0g2usv gtjqHYeFINaPZC05GTLQASr1xuLICIa3xvkM9vXQFzxV9J8Kq1T4X6DA8g2A0WpC0n1Wg6Gq Mol1cMl1etiFdzD6h5bCyqJGtw7voiFjOscWCzcF3xaGTFRaUCwK2Ejldg5A8omLL4TLnP9F CCzobIplNyHxUUx3nlSERpajJ2UTwLl6dXas5KGMipAJBIzLMR53C1vUHDBYGZ9a/g6g7rPo Yw7jFgUZ6R9o6VKKZV/TkvQUOntjwlnYuFeKSq+gQFrti7dsI1HbRAiKjFvU3M2L7V2FnKaq bitnjTlqln50SYbINykTlvGlOKBRgnzK7qIzEv/M1YT3imSXkK013H5ov6/s13tOslgOdqPP PSe082yqJZSObg35pPkQ832zg6/ZnO62C3uyxOp3oTiwZlb8Bo6Q9tbZuGQrKXm8FNi3yu5Q bKe8FQHNUo0xRuk8FTMQ1VMS8H9vBOmgati6BoaPFnyb/QdcI78Fnstupn50nWGyZzejyp2X lq0798ElLsuvKWJmr3qqQFw1uMW5svFV+KJ2/i0JEgUR3rhmwAYZZ45t6HhwFO6scP6RET8z 96QOMLH8UpFY1cn8YPhWYT68nxv7hoSE6gcwAGztqiewAptDJRbW1DogiiYAFCAgDQplbmRz dHJlYW0NCmVuZG9iag0KMTUgMCBvYmoNCjw8DQovUHJvY1NldCBbL1BERiAvVGV4dCBdDQov Rm9udCA8PA0KL0Y0IDQgMCBSDQovRjUgNSAwIFINCi9GNiA2IDAgUg0KL0Y3IDcgMCBSDQov RjggOCAwIFINCi9GOSA5IDAgUg0KL0YxMCAxNiAwIFINCi9GMTEgMTcgMCBSDQo+Pg0KL0V4 dEdTdGF0ZSA8PA0KL0dTMSAxMCAwIFINCj4+DQovQ29sb3JTcGFjZSA8PA0KL0NTMSAxMSAw IFINCj4+DQo+Pg0KZW5kb2JqDQoxOCAwIG9iag0KPDwNCi9UeXBlIC9IYWxmdG9uZQ0KL0hh bGZ0b25lVHlwZSAxDQovSGFsZnRvbmVOYW1lIChEZWZhdWx0KQ0KL0ZyZXF1ZW5jeSA2MA0K L0FuZ2xlIDQ1DQovU3BvdEZ1bmN0aW9uIC9Sb3VuZA0KPj4NCmVuZG9iag0KMTAgMCBvYmoN Cjw8DQovVHlwZSAvRXh0R1N0YXRlDQovU0EgZmFsc2UNCi9PUCBmYWxzZQ0KL0JHIC9JZGVu dGl0eQ0KL1VDUiAvSWRlbnRpdHkNCi9IVCAvRGVmYXVsdA0KPj4NCmVuZG9iag0KMTkgMCBv YmoNCjw8DQovVHlwZSAvRm9udERlc2NyaXB0b3INCi9Bc2NlbnQgMA0KL0NhcEhlaWdodCAw DQovRGVzY2VudCAwDQovRmxhZ3MgNA0KL0ZvbnRCQm94IFstNDAgLTI1MCAxMDA4IDc1MF0N Ci9Gb250TmFtZSAvTEZDQ1BLK01TVFQzMWM3OWUwMA0KL0l0YWxpY0FuZ2xlIDANCi9TdGVt ViAwDQovQ2hhclNldCAoL0c2RC9HNEIvRzU1L0c2OS9HNzMvRzdEL0czOC9HNEMvRzU2L0c2 QS9HNzQvRzJGL0czOS9HNTcvRzZCL0c3NS9HMzAvRzNBL0c0NC9HNTgvRzEzL0c2Qy9HNzYv RzMxL0czQi9HNDUvRzRGL0cxNC9HMUUvRzc3L0czMi9HM0MvRzQ2L0c1MC9HNUEvRzY0L0cx Ri9HMzMvRzc4L0czRC9HNTEvRzQ3L0c2NS9HNkYvRzc5L0czNC9HMjAvRzQ4L0c1Mi9HMkEv XA0KRzY2L0c3MC9HMkIvRzM1L0c3QS9HM0UvRzUzL0c0OS9HNjcvRzcxL0c3Qi9HMzYvRzJD L0c0QS9HNkUvRzIxL0c2OC9HNzIvRzdDL0czNykNCi9Gb250RmlsZSAyMCAwIFINCj4+DQpl bmRvYmoNCjIwIDAgb2JqDQo8PA0KL0xlbmd0aCAyMzM4Ng0KL0xlbmd0aDEgNDYzOQ0KL0xl bmd0aDIgMTg3NDUNCi9MZW5ndGgzIDANCj4+DQpzdHJlYW0NCiUhRm9udFR5cGUxLTEuMDog TEZDQ1BLK01TVFQzMWM3OWUwMCAxCjEzIGRpY3QgYmVnaW4KL0ZvbnROYW1lIC9MRkNDUEsr TVNUVDMxYzc5ZTAwIGRlZiAKL0ZvbnRUeXBlIDEgZGVmCi9Gb250QkJveCB7LTgyIC01MTIg MjA2NCAxNTM2fSByZWFkb25seSBkZWYKL0ZvbnRNYXRyaXggWzAuMDAwNDg4MyAwIDAgMC4w MDA0ODgzIDAgMF0gcmVhZG9ubHkgZGVmCi9QYWludFR5cGUgMCBkZWYKL0ZvbnRJbmZvIDEy IGRpY3QgZHVwIGJlZ2luCi9CYXNlRm9udE5hbWUgKE1TVFQzMWM3OWUwMCkgZGVmCmVuZCBk ZWYKL0VuY29kaW5nIDI1NiBhcnJheQowIDEgMjU1IHsxIGluZGV4IGV4Y2ggLy5ub3RkZWYg cHV0fSBmb3IKZHVwIDAgL0cwMCBwdXQKZHVwIDEgL0cwMSBwdXQKZHVwIDIgL0cwMiBwdXQK ZHVwIDMgL0cwMyBwdXQKZHVwIDQgL0cwNCBwdXQKZHVwIDUgL0cwNSBwdXQKZHVwIDYgL0cw NiBwdXQKZHVwIDcgL0cwNyBwdXQKZHVwIDggL0cwOCBwdXQKZHVwIDkgL0cwOSBwdXQKZHVw IDEwIC9HMEEgcHV0CmR1cCAxMSAvRzBCIHB1dApkdXAgMTIgL0cwQyBwdXQKZHVwIDEzIC9H MEQgcHV0CmR1cCAxNCAvRzBFIHB1dApkdXAgMTUgL0cwRiBwdXQKZHVwIDE2IC9HMTAgcHV0 CmR1cCAxNyAvRzExIHB1dApkdXAgMTggL0cxMiBwdXQKZHVwIDE5IC9HMTMgcHV0CmR1cCAy MCAvRzE0IHB1dApkdXAgMjEgL0cxNSBwdXQKZHVwIDIyIC9HMTYgcHV0CmR1cCAyMyAvRzE3 IHB1dApkdXAgMjQgL0cxOCBwdXQKZHVwIDI1IC9HMTkgcHV0CmR1cCAyNiAvRzFBIHB1dApk dXAgMjcgL0cxQiBwdXQKZHVwIDI4IC9HMUMgcHV0CmR1cCAyOSAvRzFEIHB1dApkdXAgMzAg L0cxRSBwdXQKZHVwIDMxIC9HMUYgcHV0CmR1cCAzMiAvRzIwIHB1dApkdXAgMzMgL0cyMSBw dXQKZHVwIDM0IC9HMjIgcHV0CmR1cCAzNSAvRzIzIHB1dApkdXAgMzYgL0cyNCBwdXQKZHVw IDM3IC9HMjUgcHV0CmR1cCAzOCAvRzI2IHB1dApkdXAgMzkgL0cyNyBwdXQKZHVwIDQwIC9H MjggcHV0CmR1cCA0MSAvRzI5IHB1dApkdXAgNDIgL0cyQSBwdXQKZHVwIDQzIC9HMkIgcHV0 CmR1cCA0NCAvRzJDIHB1dApkdXAgNDUgL0cyRCBwdXQKZHVwIDQ2IC9HMkUgcHV0CmR1cCA0 NyAvRzJGIHB1dApkdXAgNDggL0czMCBwdXQKZHVwIDQ5IC9HMzEgcHV0CmR1cCA1MCAvRzMy IHB1dApkdXAgNTEgL0czMyBwdXQKZHVwIDUyIC9HMzQgcHV0CmR1cCA1MyAvRzM1IHB1dApk dXAgNTQgL0czNiBwdXQKZHVwIDU1IC9HMzcgcHV0CmR1cCA1NiAvRzM4IHB1dApkdXAgNTcg L0czOSBwdXQKZHVwIDU4IC9HM0EgcHV0CmR1cCA1OSAvRzNCIHB1dApkdXAgNjAgL0czQyBw dXQKZHVwIDYxIC9HM0QgcHV0CmR1cCA2MiAvRzNFIHB1dApkdXAgNjMgL0czRiBwdXQKZHVw IDY0IC9HNDAgcHV0CmR1cCA2NSAvRzQxIHB1dApkdXAgNjYgL0c0MiBwdXQKZHVwIDY3IC9H NDMgcHV0CmR1cCA2OCAvRzQ0IHB1dApkdXAgNjkgL0c0NSBwdXQKZHVwIDcwIC9HNDYgcHV0 CmR1cCA3MSAvRzQ3IHB1dApkdXAgNzIgL0c0OCBwdXQKZHVwIDczIC9HNDkgcHV0CmR1cCA3 NCAvRzRBIHB1dApkdXAgNzUgL0c0QiBwdXQKZHVwIDc2IC9HNEMgcHV0CmR1cCA3NyAvRzRE IHB1dApkdXAgNzggL0c0RSBwdXQKZHVwIDc5IC9HNEYgcHV0CmR1cCA4MCAvRzUwIHB1dApk dXAgODEgL0c1MSBwdXQKZHVwIDgyIC9HNTIgcHV0CmR1cCA4MyAvRzUzIHB1dApkdXAgODQg L0c1NCBwdXQKZHVwIDg1IC9HNTUgcHV0CmR1cCA4NiAvRzU2IHB1dApkdXAgODcgL0c1NyBw dXQKZHVwIDg4IC9HNTggcHV0CmR1cCA4OSAvRzU5IHB1dApkdXAgOTAgL0c1QSBwdXQKZHVw IDkxIC9HNUIgcHV0CmR1cCA5MiAvRzVDIHB1dApkdXAgOTMgL0c1RCBwdXQKZHVwIDk0IC9H NUUgcHV0CmR1cCA5NSAvRzVGIHB1dApkdXAgOTYgL0c2MCBwdXQKZHVwIDk3IC9HNjEgcHV0 CmR1cCA5OCAvRzYyIHB1dApkdXAgOTkgL0c2MyBwdXQKZHVwIDEwMCAvRzY0IHB1dApkdXAg MTAxIC9HNjUgcHV0CmR1cCAxMDIgL0c2NiBwdXQKZHVwIDEwMyAvRzY3IHB1dApkdXAgMTA0 IC9HNjggcHV0CmR1cCAxMDUgL0c2OSBwdXQKZHVwIDEwNiAvRzZBIHB1dApkdXAgMTA3IC9H NkIgcHV0CmR1cCAxMDggL0c2QyBwdXQKZHVwIDEwOSAvRzZEIHB1dApkdXAgMTEwIC9HNkUg cHV0CmR1cCAxMTEgL0c2RiBwdXQKZHVwIDExMiAvRzcwIHB1dApkdXAgMTEzIC9HNzEgcHV0 CmR1cCAxMTQgL0c3MiBwdXQKZHVwIDExNSAvRzczIHB1dApkdXAgMTE2IC9HNzQgcHV0CmR1 cCAxMTcgL0c3NSBwdXQKZHVwIDExOCAvRzc2IHB1dApkdXAgMTE5IC9HNzcgcHV0CmR1cCAx MjAgL0c3OCBwdXQKZHVwIDEyMSAvRzc5IHB1dApkdXAgMTIyIC9HN0EgcHV0CmR1cCAxMjMg L0c3QiBwdXQKZHVwIDEyNCAvRzdDIHB1dApkdXAgMTI1IC9HN0QgcHV0CmR1cCAxMjYgL0c3 RSBwdXQKZHVwIDEyNyAvRzdGIHB1dApkdXAgMTI4IC9HODAgcHV0CmR1cCAxMjkgL0c4MSBw dXQKZHVwIDEzMCAvRzgyIHB1dApkdXAgMTMxIC9HODMgcHV0CmR1cCAxMzIgL0c4NCBwdXQK ZHVwIDEzMyAvRzg1IHB1dApkdXAgMTM0IC9HODYgcHV0CmR1cCAxMzUgL0c4NyBwdXQKZHVw IDEzNiAvRzg4IHB1dApkdXAgMTM3IC9HODkgcHV0CmR1cCAxMzggL0c4QSBwdXQKZHVwIDEz OSAvRzhCIHB1dApkdXAgMTQwIC9HOEMgcHV0CmR1cCAxNDEgL0c4RCBwdXQKZHVwIDE0MiAv RzhFIHB1dApkdXAgMTQzIC9HOEYgcHV0CmR1cCAxNDQgL0c5MCBwdXQKZHVwIDE0NSAvRzkx IHB1dApkdXAgMTQ2IC9HOTIgcHV0CmR1cCAxNDcgL0c5MyBwdXQKZHVwIDE0OCAvRzk0IHB1 dApkdXAgMTQ5IC9HOTUgcHV0CmR1cCAxNTAgL0c5NiBwdXQKZHVwIDE1MSAvRzk3IHB1dApk dXAgMTUyIC9HOTggcHV0CmR1cCAxNTMgL0c5OSBwdXQKZHVwIDE1NCAvRzlBIHB1dApkdXAg MTU1IC9HOUIgcHV0CmR1cCAxNTYgL0c5QyBwdXQKZHVwIDE1NyAvRzlEIHB1dApkdXAgMTU4 IC9HOUUgcHV0CmR1cCAxNTkgL0c5RiBwdXQKZHVwIDE2MCAvR0EwIHB1dApkdXAgMTYxIC9H QTEgcHV0CmR1cCAxNjIgL0dBMiBwdXQKZHVwIDE2MyAvR0EzIHB1dApkdXAgMTY0IC9HQTQg cHV0CmR1cCAxNjUgL0dBNSBwdXQKZHVwIDE2NiAvR0E2IHB1dApkdXAgMTY3IC9HQTcgcHV0 CmR1cCAxNjggL0dBOCBwdXQKZHVwIDE2OSAvR0E5IHB1dApkdXAgMTcwIC9HQUEgcHV0CmR1 cCAxNzEgL0dBQiBwdXQKZHVwIDE3MiAvR0FDIHB1dApkdXAgMTczIC9HQUQgcHV0CmR1cCAx NzQgL0dBRSBwdXQKZHVwIDE3NSAvR0FGIHB1dApkdXAgMTc2IC9HQjAgcHV0CmR1cCAxNzcg L0dCMSBwdXQKZHVwIDE3OCAvR0IyIHB1dApkdXAgMTc5IC9HQjMgcHV0CmR1cCAxODAgL0dC NCBwdXQKZHVwIDE4MSAvR0I1IHB1dApkdXAgMTgyIC9HQjYgcHV0CmR1cCAxODMgL0dCNyBw dXQKZHVwIDE4NCAvR0I4IHB1dApkdXAgMTg1IC9HQjkgcHV0CmR1cCAxODYgL0dCQSBwdXQK ZHVwIDE4NyAvR0JCIHB1dApkdXAgMTg4IC9HQkMgcHV0CmR1cCAxODkgL0dCRCBwdXQKZHVw IDE5MCAvR0JFIHB1dApkdXAgMTkxIC9HQkYgcHV0CmR1cCAxOTIgL0dDMCBwdXQKZHVwIDE5 MyAvR0MxIHB1dApkdXAgMTk0IC9HQzIgcHV0CmR1cCAxOTUgL0dDMyBwdXQKZHVwIDE5NiAv R0M0IHB1dApkdXAgMTk3IC9HQzUgcHV0CmR1cCAxOTggL0dDNiBwdXQKZHVwIDE5OSAvR0M3 IHB1dApkdXAgMjAwIC9HQzggcHV0CmR1cCAyMDEgL0dDOSBwdXQKZHVwIDIwMiAvR0NBIHB1 dApkdXAgMjAzIC9HQ0IgcHV0CmR1cCAyMDQgL0dDQyBwdXQKZHVwIDIwNSAvR0NEIHB1dApk dXAgMjA2IC9HQ0UgcHV0CmR1cCAyMDcgL0dDRiBwdXQKZHVwIDIwOCAvR0QwIHB1dApkdXAg MjA5IC9HRDEgcHV0CmR1cCAyMTAgL0dEMiBwdXQKZHVwIDIxMSAvR0QzIHB1dApkdXAgMjEy IC9HRDQgcHV0CmR1cCAyMTMgL0dENSBwdXQKZHVwIDIxNCAvR0Q2IHB1dApkdXAgMjE1IC9H RDcgcHV0CmR1cCAyMTYgL0dEOCBwdXQKZHVwIDIxNyAvR0Q5IHB1dApkdXAgMjE4IC9HREEg cHV0CmR1cCAyMTkgL0dEQiBwdXQKZHVwIDIyMCAvR0RDIHB1dApkdXAgMjIxIC9HREQgcHV0 CmR1cCAyMjIgL0dERSBwdXQKZHVwIDIyMyAvR0RGIHB1dApkdXAgMjI0IC9HRTAgcHV0CmR1 cCAyMjUgL0dFMSBwdXQKZHVwIDIyNiAvR0UyIHB1dApkdXAgMjI3IC9HRTMgcHV0CmR1cCAy MjggL0dFNCBwdXQKZHVwIDIyOSAvR0U1IHB1dApkdXAgMjMwIC9HRTYgcHV0CmR1cCAyMzEg L0dFNyBwdXQKZHVwIDIzMiAvR0U4IHB1dApkdXAgMjMzIC9HRTkgcHV0CmR1cCAyMzQgL0dF QSBwdXQKZHVwIDIzNSAvR0VCIHB1dApkdXAgMjM2IC9HRUMgcHV0CmR1cCAyMzcgL0dFRCBw dXQKZHVwIDIzOCAvR0VFIHB1dApkdXAgMjM5IC9HRUYgcHV0CmR1cCAyNDAgL0dGMCBwdXQK ZHVwIDI0MSAvR0YxIHB1dApkdXAgMjQyIC9HRjIgcHV0CmR1cCAyNDMgL0dGMyBwdXQKZHVw IDI0NCAvR0Y0IHB1dApkdXAgMjQ1IC9HRjUgcHV0CmR1cCAyNDYgL0dGNiBwdXQKZHVwIDI0 NyAvR0Y3IHB1dApkdXAgMjQ4IC9HRjggcHV0CmR1cCAyNDkgL0dGOSBwdXQKZHVwIDI1MCAv R0ZBIHB1dApkdXAgMjUxIC9HRkIgcHV0CmR1cCAyNTIgL0dGQyBwdXQKZHVwIDI1MyAvR0ZE IHB1dApkdXAgMjU0IC9HRkUgcHV0CmR1cCAyNTUgL0dGRiBwdXQKcmVhZG9ubHkgZGVmCmN1 cnJlbnRkaWN0IGVuZApjdXJyZW50ZmlsZSBlZXhlYwoCJYFKBeUcBYY/PHyoeahKSSqdP9mD ZQNDW44vU7rBfNeE2WKhwGFVadHJmMN1COv0XF4MhHu1xTAheNbnXlOBHngBOqT7e5wRMbaH wc6ncbDRRAGDbeB+1j8QqIKR+Sf1ytNEmSW+ooexpm61FWgB4+gtJL5OdCRZBxirICTh/xRL xearlYwDBh4tO0qTuCFrCWcV9qWxbnxkhNMkz8HUfOg94T48zTT3r85Nk8hjECVGBgFB2ZRB oSX0pKKJT5avHXktR8ZdCYjh6XLBSk5S1UAQmhD+AOG4MQxj9HmWN5wG4L35JZe7wq1cGT5J kEEZJmWjAkR3BDHq7jc/u7WRNuuFbUq52G+9tfulvhnJr+VusDJLcrE6qJRBntNmdiICctVL bLw9ryudU27ISaRjD08SJomxbNd/NqWqdr/7HAfSGFf6UX+ZDw4GYtoYO0FtB+UkNgxm9Wgx PdmGshO/bNlnGC0HwTmi+HwmFNNTyYS7EqGpZguLELDoV1MuwRfVA5h2Qu90evs1EAvJ3EpZ /F0clb1uIhsCsKUw73H0+mjWJqmSm/a4ooGtB9pRCxOclqTY04QHkgZd7xjaMuqXxCzBMv5V ++yge+b7U5akyGcb3xBK76F9dTW1KaCXQhgr0xzBsNDZuEQm8kEuxdFMbW9FeyerZftvTUl1 uBV3zLwha/lxilrtXxv+X0TPRM+qgYH05Bfur8blT54eq7KIK1Ho3s0LMy3jKsEASG0qHbEs /9xiLjGgsNZGN8eKryUzb9AHLcDBeefb2Vcws8Y2FhpqNhKP4dhCOArtqUyhz6clKMSyBcLk JKx4n18Qf3evzheMWdjG+IXqImtW+77HWAQ9JFNWpKQ3WJVScA8snSAuhP2nXAMFyWi+WnuE OCJe5q7kzC2KP6idX+zArAMbKZw22ABgbKhRzTU6p2Zt2IKhoFu+bQ7k6M0BURyY3EwTmflY +sy684O3DeEo5F5H7J51xEFJI1eQI5joW0sCSBZzWk2cw5yPCHp1zQdT4+InPEynDB5JtQTB qbBuQ2pxQ9Pk9fsNyTXN0+r/wML45JqbXmbwyLWPZAT4M5t4Ubr4ZuLC9/NkktJlpdJOtrK0 NxqhtJwnItqNiUXt131Lec3l1bHkuc2RWNdXsbEArdN8HkaiNJti4oeOYmODlrXdLM6ZGSIS kYn7GhX0RmRAxIL1zpFAEpeZG10FumW8ApI+Ya2bT4H+49rVMPrNczA2Z9zFUp8SGsZumIbA /Alx14DZnAD3v5zhVHfg+KzkMO5Me9eDCqZTLAm8JqZyi3yT6z+0OFHKWERwubv6qnhytv/a YhtHXHSntAS7QDBElfw38T8pmR97iFSuPU3VVIcVBbmzJpFyhTguYbsPlnRZldilSrOnX4d9 fv4v+k9ZMhUCPi3PgxCZ3CWXkw+LzZvYYzDY4AxkLhvDCYUevCd1u9tHWKku4ERObjr92bDp OxAyjra82RFpWihw4N7FpqgYpRWHK4p6ueJEVUX0eNYeSO1O0FjOGJIdeB1HSD7MRnhe3KXR jtpIJXel2/EmoCyA/o1rXSeqrwaZb0ay/1Aw9hRlZEOSw0uUPtOi7xaAP2UDCNpC5lEJgP/V WUjDdY/SuZIXwbAHrucilPC7tISuhPiJer3bidGM8tAv8oAJxLNWHBY9WBvL2I5Dn7TCRGLe ohLE+5keAaOoZaP4aPjrfEdpgS7ldFF09/Zp33wF26whAl2XWh8UWteD7V2I7QYQDvpEJDQK iS5A4Fq72yeLZ2Om91QzLgDz2bCKyqNssh9iZSgYDkHNtQTYXKttCeqFaasbnE8LpzeDIyUA aEIKeBUjnV/ngMDOJOwnvajLvagJuWRnsL3NVcjYpJKzyOrnehHSvmQBEvMPz9eCPZZ2IyGb qTUplS+UnWFfAbuF7Vmp9FdEK02/1gqcMUzeEpxnahZiQ0fxFUAObnSRTXd54utnh4Ix1J5P ruBy93QzdZE0P0QDISGIyVLMN4SyUNCm8+ZWvmpP17yiBW8uD9PnUALDq0FDOgtrydxhyulN F1Ry/OXdmqRpLNOiGdiAj+/dntWGstUP7hhjB8kjzqwIjq8kcA64uYL2OKw1SqOBkaysLsGM TzTOHYsvHIizokQ8wDlty2lRHLcytsutfrOncEvDeD9gjx6s6RzvRu82NISuGxjxwEBgUJ1m Ul5oX+sf0IHp63uFMlRUJtFdC8jtQMqS/I33pYQejfJJRfFfQHNVS2dqo6nj7k9Yb1Tncxr8 K/UlIZBq9BZBVjyz2aGXF+W8St35tER3WDoEXPVH1PCXZKE43tMUZ4XQ5ZXdA0Otej4zEPb8 H/w9goxRnaWxYgfb2kdQtz46ft6Tl4IxBc/yTL6bg/YCo4V74pmw26wJ11AMvV/93m1+r7V1 YouQ59mSonCNCixZrpBDUHVVAIhMm+ghnQJ+RjddCoFodG82GPyvuXysJY7GKpxmb8NrVZh7 gLC2LKx8NwstXg2+rhHtXWW66CzTo57+jFphJQvT4p2r7k1MjyAnIeUelQdh9oksIZ5R8ci5 38m/jtXLEWZselEAtuccI9DbctlytOy+ckeZYjdU+FnQ0ZOHKSgVeb+ONHzYWdqDOq3FSGE5 u723vU6T7vaqFshLLBWofadtdwlGCmJk+8Y91lSncdjfac5Cx5hu/OgJ8reDg7QrfehsGxNT PNraCoF+KUsV7DBgFIeu+sKApgVp7htEFwYmehZDDlXav+x2GNlrMFD2+ZmVnpql807zpg97 6AJpp5WRo+Vf/wx96lom40giMQlePhuWWnHnFALe414p5eWC8ktl+0ISbbqqvaKH+MQzlgEQ qGsWvdajyFg8NNbYFCBxhiljJOc2q3kzLVNQ99anlLe/I40EHkRCkKN7pgeNsvEDEMOlGx6D OG8Sqem/ApH5KeeXBUitLBqVvNatqrpOCLhN1BVgcZzFs4QX7WN3rGVUzbGtfI+MAObbBnXX ZSuBKqvw2hfsh6vuuneut8MWLdk1NLG4su+1w5jZ2dyzFTDdPfzQzKAPuKF0nXszyeSX+PsH L2VoJiS5d+Y7Kr9vXzvKJK7RW9Gl9o2I5VNiUMtXgOQQoXvl/PnR+rLUGL7pu2Wi0tA/9obx YzXQXa85q6BK9GDsCO1fIXa20N1NnaMgluebOjv9opHsNEXkpmxiG8WbnTJQYOTdLFC3j3w5 Oc4EHRYXsQj2ElVjEDMM4y0pQyXfuqAiPEr/qHz7rVlwg4IUNn7wasXQc+LHf7xeMm9XKIDr wnf9z0B2APmLUf324UD9kwb1HQSZmIghLHKaFbqH15utUQvdPne3oLdojGdQ5HweJBcnA+2S PEJKc5/AjhYBai1CNOWWjYJCCizl89agxxlRWau5UbhP37lEAtSabSBoCZkYXTNYDTsfu4/z QkGrKczAEvNVfIuaBe646p8VALauZDDG37DgBRG3Np7NVJA4vjIK178fwLl4JZifIz4WmAZY TbAWErTb40csDPrtf8+GfR7rzHMQvXgR1oVSAfgukHTbn0oAvLQ3zxWbVHfgtEUI17C/K4IK yPDvpxN0SG+9Wpbru/g3VvJqwAS1CZGQkEuqZ0Mmn7APl2d3/Zh0zJX/WVCWDt2EtWsPAd2O v9h86GNQDQ03pzvySXxODOfkMSz12ICEohNcBsenlcBy68B2xbcBfxd1o0bONXmUk2JDs95F 0cj3eyOuRyBkTCYNkaSBY1aJoxKiDRfd9fC4SgXq9X9mJDjStZsOFYpfCR9VRVVc6SODp+eK i+3NwTIqipMOcKkOsExv4sc7i43mRd/IrIlED3xOcNwZrVAZ2LOrZbM6/oNHyh2wkHsKvuZy GSIpvFreW1ANEkMTBos3+1YPTLvttqamMb/DUIHGDiVSe4yu13FMqt2rLeueskHzldGfsSMG INRZk7jhD9HTDi8cgzkT+GCgBIUNC5gw48AOT8m0M3P44+Pxu0ogwHDlK4uDwjd9rbjWOjL/ rRWSHjbQ9AsFxW1SHd7xGUKXmHmRsA53Yz0sfLg1LMS8m6oNUdDowI9qMXom2n47wY20bI/b ggDAPgTuhBvDZM597i1eIACzid9d2BGtV2lADNuf8g493qF6+RVomaHroEwQWhTbSo5GR99U jmcGi8jIEjqW5rj4Y8uzyqM/JdhROjdxOoLx5SwQiZKeL0PFS4iEvlZJc0/7SpNA1/fZ9Gu3 hkz+7Kkf7kR0A/LqzOn+oeoA5WZ0MtvnB/g/xDko0tSB13g0tkskHR6UCVxw2HmPur8GDxaz KJ85HdhpzbCVCkSeEUY1S4zXoNBG5LN39W+r08ybOw80pZGvHiIa4QVVgEersxZPPpm9GbvM yQ+aOCuzgvJOLVDEUPmU5qTaV2V1wdHkBR25rFEKD8yhcRcXFmzHg08yPTHK601GurzV9SUf Mrd+wC6AChVKib9vEWdlyz9br2TYhy5tsDNPX7VKd1FJSd0KsvY7OvR0/yw37d6IdfZJJXRL CDd/pnGb0ifklQTXR+LDtshIAJ7jBzUKBNWnWYCq0eiCHOzLFtJWgWrUiiVW94LapBJZBw7F ICJeqZnttciIKQVjFPjusx+WS/igHp8H7xPkAbBKMMQcDvE+9rp4MswpELb6c7Dv583A3KvF DrL4HZxBYVZoScvFId5KsDsMByaByJS3ru2NfTVCtFCgIzJm3CQ0bnF0cwOr6HA8V2X++nVL mwsisjKT7Q77KzZJ/fqs704H8dhCJgQxzJN0Obvg2YokC+3+hOBx1Jy+KDDJBUPSPzvhUYOv oNWbAyr1TxXgwjPXwdRXQ9vVSRTsJe2y9TzVYyC9AekSplTioPLW1/6JrxCdR33VcWTfcR4T PQyk6DnLGc7kVXZsCx4hyxwsUPFB9xs55fO0qcmhijZgbMjYslW1J1USDhaukhgmGTrU9hps y1B6jQ6Sax3Llh+nPeLPyO14bkksawAeA1PhAeVjg0HUiBPr0eXWuAcxhSSUHiXbFLxsvUSc gbajZlb3oEUYjcuSc+5K5B87aykXyVMTM3/j1dffUkcA8IU73kH3kIAm1zCuJ16BBeXr3uyN 9PU9peY6JPTsJ7tDS44IrzxjdSo/ecz/oRcBtjQDfnk6gsPpzBCyttLPO9j0O++NJOk/pDOl 3M9Po7DIeb5pV7HBQlxtx61qT+8tACLtAx6miR7aDZ2qeMamNBerf0ABT6y1Lm4EL7ItiMcT NyDefYf7xfyv7x/NdvKpbGIhjh5C+0EVtIG+hANH6bTgGmjUaZgg3fpz7U3znpniH5CAacyW Br2XFkfn6yy6Ww0TNGUKcrXAtKoVnwCvYE733BO9U6C9hT1mpG5vNMWS3OhDWwf+Gx1tVuR7 D8cIPhK5l6GG2y67bf/MEWwPZCB0khMDFtTZ8Ez4wna8kXvGjHKMmDKxL4j1+TLrTE8FDuqm usVJC1hp0hcosvTZCwAq/J4dUViE5NZtF41YFMR9sE2s6D+mthUEoMM+z30qDiSZKYAyS8SG 4f8KesPXOGbjalLR/dW6nPtpVnns3+XUD10HhgRBEtuMcg3gvitRyh+PBDYkL3EEPFTa4tM6 0HPN0pueEJDbtQKmWsUZXoPHMlj65p6BqGhu9B5FOcT1OVjIHoLNIHzuX3z+mSRMQFCCiBCO f69M+1uxXM/mcsHZYXXpkymueho/PLbTRC6+TuiijgMDBg9JEjpbtSGRHMpCyjd+piMoEnvS 7nDd59mCD9JI1cVWdaTkce/KryYuV+5Rfz+fValZpLYeUQ9KfCQOs3ECTc3HkxH6yp0QHs3D F+O8vgqcx5M580FFuTKUu0G6RaphnjJMeBMnoyYMs3mtTb3VU6YfQdZjU5QoEaNufbGAdX3C VmndXXHExoXa+JNZzzUi09SR7dqNENprHlsEfgT8Tej5PhODnF2tic5AMChhYTTGq73aPRSg HGPdtAkoEvMf0jtf/MUARhgHutz2yIMA4zoJVl7uXGY68O1JbyfZIgwmnWQT3lMabXBCXzf0 041k0Hfq0jImJ4ibLK+vxkbthu7ilbXcK2eVKiHF+Gs52qEKGQiTqTCf0gisgT1HLJiOT+2D paATwVkOOkp7MJ4wW6O/GkRQUvVT63WKn8+7YLLO/kDLiXWFnN7goifKY7lDdonpEQ7pQjXD xaRi1+Qent6H2nIlUdEIyyGIA1UdeyY5te7jyfF4k4s/ahypj6ClbElxUIu87c+Ouz/NQL5U 5GRt34l2b2cbKc3FffEm1MNAq8d+fED1rn1/LUqhMS2vFkNxO1/dU4a2v3CvjF3Q3PZFqNjz ZnlAhQdqkYN29pT2/i4yxGZX+CMZQvPmGRAYPV0mS//BhqPaMbcS3tShcCfkeKTXJgszWr/5 Vpe2vx+zgc+d0u8yyocbM/do0BX6dPPPFpFv22e/hhjWQwrNcniy8vHNtAwT3bZOGioPu33n IMh3bvur/qOt/8fv6eIwgpFHno5h6b773QAd3nq2qIzhv8IxGhY9fzfQXCFK2n9gBdbfi3vn beLWwy/EdB1ELfJs34DGKyPGK2EMLf3OASk5FVxlRqJzkxXYM8uG3od8njV438sUv1h3+ZqX T+sGtztiJyjZI0zmyptgD0UlL/vCfu9f+7nspRtZrda0EbDmgyuo1mEr51tnwnlMxonR4T0t cO/48BHtNO63LcpSP9nviKnb6aX8x6HVPXkqJ6Iec3f8CJh6MjzQSzjBVWAN/8/WATotFnQi gC6XxljUz0vrBFX/J2X43AuX0eUBQ7n04SY2m+ByR3kKH9+vWnkEvT/Ft5RLdcOHvv4JCV8W F1LJircIDv38RHu1bBo4418mFK8nsAzN2sNYRXg4FCc69FC0tP31exFh3CjE9Ro4hoO8fM5b c3ZbtIIF6LldhBjLvxEU8DceAixremVjlCItgNkHorVQdt3yDNJEXEC8B+2Dt370caU+9sXK 5UJYQreLCrr+cEjWdbXNZ5rKTj1fGSjjdjL6KArpF1gntZdfi0538k1XS3yKDhLbLyWD4sBU j/seDHefLwARI2wJT6NRVHNVoE8RDA+0s3ICm+wT0TXt9WiFiHIgg0AMVUELxCq+zRT5Eepc 7w1p1dHlRdxBDekSrMc+GtM6uInIkqTLqDr5GrlkeMnAw/Koo8bPufBo6yrMbys3vhwK9tWJ Y766eQcvgkb0yhSGQupZF/59gh3q1zXq/Fi92PZI6t9BGxcck6AsHAA4t/fRpJcD8ENTcMMp 0uQ/42/IVrJFA+A4GEK+sOnEa/RoqAfy3b/6nE9+lXoq0vizOOjeuxf8jzIfjLrliz2y/li4 YSZtb/U+ZArDUrLpQ8e3AFM/8fmXt6L0sZbOhYdP596Pj19w/3d9vjEgHSDp5UFxeYXu0PLV JAy9UbnjivXNOhaCQTjnQnQS1+SJCvKQjUO9J59ofRy2VjQZfBYzlqx8mJDGFTIw/SE2cMy5 ei8r44OpnpmSTCT7HQQHgKeO9jH/X1eI7XUTIyILS3303O3Bfdn4axYSCtrI24bNf539jnMs lenU1PdMTC4Xc/PewtZ6V8bAxBWvsWmBjDBF/CRz/uoRHJXdBqcvrwpsWAVkdyQ1vnIHN1GT Bnpggy9uX99Wt+gOIGvpOQYP+qlUhFPEEnkQDwl1ehJCW8RTfaFE8atEgTsaqszh4YyKmTtc wS9cNttPKaN+jXj5ek9K78UXlKKNLh9u2ZCqxaLtcbixzqn/ggjKiq1iZ7ahyfqaG12VpYX8 cT0E36L+SamxLX+tZB6UMS7icn/Tw2t7hd0J86Dwixj+n3h+OvOgIaL9YBoNQR/Dv7WBBVXB h1BtEsy4ccAzHC5ydFbq6ixuMQfgfsQBjzuHYT/9s6j+P4GHAPZKzi1y9tafrdIsnRC9Z9uF H79lFYMICd/YaM/iaUnc8gfobK/NKvL/wvVxmcz0YrFmiW8G5oMPK0agkB7/aYqJeJU3xGgS l5WvLT6XEiS74G++h49tg/DTCxjMcYjoFW0fwOp1VW9sqnIylzva2YVh0Khu9UxnqPY4KxLt UNMVc8czNGjZCLdEQnR7jRuejaqV/FHGsm74RU41i+3OmhIbF7ESV50Covof+4X/o1wXU5jC EXXGEp+IeH3Do/6mrmDMSnuTRAogU9ggCZIaa7ZuA518/hnynrdm0aiOVRylt8YSJyUh7ER4 q+692nOnYvb6DmUtR8nkdr6JtF9cyMOJXOPzfnJd54OwvrNDZbN/gCBbsO8Y4PfFJTb+5c7q hrooKKc+Ef7O/DsymyLd7dHQVkfLkZdg1jYlBgDfG0oe4yVlogGTZHDmuQ8j6BQnxY5mfnhX YRiaUz9VJas2bHQ5DXOdRrnkRYuvGz3OnSQl8jgyffabkRzRfMH2mza1Ti38kkCcwqlWd+iZ myUtOLBotlSSv4WDbcujY+AZrXWG65gNeS10l+HT/JnbzqPUkPtDEp4rBmIJdl6Kj6Vgv64P V0D/F+e9VMz6FqBYXqZsuhToZPGjppMecI26TZzjAy5RZujwoUw3Ufk/lONj2SxzFtRS9kk7 IpSdcCMb7+2o+PgnjUxCrgHpuvJ5cFDek1zGyyI5iJbI4yutq+Sp1/LVVhiMB5rjGvPwTFxs w73tWKJEbF1st6cXiZdE1D4ej3Qtbo8h7V73MtscN/qMa95gDx+zPgrc37W93fapvpMTNJVf PnKaUJKUONys6Bz8/CQ1dAj+vaCuVGyZC8jA1Wtg+4Aea78Xd8zVX2UVlR5qmNeyCOBMsUT8 dNv1RVrjSUx/rHlP+fvQyiDsv879Ry51wcv/o6uoNUr4dnloJ2jrGiMhUdrgQ+0nFm4iZHnZ 0jE8QaorCaBu46iv7abMKWJ0y5Etlm8DE1cizeN4U0Qnz5qbpY8d6W9gA9jlhOAc10mAxAy2 sh51Fs4+YhmHBO26cGHFo0s42yChKRdWmjeI/8Sij3rjTCXRyxNxowFRwLiqfK6YJAVea/DD xw3kj3YY4aVBWRXDRRoEDQgc/ZVAA7+T7tcgqRtWZkCKvutGLjfYJdsjNy9udlAnANw24KNX 01VM7JXlutbxB//7oBM5/yZ/rgAYMy/bYiJovC8vB+W9VA5W6VoXDpKMV0FTLVF5WJ7jZPtd bWmq9hPPImbSQl/YkeGbqZEKYTbxCfVinlXqIayAErm8/Z/TkadOrstQ3+ZoA95jKXM0VH0X BMwPh57bfO6i7Ti1PUoZxh0gUKc5ThfwSwI2XmpXT/vtcqiIwGeQ3ETAroAWQk/YOUiaKkh/ FE5Dk5v+Mx9qDwXkieF+LJ2CrPXgekLdN97SwaHGpzSkeWD032GOUr5V/VpQUdwlMry4Bx/a 4gBAk7zO4o2Q+abvp3F7jCRHJQcFrBGayNZSXNz5Np6dn6v+zZ0OchR0iEgBF50ZhOeWtyUJ GnPmG4PCOy7YKUrk+ZkG1EzTVCi10tycmCyB8hqOv3g4fF4DSAp9KwjLR0O6ycQLUWeg8ZKW yMsDWOeuRe7zyUfMxkccqmXSvO2O8s6yGFHIGQqV7flOA5UL7cUaA+27B3FKPmP6w9qduj0m 4BC5DxDzJtgTn1KwwtSgbM6I0x6qST/WN/sHoU9SdjfEogJjox/EHp4MCD4mBx3Tlo4X8a1X f59mbsS+n8z96IpHsX0BgOqbbmxtaFtbapebkrgnfhONvgfOBrYq7nvdLKqjcjfyDPEjkUw4 CT+DcPhGrERZseqd7Doqfl8jmwi8/VG4xh8WkJxbfFXotxYqOngYZRaI+tGvz5cjFQmTCq35 URkLEfy8LKDDnUFONa22wzYHi9ziJHtuwZQc2it/7krB03glsLn2aaQBIp8W1ikIrWgBg7zt VEaw89YBa4oeuuMIKb0XXMSZvkyfgQ7WVJ31y+k/Xvq5qhjSfLh62NJkOyAxmGu5OulrRVEL xUra2NMxIW7bHKKUZ6EmB4J3tuRClN92Z/isy+8GNLjaNhiFJ3woZViYuJY5XVD4Hl1xtwSJ KhzlLMclEZB2GdY+FDYLCdMcnXk9B1eV81EeDau+9LrYUhgZJ8xtLhtaOkGJtlW5hjfvLgeM NtwT2h9TIbmoawhj4GsjId32T4+xMswEo5EsM/YPrZfjg/SCtzaEVptSc9iyJWS85POn5oK2 DRh+7Vphi/kqJhYH0KoGHg/HKCueP+L/2sEBkIGwRB3IkiAaFgFFTcOJ9o/RnXLVfwsN2QeZ B9QMWz5Qhh15tPQRZQE9AwGzlCqk3M+c2SJ4DL5U4KFMsjvuwKBfKxeop/GkltMmZH5KPam4 mWbr1k/QOtIEnODqx8vFeoh9OeEBi/GWLiRi/h16GhrINVZAY5utF5fpcLk7Ey1vdeikLcbv xa5Wh76uq1my9PMdQWr3MxyA8dLIYJ/z/BO2aqXZ7/dwhwwnEjgJqGJYC03k3yfOjhLUlT46 pa/2WqD58SiQxCBDxWdw8c33dE+3GIn6b4T+nrN0Kt5tH9l9cO5BQ9WVE0ZsOI+z9u9EN3WU KaqFMyavF+JPKVnsLBeN44M+TktYL2dN7hNwUcGp7kiz1RhqEK/a+l9yKAihgQGpydovu7ZO T7gCeznyasfNNzj6TrcQU860MAuGwpdQCZVlO+O0PWTqTxSE0yU9ddK9K5dKXYWP1lxESTyX D0QFcjKMlDvdYyuvXO1Dsljt3PqpUIJmGnFWo0V7JdAYYo6Jj2rDTsRnWRQJQU55A9CjccNY 3neDIDBGQuekwN5TWB5cQ6P+/pIM1wdYAGvDJ4DmVuuq6fzsSX7i1I5gpMJJy4cxeSm4Gfj9 uWaLZbuEidSx+dWEg6y0QDrISqAxQg/FUwTIv7FpYcjoLhqLa2NnKRzKadQHEb/yneQRK3lH gweitV9oIEVQjZKWkoaG0EFO2LmX+QDWtyfYmfbkUO4n9eWmlnuB0V87nLWPVo/1f5rfJLf9 5XivmjWYGuzmFYJgq1uTbm0/wD3xxBlx7OtjjYMro9+UfjSAkBEZJoPI5ACMsj5r2FLlC550 AHJGk9rgV0n5K1OHw5BPDFTrfmLvc9uYNOKkTTfuvuFFCSY/FUwkA+5S1kdiqbQO8tD0T8ot g6UNThxE/NzXKpdcOvyYO2afxOhfy7Py6OCvPJ8uJJCgWMwZI+V06TsfiKDIO/nBOCv1GqYS OK3EfSo8kKT6rAKGEmTq96IVUYDgBrd/QuBoFnzge2a3oIRP/LKq8tK+osN7kXcnNAOqCWkB YdrUFEW+A6xzZV1GmRHCBsYHtupbUDxp/GAJ6eSECFm3Bi544lpZtatwl0EYtsQbH7ZtNTof oDuvI6QXmgMZQ4EPyJr2dRUUgyPrXAl036P0o3PF8ThAMwHS8nzFbA79AsZEZ7OmWwV9x2kK 9NZB98eZdtssG5XIuUVS6PlT/jXrxgESTRPtKZFZhxv4sC65qIyGsUTybY+rNop3c9DcZZqz wiPpj2jP2TzmRPKZx2hBzHJY7NKSRPdNAnB/niYP6n6vdsYbUcbzNfeC9ILd5HkIIfScL55M O4z8bUXDsXet4FiRLigSmQMjZDOFelZk0YKGQl53R2h9X4oggKUimKOMyatCsFHDw/KvZik+ K82+iHVG3A83Wpz/TY79F55IONDWUtMon6Wo/iJIFYErXI2fI9Cr0clNRmURa5QvtChtSPTg Hmyree/6UiwnD/wGwvnWp9WdVayD4juCJFXgVZqXuutbqOMW1xsfhkHnzTa0bDA9cMb/YDXz EgSHxqOrm+YZ6UtABUsQpm5ojlW4FmzALwT8qvXXdlPMC3od3A6PBuYD5vxwoBUZCh22MDFT YkmgwM0G2wfdGVO7ZSQnklDfBicfG8ZJMUbkKhgpX5yEy1bgiG/7N86qHS0twCvyzl0u4wBf HVqVUiJXsxziiVsbOD4m8sb+8cJRLsKOkK5CwBxMIoI+3MvsPc7Dpl/9OcNORrFZdczyB2By XHeF+YPGpCtcDANgHLha3W6U95ltc9+i1HTe1v6cazsGvr+ZDhJjJh94XvYLcZlwV0vZv4dr VqAOEarvHsktvqAcnB4l2NuoViNQheCL0yNBYJXMtRn0rLm5tAq7whoTI5VtDZGJSXhA/zMX PQ79J5xI9IhlggH1JY9zjSZp0CpNLpIMEvVqIWGaaD31qy/pabU1wa2Q7S0VDr/KxaOo/SUJ /tJoRvS0FmvgoykP7OByHlHlW9fIfgyE/VjgHiTWH6H57sHUdeFdURa1zL3hCF6OM9iXlKys GbmR8kTlqukU8/ZdUDA/f+Z7y23kKKk/FtVXbQjVXblu4ByVDtHQwTq43mwN0u3f1yT7kRXV 9SzYyCnDBDwLMHJMgPu5HWieWqJoOhBOwpOWRJcrnW7v2OpP0opAyCW0tesEgyxoQU5qC8/a C1vbd6pTuDASiloBHrbGo+XaLU/QQvtfUBQIz5uohDlzcqKDMTN3aGY/7EpvXDoI3ifQJQ4J dYDLByKC0/kAS/AYLWZiq/pGjMASwVePjFWLYtb4rvImWSsyzW4mc45KkyCZzGP3gxv7hqiw LRIz1KzUPHdddQ7cpKQ+yLxK4b3KuWoInFspPtBbQodCEg8Fi33jIGbd+mti6nmh51/O9pTo hOuFN5hSi3XpcHV0EYISccEMh55w42xXq++C0urLYGOeQJm1HiMAZqGzcGOHrOUYIafcJY2f 1eQNtjzO4gVRGpBGBSGP4EugiT0FMbTwnZ9C2wtCSPAD0QLrgo7PUydrSk2YatHI3W9N+2gs zPeluWqpB7ujlf0Ewxo2fd1iNHNKEua3J5Wm34svbwr5Fp9L92dcA0YjJVZbCcomJrkyO2iX UxDVoS3N12pxOTfG3Gnj1K5Uv/MnGhxh1jkNUlmHF/nWdylRw6e7Lr4VRLcMcAkPilNuH4tX tE9Zsh7KelvUiPBFDGLIm3OK8WlwKp4lpmSTFIACYHQ9jJ++qoBUPDle9oFa60lftfkLiTr8 NjEhiZSC8AypQUu4JZNne/pkqDlsqiUIIUOe0w3SaX6AdDyHU7z85w3GTaJh0KrGzBx9FfNq Lcs8E7bX8me9j4H9d8MiZGLbYd3T1qyTU2qbMLVuwVXwXqM9KOMC94ntQWnlUlU/fC7wLu5N 1qUGj7UzbKw8ikj7WDKer0RBVzYoBwYVzK6g37CzgUormY2S1EsVFbfVm+/msauBFcFUiBoV tYTTCqPyFGhbBF6RE5/GQ596zqLXM8TgcxQMVaodheAr4KK53K3tVpvm7m8U23ope9Wzj93i AKk7ZwEw/pdrfJIv3r+80fZiKdQwM89vamjVMQTcXg+4EjVdYV/o+V/20eyU4d3nfNFfjRA/ 27QNigJHFYrGSnOg6A0zqpoDJvEiQSWFGvdHFOM0hdD/Z1aLtmuSKx3TgefBdCh0A44yDVTm 5da+NMAogPm4+c8mkXd3yuK4P8GYPpTB8d4+V66POjT9h8TAelYcyGQp41czxNX347lDmNDK w+Xz1kkGdm5/MKHyBSZCxVGeknDGJdAxHuelVIm4hNNg5BpzTg9/GtvJD3OTNDTwVoIqdagd 0ORGcCybpXOPcIW52mKq/IX7hwYikAkUFNUJZW8SmvLbWdgnbyUW/Dx85iiC6x/6Vxw8/Rjo p3vbm5zsoPd0OrvcyoOsGFi7X6VwDYwQU6/pwJSi7Z7sRmlZ5fLsS9rU25VcbFJh85i1VLbq OXulePRLjTFrJ13vM0WozBKJGKbJA7OzvuehE7BsyZs2xttb4VLtMVmE72BTHR4EGifWbsH8 TnE1Fmo1eqFWPD4442xrTzM7JMLWeGvmNKwsDoeULml/JNGKRHguMhzf1K1aJeCjxTvFYN2g LxhA6qg9Bar1Ii0VHdSZamUhGa/VLYdOTLutz8Q2wSa+xco8PF9HC/1y4P4EJmqJCgOMqvk8 BwO/AfOTVl50z4CTXhdYFmNDGmiUDCKih7qxIxoXLtMUl/dyUKhJTjLm2shDvQysvUMieHi2 K/FNg0oDxfzrRGBSkk79FnaCVjcwHRanmz9TKpL8y9OTM8qVM31cz+SOYY1tmM5Y0zv8jCbE C7V9NRgNRMcuzEVGcBnSQqLvn1gDXkFHQO7Zg/r0nIL9n5I6H/NNjXJyyfxITRvWED0VE2Jt DE/h6KeG/5RJ5orqDIPbUS7EEPFOGETb/D+ogyU0R9+hAUlFvzkJODnBi53KF6gREdJJIoqq tW9v3FSAppcxCoJD+7dnDW7RwcsLoveqX6nrAna+qeUKqkw2iCfbLZRsbHqkEwZ0dqfRZbSv jR99pFSDD2ZOM4RDjTbmVs+NvPDPLYBPljT/Z1DfhC1J9ZzvegV+mil7H/xd9v8rO43hRAVK /lJTz/Nmqt6MPdtQ1HI5TIeHCx+JjrJfFQrJfX4TMcJpeYEnLFA5ze32UectqwfR9AhjPWii NddEIg93G0rk2l7IZM4Vb79WawlDnh6B6fjZklncE/CE3Kotf9xkc69D2TNzvNyIzDpEQs9y TbouY1Z+6KpUqiSUfiUOoqrl9ganJt1qhV90f3SfRgpPj/r1eZYA1YdtMtCD8D8CxzD6mnLH Qi0UowE1BXhFZe7rOivKWysBrSLpvVPBqYOtyGZlNCnJodk9X3OjXhNWZSFYt/nNbiilA4I/ xZjXwly92sx0wQiwZXV5ZgJSOKJAAj9TFSSdoCwOQf4ZNXocdic5XIvEISWBYlvolmI7d6Pw p9MpQEihtX9Nm+AuNAs6qbi24vl44QBSPSGdAyRacLfwFSySfUWl5yUoOtfnhgC6qck47xvT ZIot5KeKiVlkGX+Caxkk9a13O+RcvmEWrMEATg6hUdCdAzH6FQU4uQNY9XcSue3tVWYUJMCu eiUXG5FI35QLrfS3TA/zdNTS61Zjf1nIMA+lwUBncfEy5tNeajzm1wJygcZnkbzFBJmxklN8 VCjf1RU+FWeeaJWFYlB9xHYOTpZw2m0VK7BU/3QbCqSSZatzxISR683VIvZbj4SrrKuL7cjO MLeD6g//WDh+/0bF0MZSSXL2nqMZFA3wKu8O5JMM5s0W0u7Atoy5DBpBc41Ze/nJVtpuJGFq p16wTMMfFSzzpt6a5+yC5X4d7vobbOiZzTSQdPk4ueTNvbBclS29ZM+D2s7Y9NnfZWq++H0k NqBUAZ5fYIIoGld/OzaEHmTen8mPt2y5fZHFkc0DSsfdLBtq/4kzJ5ydOAxNzdITOEgopg3b n6rEBThd6wYpMARH4+8arqPFrj9ggiWjgJPkB6sME9janvQca4CtkERPa/2HR3FqJrvzVBXR 42fLLc3WjuEl6wq6kkwBeDxTURY7VbNs0/RpQVMgYTUyx4qFOkAawgpT6XIVjaE+zWqZBe94 Ffp76ARebKrSltW9brxiJtwPBDYAlEFV3HgcqasAYr8ntM9ZhQFdfQvAKOc3hFpa5lSXwRtV tp7yGm3Ytj6HfY58ILZQh1M64beEzSrxDKMtU7R2KnaxmrYbo43LlQhdCjuopLRCtXW7CIKD 0SWCedAoNhzVJhl/u6ZDJYIwtUu2TAIbT2ebmkFScJgvGrpl8HnM0USfXm9d4Z3VzyIZg367 DV3cdJgVfH7L2xGLz4ShT/T1CSWR04CrU9tsVn75+v0M3CfhivZUhZnGBwMeX2fSMU1xaSeq pJXRE0wejiqS9+VKF9VydEZNi0O+w4k9Td4lGYqwR868u6Sziqn9PGSB8sZgT55OriFOCCGi bPzjv3W7pa6a687gdHA7LNMktEz3uBSTzKdMRBGzrpos/pEvFcn/GVw41mzhDlBFea9Fvfq1 vLzRXNaTbDX6ATiwukaD45e+CkoeOUS3+3hXBJA1IKRJTf7nD1+WUNwWqsDVIFyr0U5YngIt 54F+YZjWGyIgAOLWtWy4FcmthUHNvGhE/hZ11ozxS7v4s02vwnxp3xOM2gXx8hS02dt5qmz4 7emnaOiWlRFijowDTWugAFC1BNF/jvflQ0yhDWZ3qs6O1CrJUBruCQLBKJKSuLQu6ru0HEhh 7esyDBn55lxGtOL6yuPNfa1lU9rkkTkPJ7fRTYz3TZ6HIom9jNUj/JeVE52y/ZJ1Ldi8KCXt IpVJnHRQUVokfu3QshGuwkWpDOSVK0xNj02SGj/tdso4F2xr2+SvTmW4HRUfNsp+hzNU1vf/ zlbzUU1nx0IZldyNmbE/7QiBMdnLSRjxsRt09za7J6gAHCE6dTYJaOq39rqCe55g1BR65gZs oRoKhO0DnV4puchX1ymYVUm9yOLtqc4xxc6weKcP1EMGlm80G9ATODQhLxoxE1+VcYcD3MUL HyQkL5XoXOtyukfrLrKJZcw61x5fsKK4Bt6gwyGnPs82fA7kS0eFIJT1pz2ONv7bqM/jEyHG C5gdMYFJcYKlbWx5tZoS44+OFy6+BRtLgC3eqo4iw87nDWbz344jTYux8ZV6OXQedMcIgCq/ Hizp5HomI/64wGFYwpXPQY6tCFaXDHBgXi73WL09encrQ3o6FXjgep/eIvzdZ1QPtEHrtxiQ 42eHC5JMOnqNlppSNQ5lnHfD2KQQaAxNIr6mZcptB8w8z5L4wmie2EG+CCk3QlCci2RWrX82 +WuuCPBPBTruuhaNMRIx0Cg8x1yF9gMzUFQ1yj51CJJqOxEaj5sTB1T4zeex3DLAkMPJUdaM LxYdJvIVECnJrkcU/7rhvi4t//9/UJiDzzlEl5AYAgwItqQ4XwF/XCNxIuipgQdvvj/+lTZ9 HyNy0lLdjSAGLzExHjzVy/ITs/bOVa26JnCKVeFWe3BWezRpxrnZ0h3He839HvQdvKBBRRsW qZtVQWKlFjiL5jBhbESYDA18Ve/qWya1zLLMA64N5/c3QM+LkxDRh5ec3QYYcI0wOjGtHIQa TPjyjv1SGt8vD/nwqlzI9AkMBqWfDYuPfKXYZEQ4ozaT+YGXC//AF3YYzlnIfTifIsN1rckR gl2KJdIfdolnfnatN0P6bPZPxcVCx4x93l3OrQbBlvzHAxYXKuxesjkFTwLX+ql1I4f5vxEp 0nYZ/5y9rDKJFaht0A1eUAVC1E1geyxeDbeW0desNLV6kSCu588HQajd5Xx2QwPLtdrENzxb +veIIznWHxpTvO3Hc7tefmaJ3sGqsEGH7p0dt/X9G3U8tVjp/M36g21E61c8GDXbHiqtTZiQ xzP+Ife0etM27mkDCEbPNrFXanhYbQ7CLVpzkB8btPCpR8XF1IcF3z+c3W5w43zaUdI30rIY EQ1xU8BIaJkOnKRCy2s7UEQPdPFed4I+glhtYPL+n0CPd7FvFodm+Uh5B7i5b+bqj+H8U0Jr ZBKt+GOvFQ/+wqZ/dSf8NWN9KzbMQY1s29BkuyZ02DZsOE+CQiJETZ73kdFzYsMSa/SGRAb3 5JId6tCtyZn+ItsuYhKj/08a4+oMD7fksR+x/CAoNYx5UCfig3Ie2UPUU4FX7fUZRgIbvQ98 7bKQfcTFjq70SlwG+6svTJCUtVnAOnTVYG0aUsEQl1UqabIh0ZDSTcUhhEc6Nf5nrfF3QFww SI4y2GGWBZ4NwVt26a8vdJRMUCyzrllCVquWyYg3ZSCGvqyMFZTqyrZtOXoQ/bG7H2GSPM5A saG6jIakihvRjqzaQCd7QVB/8SsJS5D6dC+FeCe4rIZ4LJD0zwzQJlhiHX59Cnrtpz47Zkkb /VMrX+RmPyPypn53uTfewjE1fh1rkD2C65Jx4TmpJpnq6G6kieITpW0/m5wty/zivy4osu0r yZk0p+szTZzKO5gIsBTPS1Kq/wLWDwX2tQ8eF2kb7GQ80e1HXc9he6AFcaAZQ1OawsEsMvXe JEVp/DynY2GhiMEYx+cgVHR21yfDeXZgkTXFKM/9m6VVz20QPP3XU8RZNS23ZhECAfhR466t 95WcTSsmXlZR+lyxrCK8pr3zvnAUAEyURPS2q1LN/M5gnVADj6gOUT5HGdkT/gpzKEgr+p1t CdYwDMCDvxEiw2FfIZslZRZbbq/QM583Jozg0v46funO+DEojKcH5iy2safhg0FODK44YzFf 25XAisGZz3MKanZnbWWdkGeS2gRblxBoIIByemTUGjh1nej0l8MbJ3/UN++x4ksmvJm+NkA/ hpuHmX0ebYJtrkq8IaVH1fC0SgPyki+GBt4/xzT5ITjiY6HEuVhgRaProfz+jmJLHVbB7wke W0BeNwjuCHQBwRONzQzp/yH/fv6J32iszHzidJYgAQWdtbGzF84z/PkeW4wlsY+HCHVTTCGb CTBUj5QSGnSjMy16Lptb4k9OcS6+unxTTEgmoF5bc3KnxfhIy/q1JIdRat5jAnfbej5AdpsH YG0S5C1ltrIvSN664zugFZxsNo2aChOwEcFx/OXoxZgNngPmCp21qqjlC4r6xfT05Ew3euh6 TKwuc8+WARll38bn2oHB2ljKti4Y9vYfPw8QmogtycDlVPjzzuMXUWMCuIYcb19e9VhsDPVV KYWeTG8nD6ZVnLsdKF6eA2m7SqcbauRA2vc34Qa8/TEMgflQOHxXuAGY3gUhRKMbXch4Q8y5 smFpRuxHnka1oDkivpmUBZl1vj2L5KlKjCuJoQRywN1KVNrlS57cug6zqL3hVY7J88iIJZfi fGmqSoHqVmsBargJsrbv/AP/sVEMKKrwW4p5a/sc5yzaMdHEPo52GGbzLxgXrT77waT9HorJ ouJCqLF9bk1fN/P+i1aliZ//fIyFdS/6YlfJHuS/MJ9F1NHSdbjkQy8hHynla/7Y4U3l98Ji bpaDoD/4EpIsv0x8gNhl57Hh4D8LN162p+xSLYkWkpFPNFPhP4WPaZ8eiF+29BrIf6nO2YgU V5Q5JTMAafmLfH8Z6V4yfBtf3+3nVxDVCGIJ/UAbDpSPwvG81qK/RLDFz1ED+VuMW3uwgHFR funu5Mr2GXxedDTnRwkXz9LqAZFnpAceegZbqiInR3ioQ7JRvtWa8r7/bvarAa9XutQ4b562 AQI/2vNYisuXKE+w48P58hAkzjaaj5dRGM4JZYwyIxdutB/BeGM197Odgn/doPVgy4y8eSXD oEtIlfA39OzA4zTQEchrkClKk0BUql33sUty4n7OV8lqxCX/1/Yl7BPWCXjweKdjIw3waGXG ZJP5ppFziiS1fku7OdzoMTB2+KDxhVaFkop03mSOEVgCmwjmKCgqtqQmuI1OQql1JjZqQmaL kS3tWQyXOeawcqcBj4RPImQJ6Af4pwnuzaNq34GaEnP24qdQfiDdla66cdXuYB1PhB60wDzp RQP4R8SqlGR5d/YdknSggVXoigjaOJOR859H0NtV+hqZEefPlfSxCYmXOw667RjXwA+/eejT CUWQ5Xa9rqMyGGpIpIoGFhEcKiHLiKWbV2sy7V5LHz4GNq5mvW+PsCHlCd/2voj/9I7m8xMl 87s5Yj/LBDDzcVIVrR0FlqydHEphdpWbpBDnUgYKwQhKZql9Uw2A5J0gVSr9E7AMuGBIe3jx bE9zpPznc31He62wQMCzmdg6Mg6+gZSODDZDp5aOEMholueKJc0nhQhnUNPtMPWoBTzKXhMb 6od+5dO9FuM/KTTrKBzFxzrb64VmtNJ4PbxWCNod0Qe7H7tGzvaLPG8W+JpkuQ778xlegz2v 2FdZLNGMyjpWvaTCe/P42OjPDC07trQzec68inDq4XLZZlMawuGBVLkEnFj+FllKsxpG5Auf zlhlhj0DMf0ebVDt2DpkTHTXw7VM5D2sUvWcG8LhnoTEYsIZGTBSVP32u4Wr11tGL0G68tR0 GkWMOkL/vZhgWkIKuskvB/aC9Lfg1wUjMZe7hjJNsGYER3Nnywevmk+MdtoR7Xt3DPzDjbbH WRmQawcCMhqqwutTZPSxFjNeM/bQ1VIWqPEad0pSkAgpBwFMRqHR06JdyjBQzJExLI7czhZu vZXy3bdkUJBS78SShwfhRhKTnTQ85zEdaeSn0u+Wrgn+cbvsL3wMY1qqS9EPuWQercH4cKYJ ICnCuo2LUXP3QxsUNJ5s6pfNltF0cYP+JDytSyI5yP5t4uS6w5qXFnmob5TNPcbfgszqnYsL bk8zi03uoPRQ3kJ7aEJkFOpEyPYWYMkT4j05m011dYCg6+WvcJRUSQRpIkywQmNrA+MlbG2C yn8UMvxtXLbmX0QKVHdCcIw9sBXwjViYc6lvJelE/c4gFJXit6S78yZ6M3RGmGCSRKk4VmRM FJlh6OSNjVtAWBE+OGjMVwWeOggnyongjqEqH7aK9U54nJ/UnuCWcwdc2kY37OlsTsksPCKk M3kbN1olV8YX3wmLjQrVBbQf9AryGnP6XxRV6+0V196zL0qNAiddRaIeRwrpUvBEQiTXq8W7 OjCEE37v439FgQNAdRpNnB0bIr0PLFjFIe1uwgVDSC+cPIujbDaNMsEcNQyZM52sb7sPiFFw HWKdS50jjKTJjN76Dm5DPRDo6L88yQxXxCC3SYsO5V32d3XtOSaD97Ko9cqI6qcoipvCH1Jx /6pHVWUUMg+c1GDJayTzqVki9swa/2rFQfmLZO+62HA31y/SSFXmBtdZADZQDJe3dInqJTqv Rit5+jJPV+fFDF5Tob0uLEmkOokOGhQPOo/W55DhPsmdVdyWtcLKVED0w0RRQ0OivdfGm1Ap ao9l72R+4dopaEt3qForD1AFYfMzHlnIPD5YvJMFl4n3AnGu/UhZ83FODXxkFgS0V1Z9Kxlh w+1rBxXEiF5iBW67l+xnpHlz/A/9F0LwfB4z+ToJI8vkaZCmYPbqClxFFOtjaUqsVDH3Y9jX hzpyLvvBa6YyNItCYe/G2qxWKU4/XS3asP4YpfUcrjQEQdP7f5cw2lXIfTxTFwSDjX6wPx42 55I8vA1Mre4mXcz4aMu8elP/iFyf0ALcfDt6WmeHSc9eJEoUzf+f4Tn+X1uxckx44gTD8d2K OI+dFeeE88WDreD12Nuey2MGCq8FM3Ryl4S+G5ChdnUbZA4Kplr7hxtX57azQJL1aVBzkVS/ TBlzFMC6JN2ewmIV8Wn+86aKVRO97vuIdYBNRyRYDYrjSM5Ja3Ffx0fDVGv+ZL/a91lMUec1 Jnep6yO1yvgWaLoDDxUMnDgpP/NIVWKgOdf0GLjzDP2iX0ZA67RsbLRdP1d3b/Tr0tM5PN+3 aGZj653xNDPDCENNboQ+GiFoFaWuCcBjb/mDhuuhkiwAkDgxhQeJbcZEikwYCyrrReaubn1C knQha0gM/8oyXHtTDj0SdaOAETOJxIJ1iapfe3sV2pmgY4Agvi/5rm4xLURyxqgn+ggU7JxQ tqtpsBTNpeCBEJDVtt1uFW1pfAs9tTrsj/u8GAS/dy1+gT/eKcrvG4cDOhYWB7Q29z3u0xI/ J7MyAgwnW7aSS1MaEjmhJDwW+yUW9opxL/Sz6EeOhjKFiNkJYM2JI7GskG/W7/lmGeqLYbE/ V3/9Op6x0w2RsD/L4ediTpbeq75cUnTrkt/kpCpmInfvRYC/h34GBAJymBfJlAvh0qDJvAx2 NGLtkwV0Nj8gXundFlpcNNeq7EIhcbw9qPcN87xZfeSAOtOJ4B6TIG5B4gzW3FgyoOJH0ien n3bPxOyPwerapvHBWN61gHkaCpbRd0N5u95we7kkJfmGzAqn/NjcMAbyGcEq7gnX5yBM4vxf P+JULKZcUBt/o/chcchnv4tBAUbS4EoLhSMXdcP/0RTA9wt0YzgCX12QYW9/ZsHZjxaYl1fH Uh3x8xf8+KHgCUp4NW+CakDay8y35ircUANGDwSguJ8aF8XfNZIlMyX3kZ0ArB32gXNO5fYs OhHdu6JFthkPNVJWzC5iGQfb0UHtbZgBkP01+ceysQ0UTQAEnBI6r9H1tLf8MQbPGMmjuDnD oPOGluAFUNpRc0iwWSdnrh3wqOqW69yId50uZQ0DiyLylrTxkGY/aYtBPqOshPZkdmGxvcBW mfMHhbScPz/cTtSQ+cExaDQZ89hw0Ds6ajgqTOgvbYazvX7D9iWOR1bIQEEHAtkzC6/xiIu5 wbUQn0nWDMAhiz6oUs2nEAUcXOGntCwzzI13GELb4BQCa9f83zrJfSUvDXqsP5T4OLNLLaf/ nrC8VbXi7rtPNPMyxm/5/G25QJe2wy9RPoxU+0sSD2g6MXZSW0NOhbMF+zNbqABOTO/e8Xne 2ErBQqlU7STAGygreFiCdKTS5QvVaUZbtYC8T2SisqNlpG/OhCcc/TupIMqW0vtl5W0GlF+c 4u19ldZGARk+1VTp4EBsxmFENxr3WQ2fJzWEevQdFk6JYbumys+q5sdXhyAWKfo/Td+//S0J o15awYBZ/HEIKUqe4tgATGQt/fv2Pc/HXQv8zpQSmTq/ivGXaG4jCsHNkwYYQWlvMx13djrP 7MigCDV3TRSl3VqvGmDvgyOhqMC27Mb94DtxQXNJTIa77zNRohtRRsHkuTyE9BIr+uCqd1pD VdAx13K46c7oS9vLKAvZ2j1q1LM4lI3tmoo2F0j/0f7Ui2kzL89tMRQsLR6PU2b67rsIcxuz zSrHO1HNp4wqvPndGRJ/iTsZXOL4weggALZYMc8GXEDIiiZETeIsN7Tg5UyLfVC0wNXdYzx4 OflrUqaLQUKl4H9yxu/buBlirXuultsm7+LRtPpAX2JWaxabl6pI0dg+0tGi9Gi+pa3W0H5t q/1bNYFJp67QnN56KbfapWsYPrwPwo49+EJ360ciRJmx6POv+vpvl+ryq4OaoQ3y0ZPSThts sjTD0GottmHkxGDsvr3I8TqqRvbIbDd9ki8R/0aj/3zZO5oV0J0STHR3IKFQwLP+bpHl96U+ QfE8fOvRD1RPUcEDQj7x2pY5Freoc7M16X76znFlERrEFFIeyq8h/RTzfUcALzCXpj+Mcvt9 7ZAVE/cOGLCMb5a4ctUKxJlKP9N7pmaMqB0CGKekWqmmZguFJthuNcSJ5nLtox/tux7jl7t5 06yoD47EbfvMZDhDpbAFlsZRzah5X3uGDXntN6DrBT8hvWCmuVBQSzCiZQk8ZhY+yBcnJQhY IwAqsV5m0I3CeXZpfAmaH2+VaL5LjeVBEOHXFefnk6ZM606wvuie0YEBCA/60taJjBzdeYTG nbop8aFMhRUTZfmkk57o8Mvai+Apu9MY+0u8l7NCcMW6gi+7CbNC9lRRtDsXexKxY2Rf3d59 lEl27qYzzSgXmCGLRjTqdMcYnNnqrRlqkN73YEq5v79XSuvEr2O9GKF+f58nEgNDi7J2IWJF S5enb97WvQ3DwSPEcuN2xdWKnNCLH9kHXM954JWp4enYtNIbbS0XgDI1MtuwTfEo1tTokJia DKVLm6O74ol5w1rQ4WMv1qf30ocYH96I1ZCoY+vcRYGdSFJVeOaKcjrfN5GGd+ECM7zoDm2F /tD0TeMFdEP5HGokSjFpzLjwbZHs6hUSO76uSkoqncpAohJv1W8eF7ndo7IzJWpPlVHwqYV0 BMwcqcR/trVmbA9/MEqLJguYFwx8jBM+FicY6pz6+X260FkOxQWa16Ts5ugwvUcCkW/AbaHf xuUfG9k5qpk3AxTAF3FTVoFbj2AREU8JmmfzS+lFcOkye/Ogt2S5cPhNXCX/KmK260WhBQ8D fNfcaSkPNXamrd6SWHJfzR6Bwv/d4nnCAxzh1Ddu3HjhVasqi12enFWkhdAdKqJ0lXOPumnE nve7l3bK4ShYBJ0ib7U0zHCHbHhQeMgVByLQFrRLVUBUtpnYLKo0aCfyqb3gFzrjuHVxeMfT P2DS+nlOFUOd7waxZSf8R6XIEZQ68OH6xE5qfPmmx8HvdDI2gKtVlOS9Pm5ycpPtF9dNukwY GodtEmBwvp8j+H5NizN2/+XOY9QwSEa4jsDZLK4MKoFj1Vk5EJbFZkCSENXLw1Opk76vd+sG Sp3tpcsCQZihFUVaim728+zU5IUhlIdTlKcRDZFKems8YU4gxv/rcRPFt/ElidxjoC+WHGl9 vlsU1ILr90KYJil1kgABhp5cpAoeTNx9PLiWEsAvqXZo34VJE09pcc2PR1nxfrXfTHb4IX7d rQWJNBwv6AFvitfJrq9U1UMT1R0/tkW3XWPdzLX947eQ7YUSuqRNIaqvtkamDAkFYrmgBdwJ wem+Y4oldbiwFsJfdSN6V8jd8EBLkKxKPNsSa/hPjlcj9hZ3whPVeIeIK7DQoP/QdT/IvZrz mfePhP2jV57fbSA2p5TezBrJAqWS7RKsY/gQMK01jPrIcYrYVNJshWvLMUurrRGityayO+O0 3qjmH/+TL6iXhwx1q07FatDT2EKOaya8MHzFmIuAxvtZK0TfZM18QZlUpZJf5mIOkEh8G1eO ndQ+jEQLQ/yo7BIZOepkreUDGs0p6QjUA4csMYB0t9BnphfH5tJYsdNer7nRltsYoB9foklW JS+sWQOuFH1p+c1sXkhSgxf0wDYPSza2bvsbEmuFFfgclnAs1J0rryaMkevU9yS+viDLUlip hKzAoaMFymiFxhc6qHFkC2aYVWllsp9Mctu7deVqbhOhZEeAC5rRNXtZcTSg9wxzNLAU/4V0 6FNjmzfkqBziBCIeWe4ley5CUL0BPhoxxz3UAkm/RIFQg6bxIuMkOb1SU41NSi+uCxsULIwi yiOTQVaTc48jZVO4zP0etq+gnT0YHXVCP2zECyvjsVwMYcIhHaFggsXAo/7o9UkaBbslGhwC bbdlHMln3MBSFnc8CaUTZnvKJebSGN65QnH8VNqI2pH0LGofnSgKZwYKKgmDk8wz3l6XdLbI 8useA/9g5VUV7L2t4l3/BIsWOvir+8Cg3Egh7Ci0DaCgrMMW+HHraPCCYndBrfutrosctkiy eDXMZW8wSBPdFCXCe1MFAWliUJkVaafAwQwGXR+OCQL7RoEGnIKmL3ibqGmmlAU1+gukBrKs RWGgm+yi+BM50Zpwi3U1pH+2oylqqLAGWJgurVVzmJ5nsvm3h8Oh63f8EexeGYf9SFYYEV2/ 4eUf+hWEc+ijWUnMT4BCkdseBbUyBw+MgN8FetnEIwf3TBnqjV1TgC8f9V01r7vs38ilNq+J PzB+lSI0B8APDbfcVAmupkjuXU78zonvW0kjFDUxKD91GAj9yugGDQplbmRzdHJlYW0NCmVu ZG9iag0KMjEgMCBvYmoNCjw8DQovVHlwZSAvRm9udERlc2NyaXB0b3INCi9Bc2NlbnQgMA0K L0NhcEhlaWdodCAwDQovRGVzY2VudCAwDQovRmxhZ3MgNA0KL0ZvbnRCQm94IFsyMSAtMTk0 IDkzNCA2OTRdDQovRm9udE5hbWUgL0xGQ0NQTStNU1RUMzFjN2E5MDANCi9JdGFsaWNBbmds ZSAwDQovU3RlbVYgMA0KL0NoYXJTZXQgKC9HNTUvRzczL0c3Ni9HMzEvRzY0L0czMy9HNzgv RzZGL0cyQS9HMzQvRzQ4L0c3MC9HMzUvRzdCL0c2OCkNCi9Gb250RmlsZSAyMiAwIFINCj4+ DQplbmRvYmoNCjIyIDAgb2JqDQo8PA0KL0xlbmd0aCA5MjA5DQovTGVuZ3RoMSA0NjM4DQov TGVuZ3RoMiA0NTY5DQovTGVuZ3RoMyAwDQo+Pg0Kc3RyZWFtDQolIUZvbnRUeXBlMS0xLjA6 IExGQ0NQTStNU1RUMzFjN2E5MDAgMQoxMyBkaWN0IGJlZ2luCi9Gb250TmFtZSAvTEZDQ1BN K01TVFQzMWM3YTkwMCBkZWYgCi9Gb250VHlwZSAxIGRlZgovRm9udEJCb3ggezQ0IC0zOTcg MTkxMyAxNDIxfSByZWFkb25seSBkZWYKL0ZvbnRNYXRyaXggWzAuMDAwNDg4MyAwIDAgMC4w MDA0ODgzIDAgMF0gcmVhZG9ubHkgZGVmCi9QYWludFR5cGUgMCBkZWYKL0ZvbnRJbmZvIDEy IGRpY3QgZHVwIGJlZ2luCi9CYXNlRm9udE5hbWUgKE1TVFQzMWM3YTkwMCkgZGVmCmVuZCBk ZWYKL0VuY29kaW5nIDI1NiBhcnJheQowIDEgMjU1IHsxIGluZGV4IGV4Y2ggLy5ub3RkZWYg cHV0fSBmb3IKZHVwIDAgL0cwMCBwdXQKZHVwIDEgL0cwMSBwdXQKZHVwIDIgL0cwMiBwdXQK ZHVwIDMgL0cwMyBwdXQKZHVwIDQgL0cwNCBwdXQKZHVwIDUgL0cwNSBwdXQKZHVwIDYgL0cw NiBwdXQKZHVwIDcgL0cwNyBwdXQKZHVwIDggL0cwOCBwdXQKZHVwIDkgL0cwOSBwdXQKZHVw IDEwIC9HMEEgcHV0CmR1cCAxMSAvRzBCIHB1dApkdXAgMTIgL0cwQyBwdXQKZHVwIDEzIC9H MEQgcHV0CmR1cCAxNCAvRzBFIHB1dApkdXAgMTUgL0cwRiBwdXQKZHVwIDE2IC9HMTAgcHV0 CmR1cCAxNyAvRzExIHB1dApkdXAgMTggL0cxMiBwdXQKZHVwIDE5IC9HMTMgcHV0CmR1cCAy MCAvRzE0IHB1dApkdXAgMjEgL0cxNSBwdXQKZHVwIDIyIC9HMTYgcHV0CmR1cCAyMyAvRzE3 IHB1dApkdXAgMjQgL0cxOCBwdXQKZHVwIDI1IC9HMTkgcHV0CmR1cCAyNiAvRzFBIHB1dApk dXAgMjcgL0cxQiBwdXQKZHVwIDI4IC9HMUMgcHV0CmR1cCAyOSAvRzFEIHB1dApkdXAgMzAg L0cxRSBwdXQKZHVwIDMxIC9HMUYgcHV0CmR1cCAzMiAvRzIwIHB1dApkdXAgMzMgL0cyMSBw dXQKZHVwIDM0IC9HMjIgcHV0CmR1cCAzNSAvRzIzIHB1dApkdXAgMzYgL0cyNCBwdXQKZHVw IDM3IC9HMjUgcHV0CmR1cCAzOCAvRzI2IHB1dApkdXAgMzkgL0cyNyBwdXQKZHVwIDQwIC9H MjggcHV0CmR1cCA0MSAvRzI5IHB1dApkdXAgNDIgL0cyQSBwdXQKZHVwIDQzIC9HMkIgcHV0 CmR1cCA0NCAvRzJDIHB1dApkdXAgNDUgL0cyRCBwdXQKZHVwIDQ2IC9HMkUgcHV0CmR1cCA0 NyAvRzJGIHB1dApkdXAgNDggL0czMCBwdXQKZHVwIDQ5IC9HMzEgcHV0CmR1cCA1MCAvRzMy IHB1dApkdXAgNTEgL0czMyBwdXQKZHVwIDUyIC9HMzQgcHV0CmR1cCA1MyAvRzM1IHB1dApk dXAgNTQgL0czNiBwdXQKZHVwIDU1IC9HMzcgcHV0CmR1cCA1NiAvRzM4IHB1dApkdXAgNTcg L0czOSBwdXQKZHVwIDU4IC9HM0EgcHV0CmR1cCA1OSAvRzNCIHB1dApkdXAgNjAgL0czQyBw dXQKZHVwIDYxIC9HM0QgcHV0CmR1cCA2MiAvRzNFIHB1dApkdXAgNjMgL0czRiBwdXQKZHVw IDY0IC9HNDAgcHV0CmR1cCA2NSAvRzQxIHB1dApkdXAgNjYgL0c0MiBwdXQKZHVwIDY3IC9H NDMgcHV0CmR1cCA2OCAvRzQ0IHB1dApkdXAgNjkgL0c0NSBwdXQKZHVwIDcwIC9HNDYgcHV0 CmR1cCA3MSAvRzQ3IHB1dApkdXAgNzIgL0c0OCBwdXQKZHVwIDczIC9HNDkgcHV0CmR1cCA3 NCAvRzRBIHB1dApkdXAgNzUgL0c0QiBwdXQKZHVwIDc2IC9HNEMgcHV0CmR1cCA3NyAvRzRE IHB1dApkdXAgNzggL0c0RSBwdXQKZHVwIDc5IC9HNEYgcHV0CmR1cCA4MCAvRzUwIHB1dApk dXAgODEgL0c1MSBwdXQKZHVwIDgyIC9HNTIgcHV0CmR1cCA4MyAvRzUzIHB1dApkdXAgODQg L0c1NCBwdXQKZHVwIDg1IC9HNTUgcHV0CmR1cCA4NiAvRzU2IHB1dApkdXAgODcgL0c1NyBw dXQKZHVwIDg4IC9HNTggcHV0CmR1cCA4OSAvRzU5IHB1dApkdXAgOTAgL0c1QSBwdXQKZHVw IDkxIC9HNUIgcHV0CmR1cCA5MiAvRzVDIHB1dApkdXAgOTMgL0c1RCBwdXQKZHVwIDk0IC9H NUUgcHV0CmR1cCA5NSAvRzVGIHB1dApkdXAgOTYgL0c2MCBwdXQKZHVwIDk3IC9HNjEgcHV0 CmR1cCA5OCAvRzYyIHB1dApkdXAgOTkgL0c2MyBwdXQKZHVwIDEwMCAvRzY0IHB1dApkdXAg MTAxIC9HNjUgcHV0CmR1cCAxMDIgL0c2NiBwdXQKZHVwIDEwMyAvRzY3IHB1dApkdXAgMTA0 IC9HNjggcHV0CmR1cCAxMDUgL0c2OSBwdXQKZHVwIDEwNiAvRzZBIHB1dApkdXAgMTA3IC9H NkIgcHV0CmR1cCAxMDggL0c2QyBwdXQKZHVwIDEwOSAvRzZEIHB1dApkdXAgMTEwIC9HNkUg cHV0CmR1cCAxMTEgL0c2RiBwdXQKZHVwIDExMiAvRzcwIHB1dApkdXAgMTEzIC9HNzEgcHV0 CmR1cCAxMTQgL0c3MiBwdXQKZHVwIDExNSAvRzczIHB1dApkdXAgMTE2IC9HNzQgcHV0CmR1 cCAxMTcgL0c3NSBwdXQKZHVwIDExOCAvRzc2IHB1dApkdXAgMTE5IC9HNzcgcHV0CmR1cCAx MjAgL0c3OCBwdXQKZHVwIDEyMSAvRzc5IHB1dApkdXAgMTIyIC9HN0EgcHV0CmR1cCAxMjMg L0c3QiBwdXQKZHVwIDEyNCAvRzdDIHB1dApkdXAgMTI1IC9HN0QgcHV0CmR1cCAxMjYgL0c3 RSBwdXQKZHVwIDEyNyAvRzdGIHB1dApkdXAgMTI4IC9HODAgcHV0CmR1cCAxMjkgL0c4MSBw dXQKZHVwIDEzMCAvRzgyIHB1dApkdXAgMTMxIC9HODMgcHV0CmR1cCAxMzIgL0c4NCBwdXQK ZHVwIDEzMyAvRzg1IHB1dApkdXAgMTM0IC9HODYgcHV0CmR1cCAxMzUgL0c4NyBwdXQKZHVw IDEzNiAvRzg4IHB1dApkdXAgMTM3IC9HODkgcHV0CmR1cCAxMzggL0c4QSBwdXQKZHVwIDEz OSAvRzhCIHB1dApkdXAgMTQwIC9HOEMgcHV0CmR1cCAxNDEgL0c4RCBwdXQKZHVwIDE0MiAv RzhFIHB1dApkdXAgMTQzIC9HOEYgcHV0CmR1cCAxNDQgL0c5MCBwdXQKZHVwIDE0NSAvRzkx IHB1dApkdXAgMTQ2IC9HOTIgcHV0CmR1cCAxNDcgL0c5MyBwdXQKZHVwIDE0OCAvRzk0IHB1 dApkdXAgMTQ5IC9HOTUgcHV0CmR1cCAxNTAgL0c5NiBwdXQKZHVwIDE1MSAvRzk3IHB1dApk dXAgMTUyIC9HOTggcHV0CmR1cCAxNTMgL0c5OSBwdXQKZHVwIDE1NCAvRzlBIHB1dApkdXAg MTU1IC9HOUIgcHV0CmR1cCAxNTYgL0c5QyBwdXQKZHVwIDE1NyAvRzlEIHB1dApkdXAgMTU4 IC9HOUUgcHV0CmR1cCAxNTkgL0c5RiBwdXQKZHVwIDE2MCAvR0EwIHB1dApkdXAgMTYxIC9H QTEgcHV0CmR1cCAxNjIgL0dBMiBwdXQKZHVwIDE2MyAvR0EzIHB1dApkdXAgMTY0IC9HQTQg cHV0CmR1cCAxNjUgL0dBNSBwdXQKZHVwIDE2NiAvR0E2IHB1dApkdXAgMTY3IC9HQTcgcHV0 CmR1cCAxNjggL0dBOCBwdXQKZHVwIDE2OSAvR0E5IHB1dApkdXAgMTcwIC9HQUEgcHV0CmR1 cCAxNzEgL0dBQiBwdXQKZHVwIDE3MiAvR0FDIHB1dApkdXAgMTczIC9HQUQgcHV0CmR1cCAx NzQgL0dBRSBwdXQKZHVwIDE3NSAvR0FGIHB1dApkdXAgMTc2IC9HQjAgcHV0CmR1cCAxNzcg L0dCMSBwdXQKZHVwIDE3OCAvR0IyIHB1dApkdXAgMTc5IC9HQjMgcHV0CmR1cCAxODAgL0dC NCBwdXQKZHVwIDE4MSAvR0I1IHB1dApkdXAgMTgyIC9HQjYgcHV0CmR1cCAxODMgL0dCNyBw dXQKZHVwIDE4NCAvR0I4IHB1dApkdXAgMTg1IC9HQjkgcHV0CmR1cCAxODYgL0dCQSBwdXQK ZHVwIDE4NyAvR0JCIHB1dApkdXAgMTg4IC9HQkMgcHV0CmR1cCAxODkgL0dCRCBwdXQKZHVw IDE5MCAvR0JFIHB1dApkdXAgMTkxIC9HQkYgcHV0CmR1cCAxOTIgL0dDMCBwdXQKZHVwIDE5 MyAvR0MxIHB1dApkdXAgMTk0IC9HQzIgcHV0CmR1cCAxOTUgL0dDMyBwdXQKZHVwIDE5NiAv R0M0IHB1dApkdXAgMTk3IC9HQzUgcHV0CmR1cCAxOTggL0dDNiBwdXQKZHVwIDE5OSAvR0M3 IHB1dApkdXAgMjAwIC9HQzggcHV0CmR1cCAyMDEgL0dDOSBwdXQKZHVwIDIwMiAvR0NBIHB1 dApkdXAgMjAzIC9HQ0IgcHV0CmR1cCAyMDQgL0dDQyBwdXQKZHVwIDIwNSAvR0NEIHB1dApk dXAgMjA2IC9HQ0UgcHV0CmR1cCAyMDcgL0dDRiBwdXQKZHVwIDIwOCAvR0QwIHB1dApkdXAg MjA5IC9HRDEgcHV0CmR1cCAyMTAgL0dEMiBwdXQKZHVwIDIxMSAvR0QzIHB1dApkdXAgMjEy IC9HRDQgcHV0CmR1cCAyMTMgL0dENSBwdXQKZHVwIDIxNCAvR0Q2IHB1dApkdXAgMjE1IC9H RDcgcHV0CmR1cCAyMTYgL0dEOCBwdXQKZHVwIDIxNyAvR0Q5IHB1dApkdXAgMjE4IC9HREEg cHV0CmR1cCAyMTkgL0dEQiBwdXQKZHVwIDIyMCAvR0RDIHB1dApkdXAgMjIxIC9HREQgcHV0 CmR1cCAyMjIgL0dERSBwdXQKZHVwIDIyMyAvR0RGIHB1dApkdXAgMjI0IC9HRTAgcHV0CmR1 cCAyMjUgL0dFMSBwdXQKZHVwIDIyNiAvR0UyIHB1dApkdXAgMjI3IC9HRTMgcHV0CmR1cCAy MjggL0dFNCBwdXQKZHVwIDIyOSAvR0U1IHB1dApkdXAgMjMwIC9HRTYgcHV0CmR1cCAyMzEg L0dFNyBwdXQKZHVwIDIzMiAvR0U4IHB1dApkdXAgMjMzIC9HRTkgcHV0CmR1cCAyMzQgL0dF QSBwdXQKZHVwIDIzNSAvR0VCIHB1dApkdXAgMjM2IC9HRUMgcHV0CmR1cCAyMzcgL0dFRCBw dXQKZHVwIDIzOCAvR0VFIHB1dApkdXAgMjM5IC9HRUYgcHV0CmR1cCAyNDAgL0dGMCBwdXQK ZHVwIDI0MSAvR0YxIHB1dApkdXAgMjQyIC9HRjIgcHV0CmR1cCAyNDMgL0dGMyBwdXQKZHVw IDI0NCAvR0Y0IHB1dApkdXAgMjQ1IC9HRjUgcHV0CmR1cCAyNDYgL0dGNiBwdXQKZHVwIDI0 NyAvR0Y3IHB1dApkdXAgMjQ4IC9HRjggcHV0CmR1cCAyNDkgL0dGOSBwdXQKZHVwIDI1MCAv R0ZBIHB1dApkdXAgMjUxIC9HRkIgcHV0CmR1cCAyNTIgL0dGQyBwdXQKZHVwIDI1MyAvR0ZE IHB1dApkdXAgMjU0IC9HRkUgcHV0CmR1cCAyNTUgL0dGRiBwdXQKcmVhZG9ubHkgZGVmCmN1 cnJlbnRkaWN0IGVuZApjdXJyZW50ZmlsZSBlZXhlYwpwUmKtrINIBbRJeMIVVwS8/ao129y7 EbsCTkhzv3OEL5MUqMiWQORDflRvbnjtfmyc2lMe3PvFh9ZcAwezslTOWzkA/NwUkR3Q3eyn Fk1cHOuNWcm+Yx1tglAXYJjZjkE81y1Y9xmZgGvrr9wDFgB/RevRWFP8X7Q7Pz9+Mfn09GWA iW9j139e55UErM7SSLVSm3ba+zQvg9G5fSsvreMvdDX7if4fM96q0/4yQp/i4nNQaIzg2gDH 5xDyNOFSl8ixtNNClHgRUfSzSb2+uojEt67vhAS5BW0aTxEXd+I0ZGwZg/U6waVGvFTC8o/F D2/OwsE24DUsifoHTh0KEiqvT0TfQazdaiM3BsZQ2SRcYclnW4qUn9IhCbt0B76CAIQ4e1LG 6LtjeLB2GNn/e1YJGrZimPP/6y7jPcGsKKlJYnjDy0BQv8JVZEtqp1WlNn295H/A5mWPdCfg IwklVQZoxAH0UXsi7wRoBMLnfeV3uEEKU369lmDP+7fkF22qc694Cj2JiJ5jFThBbeJx6GFn fsQbTmFsdPNQ9X167qUAGJC1FxcjiQ/H416AITwP3ucthSliuLM0IET7uSm2uFq7z0iezyL1 wkMd7snBM5e13eJ26w2LErJWkT5S0NISM5XnmMFsgv3BGDPJMVkB3D8Hgq9o+N7psKhhOawa Z6/aU40zvCZDJWZYDn2numzWYW5nQDcnTRUNLoC1O2HvZkfa9sjkVinRBCvPDy8t7HpwGkh9 NPI5c2xMdmJScKl6xiCmbKtAIWu6xv14/gmwz+XI+4/1OLEJPh+ZkEuk+u02ES0CGZloNSXQ IWX8GSdOwASk7t2OPGjfMRJAdcqnzzgFZQJYhkL6WY+S9tisIMH5y+RYdt28Dd3sqKUgD/Bc W4Fu8T/ThAjF9sv4OGM/yp0PNy6Ks2iHMPyUr1k+MqScyiNN/r2pFrlaP0Fkxli8Eb8tuNcT Su44Anv1A1vD9VjiP5gDJPpBlbFwp55dtdQDsLQIdBKKpnaCoF/lIhhH2xuN2cClvhn6EBuI 6QBhas3pC0tyxPLd8guxvDZJKtmuLbyYlme+UlOtyUbOztX9auJNMuLz3KXQ/vucCbeiyv5E NKlX2sRyDMEo3Km/71cpXjyFw9b8K7KlonJwJfZ4WIgzDiwjE0LmZ7nYb+P//1ZuVdyTCWKM Nr+mf3HJmGBLKShiYfMF0smV4B6mtouwLRs3OmxH0dxV9UkzO3+MoZCaSXFS3gn7ApIEZqs9 T2TgqYl2qKiDxZYZRNo9Xy0Cc1lq7GLSZ5HyMbni6BIT4/wnEjS3kCT+BCGTTwYG8H/xbTFX PewLeRP57/YQH1ovtCyKADxS6UtuCQo/Y4fz7RavEIGF0iRfmX8MKo+alcIiP58NQesBoQ2L n9ZQWWQdyd7FH5/nCDdR9zIt1TadLesAInRHVq6Th67sCqVfKme2JdG18i0ICVsDG/FSz19o tLuh8fRQO5m9TH3BZINFll3YtSRrBLrli6yer3pMOKUJN40oRTNj4hjySrnxWT5/DLzrxXx5 j8BTicCNtEvcqclzaWE7a/qpfybkrOokueVUgBOyEVpJ0n7OIZSHI6Qy1etjBJS9AGlXazTl GW/LPJkfeepJT7Sm0krPqZXI0ZFURUVheFVGwfPntRQu1Bj5LR60d/KIoFKzlN83vq8EOyeG H8JfuHfdAS1oN111EGP15yV57bVmtQ8u47y/nCoQIR5XhcBlocEXVywllrsEfFTDUUiqY21h y5+1+ai449LICPJ61jHKu7vDA69Sjqt5JA1pr8RsjN+yJxnXGF1XG75VSPE2l/rjAufkJl9u 7sXqIKqMmU07QoWIj7WUQX8zS66V4YM5T4ZIm115T3Jz1to8VCOBVKWDNs6jPZAU+srPNzH0 yACCxZg5/c+eu0pyPgo7G1ckeZshW1XbpWsmu8qjKIs9QtdKmBI7i2DTYY0ioKi3NHSx1Vtr wQSLJYm6wvvTT6Oaffh1G2qMAFL5fEDHM35MEKkNvOFcJSY2BJjtotRTd6c8z5K/gb8kjTC7 Jw1p4mLB+bpWuEmyFSG/szNqlguVN9lJjwiQCHuJe8ymqlZhxYj6Pe9qI4ZY3g1KolIanDSt 3jVsM+4FbSq3tqcgbsVahwV9Mw+tqgmD5p9I5pak0ehg6WYZj2Vai2phhPmopgITZrckByv7 vyG6MUN0B/2B9SjrtR2+6hMwm4VcEK0AbCggBT3BEyF90eNpsj4htWPd9w6d7ynpzPC8cXh0 roNHQMEXtdYJEgskGJfOZb8XcWR9u34K8V2GVUtIIsOBW1b6kJhjeucb2M36hhxqNYZAGPyk MVjWrFGUm28QqbWwOJ0eD2azL6Mal7iVQDxuVxS5yG4W42GkeEbMD0O3pTnflwJu/PLNPPXK yhdlKHOJzltYoeOTJfMeId3NhjmzhCD33AtDcQBgrlPTfjYRIAQkOEDqbbrrtX3+OfKoLjnq 6HIOxlOndzcvKA8/BgucvYhyqcKESn2uE0KyiODREbaeDXICaooVnw9Zn1+3dnsihtHHCyTC 2rmdo25Rl9j3LaWmymf06C3xlK0BXBwFx5NdFpVbbbJHJGjoyEB2afCout7j/F9H3kwBQhc9 WD80ECjFw2bIKK5hlEktEs2yOz+ZrYVHxHpJnEY0/XsJfHBAX0WAI2eQm/RhgWmqheiAtJJI Qn6E4qqCn2ncpLHP5Ar1DHaxP6LzrQgRmeUP4AiTbIolvcPomMx6JStj/Fi4jx0A4uQWB+wV gX5c6rsrnYvK9des7n8Zd8UW7/GC9/msOdoO5EUgayTTNZymVkX1VnbTR/G79dj3mHQCJ4v3 Gm/cNqP4hB3NceF3EBbEADNI+JHwy+ahBkw6uWt/1iENP/2Pfr5JvnDmQB2Fqmjtzn++dQ6R XgmVv8JzjH8m70ZLInK48wdiyQf54cJ6B5o+ZapKKACkmFGLfWoocOj4+UnBQUxBtI7gh+PJ wMJG8mWf8xn9su9JfKwPzkQYL0ZhuRuLaNPbg32CG0XxDIB5ZKsz2d7IBuGfiQgEbsTTaYn9 5QCoWaOemfjOmjn/+SZjHKkyoU2BJyn9uWIu5B3h2xO2NP88m5mSy8ZxFgD1C8fHUqw8GbF2 M0MkKqlmfiO5NMDpZorJWLvDPdO1mugKRs91PvIKgRv8toq4w+b2wA77kSoOUMZyslr9KsR7 Gji/LYMH2Tcy5bwIaITuAkTPYG78lYjVlOLGyvC+093fmyoM2w+tnh53yGWDnLjr5cl/UbRQ uMk+we90OPxWDht/6CjVRC2P/5OuujDyjw8UT2u6cRo6Rgf8d61WYmSxKm927GrNLVw/gZjN Rc/JYD8WY6wk5CMcVqvY9n/lqPkqzxFFKu/XuBFNe0bWmktThspcKbFb2G7DX5m93esrXntO 6fMpIRXbHLe48C4BTgAK5Psr8hDjFGEPb4RCBvRgpVvdQXvR61ZicbeGEUzWQ4obR13qb04e V0ysoAnZFNL5BtBXiFHaTBLLkKj6LmgaD151/Mu3JoZQ93zCCC0EWVcKqRPCbXePG3saaFrM AUU3vRpu+Rfnw0cEqx0AzdpVZQz/hnrD6Wj9xWcErnAX8rdHVM1JY9buRWFJY/Wfhx/2jWGU 8iM5lyEVtkuy6tNgN14kka6hKLZcEeLhVH/cTX0jDVZCDUFk0kZdLOP/4WVcLRuCGCk8raII tFVM/6iJGL6km3WUJkeXqNKSB3dN/xz1SkL+Q0a/kz9eQ7lpg5s2d84vTHKLCDcUQiljhRVE yeqP+ncSF1iJed3xAUqAmUvy/3aWXOo1lYak9x9HPY8+xQMkebXysA528u6GyDJ01kC1FZaa yNukuKoXFxmf/qBxTRp3SD8G0EpkJaOpT9c2ObFvpQlZZa4Hp1qSW74FW8G7IocmJRYJ6NGX RaE2ljZo5tFSQer58Lq/WI7SE2Ps6uDkG4pRlTx3liHiuHmu4H+Km//Q2vb+VJxOjBzoExq4 OnLFgEf8WBqqFLxamCTsiI2Eqtg+f/zJ39jG3mAjB2CkIbBaAyenJe0DqD2fg+dWPJzOyGA0 B3HcboEeZDfDo+L5OzBx533gE4bIGHAZURpH82QULQirN8HRpM7qzMF3tFeicTgP1j5+KIAw i86DEuxHNLTNvu5yG4tIQ163HJ4nLHDFRcOAouI/Ib8uH3smCIKcGo93w3h9vq3mnTKpe9du iao3yjP6xsq8UKYF8C+gmj36prhh8s9q2AVVYayU3aJwqYD1c5hlbrd0B2aXvcXQBnapiUsX N5UvMlkWqW5vnn8Ww32fGXs+/G1Fs4AvWuuL2oPZr8gUlO0GFCg/Pts6RyHUD2cLyqoYrLql fRBhE7KmQ16+Ku3sZMHe5XGo50VUezb38vNf8nqD/9HsmOuVVUts1jC1Arym08nPzA6TUg1h XaztEvHJPLq/XEXRE3ZSRxmInQqhfNOIHcLkGRTp1nd8Y9Mq43CWmcagcxqCqhBieURknk7O EyflgsxilN+DRBWkeXceKudUpW43AGhZpdn/PLsGGeskaM0GcO3/RLAn+tmvQWQTU8pQOEX/ QcWPe0avqHYhBfgO2aN4iOzW1VXLcqygs82nJPHdIhZ0b68ic3q2+9Yhn/EKcg699UhlwxKP q3f+k22KlkROofLBh6EyoVNJ76euT797TQTVUtVOeyhQMe+wMDg8OM4BbxxBuLilfBR/2bXq zaHBquQ5UlfZCTDGZHgOgidBObNsLTuR3p6H4j79NKLc0rViKtMFPMwG9CObOaZoclIO/qHj bNLBPzSbQi8jzzR4exH4uBgEDWYR4ppAGD+tvtB1Ee1ZpKlwZtS2lR+BrvU/AzjreI8a/NLn 1ajC9axfbQ9ienmgRG9ROOpuTG/xoetIdD2QxNg48N7WEGTqM6LmvPoR8OqnjM9nRBzNrdEp BxQjOvYhy3HN2e7vMLJfT8UgNjj62WhAUL0Py37xW0jmwPwOyQKpCGcxyRTtfg9M7o+YSXla V+jP7Ffi/J/xIPowLA/+8np2Q2n7/XbcNBtTbcRsI0e7VrMXn6lrurutB+lU6GAiqh0SYbLk FvJL//h0hYBbE25M9CxAjLrX1/5erKu2Jxn2xWlHdFBNM2o0y1p7QGpStwpHf+/WViyA3RTM F/m4LFERaPTx6xcMNmoKcZw0HzbxckMDYsjPErmBRCAvh2a415iq0t+qNTptAL/dp+euQ/wv 7NWwyDkFjHSyVEJyiNcg7WYSYpBoLGw/PpRpnSihKFf3/fM5G+LGXB9WkAlgavGuaO9tEOSd /31ap1SyMbfuh3GDv6TjJNptPT2UObXSTbwdCL0RtGGdZRZTaM5UmNJWvjPdheqfG4+xTs5+ AKdE0HATYBRLPnxUy9gsSX5nMGlf/cmKLsv+acC58z0nGcaizw61jGugpBCVUyigCEMfkZBV dO3JY/McyVOBXp3w201k/LYFx/J3ayfj3pRlRRu4oBnAKSRNnPZFvVZEyQ0lsfliQwX5HzpV xLhPoZnsA707dBtnC0Vq6R4rnn96L+BzY6pPvwMEW2WGPEXiIexm167w8Td3yzTRfB6TDNui ozSLkjBKzReA+MwuXYZTZosV89+ExwgDRZq4IE2yf7qSVBprVYrkFmKhUL8m6R1GwIqXKiwo yyxKl/q8dVeo4oCKZx1yBc8OIEDcPBSdy6hty5nOHMjBbHVj/qSNJSOqHxTkngi7SzzSb+7u oEyefV7U5psf2qqTMqebipqqCDbCzegHDyMhdZFlFtEMkVBtF/fC3hTGKKT/SneQhJBs2Jn0 Gnme8YU6tkadStQY1qdYrRg+oBbQ+5NFiDFJ30A0xxpSnf3CTy5hYNgNJBfpNqjtTFWL3+DE xDq1xCvJ0DweJixH6o/dPSfc1Vk0F8PRU/GmVSBx1ZeuPG6FTPZlBlXxbrMt6TTlJTT+CqUX sSi+PKfO8NYAQsAv5o2ZHZs0Yqjg+c5noo6WMCGeMEXUKGsi87IJ6rw/p4PO9+wa1vm+ZK/S eOZ2lgsKq/Pftlt94ETQzeE8cdp6U81q5Hu/IDe//mNomyO8y+St8jIhm9CcR7frFUn/aCcX 8BqktArrAkVt4jUNCmVuZHN0cmVhbQ0KZW5kb2JqDQoyMyAwIG9iag0KPDwNCi9UeXBlIC9G b250RGVzY3JpcHRvcg0KL0FzY2VudCAwDQovQ2FwSGVpZ2h0IDANCi9EZXNjZW50IDANCi9G bGFncyA0DQovRm9udEJCb3ggWzAgLTIwNiA1NTUgNzA2XQ0KL0ZvbnROYW1lIC9MRkNDUE8r TVNUVDMxYzdiNDAwDQovSXRhbGljQW5nbGUgMA0KL1N0ZW1WIDANCi9DaGFyU2V0ICgvRzY5 L0czRC9HM0UvRzdCL0c3QykNCi9Gb250RmlsZSAyNCAwIFINCj4+DQplbmRvYmoNCjI0IDAg b2JqDQo8PA0KL0xlbmd0aCA1MDY5DQovTGVuZ3RoMSAyNTI5DQovTGVuZ3RoMiAyNTM4DQov TGVuZ3RoMyAwDQo+Pg0Kc3RyZWFtDQolIUZvbnRUeXBlMS0xLjA6IExGQ0NQTytNU1RUMzFj N2I0MDAgMQoxMyBkaWN0IGJlZ2luCi9Gb250TmFtZSAvTEZDQ1BPK01TVFQzMWM3YjQwMCBk ZWYgCi9Gb250VHlwZSAxIGRlZgovRm9udEJCb3ggezAgLTQyMiAxMTM3IDE0NDZ9IHJlYWRv bmx5IGRlZgovRm9udE1hdHJpeCBbMC4wMDA0ODgzIDAgMCAwLjAwMDQ4ODMgMCAwXSByZWFk b25seSBkZWYKL1BhaW50VHlwZSAwIGRlZgovRm9udEluZm8gMTIgZGljdCBkdXAgYmVnaW4K L0Jhc2VGb250TmFtZSAoTVNUVDMxYzdiNDAwKSBkZWYKZW5kIGRlZgovRW5jb2RpbmcgMjU2 IGFycmF5CjAgMSAyNTUgezEgaW5kZXggZXhjaCAvLm5vdGRlZiBwdXR9IGZvcgpkdXAgMCAv RzAwIHB1dApkdXAgMSAvRzAxIHB1dApkdXAgMiAvRzAyIHB1dApkdXAgMyAvRzAzIHB1dApk dXAgNCAvRzA0IHB1dApkdXAgNSAvRzA1IHB1dApkdXAgNiAvRzA2IHB1dApkdXAgNyAvRzA3 IHB1dApkdXAgOCAvRzA4IHB1dApkdXAgOSAvRzA5IHB1dApkdXAgMTAgL0cwQSBwdXQKZHVw IDExIC9HMEIgcHV0CmR1cCAxMiAvRzBDIHB1dApkdXAgMTMgL0cwRCBwdXQKZHVwIDE0IC9H MEUgcHV0CmR1cCAxNSAvRzBGIHB1dApkdXAgMTYgL0cxMCBwdXQKZHVwIDE3IC9HMTEgcHV0 CmR1cCAxOCAvRzEyIHB1dApkdXAgMTkgL0cxMyBwdXQKZHVwIDIwIC9HMTQgcHV0CmR1cCAy MSAvRzE1IHB1dApkdXAgMjIgL0cxNiBwdXQKZHVwIDIzIC9HMTcgcHV0CmR1cCAyNCAvRzE4 IHB1dApkdXAgMjUgL0cxOSBwdXQKZHVwIDI2IC9HMUEgcHV0CmR1cCAyNyAvRzFCIHB1dApk dXAgMjggL0cxQyBwdXQKZHVwIDI5IC9HMUQgcHV0CmR1cCAzMCAvRzFFIHB1dApkdXAgMzEg L0cxRiBwdXQKZHVwIDMyIC9HMjAgcHV0CmR1cCAzMyAvRzIxIHB1dApkdXAgMzQgL0cyMiBw dXQKZHVwIDM1IC9HMjMgcHV0CmR1cCAzNiAvRzI0IHB1dApkdXAgMzcgL0cyNSBwdXQKZHVw IDM4IC9HMjYgcHV0CmR1cCAzOSAvRzI3IHB1dApkdXAgNDAgL0cyOCBwdXQKZHVwIDQxIC9H MjkgcHV0CmR1cCA0MiAvRzJBIHB1dApkdXAgNDMgL0cyQiBwdXQKZHVwIDQ0IC9HMkMgcHV0 CmR1cCA0NSAvRzJEIHB1dApkdXAgNDYgL0cyRSBwdXQKZHVwIDQ3IC9HMkYgcHV0CmR1cCA0 OCAvRzMwIHB1dApkdXAgNDkgL0czMSBwdXQKZHVwIDUwIC9HMzIgcHV0CmR1cCA1MSAvRzMz IHB1dApkdXAgNTIgL0czNCBwdXQKZHVwIDUzIC9HMzUgcHV0CmR1cCA1NCAvRzM2IHB1dApk dXAgNTUgL0czNyBwdXQKZHVwIDU2IC9HMzggcHV0CmR1cCA1NyAvRzM5IHB1dApkdXAgNTgg L0czQSBwdXQKZHVwIDU5IC9HM0IgcHV0CmR1cCA2MCAvRzNDIHB1dApkdXAgNjEgL0czRCBw dXQKZHVwIDYyIC9HM0UgcHV0CmR1cCA2MyAvRzNGIHB1dApkdXAgNjQgL0c0MCBwdXQKZHVw IDY1IC9HNDEgcHV0CmR1cCA2NiAvRzQyIHB1dApkdXAgNjcgL0c0MyBwdXQKZHVwIDY4IC9H NDQgcHV0CmR1cCA2OSAvRzQ1IHB1dApkdXAgNzAgL0c0NiBwdXQKZHVwIDcxIC9HNDcgcHV0 CmR1cCA3MiAvRzQ4IHB1dApkdXAgNzMgL0c0OSBwdXQKZHVwIDc0IC9HNEEgcHV0CmR1cCA3 NSAvRzRCIHB1dApkdXAgNzYgL0c0QyBwdXQKZHVwIDc3IC9HNEQgcHV0CmR1cCA3OCAvRzRF IHB1dApkdXAgNzkgL0c0RiBwdXQKZHVwIDgwIC9HNTAgcHV0CmR1cCA4MSAvRzUxIHB1dApk dXAgODIgL0c1MiBwdXQKZHVwIDgzIC9HNTMgcHV0CmR1cCA4NCAvRzU0IHB1dApkdXAgODUg L0c1NSBwdXQKZHVwIDg2IC9HNTYgcHV0CmR1cCA4NyAvRzU3IHB1dApkdXAgODggL0c1OCBw dXQKZHVwIDg5IC9HNTkgcHV0CmR1cCA5MCAvRzVBIHB1dApkdXAgOTEgL0c1QiBwdXQKZHVw IDkyIC9HNUMgcHV0CmR1cCA5MyAvRzVEIHB1dApkdXAgOTQgL0c1RSBwdXQKZHVwIDk1IC9H NUYgcHV0CmR1cCA5NiAvRzYwIHB1dApkdXAgOTcgL0c2MSBwdXQKZHVwIDk4IC9HNjIgcHV0 CmR1cCA5OSAvRzYzIHB1dApkdXAgMTAwIC9HNjQgcHV0CmR1cCAxMDEgL0c2NSBwdXQKZHVw IDEwMiAvRzY2IHB1dApkdXAgMTAzIC9HNjcgcHV0CmR1cCAxMDQgL0c2OCBwdXQKZHVwIDEw NSAvRzY5IHB1dApkdXAgMTA2IC9HNkEgcHV0CmR1cCAxMDcgL0c2QiBwdXQKZHVwIDEwOCAv RzZDIHB1dApkdXAgMTA5IC9HNkQgcHV0CmR1cCAxMTAgL0c2RSBwdXQKZHVwIDExMSAvRzZG IHB1dApkdXAgMTEyIC9HNzAgcHV0CmR1cCAxMTMgL0c3MSBwdXQKZHVwIDExNCAvRzcyIHB1 dApkdXAgMTE1IC9HNzMgcHV0CmR1cCAxMTYgL0c3NCBwdXQKZHVwIDExNyAvRzc1IHB1dApk dXAgMTE4IC9HNzYgcHV0CmR1cCAxMTkgL0c3NyBwdXQKZHVwIDEyMCAvRzc4IHB1dApkdXAg MTIxIC9HNzkgcHV0CmR1cCAxMjIgL0c3QSBwdXQKZHVwIDEyMyAvRzdCIHB1dApkdXAgMTI0 IC9HN0MgcHV0CmR1cCAxMjUgL0c3RCBwdXQKZHVwIDEyNiAvRzdFIHB1dApkdXAgMTI3IC9H N0YgcHV0CmR1cCAxMjggL0c4MCBwdXQKZHVwIDEyOSAvRzgxIHB1dApkdXAgMTMwIC9HODIg cHV0CmR1cCAxMzEgL0c4MyBwdXQKcmVhZG9ubHkgZGVmCmN1cnJlbnRkaWN0IGVuZApjdXJy ZW50ZmlsZSBlZXhlYwqhzUOdz/pW2w7O5y4PBfhf6SKVZ3PhdrEgNayCYmhCNeLnv5F0QvmG FBcbZtD994gXgTX1Iph0fc/iOyph+Xmb2qRX8fSB5oazkHiZOagVOXHc+J/wS6yyrQHFsxHz TTzDyPl7/JbY++ilfrA0kPohpbaM3aBzDInCJezmh7L2hFvvVWK4OBDc7VnP73cvNP/t+DLF f+8Zt6GyhI+tEyVvIHyZQAFPhVTQBru2FwpztRQx10gsm98tys7pLwuubumgsTkypauoD+jL UFYCTEKGywMxwBu1wj7Xnk4QXMAY0mFKQL2XUvKb+ePMZLDB+F8+J/qmbycHSB5KeG8Liciy 8ldARHZyC/lYiLtpX0qFGCRdAlq93u7tiG8ecqNyL3HpZN/htl/iPcEU7V9HGMrPV1k+g+56 OpcVacYOtk5ofpNMXpdEQGWsl0d5eWz/9V0NlMxTm4brMznbvj4hEb8DBLEoP22oOMidBPMv 8xViONxSEgbidDOzgR6TPv4HjmzRUr5EaWX4OMGocK8dS5I7eBRTnm0pfb0sRi9sMESxCYeU Wvwyj+b0Dx4GOP8B9zHdxXOvPugt3AvGLvbLP/ZmoOs4cnbGnz5x5aeZ+J7uTugrEFKLiFXO u5k2KYVDLaCzlVI+/rbhGP0dRkNcRcG3sJ9c09A0MfoTszBA3GOtnvk6Lo2NePcK/+6dWv39 06+PtIUTWDTlPyjASYqLcL+t1pE8JhqQVNxWlTwouqY0ttQTy8gUrwNNiIboIRc7Evt7Vfxo 4z9cdSjno5Dbqc+aky2uDUY6JTyWe9DGswLjXa9l6Lxqei1ZFw5y9fJFhOYgNMTalh4zxPFT vuKj7wULGRWGZLY9gbHS/et3HvmHHdCOIpSB8xmqUPBXW1hpicRrgyYx9GGqBHimDUu6VeSx pGph7t0RHEQqfPSLYnvfvXU+3GLUtTQkmw3IRXnrJ0cYowmPvVBQ8Q8b1sZomfsupr+1bq+a Cy3r70Hi6Q4kxzg5tVmdSN0jebraGv1RBjfatzzfoLl3e+ykCnYNjBAWu37RJMBnKufov8a9 VV8t8dFwQ4EOWE1HOhqIjd9tGuo57o82qZ8hFpZCkWdzCeu6hYvmna8FMuzHVIUKHKRMpIur oAeKCsG4rvXjKTl5RGUgHXzaDaZYAG5q0A+o530miQyQJWEJC1SpVbSatOco+JBRP/AempaO OGPeDMjYhUyFywU5NMvhH42uPlAlOctXMWe4KxqczGWH+aGgUv0F4bv/bf0EETbWzzQn2eah VAyfzWa9seBQq6Fuun/6FeHMyK44VQ6oJNd/haUVpYmD5Rb47pBHOBBUNYoaCY9qUXn7mrO3 SnvnJl8nm7cEcEp0XxV/BrZpa+8bxaRN7PqgYnKavcBrNa9Xoz6ok3nML09oMm41BCxh3rZ2 de2eGaCVE8gscsnGcX/Tq+45Gf3Hkye7/7Y9MAjWgOvnvjoSgFLaESYRaPUWZCQaoxuAj5If zFKi7xJ2qmz8ewH8zjtoG5KeXRfEHrZ6BDm+ZtULli2uWsSJX/kAUzUD9W889PUI3QUMeJPS taXlEytGQX2Kb3QTXIr6V1ldlK7kt/bEjWRf++1dS09kvTcujjgXFltIXM9OSm8mkM1FfQtz /gl/MQTgjV/Lkl9xhyMFxXaMH47QFBgTJ2h60++j0OsemhmJqRhURoxQgf9z5QlPcQ5k5zHH fK4ykmqbU++w1MXx3sh35DCKfdXwS1kqfTcRw/6VQFsomesFHZZXfVMJiSlYQhI3FOKMjTG/ nQfKWAr7fB8QmMSxp87wLIC6R4lk3p/Jj96lZTb8IPRGrAq5yH7qX/UTy0D4pHbHthXB0qxN f+PBHFlwH+whFv1yVko69kXMEy6SQpzua0POmZKjmpi+IqGMI0aWQKUUd9i7Y0yBdiKUfbta fhrjZBvCvt6JRxxsnRHy0aMXmbDjgOKijRdkqX5SOYsUY8lNSfHQY531XcYhi5Omv/3YXzDN kGH3VCWojY4iG8RvVPkhiLMP29IToWOfrbqr2LRK0KzbWiW1KJnHNNkfBHoG5epYAbWwYNiA Z/QDsEugCIvYwLXitDON8cyH0KTLFV0IuDqGojoF37VJ1qT0rxV0qeZPFBzY8VpcWl9RnB50 xqi7YeLXuMs/3qC98iYIG2Cwjaw8YUdHti3KW+azbJX4Or3B/9QPJdpkUPAZJ64uVm8ro6lY WayBHJFhwwYIbMJ7Txm8TGnYh+im5CCvNjD9zNpRwEUAe9OkiUoQ1V4cI1UMIiQXhh9dgiVU U5c2loah1Ze81gOMazoeEdL1kviLvMkQfzWJ2m4Ol2O0Yjoxj0nqTRrVp2VjOS40jqArzNld Y66ks0cZg2XBzF6DrX7kaIY4jlfgh2UDu49DS1DOimvqYLjv+VybDVjv+84YR/OgVzWCsS5Y H8/iEw9Rgw+iecFk/eOHF3yOixfSJ7/o9M1aemBXDaldlYN9ICAfFM0I6AjiFrrPIpQvW5pi zd0BUKBDyi48PBTg/yMfXlLdxBUIlQpCBOgkv6hvnwnccDD3ZI7foP/2NrHQ+3APZwmkG24F QFLxKf9TbbTW6qZgpTmSDurvx/qREtbFnQsO7nKXGF4lQM3Tt0eyPyKrWs2oRKYpn7nX3tUo 7JpnwqSjTXAxl8pBvg177A9u79gxakgNRJKLDGjRdm0skHqSHIHJ2OW3ZENQR39+8Lv8tdn8 dPOXtJGjRpokN3rK3CYEiPsqgFDyNB5HCJvs8/JXWcM30Hi+q2JhzrYEKANLQvqxm3BevXrC jDrOBRDplduJDnDeHo1qKkRmzPTc7us5SzgDpdLDkTlbx9cSCsP1305lL/a5hwuJq3hnWx/7 fJFFFCMg5ZMoYbKRz+MPc6Ei9jcyoJsMVIx27v1GoXEUBUbWRJ5ya7eZrk/5fWZw9ruRWdMG I1fV5Dpt/p1Rtvu1rvXR9SnV7y1jpy+yhGJMF2XSMQR7B8uaOut8GDQsUJfcYntBum/2mzum oFp9W+oyzmJQTtQ58M99MCfnYWAS5jHPYG3Q6mK6a+/ft3Xto2QvDOSl/xUwJbCBkkOPKjt1 rw+H3stHykViYm53pcWLOeai3Vz/10ixlOkBsOs3jPSGmV1WmZrkzZPmHFwcxLXo91Lr8ck6 LqjvW8qisKe/yOlCFhnm8UtcX9DEGBG354XhLUHn1AG7chcO9dWvaUvCtAozk+A5tTBKFC0E OOJLSl3OKSddPjh890eXbsS0QzaSO1uwx3Qusa4+35mseeCrxwQoW4OZH5cO1VRNn22tt13W Cu9n/C9F032R/jKuxvVUL3chJshuGK7YAAT1gAx1x044Fnx/zmjtKObPt8IaLpQjCm+vjoSU p7agri8FlOrDg0TJxEgNCmVuZHN0cmVhbQ0KZW5kb2JqDQoyNSAwIG9iag0KPDwNCi9UeXBl IC9Gb250RGVzY3JpcHRvcg0KL0FzY2VudCAwDQovQ2FwSGVpZ2h0IDANCi9EZXNjZW50IDAN Ci9GbGFncyA0DQovRm9udEJCb3ggWzAgLTIwIDUyNSA2NzRdDQovRm9udE5hbWUgL0xGQ0RB QStNU1RUMzFjN2JmMDANCi9JdGFsaWNBbmdsZSAwDQovU3RlbVYgMA0KL0NoYXJTZXQgKC9H MzkvRzNCL0czMy9HMzUvRzM3KQ0KL0ZvbnRGaWxlIDI2IDAgUg0KPj4NCmVuZG9iag0KMjYg MCBvYmoNCjw8DQovTGVuZ3RoIDUxMzQNCi9MZW5ndGgxIDI1MjgNCi9MZW5ndGgyIDI2MDQN Ci9MZW5ndGgzIDANCj4+DQpzdHJlYW0NCiUhRm9udFR5cGUxLTEuMDogTEZDREFBK01TVFQz MWM3YmYwMCAxCjEzIGRpY3QgYmVnaW4KL0ZvbnROYW1lIC9MRkNEQUErTVNUVDMxYzdiZjAw IGRlZiAKL0ZvbnRUeXBlIDEgZGVmCi9Gb250QkJveCB7MCAtNDEgMTA3NiAxMzgwfSByZWFk b25seSBkZWYKL0ZvbnRNYXRyaXggWzAuMDAwNDg4MyAwIDAgMC4wMDA0ODgzIDAgMF0gcmVh ZG9ubHkgZGVmCi9QYWludFR5cGUgMCBkZWYKL0ZvbnRJbmZvIDEyIGRpY3QgZHVwIGJlZ2lu Ci9CYXNlRm9udE5hbWUgKE1TVFQzMWM3YmYwMCkgZGVmCmVuZCBkZWYKL0VuY29kaW5nIDI1 NiBhcnJheQowIDEgMjU1IHsxIGluZGV4IGV4Y2ggLy5ub3RkZWYgcHV0fSBmb3IKZHVwIDAg L0cwMCBwdXQKZHVwIDEgL0cwMSBwdXQKZHVwIDIgL0cwMiBwdXQKZHVwIDMgL0cwMyBwdXQK ZHVwIDQgL0cwNCBwdXQKZHVwIDUgL0cwNSBwdXQKZHVwIDYgL0cwNiBwdXQKZHVwIDcgL0cw NyBwdXQKZHVwIDggL0cwOCBwdXQKZHVwIDkgL0cwOSBwdXQKZHVwIDEwIC9HMEEgcHV0CmR1 cCAxMSAvRzBCIHB1dApkdXAgMTIgL0cwQyBwdXQKZHVwIDEzIC9HMEQgcHV0CmR1cCAxNCAv RzBFIHB1dApkdXAgMTUgL0cwRiBwdXQKZHVwIDE2IC9HMTAgcHV0CmR1cCAxNyAvRzExIHB1 dApkdXAgMTggL0cxMiBwdXQKZHVwIDE5IC9HMTMgcHV0CmR1cCAyMCAvRzE0IHB1dApkdXAg MjEgL0cxNSBwdXQKZHVwIDIyIC9HMTYgcHV0CmR1cCAyMyAvRzE3IHB1dApkdXAgMjQgL0cx OCBwdXQKZHVwIDI1IC9HMTkgcHV0CmR1cCAyNiAvRzFBIHB1dApkdXAgMjcgL0cxQiBwdXQK ZHVwIDI4IC9HMUMgcHV0CmR1cCAyOSAvRzFEIHB1dApkdXAgMzAgL0cxRSBwdXQKZHVwIDMx IC9HMUYgcHV0CmR1cCAzMiAvRzIwIHB1dApkdXAgMzMgL0cyMSBwdXQKZHVwIDM0IC9HMjIg cHV0CmR1cCAzNSAvRzIzIHB1dApkdXAgMzYgL0cyNCBwdXQKZHVwIDM3IC9HMjUgcHV0CmR1 cCAzOCAvRzI2IHB1dApkdXAgMzkgL0cyNyBwdXQKZHVwIDQwIC9HMjggcHV0CmR1cCA0MSAv RzI5IHB1dApkdXAgNDIgL0cyQSBwdXQKZHVwIDQzIC9HMkIgcHV0CmR1cCA0NCAvRzJDIHB1 dApkdXAgNDUgL0cyRCBwdXQKZHVwIDQ2IC9HMkUgcHV0CmR1cCA0NyAvRzJGIHB1dApkdXAg NDggL0czMCBwdXQKZHVwIDQ5IC9HMzEgcHV0CmR1cCA1MCAvRzMyIHB1dApkdXAgNTEgL0cz MyBwdXQKZHVwIDUyIC9HMzQgcHV0CmR1cCA1MyAvRzM1IHB1dApkdXAgNTQgL0czNiBwdXQK ZHVwIDU1IC9HMzcgcHV0CmR1cCA1NiAvRzM4IHB1dApkdXAgNTcgL0czOSBwdXQKZHVwIDU4 IC9HM0EgcHV0CmR1cCA1OSAvRzNCIHB1dApkdXAgNjAgL0czQyBwdXQKZHVwIDYxIC9HM0Qg cHV0CmR1cCA2MiAvRzNFIHB1dApkdXAgNjMgL0czRiBwdXQKZHVwIDY0IC9HNDAgcHV0CmR1 cCA2NSAvRzQxIHB1dApkdXAgNjYgL0c0MiBwdXQKZHVwIDY3IC9HNDMgcHV0CmR1cCA2OCAv RzQ0IHB1dApkdXAgNjkgL0c0NSBwdXQKZHVwIDcwIC9HNDYgcHV0CmR1cCA3MSAvRzQ3IHB1 dApkdXAgNzIgL0c0OCBwdXQKZHVwIDczIC9HNDkgcHV0CmR1cCA3NCAvRzRBIHB1dApkdXAg NzUgL0c0QiBwdXQKZHVwIDc2IC9HNEMgcHV0CmR1cCA3NyAvRzREIHB1dApkdXAgNzggL0c0 RSBwdXQKZHVwIDc5IC9HNEYgcHV0CmR1cCA4MCAvRzUwIHB1dApkdXAgODEgL0c1MSBwdXQK ZHVwIDgyIC9HNTIgcHV0CmR1cCA4MyAvRzUzIHB1dApkdXAgODQgL0c1NCBwdXQKZHVwIDg1 IC9HNTUgcHV0CmR1cCA4NiAvRzU2IHB1dApkdXAgODcgL0c1NyBwdXQKZHVwIDg4IC9HNTgg cHV0CmR1cCA4OSAvRzU5IHB1dApkdXAgOTAgL0c1QSBwdXQKZHVwIDkxIC9HNUIgcHV0CmR1 cCA5MiAvRzVDIHB1dApkdXAgOTMgL0c1RCBwdXQKZHVwIDk0IC9HNUUgcHV0CmR1cCA5NSAv RzVGIHB1dApkdXAgOTYgL0c2MCBwdXQKZHVwIDk3IC9HNjEgcHV0CmR1cCA5OCAvRzYyIHB1 dApkdXAgOTkgL0c2MyBwdXQKZHVwIDEwMCAvRzY0IHB1dApkdXAgMTAxIC9HNjUgcHV0CmR1 cCAxMDIgL0c2NiBwdXQKZHVwIDEwMyAvRzY3IHB1dApkdXAgMTA0IC9HNjggcHV0CmR1cCAx MDUgL0c2OSBwdXQKZHVwIDEwNiAvRzZBIHB1dApkdXAgMTA3IC9HNkIgcHV0CmR1cCAxMDgg L0c2QyBwdXQKZHVwIDEwOSAvRzZEIHB1dApkdXAgMTEwIC9HNkUgcHV0CmR1cCAxMTEgL0c2 RiBwdXQKZHVwIDExMiAvRzcwIHB1dApkdXAgMTEzIC9HNzEgcHV0CmR1cCAxMTQgL0c3MiBw dXQKZHVwIDExNSAvRzczIHB1dApkdXAgMTE2IC9HNzQgcHV0CmR1cCAxMTcgL0c3NSBwdXQK ZHVwIDExOCAvRzc2IHB1dApkdXAgMTE5IC9HNzcgcHV0CmR1cCAxMjAgL0c3OCBwdXQKZHVw IDEyMSAvRzc5IHB1dApkdXAgMTIyIC9HN0EgcHV0CmR1cCAxMjMgL0c3QiBwdXQKZHVwIDEy NCAvRzdDIHB1dApkdXAgMTI1IC9HN0QgcHV0CmR1cCAxMjYgL0c3RSBwdXQKZHVwIDEyNyAv RzdGIHB1dApkdXAgMTI4IC9HODAgcHV0CmR1cCAxMjkgL0c4MSBwdXQKZHVwIDEzMCAvRzgy IHB1dApkdXAgMTMxIC9HODMgcHV0CnJlYWRvbmx5IGRlZgpjdXJyZW50ZGljdCBlbmQKY3Vy cmVudGZpbGUgZWV4ZWMKn27wbv3pm9faczWH4mfoj93bKD9ZMyoe4LjVGtyW1J0zsP/e8dvX 8CxIt1V8NxXx1SOX74Sx+XLdiMXlQ7SpvVN4Kfejvl3HOmazr12etSup4FIV3rIocr0Q5xH7 4/ib5hTDjBY7kOnjSEDwyJpx5Std7MwT+5UtBCSIk+C4vUW2shQHGDdX1DglgVkM4DU/7wqZ 9qnTAf2wAvGOHG3l6KgtUBG1b7BjE/oPAaQ4PTyRCZKzHZCNiTI+Wykc0df4R7jJAUHfx02I 3T7zVscJr47GvIu+7MeaTxzSh3rPOBRGmPkCPhspVOj+N8U5WD4+IoRQLS68F6iVBqdb2CSY XDNL9VNt30tDLf9bemQJXl2M+2fW3oekGv8Qw2sB4ZM+7xzyhebZUQu2EyMsyIEXrVlIMkP0 PnDW3WcIK51/9ZKM8bcfJjjecirdLW2JO1GYhE75mXuC0Ol5dPxS7kiSTEPB2mk4+/iMvVaX IH+uU+nxlPyi904ABy2FVCZl/NbkUTfC3HBCBPidDJLeb5sgjRB8jNxXzXOxXFIAgIMoO/1N Mcbfv1M2HPOwVSdlb60/m9DyacWggTSM4nbY6Xg7zH/5JDwtFDbZ1QiL+Zjdo8upFp8yd3Ej lIBL044hjnMoa4lmDuuXf1v8Rtj5kpsP62cXE3eniy3y/jJbBcOFAMFx+fcPAqWbrsCkdMCw 0RmvbMoX2xsDqx6SgcYQh2Vtbrn0trC7KET7poKH01T12eqki1zI6fd/6zeVRCcqRMP9DkSw gUvBYiHszQJ/2+BFxxqzG8tmxMUBAVfff2OoqT7Jgf/AgPirFEQYHkiAOu74UsEqEyv/ecDP hc2z0dduzTstYzXPhRiBmRa9SIlhY7XLjxvSVT9vyS2hHqbmA3PioZ0GGwp23WE8JwYl6KDH v0TrjMEyi3eLCq+unGBa4j4PGa5iM8p85XW8QpFGk4AP6L9eH57nJ2Dv8iJqqx4HXwVIDhMs UNPBHdV6/GENDe9ds42DGEvKbOwQtgw1dYs7mvIwscIiY5f5juFYK5lMzn5P7JvcU45kkeT3 bTcBxP1YPIzVhAsWn/JuQZktex5btmiCwTBAfGOFcbxSwmlZMNPVr3gCGHwi3J+i4aFnP9eQ +NW9sHqHyo0k/3XXWoj3AJPkScVrYHS4oiUrx1vuaxtP0TJbIrPsrXytUZ2TQ37D9i6wMrBh q1hoPyTE+0sCoQ6imO36ZsrDSxFmC6B2aEssr48zh3Y1rRUhR9nOM32PpLQz9YwApaTOo4Il 3/8BwrPr48MC5YZAtbDIPVp8Xu1I4Py+8W5H44X/xPPMs9T7HV1TwmWdDjx+K4vFrT0EjYie IISWQeb/TxkPYo7IXayKkk7zvugmj5veEmYhjSPDAM+rrcOuzLqF2hElbtqYHCK4X9Ph+PLJ w+47lfrKZZUBHn6AFEUKzkBpjJEZnyV3qOsSxlwK7A7wNMowueECcCR8AZk3+G42tLfzmHHD C4Ix2HoaM4fleKXg/FFQIpLfXMMrdhSO7cu0+F5RpaRR3vauU3C0jRvnIgZtN/jDoFYlE2xo kCY8YY62j81KArB7vBbB4DehvOrRF8jhS1nLuAqJ+jwOcKKeVM2J3UBoZ+va/8dNMcOm/xL4 WCRVtLyjAwIUL2kwtW5pWhtoevDNeoIHOG44OpQqN32fbJIRXYpdXt3WGWJqtANF2zI/+3zx Mt6f1NrBvdzdJ5U+aoB2yX8PwoLymvDC2qLoqVacdFfVvHD8PxEMhPX1Kba4eG8RqbHorXLD 8Y07OlESiX3LIAGQfwXnz7PGnanVlh8K0NlqS54pk2Ksnt9HSv9kfbdD4GJZgZS5wBNUA906 HaILvOu5E6+Hr6udM1n3D0JFUuHVPt8FP+txFjG8lqG23xDErNEnOZB2o51WrcHXld91xqxi TjrbsoW3G8mQOhkuKnw1E9/hWXLzrHxDv+GYe8U7elujaTkIMYb3+7Ytlo9Xway9hXZgp3m2 1dmU+V9/UvJEUxtv34adN3fRvCWrmURpsTim+2CLvHaC6pkTxi91TP6IZjUljqZTYG8KicLg T17Au0NbYTMTVCnMV9cJbl9hjPWiZhd/CTIugDY4XP+lR4b2QO3YflAuSSPyNkAevjTSL5Rt yrteoZRg6L2+QSPIONn09ye46rXleiE3Xglct/3Fh4MO68cE8MVZLUFLB/X+Pr1WCvEIquGv eo6jDGt76G6i/2uSnjQNQZQ1OCFrsHFL+VeR8+xUBnZ7N62uhCKN/rvqTPSyXs5jiepT1ds7 YipQq+Nrib3yCIK1DZ6VtNqJ7i5nIOswxdc+6v0F4vqrhHpVgrOH1hlz/whU0GsNmtJcpOpJ XYR0gM8ItLo8pOXX2l1p9t6wx3e7eRwiiL+4LCqD95pAkn/PMXEGg0R/oD3roZUH5j5EeRtz M3cg/z6dvSWjGzYZwJyfKsrOCMnP4PU8SQJ9BRgEyoyPrblwgvXz94ErNIkB+H3vQcESShwE 7Wx3LMLPyFzujZqwGzlJ4A7IhkTSJ5++VFVwi5zE0cuLJl4ODGJFhvmsm0JAmxbTtZJMWw/B jQf09yiKr1leAqWiXXJ/VezcHbs3vE1d0ZwLa4nfM45XF1cgZdwwinsJPDPf4J/u32hxL/Lg WYWqUFSz3+g6IOYVQtIuRpwZMZY7xhYUFodZE4qktbUCgt1NaGBFw/8rOdLxCK2o3rkZkpep LCXb5UWYYCm3GkfHa/ZO9GWHQdkEbpRE9xiW6lkr7YLU1RtbjR4rYho1GAWp3IMeVwd0jX0O iU+j1dJVJCkFGqSjrr6nBJPsmr82xPSqMofhO9ljSWg2Uv2eRZ3EVAlS4esdv2/aYSZtGYPN JWrl8ejN7nUsb+N+4AQcNaLYhDbC8qlZ+wcc9cG/09Yl+6EopBLTzIy/+FpBjUcwyuqsNqD9 AEYNnjSe0Qvdns5Dv6jrNHTj7LdEdzzwJs8jPFrR4++MY4/BNTR/lwUzpXwpxiEQF1QeUZZD zZAmD0B/lbXHdTBMfoVIHU6C+zztb9e2fSBMjmnEH8ovu8NUzhiC+0cSCre5FutaXxHfBBmH CrupMXAeJ48GqF3HAxEgmYKmUcJkG7kif92cVjcMxrT+GtZk/ZGDFWWtgsV9GpYswAFTi5yT OMCcQd5l3sQePsp8QMT0DS2Cs4I8KjgNCSKNtXAx/rTIsMIONhWgrz9Ea3fAmRNHfxcidXy5 l6NU4aRWwOSga2UpIJZPBB8TI2TcQFhjdPT+hxIaCTnz7iCrbm7rfD5Fq4NMyz38qOFeaJX+ s2PYN3Hl92XKlSCET9ZOTafJxhyBrUwSLCIAwyU2BqvfFTWGU/Slh+dqogjP4rSVJg5XO5JW FpbAJb5xZMulxB3TYVbqLfB6MPdYnUn58kc7w0p2FgUp9Z0LmwDAREfzokW305eJEBhN2sm7 DNr4o0bFB9iAIGnLV302YQF+cmEjAZZpL4cbDQplbmRzdHJlYW0NCmVuZG9iag0KMjcgMCBv YmoNCjw8DQovVHlwZSAvRm9udERlc2NyaXB0b3INCi9Bc2NlbnQgMA0KL0NhcEhlaWdodCAw DQovRGVzY2VudCAwDQovRmxhZ3MgNA0KL0ZvbnRCQm94IFswIC0yNTAgNzIzIDc1MF0NCi9G b250TmFtZSAvTEZDREFDK01TVFQzMWM3YzkwMA0KL0l0YWxpY0FuZ2xlIDANCi9TdGVtViAw DQovQ2hhclNldCAoL0cyRS9HMzgvRzM5L0czQS9HM0IvRzNDL0czMy9HMzQvRzJCL0czNS9H MkMvRzM2L0c0MC9HMzcpDQovRm9udEZpbGUgMjggMCBSDQo+Pg0KZW5kb2JqDQoyOCAwIG9i ag0KPDwNCi9MZW5ndGggNzMxOQ0KL0xlbmd0aDEgMjU0Ng0KL0xlbmd0aDIgNDc3MQ0KL0xl bmd0aDMgMA0KPj4NCnN0cmVhbQ0KJSFGb250VHlwZTEtMS4wOiBMRkNEQUMrTVNUVDMxYzdj OTAwIDEKMTMgZGljdCBiZWdpbgovRm9udE5hbWUgL0xGQ0RBQytNU1RUMzFjN2M5MDAgZGVm IAovRm9udFR5cGUgMSBkZWYKL0ZvbnRCQm94IHswIC01MTIgMTQ4MSAxNTM2fSByZWFkb25s eSBkZWYKL0ZvbnRNYXRyaXggWzAuMDAwNDg4MyAwIDAgMC4wMDA0ODgzIDAgMF0gcmVhZG9u bHkgZGVmCi9QYWludFR5cGUgMCBkZWYKL0ZvbnRJbmZvIDEyIGRpY3QgZHVwIGJlZ2luCi9C YXNlRm9udE5hbWUgKE1TVFQzMWM3YzkwMCkgZGVmCmVuZCBkZWYKL0VuY29kaW5nIDI1NiBh cnJheQowIDEgMjU1IHsxIGluZGV4IGV4Y2ggLy5ub3RkZWYgcHV0fSBmb3IKZHVwIDAgL0cw MCBwdXQKZHVwIDEgL0cwMSBwdXQKZHVwIDIgL0cwMiBwdXQKZHVwIDMgL0cwMyBwdXQKZHVw IDQgL0cwNCBwdXQKZHVwIDUgL0cwNSBwdXQKZHVwIDYgL0cwNiBwdXQKZHVwIDcgL0cwNyBw dXQKZHVwIDggL0cwOCBwdXQKZHVwIDkgL0cwOSBwdXQKZHVwIDEwIC9HMEEgcHV0CmR1cCAx MSAvRzBCIHB1dApkdXAgMTIgL0cwQyBwdXQKZHVwIDEzIC9HMEQgcHV0CmR1cCAxNCAvRzBF IHB1dApkdXAgMTUgL0cwRiBwdXQKZHVwIDE2IC9HMTAgcHV0CmR1cCAxNyAvRzExIHB1dApk dXAgMTggL0cxMiBwdXQKZHVwIDE5IC9HMTMgcHV0CmR1cCAyMCAvRzE0IHB1dApkdXAgMjEg L0cxNSBwdXQKZHVwIDIyIC9HMTYgcHV0CmR1cCAyMyAvRzE3IHB1dApkdXAgMjQgL0cxOCBw dXQKZHVwIDI1IC9HMTkgcHV0CmR1cCAyNiAvRzFBIHB1dApkdXAgMjcgL0cxQiBwdXQKZHVw IDI4IC9HMUMgcHV0CmR1cCAyOSAvRzFEIHB1dApkdXAgMzAgL0cxRSBwdXQKZHVwIDMxIC9H MUYgcHV0CmR1cCAzMiAvRzIwIHB1dApkdXAgMzMgL0cyMSBwdXQKZHVwIDM0IC9HMjIgcHV0 CmR1cCAzNSAvRzIzIHB1dApkdXAgMzYgL0cyNCBwdXQKZHVwIDM3IC9HMjUgcHV0CmR1cCAz OCAvRzI2IHB1dApkdXAgMzkgL0cyNyBwdXQKZHVwIDQwIC9HMjggcHV0CmR1cCA0MSAvRzI5 IHB1dApkdXAgNDIgL0cyQSBwdXQKZHVwIDQzIC9HMkIgcHV0CmR1cCA0NCAvRzJDIHB1dApk dXAgNDUgL0cyRCBwdXQKZHVwIDQ2IC9HMkUgcHV0CmR1cCA0NyAvRzJGIHB1dApkdXAgNDgg L0czMCBwdXQKZHVwIDQ5IC9HMzEgcHV0CmR1cCA1MCAvRzMyIHB1dApkdXAgNTEgL0czMyBw dXQKZHVwIDUyIC9HMzQgcHV0CmR1cCA1MyAvRzM1IHB1dApkdXAgNTQgL0czNiBwdXQKZHVw IDU1IC9HMzcgcHV0CmR1cCA1NiAvRzM4IHB1dApkdXAgNTcgL0czOSBwdXQKZHVwIDU4IC9H M0EgcHV0CmR1cCA1OSAvRzNCIHB1dApkdXAgNjAgL0czQyBwdXQKZHVwIDYxIC9HM0QgcHV0 CmR1cCA2MiAvRzNFIHB1dApkdXAgNjMgL0czRiBwdXQKZHVwIDY0IC9HNDAgcHV0CmR1cCA2 NSAvRzQxIHB1dApkdXAgNjYgL0c0MiBwdXQKZHVwIDY3IC9HNDMgcHV0CmR1cCA2OCAvRzQ0 IHB1dApkdXAgNjkgL0c0NSBwdXQKZHVwIDcwIC9HNDYgcHV0CmR1cCA3MSAvRzQ3IHB1dApk dXAgNzIgL0c0OCBwdXQKZHVwIDczIC9HNDkgcHV0CmR1cCA3NCAvRzRBIHB1dApkdXAgNzUg L0c0QiBwdXQKZHVwIDc2IC9HNEMgcHV0CmR1cCA3NyAvRzREIHB1dApkdXAgNzggL0c0RSBw dXQKZHVwIDc5IC9HNEYgcHV0CmR1cCA4MCAvRzUwIHB1dApkdXAgODEgL0c1MSBwdXQKZHVw IDgyIC9HNTIgcHV0CmR1cCA4MyAvRzUzIHB1dApkdXAgODQgL0c1NCBwdXQKZHVwIDg1IC9H NTUgcHV0CmR1cCA4NiAvRzU2IHB1dApkdXAgODcgL0c1NyBwdXQKZHVwIDg4IC9HNTggcHV0 CmR1cCA4OSAvRzU5IHB1dApkdXAgOTAgL0c1QSBwdXQKZHVwIDkxIC9HNUIgcHV0CmR1cCA5 MiAvRzVDIHB1dApkdXAgOTMgL0c1RCBwdXQKZHVwIDk0IC9HNUUgcHV0CmR1cCA5NSAvRzVG IHB1dApkdXAgOTYgL0c2MCBwdXQKZHVwIDk3IC9HNjEgcHV0CmR1cCA5OCAvRzYyIHB1dApk dXAgOTkgL0c2MyBwdXQKZHVwIDEwMCAvRzY0IHB1dApkdXAgMTAxIC9HNjUgcHV0CmR1cCAx MDIgL0c2NiBwdXQKZHVwIDEwMyAvRzY3IHB1dApkdXAgMTA0IC9HNjggcHV0CmR1cCAxMDUg L0c2OSBwdXQKZHVwIDEwNiAvRzZBIHB1dApkdXAgMTA3IC9HNkIgcHV0CmR1cCAxMDggL0c2 QyBwdXQKZHVwIDEwOSAvRzZEIHB1dApkdXAgMTEwIC9HNkUgcHV0CmR1cCAxMTEgL0c2RiBw dXQKZHVwIDExMiAvRzcwIHB1dApkdXAgMTEzIC9HNzEgcHV0CmR1cCAxMTQgL0c3MiBwdXQK ZHVwIDExNSAvRzczIHB1dApkdXAgMTE2IC9HNzQgcHV0CmR1cCAxMTcgL0c3NSBwdXQKZHVw IDExOCAvRzc2IHB1dApkdXAgMTE5IC9HNzcgcHV0CmR1cCAxMjAgL0c3OCBwdXQKZHVwIDEy MSAvRzc5IHB1dApkdXAgMTIyIC9HN0EgcHV0CmR1cCAxMjMgL0c3QiBwdXQKZHVwIDEyNCAv RzdDIHB1dApkdXAgMTI1IC9HN0QgcHV0CmR1cCAxMjYgL0c3RSBwdXQKZHVwIDEyNyAvRzdG IHB1dApkdXAgMTI4IC9HODAgcHV0CmR1cCAxMjkgL0c4MSBwdXQKZHVwIDEzMCAvRzgyIHB1 dApkdXAgMTMxIC9HODMgcHV0CmR1cCAxMzIgL0c4NCBwdXQKcmVhZG9ubHkgZGVmCmN1cnJl bnRkaWN0IGVuZApjdXJyZW50ZmlsZSBlZXhlYwrMM+2mLtgGn6sWCqsSQHlrJmyHFPKYmii6 y9w9JntPOSG3LsN3K3lTGXajjWs19fnTuWM5NqUiyDbiIqgSiX/jxHtQ97XbgwkegT5j0ur7 3Mk+DJJlZXAa3o/moBguJ5WN+2no2tpQhTz7Sj9fz46QqCfwRjTHt70DwpJqnbZhXoaFlR/g rMp3Qy3IxbkU3nLgzpofNneaN2i69uV290qD/g5vVsnoFy+tp2iP5figJwM6i0TThqQYtdjH O8oqDztCF5iHSkeeLuS68otZXD68U9Sx+hr9IH50a7wS7AUOhDhYBjastaIymKeBnQ4/CeDB tRGE3uqF200t2EGzPTQRTF315/UbCHhHTLUerVBcGduxn0WS8vO5SOquIt2DQGVtk9Yd0ruF E8G0xXRZVnftvakQK79KehRScrLoYHQhOJgn9ssF8hF8O5wQ0CkxkyvnbVo/KHReN54z4zFt K51wW3mm1HiqUahT73MTPwHezXkjUWYjcuasFoTSwzVHCk/pVmooizggJjBvkVFB6zJLFvim cz5Elr8xaGZBSCTQ0eHGz63w17m0WudPl97X2EwN605VzFWbs5GLxf5tXIMs6k7bpJHMy37B 31CVfKG7oa/e4OWbLPdv6zMB1U1d+7nhNX3q8toQnLhZ2W43sX1qYCah85lBBhWYyEBEX1Qc 1JD4AKOEjov/DUa/69xB3vc849e1KdV/kit/KIVnRL5eX3CdN54xp86OUCSmiNgYPpJEz5WE n8gv/t/LsXuMsPgPXcQ3KtmrKKIDcf7pc3yQtAsUxGYqjZ+LD+gIPuiyP5/V2PVrknk2T7hA xdnmhl1n4fkFXzOA3f8RcRiUkJA/5wwMVKYXkrJwpGgUuIrnnwsxXRtaejNRPELz+xg8yPoR j/Myo2opb0LGN1T9Ljo7z68WJwYJ8QJnzWk7oSnIrrtY1ty7+nRcr0M9OFdBVZoLLr4rGScO pnh6SSWh8brihG8TQK+Xs2EhMaDZjyHxCEgiTyNau4WBy4TVqrsJxbRtfESl5XtYzVZ/OcvH U3digtXRxyxLxwCfb5tAv6ElN4wEDOdZ0lEPJwMpalC35UBHJmp1D67O0Z+OkNxV00oxqAgB 7WrF2yxfof9dkVzkZIJmauHa3zDWljvzL1mBtkYZvtFREx4foAPwGAAFqihLZzRojrSIe8hF 5j3qSZUC5f0KkCb6Q5c8bJGQBCcdurA9A9gWVran4aa+oeQi0poG1yjaES/xW6heHNhg59MQ w9xJjuIJP/bMrNolCcg9p6F+Gn+2+W3gafz9tMxoagJyq+Naamy3YKVGiIkbVhBWIi6aUWKa +b3gL5gs3f4SzWly/3jWyCkNE7fk/nd2joi8O/8bRPxHWENxHvIQdPkpWKg5Z1djRrC7WC1a wasOcIK7Frt4YIrJnMGe12m8t8Nhk7gRRVIlOa4X2MhKC/vpATnab5qfdHONwyMX0fuFbNm/ ZCKOm7JGxqnwPwhWvRyyC6c3uPr+PyMgrF5PHMdkyECTYfbm/7L7S3BEX9Mj4hJBXyWxfK/P NwVvUVl0xIXqYiAmwHETKt2cMXAm5s2SkQMg/ZlyLX+xvlE6sqyzqonDa/3lReqFzVssQIXC PuKJuYh3ZbgPow6SzlZ/ACt/LBxjtqgIH6O+4hUs/A+GE5zl/FQZkKAy3Dfkfp+rVlujeZ0s hEigWAqjFIsupk9nYMaBI52/cP/MhKda8bW3peEnAFldlbPD/caaBzNbPfX3nvKoLFcn7Z1L sq+DhGdLSQyDcYstI8dVyLWRpQPbSYbW39ey3rLQv4gVLvRWwkbv03C+yp6dv5Go7XDzk9ev LY6GRLKwhLmIm292u6Ny/+bSO8hwgamR+h1oUZaZ5SWHhHeHaXozRfamHgaivm3DmtPUHllB MwoX+0qmmXjNep734GhjRf6I3f7oWrDyMpKdfLveUP1QzBYnKxlZHCktsXx3C4Iko5PkzefR kFHpRmP1YcXZNe72xEe2kYZUcGUPwdKzNSlTvsf78r1nHhwTUdjcOMUiZ1wT4Fw/wlLBqOnO W2RLmolss+xzFMFbcIKu9a13uFO8MKpxMYmiV3z1bJiQi4UZTFMMuqbhjC9G7FEEDaTyHKs8 BUMW8JgC5/VauwaGivCjLTKsipbVz9PtIh2JWdc3bfvt8zlAYxu+WSTTfdzhIeeYXzdAhCF2 /d9q4KLoLWe5efRpmtyMsAFDDIqUpBPuNEklX5R8pr15doivHQCdGHoH1iifHlu0yWaUfsua +AplvqsdvqLMdcyAIJtLqB7UdFU1pXp0R2wSO3miOtcqTCI1MObLfkfODcl6b24IFHrOW+MY h8+jPiY+0RYFCTyYHuNEboLuI2rT9wK6yuFrgS/zNtDsOkMermK8LI1pt+JrYmGa4mNO9Ju/ F2wb1yKioWjxKejOOAsEQdPu2qN3auS9wfq+FHOonvVbLB1v9aS96vSixKOIwHhFdVDnE6n3 YE3XUXTR7od6GNTWZ+KoHZPBKXuqfgHfrh4mv1xny7/E+xUMEhN2PKQS0HZfALVyK1eEEEj0 2/crF8mw5JkidO++nu7wBhM+4WWOs4ikAZKxWS5UGPeNFbphWV2SU9phSfxFI4qTj2Wave10 imxS+7epdHCsQKXLhEPqgYzHorpDhubb2ZUuAlxOC9mxf1ugv0c56H4GM1v0u28UpotFayOJ 34iBE9fWG9N3L4pZE6j5uUzNyMJJAhMytunqH/cRZ2nYKdIJPTij0tZw9i5uPYgze3U/wzmG EuS6FlMJday7MkLc/nteXDxWlBtAuM7Jm6HhIeIgf3R6DydX57afuVjqgUzYFqiR7QjIN4iJ x7K+AftZx6O0IGekEe9zUo43RWOaXCb9I2CtmbWHSWzkIkw+Q9rPPrgIjYExl6wW1OrOAZF1 uiWbsIsj+W/Dk5P2XN3GktFkPT19rJMFCtKqK/NZDW13knuPOEWNm+EqqERKEYTNMpNRlr50 DiERiHMNy6MiVe113skMrF9abWZnJaf8RZkEMZwqauaEqLGKmltt3grqiS7qmmRZ+bwk7N5a 3zw5mZj8T2EJ6Fyvod1MPLElwtfPvWqLzTolglyp/TDk8W9g/6h9h8Q6ikVWOwGyvwSUKILS Piv5Re49nPHmt5/yjsjD9fq+PQX8Bh9cpu+RdKg43gklfnmszcJ7D99CNxab/co14bk+8hRN zGS0eAaeX6qMhI7RHobIpM/h93hvtqm1/hLktBA7WmFmHRczRI1LxUxuKs3++/aejNkVV4U0 ew92fzQDXOY8RY/MgSCEMMnfZYQ5ZroYsaoOG3t4Gkix8qj0fEBxWkXHFgm7p+Az5aEvE8IU SV1zkPgbHAbCuIvfVVqdeMFI4VfOiTpxouPFPlfA5hJmCl54Q8EaIkFUuIIPFlGfzVb9YYqb latlSwJ6TgbPkeI2CBZrrSK3Jbg8P0gdlGTMf6Cv7yREHl819AR1zyhDnz9pLNBuFzagLhW1 PbVEi6R9Vmth6Tpa1uBjr/9nG3naKsdGtR68pu2t2h9aWCFRXv5IDMQF57YNAG8NdM6IokBa jTlvietICHlpGArbG6msEYq1OCDV0/zQ0RuLv2KmwewLqP4XsO+7hA42qhzcVfuSpT5V0085 9vPc6Uh4Qg0BZpqpFDBXQbUkgYGt3RYoMLjBZDoNSM6x7M2zQ8Um8rXLK0pH15oFGbLUIvI8 +il6qT54MVm3qr26IXPXrzZr3GD9xCLpoogsxydCWXrU81iEZMh2ljhlAeYZPMAXoBBzEXCI YExgqKPNo7NqFWtbmt+WXRCviAfFpjNCdLAmOICRpCJNXXRcgQxtuc3n2jLQMX8OIcAiruxC P+cgRWKo1HMmXlkas09VAfFZoCGNuBHtx2EAYrzub7kDLUdobNAvJRYq+eMb0Pg1WMzZppW+ iRzuNiJB8yBfQ9pli2Ci+atxW1MG1yBpbRiAcPzmvtJvSineYbo9H7XtFppU4gLpExVLTrXI fPBhVC6Og7eCo/IwC8PAynOkcHRIPAapHew3f+/BlC19INb3ogRV9/KD5rc9tyo1NWeJEmEC +V8S0mBTMmzvwkTyL35juAE/5obweCpl7UIg0qOM3BLFjhU31S2NJSBJkb3ChCrIouWA4knv dCLqU794YtAvvPiz4ATraTruupa+8y8ZAGZJ8qf0BpqBPXwiTOKKL8pvfJfiCcR9p7Rn5zRT 5Sz028B4O+R0Iwe6C0iKtNez5MlHod4syAyjHDVtRecFhl/k9cVdNCKTBtNxlLL4CrwOtk65 plx2/4h6nFyTvLdaNcNQCpn/7NuefVDi0RfPAsaURif9XSbSVSpcbfbcK0fpIDEAT8uHqTR1 Z7FxwsmWo4Unq6T0O/RNYlwukJB6GFphzyoAUoDCPzG+Jlt1xQr+/axmOLUXN0t9so7skvjB PlqflhVOGolgiYKUyR1cAyssJ/HbbDlKUMuIFowjmJGKIX0dXgo+1n7rclpCVFtgDrQeSZvl nHsOWO6lyWwK+ihArwqvVg+mcbpr8h9BE4oz5aPfPJtt+pIgPRzJiqHgqumyAWmIo/1lLWFS Xys/LSuhkNMA6G1wqFTBGJAvpKe1bz22uDk0t5T2AAoO22IYhBS+W7r3X3AQ7vBzBiZ4lwJB SkZvUtOifrvQHUndkw/Boy07+mzjX1wDJ7YGQDr6f5WOIYgG+jiHE9rUdmJM7yfGxj5ee5ib z+8vNFwm40gjwCPrx6CpxZU699LYlY/oFV+1QmWzFhR9eHsr3fDSTN40T5GffmmiU1CwrHLx /hRIQpifXxX4b8y35VpIxkzZJgmAF15hQa6+gM03tZapH42wri6VbbtaPtB/+7mnCjTlM6ZE sXbCcWOoGzVsyDpTqgrx0anu5vDel2Oc3e7vplIiMzlmCvny1sCYy7J2r212kvn5dsxA1Nm5 Jf3ToDFU7fnBW0QU0uDg5mP2Wr4H9WIpTnZn2XoCnwKfOZGHZ6B3Bk8Q/BtH/iDOkBc8zKwq lhpxujx07tfxz0KkTLFQ/0L6wn9Ovc40AQc/kKfiDYSeLl3zrgeaKpc18qHiCOGa7p/Hjjai upeS36dAWCJT9qgJs0D5K9pAtdIOaca08DTXogHhhA28DDxgokLTmB/THl6+povGv8cAEF8Z nx2yoqCPGdDyA3IygFbcHdUHNnDli7XMYEV+Jff6Ikf8EwrCXwalaSZjUmAaPUM2aKhDzjYh yqaSfyxM7AixfVZzSCl8cbWxJfAW8ktBTaEZCUDr1REqucNuqhPi2508p+ye9gl0q1m455ni 2UzLL8+uuhETtb9jGjoXXwr/xtE9T12MfBV6u1ykGOHQvIdgNTSPgLsNvJ1s3Uu0+KOm6FM2 nQvso5uLdZgvgSU5ge3BO0qFgvxK0RtmDIQhOOs8OC1bJZVaar8S3hOWKM8gZ3O+jjyqn5jN WgjHorwsRoWOV/3AH8M7suNtOBYSZQzLLq50CL9SgViuP1HuaTJ+yJ4gmix+cLM436nQYlpK PIwtG+teN5KljqDND88K9ZlhcsdmqCbZcCu28l8urYf8YgDNIB0/MwuDx/jKCIYsF5kwXDXX Udcy7VxHXuLwJ8f9iBZFFX94yX0EXQtqPvjrVczTnP2T7eyxlxC8nPRqHM91KxN7H0cVpIMq FLEUc9+eCXSXCMu7zTk+xq7dFZ+AXBFj83Sy4JY1Xx3sk5cH+V3ZJEV8ryxix7etbLScdLp5 txi6G4MI19EzUVw5v2t16Qf/OFVa4Au1mnlbk3RewfQX+IkMTyYvDqTzCsee9ca2SKKaip2k qWAGMjYhe0BWRWfT3U43MQf+rpCU6XA+NXAmBn83y5uxV9ahTTA1QEaGIWybYOZyRI/eV13x xiGqRQAJzV4Qw6hSQ16ENlgwB+QS5/K69ovxU7IAQVP5ho2oHbBDrQ8QEs1wHZvt/SyHAiHk hXRwxdi30lzKQGUI2qtHhkwpLjPARfrGEqDdV1QwtjIMkIOtdVvARmKQ3icQFBMW7Anl8yBt 6R4eTg30vknh+a6dMeFbdVkjkTdl5fezjPLubF3GEXWh8WGRoQDWHFSuB7YQMaHT6mn9641E YP24t/eniZ/2+uCSSzw30Qy+ZUJdN10jDHFhyqGe9HSYUcU69QfEEmzmxv5IaaK6QSI23k1l AYM59qPwLztMjMQsfwrGZZ9lNG2qmr1OABnbEE3VVG/Kw+g//2/wnrqjmnY8WrwL/kU7cfF2 HdDtYcrb1/EQfAS6tj5DisUFFe8RvY64ZgmCaGBY9vF/DEMsBHWIYaIfpex4xrH3h74foa8f lGiguGbOrYGRvIz/vOKZ8THSxxYx9BEHuUu+deXGF4Bw315LwsjUagCHcNuzk6x6DQplbmRz dHJlYW0NCmVuZG9iag0KMjkgMCBvYmoNCjw8DQovVHlwZSAvRm9udERlc2NyaXB0b3INCi9B c2NlbnQgMA0KL0NhcEhlaWdodCAwDQovRGVzY2VudCAwDQovRmxhZ3MgNA0KL0ZvbnRCQm94 IFswIDAgNjg5IDI3MF0NCi9Gb250TmFtZSAvTEZDREFFK01TVFQzMWM3ZDQwMA0KL0l0YWxp Y0FuZ2xlIDANCi9TdGVtViAwDQovQ2hhclNldCAoL0cwMykNCi9Gb250RmlsZSAzMCAwIFIN Cj4+DQplbmRvYmoNCjMwIDAgb2JqDQo8PA0KL0xlbmd0aCAzNzM5DQovTGVuZ3RoMSAyNTI1 DQovTGVuZ3RoMiAxMjEyDQovTGVuZ3RoMyAwDQo+Pg0Kc3RyZWFtDQolIUZvbnRUeXBlMS0x LjA6IExGQ0RBRStNU1RUMzFjN2Q0MDAgMQoxMyBkaWN0IGJlZ2luCi9Gb250TmFtZSAvTEZD REFFK01TVFQzMWM3ZDQwMCBkZWYgCi9Gb250VHlwZSAxIGRlZgovRm9udEJCb3ggezAgMCAx NDEyIDU1M30gcmVhZG9ubHkgZGVmCi9Gb250TWF0cml4IFswLjAwMDQ4ODMgMCAwIDAuMDAw NDg4MyAwIDBdIHJlYWRvbmx5IGRlZgovUGFpbnRUeXBlIDAgZGVmCi9Gb250SW5mbyAxMiBk aWN0IGR1cCBiZWdpbgovQmFzZUZvbnROYW1lIChNU1RUMzFjN2Q0MDApIGRlZgplbmQgZGVm Ci9FbmNvZGluZyAyNTYgYXJyYXkKMCAxIDI1NSB7MSBpbmRleCBleGNoIC8ubm90ZGVmIHB1 dH0gZm9yCmR1cCAwIC9HMDAgcHV0CmR1cCAxIC9HMDEgcHV0CmR1cCAyIC9HMDIgcHV0CmR1 cCAzIC9HMDMgcHV0CmR1cCA0IC9HMDQgcHV0CmR1cCA1IC9HMDUgcHV0CmR1cCA2IC9HMDYg cHV0CmR1cCA3IC9HMDcgcHV0CmR1cCA4IC9HMDggcHV0CmR1cCA5IC9HMDkgcHV0CmR1cCAx MCAvRzBBIHB1dApkdXAgMTEgL0cwQiBwdXQKZHVwIDEyIC9HMEMgcHV0CmR1cCAxMyAvRzBE IHB1dApkdXAgMTQgL0cwRSBwdXQKZHVwIDE1IC9HMEYgcHV0CmR1cCAxNiAvRzEwIHB1dApk dXAgMTcgL0cxMSBwdXQKZHVwIDE4IC9HMTIgcHV0CmR1cCAxOSAvRzEzIHB1dApkdXAgMjAg L0cxNCBwdXQKZHVwIDIxIC9HMTUgcHV0CmR1cCAyMiAvRzE2IHB1dApkdXAgMjMgL0cxNyBw dXQKZHVwIDI0IC9HMTggcHV0CmR1cCAyNSAvRzE5IHB1dApkdXAgMjYgL0cxQSBwdXQKZHVw IDI3IC9HMUIgcHV0CmR1cCAyOCAvRzFDIHB1dApkdXAgMjkgL0cxRCBwdXQKZHVwIDMwIC9H MUUgcHV0CmR1cCAzMSAvRzFGIHB1dApkdXAgMzIgL0cyMCBwdXQKZHVwIDMzIC9HMjEgcHV0 CmR1cCAzNCAvRzIyIHB1dApkdXAgMzUgL0cyMyBwdXQKZHVwIDM2IC9HMjQgcHV0CmR1cCAz NyAvRzI1IHB1dApkdXAgMzggL0cyNiBwdXQKZHVwIDM5IC9HMjcgcHV0CmR1cCA0MCAvRzI4 IHB1dApkdXAgNDEgL0cyOSBwdXQKZHVwIDQyIC9HMkEgcHV0CmR1cCA0MyAvRzJCIHB1dApk dXAgNDQgL0cyQyBwdXQKZHVwIDQ1IC9HMkQgcHV0CmR1cCA0NiAvRzJFIHB1dApkdXAgNDcg L0cyRiBwdXQKZHVwIDQ4IC9HMzAgcHV0CmR1cCA0OSAvRzMxIHB1dApkdXAgNTAgL0czMiBw dXQKZHVwIDUxIC9HMzMgcHV0CmR1cCA1MiAvRzM0IHB1dApkdXAgNTMgL0czNSBwdXQKZHVw IDU0IC9HMzYgcHV0CmR1cCA1NSAvRzM3IHB1dApkdXAgNTYgL0czOCBwdXQKZHVwIDU3IC9H MzkgcHV0CmR1cCA1OCAvRzNBIHB1dApkdXAgNTkgL0czQiBwdXQKZHVwIDYwIC9HM0MgcHV0 CmR1cCA2MSAvRzNEIHB1dApkdXAgNjIgL0czRSBwdXQKZHVwIDYzIC9HM0YgcHV0CmR1cCA2 NCAvRzQwIHB1dApkdXAgNjUgL0c0MSBwdXQKZHVwIDY2IC9HNDIgcHV0CmR1cCA2NyAvRzQz IHB1dApkdXAgNjggL0c0NCBwdXQKZHVwIDY5IC9HNDUgcHV0CmR1cCA3MCAvRzQ2IHB1dApk dXAgNzEgL0c0NyBwdXQKZHVwIDcyIC9HNDggcHV0CmR1cCA3MyAvRzQ5IHB1dApkdXAgNzQg L0c0QSBwdXQKZHVwIDc1IC9HNEIgcHV0CmR1cCA3NiAvRzRDIHB1dApkdXAgNzcgL0c0RCBw dXQKZHVwIDc4IC9HNEUgcHV0CmR1cCA3OSAvRzRGIHB1dApkdXAgODAgL0c1MCBwdXQKZHVw IDgxIC9HNTEgcHV0CmR1cCA4MiAvRzUyIHB1dApkdXAgODMgL0c1MyBwdXQKZHVwIDg0IC9H NTQgcHV0CmR1cCA4NSAvRzU1IHB1dApkdXAgODYgL0c1NiBwdXQKZHVwIDg3IC9HNTcgcHV0 CmR1cCA4OCAvRzU4IHB1dApkdXAgODkgL0c1OSBwdXQKZHVwIDkwIC9HNUEgcHV0CmR1cCA5 MSAvRzVCIHB1dApkdXAgOTIgL0c1QyBwdXQKZHVwIDkzIC9HNUQgcHV0CmR1cCA5NCAvRzVF IHB1dApkdXAgOTUgL0c1RiBwdXQKZHVwIDk2IC9HNjAgcHV0CmR1cCA5NyAvRzYxIHB1dApk dXAgOTggL0c2MiBwdXQKZHVwIDk5IC9HNjMgcHV0CmR1cCAxMDAgL0c2NCBwdXQKZHVwIDEw MSAvRzY1IHB1dApkdXAgMTAyIC9HNjYgcHV0CmR1cCAxMDMgL0c2NyBwdXQKZHVwIDEwNCAv RzY4IHB1dApkdXAgMTA1IC9HNjkgcHV0CmR1cCAxMDYgL0c2QSBwdXQKZHVwIDEwNyAvRzZC IHB1dApkdXAgMTA4IC9HNkMgcHV0CmR1cCAxMDkgL0c2RCBwdXQKZHVwIDExMCAvRzZFIHB1 dApkdXAgMTExIC9HNkYgcHV0CmR1cCAxMTIgL0c3MCBwdXQKZHVwIDExMyAvRzcxIHB1dApk dXAgMTE0IC9HNzIgcHV0CmR1cCAxMTUgL0c3MyBwdXQKZHVwIDExNiAvRzc0IHB1dApkdXAg MTE3IC9HNzUgcHV0CmR1cCAxMTggL0c3NiBwdXQKZHVwIDExOSAvRzc3IHB1dApkdXAgMTIw IC9HNzggcHV0CmR1cCAxMjEgL0c3OSBwdXQKZHVwIDEyMiAvRzdBIHB1dApkdXAgMTIzIC9H N0IgcHV0CmR1cCAxMjQgL0c3QyBwdXQKZHVwIDEyNSAvRzdEIHB1dApkdXAgMTI2IC9HN0Ug cHV0CmR1cCAxMjcgL0c3RiBwdXQKZHVwIDEyOCAvRzgwIHB1dApkdXAgMTI5IC9HODEgcHV0 CmR1cCAxMzAgL0c4MiBwdXQKZHVwIDEzMSAvRzgzIHB1dApyZWFkb25seSBkZWYKY3VycmVu dGRpY3QgZW5kCmN1cnJlbnRmaWxlIGVleGVjCjolyeJPvwHN4HEsSXPg97dXIqDKgPv/2SqT oqNJxsDsNiP9lGWuWAMJn7h7ai+OBsOsXPtUMN2G493yfJEV/KXVzo7DhWpE3DU4+OeDS2SN jq/BfwLqiWMs65QQKMUpK+ldvZ4VFVaBlxoYmKVmab8ST/bXg9VKTM8iEb7pQKEde6VsYsMH 3Q4wA2NUVKaQAWq8KnCoFnMB5QqRdj/Gvvun4PVhHHVxBHgC8EaTGkvM6yKGYiqwJrUX7u38 e3TTF49zA+h3RrJzxX71riYxLKFqSowetPmitSmffXhtTNzHiOHd/W/Fa0L3HKMU1pWyFTSb PDc3AbH8HUa/FTZmSDvQI7449XSbfUkH6xolTW5zkWt1PwM2glhZLcH+OUDiSd7cpKUnf2s9 OOxGJXfb3f5L1pP0yE7MtumJ1YeruXFcSSla+jqzr3guSNfOIqvkaO2PhP4tqR37JLqJWGcR xlAAElM2QMfW6FsFf4xXxpHYl/t/YslWydCYbDTAg1veG0k27IiWvgdEgkLWNhcoei5pLJQd iMY9WD0ap/OlWQhhlIMwEQ1mqSAsZ87tN2bvJEfFjMjWi+K+E28VDNMxOW7LyozFdenCRZ7q U2MyzTlrTzCkmdVePWMRxNADU/cRfdebztfhPAw8OrHzGWR9osDRi/4rrrcMa0Q7uGRl/7ZC 0aNjYbiwVuOqK0Kcr5xdsECf3vPto/vE62Oe3RuC/RnIHNrTqx3W7dsv4cqD5sPf4oLt3tM3 QOXAmTFlWSufPcOYlme+UlOtyUbP+E2F01Q9nDkKFBtIhv4Oke/7IEMN3bXvdsippEybBFVN FZUz8Kd2RXfjmxjTZIzg2ZZaHIpkwBB9sA7UINNar4hUVP4UBw+vNqTLY7ZiDW23FZOuyTXu igZWpHrAe2IgKpktQp1cPsp9cnnRTbET6tu8WRHya2TuLrFdQmniEm8MQAWZCKK9PI3oe1st +mW/tDYXJM/PX8sg0zZsv81YEjCLmt+f/3cfGGbsxQlG416LxujyrESetygPh6HmMhKmbhma onP+Vjcwbq7jnKPjf8oWBnqgmt5PJgXi+LnCjf9LmOUYli43yIwaL7wC8ZZ9rwYUeTVNWaMk Hqr358MqlX13dMOTKn4bC7JyFhaJBPe1SySBKhhBtcBHi96q/9FxG0q5u/MU2U71NtFlPo32 wypGQ3INOn7pBQIIEznNvUVR01ed3X7s6YVy0E3M6UyzGZhvdq7o4UFJBQJpav0GTvoSW8Y/ J6d19/Y4c7PaUHBSbbnY0Xx65OUI6FD1fnYheTgdXn7hJAnl9WtE0W1gVwpxasIJilyG0Pcl pAN3ec+yQfllR+zFNC70R1pNQcYO+su7dXe2yzwSF2AGePgBfFmshietljar2ECgWedjYYIp bi6POsXZmqDZUNiov430Tik1wWj7pZLr5sNLKXBjo/S/dD0cGYXawBA6XxVmpij094CuJqVW GQIpsvbllD92/jpaei/RV/vs/H8o7HpCK/zndGMPLFtYycfrLFPxrL7cwlOfksFWU/+uYhVT YhI1cw1kPMkRAgIjXTdoO1ToSWtDDIgdD8VZY7e5DyIDfLlWHIcerqqhygUCZEYAwU3Avg0K ZW5kc3RyZWFtDQplbmRvYmoNCjMxIDAgb2JqDQo8PA0KL1R5cGUgL0ZvbnREZXNjcmlwdG9y DQovQXNjZW50IDANCi9DYXBIZWlnaHQgMA0KL0Rlc2NlbnQgMA0KL0ZsYWdzIDQNCi9Gb250 QkJveCBbLTUgLTIyMiA1MjkgNjk0XQ0KL0ZvbnROYW1lIC9MRkNEQUcrTVNUVDMxYzdkZjAw DQovSXRhbGljQW5nbGUgMA0KL1N0ZW1WIDANCi9DaGFyU2V0ICgvRzczL0c2Qi9HNzUvRzZD L0c3Ni9HMzEvRzc3L0czMi9HNjQvRzc4L0c3MC9HN0EvRzY3L0c3MS9HNjgpDQovRm9udEZp bGUgMzIgMCBSDQo+Pg0KZW5kb2JqDQozMiAwIG9iag0KPDwNCi9MZW5ndGggOTc3OQ0KL0xl bmd0aDEgNDYzOQ0KL0xlbmd0aDIgNTEzOA0KL0xlbmd0aDMgMA0KPj4NCnN0cmVhbQ0KJSFG b250VHlwZTEtMS4wOiBMRkNEQUcrTVNUVDMxYzdkZjAwIDEKMTMgZGljdCBiZWdpbgovRm9u dE5hbWUgL0xGQ0RBRytNU1RUMzFjN2RmMDAgZGVmIAovRm9udFR5cGUgMSBkZWYKL0ZvbnRC Qm94IHstMTAgLTQ1NSAxMDgzIDE0MjF9IHJlYWRvbmx5IGRlZgovRm9udE1hdHJpeCBbMC4w MDA0ODgzIDAgMCAwLjAwMDQ4ODMgMCAwXSByZWFkb25seSBkZWYKL1BhaW50VHlwZSAwIGRl ZgovRm9udEluZm8gMTIgZGljdCBkdXAgYmVnaW4KL0Jhc2VGb250TmFtZSAoTVNUVDMxYzdk ZjAwKSBkZWYKZW5kIGRlZgovRW5jb2RpbmcgMjU2IGFycmF5CjAgMSAyNTUgezEgaW5kZXgg ZXhjaCAvLm5vdGRlZiBwdXR9IGZvcgpkdXAgMCAvRzAwIHB1dApkdXAgMSAvRzAxIHB1dApk dXAgMiAvRzAyIHB1dApkdXAgMyAvRzAzIHB1dApkdXAgNCAvRzA0IHB1dApkdXAgNSAvRzA1 IHB1dApkdXAgNiAvRzA2IHB1dApkdXAgNyAvRzA3IHB1dApkdXAgOCAvRzA4IHB1dApkdXAg OSAvRzA5IHB1dApkdXAgMTAgL0cwQSBwdXQKZHVwIDExIC9HMEIgcHV0CmR1cCAxMiAvRzBD IHB1dApkdXAgMTMgL0cwRCBwdXQKZHVwIDE0IC9HMEUgcHV0CmR1cCAxNSAvRzBGIHB1dApk dXAgMTYgL0cxMCBwdXQKZHVwIDE3IC9HMTEgcHV0CmR1cCAxOCAvRzEyIHB1dApkdXAgMTkg L0cxMyBwdXQKZHVwIDIwIC9HMTQgcHV0CmR1cCAyMSAvRzE1IHB1dApkdXAgMjIgL0cxNiBw dXQKZHVwIDIzIC9HMTcgcHV0CmR1cCAyNCAvRzE4IHB1dApkdXAgMjUgL0cxOSBwdXQKZHVw IDI2IC9HMUEgcHV0CmR1cCAyNyAvRzFCIHB1dApkdXAgMjggL0cxQyBwdXQKZHVwIDI5IC9H MUQgcHV0CmR1cCAzMCAvRzFFIHB1dApkdXAgMzEgL0cxRiBwdXQKZHVwIDMyIC9HMjAgcHV0 CmR1cCAzMyAvRzIxIHB1dApkdXAgMzQgL0cyMiBwdXQKZHVwIDM1IC9HMjMgcHV0CmR1cCAz NiAvRzI0IHB1dApkdXAgMzcgL0cyNSBwdXQKZHVwIDM4IC9HMjYgcHV0CmR1cCAzOSAvRzI3 IHB1dApkdXAgNDAgL0cyOCBwdXQKZHVwIDQxIC9HMjkgcHV0CmR1cCA0MiAvRzJBIHB1dApk dXAgNDMgL0cyQiBwdXQKZHVwIDQ0IC9HMkMgcHV0CmR1cCA0NSAvRzJEIHB1dApkdXAgNDYg L0cyRSBwdXQKZHVwIDQ3IC9HMkYgcHV0CmR1cCA0OCAvRzMwIHB1dApkdXAgNDkgL0czMSBw dXQKZHVwIDUwIC9HMzIgcHV0CmR1cCA1MSAvRzMzIHB1dApkdXAgNTIgL0czNCBwdXQKZHVw IDUzIC9HMzUgcHV0CmR1cCA1NCAvRzM2IHB1dApkdXAgNTUgL0czNyBwdXQKZHVwIDU2IC9H MzggcHV0CmR1cCA1NyAvRzM5IHB1dApkdXAgNTggL0czQSBwdXQKZHVwIDU5IC9HM0IgcHV0 CmR1cCA2MCAvRzNDIHB1dApkdXAgNjEgL0czRCBwdXQKZHVwIDYyIC9HM0UgcHV0CmR1cCA2 MyAvRzNGIHB1dApkdXAgNjQgL0c0MCBwdXQKZHVwIDY1IC9HNDEgcHV0CmR1cCA2NiAvRzQy IHB1dApkdXAgNjcgL0c0MyBwdXQKZHVwIDY4IC9HNDQgcHV0CmR1cCA2OSAvRzQ1IHB1dApk dXAgNzAgL0c0NiBwdXQKZHVwIDcxIC9HNDcgcHV0CmR1cCA3MiAvRzQ4IHB1dApkdXAgNzMg L0c0OSBwdXQKZHVwIDc0IC9HNEEgcHV0CmR1cCA3NSAvRzRCIHB1dApkdXAgNzYgL0c0QyBw dXQKZHVwIDc3IC9HNEQgcHV0CmR1cCA3OCAvRzRFIHB1dApkdXAgNzkgL0c0RiBwdXQKZHVw IDgwIC9HNTAgcHV0CmR1cCA4MSAvRzUxIHB1dApkdXAgODIgL0c1MiBwdXQKZHVwIDgzIC9H NTMgcHV0CmR1cCA4NCAvRzU0IHB1dApkdXAgODUgL0c1NSBwdXQKZHVwIDg2IC9HNTYgcHV0 CmR1cCA4NyAvRzU3IHB1dApkdXAgODggL0c1OCBwdXQKZHVwIDg5IC9HNTkgcHV0CmR1cCA5 MCAvRzVBIHB1dApkdXAgOTEgL0c1QiBwdXQKZHVwIDkyIC9HNUMgcHV0CmR1cCA5MyAvRzVE IHB1dApkdXAgOTQgL0c1RSBwdXQKZHVwIDk1IC9HNUYgcHV0CmR1cCA5NiAvRzYwIHB1dApk dXAgOTcgL0c2MSBwdXQKZHVwIDk4IC9HNjIgcHV0CmR1cCA5OSAvRzYzIHB1dApkdXAgMTAw IC9HNjQgcHV0CmR1cCAxMDEgL0c2NSBwdXQKZHVwIDEwMiAvRzY2IHB1dApkdXAgMTAzIC9H NjcgcHV0CmR1cCAxMDQgL0c2OCBwdXQKZHVwIDEwNSAvRzY5IHB1dApkdXAgMTA2IC9HNkEg cHV0CmR1cCAxMDcgL0c2QiBwdXQKZHVwIDEwOCAvRzZDIHB1dApkdXAgMTA5IC9HNkQgcHV0 CmR1cCAxMTAgL0c2RSBwdXQKZHVwIDExMSAvRzZGIHB1dApkdXAgMTEyIC9HNzAgcHV0CmR1 cCAxMTMgL0c3MSBwdXQKZHVwIDExNCAvRzcyIHB1dApkdXAgMTE1IC9HNzMgcHV0CmR1cCAx MTYgL0c3NCBwdXQKZHVwIDExNyAvRzc1IHB1dApkdXAgMTE4IC9HNzYgcHV0CmR1cCAxMTkg L0c3NyBwdXQKZHVwIDEyMCAvRzc4IHB1dApkdXAgMTIxIC9HNzkgcHV0CmR1cCAxMjIgL0c3 QSBwdXQKZHVwIDEyMyAvRzdCIHB1dApkdXAgMTI0IC9HN0MgcHV0CmR1cCAxMjUgL0c3RCBw dXQKZHVwIDEyNiAvRzdFIHB1dApkdXAgMTI3IC9HN0YgcHV0CmR1cCAxMjggL0c4MCBwdXQK ZHVwIDEyOSAvRzgxIHB1dApkdXAgMTMwIC9HODIgcHV0CmR1cCAxMzEgL0c4MyBwdXQKZHVw IDEzMiAvRzg0IHB1dApkdXAgMTMzIC9HODUgcHV0CmR1cCAxMzQgL0c4NiBwdXQKZHVwIDEz NSAvRzg3IHB1dApkdXAgMTM2IC9HODggcHV0CmR1cCAxMzcgL0c4OSBwdXQKZHVwIDEzOCAv RzhBIHB1dApkdXAgMTM5IC9HOEIgcHV0CmR1cCAxNDAgL0c4QyBwdXQKZHVwIDE0MSAvRzhE IHB1dApkdXAgMTQyIC9HOEUgcHV0CmR1cCAxNDMgL0c4RiBwdXQKZHVwIDE0NCAvRzkwIHB1 dApkdXAgMTQ1IC9HOTEgcHV0CmR1cCAxNDYgL0c5MiBwdXQKZHVwIDE0NyAvRzkzIHB1dApk dXAgMTQ4IC9HOTQgcHV0CmR1cCAxNDkgL0c5NSBwdXQKZHVwIDE1MCAvRzk2IHB1dApkdXAg MTUxIC9HOTcgcHV0CmR1cCAxNTIgL0c5OCBwdXQKZHVwIDE1MyAvRzk5IHB1dApkdXAgMTU0 IC9HOUEgcHV0CmR1cCAxNTUgL0c5QiBwdXQKZHVwIDE1NiAvRzlDIHB1dApkdXAgMTU3IC9H OUQgcHV0CmR1cCAxNTggL0c5RSBwdXQKZHVwIDE1OSAvRzlGIHB1dApkdXAgMTYwIC9HQTAg cHV0CmR1cCAxNjEgL0dBMSBwdXQKZHVwIDE2MiAvR0EyIHB1dApkdXAgMTYzIC9HQTMgcHV0 CmR1cCAxNjQgL0dBNCBwdXQKZHVwIDE2NSAvR0E1IHB1dApkdXAgMTY2IC9HQTYgcHV0CmR1 cCAxNjcgL0dBNyBwdXQKZHVwIDE2OCAvR0E4IHB1dApkdXAgMTY5IC9HQTkgcHV0CmR1cCAx NzAgL0dBQSBwdXQKZHVwIDE3MSAvR0FCIHB1dApkdXAgMTcyIC9HQUMgcHV0CmR1cCAxNzMg L0dBRCBwdXQKZHVwIDE3NCAvR0FFIHB1dApkdXAgMTc1IC9HQUYgcHV0CmR1cCAxNzYgL0dC MCBwdXQKZHVwIDE3NyAvR0IxIHB1dApkdXAgMTc4IC9HQjIgcHV0CmR1cCAxNzkgL0dCMyBw dXQKZHVwIDE4MCAvR0I0IHB1dApkdXAgMTgxIC9HQjUgcHV0CmR1cCAxODIgL0dCNiBwdXQK ZHVwIDE4MyAvR0I3IHB1dApkdXAgMTg0IC9HQjggcHV0CmR1cCAxODUgL0dCOSBwdXQKZHVw IDE4NiAvR0JBIHB1dApkdXAgMTg3IC9HQkIgcHV0CmR1cCAxODggL0dCQyBwdXQKZHVwIDE4 OSAvR0JEIHB1dApkdXAgMTkwIC9HQkUgcHV0CmR1cCAxOTEgL0dCRiBwdXQKZHVwIDE5MiAv R0MwIHB1dApkdXAgMTkzIC9HQzEgcHV0CmR1cCAxOTQgL0dDMiBwdXQKZHVwIDE5NSAvR0Mz IHB1dApkdXAgMTk2IC9HQzQgcHV0CmR1cCAxOTcgL0dDNSBwdXQKZHVwIDE5OCAvR0M2IHB1 dApkdXAgMTk5IC9HQzcgcHV0CmR1cCAyMDAgL0dDOCBwdXQKZHVwIDIwMSAvR0M5IHB1dApk dXAgMjAyIC9HQ0EgcHV0CmR1cCAyMDMgL0dDQiBwdXQKZHVwIDIwNCAvR0NDIHB1dApkdXAg MjA1IC9HQ0QgcHV0CmR1cCAyMDYgL0dDRSBwdXQKZHVwIDIwNyAvR0NGIHB1dApkdXAgMjA4 IC9HRDAgcHV0CmR1cCAyMDkgL0dEMSBwdXQKZHVwIDIxMCAvR0QyIHB1dApkdXAgMjExIC9H RDMgcHV0CmR1cCAyMTIgL0dENCBwdXQKZHVwIDIxMyAvR0Q1IHB1dApkdXAgMjE0IC9HRDYg cHV0CmR1cCAyMTUgL0dENyBwdXQKZHVwIDIxNiAvR0Q4IHB1dApkdXAgMjE3IC9HRDkgcHV0 CmR1cCAyMTggL0dEQSBwdXQKZHVwIDIxOSAvR0RCIHB1dApkdXAgMjIwIC9HREMgcHV0CmR1 cCAyMjEgL0dERCBwdXQKZHVwIDIyMiAvR0RFIHB1dApkdXAgMjIzIC9HREYgcHV0CmR1cCAy MjQgL0dFMCBwdXQKZHVwIDIyNSAvR0UxIHB1dApkdXAgMjI2IC9HRTIgcHV0CmR1cCAyMjcg L0dFMyBwdXQKZHVwIDIyOCAvR0U0IHB1dApkdXAgMjI5IC9HRTUgcHV0CmR1cCAyMzAgL0dF NiBwdXQKZHVwIDIzMSAvR0U3IHB1dApkdXAgMjMyIC9HRTggcHV0CmR1cCAyMzMgL0dFOSBw dXQKZHVwIDIzNCAvR0VBIHB1dApkdXAgMjM1IC9HRUIgcHV0CmR1cCAyMzYgL0dFQyBwdXQK ZHVwIDIzNyAvR0VEIHB1dApkdXAgMjM4IC9HRUUgcHV0CmR1cCAyMzkgL0dFRiBwdXQKZHVw IDI0MCAvR0YwIHB1dApkdXAgMjQxIC9HRjEgcHV0CmR1cCAyNDIgL0dGMiBwdXQKZHVwIDI0 MyAvR0YzIHB1dApkdXAgMjQ0IC9HRjQgcHV0CmR1cCAyNDUgL0dGNSBwdXQKZHVwIDI0NiAv R0Y2IHB1dApkdXAgMjQ3IC9HRjcgcHV0CmR1cCAyNDggL0dGOCBwdXQKZHVwIDI0OSAvR0Y5 IHB1dApkdXAgMjUwIC9HRkEgcHV0CmR1cCAyNTEgL0dGQiBwdXQKZHVwIDI1MiAvR0ZDIHB1 dApkdXAgMjUzIC9HRkQgcHV0CmR1cCAyNTQgL0dGRSBwdXQKZHVwIDI1NSAvR0ZGIHB1dApy ZWFkb25seSBkZWYKY3VycmVudGRpY3QgZW5kCmN1cnJlbnRmaWxlIGVleGVjCmgZqS/gWyvn oB0N+7+UJgf9X3DsOsx+geVdJRMGeqAtmF4ldnRixA6BJ7/UdUbpNhxBCap0Z5+laPXA4f4+ HnHHF4wAc7vXEDjrNXzuDOAeWQ+0dxWll+wfb14Y5g8Ayaf3qEVbWZ9PHsrc1Wwr9KRL676/ jkbSyBzSLGE90ZCk8yUL+KQm5vSwzlIHp6FUNiULIgPCB2kGjIVoZk6Jegn+gSo/vKCfvVgl ovy0rMwBA+/3EoJMLoD1b67IfMVuqhQgexlhMs7Vml7fdl2cSksqo5Yza8xKORqVmhI/POCT MWSF60LEA+ezaZRcOqBtV3Qx5T+9bhQ7jp85BKR3eDMbQXp5UFT+/3GeOSySbtJNq1OMKAuk Jg1x+/uAMdD9upGZ+0FoQjntP3x0YWtsd4K/Ec8nL94+TECfVb6yUpVnnegp3+8UY2qOK3Zu D4nJ7cf0POaHGgYNZgZ6earAoccC7hXq6bQ3NZW4MX5OUPlO+pyKjVaw3G6T7Z0kIjytesOX cenmVZFhxSZHXD+BbyAeWKsHjPwC36W7MTN5b8VmRJBZfaXyysfAATsZ/VDSZulLdr+c0q9k Wq+2ecTpdFuovETRTc1RGLXds+s9fT4fIW8z0ClYR0qpWm+oV2ETIyG70VPaBrgeMGqoA+Jj orHGq1ZX++jWohnODNBV1c6e27ScIOpkrlM9D5AR3+3qKrTXWtmMZJUNxafQPKnsOz6Uxcs/ WDjmz2O7gPHef77tGl2ZnPJ5v5RRy7O5v93oBno/KU346XkvKYT26ctWrgp8ggPZ8Rm4E+Fd pBQEFIhc5Tk/e+EAFQ7jnwn/vU7kjip36QoqJsFbYcBvE1vTRk28CcYo+M7wmw7Q/i8r0TbA nIIOg/A+p3hr+XrztZeakJGlB92FwEtevSmjOlLG6KdeHbLSzrSFJZ6gdqGOHvzZfY7mm+Ab ioajUIEmiR0Jhns0AeFMk+o5CR1fjTp2jM+MN6rwmmK9i8xM3WNedemQNUogtallMPFGUBHg OPnVJJG8fD2s1faW/G4tTpWyHhlZeQPAd2E8cXn0JGsEBFwkCkzIUHy2O8YWuj1GaBWv9UG3 Q5xX87kMWWR8rtI5sXvnabgqN7JuX5lj9bWWqyqzBI5suE1YFAws2x3gsXSA0Ci02pWQsZtM Kc4ckxF7hamT3f1wa8gyuj2ZHqZswodogmnf+pGAccl5mfZ5G+e0tbc4AtMxeUfdOpRjc//c k+T8LS3vKD5hfE6enPLujCykqNV8suqogF5qpusSrJDdib+iOPHD3SkzaZgMkiuP+Ee8a6k7 jj9TqcPf8IWiFzb99mKoG8bkLhpmLJaMk86FRZ8nDB8fZzpd7kgpY6LTWQ+nBYw+Nw4t