United States v. Flynn
This text of 106 F. Supp. 966 (United States v. Flynn) is published on Counsel Stack Legal Research, covering District Court, S.D. New York primary law. Counsel Stack provides free access to over 12 million legal documents including statutes, case law, regulations, and constitutions.
Opinion
DIMOCK, District Judge.
Defendants’ challenge to the array was disposed of from the bench adversely to the challenge. In view of the possibility of such challenges in future cases, however, I have thought best to record in this opinion the reasons for my action.
We are enjoined by the Supreme Court of the United States, in Thiel v. Southern Pacific Co., 328 U.S. 217, 220, 66 S.Ct. 984, 985, 90 L.Ed. 1181, that “The American tradition of trial by jury, considered in connection with either criminal or civil proceedings, necessarily contemplates an impartial jury drawn from a cross-section of the community.” While the general principle undoubtedly is that the various classes of the population must be fairly represented in this cross-section, it need not be a mathematically correct proportional representation of every class, the rich and the poor, the illiterate and the educated, the young and the old, the dull and the bright. 'In the Thiel opinion itself, Mr. Justice Murphy said, 328 U.S. at page 224, 66 S.Ct. at page 987: “It is clear that a federal judge would be justified in excusing a daily wage earner for whom jury service would entail an undue financial hardship.” Similarly in United States v. Dennis, 2 Cir., 183 F.2d 201, the Court of Appeals relied upon state statutes which fixed property and character qualifications of jurors. When hereinafter using the term “cross-section of the community”, therefore, I shall refer to the result of a random selection tempered by such matters as releases on account of financial hardship or disqualifications for defect of estate or want of proper character.
In support of their challenge to the array defendants stated that the “essence of the challenge is that the method of jury selection is such as to discriminate against manual' workers (and the poor) and against Negro and Puerto Rican citizens, and in favor of the owning, well-to-do classes in the community and their agents and representatives.”
The subject is complicated and perhaps belongs philosophically in the realm of statistics rather than law. At the risk of oversimplification, therefore, I shall attempt to state in advance my conclusion and the method by which it was reached, in the hope that it will make the detailed statement which follows intelligible.
My conclusion is that the list from which the panel of veniremen available for the choice of jurors in this case was drawn by lot constitutes a cross-section of the community. Defendants have, therefore, no tenable ground of complaint.
That list was the subject of defendants’ attack. The list was made up by selection in two steps: in the first step persons were selected to whom should be-sent notices to call at the clerk’s office for consideration of their qualifications; in the second step the qualifications of those who appeared were considered and those whose names were to' be put on the jury list were selected.
Objection was made that, in the first selection, names were chosen in such way as to discriminate against manual workers and non-whites' and that, in the second selection, there was deliberate discrimination against manual workers and non-whites.
To substantiate this charge, defendants offered to prove a discrepancy between the relative proportions of certain classes in the population and on the jury list. In examining the discrepancy the question arose whether it was the result of selection of those who were to receive notices in the first step or was the result of selection from *968 among those who had received notices in the second step or was the result of both.
The jury list from which the veniremen on the panels had been chosen by lot was made up in the past with no attempt to reach a cross-section of the population but in recent years notices have been sent out to persons selected at random and .in accordance with the voting population of the various localities with a modification which I will mention later. Such a method of sending out notices satisfies me as an ideal system. Therefore, if the veniremen resulting from those notices could be separately ranged in classes, the relative proportions of the classes would constitute a norm for the result of an ideal system of selection in the first step combined with the actual practice of selection in the second step.
Such a study proved possible and was made. In the norm resulting from the combination of this ideal first step selection with the actual second step selection the relative proportions of the various classes were substantially the same as on the jury list. It thus appeared that the disproportion between the representation in the population and in the panels arose as a result of selection in the second step.
Accordingly the actual practice of selection in the second step was studied. It appeared that lawful standards of qualification were applied and that there was nothing improper in any of the procedures involved in selecting from among .those who responded to. notices persons whose names were placed upon the jury list. Rejection of the challenge followed.
A few words should be said as to the modification mentioned earlier. While notices were sent to persons in Manhattan and the Bronx during the norm period in strict accordance with the voting population of each" assembly district, notices were going to Westchester without such a careful reference to voting population. This raised a problem with respect to the validity of the result ás a norm since the raw figures may not have reflected an ideal system of selection in the first step. The attempt to solve this problem led to an analysis of the effect on the ideal system of the sending of the notices to Westchester.and also to a method of adjustment to allow for it. A large part of the succeeding discussion is devoted to that attempt at solution. The problem was solved to my satisfaction and I concluded that, notwithstanding the treatment of Westchester in the data, I was justified in holding that the relative proportions of the classes which would have resulted from the combination of an ideal first step selection system and the actual second step practice .would not have differed substantially from the relative proportions of the classes on the jury list.
The Procedure.
To substantiate their allegations, defendants made three written offers of proof. These were supplemented by the testimony of the jury clerk whose duty it is to obtain qualified veniremen in' the Southern District of New York and of the deputy clerk who assists him. These two witnesses were examined by defendants and cross-examined by the Govérnment. In addition, the Government submitted certain tables or analyses and testimony in ‘explanation of them.
The Government moved to dismiss the challenge on the ground that, even if defendants should establish the facts which they offered to prove, the sum total of those facts and the facts as to which oral testimony was given would not suffice to support the challenge.
There was no direct evidence of any intention to discriminate against or in favor of any group, race or class.
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DIMOCK, District Judge.
Defendants’ challenge to the array was disposed of from the bench adversely to the challenge. In view of the possibility of such challenges in future cases, however, I have thought best to record in this opinion the reasons for my action.
We are enjoined by the Supreme Court of the United States, in Thiel v. Southern Pacific Co., 328 U.S. 217, 220, 66 S.Ct. 984, 985, 90 L.Ed. 1181, that “The American tradition of trial by jury, considered in connection with either criminal or civil proceedings, necessarily contemplates an impartial jury drawn from a cross-section of the community.” While the general principle undoubtedly is that the various classes of the population must be fairly represented in this cross-section, it need not be a mathematically correct proportional representation of every class, the rich and the poor, the illiterate and the educated, the young and the old, the dull and the bright. 'In the Thiel opinion itself, Mr. Justice Murphy said, 328 U.S. at page 224, 66 S.Ct. at page 987: “It is clear that a federal judge would be justified in excusing a daily wage earner for whom jury service would entail an undue financial hardship.” Similarly in United States v. Dennis, 2 Cir., 183 F.2d 201, the Court of Appeals relied upon state statutes which fixed property and character qualifications of jurors. When hereinafter using the term “cross-section of the community”, therefore, I shall refer to the result of a random selection tempered by such matters as releases on account of financial hardship or disqualifications for defect of estate or want of proper character.
In support of their challenge to the array defendants stated that the “essence of the challenge is that the method of jury selection is such as to discriminate against manual' workers (and the poor) and against Negro and Puerto Rican citizens, and in favor of the owning, well-to-do classes in the community and their agents and representatives.”
The subject is complicated and perhaps belongs philosophically in the realm of statistics rather than law. At the risk of oversimplification, therefore, I shall attempt to state in advance my conclusion and the method by which it was reached, in the hope that it will make the detailed statement which follows intelligible.
My conclusion is that the list from which the panel of veniremen available for the choice of jurors in this case was drawn by lot constitutes a cross-section of the community. Defendants have, therefore, no tenable ground of complaint.
That list was the subject of defendants’ attack. The list was made up by selection in two steps: in the first step persons were selected to whom should be-sent notices to call at the clerk’s office for consideration of their qualifications; in the second step the qualifications of those who appeared were considered and those whose names were to' be put on the jury list were selected.
Objection was made that, in the first selection, names were chosen in such way as to discriminate against manual workers and non-whites' and that, in the second selection, there was deliberate discrimination against manual workers and non-whites.
To substantiate this charge, defendants offered to prove a discrepancy between the relative proportions of certain classes in the population and on the jury list. In examining the discrepancy the question arose whether it was the result of selection of those who were to receive notices in the first step or was the result of selection from *968 among those who had received notices in the second step or was the result of both.
The jury list from which the veniremen on the panels had been chosen by lot was made up in the past with no attempt to reach a cross-section of the population but in recent years notices have been sent out to persons selected at random and .in accordance with the voting population of the various localities with a modification which I will mention later. Such a method of sending out notices satisfies me as an ideal system. Therefore, if the veniremen resulting from those notices could be separately ranged in classes, the relative proportions of the classes would constitute a norm for the result of an ideal system of selection in the first step combined with the actual practice of selection in the second step.
Such a study proved possible and was made. In the norm resulting from the combination of this ideal first step selection with the actual second step selection the relative proportions of the various classes were substantially the same as on the jury list. It thus appeared that the disproportion between the representation in the population and in the panels arose as a result of selection in the second step.
Accordingly the actual practice of selection in the second step was studied. It appeared that lawful standards of qualification were applied and that there was nothing improper in any of the procedures involved in selecting from among .those who responded to. notices persons whose names were placed upon the jury list. Rejection of the challenge followed.
A few words should be said as to the modification mentioned earlier. While notices were sent to persons in Manhattan and the Bronx during the norm period in strict accordance with the voting population of each" assembly district, notices were going to Westchester without such a careful reference to voting population. This raised a problem with respect to the validity of the result ás a norm since the raw figures may not have reflected an ideal system of selection in the first step. The attempt to solve this problem led to an analysis of the effect on the ideal system of the sending of the notices to Westchester.and also to a method of adjustment to allow for it. A large part of the succeeding discussion is devoted to that attempt at solution. The problem was solved to my satisfaction and I concluded that, notwithstanding the treatment of Westchester in the data, I was justified in holding that the relative proportions of the classes which would have resulted from the combination of an ideal first step selection system and the actual second step practice .would not have differed substantially from the relative proportions of the classes on the jury list.
The Procedure.
To substantiate their allegations, defendants made three written offers of proof. These were supplemented by the testimony of the jury clerk whose duty it is to obtain qualified veniremen in' the Southern District of New York and of the deputy clerk who assists him. These two witnesses were examined by defendants and cross-examined by the Govérnment. In addition, the Government submitted certain tables or analyses and testimony in ‘explanation of them.
The Government moved to dismiss the challenge on the ground that, even if defendants should establish the facts which they offered to prove, the sum total of those facts and the facts as to which oral testimony was given would not suffice to support the challenge.
There was no direct evidence of any intention to discriminate against or in favor of any group, race or class. The claim was that the representation of manual workers, Negroes and Puerto Ricans was disproportionate to their numbers and that that disproportion was circumstantial evidence of discrimination against them.
In support of that claim defendants offered to prove various facts as to the composition of the jury list from which the array was drawn and to prove the breakdowns of the last thirteen panels drawn from that jury list, and of certain portions of the jury list itself, as among various employment groups and geographical areas.
History of the Current Jury List.
On the matter of the make up of the jury list defendants offered to prove .that the *969 basic jury list was established as the result of a reorganization of the method óf jury selection undertaken in about 1938 and continuing to about April, 1943. The purpose and methods of reorganization were said to be accurately described in the Report of Leland D. Tolman, of the Administrative Office of the United States Courts, dated January 2, 1941. The Report states that, prior to the reorganization, the juries were overcrowded with relief workers and housewives due to general economic conditions in New York City. Defendants said that the reorganization required (1) the “requal-ification” of all then on the list, i. e., the elimination of all those failing to meet the standards of “superiority”, “high class” and the like, reflected in the new sources of names referred to below and (2). the substitution of new names of those who met the standards.
The new names were said' to have been supplied from two sources: 18,291 names were furnished by the Federal Grand Jurors Association until April 1943, 1 and an unspecified number of names were obtained by the jury clerk himself.
Both used similar sources of names. They were Who’s Who in New York, Who’s Who in Engineering, The Social Register, Poor’s Register of Executives, the Directory of Directors, the alumni directories of Princeton, Columbia, Harvard, Yale and Dartmouth, the subscription edition of the telephone directory (“arranged by street numbers rather than alphabetically by names” and thus “especially valuable, since it permits the jury clerk to select names from neighborhoods where he knows-that persons who are most likely to be suitable material reside”), volunteers nominated by the Association and the like.
After the reorganization the list was kept replenished by requalification of certain persons on the original list, by drawing for a time on the lists supplied by the Grand Jurors’ Association, by use of a list of se-. lected Negroes, by accepting volunteers and recommended persons, and, to the largest extent, by the use of voting lists. It was charged that the choices from.the voting lists were made in such a way as to favor the neighborhoods inhabited by whites and members of the higher income groups. j
That is the alleged history of the jury list up to December 31, 1948, when it was considered under a similar challenge in U. S. v. Foster, D.C., 83 F.Supp. 197, affirmed sub. nom. U. S. v. Dennis, 2 Cir., 183 F.2d 201.
At that time there was in existence a backlog of qualification notices prepared by the clerks from voting lists under instructions to select the names at random. These' notices were sent out for the purpose of bringing in candidates to; qualify for the jury list. The backlog was ’ exhausted in October of 1949. The breakdown of this backlog by assembly districts in Manhattan- and the Bronx discloses a- widely disproportionate distribution.
Beginning on November 1, 1949, the voting lists were practically the only source of new names, the only exceptions being in the case of volunteers or recommended persons who, it was estimated by the jury clerk, did not run over 100 a year. The procedure employed was calculated to keep the number of notices sent to each voting district substantially equal and to make the choice of names haphazard.
On April 14, 1950, in order to insure the haphazard character of the choice, the procedure for choosing the names from the voting lists was changed by the introduction of the “punch system”. This method was tc punch holes through each pamphlet, voting list at points fixed on the top page by an arbitrary rule and then to take off the names on each page which had holes opposite them. *970 This system has been employed since then for the selection of the names of persons to whom notices are to be sent in Manhattan and the Bx'onx.
Continuously after November 1, 1949, the number of notices sent to each district has been substantially equal. In September 1950, it was, however, decided to send exactly the same ntimber of notices to each district and thereby avoid even minor discrepancies between the numbers of notices that the respective districts received.
A month before this, in August 1950, the jury clerk first started regularly sending notices to persons residing in Westchester County. These names were all taken from voting lists by use of a haphazard method of selection and an equal number of notices were sent for each voting list representing a Westchester municipal subdivision.
Defendants conceded that an attack on the make up of the jury list up to December 31, 1948, was rejected by this court and the Court of Appeals in U. S. v. Foster, 83 F.Supp. 197, affix-med sub. nom. U. S. v. Dennis, 2 Cir., 183 F.2d 201, but they, said that the attack was unsuccessful because (1) it failed to show that what they call the improper methods of selection employed up to 1943 had any effect on the panels in question and (2) because the methods of sampling were held to be capricious.
Defendants proposed to show the relation between the “improper” lists and the present panel and at the same time to avoid the criticism of capi'iciousness by analyzing the names on all of the last thirteen panels. 2
Content of the Current Jury List.
Defendants had tabulated the home addresses and occupations listed on the cards-of the 3,725 veniremen on the current panels.- They had also tried to determine the initial qualification dates of veniremen on those panels- but these dates appeared for only 3,318 of them.
Of the 3,318 where dates could be obtained, they said that 924 qualified before July 1942, and they thus developed the proposition that more than one-quarter of the / veniremen serving as the regular jurors in the court were the product of the methods used during the reorganization. They added that all but 1,086 were qualified before December 31, 1948, and were thus “survivors of the selective and discriminatory pre-Dennis methods”..
To support the charge that what they called discriminatory methods of selection had actually resulted in discrimination, defendants attempted to show that the veniremen from Manhattan and' the Bronx in the current panels were drawn predominantly fi-om the neighboi-hoods inhabited by whites and by the higher income groups and from the higher paid occupations. A specific example of their geographical charge is that the predominantly Negro 14th Manhattan Assembly District produced but %o of 1% of the veniremen from Manhattan although it contained 3% of the.voters in Manhattan. An example of their occupational charge is that labofers made up but a small fraction of 1% of the veniremen although they made up 6% of the population in the New York Metropolitan area.
The defendants offered to prove numerous breakdowns showing the relationship between representation of geographical areas or employment groups on the jury list and the current panels, on the one hand, and on the voters’ lists and in the .population, on the other.
While -it is doubtful that general population or even voting population is a proper standard of comparison to determine whether the jury list constitutes a fair cross-section of the community, in view of the special standards of juror qualifications, I pass that point.
Alleged Overweighting of White and Wealthy Neighborhoods in Sending out Jury Notices in Manhattan and the Bronx.
The results of the geographical breakdowns are summarized in Appendix A. Assembly districts are grouped in classes defined by defendants and called by them *971 the “favored”, “middle” aiid “excluded” 3 districts within Manhattan and the Bronx.
Defendants complained that the small representation of the “excluded” districts on the current panels in relation to their voting population evidenced discrimination against those districts or against the classes of persons that resided within them and was in part the result of the pre-1942 and pre-Dennis methods of selection. The veniremen selected under the more recent haphazard and equal distribution methods, however, fell into substantially the same disproportions among the districts as the veniremen selected during the earlier periods and the veniremen on the current panels. -To be sure, the “excluded” districts have increased their representation to some extent under these methods. Further analysis, however, suggests that the increase in representation of the “excluded” districts is due to the fact that, since September 1950, they have been receiving a higher proportion of notices than their proportion of the voting population would warrant. 4 These breakdowns appear in Appendix A. At any rate, as a result of the distribution methods adopted since September 1950, the excluded districts have been favored rather than discriminated against if voting population breakdowns are accepted as a proper standard for comparison.
It would be natural to conclude therefore that the representation of the “excluded” districts on the current panels is substantially what it would be under a method of sending out notices designed to obtain as veniremen a fair geographical cross-section of the community.
Since defendants’ claim of discrimination against Negroes and Puerto Ricans rests primarily on the alleged neighborhood or geographic discrimination, it does not require any separate consideration.
Alleged Overweighting of Non-Manual Employment Groups in Sending out Jury Notices.
Turning now from geographical considerations to the results of the breakdowns into employment groups or occupational classifications, a very similar picture appears. These results are summarized in Appendix- B. The proportion of “manual” workers among the veniremen that have been selected during the operation of the recent haphazard and equal method is substantially the same as their proportion of representation among the veniremen selected theretofore and their proportion among the veniremen on the current panels.
Again, as in the case of representation of geographical districts, the natural conclusion would be that the representation of “manual” workers on the current panels was substantially what it would be under a method of sending out notices designed' to obtain a- fair cross-section of the community.
Ways in which Treatment of Westchester is Alleged to Disqualify the Recent Method of Sending out Notices as One Designed to Obtain a Fair Cross-Section.
*972 Except for a qualification with respect to notices sent to Westchester County, these conclusions were practically conceded by the defendants when it was stated, at a later state of the challenge, in their Supplemental Offer of Proof, p. 15 “The names taken from voting lists by the present mechanical methods of selection, apart from the unfair representation accorded West-chester, appear to afford a random and fair method of selection.”
The argument urged in opposition to the conclusion that the representation of “manuals” is what would result from a fair method • of sending out notices was that the recent method of distribution does not qualify as a fair method because the distribution of notices to Westchester County was improper in two respects.
1. Alleged Favoring of Westchester : with Higher Share of Notices.
First, it was urged • that Westchester County received a higher proportion of notices that its proportion of the combined New York, Bronx and W.estchester County population would warrant. 5 That is, as shown by Appendix C, there were sent to Westchester County about 23% of the notices, while it has only 15.5% of the population in the three counties. Actually, however, population figures aré probably not •truly indicative of the numbers of qualified veniremen in any area. The total voting registration is, I should think, a better standard. Indeed the defendants have so contended with respect to Manhattan and the Bronx. In the Defendants’ Offer of Proof, p. 25 it is stated, “Since the populations of the various assembly districts are not equal and because there are larger percentages of children in the working class, Negro and Puerto Rican districts, it is more appropriate, and more fair to the jury officials, to use voters as the basis of comparison. Moreover voters, more than any other group, approximate the qualifications for jury service and the clerk himself claims to use voting lists as the source of names.” By this standard, Westchester County should receive about 19% of the notices. ,■ ’
This difference of about 4% of all notices sent out is hardly large enough to have produced a significant result in the breakdown by occupational classification and thus vitiate the conclusion that current panels are substantially what would be obtained by a method of sending out notices designed to achieve a fair employment cross-section of the community. ;
It is worth noting that, even on the defendants’ theory that Westchester County should be sent notices only in proportion to its population, during the periods of September 1950 through March 1952 and April 1951 through March.1952, the “excluded” districts in Manhattan received more notices than their proportion of the voting population would warrant and that excess much more than makes up for the number of notices by which Westchester exceeded its proportion of the total population. For these figures see Appendix C.
I might add also that it appears that no notices, except for some volunteers or recommendations, were sent to Westchester County from January 1949 to, July 1950. This may also have been true in 1947 and 1948. At any rate, it seems that West-chester County ' received less rather than more notices than its proportion of even general population would require during the period from January 1949 through March 1952.
*973 2. Alleged Favoring of Non-úrban Parts of Westchester.
Second, it was urged that breakdowns of occupational classifications of Westchester veniremen selected under the haphazard and equal methods of selection were not truly indicative of the result that would be obtained under a proper system because the numbers of notices sent to the residents of the respective municipal subdivisions did not vary in direct relation to population.
Defendants pointed out that the three largest cities ' in Westchester, Yonkers, Mount Vernon and New Rochelle, have 45.4% of the population but received only 14.9% of the notices sent to Westchester County from August 1950 to the time of the challenge. That came about in this‘way. At the time when notices were first regularly sent to Westchester in August- 1950 the practice of sending an equal number of notices to the persons listed on the voting register in each Assembly District in Manhattan and in each in the 'Bronx had been started. The voting. registers of the various municipal subdivisions in Westchester were similarly used. To determine who should be sent notices, an equal number of names was picked at random from each voting register without regard to the relative numbers of names in the various registers or the relative populations of the various municipal subdivisions. Later this was modified by the elimination of places in Westchester so far away that the transportation and lodging involved in jury service cost more than the pay. The net result was, however, as claimed by defendants, that the larger places received a disproportionately small number of notices. Indeed, this is .true whether-we relate -this number to the total population, as did the defendants, or to the voting population. This, said defendants, rendered the occup-ation&l- -breakdowns of veniremen -qualified' since September 1950 useless as a suitable basis for comparison with the results of the prior methods of selection.
This conclusion however is rather doubtful for it rests on the premise that there is a considerable difference between populations of the three largest cities and the rest of the county with respect to their “manual” worker and “non-manual” worker 6 breakdowns. According to the census data for 1940, which are .the latest ones available at this time, that difference between the percentage of “manuals” in the three largest cities and the percentage of “manuals” in the rest of the county is 1.5%. For these breakdowns see Appendix D. The fact, then, that the threé largest cities received less than their proportionate share of notices, while the rest of the county received more than its share, should not in any significant degree change the breakdowns of veniremen or prevent the breakdowns of veniremen qualified since September 1950 from being regarded as a fair indication of the results of a method of sending out notices directed towards obtaining a fair cross-section of the community.
This is not to say that there are not other instances of disproportionate distribution within Westchester County or that these disproportions would have no effect on the occupational breakdowns. No. doubt, the occupational breakdowns among various of the subdivisions of Westchester County vary appreciably. I cannot say that an ideal method of distribution was employed with respect to Westchester County. To be sure, I would not attempt to define an ideal method. Nevertheless, all the variations with respect to Westchester County, taken together, do not impress me as being significant enough to prevent the acceptance *974 of the recent figures as the result of a haphazard and equal distribution of qualification notices.
This conclusion is further supported by a mathematical process of adjustment t'o eliminate the disproportions of distribution of notices within Westchester County and any excess of the total notices sent West-chester County over what its proportion of voting population would ' require. This mathematical process is discussed in Appendix E.
The distribution of notices largely in proportion to voting population since September 1950 would satisfy me as being so reasonably close to the method of sending out ¡notices which would be adopted in properly seeking a fair cross-section of community as veniremen than the resulting occupational breakdown should afford a fair standard of comparison.
The Attack on the Qualification Process.
The above discussion actually serves to introduce the main point raised by the defendants. The list of persons to whom qualification notices were sent was not the subject of vigorous attack by the defendants. It was on their attack on the qualification process that defendants finally placed their main reliance.
Their argument was that, while the recent methods of selecting the names of the prospective jurors might perhaps be said to have been directed towards obtaining a .fair cross-section of the community, there was discrimination or favoritism in the qualification process administered by the jury clerks. It is on this feature of the challenge that defendants in the end placed their greatest emphasis.
The Qualification System.
The notices that are sent out to prospective veniremen direct them to appear before the jury clerks for qualification as veniremen. It has been found that in 97 or 98% of the cases the person to whom the notice is sent responds either in person or by mail, or the notice is returned as not delivered. There are two- stages in the process. When the prospective venireman appears the clerks ask him if he knows of any reason why he should not serve. At the close of this stage more than half of the persons to whom notices have been sent are eliminated either because of statutory exemptions or- disqualifications that are immediately made known or because of physical or financial hardship or because the notice was returned as not delivered. After this, each remaining prospective venireman is given a qualification questionnaire to fill out. When the prospective venireman returns with the questionnaire, the clerk examines the questionnaire and sometimes converses with him. At this time, some prospective veniremen raise for the first time claims of hardship or statutory exemptions and some of them are then excused for these reasons. The questionnaires of those remaining are then considered and the clerk determines whether the prospective venireman is qualified. The basis of the determination of disqualification is generally noted on the questionnaires. The reasons for disqualification vary and include the following: government employment, incomplete questionnaires or statutory grounds, such as age, residence, citizenship and prior criminal conviction. In some instances, the defendants assert, no ■reason for disqualification has been noted on the questionnaires. However, the jury clerk stated that in such cases the reason was usually apparent on the face of the questionnaire and when examined concerning three particular questionnaires he pointed out that two contained mis-spellings and one showed that the prospective venireman would in all likelihood be over age by the time he was called to serve.
Three Grounds of Attack on the Qualification Process. '
After studying the jury clerk’s records and offers of both sides to prove various statistical analyses of the qualification process, defendants made three contentions.
1. Alleged Higher Proportion of Manual Workers Just After Dennis Case as Evidence of Manipulation.
First, it was asserted that the proportion of “manual workers” qualified as venire *975 men was higher during the period from January 1, 1949 to August 1950 than for the period September 1950 to date when the haphazard and equal method of selection was employed. It was claimed that the explanation for this discrepancy is that jury clerks qualified more “manual workers” in the former period as an immediate result of the Dennis case and that in the latter period they returned to- discriminatory methods. I find, however, that the sounder explanation is that during the former period a very small number of notices were being sent to Westchester 'County and during the latter period the practice of regularly sending notices to Westchester County was in full operation. It has been defendants’ position, and I would say to some extent it is reasonable to assume, that West-chester County has a higher proportion of “non-manual” workers than Manhattan and the Bronx and that the result of sending a fair proportion of notices to Westchester County would be to raise the proportion of “non-manual workers” on the jury list. That would mean that during the recent period a better cross-section of the Southern District of New York was obtained than during the period from January 1949 to August 1950 when Westchester County was not afforded its proportional representation.
2. Alleged Suppression of “Excluded District” Names and Introduction of “Favored District” Names at the Questionnaire Stage.
Second, the defendants contend that somehow more prospective veniremen from the “favored districts” and Westchester County received questionnaires than their proper proportion while fewer prospective veniremen from the “excluded districts” received questionnaires than their proper proportion. This result was said to flow from a statement by the Government, in a footnote to one of the tables that it presented, that the total number of persons excused prior to the questionnaire stage in the qualification process during the period of April 1951 to March 1952, being 6,398, was excessive by about 900. It was as a result of an attempt to recast the figurés into a corrected form that defendants purported to find that an excessive number of prospective veniremen from the “favored districts” and Westchester received questionnaires.
Defendants reached that result in this way. They said -that 90%398, or 14.5% 7 of those persons excused prior, to questionnaire should be eliminated. In other words, defendants said that, in order to get the correct number of persons excused prior to questionnaire, the number listed as excused prior to questionnaire should be reduced by 900, or 14.5%. Defendants assumed that these 900 cases were distributed ratably among the geographic districts, Manhattan, Bronx and Westchester and further among the “favored”, “middle” and “excluded” districts in each and so reduced by 14.5% for each area the number of persons listed as excused prior to questionnaire. Reducing each number by 14.5% was supposed to give the correct number of persons for each area who were -excused prior to questionnaire. By deducting these numbers from the number of qualification notices actually-'sent out, defendants produced a set of figures which they called the numbers of those “available for questionnaire”. It appears that the total number of persons who filled out questionnaires was substantially the same as the total “available for questionnaire” but the number calculated by the defendants as “available for questionnaire” for each area varied widely from the actual number of questionnaires from that area. Indeed, as defendants claim, there were more actual questionnaires from “favored districts” and Westchester than the number found by defendants to be “available for questionnaire” from those areas and fewer actual questionnaires from the “excluded districts” than the number found to be “available’ for questionnaire” from those areas. The fact, however, that the totals are substantially the same indicates that these discrepancies are the result of improper allocation of the alleged excess between the various areas rather than the result of some irregularity at the question *976 naire stage by which there were included among the questionnaires for the favored districts questionnaires of persons who had not been sent notices and there were abstracted from among the questionnaires for the “excluded” districts questionnaires of persons to whom notices had been sent.
This contention of defendants that the inhabitants of the “favored districts” were given more than their fair share of the questidnnaires rested on the basic assumption that the rate of over-statement of persons excused prior to questionnaire was the same in each district. This may or may not be true and the records showing the prospective veniremen excused prior to questionnaire were available to the defendants so that they could have offered to demonstrate whether the assumption of the uniform rate of over-statement among all the districts was or was not borne out by the facts. In the ■ absence of such an offer I would not infer that somehow questionnaires were given to persons from the “favored districts” to whom qualification notices had not been sent while approximately the same number of questionnaires of prospective veniremen from “excluded districts” were being destroyed. The more reasonable inference is that cases of overstatement did not occur uniformly for all geographic areas.
Incidentally, some attempt should be made to account for the Government’s admission that the records indicated an over-statement by 900 of the persons excused prior to questionnaire. That over-statement appears when one takes the sum of those excused prior to questionnaire plus the number signing questionnaires and sees that this sum exceeds by about 900 the number of notices sent out.
Properly speaking it is not accurate to say that the discrepancy of 900 in the number of persons excused is wholly an overstatement. Some at least of this so-called over-statement is due to the mis-matching of the time periods during which notices were sent out and the number of persons processed. Or, putting it in other words, the discrepancy arose to some extent from an error in the statement of the total of notices sent out for the period, rather than from an actual over-statement of the number .of persons excused prior to questionnaire. The reason for this is that the number of notices used in the calculation was the number sent out during the period April 1951-March 1952. There is, 'however, always a lag of two. or more weeks between the sending out of notices and the processing of the prospective veniremen who respond. Hence, the number of notices sent out during a year would never correspond exactly with the number of persons processed during that year. The number of persons processed for the two-week or longer lag at the beginning of the year will correspond to the number of notices sent out during the corresponding two weeks or longer at the end of the preceding year. With respect to the period in question, larger numbers of notices were being sent out at the end of the previous period than at the end of the period in qustion, thus a smaller total of notices were sent out during the April 1951-March 1952 period than the total of persons processed. This would indicate that the number of notices sent out was understated rather than that the number of persons excused prior to questionnaire was overstated. It is most likely, therefore, that the over-statement of the number of persons excused prior to questionnaire was not nearly as much as 900.
In addition, the government’s footnote indicates that this excess of 900' was due to duplicates in the files and the testimony of the jury clerk was that, for various reasons, there were many situations which would result in the presence in the qualification notice files of more than one paper relating to the excuse of a particular prospective venireman. One such situation comes about when a person receiving a notice responds to it by returning the notice on one occasion and on another occasion communicates his excuse by mail. All communications relating to excuses are filed chronologically in the file of qualification notices and, where they are received on different dates, the result is that the qualification notice file contains two or more different excuses for the same prospective venireman with no indication on their face that they *977 are for the same prospective venireman. Another situation arises because some persons receiving notices ask for postponements. For each ’postponement date a new notice is sent and on the occasion of each postponement for such a person the notice or a written notation is placed in the qualification notice’ file. This will thus again produce duplications. It seems unlikely that extra riotices or other papers resulting from this- sort of situation were counted in computing the total of persons excused prior to questionnaire because they would not contain entries showing that the persons were excused. This is, however, not entirely clear. On the other hand it does seem likely that duplications resulting from the first type of duplication-producing situation discussed above were included in the total of persons excused prior to questionnaire and would therefore account for some overstatement. The combination of this factor with the mis-matching of time periods does, I believe, explain the so-called over-statement of 900.
3. Alleged Improper Rejections of Prospective Veniremen by Jury Clerks.
The third and main contention with respect to the qualification process was that the jury clerks discriminated against manual workers and certain areas in excusing prospective veniremen.
First, it was said that the percentage of manual workers excused exceeded the percentage of non-manuals, thus evidencing discrimination.
Second, it was said that various grounds on which prospective veniremen were excused were invalid and that the excuses on these invalid grounds worked discrimination against manual workers and- certain areas.
It will be remembered that the qualification process consists of two steps. In the first step persons to whom notices were sent are excused without filling out questionnaires either because óf statutory exemptions or disqualifications or because of physical or financial hardship or because the notice was returned as not delivered. As was mentioned earlier, á majority of the -persons sent notices are eliminated at this step. No complete occupational breakdown of this group was made. In an occupational breakdown of all employed males filling out questionnaires at the second step during the April 1951-March 1952 period which defendants offered to prove, the representation-of “manual”- workers among employed males is 27.5%. I have found the method of sending out notices during this period to be a fair one. Defendants argued that “manual” workers would represent 54% of the persons receiving notices under a fair method of sending notices. If that assumption be granted it follows that almost one-half of the “manual” workers’ representation is lost at this first step. Yet, with one exception, defendants have not criti-cised the actual practice of the jury clerks in excusing persons at this first step or offered to prove any facts with respect to it. The exception is that defendants did offer to prove the numbers of “manual” and “non-manual” workers excused at the first step because of financial hardship. This offer would show that a large number of “manual” workers were excused for that reason and that it was three times the number of “non-mánual” workers excused for that reason. That is just what one might expect, however, for it is reasonable to assume that the financial hardships of jury service will fall hardest on the “manual” workers since they are more likely to be employees, salaried or non-salaried, who would lose their normal earnings by service on juries. The number of “non-manuals” excused for this reason, though only one-fourth of the total, was substantial and this too points to the conclusion that the offered proof does not even tend to show a discriminatory practice by the jury clerks.
With .respect to the second step of the qualification process defendants offered to prove the numbers of “non-manual” and “manual” worker employed male veniremen eliminated. They divided those eliminated into two categories: first, those that they described as “Rejected for Statutory Grounds” which they said included the reasons noted on the questionnaires: “non-residence, non-citizens, those-unable to, read *978 or write English, those not having $250 in property, conviction of a felony, over age, all exemptions based upon occupation or otherwise, employees of federal, state or local governments, and physical defects”; second, those that they described as “Rejected for grounds not provided in the statute” which they said included (a) the reasons noted on the questionnaires: incomplete questionnaire, financial hardship, appearance or manner, and any reason other than these three or the “Statutory Grounds” ■and (b) the reasons where none was noted on the questionnaire.
Defendants contended that, in the eliminations for “non-statutory” reasons particularly, the elimination was accomplished so as to discriminate against “manual” workers. They based this contention on tables showing that, after elimination of prospective veniremen at the first step and for “statutory grounds”, there were eliminated for “grounds not provided in statute” a higher percentage of the “manual” workers than of the “non-manual” workers. While this may be so, the absolute number of “non-manual” workers eliminated for “grounds not provided in statute” exceeded the absolute number of “manual” workers eliminated for these reasons. This is also true of prospective veniremen eliminated for “statutory grounds”. The reason for the higher percentage of elimination of “manual workers” after eliminations at the first step and after eliminations for “statutory grounds” is that at that point there were almost three times as many “non-manual” workers left as “manual” workers and therefore the elimination of any given number of “manual” workers would produce a much higher percentage of elimination than the elimination of the same number of “non-manual” workers. To carry their argument to its logical conclusion defendants would have to contend that many fewer “manual” workers should be eliminated at this point than “non-manual” workers, but there is no plausible basis for such a contention. If anything, the absolute numbers of “manual” workers and “non-manual” workers eliminated for both “grounds not provided in statute” and “statutory grounds” tend to show that these standards were applied fairly rather than discrimina-torily. The major elimination of “manual” workers appears tp be at the first step in the qualification process and I find no facts even tending to show discrimination by the jury clerks there.
These same arguments apply in principle, though there is a difference of degree, to the part of the offer of proof similarly designed to show discrimination against the “excluded” districts and I regard an extended discussion of their application unnecessary.
I turn to the objection that some of the reasons assigned for disqualification were not valid and worked to discriminate against manual workers. In these cases defendants must show, first, that the reasons were invalid and second that their application worked discrimination. Defendants urged that incomplete questionnaires and mis-spellings on the questionnaire are not proper grounds for disqualification and that eliminations made where there was no reason noted or the reason noted was “appearance or manner” were not proper.
In view of the standards of qualification fixed by statute that the jurors be able “to read, write, speak and understand the English language”, not have been convicted of a felony, 8 be the owner or married to the owner of property worth $250, “be intelligent; of sound mind and good character; well informed”, if from New York City 9 and “of fair character; of approved integrity; of sound judgment; and well informed” if from other New York counties, 10 all the grounds for disqualification considered, except perhaps “appearance and *979 manner,” impress me as being proper. The omissions in the vast bulk of incomplete questionnaires were of answers to questions seeking to elicit information on statutory standards such as the property requirement and clear criminal record, while the fact of omissions in other parts of questionnaires and the mis-spellings in questionnaires bore directly on the prospective venireman’s qualifications under the more general statutory standards.
That leaves the instances where no reasons were stated or where the reason noted was “appearance or manner”.
With respect to the. instances where no reason was noted on the questionnaire the jury clerk stated that the reason was usually apparent on the face of the questionnaire and, when defendants examined him concerning three questionnaires where no reason was noted, he pointed out that two contained mis-spellings and one showed that, the prospective venireman’ would in all likelihood be over age by the time he was called to serve on a jury. No further attempt was made by defendants to show that there was anything improper done by the jury clerks in eliminating persons where they did not note any reason.
In those instances where the notation indicated “appearance or manner” as the reason for elimination that designation did not indicate discrimination whatever we may think of its validity as a basis for disqualification. Defendants did no more than offer to prove the number of questionnaires bearing such notations, 11 and surely from that, no inference of discrimination can be drawn.
On the other hand, defendants did make various occupational and geographical breakdowns of the questionnaires for the April 1951-March 1952 period and also various analyses of these same questionnaires showing the reasons for disqualification. Yet, despite the availability of the information necessary to make such breakdowns and analyses, defendants did not offer to prove that eliminations because of “appearance or manner” showed any discrimination against “manual” workers or any geographical area.
In the face of this lack of any evidente of discrimination in connection with the “appearance and manner” cases offered on behalf of defendants who had the burden of proof, there was the emphatic testimony of the jury clerks to the effect that there had been no conscious discrimination against “manual” workers or any other group.
Thus, even assuming that “appearance and manner” was an improper ground for rejection, the evidence was overwhelming that it was not used as the basis for discrimination.
Likewise with respect to the other criti-cised grounds: incomplete questionnaires, mis-spellings and .“no reason noted”, they were not only clearly valid but were, like “appearance and manner” overwhelmingly shown not to have been the basis of discrimination.
The testimony of the jury clerks convinces me and I find that there has been no intentional or systematic discrimination against any race, creed, color of residential area in the qualification process. On the contrary, I find that they have made an honest effort to obtain a fair cross-section of the community and have determined the qualification of each prospective juror on an individual basis without regard to race, color, creed or residence.
Conclusion
It seems to me obvious that the requirements for a “cross-section of the community” are satisfied if the names are selected for the jury list at random from among the persons residing in the district reasonably convenient to the court house and the persons finally placed thereon are chosen by rules and standards of qualification which, except as permitted by established criteria such as financial hardship *980 or the property or character requirements, do not deliberately or systematically discriminate against either sex or any geographical area or any economic, occupational, social, racial, religious or other group. Defendants have no vested right in any method of selection. If the list from which the panel is taken is substantially the same as would have resulted from a random selection subjected to these proper methods of qualification defendants can have no complaint.
That is the state of the case here. Ever since September 1950, jurors have been added to the list in the Southern District of New York by just such a random selection subjected to just such methods of qualification. Yet, as above stated, the breakdown into geographical areas and occupational groups of the prospective jurors thus obtained reveals substantially the same numerical ratios as in the case of current panels taken from a jury list which contained the remains of the pre-Glasser 12 and pre-
Foster 13 eras.
I therefore 'find that the list from which the panel to be used in this case was drawn by lot constitutes a cross-section of the community.
In reaching this conclusion I have treated as before me all of the records of this court with respect to the make-up of the panel drawn for this case and of the jury list from which it was drawn including, without intention of limitation, all of the jury list cards, the “wheel” cards (used for choosing panels by lot from the jury list) the qualification notices, the questionnaires and the jury clerk’s record books. The various analyses prepared by both the Government and the defendants, the accuracy of none of which has been seriously impugned, are accepted as in the nature' of argumentative compilations. I have treated as established everything that defendants offered to prove except where the subject matter was covered by the testimony of the court clerks. There I have substituted the proof for the offer. I have also made a few minor additions and corrections to the offer which are indicated herein or in the Appendices.
*981
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ttO7Qvquy34nXp1jWiKewBGowbcNvEzFsIdF8IHsmCccuOztOxwFaUcL6RlBWqv+JkB4BszPeajppjg4jV5cTlIyNDMJY2Ow7x36vBjsKZNDGh+bD3Az8qvnOkS/L5SRL+ip5nppijxNvbxVIgFw9EhawwvWAm7yw1TeBRgs3QR8RHDy45n1OP+DH2/swckAjEKXyElKua/yo4BcT+sB/qZ68pmMJd+8BB3c9AyZAGMJlZh26zZ482eVGudMQWk+Tz7/ksCyjLTVpN32sBd8zDfFF0+SKp8GhlmQwxAwESBi8WJb8JZfKBqLV2zeb98E8470d7t91gHs0YTozqG62NFnPPhg/OJu/ANAKScuMK/SSJ8wqwaCiJEXS39rD/UNj+BCQASf3Yz3BxjLEWrh8r1xJdtURDBYgdUDwpavvjg5RfOW78mpttpPP2lpwLslvPTMe7n2gfhYIrykszKa3p63AD4DZqyLfA9gbnG4zqkSpShPATlaEpYhEqPnEQ6vRl2cf7FDosVGyyHKtOa5o45SSZ81F7Y9DM8QOAcMDH8svFoBJQcI49UiGWz/THpvjwBnZZvlSzBvOwCE3Zav+LXHf9LgCLjTB3rsCwh3BnBJm2shR1Q+6k9We/ThIU8Bu/I0NjbcME/LtwHoCdZyrCEDILzTR4ZrQKDuJwpNXxnD4A6MzUnKOPX6qNuWYP9D3s+C6aBEqCozsuO/7fRVNgVR14UX/IR9RUpSvmOiOIr7x43R/TwCmxMTc0AnRoa1TZS/MLA/T2yR2AA+73LtvderkZdThZIGtrqAiO+hjXowXDGc9pXb6RE/9wBlzAXkwKkoKo3Fh52x0qGvj5eZYfCHc6l7JOvv5JsPSPmI3JWq3rlVgA1y2aO9+FN03rUHg0py6eYzAO31ETF/ov6+ajlzqObyIsmUdoQEouSM+BNv9wCJA495KsLr4xtYU6zbxQesR9zJajvD+peHxzzSy4/Dbw+/iw0Y3KM2OxZg/SPg6Y24tEPr3KtFLSZd4sVHxPBIO3njzyRHLAQ2wyFv6vQPWwJMB+iyn+m6Tc/F/Fq8051TDeqb1fwqAY2bR+b04AXhoqL1OsD1qSqGBxYEGXO1gcCgi+Env9CKc96XUpLqy3wwp3g422hLAc8Ol5YuAam8vM4LsDUQ+P9RpM87D2wJ+BAgt+OhZSvzMzWUrLjVeaNbJG3FSRTsEODhdUgmMvvvUZq/dcehkHufV0m9Wg+6qvBrISo9084dwMEtcKRNI51HKZG/I/pD4dGNDw+uNcE+JURseziEF1Ul8/x1RKEQPP4Vn/mfUqf13A7IP8FVP1MAezeB1A6YximZ2SYDfD3Rz4Jg/KRDXCCgF3QBMDDh7fMeizevDaeClfj6Lj7/D3Zg0YVLNhw8mJeaPY+S8ji+MU9oPOCkH54vBDvssAdcfuaX44PzcO56cDou/GJcU24Dt8Xk5A9twh+2ITgBvSlStHwnp9YIMoTg/mdhq9/ja4YDiT11OSC0HCCAV59zAejzLohcnPixxQ3hQhv2X0KDvLbkD/fbifBY9pOnZALvfs7hxJw32XWGLtzzkkuyhqwDYH7eUCcb2OqVXvUCzC2JSpR84LpcO8WJhJIhVGAumzguQPwCDmDHAth3bexEE6AFxg5wyXBRpniUB8vodxdTSmgl6CTFjjm8A3BTSxAAHelzYlOVSPDoeOB2HE54DgBzHBKbA4aXTkbH+h4lPUvCqpSyL5/NzGksJ6XrMlqXcl/KJ+QTUm79L23Zl5dlMK+mjHpStiPjk+sqae0gPullX0q5L5dl+I+ekJKWlF+8V8rzp6U8Xym5bRj/MldHP4LBOsAayP+T+sI7wzkFuWjMvUuodDCJbdRfTJ7Hv3MBazTarbacoTfxLEdq2QE8ZNgQdgS4HLGCjcU8c7cpAWNtP+LQi4O29Z3YmIMlVIZrAMkptdTxhv8PvLlSt2sMfMgsYT/KjrYOofdFD3x6LJzxMQFj61PqQn3hqsDdbLIX93FRB8Ciq1I+rW4qDcq+AAuVvCmU1nyU6uP2kq/PsOMv7sAFIICHid9PV9UQoyz2+KGq9tp/kSujO1Zsfi6lFPMe2hhguIwUUGLyqjPAGhfYBvrOY6PS7hySR9Qi0nZiTlHJ1n5JwCsuhht7Zrd7YFXyppATiR2/NS/S9UyU+eNQ3kUI9NKMl3uwui7kncbI21WptFJ48P/0QXTgubh6RSvcOmRHM0O5tHwJWiSv5p+YHQZA8D0ve7VvJC89nhpqnfsWEL3mgwigL2DVBWDg6hhlKT3iAC/nvWQnGVuGkRtb9h0wxmLaGsHDStOXdyV3XER1MxIP9jYZ4MO+INzbqEIpeZTOZDUvefPX3gLMtzzedmmHuZXxqmd8Hn8ZDi5mLrUpiNZ6sLlHyB7sbNB7sdJaS77oA0FKVGY0CIEUdHwBrRB46CS0/ckrdhbBSbTlRRh1ITwe0pMeuD061k9VonQ7wNWpWakbhvKsB4JopAyrb/+WEXuIE0P0ALubKfBRImTRx2AJOMJa1MY6SVOajKthJ/LszA4MHrCglQRq8xfhtXEvu8N3CO5GsCCc1NFOJ/5hjq0+2mUfupVWWMzhk53WGcoz8V4gQIxsEdFcnnUscFihS9alQDqAULaUAJuo+6aiib08FWFzRHJo5JyQws+e6WGPCTJDum7O9awfdDCfFmQGx1pGWUqxkUGM7hHEG8s9lI6ZWbQ674wr0J8STMIMXYyv2+DHdxOFVoHsr/eBwY/mVHOIk9AFie37YQNa3viXnDt7boh1VPaunSqREjKUDAGEX075vCbhrz2AxfQMsfqZJW69MdaBf2HBvXdVce7J9HF3iInyzBGjaOK6BBbgjhSSAtezNKXoeQBGM+D93WG3m8CuR0jBsTlhrJTVyiq6DmDc4CQHet34QV8CEM9RL0jzoIOy1y37yHi4YYlKQf6JxaBfdPmKky5pHB7B4IbUhID1ScwNbSS2jKJ7s4/yNorReTytIAuoFxNmRyoTwis3OhhPBTA4jbGv/W2ZgnGDdDurH7+delGjPNSwzyfgoBsf1IdjzeM6IuaYavtawwZ6UmRyKVdFO/QwpA9v6wERfLUH3UoLG+WPg0rIk2DkitUDwqhe2IyO6XOBNRzvc+qOP2TFSdtKKfH1NSDfcB8CA0uwmBqHVMFAwIahfkoLR7lDFiNjGIy1eG90KP5IWvK7IPypyIzgEM/4VstMm7HC3eIA/ZRgTcZxAQBGh4Ko83kYDGd3PHpD4dIqCnU0JgSe9xhb1zWU3h1g7hCKFJ1JhLfXaHuTX9ycrlh0TMD0pr9JjIqLMwqF8SDRuZY4C6zUEUrTEsqlNLIAhzcMn/sj8d/+dB3Ky6pPFLhAr5oLQE6lkeOZNWo5yd/JZDfl2DIEvQueWppeh5IcNeDWc8NX9eMoU7XxpJg4Qw9rL7l+f1lwE79Zj1KUkktXDbuPFdRA03zvNJRambHpwp2Vzp2kFEp/uO1kSgIJh94UjGLcJOGFjgCvUhOPKaUsb+nPKts1vtwD3jEVpxtOCbgZeMOrJ+lU00mLNYGxNaqWqKj/j/AHjFTC9oT9qg6ljPLfAQZT+FYyfMzxVb4udPWKKbXSLSjTH/S4u2A1eS6sOcCni8ut8NZq56g/qcC27nuzNYLhloNapDgFhua/lepjm/gpbY6OvPCIP9zet9KvsR9u03pitF8F2vGaPuqEotTX3CfI9vsdTbUCROOn9B3196DEMyex5W8CV/uj0tbtNI2zl2DgwE5xup+hVBLA30hpxZcyE/Qvy2ekmG4Avj+uL0kxgEuX4H3F/t6hslANJlxmOhcmCXjTUBp78xAtbAqcAXJp6AuSSymjNaRvmTV0nEw8Sapj4rZh8H5PXuWLqJudeRinROsBwWi8vzm6QLayBK9wPn0C7fRwGaBPy4/eveSFgtOFlEa3zOLGv5nZjQRbb3an4jSOAyD0Wj76RcImQCe6VqB8Q+CJ1JoVe+ZsVqbI7tvqT9BG2wpMGLNjRV8bbXecZCvpfxcibxpGE8bMefAN/TrTISX8AJK55BQ72svJnoSCeI5a5D7lOeiOppw0lJbVzYQgtnOOY3AnwIYwftuYKs7J1yfGcfsCGMAjZUtjPFDe63BP+jkld39hG5BC8KOuWy9WbAxpnwUQcn1FVyWmlGpqN6dK+39BivLBCWj7KcI1EOelFFEq90c+JSt9pDvc7j0RbywEwI6Nco2qDiWXgnjvOQgXHlZxTh70dBcqlktBYhOwOzC4UwCb3jSUALVwkC2Qqy694GNADx7VPm4z9UfJpXSz8x9JCFjACUMwEO40zbvTErAnBdJ1OGKbFlb8SgLdOTGl2JDlksS7Vxh65gdJ1cOFFONyrABssskRe+wREhFiLvTg0jO6c7KTX85EuRv/CcH4HSmw19xpKHEEPBEF9FYfZFd+AUkPY8USdCfvlUMpfmfe6MDwFe2fBm6MkKllgZUgQdrGaS57P8cX+WE6hrJ3m14l8ylnPcBIP6thf7ABLK1VnccZwRJEDqf4Tdp06GAp+5Uw7qtkZJZnfLK53m9IkgN4oGa/9s2p4i/1AVwcOoCNRYtF5YT8Ka1xMM01nhdN5U+xd86rjYUA4P/6VHUyCj1b0hIsYnMVXVppQSM052RenB8AYao/nB/+lA7AEqxPZ4oP1B+LbjVz1zilyW679WS8YU9FZISXP06spfbd+NARyiw8TULSi6ntbvK3blrZ9wH3A/s9n0HGjF8xQA3Ei6ISZLsYk2nndTqmUM9lRwSEbHBAjz7Q/8++Wo3SBpBpd8lgVbUgQxgCb5oOpeejFn29sP8MPXpIwwbDghdXqlCy1gHmRprA6Bk96lankcZRBMxvwhs+C5f7ytvSonB6SFEKpADMTzuQbnfRvYkXhthJnSOqU1q+BWjtOtiPOVyyHOYBkw6mvVJM6asRqDAEkBrNhJvxxiD9sqaPeSYYydf1V2H1EOOHt3W1FaW49T8vgL7pD0v/XnKh1XT43ClGUouOALoQnIal0E8VlXQosfxTvg6pb+o34mnq0Hv7qDs2UvaxMrTfApxegv8i9Bl0vOQGuWOODKUk69UmkGorhh9v9zP9mrYPz4EF8qjlE/7du7GxQLTBawnD0/ZxilLac37jo2K8mNkzlDl323TMTvFMXPqeflftCaD3S8NCeWssl0yoH8LeiIDlxxzmwrUq9eMX9wLkRa4+XZDwdUq0vIzgPxpbrzZBiTeoW/vA6df8pND6tdg6GTHT3OAI4hI4fdhoCa3tLKb0qgDCjygi6UIx3KmdEEwKDthAiu1s4kzd2DKm9H8H4JMCSIUjNM7FH1dnfJHgVFA81E86wcCxBBvm5shxO5/SBiQCKdMRXxIAyGm62nEYPgecJBRLRG5bHLrm5gBjVxuqTxEw5tWNFcQEY8u061Nij9/kzXybazkU1iO2MHatbjy3qqek/leNaHC7Nyw9mShd0pzSlJvGx42zLBhvpWNcR0vGK0C6lXRv0xUQ6zjjter4miToYtPBwsIEMQfdlmCxJNOHpf5sAXDnWqpcAGCIaTqRcSzSZY4OLebh9GlYXCoKNJpd9LEspQw/8MToUDQKkxWuaiJdleO8lPJTUkpPs86jYNHHvakHA2MThBnNYjqsoOKCrIriegomgIxv/usCCEfNJvqt6RwVmoEJMIyf2wWIF3WD8v1O3nmeb/fxUQozubdTb+59l7yZjKW1EE/rAGCxCVgjm4Ap3jzr9WsEbpx8POZoYGtY0w0kJ7H36vTnqDmUluoTzwjAOBiVLtYX2QphjblXE8BSCn6sgBdE/5sedb28WvaUfoBTXr/OKfFXFYvGzVFxUcKGChjceFNdSqplG6ntpjDFmC9BTCnWQLvAZ0Zf2cB4PaS3BTBovwaJr+Baptyd6eqtLWf6N6ekd+zFfc5jbMQ/bd6acbx1hrbkArhdssN1qQlbVxnTZU/JUPIApLEGrKWbt+HOQmi08nMaxH4CdwBE74fMoKk1+9eXGblVg5Ku96nHtPEi2SnCWSGNGgNS1aHETXrSHVDnRVQR0QPz7tQnqQ5F3TjHujZl2ppxhE/XGJKmrXAq43MmfNSsotKvcU5s8rrGBVjfAI7SMwnubIwsozhgQgEltdzD/JcO2XTB+1qXh4pXv2DXpbRkwNDIvOKlyt2ZKBlWjTevGk5LrTPwVoGLXxk5L1x+8mLeSdWxWFtUxvNmqwBfSunbdX7kzEiUE6Ep964QjxTSjyFxHBSjQ5OhtI4fqi1FEpKYinaqdGYnrzpQT0mXQ3XwsStIJUE8+QWA4W2Oldozj3ZrIO0sNPmoOrZ7JckoKEoXjyAxnwap0vYnxRXmQ/Jk4udz1Vu6sG8sd4fFFWZhGke6Qwlflw9sArFyopvbmnG0WwuxcqIUJimAhVmn3mbFpHuenRpc7r5+fZwPwFV0qbcyt1HEj+FLqe0M3CtHJUE8jrvNA1QowbXUi2u/Ht9gMqHjAjmqyIxtqX9rjbX+8dyuCnY/UTqzrdKvcQEzvjXA/zRpHpzVolsHsVy6AeD7k8WzyqiuPnGfForSkUZrn1kueTXOUffsWQB3T7RFf1Yh8LXaS4mDD3vAbRMEnBmNObXmOuPp5nhouTJW2p6xLdV67+np5uZhnKsha2MysRf1zljpLDO6QCoX/RSI9SV31ntrUGcyT1FaiDl5E+VubTYA1JmciR/sLcDQN36E/ubOLITqIZ7WMQRDUZ2STnv3z+BrAiBrvDklOebUrLM6tO0NSzePvjgbJW2inlJKhvpW5QbAqVEmJjFrt9urIdiyH6kLyNHyPrlcY2o2g8ARU58TU0r7pY2uEjmWOxMjWi9Of46i1FeLYQVAdM+IhhvO6CVg1fCgizUBBGB6Y+eH4i9WZqNUB7EmsAQQxUuovFHxN680H4YjlAvHdPmw7rROAlcdEcm+xVv0qzQrYXDKrU3pEsCzAuJkmAqiemamfNjboialhITL2AKieXe2YVN9TeC7kQAVWBaEOyzcRMzmLGTVsJmpDkVaKW+50douY9eaUdNtVYngN4ZM8z4nxkrFdEtic+BNf0r2i/OAQcqf152r1xxmQiyXpA9I5SYkRjHvT82sfNdA2qIbOfcDpFacpQIPXzkkZopFiJOYmMHbMzVmEpV66MX6pL5E78q0HqErUJSSYIYeEHWzNbzj4FNkk1FcQlVBNSmRreEeB6GieYe0vsTGBsC1V8bwLXQFitJ+CGD+Ql6N10kumabHsMOV2WGJdmH0cSH54noAwoOJnGXuFWQDjCj9HYCPqCPaVZBXBhm5pFZTzwevB5ERYrkUQbL40/hXx6WIV0RsTbSGi4ZH6eheL6Rf3IRmJF1mlt5i6jNi8+kr6RsPrxIdSnemLy6q454XyyXbAd4dH9sYlgYz0AGQP197HPdG3shoIlckheEL5L04bYSSSTxe2xTf4Szwwg7kcgBg4O7aauuYJ+nSnsxv+DAAo3iJ1g3p1u9vplz4K8KpTwkA+6cFIEe+OSakOhQn8d6d4mX8X2a0CXSXzgLRB4YHsv3v6kIgpr389OGRhpTStvhfHd34YvpHRmtx+I7qbckUJRUKKL0AsKF+UxgMS99IurWL2KRW/cXVsr7GA3B3FWBnfNg2pr5NP69R5+NMZi0BBnf6MJYN20vVdKZvGYMT7tTnWACycwLgUC0e2PxvMzWG13xu155aK7drtO/0tIAaX5qviFHxxmgzurOGYX6+7rROGK+y9AGMN9rDUvnhdN21O6a+fKtGMJLYTIHHMNvDRX9YeiTTJq8FL/8a2pAO1DIFpVdYTA6PeiujizayLL0KTIC5Qbrdpjy7jK+keMg6U8CpAL1TUTJEyjwySAVgP0g36OghzQdXNPisPWGRidOYCSTpjxi910PkGmFWpr5rIZT0bh/BcDWTsFOlznDbNF6M7oRffFVMcfl+Z7W80hjSq3Umke0PDEA+pqmajzrhwGNKKVvwY/9ADLdPZJo9BkSpwCwVsL8oSutoKKW/pWfsEYtMDmLZg3BrqsvPfaK8joZSCnentudTw7CIvX8GxrSyT5TW0FFyh0felQo5bqaFgNHDTYxj1eHVpaTpHY1vpGu2hWDan70yNaOcF5c54ow2I3gL0445+rrItFNRMrRj3NCCdtuZ6vo13Orzui1fU/O/s8H6x1PZ5RZHM2kzUdJAigMwvanu0V4UZNYmV0DOaMNQ0fsmsXg3GFOqiW2Y1tqRfkqPs0OR2f7lJwVENb5ruzalqPhmUR196c0wrVE4fRchAKJ1kV81xAMG+sgyORhIwbSCI02pE38e2VsOFbQX+LBKV+pVv3xY276UwJs4klLo32sgpp017A9DzFZH+ld81FhYK6x8p/rjVr/8QQ2rQPqZxLfyUofSaq2x9ctTT1/U8YhNU1KO6GyNbivfkir27gDO3exMdf0ZHc+WJlT9aDLJmz/V5eO8u1MhZ243fcX0mMdFiky60go4MV3/M0FJ+Zyk5VL/zHj9q6U+eloO2j8zNaOytSeunynxxstzsAGkjJSiAUpm6iIZq7J860mXOcqmVpUci5vlXA3PnAylfchzRE/w0h8CMhNM2yHz0J7bdTkC9pIfY9T391bYEAAvi+EBO909jUk9uYdcvkQmeNCzGz6rAr42Ea2wLiWxCfDk5vBAZt2c91REKqJO9CBH3x4TG2KbMd2l9mqd+PTFAOBMMDzSvs3N1PYgjNvSHtALL7C/PnqPkm9Be8uezXCR0Zc+6AHSFsMj1sQHM7jKUW3pax0IIuCRnVHp8hogenSSseB+bcNgBl13VDr2wbgpw0X4m7ReSX1+UoBzO3FAMFsdDGZt3kIleRy9iewH0zXEMEOoavfzAutEfPcLG8AShMqTNa7WSJa9YuFm3AxwARbveD88B63k49o8xNi/Fu5oncXgGnWwPXMsr844pT5j6PW6MFh2mfPBvncTnlGvTooB7HRAssbcLDEJM5Q8DzBTRuPeRBpqE+CQHYwABoE/TBwd4QrEXAM+KmlK5tYGgLcxPPKdCevn3INA8A978NtvVNm2flAVhMtf8LjXCPj7TVJSnjnGl0dH7IlQY/K8YPvl3/ocrHSYc07Eab2g5xs+N43lXommDcA/Tgml3Iju8P5r0hmrb0XANm+HjsDwOiNzkGqEybkqYNZr/3xWuXS7AHhAJPvRr098kK2Og2vTiYfWc2B4edeNDVObs8ol9ZNG/kumnNSNvuiRG3romtHhkR/BDOvjMhh1tXkPQOkDyfsYPYT47g7pv+H6rE8pvpo73HxbbkceS/c9VDbsRM87L1I/J47hJWqYESZPkYE/3LZzztgknqxJlpi8kpR48d+7mQk5v8JYG25OtgUXQWf3cDNdH4CB00u2V4ZF2iiw01GKUra8CS1bbhmAvxbE+9eTPMpw9+ddkmFe/z12wlc0QCkTq27gTpYDmz3Umrp5ki90+ED3zrj0XpzBcb2k+U2YDE7+tw4sLHgMF2LGaojZegz68/AiPsHeDN47eZS80eaE4cP4BWBHOGrvjIA4EZ/hXQPb34MvPBnQev4b4yfOREmMNvtprdJTFLsQuDAwXAZiAyniBncTKgn5dW8TWLfYTVIyz2wMt3sThotnAG77fJdDfEJ6HCXicsGDJyKPeXdGp/W82YEUJtbHdYCedD0CAnrrL8Jy/I5OfFzIFaPLPh1eq2Mx0VPCc4ablz+dKZECEOCsdGi99gyXxQn6ftzMDaG+gJ7nEa3/cTOU5I0OZMOL3emlK0ce8oMOXIfNpWhpTJL+pGpLt28J7CXqI/OUlOfSQupIMF5/7tMunDC7qn7WiLdn7HzdYQWbjjGlDpB26M5QqixqDYHDJteesmm7ShfpAzxwi8s8XdpiSlEZpY0Pqcja0TvOSSmj5dGR4IPL8two1He0LuXjUp57Vvakbzwue7RlD0NKKWVvXZ6TXvpy1fCUlBelDGjnxvdWTlyZ777vpPcMobwcuoBtgsVJ5kbp1T3eE+ebk07FF+ciwIcgpXkVdyiahL9r9OlYd9OSHoavJiatTc5irgFsc7AnKjFSvbgD6SeRoWR2ALMbDA9YHTddngw0TxLQ7q0AAcRJHVu7d474B2G3EqUhdlPuxRlKgTruDw+0Hhep4sOkr7lBJllBU+FWhdclOKOMq62pujgfzLQnSarIVMnMPXSYi4tsswOXXQadhxla4VwEnxIeW/e68Kb4DFkxYe8tqeeQGVoqTXvL1Z2YCI95bPa/u02Yfj8nW7B+0odNsKwNdfDIpxDLEnDUBFYeJX5tfAo988GNhgYt4RP8U+jdCPuD+KR2iIz+vsClQ/jZmNKkUSELJ24L0ngkdaNxRCyM5FJLjuTSHbAqpYyP+E9clJd7D8gglktSShmdkk/J3VfOyQsLj6oj+7++WiiV1qWckEs58mPdHm7ujCkn7mizbdtgZdVzwwRMG04QOxdmDOc5EADfWGKh86PDY5kXt+0CUerGXtqia+xOWMMueNmud0fNxdKxHXWgSrB5+V7gf/byKZ3yATPRYoEnemkS16cJhm4Av7UD68Ho4IfPONg4tLt2cr8yQptEY2pDRjlhDTBGA0ppJ5/9JK592y1cJTwyr87FZZ5Fhiu1S0YF0kSAv8PgTieXUjz0ftkdHchcIGMs6kSdjKscACvjrzYqcX6Whlq8FzLKFJXpdu/pAXzGHx3SN4U5bMwVGzNNYuGlsVoL9xUyiuHRe2C0MjeTfOQj56SUsjUUAtG6TCsnYfy5nxud0i/8xKXsFWsr0dXryzJiQV7s72qEQAeQK+/P/y1uONlWy4xsZcvSf0Q4RIDVsofHJv0E0A16vN5pUcJgAq0Z7UumJxh3L3VS25c/AUxMrc4Cw6ML64Iw3MmlxJftwgtYeMhYGFZFlckv4XLt1V4upSTewkjQnBkzELll3juTKKk+NBmPiOTMDqSjnWSmQObZI1z3pyE0mHOLKxiAgPm5kbTIUFIuvl/e0F5AJB5FVXFY2rzXMD1oPTNab5JnE3CG2yuZksGRmHaxS1BSLg1BtOVldIwMJWVzSzlALKQ8USHobE5HCFoly6wibJSKMlodkhaVA+cywOr0LtpalA3nwjkHbEla9mRi1U+k+XMzey25h0xNsTSBb9vwIRsIhpMSmRd380nGFyA4qW0LsKZzFrJLyo2eD8LlQsqxIEPpJKSykIKRdXlZZHFqZ6HSGuoGw9UbwbgTIxQuZjmC1lTOQoOHS2rLMKDFyuhpfjVvAB4E2gssE+dRrYrwiedKatyj/nTSTubp8sAD2tp7DiyRWWVQjnL7yTc3UBNEqSm3iUq+qzs9NL1pnUjuuKu43AKCsfaZUU7+h+Lb9c/AUeRNQ+nO3ysub33L4U8tFyMhcnJME4hVgB3N+SY0HiDG8PAGMJcaa2Te9r4PdLTfXABM6UkmKlR3x/azbckpPjkAK2PDK0MFt7NNnHVPS0lp+3q5dOInoGVsTDOJfFiadkZMhA7NiEp1+od1qwLb363OZQivsFS2YPzlZLVKgLeouGd5eJdLMKXzdsmoKoqciaSZGU3Ah6IoRHMIrm42ek2Y02dmXpzyykjPETySrms8Pu0d5R1ucYW8eJaZpvpPAF6OdoYHButJUnkATpcPb8dwnmJ90MgREhnp/aQDZL85bzoKk3CnPiMrl/bIWvrCB/10cawfV5dLxm7JgCbPtzZD6cEjwEnV6z+aKpXWLzEtrl8vLm+VvLho+xUg7ahpZs+YcvBdBTmPKSMEni/0UTast3jT3rHUyys7lTVBCas3ZgPIyqgfpXHkNcvMizu5P1EuRpvyOILZ58ilTPN2x0vb6dY5tIR6jRGin+MxlhUC14+VWo84qT0Riz23OUp5yIjKidZoatfJNITh95S6T0bRFeNnGM9n993x02fFsCl18ynl2F6yQ6SH1R/RGCNILLKpO2cp+eNnpD8I88HmQwr1kuh4qZnANKXHxgMgc5iZbzi7p8ofa5oYGZfpNKXBKMxRXrGROOzWWB6sg3Eu3vjU6NYljmcZt+EZI6Ln4mRM5W9uDg+lO5T5y08yCWd0evOxK5UQ2FsklfY7/ZRa43kx5bU/5HCsmKML+LA6+pCyL25l/JQ/q5eacjr8qgd/PHocaUprOdXdnGPToGRo2b/dRf5zAcHo5tmR62Y3s2vsvqmWS+sIB22vuELv5fhW4m35lIKxE66fkVGp0ct639+B1bNwInSSY9m21HjM3PHlPuMI1yBynYzpt1gu1VmNkIHITC9MYhRPYeQumaF0xqVZyF8qc0G/FFNwTJEcypwiCocD9eCUlNtqOYA3UqIz9qV3bFxpQmwL+EM/cyj9RUT/5UqTdADjf+sUf3Jy/Trkq24PjobWhvLlBs5MpMqEwDXJ1sgQkaEU5RkF/ZkolaDlAOse+4Y9PNZkouYczMuSsd855Xka6Gg80TynCjEATQG84Az3swNwd7bw+Xn3K69isMgPLO/knmI8kieXZhwD+KU11KzM6NaZX/GP7LHaB9RcKpVgULF9zqcsjlmXKn+sphxbgjc1wioyxMvulv2Kj9bkEqNXJaf8C5D225i0VaZRI4RaFq0qawgi0IrKybb4jZ+bjRL7XnmdD7mZ3RLlJBhtHl/yxl/ehi17uJvRBMR45b4Y9XH1Zip6fnkdEWqNOZMw31uLxwiDp8rrcDAPT6yMbpoqila9nBP8mTj9nlNeZz8Ae3TrrK3y1pwTZpLemsUHWVzI7mYNg+NdXMYU6NegZFVed6npUCYgf6QGjTRan/DKK90uQOTLJWPTH6vcmf/tGbXKKqLjkbPJAu0JSnLXG6tsdfzjN1MYhPCQnUtpMuR665svjDQB5zj4nBPID1tZImknXmdh0rNXvjx0Gy5xEa6FuWUZsS7lZ/aHbsOZp/TTVzyrhzSAlshMIpcpaJ57rMHqo+6tKpSEMzpWZjnpHnPzFm4cCyjffylvxZ4zm6JbhjBEWfpSt84+pXHXjv7xaSQxkh+cWtldLL0bH0NNwlMv53BneCRLaWLQ9K3Utn8MhCyBUrt3dIquN3ZG64YUD+cYKJm/tgHnXHj+c8NjZY1XHAOPFAwccBdgaZRoLPOUJozUEcMANMcE4SFN9F/chC9VJ13VPw5K68B7ngFn1IzTL06658dOkI5zHDyyiNzsTdJP6bmJgMu9XY5ZE2BTvTQ//4vzxh3lgNH7mojoUwX7o2g3BQjh/iCXEv54H2dEo+ZdaxwXxOtudZCmAGHB7W/NpzSJ9KNx6nDyi4uPJJieD6kV2mlKXbEzdkZ6hW+tL+66T1Q4a+tusPNHKEvjei7zeLNpAuYfOIXlhhQYwoZUhOPMRKrIO8ufhVJQpQUWCAF+2p2ovjnadCYKm4ALY+P+DKVj6Ds2iotbOZPqGUrjb07eYKaO+DUYXVvizaMIZY1/Jbq3nPV9FT94ebchJn5quRF4phHKfMnUbAjsGpuZY4VapWGkPphaanireA4kvqbIHMxKnZWxU/xU7ZouzF5h6bK/OXGsxDBYj0Z1OEC7SAhMwEo5hNUcP7kl5V3aRaKyeQj96x645L/WMkqbtdkAyH+v7yHVlyxdVsY+6mJK7ozNOyqQlBbAAJ/xb6yQkgrWMhNK9IjQADw/c+x425LZmohUOkTLAyzWMLayZt9iSvaMlAyvpMJvAOn8QlBG6W+l2kLN9HSerui8AC4aArr+FJTMlMmplvQucmLxALDV4kQtpfEpwnSn2aypSd7Wc4HTiaJxduQ/mqa0M64n1IhZOAbtOC46/24AS3pIAggfzqW0f603fuLMlhPdOC59X9GD963kFjER0l+WrNkoxz/VHDce/aJHvIDO86Ez8rorbt6djFd8DVytbYHdVMv3YMfNp/SSN3aehTvboOko/5MbuJk82iIzzZamdG7CdNOaeTGMl3s0hDeMgod+DdLe1WlKnYk1hszaloL8wxaYvyuSvbt90spumUPsrCOUhdyjppsWMNctwqVgVFo4D9Rbl/JiMtO0XGMiKVha1RWdH271pZR/tJU/0zQxNu15o+1a0rvzxryjj+2RHnpYezAyL5UNLd0ZnYXy4zc9lDEdby10YX6UxjN7Q2fsXEPMOLQ08tJzSjYydtrDbJ3MU9oap3TNjLq3Dl+aODLIDwk1OcCZvY+7IedYNO+ndxekB5fyIzHKCfXm1fxvuDr6ub/pnYGb2vNwwM/XBPgxb/zkgxkp9fI+DpPn0rsui/Cl/AA10+dBLoWRmzrq4Pr00RUAU7PCIhrvUFop7baWopufOuq8nd3fSIemGHODKYRTh1M+xtrsSf1M0xP+xMmbM94879kaZ4ZOlBsALMEjH8+nNBHueD4lF2rNodi5R0df1/fYga37MtFVMpPyZuOO+oP8T2Y45HwMD1wPbtPMofyKPX5uyoen3jiuJHqeTSwONXMo48NOGNw4MrjWG+3qx13SBa7CVuJwsShwVhphSlGt9ZQKkm5HPvDaNnDHIrT9fEqpRNIxHhw/MC0KnB8c4H6VNiODDKUNf6y0/6g/qzhydQWmS7w2VoZkwr2ldc/2wpgyui+fGiq6so6iW+aIFconZDR/UUqZ71IlJ5LQmBNhf44DR05mhUWm2+06Y5XTpsZazbtfNlGLiyEKbJU5M+Abw7ZUSwgUufa4gMkKfPxsgf/SONrHNLQcwxrGT4xqlrigz2qm0EfPM9RTj7sHcyffcjKRAYm5mZd/al73cNbgMwIOnUxZdoQy7uRr5MzgTQU7//DAQwU7lWITvrKWWa6Y0QQmr5B67o3G7/E5pZLNrKCCrQb5lNJ5GUZIjtX64qKCH+ID0U+swaIDvXxjzvjsOMiPi5xr6UJGTWKwkK+BmY7hqRYhXKQTwu3X51OawJHn5bSlksBq2SvkHs3OGrjo9aUJydabyxsFiCkolWA4xn2LTl8Kxs4wswr3zrSUzFWNXPLiO1sAngXW6EZpStsTL2nfy4hPT/1xK1PSDlYFA9XwXY9xCZ2mFBolN+tNnlOGvO9UGqsQdgRgbm1gbN9NeqifvvyzhReXwxFh9RAsLU3a2eeg/32P2K8yFZwesiYvd2L67MRE/jiYbtg70K1Lj5upkNB9Fn7h2788LEnTu2P8ZvK2lKh0puYD4USEV4BUo39AwLIHpzdGZFM1T7rj53Z2xmcH+hemctH5fr6xctTCTkwWZl3Qx3W4yYSke9OF0DRzuyEXL50nZhNYvpBLaRKtdNQFX52+OQ2l3EdqCKA96v3WyMzvFs9apsW5auniJzM26hLIE29zdGWmm1QCtClaKrSS5f/3WmVCgMxPjLI9EYavO9osfnEZTcDHrWH3cvMu+26lHcZYO8yUZsZxxvj5USrWkBHzmuaLMy7nfg2RFKk3ZXgB/EJV5eQwvXbAwYM9uVGZELpJ+XBtQmtJNdrM0HLy5Jv91E5/Az5uOtO8vPwJmP4ve1ijT2nrlkzNzFMai4sKLU8MtyXbu9BtJN6ZgbT+bHThEJ7K9/c2c3//8JjvIVjM95rVw805ZgLmKBafPVlahLQV7nHbo31YVHsSuuGAMT+y1f5wkLEcZzqUMUeiMTPF8smvMO1c2CDXChehEnSl4I420wVnxxkhV5307r6P80tTTdPnB/lvS6Qz7P0Mbx6d9O7vjo92O6n5FwMXCMQUhLQw3QkHhNdyKU3CSj9QY/N34WMnpoqkezlXA7UQ6eP7cJiaZUtTcidUkVZLpJal7244uO8Yr1OMXOWkNTr6sgsbDoODq3MpnZcT082ZpiAsN50noQrs/MOjZ7TkgwgyhcXTzZlUDbhy6oQRuZDfXsSMb2YEyOX7MWS+TcDNsQzGay0vPQesCvgXX5jq7lflyqWHLiVZqSQCHFc7B7eiv/TXkwFV9Ocb01DKnUiNnhwQDzujNTdezqDp474sci7gABhnAfacKaPX54/5TBaFsa2aabwcfT4VnTxD6Xc2x8+WjrImvvWNCZX7utNxyoHRnvP4eEddscO4EpahNNb0gQNfef9bJgZOiG6xig6t3La0CLjDD+WPPODg3+ZSkpNhHzcc9dcH+dLujZSu4qgEx4AVNYITHvvP3w07786lxKo7dm7/UV9tdG6KrcL7D0x188mBICDBZ94GVB6mqAfeSj6lMfd02Eu+g1cROBcvws50KlxHWz3OX/BlGwjB1nkMjn1P0iFud28OgRNvBO+X/Wko5fqcpGAIh7a8BL88Opa16H4re0Jk+AmBW4X028BT965No5wMAlFcYd1jfO4ma/ceh0ksl2yB4cz9HYHTgymGTaEmJ56T2h4fxWSe0u+/a6IwvuLCssA3uqp7cQ6erkqpl5sizM+IhhWAtfw+To6rgMbX/W56/0EIXhLTJCBpuTkHjWBow4wTIcO7glxKk1iauOIDf8/W1P0lkXMw32v4oyNP6jCmMtC4LqyOP6bWUP2UAmf/XeDiYHXyZv7yhy5u3sHByKn1Bk/9XdF0u+7EM4sNHwMiYOfL4Fzvwnb1YUqum1h31McmYd86qa+ueAI7XJYXpZQ9kHJVxAlJn/daOVVF3vn7L+bNgPcWhtffPzdeWOLqmX2IT8WRf3OzIMuRG3AK7VtyjwbDzaG5W2fymtAqE1F1w5eEfMl5pw/swMmctYFRWlhJ21YbuX1cBolU0oX21WY8+tZtJCMYD75XchuiXvzr882naXjxX0066cHkGZvDLcPxhvJoLf6btgrNO9DvgDQhDOM7GWUzCbfEl9zVxF967JaJU1wA2hhCGtvvBmMkPxz4Axdpq5ZlCE/NPR5Jl160UkIluX2iAPipY+kKueFYh5X9vu0gXhJwYg0Gq3vgbRO9mnoQO7j02IFXytlIBzB2cmimK93u5ZzqAAxuw8C7xR2JvvAbIQMREh6lOizP8gno0YoVa/aFllIUANwcqL3zuqAiuTEOAA4lOLc/L3hb0p5634kIiSCIo4b/ALBwMeDly97IqTrQMhoX9xrprU/QFcwL/DnShoueD/IVjE58VhfYsD0680vwKzvqoDHqOsZhqjAiO5MF6Z0JVWCIi4ngCEFZo43dN9GnQ3ZeNXaOihIm1+tXfhkeKQHpaihpT8e6gOEMNa8jdjBjQ7XspTgFgUuvdwvtobW/wE/gBClTey9fLhnbju70TlKyxArs0YMI2vQgUWUHO/DAzdC/Hqyku82RdPnoa+SSqV0n0ItIiYOL8vcYuL8AxnVYb42j3bxwG7imjQO0gvgOQYE+5qR3vBQLzfFxCAE3J73Dl3DgGqGmZq76A3XQleB0HBatbaA0VrmrKMkN8C5ljEoZSrdrnbLenNHKbRboWCYGJ2idfQQAafRcbtz3aJ9eGdXUdCipKeMV9X28QSeXJhGfu3Ad0k/UUx86zLmPQbQGa0oIRDfN+epqInW65vJR4vXXYvILqLjKpGMCbxj9uLaaejQEyeKVsGvcnzrhQA35bY0mcDhk0lMu0vdoRKU9fqZM+uG5JQzHjXecbt5dRGYvUH/m89unuZbZ3QQn3740KUOOvh5vZHxPcp+socSdjwpCW+yAZQiCI4DBDXdDkE0dMo2zxkrcAMJg10HNqPVJZM+ceoMrqA/Xjs/JZlwdYp5DNfm1Z8ObQnTr41gbn27uDWcHMjLvT/w91Nx0j7EVmsmHMzRje7mUBG/fVVsO/PhKpqz4KRnvSV1khJsImXw3PqhYjncxmk3O9e8Q0qPznA/KTBG59LdSqlp6vLI6PoIZug1HJ1Nuwxd3n3heSrkrpQyklD01/FnNXOqpeFB0ImfQFK1GqzJceFZKGUopJR+RgTc8O+s2vOXqHteFrP+b7aCSOi2QCGrzpfhB7gGD5VgghAfoYA9v1trIFBTP7eq6BdcG5jMNzPguHpf2BS8C4Xfjjr2XNwI3AE65HvF7nRd0tFrlBBK95+VMV+M/LaAHyu0mCZEUwdeDTQZA7zD+ea1f1/wmeverSztgdzO/PTNTMDHd3DvjqI2Ole655cc2GFxtwyZIO3HmN3y8DwTggrEVjwcyS7xGPyny+KN4eBrEHd5I2JVN6/gxt1Z6XOMAIT2sPYheTb2+X//2Ns42mCvJz94f/5UAQQjmtWr7GfUe78mX3sZY+AMwPpGQyHzxy3sWPfuUkpaxD4Lko3DzWWh9PPUzxy8Y/8A9mFduuX5cyxmVZr64yXFcstIozCSTxF7E+M6/gw8ZsBa/uMPPO0APuimZPchTd+Tfvgi2eiw3u+Blc5xkvjhv8vT42teOHMg2AcPHNM9CB8Kz6hMwTgJsdNTIy0uk97/JoWR0Xhiyo8P48Cn7xT1HFtckav3O6PmtgbJW9pSMTl7TOc/l6wH4c9AZ+pPlqnDPnx2+qXduTJRm5+bG3Rjz+vwe7BARRfcQGh6WsxMXLAju+0mPz595ASsJQpsfudpd8RLG7grjA9DsCGUiCltiE22PXlxH9a1tC3qb74Dgd4ZFuL+wAZyk5cWfePsJjwnIcKh6IYBE0uRQIhxfHi1zlkoGEBIqG0pwYpjQR0KP1Y7DwwJKXK/27gdP6Qhzi5M+v+nusPXkuFGxrWyV8pUo7nYHUsqLu9KX0cKrUr4kpVxWC8rD9dG6HhkuXDfZ245gXEw64/DjL0v5Nz6QLsx0u9HaxO/Zjg8NdejXABaMmzGCjnoWfmxx8lKL6cLDp+MfnNthmdeCMsKYT/jIv7WRLdU/XtKzKqPvdDuVccL/BZKgZqaPh3sEcIle5KgKR7kTCfOj79dyJ6ZuMpQSq9AQI0E36jVOgUmHQ+nALb1hkRHIs/iPu2rnFTu5gJdDyZ5Luqe8tZQZSr1xSvTiSzMvABys5RNgeGtER5cYRN1R1QdMmzXhs4WPOR9faT/XFuMA96rPYv/piXmrDKVJt4RbMyQlENwKrApkLyKUiyM1vkuX3k0BGHcRJKtszLyGITEYOsK4mdWo45TM8+OLBtvJYHJ/Qmh2HvCVwmTERYstgXuzB4TYiclLN7WSwb6jpWSkvPgUrNiIRIAADAi/mpTNLWKYqXCW7RA8t4dDNz1vs6LhsaiUjKfciZLMg/2XN0+Uxme0bgfAT38eEvb9sU/Wg+ukYGhVs/PYGB4eP6SUjDUfbvf1lB744/GTzwi1cWli8NMLQX71flhO6R8Pfl19QJ0kplK+4UQAKlT/wFhDzmWJZ3/kRHK2ZGl0/Kl6qaJoE+tUN9t1DEIXgLm169QBaW5OMpKedIlPHE4GjaRM2QDcj/92BUA3NZ7c/GexppBq+QNsrsHCELF2EhRc2ANY68XueZo5lJxchp76o17OMlhvjt/TwgVopSf2AEzfwUpbz4dvXoMhkUPNoGl7U3fqDQFq3QCdnfjQ0idgx8Lw0s1sfA7efLibc7X9oT+CRUe9s7+08yl5wdi5cuiUrQyDDuYbh5rn3RmrkMJ4f2WTEw7G2BnN5wlPrSL0R8UZEfiL4wJx6AF9HQL60sG4OJRdK5M3m8B8YcwzwPPX2NnTWnTlu8aXEg9x4rrhpnaepTLkqaFsMLccOP8gLIzaUua5THjm9OVmwgmYPSxEjNHrNgA2/mSKJJJZqHew2QCnES56SkAFo0MVKW0AtOgyqSnPhiWAuYJpHX/shKFBQ6X++gHoN5A/wvAML3Ogp107YPjZmnGmAkBGAIffdaaYqKmGF1z1t5/vo8vu+DjOjIWvWuta8j3XguvDPQf6aZ0Jy8nwYz0N0Jfo+va6kKzB/i0wP3JOyVDqdsfOGHZBQ2O50VhAxl+CxCG2A62R9pihtCzGTuvLNDUTQWNfnIwVz57qBuxRSZkQSBOYxx1fHVUfkRoxCFRQ9u7o6WcoHW5orzAQxMZ30QylcMsHRt+MJj3iL4/bzJL6iTBy6e82wygdtHqTzNx89otzx84b0z0MHxrJavHpRDaanquMaN6oMCOMF8f9ksL2UhcAET4O6je5szOSHxpG1vzyMrIH3DYqzSgnjj12bi+R1Y+dBQaGmMaBUV0zr/qRJeI2YQhHqTup2DjTaAIhzvTLGby8g494SDWQvtPDcA7h2h0NpWDs1GFg9o+cBPjKtHw0sa36d4KlXlUSWvD638qlZKRW0CcQAISffAEwboa5KfWl5byDJmCOnp8fjO40RokXxzWPa0abAo6qZITMon9BV5LqmXadzGRLhtIHx5dZpQKzC+hMmMRK0dPbUVX/fSam0l8dXjtDybXHz4uXgVi0SFI7T01rEnOjMbPw1SKinjs8lKE04QAgXQ9IRKb88anpGK/mHZUPJwTkfABvszOlmdYzoQlEGfFtXUV68rwK8sO6Wb/z71HZfaOP3KMOpUJtFsulMKONmzBNkBPQKXymMfTMEb14punOfEoTRrxsuEnjnJM+tQpkrk+/8RUYD0PvDu+UfnG//89/e7yTG1MNPPqmmErXzQ/gtDLaTN6sO2zy6acUtp3xcyeawtlJB9VCaAM4LUsfMAXwkAN9je6dAzHalC5wxpuOkhYOABueR/9zAfRW8imdHJfe7QlJZ6ZX/teHi9LcDrYEPb9HuntOU3ImH8HtqW0DQEzXxxl5TkPSluCNFK//6GUc+tOUJnXYuZRQVfjnYhpGmnaxPzzcvgyXvexHlZ1IHf/gUqERUN/t/JPTcSpBZz7AusXWUrImf9PY9y5oRVPFOcl3PLvjFQry1WRYTJi9sy3hOrxKntwZuDnHDBvkAPJDimfk0kRpNljR26UPnQZCxbrAUVal6Nu5lCYx6mH6xN9pqVtpGvLH8h6qIfDGHSX2RrfK+i/5umtfqqcmRd+/XleUynVqA37+OpTJ1S2GtxlvWTC0Oft12GVggTm8TLgWZCy6mRd3Zlx6D7s4y/SAVwGcqYLW5TaMfqQupDD4NSdZfz1xijPRy8r4fSUDRIeyXFBjhFq5dsSezAaosdCtHbBzrqn+RL82DLozFcbMpMPDoUiPb40Xtfal+ck5iOw76tTiNYkD06M9vHMrsrV2b3NCriVtrnVRxUwSwFTRBjVuwwZy1AIsADd/Ps6YsCkMO3Ir3bO8tTqlfNgynVguBNj/hVxKeRA5xxrID+jyNunF2xaA80hFSp1EYZt9eikF+V9GIvXyF5mHRV3Yw3GMvtQmA2hGz1sTsqT9qYqUIBEYWY/BmSEmAkB3h1tllDZnSxiRD8tlwvJUdVLeTTYmfU5mwY8VRjMspCS/lii6QVNsALVoU6ofeckB4OiXX6xGacTk4inRHCMTIBo4AJvqFq9+5NaKlOxkw/IapGQIIFSt4uHJ65b4wo0G6pOnzoCBwPgKgFxRC2s65yq6oBubifR2miQEpseR0hLiVW5Gp6qodJONsFm5ZAy1andSxpRJ767aaHiusg2dRQHDoDqWNyqtKG+k1WSPcnQE8lTYBbb9RYC0hlguvYH6I4D93KO9T7pwbeb+mrWWBWjX7VZE3kHjH4OFAxj/e2xq1621nIBMHAXqmruD3KNm4NG62wX4kK0OpdyOitvSUdIZOtPMSKUhco8awOMAuHF3V1lfGmKxZlrCHyibUUzid7dHSmIJpaE2rl0YWgeDT2VM+oMdtCGhJjBURwfvLqpWcPNc2dH53dFAeg8u9cCv7sQY43Aqg0kKude37NGv9cEP4emqQmD0G0efXgNobQ9zEZ65OxMOqpzS8DO43CAhoC1YUQ1fAPMRuJq1lhMITaE2LtUUArlG5lid94lfw0YHeqP0bWVfXPy3rutS7uWlGos5QLTuoZbauRUpjTxka6sCQlcQ/1pH/fn9ynIpmSiqK5bmc0WlCWyZmSvfdUumtAhC/Vl6UNTj9Gt5B0dGBRNg7QBucytSGjaha/9BPUbyoyLnqDN8doa3B++6BYzPVaQ01Co7Nb+4o2SpRRoGDIOz/4EHTocDx69IaTgsbn+yHqXc8GJyRIyeANtG7n67KqWhKXCtHiXD9fMOS0jJpYVsnZIOxcy9YnXkXt7YMiHOSRUrJ8ZrXy0+Z4gome/Wuo7UgQQI8EgmaQ7BcobFxVplKGK3gJduapITTA4UNFE9JxCnP6iZMgaIrnVyjqbzfyzCGZsTlSkNvYPqulPKv+PmHTaSSGYmfiyO82eacpDISkcT0KEM85Pr2wAkfEhtfTq5gy7i2TjazoZSiusO4zThxQw32XrZhdWA9DxfyQD8/5ZEqZrP9fopRX40mJ2hfjAsPjj395PNEq3y95x4q9ERSuprW40XdbwycjArptRKzNN+3bvvlJS78V9f4zY8iTMxm9Tq5qnQvy3noFrBPrp9FxVELUZJW3q4uLgUvVzd+9xoU8Bzu3bIVkUzxWgZW22bQInqZ5CMqEem8GO2CVzOWUQ+DnXpqmYKP9mYMtvAVHjrup8JuFhuivcBvlvzdlXsjhY2fH8Uk66Y0kFy1bqWygkvFh2c0WaJCufGGzXd82WVfNCLzIOtcWKcgJ14Uc5NG/1cIbq2wpuTNnBilMC9mNIPJ8sIndqjXa+0xrjGWiIEXnLV3/zMdLPBTe9IzWqdcRje4k4c6LDmFOHqZL7cIYmkURgBcPTz1SjVtCyn4BZcItHBTA5hb2TMKfz10kv6Z8MrzuQ2JeTN/khXsiDj8lz8lL6exNj/1h2F9XRoifynFAVgrKSPfLNqhzJcIO/UYgS6L+6QtJt2AJ0R9+LmPTS72DW7Xb3Y3xyOevqGM/KUKqM0ciioaxgc5F/f/Dl+JisEjNFesb40ejb1pLduZGP8Szm6rA1TTH7l5MCbDrmjCOlixfEbI6AlA7jh31akZMdnDS2L02I/tyP6E9zUjQMAe9Sx11VgKyJ3aBkFEr5/DmBopOtoVhFeKayAcV5NFCUhtOYqGplHP7HuoMnWXlhdL/opGDCf8dOpqHvXfZq5KYDNwVUuD6hW9iZ36jRksR3nZM0euJ0r0HbCpJWZn9hRh14YlZZTOllaowh5ipYB3jDf6iFvAv0a8OaRb924HIGlWNz3dyeiolWktPXmepSMstHW5rbq0+ennkhlT9SidHBd7uHUp4x6aRrv0yI0tkAeYjdZdefFyVdb1cGj5gjF1pZICRB1A7X7W1W1yiFuLa+iuXPu0QCOYuVk8s1WpGTXI8Qg90R5PYnqLSaVjYqUtoOanA7yDkYSXjMgNSegWztQAL8eoyD3aHhSFFyyGqX+qd+pwQdo5X4W/S96xB3vllCH7rlqSkqjtdrTIld4mAZEipK7qQ6lLI/VKFnWj5ZXyoenOd7BAeTw1W5OSck8WdPnRO9AL6+GxHUB2L9/SkqRW7t7djXHrXg1YPylBSOV4XVRdCMJrexg1R9tVqMUanLDl2L/h/KOtk13KCqTQ6Npo2N+SsGf5R0dJQEyk2jC21O759WFnXu0NRTaG6l81QkqU3KnpgO6EJqExJGBDhJRObW+ZImaykluCE0A5EPqb219yas5jtOrfkp6tzy1d/CuyvNxSa0XxoNrVkS+qJQWhJEHzK+qpyQHuvSIWgzGg2vOhGiQWWs5hmqUjLpf5mG+e54jUNJxLTmUCuZQ8VZ+5fmZLP4ye/241+h/wxurZ/yoLg2ZFktr9SiNCY9YRBcnJ6tIqSXq0JkwuwajzWyoglTYmeOW3vkZrgG4Mb2TnhG4sh2KDQxUvA3rkqs555gpjX0VC0DogYSW1jx03PpSZqbJi/+2TRee151yJVW4wV0mYMXKiTbJ4JWkFEbrQMtrRgjUxX7m+rKr/hpRbszeGMf+lNzRZi+dNzOtlGQVlONWdDN7PRism3F0QDE6/odXklIrHe7BsDz4glSzcS3tAGMqSnHUytphBv83H0IECJhrRC7Jh9RV/OqnZHN+PbOG6YceBtnwhW+tTSnpr6axW6b1pba4EwxTpeIOU1fRB6gpQ3jTJqAJP5ePsV43dWb8lYWbE+dUpSQFdRpRJmHEXGeRSPZAfnAYI+KLa9DPxger/JQ8eC3WS6d4cUF6xwgs2PtvXHqfBlYBFcJrLzvYO+ah5RhamN2xG5+ETYdpYsXH2DeptRZlwjBoPCUERpSoLfKkA/NZq25FSoH6Y09PagIt4lfvgXr4G6ANX6+HAdCuMTM/2e6WcifWdSmA9ZiLAMt0pmW0pOk2PLbiLT9O9DLtADyp605LSQ9PACoNkw9pmVp5pkmMkgjM3FWPMt4Y/rZaQFQ9WVuCeSmY3WM3wWeAX/cA+K+FCvKfwjRtqTEIAbHdw10B11L/pqP0sgXajJCz4KQHK9nALtO0i2TN1KxPTKZmP9qoBUTTL0tvnQVk7PIwuybqwVgXVXlZ+hDWRwGj7mKrcaTJ7MGqRVqmVv3FJrA5u1PjJHykT2btTUVKi4aADxr2jPdPllZIF18CnDFcla/w6rqzA86MlGIYLuw+p7Y7GIcOePVmB3qeOysZAYOJ+ZiIYWrCKSjtuoAxClI8A6XQM/70PyT7C/IZFXmhO6pT1Xw6XfhcHVTsTvkXX3JZHfWz2yukXXgqUnJD6q9Iy8JyUztrnIS5S6S9JKs5TUujbjaNaOI3t4S8Les9sJslUnEi9aa6y6zekN7Zj0X0//nPvNRReelzsD/K31zxxTk1p5sHmdcUhIBQ/UmcHnFLKLkdiOENqralafs1T/0JM+f5EfSz6azdTQhvqTG3y0iWVHqDA3lPXHsrdS/OvQK9AamsMcpPYBHaVaMu5NzsmrIaB32H8KZ4xVF2gNLpJJnohkiW7HXeNyWlkTJzKflYlRx586Re94oI6IVR7uUtaGVG25FLohgMLzPtANxPPDFUNynjxA0JngP/mR7796/lXmSwMxGs14nbkT88Mu3c7rlzQbwl1B8pUj/bedal/Uc+ZjcuzSqgLh4qBfbo9r4aSh+IKSmN8KlhEtgN2G9DlL5tIHy2vRCMRElM8isCsH9akF1JYoiOcruS///23ja4kuu87/ydvk3cpnUJ9FB0hFHAQYumY24qkSCSsUFpxNsjcePJbhJzU7tbcS1jg5I25gevBdrceKiMiDNjrjjZlTWwndpQDi3Cjj64Usmu5CS7lIsRe8hZDpSiOLDXm2USimzMwBooK5GNGcjsC/btsx/O6bf7hot7AbhS5adq5nafc7rPH92nn/Oc5zwvxUJqSEjLG9mNsmfibAM7mySzpVC5p4hgohRKuX53+S7BeZek8uCecgOYkjDn50iH3G7eyvZ2ZZ5G4V8bcGEpQYJqSliF5A3DK+yuBXg7WSid7WxJ+OAKvKfYPB5ybzfv9Y+vZkdzE0xOuYjKNOn7+Kfglv/SXFAra0mpX47A0SA9UxaD2AC7cMUflntnUsDxPINDQyHk76JMgnK2HM2GfnDrBo7166ZV5ZN35vI/jf+ID0ypIIsYvVdIx7PV39zNXunK+YViJK/twN//MszM+thzPe/yIA9SUwEeqNh89vOwMyPLegK6E/T2o9lkSalEKXVVySUVLV1QKppWKUJXw5JiekpFyZRS6nyWVrjZcZdr6lr6XtVMj6jZrBlPNNX59TwF8F4gxeb2VxNmVTirlEpmlaKu+yZqqtb0rFLqhdJFXZDeVio9kjbT+1ReI5KmOh/nkPa8ck3WYJtNrSO0PNg4BUDanAyZCD0qK8etrijFLmY3QOXnk0n//HG7kAiAOIBIPa8jgYgA/rzUUH0RVQPzQRYUo5N8EJdMz6fAxd8pNdsLpAlWdA/X6I6eEUuC0s6IEWXFk36PGwVWwcN98GzUoyUngz2MpUTNKphVV8+rqKu2pdTb5vCSUio0wy5d6nEnaKotPcpkXammUnH2kexxLCVZYIQ5HDTLScnCqU3oYbJiKnL3w143kh44IgSUdCCA+rkRvXV+H4JHJfjUge+gY3xlETGUT2Fq41Z+OuiX3gRL82+xrIt+cu+WzBnddzbKDjvzpK2V22Wq7Tzycifp2S/97BywPbLTh1BG91WQQ+7OJ/NkQMfJp7Deq9Fy7KqYK1+KqmGC9jC8W+pZld7zprqaJFNKqS2l87m3lFJKJVbQ1O3SpoqPa/6ZQK/hrZRS6h2llEpa11RITSl1Y6ThPcEC4kEPto/H6HdjFU8i9dH5nHdC4nD3wCg6opM0Z1HhsLJn7r0ARK8lWhT0zqG12OZG69c9olObiJ//BdO8usAv2SvojWLrfKi9CF8bPmtMF80A1g2JAnbeLtykRe1lCE9GXPna81z/26bvpTJbhngl7zXVI9r2NWqnsOHZE6Sz5jP744uL+pH/yRQtafKZh3dBcM3llk962Ef6uLBG0PZRCiPw6pzyDXBL3+4ehnfKkopYUlcvXbuh4imltsJ5FWe3iF5uqvrUrDr/0lV1bd2M+Y7BvfWUUvGUiueaqqVm9V2VUkrFjddGGt4pAWsE4CHh9wA3KGobJ3ymnwNslyTUZZ0mdJMngdgkotN1oX5228OGia5SshRy4eUQWkvLMPtxuNlmIpPLa0i8GfD9iFuaGTMuG+DswBzYx2mJsKJbeI1R9N4AQkq+/IFFcB0b3BdgE8RkJgMHEE5Kthob0HB12fUyW9YL5NoKFoW/dAT8ReqXh42/VKUJANcHx3WgAfU3T0PuoTIPjz+1iPejPs6tVvmajNwWZLYTmdP3yJbMBUVAXbrEtTPwV5+GD2qFUQs22ZyAad+hvm74kVWOCB9RUknawgNW2YR1t39unV1oB1h80wdESOT/Peybt8rhpUIWzkjqT7rkf7xfvl64kP5uW5Iqz9S9IHxYqYLYk2UOkq28k9ChFp0F2wQhlQTM4kMtLK74Vh4zDXCl0N9i3aoYMZ57gEoInD3wpWSmqcLJprpaZTiGszTVlFLvVi9JoVniSggVX0uWVMKSUi8ppdTLS5eUmn1HqUJO3dsKZQPcDwedBkz6bOJiy2XjB36lSoTlL64O2A7GB+U4GFfA2YRyxIK9QKrh6wR9EYnRwhVLZ9F2PH4rqqLlSNl+yXaAIzYlrzmXuyBNYcsr2u3hxWXy/tV1FapoSSml4qJasqScT5yvXnOjcnY+VmkPCer+R8tr3L29OBdgDaaOfABWOlIlLtQk77vY4QtdtdF1e6e2WQ0gGI17awqgEU3gbFBStgGz/4zdYlgs1OHHOoS7CD63BpOnRoRklpY16cC3P95R+eBw9+joMQb/bGVx1R/SRs/SC0ICCxG4L3TvCSl3V0TTFV3lJRnCX3qA4cKLhV0lR3Nl3CzQAOujHTPUf9wcuG/X9uhYbO6c8cGdB3eYnaagZ6mfHcQ1UXbh0nTqv1seBGm795MHuGPYTB/d9GPZvBmVwszm5H9x4MVRSsefWlcBevIcxrGxe/NQh6HOP/Nj4HfsiTVNSoo+dO1taD9QLlnFA+Ihw4ud7CoJgTD7zl0PhOzIyk09GARp1YGkoiv1ceCCD7c+PASkua4SixAvKy+lfh2e7EJk0xLwHC4EfwiNPSuZsxuOn7DpRiI7E+TK6une5KV9INvS6h/ztDY8mI0YwdQTgB05Lp4Esk9OyADgy4tmaO89EATFzUane7M3JvPcXXJeu5Xt3a4SqKR8Ho1udwF8BF6WDnraA7k9ynYzAO+RY0JyJ7QuwAH4gAdGEZNWXsBeQtCl9OLYe6BJB26xFUp5JGFuj751azJK1AWomvsPSdXQmyICR/Tsdahpt5uSnhrjgRR1ldxkaQbQUiGAFWq+FI0mVQ7Ixt6PqtvNygfrdRDfyYu+Asy+NfIeyghU5a7XL6K50FGwWAHEd0/qkul3R4Ik9LPeC7le+SwqPTSjaD7lApNsHb05r9nLFzdKvLqKV/TVCNr1hEkC+PaPACyoLg68152mPdKNr5fPGnXyTXk154NJY79aeTR7G0v+XiHd5nmls8CGeAn4UtX8awbx7mh8KdorIEjCqj+75MZpiXpMkpwIyYwtJhnFKh7gD/YOKT3xfOVcYluQWgE8LTFMYhMs9uwpD+Tm/nshVd3XMcJJsh6WA8F96dbKQv2ghZOKjuBeM2BaBZwF0GN7NBHuy+Z3LybolXAYtx+B/Bu5SwIWq/BMCCVxdy98aYQwB7VKB65VYgJ6ZAcn4Y4GtLxro0AagaJGmb82Glo4cZSH/d/6AC+uzkMKm8WXecBznFvZjqxFWjhRgPU8QKokIEeWKkew9+4O1jBhAXf7uZ8NOghEffTt5r1SZSt4FRCJ8UbVfjZLz8HpZRg6ieTY1P5s+Sxjm6Iqn4rRVfFvvuntFVLS5b92HSDJOYoEHqi22QOk6MqVcK+Q4oXy2UwKmTHfaeUDNeZh/nRlYX/Aw7tmvKk0zb0L66B8ZOdi/t8NnQ+lQu5eAfVwS+cvAH+kAu7oWFsUN98LpA+N4MxU6fjrRwEl8YAf76v02AOkqc50y0OQW+04MvnrQJyv9jza0jLaKx66xHUF3wfalt+pVNqz7wBQsvd8c+hL1A8/4ZdOp2LA1gZCj/cdBXsZS76ZiYKhr0ijJ3s1DoAXq8N7bs/eOpSF45GDji7eBPXX4Z404Gf2J+qCoT14gd7f7XHuwV3CV53LneuVRsPRSJ5oohaUzgLdo4KKExPAaMuBcO+Iqoae6iTa084Dgmu9LtgjpBGoGn1pJ3uL8yoQl6rBgkfUVe4LnQP+dTdfGtEHfGx6A/gsiDXIgmlnNHF3fnjAy4EKtaZNRN+vXVadrGT1r/6pQAK0YPTxv3GCDq8f9//LDw97LKWfBhCFo4NZLIlRuPf4ZLGjJdtGiXOH+icKSs0Om6T+0fPThezhXB99I3V8MguE63oUZxmK/GE2UvefUmIg1T7oUdXTw/vT4Uuq7oIlYXMHfjuQwMRPSD3/HvvknwokQGuCQgWTxwPAOembEd6R8PawSAK2DZdiybLp2QOqRnqHCUksmZ6fnsCkiPggwCbsDJ1E8oDII9AWrJpaAKNtWOwLLb2OKs1vQQjzstLicLk32rvCz8VB6+JJ+MllyqPpwCF5xWGDic5kO6LtQnCduigi6x/2i8slj1CPpcCDdlXjc8CQvM6Vfw1LAl62V/HRAP7ofuLQPSRIar37/gIRKFjUX70NREHA7KFpdHurpMLU58MP+AD4UE9xFovHecBSZe9d/E9vgKdzrV0MYTqhfuuhTbtHeyojPubJLNTfNx4B8PnZQ5O9P9JTf+AJmHoCQMmXwatLjq3ltQc+lsIeZdMTZNLlvKfX5WIUncAo1CfFW2krwAcIy3tfB/2U0rJbes9xlcCV34UnC8OzAx5Lqpz988bb7+92TrPhzeO0zxzaHOdU/uRAdT2pF0MIPLZL0esOGFLFPHULwbsdCtV/uwrPTMLioT2liqe87qsSy0J8Zlkz+PbcYUHqEEa6/WB8T/8+GeZFhymc3LLuds15Jq6EKJUftObkaEn/50jRZWc7BYQWqK286KC3CMt2xXXHhf+nYmmsfODn1xjV6WMESvJkgwD/aBP7+YpKMA2B+Ypx80FDap3pKOjVnzeNNaJ/3Ah0PSqfLUwgLiVQMp1YAKZDGozoA75/FGC+PblW1sIDfwqQPIvSNqolodP68qCtKabKZy0PpoVUZywQevCsBF3XDAnpyoiQ0so0G2921a9L2IxGgeSOCKn+ab90FiX6Vkd/s9LoS171ouG8vn4wXLMuSir3f+mMUklNpU3VVLNqVimVLFxWilq50bCeX7uGy+pDVuVreuWXYbutpVvzZGZjmA1HeUr7Q+djpaJKj4m6ptJmxYtub/5xI1BleLu29rAoSPRw2ThgSNvvlgFEiemxLaHtm5POpd5Br1A6w8OYROSBtvTWYX3loUKq3v/uL5kDEWYP54uLbKxFhwmp4nDN7KI5+BUPpQeRnOfbj955iJCUe9krnboW7KTQegLS7wKoOY9OAf2ABd323c3SmdOAqTvfBHwSzZlOQm3xRuWaA5cE/NLxRATOOrTeAPEqADHY55cpL14O9imJrf9wT/kU7WZlRUFmy5qJ3K2/lG/IHfTwvqezpNYw2OIQwArxaEBcrMAPX6qMMjgS4LmAadYgGs3eewQy1qfL1dL2CqjjAOJb54AYrhQM86CfUtsHOFUtdL7pwS0SQM6Bl+g9MUMHvdot/uS4WDzagK2ZwIICPliZ6A4aUsG9XykSM1kn69RO1sE4fVAxmjtoVmnPQofxMIingKeys22iyRECs49Khkcv9m+xyffXPYqIYLtDCseDFNvQI4t7y/zjLCEuFhxdyyt3gxSMB0n8Gx905LHjRcLBbaTJcK2Ez9R0o5IXaDdIcjxIlghK2X0ze+/UDsqha56KwBmWe6vF8RBpWq8HtNteqaSG+RYnmuZTq0dDQtr5tTHRpHowSraPm5XuFdOpD4DjB8Z48NA25dXTpvfoNWBmB94ENSngkvDIzFCgvCk/mC91a2D3SM7XPZhr/Vu4EQDG3kwrTnxgsdalJtjlKYkRUtdU6UGgIWPqFz0tBEwoxBx+bhcd6p/DM2KccHywViDEha2bwUslF5TM/tosK/HKsExgFO/KComnJIhHHPCkLslWl/rW1lnfYC+u2YUJoLPfjEn+HEeXvqqP266GozGILDLK8AkjpBwTzXUwTnIb2gQ93AYbH8MG5IJu5xaX7CIJnNlDFrRe1FqakmCDO02XJwHAUvc1g59SvGuLXSgOgPWPLnPzmaeBm6CekKUgK9PcsJDoawE9JEWTAbAhmWABFkxvrRehbT4xw5dKHHAXvjQtx4NknQDCG1JPGOrTWskc07no3rqvcLg+YCZwkwQunYLWR124nmoTdHEWYgtgNRfIhvXbnWBhzLQsCnAegjiMIXLR0fY+BzorhhK+bieLSw5c0AWYhCh+CL5Th/YT3fUlyz12ZQIbvRN5D02TqQ/tp95H7V94urftio7X/gm365pdntLtcjxIygaSi5KNO10dRwxblkLdeidXDIybhoTUHlf2dpAw/4nsrRyHG58E9bPGPD7X5zaGzhqTuOF4kGwBeFJydGlOTyE2AfHvZF+ckAbU2n8srhl8y7dXxlt81lLQwRAJIW5Bv6AEwwatbY3kgdZN87jTNrx/WZ/e/Abw1WqTQ/RD8U2P0ikC1MQfAPFTAOp2T+ckKXGmwa/FoZPz75UyplxH6r4nE4lF/iySG6Da1SBRffdgbiilWuq8ujrGPk7y97si6seifJbOX1Jq4lNFNN6B2zpvAUouj/WM+Fdd9+/IhyI8+F+Hzf4ZKg/7jDcepAq1SKH2BeBLwFEgj8pQl8MsB2bTCAt/TA1U+f7xSgIsQvsxSTuSmNXwioSfGmq1ezpGjCl7W/eUPw6BsV5KrIBkIgA4S2fmpEGQgqCnaLwX6mExuNGWiAt5ZORFR0OdGwpS6AFjMoHbRFA6+/40iQ3EhHACwFrx4bWock1/SDrq9Mo4gDrE0vpjUcERtcmOqMWwWO2kP6R9UqzK4tBYLpkSPanFPvzdqhnh4Anl9kfkmNB03oE1AGqzMfZP2+AoL3NmEs/BdNXrtj+kBEi+1Ld6KGoL7ZP6HKC1qFYAqLTDCHRISPuUqDXIDjaImUBcmwbu9XHM7ulq1xX9IalH5D7olwrpKAR2UHJDG4BmRqAOXPCGhOR6K4TA631bDEHG7/QkkGJl/9dquRGo8CGoauL6Q3L+K4ex03+b288BFum6Q0KKGaeQpeboYH0DRIvLSimlro0jnPxcIZxcVXEWTFupJaVUUymVMKXUbEX+GWhzMjd21EwT5P8MlBzgpGENIcBSgPLcykWD+VLa90sdkvQrkT7sQP3ZGPuoDeBnrjESRMfwGABJSd7VhiGjU5r6oAjh2krRX8uCtu5ZQudEMYA3p4BYHG/afZ+tIG0+CCvnF1hIEJc+UFbmWABhVLmm/1Pass3G/jiQiuzx8pS+l3ebhJch/kWyqj+uxt/oDylCT417fkpKQsvXxxNacduEWb9ocWMexK8CpBd8SD4yJKQQiMVyn6DahXnmuiwKXWB9Z5l2vKYLhNYGpChvRocVnq5JBm/z9YdUT/Tg83pX51NguFwUxkB4CrYLVwAJSBd8JP5agdzQAqhmqnWGu0LCgsd/x+u3laoA3HPgl8xZPwvw1ecgyF63CoDgeQjsQLEN73j6zzFqptkAgYDr3xgCkgfwd4K+Y8kHFX+WdllkUBIuPzxD9GNvlZteeASBIm0+Af9mE+r4uTwX6wyu0TBqit2G9SqQLJFMlTI2OivAX4XaOSPhFsnaXg9J/EUIdszy8ozu/i5gu5JksT+kdqo3Ovoxgd/fYCd9FKKSfD2x7vZqWtuEP/6IL2RdWzHnsy4iG1cldeGAsWTTkSmzSrPH4DOymrGxJQHfZnLayBsRAKdvAwhiQDVjaNkBvAzofdXaYmXdMJgJDAiPI6ZBfAFU/oZtYAF+IoH6UfOn1NIA/dXNTAI2qW9LrB1o3QfAsxLSlcqdByyaAJB99d4TgBVgT92lz7faEjkN758MsSvZYmrU4Eci05dV+UulgTPUhoWdgPq7sucwFz5vixfhD0K4vq7LIlbUTv2ruD93UudK0OSXrtF/Y7lj1YiguVGu2IUvpf+4Z02bNfDvRD23SC17ICERCp+pJ11YinRhoaYJkGn5z9NXpb8UAA3488WyoP+kepeQJHi9WmyzDUxjy38Pgcmr8JJyRAsqWWOq5GQvbEJlSmbkqn6Qk8Moc1woAstVKZp9glkJlvM+yLK6f4Hnu9PFvV2+v5KbQArvWH7eswcyBWcopw8laWmzvk66Gn5Ghy37aqm12yONY5Xss79TDJmOOAv1+WEgAX3MYC5mbMQvthnwuNUcJRSCQvm1W+pJLBIJbpDpeWQIXkRZNugLqfXfm5V62F3nZDMUFlmiFhHQQGtaVtGaTqBWNl0SOLASQ+0tMtVVsArrHsPZnCRPQv0LPatckEV2EvMK/uEVGGjPBYGLWD9CiS9ZFyT6UQ4dCOK3enaxQA39d+1A9nLfUrK0yHL1z20VPVwzBGnpmDmT2oMYF2afrjC/ASLc62A77mDI7wBmDl8iMH/+neQMqXsn1rfNX6JfabAA3gI/EEPs7dryDqjd6fWuLa/i9RBX/AxoO9Np2sXei99x5fw0MAtKC0gZCylRX0g1RwAvBf3qyb/5K+Y3hCy2WtLMNtr0mywZMdZDaCNJC2/CAIR0d4eUZwfsMccV+9bvgXO/ow+boA1UdlaJ39BXKV/XfZaKW1QyEWT+hVmLpcIiYcDwNlh6TChfFz5ckOxgo07rB5HyC7D6gtR2UyaJZLplZlcqwRJbi9AKAJhcgy1ZuXt/SJs/gD4aXR9o+8soIE0XdeHxe+F5ucKTHlz/21V703OSMte1TmDM89QOEK0wOWT0PMntvbn3nBeQEIMPyYeMJdL/MQdLM9De9LFzp+YAgMcAvRtQLgVS8RysDKvyogEbvaVK8aZH/ESi+fQrBvWaA9anFivBA61P3A9E1CT8Z6VyhZGhk03gqdMw3HbzYC5648xbxmQr0DCx4f0SppMgTyKpGeU/AioJJ9MHKaXoUfMLDGXg0V64H5p/uS8kG+DvPAZbpvsXjeJn4gykfy2b6IUPO6x1LOP/Nx/1DR/QrMNl58J9RW0/nV4MS2lTPasudde9q5S6qrZUfEqo+FKQFS+pLy7NqvToJRU1yl54jrqsXmqWFIwx+p9Sibqk0uYNFQXD5W6Wg55QW/4h2Ao783UHyd1nPMTPeTTuLrOO2wjMQsZM97aACT1M5/WzGiorcf3SFiLoinSZ0/SZB6AWYCWaUbdgEw8fFqapXegwOuciNjmrq02A+CKA9U0HiBCloLX9Nbr6Qb/Z68U9q5KlhCkVNdeVTjOvVLykLum3sdXZXDKlXl66prIExmViVqmpl1Tr2bxqwItzOh9oQRFQkxGEx8gztwRA/SnKq5KcXDjjsSW6BQPpwaMPVIT2XZYDvSkmgaVs/yoEwA5ZhVMhsEn7Y+XOT15YpIjh2ZYmgp4PsBCA78DCsD7gLUv2Kj7OdyUkEIJy9f5M7ZpYhu2vApC8Xp6s772/1x+oGdEs4M0xHKts2xD3VsXf/gEP1N8DlknfMZ/RZymEo7ic+QTwmVwKEeod2sKWSlioUujz2YChWCXbKf1Wnm44wTagnOsk2Z/ns8C/m1zmgvDY+npUal4zm8kWlFlBlYIhIEUKxI7Xq6qBw/etXyfdWYDQWNfO1SSbJuGKdUf3QHbJVt0dfSofuPDTQ0ASsexwlcypFiwCkCgXcdQUTn1PM9eFrP/q3aRDAwEqkWkzhWbxplaBIBoC0ncmoB8Hb0qmJ48Zf5bs5brw8NYitWcDlPTLzS9Ur7Z8KjK5V/0jBvGloEcmg5zq7oO0bFBeaQ/xfQ6wAOkLlf1R3zfq8SvchCVBSCGL0fO3Y5gqZKbdFuD9aRGt8uJ3izLbrOzdRkU9QQjhCuBiITptLETwvcqb7g8pxqd1k+xbPx/g/Npa9aOsrbmAh3OiXJrORmawOGgL8LXKncLqjftDOppS9sfqor/ooE4FWFZZ8pQLzDBNXVa+uAUJ508ZnwUF7btBFT1veDA3lI2uDYMyDTgT2Mwiwo+XO88OonJTOQ9zD5nH4UgSleX4AFBt2I4L07PB6zihjSF60gRYjotJL2yoS8X0m0CCg8KFwEJnbZyWHVE7vn+fOwSkhhg8+EWAiLo5YpXaS9I8bh+YjPQ2340NEEmQNWqGlWsGKJknh7D3ru9Sn2wHgAw0U3c/DxESLLhe6tkdDtIO0X5Y55wFeE6Sj67a06DVHMWKwaamCq1oX0j7ksSS1gnA+qaLWP2KLtlY0L8ufwn0BCcLy+aBkPRUMjonLd1eSA8el/DHATgPgJsu5OJACH711Q3usz2uGUwEGNFxGu5f1Rjat/moJzYAJSMU6zTCYXQCK0Ayrr23jpkziw4meIeEWEqNVOdtEBLCUJaNiPtD2gDicY2YiuvrAdz2BByNNKLI1M8jPl3V4A8QdCXU9p5upErqn/rAN82ZA3CvpKYCjqjXgYsA6Wo13lG/ZVwczSp1o/cCfHjSy2yaSmdXf1upKFTqhrqmbqirSilJXSnxxaaSS0Os4yY3gdJiZySa0H6MIaytAH8EU7PQYJoGM2iero770FjJrxkg6H5dMmEM6/ZMfnaTs0DajLQp10rXFHg840fvfXh3SHXQk/QoVPDZ06Bz4Z0P0A4V28YVRM8MS5FuvVBcPGBC8SG2l0fDVNwFSPDBNSZVUVvHOmlX/9qVISCpdn9lzvCUq46V57IlY3jN0SvWBIk2AxOvA9/NffkHjKVPT42U7xMAEfQsXr8VsGErkbTSvM3m3RCfGgLSyhxYIwfxqlIHQsvKO14HfgW/ooIeeJ/GyBGzDNWUD7YngYipZxPaTdsEYtYiu+fDA0/DpJ9f018iqu1SPxS5ANb6LGLNOJFpFcImrtYqEML8j1scOZFf0/8pzUAvC9q9UZpi4mM+6pv+BDSQpGoOoLmYgZC7Q1oA1H19q4ejt20fCHwtnCyAuJRCmoaGdaW+b1rKISAB7OwHI9Cbbsc9YEevwt62fbBWQNsvAXx3CDsB3fR0sB+QAGqBvul0XlIZpnEx1/SF5Eig9SvhmEDSAIoYEIEp/QcB8FCl5cLukEytHA9SQS5w5kF0QOi/51GrOu/Xi44GT7tj020iMAr4uLsOsJYDfTKUSdW+kO4pXEM5wLMRxDMwwzQNHEBsLXddMxhSOvaLa0kzjl2D8LublPXe0tPt3GEh1QfYwu2BZIDwtjU3DLNCzZgWAn22MCyk8d/rRACckeQL8Hqs9QFmI7pHvILBfU6M6zvQyoftpQWIYh3xu2wVtbZHSKJPsPehKd7x9YHaCeBmf5jQKbu8mf3xj8mW8Zt/qCeUWgI3jA9AsEdImz2v2QPdeAYAX+u1nFQzKdsClwBQPV7DYEj/njE9UWyLfMMO3hdBe0bPc7aA3m9hMKQXxoGTU8ocwncNE9gECAwTTXowvsGQpBzzxSVzAdwAlGyAnKSYOhSAveegIo2x8ADtGIikeUMLQH0brv9JrqusbrYMAeluxvS1dH8TYAUSH2iv6VLV8FHFIN1ZHh6SgjGHt4NvHCPWgO3t7L1J8w8JXKryqt1njGAcSLjAJcx0svZlnQz1xjsg9By3EkBwvHLJLtx73Aml/gUJziKIU6At5R4DnGJpKQtpYChIY4eq0zzJN9uCr56D1je08Fba6pt29wRpP8hF7ybhAnG9IkIuAHfuxUk23CdQa8WhqOQdSGZll9J/MKT1Ds/MUSkg36O4bqG1xdqiSfoIEe0B0v6Q7jHRPHMjRH3kt1D2b4GRgcMrlDPZ7A5pHxyvA/N7/Q/176qUaJZUYw4+FQDb/nCQZh8YWD0kbT0qgQ1YeAA4hrAmA2F/McgbPAmlcTsYkvf7+wEJ4OiXfHAdmDkNDRfOHcnrFhwobb0OfC0pIhwzqEjeh6zD5CKkpy4z9QH434v66QhKEvBASNU8xyPTe9SyPhASpv4A5YEqbWTXAW4O8l4H3UvIchbDseiqCoAEHIuu/cdtYCKHuNsXF+wTJB0290+gPqOFC78EK4KSlmf3T/z02HDcbPJug/6yfEFQzOgXPpFAK9eLHAarXMjic1x0Na5vpJDen9f71eaHAQlgnumCZSYJvF79kv+0km5PRdBqIFYfCcTmZmBK1WxYmeAPFZLrwGYMMoQzCzkKzx3OIHa/yO9R9qkfgyd+zCsXlQxiDzpydZcFxLUYZghgOjAlnWbuh/riIuj1EBYi4zGj6YAhtf5u+ey/7qnJufCw3ENwyLEpfqZ8li73bvUVmDm0+N4Rfil4fSjLOZqBIqz3t4tdgwOGJC4CWxmoF124pbqO/F9WqLVeH9LUc1/I+ghaot0EmNmkcz/tTITd4dZ94F9coJdtr0tjyxlVahWnABfeU4zvA4bkgl7ctrMIA1dfK9encz53dRi5HjAkvY4VkhezV2PfWWngg8syPHhofEk9gV5ENnxgLu5p8efH4BWpHQ+aCTzp6+F9YiuEr7m92iiOg3rksCABcEpJtpZ9fTIN5W0dAr0bfX3YqJ5jUy1Bf+AegOPCTkV0s75xThuBRO5hQTJ+YT4zzQDetwlvOihhoY5rBfZll50LHlw5tHwo4OuFv+UXM0kK8H99HIx9I3ZFA3bAkGaKDQlb+7/UHaxiPmPBZxqnsjg78KckgfXMC3YN2IafTmA+AmBWMjEbZQ0PBZIKgBUfcGA91pF1LStATIL+9IRHxbzoUKRKGerfcEa7qyBgR2/rZEjmi9YHLVVaPigi7aEfaKcwsagQgV4nhejVybvfzK854OVADJAuOToUVQNo/5VXzXa3V254rHg2Bz2h6BhGvp7IlqWOXN1OfVKzA76mG7qHNu3WfhO0+n/2dfSca0scW1K/3y93P3logq6J44Ve0soYbpzVO5b2RV0d6GYlY+CDhqTXjZkFlQQ7hJoFlpBAekGahsM4fewLuQqwskSo0XUNiyRP1taDDhiStuReNy6z995JwaXnKg3XC8Z00FKl5euYGCKMwHMgOUZJXkqWAt0w/IP8moNWUwAgFax7CdMupKcvAUG23SzXulof9AIcgNMSpK3jUkytQvuYn7fw9M/l//OwIN1IA/Q+oW/WAU4Ntjd06EOoZYZLbgHy0JQ583PADkw0TKI9U+53tTw0SPVA/4pIS+Ti8/p8Tf9EhwbJLZ8UPuAWWMaa2vz4hwaph5mLQvN0zdeXDcpbD417v919/1SWEtqoR6U5cA8LUuWNCBXTFrbm6Sa8WFYXFs0OU32a9/XOZB5e7OeWddHLi4cFqVZ4CJQo2s5yWORKw5uLyBGHI5xoajABKql8h8kcsF3Jl3TAkLqm0AQLbqQgXikKK8EtDxqS1pN45uy6W1gjW3cD8ARwwdpLnqZ9oRT0oLkcA6ISWP0fBRB4EJfe8DiuZrvTO2lTqXhWKVVXSslaZ33qTOkE7nK4aDD7QN+1QIeBdYCpt8omsgDc5kJjoVJ0GC/OlmvgxsZYaIeSwlJc9eCeZRDDZo3ZJ5IBwvse4KwQKWjvFJ7ygf6Z3I8QB0NRLQVjU2LoakyHp7yJd3RYkEzcLwlrIUQ7OtdvnHQFC104NMXgbdmLUVsPQBxJHCJqVlB2RluPKkrmA16hOArg8T+nz1q3zxLz2nz0HipTjZdw/dD24wBIPk+xlesJBwRYVUO7UkSbA4a0rje2fPixGWh8BpI8tIGXNZoNIfx/82sOemmZ+EXMHN77S3N8+/apxWqaROUBE4emX8otcUVJ70079WmXYLngJPKQIIkbABdX4LPLsMACdSVxbHByey0RNA7TmsJqAPgRrMZaf+vgm7QaVey+PCRINoBCak/CGye1YFSrZyovTduHqWTWGsi5eTg3A2sJwBzEIN6oNHy3cDY/FBHufmDKBfFEacHmZdUbRHCs0C8d1jpOecCrp2BnG9rXMQldAULhgXtoem+t2LIw84fSsSWSJWjneVLbAI1i9+tgIbVFFrdOEIErQrbehvgVSeL4pqqGD7XokCBpa2ArOAlhCBEB32lA9PGAOI+RcWdzpWIRfhiqePEt4O2L5MGJaxKua0lASd4HOk3p4UBqpgByDk6dg+1JCSxik23Atd+/UgT6PxxIvv7fB9/FzGuZhUcIsBlBUDXwOGhJQAA0mzAf6oIZobM2arZgffjLpviwrE9rksAM3TowpeCN2kkmE59JLbP5Ro3qFxcdBvf+V8X3pP0plCNRtk9J5RUdGiRloxeSG2wWKyNHmISucNasMr82bKLksSlto6W40E8KeWliIg/HGgVd1xwwpAQJywRmP24HwEfEIPQ787KWh6byipMAIiG5Kfu256u7XjWpf0sGsbtDOjsKlCfNmltYaDt8teXBr8boWfYo5olZ2bTiFteO/5QuPtmjMEsabQl0qrb3rwHbLoCkDbRnJSByEWWYhBHDtqimrO6gCOBeJYuncGEBEluaiHFk/kxDR4gdiqZ6lP3NnyidrOEz1Qyh8UngK5LW2YC474DYHZK/S/3EXT0KA/M7qczJBsBvLJtOIxA61solz7Tczz2UHiaAFPJt9lZDADkB9ecAN+tYfSwCylF0D4wJZOlNnCf9zN0bWFCQC5OGJADvXCi49/iSQNCrMIqz20uIxNRiIX/YJmyNAkgX/K677MNTOtGjbDvSv7UaJb5EDHNIwCd1Aa1V7aDxId3d09Q9C/CQoMfV9TXgzBEASf3TUP+nfrn91Dv7CIkHe5Tl8csS4LgyqZWkBVFiXqfZQdVfx5RbXDs+pA/0CiN5zHjAt/5HAHyONQPAhj9wwKrBhNYJ2IG5Ytg098PQsWbppHMvNw5kFuLAg2djQIBIQGgJ958tg78MCwe12m191DdHxvY90u7/c4isIu9xAcD6NqhLpyh7pI7OBLxNM643Cv1avGqG9Yqx6rbyP1mihaN6AmxnPEFIhdipAUlhXb3rZtFLvYtTzLbR+fWiMP7klD6YNTHsr6mmUkl5g+myUnFdqOSXzyillAqVUl/4lFIqzneaRoakckilCPrx1fP64NkMUtpUKmZJqUAppZ7VrSZQSQ2VAZm5UQYyxuZX4xfMQXmSM+NgW5rzBGCTAOX7bLf1FoDtTEuUI9FuKKrDNWV0SK7+acl3SoWh/vl+lm0vTYHwlZCUkO8blxxr8k5qjdv0n2O+g2A/IHla4Im3jgxoNGX7ULsnYns6wuVnaIU2qfdxcO/QKJ4Hoj3K3v3IbHp0yCbLAMxOW1mthFeQRNdOgbcMH0p1skcdTsxeeQLRqn72Y/ClCID6k+Vb/KT5rWeJeUzyDGo8hHjOgXQKZJrl3ObtOfjDFcrcaHRI3wz173tKO2t2xr1ldvsJ9KizAUsobPfnSfQVPkDsgDy9TzoBGehft1RWmzMHC+ZXZEMkxCVCwENIbRx3P0ASAKftSnDz0SF9XFZ7B+Cl0Bys9LrkOuSB+xK9aeIpIIav74eT7PHs71rrUbmxWTqJ8qNYxzo6FWPerXBNjV80H3/a3XiudNI2t/5ydeu2ZHAG8GvL0jq3KQEVgRY73bzxGJCW9c8bJR+8nYxXyl8oNYwJ8IhwwwihBblAiDAA9Co3gsv7sSmfJbUNPliUqWw6v7vc8iNKmnQaK9BKEJ9vwNkAwJ4BznjVO4887TZNQvFfK2WnaZmJVTXLKWtCZtW6ekrFdVS8lU356ZJSKnGOKEVdqTfb+bQ7BqQb+ne21LtQs6aynGAnoqkiplQyiYpPBKUalc6cUCn1ctHoksBGh4uifuKdnsoANPBxcKltTWG/GMhSlVg5C83X98cZrZ0HiikmTJFr1SqpvWvPShN1IaIz3OQnjptF1V6c0XYThRdLvUeBrw8+fUe5yQLUDLMXX+iQjoDgtooz2u5jqU9873wEBCeKwiLZUr2zfT9qKtW8plRwag9jqU+cE9GM0S/txWqFmnK1/ftwtKblvpeLkvFe3JMN8Eo7IBMAfxLD9OKwkCRq3QMn2AOkftFgAoAUZuZKhW3gtqASynQX8jt+x31Kn3BNNNqMjkvwvGHhgBJ+xSJ+OEj9KHLB8agsutsbgfl7s296l1wKKcCHVortnbEg/cMYgjrlhKsktdBoHfMegsF3qbn6d3trHyDJ58H1q4oJYTLJXj/jDnmXxyTCB46tjA9J4UEqeLekZUQnMQ6Luaa1G7TTcyCnwd0Pvfe9LkSnqoFDLYBfD0oYrzOQ8s95cj8EXQe4N6qWpYCoSdxZU9FRb8gnmwxt0BH0SlHPxhN0xQrO5VK3dZZhtZxobwKta9qh0La310AZKfIvBBAuDxfkf3eK4G6fmyrRmz/mwYdjcmm7FgNfR4c+3PR1lpgkJn9lb0sI4v3ZSBVrITTux3qwVGhcAyu7NIYkEEIaUk4iZ93mmoSk+wCJR02ANq+UeNsioOJq6uZwAiCCJKrcRFzxUPOLleQjYywHHAhXq7cQF0D5mx1rOzsAK4Qjy0AM9gKIOXNFoH/bch8geXPw1Z2OICH3AzwP95qOPgfwX/jwox5MLgDHofYM0OHXWE6WMaoIp5TSSsFf6ShLl64qZZSGCTSrtclVpZR6p2g9q9LmDaXe3A/FINBj52tFP7U+4sMatRlg+7vAFkD7TKT9Y4Oi0RiQttkhrvYeADtn7oTp8gabjl/vwUVjEvT9ELYWJQBSm8KUthjG4kvvPOpWe/87mWVilk5gKQXaMxGqvglBtmEYSPidAIB5eNyDt/Zhw2IjAAer2HoD1PeA+3Gqqcs1TaDVTy+ehSMNE+CHZRzwG7BdJPEZGVJ4wqN+zobrUUfNUgSPu+ZE+GD99HMI9yE46Wj3KuvWAGsVYPuMD3NzlF2/xhve9bjkiwPC7RAiaxJpGPWMhEDBj5zT0T1LhgFTQSWnwOiQZsPurbh1TAppK8N5HeAXAVyd9MS+TWLjEymA0wtzul3p0x0Z0p0eeuqoWo0tABJuX9OnrSXToSIChHlBKbguAM8GOlxciUaGNM1twHLFIlKv4y6vkP/V8ao0VS3QO5UevDsDPOFnV72TVJ736C8ucGBju/o3KoD7FiE0kkA0EQCfDoAN2IigjmQyhvZMgNk9nFioTNXjDe83/KocsiNBMU8uklknAOsFifjDAE6saG2yMwcNwzvWwJWVm44OaRmjFIyKsjiA1PfIGdNNEhATaH37HcDOPNT/saRmXPaCriBoo0+7daXUkZaqqOmiplKJuqrUs6bwqmoqpdRlpd42M3EYLpXv8tR5lc5fLZeMOe1OKlgu5RZzghWj0M50qnrumgPXSLbToFlDG0A9LrtuOjqkPORGyVhI5G/RjHltPal8U3oR6rM6efF2IjFLmsIoZ0xI3jZIAQ+WvrgaEivY4Ho27SrbhzYhcA6WzdI4cSSpHQDJT0rtgbUvkEIgiml99VRRlggX1nxUtmuprSdFBH4CfAj90mwy64HnfDjr7xOkKIIP30r86K9Vy8WWo0UEAFt3LNF83YFNZXbCdD3AX6/eYHRIWx54FL3nFLjUM8Gu9grZJO9qBU/oyPZsA+UW2dnmqlq18b44t0H9C+Xo1gkwv2BMOUC7nXzlhGSDiHcedcCzYSeG9meBYi9xPyApPGjMQa2009SqyQ6lYAsgyNTkAiXB8pHCqMae87sydI/+lJprWin4YKks//gWsgPbh2mzlfoeLNIv2Yh/0oL1lyQUKS/LNDL3bt5QJUV3mcrMOG0qlSwpld46pdR5pRLQDfQOTMyUSqcqV4/BvWWjZ6me3c4Zk9T2pC5E/OUIgFogdGhG80Aflp2hasZZgKMXTRWDWLXC6+JOSMxaZHubUoqWB4F6asIw6ZH9ZRB+SSEwFiSWeeeCi3Z2N9TKJpRPzJWQw5rW2HgmWRtKmlho4FeCjO4Oaf1h2bfOM+t++32lQiHRgcWNeH/jC3lVAsTmheU615uYQ1W0Crs+pXBAnadXt3WvKLLx+ZFmWFpP3wrkmhwAty1p2dCa0N1LaHlAkc9vMCQ3s0/ohWiSm2cjoMQqbQItIRQJGAOTZujCAlxwdVlMbqtwtbuDwZBElzItpw0sJnAgKE8HQiL82yhWaXOY1xQExbOLigs+EMJ8UHim7QqJm/vWtC+4hiOWNk8lLkinWJW3S1WAq1OT1xLY+jKAYhHqHqpWLNkH70leDwYjZgLSkub7VGyG9fai/tWS/pILsxdhalM/IRu4deG30RFi8TE6zGEgDcgGPpv5A9/73xSF9lOAG4H/Zjm1rlzTy3QXuOpQ8uHDjxGyqqfa5cW5/as+FME5wHuzWq4AfsScONnGVu7eZW+Xt/i0LUzAxNT8kJBuGeQXgL532e4s59MzxiS1/quSQgKZiwEkbooJ6GcDXHnQpN4ZBhK39a8KzO9k2SC2LTGbTZ33X4u0X56H5Pt2HghCOrByqpJRcjCkhj8Y8UxVHqQ1s4L2E8j48xsAZwJ4dAHabve+bADyONwyLKTa+aBv3ZRjei7tyNnTi0aTbXpufwgAWRiFpVH5JtavLqPmPGjsRyCIP0nM4CjtWnLCh7UNdtYMZ8gznynQeh91m+FLr4JJMutAOanILpD6MwHOLcLXlmm1y3YmZ0Ft+byThzo+GwBLEjEbar1PmHU8B+iH57uwKYeFlNka9iBXQ4gvlnlK1NGodQJA+lm4YbAa0mTZA2DJXPX+ML9mF0i1/lUrsrssAWg+Xy7Ku3K1V/VNEUwLSSPbPdGrBQoHm9HHUgLMfhDnYrnQDsD3eM+sGUtWAAjmtCZ5Qae00RuGevp7wUd85yskpS9xMKRWT3skQxEQcNN7SmbDdrisf7NBmPqAhQdyBdgAz5LwDoZxq78ZwsPniJPCl38wpHiAR1NLu1FW8+80I4ScAT/7W7QKOkIrwVKYFkBDmrygaS0C30VY54eEVJJTu2jtGa0ULMV2pTYBsAKeEbTq3wDgOTR/fh9mOyf7lhMC8BYqMHYZS3+rf9VZCaxWQqobaaa0W2E/5pujVeB6DDha0N3RPa8Bs5K09K3uAmkr6Fv1aWClc3oQcyj/eP4o0FOu8OHrHlw+AthG0NXVnv5Rk+GwkAbQdAIc07v8VfJg2deHtTqgcECa99tyYWtaIp7xs/ZtcHLT/rENYl1YWSkXrAGg8vihMZDeswxiFV79RYi3JdZXwPq4bhDARRc7l7B2lZd+Y0DlNgmgZFQUhWRyQMXuw59DzcfasCQC7CvSjB1bSvBjrFqhad5tQhlY+4PH56jsp/9z4fPGBQ+WKlv/EvA9fVzPxEc9TM+4MKldijIaLHs33L5VG88/0GBTkDZL67hTwJ3YJLJi/pKa/VbxAoSmR/d7ADU81Ie/UrnzYEi1X+37FN94uJbcjAVBh/HN+5sLpdRUAJz7mzq2YMYdWh9cp/3LAMjuJKm7vLjB1RPPJlUp8ZwKEJmitLjJb6ADaX/kNfAWJO5jgN45XYJOY7hdIP1m35o7ji2Yo1Lvi/lRVdA6idhZAR7VUqWzUh4TojwaYRctXDKz1E8Ld6N5Q6k3lWoVDlJKJeptpZRSW+VCpSanlPp8XamrLykVXlxSSaRUGmdKOjX7nebwWrikh7u/oQZ0xFYni9S70tl2y4W/Bswc13sptSkQdTCL3PXqbXaRBH5lgKlnA7227BRg6o90tQ2zNRw4nuyoVGRpsIeBJPxwYP2Tp3sUpsd7FJoqqM9SzvSh7x8MDynb7+hH/mT1vG0DT3vw7fsq5atojqqfuCr9z1oWn2VISINoE6jdhXqxvK3TlrCyTP3Oy6WWa+b3uoGSCgslzGq3+8a7QfK6hnBGR6UOjtdcLsMMerT8mpCsry+iRJ2uOSpA+MIfHtJk/3Vcm5VuR53Q02y6k1bBWwFE7juT0RkJsvqsBkNSqd9Pg5oQQdrh/117IwQBLRX3uuYVSC7Y8EsJ3NPvS94FknOv7PfiYtOrUKUVykWxiIg7mDGTSwG12RBRM8ocLPB1zw8Fe4Q00X/RJHhdB6Ask4PftS+qyfZcrDWF/alVaT2mbwDwT3o0HQhJfL4vq7QA7u0F+cXjST7ttepAKgtle/LkD+tQ2rLvMN3lizvVtybChQ/0qjm+wkRm/BNLism4Adgh5Yietc49nd0h0XeDwAY4RtE7ZLqHkkYtAkCaLfxI6Nc1mUJqNCBSN1wZFlILuHNQg/sHVeY9rqxq/53rm1gOEmVLHXMIXgrK2IeAFMu9ZbnWNy59ce/FB2t9GdbvhOYdesniCGhotCe6bz8YUsQuae53BlVmJPC0nnTOhqPbMDGBDl2jdwf2BAnZLRNVqIMjbgmfjTgqCnRAscDXqjZnGjhCKdhRuujrhiUBfjCk9/bXnCSLkY6B0UnhzV7pzAJoSmiGOmHEdEePmSWIHBJS2n8ktX/NQ/XOvlaiqPJDC+pfMABy2xgAZlbyw8GsUi15u/RZpUYwBff/aFf5a/NFpIXFalVmPr+Rl+wyls72rbHwEJnXQkbvbcKdpXCdWXgsKJ5UW9KagLaJVBMUxcNB6k9CrTHZacksyQwVstsLKKRznedXEiewraNUBQaJKiStXWTvl7N09F3U/42X4/O5uruT2hM2NKVR0eBbc/r3yrGschdI1n19+VIC3V4LK51TViP/46WtX9K62bW8kQKI/JNeKDodCGkAdbtM0i98VLoUoGYWSqEVLDCj6ILkotzUYVGHgtQg6P+Gehp4ABVFuL5/DD9i1ldXHP06XY3/flBBUnam2fUp+QMnlDzxU4nUx0qFpTDHrlY939Qpc+qn5eJBSw4DqQ+t6R9/pbtqh4B86zsxYm2VUrJMH7wOD/hgtpqCMSBp0ubJHTaIMWvkH0X8WAAEjjZvzIaSmtTZPkD9qG/KhMzqd4fk9SoMAEhneixEtpRkS5irav8zwAVg1dY5Bzx0po9oG/Tz2jlzp/ngnGEgqZ4MSBkGnVx9nZboYJcW8+QKt0gAKb42LAkgi5WBucWfD83fvIM2CdsV0pWefCmbJQQucafmxO7NH3QVfNvRfOnW0wDJxiLHCt4hh4C0MnBi1q+tY6kRAuJSVC5aClD3+6U8v1al56Vn2BK5fe1usvfZ3oLuUmAuTjt7N+QYY5ViBS+eyaqqLbOt7wiGekrnbuk5vIsg6oDjdDew1gyU+o+Xi10bJrZ1TtCGkBjVc8eLGAipLYOeY8kuQSo5b5sC7IKx26/4UJNzCH9GB+wSoO/5+wDIBOQ0NRVro53dIFGjr5oi63/C7SiKy3+EiWjg663u9io4Fqi/AupnAZIzpb8uyq4ZTLJnaZbUsHSjEtkQGf6plaQXQ7iyBtvPwFwLdjZhxzzq7xa8NhwCUi2lt+Ykn9timO9gl0fFDHzY1SfatduPIIwhmoT2z6Pf/C/qP0vBpqeTE5lwRIOf0pvG0rAPtcWt0OoKCWWDp++r/kjznACCh2CjDtuvwrtnA2NBwFIMKxHlFeFgSNN+73K3dOxVbrGmH7/Z8E09Bai5GJ4GfgiI6vpdW2tgPpRfyXYxrSEg1Xsq0+AzAfk6u7zdvEJAPX29JKwJjBuI5+oJpZY1j3T1TXD8dfPHMQQk86X2o5qKKlG4eFjIbtU8UGLxG9kXM6W7t1C42r4xSYeBNN+7WM6xca9kwBaimXbXtJYtJP8yHcBN/bI1i49+gO1h+BJ2zwlFAO1vLRtAm90N2NJirdX+GUrGeH4LQJIC6iM5pgD44t0QDjXt9q62zGu3Sr0DMLPkMs+M/twLClZhbQOuX9fDuP5pSaNkHhTBkSDvbESp8hFqkz4NJjt6l4v6N9QTn1g/C4hvLcMv++B9BbxEx0Gz8iEnQmOpdpOeO3eBJPze5T58baXSu0aUiT6vmuGspSLpgT+t/dZa34TaJIgv5let+1r9ZGi0CeUo2NLVx+UdhFx4c7MDAbAQwNwCNCKYtvQ+vr+YXxU4aLtrSw4DqSepzZOFS1NQqvC791B82JyFeEpyvQbYDQjwy3Kf5+pvZE6/yzGtKTrI7nyqDSQ8yxrfA9TKDtQi0s22n/5RoV9yQ3j8VuD3RoeU/pIshVgqs8YsqFr0eKnwCQB/g4hY62x8KpJh5jrW/tnRIYEPn17Rh2VJIOMO3LtYauwAbwICh2Stbawrqjec8SAZatFEn3VcDB/LzK2iolhkBp3HfqfU2EXHpLXMlb2o9iq0tAg12lOaQA9KgPeXe89sI9yyDiO5EoAPk+vTeoP6p21pna9arAFc14HZRtuweBaY9vTxTC8NytRc6eSRWcm7FzyUjBBoM0LRMTuGk/nybKSnZG37RbbaipYr1xUHVOlYoBcJ6sM24jvfk1z9Xvd95eiQKlQr934uOylvG5wOJTNA7Xykn4S4Fdxbq6h9MlvXkYa3+HWHQg9f6l2dCXvdYtbHar4Obsm/z9bf3FO+Pt1ahWTF3GU3utqnPNDGKO+Wvb+SnzuvlFIqany41DJRl1XavKrUs+8qdTkp3ySdmDV3qym15Q5hczKIAj2h/UCUxLxE+kAA22ulljXmEMFtmUFshTKvEJGC0naGo31xyodnXGhvUtmZF0DvnRVwtLTrZXHRPwKQLizq2lcjaAyzHOhD7QxG8lAPLe5aTO18VC7Z9IEVSE8aGeF2aFcdM1wHasdHh8QZqYf9poco964IwH+o01nkmRCu3FXEuG45OkMDJNlfJCHjHCNOKOZG4aRZj+W0DNMurFTUKXJRb96Gk6DuhNiWtM4C2HkUdMi42khPaRm0JDDpUFEY2U0P5XWqxJU3D/UZmHDg+i0gzoJV5aYS8gBIo0B67IwPiyEEHb1b/x79BMNyafog8A9dWFUQOXqLMjKVy/pnMwaGHUthd5EE2H7ADJnS3C6yP32xa8L3zG/N1m505m/4kjm471bNLoeC1INO4ur4Sz4dypx1tOvk1q3lUonRnAm92k3wzQcgsu0cD4iGWsf1pnuXQh3DJToFHy73fsysgXzDFHVQPIkJaRZnq93cdEFK3dBtaEUvjBYhNp8TXr6qVFjvqJBTSkVTuiBaaCqVfvS8UkdfUurlh5W6fFypreeUiouen1VKqaZSl5KlkSeU3A5+x6sGFUl89Pb7VGRKVgBekfCZi1rai4H6yTzMNpjd3oDhlDnQhy9JPbTnGqXe8wsWSicvA6LlQnxOv52ZbLWb95yvn0xPo61Q1Ir+czstnWMfmJXkEosQoFe7C3PguND+JtQ+n5lTt+/ziqsbRtIcbSxNKTWVnfWKNqxMaN+tS7NKJxfIKK5kuU7mZlXavGYaPz/6WAJZmJOW+ZIeZJtwxtcFOsrSbNECu8rZTywiMq200pbjI0EK8dGcZqdTkyF1Re470G3GG5n+JQC/6JOvA8bhS1/5xQAWImJiKvJSIuCCKFk8pfnYvYIWIEDbCV9fRqNHZXGLLfMAdx1LPYyrYUopPqEihFKIoiJiVgXUlZo14nE03VQqEUopllTKrC4N6lqy1Rkm8iDBlxI18ljSyakCwNLRqjIqWJRrCqZD2FaSNgEpm/oJBTsQtPOGXnaRJYd7Sr2+uCWlFDUVUSuSeiilv7jL6ppSL2SfYTyrVEhTxUypxK7pJzQVKjUVK6VU8pOzeqWglFLX0qWRnxISaIbU1FsV/VlBfnZQD0GkIfHsKZJ3Q/AT1Nw0uFoB8EL5qjHkJU0u8EPVIP8TLDBXaMNML98Ri4jQRzCJIgVsqTRjsP5chDBrnYy9jjXHNbDprQtZyYy33oMeYdfxdMNmpJ/hRywAcTUit2B8cyi9N33mOLcwwPlSVCrP4D3s6t/UlhDkbMwh9ev6/dhF9IQXFyv3HvHFZVapCWzeUSq/GbTe0dw3sUzHeeQeaXqtSUBzzMDcrd4YElLYq9CHLR/4k2qxwtfBIX/IPMLWhdLlaQ6uoHMByAVzEg0JqRddANbW2Doq4YulPtI7Q1rCgx+OhruR+lyIms92XsdY7eKf1IFLNn8djpSGd/IfzIGXlRSVbt5Xqc+0bK03ry0sRtN7A/TyWBcsMnE/PeNVR4XX3LlX4V/obZ1Fv8TEHutEPDyl9yzD3Azi8wvdY+3mitlt3YOtm+DdyxKVqaBb98KreoUkY41/9Ry0vjE6JPw5FC7W0eXuulKeT20WBIlPevdK71s5wAY8fxpMrI3RJxQALy1631QScPlg2U7AudPv0K0ImxQH5szLDRCvBVD7IGyNo2T+vLbHaUhIsgHyegrKLMu2MgsiGwkTttTbhDr7CSxtw1Sa/3GfOafTcX5H144GKUCrKUL4QZ74BbDPeFhByRKzJoBvvjuPUCG9sjueX9arrMBhLCZg/d8+bN4J0fWOCnzEt06WSjL1dg4z85zx9QWpB/eEJsjOuJLA1x141Ct1vg0CCY9BTb3d4wrTl0RapJIAs8PR6NlsbyTOOnrDwreLAaIidJwKf4482E77w8CDzOPh0mCCBKghZM1sUD2c3TMu4oGMIlUqpXQgyHBK3cjWcQFLRuYsNYrnm9Wr4kp1JosqJW8oFaJGlypBh5qdjXAyC0kPww/KnvNxhxEv9c7YEiFc8uHEq+COtWsZ5dymMrEX26PLpt2gsA0AfBWCNR3wcxwlczGL7BS9F7QBG4ZZ1YKws7ZCykdPKjP3j6lk/peBOYiL3gkAFy6sU03eNIjSC8uoZna3YHRI6m9IuAhbdRfeMN9JOIcJ1LNQTt7kDbxTAuDPQGs1N/UZCdIO6HGzY0GQJa5yQPxl0JF5DJ8e4Iao6VPmdzOBt8aAxM9J+KCvj5dNWc0GGi715sXC10l1b5dW6Rmpf8Nj+ccxEiT7aR9+ahXxbFjOPhHoZ59FDoB+KYsLEgCBDa+4+e7+6HxJJli4FLKs5aPWIuok2hgPetgydFANn4svbmqN58QYGxYA3GdTMam6U0j4hNnqXcy6fIrBJAO+oebH37AIZACrp2hIitRR0/h6Bg0oqeJPdV1cIXdFGmjkdq4jQXoQcoNttspLy0WgrIrvEF66aHsd6mmgY9sNZ3jWk9T2mWVtvvDI9aJ3tQRIEOs+zJlxvUu2HwBOinkgGs76FOhl762/jGm0qXuWuOr6WV8fSAcai/o42uXmk6eMduzeOxjLTmDCNyGDfBtmFjvA+nP5ir/DJGvL078tO3tLR56SeGzqWLb100NC6mFcffQFA2NuuuhdpIGWU5pBEdbjvfighHE3WDbi7mYbNtoAXqaemp4u7FhHEeFm1WWljMCmXjCKwatpU6l01rTIVPFpU2fZS5hVKSgV1ZWCppKlhBsVDe1oIpzHHMhlfWL+TCOARdWWSpetsc0mqZCStR1UPQmRYQh8W2L09gWHG40vsYbm2tsdIATAZrF0iSzgt5OYtWbC9hOnA3wl0/lapGaPRcBXwIy3hMxtb0TuHZM7MWexaNoKsI0xarxSavzF2vPUgrfYklao6vFK4iNTT0iDpnNqHhFSAMA2EcRZYLBbod2G14VXODy4+r8P8gp1QJDO10Eyj8888JQEPrbClnBoP/bM6JCUOAdnPN4645lksGDeoAsk8M9NWa1sOXUrSHwW63CX4Q5OSfGSfP8ROTKkFMjDTv3wclGRXEMbgOcLXgF4ODzITTRgIZHMA25NW0m7fLV84yEh9fDWSYjhyKqeZb27TOepXu1+9ENh2fFdQsAEnunpJrI4ebkoJUIQl6M9LAd6+aE0V1F3xbz/N94qxTHMmUACj5jCRq7JUS4Ey1j8lqt4Ep4tLLEWwVpzsJ8MhoTUi3zgAU8/wO+ulioaaGV/4TrpA6zScrdp/6zW/KVLn0P9gpLAuljO55naWOpT0CqQlcl8e4gGAco3JxMfz1qZ37glSd5KaXEavoFMk0VdEYHUmiqmh1WfBt1FVoAeLj46N1VG9rfNjqFjYtauT6BLomQBthWITazX5+DmzFIUhW8sQjT+3SE90F2UXggQm5KNPBQ1gA2WAzGbeQIWPRK/Jnxqb0Ark8h5NeCTAExJoLkCcQSkcjhIPUmC9RXTo+m93Qax4fa5wJ4CjphEBILM/dNdkvpZ31fIpiO9OMDk+ooKg64ECbVQn4TmviL29cQcNhYAqD35tO73heJLDtDyUmt0QVfH8f+wvvfOgrlFHAf85xKOCo+dTLOmU5MryczHA9TJFXgI0mufRKw6ktKcPTOdu2rsDsnvUXYKYIFbhFc4w4kJnbM+BOLMLMkl0I7eah2c8BFIbSD0kQIgZlHn/Moe71CQepH0uWCgvvutbDFSUiW9vWju28j6mCTQ8W7eJ6klUrI4AfC4pIjldXUYz6++NAchRD7E86WZtSbRcek7ZiEPx/OwvbMwUYPaB8p+R/rT3x7Pkrmmu8FthtqdC/Qwb6CDSLk3XtSF6xbQYJp64FILHjJCXh5pu9iIinIwIz4lDxFAY8WFRwNdlD2sGVeHDjTkZwcaLoS0i2CodYkO7iE8s/0xMqRFnelsE5Bzukjdoi3Kav9yWsf40SQBFKH2KPSuw3Iiea0teT1HbRqPswDPH/jXG4V/hbPt06oB90m4XepCcQNwSeUmiu8VIANz0SUfhHT0ax9jAQ5C6oXZcb+wCyr0Nud0djnILVsSElJSlKfAtQPOWQEPkO1SxfChjWE9v3qTwgGeA+silgx1Ya0G9fqK3i757aDU/BSxPEbiT7GD47exLrZcsdp2AM8H+B7mrQ0JKeguSmsuQvp6SAeeKU2Ap435698yZTMA97HFlFyVjrvq2xdsT3zO8XjcdjFx0LwQAjf3SB0cDQZ6c+/H7oKfnEHZdbinhNkj4kotwLbLvgOeqCXOmbOpvXUiRVsyBPh6TRqBWn+gYpAx2lg6PQd32ezwPXLpZEvCApJwZrMj5rOLjc0ENSZACpirwVyuydAUjef5hQEST3UEg314XUv3+Wq3raRmlTU5r/cBFydgKtH/DAe5R2q+NJbn1xMQxyHRYoS6xdVlEWg5ipBbjnq6MCGAWhhqf/9mVHVgNaPir/voEXT3GJBWAo4e9XH/p4Wi7IjUv23hFzJHnELVYrAXeYADrWEF3aBH2Tu6+yipVPqAFhetbHoRIhx48wlAyEQ/wNiRMMwX14MsG6561xBX1iEw/FspmA2XEWoVocxYsnbRe98XlE62Nsw1I5D4PRf7zRDrdg/RnMuAZtUzva/qR0puGFs/b0hIPVa7Weg0t3faDZtGdt90l6cElKaks0NC6vNq7yIzPCuRh88807iZ9KtKuUH7k4Oe7Z4YElIP8ghxmQYcJrOgQDUFQWf8JWeY+7vVi0ZbDlRVMAPI7n15lT4UsCXmYeeP5HCQhqVezoS7fnGA+aPXYGpYN6seFPUuDrqLxLXd75ZHSHPGkL23xPKwTad3bwKb2iZjYliLwR4kez2ntRdHuVVODlmq+/0bS//yhV6lV4a5NOSHKMwuRppQfLXcXfiE6t7patc/N8T2l6jHNsCmfssjTbtej26U6vnJ7Q7IrHZDslXUSHxpZrlH4bG7g64yy57sLuxJV343XwOPtgB/bK67UC82qiTqR8Ldb9cMIDyeh4scbXj/Dz2693sY4ai5oW7nGYuQtTEg9bQlDvr0NhRJoP6ohFEhRb0K/SHLOknrUhzAD2BEJtD6wOBEEgWlQwgnajU0PD7wR4Z0biLcvSdD/u5NWn4M8QRKx2kebUI59dXuwnPnu8sIdg1HbGgR2NkPG90y7fSynLiYqVAGUR1jETI/JKResnc54uogEkOFT54PjE1GMCSkYUfbzUGPQil3v1AEd2mLg6Htl3r0VK93l6U/3mvYfOZjPS7vIhduCXMsI81xTi9vmN6dDTtWLfL130jDe7nH+LZ6TSgjkdjNpI/V+X3qaki6MPpO04HR/gm6+0b/SULyDx5ElXaHdMij+z/RF3fo9GeQhqE/gzQM/RmkYejPIA1DfwZpGPozSMPQ/w/Gq2H7oekmiQAAAABJRU5ErkJggg==" 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" 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" width="1508"/>
*984 Appendix D
Occupational Classification of Employed Males in the Population of Two Parts of Westchester County According to 1940 Census Statistics.*
*Computed from United States Department of Commerce, Sixteenth Census of the United States: 1940, Population Vol. II Characteristics of the Population, Part 5 New York-Oregon, Table 23, p. 62, (Westchester County), Table L-42, p. 224, (Yonkers), Table 33, p. 132 (Mount Vernon and New Rochelle)'
Appendix E
The Effect,'on the Percentage of Manuals in the Norm when Adjustments are made for the Westchester Disproportions
While the pattern of Westchester notices makes it impossible to determine as a norm precisely what kind of an occupational breakdown would be obtained by an ideal method of distributing qualification notices, an adjustment can be made for the disproportions within Westchester County which would tend to provide such a norm. Making such an adjustment provides norms of 9.3% and 11.6% (depending on the period selected) for “manual”, .workers and these percentages do not differ substantially from the percentages of “manual” workers on the current panels as shown by Appendix B.
The major reason for the need of an adjustment is the fact that the three largest cities in Westchester, Yonkers, Mount Vernon and New Rochelle were not sent notices in proportion to their voting population in Westchester. That is, the three cities were sent fewer notices than their proportion of the voting population would call for while the balance of the county was sent more notices than its proportion of the voting population would call for. An adjustment is made for this by ascertaining the number of notices which should have been, sent to the balance of the counties .in a distribution proportionate to the number actually sent to the three cities and treating the number of notices by which those sent to the balance of the county exceeds its proportion as notices improvidently sent and a. factor to be discounted.
To remove the effects of these notices, the veniremen qualified as a result of receiving them must be eliminated. The proper elimination of veniremen qualified' as a result of these excess notices requires-taking into account the possibility that persons from outside the three cities qualified as veniremen at a higher rate than the city dwellers. This rate of qualification is computed by dividing the number of veniremen-qualified from the balance of the county by the number of notices sent to the balance of' the county. When this rate of qualification is applied to the excess notices, the product is the number of veniremen qualified as a result of the sending out of the excess-notices and is therefore the number of *985 veniremen to be eliminated from the occupational breakdowns to establish the norm. In making the occupational breakdowns finally relied on only the “manual” worker and “non-manual” worker employed males were classified while the veniremen to be eliminated would in all probability be made up of undetermined proportions not only of “manual” and “non-manual” employed males but of females and unemployed males as well. Thus a problem arises as to the numbers to be eliminated from the breakdowns and the classes from which they should be eliminated. The elimination can, however, be made on the assumption most favorable to the defendants that the veniremen to be eliminated were all “non-manual” worker employed males. The number of veniremen to be eliminated is thus subtracted from the number of “non-manuals” in the occupational breakdowns and the result is the norm or adjusted occupational breakdown most favorable to defendants’ contention that a disproportionately low proportion of “manual” workers appear among the veniremen.
The following is a table detailing the adjustment made.
Sources of data and further explanation follows:
1. Computed from statistics on registered voters in the Legislative Manual, New York, 1951, pp. 1174-5.
2. Jury cleric’s records. See Appendix C, Source (5) and footnote b.
3. The theoretical distribution is calculated on the premises that the number of notices to be sent to each part of the county should be in proportion to its voting population and that the three cities should be sent tho number of notices that they were actually sent. The calculation thus is made by finding the number of notices for the balance of the county that bears the same ratio to 550 (for the September 1950-March 1952 period) as the voting population of the balance of the county bears to the voting population of the three cities. For the April 1951-March 1952 period the base figure is 420.
4. Line 3 subtracted from Line 2 or, in words, the number of notices by which the balance of the county exceeded a theoretical proportionate distribution of notices.
5. The total veniremen qualified for the county for the April 1951-March 1952 period is from Defendants’ Second Supplemental Offer of Proof, Ta *986 ble III, and for tbe September 1950-March 1952 is based on estimates of* Manhattan and Bronx veniremen in Defendants’ Supplemental Offer of Proof p. 10 and subtraction of those estimates from the total number of veniremen qualified during that period. The figures for veniremen qualified from the three cities are based on an examination of the veniremen’s cards made after the challenge began. As a result, the numbers of veniremen from the three largest cities may be slightly larger than those given here because some veniremen have been taken off the jury list since the beginning of the challenge. The figures for veniremen qualified from the balance of the county are obtained by subtracting the figures for the three cities from the total for the county. The effect of thus subtracting from defendants’ figure for total for the county the number of veniremen' shown by the cards as from the three cities is to allocate to the rest of the county all veniremen taken off the list since the challenge began, which is the inference most favorable to the defendants.
6. Line 5 divided by Line 2, or, in words, the percentage of persons receiving notices who qualified as veniremen.
7. Line 4 multiplied by Line 6, or, in words, the number of notices by which the balance of the county exceeded the number of notices that would be sent according to the tlieoretical proportionate distribution developed multiplied by the rate of qualification provides the number of veniremen to fee eliminated in order to adjust for the disproportionate distribution in Westchester County.
8. Line 7 subtracted from Line 5, or, in words, the number of veniremen that are left after the elimination adjustment has been made.
9. For the September 1950-March 1952 period, Government Table 4, which was adopted by defendants in Defendants’ Second Supplemental Offer of Proof, Table VIII. For the April 1951-March 1852 period, Defendants’ Second Supplemental Offer of Proof, Table I.
10. Line 7.
11. Line 10 (or Line 7) subtracted from Line 9.
12. Computed from actual numbers in 'Line 11.
This method of adjustment of the percentage of “non-manuals” in Manhattan, Bronx and Westchester, incidentally meets another contention made by defendants that Westchester County as a whole received an excessive number of notices in relation to the other two counties. The effect of the adjustment is to reduce the total number of notices Westchester County received as well as the number received by the part of that county outside of the three largest cities. Thus, while Westchester County has 19.3% of the voting population of the three counties, under th.e adjustment its share of the notices is reduced to 1345 or 9.9% of the total of all notices that would thus be sent out for September 1950-March 1952 period and 1027 or 12.3% of the total of all notices that would thus be sent out for the April 1951-March 1952 period.
An argument could be made that since, during the April 1951-March 1952 period certain places in Westchester County were not sent any notices because of the inconvenience and expense of jury service to persons from those places, therefore in determining the proper proportion of notices for the rest of the county the populations of these places ought not to be included. Putting aside the soundness of the argument, such a further adjustment would change the final result by only 0.6 of one per cent and therefore is de minimis.
Even with the norm computed with West-chester disproportions thus eliminated, the differences between the relative sizes of the occupational groups in the norm, on the one hand, and the relative sizes of those groups among veniremen selected during earlier periods or among the veniremen on the current panels as a whole, on the other, are not substantial. These differences would not prevent the conclusion that the veniremen on the current panels as a whole are made up substantially of the persons and in the proportions that would be obtained by a method of making up the original list which attempted to arrive at a fair cross-section of the community.
. The opinion of the Court of Appeals in U. S. v. Dennis, 2 Cir., 183 F.2d 201 at page 217, indicates that the Association may have stopped supplying names as the result of the decision in Glasser v. United States, 315 U.S. 60, 62 S.Ct. 457, 86 L.Ed. 680, which frowned upon the delibate selection of jurors from the membership of particular private organizations.
. These thirteen panels will be referred to hereinafter as the current panels.
. Defendants include the following Assembly districts within each grouping:
. While such a conclusion is weakened by the breakdown for the January 1, 1949-August 31, 1950 period, I consider the geographical breakdown of veniremen on the current panels for that period unreliable for the reason stated in Appendix A.
. The basic premise in. this contention, that Westchester County is entitled to representation only according to its relationship to Manhattan and Bronx, is not free from doubt. It disregards the fact that the other counties within the Southern District of New York (Putnam, Dutchess, Columbia, Greene, Ulster, Sullivan, Orange and Rockland)- are not represented because of the inconvenience and expense of the attendance of jurors from those counties at trials in New York Oity. An argument could be made that West-chester’s population is similar to those of these other counties and that West-chester jurors can better represent these other counties.
. While the occupational breakdowns are according to the various -census classifications, in argument upon the challenge, these have been grouped in two main categories. , The “non-manual” group includes the census classifications of professional, technical and kindred workers; managers, officials and proprietors, in-eluding farm; clerical and kindred workers; sales workers. The “manual” group includes the census classifications of craftsmen, foremen and kindred workers; operatives and kindred workers; private household workers; service workers except private household workers; laborers except mine.
. So stated by defendants. The percentage would be in fact 14.1%.
. 28 U.S.C., Supp. IV, § 1861.
. § 596 N.Y. Judiciary Law, Const.Laws, c. 30 made applicable by 28 U.S.C.Supp. IV, § 1861(4). A thorough consideration of this point will be found in the District Court and Court of Appeals opinions in U. S. v. Foster, 83 F.Supp. 197 at pages 200-201 and U. S. v. Dennis, 183 Fed. 201 at page 220.
. § 502, N.Y. Judiciary Law. See also footnote 9.
. That number is small in comparison to both the numbers of questionnaires and eliminations. The use of this notation was discontinued long before his challenge because it was not regarded as an accurate description of the reason for elimination.
. 315 U.S. 60, 62 S.Ct. 457, 86 L.Ed. 680, supra.
. 83 F.Supp. 197, supra.
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Cite This Page — Counsel Stack
106 F. Supp. 966, 1952 U.S. Dist. LEXIS 4115, Counsel Stack Legal Research, https://law.counselstack.com/opinion/united-states-v-flynn-nysd-1952.