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2008年度《内陆地震》优秀论文评选结果揭晓

４天山中）部地壳及上地幔三维速度层析成像 ………………………………………
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篇论文被评为优秀论文：

08金融危机产生的原因及过程

《金融理论在中国的实践》总结论文论文名称：简述2008金融危机产生的原因、过程及影响和应对措导论本篇论文主要针对2008年全球金融危机产生的原因、过程及影响和应对措施进行阐述。

主要包括三大方面的内容：（一）2008金融危机产生的原因及过程。

其中又包括两小方面的内容。

（1）2008金融危机产生的主要原因：一是按揭贷款证券化。

二是宽松的货币政策。

三是放松金融监管。

四是全球化的负面影响。

2008金融危机产生的更深层次原因主要有：一是超前消费长期积累酿成的恶果。

二是美国的银行为高薪所累。

三是美国目前缺乏新兴的产业。

（2）2008金融危机的过程。

2007年2月13日美国新世纪金融公司（New Century Finance）发出2006年第四季度盈利预警。

汇丰控股为在美次级房贷业务增加18亿美元坏账准备。

面对来自华尔街174亿美元逼债，作为美国第二大次级抵押贷款公司——新世纪金融（New Century Financial Corp）在4月2日宣布申请破产保护、裁减54%的员工等等。

（二）2008金融危机对世界的影响及世界各国应对金融危机采取的措施。

其中又包括两小方面的内容。

（1）2008金融危机对世界各国的影响：金融危机使生产停滞,消费减少,经济发展缓慢,自然地有些企业缩减规模,有些企业倒闭,好多人下岗了。

美国股市创出自“9·11”事件以来单日最大跌幅；伦敦和巴黎股市跌幅接近4%，俄罗斯股市MICEX指数更是暴挫6.2%等等。

（2）世界各国应对2008金融危机采取的措施：一是援助或接管问题金融机构。

二是直接注资资本货币市场。

三是全力保障个人存款安全。

四是大规模收购不良资产。

五是全球主要央行同步降息。

世界各个国家应对2008金融危机采取的基本理念及具体措施：一是加大经济及投入力度，创造就业岗位。

二是加大对小企业扶持力度，避免大幅裁员。

三是加大就业投入，提高就业服务针对性。

四是采取措施促进青年就业。

《岩土力学》2008年第9期被EI收录论文(52篇,收录率100%)

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２３Ｏ２３４５一５２３５２３８５ — ５２３９— ２３４５６２３５— ２３２６７
庄艳峰，王钊ｌＩ，陈轮
大跨度连拱隧道直中墙受力现场监测分析旧路改建高速公路中地基强夯效应测试与工艺参数分析深部三维地应力实测与巷道稳定性研究软土基坑支护中的锚拉桩结构设计基于ＳＰＵ的软土最大剪切模量测试分析研究ＣＴ高层建筑桩箱基础共同作用现场测试分析基于Ｌｇｓｃ回归模型的砂土液化概率评价ｏｉｉｔ表面波法确定卵石土地基承载力模型研究
钢纤维混凝土深梁非线性有限元分析在ＡＮＹＳ中的实现Ｓ等截面桩与扩底桩抗拔承载特性数值分析研究
单螺旋空孔直眼掏槽成腔过程数值模拟研究
土坡等圆心角斜条分稳定性分析法
２４１ — ２４１５２卿启湘，王永和，赵明华２４２— ２４６２２李宏艳，李风明，彭永伟祝介旺，柏松，刘恩聪，等２４２２４３７— ２３— ８张治军，饶锡保，王志军，等２４３２４３２４９— ２４５３４李亮，迟世春，郑榕明６— ０陈炜韬，王明年，魏龙海，等２４４２４５２４１２４６５ — ５王桂尧，胡振南，匡希龙刘方成，尚守平，王海东２４７— ２４２５６２４３２４８６ — ６郭红仙，宋二祥，陈肇元２４９— ２４５６７蒋建平２４６— ２４０７８赵明华，刘敦平，邹新军卢应发，陈高峰，罗先启，等２４８１— ２４８６罗伟，朱传云，祝启虎２４７— ２４１８９２４２— ２４７９９李典庆，吴帅兵，周创兵２４８２５２９ — ０马小杰，张建明，常小晓，等吴振君，王浩，王水林，等２５３２５７０ — ００４闰静雅，张子新，黄宏伟，等２５８— ２５１５— ０邹金锋，罗恒，李亮，等２５１２５２２５１２５６２ — ２王汉鹏，李术才，张强勇２３蒋青青，李江腾，胡毅夫，等２５７— ２５０２５ｌ２５６３一３姜小春，谢和平，周宏伟２５７— ２５１３４曾胜，覃庆通，阳军生２５２２５６４ — ４吕国仁，崔新壮４５张百红，韩立军，韩贵雷，等２５７— ２５０５５李俊才，岳颖锋，茅奇辉，等２５１— ２５５５ — ６蔡国军，刘松玉，童立元，等２５６２５０６１６李成芳，吴德伦，邓小波，等２５ — ２５６２５７２５１６ — ７潘建平，孔宪京，邹德高２５２２５６７ — ７候兰杰，陈兴长，陈慧，等５５２徐礼华，池寅，李荣渝，等２７７— ２８２５３２５８８ — ８吴江斌，王卫东，黄绍铭９９郑祥滨，璩世杰，范利华，等２５８ — ２５４２５５２５８９ — ９董育烦，张发明，郭炳跃，等

外语系08届论文题目汇总(427)

04 英本一班 04 英本一班 04 英本一班 04 英本一班 04 英本一班 04 英本一班 04 英本一班 04 英本一班
Nathaniel Hawthorne’s Conflictive Religious View in the Idio-Tragedy of Reuden 实践应用型霍桑矛盾的宗教观在鲁本个人悲剧中的体现 Analysis of the Narrrative Tactics in That Evening Sun 《夕阳》的叙事策略分析实践应用型
赵晶晶
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郄立霞王云马伟霞周婷婷何佳伟严鹭
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刘向美杨志娟
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王小锦么孝颖 /韦萍么孝颖 /韦萍
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2008年金融危机以来中国的应对措施和经济现状分析

论文题目：2008年金融危机以来中国的应对措施和经济现状分析院系经济管理学院专业金融学学号学生姓名成绩内容摘要：由美国次贷危机所引发的全球性金融危机在2008年如多米诺骨牌迅速席卷全球。

美国次贷危机极大地打击了投资者信心，全球范围内流动资金骤然紧缩；另外，美国作为全球最大的消费型国家，消费需求的大规模萎缩，使得对美国消费依存度强的经济体失去发展动力。

中国对美国的外贸依存度很强，外需的骤然降温使得中国大量工厂倒闭，工人失业。

为应对金融危机引发的外需疲软、失业高企、经济衰退困境，中国出台多项经济刺激政策以扩大内需，维持经济高速增长。

然而，为确保经济增速实行的过度宽松最终引发了通货膨胀，泛滥的流动性滋长了资产泡沫，并对中小企业构成挤出效应。

新一届领导集体上台后，中央更注重经济的转型升级和结构性转变，扶持中小企业，减少政府刺激，释放市场本身的调节。

但目前看，中国经济仍未见底，工业产出和需求仍然疲软；央行终于谨慎地再次降息，释放流动性，以刺激经济增长。

本文对金融危机以来中国的应对措施和当前的经济运行情况进行探讨。

首先，对中国应对金融危机的第一轮经济刺激措施及其带来的影响进行分析和讨论，分析政府的4万亿刺激措施所留下的后遗症。

第二部份则讨论中国经济运行所存在的问题，讨论中国在应对本轮金融危机时所受到的教训和经验。

最后一部分，针对当前的经济形势，分析中国目前的政策走向，讨论中国经济的发展趋势，。

关键词：金融危机；中国；应对措施；经济现状2008年的全球性金融危机始发于美国的次贷危机，此次金融危机，一般认为浮现于2007年下半年，自美国次级房屋信贷危机爆发后，投资者开始对按揭证券的价值失去信心，人们对银行信誉和现行金融体系产生质疑，全球范围内流动资金变得紧缩，引发流动性危机，导致金融危机的爆发。

中国经济对外依存度相当高，贸易顺差逐年增大。

2008年，美国是中国的第二大贸易伙伴，仅次于欧盟，而美国次贷危机所造成美国消费的大幅萎缩使得中国以外贸为主的出口类型制造业出现倒闭潮。

08--历年数学建模优秀论文大全

Can We Assess a Health Care System's Performance?参赛队员：董希望（自动化学院），刘琳燕（城环学院）刘福亮（软件学院）指导教师：肖剑参赛单位：重庆大学参赛时间：2008年2月15∼18日Can We Assess a Health Care System's Performance?1.BackgroundHealth systems consist of all the people and actions whose primary purpose is to improve health. They may be integrated and centrally directed, but often they are not. After centuries as small-scale, largely private or charitable, mostly ineffectual entities, they have grown explosively in this century as knowledge has been gained and applied. They have contributed enormously to better health, but their contribution could be greater still, especially for the poor. Failure to achieve that potential is due more to systemic failings than to technical limitations. It is therefore urgent to assess current performance and to judge how health systems can reach their potential.The World Health Organization (WHO) is a specialized agency of the United Nations (UN) that acts as a coordinating authority on international public health. Established on 7 April 1948, and headquartered in Geneva, Switzerland, the agency inherited the mandate and resources of its predecessor, the Health Organization, which had been an agency of the League of Nations.The WHO's constitution states that its objective "is the attainment by all peoples of the highest possible level of health." Its major task is to combat disease, especially key infectious diseases, and to promote the general health of the people of the world.As well as coordinating international efforts to monitor outbreaks of infectious diseases, such as SARS, malaria, and AIDS, the WHO also sponsors programs to prevent and treat such diseases. The WHO supports the development and distribution of safe and effective vaccines, pharmaceutical diagnostics, and drugs. The WHO also carries out various health-related campaigns — for example, to boost the consumption of fruits and vegetables worldwide and to discourage tobacco use.The annual World Health Report (http://www.who.int/whr/en/index.html) assesses global health factors and World Health Statistics provides health statistics for the countries in the UN. The production and dissemination of health statistics is a major function of the WHO. To many people, these data and the associated analyses are considered unbiased and very valuable to the world community.2. Basic Assumption and Hypotheses1.Assume that in a certain interval such as 5years the main components (metrics) ofthe health care system stays steady, that is to say the metric won’t change continually.2.Assume that all the statistics we get from the database of the WHO is authentic.3.Assume that the ranking of the world's health systems in 2000 made by WHO isscientific and dependable.4.During the data processing if a data little than x we can replace it with x.5.If there existing data missing for some year’s indicator we can value it with thecorresponding value of the near years.3. SymbolsSymbol Definition and Property'Z The matrix before standardizationZ The matrix after standardizationz j The statistic of the j indicator to each countryu The main component to be evaluatedu m The m th main component of the indicatorl ij The load of the original indicatorR1 The correlation matrix of Zc i The contribution rate of the i th main componentS i The summation of the front i main components’ contribution rateQ The integrated score of each countryX The project set ( the Member States)U The attribute set which also means main component seta ij The attribute value of x i in reference to u jA The decision making matrixa i The mean value of the line I in the primitive matrixb i The standard deviation of row I in the primitive matrix4. Problem AnalysisTo determine several important and viable metrics for assessing the performance of a health care system and comparing health care systems in different countries. We have to know what metrics or indicators are there in a health care system, as is shown in the problem we search the web of the WHO and get the database of the indicators. There exists statistics for 50 core indicators on mortality, morbidity, risk factors, service coverage, and health systems, which take on more than one hundred and fifty terms of raw indicators. We must use some data mining technology or method to distill the crucial metrics.Considering the data is promiscuous and inconsistent and not all the countries have the corresponding data to each indicator from the year 1960 to 2006, we first need to choose certain year’s data as our study object. Then to the mass actual statistical data we can’t expect all the indicators are complete so what to do with the incomplete data to make sure that all the indicators or all the data we used below are universal or effective is an inevitable problem. There are 159 raw indicators how could we select the most important ones and combine them scientifically to make them more useful in measuring quality is another basal problem. Then how could we accomplish this goal? The main components analysis method which we could use to devise our first model will help a lot.Furthermore how could we assess a country’s health care system and make some comparisons with the combined metrics? This situation much agrees with the multiple attribute decision problems. So we could solve this problem by ranking all the countries health care systems using this multiple attribute decision method.5. The Establishment of Model5.1 The Primary Data and Indicators ProcessingAccording to the above problem analysis part we know that we could obtain enough raw data for almost 159 indicators from 1960 to 2006. We first choose a year 2004 whose data is much completer than other years as our study object. Then if some of the indicators of certain country in 2004 have no value and the year close to 2004 such as the year of 2005 or 2003 has the corresponding value we treat this close value as the valve of the country in that indicator in 2004.Based on these we select the indicators that 95% of the country has the corresponding data for them from all the 159 raw indicators. By doing this primary selection we make sure that all the indicators or all the data we used below are universal or effective. After the primary selection we get 48 crucial indicators as our primary outcomes (metrics).5.2 Model 1 DesignFollowing the above analysis we utilize the main components analysis method to devise our first model.When it comes to main components analysis the biggest effect to it is the dimension of the data. So in the practical application we first should make standardization to the data.Assume that 'Z is the matrix before standardization Z is the matrix after standardization z j is the statistic of the j indicator to each country; u is the main component to be evaluated, so the objective function could be:11111221221122221122p p p p m m m mp p u l z l z l z u l z l z l z u l z l z l z =++⎧⎪=++⎪⎨⎪⎪=++⎩""""""" (1)Where u 1, u 2,… u m is called the 1st, 2nd, … mth main component of the indicator z 1, z 2, z p ; l ij is the load of the original indicator z j (j=1,2, …,p) in each main component.The detailed process of this solution is as follows:Step1: Evaluate the standardized matrix Z of the matrix'Z The standardization of the 'Z is just replace the (i=1,2, …,p) and the z 'i z ijof the matrix 'Z with z i (i=1,2, …,p) and with z ij respectively, which is shown in table1Step 2: Evaluate the correlation matrix R1 of matrix ZR1 could be evaluated by the following matrix:1112121222121p p p p pp r r r r r r R r r r ⎡⎤⎢⎥⎢=⎢⎢⎥⎢⎥⎣⎦""##"#"⎥⎥ (2) Where r ij (i, j =1, 2,…,p) is the original indicator z i and z j ’s correlation coefficient specially r ij =r ji . r ij could be derived by the following formula:ij r = (3) Step 3: By formula 1 we can compute the characteristic root and characteristic vector of matrix R1 then rank the characteristic values of R1 as expression 110I R λ−= (4)(5)0≥≥≥≥λλλ"p 21Step 4: Evaluate the contribution rate and the accumulative contribution rate according to formula 1and 1of the main components, determine the proper number of the main components.(6) λ∑==="1(1,2,,i i p k k c i λ)pWhere is the contribution rate of the i th main component.i c(7) S i λ∑p λ====∑"11(1,2,,)i k k i p k k Here is the summation of the front i main components’ contribution rate. If thevalue of the accumulative contribution rate reaches to more than 80% we can approbate the effect of the main components.i S Step 5: Compute the load of the main component l ij(y ,z )(,1,2,,)ij i j ij l p i j p ===" (8)Step 6: Sum up the above five steps get our objective function11111221221122221122............p p p p m m m mp u l z l z l z u l z l z l z u l z l z l z =+++⎧⎪=+++⎪⎨⎪⎪=+++⎩"""p Step 7: Evaluate the integrated value of each country and make a ranking of them with the formula 1.112211(m m m ii )u u λλλλ==+++∑"Q u (9) Q is the integrated score of each country.5.3 Model 2 Design5.3.1 The Description of the PrincipleThe method of the multiple attribute decision making based on dispersion maximization is used to solve the multiple attribute decision making problems with the uncertain weight attribute. We can use this method to make ranking and comparison between different projects with multiple attributes.In more details the smaller the difference between certain attribute for all the projects is the less affection it has on the decision making and ranking of the projects. On the contrary the bigger it is the more affection it has on the decision making and ranking. As a result in the view of ranking the bigger of one attribute’s deviation is the bigger weight of this attribute should be given. Especially if there is no deviation for certain attribute to all the projects which means that this attribute will have little affection on the ranking we can value a zero to its weight.5.3.2 Model DevelopmentStep 1.Structure and Normalize the Decision Making Matrix1.1 Structure the Decision Making MatrixAssume that:M={1,2,…,m},N={1,2,…,n} (10)The projects set which also is the set of the Member States in WHO is XX={x 1,x 2,…,x n } (11)The attribute set which means main component set here is UU={u 1,u 2,…,u m } (12) is the attribute value of in reference to so we obtain the decision making matrix ()ij n m A a ×=whose form is shown as table 2 Table2. The form of the decision making matrixu 1u 2… u m x 1a 11a 12… a 1m x 2a 21a 22… a 2m ## # # x na n1a n2… a nm5.4 Model 3 DesignModel 3 is our predictive model, from model 1 we can get the objective function with the data of that year.When it comes to predicating for the convenience of evaluating the main components we can change the main component which is expressed by the standardization indicator z i into the form that expressed by nonstandard indicator z i ’ to predicate the main components.''''''1111122110''''''2211222220''''''11220(13)............p p p p m m m mp p m u l z l z l z l u l z l z l z l u l z l z l z l ⎧=++++⎪=++++⎪⎨⎪⎪=++++⎩"""Here(14)'()/i i i z z a b =−i Substitute into formula 1 can we obtain the formula 2.'i z i a is the mean value of the line i in the primitive matrix; is the standard deviationof row i in the primitive matrix.i bThen utilize the formula 9 to compute the synthetic score and get variability ofthe system.Normalize the Decision Making MatrixThere are many types of attributes such as benefit type, cost type, fixation type, deviate type, interval type, deviate interval type etc. In our model all the attribute could be sorted to two types the benefit type and the cost type approximately. The benefit cost requires the value of the attribute as big as possible; the cost type requires the value of the attribute as small as possible.To eliminate the impact of the different dimensions to the decision making result we should normalize the decision making matrix A whose values could be obtained from the model 1.Assume that I i (i=1, 2) stands for the subscript set of the benefit type and cost type. If the attribute is benefit type we value i in I i as 1. If the attribute is cost type we value i in I i as 2.1min(),,max()min()ij ij i ij ij ij ii a a r i a a N j I −=−∈∈ (15)2max(),,max()min()ij ij i ij ij ij i i a a r i a a N j I −=−∈∈ (16)After this step we get the normalized matrix ()ij n m R r ×= whose form is the samewith the matrix A.Step 2: Calculus the optimization weight vector w11111,n n ji kj i k j m n nij kj j i k r r w r r =====−=−∑∑∑∑∑j M ∈ (17) Where w j is the j th main component’s weight.Step 3: Computer the synthetic attribute z i (w) (i ∈N) of project x i .1(),,mi ij j j z w r w i N j ==∈∑M ∈ (18)Step 4: Make ranking and comparison to the projects (countries) using z i (w)(i ∈N)6. Applying the Model1 and Model 26.1 Applying the Model 1 to the Statistics of the Year 20046.1.1 Data for Model 1 in the Year of 2004We first select the indicators that 95% of the countries own these indicators from all 159 indicators getting 28 indicators which could be seen in appendix Ⅰ. Then weselect the countries that have the data for all these 28 indicators from all 193 Member States getting 163 countries. By doing these we have made good preparation for our model 1’s solution.6.1.2 Solution of the Model 1 for the Year of 2004Based on the above data we solve our model 1 in matlab using the function of zscore to normalize the data, and then we calculate the characteristic roots and characteristic vector. The characteristic roots are shown in the table 3.Table3. Part Valves of Model 1From the table 3 we can see that the front six red colored components’ accumulative contribution rate reaches to 81.5% which means that most of the main components are involved, so these six components are just our combined metrics. We renamed these six combined indicators with A, B, C, D, E, F metrics all of which are constituted by several raw indicators and could reflect certain performance of a health care system.In more detail the metric A is much positively related with life expectancy, per capita total expenditure on health at international dollar rate etc and much negatively related with mortality rate, incidence of tuberculosis (per 100 000 population per year) etc. The visual relationship between the metric and the 28 indicators is shown in figure 1. The x axis is the order of the 28 indicators which maps to corresponding 28 indicators in appendix Ⅰ. The y axis is the affection of each of the 28 indicator on metric A. All the rest five figures follow this instruction so we won’t explain the rest five figures again.Figure1. The affection of the 28 indicators on metric A The metric B is much positively related with expenditure on health, disease detection rate etc and much negatively related with government expenditure on health, alcohol consumption etc.Figure2. The affection of the 28 indicators on metric B The metric C is much positively related with General government expenditure on health as percentage of total expenditure on health, immunized with disease etc and much negatively related with private expenditure on health as percentage of total expenditure on health, population (in thousands) total etc.Figure3. The affection of the 28 indicators on metric C The metric D is much positively related with private expenditure on health as percentage of total expenditure on health, immunized with disease etc and muchnegatively related with General government expenditure on health as percentage of total expenditure on health etc.Figure4. The affection of the 28 indicators on metric D The metric E is much positively related with external resources for health as percentage of total expenditure on health etc and much negatively related with tuberculosis: DOTS case detection rate, probability of dying (per 1 000 population) between 15 and 60 years etc.Figure5. The affection of the 28 indicators on metric E The metric F is much positively related with immunized with disease, out-of-pocket expenditure as percentage of private expenditure on health etc and much negatively related with general government expenditure on health as percentage of total government expenditure, population (in thousands) total etc.Figure6. The affection of the 28 indicators on metric FThe above descriptions show that our metrics is reasonable, moreover all the 28indicarors we selected could be found in 92% of all Member States, which means that our metrics could be easily collected.Furthermore we get the ranking for all the 163 countries that own orbicular and effective data. The front and the back 20 countries in our ranking and their scores calculated by our model 1 are listed as table 4:Table4. Part of our Ranking by Our Model1The whole ranking is shown in appendix Ⅱ.After obtaining the six metrics we treat the still missing value’s indicator as zero then recompute the ranking of the year 2004 with the model 1 and get another ranking for all the Member States which we list in appendix Ⅲ.In conclusion we put forward 28 important indicators from all the 159 indicators furthermore we combine the 28 important indicators getting 6 main components which we renamed as metric A, B, C, D, E and F. Then we assess the health care system with these six metrics and make a ranking of all the 163 countries.6.1.3 Applying model 1 to the Statistics of the Year 2000Using model 1 and the six metrics obtained from 4.11 we assess the health care system of each country in the year of 2000. This time we only utilize the data of this year, which means that we just substitute the data of 2004 with that of 2000.By doing this we get the ranking of this year as table 5 which just show out the front and the back 20 countries too.Table5. The Part Ranking of the Year 2000 by Model 1 (There are 194 Member States in 2000; the score here is just a relative value computed by our model; the whole ranking is shown in appendix Ⅳ)6.2 Applying the Model 2The six metrics obtained from model1 is ordered. Although the six metrics keep the same in the model 2 as what they are in model 1 according to our assumptions, there is no certain order among them in our model 2. The data processing methods for the raw data are the same with what we have described and used before.6.2.1 Applying the Model 2 to the year of 2004With the help of the software matlab we realize the algorithm of dispersion maximization computing the weight of the six metrics, and then we calculate the synthetic score of each of the 163 country that own holonomic statistics after our data mining process. After comparing the synthetic score of these countries we get the ranking of their health system as shown in table 6.Table6. Part of the ranking for the 163Member States(The score here is just a relative value computed by our model; the whole ranking is shown in appendix Ⅴ)6.2.2 Applying the Model 2 to the year of 2000Similar to 4.1.2 we just replace the data of 4.2.1 with the data of the year 2000 then compute the synthetic score of all the 194 Member States. After comparing the different countries we get the ranking as table7.Table7. Part of the ranking for 2000 by model 27. Comparisons7.1 Comparisons between Different RankingsFrom the above solution we obtain 4 different rankings. The precise clues of our models have already showed their validity. Besides we can load a ranking for all the 190 Member states in 2000 from the WHO’s official web which we list in appendix Ⅷ.By making comparisons between our two rankings with the official ranking of the year 2000 we can test the reliability and practicability of our model to a certain extent. The figure7 shows their relationship clearly.Figure7. The corresponding relationship between our rankings and the WHO’s We can see that the dots which stand for parts of the countries in the rankings match quite well with each other in the three polygonal lines. That means the model1 and model 2’s results not only agree with each other but also agree with the official results quite well. So we can conclude that our two models are practical and reasonable.Since the solution for the year of 2000 is dependable, we have reason enough to predicate that our solution for 2004 is authentic as the only difference between 2004 and 2000 is the substituted statistics and the data of 2004 is more holonomic than that of 2000.To make sure that both of our models’ results for the year 2004 are unitive we make a comparison between their rankings. We select some characteristic countries in both rankings and compare those countries rankings as shown in figure 8.Figure8. The comparison between the two rankings for the year 2004From the figure we find that the two rankings match quite well.In conclusion our models are scientific and our results are authentic.7.2 Comparisons between US and FranceIn the 2000’s ranking of WHO France takes the first place, which also could be seen clearly in the appendix. In the year 2004 there is no official ranking so we assess these two countries health care system with our model 1 to see which country has the better health care system then.The table8 shows their score according to our six metrics:Table8. The comparisons between US and France’s health system according to our metricsThe metric A, C, F belongs to benefit type and the rest belong to cost type. Base on this we can see that the health care system of US in 2004 is better than France in metric A, B, D. According to our table 1 we know that the synthetic score of US is better than France.7.3 Comparisons between US and IndiaIn the ranking of WHO the health system of US is better than India. With the help of our mode 1 we consider that India has the poor health care system in 2004, so we make a comparison between them.Table9.The comparisons between US and India’s health system according to our metricsSimilar to 7.2 we can see that the health care system of US is better than India in metric A, B, C, D, F. Also from the table 9 we know that the ranking of US is much better than India.8. Applying the Model 3Based on model 1 and model 2 with the help of the software matlab we realize the algorithm in model 3 and get the predictive function of the synthetic scores as follows:''''1234'7''''67891''''1112131410.0033810.00966920.0105590.00194680.000522790.00052406 3.06100.0782130.00421940.0003620.00873510.00956890.00984890.000392260.0043929Q z z z z z z z z z '5z z z z z z −=−++−−−−×−−−−++−+'5''''1617181920''''2122232425'''2627280.0053440.0257520.00377190.000143460.000182770.000109410.000136790.00534410.056440.00292340.000840420.029650.012447 3.8668''z z z z z z z z z z z z ++−++++−+−−++−z (19)Considering the affection of the weight on the synthetic score we could find that the bigger the absolute value of weight is the bigger the impact is on the synthetic score of the country. On the contrary if the absolute value of weight is small then the variation of the metric won’t produce big changes to the synthetic score. Then we take some indicators of the all 28 indicators as examples to discuss what affection it will has on the health care system if the various changes are occurred.'8z is the formula is the total fertility rate (per woman). It has a negative correlation with the synthetic score. What’s more it has a big affection on the score so this indicator should be as small as possible, which means that the government should take some measures to control the population within a proper range to improve the health care system of the nation.'24z is the total expenditure on health as percentage of gross domestic product. Itis an indicator that positively related with the synthetic score which means that the more it spend on the total expenditure on health as percentage of gross domestic product the better score it has in the system.'17z is the general government expenditure on health as percentage of totalgovernment expenditure. It is an indicator that positively related with the synthetic score which means that the bigger the general government expenditure on health as percentage of total government expenditure is the better score it has in the system'3z is the life expectancy at birth (years) males. It is an indicator that positivelyrelated with the synthetic score which means that the longer the life expectancy at birth (years) males is the better score it has in the system.'z stands for the neonatal mortality rate (per 1 000 live births). It has a negative 11correlation with the synthetic score. That’s to say the smaller the neonatal mortality rate (per 1 000 live births)is the better the health care system will become.9. The Strength and Weakness9.1The StrengthWe obtain the statistics directly from the raw database of the WHO’s official web not from the report of the WHO. We use some data mining technology to draw the available and effective data from thousands terms of data ourselves.We develop three different models to solve all the six parts of the problem, those models are built with precise logic, scientific principle which could solve the problems efficaciously.We don’t solve the problem part by part but solve them in our models’ development and solution process, which keeps the whole paper’s with a good continuity.We compare our result with the practical result, which tests our models’ practicability and validity greatly.Our models could be easily extended to other fields to solve the multiple attribute decision making problems.Our models are independent to the metric (indicators) to a certain extent as the algorithm of our models has the universal applications.9.2 The WeaknessThe raw data we get is the data from the real world, which means that there must be some imperfect data which do have some negative impact on our result.As there are so many indictors that it is hard to select proper metrics to assess the health system properly without some kind of error.Because the limitation of the time and resource it’s inevitable to have some imperfect aspects in our models, analysis and paper.10. References[1] Zeshui Xu, 8/2004, Uncertain Multiple Attribute Decision Making: Methods andApplications, Tsinghua University Press.[2] Qiyuan Jiang, Jinxing Xie, 12/2004, Mathematical Model, Higher Education Press[3] The World Health Report 2000 - Health systems: improving performance.http://www.who.int/whr/2000/en/whr00_en.pdf[4] World Health Organization, http://www.who.int/research/en/s[5] Principal Component Analysis,/jpkc/jldlx/admin/ewebeditor/UploadFile/200783101241734.ppt[6] /wiki/World_Health_Organisation,"World Health Organization"11. AppendixAppendixⅠ: The list of all the 28 indicators and their sequence numberAppendixⅡ: The Ranking of all 163 Countries in 2004 by model 1Appendix Ⅲ: The Ranking of all 194 Countries in 2004 by model 1。

《岩土力学》2008年第8期被EI收录论文(53篇,收录率100%)

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‘ 岩土力学》２０年第８０８期被Ｅ收录论文（３Ｉ５篇，收录率１０％）０
论文题名基于力平衡的三维边坡安全系数显式解及工程应用局域地应力场获取的插值平衡方法均质土坡的圆弧滑动分析非饱和土的清华弹塑性模型采用挤压边墙技术的高面板坝裂缝成因分析软岩崩解分形机制的数学模拟边坡稳定的非线性有限元分析随机波浪作用下海床动力响应及液化的理论分析海相结构软土的次固结研究考虑界面软化特性的垃圾填埋场斜坡上土工膜内力分析饱和砂土地层中隧道结构动力离心模型试验现浇混凝土薄壁管桩复合地基桩土应力比影响因素分析方形平板锚抗拉承载力的大变形有限元分析尾矿库环境影响指标体系及评价方法及其应用非均质地基中群桩竖向荷载沉降关系分析桩板结构路基动力模型试验研究考虑试验阻尼效应的一种土体动力双型抛物线本构模型从力矩效应和地下水分析缓倾角结构面成因扩底抗拔桩扩大头作用机制的数值模拟研究自然营造力作用下岩体混凝土水力劈裂分析与探讨铁晶砂胶结新型岩土相似材料的研制及其应用复合土钉墙工作性状的有限元模拟与分析脆性岩石侧向变形特征及损伤机理研究深基坑工程中的咬合桩受力变形分析两体接触面剪切力学行为的三维数值分析混凝土夯扩桩和土工格室加固铁路基床试验研究软土非线性固结计算若干表格及应用土层非线性地震反应一维时域分析双参数黏弹性地基上连续配筋混凝土路面振动参数分析水平荷载下黄土地基单片地下连续墙现场试验研究斜坡地基上加筋路堤工作性状及稳定性研究细粒含量对粉土动孔压发展模式影响的试验研究固化粉质土应力应变特性试验研究爆破振动作用下城门洞形衬砌的临界振速研究消石灰对膨胀土团粒化作用的研究临近基坑既有建筑物地基的抗剪强度指标取值问题ＡＤＮＡ有限元软件中材料本构的二次开发Ｉ地震液化条件下地面的大变形三维数值分析基于ＣＤ的地震液化研究新进展Ｆ基于ＳＦ神经网络的边坡稳定性评价ＯＭ非饱和击实粉土的强度和屈服特性研究双圆盾构掘进施工扰动土体附加应力分析精细积分法在地基震动响应分析中的应用Ｂ一０Ｓ１０型土壤固化剂在季冻区的路用性能试验研究三峡库区泄滩滑坡非饱和渗流分析及渗透系数反演非饱和重塑黏土渗透性试验研究桩承加筋路堤中路堤与褥垫层共同作用理论分析饱和黏土不排水剪切特性及双曲线模型地铁联络通道冻结加固融沉注浆研究洞庭湖区堤防垂直防渗模型研究浅埋大跨隧道施工爆破监测与减震技术基于算法的边坡稳定分析方法

吉林大学论文格式（08版）

吉林⼤学论⽂格式（08版）附件⼀：吉林⼤学博⼠、硕⼠学位论⽂答辩要求博⼠研究⽣1、要求（1）论⽂评阅：博⼠学位论⽂评阅⼈应聘请与论⽂相关学科的具有教授或相当专业技术职务的专家进⾏评阅。

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《电讯技术》2008年高贡献论文

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韦日华李兴华刘瑞华常军
肖汉波张健
赵亚男
何志强杨姚亚峰
综合化航空电子系统发展历程及重要支撑技术现代频率合成技术的研究进展模拟与数字调制方式的非线性变换识别方法
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袁海英傲丹
小波分析在信号奇异性检测中的应用无线网格网关键技术及其应用基于Ｚｇｅｉｅ无线通信技术的智能家居系统Ｂ超宽带无线通信及其关键技术ＧＳ卫星接收机的自适应抗干扰设计ＰＦＫ信号的非相干数字解调技术Ｓ超宽带穿墙探测雷达的运动目标检测技术一种点对多点无线数据传输系统的设计一种基于混沌序列的数字图像加密算法ＭＣ５Ｓ一１单片机与ＧＳ—ＯＭ板的串行通信ＰＥ机载雷达目标的大地坐标定位一种基于ＤＳ芯片Ａ９５ＤＤ８０的信号源高速Ｖｔｂ译码器的ＦＧｉｒｉｅＰＡ实现

经济危机论文：分析08年美国金融危机对我国经济的影响

分析08年美国金融危机对我国经济的影响随着2008年底以来美国次贷危机爆发，并逐步向国外扩散演变为全球性的金融危机和经济危机，使得国际金融形势急剧变化，并在越来越高的失业率和巨额的债务风险等不利因素的影响下，逐渐从发达国家向新兴经济体和发展中国家蔓延、向实体经济蔓延，使得我国承受着改革开放以来最严重的一次外部经济冲击，导致我国经济增长速率不断下滑。

因此，从对全球金融危机的趋势和状况的分析结果看来，如何做好充分的准备，实现我国经济的可持续增长和社会的稳定具有重要意义。

一、国际金融危机对我国经济负面的影响由于一个国家受金融危机传染的程度通常与这个个国家的金融体系稳健程度、市场开放程度、经济实力等因素密切相关，因此，随着美国次贷危机引发的国际金融危机的不断蔓延和加深，致使我国在金融、贸易、预期和产业联动等多种传染机制中受到改革开放以前所未有的冲击，负面影响日益显现，具体主要体现在以下几个方面：1.国际金融危机对我国经济增长的负面影响。

自国际金融危机爆发以来，受国际金融危机快速蔓延的影响以及全球济增长呈下降趋势的影响，我国面临着的经济增速下滑的主要矛盾越来越突出，可以说这次国际金融危机对我国经济下行风险比预想的要严重的多。

2.国际金融危机对我国投资的影响。

由于固定资产投资受到金融传染机制和预期传染机制的影响，大多数企业在面对当前存在的诸多不确定因素以及诸多潜在风险的经济形势下，普遍对经济增长没有信心，加之国内银行放贷更趋谨慎和国际金融市场流动性明显不足等因素，导致越来越多的企业对于企业是不是要投资的意愿和能否进行投资的能力存在一定疑虑，这就导致多数企业对于固定资产投资趋势呈不断下滑的趋势。

相关数据结果显示，在在金融危机爆发的第一年我国全社会固定资产投资实际累计增长同比回落达到5.7个百分点，而城镇固定资产投资虽然同比呈增长趋势，但是也降低了3.2个百分点。

3.国际金融危机对我国进出口贸易的影响。

我国作为个发展中国家，我国经济对于欧盟、美国等发达国家以及新兴经济体的对外依存度很高，GDP的2/3左右都来源于对外贸易的进出口总额，然而受贸易传染机制收入效应、国际金融危机的影响以及价格效应，的影响，欧盟、美国等发达国家的对外需求降低，进口数量萎缩，由于这些我国的主要出口国家经济走向衰退或增速放缓状态，使得我国在外贸出口增幅明显回落，与此同时受国内需求不旺和预期收入降低等因素的影响，我国国内进口数量也开始下降，最终导致我国在对外贸易交易中的出口总值增速呈下降趋势，进出口形势急转直下。

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实施人性化管理，实现“我要安全”
---张玉忠
摘要：安全，成为自古到今，人类生活永恒的主题。

因为安全是组成生命的另一类元素，须臾不可远离，一旦失去，属于人们的只有一次的宝贵生命便毫无保障。

人的安全素质分为两个层次：一是人的基本安全素质，包括：安全知识、安全技能、安全意识；二是人的深层安全素质，包括：情感、认知、伦理、道德、良心、意志、安全观念、安全态度等。

人的安全知识和安全技能通过日常的安全教育手段可以得以保证和提高，在社会和企业的日常安全活动中(宣传、教育、管理等)，能够得到较好地解决和保证。

而安全意识、安全观念、安全态度，以及情感、认知、意志等培养则需要一些亲情的管理。

关键词：安全管理人性化细节我要安全长治久安
引言：随着高科技的突飞猛进，产业规模日益大型化，生产过程走向自动化，各种系统变得越来越复杂。

对生产者而言，稍有疏忽便轻则伤残，重则死亡。

其中任何一种事故都会给个人、家庭、集体、国家、社会造成巨大的灾难。

可以说，许多管理者对职工的人身安全是高度重视的，不但制定了规范化的安全生产责任制、配备了三级安全员(厂、车间和班组)、开展务实求效的针对性安全教育和安全巡检活动，而且大张旗鼓地营造安全生产的氛围，运用传媒工具吸引广大职工及其家属对生产安全的高度重视，强化职工们的安全意识。

遗憾的是，管理者们这种以人为本的“婆婆式关怀唠叨”，却未能得到全员对安全的重视，有些职工在高温岗位上打赤膊操作、在易燃易爆岗位上抽烟、在多尘有毒岗位上不按规定使用个人劳动防护用品，甚至将劳保用具卖给小商贩;有的职工在从事生产操作期间不执行岗位操作法，习惯性违章(如高空作业不系安全带、不按操作规程作业等);没定时安全巡检、不对设备进行维护，甚至让设备超负荷运行，从而导致设备出现故障，井下事故，人身伤亡的事故。

那么，如何在安全管理上不断突破传统安全管理模式，大力实施人性化安全管理，使安全管理工作逐步由被动管理转变为主动管理，变员工“要我安全”向“我要安全”转变，变“他律”向“自律”转
变，把安全管理的重心逐步转移到员工的自动自发行为上来。

应从研究满足人的各层次需求入手，以最大限度激发人的主观能动性：
一、实施人性化安全管理首先要突出一个“爱”字
人性化安全管理的内涵，就是从人对生命、健康的基本需求出发，从人对生活、工作和环境的需求出发，从对人的情感、精神和价值取向出发，做到关心人、尊重人、爱护人。

推行人性化管理，必须从抓思想，提认识，转观念入手，坚持以人为本，大力倡导和培育“安全第一、生命至上”，“对生命负责、为幸福着想”、“让安全成为我们的习惯”等安全理念和安全价值观，通过培养、教育、熏陶、启发、感染、关爱等一系列“人性化”安全活动的开展，体现企业对员工无微不至的关怀，从而增强凝聚和向心力，形成“关爱生命、关注安全”的舆论氛围和社会环境，逐步规范员工的安全行为，为实现安全生产长治久安奠定坚实的思想基础和行为保证。

安全管理的对象重点是人，关键是做好人的思想工作，让人的行为更加符合安全生产规律。

要让员工真正体会到搞好安全生产是对员工生命健康的爱护，是员工根本利益的体现。

在安全管理上，要把我们的员工当成自己的兄弟姐妹看待，在工作上关心他们，生活上帮助他们，学习上提高他们，坚决消除疲劳战，消除员工情绪低落和疲劳厌战情绪，使员工时刻保持旺盛的精力和高昂的斗志，全身心地投入到安全生产中。

这就要求企业管理者要跳出传统安全管理模式的圈子，从尊重员工生命和健康的情感观出发，带着感情抓安全，时刻注重员工安全心理研究，掌握员工的安全心理动态，从员工的思想、精神源头和消除不安全因素，合理组织生产，减轻员工劳动强度，逐步改善生产生活条件，替员工着想，真正体现对员工的关心和爱护，切实做到人性化管理。

二、实施人性化安全管理必须突出一个“实”字
就是要为员工做实实在在的工作，坚持以人为本，为员工创造一个良好的工作，生活和学习环境，是实现安全生产的重要条件和保障。

工作、生活和学习环境的好坏，能够直接影响到员工的工作情绪，工作质量和安全效果，从改造员工
工作环境入手，全面推广5S管理，大力实施精细化，不断提高企业安全质量标准化水平，为员工提供一个安全的工作环境;如：在施工现场，建立临时休息室和茶水站，吸烟点等，为员工提供一个舒适的工间休息环境;员工澡堂24小时不间断提供淋浴，为员工洗澡提供方便;尤其是冬天高空露天作业，组织人员及时为一线职工送去可口的饭菜、御寒的姜汤，组织女职工服务队为一线职工洗工作服等等，使员工在温馨和谐的气氛中潜移默化地接受启示和教育，自主筑起安全防线。

三、实施人性化安全管理关键要着眼于每一个细节
细节决定成败。

生产过程与环节是连续不断周而复始的。

造成安全事故的也就是设备的一个点、人员的一个动作。

一个蚁穴般的细节问题可能损毁甚至毁掉安全生产的千里大堤。

比如生产检修，就是这次生产检修作业中各个环节综合的安全管理。

如生产任务分解上，几个人进入场地作业?完成哪几步操作?作业前、作业中、作业后各要完成什么工作?安全措施要根据任务进行分解，进入场地之前要完成哪些安全措施?作业之前必须完成哪些安全措施?作业中始终保持什么
样的安全措施?作业结束做哪些?退出过程有哪些?哪些过程和设备对人身和设备安全可能造成什么样的威胁?这些部位怎么防范?对安全细节进行了全面分析，逐一进行了落实，安全的成功率就会极大提高。

四、加强企业安全文化建设，诱发人们对生命安全健康的渴望，提高员工安全思想境界
1、加强安全宣传阵地建设，开展经常性安全教育。

借助广播、电视、网络、黑板报等宣传媒体和工具，借助班前会、班后会和周一的安全例会，及时宣传党和国家安全生产方针政策、法律、法规，宣传集团公司安全理念和安全工作部署、政策、措施等，提高员工的安全意识和安全法制观念，班组开展集体安全宣誓，颂读安全誓词，利用五一，十一等法定节假日和安全生产月等特殊日期、时段，掀起群众性的安全宣传教育热潮，吸引广大员工直接参与，增强员工做好安全工作的责任感、紧迫感和使命感。

2、开展典型事故案例教育，震撼员工心灵。

把公司历年发生的典型事故案例和具有代表性的兄弟单位的事故案例汇编成册，发放到每个员工手中，各单位充分利用多种形式和各种场合，从正反两方面的典型事故案例对员工进行教育，通过现身说法，言传身教，事故分析，危险预知等多种方式的案例教育，让职工有一种“身临其境”的触动，认识到“不重视安全生产不得了”，而安全生产的受益者首先是自己，从而增强对事故的“免疫力”，以此来震撼员工的心灵，起到了警钟长鸣的教育效果。

3、定期召开“三违”人员恳谈帮教会。

就是让“三违”人员真正体会到“严是爱，松是害”的道理，让“三违”人员自己讲述“三违”的危害及其造成的后果。

定期排查“三违”人员，研究分析“三违”产生的原因，制定防范措施，保证“三违”人员思想认识到位、态度端正，不再重犯。

4、开展安全亲情教育。

通过组织开展家访、谈心、结对子等活动，构筑多道安全生产连心桥，单位与家庭之间、领导与员工之间，结安全对子，并对其负责。

如在厂区醒目位置设置带有员工亲人家属安全寄语的“全家福”照片宣传牌板，充分发挥员工家属的亲情感染作用，使其懂得“一人安全，全家幸福”的道理，促使其内心重视自身安全、重视安全生产，自觉变“要我安全”为“我要安全，我会安全”，这就是“亲人安全寄语”教育的作用，让员工时刻牢记全家的关心，夫妻的关家，子女的期盼和领导的关怀，激发员工“关爱生命、关注安全”的工作热情。

5、发挥女工督导作用，构筑安全生产第二道防线。

女工安全监督导成员经常组织开展“夏送清凉，冬送温暖活动，送去员工家属的关爱亲情。

6、切实维护员工利益，保障员工身心健康。

美国组织行为学教授亚当斯于上世纪60年代提出公平理论：人们不仅关心个人努力所得的绝对报酬量，而且还关心自己的报酬量与别人报酬量之间的关系，即相对报酬量。

因此，分配的合理性直接影响人的积极性，人的积极性对安全生产起着决定作用。

合理确定利益分配机制，使企业员工得到心理平衡和适当激励，提高人的主观能动性以促进安全生产工作。

总之，人性化安全管理，就是要在教育人上下工夫，在调动上作文章，在聚人心上求实效。

在企业安全生产人、机、环境之要素中，人是最活跃的因素，同时，也是导致事故发生的主要因素和主导角色，因此，能否做到安全生产，关键取决于人的主观能动性，取决于员工对安全工作的认知、价值取向和行为准则，取决于员工对安全问题的个人响应和感情认同。

所以只有坚持以人为本，把“人本”思想作为安全工作的灵魂主线，大力推行人性化安全管理，引导、教育员工树立科学的安全观，使安全生产成为员工的第一需要，变成员工的自觉行为和习惯，才能保证企业安全生产长治久安
参考文献人的安全素质安全文化网
企业人员安全素质如何提高安全文化网
安全生产关系论安全文化网。