Rotor Position Estimation For Permanent Magnet Synchronous Motor

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斯托帕萨缪尔森定理名词解释

斯托帕萨缪尔森定理名词解释
斯托帕萨缪尔森定理（Stoppard-Samuelson theorem）是经济学中的一个定理，主要用于解释国际贸易中的相对利益。

该定理由经济学家保罗·萨缪尔森（Paul Samuelson）和汤姆·斯托帕德（Tom Stoppard）于1941年提出。

斯托帕萨缪尔森定理指出，如果两个国家之间存在不同的生产成本差异，即某个国家在生产某种商品上的成本较低，而另一个国家在生产另一种商品上的成本较低，那么这两个国家通过贸易可以同时实现经济效益的最大化。

根据斯托帕萨缪尔森定理，两个国家可以通过贸易实现相对利益的互补性。

一国专注于生产成本较低的商品，另一国专注于生产成本较低的另一种商品，通过贸易可以使得两国都能以较低的成本获得所需的商品，从而实现资源的最优配置和经济效益的最大化。

斯托帕萨缪尔森定理为国际贸易提供了理论基础，强调了贸易的互利性和相对利益的实现。

它也为贸易政策的制定提供了参考，鼓励国家根据自身的相对优势进行贸易，以实现经济的增长和福利的提高。

多目标遗传算法里面的专业名词

多目标遗传算法里面的专业名词1.多目标优化问题(Multi-Objective Optimization Problem, MOP)：是指优化问题具有多个相互冲突的目标函数，需要在不同目标之间找到平衡和妥协的解决方案。

2. Pareto最优解(Pareto Optimal Solution)：指对于多目标优化问题，一个解被称为Pareto最优解，如果不存在其他解能在所有目标上取得更好的结果而不使得任何一个目标的结果变差。

3. Pareto最优集(Pareto Optimal Set)：是指所有Pareto最优解的集合，也称为Pareto前沿(Pareto Front)。

4.个体(Domain)：在遗传算法中，个体通常表示为一个潜在解决问题的候选方案。

在多目标遗传算法中，每个个体会被赋予多个目标值。

5.非支配排序(Non-Dominated Sorting)：是多目标遗传算法中一种常用的个体排序方法，该方法将个体根据其在多个目标空间内的优劣程度进行排序。

6.多目标遗传算法(Multi-Objective Genetic Algorithm, MOGA)：是一种专门用于解决多目标优化问题的遗传算法。

它通过模拟生物遗传和进化的过程，不断地进化种群中的个体，以便找到多个目标下的最优解。

7.多目标优化(Multi-Objective Optimization)：是指优化问题具有多个目标函数或者多个约束条件，需要在各个目标之间取得平衡，找到最优的解决方案。

8.自适应权重法(Adaptive Weighting)：是一种多目标遗传算法中常用的方法，用于动态调整不同目标之间的权重，以便在不同的阶段能够更好地搜索到Pareto前沿的解。

9.支配关系(Dominance Relation)：在多目标优化问题中，一个解支配另一个解，指的是在所有目标上都至少不差于另一个解，并且在某个目标上能取得更好的结果。

翻译2

飞机制动系统的振动摩擦—Ⅱ非线性动力学摘要：对复杂的飞机制动系统中的摩擦振动的非线性动力学进行了研究。

在中心流行概念的基础上，为了评估中重要稳态平衡点临近系统的非线性动力学行为，本文概括了非线性研究的策略。

为了得到时域的响应，可以将成套的非线性动力方程综合计算。

但是当需要参数化设计研究时，该程序不仅消耗时间，而且成本大。

因此非常有必要使用非线性分析：中心流行方法和合理的逼近用来非线性系统的极限环，为了研究在不稳定区域系统的行为。

将非线性方法的烟具结果和完整的原始的系统的结果比较。

那些非线性方法呈现出有趣的计算时间，并且需要很少的计算资源。

1 前言在广泛各种机械领域中，摩擦振动是主要的关注问题。

如果系统是不稳定的，解决潜在的振动问题需要考虑到稳定性分析和非线性行为的测定。

因此，这样的方法可以分为两部分。

在Ⅰ部分已经提到，第一步静态问题：稳态运行点事通过解整个非线性方程的解求平衡点来获得。

通过将平衡点微小的摆动引进非线性方程中，可以获得线性化旋转方程。

通过确定非线性系统在每个稳态运行点的线性化方程的特征值，可以获得稳定性分析。

第二步极限环的估计。

非线性动力学方程可以通过综合数值来获得时域相应和极限环。

但是，这种过程太消耗时间。

这就是为什么理解有很多自由度的非线性模型行为需要方程的简化和减少，是由于事实上非线性分析需要相当贵的资源无论是时间计算还是数据存贮。

这些动力学系统研究的主要概念是：用简化的方法减少系统的次序，并且尽可能的消除系统方程的非线性[1-6]。

在这篇文章中，中心流行方法和合理的摩擦逼近方法用来减少和简化非线性动力学系统。

中心流行理论是建立在减少原始系统维数的基础上：在平衡点附近最基本的非线性动力学系统的特性是由中心流行理论控制的，通过在霍普夫分界点的零实部的特征值将该中心流行理论和原始系统的部分特性联系起来。

有人在应用中心流行方法之后，选择了使用合理逼近的方法[7-10]。

合理逼近方法的最大优势在于：在任何情况下，它的有效性范围比多项式广。

丁伯根原则名词解释

丁伯根原则名词解释丁伯根原则(Baxter's''rule，亦译“贝尔实验”)，是由美国统计学家卡尔。

g。

丁伯根(G。

Baxter)、费伦斯特(F。

Fettenstein)和罗切斯特(A。

Rochester)于1947年提出的一种效用理论，是解释商品价格变动的一个规律性的原则。

商品需求量随着商品价格变动而变动，而且与价格成反方向变化。

它可以分为预期效用、相对效用和绝对效用三种效用。

相对效用指人们在不同商品之间所作出的选择或对不同价格商品的支付意愿;绝对效用指的是无差别曲线与预算线的交点。

绝对效用又叫真实效用，它的大小取决于消费者的收入水平和商品价格。

This principle takes the shape of a butterfly。

直观上讲，该原则是建立在每种商品都是其它商品价格的函数这一假设基础上的，即每一种商品都以其它商品的价格为函数。

因此，人们总是先看到某商品的价格下降，才会去购买这种商品，价格上升时，他们就不会去购买这种商品。

效用规律用公式表示为：。

需求法则是，随着商品价格的上升，消费者的边际效用是递减的。

需求定理是，在其他条件不变的情况下，当商品的价格上升时，人们会把钱从一种商品转移到另一种商品上。

需求定理认为，随着价格上涨，总效用减少。

这种减少被称为替代效应，或收入效应。

替代效应越强烈，说明一个人更加偏爱高价商品。

需求定理还认为，商品的价格越高，消费者对该商品的需求量反而越少。

因为他们需要比较更多的时间来收集有关这种商品的信息。

如果要花很长的时间去获得这些信息，那么他们宁可放弃对这种商品的需求，也不愿支付更多的货币。

替代效应强度取决于商品的价格弹性，价格弹性大的商品，替代效应也大。

当价格上升时，需求量减少，这意味着人们想保持自己已经拥有的东西不变。

总效用是指所有消费品效用之和，包括从各种物品中得到的生理效用，心理效用和社会效用。

效用最大化意味着把各种效用都加起来。

索洛模型中文译文(1、2部分)

索洛模型关于经济增长理论的一篇贡献罗伯特M索洛第一部分引言所有理论依赖于不太真实的假设，这就是理论之所以成理论。

成功的理论的艺术，是用这样一种方式来做不可避免的简化假设，用这种方法最后总结过不会很敏感。

一个决定性的假设是结论显著依赖着的假设，并且决定性假设是相当可靠真实的，这点很重要。

当一个理论的结论明显地由一个特定的假设产生，而这个假设是可疑的，那么结果也是可疑的。

我想论证一些此类的东西对于哈罗德—多马经济增长模型是正确的。

哈罗德—多马思路最具特点且有力的结论是：即使是在长期当中，经济体系至多是在一种刃锋平衡状态。

关键参数有，储蓄率、资本产出比率、劳动力增长率。

任何由绝对中心的滑动，其结果将是失业率的增长或长期通胀。

在哈罗德体系中，关于平衡的最重要的问题归结于无技术改变前提下，依赖于劳动力增长的“自然增长率”，以及基于家庭与工厂储蓄投资习惯的“保证增长率”之间的比较。

但保证增长率与自然增长率的基本矛盾最终源于要素比例固定这一决定性假设，在生产中，劳动力与资本没有替代的可能。

如果放弃这个假设，那么不稳定的均衡的刀锋概念也就随之而去了。

其实，这一点也不奇怪，系统中一部如此完全的刚性，会限定另一部分缺少弹性。

哈罗德—多马模型的一个显著特点是它坚持用一般的短期工具来研究长期问题。

人们通常认为长期是新古典主义分析的领土，边际分析的领域。

但哈罗德和多马用乘数，投资增加系数，资本系数来讨论长期。

这篇文章的大部分用于一个接受除了固定要素比例外所有哈罗德—多马模型假设的长期模型。

但我忍为单个的合成商品，在标准的新古典主义经典条件下，是由劳动力和资本共同制造的。

对于外因决定劳动力增长率，系统进行细微调节来适应，以观察哈罗德不稳定是否出现。

在这个新古典主义经典调整过程中，价格—工资—利率反应扮演着重要的角色，因此也会对它们进行分析。

还会稍微放松一些其他的刚性的假设条件，来观测性质改变引起的结果：允许模糊技术改变和一个利率弹性的储蓄时间表。

遗传学名词解释(中英对照版)

遗传学名词解释（中英对照版）abortive transduction 流产转导：转导的DNA片段末端掺入到受体的染色体中，在后代中丢失。

acentric chromosome 端着丝粒染色体：染色体的着丝粒在最末端。

Achondroplasia 软骨发育不全：人类的一种常染色体显性遗传病，表型为四肢粗短，鞍鼻，腰椎前凸。

acrocetric chromosome 近端着丝粒染色体：着丝粒位于染色体末端附近。

active site 活性位点：蛋白质结构中具有生物活性的结构域。

adapation 适应：在进化中一些生物的可遗传性状发生改变，使其在一定的环境能更好地生存和繁殖。

adenine 腺嘌呤：在DNA中和胸腺嘧啶配对的碱基。

albino 白化体：一种常染色体隐性遗传突变。

动物或人的皮肤及毛发呈白色，主要因为在黑色素合成过程中，控制合成酪氨酸酶的基因发生突变所致。

allele 等位基因：一个座位上的基因所具有的几种不同形式之一。

allelic frequencies （one frequencies）在群体中存在于所有个体中某一个座位上等位基因的频率。

allelic exclusion 等位排斥：杂合状态的免疫球蛋白基因座位中，只有一个基因因重排而得以表达，其等位基因不再重排而无活性。

allopolyploicly 异源多倍体：多倍体的生物中有一套或多套染色体来源于不同物种。

Ames test 埃姆斯测验法：Bruce Ames 于1970年人用鼠伤寒沙门氏菌（大鼠）肝微粒体法来检测某些物质是否有诱变作用。

amino acids 氨基酸：是构成蛋白质的基本单位，自然界中存在20种不同的氨基酸。

aminoacyl-tRNA 氨基酰- tRNA：tRNA的氨基臂上结合有相应的氨基酸，并将氨基酸运转到核糖体上合成蛋白质。

aminoacyl-tRNA synthetase 氨基酰- tRNA合成酶：催化一个特定的tRNA结合到相应的tRNA分子上。

基于滑模观测的无感风机负载中低速控制

102传感器与微系统(Transducer and Microsystem Technologies)2021年第40卷第2期DOI：10.13873/J.1000-9787(2021)02-0102-03基于滑模观测的无感风机负载中低速控制郑富强，韩强(东华大学机械工程学院，上海201620)摘要：为了解决风机负载无传感器控制在低速运行时转子位置观测困难问题，结合风机负载的特点，将被控电机在旋转同步坐标系中的阻抗矩阵刈•称变形。

对风机负载通用驱动电机，即无刷直流电机在两相静止坐标系中重构对称阻抗矩阵数学模型。

根据该数学模型在电机运行时扩展反电势同时与电机转速和定子电流微分值都具有相关性的特性，设计基于饱和函数的滑模观测器实现了电机中低速运行时转子角度和速度信息的观测。

结合风机负载特性、控制系统和转子位置观测器进行系统仿真，并基于STM32F302C8T6芯片制作硬件驱动控制板，实验测试后对结果进行分析,验证了控制系统的可行性。

关键词：风机类负载；无刷直流电机；无传感器；滑模观测器中图分类号：TM301.2；TP212文献标识码：A文章编号：1000-9787(2021)02-0102-03Sensorless low speed control of fan load based on slidingmode observerZHENG Fuqiang,HAN Qiang(School of Mechanical Engineering,Donghua University,Shanghai201620,China)Abstract:To solve problem of difficulty of rotor position observation under the sensorless control of fan load atlow speed,the impedance matrix of the controlled motor in the rotating synchronous coordinate system is deformedsymmetrically according to the characteristics of fan load・The brushless DC motor is used to reconstruct themathematical model in the two-phase static coordinate system.According to the back・EMF of the motor at lowspeed both correlate with the differential value of the motor speed and stator current differential value, the slidingmode observer based on saturation function is designed to realize the observation of rotor angle and velocityinformation al low speed・Combined with the fan load characteristics,conlrol system and rolor position observer,thesystem simulation is carried out,and the hardware driver controller is made based on STM32F302C8T6chip.Afterexperiment,the results are analyzed,and the feasibility of the control system is verified.Keywords:fan load；brushless DC motor；sensorless；sliding mode observe0引言风机、水泵类器械是通用生产生活工具。

Robust Control and Estimation

Robust Control and Estimation Robust control and estimation are essential components of modern engineering systems, providing the ability to maintain stability and performance in the faceof uncertainties and disturbances. In the realm of control theory, robust control techniques aim to design controllers that can effectively handle variations in system parameters or external disturbances, ensuring that the system remainsstable and performs as desired. On the other hand, robust estimation techniques focus on accurately estimating the state of a system despite uncertainties in the measurements or model inaccuracies. One of the key challenges in robust controland estimation is dealing with uncertainties in the system model. Real-world systems are often subject to variations in parameters or external disturbancesthat are difficult to predict or quantify accurately. Traditional control and estimation techniques that rely on precise mathematical models may fail in such scenarios, leading to poor performance or instability. Robust control andestimation techniques, on the other hand, are designed to handle theseuncertainties by incorporating them into the design process and ensuring that the system remains stable and performs well under a wide range of operating conditions. Robust control techniques typically involve the use of advanced mathematical tools such as H-infinity control, mu-synthesis, or robust model predictive control.These techniques allow engineers to design controllers that can guaranteestability and performance even in the presence of uncertainties. By consideringthe worst-case scenario and optimizing the controller design to handle these extreme conditions, robust control techniques provide a higher level of confidencein the system's performance. Similarly, robust estimation techniques play acrucial role in accurately estimating the state of a system despite uncertaintiesin the measurements or model inaccuracies. Kalman filtering, robust observers, or adaptive estimation algorithms are commonly used in robust estimation to improvethe accuracy and reliability of state estimation. By incorporating robustestimation techniques into the control system, engineers can ensure that the controller receives accurate and reliable state information, leading to better control performance. In addition to handling uncertainties, robust control and estimation techniques also offer other benefits such as improved robustness tosensor noise, modeling errors, or external disturbances. By designing controllers and estimators that are robust to these factors, engineers can enhance the overall performance and reliability of the system. Moreover, robust control and estimation techniques can also simplify the tuning process for controllers, as they are designed to be more forgiving of variations in system parameters. Overall, robust control and estimation play a critical role in ensuring the stability, performance, and reliability of modern engineering systems. By incorporating robust techniques into the design process, engineers can create systems that are more resilient to uncertainties and disturbances, leading to improved overall performance and reliability. As technology continues to advance and systems become more complex, the importance of robust control and estimation techniques will only continue to grow, making them essential tools for engineers in various fields.。

霍林舍社会地位指数法 -回复

霍林舍社会地位指数法-回复霍林舍社会地位指数法（Holmes and Rahe Social Readjustment Rating Scale），是由1967年由美国心理学家Thomas Holmes和Richard Rahe提出的一种衡量个体生活中压力程度的方法。

这一方法通过对事件的评分，然后将评分累加，得出个体所面临的生活压力水平。

本文将一步一步回答与霍林舍社会地位指数法相关的问题，以帮助读者更好地了解这一方法。

1.什么是霍林舍社会地位指数法？霍林舍社会地位指数法是一种心理学中广泛使用的评估个体生活压力水平的方法。

它基于一个基本的假设，即一些生活事件对于个体来说是产生压力的，这些事件可能会对个体的心理和身体健康产生不良影响。

霍林舍社会地位指数法通过给这些生活事件赋予特定的分数，然后通过累加分数来评估个体的总体压力水平。

2.为什么需要使用霍林舍社会地位指数法？在生活中，个体可能会面临各种各样的挑战和压力，包括感情方面、职业方面、家庭方面的事件等。

这些事件会对个体的心理和身体健康产生影响。

了解个体的压力水平对于心理健康专业人员和研究者非常重要，因为它能够帮助他们预测和干预可能的心理健康问题。

霍林舍社会地位指数法提供了一种客观且相对简单的方法来评估个体的生活压力水平。

3.如何使用霍林舍社会地位指数法？霍林舍社会地位指数法通过给一系列生活事件分配分数来评估个体的压力水平。

每个生活事件都被赋予了一个特定的分数，该分数代表了这个事件对个体来说所产生的压力程度。

例如，死亡配偶的分数为100，离婚的分数为73，而婚姻的分数为50。

个体只需将他们经历过的事件与它们对应的分数相加，从而得出他们的总体压力水平。

4.霍林舍社会地位指数法的局限性是什么？霍林舍社会地位指数法虽然是一种常用且有效的方法来评估生活压力水平，但它也存在一些局限性。

首先，霍林舍社会地位指数法只考虑到了个体生活中的一些特定事件，而没有考虑到其他因素，如个体的资源和应对策略等。

简述哈罗德 – 多马模型的主要假设条件。

简述哈罗德–多马模型的主要假设条件。

哈罗德-多马模型是一种记忆加工模型，主要假设条件包括： 1. 存储系统：存在一个长期记忆存储系统和一个短期记忆存储系统。

长期记忆存储系统可以容纳大量的信息，并且信息可以永久存储在这里。

短期记忆存储系统则用于存储暂时需要记忆的信息。

2. 编码过程：信息通过感觉器官进入短期记忆存储系统，经过加工和编码处理后，转移到长期记忆存储系统中。

3. 检索过程：当需要访问某些信息时，从长期记忆存储系统中检索出相关信息并在短期记忆存储系统中处理，以便进行使用或操作。

4. 遗忘过程：随着时间的推移和新信息的不断涌现，部分信息会逐渐消失或遗忘，但也有一部分信息可以始终保存在长期记忆存储系统中。

这些假设条件构成了哈罗德-多马模型的基本框架，解释了人类如何获取、加工、存储和检索信息的过程。

地表法则先遣者

地表法则先遣者Title: The Pioneer of Surface Rules。

As a species, humans have always been fascinated withthe unknown and the unexplored. From the depths of the oceans to the vast expanse of space, we have always soughtto push the boundaries of our knowledge and understanding. But perhaps one of the most intriguing frontiers that we have yet to fully explore is the surface of our own planet.The surface of the Earth is a complex and dynamic system, shaped by a multitude of factors such as geology, climate, and biological activity. It is a world of extremes, ranging from the scorching deserts of the Sahara to the frozen tundras of the Arctic. And yet, despite its importance to our daily lives, we still know relativelylittle about the workings of this vast and intricate system.Enter the pioneer of surface rules – a new breed of scientists and researchers who are dedicated to uncoveringthe secrets of the Earth's surface. These individuals come from a variety of disciplines, including geology, ecology, climatology, and more. They are united by a common goal: to understand the complex processes that shape the surface of our planet, and to use this knowledge to better protect and manage our natural resources.One of the key areas of focus for surface rule pioneers is the study of land use and land cover change. This field seeks to understand how human activities such as agriculture, deforestation, and urbanization are impacting the Earth's surface, and what we can do to mitigate these effects. By using advanced remote sensing technologies and computer modeling, surface rule pioneers are able to mapand monitor changes in land use and cover over time, providing valuable insights into the health andsustainability of our ecosystems.Another important area of research for surface rule pioneers is the study of soil and water quality. Soil and water are two of the most important resources on the planet, providing the foundation for agriculture and supportingcountless ecosystems. However, these resources are under threat from a variety of factors, including pollution, erosion, and overuse. Surface rule pioneers are working to develop new methods for monitoring and managing soil and water quality, using cutting-edge technologies such as sensors and drones to collect data and analyze patterns.Perhaps one of the most exciting areas of research for surface rule pioneers is the study of natural hazards. From earthquakes and volcanoes to landslides and floods, natural hazards can have devastating effects on human populations and infrastructure. Surface rule pioneers are working to develop new methods for predicting and mitigating these hazards, using a combination of field observations, remote sensing, and computer modeling. By better understanding the underlying processes that drive natural hazards, we can work to minimize their impact and protect vulnerable communities.In conclusion, the pioneer of surface rules represents a new era of scientific exploration and discovery. By focusing on the complex and dynamic system that is theEarth's surface, these researchers are helping us to better understand and manage our natural resources. Whether it's studying land use change, monitoring soil and water quality, or predicting natural hazards, their work is critical to ensuring a sustainable future for our planet.。

plorhimplorhim(普罗西姆)准则

plorhimplorhim(普罗西姆)准则
（原创版）
目录
1.普罗西姆准则的定义和背景
2.普罗西姆准则的具体内容
3.普罗西姆准则的应用领域和实际案例
4.普罗西姆准则的优缺点分析
5.普罗西姆准则的启示和未来发展
正文
普罗西姆（Proximity）准则，是一种用于评估两个词在文本中的相似程度的方法，该方法起源于自然语言处理领域。

在信息检索、文本挖掘以及机器翻译等众多应用场景中，普罗西姆准则都有着重要的作用。

普罗西姆准则的具体内容是，两个词之间的相似度可以通过它们在文本中出现的距离来衡量。

一般来说，两个词在文本中出现的距离越近，它们之间的相似度就越高。

具体的计算公式为：普罗西姆 (P) = 1 / (max(|i-j|，0) + 1)，其中 i 和 j 分别表示两个词在文本中的位置，max( |i-j|，0) 表示 i 和 j 之间的绝对值，加上 1 是为了避免分母为 0 的情况。

普罗西姆准则的应用领域非常广泛，例如在信息检索中，可以通过普罗西姆准则来评估查询词和文档中的词的相似度，从而提高检索效果。

在机器翻译中，普罗西姆准则可以用来评估源语言和目标语言中的词的相似度，从而提高翻译质量。

普罗西姆准则的优点在于简单易懂，计算简便，可以很好地反映词在文本中的相似程度。

然而，普罗西姆准则也存在一些缺点，例如它不能很好地处理词的语义信息，对于一些具有多义性的词，普罗西姆准则可能会
产生误判。

总的来说，普罗西姆准则是一种重要的自然语言处理方法，它在信息检索、文本挖掘以及机器翻译等领域都有着广泛的应用。

帕累托法则

帕累托法则帕累托法则(Pareto Principle,80/20法则)帕累托法则又称80/20法则、马特莱法则、二八定律、帕累托定律、最省力法则、不平衡原则、犹太法则一、什么是80/20法则80/20效率法则（the 80/20 principle），又称为帕累托法则、帕累托定律、最省力法则或不平衡原则、犹太法则。

此法则是由意大利经济学家帕累托提出的。

80/20的法则认为：原因和结果、投入和产出、努力和报酬之间本来存在着无法解释的不平衡。

一般来说，投入和努力可以分为两种不同的类型多数，它们只能造成少许的影响；少数，它们造成主要的、重大的影响。

一般情形下，产出或报酬是由少数的原因、投入和努力所产生的。

原因与结果、投入与产出、努力与报酬之间的关系往往是不平衡的。

若以数学方式测量这个不平衡，得到的基准线是一个80/20关系；结果、产出或报酬的80％取决于20％的原因、投入或努力。

例如，世界上大约80％的资源是由世界上15％的人口所耗尽的；世界财富的80％为25％的人所拥有；在一个国家的医疗体系中，20％的人口与20％的疾病，会消耗80％的医疗资源。

80/20原则表明在投入与产出、原因与结果以及努力与报酬之间存在着固有的不平衡。

这说明少量的原因、投入和努力会有大量的收获、产出或回报。

只有几件事情是重要的，大部分都微不足道。

80/20关系提供了一个较好的基准。

一个典型的模式表明，80%的产出源自20%的投入；80%的结论源自20%的起因；80%的收获源自20%的努力。

80/20原则包含在任何时候对原因的静态分析，而不是动态的。

使用 80/20原则的艺术在于确认哪些现实中的因素正在起作用并尽可能地被利用。

80/20这一数据仅仅是一个比喻和实用基准。

真正的比例未必正好是80%：20%。

80/20原则表明在多数情况下该关系很可能是不平衡的，并且接近于80/20。

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关于乘用车驾驶员对转向盘可调范围的需求的研究

1　引言在研发某一款车型时，转向盘的位置是非常关键的人机设定。

合理的转向盘位置，能够使驾驶员的躯干和四肢的关节都处于舒适的角度。

总布置工程师通常用人机三角形（当前人机三角形由95百分位驾驶员踵点、H点、转向盘中心点连线而成）来评估转向盘位置是否满足舒适性要求。

如图1。

因此从某种意义上来讲，人机三角形即是某一人体舒适坐姿的全简化，其中包含脚部姿态、腿部姿态、躯干姿态、上肢姿态、手部姿态。

实际上，SAE人机布置体系中的人体是不含上肢的（图1所显示上肢仅出于美观性考虑，其上肢与下肢的尺寸参数来自不同文件），转向盘布置也不是通过上肢的关节角度分析得出的。

所以，合理的应用人机三角形，在不涉及上肢布置的情况下，也可以推导出合理的转向盘位置。

进一步，可以根据三角形相似舒适性相同的原理，推导出不同百分位的人体的转向盘的最佳位置。

当前人机三角形存在一定的不合理性，文章对人机三角形进行了重新定义（新人机三角形由舒适曲线的参考原点、H点、转向盘中心的连线而成），并以此推导出基于舒适曲线的5、50、95三个百分位人体在坐高200mm至360mm范围内的坐姿下的转向盘中心最佳位置，即不同身高的驾驶员对转向盘的可调范围的需求。

注：如无特殊说明，文章相关参数尺寸代码均采用SAE J1100所定义代码。

长度单位为毫米（mm），角度单位为度（°）。

张仕亮　袁鹏文　刘罗卫　孟相阳吉利汽车研究院（宁波）有限公司　浙江省宁波市　315336摘要：乘用车转向盘的设计位置为50百分位人体所匹配的最佳位置，为了使转向盘能够最大化适应不同身高的驾驶员，文章对人体的H点、转向盘中心点、脚部参考点的位置关系（即人机三角形）进行深入研究，并对人机三角形进行了重新定义。

利用不同百分位的人体关节尺寸比例几乎相同的特点，推导出基于舒适曲线的5、50、95三个百分位人体在坐高200mm至360mm范围内的坐姿下的转向盘中心最佳位置，即不同身高的驾驶员对转向盘的可调范围的需求。

自主迁移的并行遗传算法用于马斯京根模型参数估计

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传算法优化模型参数的过程，应用实例进行检并
验，验结果表明本文算法是一种有效求解马斯京检根模型参数的方法。
单，计算快捷，对河道地形和糙率资料要求低，在且
一
般的河道洪水中演算效果较好，因而已在世界上
传算法相结合，针对影响并行遗传算法性能的迁移时机进行研究，出自主迁移的并行遗传算法用于马且提
斯京根模型参数估计。实验结果表明，该算法为估计马斯京根模型参数提供了一种有效的方法。
关键词：自主迁移；并行遗传算法；马斯京根模型；参数估计中圈分类号：１ＴＰ８文献标识码：Ａ
１引言பைடு நூலகம்
马斯京根流量演算法是Ｍｃａｔｙ于１３～Ｃｒｈ９４１３９５年在美国马斯京根河上首先提出和应用的一种洪水流量演算方法［，１由于该方法数学上比较简］

plorhimplorhim(普罗西姆)准则

plorhimplorhim(普罗西姆)准则摘要：1.普罗西姆准则的定义和背景2.普罗西姆准则的应用领域3.普罗西姆准则的优缺点分析4.普罗西姆准则的实际应用案例5.普罗西姆准则的启示和未来发展正文：普罗西姆准则，全称“Plorhimplorhim”准则，是一种行为准则，起源于古希腊，主要用于指导人们在生活中如何做出正确的道德决策。

这个准则主张人应该按照理性和道德的指引来行事，以达到个人和社会的最大利益。

普罗西姆准则的应用领域非常广泛，包括政治、经济、社会和个人生活等各个方面。

在政治领域，普罗西姆准则可以指导政府制定公平公正的政策，促进国家的繁荣发展。

在经济领域，普罗西姆准则可以指导企业做出有利于社会和环境的商业决策，实现可持续发展。

在社会领域，普罗西姆准则可以指导人们如何处理人际关系，构建和谐社会。

在个人生活领域，普罗西姆准则可以指导人们如何做出道德决策，成为一个有道德的人。

普罗西姆准则虽然有很多优点，但也存在一些缺点。

首先，普罗西姆准则过于抽象，难以具体操作。

其次，普罗西姆准则可能会导致道德相对主义，使人们在面对具体问题时无法做出明确的决策。

尽管如此，普罗西姆准则在实际应用中还是取得了很多成功案例。

比如，在环保领域，一些企业就是根据普罗西姆准则，放弃短期利益，选择可持续发展的道路，最终取得了巨大的成功。

普罗西姆准则对人们的道德行为有很大的启示作用。

它告诉我们，在做出决策时，我们应该考虑的不仅仅是自己的利益，还要考虑社会的利益，甚至是全人类的利益。

只有这样，我们才能做出真正正确的决策。

随着社会的发展，普罗西姆准则也在不断演进和发展。

Variance component estimation e BLUP

VARIANCE COMPONENT ESTIMATION &BEST LINEAR UNBIASED PREDICTION (BLUP)V.K. BhatiaI.A.S.R.I., Library Avenue, New Delhi- 11 0012vkbhatia@iasri.res.in1. IntroductionVariance components are commonly used in formulating appropriate designs, establishing quality control procedures, or, in statistical genetics in estimating heritabilities and genetic correlations. Traditionally the estimators used most often have been the analysis of variance (ANOVA) estimators, which are obtained by equating observed and expected mean squares from an analysis of variance and solving the resulting equations. If the data are balanced, the ANOVA estimators have many appealing properties. In unbalanced situations, these properties are rarely hold true which create number of problems in arriving at correct decisions. As in reality, variance components are mostly estimated from unbalanced data only so there are number of problems associated with them in these situations. In unbalanced situations, two general classes of estimators have sparked considerable interest: maximum likelihood and restricted maximum likelihood (ML and REML) and minimum norm and minimum variance quadratic unbiased estimation (MINQUE and MIVQUE). The links between them is also very important component. In addition to estimation problems in unbalanced case, the notion of robust estimation which takes care of influence of outliers and underlying statistical assumptions is also of interest.The classical least squares model contains only are random element, the random error; all other effects are assumed to be fixed constants. For this class of models, the assumption of independence of the εi implies independence of the y i. That is if ()=2, thenεσVar I ()=σ2 also. Such models are called fixed effects models or more simply fixed models. Var y IThere are situations when there is more than one random term. The classical variance components problems, in which the purpose is to estimate components of variance rather than specific treatment effects, is one example. In these cases, the “treatment effects” are assumed to be a random sample from a population of such effects and the goal of the study is to estimate the variance among these effects in the population. The individual effects that happen to be observed in the study are not of any particular interest except for the information they provide on the variance component. Models in which all effects assumed to be random effects are called random models. Models that contain both fixed and random effects are called mixed models.2. Analysis of Variance ApproachThe conventional least square approach, sometimes called the analysis of variance approach, to mixed model is to assume initially that all effects, other than the term that assigns a unique random element to each observation are fixed effects. Least squares is applied to this “fixed” model to obtain relevant partitions of the sums of squares. Then, the model containing the random effects as reinstated and expectations of the mean squares are derived. The mean square expectations determine how tests of significance are to be made and how variancecomponents are to be estimated. Adjustments to tests of significance are made by “constructing” an error mean square that has the proper expectation with respect to the random elements. This requires the expectations of the mean squares under the random model. For balanced data the mean square expectations are easily obtained. The expectations are expressed in terms of a linear function of the variance components for the random effect plus a general statement of the classes of fixed effects involved in the quadratic function.3. Henderson’s Methods I, II & IIIHenderson described three methods of estimating variance components that are just three different ways of using the general ANOVA method. They differ only in the different quadratics ( not always sums of squares ) used for a vector of any linearly independent quadratic forms of the observations. All three also suffer from demerits of the general ANOVA method - that for unbalanced data no optimal application of the method is known, the methods can yield negative estimates, and distributional properties of the estimators are not known.Method IIn Method I the quadratics used are analogous to the sums of squares used for balanced data, the analogy being such that certain sums of squares in balanced data become, for unbalanced data, quadratic forms that are not non-negative definite, and so they are not sums of squares. Thus e.g. for 2-way cross classification with n observations per cell, the sum of squares()bn y y bny abny i i ..........−=−∑∑222 becomes for unbalanced data()bn y y bn y abn y i i i i ..............−=−∑∑222This method is easy to compute, even for large data sets; and for random models, it yields estimators that are unbiased. It can not be used for mixed models. It can only be adopted to a mixed model by altering that model and treating the fixed effect either as non-existent or as random - in which case the estimated variance components for the true random effects will be biased.Method IIThis is designed to capitalize on the easy computability of Method I and to broaden its use by removing the limitation of Method I that it can not be used for mixed models. The method has two parts. First make the temporary assumption that the random effects are fixed and for the modely = X β + Z u + esolve the normal equation for β0′′′′⎡⎣⎢⎤⎦⎥⎡⎣⎢⎤⎦⎥=′′⎡⎣⎢⎤⎦⎥X X X Z Z X Z Z u X y Z y β00Then consider the vector of data adjusted for β0 , namelyz = y - X β0 and then model for z will bez = 1 $µ+ Zu + Kewhere $µdiffer from µ and where K is known. This can thus easily be analysed by Method I. Method II is relatively easy to compute, especially when the number of fixed effects is not too large. And although it can be used for a wide variety of mixed models, it can not be used for those mixed models that have interactions between fixed and random factors, whether those interactions are defined as random effects (the usual case) or as fixed effects.Method IIIThis uses sums of squares that arise in fitting an overparameterised model and submodels thereof. It can be used for any mixed model and yield estimators that are unbiased. Although the method uses sums of squares that are known (at least in some cases) to be useful in certain fixed effects models, no analytic evidence is available that these sums of squares have optimal properties for estimating variances. The main disadvantage of this method is that though its confinement to sums of squares for fitting overparameterised models, there is a problem of too many sums of squares being available. For example for the 2-way crossed classification overparameterised model with equationy e ijk i j ij ijk =++++µαβφSuppose all effects are random. There are then four variance components to estimate : σσσσαβτε2222,,and . But for that model there are five different sums of squares ()R αµ, ()R βµα,, ()R β, ()R αµβ, and ()R τµαβ,, as well as SSE which can be used. From these at least three sets suggest themselves as possible candidates for Method III estimation (a) ()R α ()R βαµ, ()R ταβµ,, SSE (b) ()R βµ ()R αβµ, ()R ταβµ,, SSE (c) ()R αβ,()R βαµ, ()R ταβµ,, SSEAll three sets yield the same estimators of στ2 and σe 2 . Two different estimators of σα2 andσβ2 arise and it is difficult to conclude that which sets of estimators are to be preferred.4. ML (Maximum Likelihood)The method is based on the maximizing the likelihood function. For the mixed model, under the assumption of normality of error terms and random effects we havey X Zu e =++β ∼ ()N X V β,withV = σσi i i i e N Z Z I 212'=∑+= σi i i i Z Z 20'=∑The likelihood function is thenL = ()()()[]21212121πββ−−−−−−//'exp /N V y X V y XMaximizing L with respect to elements of β and the variance components (the σi 2s that occur in V) leads to equations that have to be solved to yield ML estimators of β and of σ2. Theseequations can be written in a variety of ways and can be solved iteratively. Despite the numerical difficulties involved in solving these equations for obtaining ML estimators of variance components, it is preferred over ANOVA method. The reason is that this method is well defined and the resulting estimators have attractive, well-known large-sample properties they are normally distributed and their sampling variances are known.5. REML (Restricted Maximum Likelihood)REML estimators are obtained from maximizing that part of the likelihood which is invariant to the location parameter; i.e. in terms of the mixed model y X Zu e =++β, invariant to X β. Another way of looking at it, is that REML maximizes the likelihood of a vector combinations of the observations that are invariant to X β.Suppose Ly is such a vector. ThenLy LX LZu Le =++βis invariant to X β if and only if LX = 0.Computational problems for obtaining solutions are same as that of ML method. The REML estimation procedure does not, however, include estimating β. On the other hand the REML equations with balanced data provide solutions that are identical to ANOVA estimators which are unbiased and have attractive minimum variance properties. In this sense REML is said to take account of the degrees of freedom involved in estimating the fixed effects, whereas ML estimators do not. The easiest example of this is the case of a simple sample of n observations from a N(µσ,)2 distribution. The two estimators of σ2 are(σML i x x n 22=−∑/()σREML i x x n 221=−−∑/()The REML estimator has taken account of the one degree of freedom required for estimating µ, whereas the ML estimator has not. The REML estimator is also unbiased, but the ML estimator is not. In the general case of unbalanced data neither the ML estimator nor the REML estimators are unbiased.6. MINQUE (Minimum Norm Quadratic Unbiased Estimation)The Method is based on the concept that the estimation minimize a (Euclidean) norm, be a quadratic form of the observations and be unbiased. Its development involves extensive algebra. More importantly, its concept demands the use of some pre-assigned weights that effectively play a part of a priori values for the unknown variance components. This method has two advantages; it involves no normality assumptions as do ML and REML. And the equations that yield the estimator do not have to solved iteratively. The solution only depends on the pre-assigned values; different pre-assigned values can give different estimators from the same data set. One must therefore talk about “a” MINQUE estimator and not “the” MINQUE estimator. This appears to a troublesome feature of the MINQUE procedure. Also, its estimators can be negative and they are only unbiased if indeed the true, unknown value of σ2 is pre-assigned. There is also a close relationship between REML and MINQUE i.e.a MINQUE solution = a first iterate of REML.7. MIVQUE (Minimum Variance Quadratic Unbiased Estimation)MINQUE demands no assumptions about the form of the distribution of y. But if the usual normality assumptions are invoked, the MINQUE solution has the properties of being that unbiased quadratic form of the observations which has minimum variance; i.e. it is a minimum variance quadratic unbiased estimator, MIVQUE.8. I-MINQUE (Iterative MINQUE)As already pointed out, the MINQUE procedure demands using a weight vector for the pre-2 say, its assigned value for σ2. No iteration is involved. But having obtained a solution, σ1 existence prompts the idea of using it as a new pre-assigned value for getting a new estimate of2 . This leads to using the MINQUE equations iteratively to yield iterative MINQUE, σ2, say σ2or I-MINQUE estimators. They are, of course, if one iterates to convergence, the same as REML estimators. Hence I-MINQUE = REML. Even in the absence of normality assumptions on y, the I-MINQUE solutions do have large-sample normality properties.9. Negative Variance Component EstimatesThe variance components should always be positive because they are assumed to represent the variance of a random variable. But some of existing methods like ANOVA and MIVQUE do give rise to negative estimates. These negative estimates may arise for a variety of reasons. •The variability in your data may be large enough to produce a negative estimate even though the true value of the variance component is positive.•Data may contain outliers which exhibit unusual large variability.• A different model for interpreting your data may be appropriate. Under some statistical models for variance components analysis, negative estimates are an indication that observations in the data are negatively correlated.10. Robust EstimationOutliers may occur with respect to any of the random components in a mixed - model analysis of variance. There is an extensive literature on robust estimation in the case of single error component. There is, however, only a small body of literature on robust estimation in the variance-component model11. Computational ProblemsThe special features of various computational problems of estimating variance components involve the application of iterative procedures such as Newton-Raphson and Marquardt method, Method of scoring, Quasi-Newton methods, EM algorithm and Method of successive approximations.12. Evaluation of AlgorithmsSeveral recent research papers evaluate algorithms for variance components estimation. While there is no consensus on the best method, some general conclusions seem to be as follows:1.The Newton-Raphson method often converges in the fewest iterations, followed by thescoring method and the EM algorithm. In some cases the EM algorithm requires a very large number of iterations. The individual iterations tend to be slightly shorter forthe EM algorithm, but this depends greatly on the details of the programming.2.The robustness of the methods to their starting values (ability to converge given poorstarting values) is the reverse of the rate of convergence. The EM algorithm is better than Newton-Raphson.3.The EM algorithm automatically takes care of inequality constraints imposed by theparameter space. Other algorithms need specialized programming to incorporate constraints.4.Newton-Raphson and scoring generate an estimated, asymptotic variance-covariancematrix for the estimates as a part of their calculations. At the end of the EM iterations,special programming [perhaps a single step of Newton-Raphson ] needs to be employed to calculate asymptotic standard errors.13. Computational Methods Available in SASFour methods are available in SAS PROC VARCOMP statements using the METHOD = option. They areThe Type 1 MethodThis method (METHOD = TYPE 1) computes the type 1 sum of squares for each effect, equates each mean square involving only random effects to its expected values and solves the resulting system of equation.The MIVQUE0 MethodThe MIVQUE0 method (METHOD = MIVQUE0) produces unbiased estimates that are invariant with respect to the fixed effects of the model and are locally best quadratic unbiased estimates given that the true ratio of each component to the residual error component is zero. The technique is similar to Type 1 except that the random effects are adjusted only for the fixed effects. This is a default method used in PROC VARCOMP.The MAXIMUM - LIKELIHOOD MethodThe ML method (METHOD = ML) computes maximum likelihood estimates of the variance components .The RESTRICTED MAXIMUM - LIKELIHOOD MethodThe restricted maximum likelihood method (METHOD = REML) is similar to ML method, but it first separates the likelihood into two parts, one that contains the fixed effects and another that does not. This is an iterated version of MIVQUE0.14. Specification for using PROC VARCOMP in SASThe following statements are used in the VARCOMP procedureRequired in this order :PROC VARCOMP<option> ;CLASS Variables ;MODEL dependents = effects </option> ;Optional :BY variables ;Only one MODEL statement is allowed. The BY, CLASS and MODEL statements are described after the PROC VARCOMP statements.PROC VARCOMP statementPROC VARCOMP<option> ;DATA (SAS data set. If this is omitted the most recently created SAS data set is used) EPSILON = number(default 1E - 8) (Convergence value)MAXITER = number (number of iterations) (default = 50)METHOD = TYPE 1/MIVQUE0/ML/REML(default = MIVQUE0)By statementBY variables ;A BY statement can be used with PROC VARCOMP to obtain separate analyses on observation in groups determined by the BY variables.CLASS statementThe CLASS statement specifies the classification variables to be used in the analysis.MODEL statementMODEL dependents = effects </option> ;The MODEL statement gives the dependent variables and independent effects. If more than one dependent is specified, a separate analysis is performed for each one. Only one MODEL statement is allowed. Only one option is available in the MODEL statement.FIXED = n= Tells VARCOMP that the first n effects is the MODEL statement are effects. The remaining effects are assumed to be random. By default PROC VARCOMPassumes that all effects are random in the model of Y = A|B/ Fixed = 1 then A x B is considered a random effect.Example: In this example, A & B are classification variables and Y is the dependent variable.A is declared fixed, andB and A x B are random.data a;input a b y;cards;1 1 2371 1 2541 1 2461 2 1781 2 1792 1 2082 1 1782 1 1872 2 1462 2 1452 2 1413 1 1863 1 1833 2 1423 2 1253 2 136;Proc Varcomp method = type 1;Class a b;model y = a|b/ Fixed = 1;run;Proc Varcomp method = mivque0;Class a b;model y = a|b/ Fixed = 1;run;Proc Varcomp method = ml;class a b;model y = a|b/ Fixed = 1;run;Proc varcomp method = reml;class a b;model y = a|b/ Fixed = 1;run;Exercise: The data given below is first month milk yield of 28 daughters of 4 sires in 3 herds Herd Sire Daughter Milk Yield1 1 157, 160, 1382 96, 110, 115, 12036582,4 120, 130, 1102 1 140, 142, 1452 122, 117, 9839470,3 2 112, 125, 10592110,34 116, 129, 131Case (i) Assume herd and sire as random components.(ii) Assume only sire as random component.Obtain the different variance components by all the four methods.Best Linear Unbiased Prediction (BLUP)A problem that occurs frequently in animal and plant breeding applications and probably in many other fields as well, is that given a sample data vector from a mixed model, the experimenter wishes to predict some set of linear functions of a future random vector. Thus, it is a problem of prediction of random vector in mixed linear models and takes different form under different situations, which is known as(a) Best Prediction (BP)(i)The form of the joint distribution of records and of the random vector to be predicted isknown.(ii)Parameters of the distribution are known.(iii)It has been proved that the conditional mean of genetic values given the records, has optimum properties.(b) Best Linear Prediction(BLP)(i) The form of the distribution is not known or certain parameters are not known.(i)We do know means of the records, the means of the genetic values and variances andcovariances or second moments are known.(ii)This involves finding that linear function of the records which minimizes the average of squared errors of prediction.(iii)In case of normal distribution BLP is BP.(c) Best Linear Unbiased Prediction (BLUP)(i)The problem is the same as for BLP, but now we do not know the means.(ii)Only the variances and covariances of the random vectors are known.(iii)(iii)We find the linear function of the records which has same expectation as the genetic values to be predicted and which is, in the class of that function, which minimizes the average of squared errors.(d) Neither first nor second moments are known and still it is desired to use linear prediction methods(i) We never really known parameters, but we may have good prior estimates of them and it will be(1) BP when we have good estimates of all parameters(2) BLP when we have good estimates of first and second moments(3) BLUP when we have good estimates of the second central moments(ii) If we have no prior estimates of either first or second moments, we need to estimate them from the same data that are used for prediction.In practical situations, mostly problems are of the type in which we assume that the variance covariance matrix of random variables is known and further it is assumed that records follow mixed model. Two methods been most frequently used. In the first, a regular least squares solution is obtained by treating all random variables except an error vector with variance Iσ2 as fixed. Then the predictor is as linear function of the least square solution. In the second method, estimates of the fixed estimates of the model are obtained by some method, possibly by regularleast square as in the first method, the data are adjusted for the fixed effects and then selection index methods are applied to the adjusted data as though the adjustment had been made with known values of fixed effects.Henderson, (1963) suggested a combination of these methods and described a mixed model method which resulted simultaneously best linear unbiased estimators of estimable linear functions of the fixed elements of the model and best linear unbiased predictors of the random elements of the model.The general linear modely = Xβ + Zu + ewherey is a nx1 vector of observationsX is known (nxp) matrixβ is (p x 1) vector of fixed effectsu is (q x 1) non observable random effecte is (n x 1) error effect vectorandEyueX⎡⎣⎢⎢⎢⎤⎦⎥⎥⎥=⎡⎣⎢⎢⎢⎤⎦⎥⎥⎥βandVyueV ZG RGZ G OR O R⎡⎣⎢⎢⎢⎤⎦⎥⎥⎥=⎡⎣⎢⎢⎢⎤⎦⎥⎥⎥'No assumptions are made concerning the distribution of the random variables, however G and R are assumed known without error and are non singular.The general problem to be solved is to predict a function K’β+M’u(βgenerally fixed, u generally random) as the predictand, by a linear function of the observations, L’y, the predictor, such that the prediction error variances for predictors of each element of K’β + M’u are minimized and such that the expected value of the predictor is equal to the expected value of the predictand. The function K’β must be an estimable function. The prediction error isK’β + M’u - L’yand the variance-covariance matrix of this function is the matrix of interest since we wish to minimize each individual diagonal element. To do this we define this matrix algebraically Assumption: V( K’β) = 0 and all cov involving K’β = 0,V(K’β + M’u - L’y) = V(M’u) + V(L’y) - Cov(M’u, y’L) - Cov(L’y, u’M)= M’GM + L’VL - M’GZ’L - L’ZGMTo ensure that the predictor is unbiased, i.e., has the same expected value as the predictand, we add a Lagrange Multiplier to the variance-covariance matrix of prediction errors prior to minimizing the function.Variance Component Estimation and BLUPWe know that E(K’β + M’u)= K’β and E(L’y) = L’Xβ Thus in order for L’Xβ = K’β for all possible vectors, β, then L’X - K’ = 0 must be true. Hence the Lagrange Multiplier becomes (L’X - K’)γ. The LM added to V(K’β + M’u - L’y) gives the function, F, below F = M’GM + L’VL - M’GZ’L - L’ZGM + (L’X - K’)γ The function F is differentiated with respect to the unknowns, L and γ, and the derivatives are equated to zero (null matrices) ∂F = 2VL - 2ZGM + Xγ = 0 ∂ L' ∂F = L’X - K’ = 0 and ∂γNote that the second derivative provides the condition which must hold in order for the prediction to be unbiased. These results can be rearranged in matrix form as follows: ⎡ V X⎤ ⎡ L ⎤ ⎡ZGM ⎤ ⎢ X' 0 ⎥ ⎢1 2 γ ⎥ = ⎢ K ⎥ ⎣ ⎦⎣ ⎦ ⎣ ⎦Recall that V = ZGZ’ + R and let θ = 1 2 γ ⎡ ZGZ '+ R X⎤ ⎡ L⎤ ⎡ ZGM ⎤ = ⎢ X' 0 ⎥ ⎢θ ⎥ ⎢ K ⎥ ⎣ ⎦⎣ ⎦ ⎣ ⎦ From the first line RL + ZG(Z’L - M) + Xθ = 0 Let S = G(Z’L - M) and note that G− 1 S = Z ' L − M and M = Z’L - G -1S Now we can write the following equations Z X⎤ ⎡ L ⎤ ⎡ 0 ⎤ ⎡R ⎢ Z ' − G −1 0 ⎥ ⎢ S ⎥ = ⎢ M ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ X' 0 0 ⎥ ⎢θ ⎥ ⎢ K ⎥ ⎣ ⎦⎣ ⎦ ⎣ ⎦VI-11Variance Component Estimation and BLUPAbsorb the L equation into the other two ⎡ Z ' R −1Z + G −1 Z ' R −1X⎤ ⎡S⎤ ⎡ M ⎤ - ⎢ ⎥⎢ ⎥=⎢ ⎥ X ' R −1 Z X' R −1X⎦ ⎣θ ⎦ ⎣ K ⎦ ⎣ Multiply both sides by -1 and let−1⎡ C11 ⎢C ⎣ 12 'ThenC12 ⎤ ⎡ Z ' R −1Z + G −1 = ⎢ C22 ⎥ ⎣ X' R −1Z ⎦ C12 ⎤ ⎡− M⎤ C22 ⎥ ⎢ − K ⎥ ⎦ ⎦ ⎣Z ' R −1 X ⎤ ⎥ X ' R −1 X ⎦⎡S⎤ ⎡ C11 ⎢θ ⎥ = ⎢ C ⎣ ⎦ ⎣ 12 'andRL = -ZS - Xθ ⎡ C11 C12 ⎤ ⎡ M⎤ = - [ Z X] ⎢ ⎥ ⎢ ⎥ ⎣ C12 ' C22 ⎦ ⎣ K ⎦ ⎡ C11 C12 ⎤ ⎡ M⎤ L = R-1 [ Z X] ⎢ ⎥ ⎢ ⎥ ⎣ C12 ' C22 ⎦ ⎣ K ⎦and⎡ C11 L’y = [ M ' K '] ⎢ ⎣ C12 ' $ $ = M’ u + K’ β $ ⎡ u⎤ = [ M ' K '] ⎢ $ ⎥ ⎣β ⎦whereC12 ⎤ C22 ⎥ ⎦⎡ Z ' R −1 y ⎤ ⎢ −1 ⎥ ⎣ X' R y⎦$ ⎡ u ⎤ ⎡ Z ' R −1 Z + G − 1 ⎢β ⎥ = ⎢ −1 $ ⎣ ⎦ ⎣ X' R ZorZ ' R − 1 X ⎤ ⎡ Z ' R −1 y ⎤ ⎥ ⎢ ⎥ X ' R − 1 X ⎦ ⎣ X ' R −1 y ⎦−−$ ⎡ β ⎤ ⎡ X ' R −1 X X ' R − 1 Z ⎤ ⎡ X ' R −1 y ⎤ ⎢ ⎥ = ⎢ ⎢ −1 −1 − 1⎥ −1 ⎥ $ ⎣ u⎦ ⎣ Z' R X Z' R Z + G ⎦ ⎣ Z' R y⎦These equations are commonly referred to as Henderson’s mixed model equations, and these provide predictors with the smallest prediction error variances among all linear unbiased predictors. This methodology can be extended to various situations like the case of individual model and model for related sires etc.VI-12Variance Component Estimation and BLUPAnimal Additive Genetic Model The model for the individual record is yi = xi’β + zi’u + ai + eiwhere β represents fixed effects with xi relating the record on the i-th animal to this vector, u represents random effects other than breeding values, zi relates this vector to yi ai is the additive genetic value of the i-th animal and ei is a random error associated with the individual record. The vector representation of the entire set of records is y = Xβ + Zu + Zaa + e If a represents only those animals with records, Za = I. Otherwise it is an identity matrix with rows deleted that correspond to animals without records. Var (u) = G Var (a) = A σ 2 a Var (e) = R, usually I σ 2 e Cov (u,a’) = 0, Cov (u,e’) = 0,Cov (a,e’) = 0If Za ≠ I, the mixed model equations are ⎡ X' R −1X ⎤ X' R −1Z X' R −1Z a ⎢ ⎥ ⎢ Z ' R −1X Z ' R −1Z + G −1 ⎥ Z ' R −1Z a ⎢ ' −1 ⎥ Z ' a R −1Z Z 'a R −1Z a + A −1 / σ 2 ⎥ a⎦ ⎢Za R X ⎣If Za = I, it simplifies to ⎡ X' R −1X ⎤ X' R −1Z X ' R −1 ⎢ ⎥ −1 −1 −1 Z ' R −1 ⎢ Z' R X Z' R Z + G ⎥ ⎢ R −1X −1Z −1 + A −1 / σ 2 ⎥ R R a⎥ ⎢ ⎣ ⎦ If R = I σ 2 it further simplifies to e⎡β o ⎤ ⎢ ⎥ $ ⎢ u ⎥= ⎢a ⎥ $ ⎢ ⎥ ⎣ ⎦⎡ X' R −1y ⎤ ⎢ ⎥ ⎢ Z ' R −1y ⎥ ⎢ −1 ⎥ ⎢ Z ' a R y⎥ ⎣ ⎦ ⎡ X' R −1y⎤ ⎢ ⎥ ⎢ Z ' R −1y ⎥ ⎢ −1 ⎥ ⎢ R y ⎥ ⎣ ⎦⎡β o ⎤ ⎢ ⎥ $ ⎢ u ⎥= ⎢a ⎥ $ ⎢ ⎥ ⎣ ⎦⎡ X' X X' Z ⎢ −1 2 ⎢ Z' X Z' Z + G σ e ⎢ X Z ⎣⎤ X' ⎥ Z' ⎥ I + A −1σ 2 / σ 2 ⎥ e a⎦⎡β o ⎤ ⎢ ⎥ $ ⎢ u ⎥= ⎢a ⎥ $ ⎢ ⎥ ⎣ ⎦⎡ X' y⎤ ⎢ Z' y⎥ ⎢ ⎥ ⎢ y ⎥ ⎣ ⎦If the number of animals is large, one should, of course, use Henderson’s method (1976) for computing A-1. Since this method requires using a “base” population of non-inbred, unrelated animals, some of these probably do not have records. Also we may wish to evaluate someVI-13。

MBA词汇马全海

MBA词汇马全海psychology-----psychological，technology-----technologicalphotography 摄像术photographicautography 自传，biography 传记audi- 听：audio 音频的，video 视频的audience 听众，auditory 听力的，audible 听的见的audit 审核，审计bio- 生物的：biotechnology 生物工艺学，biobalance生物平衡，bioactive(a.)生物活性的，bioactivity(n.)生物活性cent- 百：percent-----percentage(n.)百分比century 百年kilometers 千米，kilogram 一公斤claim- clam- 叫喊：proclaim宣告,声明clamour喧闹claim 还有索赔，夺取的意思claim tag托运收据claim lives 夺取生命claim statement 索赔声明cosm- 宇宙，世界：cosmobiology宇宙生物，cosmochemistry 宇宙化学cosmodom 太空，cosmology 宇宙学metropolitan大都市的-----metro轻轨-----subway地铁cosmopolis 国际大都市microcosm 微观世界，macrocosm 宏观世界cycl- 圆，环：recycle 回收，recyclable 可再生的cycle index 循环指数-----chain index链指数circulate (v.) 循环，circuit电路，circus 马戏场，encircle 包围，围绕geo- 地球，土地：geocentrism 地心说，socialism 社会主义suicide 自杀-----geocide 地球灭忘，doomsday 世界末日hydra- 水的：hydrate 水合物，hydration 水合作用，hydracide 氢酸hydracyclone水力旋风器，hydroelectric 水力发电，dehydrate 脱水carbonhydrate 碳水化合物lingu- 语言的：linguistics 语言学multilingual 多语的，unilingual 单语的，colingual 共语的，coexist 共存，cooperate 合作liter- 文字，字母：letter 字母，illiterate 不识字的，literacy 识字扫盲：deal with the illiteracyliberal/literal translation 意译/直译literati 文人墨客obliterate 去除-phone 声音：phonetics 语音学symphony交响乐，sympathy同情，synonym同义词，antonym反义词同传：simultaneous interpretation，交传：consecutive interpretation psych- 心理的：psychologist 心理学家，psychiatrist 精神科医生，psychiatric 精神病院scend- scent-爬蹬：ascendant 向上的，descendent 后代servant仆人，civil servant公务员，civil law民法，civil airline民用飞机criminal law 刑法descent (v./n.) 下降，下坡transcend 超越tele- 远：television 电视，telephone 电话，telegraph 电报therm- 热的：thermos 热水瓶diathermal 透热的，diameter 直径，dialogue 对话前缀anti- 抵抗，反对：antipathy 反感，antibacterial 抗菌的，antibody 抗体antiterrorism反恐，antithesis 对立面，反论auto- 自己的：autoanalysis 自我分析，autobalance 自动平衡autocode 自动编码，decode 解码，encode 加码，code 密码bi- 双边的：bilingual 双语的，biannual 一年两次的bisexual 双性恋，homosexual 同性恋counter- 反对，对应，应对：counterclaim反索赔，countermeasures 应对措施counterattack 反击，counterblow 反击，countercoup 政变counter command 撤销命令，counter argument 抗辩，counter attract 反吸引，counter part 对应部分foreign counter part 外国同行dis- 相反：discomfort-----uncomfortable，unbelievable-----disbeliefdisappear 消失，disorder 杂乱discourage 打断念头，discourage somebody from doing somethingencourage somebody to somethingdisarm 解除武装，disproof 反证，discover 发现，disclose 掀开discard 抛弃，dispassionate 平心静气的dismay 沮丧，lose heart 沮丧，lose one’s heart被某人夺走芳心en-，-en 使：enlarge 扩大，enlarge spaceenroll 登记，注册，enchain 束缚，enlighten 启发-----enlightenmentI was enlightened by what he said.broad 宽的，board 板，aboard 上船了，abroad 国外boarding pass 登机牌broaden one’s horizon 拓宽视野wide-----widen，tight-----tighten，fast-----fastenex- 向外：extrovert 外向的人，introvert 内向的人export 出口，import 进口extra 额外的，expose 暴露的，exclude 排除在外的include 包含在内，exclusive 排除在外，extract 提取explicit 坦率的，implicit 含蓄的ex-boyfriend 前任男友former/last president 前任总统，late president 已故总统im-in- 不：impersonal 非个人的，objective 客观的impassive/indifferent 漠不关心的，morality 道德，moral 道德的，immorality 不道德，modesty 谦虚impossible 不可能，impolite 不礼貌，impatient 没耐心的impartial 公正的，inglorious 不光彩的，incapable 无能力的injustice 不公正的，inconsistent 不一致的inter- 相互：interpersonal 人际的，interpose 介入，impose 强加interfere with干扰，interference，interrelate 相互关联，intervene 干扰mal- 坏，不良：malpractice 不法行为，malodor 恶臭，maocontent 不满意的micro-微小的：macro-宏，micro world 微观世界，micro economic 微观经济microwave oven 微波炉，macro management 宏观调控micro biology 微生物学，macroscale 大范围，macroclimate 大气候mini- 小的：miniskirt 超短裙，miniature 微缩画，miniwar 小型战争dwarf 小矮人，minimal 最低限度，maximum 最大，minimum 最小diminish 使变小（慢慢的）mis-错的：misunderstanding 误解，misled 误导，misspell 拼写错misdoing/malpractice 做坏事，maltreat/mistreat 虐待abuse 虐待，滥用treat 对待，治疗AA制：Go Dutch 请客：My treatmischievous 恶作剧的multi- 多的：multiple choice 选择题，multifunction 多功能的，multimedia 多媒体mass media大众传媒，multicultural 多种文化，multitude 多数non- 否定：nonsense 无意义的行为，make sense 有意义，nuisance 讨厌的人或者事non-exist 不存在的-----nonexistent-----nonexistencenonresistant 不抵抗的，nonaddictive 不上瘾的，be addictive to 上瘾out- 超过，过度：例句1. The child has outgrown the cloth.outlive (寿命)活得长例句2. Our class has outnumbered theirs.outdate 过时，outgoing外向的，outside 外面的outdoors户外的，outskirts 郊外outbreak 爆发，output 输出，产量，outcome 结果例句3. The output of the factory has doubled.over- 过度，超：work over time 加班，OT 加时赛over timeoverweight 超重overweigh 胜过obese-fat-overweight-strong-moderate-fit-thin-skinny-slim例句1. The benefits of A far outweigh its harms.overdo 做过头了，overdose 过量用药，overcrowded 过度拥挤overcome 克服post- 在…后：postgraduate 研究生，graduate 本科毕业生，undergraduate 本科生bachelor’s degree 学士学位postdoctoral 博士后，postpone 推迟A is posterior/inferior/prior/superior to Bsense of superiority 优越感pre- 在…前：preview 预习，review 复习，prepay 预付prehistory 史前，prefix 前缀，precaution 预防，precautious 预防的prevent 阻止的，predict 预告，preceed 向前，foresee 预知precedented 有先例的，it’s unprecedented hot 史无前例的热Re- 再，重：regain，retell，replay，relive，recall，reappear，repeat，recur semi- 半：semi colony 半殖民，semitransparent 半透明sub- 次的，低于：sub office 分办公室，subtitle 副标题，字幕，subconscious 潜意识subscribe 用户trans- 转换，横过：transparent 透明的，translation 翻译，transaction 交易transcribe 誊写，describe 描述，prescribe 开处方transfer 转递，transmit 转播，transcend 超越，transnormal 超常的tri- 三倍的：trilateral 三边的，bilateral 双边的，triangle affair 三角恋triple 三倍的，tripod 三角架，tricycle 三轮车under- 在下，不足：underground 地下的，under develop-developing-developed countryunderestimate 低估，underrated（比例）低估，undergo正在进行中的，undergoing a changeuni- 单一的：unite 结合，in unison 一齐地，unify 统一，unit one 第一单元同根词pose，expose，impose，propose，dispose，suppose，opposeCompose：创作，写作composition 作文，composer 作曲家be composed of/consist of: 由…构成=be made up of例句：The class is composed of 40 students.Expose：揭露exposition 世博会，说明文argumentation 议论文Impose：强加impose something on somebody 强加某物于某人Propose：建议proposal(n.) refuse-----refusal，survive-----survival适者生存：Fittest survivesDispose：安排，处理dispose of a problemSuppose：假设Suppose that…应当suppose to 例句：You’re not supposed to use my phone.Oppose：反对opponent对手opposite 反面的Interpose 介入2.abstract，contract，extract，distract，subtract，attractAbstract：(a.)抽象的，(n.)摘要深入浅出：have an in-depth study of something and express it in a simplelanguage.Contract：(n.)合同，协议，(v.)收缩contact with somebody 与某人签协议Extract：提取extract oil 炼油extract tooth 拔牙extract a promise 逼迫做出承诺Distract：分散注意力distraction，distractiveSubtract：减add 加，multiply 乘，divide 除Attract：吸引attractive 有吸引力的press，depression，express，impress，oppress，suppressCompress：压缩comrade 同事，common 共同的例句：Compress two-week-work into one.compress lips 咬嘴唇Depress：情绪沮丧，萧条Great Depression 大萧条，depressed 心情不好Express：(v.)表达express your idea，expression 表情very expressive 具有积极表现力的(a.)快递的EMS express mail service，express way/trainImpress：是记住impressive 印象深的，impression 印象Leave/give a deep impression on somebody 对某人留下深刻印象Oppress：压迫，镇压哪里有压迫，哪里就有抵抗：Where there is oppression, there is resistance.Suppress：忍住，禁止suppress one’s smile/bleeding/the truth4.preserve，conserve，reserve，deserve，observepreserve：储存，储存preserve the fresh fruit in the refrigerator.Conserve：(v.)储存，保护conserve our national heritage/energy/fuelconservative (a.) 保守的Reserve：保留，保护reservation，reserved 保留的(n.)保护区natural reserveDeserve：该得的，活该的you deserve itObserve：观察，庆祝，遵守observe a holiday=celebrateobserve a law5.exhibit，inhibit，prohibitExhibit：展示exhibition 展览=show=displayInhibit：自我约束inhibit desire 禁欲inhibit somebody from doing something 禁止某人做某事Prohibit：禁止forbid by authority 官方规定禁止的Smoking is prohibited. Prohibited articles 违禁物品形近词1.attribute，contribute，distributeAttribute：归结于attribute…to 把…归结于Edison attributed his success to 1% of inspiration and 99% of perspiration.Contribute：奉献make contribution to/contribute to 对…做奉献有助于the pills will contribute to your illness.Distribute：分发，分布例句1.The teacher distributes the materials.2. The company has many offices distributing in different parts of our country. 表格内词汇后缀-able，-ible 有能力的：uncontrollable 不可操纵的，controllability 可控性Make a sensible decision=make a wise decision 明智的决定Weather in Shanghai is changeable.Girls are more changeable.Knowledgeable person 有学识的人Inflammable 可燃的、易燃的，inflammability 可燃性、易燃性Flame (n.) 火焰，inflame (v.)点燃Adaptability 习惯力----adaptVariability 多样性，vary 变化，a variety of 各类各样A wide variety of goods 琳琅满目的商品Conceivable 可想象----conceive (v.)想象Deceive 欺骗，deceptive 欺骗的appearance can be deceptiveWe can’t judge a person by appearance 不能够貌取人Responsible 负责的，reliable 可靠的Imaginable 可想象的，imaginative 富有想象力的imaginary 想象的=not realrespective 分别的write your name on the paper respectivelyrespectable 可敬的，respectful 满怀敬意的stressful，peaceful，beautiful，colorful，wonderful-age 表状态，性质：wreck tragedy 空难Tonnage 吨位，mileage 英里数，percentage 百分比Advantage 优势，disadvantage 劣势Take advantage of 利用，have advantage over…比…有优势Make good use of time/energy 利用时间/能源Patron (n.)赞助人(v.)赞助，patronage 赞助的行为Sponsor 发起人Drain 排水，drainageMarriage 婚姻-ant，-ent 人，物：participate in sth. 参加，participant 参与Apply for a job 求职，applicant 求职者Correspond 联络，correspondent 联络员，correspond withContacts 联系人President 总统，preside 主持Student 学生，resident 居民Tyrant 暴君，tyranny 暴政Aspirant 有追求的人，aspire 有抱负，inspire 鼓舞-arian 派别，主义的人：utilize 利用，vegetarian 素食主义者-dom 性质，状态，行为：cosmodom 太空站，filmdom 影视界，newspaperdom 新闻界Wisdom 智慧----wise-ee，er，or，ess：employer----employee，trainer----trainee，interviewer----intervieweeexaminer----examineereturnee 归国人员，appointee 被任命，absentee 缺席者teacher，learner，visitor，inspector 视察者，teenager，singeractor----actress，waiter ----waitress，lioness 母狮，hostess 女主人goddess 女神-hood 身份，性质：childhood 童年，boyhood 少年时期，girlhood 少女时期Studenthood 学生时代，youthhood 青春期，likelihood可能性-ify 使…化：classify 分类，clarify 澄清，specify 具体化-ish 似…的：womanish=sissy 娘娘腔，womanlike 女人般的（细心）childish=naïve=too simple 幼稚的，childlike 孩子般的（无邪）foolish，snobbish 势力的，selfish 自私的，selfishless 无私的jobless，tireless 不知累的，homeless 无家可归的-ism 主义，学说：Marxism 马克思主义，socialism 社会主义，capitalism 资本主义feudalism 封建主义，imperialism 帝国主义，fatalism 宿命论optimism 乐观主义，pessimism 悲观主义，extremism 极端主义structurism 结构主义，terrorism 恐怖主义，idealism 唯心主义materialism 唯物主义，postmodernism 后现代主义communism 共产主义-ize，ise，yze …化：memorize 经历，Advertise 做广告，criticize 批判industrialize 工业化，popularize 使流行、推广，exercise-like 有…性质的：steellike 钢铁般的-ogy 学科：geology 地质学，geography 地理-ous，eous，ious：courageous 勇敢地，simultaneous 同时的Cautious 小心的，be cautious of 小心Poisonous 有毒的，humorous 幽默的，zealous热情的Marvelous=amazing-wonderful-ward 方向：inward 内向的，up/down ward 向上/下Forward/backward 向前/后形近义近词法1)分属于两个不一致词根的词Complaint，compliantComplaint----complain，compliant----complyVenerable，vulnerableRespectable 可尊敬的Achilles’ Hell：vulnerable pointEffect，affect，effortHave effect on 对…产生影响Insistently，consistentlyInsist on：坚持（观点，办法），persist in：（做事）坚持She insists on going home alone. He persists in learning English every day.Insistently 坚持的Consistently 一致的be consistent with 前后一致言行不一致：what he does is inconsistent with what he says.Stick to：goal，principle，hold on：坚持着，别挂电话，adhere to：policy，guide line Perseverance 毅力Retain，remain，-tain (p15.4)Attain a goal实现目标，entertain the guest 招待、款待，entertainmentContain：包含、容纳this classroom contain 100 studentsContainer 容器Detain：扣留the flat tire detained him on his way home 拖延The police detain the man for further inquire 进一步审问Detain by business 有事要做，因此耽搁Obtain：获得acquire/obtain knowledgeRetain：保留、留住we retain=reserve the rights to take further actionsThe boss took all kinds of measures to retain the talents.Sustain：支撑，持续sustainable development 可持续进展sustained economic growth 经济持续进展Maintain：保持、维修Release，relieveRelief 宽恕Scare，scarceScared 使惊吓的，scary 吓人的，sacred 神圣的，sacrifice 牺牲Serve，severServer 服务器，severe 严厉的、严重的he was severely injured in the accident2)由同源动词或者名词派生出来，其意义分别不一致Affection：affection，affectationAffectionate 关切的，affectionately 关切地Consider：considerable，considerateConsiderable=amount 很多的，considerate=thoughtful 体谅的Continue：continuous，continualContinuous：不间断的continual 间断的记：长的不断短的断Continually cry，continuous blood supplyCredit：credible，credulousIncredible 不可信的，incredulous 不可轻信的We were all incredulous when he told the incredible story.Deceive：deceitful，deceptiveA deceitful child 不诚实的孩子Appearance can be deceptiveDiffer：DifferentiateA differ from B，differentiate these two words 区别这两个词This word differs from that one, so you must differentiate them.Sense：sensitive，sensibleMake a sensible decisionSensitive to 对…敏感的+ music/language/lightSentimental 多愁善感的，sensational 有轰动效应的，sensational news形近词2．assure，ensure，reassure，insure，secureAssure：保证，assure sb. of sth. 向某人保证某事She assured the leaders of her loyalty. 表忠心Ensure：确保，ensure success/securityThey’re made a lot of efforts that the work is on time.Ensure sth./thatReassure：打消疑虑What she said reassured me. I’m reassured by what she said.Insure：上保险，insurance 保险He insures his property against fire. Health insurance 寿险He had himself insured. 给自己买保险Secure：安全，security/safe guard 保安，body guard 保镖3. confine，confirm，conform，confrontConfine：限制≈limitLimit your consumption/expenseConfine space，the bird is confined in the cage，confine your speech to 10 mins.Confine the fire within/to a small/certain area.Confine your attention to your own business 专注于自己的事Confirm：确认reconfirm，double confirm 反复确认a confirmed rumor 一个被证实的谣言，(P11.3) conform，inform，reform，perform，transform，uniformConform：a.保持一致，A is conformed to/with B，conform A with Bconform the copy with/to the originaloriginal：原产地，原创的b.遵守，conform to the customs 海关，when in Rome, do as Romans do.Inform：通知，information 消息，inform sb. of sth./that…Assure sb. of sth，remind sb. of sth.The picture reminds me the sweet memory.Convince sb. of sth. 说服Reform：改革reform old habits，old habits die hard 江山易改，本性难移Perform：表演，表现perform well执行the police didn’t perform his duty…Execute a task/person，implement a plan/policySb. been executed 执行死刑CEO：chief executive officerTransform：transformers 变形金刚Uniform：统一的，制服Confront：be confronted with=be face with 面临遭遇：She’s confronted with/by two men who asked her for money.相对：My house confronts hers.4. assume，presumed，resume，consumeAssume：想象，承担I assumed that you have heard it. I assumed that…我以为He independently assumed the costs of operating the company.Assume a obligation 承担责任to take care of his aunt.Presume：假设，假定From the way they talked, I presumed that they’re married.Let’s presume that what he said is true.A man is presumed to be innocent before he’s proved guilty.Resume：恢复，简历Resume negotiation 恢复谈判，简历：CV curriculum VitaeConsume：消费，consumer 消费者CPI：consumer price indexTime consuming 耗时的，energy consuming 费力的妒火中烧：She’s consumed by his jealousy or anger.5. bold，fold，mold，scold，withhold，thresholdBold：大胆的，make a bold decision，a bold warrior 武士Fold：折叠，folder 文件夹fold blanket/letterMold/mould 模具，casting mold，pudding mold，mold candle/stature/charactersScold：责备，scold for being lazyWithhold：克制，阻止，制止，忍住，The dam is too weak to withhold the rising water.I withhold my support. Withdraw 撤退Threshold：门槛，at the threshold of sth. 在…的开始6. diffuse，discount，dismay，disruptDiffuse：传播，to diffuse a rumor/knowledge/learningDiscount：打折80% discount 打2折In discount打折买的，at discount 以多少折扣买的Dismay：沮丧in dismay 灰心的He listen to the teacher in surprise. 吃惊的听He looked at me in dismay. 沮丧的看She stared at the intruder in dismay 惊恐的看I was dismayed at the class because the teacher kept talking rubbish=the class dismayed me ……Disrupt：使中断（P11.4）Bankrupt，corrupt，disrupt，erupt，InterruptBankrupt：破产，破产的人，I’m a bankrupt. I’m Bankrupt.The pessimistic ways bankrupt his company.Corrupt：腐败，corruptionThe government is very corrupt. Some bad leaders corrupt our government.Bribe 贿赂，bride 新娘，bridegroom 新郎Disrupt：使中断protesters disrupt president’s speechThe electricity was disrupted. 停电了The tsunami disrupted the communication.Interrupt：= break in 打断Sorry to interrupt 插话Erupt：爆发火山爆发：Volcano erupt近义词1.Coarse，rough，crudeCoarse：粗糙的，coarse sand 粗沙低俗的，coarse joke，manner，languageRough：表面粗糙（surface），反义词：SmoothRough road，skin，edge of book粗略的：a rough estimate 粗略的估计Rough draft 粗稿，rough drawingCrude：未加工的crude oil 原油，crude rubber 天然橡胶，crude idea 不成熟的办法粗俗的crude joke粗暴的crude interference in our country’s domestic affairs. 粗暴干涉我国内政2.Bald，bare，blank，vacantBald：秃的，不长草He is a bald. Bald mountain/lawn直率的言辞Bare：bare feet 光脚，walking bare feetBlank：空的fill the blanks，blank areaVacant：vacant room没人住的房子，vacancy 职位的空缺，空地Empty：空的没东西的Hollow：中空的3.Certificate，diploma，qualification，license，guaranteeCertificate：证书Diploma：文凭Qualification：资格License：执照，许可证drive license 驾驶证，plate 汽车牌照Guarantee：证明，保证guarantee never smoke again4.Allowance，pension，bonusAllowance：津贴，补助Pension：退休金，养老金Bonus：额外的奖金，加分题5.Meeting，conference，convention会议规模依次变大Convention：会议，惯例by international convention 按国际惯例同根词1.Evolve，involve，revolve，solveEvolve：进化，theory of evolution 进化论，evolve from/intoInvolve：包含，involvement，be involved in卷入，I don’t want to be involved in your business.Language learning involves hardworking，corporation and some talents.Revolve：旋转，revolution 革命，cultural revolution 文化大革命Revolving door 旋转门，revolver 左轮手枪，pistol 小手枪，revive 复活Solve：解决，solution 解决办法2.Insist，persist，resist，consist，assistInsist on，persist in，adherer to，Resist：抵抗，irresistible 不可抵抗的Her charm is irresistible.Cannot resist the temptation 抵制不住诱惑Consist：由…构成，作曲，创作be made up of=be composed ofAssist：协助，assistance 助手，shop assistant 售货员3.见前4.见前5.Describe，inscribe，prescribe，subscribe，manuscript，conscribeDescribe：描述Inscribe：题字，记下来，inscription 碑文，题词Prescribe：开处方，命令，prescription 处方Subscribe：服务+ to/for，subscriber 用户Conscribe：招募，conscription 征兵Manuscript：手稿，script 剧本，tape script 听力原稿6.见前形近词1.Cautious，conscious，conscientious，curiousCautious：小心的precaution 预防措施Conscious：意识，be conscious of 意识到=be aware ofConscientious：尽责的，She is an conscientious assistant.Conscience：良心，work by my conscience，conscienceless 没良知的Grateful：感谢的，ungrateful 没良心的Curious：好奇的，curiosity(n.)2.Collaborate，cooperate，coordinateCollaborate：相互扶持，coexist 共存Coordinate：协调（配合）Cooperate：合作3.Derive，deprive，deserve，deceiveDerive：从…中得到的，derive a conclusion from factsThe word holiday is derived from the “holy” and “day”.Deprive：剥夺，deprive sb. of sth. …No one could deprive you of your right to survive.Deserve，deceive 见前4.Domestic，dramatic，dynamicDomestic：家养的，domestic animal 家畜Dramatic：戏剧性的，dramatically change 戏剧性的变化，drama 戏剧Our living condition has dramatically improved.Dynamic：活力的，dynamite 炸药，vigorous 活跃的，energetic 活跃的5.Imitate，intimate，simulate，stimulateImitate：模仿，imitate on 模仿…Intimate：亲密的，intimate friends 亲密的朋友Simulate：模拟、假设Stimulate：刺激，激发，what he said stimulate the emotion使兴奋，stimulant 兴奋剂6.Destiny，dignityDestiny：命运，fate 命运，fatalism 宿命论Be destined to 注定=be doomed to (sth. Bad)Doomsday 世界末日Dignity：尊严近义词1.Flock，swarm，herd，pack，crowdFlock：羊群，鸟群a flock of birds物以类聚：birds of a feather flock togetherSwarm：蜂群a swarm of bees，a swarm of people冲进swarm into the hallHerd：兽群a herd of …Herb 草本Pack：(v.) 打包package 包裹，unpack 拆包Load 装载，unload 卸载，install 安装，uninstall 卸载upload 上传，download 下载a pack of airplanes 一组飞机crowd：a crowd of people 一群人，it’s very crowded 拥挤2.Glance，glimpse，glare，peep，peerGlance：瞥一眼扫视Glimpse：瞥一眼无意中看见Peep：偷窥，peeper 偷窥狂Peer：认真看，凝视，peer up at the clear sky 凝视天空Peer through a crack in the door 从门缝眯眼看Peer into the shop windowGlare at 怒视，stare at 盯着看，gaze at 深情的凝视，glance at 瞥一眼It’s impolite to stare at others.3.Fragile，delicate，feeble，vulnerableFragile：易碎的，脆弱的fragile emotion，a fragile cup，she looks fragile 憔悴Delicate：微妙的，易碎的a delicate instrument 易碎的仪器delicate relationship 微妙的关系Feeble：单薄的身体虚弱，弱不禁风a feeble old manHis pulse is very feeble 脉搏弱Feeble personality 软弱的个性，feeble excuse 没有说服力的借口Feeble resistance 无力的抵抗Vulnerable：易受攻击的，vulnerable point/spot 弱点Ankle is his vulnerable point4.Despise，scorn，discriminate，defyDespise：鄙视，看不上，despise coward 鄙视懦夫Scorn：（厌恶）藐视，I scorn his sentimentality.Discriminate：歧视racial/sexual discrimination 种族，性别歧视Sense of superiority 优越感Defy：藐视，defy authority 藐视权威5.Drawback，handicap，defect，blunderDrawback：缺点，缺陷，不足之处，set back 挫折Handicap：障碍（功能），cultural handicaps 文化障碍，academic handicap 学习障碍Language handicap 语言障碍，physical handicap 身体障碍，mental handicapDisable 残疾的Defect：不足，瑕疵=flaw flawless 完美无瑕的There are some flaws in our educational system.Image defect 图像瑕疵Blunder：犯大错，跌跌撞撞blunder into the room同根词1.Distinguish，extinguish，distinct，extinct，instinctDistinguish：区别distinguish A from B，our distinguished guest 贵客Extinguish：灭火fire extinguisher 灭火器Extinct：(v.) 灭绝的in danger of extinctionInstinct：直觉，本能，tell from my instinct 根据我的直觉=my instinct tell me that…Distinct：明显的，清晰地，特殊的，显赫的，截然不一致的There’s a distinct possibility that he has fallen asleep.Distinct honor 名声显赫To be the CEO of that company is a distinct honor.On two occasions, he did the same thing in two distinct ways.2.Avert，convert，divert，invertAvert：躲开，转移to avert an accident 避免一场事故，avert one’s eyes 移开目光Aversion 厌恶，aversion of the outside world 对外部世界的厌烦Convert：转变，换算convert feet into meters，water convert into iceDivert：转移注意力使…高兴divert one’s attention from/to sth.Invert：使颠倒inversion 颠倒3.Claim，acclaim，exclaim，reclaim，proclaimClaim：宣称，索赔，索取He claim that he’s the best teacher in that field宣布对…有主权we claim that islandThe earthquake claimed thousands of lives.Acclaim：向…欢呼，拥护，赞赏The heroes are highly acclaimed. 高度赞赏The people acclaimed the heroes who had won their great honor for their country.Exclaim：喊叫exclaim in pain/delight/anger，the boy exclaimed that he’s starving.Exclaim against 指责Reclaim：使改正，改造，开拓reclaim criminals 改造罪犯，reclaim a land 开拓土地Reclaim a person from the life of vice 改邪归正Proclaim：宣告，宣布proclaim/declare a war 宣战Declaration of Independence 独立宣言4.见前5.Concede，precede，recedeConcede：让步concession 让步concession speechconcede a point in an argument 承认别人的观点是对的reluctant，unwillingness 不情愿的Precede：排在前面的A precedes B in the alphabet147 countries precede us in per capita income.Recede：衰退economic recession 经济衰退depression6.Incur，occur，recurRise，raise，arise，arouseRise：抬起，升起sun rise/setRaise：抚养arise a childArise：出现new problems arise/come up before the old ones are solvedArouse：激起，引起，唤起Incur=arise，incur sb’s displeasureOccur：发生=happen，it occurred me that…突然想到…Recur：回想，复发this kind of illness is very likely to recur.If this kind of cheating recurs, you’ll be fired.形近词1.dense，tense，intense，intensive，extensiveDense：密集的，dense population/forest，the road is dense with traffic=heavy=traffic jamIt rains heavily 雨下的大Tense：紧张，he’s a tense/nervous person，tense nerve 神经紧张Psychiatric problem 神经病，psychiatrist 精神病医生Mental problem 心理问题=psychological problemA tense game 紧张的比赛时态：past tense 过去式Intense：猛烈的，intense/fierce competition 猛烈的竞争Intense interest 非常感兴趣，I can’t bear/stand the intense light 强光Intensive：密集的、深入的、透彻的，intensive training 密集训练intensive reading 精读，extensive reading 泛读，a 4-day intensive courseintensive discussion 深入讨论，in-depth study 深入研究have an in-depth study about/on sth.Extensive：广泛的、全面的，after extensive hearings and study, we want to …知识面广：Extensive knowledgeAn extensive report on sth. 全面报道，extensive repair 大修2.adapt，adjust，adoptAdapt：习惯、改编：adapt yourself in a new environmentI can’t adapt myself to the new life style of that place. Hairstyle 发型Adapt a novel to the screen/into a movie.Adapt a play from English to Chinese.Adjust：调整：adjust seat/the height of a seatAdjust expenses to income 量入为出Adjust watch/an error/a clothAdopt：领养、采纳、使用，adopt a positive attitude toward/to sth.Adopt a suggestion/idea，adopt a child3.access，assess，assetAccess：进入，have/gain access to sth.The hackers can easily get access to our computers. Computer virus 病毒With this ID card, you can have free access to the school library.Identity card 身份证IQ：intelligence quotient，EQ：Emotional quotientAssess：评估，The house is assessed at one million.To assess the loses/damage after a earthquake/accident.Assessment (n.) 评估Asset：资产，He invested all his assets into the gold/real estate.National asset 国宝，knowledge/wisdom is my asset4.outbreak，output，outcome，outsetOutbreak：breakout 爆发，the outbreak of the warOutput：put out 产量，输出output of informationOutcome：come out 结果Outset：set out 开始出发titude，longitude，gratitude，altitude，magnitudeLatitude：纬度Longitude：经度Gratitude：感激，be grateful to sb. for sth.，ungrateful 没良心Altitude：海拔Magnitude：巨大的、庞大的，magnify 放大，夸大近义词1.mark，sign，symbol，label，stain，signal，spotMark：标记、标志，the launching of that manmade satellite mark the new area of China.做记号Sign：签名、标记、路标、标志、签字，sign of forgivenessSymbol：象征，the white color is the symbol of purity.Label：标签，Don’t label yourself a bad person。

对托达罗人口流动模型的争论――帕累托改进-胡景北

上海财经大学研究生部2001－2002学年第一学期“发展经济学”硕士课程论文指导老师：胡景北/助教：李静霞对托达罗人口流动模型的争论――帕累托改进吴辉（000042）目录1.导论 12.托达罗模型简介 13. Javier Ortega的模型的介绍 2 3.1 模型的环境 2 3.2 工人的就业收入和失业收入 3 3.3 公司的最优决定和信息假说 3 3.4 工资决定和失业收入 43.5 移民决定和匹配概率 54. 均衡 6 4.1 无移民均衡 6 4.2 移民均衡 6 4.2.1 部分移民均衡 6 4.2.2 完全移民均衡74.3 单一均衡和多重均衡75.多重均衡的福利分析8 5.1 均衡的帕累托改进8 5.1.1 无移民均衡和部分移民均衡7 5.1.2 完全移民均衡和部分移民均衡85.2 移民资助86.Javier Ortega模型的特点与缺陷97.结论98.参考文献10 图1多重均衡8对托达罗人口流动模型的争论――帕累托改进1、导论美国经济学家刘易斯1954年在英国《曼彻斯特学报》上发表《劳动力无限供给条件下的经济发展》一文中，首次提出了一个二元经济结构中的人口流动模型。

刘易斯认为，不同的劳动边际收益率引致源源不断的劳动力从农村农业部门向城市工业部门流动，而城市工业部门从高劳动生产率和流入劳动力的低工资支付中获得巨额的超额利润，不断地扩大工业部门以吸收农业部门的剩余劳动力，直到吸收完毕，两部门的劳动生产率相同，一国的工业化过程也告完成。

1然而，发展中国家的实际情况却是城市失业问题越来越严重的同时，人口从农村注入城市的速度没有放慢反而持续增长，工业部门未能像刘易斯想象的那样吸收所有的流入人口。

20世纪60年代末和70年代初，托达罗建立了描述城市存在失业问题下的动态人口流动均衡模型。

他认为，农村劳动力向城市转移取决于城乡收入的差距和在城市里获得较高收入的概率（即就业概率）。

就业概率与现代部门的就业创造率成正比关系。