地方官员的晋升激励与外来务工人员的社会保障

通过建立一个多任务下的地方政府官员晋升激励模型,从中央和地方政府委托—代理关系的角度分析了外来务工人员社会保障缺失的原因,结论认为,以GDP为主的绩效考核机制下的晋升激励扭曲了地方政府应当为外来务工人员提供保障的职能,这是外来务工人员社会保障缺失的根本原因。     

Optimal choice of sample fraction in univariate financial tail index estimation

This study introduces a technique to estimate the Pareto distribution of the stock exchange index by using the maximum-likelihood Hill estimator. Recursive procedures based on the goodness-of-fit statistics are used to determine the optimal threshold fraction of extreme values to be included in tail estimation. These procedures are applied to three indices in the Malaysian stock market which included the consideration of a drastic economic event such as the Asian financial crisis. The empirical results evidenced alternating varying behavior of heavy-tailed distributions in the regimes for both upper and lower tails.     

莫尔条纹图像处理和分析的研究

本文描述利用莫尔拓扑波(moire topography)原理和数字图像处理技术研制成的分析系的原理和组成及其在颜面三维形态测量中的应用.     

全球化时代的日本法学教育与发展

法学教育是各国高等教育体系中不可或缺的有机组成部分。日本法学教育与法律职业培训制度改革是日本司法制度改革的重要组成部分,其核心是改革现有的建立在大陆法系基础上的法学教育和法律职业培训制度,引进以美国法学院(law school)模式为标准的法科大学院制度。就日本近年推行的司法制度改革进行分析,重点探讨法学教育改革以及对我国法学教育改革的借鉴与启示。     

高校学生生态型管理模式初探

按照传统的管理模式管理学生,已经在一定程度上抑制了学生活跃的个性和创造性的发展,损害了法律赋予学生的一些权利.构建高校学生生态型管理模式,是解决目前我国高校学生管理中存在问题的有益探索和尝试.     

“水利社会史”探源——兼论以水为中心的山西社会

学界通常将魏夫特的"治水社会"理论和日本学界的"水利共同体"理论作为中国水利社会史的学术源头.但事实上,水利社会史的学术源流至少可以追溯到19世纪末20世纪初的法国人文地理学,随后兴起的法国年鉴学派也受其影响颇深.这一传统持续影响到近年来由蓝克利、魏丕信等法国学者对中国山陕民间水利问题的研究.文章以山西为例,提出了水利社会史研究的内容和体系.     

LTCC小型化Balun设计

介绍了一种基于LTCC(低温共烧陶瓷)技术的小型化balun的设计。设计的Balun采用Marchand balun结构,使用LTCC技术实现多层结构、上下耦合的方式减小balun的体积并拓展带宽。该balun工作在1.7～2.2GHz,体积为2.8mm×3mm×1mm,并且平衡端口输出具有良好的幅值平衡和180°相位差。     

Dynamic Pricing to Minimize Maximum Regret

We consider a dynamic pricing problem that involves selling a given inventory of a single product over a short, two‐period selling season. There is insufficient time to replenish inventory during this season, hence sales are made entirely from inventory. The demand for the product is a stochastic, nonincreasing function of price. We assume interval uncertainty for demand, that is, knowledge of upper and lower bounds but not a probability distribution, with no correlation between the two periods. We minimize the maximum total regret over the two periods that results from the pricing decisions. We consider a dynamic model where the decision maker chooses the price for each period contingent on the remaining inventory at the beginning of the period, and a static model where the decision maker chooses the prices for both periods at the beginning of the first period. Both models can be solved by a polynomial time algorithm that solves systems of linear inequalities. Our computational study demonstrates that the prices generated by both our models are insensitive to errors in estimating the demand intervals. Our dynamic model outperforms our static model and two classical approaches that do not use demand probability distributions, when evaluated by maximum regret, average relative regret, variability, and risk measures. Further, our dynamic model generates a total expected revenue which closely approximates that of a maximum expected revenue approach which requires demand probability distributions.