大都市循环型社会建设的路径——基于"日本经验"的借鉴

可持续发展理念已经成为经济社会发展的主流价值取向,各城市、地区纷纷积极探索符合自身特点的建设路径和模式,特别是近两年受金融危机和全球气候变化影响,发展低碳经济、循环经济成为经济社会转型的重要战略.日本作为全球经济最为发达的国家之一,十分重视资源的循环利用,是循环型社会意识最超前、行动最迅速、体系最健全、发展最富有成效的国家.因此,本文在总结日本建设循环型社会的实践历程、主要做法与突出特点的基础上,以南京为案例提出大都市建设循环型社会的基本思路和实践途径.     

基于改进遗传算法的电力系统无功优化

在简单遗传算法的基础上，针对无功优化的动态、多目标、多约柬以及非线性特点，提出了基于简单遗传算法的改进算法．改进遗传算法中采用了个体适应度函数的线性变换、归一化的选择方法，以及定向变异策略的应用．以Ward＆Hale6节点标准测试系统为例对该算法进行了有效性验证．仿真结果表明，该方法对电力系统的无功优化效果良好．     

辽宁中小企业知识产权战略分析

目前绝大部分中小企业利用知识产权制度的状况存在很多问题，为提高辽宁中小企业的自主创新能力，正确建立知识产权战略，运用实证分析和逻辑推理的方法，分析辽宁中小企业知识产权发展存在的问题及内外部原因，并提出辽宁中小企业制定知识产权战略的若干建议。     

MOSUM monitoring for variance change in nonparametric regression models

ABSTRACT

This article considers the monitoring for variance change in nonparametric regression models. First, the local linear estimator of the regression function is given. A moving square cumulative sum procedure is proposed based on residuals of the estimator. And the asymptotic results of the statistic under the null hypothesis and the alternative hypothesis are obtained. Simulations and Application support our procedure.     

Solution to the hazard function of some two-stage carcinogenesis models when normal cell growth is piecewise linear

A closed form expression is obtained for the hazard function for three stochastic two-stage carcinogenesis models, when the normal cell growth is assumed to be piecewise linear.     

单面与双面信息广告重复效应的实证研究

通过引入企业道德感知变量,并在整合最优唤醒理论、加工流畅模型、归因理论和两因素理论的基础上,深入剖析了单、双面信息广告重复效应的差异。研究结果表明:双面信息广告比单面信息广告加工更困难,但消费者对其广告正确回忆率更高,广告反应也更积极。单面信息广告的重复效应呈现先增后减的倒U趋势,而双面信息广告的重复效应呈单调递增趋势,疲劳效应延迟出现。消费者对企业道德感知的差异在很大程度上决定了单、双面信息广告重复效应的差异。     

大地上的疤痕

村子西边,原来没有任何建筑.十几年前的一个晚春,也许是晚春过后的初夏,台子地靠近村子的这一侧,一座高高的砖窑拔地而起,横空出世.一时间,祖祖辈辈以耕为生、以农为本的生活常态,遭遇到了一波前所未有的冲击,不啻一场地震降临.     

Experiences of Families Inquiring About Donating or Adopting Embryos: Results of an Online Survey

Four hundred thirty-four individuals who had previously inquired with the National Embryo Donation Center (NEDC) or Nightlight Christian Adoptions about donating or adopting embryos responded to an online survey regarding which issues had been raised with them during their inquiry and how well they perceived NEDC and/or Nightlight had answered their questions regarding those issues. In general, respondents perceived that the operational issues surrounding donating or adopting embryos received more attention than the longer-term issues involving family dynamics and relationships. Overall, the inquirers were pleased with the responses to their questions (average >8 on a satisfaction scale of 1–10).     

A tutorial on rank-based coefficient estimation for censored data in small- and large-scale problems

The analysis of survival endpoints subject to right-censoring is an important research area in statistics, particularly among econometricians and biostatisticians. The two most popular semiparametric models are the proportional hazards model and the accelerated failure time (AFT) model. Rank-based estimation in the AFT model is computationally challenging due to optimization of a non-smooth loss function. Previous work has shown that rank-based estimators may be written as solutions to linear programming (LP) problems. However, the size of the LP problem is O(n ²+p) subject to n ² linear constraints, where n denotes sample size and p denotes the dimension of parameters. As n and/or p increases, the feasibility of such solution in practice becomes questionable. Among data mining and statistical learning enthusiasts, there is interest in extending ordinary regression coefficient estimators for low-dimensions into high-dimensional data mining tools through regularization. Applying this recipe to rank-based coefficient estimators leads to formidable optimization problems which may be avoided through smooth approximations to non-smooth functions. We review smooth approximations and quasi-Newton methods for rank-based estimation in AFT models. The computational cost of our method is substantially smaller than the corresponding LP problem and can be applied to small- or large-scale problems similarly. The algorithm described here allows one to couple rank-based estimation for censored data with virtually any regularization and is exemplified through four case studies.