我国建立慈善认定制度的必要性

慈善认定制度的目的是由特定机关对某一组织体的慈善性给予确认并赋予其一定法律地位的制度。慈善认定制度是慈善法的核心组成部分,该制度有助于区分慈善组织与企业和一般的非营利组织、促进公益慈善行业的形成并对其实行统一管理、扩大慈善免税资格和公募资格的范围、推动慈善信托的实施。慈善认定制度对慈善法的制定以及我国慈善事业的发展有着重要意义。     

基于小样本的安全第一投资组合优化模型

为了解决小样本情况下安全第一投资组合选择问题,将结构风险最小化原则引入投资组合选择过程中。根据结构风险最小化原则的直接实现,构建了含有范数约束的安全第一投资组合优化模型,并研究了模型参数的选取方法。实验结果验证了本模型的有效性。     

《论语》不同语篇类型的结构特征研究

学界普遍认同《论语》是孔子及其弟子的言论集.从现有文献上看,有关《论语》的语篇类型及其结构特征问题的系统研究不多.本文在系统功能语言学框架下探讨《论语》不同语篇类型及其结构特征.本文认为,《论语》的语篇类型可归纳为“引述”、“对话”和“描述”三大类,另外还有少数的“叙述”;不同语篇类型具有不同的结构特征.本文的研究结果可为我们后续进行《论语》各种语篇类型的汉英对比研究提供可借鉴的评估参数.     

基于GIS铁路网络运营管理和分析系统

随着地理信息系统发展,海量数据的读取和存储得以空间化。以地理信息系统为平台,以铁路运营网络与空间地理信息为基础,实现高效的铁路运营管理决策系统。以2013年4月19日的铁路运营图为基础,共采集2 614个普通车站,161个高铁车站和58个综合车站的空间信息,3 065条铁路网络物理空间信息,83 903条普通铁路运营网络和4 085条高速铁路运营网络空间信息,构建铁路网络GIS平台,并以SQL Server2005进行数据管理,应用Visual C#和SuperMap Objects进行系统开发,实现数据的存储、读取、更新及计算等功能。     

多孔介质内流体相态特征超声波探测研究进展

目前有关烃类流体相态的研究已较为成熟,能为油气田的高效开发提供相应的理论指导和技术支持。但研究成果大多忽略多孔介质对流体相态的影响。为此,在充分考虑流体与多孔介质相互作用的基础上,开展多孔介质内流体相态特征的研究十分必要。随着研究的不断深入,超声波探测技术不受压力温度限制、不改变流体成分、检测成本低、对人体无害等优点逐渐得到重视,并在多孔介质内相态研究领域逐渐得到了应用。在参考国内外相关文献资料的基础上,简述了超声波的探测机理,重点综述了超声波探测在多孔介质内天然气水合物、凝析油气相态特征研究的应用进展,并指出了目前存在的主要问题及今后的研究重点。     

Gender and Children's Housework Time in China: Examining Behavior Modeling in Context

Differentiated gender roles in adulthood are rooted in one's gender role socialization. In order to understand the persistence of gender inequalities in the domestic sphere, we need to examine the gendered patterns of children's housework time. Although researchers have identified behavior modeling as a major mechanism of gender role reproduction and characterized gender socialization as a contextually embedded process, few have investigated contextual variation in behavior modeling, particularly in non‐Western developing countries. Analyzing data from the China Family Panel Studies 2010, the author examined the differences in behavior modeling between boys and girls age 10–15 from 2‐parent families (N = 1,903) in rural and urban China. The results revealed distinctive gendered interplays in the way parental housework and employment behavior helps shape children's housework time. This analysis is a crucial illustration of how the distinctive sociocultural contexts of rural and urban China moderate the effects of housework‐behavior modeling on intergenerational gender role socialization.     

ON THE INTERNAL REVENUE SERVICE'S SERVICE AND ENFORCEMENT

Built on the seminal work of Graetz et al. (1986), this study considers a model of tax compliance in which the Internal Revenue Service (IRS) services as well as audits taxpayers as in the real world. Taxpayers are uncertain about whether their incomes are eligible for deduction/exemption, and may seek advice from the taxpayer service provided by the IRS to mitigate the uncertainty. We show: (1) while the society's preferred quality of taxpayer service is likely to be sufficiently high, the IRS's preferred quality of taxpayer service is only at an intermediate level; and (2) the conflict between the society and the IRS tends to be intensified as the cost of tax audits becomes lower. Evidence in support of our results is addressed. We also extend our model to addressing the issue of corruption in tax administration and to considering a dynamic model. (JEL D82, H26, K42)     

论农民工“回流”现象的原因及其消极影响

20世纪80年代开始,改革开放的不断深入和联产承包责任制在农村的实施,使农民获得了自由流动的机会,大量的农村劳动力移向城市。长期的异地工作,在多种因素的作用之下,很多的农民工无法融入当地城市,他们在积累了一定的资金、知识、管理经验后选择回流,这在一定程度上对农村和城市的发展具有深刻的现实意义,但其负面的影响也是巨大的。农村劳动力的"回流"顺应了社会和时代发展的需要,是加速农村城镇化建设的必由之路。因此应该制定一系列有针对性、可操作性的政策,鼓励农村劳动力积极、合理和有序地回流,以减少或降低其产生的消极影响。     

Nonparametric multiple expectile regression via ER-Boost

Expectile regression [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847] is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. Expectile regression at 50% level is the classical conditional mean regression. In many real applications having multiple expectiles at different levels provides a more complete picture of the conditional distribution of the response variable. Multiple linear expectile regression model has been well studied [Newey W, Powell J. Asymmetric least squares estimation and testing, Econometrica. 1987;55:819–847; Efron B. Regression percentiles using asymmetric squared error loss, Stat Sin. 1991;1(93):125.], but it can be too restrictive for many real applications. In this paper, we derive a regression tree-based gradient boosting estimator for nonparametric multiple expectile regression. The new estimator, referred to as ER-Boost, is implemented in an R package erboost publicly available at http://cran.r-project.org/web/packages/erboost/index.html. We use two homoscedastic/heteroscedastic random-function-generator models in simulation to show the high predictive accuracy of ER-Boost. As an application, we apply ER-Boost to analyse North Carolina County crime data. From the nonparametric expectile regression analysis of this dataset, we draw several interesting conclusions that are consistent with the previous study using the economic model of crime. This real data example also provides a good demonstration of some nice features of ER-Boost, such as its ability to handle different types of covariates and its model interpretation tools.     

Automatic clustering algorithm for fuzzy data

Coppi et al. [7 R. Coppi, P. D'Urso, and P. Giordani, Fuzzy and possibilistic clustering for fuzzy data, Comput. Stat. Data Anal. 56 (2012), pp. 915–927. doi: 10.1016/j.csda.2010.09.013[Crossref], [Web of Science ®] , [Google Scholar]] applied Yang and Wu's [20 M.-S. Yang and K.-L. Wu, Unsupervised possibilistic clustering, Pattern Recognit. 30 (2006), pp. 5–21. doi: 10.1016/j.patcog.2005.07.005[Crossref], [Web of Science ®] , [Google Scholar]] idea to propose a possibilistic k-means (PkM) clustering algorithm for LR-type fuzzy numbers. The memberships in the objective function of PkM no longer need to satisfy the constraint in fuzzy k-means that of a data point across classes sum to one. However, the clustering performance of PkM depends on the initializations and weighting exponent. In this paper, we propose a robust clustering method based on a self-updating procedure. The proposed algorithm not only solves the initialization problems but also obtains a good clustering result. Several numerical examples also demonstrate the effectiveness and accuracy of the proposed clustering method, especially the robustness to initial values and noise. Finally, three real fuzzy data sets are used to illustrate the superiority of this proposed algorithm.