发展用户驻地网与打破本地固话垄断

本文提出了一个发展用户驻地网的思路,着重论述了发展用户驻地网可有效地打破本地固话垄断,兼述了发展驻地网对电信管制的影响及发展驻地网所需解决的问题。     

A non-profit approach to addressing the effects of urbanization in the Lake Pontchartrain Basin, Louisiana

The Lake Pontchartrain Basin in southeast Louisiana is an estuarine watershed encompassing New Orleans, Baton Rouge, and wetlands that are an integral part of Louisiana's unique culture. In recent decades rapid growth and poorly planned development have resulted in a decrease in water quality and habitat loss, especially in St. Tammany Parish on the north shore of Lake Pontchartrain. In 1989, the Lake Pontchartrain Basin Foundation (LPBF), an environmental, non-profit organization, was formed to address and rectify environmental issues in the Basin. LPBF accomplishes its mission through programs addressing water quality, habitat protection, geographic information science, public access, and education.LPBF used the recent, rapid development occurring in St. Tammany Parish as a case study to examine the relationship between land use, water quality, and development in the Basin by investigating habitat change/urbanization, development in flood plains, and water quality. Through the integration of LPBF programs, it was found that urbanization is having a devastating impact on the parish's natural habitats and severely degrading water quality. Using these results, LPBF is educating Basin citizens on the protection of their habitats and water quality to insure that these resources may be available for future generations.     

Bayesian inference for a discretely observed stochastic kinetic model

The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observed by means of discrete-time sampling systems, which are often limited in their completeness. In this paper we explore how to make Bayesian inference for the kinetic rate constants of regulatory networks, using the stochastic kinetic Lotka-Volterra system as a model. This simple model describes behaviour typical of many biochemical networks which exhibit auto-regulatory behaviour. Various MCMC algorithms are described and their performance evaluated in several data-poor scenarios. An algorithm based on an approximating process is shown to be particularly efficient.     

Risk,trust and knowledge networks in farmers’ learning

This study reports on New Zealand dairy farmers’ access to and use of information as mediated through conditions of risk and trust within the context of their interpersonal social networks. We located participants’ reports of their information use within their perceived environments of trust and risk, following Giddens's [1990. The consequences of modernity. Polity Press, Stanford, CA] typology of trust and risk in pre-modernity and modernity. The research participants were constant users of interpersonal and print information from numerous sources, and monitored their incoming data in the light of strategic needs, reflecting their roles as both farming practitioners and business owners. Socio-spatial knowledge networks (SSKNs) combine individuals’ explanatory cognitive models of information acquisition and use with a micro-geographical analysis of their interpersonal networks. The participants showed characteristics of pre-modern, modern and even post-modern society in respect of their use of complex interactional forms, as well as a blending of individualistic and communitarian practices and concerns in their professional and personal lives.     

论电话监听的合理性及其限度

电话监听是技术侦查手段的一种。适当运用电话监听,能够提高对抗犯罪的效能。然而,电话监听具有对公民隐私权、言论自由权等权利造成侵害的很大的危险性。因此,既要认识到其合理性,又要对其适用确定必要的限度,并在运用过程中,保障犯罪嫌疑人、被告人的基本人权和诉讼权利。     

经济责任审计与执政能力建设的若干思考

从经济责任审计的实际出发，联系工作实践，对目前经济责任审计中存在的问题进行了探讨，并提出了相应的对策，试图能解决当前经济责任审计中碰到的一些问题，进一步加强经济责任审计对权力制约和监督的作用，促进领导干部提高执政能力，推动经济社会全面和谐发展．     

基于SRFML-Lift的流程制造产品质量状态监测

对流程制造型企业而言,产品质量状态的监测精度直接影响了企业的生产与运营成本。面对流程工业的多变量监测要求和数据不均衡性,以往研究主要采取局部建模策略或多输出模型,存在特征选择偏差和分类精度不高的问题。对此,本文设计了一种结合SRFML特征选择和Lift学习策略的质量状态监测模型,通过共享不同目标之间的信息以期提升模型的监测效果。首先,根据ReliefF过滤机制,引入重采样赋权思想对工业特征的选择过程进行优化(SRFML);然后,将选择结果作为Lift学习框架的输入,通过类属属性学习方式重塑各待监测特性的特有关联属性;最后采用多个SVM分类器进行训练,得到各目标的质量状态结果。结果表明,本文构建的SRFML-Lift充分学习了原始特征的关键信息,与其他组合策略相比,对质量状态的监测效果更佳,可应用于流程工业的生产管理实践。     

Origins and destinies: Immigration to the United States since World War II

Contemporary immigration to the United States and the formation of new ethnic groups are the complex and unintended social consequences of the expansion of the nation to its post-World War II position of global hegemony. Immigrant communities in the United States today are related to a history of American military, political, economic, and cultural involvement and intervention in the sending countries, especially in Asia and the Caribbean Basin, and to the linkages that are formed in the process that open a variety of legal and illegal migration pathways. The 19.8 million foreign-born persons counted in the 1990 U.S. census formed the largest immigrant population in the world, though in relative terms, only 7.9% of the U.S. population was foreign-born, a lower proportion than earlier in this century. Today's immigrants are extraordinarily diverse, a reflection of polar-opposite types of migrations embedded in very different historical and structural contexts. Also, unlike the expanding economy that absorbed earlier flows from Europe, since the 1970s new immigrants have entered an hourglass economy with reduced opportunities for social mobility, particularly among the less educated, and new waves of refugees have entered a welfare state with expanded opportunities for public assistance. This paper seeks to make sense of the new diversity. A typology of contemporary immigrants is presented, and their patterns of settlement, their distinctive social and economic characteristics compared to major native-born racial-ethnic groups, and their different modes of incorporation in—and consequences for—American society are considered.     

A hybrid ARIMA and support vector machines model in stock price forecasting

Traditionally, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Support vector machines (SVMs), a novel neural network technique, have been successfully applied in solving nonlinear regression estimation problems. Therefore, this investigation proposes a hybrid methodology that exploits the unique strength of the ARIMA model and the SVMs model in forecasting stock prices problems. Real data sets of stock prices were used to examine the forecasting accuracy of the proposed model. The results of computational tests are very promising.     

基于ARTⅡ网络的煤炭资源资产分类研究

本文建立了用于煤炭资源资产分类的ARTⅡ神经网络模型,编制了相应的计算机和软件,并将ARTⅡ模型与模糊分类模型和基于BP网络的分类模型进行了对比分析,实例运行结果表明,用ARTⅡ网络进行分类具有分类稳定、结果可靠等特点。