随机型细胞神经网络的稳定性

借助于李雅谱洛夫理论、矩阵分析方法和It?公式,结合不等式分析技巧,研究了随机细胞神经网络系统的均方指数稳定性,给出了系统的解的二阶矩Liapunov指数估计式和均方指数稳定的充分条件。     

基于定位辅助按需拓扑维护的超宽带自组网路由算法

提出了一种基于定位辅助按需拓扑维护的超宽带自组网路由算法,该算法利用超宽带技术精确定位信息所获得的网络拓扑信息和路由信息进行分组转发,路由维护阶段在定位信息辅助下采用按需方式进行断链路由的修复和拓扑维护,通过基于位置信息的按需路由发现和限制路由查找范围,以及定位信息和网络拓扑信息的及时更新,在降低协议开销的同时保证了算法的有效性。仿真表明,该算法在分组丢失率、平均端到端时延和路由附加开销等方面具有良好性能,其优良的分布式控制特征能适应超宽带自组网的动态环境。     

First Housing Moves in Spain: An Analysis of Leaving Home and First Housing Acquisition

This paper examines young people's firsthousing acquisition in Spain. The majority ofyoung Spanish people leave home to buy aproperty and to live with a partner and/orchild. We examine the extent to which thistransition dominates first housing moves duringthe 1980s and consider the ways in which widerfamily networks facilitate leaving home to buya property. We then examine characteristicsassociated with deviation from this normativeroute. We find alternative transitions,particularly moving into rented accommodation,are more common among young people from moreadvantaged backgrounds, and those living innorthern Spain.     

‘The Effect of Social Networks at Origin Communities on Migrant Remittances: Evidence from Nang Rong District’

The literature on the effect of social networks on migrant remittances has ignored the influence of a household’s embeddedness in social networks in the origin community. This paper, using multilevel statistical modeling and social survey data from Nang Rong district in Thailand, examines how remittances between migrants and origin households are associated with social networks. Social network measures come from data on sibling and rice harvest network ties. Rice harvesting is very important to the rural economy, and households participating in the network are privy to vital sources of news and information that spread through the network. Results support the notion that migrants remit less to households that are isolates in the rice-harvesting network, which is theorized to be associated with access to information about employment options.     

Identifying regular blocks in valued networks: A heuristic applied to the St. Marks carbon flow data,and international trade in cereal products

While the concept of regular equivalence is equally applicable to dichotomous as well as valued networks, the identification of regular blocks in regular blockmodels is somewhat problematic when dealing with valued networks. Applying the standard procedure for identifying ties in such blockmodels, a procedure perhaps most suited for dichotomous networks, does tend to generate block images and reduced graphs that differ from intuitive notions of such structures.     

关于移动自组网自安全证书管理模型

对目前建立移动自组网信任模型的各种主流方案进行了分类和评述,重点阐述了自安全证书管理模型各种方案的优缺点,并指出了今后的研究方向.     

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.     

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.