Knowledge search and people with high epistemic curiosity

Porter sites have tried to increase their business potentials by helping users easily engage in diverse online activities such as search, entertainment, online communities, and others. Knowledge search engine is one of the most successful killer applications increasing user loyalty. However, most studies have independently focused on search engine technology such as search keyword algorithm, presentation of information and user characteristics such as cognitive style, search experiences, and skill levels. However, these previous studies lack an integrated understanding of causal links among critical factors predicting user motives and intention involving more situation-specific user characteristics. To fill these gaps in the literature, the present study focuses on whether knowledge characteristics such as specialization, preciseness, and diversity, and system characteristics such as interactivity, responsiveness, and communication richness are important in forming user motives and intention. The present study also identifies a moderating role of epistemic curiosity in the links between motives and intention. Two hundred and fifty-five data from Korean university students confirmed that knowledge and services characteristics such as specialty, preciseness, diversity, communication richness improve user motives, which have a positive impact on intention. Epistemic curiosity has a moderating effect on the links between motives and intention. Theoretical and practical implications are discussed in conclusion.     

Co-evolutionary particle swarm optimization to solve constrained optimization problems

This paper presents a co-evolutionary particle swarm optimization (CPSO) algorithm to solve global nonlinear optimization problems. A new co-evolutionary PSO (CPSO) is constructed. In the algorithm, a deterministic selection strategy is proposed to ensure the diversity of population. Meanwhile, based on the theory of extrapolation, the induction of evolving direction is enhanced by adding a co-evolutionary strategy, in which the particles make full use of the information each other by using gene-adjusting and adaptive focus-varied tuning operator. Infeasible degree selection mechanism is used to handle the constraints. A new selection criterion is adopted as tournament rules to select individuals. Also, the infeasible solution is properly accepted as the feasible solution based on a defined threshold of the infeasible degree. This diversity mechanism is helpful to guide the search direction towards the feasible region. Our approach was tested on six problems commonly used in the literature. The results obtained are repeatedly closer to the true optimum solution than the other techniques.     

Diversity of Agents and Their Interaction

Diversity of agents occurs naturally in epistemic logic, and dynamic logics of information update and belief revision. In this paper we provide a systematic discussion of different sources of diversity, such as introspection ability, powers of observation, memory capacity, and revision policies, and we show how these can be encoded in dynamic epistemic logics allowing for individual variation among agents. Next, we explore the interaction of diverse agents by looking at some concrete scenarios of communication and learning, and we propose a logical methodology to deal with these as well. We conclude with some further questions on the logic of diversity and interaction. This work was supported by the Chinese National Social Science Foundation (Grant Number: 04CZX011) and the Dutch Science Organization NWO.     

The effect of diversity maintenance on prediction in dynamic multi-objective optimization

There are many dynamic multi-objective optimization problems (DMOPs) in real-life engineering applications whose objectives change over time. After an environmental change occurs, prediction strategies are commonly used in dynamic multi-objective optimization algorithms to find the new Pareto optimal set (POS). Being able to make more accurate prediction means the algorithm requires fewer computational resources to make the population approximate to the Pareto optimal front (POF). This paper proposes a hybrid diversity maintenance method to improve prediction accuracy. The method consists of three steps, which are implemented after an environmental change. The first step, based on the moving direction of the center points, uses the prediction to relocate a number of solutions close to the new Pareto front. On the basis of self-defined minimum and maximum points of the POS in this paper, the second step applies the gradual search to produce some well-distributed solutions in the decision space so as to compensate for the inaccuracy of the first step, simultaneously and further enhancing the convergence and diversity of the population. In the third step, some diverse individuals are randomly generated within the region of next probable POS, which prompts the diversity of the population. Eventually the prediction becomes more accurate as the solutions with good convergence and diversity are selected after the non-dominated sort [1] on the combined solutions generated by the three steps. Compared with three other prediction methods on a series of test instances, our method is very competitive in convergence and diversity as well as the speed at which it responds to environmental changes.     

Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front

In evolutionary multi-objective optimization (EMO), the convergence to the Pareto set of a multi-objective optimization problem (MOP) and the diversity of the final approximation of the Pareto front are two important issues. In the existing definitions and analyses of convergence in multi-objective evolutionary algorithms (MOEAs), convergence with probability is easily obtained because diversity is not considered. However, diversity cannot be guaranteed. By combining the convergence with diversity, this paper presents a new definition for the finite representation of a Pareto set, the B-Pareto set, and a convergence metric for MOEAs. Based on a new archive-updating strategy, the convergence of one such MOEA to the B-Pareto sets of MOPs is proved. Numerical results show that the obtained B-Pareto front is uniformly distributed along the Pareto front when, according to the new definition of convergence, the algorithm is convergent.     

无线传感网协作能量感知路由方法*

将多跳分集引入无线传感网中以节能,提出协作能量感知路由方法（CEAR：Cooperative Energy Aware Routing）。协作能量感知路由方法利用多跳分集方法对无线传感网经典的能量感知路由（EAR: Energy Aware Routing）方法加以改进。分析了多跳分集方法中各跳的总能耗,并基于能耗分析结果设计协作能量感知路由方法。仿真结果表明,相比于能量感知路由方法,协作能量感知路由方法有更好的节能性能。     

日本企业公司治理结构的改革——基于近年来多元化改革的分析

一直以来,日本企业所执行的公司治理结构都是"监事会制公司",这种治理结构是基于金融机构和相关企业为股东的股份制结构。但是,由于2002年修改的商法和2006年的公司法的实施,使得企业也可以选择"委员会制公司"这种新的治理结构,而这种结构的特征就是引入了社外董事。为了确保企业经营的完善和高效,日本企业在决策、监管以及经营方面,一直没有拘泥于某一个公司治理结构上,各个公司都根据本公司的特征,进行着多样化的企业制度改革。     

核电站反应堆保护系统防共因故障设计研究

共因故障是进行核电站保护系统设计时需重点关注的风险.为了使保护系统设计能避免共因故障的发生或减轻共因故障的后果,分析了共因故障的产生原因,并对国际法规、标准的相关要求进行了研究,总结出为应对共因故障所应遵循的设计准则.最后,设计了可防止共因故障的典型反应堆保护系统架构.该保护系统架构所采用的纵深防御和多样性设计手段可作为今后核电站保护系统设计的参考.     

相关瑞利衰落信道MRC接收性能分析

通过一种一般性方法研究两种天线设置时相关瑞利衰落信道上MRC接收的BPSK性能，并利用衰落信道上数字信号传输的归一化性能研究结果，分析MRC接收的MQAM的性能，给出平均比特差错率的解析表达式。     

中国电解金属锰行业的发展趋势

以大量数据为基础，阐述了近10年来国内电解金属锰的产能、产量、消耗量及开工率状况。通过对电解金属锰行业内主要企业的投资动向分析，得出规模化发展是行业的必经之路。非主流矿市场份额逐年增加表明国内锰矿石供应正在向多元化发展。在简述产业政策和企业发展成果的基础上，对电解金属锰行业的发展方向进行了展望。