基于FLAC3D的黄土基底排土场稳定分析

我国大型露天煤矿主要分布在西北部的黄土高原地区,以黄土为基底的排土场形成后,黄土基底的工程性质将发生改变并形成演化弱层,演化弱层对排土场边坡稳定性起到控制作用,由于弱层的存在,边坡容易形成"坐落-滑移式"破坏。本次分析是基于强度折减理论,应用FLAC3D对排土场边坡变形进行数值模拟,展现出排土场边坡变形机理。依据数值模拟结果,获得排土场边坡的最危险滑面和多处潜在滑动面。为排土场边坡失稳的预测及防治工作提供科学依据。     

一种无线传感器网络目标的分布式最优覆盖算法

为了最大化目标覆盖的生命期,针对无线传感器网络的分布式自组织的特点,提出一种简单高效的分布式最优覆盖算法,引入关键目标的概念,设计基于目标覆盖能量的能量效用函数,在关键目标优先和能效优先的原则的基础上设置节点等待时间,建立节点等待时间自适应调整的机制.实验表明该算法网络生命期延长20%左右.算法复杂度低,效率高,可扩展性好.     

P2P网络中Free-rider的平衡机制

针对P2P网络中出现的free-rider现象,提出一种控制点位于服务提供节点上的平衡机制。该机制充分考虑P2P网络拓扑结构不一致的问题,并允许一定数量的free-rider节点存在。实验证明,该机制既能有效利用网络资源,提高交易的成功率,又能对严重的free-rider行为进行抑制。     

基于信任机制的网格资源调度算法

针对目前网格资源调度中忽视信任机制的缺陷,在行为信任模型和能力信任评估的基础上,提出一种信任驱动的资源调度算法TDS。该算法同时兼顾了性能QoS和信任QoS。仿真实验结果表明,TDS算法与传统的基于性能QoS的算法和基于信任QoS的算法相比,在最小完工时间和服务请求提交成功率方面具有较好的性能。     

基于信任机制与遗传算法的渲染网格作业调度研究

信任关系是网格作业调度中一个很重要的因素,也是影响网格计算有效性和性能的关键技术之一。将信任机制引入到渲染网格作业调度中,建立渲染网格环境中基于信任机制的作业调度模型,在调度策略上对基本遗传算法进行了改进,提出了基于信任机制的遗传算法。实验结果表明,该算法可以提高任务完成率和平均信任效益,是适用于渲染网格的一种有效作业调度方法。     

基于WSDL-S的轻量级语义Web服务匹配模型

针对现有大多数基于语义的Web服务发现方法实施难度大,实用性不强的问题,提出了一种基于WSDL-S描述的轻量级语义Web服务匹配模型.该模型最大的特点是只需用户输入简单的服务查询字符串就能自动实现Web服务的匹配和调用.服务匹配经过领域本体匹配和基于WordNet词典的同义词匹配两个步骤,特别是领域本体匹配过程中的学习模块能有效提高系统性能.实验结果表明,该系统性能提高在20%到82%之间.     

基于综合度量的Ad Hoc网络跨层路由协议

Ad Hoc网络路由协议普遍采用最短路径准则,无法有效支持多速率传输并容易导致网络局部拥塞。针对该问题,利用协议栈跨层设计的思想,提出一种基于综合度量的路由协议SMRP。该协议综合考虑链路速率值、MAC竞争度和网络队列长度多种因素选择路径,能有效利用多速率传输能力,避开拥塞节点。仿真实验结果证明,该协议能从网络吞吐量、分组时延等方面有效提高网络性能。     

Borland C++Builder响应Windows消息机制的分析

Windows是一套以消息驱动的操作系统。C++Builder的可视化组件库(VCL)提供了对大多数Windows消息的处理机制,程序开发人员在一般情况下无需理会Windows消息的细节。然而作为一个windows程序开发者,实有必要掌握C++Builder中如何进行Windows消息的捕获。该文阐述了Windows消息的概念,对C++Builder的消息映射机制进行了系统分析,同时结合一个实际范例,仔细说明了其中之技巧。     

计算机网络安全现状及对策

网络已成为获取各种信息的重要手段,人们在享受着网络带来快乐的同时,也面临着越来越严重和复杂的网络安全威胁和难以规避的风险。该文从网络安全定义入手,全面分析了影响网络安全的因素,从而提出计算机网络安全防范相关措施,以确保网络安全有效运行。     

The moderating effects of psychological reactance and product involvement on online shopping recommendation mechanisms based on a causal map

The use of the Internet in the daily activities of individuals and firms has become entrenched, and online shopping has become commonplace. However, debates about how online shopping recommendation mechanisms (OREMs) should be designed have not been completely resolved. The challenge with traditional online shopping recommendation mechanisms (TR-OREMs) is that they focus too much on quantitative factors. Thus, they ignore causal interrelationships with qualitative factors that are believed to significantly impact quantitative factors. Considering only quantitative factors and ignoring qualitative ones likely distorts the final recommendation results. Another problem with TR-OREMs is that they ignore the perceived psychological reactance of consumers against the recommended products. Such consumer reactance may be reduced when the causal interrelationships among all the quantitative and qualitative factors are analyzed and incorporated properly into the OREM. To overcome these problems, we propose a causal map – online shopping recommendation mechanisms (CM-OREMs) based on a causal map. We analyzed possible causal relationships among quantitative and qualitative factors and incorporated them in the recommendation process to reduce consumer reactance against the recommendation results. Furthermore, an elaboration likelihood model (ELM) was used to build hypotheses about how the online shopping behavior of consumers is affected by OREMs based on the proposed causal map. Specifically, the performance of the proposed OREM was empirically analyzed by gathering experiment data from qualified respondents who were asked to refer to the proposed OREM before making purchasing decisions via mobile phones. Statistical results showed that the proposed OREMs could enhance consumer decision satisfaction, decision confidence, and attitude toward the recommended products. It could also positively affect consumer purchasing intentions. The OREM had a greater effect on the high-reactance group of participants than on the low-reactance group as well as on a high-involvement product versus a low-involvement product.