Statistics for Studies of Human Welfare

Developments in the recent past have substantially increased our ability to measure, compute, and communicate. We take the view that a corresponding improved understanding of processes in the life sciences will come about only through more intensive studies of properties of statistical methods and algorithms and transparent, open source computing environments.     

浅谈网格计算及其应用

介绍了网格计算的概念、特点、起源、研究现状，将网格计算系统与相关技术进行了比较；分析了网格计算的发展趋势。表明了网格计算能使互联网上所有资源实现全面共享与协同工作，使整个因特网整合成一台巨大的超级计算机，为用户提供“即连即用”式的服务。     

分布式并行计算网格系统的设计与实现

基于网格计算技术提出了一种分布式的有限元并行计算网格系统的原型DPFEM的构建方法.探讨了DPFEM的系统设计目标,系统平台,软件配置以及系统运作机制.     

基于云计算与物联网技术的数据挖掘

在科学技术快速发展的今天,信息通信行业已经与我们的日常生活完全融为一体,并且推动了整个社会的发展,所以在科学技术快速进步的过程中,信息通信相关技术也在不断发展,其中,基于云计算的物联网发展越来越受到人们的重视。论文对基于云计算的物联网数据挖掘模式进行分析及探讨,从而为互联网数据挖掘技术发展提供相应的参考。     

云数据中心网络虚拟化技术的实现路径

当今社会,随着科学技术的迅猛发展,信息技术实现了持续快速发展。在大环境的影响下,云计算与网络虚拟化技术应运而生。该技术的产生和发展促使云数据中心网络的变革与发展成为新一轮技术革命的核心内容之一。当前,IT行业对数据中心网络的需求越来越大。基于此,论文论述了云数据中心网络虚拟化技术的实现路径。     

基于交易或事项分类设计所得税会计债务法分析

本文从影响所得即净资产变动(基于经济收益观)的交易或事项出发,分析了时间性差异与暂时性差异的成因,探索利润表债务法与资产负债表债务法的设计思路。在此基础上,提出易于与税法衔接简化的资产负债表债务法——发生额资产负债表债务法。     

基于云重心Shapley值的供应链融资联盟收益分配研究

针对Shapley值法在供应链融资联盟收益分配过程中分配因素考虑不全以及各分配因素随机性和模糊性强的缺点，本文利用云重心法修正Shapley值的收益分配。根据供应链融资联盟收益分配的主要影响因素，在传统的Shapley值法的基础上引入了风险系数、投入成本、努力程度以及信息对称水平4个分配修正因子，弥补了影响因素的缺失，并利用云重心法计算各联盟企业的收益修正值，云重心法能够解决定性指标和定量指标之间的模糊性转换，极大克服了Shapley值法自身的局限性。算例对比结果发现，改进的Shapley值法能更加合理的分配收益，有利于维持供应链融资联盟的稳定。     

云计算相关问题及其在高校中的运用价值

云计算是计算机科学技术与网络互联技术结合创新发展的产物,已经成为引领未来世界信息产业的关键性技术和手段。云计算在教育领域体现出了很大优势,改进学习方式,改造学习环境,同时可以降低IT成本,对于提高高校现有资产的效率、利用率和灵活性具有重要影响意义。     

云财务共享服务模式设计——以费用报销流程为例探讨

在企业数量增加的同时,企业之间所具有的竞争情况也愈发激烈。其中,财务共享服务是目前企业提升运营效率、降低运营成本的一项重要方式。本文中就以企业的费用报销流程为例对云财务共享服务模式进行一定的研究与设计。     

A HPC based cloud model for real-time energy optimisation

Recent research has emphasised that an increasing number of enterprises need computation environments for executing HPC (High Performance Computing) applications. Rather than paying the cost of ownership and possess physical, fixed capacity clusters, enterprises can reserve or rent resources for undertaking the required tasks. With the emergence of new computation paradigms such as cloud computing it has become possible to solve a wider range of problems due to their capability to handle and process massive amounts of data. On the other hand, given the pressing regulatory requirement to reduce the carbon footprint of our built environment, significant researching efforts have been recently directed towards simulation-based building energy optimisation with the overall objective of reducing energy consumption. Energy optimisation in buildings represents a class of problems that requires significant computation resources and generally is a time consuming process especially when undertaken with building simulation software, such as EnergyPlus. In this paper we present how a HPC based cloud model can be efficiently used for running and deploying EnergyPlus simulation-based optimisation in order to fulfil a number of objectives related to energy consumption. We describe and evaluate the establishment of such an application-based environment, and consider a cost perspective to determine the efficiency over several cases we explore. This study identifies the following contributions: (i) a comprehensive examination of issues relevant to the HPC community, including performance, cost, user perspectives and range of user activities, (ii) a comparison of two different execution environments such as HTCondor and CometCloud and determine their effectiveness in supporting simulation-based optimisation and (iii) a detailed performance analysis to locate the limiting factors of these execution environments.