Solving large sparse linear systems in a grid environment: the GREMLINS code versus the PETSc library

Solving large sparse linear systems is essential in numerous scientific domains. Several algorithms, based on direct or iterative methods, have been developed for parallel architectures. On distributed grids consisting of processors located in distant geographical sites, their performance may be unsatisfactory because they suffer from too many synchronizations and communications. The GREMLINS code has been developed for solving large sparse linear systems on distributed grids. It implements the multisplitting method that consists in splitting the original linear system into several subsystems that can be solved independently. In this paper, the performance of the GREMLINS code obtained with several libraries for solving the linear subsystems is analyzed. Its performance is also compared with that of the widely used PETSc library that enables one to develop portable parallel applications. Numerical experiments have been carried out both on local clusters and on distributed grids.     

Design and development of a fuzzy agent-based model to measure interest rate expectations

The financial system, which has been investigated by various researchers, is a rather complicated environment. Most research has only been concerned with quantitative factors (technical indexes), though qualitative factors (e.g., political situation, social conditions, international events, government policies, among others) play a critical role in the financial system environment, determining the regulatory policies within an economy. This paper presents a fuzzy knowledge-based model to measure the qualitative aspects related to one of the most important financial instruments used to regulate an economy, the base interest rate. The development and assessment of the proposed model was based on the Brazilian economy. Evaluation of the results obtained indicates that our approach gives good results when compared with real data and statistical-based forecasting tools. The main advantage of our approach is its capability to forecast long term interest rate expectations when combined with a powerful econometric model.     

一种优化的AP-CAPSA中文文本结构分析算法*

从段落向量空间模型、特征词权重等多个侧面分析比较了已有的文本结构分析算法,提出了优化的AP-CAPSA算法;给出了算法性能的评价准则,并在不同的语料集合上进行了测试。实验表明该方法能够更有效地反映文本的结构,为中文自动摘要系统奠定了重要的理论基础。     

Effect of radiation heating on the creep of polymeric materials

Strength of Materials -     

The use of energy windowing to discriminate SNM from NORM in radiation portal monitors

Energy windowing is an algorithmic alarm method that can be applied to plastic scintillator-based radiation portal monitor (RPM) systems to improve operational sensitivity to certain threat sources while reducing the alarm rates from naturally occurring radioactive material. Various implementations of energy windowing have been tested and documented by industry and at Pacific Northwest National Laboratory, and are available in commercial RPMs built by several manufacturers. Moreover, energy windowing is being used in many deployed RPMs to reduce nuisance alarms and improve operational sensitivity during the screening of cargo. This paper describes energy windowing algorithms and demonstrates how these algorithms succeed when applied to “controlled” experimental measurements and “real world” vehicle traffic data.