Predictive capability of cognitive ability and cognitive style for spaceflight emergency operation performance

This study explores the effects of cognitive ability (information seeking, inference, spatial recognition, attention span, and attention allocation) and cognitive style (active-reflective, sensing-intuitive, visual-verbal, and sequential-global) on task performance of simulated spaceflight emergency operations that require judgment and operation on a Chinese spaceflight instrument board and the possible interaction effect with training experience. The performance criteria included task completion time and number of human errors. It was found that inference ability, spatial recognition ability, and attention span had significant effects on task completion time, while attention allocation ability had significant effect on the number of error. The participants with a sequential cognitive style made significantly fewer errors than those with a global cognitive style. Training experience significantly decreased task completion time. The participants with sequential cognitive style learnt faster than those with global cognitive style in the spaceflight instrument operations. With increasing training experience, the predictive capability of cognitive ability on performance decreased, whereas the predictive capability of the sequential-global cognitive style on performance increased.     

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.     

Simulated environments with animated agents: effects on visual attention,emotion, performance,and perception

This research assessed how emotive animated agents in a simulation‐based training affect the performance outcomes and perceptions of the individuals interacting in real time with the training application. A total of 56 participants consented to complete the study. The material for this investigation included a nursing simulation in which participants interacted with three animated agents. The results of this investigation indicated that both experienced and novice participants focused more visual attention time on the body of the animated agent than the other defined areas of interest in the simulated environment. The results also indicated that novice participants conveyed more neutral facial expressions during the interaction with the animated agents than experience participants. The results of the simulation performance scores indicated that novice participants achieved higher simulation performance scores on the simulation task than experienced participants. Lastly, the results of the agent persona instrument showed that experienced and novice participants perceived the animated agents as facilitators of learning, credible, human‐like and engaging.     

The role of creation mode and social networking mode in knowledge creation performance: Mediation effect of creation process

This study proposes and empirically examines a research model that incorporates the knowledge creation mode and social networking mode to describe knowledge creation performance. The mediation effect of the knowledge creation process is explored in terms of socialization, externalization, combination, and internalization (SECI). The data collected from the manufacturing and service industries in Taiwan were analyzed. The goal-driven mode and web topology are found to be significantly associated with product or service creation primarily because of the creation efficacy aspect. The SECI with web topology has a mediation function when the goal-driven mode is adopted. Implications and suggestions are also provided.     

Optimal back-off point determination and controller weight selection for multivariate systems under finite-horizon control

Plant economic performance is most often related to the operating point, specifically the mean values of the process variables; meanwhile, most existing performance assessment techniques involve examining the variances or covariances of the controlled variables. A combined approach is to determine the appropriate trade-off between variances of different process variables in order to operate the plant at the point that provides maximum economic benefit while satisfying the operating constraints. This problem is referred to as the minimum backed-off operating point selection, and previous works have formulated it as a non-convex constrained optimization problem. In the current work, a new technique is introduced that can provide the optimal plant operating point. Additionally, this method provides the weights for a finite horizon controller that results in the optimal trade-off in process variable variances that will allow satisfaction of the operating constraints at the optimal operating point. In this method, the plant and disturbance models for the given process are used to generate data representing possible trade-offs between process variable standard deviations. Employing a piecewise linear regression to describe the sample points of this standard deviations data allows for the operating point selection problem to be solved as a small number of linear programs. The advantages of this approach are demonstrated through the use of mathematical and simulation case studies.     

面向电网流式数据处理的性能测试平台

提出一种面向电网流式数据处理的性能测试平台的系统架构及关键技术.针对电网流式数据的业务场景特点,测试平台支持基于浏览器的测试脚本设计和测试场景设计,并支持测试任务的全生命周期管理和高可用保障.为了满足大规模负载测试需求,测试平台提供基于Linux容器的虚拟化测试资源池,实现了测试资源的轻量化、弹性管理.     

面向在线支付业务的容器资源弹性管理框架

随着在线支付业务的大规模应用,系统运维人员需要更便捷的服务器资源管理机制来满足系统扩展需求.本文提出一种基于容器技术的轻量化、弹性资源管理框架,并给出应用服务的性能诊断方法以及集群规模的动态调整方法.利用在线支付业务的典型数据处理场景进行实验,实验结果验证了方法的有效性.     

面向HBase的大规模数据加载研究

分布式数据库HBase在大规模数据加载中较传统关系型数据库有较大的优势但也存在很大的优化空间.基于Hadoop分布式平台搭建HBase环境,并优化自定义数据加载算法.首先,分析HBase底层数据存储,实验得出HBase自带数据加载方式在效率和灵活性方面存在不足;进而,提出了自定义并行数据加载算法,并针对集群进行优化.实验结果表明,优化后的自定义并行数据加载方式能充分发挥集群性能,具有较好的加载效率和数据操作能力.     

基于程序分析的分布式应用自动化追踪方法

提出了一种零配置的端到端细粒度追踪的方法-EasyTrace,应用于分布式系统发生性能降级时的问题诊断.传统工具往往存在监测粒度与服务组件源码紧耦合或配置信息复杂、修改代价高等不足,EasyTrace能够做到零配置下性能问题的准确定位.基于开源电子商务系统的实验结果表明,EasyTrace相对传统监测工具对系统造成的扰动很小.