蓄能器在机床液压载重补偿系统中的应用

带蓄能器的液压载重补偿系统在机床中有着广泛的应用。文章分析了机床液压载重补偿系统的特点,介绍了系统的原理、组成和蓄能器的作用及其详细选用过程。实践证明,这套系统对提高机床精度是行之有效的。     

CMM模型和我国软件业生产过程标准化

CMM模型是国际上最流行的一种软件生产过程标准。本文比较了国内外软件业生产过程标准化的现状,分析了阻碍我国软件业开展CMM评估的原因,并在此基础上,提出在我国推行CMM标准亟待解决的几个主要问题。     

Assessing the Effects of Climate Change Regulations on the Business Community: A System Dynamic Approach

Based on the business environmental literature and system dynamics, this paper develops a simulation model for managing the business risks derived from climate change. In particular, the purpose of this paper is to transform the valuable findings from the literature regarding climate change and corporate implications into an effective business management model with a broad applicability, regardless of the size of the business or the sector in which it operates. A methodology consistent with the basic principles of the system dynamic modeling process is developed, and a case study is designed to determine the level of completeness of the simulation model and its ability to address different aspects of business performance. To do so, three different scenarios have been simulated to analyze the reactive, proactive and inactive stance of managers against climate change risks. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment     

GeoEast软件项目管理

GeoEast软件是由中国石油天然气集团公司下属的东方地球物理勘探责任有限公司开发出的一种地震勘探数据采集、数据处理和数据解释一体化软件,1.0版于2004年12月31日正式发布。本案例介绍了东方地球物理勘探责任有限公司和GeoEast软件的背景,讨论了GeoEast软件项目研发的组织管理和时间管理,总结了项目经理刘超颖如何在传统的螺旋加瀑布模型的基础上创新出14331模型。最后,案例提出一个问题:刘超颖是再次担任2.0版的项目长呢,还是负责1.0版的推广应用工作?     

OSGi及其QoS获益驱动动态软件体系结构研究

随着软件领域中移动计算、分布计算和Web Service等技术的迅速发展,软件计算环境变得越来越开放、复杂、多元。如何应对不断变化的计算环境,构建一个自适应的动态软件架构,受到了越来越多的关注。文章首先介绍一个成功的软件系统结构框架实例——Open Service Gateway Initiative(OSGi)。然后,以请求质量QoS为核心,对面向服务的OSGi框架进行扩展,提出了一个动态自适应软件架构解决方案,它以用户QoS属性为目标驱动,通过动态调整软件自身结构,以进一步提高用户期望的服务质量。实验结果证明了该调度框架的有效性。     

论信息化条件下虚拟物流中心的构建

随着信息化、网络化和经济全球化的进一步发展,物流的实体网络与虚拟网络的无缝结合已成为现代物流需求的首要目标和物流行业的发展方向。文中首先探讨了物流虚拟化三个方面的成因,从而引出对虚拟物流中心解释,并提出了虚拟物流中心的构建是通过集中分散的制造商、供应商、销售商、物流企业、相关部门、银行、保险公司形成一个多功能的虚拟物流交易平台。最后从企业、第三方物流、人才以及管制四个方面提出了建议。     

人工智能潜在替代风险与员工职业能力发展:基于员工不安全感视角

随着智能科技的蓬勃发展,人工智能+在有助于生产和生活的同时,也为员工的职业发展带来了挑战。本文采用机器学习算法计算我国各类职业潜在替代风险,在此基础上深入探讨了潜在替代风险对员工不安全感与职业能力发展的影响。基于13158条职业数据和808份调查问卷的分析,研究发现:我国职业面临着较强的人工智能潜在替代问题的威胁,潜在替代风险高中低的比例分别是52.80%、25.72%和21.48%;潜在替代风险最低的职业大类是企业、事业单位负责人,最高的是生产、运输设备操作人员及有关人员。进一步地,人工智能潜在替代风险在质量性不安全感与职业能力发展间起负向调节作用,当潜在替代风险高时,质量性不安全感对职业能力发展的正向影响作用更小,当潜在替代风险低时,质量性不安全感对职业能力发展的正向影响作用更大。人工智能潜在替代风险对数量性不安全感与职业能力发展的U型关系起正向调节作用,当潜在替代风险高时,数量性不安全感对职业能力发展的U型效应更强,当人工智能潜在替代风险低时,员工数量性不安全感对职业能力发展的U型效应更低。本研究丰富了人工智能领域与职业能力发展的实证研究,为新时代人才培养和技能转型提供建议。     

Comparing the accuracy of several network-based COVID-19 prediction algorithms

Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

Post-script—Retail forecasting: Research and practice

This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.