Improved Transferability of Data‐Driven Damage Models Through Sample Selection Bias Correction

Damage models for natural hazards are used for decision making on reducing and transferring risk. The damage estimates from these models depend on many variables and their complex sometimes nonlinear relationships with the damage. In recent years, data‐driven modeling techniques have been used to capture those relationships. The available data to build such models are often limited. Therefore, in practice it is usually necessary to transfer models to a different context. In this article, we show that this implies the samples used to build the model are often not fully representative for the situation where they need to be applied on, which leads to a “sample selection bias.” In this article, we enhance data‐driven damage models by applying methods, not previously applied to damage modeling, to correct for this bias before the machine learning (ML) models are trained. We demonstrate this with case studies on flooding in Europe, and typhoon wind damage in the Philippines. Two sample selection bias correction methods from the ML literature are applied and one of these methods is also adjusted to our problem. These three methods are combined with stochastic generation of synthetic damage data. We demonstrate that for both case studies, the sample selection bias correction techniques reduce model errors, especially for the mean bias error this reduction can be larger than 30%. The novel combination with stochastic data generation seems to enhance these techniques. This shows that sample selection bias correction methods are beneficial for damage model transfer.     

Physiologically Based Liver Modeling and Risk Assessment

Because of the inherent complexity of biological systems, there is often a choice between a number of apparently equally applicable physiologically based models to describe uptake and metabolism processes in toxicology or risk assessment. These models may fit the particular data sets of interest equally well, but may give quite different parameter estimates or predictions under different (extrapolated) conditions. Such competing models can be discriminated by a number of methods, including potential refutation by means of strategic experiments, and their ability to suitably incorporate all relevant physiological processes. For illustration, three currently used models for steady-state hepatic elimination--the venous equilibration model, the parallel tube model, and the distributed sinusoidal perfusion model--are reviewed and compared with particular reference to their application in the area of risk assessment. The ability of each of the models to describe and incorporate such physiological processes as protein binding, precursor-metabolite relations and hepatic zones of elimination, capillary recruitment, capillary heterogeneity, and intrahepatic shunting is discussed. Differences between the models in hepatic parameter estimation, extrapolation to different conditions, and interspecies scaling are discussed, and criteria for choosing one model over the others are presented. In this case, the distributed model provides the most general framework for describing physiological processes taking place in the liver, and has so far not been experimentally refuted, as have the other two models. These simpler models may, however, provide useful bounds on parameter estimates and on extrapolations and risk assessments.     

Ranking Nuclear and Radiological Terrorism Scenarios: The Italian Case

A quantitative criterion for ranking the different scenarios of nuclear and radiological terrorism has been developed. The aim of the model is not to predict terroristic events but only to indicate which scenario has the higher utility from the point of view of a terroristic organization in terms of balance between factors favoring and discouraging the attack, respectively. All these factors were quantified according to a scoring system that takes into account the logarithmic relationship between perceptions and stimuli. The criterion was applied to several scenarios, each of which was modeled in a simple but not trivial way in order to estimate the expected damage in terms of probable life losses from both radiative and nonradiative effects. The outcome from the ranking method indicates that the attractive scenario appears to be the detonation of a low yield improvised nuclear device in the metropolitan area of a major city.     

浅析凤翔泥塑对现代设计的启示

中国的民间手工艺文化一直延续着中华本元文化的特征,保持了精神和物质统一的原生状态,同时包蕴了中华民族传统的自然观和思维方式,无论是其中的思想智慧还是造物工艺都为我们的现代设计在如何继承本土文化方面打开了一个可供吸收营养和借鉴的宝库。凤翔泥塑作为一种符号现象被现代设计加以运用,不能不说是民间传统手工艺术在现代的一次回归浪潮。文章探讨的是凤翔泥塑在纹饰、色彩、造型三个方面带给现代设计的启示。     

基于规则的客户关系管理系统建模方法

客户关系管理系统(CRM)是提高企业核心竞争力的有力武器。以工作流和过程建模技术为基础,研究CRM系统的建模,是CRM系统的建立和顺畅运行的关键。因此,将动态过程建模的思想引入CRM系统的建模过程中,按照逐层分解的建模方式来简化系统的复杂性,并采用事件驱动的、基于扩展业务规则的方法进行CRM系统的过程建模和系统性能评价,是针对CRM系统进行建模的有效手段。通过以上方法,可以建立出全面支持CRM系统运行、监控、仿真和性能评价的过程模型。根据该方法建立的CRM系统的模型经过仿真实验和企业实际应用,证明具有良好的应用效果。     

制造业设备维护与管理的相对有效性评估

在设备维护与管理的评估中引入数据包络分析,以此为核心构建了设备维护与管理相对有效性的评估方法。该方法在系统功能建模的基础上,全面分析了设备维护与管理作为服务系统的投入与产出构成,以数据包络分析为计算模型完成了设备维护与管理有效性的评估。该方法满足了设备维护与管理评估全面性和客观性的要求,其有效性和可信度通过在某石化企业的应用得到了验证。     

基于工作流的企业业务过程集成建模方法

工作流管理是一种对业务过程进行支持、控制、监视和优化的先进工具. 将生产制造过程 BOM和Petri 网技术应用于企业业务过程,对于相关定义、建模分析以及从BOM到Petri 网的映 射实现算法进行了说明;对基于BOM- Petri 的建模方法进行了扩展优化;最后结合算法进行了 仿真研究.     

The impact of supply chain management practices on competitive advantage and organizational performance

Effective supply chain management (SCM) has become a potentially valuable way of securing competitive advantage and improving organizational performance since competition is no longer between organizations, but among supply chains. This research conceptualizes and develops five dimensions of SCM practice (strategic supplier partnership, customer relationship, level of information sharing, quality of information sharing, and postponement) and tests the relationships between SCM practices, competitive advantage, and organizational performance. Data for the study were collected from 196 organizations and the relationships proposed in the framework were tested using structural equation modeling. The results indicate that higher levels of SCM practice can lead to enhanced competitive advantage and improved organizational performance. Also, competitive advantage can have a direct, positive impact on organizational performance.