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.     

Loss and grief: The experience of transition to residential aged care

There is growing recognition of the experience of non-death-related loss and grief. One unexplored area of non-death-related loss and grief is associated with older people’s transition from home to residential aged care (RAC). This paper reports findings from a qualitative study that explored the experiences of people living in RAC, carer-relatives of people living in RAC and RAC staff. Using theories of loss and grief, the paper identifies features of the transition to RAC that are associated with unresolved loss and maladaptation to changed circumstances, and the types of support that would mitigate these. Using a series of focus groups with both residents and carer-relatives, as well as staff, the study found that residents and their families need more support to negotiate the multiple, often necessarily hasty decisions and bureaucratic requirements associated with transitioning to RAC, while simultaneously experiencing loss and grief. The types of support that families would welcome from service providers include facilitating shared decision making, valuing family and community carer expertise and providing practical information and assistance to fulfil administrative obligations.     

A new financial stress index model based on support vector regression and control chart

Financial stress index (FSI) is considered to be an important risk management tool to quantify financial vulnerabilities. This paper proposes a new framework based on a hybrid classifier model that integrates rough set theory (RST), FSI, support vector regression (SVR) and a control chart to identify stressed periods. First, the RST method is applied to select variables. The outputs are used as input data for FSI–SVR computation. Empirical analysis is conducted based on monthly FSI of the Federal Reserve Bank of Saint Louis from January 1992 to June 2011. A comparison study is performed between FSI based on the principal component analysis and FSI–SVR. A control chart based on FSI–SVR and extreme value theory is proposed to identify the extremely stressed periods. Our approach identified different stressed periods including internet bubble, subprime crisis and actual financial stress episodes, along with the calmest periods, agreeing with those given by Federal Reserve System reports.     

Estimation of geodesic tortuosity and constrictivity in stationary random closed sets

We investigate the problem of estimating geodesic tortuosity and constrictivity as two structural characteristics of stationary random closed sets. They are of central importance for the analysis of effective transport properties in porous or composite materials. Loosely speaking, geodesic tortuosity measures the windedness of paths, whereas the notion of constrictivity captures the appearance of bottlenecks resulting from narrow passages within a given materials phase. We first provide mathematically precise definitions of these quantities and introduce appropriate estimators. Then, we show strong consistency of these estimators for unboundedly growing sampling windows. In order to apply our estimators to real data sets, the extent of edge effects needs to be controlled. This is illustrated using a model for a multiphase material that is incorporated in solid oxide fuel cells.     

政治关联如何影响社会组织有效性？——组织自主性的中介作用和制度支持的调节作用

社会组织领域的政治关联现象日益显著，但政治关联究竟如何影响社会组织有效性，其隐藏的作用机制又是什么？这是学术界尚未明晰的重要课题。针对254家社会组织的问卷调查发现，社会组织政治关联会正向影响组织有效性，这一关系受到组织自主性的中介作用影响。更进一步的研究显示，制度支持能调节上述中介关系。具体而言，当制度支持水平较高时，这一效应将会增强；当制度支持水平较低时，政治关联通过组织自主性间接影响组织有效性的效应就会减弱。这一发现，一方面增进了我们对社会组织政治关联的理解，另一方面也为提升社会组织有效性的理论和实践提供了重要启示。     

从WTO规则析我国农业国内支持制度的完善

WTO相关协议允许各国采用一定的农业国内支持措施，尤其是《农产品协定》的绿箱条款更指明了农业国内支持的方向。我国在入世协议中也对农业国内支持作了承诺。为此，要发展农业，应在遵守上述WTO规则的基础上逐步完善我国现行的农业国内支持制度。     

社会主义精神富裕论

马克思主义经典作家的相关阐述 ,社会主义本质的实现和比较优势的获取 ,精神富裕和物质富裕特殊的关系均表明 ,精神富裕是建设社会主义的应有之义。而精神富裕的导向、认识、实践和凝聚作用 ,又反过来在促进社会主义事业快速、健康与持续发展中扮演着重要角色。同时 ,社会主义精神富裕还具有向度的特性 ,是一个动态开放的发展过程     

一个汉英机器翻译系统中的语义处理模型

简略阐述了三种语义分析理论 -配价语法 ,格语法 ,论旨理论后 ,重点介绍了机器翻译系统所采用的语义模型     

设计语言与传统绘画

民族文化除了它的民族性以外,还有它的时代性。运用现代的美学观念与传统文化相结合,是我国艺术设计发展的必然之路。     

开放式数控切割机神经网络误差补偿研究

分析了影响数控火焰切割机加工精度的主要因素,利用开放式数控系统的软件开放性,提出了采用IGCAQBP学习算法的神经网络方法来对包括金属热变形、机械传动误差等非线性因素在内的多种因素造成的加工误差进行误差补偿,设计了嵌入开放式数控系统中的神经网络误差补偿器,给出了实用的补偿器使用方法,并对误差补偿功能进行了扩展,仿真结果和实际应用表明该方法稳定有效。