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.     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

论数学教育中的数学观念体系

本文从培养数学观念的角度，就数学教育如何培养人们的科学思维方式和形成良好的思维习惯作一论述。     

数学素质培养

文章从素质教育的要求出发,结合数学教学实践,着重阐述了数学素质的内涵及其特征,并就如何培养学生数学素质的初步探讨.     

大学数学教学改革的探索与思考

文章分析了当前大学数学课程教学中存在的一些问题,结合教学实际,对大学数学教学改革的目标定位进行了探讨,总结了我校数学教学工作在教学观念、教学方法、教学模式、教学内容及教材建设等方面的一些改革措施与效果。     

中国传统文化中的"尚三"理念对巴金《家·春·秋》创作的影响

巴金自称是受外国文学影响最多的作家之一,但是中国传统文化对他的创作在无形中的影响恐怕连他自己都没有意识到,在巴金的小说尤其是在长篇小说《家.春.秋》创作中就处处可见"尚三"传统的影响,形成了几种独特的创作现象:三事话语,三复情节,三变情节,三极建构,三而后成,传统文化与外来文化的相互渗透融合,是巴金能够成为文学大师的文化基础。     

Estimation of Metabolic Rate Constants in PBPK‐Models from Liver Slice Experiments: What Are the Experimental Needs?

A mechanistic model is presented describing the clearance of a compound in a precision-cut liver slice that is incubated in a culture medium. The problem of estimating metabolic rate constants in PBPK models from liver slice experiments is discussed using identifiability analysis. From the identifiability problem analysis, it appears that in addition to the clearance, the compound's free fraction in the slice and the diffusion rate of the exchange of the compound between culture medium and liver slice should be identified. In addition, knowledge of the culture medium volume, the slice volume, the compound's free fraction, and octanol-water-based partition between medium and slice is presupposed. The formal solution for identification is discussed from the perspective of experimental practice. A formally necessary condition for identification is the sampling of parent compound in liver slice or culture medium. However, due to experimental limitations and errors, sampling the parent compound in the slice together with additional sampling of metabolite pooled from the medium and the slice is required for identification in practice. Moreover, it appears that identification results are unreliable when the value of the intrinsic clearance exceeds the value of the diffusion coefficient, a condition to be verified a posteriori.     

研究性学习在数学教学中的应用探讨

研究性学习是一种新的学习理念,是一种全方位的学习变革。教师在日常的教学中注重对学生研究性学习的培养和训练,将会极大地提高他们学习数学的浓厚兴趣,从而引导学生去探索、创新。从数学教学角度对培养学生研究性学习的教学原则、研究性学习内容的基本特征、研究性学习课题的设计应用等方面作了探讨。     

理想点法综合评价学生数学成绩

应用多目标决策———理想点法 ,建立了一个综合评价学生数学成绩的数学模型     

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.