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.     

外语能力与科学研究的密切联系——周立伟院士访谈

近日对我国著名电子光学和光电子成像专家周立伟院士进行了访谈。访谈中，周院士首先分享了自己学习英语和俄语的经历，强调了自主学习能力的重要性；其次，周院士详细论述了外语教学，尤其是英语教学的必要性，指出英语能力与科学研究紧密相关，英语教学不可或缺；之后周院士分析了研究生在英语学习和科学研究方面存在的问题，并讨论了大学中开设人文课程的迫切性；最后，周院士对上理工师生提出了建议与期望。     

劳动力转移对城乡居民收入差距的非线性影响研究

借鉴已有相关研究成果,将劳动力转移、农业技术水平与城乡居民收入差距联系起来思考,可以具体地考察劳动力转移对城乡居民收入差距的影响.文章以农业技术水平作为门槛变量,选取2009—2019年中国31个省份的面板数据,利用泰尔指数测定城乡收入差距,以劳动力转移作为核心解释变量构建面板门槛模型,实证得出劳动力转移对城乡居民收入差距的非线性影响.研究发现:劳动力转移可以缩小城乡居民收入差距,且该影响具有单一门槛效应.在农业技术水平较低时,劳动力转移对城乡居民收入差距收敛效应较小;当农业技术水平越过门槛值达到较高水平后,劳动力转移对缩小城乡居民收入差距的效果几乎增加了一倍.此后,通过替换解释变量泰尔指数为城乡人均可支配收入比、替换核心解释变量农业技术水平为农业生产效率重新建立面板门槛模型,发现上述结论仍然成立,其检验结果具有较强的稳健性.文章将农业技术进步、劳动力转移、城乡居民收入差距这三个重要变量纳入同一模型框架中,在加深劳动力流动影响城乡居民收入差距这一视角的研究的同时,对缩小城乡居民收入差距政策的制定提供了重要的参考意义.     

学习分析视域下S-ISAL在大学英语混合式教学中的构建与评估

学习分析技术在教育教学中的应用，其根本目的是为了构建新的教学法，更新教学与管理的方式。针对高校大学英语线上、线下混合教学无法有机融合的现状，S-ISAL（Space-based Information search and Analysis Learning）应运而生，它是基于网络学习空间的信息搜集与分析的教学模式，将学习分析技术引入大学英语混合式课程中。探究该教学模式在大学英语教学中的适切性和实效性是文章的研究重点，目的是为了实现大学英语线上、线下教学优势互补，为大学生在线英语学习提供更具个性化的学习内容和路径，并提升大学生自适应学习能力和在线学习绩效。     

概念迁移视阈下基于语料库的非英语专业学习者动名词搭配错误研究

文章拟从概念迁移视角研究大学英语写作中的动-名词搭配错误，研究采用语料库定量与定性相结合的分析方法。语料取材于中国学习者英语语料库（CLEC），包括大学英语低年级学习者（St3）和高年级学习者（St4）两个子语料库。利用检索工具Ant Conc3.58检索中国学习者作文中的动-名词搭配错误，从而对大学生英语写作中的动-名词搭配错误进行描述与分类，进而分析造成大学生英语写作动-名词搭配误用的潜在原因，并对现有大学英语教学做出反思。     

“省直管县”改革能否提升县级财力水平?

将“省直管县”财政体制改革作为拟自然实验，基于全国县级面板数据，利用可有效校正选择偏差问题的 PSM-DID 方法构造“反事实”状态，估计改革对县级财力水平的平均处理效应。结果显示: 改革增加了人均本级财政收入，却减少了人均转移支付水平，且下降幅度较大，致使人均财政总收入减少，表明“省直管县”改革不但无法提升，甚至会在一定程度上恶化县级财政状况。东、中部地区的平均处理效应与全国类似，上述结论具有比较好的稳健性。此外，还进行了影响机制检验，地级市与县、县与县之间的财政竞争以及省对各市、县的协调机制会在一定程度上影响财政体制改革的效果。     

Validation of the Australian Midwifery Standards Assessment Tool (AMSAT): A tool to assess midwifery competence

Background

There is no current validated clinical assessment tool to measure the attainment of midwifery student competence in the midwifery practice setting. The lack of a valid assessment tool has led to a proliferation of tools and inconsistency in assessment of, and feedback on student learning.

Inference and optimal design of multiple constant-stress testing for generalized half-normal distribution under type-II progressive censoring

The generalized half-normal (GHN) distribution and progressive type-II censoring are considered in this article for studying some statistical inferences of constant-stress accelerated life testing. The EM algorithm is considered to calculate the maximum likelihood estimates. Fisher information matrix is formed depending on the missing information law and it is utilized for structuring the asymptomatic confidence intervals. Further, interval estimation is discussed through bootstrap intervals. The Tierney and Kadane method, importance sampling procedure and Metropolis-Hastings algorithm are utilized to compute Bayesian estimates. Furthermore, predictive estimates for censored data and the related prediction intervals are obtained. We consider three optimality criteria to find out the optimal stress level. A real data set is used to illustrate the importance of GHN distribution as an alternative lifetime model for well-known distributions. Finally, a simulation study is provided with discussion.     

Random effects regression mixtures for analyzing infant habituation

Random effects regression mixture models are a way to classify longitudinal data (or trajectories) having possibly varying lengths. The mixture structure of the traditional random effects regression mixture model arises through the distribution of the random regression coefficients, which is assumed to be a mixture of multivariate normals. An extension of this standard model is presented that accounts for various levels of heterogeneity among the trajectories, depending on their assumed error structure. A standard likelihood ratio test is presented for testing this error structure assumption. Full details of an expectation-conditional maximization algorithm for maximum likelihood estimation are also presented. This model is used to analyze data from an infant habituation experiment, where it is desirable to assess whether infants comprise different populations in terms of their habituation time.     

Decision‐making under uncertainty in child protection: Creating a just and learning culture

The argument is made for having a positive error culture in child protection to improve decision‐making and risk management. This requires organizations to accept that mistakes are likely and to treat them as opportunities for learning and improving. In contrast, in many organizations, a punitive reaction to errors leads to workers hiding them and developing a defensive approach to their practice with children and families. The safety management literature has shown how human error is generally not simply due to a “bad apple” but made more or less likely by the work context that helps or hinders good performance. Improving safety requires learning about the weaknesses in the organization that contribute to poor performance. To create a learning culture, people need to feel that when they talk about mistakes or weak practice, there will be a constructive response from their organization. One aspect of reducing the blame culture is to develop a shared understanding of how practice will be judged and how those appraising practice will avoid the hindsight bias. To facilitate a positive error culture, a set of risk principles are presented that offer a set of criteria by which practice should be appraised.