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.     

Impression management in corporate annual reports during the global financial crisis

This paper analyses impression management (IM) during the global financial crisis (GFC). It examines the differences in multiple textual characteristics and attributions between a highly positive performance period (2002–2007) and the GFC period (2008–2012), within the setting of Spain, where these two economic cycles were extreme. In contrast to previous research, companies' extreme poor performance in our sample is driven by an exogenous event. The findings do not show clear evidence of IM based on textual characteristics specifically linked to the GFC. Companies tried not to use overt IM and, to some extent, tried to clarify the impact of the crisis on performance. They were under great scrutiny and probably preferred to tell a more careful story. However, a general pattern of IM was still present during the GFC in the form of consistent positive attributions, favourable benchmarks and enhancement practices. In essence, the crisis did not fully stop IM practices, but rather influenced the way IM was produced. Overall, our results show that IM was lower during the GFC than in the case of poor performance in normal macroeconomic conditions found by previous literature. The results also show that the narratives of firms in the finance and real estate sectors were the most reactive to the GFC, probably linked to their key role in the crisis.     

Factors associated with a positive childbirth experience in Brazilian women: A cross-sectional study

BackgroundHaving a positive childbirth experience is an increasingly valued outcome. Few studies evaluated the women’s satisfaction with childbirth through face-to-face interviews out of the health service environment. The objective of this study was to identify factors associated with a higher level of satisfaction with the childbirth experience among Brazilian women.MethodsThis cross-sectional study involved 287 women giving birth in two hospitals in southern Brazil. Women who gave birth to healthy newborns at term were randomly selected. Face-to-face interviews were conducted 31–37 days after delivery, at the mothers’ homes, using a structured questionnaire. Satisfaction with the childbirth experience was measured using a Likert-type scale ranging from very satisfied to very dissatisfied. Prevalence ratios (PR) were estimated using Poisson regression with robust variance.ResultsFollowing hierarchical multivariate analysis, the following factors remained associated with a higher level of satisfaction with the childbirth experience: being satisfied with antenatal care (PR = 1.30; 95% confidence interval [95%CI] = 1.06−1.59), understanding the information provided by health professionals during labor and delivery (PR = 1.40; 95%CI = 1.01−1.95), not having reported disrespect and abuse (PR = 1.53; 95%CI = 1.01−2.31), and having had the baby put to the breast within the first hour of life (PR = 1.63; 95%CI = 1.26−2.11). No association was observed with type of delivery or hospital status (public or private).ConclusionsA higher level of satisfaction with the childbirth experience is related to satisfactory antenatal care, a non-abusive, respectful, and informative environment during childbirth, and to the opportunity to breastfeed the baby within the first hour of life. In clinical practice, greater attention to these basic principles of care during pregnancy and delivery could provide more positive experiences during birth.     

科技保险的困境—一个制度供求非均衡分析

现阶段，我国步入了创新驱动引领升级的关键时期，科技创新受到了整个社会的空前重视。科技保险作为分散和转移科技风险的重要手段，可以为科技创新提供有力的风险保障。目前，我国科技保险工作已经进入全面推广阶段，虽然取得了一定的成效，但整体运行并不理想，科技企业参保率低，科技保险缺失严重。究其原因，主要在于制度供给不能适应科技企业对科技保险的需求，科技保险制度供求非均衡，在现行的科技保险制度下积聚着大量的潜在利润，而这些潜在利润既是科技保险制度非均衡的原因，又是科技保险制度创新的动力。     

Local mobilisations and the formation of environmental networks in a democratizing Tunisia

ABSTRACT

This study examines the environmental protests that occurred in Tunisia after the 2011 uprisings. It analyses the factors underpinning the rise of the environmental networks during the period of transition (2011–2014). It details the mobilising strategies that were crucial for the networks’ growth or survival during this period of institutional instability. The study shows how networks leaders were able to bring together social and political actors from different backgrounds and ideological orientations. It is argued that the ability of networks to develop new distinctive collective identities was crucial for network sustainability. Those networks and actors who did not develop new clearly defined environmental identities and continued to rely importantly on pre-existing (authoritarian) structures and practices were more negatively impacted by ideological cleavages and political calculations. Empirically, the contribution builds on interviews and observations, as well as documents collected from Tunisian municipalities between 2013 and 2015. Conceptually, the research proposes a bottom-up perspective that highlights the interplay between micro- and macro-dynamics and strategies during a political transition. The analysis details the actors’ capacity to build alliances via interpersonal relations at the micro level, and their strategies to engage with institutional actors and processes.     

客户集中度、市场环境与存货管理效率

客户处在供应链的下游,对企业存货的采购、生产和销售都会产生重要影响,而多变的市场环境也在无形当中影响着客户与企业之间的关系。从企业所处行业、地区产品市场和客户三个角度所带来的市场环境变化入手,采用2011—2017年我国沪深两市A股制造业上市公司的数据,实证分析客户集中度与企业存货管理效率之间的关系。研究发现:(1)客户集中度越高,企业存货管理效率越高；(2)产品市场竞争力度越强,客户集中度对企业存货管理效率的影响越大；(3)产品市场发育越好,客户集中度对企业存货管理效率的影响越大。进一步研究发现,当企业因客户生产经营原因导致需求发生较大变化时,客户集中度对企业存货管理效率的正向影响更加显著。研究结论表明,政府应当注重市场竞争力的提升和良好经营环境的建设,使得企业在维护客户关系的同时充分发挥客户集中度对企业存货管理效率的积极作用。     

"指标困局":农村贫困治理的实践与反思

在国家的扶贫实践中，地方政府的行为策略是影响扶贫绩效的关键变量。通过对精准扶贫政策实践的调研发现，中央政府不断升级的治理手段为重构央地关系创造了条件，地方政府对国家扶贫理念转型认知的偏差以及在形成路径依赖之后的策略行为不仅容易产生对总体局势的误判，还为后期的政策困局埋下了伏笔。在此背景下，地方扶贫工作遭遇“指标困局”，即地方政府很难实事求是去识别贫困户，只能在既有的指标约束下进行精准识别，扶贫工作陷入了反复识别，却仍难实现精准的怪圈。     

Impact of competitive conditions on supplier evaluation: a construction supply chain case study

This research proposes a multi-criteria decision-making (MCDM) framework and demonstrates the impact of competitive conditions on supplier evaluation process for construction supply chains. The paper focuses on the supply chain of a large-scale housing project in order to illustrate the role of competitive capability and suppliers’ profile and its influence on supplier evaluation based on prevailing supply/market conditions. Various scenarios are investigated to demonstrate the impact of competition on supplier evaluation. The contribution of the study lies in highlighting the impact of supply/market conditions on MCDM decisions causing supplier evaluation ‘imbalance’ and MCDM usage. It is expected that the study will be useful for project management, construction, supply chain management, sourcing professionals. The findings of the study are generalisable to projects-based situations such as petroleum refinery and ship building where bill of materials typically consists of thousands of items and a large number of suppliers are involved.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

嵌套并联结构两阶段DEA下科技创新效率测度与分解

目前有许多研究将两阶段DEA模型应用到科技创新活动当中，一般将创新活动分为研发和转化两个阶段，为了进一步探讨不同转移转化方式下科技成果的转化效率以获得更多的详细信息，本文拓展了传统的两阶段DEA模型，考虑了嵌套并联结构的两阶段网络DEA模型，将科技创新活动分为科技成果研发阶段和科技成果转化阶段，并将科技成果转化阶段分为内部转化和外部转化两个并联子系统。本文选取14家中科院院属单位作为被评价对象，应用加权加法分解方法计算整体效率和各阶段子效率。通过将本文模型与传统两阶段模型进行对比发现，传统模型会低估科技成果转化效率。结果表明：整体科技创新效率均值偏低，科技成果研发效率均值高于科技成果转化效率均值，科技成果转化效率仍有很大的提升空间。科技成果转化阶段中内部转化效率普遍较高但是外部转化效率普遍较低，较低的外部转化效率是导致科技成果转化效率低下的主要原因。最后，本文将所有被评价单位分为四类机构，分析提高科技创新效率的实现路径，可以通过重点突破模式和渐进突破模式来实现整体效率的提高。