Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

Topological Performance Measures as Surrogates for Physical Flow Models for Risk and Vulnerability Analysis for Electric Power Systems

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically‐oriented models to advanced physical‐flow‐based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large‐scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

盛名难副: 明星 CEO 与负面信息隐藏

以 2009 年 ～ 2016 年间 A 股上市公司为研究样本，采用双重差分法研究明星 CEO 成名后负面信息的披露策略． 以 CEO 是否登上媒体发布的榜单作为明星 CEO 的代理变量，研究结果表明: 明星 CEO 上榜将显著增加隐藏负面信息的可能性，验证了“名声俘获假说”; 明星CEO上榜后发生财务重述的概率显著增加，而一旦退出榜单其隐藏负面信息的可能性则显著降低，验证了名声的负面效应． 机制检验表明明星 CEO 通过管理层权力抑制负面信息披露． 另 外，上榜前盈余质量较低、代理问题较严重的公司，其明星 CEO 隐藏负面信息的可能性更高．文章丰富了 CEO 个人特质对信息披露行为的影响及其经济后果的研究，对于资本市场的健康发展具有一定的启示意义．     

Modeling stereotypes and negative self‐stereotypes as a function of interactions among groups with power asymmetries

Stereotypes is one of the most researched topics in social psychology. Within this context, negative self‐stereotypes pose a particular challenge for theories. In the current work, we propose a model that suggests that negative self‐stereotypes can theoretically be accounted for by the need to communicate in a social system made up by groups with unequal power. Because our theory is dynamic, probabilistic, and interactionist, we use a computational simulation technique to show that the proposed model is able to reproduce the phenomenon of interest, to provide novel accounts of related phenomena, and to suggest novel empirical predictions. We describe our computational model, our variables' dynamic behavior and interactions, and link our analyses to the literature on stereotypes and self‐stereotypes, the stability of stereotypes (in particular, gender and racial stereotypes), the effects of power asymmetries, and the effects of intergroup contact.     

Feminist power and its implications on Uganda’s malaria communication campaign

This paper examines power and its manifestation in Uganda’s “Stop Malaria Campaign.” It specifically questions the apparent radical feminism, which is conceptualized as a quest for power, and how such excesses drive implementation of the campaign. The paper explains data collected through focus group discussions and key informant interviews using feminist communication theory as informed by the critical ideas of feminism and power. These three ideas help to put into perspective: domination of the campaign by one gender, interpretation of the campaign’s objectives to suit that one gender, and communication methods used in the campaign. Analysis of the above three processes shows that men are the weaker gender in the malaria prevention drive, an idea that has clashed with the existing male chauvinism on which several families still thrive in Africa. A view that radical feminism should be seen as an organized form of power that needs to be checked if Uganda’s malaria communication campaign is to be implemented successfully is herein proposed, along with some solutions to the challenges.     

Celebrating Joan Acker: Sociologist,fearless feminist,friend

Joan Acker's life reflects a time when middle‐class women were expected to be satisfied with maintaining the home front, serving husbands and children, not having paid‐work careers. After living “the ideal” for 37 years, Acker took a new path by earning a Ph. D. and producing path‐breaking scholarship that challenged taken‐for‐granted beliefs about gender, family, work, and organizations. Acker spoke “truth to power” and was an academic heroine in posing feminist challenges to injustices involving gender, social class, and race/ethnicity, particularly (but not solely) related to the workplace. This overview lets Joan tell her story and offers reflections on her milestone publications as seen by Pat Martin.     

Trying to stay ahead of the curve in Chile's economic development: exploring a way out of the middle‐income trap through pragmatic export development

Underneath impressive growth levels, the Chilean economy displays signs of being caught in a middle‐income trap. It has been unable to improve its productivity, increase the added value of its exports or upgrade its value chain. Its economy cannot compete either with low‐wage countries or highly productive, innovative countries. Its export strategy based on export promotion seems to have outlived its usefulness. It achieved remarkable quantitative success, but must now attend qualitative attributes. Instead of regarding market‐driven export promotion and state‐led export development as substitutes, this article proposes to view them as alternatives in different stages of development. To choose the appropriate time, more attention should be given to non‐traditional, structural indicators, like export sophistication and political‐institutional capabilities.     

Changes in the Factors Influencing Public Acceptance of Nuclear Power Generation in Japan Since the 2011 Fukushima Daiichi Nuclear Disaster

Public support for nuclear power generation has decreased in Japan since the Fukushima Daiichi nuclear accident in March 2011. This study examines how the factors influencing public acceptance of nuclear power changed after this event. The influence factors examined are perceived benefit, perceived risk, trust in the managing bodies, and pro‐environmental orientation (i.e., new ecological paradigm). This study is based on cross‐sectional data collected from two online nationwide surveys: one conducted in November 2009, before the nuclear accident, and the other in October 2011, after the accident. This study's target respondents were residents of Aomori, Miyagi, and Fukushima prefectures in the Tohoku region of Japan, as these areas were the epicenters of the Great East Japan Earthquake and the locations of nuclear power stations. After the accident, trust in the managing bodies was found to have a stronger influence on perceived risk, and pro‐environmental orientation was found to have a stronger influence on trust in the managing bodies; however, perceived benefit had a weaker positive influence on public acceptance. We also discuss the theoretical and practical implications of these findings.     

Utilizing Bayesian predictive power in clinical trial design

The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP-based phase II-to-phase III design framework.