Religion,Religiosity, and the Attitudes Toward Homosexuality—A Multilevel Analysis of 79 Countries

Although attitudes toward homosexuality have become more liberal, particularly in industrialized Western countries, there is still a great deal of variance in terms of worldwide levels of homonegativity. Using data from the two most recent waves of the World Values Survey (1999–2004, 2005–2009), this article seeks to explain this variance by means of a multilevel analysis of 79 countries. We include characteristics on the individual level, as age or gender, as well as aggregate variables linked to specificities of the nation-states. In particular, we focus on the religious denomination of a person and her religiosity to explain her attitude toward homosexuality. We find clear differences in levels of homonegativity among the followers of the individual religions.     

青岛市乡村休闲旅游 SWOT 分析研究

运用SWOT 分析法对青岛市乡村休闲旅游发展进行分析，结果显示：青岛具有资源丰富、经济发达、交通便利等优势，但同时也存在认识水平不高、农村基础设施落后、产品同质化严重、季节性明显、人才缺乏等劣势；面临旅游新热点形成、政府出台鼓励政策、新型农村社区的建设等新的发展机遇，但周边地区的竞争、生态环境的恶化也对其构成了威胁。在此基础上，提出了提高认识水平、完善基础设施、打造特色品牌、保护资源、培养人才等可行性建议。     

Translation and factor analysis of structural models of Edinburgh Postnatal Depression Scale in Serbian pregnant and postpartum women – Web-based study

BackgroundThe Edinburgh Postnatal Depression Scale (EPDS) is well accepted for detecting symptoms of postnatal depression. The aim of this study was to examine psychometric properties and to evaluate structural models of the Serbian translation of EPDS in pregnant and postpartum women.MethodsThe original English version of the EPDS was translated into Serbian, and checked by means of back-translation. Data were collected via an anonymous online questionnaire posted on a Serbian website devoted to pregnancy topics. The study sample included 201 women (76 pregnant, 125 postpartum). The internal consistency of the scale was measured by Cronbach's α coefficient. Principal component analysis was used to determine scale dimensions while confirmatory factor analysis was used to evaluate model fit.FindingsCronbach α coefficient was 0.84 and 0.83 in pregnant and postpartum women, respectively, which indicated good internal consistency of the Serbian EPDS. Three dimensions of the scale were revealed in both groups of women. Goodness of fit indices described good and excellent model in pregnant and postpartum women, respectively. High level of depression symptoms (score ≥13) was recorded in 27.6% and 24.8% (p > 0.05) of pregnant and postpartum women, respectively. Moderate level of depression symptoms (score 10–12) was recorded in 21.1% and 16.8% (p > 0.05) of pregnant and postpartum women, respectively.ConclusionThe Serbian translation of the EPDS showed good consistency and good model characteristics in pregnant and postpartum women. However, cut-off values, sensitivity and specificity of the scale should be determined in the further studies with more representative samples of women.     

Risk Analysis for U.S. Offshore Wind Farms: The Need for an Integrated Approach

Wind power is becoming an increasingly important part of the global energy portfolio, and there is growing interest in developing offshore wind farms in the United States to better utilize this resource. Wind farms have certain environmental benefits, notably near‐zero emissions of greenhouse gases, particulates, and other contaminants of concern. However, there are significant challenges ahead in achieving large‐scale integration of wind power in the United States, particularly offshore wind. Environmental impacts from wind farms are a concern, and these are subject to a number of on‐going studies focused on risks to the environment. However, once a wind farm is built, the farm itself will face a number of risks from a variety of hazards, and managing these risks is critical to the ultimate achievement of long‐term reductions in pollutant emissions from clean energy sources such as wind. No integrated framework currently exists for assessing risks to offshore wind farms in the United States, which poses a challenge for wind farm risk management. In this “Perspective”, we provide an overview of the risks faced by an offshore wind farm, argue that an integrated framework is needed, and give a preliminary starting point for such a framework to illustrate what it might look like. This is not a final framework; substantial work remains. Our intention here is to highlight the research need in this area in the hope of spurring additional research about the risks to wind farms to complement the substantial amount of on‐going research on the risks from wind farms.     

Portfolio Analysis of Layered Security Measures

Layered defenses are necessary for protecting the public from terrorist attacks. Designing a system of such defensive measures requires consideration of the interaction of these countermeasures. In this article, we present an analysis of a layered security system within the lower Manhattan area. It shows how portfolios of security measures can be evaluated through portfolio decision analysis. Consideration is given to the total benefits and costs of the system. Portfolio diagrams are created that help communicate alternatives among stakeholders who have differing views on the tradeoffs between security and economic activity.     

Extended pairwise local alignment of wild card DNA/RNA sequences using dynamic programming

Optimal string alignment is used to discover evolutionary relationships or mutations in DNA/RNA or protein sequences. Errors, missing parts or uncertainty in such a sequence can be covered with wild cards, so-called wild bases. This makes an alignment possible even when the data are corrupted or incomplete. The extended pairwise local alignment of wild card DNA/RNA sequences requires additional calculations in the dynamic programming algorithm and necessitates a subsequent best- and worst-case analysis for the wild card positions. In this paper, we propose an algorithm which solves the problem of input data wild cards, offers a highly flexible set of parameters and displays a detailed alignment output and a compact representation of the mutated positions of the alignment. An implementation of the algorithm can be obtained at https://github.com/sysbio-bioinf/swat+ and http://sysbio.uni-ulm.de/?Software:Swat+.     

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.     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

The Multiscale Importance of Road Segments in a Network Disruption Scenario: A Risk‐Based Approach

This article addresses the problem of the multiscale importance of road networks, with the aim of helping to establish a more resilient network in the event of a road disruption scenario. A new model for identifying the most important roads is described and applied on a local and regional scale. The work presented here represents a step forward, since it focuses on the interaction between identifying the most important roads in a network that connect people and health services, the specificity of the natural hazards that threaten the normal functioning of the network, and an assessment of the consequences of three real‐world interruptions from a multiscale perspective. The case studies concern three different past events: road interruptions due to a flood, a forest fire, and a mass movement. On the basis of the results obtained, it is possible to establish the roads for which risk management should be a priority. The multiscale perspective shows that in a road interruption the regional system may have the capacity to reorganize itself, although the interruption may have consequences for local dynamics. Coordination between local and regional scales is therefore important. The model proposed here allows for the scaling of emergency response facilities and human and physical resources. It represents an innovative approach to defining priorities, not only in the prevention phase but also in terms of the response to natural disasters, such as awareness of the consequences of road disruption for the rescue services sent out to local communities.     

Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents’ relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.