Global adjustment to US disengagement from the world trading system

US trade policy in the Trump administration is protectionist, raising tariffs outside of WTO rules, threatening trade wars, withdrawing from existing trade agreements, and negotiating new bilateral trade deals rather than free trade agreements. The reaction of the rest of the world has been to support the WTO rules-based trading system and to pursue new and expanded regional trade agreements that do not include the US. The result may be US disengagement from the world trading system, with countries diverting trade around the US. We use a global computable general equilibrium (CGE) model to analyze changes in trade and production patterns for a variety of scenarios, including US trade wars. We find that adjustment to US disengagement is feasible—the changes in trade shares as countries divert trade are modest and achievable, with very little impact on the volume of global trade. If the US engages in trade wars with major partners, the best response for other trade partners is to sit out the trade war, accept higher US tariffs, and gain markets from global trade diversion around the US. Finally, because of indirect effects, widespread tariffs fail to benefit the protected sectors—a fallacy of composition in a protectionist trade policy.     

Bayesian bridge-randomized penalized quantile regression estimation for linear regression model with AP(q) perturbation

Bridge penalized regression has many desirable statistical properties such as unbiasedness, sparseness as well as ‘oracle’. In Bayesian framework, bridge regularized penalty can be implemented based on generalized Gaussian distribution (GGD) prior. In this paper, we incorporate Bayesian bridge-randomized penalty and its adaptive version into the quantile regression (QR) models with autoregressive perturbations to conduct Bayesian penalization estimation. Employing the working likelihood of the asymmetric Laplace distribution (ALD) perturbations, the Bayesian joint hierarchical models are established. Based on the mixture representations of the ALD and generalized Gaussian distribution (GGD) priors of coefficients, the hybrid algorithms based on Gibbs sampler and Metropolis-Hasting sampler are provided to conduct fully Bayesian posterior estimation. Finally, the proposed Bayesian procedures are illustrated by some simulation examples and applied to a real data application of the electricity consumption.     

Complementary person-supervisor fit: An investigation of supplies-values (S-V) fit,leader-member exchange (LMX) and work outcomes

By applying the supplies-values (S-V) fit approach from the complementary person-environment (P-E) fit literature to the leader-employee perspective, and drawing upon social exchange theory, we examine how fulfillment of different work values is related to Leader-Member Exchange (LMX) and work outcomes. First, polynomial regression analyses combined with response surface analysis of data collected at two time points (N = 316) showed that LMX (Time 2) was higher the more the leader fulfills the employee's work values (Time 1). Second, LMX (Time 2) was higher when leader supplies (Time 1) and employee work values (Time 1) were both high than when both were low. Third, analyses of data from a sub-sample of matched leader-employee dyads (N = 140), showed that LMX (Time 2) played a mediating role on the relation between S-V fit (Time 1) and work outcomes (Time 2). Specifically, we found eight out of 10 relationships between S-V fit (Time 1) and leader-rated task performance and OCB (Time 2) to be fully mediated by LMX (Time 2). LMX (Time 2) partially mediated the relation between S-V fit (Time 1) and job satisfaction (Time 2) as only two out of five relationships were fully mediated.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.     

On the enumeration of rectangular (0, 1)-matrices

The construction and enumeration of (0, 1)-matrices with given line-sums is described for the rectangular cases often encountered in applications. Improved approximations are provided for the number of such matrices. Some new enumeration results for semi-regular bipartite graphs are included, and the related category of the quasi-semiregular bipartite graphs is recognized. The range of certain elements of products of a (0, I)-matrix is considered as a function of the line-sums. This, in turn, is related to the range in the numbers of interchanges available. Improvements in statistical practice that come from these constructions and enumerations are described.     

语义场理论对科技术语翻译的启示从——“水资源管理”一词的英汉互译谈起

科技术语在不同语言之间的翻译是一个复杂的语义匹配过程，如何在考虑相关术语使用现状的基础上将新产生的科技术语准确翻译为其它语言是科技工作者和术语研究者关注的重要问题。以语义场理论为分析框架，通过分析以"水资源管理"及其相关术语为代表的英汉互译，结果发现：不同科技术语在同一语义场所涵盖的语义范围将会随着相关研究深化带来的术语界定细化而发生相应的动态变化；新兴科技术语的翻译要充分考虑其所在语义场其它相关科技术语的翻译先例和使用现状所带来的影响，在翻译准确性和解读便利性之间做出适当的平衡。这是造成"水资源管理"及相关术语英汉互译处理极为复杂的重要原因之一，需要科技翻译者在工作实践中多加注意。     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

Smoking during pregnancy,stigma and secrets: Visual methods exploration in the UK

BackgroundMoral judgements are commonly directed towards mothers through reference to health behaviour in pregnancy, and working-class mothers are particularly subject to this moral gaze.AimTo gain an in-depth understanding of the health issues affecting 10 low income pregnant women from deprived areas of south Wales, UK.MethodsParticipants completed visual activities (timelines, collaging or thought bubbles and dyad sandboxing) prior to each interview. Participants’ visual representations were used in place of a topic guide, to direct the interview. Guided by feminist principles, 28 interviews were completed with 10 women. Data were analysed thematically.FindingsSmoking was discussed at length during interviews, and this paper focuses on this issue alone. Five of the participants had smoked during pregnancy. Negative reactions were directed towards pregnant women who smoked in public, resulting in maternal smoking being undertaken in private. Participants also reported awkward relationships with midwives and other health professionals, including receipt of public health advice in a judgemental tone.DiscussionSmoking during pregnancy is a particularly demonised and stigmatised activity. This stigma is not always related to the level of risk to the foetus, and instead can be seen as a moral judgement about women. We urgently need to move from individualised neo-liberal discourses about the failure of individual smokers, to a more socio-ecological view which avoids victim blaming.ConclusionStigma from friends, family, strangers and health professionals may lead to hidden smoking. This is a barrier to women obtaining evidence based stop smoking support.     

Evaluation of bank branch growth potential using data envelopment analysis

Banks occasionally employ frontier efficiency analyses to objectively identify best practices within their organizations. Amongst such methods, Data Envelopment Analysis (DEA) was found to be one of the leading approaches. DEA has been successfully applied in many bank branch performance evaluations using traditional intermediation, profitability and production approaches. However, there has been little focus on assessing the growth potential of individual branches.This research presents five models that examine three perspectives of branch growth. Each model was applied to the branch network of one of Canada׳s top five banks to gauge the growth potential of individual branches and to provide tailored improvement recommendations. Using various analysis methodologies, the results of each model were examined and their functionality assessed.