Clergy views on their role in city resilience: lessons from the Canterbury earthquakes

Successful emergency management requires effective communication between the civil defence agencies and the community to provide and receive timely and accurate information, and identify needs and resources. Religious communities encompass a wide cross-section of social classes, socio-economic indicators, ethnic groups and ages, and have their own internal communication structures. In this study, clergy in different Christian denominations were asked about their roles in the Canterbury earthquake sequence (CES), the needs of their congregations and the possibilities and obstacles to deeper collaboration with Civil Defence and Emergency Management (CDEM) authorities. Results show a lack of communication between CDEM and religious communities prior to and during the CES, and record suggestions from the participants on how links between organised religion and CDEM can be strengthened to build resilience for future disasters.     

Russian Bioscience Publications and Journals in International Bibliometric Databases

This article analyzes shares and rankings of Russian bioscience publications and journals in international bibliometric databases. Studying scholarly output, international collaboration, and the share of Russian papers both in national and international journals enables the authors to highlight the current state of biological science and journals in Russia and outline some pronounced trends. A comparative analysis with the leading countries confirms good representation of Russian bioscience journals among other international serials. Certain problems remain unsolved, however: low impact factors, overrepresentation of national papers, and poor online services. Thus, Russian publishers should correct their editorial policy to improve availability and performance of serials.     

Young,Black, and (Still) in the Red: Parental Wealth,Race, and Student Loan Debt

Taking out student loans to assist with the costs of postsecondary schooling in the US has become the norm in recent decades. The debt burden young adults acquire during the higher education process, however, is increasingly stratified with black young adults holding greater debt burden than whites. Using data from the NLSY 1997 cohort, we examine racial differences in student loan debt acquisition and parental net wealth as a predictor contributing to this growing divide. We have four main results. First, confirming prior research, black young adults have substantially more debt than their white counterparts. Second, we find that this difference is partially explained by differences in wealth, family background, postsecondary educational differences, and family contributions to college. Third, young adults’ net worth explain a portion of the black–white disparity in debt, suggesting that both differences in accumulation of debt and ability to repay debt in young adulthood explain racial disparities in debt. Fourth, the black–white disparity in debt is greatest at the highest levels of parents’ net worth. Our findings show that while social and economic experiences can help explain racial disparities in debt, the situation is more precarious for black youth, who are not protected by their parents’ wealth. This suggests that the increasing costs of higher education and corresponding rise in student loan debt are creating a new form of stratification for recent cohorts of young adults, and that student loan debt may be a new mechanism by which racial economic disparities are inherited across generations.     

Computing first-order sensitivity indices with contribution to the sample mean plot

In this paper, we investigate the use of the contribution to the sample mean plot (CSM plot) as a graphical tool for sensitivity analysis (SA) of computational models. We first provide an exact formula that links, for each uncertain model input X_j, the CSM plot C_j(·) with the first-order variance-based sensitivity index S_j. We then build a new estimate for S_j using polynomial regression of the CSM plot. This estimation procedure allows the computation of S_j from given data, without any SA-specific design of experiment. Numerical results show that this new S_j estimate is efficient for large sample sizes, but that at small sample sizes it does not compare well with other S_j estimation techniques based on given data, such as the effective algorithm for computing global sensitivity indices method or metamodel-based approaches.     

Inference for the Birnbaum–Saunders Lifetime Regression Model with Applications

The Birnbaum–Saunders distribution is a positively skewed distribution that is frequently used for analyzing lifetime data. Regression analysis is widely used in this context when some covariates are involved in the life-test. In this article, we discuss the maximum likelihood estimation of the model parameters and associated inference. We discuss the likelihood-ratio tests for some hypotheses of interest as well as some interval estimation methods. A Monte Carlo simulation study is then carried out to examine the performance of the proposed estimators and the interval estimation methods. Finally, some numerical data analyses are done for illustrating all the inferential methods developed here.