Mapping vulnerability to climate change-related hazards: children at risk in a US–Mexico border metropolis

There are significant human impacts associated with climate change. This paper introduces a model for identifying small area risks associated with children’s vulnerability to climate change-related hazard exposures, which is transferable to other regions and adaptable to varied population and exposure scenarios. The cross-national El Paso-Ciudad Juárez (US-Mexico) metropolis serves as the study area for model implementation, which involves mapping social vulnerability, hazard exposure, and cumulative climate change-related risks. This study addresses two limitations of extant fine-scale climate change vulnerability mapping studies. First, rather than focusing on one exposure variable, it assesses the combined risks of multiple exposures (extreme heat, peak ozone, and floods) and, thus, offers a model for mapping neighborhood-level cumulative climate change exposure risks. Second, it provides a model for small area spatial analyses of climate change vulnerability within low-/middle-income countries and in contexts where climate change risks (and appropriate responses) are cross-national in scope.     

Flexible manufacturing systems performance in U.S. automotive manufacturing plants: a case study

Flexible manufacturing systems (FMS) philosophy is a key weapon in achieving global manufacturing competitiveness. It encompasses a wide range of dimensions to improve all aspects of operational performance metrics. The aim of the study is to examine the current state of flexibility adoption in U.S. automotive manufacturing facilities and its impact on operational performance metrics. It utilizes survey questionnaire developed based on previous work in U.S. manufacturing industry. The survey was originally distributed to 420 facility managers in the U.S. domestic automotive industry. It was revealed that 70% of the respondents had implemented all 15 flexibility dimensions listed in the questionnaire. The data analysis conducted shows that implementation of certain flexibility dimensions will lead to significant improvement in specific operational performance metrics. This considerable finding can be used as a guide for manufacturing managers to achieve certain objectives in operational performance improvement in a rapidly changing environment.     

Statistical Inference Based on Progressively Type II Censored Data from Weibull Model

In this article, we consider the problem of estimating the shape and scale parameters and predicting the unobserved removed data based on a progressive type II censored sample from the Weibull distribution. Maximum likelihood and Bayesian approaches are used to estimate the scale and shape parameters. The sampling-based method is used to draw Monte Carlo (MC) samples and it has been used to estimate the model parameters and also to predict the removed units in multiple stages of the censored sample. Two real datasets are presented and analyzed for illustrative purposes and Monte carlo simulations are performed to study the behavior of the proposed methods.