A non-homogeneous skew-Gaussian Bayesian spatial model

In spatial statistics, models are often constructed based on some common, but possible restrictive assumptions for the underlying spatial process, including Gaussianity as well as stationarity and isotropy. However, these assumptions are frequently violated in applied problems. In order to simultaneously handle skewness and non-homogeneity (i.e., non-stationarity and anisotropy), we develop the fixed rank kriging model through the use of skew-normal distribution for its non-spatial latent variables. Our approach to spatial modeling is easy to implement and also provides a great flexibility in adjusting to skewed and large datasets with heterogeneous correlation structures. We adopt a Bayesian framework for our analysis, and describe a simple MCMC algorithm for sampling from the posterior distribution of the model parameters and performing spatial prediction. Through a simulation study, we demonstrate that the proposed model could detect departures from normality and, for illustration, we analyze a synthetic dataset of CO\(_2\) measurements. Finally, to deal with multivariate spatial data showing some degree of skewness, a multivariate extension of the model is also provided.     

Spatio-temporal modeling and prediction of CO concentrations in Tehran city

One of the most important agents responsible for high pollution in Tehran is carbon monoxide. Prediction of carbon monoxide is of immense help for sustaining the inhabitants’ health level. In this paper, motivated by the statistical analysis of carbon monoxide using the empirical Bayes approach, we deal with the issue of prior specification for the model parameters. In fact, the hyperparameters (the parameters of the prior law) are estimated based on a sampling-based method which depends only on the specification of the marginal spatial and temporal correlation structures. We compare the predictive performance of this approach with the type II maximum likelihood method. Results indicate that the proposed procedure performs better for this data set.     

Classification when the covariate vectors have unequal dimensions

This article deals with the problem of statistical classification when the covariate vectors can have unequal dimensions. Representations of the theoretically best classifier are given. We also propose a number of procedures for constructing consistent classifiers. Both parametric and nonparametric situations are considered but the emphasis is on the latter case. Numerical examples are also given.     

The Impact of in-Service Training and Motivation on Job Performance of Technical & Vocational Education Teachers: Role of Person-Job Fit

In-service education and training (INSET), motivation and person-job fit of workers are considered vital for the performance of employees. The focused purpose of the study is to examine the associations between in-service training and motivation impact on job performance of Technical Vocational Education and Training (TVET) teachers, while examining the moderating role of person-job fit. Using convenience sampling technique, data is collected from TVET teachers in South Asia. The finding of this study revealed a significant impact for in-service training and motivation on job performance. This research study is useful for the HR managers and practitioners to utilize their employee’s true potential to get the desired results.

     

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method–local linear neuro-fuzzy–adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.     

The Impact of Organizational Change on Employee Turnover Intention: Does Stress Play a Mediating Role?

This study examined the relationship between change interventions and employee turnover intention to see if change interventions lead to increased employee turnover intention. Moreover, the mediating role of employee stress was tested. Data were collected from 162 respondents working at various administrative positions in a large public organization. The results indicate that employee turnover intention is positively correlated to human process, techno structural, human resources, and strategic interventions. Limitations and direction for future research are provided.     

Breaking Bad,Making Good: Notes on a Televisual Tourist Industry

This article explores emerging intersections between the consumption of mediated popular culture and the real and imagined topographies within which those representations are framed. Through an examination of the ‘televisual tourism’ centred around the successful TV series Breaking Bad, we scrutinise the multiple modes of sensorial and embodied travel experience enjoyed by fans of the show as they consume their way around the show’s sites, scenes and tastes in the city of Albuquerque. This exploitation of media textuality through fan tourism is, we suggest, centred upon a carefully managed commodification of crime, criminality and transgression.     

Randomized heuristic algorithms for orthogonal projection of a point onto a set

Abstract

The problem of orthogonal projection of a point onto a set is an essential problem of computational geometry. This problem has many practical applications in different areas such as robotics, computer graphics and so on. In the present paper three algorithms for solving this problem are proposed. This algorithms are based on the idea of heuristic random search. Numerical experiments illustrating the work of the proposed methods are presented.     

Household welfare and women empowerment through microcredit financing: Evidence from Malaysia microcredit

Microcredit financing is considered a crucial vehicle to flourish the socioeconomic conditions of women and women empowerment, especially among those who are from low-income households and deprived of access to financial services. Nevertheless, the literature arrived with a conflicting conclusion about its effect and women demonstrate that microcredit has small or no effect. This study is intended to scrutinize the impact of a productive loan provided by Amanah Ikhtiar Malaysia (AIM) on women household welfare and empowerment. A cross-sectional survey was employed through the distribution of a questionnaire to 495 of old and new borrowers. We observe that microcredit has significant positive effects on borrowers’ household income and personal asset acquisition. However, even though the effect of microcredit empowers women borrowers in households’ decisions-making, it has no effect on women control over minor financials.