The impact of short-term exposure to ambient air pollution on test scores in Iran

Population and Environment - Air pollution in urban centers has become a key public concern around the world. Apart from its adverse health effects, air pollution could impact less visible outcomes...     

A Comparison of Bayesian Models of Heteroscedasticity in Nested Normal Data

We consider the fitting of a Bayesian model to grouped data in which observations are assumed normally distributed around group means that are themselves normally distributed, and consider several alternatives for accommodating the possibility of heteroscedasticity within the data. We consider the case where the underlying distribution of the variances is unknown, and investigate several candidate prior distributions for those variances. In each case, the parameters of the candidate priors (the hyperparameters) are themselves given uninformative priors (hyperpriors). The most mathematically convenient model for the group variances is to assign them inverse gamma distributed priors, the inverse gamma distribution being the conjugate prior distribution for the unknown variance of a normal population. We demonstrate that for a wide class of underlying distributions of the group variances, a model that assigns the variances an inverse gamma-distributed prior displays favorable goodness-of-fit properties relative to other candidate priors, and hence may be used as standard for modeling such data. This allows us to take advantage of the elegant mathematical property of prior conjugacy in a wide variety of contexts without compromising model fitness. We test our findings on nine real world publicly available datasets from different domains, and on a wide range of artificially generated datasets.     

Validation Study: The Iranian Version of Caregiver Abuse Screen (CASE) among Family Caregivers of Elderly with Dementia

Since abuse screening is difficult among the elderly with dementia, detection of elder abuse is a serious issue; meanwhile, the number of available tools to assess elder abuse by caregivers is limited. The aim was to evaluate validity and reliability of the Iranian version Caregiver Abuse Screen (CASE) tool. After the translating process of the tool to the Farsi language for 236 dementia participants ≥60 years old and their informal caregivers’ from outpatient’ clinics, in a cross-sectional method then the validity, reliability and factorial structure of the CASE was evaluated. The translated Iranian version CASE reveals a good psychometric property (α = 0.868) with strong internal consistency and reliability. Some variables (depression, burden, dementia intensity, and ADLs) were associated with abuse in dementia elderly. The two-factor structure was obtained in exploratory factor analysis labeled interpersonal abuse and neglect. Generally, the Iranian version of CASE approved with sufficient validity and reliability as a suitable instrument to find the risk of elder abuse among dementia caregivers.     

The concept of development: Smith and Marx revisited

This paper attempts to provide a framework for understanding and analyzing the social origins of underdevelopment in contemporary third world societies. In critically examining Adam Smith, Karl Marx, the dependency school, and the sociological modernization theories, it shows that the considerable dispute between the two commonly advanced explanations, “conservative” modernization theory and “radical” dependency theory is spurious. It is argued that neither Smith and the modernization theories that draw upon him, nor the dependency school, provide an appropriate framework for analyzing the historical rise of capitalism and examining the obstacles thereto.     

Returnable transport packaging in developing countries: drivers,barriers and business performance

Abstract

This study, drawing on natural resource-based view, identifies drivers, barriers and the potential benefits of returnable transport packaging (RTP) – that is, the repeated use of packaging items – and conceptualises RTP as a technology and resource that supports organisational competitiveness. Specifically, it investigates the impact of RTP adoption on business performance, the effects of drivers, barriers and size of organisations. The data collection took place in Nigeria and South Africa. The findings suggest that RTP has a significant positive impact on business performance. Whilst prior studies seem to suggest that shrinkage and attrition are the major problems identified with the usage of RTP, our findings indicate that there are several other barriers affecting RTP adoption and the resultant performance advantage. The results also show that there is an increasing move towards adoption of RTP but some organisations are faced with financial constraints, especially the small and medium size enterprises. In addition, the results show that RTP is largely a ‘sustainability facing’ initiative with adopters motivated primarily by potential environmental, economic, social and operational benefits of adoption.     

An efficient spread-based evolutionary algorithm for solving dynamic multi-objective optimization problems

Dynamic multi-objective optimization algorithms are used as powerful methods for solving many problems worldwide. Diversity, convergence, and adaptation to environment changes are three of the most important factors that dynamic multi-objective optimization algorithms try to improve. These factors are functions of exploration, exploitation, selection and adaptation operators. Thus, effective operators should be employed to achieve a robust dynamic optimization algorithm. The algorithm presented in this study is known as spread-based dynamic multi-objective algorithm (SBDMOA) that uses bi-directional mutation and convex crossover operators to exploit and explore the search space. The selection operator of the proposed algorithm is inspired by the spread metric to maximize diversity. When the environment changed, the proposed algorithm removes the dominated solutions and mutated all the non-dominated solutions for adaptation to the new environment. Then the selection operator is used to select desirable solutions from the population of non-dominated and mutated solutions. Generational distance, spread, and hypervolume metrics are employed to evaluate the convergence and diversity of solutions. The overall performance of the proposed algorithm is evaluated and investigated on FDA, DMOP, JY, and the heating optimization problem, by comparing it with the DNSGAII, MOEA/D-SV, DBOEA, KPEA, D-MOPSO, KT-DMOEA, Tr-DMOEA and PBDMO algorithms. Empirical results demonstrate the superiority of the proposed algorithm in comparison to other state-of-the-art algorithms.