Culture,Structure, and the Refugee Experience in Somali Immigrant Family Transformation

This analysis suggests a theoretical refinement of migrant acculturation theories to deal specifically with refugee acculturation experiences. Using the case of family dynamics among Somali refugees in Minnesota, we find that the same factors that are theorized to affect voluntary migrants are also important for refugees. Specifically, the nature of exit from the sending society, the reception in a new location, and group characteristics all appear to be important. However, within the category of exit from the sending society, there are specific concerns that will be more relevant to refugees than to “voluntary” migrants. Specifically, the ongoing condition of the sending society and the effects of any transitions on transnational ties are critically important in the refugee context. We demonstrate how the societal upheaval that created the Somali refugee community also affected culture and connections within Somalia, and how this has an ongoing impact on the US Somali refugee community. We argue that it is valuable to refine the acculturation framework when considering refugees.     

The effect of Phase I sample size on the run length performance of control charts for autocorrelated data

Traditional control charts assume independence of observations obtained from the monitored process. However, if the observations are autocorrelated, these charts often do not perform as intended by the design requirements. Recently, several control charts have been proposed to deal with autocorrelated observations. The residual chart, modified Shewhart chart, EWMAST chart, and ARMA chart are such charts widely used for monitoring the occurrence of assignable causes in a process when the process exhibits inherent autocorrelation. Besides autocorrelation, one other issue is the unknown values of true process parameters to be used in the control chart design, which are often estimated from a reference sample of in-control observations. Performances of the above-mentioned control charts for autocorrelated processes are significantly affected by the sample size used in a Phase I study to estimate the control chart parameters. In this study, we investigate the effect of Phase I sample size on the run length performance of these four charts for monitoring the changes in the mean of an autocorrelated process, namely an AR(1) process. A discussion of the practical implications of the results and suggestions on the sample size requirements for effective process monitoring are provided.     

Comparison between Bayesian approach and frequentist methods for estimating relative risk in randomized controlled trials: a simulation study

Relative risks (RRs) are often considered as preferred measures of association in randomized controlled trials especially when the binary outcome of interest is common. To directly estimate RRs, log-binomial regression has been recommended. Although log-binomial regression is a special case of generalized linear models, it does not respect the natural parameter constraints, and maximum likelihood estimation is often subject to numerical instability that leads to convergence problems. Alternative methods for solving log-binomial regression convergence problems have been proposed. A Bayesian approach also was introduced, but the comparison between this method and frequentist methods has not been fully explored. We compared five frequentist and one Bayesian methods for estimating RRs under a variety of scenario. Based on our simulation study, there is not a method that can perform well based on different statistical properties, but COPY 1000 and modified log-Poisson regression can be considered in practice.     

On a class of shrinkage estimators of vector of regression coefficients the

This paper studies a class of shrinkage estimators of the vector of regression coefficients. The small disturbance approximations for the bias and the mean squared error matrix of the estimator are derived. In the sense of mean squared error, these estimators dominate the least squares estimator and the generalized Stein estimator developed by Hosmane (1988).     

Determining Six Sigma success factors in Turkey by using structural equation modeling

Since it includes strong statistical and executive techniques, Six Sigma (SS) succeeded in many countries and different sectors. Especially successful SS applications of many international companies have increased the interest of other companies. As a result of this, the number of implemented SS projects in various countries has increased. Although successful SS projects are often in mind, the number of failed projects because of various reasons is not as low as to be ignored. As well as there are many factors that affect the success level of SS projects, and these factors vary according to countries. In this study, a survey was applied to 117 people who have 1 of SS belts in order to determine success levels of the SS projects in Turkey. By using explanatory factor analysis and structural equation modeling, critical success factors were determined. According to the results, project selection and its scope, quality culture and defining and measuring of metrics were determined as the top factors that are affecting success levels of SS projects applied in Turkey. The results of the study were also compared with the results of similar projects implemented in other countries.     

基于扩展线性支出系统模型的我国城镇居民消费结构分析

根据统计资料,运用扩展线性支出系统模型,对1993～2006年间我国城镇居民消费结构进行计量研究,对居民边际消费倾向、收入弹性、自价格和交互价格弹性等进行数量分析,对我国城镇居民消费趋势进行预测,并提出一些政策性建议,旨在为促进国内市场与消费需求持续稳定增长提供参考。     

Human resource development competencies as predictors of agricultural extension agents' performance in Yemen

Human resource development (HRD) issue is recognized as a significant factor for survival of organizations and professionals, including those in the sector of agricultural extension. Few empirical studies have examined the influence of HRD competency variables on agricultural extension agents' performance. The authors argue that HRD competency variables such as leadership development, communication, programme planning, programme implementation and programme evaluation could predict agricultural extension agents' performance. Questionnaire data were collected from a sample of 290 extension agents in Yemen to discover if HRD competencies affect the performance of extension agents. Regression analysis shows that three variables – competencies in programme implementation, programme evaluation and programme planning – contributed significantly to the performance of extension agents. These predictors explain 42.2% of the variance in the job performance model.     

Organizational wisdom practices and firm product innovation

Review of Managerial Science - The manner in which organizational wisdom-related variables or practices (e.g., reasoning, intuition, virtue, prudence, and aesthetics) are related to firm product...     

Aviation Risk Perception: A Comparison Between Experts and Novices

This article describes an exploratory investigation of the risk perceptions of experts and novices in relation to helicopter operations, under conditions where the participants are matched on various characteristics previously found to affect perceptions, such as demographic, gender, and background factors. The study reports considerable evidence of perceptual differences between the two participant groups (i.e., expert pilots and candidate pilots). We find that the experts' perceptions of relative risks are more veridical, in terms of their higher correlation with the true relative frequencies. A significant positive correlation between the flight hours and the contextual risk-taking tendency is also shown, leading the experienced pilots' choices toward risky alternatives in scenarios--a potential result of their overconfidence based on superior task performance. Possible explanations are offered for the findings and potential avenues for future research are identified.     

A sustainable supply chain network considering lot sizing with quantity discounts under disruption risks: centralized and decentralized models

This study proposes a framework for the main parties of a sustainable supply chain network considering lot-sizing impact with quantity discounts under disruption risk among the first studies. The proposed problem differs from most studies considering supplier selection and order allocation in this area. First, regarding the concept of the triple bottom line, total cost, environmental emissions, and job opportunities are considered to cover the criteria of sustainability. Second, the application of this supply chain network is transformer production. Third, applying an economic order quantity model lets our model have a smart inventory plan to control the uncertainties. Most significantly, we present both centralized and decentralized optimization models to cope with the considered problem. The proposed centralized model focuses on pricing and inventory decisions of a supply chain network with a focus on supplier selection and order allocation parts. This model is formulated by a scenario-based stochastic mixed-integer non-linear programming approach. Our second model focuses on the competition of suppliers based on the price of products with regard to sustainability. In this regard, a Stackelberg game model is developed. Based on this comparison, we can see that the sum of the costs for both levels is lower than the cost without the bi-level approach. However, the computational time for the bi-level approach is more than for the centralized model. This means that the proposed optimization model can better solve our problem to achieve a better solution than the centralized optimization model. However, obtaining this better answer also requires more processing time. To address both optimization models, a hybrid bio-inspired metaheuristic as the hybrid of imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) is utilized. The proposed algorithm is compared with its individuals. All employed optimizers have been tuned by the Taguchi method and validated by an exact solver in small sizes. Numerical results show that striking similarities are observed between the results of the algorithms, but the standard deviations of PSO and ICA–PSO show better behavior. Furthermore, while PSO consumes less time among the metaheuristics, the proposed hybrid metaheuristic named ICA–PSO shows more time computations in all small instances. Finally, the provided results confirm the efficiency and the performance of the proposed framework and the proposed hybrid metaheuristic algorithm.