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Challenges to a robust and accurate implementation of electric‐field‐enhanccd thermal‐generation mechanisms in a drift‐diffusion‐based semiconductor‐device simulation code are discussed and solutions proposed. The implementation of the physical models and associated numerical methods is applied to the simulation of leakage currents in trench‐DRAM cells.

The refugee journey is fraught with challenges before, during and after resettlement. There is a critical need for mental health support upon arrival, and refugees face language, cultural and logistical barriers. Arts-based therapies are a promising approach to mitigating such barriers. The purpose of this study was to elicit professional stakeholder perspectives on mental health challenges among refugees, the value of arts-based programs and future directions.

The authors conducted three 90-min focus groups with 19 professional stakeholders in North Carolina, USA. This included mental health professionals, professors and community services/resettlement workers. Participants were identified from professional networks and snowball sampling. Each group was held by videoconference, audio recorded and transcribed. Data were analyzed through a team-based approach using applied thematic qualitative analysis.

Interviewees described a need for targeted, culturally compatible mental health services for refugee families, including trauma-informed, family-focused services with language interpretation. Arts-based therapies were viewed as highly acceptable and culturally responsive approaches for understanding distress and building resilience and less stigmatizing than traditional mental health services. Services in schools and community settings would further reduce stigma and minimize logistical barriers. Participants identified needing strong, culturally sensitive assessment tools to measure treatment progress as a key future direction.

The study offers novel insights into the value of arts-based approaches and considerations for program development. The next phase of the project will obtain the perspectives of refugee parents and children to understand client preferences for arts-based therapies.

Acute and chronic pain affects more Americans than heart disease, diabetes, and cancer combined. Conservative estimates suggest the total economic cost of pain in the United States is $600 billion, and more than half of this cost is due to lost productivity, such as absenteeism, presenteeism, and turnover. In addition, an escalating opioid epidemic in the United States and abroad spurred by a lack of safe and effective pain management has magnified challenges to address pain in the workforce, particularly the military. Thus, it is imperative to investigate the organizational antecedents and consequences of pain and prescription opioid misuse (POM). This chapter provides a brief introduction to pain processing and the biopsychosocial model of pain, emphasizing the relationship between stress, emotional well-being, and pain in the military workforce. We review personal and organizational risk and protective factors for pain, such as post-traumatic stress disorder, optimism, perceived organizational support, and job strain. Further, we discuss the potential adverse impact of pain on organizational outcomes, the rise of POM in military personnel, and risk factors for POM in civilian and military populations. Lastly, we propose potential organizational interventions to mitigate pain and provide the future directions for work, stress, and pain research.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

In this paper, we study a partially linear dynamic panel data model with fixed effects, where either exogenous or endogenous variables or both enter the linear part, and the lagged-dependent variable together with some other exogenous variables enter the nonparametric part. Two types of estimation methods are proposed for the first-differenced model. One is composed of a semiparametric GMM estimator for the finite-dimensional parameter θ and a local polynomial estimator for the infinite-dimensional parameter m based on the empirical solutions to Fredholm integral equations of the second kind, and the other is a sieve IV estimate of the parametric and nonparametric components jointly. We study the asymptotic properties for these two types of estimates when the number of individuals N tends to ∞ and the time period T is fixed. We also propose a specification test for the linearity of the nonparametric component based on a weighted square distance between the parametric estimate under the linear restriction and the semiparametric estimate under the alternative. Monte Carlo simulations suggest that the proposed estimators and tests perform well in finite samples. We apply the model to study the relationship between intellectual property right (IPR) protection and economic growth, and find that IPR has a non-linear positive effect on the economic growth rate.

This chapter develops an asymptotic theory for a general transformation model with a time trend, stationary regressors, and unit root nonstationary regressors. This model extends that of Han (1987) to incorporate time trend and nonstationary regressors. When the transformation is specified as an identity function, the model reduces to the conventional cointegrating regression, possibly with a time trend and other stationary regressors, which has been studied in Phillips and Durlauf (1986) and Park and Phillips (1988, 1989). The limiting distributions of the extremum estimator of the transformation parameter and the plug-in estimators of other model parameters are found to critically depend upon the transformation function and the order of the time trend. Simulations demonstrate that the estimators perform well in finite samples.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

Statistical inference (estimation and testing) for the stochastic volatility (SV) model Taylor (1982, 1986) is challenging, especially likelihood-based methods which are difficult to apply due to the presence of latent variables. The existing methods are either computationally costly and/or inefficient. In this paper, we propose computationally simple estimators for the SV model, which are at the same time highly efficient. The proposed class of estimators uses a small number of moment equations derived from an ARMA representation associated with the SV model, along with the possibility of using “winsorization” to improve stability and efficiency. We call these ARMA-SV estimators. Closed-form expressions for ARMA-SV estimators are obtained, and no numerical optimization procedure or choice of initial parameter values is required. The asymptotic distributional theory of the proposed estimators is studied. Due to their computational simplicity, the ARMA-SV estimators allow one to make reliable – even exact – simulation-based inference, through the application of Monte Carlo (MC) test or bootstrap methods. We compare them in a simulation experiment with a wide array of alternative estimation methods, in terms of bias, root mean square error and computation time. In addition to confirming the enormous computational advantage of the proposed estimators, the results show that ARMA-SV estimators match (or exceed) alternative estimators in terms of precision, including the widely used Bayesian estimator. The proposed methods are applied to daily observations on the returns for three major stock prices (Coca-Cola, Walmart, Ford) and the S&P Composite Price Index (2000–2017). The results confirm the presence of stochastic volatility with strong persistence.

This article attempts to examine the impact that traditional island cultural events have on tourism development in some of the West Indian (Commonwealth Caribbean) islands, how national tourist boards and hotels use these mega‐events for promotional purposes, how these cultural events are looked upon by the tourists themselves, and the possible dilution of the basic traditions and cultures of the territories if they are abused for tourist gain only.

This chapter studies the dynamic responses of the conditional quantiles and their applications in macroeconomics and finance. The authors build a multi-equation autoregressive conditional quantile model and propose a new construction of quantile impulse response functions (QIRFs). The tool set of QIRFs provides detailed distributional evolution of an outcome variable to economic shocks. The authors show the left tail of economic activity is the most responsive to monetary policy and financial shocks. The impacts of the shocks on Growth-at-Risk (the 5% quantile of economic activity) during the Global Financial Crisis are assessed. The authors also examine how the economy responds to a hypothetical financial distress scenario.