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Purpose: Pain and mood disorder frequently coexist. Yet, for Asian Americans (AAs), scant information about pain and mood disorder is available. Our aims were to compare (1) the rates of pain and mood disorders and (2) the magnitude of associations between pain and mood disorders between AAs and European Americans (EAs), and across different Asian subgroups. Methods: An analytical data was constructed from the Collaborative Psychiatric Epidemiology Studies (CPES), a representative sample of community-residing U.S. adults (n = 9,871). Pain morbidity was assessed by self-report. Mood disorders, including major depression and anxiety disorders, were assessed using the diagnostic interview. Analysis included descriptive statistics and multivariate logistic regression modeling. All analyses were weighted to approximate the U.S. populations, and controlled for sociodemographic and immigration characteristics. Results: Greater proportion of EAs, compared to AAs, endorsed lifetime pain (56.8% vs. 35.8%). Having life pain disorders elevated the likelihood of lifetime mood disorder by more than 2-folds (weight adjusted odds ratio (WAOR): 2.12, 95% CI: 1.77, 2.55). Having pain disorders over the past 12 months elevated the likelihood of mood disorder in the same time period by more than 3-folds (WAOR: 3.29, 95% CI: 2.02, 5.37) among AAs. The magnitude of the association between pain and psychiatric morbidity were greater in Vietnamese Americans compared to other AAs and EAs. Discussion: The conventional belief that rates of pain and mood disorders are greater in AAs than EAs may need to be further examined. Vietnamese Americans may be particularly vulnerable for experience of comorbid pain and mood disorders.

In shale gas field development, the major endogenous uncertainty comes from the production profile of candidate wells and its realization is dependent on the development decision. In this work, we apply multistage stochastic programming to address the shale gas field development planning problem under production profile uncertainty. Business realities such as budget limit and uncertainty resolution delay are also considered in the proposed model. To solve the multistage stochastic model, a Lagrangean decomposition method and a heuristic method are proposed. Computational results demonstrate the efficiency of the proposed methods on examples that are otherwise intractable. The optimal development strategy from the proposed model is analyzed under different levels of uncertainties. Different cases are further tested to figure out the impact of budget and resolution delay on development decisions. [ABSTRACT FROM AUTHOR]

Summary: Evaluation information generated by various users is processed using various requirements and data to make recommendations for solving the problems, and it analyzes satisfaction with the results. Despite people normally utilizes the processed information for decision making, not all information, however, brings positive outcomes to users. There are some users who perceived it negatively. In order to minimize the occurrence of such negative effects, the analysis of various user requirements is essential as well as diversifying user inputs for each requirement. Consequently, the results from individual inputs must be predicted. In the past, since the system relies on a single‐expert system, it is necessary to accept and process various limitations of recommendation and multiple requirements. Therefore, the results of the recommendation also have various problems. In order to solve this problem, this study applied an analytic hierarchy process to multiadvisor configuration. In the proposed system, one or multiple advisors are defined, and after analyzing the predefined requirements, the system accepts only the requirements that can be processed and calculates the individual recommendation results. A recommendation system was going to be studied by learning all situation. [ABSTRACT FROM AUTHOR]