Religion in the Lives of Older Women Serving Life in Prison

This exploratory qualitative study examined the roles of religiousness in the lives of 21 older women (mean age = 63) serving life sentences in one southern state. Based on survey and focus group methodology, several themes emerged as important focal points, including early religious experiences prior to incarceration, imprisonment and religious doubt, participation in formal and informal religious activities, and use of religion in coping with stressors such as interpersonal relationships, trauma, and health statuses. Irrespective of their religious upbringing, all participants agreed that religiosity/spirituality played a key role in their ability to cope with their prison experience as well as for maintaining hope and the opportunity for release.     

转变发展方式必须率先转变调整结构的方式

面对新的发展形势和经济任务，应该明确提出产业结构调整的新思路，即政府创造条件，靠市场来调结构。“十二五”期间，最优先考虑的问题应该是如何改革、如何转变结构调整的方式。倘若“调结构”的方式、特别是实体经济中制造业“调结构”的方式不能率先改革，转变发展方式的诸多目标就有可能落空。“十二五”期间的政策落脚点在于继续加快培育“统一开放、竞争有序”的现代市场体系的进程，切实贯彻中央一再强调的“资源配置要建立在市场调节的基础上”的施政原则，把产业结构的市场化调节落到实处。对于以制造业为基础的结构存量调整的方式，要基本实现从行政化到市场化的转变，强化金融、商贸等服务业的疏导、中介功能，创建有利于推进服务业深度参与存量结构调整和产业升级的体制政策环境。     

Optimal cut-off for rare events and unbalanced misclassification costs

This paper develops a method for handling two-class classification problems with highly unbalanced class sizes and misclassification costs. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional classification methods tend to strongly favour the majority class, resulting in very low detection of the minority class. A method is proposed to determine the optimal cut-off for asymmetric misclassification costs and for unbalanced class sizes. Monte Carlo simulations show that this proposal performs better than the method based on the notion of classification accuracy. Finally, the proposed method is applied to empirical data on Italian small and medium enterprises to classify them into default and non-default groups.     

Production and changeover control policies of a class of failure prone buffered flow-shops

This article deals with a stochastic optimal control problem for a class of buffered multi-parts flow-shops manufacturing system. The involved machines are subject to random breakdowns and repairs. The flow-shop under consideration is not completely flexible and hence requires setup time and cost in order to switch the production from a part type to another, this changeover is carried on the whole line. Our objective is to find the production plan and the sequence of setups that minimise the cost function, which penalises inventories/backlogs and setups. A continuous dynamic programming formulation of the problem is presented. Then, a numerical scheme is adopted to solve the obtained optimality conditions equations for a two buffered serial machines two parts case. A complete heuristic policy, based on the numerical observations which describe the optimal policies in system states, is developed. It will be shown that the obtained policy is a combination of a KANBAN/CONWIP and a modified hedging corridor policy. Moreover, based on our observations and existent research studies extension to cover more complex flow-shops is henceforth possible. The robustness of such a policy is illustrated through sensitivity analysis.     

Risk Assessment for Hospital-Acquired Diseases: A Risk-Theory Approach

We introduce a new approach to hospital-acquired disease risk assessment from public health databases. In a spirit similar to actuarial risk theory, we define an adjustment coefficient that can quantify the risk associated with a hospital department, allowing comparisons of similar departments. The adjustment coefficient characterizes the tail of the distribution of the total patient length of stay in a department before the first disease event occurs. We show that this coefficient is the solution of a Lundberg-like equation, and we provide a nonparametric estimation procedure for this measure, based on a Cramér-Lundberg approximation for the tail of the distribution. Using simulations, we provide evidence of the robustness of the approximation to various individual risk models. In addition, we illustrate the relevance of this approach by evaluating the risk associated with a standard patient safety indicator in 20 hospitals of southeastern France.     

广告语篇连贯形成的动态模式分析

指出作为一种特殊的交际形式,广告语篇具有隐含性特点,因此,其连贯也呈现出鲜明的特征,即其连贯是隐性的;广告语篇连贯的形成过程是一个语言使用者不断建立心理空间的动态过程。     

兰州城关区回族居住格局研究

本文运用都市人类学的研究方法,对兰州市城关区回族的居住格局进行了研究。研究表明,随着民族迁移、社会发展、城市化进程的加快,聚居区转变为杂居区,以及城区边缘地带出现的新聚居区,这种变化还在进行之中。这种居住格局的变化虽然促进了民族团结、社会安定与发展,但是也出现了一些新问题。为此,本文对未来回族的居住格局进行了初步设想。     

从国际视野看研究生教育成本分担

在西方国家,研究生交费入学,即生均教育成本分担被视为必然现象。如何联系我国研究生教育发展的实际,积极借鉴发达国家发展研究生教育的经验,把制订研究生教育成本分担与资助政策作为推进我国研究生教育运行机制改革的有效手段,已成为人们关注的焦点。建议在构建我国研究生教育投资主体多元化与资助政策的过程中,积极引进政策调节与市场竞争相结合的机制;实行研究生教育成本分担与资助的政策措施要与高校内部管理体制的改革有机结合起来。     

Comment on “Constrained Optimization Approaches to Estimation of Structural Models”

We revisit the comparison of mathematical programming with equilibrium constraints (MPEC) and nested fixed point (NFXP) algorithms for estimating structural dynamic models by Su and Judd (2012). Their implementation of the nested fixed point algorithm used successive approximations to solve the inner fixed point problem (NFXP‐SA). We redo their comparison using the more efficient version of NFXP proposed by Rust (1987), which combines successive approximations and Newton–Kantorovich iterations to solve the fixed point problem (NFXP‐NK). We show that MPEC and NFXP are similar in speed and numerical performance when the more efficient NFXP‐NK variant is used.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.