Changes in Family Relationships among Substance Abusing Runaway Adolescents: A Comparison between Family and Individual Therapies

Eligible adolescents (12–17 years old) were recruited from a short‐term crisis shelter for runaway adolescents in a large Midwestern city. Adolescents (N = 179) were randomly assigned to Ecologically‐Based Family Therapy (EBFT, n = 61), the Community Reinforcement Approach (CRA, n = 57), or brief Motivational Enhancement Therapy (MET, n = 61) with the primary focus on substance abuse. A significant increase in perceived family cohesion and a significant reduction in perceived family conflict were found among all treatment conditions from baseline to the 24‐month follow‐up. Adolescents who received EBFT demonstrated more improvement in family cohesion after treatment than those who received CRA or MET, and more reduction in family conflict during treatment than those who received MET.     

Tail Behavior and Limit Distribution of Maximum of Logarithmic General Error Distribution

Logarithmic general error distribution, an extension of the log-normal distribution, is proposed. Some interesting properties of the log GED are derived. These properties are applied to establish the asymptotic behavior of the ratio of probability densities and the ratio of the tails of the logarithmic general error and log-normal distributions, and to derive the asymptotic distribution of the partial maximum of an independent and identically distributed sequence obeying the log GED.     

Feature screening in ultrahigh-dimensional additive Cox model

The additive Cox model is flexible and powerful for modelling the dynamic changes of regression coefficients in the survival analysis. This paper is concerned with feature screening for the additive Cox model with ultrahigh-dimensional covariates. The proposed screening procedure can effectively identify active predictors. That is, with probability tending to one, the selected variable set includes the actual active predictors. In order to carry out the proposed procedure, we propose an effective algorithm and establish the ascent property of the proposed algorithm. We further prove that the proposed procedure possesses the sure screening property. Furthermore, we examine the finite sample performance of the proposed procedure via Monte Carlo simulations, and illustrate the proposed procedure by a real data example.     

Agent‐Based Recovery Model for Seismic Resilience Evaluation of Electrified Communities

In this article, an agent‐based framework to quantify the seismic resilience of an electric power supply system (EPSS) and the community it serves is presented. Within the framework, the loss and restoration of the EPSS power generation and delivery capacity and of the power demand from the served community are used to assess the electric power deficit during the damage absorption and recovery processes. Damage to the components of the EPSS and of the community‐built environment is evaluated using the seismic fragility functions. The restoration of the community electric power demand is evaluated using the seismic recovery functions. However, the postearthquake EPSS recovery process is modeled using an agent‐based model with two agents, the EPSS Operator and the Community Administrator. The resilience of the EPSS–community system is quantified using direct, EPSS‐related, societal, and community‐related indicators. Parametric studies are carried out to quantify the influence of different seismic hazard scenarios, agent characteristics, and power dispatch strategies on the EPSS–community seismic resilience. The use of the agent‐based modeling framework enabled a rational formulation of the postearthquake recovery phase and highlighted the interaction between the EPSS and the community in the recovery process not quantified in resilience models developed to date. Furthermore, it shows that the resilience of different community sectors can be enhanced by different power dispatch strategies. The proposed agent‐based EPSS–community system resilience quantification framework can be used to develop better community and infrastructure system risk governance policies.     

产品服务供应链的“双重收益共享”合作机制

产品与服务融合缓解了"供需错位"矛盾，使得产品服务供应链探索成为"供给侧结构性改革"下供需结构性匹配的重要路径。本文刻画产品服务视角下供应链组织结构的本质特征，构建产品服务供应链合作模型，探讨合作机制下"帕累托改进"区间存在性，揭示合作机制对产品服务供应链效率的作用机理。核心研究发现：供应链多元化组织结构加剧收益分配冲突，并导致服务规模与供应链效率"倒挂"现象，而合作机制下"双重收益共享"合作模型能提升产品服务供应链效率。     

日本思想史脉络中的中国--以竹内好的中国论述为例CSSCI

日本战败之后,经历战争的一代知识分子曾经为实现中日邦交正常化而致力于思想与知识生产,但随着世代更迭,战争记忆的传承在思想史上经历了一个断裂时期,而日本的学院体制也妨碍了其中国观的建设性发展。竹内好在这个时期敏锐地察觉到,日本社会对中国日益失去探求心的现象背后隐藏着一个巨大的危险,即在日本,中国有史以来第一次成为“未知国度”。中日之间为了真正消除战争,相互理解并不能仅仅停留在友好的层面,竹内好设想的“内在于日本的中国”至今对我们仍具有认识论的启示意义。     

A class of partially linear transformation models for recurrent gap times

In this article, we propose a general class of partially linear transformation models for recurrent gap time data, which extends the linear transformation models by incorporating non linear covariate effects and includes the partially linear proportional hazards and the partially linear proportional odds models as special cases. Both global and local estimating equations are developed to estimate the parametric and non parametric covariate effects, and the asymptotic properties of the resulting estimators are established. The finite-sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease is provided.     

A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach

The generalized linear model (GLM) is a class of regression models where the means of the response variables and the linear predictors are joined through a link function. Standard GLM assumes the link function is fixed, and one can form more flexible GLM by either estimating the flexible link function from a parametric family of link functions or estimating it nonparametically. In this paper, we propose a new algorithm that uses P-spline for nonparametrically estimating the link function which is guaranteed to be monotone. It is equivalent to fit the generalized single index model with monotonicity constraint. We also conduct extensive simulation studies to compare our nonparametric approach for estimating link function with various parametric approaches, including traditional logit, probit and robit link functions, and two recently developed link functions, the generalized extreme value link and the symmetric power logit link. The simulation study shows that the link function estimated nonparametrically by our proposed algorithm performs well under a wide range of different true link functions and outperforms parametric approaches when they are misspecified. A real data example is used to illustrate the results.     

Digital Strategies for Building Spiritual Intimacy: Families on a “Wired” Camino

Qualitative Sociology - Researchers have explored the role that information and communication technologies (ICTs) play in mediating both religious practice and intergenerational family...     

Multi-neighborhood based iterated tabu search for routing and wavelength assignment problem

The routing and wavelength assignment problem (RWA) has shown to be NP-hard if the wavelength continuity constraint and the objective of minimizing the number of wavelengths are considered. This paper introduces a multi-neighborhood based iterated tabu search algorithm (MN-ITS), which consists of three neighborhoods with a unified incremental evaluation method, to solve the min-RWA problem. The proposed MN-ITS algorithm is tested on a set of widely studied real world instances as well as a set of challenging random ones in the literature. Comparison with other reference algorithms shows that the MN-ITS algorithm is able to improve five best upper bounds obtained by other competitive reference algorithms in the literature. This paper also presents an analysis to show the significance of the unified incremental evaluation technique and the combination of multiple neighborhoods.