浅析经济新闻的民生视角

随着我国市场经济的迅猛发展并趋于成熟,经济新闻越来越为人们所关注、重视。目前中国政府提出"改善民生",为"民生视角"的新闻报道提供了大量素材和广泛受众。大势所趋,媒体应以"民生视角"来报道经济新闻。电视民生新闻的崛起为"民生视角"的广播报道提供了借鉴。而"小角度"报道、"百姓立场"关注、"零距离"解读和"大容量"覆盖则是可行的方式。     

论北宋天圣年间四川仕风的嬗变——三苏文章汇入北宋古文运动的地域背景

北宋天圣以前川民「怀土罕趋仕进」。天圣以后,「应时务用」的观念成为了时代的主导观念,因而促成了「通经学古,以西汉文词为宗师」的蜀学汇入时代的主流。这是三苏文章与欧阳修古文革新思想合拍的重要背景。     

基于VaR风险控制的半log-最优资产组合模型

通过建立基于VaR风险控制下的单周期半log-最优资产组合数学模型,证明了最优解的存在性与唯一性。利用遗传算法对半log-最优资产组合模型进行了实例计算与分析,并与log-最优资产组合模型进行了比较,结果表明半log-最优资产组合模型具有计算方便的特征。     

‘Pains of imprisonment’: narratives of the women partners and children of the incarcerated

This paper focuses on an important line of inquiry in contemporary criminal justice social work research: the collateral consequences of incarceration in the Chinese context. It gives an insight into the impact of incarceration on families from an insider's perspective. An analysis of interviews with 10 partners and 10 children yielded three major themes concerning the impact of imprisonment: the problems associated with the imprisonment of a family member, the coping strategies used and views on the importance of maintaining family ties. It is hoped that this study will give practical guidance regarding what should be done, and how, to reduce the negative consequences brought about by the absence (temporary or permanent) of the imprisoned on their families, including both their partners and vulnerable children.     

王先谦《诗三家义集疏》探义

今古文两学派的较量是《诗经》问题争论史中的一大焦点,但因三家《诗》的陆续亡佚,使得后人要支持今文《诗》,得同时致力于辑佚与诠释两个工作才行,清末王先谦的《诗三家义集疏》就是这方面的名著。此书在前人的研究成果上继续前进,尽量铺陈三家原貌,以《毛诗》作为理解今文的参照系,条理清晰,而其分别家数时,主要是依照陈寿祺父子《三家诗遗说考》的成果,甚至陈氏取材不够精审的缺陷也被王氏所承续,所幸,其在面对旧有材料时仍能注意到筛选与考辨的工夫,所以呈现出来的材料仍较陈书稍显精审。     

Mixed integer (0–1) fractional programming for decision support in paper production industry

This paper presents an effective and efficient method for solving a special class of mixed integer fractional programming (FP) problems. We take a classical reformulation approach for continuous FP as a starting point and extend it for solving a more general class of mixed integer (0–1) fractional programming problems.To stress the practical relevance of the research we focus on a real-life application in paper production industry. The constantly advancing physical knowledge of large scale pulp and paper production did have a substantial impact on an existing DSS in which mixed integer (0–1) fractional programming is introduced. We show that the motivation to solve a real-life fractional programming problem can provide the basis for a new approach in a new context that has an added value of its own, even outside the given application area. We describe the main characteristics of the DSS, the necessity to develop a non-iterative solution procedure and demonstrate both the effectiveness and efficiency of the proposed approach from practical data sets.     

The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein's loss function

For the hierarchical Poisson and gamma model, we calculate the Bayes posterior estimator of the parameter of the Poisson distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the parameter under the squared error loss and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior by two methods. In numerical simulations, we have illustrated: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we exploit the attendance data on 314 high school juniors from two urban high schools to illustrate our theoretical studies.     

A Diversity Index for Model Space Selection in the Estimation of Benchmark and Infectious Doses via Model Averaging

In chemical and microbial risk assessments, risk assessors fit dose‐response models to high‐dose data and extrapolate downward to risk levels in the range of 1–10%. Although multiple dose‐response models may be able to fit the data adequately in the experimental range, the estimated effective dose (ED) corresponding to an extremely small risk can be substantially different from model to model. In this respect, model averaging (MA) provides more robustness than a single dose‐response model in the point and interval estimation of an ED. In MA, accounting for both data uncertainty and model uncertainty is crucial, but addressing model uncertainty is not achieved simply by increasing the number of models in a model space. A plausible set of models for MA can be characterized by goodness of fit and diversity surrounding the truth. We propose a diversity index (DI) to balance between these two characteristics in model space selection. It addresses a collective property of a model space rather than individual performance of each model. Tuning parameters in the DI control the size of the model space for MA.     

An Integrated Risk Assessment Model of Township‐Scaled Land Subsidence Based on an Evidential Reasoning Algorithm and Fuzzy Set Theory

Land subsidence risk assessment (LSRA) is a multi‐attribute decision analysis (MADA) problem and is often characterized by both quantitative and qualitative attributes with various types of uncertainty. Therefore, the problem needs to be modeled and analyzed using methods that can handle uncertainty. In this article, we propose an integrated assessment model based on the evidential reasoning (ER) algorithm and fuzzy set theory. The assessment model is structured as a hierarchical framework that regards land subsidence risk as a composite of two key factors: hazard and vulnerability. These factors can be described by a set of basic indicators defined by assessment grades with attributes for transforming both numerical data and subjective judgments into a belief structure. The factor‐level attributes of hazard and vulnerability are combined using the ER algorithm, which is based on the information from a belief structure calculated by the Dempster‐Shafer (D‐S) theory, and a distributed fuzzy belief structure calculated by fuzzy set theory. The results from the combined algorithms yield distributed assessment grade matrices. The application of the model to the Xixi‐Chengnan area, China, illustrates its usefulness and validity for LSRA. The model utilizes a combination of all types of evidence, including all assessment information—quantitative or qualitative, complete or incomplete, and precise or imprecise—to provide assessment grades that define risk assessment on the basis of hazard and vulnerability. The results will enable risk managers to apply different risk prevention measures and mitigation planning based on the calculated risk states.     

A Tiered Framework for Risk‐Relevant Characterization and Ranking of Chemical Exposures: Applications to the National Children's Study (NCS)

A challenge for large‐scale environmental health investigations such as the National Children's Study (NCS), is characterizing exposures to multiple, co‐occurring chemical agents with varying spatiotemporal concentrations and consequences modulated by biochemical, physiological, behavioral, socioeconomic, and environmental factors. Such investigations can benefit from systematic retrieval, analysis, and integration of diverse extant information on both contaminant patterns and exposure‐relevant factors. This requires development, evaluation, and deployment of informatics methods that support flexible access and analysis of multiattribute data across multiple spatiotemporal scales. A new “Tiered Exposure Ranking” (TiER) framework, developed to support various aspects of risk‐relevant exposure characterization, is described here, with examples demonstrating its application to the NCS. TiER utilizes advances in informatics computational methods, extant database content and availability, and integrative environmental/exposure/biological modeling to support both “discovery‐driven” and “hypothesis‐driven” analyses. “Tier 1” applications focus on “exposomic” pattern recognition for extracting information from multidimensional data sets, whereas second and higher tier applications utilize mechanistic models to develop risk‐relevant exposure metrics for populations and individuals. In this article, “tier 1” applications of TiER explore identification of potentially causative associations among risk factors, for prioritizing further studies, by considering publicly available demographic/socioeconomic, behavioral, and environmental data in relation to two health endpoints (preterm birth and low birth weight). A “tier 2” application develops estimates of pollutant mixture inhalation exposure indices for NCS counties, formulated to support risk characterization for these endpoints. Applications of TiER demonstrate the feasibility of developing risk‐relevant exposure characterizations for pollutants using extant environmental and demographic/socioeconomic data.