基于企业社会责任的闭环供应链超网络均衡模型

文章为研究企业社会责任对闭环供应链总体效率及决策行为的影响,考虑到多层多成员多目标决策的闭环供应链网络中同层非合作竞争决策者,以利润最大、排放废气最小、风险最小为目标,构建了由供应商、制造商、零售商、需求市场和回收商组成的超网络模型.该模型利用均衡理论及变分不等式的方法分析各层决策者的独立决策行为及其相互作用,并通过改进的投影梯度算法求得均衡点.通过三个算例分析得出企业社会责任水平对产品的交易平衡和价格水平的影响.     

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.     

薪酬参照体的效应研究:基于多表征的路径模型与相对重要性

参照体选择在分配公平、薪酬满意研究中扮演重要角色,而以往研究多是关注焦点对象与参照体在薪酬上的数量差异。本文认为,参照体"数量差异"并不能完整表征参照体的效应,为此提出了"信息知晓度"、"判断关联性"、"比较频率"、"数量差异"等四类表征,在理论基础上构造路径模型与研究假设。实证研究表明,信息知晓度、判断关联性正向影响特定参照体的比较频率;数量差异正向影响分配公平、薪酬水平满意,比较频率负向影响分配公平、薪酬水平满意。通过三种研究路径的比较研究证实,自我生活成本等七类参照体相对重要。未来亟需就参照体选择的相关机制在纵贯上、在不同的组织情境下及考虑个体差异等方面予以深入考察。     

《鹿特丹规则》中“门到门”运输及其影响

《鹿特丹规则》的立法宗旨在于实现海上货物运输法律的统一。它对海上货物运输中相关主体的权利义务进行了重新分配,尤其是其中的"门到门"运输,加重了承运人的义务,这种权利义务的再分配,对海上保险法律制度必然会造成一定的影响。文章从《鹿特丹规则》"门到门"运输的基本内涵的研究出发,探讨了"门到门"运输下承运人义务的变化,分析了该规则的主要影响尤其是对海上保险可能造成的影响,以及海上保险法律制度的完善路径。     

类文明:全球化时代中国式民主的应然走向

中国式民主是人类民主类型中的一支新秀。如同任何类型的民主一样,中国式民主包含着内在精神和外在形式两个主要方面。就其内在精神来看,中国式民主以其创新的理论体系演绎着民主的普遍精神;就其外在形式来看,中国式民主又以其独特的实现形式丰富了人类民主的具体形式。因此,中国式民主是中国的,但又是人类的。在全球化时代,发展中国式民主必须具有开放的胸襟和广纳的气魄——即具有类文明的自觉。只有准确地把握中国式民主的精神实质和具体实现形式,在坚持中国特色的同时,不断增强"类文明"的成分,才能发展好中国式民主,建设社会主义政治文明。从一定意义上讲,不断走向"类文明",正是中国式民主的基本走向和最终归宿。     

多重假设检验中参数估计问题研究

在多重假设检验中,真正原假设的个数m0是未知的,但是它有着很重要的影响,因此,它在最近的统计文献中备受关注.文章综述了三种主要的估计方法:最低斜率法、三次样条法、均值估计方法.然后将上述三种方法结合起来,提出了新的估计方法:均值三次样条法,并主要研究了其在微阵列数据上的应用.大量的模拟研究表明,和其他方法相比,新的估计方法具有较小的偏差和标准差.最后利用真实数据来对估计方法进行评估,并找出了差异表达性基因.模拟和实际数据表明此方法具有显著性提高.     

汉语同源字考

本文旨在为王力著的《同源字典》拾遗补阙。有些字王力的著作虽已收录、考释，但这些字还有别的意义，尚可与另一些字构成另一对（或一组）同源字（即“同源词”）。本文依照“音近义通”的原则考释同源字。     

大城市边缘区人居环境问题及其优化调控研究——以广州市为例

以广州市边缘区为典型案例,深入剖析了大城市边缘区人居环境问题,包括过度依赖外资、生态环境隐患、建设用地无序扩张、违章建筑、房地产开发失衡与基础设施配套滞后、传统文化衰落等,并针对性地提出优化调控措施:促进产业结构改造与升级,完善基础设施和公共服务设施配套,实施生态环境保护与综合整治,加强房地产开发的政府引导,推进边缘区管理体制改革;完善边缘区规划体系。     

Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.