制造系统生产单元碳排放核算模型

制造业是我国国民经济的支柱产业，但也是环境污染物排放的主要来源、能源消耗主体和温室气体排放大户。制造系统高能耗、高物耗、高碳排放的加工过程是造成制造业碳排放量大的重要原因。生产单元作为制造系统的加工的主体，其碳排放量的核算是确定整个制造系统乃至制造业碳排放总量的关键。本文首先对生产单元的原材料、电能、辅助物料及废屑处理所引起的碳排放进行分析，确定生产单元的碳排放源；其次，产品合格率的不同会造成生产单元输入及输出的成品/半成品数量的差异，进而影响生产单元单位产品的碳排放量，在此基础上，综合考虑了原材料、电能、辅助物料及废屑处理的碳排放情况，构建了给定工艺流程下生产单元碳排放核算模型。最后，结合一汽车排气装置加工实例，分别核算加工过程中九个生产单元的碳排放量，验证了模型的可行性。     

Entrenchment in publicly traded family firms: Evidence from the S&P 500

Family involvement in corporate governance through ownership, management, and board membership presents a unique dilemma for understanding the strategic impetus and costs of entrenchment decisions. The presence of shared family ties and the family-centered goals of firm principals call to question the applicability of extant agency arguments regarding the nature and antecedents of managerial entrenchment. Exploring this, we develop and test a model of family firm-specific determinants (i.e., family ownership and family's involvement in management and governance) of entrenchment in publicly traded firms by drawing upon principal-principal agency theory. Findings of the empirical analysis of family owned S&P 500 firms suggest family firms are motivated to entrench managers when doing so supports the pursuit of family-centric goals. However, the extent to which entrenchment supports such goals varies at different levels of family ownership.     

Environmental Justice in the Context of Scientifi Uncertainty: On the Safety of Genetically Modifi Organisms

应对风险特别是科学不确定性所导致的风险，实现和维护环境正义是当今时代的 一个前沿核心议题。转基因生物安全为代表的科学不确定性背景下的环境正义，需要 建立在相应的科学研究基础之上，但在本质上乃是一种社会构建。对转基因生物技术 及其产品等现代科技的研发、应用和推广等活动，应当维护其市场自由，与此同时， 为了公共安全以及社会弱势群体的最大利益，还应当对相关科技活动进行必要的政府 干预和法律规制。     

Closure property of consistently varying random variables based on precise large deviation principles

Let X = {X₁, X₂, …} be a sequence of independent but not necessarily identically distributed random variables, and let η be a counting random variable independent of X. Consider randomly stopped sum S_η = ∑^η_{k = 1}X_k and random maximum S_(η) ? max?{S₀, …, S_η}. Assuming that each X_k belongs to the class of consistently varying distributions, on the basis of the well-known precise large deviation principles, we prove that the distributions of S_η and S_(η) belong to the same class under some mild conditions. Our approach is new and the obtained results are further studies of Kizinevi?, Sprindys, and ?iaulys (2016) and Andrulyt?, Manstavi?ius, and ?iaulys (2017).     

Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation

This paper focuses on bivariate kernel density estimation that bridges the gap between univariate and multivariate applications. We propose a subsampling-extrapolation bandwidth matrix selector that improves the reliability of the conventional cross-validation method. The proposed procedure combines a U-statistic expression of the mean integrated squared error and asymptotic theory, and can be used in both cases of diagonal bandwidth matrix and unconstrained bandwidth matrix. In the subsampling stage, one takes advantage of the reduced variability of estimating the bandwidth matrix at a smaller subsample size m (m < n); in the extrapolation stage, a simple linear extrapolation is used to remove the incurred bias. Simulation studies reveal that the proposed method reduces the variability of the cross-validation method by about 50% and achieves an expected integrated squared error that is up to 30% smaller than that of the benchmark cross-validation. It shows comparable or improved performance compared to other competitors across six distributions in terms of the expected integrated squared error. We prove that the components of the selected bivariate bandwidth matrix have an asymptotic multivariate normal distribution, and also present the relative rate of convergence of the proposed bandwidth selector.     

Measuring and Achieving Equity in Multiperiod Emergency Material Allocation

Emergency material allocation is an important part of postdisaster emergency logistics that is significant for improving rescue effectiveness and reducing disaster losses. However, the traditional single‐period allocation model often causes local surpluses or shortages and high cost, and prevents the system from achieving an equitable or optimal multiperiod allocation. To achieve equitable allocation of emergency materials in the case of serious shortages relative to the demand by victims, this article introduces a multiperiod model for allocation of emergency materials to multiple affected locations (using an exponential utility function to reflect the disutility loss due to material shortfalls), and illustrates the relationship between equity of allocations and the cost of emergency response. Finally, numerical examples are presented to demonstrate both the feasibility and the usefulness of the proposed model for achieving multiperiod equitable allocation of emergency material among multiple disaster locations. The results indicate that the introduction of a nonlinear utility function to reflect the disutility of large shortfalls can make the material allocation fairer, and minimize large losses due to shortfalls. We found that achieving equity has a significant but not unreasonable impact on emergency costs. We also illustrate that using differing utility functions for different types of materials adds an important dimension of flexibility.     

考虑项目关联关系的R&D网络风险传播建模与仿真

考虑同一企业内不同项目间关联关系的情况下，基于研发项目的数量将研发网络分为不同子网络。借鉴SIS模型构建了R&D网络风险传播动力学模型，并对该模型进行了数理解析与仿真分析。结果表明：R&D网络风险传播阈值是由不同子网络的拓扑结构以及企业内不同项目间的关联程度共同决定的；考虑项目关联的研发网络风险传播阈值小于不考虑关联关系的研发网络风险传播阈值；项目间关联关系的企业个数及项目间的关联程度对风险在不同子网络上的传播速度与传播范围均具有影响。该研究成果对于提高研发网络抗风险能力具有重要意义。     

Controlling type 1 error rate for sequential,bioequivalence studies with crossover designs

Sample size reestimation in a crossover, bioequivalence study can be a useful adaptive design tool, particularly when the intrasubject variability of the drug formulation under investigation is not well understood. When sample size reestimation is done based on an interim estimate of the intrasubject variability and bioequivalence is tested using the pooled estimate of intrasubject variability, type 1 error inflation will occur. Type 1 error inflation is caused by the pooled estimate being a biased estimator of the intrasubject variability. The type 1 error inflation and bias of the pooled estimator of variability are well characterized in the setting of a two‐arm, parallel study. The purpose of this work is to extend this characterization to the setting of a crossover, bioequivalence study with sample size reestimation and to propose an estimator of the intrasubject variability that will prevent type 1 error inflation.