数据驱动下收货方质量偏好与电商配送服务质量优化

针对"互联网+大数据"优化电商配送服务质量问题，明确不同收货方的质量需求稳定性，引导电商根据收货方不同质量敏感性提供相对精准服务，提升配送服务质量。模拟投票结果的形成过程聚类得到收货方对服务质量偏好的记忆性特征：（1）"无记忆"型收货方；（2）"记忆"型收货方；（3）"不确定"型收货方；（4）收货方总体。进一步推导不同规划类型求解空间，设计得到"无记忆"型收货方动态规划精确求解方法，及其他三种类型近似求解粒子群算法。研究过程中，配送资源质量感知度被嵌入到模型约束；"无记忆"型收货方的质量需求规划问题转化为零库存策略最优解问题，进而证明存在精确解；"记忆"型收货方呈现出对质量感知的累积；"不确定"型收货方模型通过赋值即可得总体收货方表达式。结果表明：数据驱动研究框架借助大数据资源，使得电商更易通过收货方的质量偏好设计更加匹配的配送方案；不确定服务需求得到有效满足，投入成本的利用率更高；通过特征分类的方式，尽可能地抽取能够精确最优化的部分，缩小NP范围，提高整体求解的精确度。     

习近平总书记关于高校体育工作重要论述的逻辑意蕴与实践路径

习近平总书记关于高校体育工作重要论述是新时代中华民族所认同体育文化的价值取向、精神气质、思维方式与行动纲要的总和，它是大学生思想教育工作发展创新的内在需要。高校通过以中华体育文化精神为基石，融入社会主义主流价值；优化体育文化育人环境，让体育文化叫好又叫座；深入挖掘体育文化活动内涵，打造经典体育文化品牌；丰富体育文化载体形式，使体育文化育人产生倍增效果，多种实践途径践行习近平总书记关于高校体育工作的重要论述，促推中华民族体育强国梦的实现。     

历史变局、国家兴衰与发展之路——三次千年变局叠加下的中国特色社会主义发展道路

每当先进的发展制度开始取代落后的发展制度时，整个世界格局就会进入重大的转换阶段。在中国五千年的发展历史中，有两次重大的历史变局完全改变了中国历史发展轨迹并深远地影响了当前中国的发展模式。新时代下的中国作为前两次历史变局影响的客体，曾经既是先进发展制度取代落后发展制度的受益者，也经历过作为落后发展制度主体而被先进发展制度冲击的过程。目前世界正处于第三次先进发展制度与落后发展制度交替的历史变局阶段，作为第三次重大历史变局的主体，在三次千年历史变局叠加的背景下，能否有效地认识、适应并改造利用前两次历史变局的经验与教训，构建系统性、科学性、可行性、领先性的中国特色社会主义制度，将决定中国在本次历史变局中能否顺利成为先进发展制度的主体从而实现民族复兴。     

Integrating Operational and Organizational Aspects in Interdependent Infrastructure Network Recovery

Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.     

Bi-objective multi-resource scheduling problem for emergency relief operations

Abstract

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the completion times in emergency relief operations. Despite the importance of such decisions, only a few mathematical models of emergency relief operations have been studied. This article presents a bi-objective mixed integer programming (MIP) that helps to minimize both the total weighted time of completion of the demand points and the makespan of the total emergency relief operation. A two-phase method is developed to solve the bi-objective MIP problem. Additionally, a case study of hospital network in the Melbourne metropolitan area is used to evaluate the model. The results indicate that the model can successfully support the decisions required in the optimal resource scheduling of emergency relief operations.     

高性能可重构DSP处理器的数据通路设计

介绍了高性能定点可重构DSP处理器的数据通路设计。该数据通路以功能强大的16位定点计算单元为基础,搭建起高速16位数据处理平台;并能以单指令流多数据流的方式灵活支持多维向量运算;通过重构的方法有效地支持了32位数据处理。     

反梯度推进:西部产业升级的一种思路

反梯度推进理论的核心内容强调 :落后地区要根据自己的实际情况 ,直接引进世界最先进的技术 ,发展自己的高新技术 ,带动传统产业实现超越发展。近几年 ,我国提出的西部大开发战略 ,其实质就是要解决中国区域经济的均衡协调发展问题 ,实现区域经济的跨越式发展。而实现结构优化和产业升级则是根本出路。在这方面 ,反梯度推进理论为我国西部大开发过程中的产业升级提供了非常重要的启示。     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

一类带有分布参数系统的最佳控制

本文给出一类非均匀弦的横向振动的最佳控制，推广了P C Park的结果。这一结果同样适用于同类型振动问题。     

论我国法院调解的促进和规范

随着经济的发展 ,法制的完善以及审判方式改革的深化 ,法院调解这一纠纷解决方式以其独特的优点 ,在司法实践中 ,越来越显示其重要性。时代赋予法院调解新的价值的同时 ,也使其原有的法律规定弊端凸现。本文拟从法院调解三原则的地位分析中寻找促进和规范法院调解的途径 ,并运用新证据规则加以落实。