基于文献关键词统计分析的我国开放与远程教育研究现状探析

以中国知网（CNKI）为平台,利用共词分析方法,对2006年1月至2015年6月间公开发表在开放与远程教育领域期刊论文的关键词进行统计分析,结合SATI软件和UCINET软件进行数据处理,探讨高频关键词之间的内在联系,揭示我国开放与远程教育研究的主题结构和核心领域。结果表明：此间我国开放与远程教育领域主要存在七大研究主题;远程学习、网络课程、学习支持服务、学习者是该领域的研究核心;移动学习、内容分析、学习科学和远程学习者将成为未来的研究热点。     

基于Web的远程教学管理系统的设计及实现

远程教学管理系统是进行远程教学的重要保证,利用它可以合理地调配教学资源、组织教学活动、总结教学数据,并有效地对远程学习中心实行业务指导和监控,保证教与学过程管理的时效。本文结合北京邮电大学网络教育学院学生管理的需求,分析了远程教学管理系统的设计及实现方案,利用www上的应用系统,实现了跨平台的、灵活的、通用的远程教学管理系统。     

特征AHP在高校教师教学质量评定中的应用

本文针对高校教师教学质量的评定提出了一种特征层次分析法。首先通过分析教学质量的影响因素,建立三层层次分析模型。在其基础上引入特征法,选取特征群并确定特征群中个体的权重,利用反算法根据组合权重及方案层权重,求取准则层评定指标权重,避免了不同质指标间对比的不确定性,摒除了专家直接给出评定指标对比矩阵的主观性,减少了对教师评定过程的不一致性和不完备性。     

城市交通中车辆择路行为实证研究

随机、时变的交通流分布,偶发的交通事故等因素导致了路径随机的车辆旅行时间,也决定了现实城市交通网络中只存在随机最短路.已有的路径选择研究大都假设人们力求选择最短路,而通过实际调查发现:人们的择路行为依赖出行情景,随出行目的、约束时间、对路径的熟悉程度以及路径的不确定程度而变化.验证了城市交通中人们在不确定环境下的择路行为也符合展望理论.     

A semiparametric approach for modelling multivariate nonlinear time series

In this article, a semiparametric time‐varying nonlinear vector autoregressive (NVAR) model is proposed to model nonlinear vector time series data. We consider a combination of parametric and nonparametric estimation approaches to estimate the NVAR function for both independent and dependent errors. We use the multivariate Taylor series expansion of the link function up to the second order which has a parametric framework as a representation of the nonlinear vector regression function. After the unknown parameters are estimated by the maximum likelihood estimation procedure, the obtained NVAR function is adjusted by a nonparametric diagonal matrix, where the proposed adjusted matrix is estimated by the nonparametric kernel estimator. The asymptotic consistency properties of the proposed estimators are established. Simulation studies are conducted to evaluate the performance of the proposed semiparametric method. A real data example on short‐run interest rates and long‐run interest rates of United States Treasury securities is analyzed to demonstrate the application of the proposed approach. The Canadian Journal of Statistics 47: 668–687; 2019 © 2019 Statistical Society of Canada     

Diagnostics of multiple group influential observations for logistic regression models

In this paper, two new multiple influential observation detection methods, GCD.GSPR and mCD*, are introduced for logistic regression. The proposed diagnostic measures are compared with the generalized difference in fits (GDFFITS) and the generalized squared difference in beta (GSDFBETA), which are multiple influential diagnostics. The simulation study is conducted with one, two and five independent variable logistic regression models. The performance of the diagnostic measures is examined for a single contaminated independent variable for each model and in the case where all the independent variables are contaminated with certain contamination rates and intensity. In addition, the performance of the diagnostic measures is compared in terms of the correct identification rate and swamping rate via a frequently referred to data set in the literature.     

连续物料订单分拣完全-并行限定(k=1)轮询控制机理

中国是粮食生产大国和人口大国,粮食问题对于我国经济社会的发展具有举足轻重的影响。现有粮食供应链"层层批发"的流通体制不能满足消费者个性化需求。所以,本文基于客户的个性化需求,应用轮询系统理论来研究面粉、大米等粮食连续物料的订单分拣自动化控制机理。本文根据实际业务需求设定普通订单、优先订单,对优先订单的分拣实行完全服务控制策略,对普通订单分拣实行限定（k=1）服务控制策略,建立完全-并行限定（k=1）的二级优先级订单分拣轮询控制系统模型,确保优先业务订单顾客得到更优质服务,同时保证订单分拣系统的公平性。采用嵌入式Markov链、多维概率母函数和拉式变换求解理论模型,并通过数值计算分析,精确求解其一阶系统特性、二阶系统特性等关键性能参数,从而深入研究连续物料订单分拣的控制机理。     

基于功率约束及节点频谱选择的认知协同路由

为了提升认知无线网络的数据传输效率,优化节点能量负载,提出一种协同路由算法。基于覆盖与底层技术的协作设计一种协同网络架构,该架构针对网络频带表现多样化提供了一种新的频谱选择策略,提升频谱资源效用。结合协同网络频谱选择时的干扰特性,根据频谱、干扰和功率约束关系,提出最大化链路容量的频谱及节点功率分配方法。在协同路由设计上引入了频谱效用参数,参数的度量标准涉及节点剩余能量和链路容量,以优化路由节点能量负载和传输效率为目的。仿真对比结果表明,算法有效地利用信道接入机会进行数据转发,提高了传输效率,相比基于竞争进化算法的多播路由方法,数据包传递成功率提升了1．4％,平均网络吞吐量高出8．4％,平均节点剩余能量高出3．2％,在均衡节点负载上性能良好。     

Neighborhood change from the bottom Up: What are the determinants of social distance between new and prior residents?

An important source of neighborhood change occurs when there is a turnover in the housing unit due to residential mobility and the new residents differ from the prior residents based on socio-demographic characteristics (what we term social distance). Nonetheless, research has typically not asked which characteristics explain transitions with higher social distance based on a number of demographic dimensions. We explore this question using American Housing Survey data from 1985 to 2007, and focus on instances in which the prior household moved out and is replaced by a new household. We focus on four key characteristics for explaining this social distance: the type of housing unit, the age of the housing unit, the length of residence of the exiting household, and the crime and social disorder in the neighborhood. We find that transitions in the oldest housing units and for the longest tenured residents result in the greatest amount of social distance between new and prior residents, implying that these transitions are particularly important for fostering neighborhood socio-demographic change. The results imply micro-mechanisms at the household level that might help explain net change at the neighborhood level.     

具有模糊旅行时间的VRP的一种混合遗传算法

传统确定性车辆路径问题是近几十年来运筹学领域研究的一个热点问题.但在许多实际的应用中,由于受客观世界中存在的不确定性因素以及人类观察、认识事物的模糊性的影响,车辆路径问题的某些参数可能是模糊的、不确定的.文中传统确定性车辆路径问题被扩展为具有模糊特征的模糊车辆路径问题.在对具有模糊旅行时间的车辆路径问题进行简单描述的基础上,构建了该问题的数学模型,并通过将模糊逻辑、模糊控制方法与传统车辆路径问题的遗传算法进行有效结合,提出了解决该问题的一种混合遗传算法.最后给出了该问题的一个计算实例,并通过随机模拟试验验证了该算法的有效性和优越性.