综合性物流企业信息系统集成度研究

本文从综合物流企业信息系统集成的角度出发,将集成部的定义引入,利用矩阵原理将复杂物流系统整体结构分解为多级递阶的等级结构,分析了各个信息系统之间的集成程度.仓储、海运、陆运、空运、快递业务管理信息系统之间是有循环存在的强联结关系,安全和客户管理信息系统是物流管理信息系统的最高级,外部资源辅助管理系统和呼叫中心处在同一级,财务管理信息系统位于第2级,人力资源管理信息系统位于最低一级.     

加权有向图生成算法研究及其计算机实现

提出了加权有向图的生成算法及其在计算机中的实现，定义了加权有向关联矩阵，并据该矩阵解决了加权有向图的生成、绘制问题，从而为可视化教学提供了基础.     

具有定号解的线性方程组的若干性质

对有定号解的线性方程组或有定号零空间的实矩阵进行了更深入的研究．讨论了有定号解的线性方程组与符号可解(或符号不可解)线性方程组之间的关系，证明了有定号解的线性方程组具有某种继承性；同时也给出了具有定号解的线性方程组其任一类解中均无零元素的特征．     

加速变质库存系统的最优存贮策略

对具有加速变质的一般库存系统,在需求率为常数的前提下,利用定义目标清单和对目标函数求最优值的方法,得到了不允许缺货、允许缺货但不拖后条件下的最佳订货量及最小平均费用,给出了在上述条件下的最优存贮策略,所得结论推广了经典PLS模型的有关结论。     

一类变质物品的最优存贮策略

文章研究了一类变质性物品的最优存贮问题,假定需求率是一个变量,且依赖于库存水平,在瞬时到货和常数变质率的前提下,就允许缺货情形,建立了库存模型,模型解的存在性和唯一性,找到全局最优解,就得到了最优订货策略的求解方法.     

突出数形结合思想搞好微积分教学

突出数形结合思想搞好微积分教学王保全（南阳师专数学系）众所周知，在思考数学问题时，把数学式子与其几何图形结合起来考虑，以“形”助“数”或者以“数”助“形”达到解决问题的目的。这种思考问题的方法叫做数形结合法。微积分的全部内容几乎都具有明确的几何意义，...     

基于形态学矩阵与模糊综合评价的自动穿鞋带机构设计

为了提高制鞋业中穿鞋带工序的自动化水平,设计了一种新型自动穿鞋带机构。方案设计过程采用形态学矩阵 法寻求结构解,给结构解的不同组合形式加上约束后取舍获得多种设计方案,以备模糊评价。方案评价和优选过程运用 二元对比排序法对模糊评价的指标权重进行分配,采用专家和统计结合的方法获得评价数据,最后使用所构造的权重向 量对自动穿鞋带机构的各方案进行模糊综合评价,选出最优方案。该方法应用在穿鞋带机构上,较系统地考虑了机构设 计方案,并较快地对方案进行评价和优选。物理样机实验表明,所设计的机构较好实现了自动穿鞋带功能,并兼顾了结 构紧凑性、经济性和柔性等要求。     

互联网对中国贸易的影响--基于29个省市的空间动态效应实证分析

以经济贸易关联的新视角分析了互联网对中国不同地区出口贸易所产生的影响,采用2003—2012年29个省市的年度面板数据,通过区域间贸易关联空间权重矩阵测算中国不同地区的区域内和区域间贸易溢出效应,并进一步将区域间溢出效应分解为前向溢出效应和后向溢出效应,系统地测算了互联网产业对其他区域出口额的溢出效应。实证结果显示,互联网产业既存在正向的区域内溢出效应,又存在正向区域间溢出效应,并且互联网产业前向溢出效应和后向溢出效应存在明显差异,前向和后向溢出效应均显著为正。     

Power Enhancement in High‐Dimensional Cross‐Sectional Tests

We propose a novel technique to boost the power of testing a high‐dimensional vector H : θ = 0 against sparse alternatives where the null hypothesis is violated by only a few components. Existing tests based on quadratic forms such as the Wald statistic often suffer from low powers due to the accumulation of errors in estimating high‐dimensional parameters. More powerful tests for sparse alternatives such as thresholding and extreme value tests, on the other hand, require either stringent conditions or bootstrap to derive the null distribution and often suffer from size distortions due to the slow convergence. Based on a screening technique, we introduce a “power enhancement component,” which is zero under the null hypothesis with high probability, but diverges quickly under sparse alternatives. The proposed test statistic combines the power enhancement component with an asymptotically pivotal statistic, and strengthens the power under sparse alternatives. The null distribution does not require stringent regularity conditions, and is completely determined by that of the pivotal statistic. The proposed methods are then applied to testing the factor pricing models and validating the cross‐sectional independence in panel data models.     

Dynamic principal component analysis with missing values

Dynamic principal component analysis (DPCA), also known as frequency domain principal component analysis, has been developed by Brillinger [Time Series: Data Analysis and Theory, Vol. 36, SIAM, 1981] to decompose multivariate time-series data into a few principal component series. A primary advantage of DPCA is its capability of extracting essential components from the data by reflecting the serial dependence of them. It is also used to estimate the common component in a dynamic factor model, which is frequently used in econometrics. However, its beneficial property cannot be utilized when missing values are present, which should not be simply ignored when estimating the spectral density matrix in the DPCA procedure. Based on a novel combination of conventional DPCA and self-consistency concept, we propose a DPCA method when missing values are present. We demonstrate the advantage of the proposed method over some existing imputation methods through the Monte Carlo experiments and real data analysis.