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。     

Linear model estimation errors with estimated design parameters

The problem of error estimation of parameters b in a linear model,Y = Xb+ e, is considered when the elements of the design matrix X are functions of an unknown ‘design’ parameter vector c. An estimated value c is substituted in X to obtain a derived design matrix [Xtilde]. Even though the usual linear model conditions are not satisfied with [Xtilde], there are situations in physical applications where the least squares solution to the parameters is used without concern for the magnitude of the resulting error. Such a solution can suffer from serious errors.

This paper examines bias and covariance errors of such estimators. Using a first-order Taylor series expansion, we derive approximations to the bias and covariance matrix of the estimated parameters. The bias approximation is a sum of two terms:One is due to the dependence between ? and Y; the other is due to the estimation errors of ? and is proportional to b, the parameter being estimated. The covariance matrix approximation, on the other hand, is composed of three omponents:One component is due to the dependence between ? and Y; the second is the covariance matrix ∑_b corresponding to the minimum variance unbiased b, as if the design parameters were known without error; and the third is an additional component due to the errors in the design parameters. It is shown that the third error component is directly proportional to bb'. Thus, estimation of large parameters with wrong design matrix [Xtilde] will have larger errors of estimation. The results are illustrated with a simple linear example.     

Energy Substitution,Separability, and Technical Progress in the Australian Iron and Steel Industry

The effects of the “energy crisis” on the Australian iron and Steel industry are analyzed by using a flexible functional form, the transcendental logarithmic (translog) function, which represents a second-order approximation of any twice differentiable function. The robustness of the translog is analyzed with special attention paid to factor aggregation and separability conditions as they relate to the specification of the functions. The estimation results suggest that capital and energy are substitutes, that aggregation of administrative workers and production labor is inappropriate, and that materials are weakly separable, thereby allowing the estimation of a capital, labor, and energy model. The data set developed covers the financial years from 1946–47 to 1978–79. This 33-year time series allows the use of different temporal subsets. Technical progress is shown to be capital and energy using and labor and material saving. An energy submodel is specified using solid fuels, oil, electricity, and gas; and the interfuel substitutions are estimated.     

Bayesian multivariate normal analysis with a wishart prior

This paper considers the Bayesian analysis of the multivariate normal distribution when its covariance matrix has a Wishart prior density under the assumption of a multivariate quadratic loss function. New flexible marginal posterior distributions of the mean μ and of the covariance matrix Σ are developed and univariate cases with graphical representations are given.