药物信息服务实践应用的信度与效度分析

为建立科学的药物信息服务评价体系,我们采用问卷调查法对新疆医科大学第四附属医院药房及患者信息进行系统性调查,利用Excel和SPSSStatistics进行数据整理分析,采用Cronbach＇sAlpha系数、因子分析等方法对问卷做了信度和效度评价分析,初步提出了医院药物信息服务的要素．结果,本次研究的过程和方法为他人在药物信息乃至其它信息分析中提供重要的参考依据．     

增量统计方法在二代身份证系统中的应用

报表统计在二代身份证系统中非常重要,而且要求以天为单位按行政区划统计。当数据量比较小时,可以实时统计,但是当数据量比较大时,实践表明如果采用常规的统计方法,性能较差。因此,针对这种情况,提出了一种基于ORACLE的新统计方法—增量统计法,提高系统性能,来满足省、地市、区县、派出所各级公安机关工作人员的统计需求。     

Estimating inter-group interaction radius for point processes with nested spatial structures

A statistical procedure is proposed in order to estimate the interaction radius between points of a non-stationary point process when the process can present local aggregated and regular patterns. The model under consideration is a hierarchical process with two levels, points and clusters of points. Points will represent individuals, clusters will represent groups of individuals. Points or clusters do not interact as soon as they are located beyond a given interaction radius, and are assumed to interact if their distance is less than this interaction radius. Interaction radius estimation is performed in the following way. For a given distance, observations are split into several clusters whose in-between distances are larger than this distance. For each cluster, a neighbourhood and an area in which this cluster is randomly located is defined under the assumption that the distance between the cluster and its neighbourhood is larger than the interaction radius. The p-value of a test of this assumption is then computed for each cluster. Modelling the expectation of this p-value as a function of the distance leads to an estimate of the interaction radius by a least-square method. This approach is shown to be robust against non-stationarity. Unlike most classical approaches, this method makes no assumption on the point spatial distribution inside the clusters. Two applications are presented in animal and plant ecology.     

浅谈Debug调试汇编语言程序的方法

本文浅谈了Debug在汇编语言程序上机调试中的基本步骤和方法,帮助学生如何利用Debug工具更好地学习汇编语言,对于汇编语言教学具有一定的参考价值。     

Order management in transformable business structures—basics and concepts

An increasingly dynamic environment and the ongoing trend towards customized products are heightening the requirements which companies have to meet, leading to constantly changing structures and processes. As a result, successful production management and the transformability which an enterprise and its order management system has to display are closely connected. The present article describes an order management system for transformable manufacturing enterprises based on systems theory and management cybernetics.     

Real-time 2D–3D filtering using order statistics based algorithms

The paper presents a review of the author’s own results obtained in the last several years. Some examples of real-time processing of 2D and 3D images are described. In particular, we discuss the noise model and objective criteria that can be applied to characterize the performance of the processing algorithms. Several proposed algorithms based on RM approach are compared with other known ones, demonstrating the advantages in noise suppressing and preservation of fine image details and edges. A number of 2D and 3D image denoising filters are implemented on DSP, realizing real-time mode in the image processing. The performances of the proposed processing algorithms and the known ones are discussed and evaluated here.

Using online pickup site inclusion policies to manage demand in retail/E-tail organizations

A major development in online retailing is the significant increase in the number of traditional “offline” retailers extending their brands online. Many of these retail/e-tail firms are attempting to leverage channel synergies by allowing customers to purchase products over the internet and then pick their orders up at one of the firm's local stores. This paper proposes that the firm presents only a subset of its stores to online customers as available pickup locations, rather than simply listing all local stores with inventory. By doing so, the firm can protect stores with critically low inventory levels and thereby reduce backorder costs. Specifically, we develop and evaluate a dynamic pickup site inclusion policy that incorporates real-time information to specify which of the firm's e-fulfillment locations should be presented at online checkout. Computational results indicate that managing in-store demand via such policies can decrease total cost (holding, backorder, and lost or redirected pickup sale costs) by as much as 18% over allowing customers to pick online orders up from any site with available inventory. The percentage of pickup sales and customers' sensitivity to travel are critical in determining the magnitude of the benefit.     

Parallel R-Point Explicit Block Method for Solving Second Order Ordinary Differential Equations Directly

A new parallel R-point explicit block method for solving second order ordinary differential equations (ODEs) directly is developed. The method computes the numerical solution of the equations at three points simultaneously. Each problem was tested on the shared memory parallel computer Sequent S27 using both the sequential and parallel implementations of the new method and the conventional 1-point method. Numerical results are presented comparing the two methods in terms of the number of steps taken, accuracy and execution times. The results indicate the advantages of using the parallel implementation of the new method for solving second order ODEs directly particularly when the step size becomes finer.     

Systematic benchmarking of microarray data feature extraction and classification

A combination of microarrays with classification methods is a promising approach to supporting clinical management decisions in oncology. The aim of this paper is to systematically benchmark the role of classification models. Each classification model is a combination of one feature extraction method and one classification method. We consider four feature extraction methods and five classification methods, from which 20 classification models can be derived. The feature extraction methods are t-statistics, non-parametric Wilcoxon statistics, ad hoc signal-to-noise statistics, and principal component analysis (PCA), and the classification methods are Fisher linear discriminant analysis (FLDA), the support vector machine (SVM), the k nearest-neighbour classifier (kNN), diagonal linear discriminant analysis (DLDA), and diagonal quadratic discriminant analysis (DQDA). Twenty randomizations of each of three binary cancer classification problems derived from publicly available datasets are examined. PCA plus FLDA is found to be the optimal classification model.