基于数据仓库的多式联运物流决策支持系统

为解决业务数据量的不断增加导致传统物流决策支持系统很难满足企业要求的问题,结合数据仓库的特征和决策支持系统的要求,提出了基于数据仓库的多式联运决策支持系统结构。该系统包括三个层次:模型库系统和数据库系统组成的基础层、数据仓库组成的加工层以及联机分析处理(OLAP)、专家系统和数据挖掘等组成的应用层,可以实现定性与定量相结合的物流决策支持。为提高多式联运物流企业决策效率和实现物流系统的协调运作提供了有效手段。

Assessment of different genetic distances in constructing cotton core subset by genotypic values

One hundred and sixty-eight genotypes of cotton from the same growing region were used as a germplasm group to study the validity of different genetic distances in constructing cotton core subset. Mixed linear model approach was employed to unbiasedly predict genotypic values of 20 traits for eliminating the environmental effect. Six commonly used genetic distances(Euclidean,standardized Euclidean,Mahalanobis,city block,cosine and correlation distances) combining four commonly used hierarchical cluster methods(single distance,complete distance,unweighted pair-group average and Ward's methods) were used in the least distance stepwise sampling(LDSS) method for constructing different core subsets. The analyses of variance(ANOVA) of different evaluating parameters showed that the validities of cosine and correlation distances were inferior to those of Euclidean,standardized Euclidean,Mahalanobis and city block distances. Standardized Euclidean distance was slightly more effective than Euclidean,Mahalanobis and city block distances. The principal analysis validated standardized Euclidean distance in the course of constructing practical core subsets. The covariance matrix of accessions might be ill-conditioned when Mahalanobis distance was used to calculate genetic distance at low sampling percentages,which led to bias in small-sized core subset construction. The standardized Euclidean distance is recommended in core subset construction with LDSS method.

菜用大豆核心种质构建策略研究

核心种质是种质资源研究的新领域,构建核心种质是研究和利用植物遗传资源的便捷途径。本研究采用基于田间实际数据的计算机模拟方法构建两个菜用大豆种质资源群体,应用最小距离逐步取样法研究菜用大豆核心种质的构建策略,采用极差符合率和变异系数变化率两个参数对构建策略进行评价,利用主成分分析确认核心种质的代表性。研究表明,采用最小距离逐步取样法构建菜用大豆核心种质的最佳遗传距离为欧式距离,最佳取样比例为95%。