共轭斜量法在某些特征向量上收敛速度的估计

设 Ａ是对称正定矩阵，λ_1是 Ａ 的最大或最小特征值，χ_1是对应的特征向量．｛zk｝是用共轭斜量法求解方程组 Αχ＝ｂ时的近似解序列，ｅi＝Ａ～(_1)b-zi,本文给出了|x_1～Tei|较合理的上界估计式。从而为分析预处理共轭斜量法提供了进一步的理论基础。     

CGPSS-F仿真系统数据的采集与分析

本文详细地叙述了“医院门诊部为门诊病人服务的CGPSS-F仿真系统”数据采集与分析的方法.利用CGPSS-F仿真系统的驻留在队列中的实体数和资源处于繁忙状态的单元数,合理调整实体在系统的时间.根据资源容量的利用,实体排队的长度和实体在系统的时间,确定实用的系统参数.     

一种新的基于Kohonen网络的图像矢量量化

矢量量化是一种重要的数据压缩方法.本文利用Kohonen自组织映射神经网络进行矢量量化,首次较为详细地讨论了具体实现的步骤与细节,并在此基础上为改善边缘质量,提出一种基于Laplas算子检测边缘弓引导神经网络训练的方法,并通过实验证明其效果是明显的.     

并行算法软件库PASL

本文介绍了并行算法软件库ＰＡＳＬ。ＰＡＳＬ库运行在具有共享存储的多处理机上，它可以用于科学工程计算和仿真。     

权重分析系统

本文利用加权模糊区间统计方法,对决策指标权重进行了讨论,并引入关联度的概念讨论了各专家的偏离度及一致性。     

部分充满介质的理想螺线管条件

一般教科书中都提到，理想螺线管（指内部磁场均匀，无漏磁）的条件是无限长（相对而言），绕线蜂蜜。但对绕线是否应该均匀则未做详细论述。本文试图指出，在无介质情况下理想螺线管的绕线都是均匀的，但在部分充满介质的情况下，均匀的绕线反而会破坏“理想”的条件。     

Probing met repressor-operator recognition in solution.

The three-dimensional crystal structure of the Escherichia coli methionine repressor, MetJ, complexed with a DNA operator fragment is described in an accompanying article. The complex exhibits several novel features of DNA-protein interaction. DNA sequence recognition is achieved largely by hydrogen-bond contacts between the bases and amino-acid side chains located on a beta-ribbon, a mode of recognition previously hypothesized on the basis of modelling of idealized beta-strands and DNA, and mutagenesis of the Salmonella phage P22 repressors Arc and Mnt. The complex comprises a pair of MetJ repressor dimers which bind to adjacent met-box sites on the DNA, and contact each other by means of a pair of antiparallel alpha-helices. Here we assess the importance of these contacts, and also of contacts that would be made between the C-helices of the protein and DNA in a previous model of the complex, by studying mutations aimed at disrupting them. The role of the carboxy-terminal helix face in operator binding was unclear, but we demonstrate that recognition of operator sequences occurs through side chains in the beta-strand motif and that dimer-dimer interactions are required for effective repression.     

On the limitations of comparing mean square forecast errors

Linear models are invariant under non-singular, scale-preserving linear transformations, whereas mean square forecast errors (MSFEs) are not. Different rankings may result across models or methods from choosing alternative yet isomorphic representations of a process. One approach can dominate others for comparisons in levels, yet lose to another for differences, to a second for cointegrating vectors and to a third for combinations of variables. The potential for switches in ranking is related to criticisms of the inadequacy of MSFE against encompassing criteria, which are invariant under linear transforms and entail MSFE dominance. An invariant evaluation criterion which avoids misleading outcomes is examined in a Monte Carlo study of forecasting methods.