Markovian network processes: Congestion-dependent routing and processing

A Markovian network process describes the movement of discrete units among a set of nodes that process the units. There is considerable knowledge of such networks, often called queueing networks, in which the nodes operate independently and the routes of the units are independent. The focus of this study, in contrast, is on networks with dependent nodes and routings. Examples of dependencies are parallel processing across several nodes, blocking of transitions because of capacity constraints on nodes, alternate routing of units to avoid congestion, and accelerating or decelerating the processing rate at a node depending on downstream congestion. We introduce a general network process representing the numbers of units at the nodes and derive its equilibrium distribution. This distribution takes the form of a product of functions of vectors in which the arguments of the functions satisfy an interchangeability property. This new type of distribution may apply to other multi-variate processes as well. A basic idea in our approach is a linking of certain micro-level balance properties of the network routing to the processing rates at the nodes. The link is via routing-balance partitions of nodes that are inherent in any network. A byproduct of this approach is a general characterization of blocking of transitions without the restriction that the process is reversible, which had been a standard assumption. We also give necessary and sufficient conditions under which a unit moving in the network sees a time average for the unmoved units (called the MUSTA property). Finally, we discuss when certain flows between nodes in an open network are Poisson processes.This research was sponsored in part by Air Force Office of Scientific Research contract 84-0367.     

Minimum concave cost production system: A further generalization of multi-echelon model

We will consider a concave minimization problem associated with a series production system in which raw material is processed inm consecutive facilities. The products at some facility are either sent to the next facility or stocked in the warehouse. The amount of demand for the final products during periodi, i = 1,,n, are known in advance. Our problem is to minimize the sum of processing, holding and backlogging cost, all of which are assumed to be concave.The origin of this model is the classical economic lot size problem of Wagner and Whitin and was extensively studied by Zangwill. This model is very important from the theoretical as well as practical point of view and this is one of the very rare instances in which polynomial time algorithm has been constructed for concave minimization problems.The purpose of this paper is to extend the model further to the situation in which time lag is associated with processing at each facility. We will propose an efficient O(n ⁴ m) algorithm for this class of problems.     

Hydrogel nanonetworks with functional core-shell structure

Nanohydrogel particles of poly(acrylonitrile-co-N-isopropylacrylamide (p(AN-c-NIPAM)) were synthesized using a microemulsion polymerization technique. Highly monodisperse nanohydrogel particles e.g. 50-150 nm, and various morphology such as core-shell and connected beads were obtained. It was shown that the shell thickness and the size of particles can be tuned by the monomer concentrations and their ratios as well as by the utilization of different crosslinkers. The hydrophobic core monomer, AN was converted to amidoxime groups to increase the hydrophilicity of the nanogels which provide more hydrophilic character and impart new functionality to the nanonetwork. Transmission electron microscopy (TEM), and dynamic light scattering (DLS) techniques were employed for the particle size characterizations. The amidoximation reaction was confirmed by FT-IR spectroscopy.     

卤代化合物的碳—13化学位移模拟

用人工神经网络和多元回归方法对含２个碳的２１个卤代化合物的３５个化学位移进行计算机图像模拟，结果表明，人工神经网络方法优于多元回归方法，同时此种方法处理这类问题有明显的优势，波谱模拟技术在有机化合物结构解析中是非常有用的方法。     

人工神经网络方法预测气相色谱保留值

本文运用一典型的人工神经网络模型－“反向传播“模型的改进形式，研究了诱导效应指数Ｉ，摩尔折射度Ｒｏ，疏水亲脂参数ＩｇＰ，以及分子联通性指数与气象色谱保留行为的关系，实现了对色谱保留植的预测。神经网络预测模型的最大相对误差不超过８．７％。结果表明，该方法性能良好，可望成为色谱保留值预测的有效手段。     

PAA/PVA SIPN高吸水性树脂的制备及性能研究

采用水溶液法合成了聚丙烯酸-聚乙烯醇(PAA-PVA)半互传网络型高吸水性树脂,其工艺条件的优化结果为:丙烯酸(AA)与PVA质量比为10∶0.5,AA中和度为80%,加入AA单体质量0.14%的引发剂和0.025%的交联剂,产物吸水率为530g/g,吸盐水率达63g/g。并采用红外光谱法表征了半互穿网络结构的形成。     

{Cu[H_2NC(CH_2OH)_3]_2}{Pic}_2·H_2O的合成与晶体结构

三羟甲基氨基甲烷与苦味酸(Pic)铜在乙醇水混合液中反应,制得少见的不对称氢键连接的超分子化合物{Cu[H2NC(CH2OH)3]2}{Pic}2?H2O。晶体属三斜晶系,空间群为P ,晶胞参数为a = 6.894(1),b = 8.219(1),c = 13.543(3) ? ?= 79.290(1),?= 8 3.36(2), ?= 84.590(1)埃轿狢20H30CuN8O22,V = 746.9(2) 3,Z = 1,Dc = 1.774 g/cm3,F(000) = 411,?= 0.844 mm-1,R = 0.0334,Rw = 0.0863。在配合物的结构单元中, Cu2+位于对称中心,分别与2个四齿配体(三羟甲基氨基甲烷)中的2个OH,1个NH2,三齿配位,呈笼状螯合。而另一个OH,因配体和中心离子构型的限制,不参与配位。     

用人工神经网络处理谷物成分分析

本文用人工神经网络处理谷物的付立叶变换近红外漫反射光谱,对谷物中含量在10~(-1)～10~(-3)的蛋白质、脂肪和6种人体必需氨基酸定量分析数据进行了解析,分析结果与经典化学方法没有系统偏差,且优于逐步回归分析法的结果。     

人工神经网络及其在分析化学中的应用

人工神经网络是一种新兴的计算方法，有着广阔的发展前途，目前在分析化学领域已经有了多方面的应用。本文简要介绍了人工神经网络的原理及其在分析化学中的应用。     

新颖的二维超分子化合物[CoⅢ(aea)(2,2''''-bipy)]Cl·H2O的水热合成及结构表征

0IntroductionInrecentyearsgreatattentionhasbeenpaidtosupramolecularchemistryandawidevarietyofnet鄄workshavealreadybeenconstructedbasedupontheprincipleofcrystalengineering[1~3].Usually,variousmultifunctionalorganicligands,bothrigidandflexiblemulticarboxyli…