概率神经网络和FTIR光谱用于食道癌的辅助分析

利用正常与相应癌化食道组织的主要FTIR特征峰aυs,CH3、sυ,CH2、σCH2、aυs,po4-、υc-o、sυ,po2-及sυ,磷酸化蛋白作为概率神经网络的输入向量,对网络的主要参数(网络径向基函数分布spread(0~5))、输入向量和网络表现(m ean accurate rate of recogn ition)之间的关系进行了研究。主要结论如下:i)无论输入向量是哪种特征频率的组合,其平均识别正确率都高于71.40%;ii)当输入向量为特征频率sυ,po2、sυ,磷酸化蛋白或υc-0、sυ,po2、sυ,磷酸化蛋白时,网络表现较佳,平均识别正确率较好。当spread介于1.4~2.3时,两者均达到网络具有的最高平均识别正确率(85.71%);iii)大多数情况下,网络的平均识别正确率与spread之间呈现二个高峰的特征,即spread介于0.1~0.3和1.5~5.0之间时,网络均具有较高的平均识别正确率。研究表明,以傅里叶变换红外光谱的主要特征峰为概率神经网络的输入向量,用于食道组织样品的癌化识别分析是完全可能的,其平均识别正确率可达85.71%。     

模糊神经网络在复合地基沉降量预测中的应用

复合地基后期沉降变形对于建筑物设计及安全具有重要意义,针对通过长期沉降观测以得到复合地基的最终沉降需要耗费较多资源的问题,提出了一种基于模糊神经网络的预测方法.该方法考虑沉降变化过程有较大的随机性和模糊性,直接将样本数据进行模糊化,所得的模糊数代表了样本点集与控制点集中各分量之间的相关度,并依此建立模糊BP神经网络进行学习和估算.实验结果表明了该方法对沉降进行预测是可行与有效的,且在相对误差的有效控制方面优于BP网络方法与灰色方法.     

Circuit-switching networks with FIFO plus justifying blockout service on infinite graphs

A class of circuit-switching open queueing networks is discussed. The main result of the paper is that if extra message flows are not too intensive and the path distribution is mainly concentrated on the paths of (graph) distance 1 (nearest neighbour connections), then the network has a unique stationary working regime, no matter how large the configuration graph of the network is. Standard properties of this regime are established such as decay of correlation and continuity.     

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。     

DNA与目标分子相互作用的预测性研究

以量化的分子结构参数和实验结果为依据, 运用模式识别技术、多元线性回归和人工神经网络研究了目标分子与DNA相互作用的主要影响因素, 建立了准确性较高的2个键合常数预测模型和1个作用模式预测模型. 初次量化的分子结构参数有21种, 经过筛选发现其中的10种参数对相互作用有显著影响. 研究结果可为抗癌药物的分子设计和筛选提供有价值的信息.