Cyclolinear polysiloxanes. II. Crosslinking and characterization

The synthesis and characterization of two groups of novel networks prepared from cyclolinear polysiloxanes are described. The first group of networks from cyclolinear polysiloxanes (N‐CLPSs) was synthesized by the hydrosilation of vinyl‐terminated cyclolinear polyorganosiloxanes [prepared from diacetoxydiethyltetramethylcyclotetrasiloxane (D₄Et₂OAc₂) or diacetoxytriethylpentamethylcyclopentasiloxane (D₅Et₃OAc₂)] with a copolymer of dimethylsiloxane and methylhydrosiloxane as the crosslinking agent. Hydrosilation was effected with a platinum carbonyl catalyst with a cyclovinylsiloxane moderator. The second group of networks (N‐eCLPSs) was prepared similarly with extended cyclolinear polysiloxanes. The mechanical properties of the novel networks were comparable to those of polydimethylsiloxane networks (N‐PDMS). The oxygen permeabilities were similar to or slightly higher than that of N‐PDMS. The glass‐transition temperatures of D₄Et₂OAc₂‐ and D₅Et₃OAc₂‐based N‐CLPSs were ?67.8 and ?90.8 °C, respectively, whereas the incorporation of polydimethylsiloxane spacers into similar N‐eCLPSs lowered their glass‐transition temperatures to ?109.7 and ?115.0 °C. Upon heating to 800 °C in air, N‐CLPSs yielded more residue than N‐eCLPSs, which in turn yielded more residue than N‐PDMS. These results may have been due to the presence of T units in the cyclic siloxane units, which may have inhibited chain degradation or the formation of volatile products. © 2006 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 44: 4053–4062, 2006     

k-紧优有向双环网络无限族的构建

本文给出了一种方法用于构造k-紧优双环网络无限族(k≥1),并用此方法构造出了4族3-紧优无限族,3族新的4-紧比无限族,3族5-紧优无限族及2族6-紧优无限族．     

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.     

Communication Roles, Perceived Effectiveness, and Satisfaction in an Environmental Management Program

Earlier research has shown a relationship between various forms of structural centrality and perceived leadership and role satisfaction in small experimental groups. The limited amount of research on this topic in naturally occurring social networks has yielded results that often conflict with one another. Different results have generally been attributed to possible differences in task environments. This paper examines the relationship between two types of structural centrality and perceived influence, role satisfaction, and perceived effectiveness in an environmental resource management program. Findings in this paper suggest that the observed differences in relationships between the network and other variables is partly a function of global network properties (e.g., marginality of subgroups) and related task environments. This revised version was published online in August 2006 with corrections to the Cover Date.     

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.     

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。     

Monotone Chemical Reaction Networks

We analyze certain chemical reaction networks and show that every solution converges to some steady state. The reaction kinetics are assumed to be monotone but otherwise arbitrary. When diffusion effects are taken into account, the conclusions remain unchanged. The main tools used in our analysis come from the theory of monotone dynamical systems. We review some of the features of this theory and provide a self-contained proof of a particular attractivity result which is used in proving our main result.     

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.     

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。     

Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using and nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the ‘measured’ and ‘estimated’ RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only NMR data) also showed high correlation between the ‘measured’ and ‘estimated’ RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets.