控温相变免疫分析测定雌酮

通过热引发聚合与氧化还原引发聚合，合成水凝胶，作为雌酮全抗原(E1— BSA)载体；以异硫氰基灾光素标记雌酮的单克隆抗体(McAb)作为示踪剂，建立了控温相变竞争免疫分析测定雌酮(EI)的方法并应用于分析血清样品。实验表明：以热引发聚合合成的水凝胶为载体的荧光免疫分析方法具有更高的灵敏度，它的线性范围为10-700μg/L，检出限为1μg／L(n=10，3倍空白)。经水凝胶性质的比较(包括LCST、非特异性吸附、抗原与抗体反应时间及固定化抗原活性)，证明了热引发聚合的水凝胶较氧化还原引发聚合的水凝胶更适合于作为免疫分析的载体。     

BP人工神经网络用于除草剂活性预测

采用AMI方法研究了10种新型的磺酰脲类除草剂的电子结构，并以原子的Mulliken净电荷和除草剂在不同浓度(100，10mg／L)下对油菜、稗草两种作物的根、茎部位的抑制率为训练样本集。构造并训练得到具有活性预测能力的BP神经网络．结果表明，该BP网络不仅能对训练样本很好拟合。亦能对未知化合物的活性作出很好的预测．     

全光网中基于FBG的OADM设计研究

提出一种全新的OADM(Optical Add—Drop Multiplexers)结构设计，此结构仅采用一个多端口环行器，大大减少了环行器的数量，节约了成本。若要上／下路一个波长．则只需要一个FBG(Fiber Bragg Grating)；若要同时上／下路多个波长，则需要2m个FBG。计算机仿真实验证明此结构比H．Yuan提出的结构具有更低的损耗．对全光网络的实现具有重大的意义。     

网络图在产品配送中的应用

面对多样化的市场需求，企业的销售渠道也变得异常复杂，利用网络图可以简化实际问题，并能形象地描述它。本根据网络知识建立了产品分销模型，探求企业在市场需求固定和随机时的最优收益。最后，章还进行实例验算，并做出相应的结果分析。     

TG of polytetrahydrofuran/ isophoronediisocyanate reaction products

TG, swelling, and viscometric studies are presented for the residues, PTHF/IPDI polymer networks, and the extracts, the linear polymers and unreacted IPDI, after Soxhlet extraction of PTHF/IPDI reaction products. The products are obtained by reacting PTHF with 650, 1400, (2×650+1×2900), or 2900 of molecular mass with IPDI at various concentrations in bulk. The results on the swelling and the viscosity experiments suggest that the PTHF/IPDI reaction products have a usual expectable structure. All the TG curves are a double stage curve. The initial stage and the last stage seem to reflect decomposition of PTHF chains and vaporization of the remainder, IPDI, respectively. These are analyzed by a trial-and-error construction, supposing double event behavior. The values of ratio of mass loss associated with the initial event, W0₁, to the mass loss associated with the last event, W0₂, are smaller than the expectable those. This suggests that Event 2 involves vaporization of the decomposition products of PTHF moieties bonded to IPDI in addition to vaporization of IPDI. This revised version was published online in July 2006 with corrections to the Cover Date.     

Information processing,memories, and synchronization in chaotic neural network with the time delay

Information processing and two types of memory in an analog neural network model with time delay that produces chaos similar to the human and animal EEGs are considered. There are two levels of information processing in this neural network: the level of individual neurons and the level of the neural network. Similar to the state of brain, the state of chaotic neural network is defined. It is characterized by two types of memories (memory I and memory II) and correlation structure between the neurons. In normal (unperturbed) state, the neural network generates chaotic patterns of averaged neuronal activities (memory I) and patterns of oscillation amplitudes (memory II). In the presence of external stimulation, the activity patterns change, showing changes in both types of memory. As in experiments on stimulation of the brain, the neural network model shows synchronization of neuronal activities due to stimulus measured by Pearson's correlation coefficient. An increase in neural network asymmetry (increase of the neural network excitability) leads to the phenomenon similar to the epilepsy. Modeling of brain injury, Parkinson's disease, and dementia is performed by removing and weakening interneuron connections. In all cases, the chaotic neural network shows a decrease of the degree of chaos and changes in both types of memory similar to those observed in experiments with healthy human subjects and patients with Parkinson's disease and dementia. © 2005 Wiley Periodicals, Inc. Complexity 11:39–52, 2005     

基于人工神经网络的商业银行信用风险模型

在对人工神经网络的基本原理进行简要介绍的基础上 ,着重对构建商业银行信用风险的人工神经网络模型进行了研究 ,实证结果表明 ,人工神经网模型具有很高的预测精度     

资源有限网络计划的PRWI启发式优化方法

本文在综合考虑了有资源约束的网络计划结构特征、资源强度、时间约束等方面因素的基础上，提出了一种新的资源优化的启发式优化方法—ＰＲＷＩ方法，并通过分析证明了该方法处理问题的效果较现有的其它方法好。     

Use of dynamic trees in a network simplex algorithm for the maximum flow problem

Goldfarb and Hao (1990) have proposed a pivot rule for the primal network simplex algorithm that will solve a maximum flow problem on ann-vertex,m-arc network in at mostnm pivots and O(n ² m) time. In this paper we describe how to extend the dynamic tree data structure of Sleator and Tarjan (1983, 1985) to reduce the running time of this algorithm to O(nm logn). This bound is less than a logarithmic factor larger than those of the fastest known algorithms for the problem. Our extension of dynamic trees is interesting in its own right and may well have additional applications.Research partially supported by a Presidential Young Investigator Award from the National Science Foundation, Grant No. CCR-8858097, an IBM Faculty Development Award, and AT&T Bell Laboratories.Research partially supported by the Office of Naval Research, Contract No. N00014-87-K-0467.Research partially supported by the National Science Foundation, Grant No. DCR-8605961, and the Office of Naval Research, Contract No. N00014-87-K-0467.     

用于光学神经网络硬件系统误差纠正的虚拟神经网络

提出了一种用于修正光学神经网络硬件系统误差的虚拟神经网络模型，光学实验结果表明该网络能够有效地消除硬件系统误差对实验结果的影响。