Global stability of Hopfield neural networks

This paper gives a condition for the global stability of a continuous-time hopfield neural network when its activation function maybe not monotonically increasing.

Early Embryonic Exposure to Polychlorinated Biphenyls Disrupts Heat-Shock Protein 70 Cognate Expression in Zebrafish

Polychlorinated biphenyls (PCBs) are persistent environmental contaminants that have documented neurological effects in children exposed in utero. To better define neuronally linked molecular targets during early development, zebrafish embryos were exposed to Aroclor 1254, a mixture of PCB congeners that are common environmental contaminants. Microarray analysis of the zebrafish genome revealed consistent significant changes in 38 genes. Of these genes, 55% (21) are neuronally related. One gene that showed a consistent 50% reduction in expression in PCB-treated embryos was heat-shock protein 70 cognate (Hsc70). The reduction in Hsc70 expression was confirmed by real-time polymerase chain reaction (PCR), revealing a consistent 30% reduction in expression in PCB-treated embryos. Early embryonic exposure to PCBs also induced structural changes in the ventro-rostral cluster as detected by immunocytochemistry. In addition, there was a significant reduction in dorso-rostral neurite outgrowth emanating from the RoL1 cell cluster following PCB exposure. The serotonergic neurons in the developing diencephalon showed a 34% reduction in fluorescence when labeled with a serotonin antibody following PCB exposure, corresponding to a reduction in serotonin concentration in the neurons. The total size of the labeled neurons was not significantly different between treated and control embryos, indicating that the development of the neurons was not affected, only the production of serotonin within the neurons. The structural and biochemical changes in the developing central nervous system following early embryonic exposure to Aroclor 1254 may lead to alterations in the function of the affected regions.

网络控制系统稳定性的图理论

针对具有有界时延和数据包丢失的网络控制系统,提出了一种新的稳定性判据.基于Lyapunov方法和图论理论,给出非线性离散和连续网络控制系统渐近稳定的充分条件,获得保持这两类系统稳定的最大允许时延界,得到控制器设计方法.并且,利用区间矩阵的谱特征,给出网络控制系统区间稳定的充分条件.设计算法,获得比例积分反馈控制器增益.算例表明所提方法的有效性。

基于MRF模型的油气储层属性随机建模方法研究

Stochastic reservoir modeling is a technique used in reservoir describing. Through this technique, multiple data sources with different scales can be integrated into the reservoir model and its uncertainty can be conveyed to researchers and supervisors. Stochastic reservoir modeling, for its digital models, its changeable scales, its honoring known information and data and its conveying uncertainty in models, provides a mathematical framework or platform for researchers to integrate multiple data sources and information with different scales into their prediction models. As a fresher method, stochastic reservoir modeling is on the upswing. Based on related works, this paper, starting with Markov property in reservoir, illustrates how to constitute spatial models for catalogued variables and continuum variables by use of Markov random fields. In order to explore reservoir properties, researchers should study the properties of rocks embedded in reservoirs. Apart from methods used in laboratories, geophysical means and subsequent interpretations may be the main sources for information and data used in petroleum exploration and exploitation. How to build a model for flow simulations based on incomplete information is to predict the spatial distributions of different reservoir variables. Considering data source, digital extent and methods, reservoir modeling can be catalogued into four sorts: reservoir sedimentology based method, reservoir seismic prediction, kriging and stochastic reservoir modeling. The application of Markov chain models in the analogue of sedimentary strata is introduced in the third of the paper. The concept of Markov chain model, N-step transition probability matrix, stationary distribution, the estimation of transition probability matrix, the testing of Markov property, 2 means for organizing sections-method based on equal intervals and based on rock facies, embedded Markov matrix, semi-Markov chain model, hidden Markov chain model, etc, are presented in this part. Based on 1-D Markov chain model, conditional 1-D Markov chain model is discussed in the fourth part. By extending 1-D Markov chain model to 2-D, 3-D situations, conditional 2-D, 3-D Markov chain models are presented. This part also discusses the estimation of vertical transition probability, lateral transition probability and the initialization of the top boundary. Corresponding digital models are used to specify, or testify related discussions. The fifth part, based on the fourth part and the application of MRF in image analysis, discusses MRF based method to simulate the spatial distribution of catalogued reservoir variables. In the part, the probability of a special catalogued variable mass, the definition of energy function for catalogued variable mass as a Markov random field, Strauss model, estimation of components in energy function are presented. Corresponding digital models are used to specify, or testify, related discussions. As for the simulation of the spatial distribution of continuum reservoir variables, the sixth part mainly explores 2 methods. The first is pure GMRF based method. Related contents include GMRF model and its neighborhood, parameters estimation, and MCMC iteration method. A digital example illustrates the corresponding method. The second is two-stage models method. Based on the results of catalogued variables distribution simulation, this method, taking GMRF as the prior distribution for continuum variables, taking the relationship between catalogued variables such as rock facies, continuum variables such as porosity, permeability, fluid saturation, can bring a series of stochastic images for the spatial distribution of continuum variables. Integrating multiple data sources into the reservoir model is one of the merits of stochastic reservoir modeling. After discussing how to model spatial distributions of catalogued reservoir variables, continuum reservoir variables, the paper explores how to combine conceptual depositional models, well logs, cores, seismic attributes production history.