Uncertain and negative evidence in continuous time Bayesian networks

The continuous time Bayesian network (CTBN) enables reasoning about complex systems by representing the system as a factored, finite-state, continuous-time Markov process. Inference over the model incorporates evidence, given as state observations through time. The time dimension introduces several new types of evidence that are not found with static models. In this work, we present a comprehensive look at the types of evidence in CTBNs. Moreover, we define and extend inference to reason under uncertainty in the presence of uncertain evidence, as well as negative evidence, concepts extended to static models but not yet introduced into the CTBN model.     

基于神经网络的NMR谱图自动相位校正综合算法

目前,已有多种算法被应用在核磁共振谱图自动相位校正中,由于各种算法本身特性和所基于谱图的具体特性的差异,不同算法对于特征不同的谱图的适用性也各不相同. 针对这一情况,文在综合研究多种现有自动相位校正算法的基础上,提出了一种基于神经网络的,可以根据谱图的特征来选取最合适的算法进行自动相位校正的综合算法. 实验表明,本算法可以获得比以往方法更好的计算结果.     

The Existence and Global $p$-Exponential Stability of Periodic Solution for Stochastic BAM Neural Networks with Delays

In this paper, we consider a class of stochastic BAM neural networks with delays. By establishing new integral inequalities and using the properties of spectral radius of nonnegative matrix, some sufficient conditions for the existence and global $p$-exponential stability of periodic solution for stochastic BAM neural networks with delays are given. An example is provided to show the effectiveness of the theoretical results.     

Upper Bound of the Number of Zeros for Abelian Integrals in a Kind of Quadratic Reversible Centers of Genus One

By using the methods of Picard-Fuchs equation and Riccati equation, we study the upper bound of the number of zeros for Abelian integrals in a kind of quadratic reversible centers of genus one under polynomial perturbations of degree $n$. We obtain that the upper bound is $7[(n-3)/2]+5$ when $n\ge 5$, $8$ when $n=4$, $5$ when $n=3$, $4$ when $n=2$, and $0$ when $n=1$ or $n=0$, which linearly depends on $n$.     

湖南辰溪特高有机硫煤的稀土元素特征及其成因

采用高分辨率电感耦合等离子质谱(HR-ICP-MS)技术测定辰溪晚二叠世高有机硫(7.75%)煤中的稀土元素(REEs)。辰溪煤中∑REE变化较大,从38.84 μg/g至305.85 μg/g,加权平均值为104.57 μg/g,高于世界煤均值,与中国煤相近。辰溪煤中有明显的Ce负异常(δCe=0.74~0.84)和Eu的负异常(δEu=0.55~0.69)。煤层剖面上稀土元素含量和分布模式的变化反映了成煤环境的波动,从底板到顶板陆源物质的影响减弱,而海水的影响增强。沉积环境的阶段性变化是导致剖面上下有机硫与黄铁矿硫比值相差悬殊的原因。辰溪煤中稀土元素与铁呈现了显著的正相关性(n=11,r=0.95),说明其物质来源和富集条件有一定相似性,即陆源物质和海水共同影响的结果。稀土元素的分布模式以及∑REE与灰分、Si、Al的相关性说明了其主要来源于陆源物质;辰溪煤中δCe与δEu在剖面上变化很小,说明海水对煤中的稀土元素存在重新改造的作用。     

Banana-shaped mesogens: effect of lateral substituents on seven-ring esters containing a biphenyl moiety

The synthesis and characterization of five homologous series of symmetrical compounds composed of banana-shaped molecules containing a biphenyl moiety are reported. All these compounds are non-Schiff's bases and are esters. The effects of lateral substituents such as fluoro, methyl and ethyl in the side arms of these molecules are examined. These substituents have a strong influence in reducing the clearing temperatures. Banana phases such as B1, B2 and B6 were observed in the above series of compounds. The mesophases were characterized by a combination of polarizing optical microscopy, differential scanning calorimetry, X-ray diffraction and electro-optic studies.     

Open tubular columns with mixed‐mode reversed‐phase and weak anion‐exchange stationary phase for capillary electrochromatography

Columns for open tubular capillary electrochromatography, coated with a mixed‐mode (RP/ion‐exchange) stationary phase, were prepared by using the sol–gel method. The synthetic procedure was optimized by changing the ratios of tetraethoxysilane, octyltriethoxysilane, and 3‐aminopropyltriethoxysilane in the initial sol. SEM studies reveal that a coating with about 400 nm thickness can be obtained. The inner surface properties of these capillaries were probed by measuring the EOF as a function of pH. The surface of this stationary phase contains octyl, amine, and residual silanol moieties; the amine and silanol groups determine the net charge on the inner surface of the capillary and can produce a switchable EOF (anodal/cathodal). The performances of the columns were evaluated by open tubular capillary electrochromatography using a wide range of compounds (polycyclic aromatic hydrocarbons, aromatic acids, and aromatic amines).     

Gelatin‐Alginate Gels and Their Enzymatic Modifications: Controlling the Delivery of Small Molecules

The release of molecules entrapped within biogels is dictated by diffusion laws. Innovative biogel architectures are conceived and tested to control small molecule delivery from gelatin gels. The ionic interactions modulate the release of small molecules. Alginate is then added to gelatin gels and further hydrolyzed; the influence of viscosity is discussed. Next, various mixed gels are compared, such as a gelatin‐alginate IPN and the original architecture of an alginate gel entrapped in a gelatin gel with or without a polysaccharidase. The relative influence of ionic interactions and diffusional constraints on the delivery of small charged molecules is explored, and a solution for controlling diffusion is proposed for any situation.

     

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients logP, acid dissociation constant pK_a, water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.     

Protein Secondary Structure Prediction with Partially Recurrent Neural Networks

Abstract

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures α-helix, β-sheet, and “coil” for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 × 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.