恒温截面法构制三维相图判误用数点分析法

以相图拓扑学规律为基础，提出了利用系列恒温截面分析相平衡空间结构以及绘制立体相图时的判误数点分析法，以实例说明数点法在相图分析中的应用，并提出了三维相图的构造的实用判误方法。     

共轭高分子体系的分子轨道理伦V:二维重复单胞体系本征方程的因子分解方法

本文将一维重复单胞体系本征方程的因子分解方法向二维体系作了一般的推广,从而解决了一些重复单胞构成的二维大分子体系的能谱问题 , 利用因子分解法,本文研究了石墨二维平面分子边缘的化学反应活性以及体系的电子结构, 导电性与分子尺度的关系, 结果表明, 对于石墨类分子, 边界效应是非常显著的, 且体系的反应活性, 导电机制与边界的结构紧密相关。     

The reduction of the multi-dimensional schrödinger equation to a one-dimensional integral equation

A method is proposed for reducing the multi-dimensional Schrödinger equation to a one-dimensional integral equation. The reduction is exact; and the resulting integral equation although complicated, may be treated by any of a number of numerical methods. Two 2-dimensional problems, the harmonic oscillator and the S-limit for helium and one 3-dimensional problem, the helium ground state are treated in this way.     

A novel method for multivariate data modelling: Piecewise Generalized EMPR

A multivariate data modelling problem consists of a number of nodes with associated function values. Increase in multivariance urges us to use divide-and-conquer algorithms in modelling process of these problems. High dimensional model representation based methods can partition a given multivariate data set into less-variate data sets and have the ability of building a model through these partitioned data sets. Generalized HDMR (GHDMR) is one of these methods and it is known that it works well for dominantly and purely additive natures. Piecewise Generalized HDMR is an alternative method and was developed to increase the efficiency of GHDMR but the performance of the method for modelling multiplicative natures is still not sufficient and acceptable. This work aims to develop a new piecewise method based on enhanced multivariance product representation which works well for representing multiplicative natures.     

Thermal and dynamic analysis of the reactions between formic acid and triethylamine

It is possible to study the problems of thermal regulation and energy storage by utilizing chemical reactions. For this purpose, a method of dynamic and global modelling is used to determine the enthalpy of reaction of formic acid and triethylamine.     

Path modelling by sequential PLS regression

This paper presents a new approach to path modelling, based on a sequential multi‐block modelling in latent variables. The approach is explorative and focused on interpretation. The method breaks with standard traditions of estimating all paths using one single modelling. Instead, one separate model is estimated for each endogenous block. Each separate model is constructed by stepwise use of the standard PLS regression on matrices that are orthogonalised with respect to each other. The advantages of the approach are that it can allow for different dimensionality within each block, it is invariant to relative weighting of the blocks and it is based on simple and standard methodology allowing for simple outlier detection, validation and interpretation. No convergence problems are involved and the method can be used for situations with many more variables than samples. An application based on sensory analysis of wines will be used to illustrate the method. Copyright © 2010 John Wiley & Sons, Ltd.     

Pure component spectral recovery and constrained matrix factorizations: concepts and applications

We present new ideas underlying a self‐modelling factor analytical method which allows to extract pure component spectra and the associated concentration profiles from a set of spectroscopic measurements. The usefulness of the method is demonstrated and compared with established tools for model problems and for a system from catalytic hydroformylation by Rhodium complexes both with overlapping component spectra. Self‐modelling methods tend to minimize the overlap of the recovered spectra, which can result in an unwanted distortion of the spectra and concentration profiles. For strongly overlapping spectra a penalty condition on a specific singular value of the absorptivity matrix factor and a global decomposition approach are appropriate tools to construct improved factorizations. Copyright © 2010 John Wiley & Sons, Ltd.     

Successive projections algorithm improving the multivariate simultaneous direct spectrophotometric determination of five phenolic compounds in sea water

This paper proposes an analytical method to determine directly and simultaneously five phenolic compounds (4-nitrophenol, 2-nitrophenol, phenol, 2,4,6-trichlorophenol and 4-chlorophenol) in sea water (Ria de Bahía Blanca, Argentine). The advantages of this method is that only requires spectrophotometric measurements (separation steps and derivatization reagents are avoided) and chemometric modelling (PLS and MLR–SPA).The statistical comparison between PLS — a well established multivariate method — and MLR–SPA — a recently presented chemometric modelling — demonstrated better analytical performance for the later one. This fact is indicative of the potentiality of MLR–SPA for solving complex analytical problems.     

Trajectory-guided configuration interaction simulations of multidimensional quantum dynamics

We propose an approach to modelling multidimensional quantum systems which uses direct-dynamics trajectories to guide wavefunction propagation. First, trajectory simulations are used to generate a sample of dynamically relevant configurations on the potential energy surface (PES). Second, the sampled configurations are used to construct an n-mode representation of the PES using a greedy algorithm. Finally, the time-dependent Schr?dinger equation is solved using a configuration interaction expansion of the wavefunction, with individual basis functions derived directly from the 1-mode contributions to the n-mode PES. This approach is successfully demonstrated by application to a 20-dimensional benchmark problem describing tunnelling in the presence of coupled degrees of freedom.     

Dimensionality of amino acid space and solvent accessibility prediction with neural networks

Solvent accessibility prediction from amino acid sequences has been pursued by several researchers. Such a prediction typically starts by transforming the amino acid category (or type) information into numerical representations. All twenty amino acids can be completely and uniquely represented by 20-dimensional vectors. Here, we investigate if the amino acid space defined in this way really requires twenty dimensions. We tried to develop corresponding representations in fewer dimensions. A method for searching optimal codification schema in an arbitrary space using neural networks was developed. The method is used to obtain optimal encoding of amino acids at various levels of dimensionality, and applied to optimize the amino acid codifications for the prediction of the solvent accessibility values of the proteins using feed-forward neural networks. The traditional 20-dimensional codification seems to be redundant in solving the solvent accessibility prediction problem, since a 1-dimensional codification is able to achieve almost the same degree of accuracy as the 20-dimensional codification. Optimal coding in much fewer dimensions could be used to make the predictions of accessible surface area with almost the same degree of accuracy as that obtained by a fully unique 20-dimensional coding. The 1-dimensional amino acid codification for solvent accessibility prediction obtained by a purely mathematical way based on neural networks is highly correlated with a physical property of the amino acids, namely their average solvent accessibility. The method developed to find the optimal codification is general, although the codification thus produced is dependent on the type of estimated property.     

Thermodynamic modelling of liquid-vapour equilibrium and thermophysical properties of molten Pb–Bi systems

A lead–bismuth alloy is an advanced coolant for nuclear power plants. The study of this alloy is of current importance. However, performing high-temperature experiments involves many problems. In this work, to study evaporation of a lead–bismuth alloy in the presence of intermetallic compounds in the condensed phase and dimers and trimers of metals in the vapour phase, a method of thermodynamic modelling was used. The investigations were carried out at temperatures from 400 to 3000 K with the lead content from 0.1 to 0.9 weight fractions in the lead–bismuth alloy. In the method of thermodynamic modelling a software package TERRA and a model of an ideal solution of interaction products were applied. Concentration and temperature dependencies of the content of components in the melt and in the gaseous phase over the melt were calculated. Temperatures, enthalpies, entropies of the melt–vapour transition and various thermophysical properties were defined.     

Numerical methods for inverse problems in electrooptics of polydisperse colloids

In this paper we propose a new method for the determination of the distribution of electrical and geometrical particle parameters based on electrooptical experimental data. The electrooptical method leads to the solution of inverse ill-posed problems. The main equations for the determination of the distribution of particles on these parameters are presented. To find out the distribution functions from the electrooptical experimental data one has to solve the first-kind Fredholm integral equation corresponding to the problem under study. The proposed method of its solution is based on the penalty functions method. The results of modelling that let us compare the various numerical methods are presented.     

Source contributions to ambient aerosol calculated by discriminat partial least squares regression (PLS)

Partial least squares regression (PLS) is proposed for solving ir pollution source apportionment problems as an alternative method to the frequently used chemical mass balance technique. A discriminant PLS is used to calculate linear mixing proportions for a synthetic ambient aerosol data set where the truth is known. Without sacrificing orthogonality of the source profiles, PLS can resolve the emission sources and accurately predict the emission source contributions. Further extensions of the PLS approach to environmental receptor modelling are discussed.     

A Matrix Based Indexing HDMR method for multivariate data modelling

Modelling multivariate data of real life problems from engineering, chemistry, physics, mathematics or other related sciences, in which function values are known only at arbitrarily distributed points of the problem domain, is an important and complicated issue since there exist mathematical and computational complexities in the analytical structure construction process coming from the multivariance. The Plain High Dimensional Model Representation (HDMR) method expresses a multivariate problem in terms of less-variate problems. In this work, a Matrix Based Indexing HDMR method is developed to make the Plain HDMR philosophy employable for the multivariate data partitioning process. This new method will have the ability of dealing with less-variate data sets by partitioning the given data set into univariate, bivariate and trivariate data sets. Interpolating these partitioned data sets will construct an approximate analytical structure as the model of the given multivariate data modelling problem.     

Three-Dimensional Hückel Theory for closo-Carboranes

We have recently developed a 3-dimensional Hückel method for cluster compounds. The method uses a set of approximations for Coulomb, resonance, and overlap integrals very similar to those employed in the familiar 2-dimensional Hückel theory for the pi electrons of planar conjugated hydrocarbons. The method can be adapted to heteroatomic clusters by introducing heteroatomic Coulomb integrals, alpha(Y) = alpha(X) + hbeta, whereh is a parameter for heteroatom Y. In this paper, we use the 3-dimensional Hückel method to study the properties of the closo-carboranes, C(2)B(n)()(-)(2)H(n)(). We calibrate the method by choosing a value of the heteroatomic parameter h that distinguishes positional isomers by energy and gives them relative energies in rough agreement with those established by observation and ab initio calculations. We obtain modest improvement in matching ab initio relative energies of isomers by means of a three-parameter, first-order perturbation treatment. We use the calibrated method to evaluate various mechanisms proposed for the isomerizations of C(2)B(4)H(6), C(2)B(5)H(7), and C(2)B(6)H(8), all of which have been observed to undergo intramolecular isomerizations. Rearrangements of C(2)B(6)H(8) have been satisfactorily explained by a single-DSD (diamond-square-diamond) process. Those for C(2)B(5)H(7) require at least two DSD processes, concerted, consecutive, or overlapping. Several different mechanisms have been proposed for the rearrangement of C(2)B(4)H(6). In evaluating intermediate and transition state structures, the 3-dimensional Hückel method gives higher energies to those structures with a larger number of nontriangular faces, a plausible conclusion except that occasionally it is wrong. In comparison with ab initio results, the 3-dimensional Hückel method fails to give low energies for classical structures.     

0.3 nm分辨率绿豆胰蛋白酶抑制剂Lys活力碎片—牛胰蛋白酶(MBILF-BTRY)复合物的晶体结构分析和分子模型

Crystal structure of MBILF-BTRY complex was successfully solved by molecular replacement method and its 3-dimensional molecular model was thereby derived. Mung bean trypsin inhibitor belongs to Bowman-Birk inhibitor group, which is by far the most complicated among the ten fundamental groups of serine protease inhibitor. Neither the 3-dimensional structure of Bowman-Birk inhibitor group nor the stereoscopic conformation of its complex with protease has ever been reported. The crystallographic data of MBILF-BTRY complex are found to be a=....     

Data modelling with neural networks: Advantages and limitations

The origins and operation of artificial neural networks are briefly described and their early application to data modelling in drug design is reviewed. Four problems in the use of neural networks in data modelling are discussed, namely overfitting, chance effects, overtraining and interpretation, and examples are given of the means by which the first three of these may be avoided. The use of neural networks as a variable selection tool is shown and the advantage of networks as a nonlinear data modelling device is discussed. The display of multivariate data in two dimensions employing a neural network is illustrated using experimental and theoretical data for a set of charge transfer complexes.     

Highly ordered mesoporous Cr2O3 materials with enhanced performance for gas sensors and lithium ion batteries

Highly ordered mesoporous Cr(2)O(3) materials with high specific surface area and narrow pore size distribution were successfully prepared by a vacuum assisted impregnation method. Both 2-dimensional hexagonal and 3-dimensional cubic Cr(2)O(3) mesoporous replicas from SBA-15 and KIT-6 templates exhibit enhanced performance for gas sensors and lithium ion batteries, compared to the bulk Cr(2)O(3) counterpart.