Convergence analysis of a modified inexact implicit method for general mixed monotone variational inequalities

We consider a useful modification of the inexact implicit method with a variable parameter in Wang et al. J Optim Theory 111: 431–443 (2001) for generalized mixed monotone variational inequalities. One of the contributions of the proposed method in this paper is that the restrictions imposed on the variable parameter are weaker than the ones in Wang et al. J Optim Theory 111: 431–443 (2001). Another contribution is that we establish a sufficient and necessary condition for the convergence of the proposed method to a solution of the general mixed monotone variational inequality.     

Existence, Multiplicity and Infinite Solvability of Positive Solutions for One-Dimensional p-Laplacian

The existence, multiplicity and infinite solvability of positive solutions are established for some two-point boundary value problems of one-dimensional p-Laplacian. In this paper, by multiplicity we mean the existence of m solutions, where m is an arbitrary natural number.     

A Generalization of the Mean Size Formula of Wavelet Packets in Lp

The purpose of this paper is to investigate the mean size formula of wavelet packets in Lp for 0 〈 p ≤ ∞. We generalize a mean size formula of wavelet packets given in terms of the p-norm joint spectral radius and we also give some asymptotic formulas for the Lp-norm or quasi-norm on the subdivision trees. All results will be given in the general setting,     

Photoluminescence linewidth broadening due to alloy/thickness fluctuation of CdxZn1 − xSe/ZnSe triple quantum wells

Photoluminescence (PL) linewidth broadening of Cd_xZn_{1 − x}Se/ZnSe triple quantum wells, grown on GaAs substrates by molecular beam epitaxy (MBE), has been investigated. Various quantum well (QW) samples have been prepared with different QW thickness and composition (Cd-composition). Measured and calculated PL linewidth are compared. Both composition and thickness fluctuations are considered for the calculation with the parameters such as the volume of exciton, nominal thickness and composition of QWs. Surface roughness measured by atomic force microscopy (AFM) is used to estimate the interface roughness. Results show that when Cd-composition increases additional linewidth broadening due to Zn/Cd interdiffusion is enhanced.     

神经网络在电磁场数值问题中的应用

针对目前电磁场数值处理中计算繁杂慢速和耗费资源过多的问题,本文提出了一种基于神经网络小波理论进行计算的新方法。文中着重介绍了利用小波变换神经网络的基本理论,结合电磁场数值计算的基本方法,推导变换进而得到一个通用公式和处理方法。在该方法中,神经网络学习内容少和数据处理简单,因此该方法具有计算速度快,占用资源少的优点,并有用硬件实现的前景。     

线性泛函方程解的振动性的新结果

本文研究高阶泛函方程x(g(t))=P(t)x(t)+Q1(t)x(g2(t))+…+Qk(t)x(gk+1(t))解的振动性,得到了一些新的振动条件.改进和推广了已有结果.     

A new stochastic method of reconstructing porous media

We present a new stochastic method of reconstructing porous medium from limited morphological information obtained from two-dimensional micro- images of real porous medium. The method is similar to simulated annealing method in the capability of reconstructing both isotropic and anisotropic structures of multi-phase but differs from the latter in that voxels for exchange are not selected completely randomly as their neighborhood will also be checked and this new method is much simpler to implement and program. We applied it to reconstruct real sandstone utilizing morphological information contained in porosity, two-point probability function and linear-path function. Good agreement of those references verifies our developed method’s powerful capability. The existing isolated regions of both pore phase and matrix phase do quite minor harm to their good connectivity. The lattice Boltzmann method (LBM) is used to compute the permeability of the reconstructed system and the results show its good isotropy and conductivity. However, due to the disadvantage of this method that the connectivity of the reconstructed system’s pore space will decrease when porosity becomes small, we suggest the porosity of the system to be reconstructed be no less than 0.2 to ensure its connectivity and conductivity.     

PREPARATION AND CATALYTIC ACTIVITY OF BIOACTIVE FIBERS

Two kinds of water-soluble metallophthalocyanines, binuclear cobalt phthalocyanine （Co2Pc2） and binuclear ferric phthalocyanine （Fe2Pc2）, were synthesized through phenylanhydride-urea route and characterized by elemental analysis and FT-IR spectra. Binuclear metallophthalocyanine derivatives （Mt2Pc2） were immobilized on silk fibers and modified viscose fibers to construct bioactive fibers of mimic enzyme. Mt2Pc2 was used as the active center of bioactive fibers, viscose and silk fibers as the microenvironments. The catalytic oxidation ability of bioactive fibers on the malodors of methanthiol and hydrogen sulfide was investigated at room temperature. The experimental results indicated that the catalytic activity of such bioactive fibers was closely correlative to the types ofbioactive fibers and substrates.     

Finding finer functions for partially characterized proteins by protein-protein interaction networks

Based on high-throughput data, numerous algorithms have been designed to find functions of novel proteins. However, the effectiveness of such algorithms is currently limited by some fundamental factors, including (1) the low a-priori probability of novel proteins participating in a detailed function; (2) the huge false data present in high-throughput datasets; (3) the incomplete data coverage of functional classes; (4) the abundant but heterogeneous negative samples for training the algorithms; and (5) the lack of detailed functional knowledge for training algorithms. Here, for partially characterized proteins, we suggest an approach to finding their finer functions based on protein interaction sub-networks or gene expression patterns, defined in function-specific subspaces. The proposed approach can lessen the above-mentioned problems by properly defining the prediction range and functionally filtering the noisy data, and thus can efficiently find proteins’ novel functions. For thousands of yeast and human proteins partially characterized, it is able to reliably find their finer functions (e.g., the translational functions) with more than 90% precision. The predicted finer functions are highly valuable both for guiding the follow-up wet-lab validation and for providing the necessary data for training algorithms to learn other proteins.