Effective elastic moduli of two-dimensional solids with multiple cracks

An accurate method which directly accounts for the interactions between different microcracks is used for analyzing the elastic problem of multiple cracks solids. The effective elastic moduli for randomly oriented cracks and parallel cracks are evaluated for the representative volume element (RVE) with microcracks in infinite media. The numerical results are compared with those from various micromechanics models and experimental data. These results show that the present method is simple and provides a direct and efficient approach to dealing with elastic solids containing multiple cracks.

Evaluation of influence of multiple scattering effect in light-scattering-based applications

The extinction cross sections of a system containing two particles are calculated by the T-matrix method, and the results are compared with those of two single particles with single-scattering approximation. The necessity of the correction of the refractive indices of water and polystyrene for different incident wavelengths is particularly addressed in the calculation. By this means, the volume fractions allowed for certain accuracy requirements of single-scattering approximation in the light scattering experiment can be evaluated. The volume fractions calculated with corrected refractive indices are compared with those obtained with fixed refractive indices which have been rather commonly used, showing that fixed refractive indices may cause significant error in evaluating multiple scattering effect. The results also give a simple criterion for selecting the incident wavelength and particle size to avoid the 'blind zone' in the turbidity measurement, where the turbidity change is insensitive to aggregation of two particles.

Determination of plastic properties by instrumented spherical indentation: Expanding cavity model and similarity solution approach

The present paper aims to develop a robust spherical indentation-based method to extract material plastic properties. For this purpose, a new consideration of-piling-up effect is incorporated into the expanding cavity model; an extensive numerical study on the similarity Solution has also been performed. As a consequence, two semi-theoretical relations between the indentation response and material plastic properties are derived, with which plastic properties of materials can be identified from a single instrumented spherical indentation curve, the advantage being that this approach no longer needs estimations of contact radius with given elastic modulus. Moreover, the inconvenience in using multiple indenters with different tip angles can be avoided. Comprehensive sensitivity analyses show that the present algorithm is reliable. Also, by experimental verification performed oil three typical materials, good agreement of the material properties between those obtained from the reverse algorithm and experimental data is obtained.

Determination Of Plastic Properties By Instrumented Spherical Indentation: Expanding Cavity Model And Similarity Solution Approach

The present paper aims to develop a robust spherical indentation-based method to extract material plastic properties. For this purpose, a new consideration of-piling-up effect is incorporated into the expanding cavity model; an extensive numerical study on the similarity Solution has also been performed. As a consequence, two semi-theoretical relations between the indentation response and material plastic properties are derived, with which plastic properties of materials can be identified from a single instrumented spherical indentation curve, the advantage being that this approach no longer needs estimations of contact radius with given elastic modulus. Moreover, the inconvenience in using multiple indenters with different tip angles can be avoided. Comprehensive sensitivity analyses show that the present algorithm is reliable. Also, by experimental verification performed oil three typical materials, good agreement of the material properties between those obtained from the reverse algorithm and experimental data is obtained.

A scheme for multiple sequences alignment optimization-an improvement based on family representative mechanics features

As a basic tool of modern biology, sequence alignment can provide us useful information in fold, function, and active site of protein. For many cases, the increased quality of sequence alignment means a better performance. The motivation of present work is to increase ability of the existing scoring scheme/algorithm by considering residue–residue correlations better. Based on a coarse-grained approach, the hydrophobic force between each pair of residues is written out from protein sequence. It results in the construction of an intramolecular hydrophobic force network that describes the whole residue–residue interactions of each protein molecule, and characterizes protein's biological properties in the hydrophobic aspect. A former work has suggested that such network can characterize the top weighted feature regarding hydrophobicity. Moreover, for each homologous protein of a family, the corresponding network shares some common and representative family characters that eventually govern the conservation of biological properties during protein evolution. In present work, we score such family representative characters of a protein by the deviation of its intramolecular hydrophobic force network from that of background. Such score can assist the existing scoring schemes/algorithms, and boost up the ability of multiple sequences alignment, e.g. achieving a prominent increase (50%) in searching the structurally alike residue segments at a low identity level. As the theoretical basis is different, the present scheme can assist most existing algorithms, and improve their efficiency remarkably.

A combined statistical model for multiple motifs search

Transcription factor binding sites (TFBS) play key roles in genebior 6.8 wavelet expression and regulation. They are short sequence segments with de¯nite structure and can be recognized by the corresponding transcription factors correctly. From the viewpoint of statistics, the candidates of TFBS should be quite di®erent from the segments that are randomly combined together by nucleotide. This paper proposes a combined statistical model for ¯nding over- represented short sequence segments in di®erent kinds of data set. While the over-represented short sequence segment is described by position weight matrix, the nucleotide distribution at most sites of the segment should be far from the background nucleotide distribution. The central idea of this approach is to search for such kind of signals. This algorithm is tested on 3 data sets, including binding sites data set of cyclic AMP receptor protein in E.coli, PlantProm DB which is a non-redundant collection of proximal promoter sequences from di®erent species, collection of the intergenic sequences of the whole genome of E.Coli. Even though the complexity of these three data sets is quite di®erent, the results show that this model is rather general and sensible.

Short and long peptidoglycan recognition proteins (PGRPs) in zebrafish, with findings of multiple PGRP homologs in teleost fish

Peptidoglycan recognition protein (PGRP) specifically binds to peptidoglycan and is considered to be one of the pattern recognition proteins in the innate immunity of insect and mammals. Using a database mining approach and RT-PCR, multiple peptidoglycan recognition protein (PGRP) like genes have been discovered in fish including zebrafish Danio rerio, Japanese pufferfish TakiFugu rubripes and spotted green pufferfish Tetraodon nigroviridis. They share the common features of those PGRPs in arthropod and mammals, by containing a conserved PGRP domain. Based on the predicted structures, the identified zebrafish PGRP homologs resemble short and long PGRP members in arthropod and mammals. The identified PGRP genes in T. nigroviridis and TakiFugu rubripes resemble the long PGRPs, and the short PGRP genes have not been found in T. nigroviridis and TakiFugu rubripes databases. Computer modelling of these molecules revealed the presence of three alpha-helices and five or six beta-strands in all fish PGRPs reported in the present study. The long PGRP in teleost fish have multiple alternatively spliced forms, and some of the identified spliced variants, e.g., tnPGRP-L3 and tnPGRP-L4 (in: Tetraodon nigroviridis), exhibited no characters present in the PGRP homologs domain. The coding regions of zfPGRP6 (zf: zebrafish), zfPGRP2-A, zfPGRP2-B and zfPGRP-L contain five exons and four introns; however, the other PGRP-like genes including zfPGRPSC1a, zfPGRPSC2, tnPGRP-L1-, tnPGRP-L2 and frPGRP-L (fr: Takifugu rubripes) contain four exons and three introns. In zebrafish, long and short PGRP genes identified are located in different chromosomes, and an unknown locus containing another long PGRP-like gene has also been found in zebrafish, demonstrating that multiple PGRP loci may be present in fish. In zebrafish, the constitutive expressions of zfPGRP-L, zfPGRP-6 and zfPGRP-SC during ontogeny from unfertilized eggs to larvae, in different organs of adult, and the inductive expression following stimulation by Flavobacterium columnare, were detected by real-time PCR, but the levels and patterns varied for different PGRP genes, implying that different short and long PGRPs may play different roles in innate immune response. (c) 2007 Elsevier Ltd. All rights reserved.

Correcting the systematic error of the density functional theory calculation: the alternate combination approach of genetic algorithm and neural network

The alternate combinational approach of genetic algorithm and neural network (AGANN) has been presented to correct the systematic error of the density functional theory (DFT) calculation. It treats the DFT as a black box and models the error through external statistical information. As a demonstration, the AGANN method has been applied in the correction of the lattice energies from the DFT calculation for 72 metal halides and hydrides. Through the AGANN correction, the mean absolute value of the relative errors of the calculated lattice energies to the experimental values decreases from 4.93% to 1.20% in the testing set. For comparison, the neural network approach reduces the mean value to 2.56%. And for the common combinational approach of genetic algorithm and neural network, the value drops to 2.15%. The multiple linear regression method almost has no correction effect here.