On the microstructure-tensile property correlations in bulk metallic glass matrix composites with crystalline dendrites

Bulk metallic glass (BMG) matrix composites with crystalline dendrites as reinforcements exhibit a wide variance in their microstructures (and thus mechanical properties), which in turn can be attributed to the processing route employed, which affects the size and distribution of the dendrites. A critical investigation on the microstructure and tensile properties of Zr/Ti-based BMG composites of the same composition, but produced by different routes, was conducted so as to identify ``structure-property'' connections in these materials. This was accomplished by employing four different processing methods-arc melting, suction casting, semi-solid forging and induction melting on a water-cooled copper boat-on composites with two different dendrite volume fractions, V-d. The change in processing parameters only affects microstructural length scales such as the interdendritic spacing, lambda, and dendrite size, delta, whereas compositions of the matrix and dendrite are unaffected. Broadly, the composite's properties are insensitive to the microstructural length scales when V-d is high (similar to 75%), whereas they become process dependent for relatively lower V-d (similar to 55%). Larger delta in arc-melted and forged specimens result in higher ductility (7-9%) and lower hardening rates, whereas smaller dendrites increase the hardening rate. A bimodal distribution of dendrites offers excellent ductility at a marginal cost of yield strength. Finer lambda result in marked improvements in both ductility and yield strength, due to the confinement of shear band nucleation sites in smaller volumes of the glassy phase. Forging in the semi-solid state imparts such a microstructure. (c) 2012 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Nonlocal continuum mechanics based ultrasonic flexural wave dispersion characteristics of a monolayer graphene embedded in polymer matrix

Wave propagation in graphene sheet embedded in elastic medium (polymer matrix) has been a topic of great interest in nanomechanics of graphene sheets, where the equivalent continuum models are widely used. In this manuscript, we examined this issue by incorporating the nonlocal theory into the classical plate model. The influence of the nonlocal scale effects has been investigated in detail. The results are qualitatively different from those obtained based on the local/classical plate theory and thus, are important for the development of monolayer graphene-based nanodevices. In the present work, the graphene sheet is modeled as an isotropic plate of one-atom thick. The chemical bonds are assumed to be formed between the graphene sheet and the elastic medium. The polymer matrix is described by a Pasternak foundation model, which accounts for both normal pressure and the transverse shear deformation of the surrounding elastic medium. When the shear effects are neglected, the model reduces to Winkler foundation model. The normal pressure or Winkler elastic foundation parameter is approximated as a series of closely spaced, mutually independent, vertical linear elastic springs where the foundation modulus is assumed equivalent to stiffness of the springs. For this model, the nonlocal governing differential equations of motion are derived from the minimization of the total potential energy of the entire system. An ultrasonic type of flexural wave propagation model is also derived and the results of the wave dispersion analysis are shown for both local and nonlocal elasticity calculations. From this analysis we show that the elastic matrix highly affects the flexural wave mode and it rapidly increases the frequency band gap of flexural mode. The flexural wavenumbers obtained from nonlocal elasticity calculations are higher than the local elasticity calculations. The corresponding wave group speeds are smaller in nonlocal calculation as compared to local elasticity calculation. The effect of y-directional wavenumber (eta(q)) on the spectrum and dispersion relations of the graphene embedded in polymer matrix is also observed. We also show that the cut-off frequencies of flexural wave mode depends not only on the y-direction wavenumber but also on nonlocal scaling parameter (e(0)a). The effect of eta(q) and e(0)a on the cut-off frequency variation is also captured for the cases of with and without elastic matrix effect. For a given nanostructure, nonlocal small scale coefficient can be obtained by matching the results from molecular dynamics (MD) simulations and the nonlocal elasticity calculations. At that value of the nonlocal scale coefficient, the waves will propagate in the nanostructure at that cut-off frequency. In the present paper, different values of e(0)a are used. One can get the exact e(0)a for a given graphene sheet by matching the MD simulation results of graphene with the results presented in this article. (c) 2012 Elsevier Ltd. All rights reserved.

Common Ground Method of Current Injection in Electrical Impedance Tomography

Surface electrodes are essentially required to be switched for boundary data collection in electrical impedance tomography (Ell). Parallel digital data bits are required to operate the multiplexers used, generally, for electrode switching in ELT. More the electrodes in an EIT system more the digital data bits are needed. For a sixteen electrode system. 16 parallel digital data bits are required to operate the multiplexers in opposite or neighbouring current injection method. In this paper a common ground current injection is proposed for EIT and the resistivity imaging is studied. Common ground method needs only two analog multiplexers each of which need only 4 digital data bits and hence only 8 digital bits are required to switch the 16 surface electrodes. Results show that the USB based data acquisition system sequentially generate digital data required for multiplexers operating in common ground current injection method. The profile of the boundary data collected from practical phantom show that the multiplexers are operating in the required sequence in common ground current injection protocol. The voltage peaks obtained for all the inhomogeneity configurations are found at the accurate positions in the boundary data matrix which proved the sequential operation of multiplexers. Resistivity images reconstructed from the boundary data collected from the practical phantom with different configurations also show that the entire digital data generation module is functioning properly. Reconstructed images and their image parameters proved that the boundary data are successfully acquired by the DAQ system which in turn indicates a sequential and proper operation of multiplexers.

Fast acoustic likelihood computation using low-rank matrix approximation

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for LVCSR systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication.In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on a 1138 word vocabulary RM1 task using Sphinx 3.7 system show that, for a typical case the matrix multiplication approach leads to overall speedup of 46%. Both the low-rank approximation methods increase the speedup to around 60%, with the former method increasing the word error rate (WER) from 3.2% to 6.6%, while the latter increases the WER from 3.2% to 3.5%.

Hybrid online non-negative matrix factorization for clustering of documents

When document corpus is very large, we often need to reduce the number of features. But it is not possible to apply conventional Non-negative Matrix Factorization(NMF) on billion by million matrix as the matrix may not fit in memory. Here we present novel Online NMF algorithm. Using Online NMF, we reduced original high-dimensional space to low-dimensional space. Then we cluster all the documents in reduced dimension using k-means algorithm. We experimentally show that by processing small subsets of documents we will be able to achieve good performance. The method proposed outperforms existing algorithms.

Does aluminium bind to histidine? an NMR investigation of Amyloid beta 12 and Amyloid beta 16 fragments

Aluminium and zinc are known to be the major triggering agents for aggregation of amyloid peptides leading to plaque formation in Alzheimer's disease. While zinc binding to histidine in A (amyloid ) fragments has been implicated as responsible for aggregation, not much information is available on the interaction of aluminium with histidine. In the NMR study of the N-terminal A fragments, DAEFRHDSGYEV (A12) and DAEFRHDSGYEVHHQK (A16) presented here, the interactions of the fragments with aluminium have been investigated. Significant chemical shifts were observed for few residues near the C-terminus when aluminium chloride was titrated with A12 and A16 peptides. Surprisingly, it is nonhistidine residues which seem to be involved in aluminium binding. Based on NMR constrained structure obtained by molecular modelling, aluminium-binding pockets in A12 were around charged residues such as Asp, Glu. The results are discussed in terms of native structure propagation, and the relevance of histidine residues in the sequences for metal-binding interactions. We expect that the study of such short amyloid peptide fragments will not only provide clues for plaque formation in aggregated conditions but also facilitate design of potential drugs for these targets.

Spatial Wavelet Approach to Local Matrix Crack Detection in Composite Beams with Ply Level Material Uncertainty

Wavelet coefficients based on spatial wavelets are used as damage indicators to identify the damage location as well as the size of the damage in a laminated composite beam with localized matrix cracks. A finite element model of the composite beam is used in conjunction with a matrix crack based damage model to simulate the damaged composite beam structure. The modes of vibration of the beam are analyzed using the wavelet transform in order to identify the location and the extent of the damage by sensing the local perturbations at the damage locations. The location of the damage is identified by a sudden change in spatial distribution of wavelet coefficients. Monte Carlo Simulations (MCS) are used to investigate the effect of ply level uncertainty in composite material properties such as ply longitudinal stiffness, transverse stiffness, shear modulus and Poisson's ratio on damage detection parameter, wavelet coefficient. In this study, numerical simulations are done for single and multiple damage cases. It is observed that spatial wavelets can be used as a reliable damage detection tool for composite beams with localized matrix cracks which can result from low velocity impact damage.

Hybrid aggregated-vector algorithm for efficient parallelization of fast multipole method

An efficient parallelization algorithm for the Fast Multipole Method which aims to alleviate the parallelization bottleneck arising from lower job-count closer to root levels is presented. An electrostatic problem of 12 million non-uniformly distributed mesh elements is solved with 80-85% parallel efficiency in matrix setup and matrix-vector product using 60GB and 16 threads on shared memory architecture.

Tb3+ sensitization in a deoxycholate organogel matrix, and selective quenching of luminescence by an aromatic nitro derivative

In this article, we present the discovery of a metallo-organogel derived from a Tb3+ salt and sodium deoxycholate (NaDCh) in methanol. The gel was made luminescent through sensitization of Tb3+ by doping with 2,3-dihydroxynaphthalene (DHN) in micromolar concentrations. Rheological measurements of the mechanical properties of the organogel confirmed the characteristics of a true gel. Significant quenching of Tb3+ luminescence was observed in the deoxycholate gel matrix by 2,4,7-trinitrofluorenone (TNF), but not by several other polynitro aromatics. Microscopic studies (AFM, TEM and SEM) revealed a highly entangled fibrous network. The xerogels retained luminescent properties suggesting the possibility for application in coatings, etc.

Effect of analyte concentration on the laser-induced plasma temperature and electron density in liquid matrix

In the present work, we report spectroscopic studies of laser-induced plasmas produced by focusing the second harmonic (532nm) of a Nd:YAG laser onto the laminar flow of a liquid containing chromium. The plasma temperature is determined from the coupled Saha-Boltzmann plot and the electron density is evaluated from the Stark broadening of an ionic line of chromium Cr(II)] at 267.7nm. Our results reveal a decrease in plasma temperature with an increase in Cr concentration up to a certain concentration level; after that, it becomes approximately constant, while the electron density increases with an increase in analyte (Cr) concentration in liquid matrix.

A 16-Gene Signature Distinguishes Anaplastic Astrocytoma from Glioblastoma

Anaplastic astrocytoma (AA; Grade III) and glioblastoma (GBM; Grade IV) are diffusely infiltrating tumors and are called malignant astrocytomas. The treatment regimen and prognosis are distinctly different between anaplastic astrocytoma and glioblastoma patients. Although histopathology based current grading system is well accepted and largely reproducible, intratumoral histologic variations often lead to difficulties in classification of malignant astrocytoma samples. In order to obtain a more robust molecular classifier, we analysed RT-qPCR expression data of 175 differentially regulated genes across astrocytoma using Prediction Analysis of Microarrays (PAM) and found the most discriminatory 16-gene expression signature for the classification of anaplastic astrocytoma and glioblastoma. The 16-gene signature obtained in the training set was validated in the test set with diagnostic accuracy of 89%. Additionally, validation of the 16-gene signature in multiple independent cohorts revealed that the signature predicted anaplastic astrocytoma and glioblastoma samples with accuracy rates of 99%, 88%, and 92% in TCGA, GSE1993 and GSE4422 datasets, respectively. The protein-protein interaction network and pathway analysis suggested that the 16-genes of the signature identified epithelial-mesenchymal transition (EMT) pathway as the most differentially regulated pathway in glioblastoma compared to anaplastic astrocytoma. In addition to identifying 16 gene classification signature, we also demonstrated that genes involved in epithelial-mesenchymal transition may play an important role in distinguishing glioblastoma from anaplastic astrocytoma.

Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix

Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.

Characterization, charge transport and magnetic properties of multi-walled carbon nanotube-polyvinyl chloride nanocomposites

Multi-walled carbon nanotube (MWCNT)-polyvinyl chloride (PVC) nanocomposites, with MWCNT loading up to 44.4 weight percent (wt%), were prepared by the solvent mixing and casting method. Electron microscopy indicates high degree of dispersion of MWCNT in PVC matrix, achieved by ultrasonication without using any surfactants. Thermogravimetric analysis showed a significant monotonic enhancement in the thermal stability of nanocomposites by increasing the wt% of MWCNT. Electrical conductivity of nanocomposites followed the classical percolation theory and the conductivity prominently improved from 10(-7) to 9 S/cm as the MWCNT loading increased from 0.1 to 44.4 wt%. Low value of electrical percolation threshold similar to 0.2 wt% is achieved which is attributed to high aspect ratio and homogeneous dispersion of MWCNT in PVC. The analysis of the low temperature electrical resistivity data shows that sample of 1.9 wt% follows three dimensional variable range hopping model whereas higher wt% nanocomposite samples follow power law behavior. The magnetization versus applied field data for both bulk MWCNTs and nanocomposite of 44.4 wt% display ferromagnetic behavior with enhanced coercivities of 1.82 and 1.27 kOe at 10 K, respectively. The enhancement in coercivity is due to strong dipolar interaction and shape anisotropy of rod-shaped iron nanoparticles. (C) 2013 Elsevier B.V. All rights reserved.