Intelligent prediction of ground vibration in coal formations using ANN

The present paper details the prediction of blast induced ground vibration, using artificial neural network. The data was generated from five different coal mines. Twenty one different parameters involving rock mass parameters, explosive parameters and blast design parameters, were used to develop the one comprehensive ANN model for five different coal bearing formations. A total of 131 datasets was used to develop the ANN model and 44 datasets was used to test the model. The developed ANN model was compared with the USBM model. The prediction capability to predict blast induced ground vibration, of the comprehensive ANN model was found to be superior.

Cybernetic modeling of adaptive prediction of environmental changes by microorganisms

Microorganisms exhibit varied regulatory strategies such as direct regulation, symmetric anticipatory regulation, asymmetric anticipatory regulation, etc. Current mathematical modeling frameworks for the growth of microorganisms either do not incorporate regulation or assume that the microorganisms utilize the direct regulation strategy. In the present study, we extend the cybernetic modeling framework to account for asymmetric anticipatory regulation strategy. The extended model accurately captures various experimental observations. We use the developed model to explore the fitness advantage provided by the asymmetric anticipatory regulation strategy and observe that the optimal extent of asymmetric regulation depends on the selective pressure that the microorganisms experience. We also explore the importance of timing the response in anticipatory regulation and find that there is an optimal time, dependent on the extent of asymmetric regulation, at which microorganisms should respond anticipatorily to maximize their fitness. We then discuss the advantages offered by the cybernetic modeling framework over other modeling frameworks in modeling the asymmetric anticipatory regulation strategy. (C) 2013 Published by Elsevier Inc.

A Communication-Theoretic Framework for 2-DMR Channel Modeling: Performance Evaluation of Coding and Signal Processing Methods

We develop a communication theoretic framework for modeling 2-D magnetic recording channels. Using the model, we define the signal-to-noise ratio (SNR) for the channel considering several physical parameters, such as the channel bit density, code rate, bit aspect ratio, and noise parameters. We analyze the problem of optimizing the bit aspect ratio for maximizing SNR. The read channel architecture comprises a novel 2-D joint self-iterating equalizer and detection system with noise prediction capability. We evaluate the system performance based on our channel model through simulations. The coded performance with the 2-D equalizer detector indicates similar to 5.5 dB of SNR gain over uncoded data.

Reduced Graphene Oxide-Polypyrrole Composite as a Catalyst for Oxygen Electrode of High Rate Rechargeable Li-O-2 Cells

Polypyrrole (PPY) is grown on reduced graphene oxide (RGO) and the composite is studied as a catalyst for O-2 electrode in Li-O-2 cells. PPY is uniformly distributed on the two dimensional RGO layers. Li-O-2 cells assembled in a non-aqueous electrolyte using RGO-PPY catalyst exhibit an initial discharge capacity as high as 3358 mAh g(-1) (3.94 mAh cm(-2)) at a current density of 0.3 mA cm(-2). The voltage gap between the charge and discharge curves is less for Li-O-2(RGO-PPY) cell in comparison with Li-O-2(RGO) cell. The Li-O-2(RGO-PPY) cell delivers a discharge capacity of 550 mAh g(-1) (0.43 mAh cm(-2)) at a current density of 1.0 mA cm(-2). The results suggest that RGO-PPY is a promising catalyst of O-2 electrode for high rate rechargeable Li-O-2 cells. (C) 2014 The Electrochemical Society. All rights reserved.

Linear filtering methods for fixed rate quantisation with noisy symmetric error channels

This study considers linear filtering methods for minimising the end-to-end average distortion of a fixed-rate source quantisation system. For the source encoder, both scalar and vector quantisation are considered. The codebook index output by the encoder is sent over a noisy discrete memoryless channel whose statistics could be unknown at the transmitter. At the receiver, the code vector corresponding to the received index is passed through a linear receive filter, whose output is an estimate of the source instantiation. Under this setup, an approximate expression for the average weighted mean-square error (WMSE) between the source instantiation and the reconstructed vector at the receiver is derived using high-resolution quantisation theory. Also, a closed-form expression for the linear receive filter that minimises the approximate average WMSE is derived. The generality of framework developed is further demonstrated by theoretically analysing the performance of other adaptation techniques that can be employed when the channel statistics are available at the transmitter also, such as joint transmit-receive linear filtering and codebook scaling. Monte Carlo simulation results validate the theoretical expressions, and illustrate the improvement in the average distortion that can be obtained using linear filtering techniques.

Influence of O-2 flow rate on HfO2 gate dielectrics for back-gated graphene transistors

HfO2 thin films deposited on Si substrate using electron beam evaporation, are evaluated for back-gated graphene transistors. The amount of O-2 flow rate, during vaporation is optimized for 35 nm thick HfO2 films, to achieve the best optical, chemical and electrical properties. It has been observed that with increasing oxygen flow rate, thickness of the films increased and refractive index decreased due to increase in porosity resulting from the scattering of the evaporant. The films deposited at low O-2 flow rates (1 and 3 SCCM) show better optical and compositional properties. The effects of post-deposition annealing and post-metallization annealing in forming gas ambience (FGA) on the optical and electrical properties of the films have been analyzed. The film deposited at 3 SCCM O-2 flow rate shows the best properties as measured on MOS capacitors. To evaluate the performance of device properties, back-gated bilayer graphene transistors on HfO2 films deposited at two O-2 flow rates of 3 and 20 SCCM have been fabricated and characterized. The transistor with HfO2 film deposited at 3 SCCM O-2 flow rate shows better electrical properties consistent with the observations on MOS capacitor structures. This suggests that an optimum oxygen pressure is necessary to get good quality films for high performance devices.

In-situ Stabilization of Tin Nanoparticles in Porous Carbon Matrix derived from Metal Organic Framework: High Capacity and High Rate Capability Anodes for Lithium-ion Batteries

It is a formidable challenge to arrange tin nanoparticles in a porous matrix for the achievement of high specific capacity and current rate capability anode for lithium-ion batteries. This article discusses a simple and novel synthesis of arranging tin nanoparticles with carbon in a porous configuration for application as anode in lithium-ion batteries. Direct carbonization of synthesized three-dimensional Sn-based MOF: K2Sn2(1,4-bdc)(3)](H2O) (1) (bdc = benzenedicarboxylate) resulted in stabilization of tin nanoparticles in a porous carbon matrix (abbreviated as Sn@C). Sn@C exhibited remarkably high electrochemical lithium stability (tested over 100 charge and discharge cycles) and high specific capacities over a wide range of operating currents (0.2-5 Ag-1). The novel synthesis strategy to obtain Sn@C from a single precursor as discussed herein provides an optimal combination of particle size and dispersion for buffering severe volume changes due to Li-Sn alloying reaction and provides fast pathways for lithium and electron transport.

Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: Implications for three-dimensional modeling

With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naive Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (approximate to 85%) and specific (approximate to 95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. Proteins 2014; 82:1219-1234. (c) 2013 Wiley Periodicals, Inc.

Energy based approach for the evaluation of damage under partial slip and gross sliding condition

Three possible contact conditions may prevail at a contact interface depending on the magnitude of normal and tangential loads, that is, stick condition, partial slip condition or gross sliding condition. Numerical techniques have been used to evaluate the stress field under partial slip and gross sliding condition. Cattaneo and Mindlin approach has been adapted to model partial slip condition. Shear strain energy density and normalized strain energy release rate have been evaluated at the surface and in the subsurface region. It is apparent from the present study that the shear strain energy density gives a fair prediction for the nucleation of damage, whereas the propagation of the crack is controlled by normalized strain energy release rate. Further, it has been observed that the intensity of damage strongly depends on coefficient of friction and contact conditions prevailing at the contact interface. (C) 2014 Elsevier B.V. All rights reserved.

Sodium-ion battery cathodes Na2FeP2O7 and Na2MnP2O7: diffusion behaviour for high rate performance

Na-ion batteries are currently the focus of significant research activity due to the relative abundance of sodium and its consequent cost advantages. Recently, the pyrophosphate family of cathodes has attracted considerable attention, particularly Li2FeP2O7 related to its high operating voltage and enhanced safety properties; in addition the sodium-based pyrophosphates Na2FeP2O7 and Na2MnP2O7 are also generating interest. Herein, we present defect chemistry and ion migration results, determined via atomistic simulation techniques, for Na2MP2O7 (where M = Fe, Mn) as well as findings for Li2FeP2O7 for direct comparison. Within the pyrophosphate framework the most favourable intrinsic defect type is found to be the antisite defect, in which alkali-cations (Na/Li) and M ions exchange positions. Low activation energies are found for long-range diffusion in all crystallographic directions in Na2MP2O7 suggesting three-dimensional (3D) Na-ion diffusion. In contrast Li2FeP2O7 supports 2D Li-ion diffusion. The 2D or 3D nature of the alkali-ion migration pathways within these pyrophosphate materials means that antisite defects are much less likely to impede their transport properties, and hence important for high rate performance.

Skip Decision and Reference Frame Selection for Low-Complexity H.264/AVC Surveillance Video Coding

H. 264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.

Isotropic finite volume discretization

Finite volume methods traditionally employ dimension by dimension extension of the one-dimensional reconstruction and averaging procedures to achieve spatial discretization of the governing partial differential equations on a structured Cartesian mesh in multiple dimensions. This simple approach based on tensor product stencils introduces an undesirable grid orientation dependence in the computed solution. The resulting anisotropic errors lead to a disparity in the calculations that is most prominent between directions parallel and diagonal to the grid lines. In this work we develop isotropic finite volume discretization schemes which minimize such grid orientation effects in multidimensional calculations by eliminating the directional bias in the lowest order term in the truncation error. Explicit isotropic expressions that relate the cell face averaged line and surface integrals of a function and its derivatives to the given cell area and volume averages are derived in two and three dimensions, respectively. It is found that a family of isotropic approximations with a free parameter can be derived by combining isotropic schemes based on next-nearest and next-next-nearest neighbors in three dimensions. Use of these isotropic expressions alone in a standard finite volume framework, however, is found to be insufficient in enforcing rotational invariance when the flux vector is nonlinear and/or spatially non-uniform. The rotationally invariant terms which lead to a loss of isotropy in such cases are explicitly identified and recast in a differential form. Various forms of flux correction terms which allow for a full recovery of rotational invariance in the lowest order truncation error terms, while preserving the formal order of accuracy and discrete conservation of the original finite volume method, are developed. Numerical tests in two and three dimensions attest the superior directional attributes of the proposed isotropic finite volume method. Prominent anisotropic errors, such as spurious asymmetric distortions on a circular reaction-diffusion wave that feature in the conventional finite volume implementation are effectively suppressed through isotropic finite volume discretization. Furthermore, for a given spatial resolution, a striking improvement in the prediction of kinetic energy decay rate corresponding to a general two-dimensional incompressible flow field is observed with the use of an isotropic finite volume method instead of the conventional discretization. (C) 2014 Elsevier Inc. All rights reserved.

Computational framework to integrate the effect of antigen recognition on disease epidemiology outcome: multi-scale approach

Human Leukocyte Antigen (HLA) plays an important role, in presenting foreign pathogens to our immune system, there by eliciting early immune responses. HLA genes are highly polymorphic, giving rise to diverse antigen presentation capability. An important factor contributing to enormous variations in individual responses to diseases is differences in their HLA profiles. The heterogeneity in allele specific disease responses decides the overall disease epidemiological outcome. Here we propose an agent based computational framework, capable of incorporating allele specific information, to analyze disease epidemiology. This framework assumes a SIR model to estimate average disease transmission and recovery rate. Using epitope prediction tool, it performs sequence based epitope detection for a given the pathogenic genome and derives an allele specific disease susceptibility index depending on the epitope detection efficiency. The allele specific disease transmission rate, that follows, is then fed to the agent based epidemiology model, to analyze the disease outcome. The methodology presented here has a potential use in understanding how a disease spreads and effective measures to control the disease.

Super-Resolution Reconstruction in Frequency-Domain Optical-Coherence Tomography Using the Finite-Rate-of-Innovation Principle

The standard approach to signal reconstruction in frequency-domain optical-coherence tomography (FDOCT) is to apply the inverse Fourier transform to the measurements. This technique offers limited resolution (due to Heisenberg's uncertainty principle). We propose a new super-resolution reconstruction method based on a parametric representation. We consider multilayer specimens, wherein each layer has a constant refractive index and show that the backscattered signal from such a specimen fits accurately in to the framework of finite-rate-of-innovation (FRI) signal model and is represented by a finite number of free parameters. We deploy the high-resolution Prony method and show that high-quality, super-resolved reconstruction is possible with fewer measurements (about one-fourth of the number required for the standard Fourier technique). To further improve robustness to noise in practical scenarios, we take advantage of an iterated singular-value decomposition algorithm (Cadzow denoiser). We present results of Monte Carlo analyses, and assess statistical efficiency of the reconstruction techniques by comparing their performance against the Cramer-Rao bound. Reconstruction results on experimental data obtained from technical as well as biological specimens show a distinct improvement in resolution and signal-to-reconstruction noise offered by the proposed method in comparison with the standard approach.

De novo inference of protein function from coarse-grained dynamics

Inference of molecular function of proteins is the fundamental task in the quest for understanding cellular processes. The task is getting increasingly difficult with thousands of new proteins discovered each day. The difficulty arises primarily due to lack of high-throughput experimental technique for assessing protein molecular function, a lacunae that computational approaches are trying hard to fill. The latter too faces a major bottleneck in absence of clear evidence based on evolutionary information. Here we propose a de novo approach to annotate protein molecular function through structural dynamics match for a pair of segments from two dissimilar proteins, which may share even <10% sequence identity. To screen these matches, corresponding 1 mu s coarse-grained (CG) molecular dynamics trajectories were used to compute normalized root-mean-square-fluctuation graphs and select mobile segments, which were, thereafter, matched for all pairs using unweighted three-dimensional autocorrelation vectors. Our in-house custom-built forcefield (FF), extensively validated against dynamics information obtained from experimental nuclear magnetic resonance data, was specifically used to generate the CG dynamics trajectories. The test for correspondence of dynamics-signature of protein segments and function revealed 87% true positive rate and 93.5% true negative rate, on a dataset of 60 experimentally validated proteins, including moonlighting proteins and those with novel functional motifs. A random test against 315 unique fold/function proteins for a negative test gave >99% true recall. A blind prediction on a novel protein appears consistent with additional evidences retrieved therein. This is the first proof-of-principle of generalized use of structural dynamics for inferring protein molecular function leveraging our custom-made CG FF, useful to all. (C) 2014 Wiley Periodicals, Inc.