Low formation energy and kinetic barrier of Stone-Wales defect in infinite and finite silicene

Stone-Wales (SW) defects in materials having hexagonal lattice are the most common topological defects that affect the electronic and mechanical properties. Using first principles density functional theory based calculations, we study the formation energy and kinetic barrier of SW-defect in infinite and finite sheets of silicene. The formation energies as well as the barriers in both the cases are significantly lower than those of graphene. Furthermore, compared with the infinite sheets, the energy barriers and formation energies are lower for finite sheets. However, due to low barriers these defects are expected to heal out of the finite sheets. (C) 2013 Elsevier B.V. All rights reserved.

Prediction of the stiffness of nanoclay-polypropylene composites using a monte carlo finite element analysis approach

The present work deals with the prediction of stiffness of an Indian nanoclay-reinforced polypropylene composite (that can be termed as a nanocomposite) using a Monte Carlo finite element analysis (FEA) technique. Nanocomposite samples are at first prepared in the laboratory using a torque rheometer for achieving desirable dispersion of nanoclay during master batch preparation followed up with extrusion for the fabrication of tensile test dog-bone specimens. It has been observed through SEM (scanning electron microscopy) images of the prepared nanocomposite containing a given percentage (3–9% by weight) of the considered nanoclay that nanoclay platelets tend to remain in clusters. By ascertaining the average size of these nanoclay clusters from the images mentioned, a planar finite element model is created in which nanoclay groups and polymer matrix are modeled as separate entities assuming a given homogeneous distribution of the nanoclay clusters. Using a Monte Carlo simulation procedure, the distribution of nanoclay is varied randomly in an automated manner in a commercial FEA code, and virtual tensile tests are performed for computing the linear stiffness for each case. Values of computed stiffness modulus of highest frequency for nanocomposites with different nanoclay contents correspond well with the experimentally obtained measures of stiffness establishing the effectiveness of the present approach for further applications.

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.

Defect-Induced Enhancement and Quenching Control of Photocurrent in Few-Layer Graphene Photodetectors

A novel approach is presented for achieving an enhanced photo-response in a few layer graphene (FLG) based photodetector that is realized by introducing defect sites in the FLG. Fabrication induced wrinkle formation in graphene presented a four-fold enhancement in the photocurrent when compared to unfold PLC. Interestingly, it was observed that the addition of few multiwalled carbon nanotubes to an FLG improves the photocurrent by two-fold along with a highly stable response as compared to FLG alone.

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.

Correlating defect induced ferromagnetism and gas sensing properties of undoped tin oxide sensors

A correlation between gas sensing properties and defect induced Room Temperature Ferromagnetism (RTFM) is demonstrated in non-stoichiometric SnO2 prepared by solution combustion method. The presence of oxygen vacancies (V-O), confirmed by RTFM is identified as the primary factor for enhanced gas sensing effect. The as-prepared SnO2 shows high saturation magnetization of similar to 0.018 emu/g as compared to similar to 0.002 and similar to 0.0005 emu/g in annealed samples and SnO2 prepared by precipitation respectively. The SnO2 prepared by precipitation which is an equilibrium method of synthesis shows lesser defects compared to the combustion product and hence exhibits lesser sensitivity in spite of smaller crystallite size. The study utilizes RTFM as a potential tool to characterize metal oxide gas sensors and recognizes the significance of oxygen vacancies in sensing mechanism over the microstructure. (C) 2014 AIP Publishing LLC.

Role of Vertex Index in Substructure Identification and Activity Prediction: A Study on Antitubercular Activity of a Series of Acid Alkyl Ester Derivatives

Tuberculosis (TB) is a life threatening disease caused due to infection from Mycobacterium tuberculosis (Mtb). That most of the TB strains have become resistant to various existing drugs, development of effective novel drug candidates to combat this disease is a need of the day. In spite of intensive research world-wide, the success rate of discovering a new anti-TB drug is very poor. Therefore, novel drug discovery methods have to be tried. We have used a rule based computational method that utilizes a vertex index, named `distance exponent index (D-x)' (taken x = -4 here) for predicting anti-TB activity of a series of acid alkyl ester derivatives. The method is meant to identify activity related substructures from a series a compounds and predict activity of a compound on that basis. The high degree of successful prediction in the present study suggests that the said method may be useful in discovering effective anti-TB compound. It is also apparent that substructural approaches may be leveraged for wide purposes in computer-aided drug design.

Defect induced plasticity and failure mechanism of boron nitride nanotubes under tension

The effects of Stone-Wales (SW) and vacancy defects on the failure behavior of boron nitride nanotubes (BNNTs) under tension are investigated using molecular dynamics simulations. The Tersoff-Brenner potential is used to model the atomic interaction and the temperature is maintained close to 300 K. The effect of a SW defect is studied by determining the failure strength and failure mechanism of nanotubes with different radii. In the case of a vacancy defect, the effect of an N-vacancy and a B-vacancy is studied separately. Nanotubes with different chiralities but similar diameter is considered first to evaluate the chirality dependence. The variation of failure strength with the radius is then studied by considering nanotubes of different diameters but same chirality. It is observed that the armchair BNNTs are extremely sensitive to defects, whereas the zigzag configurations are the least sensitive. In the case of pristine BNNTs, both armchair and zigzag nanotubes undergo brittle failure, whereas in the case of defective BNNTs, only the zigzag ones undergo brittle failure. An interesting defect induced plastic behavior is observed in defective armchair BNNTs. For this nanotube, the presence of a defect triggers mechanical relaxation by bond breaking along the closest zigzag helical path, with the defect as the nucleus. This mechanism results in a plastic failure. (C) 2014 AIP Publishing LLC.

An extended Shine-Dalgarno sequence in mRNA functionally bypasses a vital defect in initiator tRNA

Initiator tRNAs are special in their direct binding to the ribosomal P-site due to the hallmark occurrence of the three consecutive G-C base pairs (3GC pairs) in their anticodon stems. How the 3GC pairs function in this role, has remained unsolved. We show that mutations in either the mRNA or 16S rRNA leading to extended interaction between the Shine-Dalgarno (SD) and anti-SD sequences compensate for the vital need of the 3GC pairs in tRNA(fMet) for its function in Escherichia coli. In vivo, the 3GC mutant tRNA(fMet) occurred less abundantly in 70S ribosomes but normally on 30S subunits. However, the extended SD:anti-SD interaction increased its occurrence in 70S ribosomes. We propose that the 3GC pairs play a critical role in tRNA(fMet) retention in ribosome during the conformational changes that mark the transition of 30S preinitiation complex into elongation competent 70S complex. Furthermore, treating cells with kasugamycin, decreasing ribosome recycling factor (RRF) activity or increasing initiation factor 2 (IF2) levels enhanced initiation with the 3GC mutant tRNA(fMet), suggesting that the 70S mode of initiation is less dependent on the 3GC pairs in tRNA(fMet).

TIME VARYING LINEAR PREDICTION USING SPARSITY CONSTRAINTS

Time-varying linear prediction has been studied in the context of speech signals, in which the auto-regressive (AR) coefficients of the system function are modeled as a linear combination of a set of known bases. Traditionally, least squares minimization is used for the estimation of model parameters of the system. Motivated by the sparse nature of the excitation signal for voiced sounds, we explore the time-varying linear prediction modeling of speech signals using sparsity constraints. Parameter estimation is posed as a 0-norm minimization problem. The re-weighted 1-norm minimization technique is used to estimate the model parameters. We show that for sparsely excited time-varying systems, the formulation models the underlying system function better than the least squares error minimization approach. Evaluation with synthetic and real speech examples show that the estimated model parameters track the formant trajectories closer than the least squares approach.

Wind speed prediction using spatio-temporal covariance

High wind poses a number of hazards in different areas such as structural safety, aviation, and wind energy-where low wind speed is also a concern, pollutant transport, to name a few. Therefore, usage of a good prediction tool for wind speed is necessary in these areas. Like many other natural processes, behavior of wind is also associated with considerable uncertainties stemming from different sources. Therefore, to develop a reliable prediction tool for wind speed, these uncertainties should be taken into account. In this work, we propose a probabilistic framework for prediction of wind speed from measured spatio-temporal data. The framework is based on decompositions of spatio-temporal covariance and simulation using these decompositions. A novel simulation method based on a tensor decomposition is used here in this context. The proposed framework is composed of a set of four modules, and the modules have flexibility to accommodate further modifications. This framework is applied on measured data on wind speed in Ireland. Both short-and long-term predictions are addressed.

Holistic Measures for Evaluating Prediction Models in Smart Grids

The performance of prediction models is often based on ``abstract metrics'' that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging ``big data'' domain of Smart Power Grids, we propose a suite of performance measures to rationally compare models along the dimensions of scale independence, reliability, volatility and cost. We include both application independent and dependent measures, the latter parameterized to allow customization by domain experts to fit their scenario. While our measures are generalizable to other domains, we offer an empirical analysis using real energy use data for three Smart Grid applications: planning, customer education and demand response, which are relevant for energy sustainability. Our results underscore the value of the proposed measures to offer a deeper insight into models' behavior and their impact on real applications, which benefit both data mining researchers and practitioners.

Insights on Defect-Mediated Heterogeneous Nucleation of Graphene on Copper

The grain size of monolayer large area graphene is key to its performance. Microstructural design for the desired grain size requires a fundamental understanding of graphene nucleation and growth. The two levers that can be used to control these aspects are the defect density, whose population can be controlled by annealing, and the gas-phase supersaturation for activation of nucleation at the defect sites. We observe that defects on copper surface, namely dislocations, grain boundaries, triple points, and rolling marks, initiate nucleation of graphene. We show that among these defects dislocations are the most potent nucleation sites, as they get activated at lowest supersaturation. As an illustration, we tailor the defect density and supersaturation to change the domain size of graphene from <1 mu m(2) to >100 mu m(2). Growth data reported in the literature has been summarized on a supersaturation plot, and a regime for defect-dominated growth has been identified. In this growth regime, we demonstrate the spatial control over nucleation at intentionally introduced defects, paving the way for patterned growth of graphene. Our results provide a unified framework for understanding the role of defects in graphene nucleation and can be used as a guideline for controlled growth of graphene.