Prediction of inter-laminar stresses in composite honeycomb sandwich panels under mechanical loading using Variational Asymptotic Method

This work focuses on the formulation of an asymptotically correct theory for symmetric composite honeycomb sandwich plate structures. In these panels, transverse stresses tremendously influence design. The conventional 2-D finite elements cannot predict the thickness-wise distributions of transverse shear or normal stresses and 3-D displacements. Unfortunately, the use of the more accurate three-dimensional finite elements is computationally prohibitive. The development of the present theory is based on the Variational Asymptotic Method (VAM). Its unique features are the identification and utilization of additional small parameters associated with the anisotropy and non-homogeneity of composite sandwich plate structures. These parameters are ratios of smallness of the thickness of both facial layers to that of the core and smallness of 3-D stiffness coefficients of the core to that of the face sheets. Finally, anisotropy in the core and face sheets is addressed by the small parameters within the 3-D stiffness matrices. Numerical results are illustrated for several sample problems. The 3-D responses recovered using VAM-based model are obtained in a much more computationally efficient manner than, and are in agreement with, those of available 3-D elasticity solutions and 3-D FE solutions of MSC NASTRAN. (c) 2012 Elsevier Ltd. All rights reserved.

Northeast monsoon over India: variability and prediction

South peninsular India experiences a large portion of the annual rainfall during the northeast monsoon season (October to December). In this study, the facets of diurnal, intra-seasonal and inter-annual variability of the northeast monsoon rainfall (the NEMR) over India have been examined. The analysis of satellite derived hourly rainfall reveals that there are distinct features of diurnal variation over the land and oceans during the season. Over the land, rainfall peaks during the late afternoon/evening, while over the oceans an early morning peak is observed. The harmonic analysis of hourly data reveals that the amplitude and variance are the largest over south peninsular India. The NEMR also exhibits significant intra-seasonal variability on a 20-40 day time scale. Analysis also shows significant northward propagation of the maximum cloud zone from south of equator to the south peninsula during the season. The NEMR exhibits large inter-annual variability with the co-efficient of variation (CV) of 25%. The positive phases of ENSO and the Indian Ocean Dipole (IOD) are conducive for normal to above normal rainfall activity during the northeast monsoon. There are multi-decadal variations in the statistical relationship between ENSO and the NEMR. During the period 2001-2010 the statistical relationship between ENSO and the NEMR has significantly weakened. The analysis of seasonal rainfall hindcasts for the period 1960-2005 produced by the state-of-the-art coupled climate models, ENSEMBLES, reveals that the coupled models have very poor skill in predicting the inter-annual variability of the NEMR. This is mainly due to the inability of the ENSEMBLES models to simulate the positive relationship between ENSO and the NEMR correctly. Copyright (C) 2012 Royal Meteorological Society

Meso-Structured Silica-Nafion Hybrid Membranes for Direct Methanol Fuel Cells

A series of novel organic-inorganic hybrid membranes have been prepared employing Nafion and acid-functionalized meso-structured molecular sieves (MMS) with varying structures and surface area. Acid-functionalized silica nanopowder of surface area 60 m(2)/g, silica meso-structured cellular foam (MSU-F) of surface area 470 m(2)/g and silica meso-structured hexagonal frame network (MCM-41) of surface area 900 m(2)/g have been employed as potential filler materials to form hybrid membranes with Nafion framework. The structural behavior, water uptake, proton conductivity and methanol permeability of these hybrid membranes have been investigated. DMFCs employing Nafion-silica MSU-F and Nafion-silica MCM-41 hybrid membranes deliver peak-power densities of 127 mW/cm(2) and 100 mW/cm(2), respectively; while a peak-power density of only 48 mW/cm(2) is obtained with the DMFC employing pristine recast Nafion membrane under identical operating conditions. The aforesaid characteristics of the hybrid membranes could be exclusively attributed to the presence of pendant sulfonic acid groups in the filler, which provide fairly continuous proton-conducting pathways between filler and matrix in the hybrid membranes facilitating proton transport without any trade-off between its proton conductivity and methanol crossover. (C) 2012 The Electrochemical Society. DOI: 10.1149/2.036211jes] All rights reserved.

Rationalization and prediction of drug resistant mutations in targets for clinical anti-tubercular drugs

Resistance to therapy limits the effectiveness of drug treatment in many diseases. Drug resistance can be considered as a successful outcome of the bacterial struggle to survive in the hostile environment of a drug-exposed cell. An important mechanism by which bacteria acquire drug resistance is through mutations in the drug target. Drug resistant strains (multi-drug resistant and extensively drug resistant) of Mycobacterium tuberculosis are being identified at alarming rates, increasing the global burden of tuberculosis. An understanding of the nature of mutations in different drug targets and how they achieve resistance is therefore important. An objective of this study is to first decipher sequence as well as structural bases for the observed resistance in known drug resistant mutants and then to predict positions in each target that are more prone to acquiring drug resistant mutations. A curated database containing hundreds of mutations in the 38 drug targets of nine major clinical drugs, associated with resistance is studied here. Mutations have been classified into those that occur in the binding site itself, those that occur in residues interacting with the binding site and those that occur in outer zones. Structural models of the wild type and mutant forms of the target proteins have been analysed to seek explanations for reduction in drug binding. Stability analysis of an entire array of 19 mutations at each of the residues for each target has been computed using structural models. Conservation indices of individual residues, binding sites and whole proteins are computed based on sequence conservation analysis of the target proteins. The analyses lead to insights about which positions in the polypeptide chain have a higher propensity to acquire drug resistant mutations. Thus critical insights can be obtained about the effect of mutations on drug binding, in terms of which amino acid positions and therefore which interactions should not be heavily relied upon, which in turn can be translated into guidelines for modifying the existing drugs as well as for designing new drugs. The methodology can serve as a general framework to study drug resistant mutants in other micro-organisms as well.

Prediction of Shallow Landslide prone regions in Undulating Terrains

Genetic Algorithm for Rule-set Prediction (GARP) and Support Vector Machine (SVM) with free and open source software (FOSS) - Open Modeller were used to model the probable landslide occurrence points. Environmental layers such as aspect, digital elevation, flow accumulation, flow direction, slope, land cover, compound topographic index and precipitation have been used in modeling. Simulated output of these techniques is validated with the actual landslide occurrence points, which showed 92% (GARP) and 96% (SVM) accuracy considering precipitation in the wettest month and 91% and 94% accuracy considering precipitation in the wettest quarter of the year.

HD resolution intra prediction architecture for H.264 decoder

High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper presents novel area optimized architecture for Intra prediction of H.264 decoding at HDTV resolution. The architecture has been validated on a Xilinx Virtex-5 FPGA based platform and achieved a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Scalable multi-dimensional user intent identification using tree structured distributions

The problem of identifying user intent has received considerable attention in recent years, particularly in the context of improving the search experience via query contextualization. Intent can be characterized by multiple dimensions, which are often not observed from query words alone. Accurate identification of Intent from query words remains a challenging problem primarily because it is extremely difficult to discover these dimensions. The problem is often significantly compounded due to lack of representative training sample. We present a generic, extensible framework for learning the multi-dimensional representation of user intent from the query words. The approach models the latent relationships between facets using tree structured distribution which leads to an efficient and convergent algorithm, FastQ, for identifying the multi-faceted intent of users based on just the query words. We also incorporated WordNet to extend the system capabilities to queries which contain words that do not appear in the training data. Empirical results show that FastQ yields accurate identification of intent when compared to a gold standard.

A sequential dual method for structural SVMs

In many real world prediction problems the output is a structured object like a sequence or a tree or a graph. Such problems range from natural language processing to compu- tational biology or computer vision and have been tackled using algorithms, referred to as structured output learning algorithms. We consider the problem of structured classifi- cation. In the last few years, large margin classifiers like sup-port vector machines (SVMs) have shown much promise for structured output learning. The related optimization prob -lem is a convex quadratic program (QP) with a large num-ber of constraints, which makes the problem intractable for large data sets. This paper proposes a fast sequential dual method (SDM) for structural SVMs. The method makes re-peated passes over the training set and optimizes the dual variables associated with one example at a time. The use of additional heuristics makes the proposed method more efficient. We present an extensive empirical evaluation of the proposed method on several sequence learning problems.Our experiments on large data sets demonstrate that the proposed method is an order of magnitude faster than state of the art methods like cutting-plane method and stochastic gradient descent method (SGD). Further, SDM reaches steady state generalization performance faster than the SGD method. The proposed SDM is thus a useful alternative for large scale structured output learning.

Structured Dispersion Matrices From Division Algebra Codes for Space-Time Shift Keying

We propose a novel method of constructing Dispersion Matrices (DM) for Coherent Space-Time Shift Keying (CSTSK) relying on arbitrary PSK signal sets by exploiting codes from division algebras. We show that classic codes from Cyclic Division Algebras (CDA) may be interpreted as DMs conceived for PSK signal sets. Hence various benefits of CDA codes such as their ability to achieve full diversity are inherited by CSTSK. We demonstrate that the proposed CDA based DMs are capable of achieving a lower symbol error ratio than the existing DMs generated using the capacity as their optimization objective function for both perfect and imperfect channel estimation.

Sequential alternating proximal method for scalable sparse structural SVMs

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.

Position control of DC motors with experience mapping based prediction controller

The paper presents a new controller inspired by the human experience based, voluntary body action control (dubbed motor control) learning mechanism. The controller is called Experience Mapping based Prediction Controller (EMPC). EMPC is designed with auto-learning features without the need for the plant model. The core of the controller is formed around the motor action prediction-control mechanism of humans based on past experiential learning with the ability to adapt to environmental changes intelligently. EMPC is utilized for high precision position control of DC motors. The simulation results are presented to show that accurate position control is achieved using EMPC for step and dynamic demands. The performance of EMPC is compared with conventional PD controller and MRAC based position controller under different system conditions. Position Control using EMPC is practically implemented and the results are presented.

Mammalian neuronal sodium channel Blocker mu-conotoxin BuIIIB has a structured N-terminus that influences potency

Among the mu-conotoxins that block vertebrate voltage-gated sodium channels (VGSCs), some have been shown to be potent analgesics following systemic administration in mice. We have determined the solution structure of a new representative of this family, mu-BuIIIB, and established its disulfide connectivities by direct mass spectrometric collision induced dissociation fragmentation of the peptide with disulfides intact The major oxidative folding product adopts a 1-4/2-5/3-6 pattern with the following disulfide bridges: Cys5-Cys17, Cys6-Cys23, and Cys13-Cys24. The solution structure reveals that the unique N-terminal extension in mu-BuIIIB, which is also present in mu-BuIIIA and mu-BuIIIC but absent in other mu-conotoxins, forms part of a short a-helix encompassing Glu3 to Asn8. This helix is packed against the rest of the toxin and stabilized by the Cys5-Cys17 and Cys6-Cys23 disulfide bonds. As such, the side chain of Val1 is located close to the aromatic rings of Trp16 and His20, which are located on the canonical helix that displays several residues found to be essential for VGSC blockade in related mu-conotoxins. Mutations of residues 2 and 3 in the N-terminal extension enhanced the potency of mu-BuIIIB for Na(v)1.3. One analogue, D-Ala2]BuIIIB, showed a 40-fold increase, making it the most potent peptide blocker of this channel characterized to date and thus a useful new tool with which to characterize this channel. On the basis of previous results for related mu-conotoxins, the dramatic effects of mutations at the N-terminus were unanticipated and suggest that further gains in potency might be achieved by additional modifications of this region.