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

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.

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.

Information content of molecular graph and prediction of gas phase thermal entropy of organic compounds

Entropy is a fundamental thermodynamic property that has attracted a wide attention across domains, including chemistry. Inference of entropy of chemical compounds using various approaches has been a widely studied topic. However, many aspects of entropy in chemical compounds remain unexplained. In the present work, we propose two new information-theoretical molecular descriptors for the prediction of gas phase thermal entropy of organic compounds. The descriptors reflect the bulk and size of the compounds as well as the gross topological symmetry in their structures, all of which are believed to determine entropy. A high correlation () between the entropy values and our information-theoretical indices have been found and the predicted entropy values, obtained from the corresponding statistically significant regression model, have been found to be within acceptable approximation. We provide additional mathematical result in the form of a theorem and proof that might further help in assessing changes in gas phase thermal entropy values with the changes in molecular structures. The proposed information-theoretical molecular descriptors, regression model and the mathematical result are expected to augment predictions of gas phase thermal entropy for a large number of chemical compounds.

Ground motion prediction equation considering combined dataset of recorded and simulated ground motions

Himalayan region is one of the most active seismic regions in the world and many researchers have highlighted the possibility of great seismic event in the near future due to seismic gap. Seismic hazard analysis and microzonation of highly populated places in the region are mandatory in a regional scale. Region specific Ground Motion Predictive Equation (GMPE) is an important input in the seismic hazard analysis for macro- and micro-zonation studies. Few GMPEs developed in India are based on the recorded data and are applicable for a particular range of magnitudes and distances. This paper focuses on the development of a new GMPE for the Himalayan region considering both the recorded and simulated earthquakes of moment magnitude 5.3-8.7. The Finite Fault simulation model has been used for the ground motion simulation considering region specific seismotectonic parameters from the past earthquakes and source models. Simulated acceleration time histories and response spectra are compared with available records. In the absence of a large number of recorded data, simulations have been performed at unavailable locations by adopting Apparent Stations concept. Earthquakes recorded up to 2007 have been used for the development of new GMPE and earthquakes records after 2007 are used to validate new GMPE. Proposed GMPE matched very well with recorded data and also with other highly ranked GMPEs developed elsewhere and applicable for the region. Comparison of response spectra also have shown good agreement with recorded earthquake data. Quantitative analysis of residuals for the proposed GMPE and region specific GMPEs to predict Nepal-India 2011 earthquake of Mw of 5.7 records values shows that the proposed GMPE predicts Peak ground acceleration and spectral acceleration for entire distance and period range with lower percent residual when compared to exiting region specific GMPEs. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.

Epoch extraction based on integrated linear prediction residual using plosion index

Epoch is defined as the instant of significant excitation within a pitch period of voiced speech. Epoch extraction continues to attract the interest of researchers because of its significance in speech analysis. Existing high performance epoch extraction algorithms require either dynamic programming techniques or a priori information of the average pitch period. An algorithm without such requirements is proposed based on integrated linear prediction residual (ILPR) which resembles the voice source signal. Half wave rectified and negated ILPR (or Hilbert transform of ILPR) is used as the pre-processed signal. A new non-linear temporal measure named the plosion index (PI) has been proposed for detecting `transients' in speech signal. An extension of PI, called the dynamic plosion index (DPI) is applied on pre-processed signal to estimate the epochs. The proposed DPI algorithm is validated using six large databases which provide simultaneous EGG recordings. Creaky and singing voice samples are also analyzed. The algorithm has been tested for its robustness in the presence of additive white and babble noise and on simulated telephone quality speech. The performance of the DPI algorithm is found to be comparable or better than five state-of-the-art techniques for the experiments considered.

CFD Analysis of two phase flow with a prediction of torque demand of a Bi-lobe rotor of a mixing chamber

An attempt to study the fluid dynamic behavior of two phase flow comprising of solid and liquid with nearly equal density in a geometrical case that has an industrial significance in theareas like processing of polymers, food, pharma ceutical, paints. In this work,crystalline silica is considered as the dispersed medium in glycerin. In the CFD analysis carried out,the two phase components are considered to be premixed homogeneously at the initial state. The flow in a cylinder that has an axially driven bi-lobe rotor, a typical blender used in polymer industry for mixing or kneading to render the multi-component mixture to homogeneous condition is considered. A viscous, incompressible, isothermal flow is considered with an assumption that the components do not undergo any physical change and the solids are rigid and mix in fully wetting conditions. Silica with a particle diameter of 0.4 mm is considered and flow is analyzed for different mixing fractions. An industry standard CFD code is used for solving 3D-RANS equations. As the outcome of the study the torque demand by the bi-lobe rotor for different mixture fractions which are estimated show a behavioral consistency to the expected physical phenomena occurring in the domain considered.

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.