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Mishra R.R.,North Orissa University | Prajapati S.,Indian Central Rice Research Institute | Das J.,Indian Central Rice Research Institute | Dangar T.K.,Indian Central Rice Research Institute | And 2 more authors.

Two Gram (+) bacterial strains, BSB6 and BSB12, showing resistance and potential for Se(IV) reduction among 26 moderately halotolerant isolates from the Bhitarkanika mangrove soil were characterized by biochemical and 16S rDNA sequence analyses. Both of them were strictly aerobic and able to grow in a wide range of pH (4-11), temperature (4-40 °C) and salt concentration (4-12%) having an optimum growth at 37 °C, pH ∼7.5 and 7% salt (NaCl). The biochemical characteristics and 16S rDNA sequence analysis of BSB6 and BSB12 showed the closest phylogenetic similarity with the species Bacillus megaterium. Both the strains effectively reduced Se(IV) and complete reduction of selenite (up to 0.25. mM) was achieved within 40. h. SEM with energy dispersive X-ray and TEM analyses revealed the formation of nano size spherical selenium particles in and around the bacterial cells which were also supported by the confocal micrograph study. The UV-Vis diffuse reflectance spectra and XRD of selenium precipitates revealed that the selenium particles are in the nanometric range and crystalline in nature. These bacterial strains may be exploited further for bioremediation process of Se(IV) at relatively high salt concentrations and green synthesis of selenium nanoparticles. © 2011 Elsevier Ltd. Source

Dash H.K.,Biju Patnaik University of Technology, Orissa | Sitharam T.G.,Indian Institute of Science
Geomechanics and Geoengineering

The effect of non-plastic fines (silt) on the undrained monotonic response of saturated and isotropically consolidated sand specimens prepared to various measures of their density was studied in detail through various approaches namely gross void ratio approach, relative density approach, sand skeleton void ratio approach, and interfine void ratio approach. Specimens of 50 mm in diameter and 100 mm in height were tested at a rate of loading of 0.6 mm/min for this purpose. The limiting silt content and the relative density of a specimen were found to influence the undrained monotonic response of sand-silt mixtures to a great extent. Undrained monotonic response was observed to be independent of silt content at very high relative densities; however the presence of fines significantly influenced this response of loose to medium dense specimens. Individual and combined analyses of undrained monotonic peak strengths which are closely related to the liquefaction related problems have been done in this paper to assess the variation patterns. © 2010 Taylor & Francis. Source

Sahoo S.S.,Biju Patnaik University of Technology, Orissa | Singh S.,Indian Institute of Technology Bombay | Banerjee R.,Indian Institute of Technology Bombay
Solar Energy

Linear Fresnel Reflector (LFR) solar thermal system is a promising technology in solar thermal applications. In LFR system, parallel absorber tubes (usually 8-16) are located inside a trapezoidal cavity, which receives reflected solar flux from the mirrors situated below it. The fluid (usually water) inside the tubes undergoes phase change due to the incident solar flux. The focus of this paper is to carry out hydrothermal analysis in an absorber tube of a Linear Fresnel Reflector (LFR) solar thermal system. In the present work, a generic methodology to deal with steady state hydrothermal analysis of the absorber tubes has been discussed. The single phase regions as well as the two-phase region of the absorber tube have been analyzed. A one dimensional model has been used for the analysis for both the regions. In the two-phase region analysis is carried out under the assumption that the homogeneous equilibrium model is valid. For this hydrothermal analysis, the radiative and convective heat losses from the surface of the tube to the atmosphere are obviously needed. To obtain the heat losses, the computational analysis of the heat transfer in the trapezoidal cavity is carried out. The present model can be used to predict the variation of bulk fluid temperature, variation of heat transfer coefficient, pressure loss along the length under different mass flux and different solar flux, in single phase region. Similarly, variation of dryness fraction, local boiling two phase flow coefficient, and total pressure drop can be predicted for two phase region. This model can be used to understand and design for a better LFR system. © 2012. Source

Dash H.K.,Biju Patnaik University of Technology, Orissa | Sitharam T.G.,Indian Institute of Science | Baudet B.A.,University of Hong Kong
Soils and Foundations

Data from cyclic loading tests on sand-fine mixtures made of Ahmedabad sand and quarry dust are presented. Tests were performed at constant void ratio, constant relative density and constant sand skeleton void ratio, for a variety of fines contents. Instead of looking at the direct effect of fines content on the cyclic response of the sand-fine mixtures, a novel approach of analysing the cyclic test data is proposed, by normalising these data with respect to state. The important difference with other similar work on plain sand is that the reference line for normalising must take account of the fines content. Simple definitions of equivalent void ratio, which take account of fines content, have been used. The results presented indicate that state plays an important role in defining the cyclic resistance ratio and pore water pressure generation during cyclic loading. Source

Senapati M.R.,Biju Patnaik University of Technology, Orissa | Dash P.K.,S o iversity
Artificial Intelligence Review

A new learning technique for local linear wavelet neural network (LLWNN) is presented in this paper. The difference of the network with conventional wavelet neural network (WNN) is that the connection weights between the hidden layer and output layer of conventional WNN are replaced by a local linear model. A hybrid training algorithm of Error Back propagation and Recursive Least Square (RLS) is introduced for training the parameters of LLWNN. The variance and centers of LLWNN are updated using back propagation and weights are updated using Recursive Least Square (RLS). Results on extracted breast cancer data from University of Wisconsin Hospital Madison show that the proposed approach is very robust, effective and gives better classification. © 2011 Springer Science+Business Media B.V. Source

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