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Tirunelveli, India

Muthuraj D.,Mdt Hindu College
Materials Research | Year: 2013

Cerium dioxide nanoparticles were prepared by solvothermal technique. The structural analysis was carried out using X-ray diffraction. It showed that the cerium dioxide nanoparticles exhibited cubic structure. Grain sizes were estimated from High Resolution Transmission Electron Microscopy images. The size of the nanoparticles is around 20 nm. The surface morphological studies from Scanning Electron Microscope (SEM) and HRTEM depicted spherical particles with formation of clusters. Thermal and electrical Insulating behaviors were determined. © 2013. Source

Innocent B.X.,Stxaviers College | Fathima M.S.A.,Stxaviers College | Sivagurunathan A.,Mdt Hindu College
Journal of Applied Pharmaceutical Science | Year: 2011

Disease outbreak in fish culture system interferes with productivity. Oral Immunostimulant containing vitamin C can result in activating the immune system in a nonspecific way thus providing resistance against pathogens. In the present study the fish Cirrhinus mrigala was fed with a feed supplemented with vitamin C (100mg/100g) for 40 days and post challenged with two different dilutions (102 and 105) of Aphanomyces invadans. The haematological parameters like TEC, TLC and differential leukocyte counts were analyzed 24hours, 72 hours and 7thday after infection. TEC counts and lymphocyte counts of infected fishes previously fed with control diet decreased significantly (43%, 36%) whereas it was minimal in fishes (21%, 8%) fed with Vitamin C supplemented diet. Post challenged fishes exhibited an increase in TLC inVitamin supplemented diet (20%, 32%) over control diet fed fishes. Indifferential leucocyte counts the lymphocytes decreased in both the experimental groups (9%, 11%) whereas neutrophils increased in both, however the increase was higher in control diet fed group (34%). The basophils and eosinophils increased significantly in Vitamin C supplemented diet only (10%, 60%). Thus supplementation of feed with vitaminC enhanced fish's tolerance to infection. Source

Stanley Raj A.,Vel Tech Dr.RR & Dr.SR Technical University | Srinivas Y.,Manonmaniam Sundaranar University | Hudson Oliver D.,Manonmaniam Sundaranar University | Muthuraj D.,Mdt Hindu College
Journal of Earth System Science | Year: 2014

The non-linear apparent resistivity problem in the subsurface study of the earth takes into account the model parameters in terms of resistivity and thickness of individual subsurface layers using the trained synthetic data by means of Artificial Neural Networks (ANN). Here we used a single layer feed-forward neural network with fast back propagation learning algorithm. So on proper training of back propagation networks it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data with reference to the synthetic data trained in the appropriate network. During training, the weights and biases of the network are iteratively adjusted to make network performance function level more efficient. On adequate training, errors are minimized and the best result is obtained using the artificial neural networks. The network is trained with more number of VES data and this trained network is demonstrated by the field data. The accuracy of inversion depends upon the number of data trained. In this novel and specially designed algorithm, the interpretation of the vertical electrical sounding has been done successfully with the more accurate layer model. © Indian Academy of Sciences. Source

Srinivas Y.,Manonmaniam Sundaranar University | Raj A.S.,Manonmaniam Sundaranar University | Oliver D.H.,Manonmaniam Sundaranar University | Muthuraj D.,Mdt Hindu College | Chandrasekar N.,Manonmaniam Sundaranar University
Geoscience Frontiers | Year: 2012

The applications of intelligent techniques have increased exponentially in recent days to study most of the non-linear parameters. In particular, the behavior of earth resembles the non-linearity applications. An efficient tool is needed for the interpretation of geophysical parameters to study the subsurface of the earth. Artificial Neural Networks (ANN) perform certain tasks if the structure of the network is modified accordingly for the purpose it has been used. The three most robust networks were taken and comparatively analyzed for their performance to choose the appropriate network. The single-layer feed-forward neural network with the back propagation algorithm is chosen as one of the well-suited networks after comparing the results. Initially, certain synthetic data sets of all three-layer curves have been taken for training the network, and the network is validated by the field datasets collected from Tuticorin Coastal Region (78°7′30"E and 8°48′45"N) , Tamil Nadu, India. The interpretation has been done successfully using the corresponding learning algorithm in the present study. With proper training of back propagation networks, it tends to give the resistivity and thickness of the subsurface layer model of the field resistivity data concerning the synthetic data trained earlier in the appropriate network. The network is trained with more Vertical Electrical Sounding (VES) data, and this trained network is demonstrated by the field data. Groundwater table depth also has been modeled. © 2012 Elsevier B.V. All rights reserved. Source

Balasubramaniam M.,Bharathiar University | Karthikraj C.,Bharathiar University | Selvaraj S.,Mdt Hindu College | Arunachalam N.,M. S. University of Baroda
Physical Review C - Nuclear Physics | Year: 2014

We study here the ternary-fission mass distribution of the Cf252 nucleus for a fixed third fragment Ca48 using the level-density approach within the framework of statistical theory. For the evaluation of nuclear level densities, the single-particle energies of the finite-range droplet model are used. Our results for temperatures T=1 and 2 MeV reproduce qualitatively the experimental expectation of ternary fragmentation of 132Sn+72Ni+48Ca. In addition, different possible ternary-fission modes are highlighted. © 2014 American Physical Society. Source

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