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.

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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 nonlinearity 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 singlelayer feed-forward neural network with the back propagation algorithm is chosen as one of the wellsuited 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 0 30"E and 8 48 0 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

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.

This paper provides tables for the construction and selection of tightened-normal-tightened variables sampling scheme of type TNTVSS (n 1 , n 2 ; k). The method of designing the scheme indexed by (AQL, α) and (LQL, β) is indicated. The TNTVSS (n T , n N ; k) is compared with conventional single sampling plans for variables and with TNT (n 1 , n 2 ; c) scheme for attributes, and it is shown that the TNTVSS is more efficient.