Massive scalar field in the Bianchi Type I space time

A class of exact solutions of Eistein's field equations with attractive massive scalar field in LRS Bianchi type I space time is obtained. It is shown that how the dynamical importance of the scalar field and the shear change in the course of evolution. This revised version was published online in July 2006 with corrections to the Cover Date.     

Bianchi type VI1 cosmological model with wet dark fluid in scale invariant theory of gravitation

In this paper, we have investigated Bianchi type VI _h , II and III cosmological model with wet dark fluid in scale invariant theory of gravity, where the matter field is in the form of perfect fluid and with a time dependent gauge function (Dirac gauge). A non-singular model for the universe filled with disorder radiation is constructed and some physical behaviors of the model are studied for the feasible VI _h (h=1) space-time.     

Stochastic simulation of droughts. Part 2: regional droughts

In Part 1 we demonstrated the applicability of stochastic models to predicting the characteristics of point drought events within any planning period by means of a case study (Mohan S, Sahoo PK (2007) Hydrological Processes 21 : this issue). In addition, studies on regional droughts are important in the context of regional level planning and evolving management strategies. The small number of drought events from a particular streamflow or rainfall series, when subjected to statistical analysis in order to predict future occurrences, produces results that are not very reliable. To overcome this difficulty, we propose using a long sequence of synthetically generated annual rainfall series at various rain‐gauge stations of a region, and multiyear regional droughts were derived from both historic and generated series. The key parameters for a successful regional multiyear drought study are the critical area ratio and the critical level, and the area affected by the drought can be ascertained using these parameters. The important regional drought parameters were determined and their suitable probability distributions were arrived at by studying a total of nine possible probability models; these models can be used in predicting the longest regional drought duration and the greatest regional drought severity with a given return period. The effect of change of critical parameters on the regional drought parameters is also studied and reported. Copyright © 2007 John Wiley & Sons, Ltd.     

Incompatibility of five dimensional LRS Bianchi type-V string and mesonic string cosmological models in general relativity

In this paper, we constructed some cosmological models in five dimensional LRS Bianchi type-V space time based on general theory of relativity. Further, it is shown that source density of the meson field does not survive either in massive scalar field or in mass less scalar field. Some physical and geometrical properties of the models are discussed.     

Geochemical appraisal of mine discharge and tailing at Malanjkhand copper mine,India

Geophysical investigation using Vertical Electrical Sounding (VES), Electrical Resistivity Tomography (ERT) and Seismic Refraction at a proposed conference center site along Ajibode-Labani road, Ibadan, southwestern Nigeria has been carried out. The investigation aims at characterizing and delineating the subsurface strata to understand the weathered profile at the site. Understanding the weathered profile is essential in determining the suitability of the site for engineering construction of the future conference center. A total of 25 VES and 10 ERT profiles were acquired in a systematic grid pattern using both Schlumberger andWenner configurations with Allied omega terrameter. TheVES data were processed and analyzed using WinResist and the ERT data were inverted using RES2DINV. The data were combined to form a 3-D data set of the site and RES3DINV was used to produce the depth slices. Seismic refraction data were also acquired with an ABEM seismograph and processed using SeisImager and Fajseis software. Seismic data were used in understanding the velocity distribution and thickness. The results of VES, ERT and seismic refraction show good correlation. Four sub-surface layers were delineated: top layer of reworked sand, clayey sand/ lateritic hard pan, clay/ sandy clay and fracture/ fresh basement. The 3-D model permits a pictorial view of the sub-surface in relation to materials that overlie the basement. The thickness of unconsolidated materials to bedrock varies from 2.7 m to 12.2 m which revealed inhomogeneity in weathering under the shallow sub-surface. It is found that the integrated geophysical tool is well suited to characterize and delineate sub-surface structure (weathered profile) for engineering site characterization.     

Pattern recognition in lithology classification: modeling using neural networks,self-organizing maps and genetic algorithms

Effective characterization of lithology is vital for the conceptualization of complex aquifer systems, which is a prerequisite for the development of reliable groundwater-flow and contaminant-transport models. However, such information is often limited for most groundwater basins. This study explores the usefulness and potential of a hybrid soft-computing framework; a traditional artificial neural network with gradient descent-momentum training (ANN-GDM) and a traditional genetic algorithm (GA) based ANN (ANN-GA) approach were developed and compared with a novel hybrid self-organizing map (SOM) based ANN (SOM-ANN-GA) method for the prediction of lithology at a basin scale. This framework is demonstrated through a case study involving a complex multi-layered aquifer system in India, where well-log sites were clustered on the basis of sand-layer frequencies; within each cluster, subsurface layers were reclassified into four depth classes based on the maximum drilling depth. ANN models for each depth class were developed using each of the three approaches. Of the three, the hybrid SOM-ANN-GA models were able to recognize incomplete geologic pattern more reasonably, followed by ANN-GA and ANN-GDM models. It is concluded that the hybrid soft-computing framework can serve as a promising tool for characterizing lithology in groundwater basins with missing lithologic patterns.     

Morphological and microstructural characteristics of bauxite developed over a part of Precambrian Iron Ore Group of rocks,Sundergarh District,Eastern India

Bauxite occurring as a blanket over volcanics of Precambrian Iron Ore Group in Kusumdih and Jaldih of Sundergarh district, Odisha, Eastern India exhibits four types of morphology viz. pisolitic, disseminated, spotted and massive. The volcanics/tuffs are altered to shale and is predominantly constituted of kaolinite. Microstructures documented in different morphologies of bauxite are collomorphous, framework, chain, stalactitic, reticulate, vesicle filled and foliated-platy types. Gibbsite, diaspore and kaolinite exhibit the abovementioned morphological and microstructural features. The morphological and microstructural characteristics are interpreted in terms of genetic evolution of aluminous minerals of bauxite in such a setup. Gibbsite and diaspore are formed through solution and precipitation/recrystallisation from kaolinite booklets present in parent tuffaceous shales. Some diaspores were formed at the expense of gibbsites. Small well-ordered kaolinites are of authigenic nature. Kaolinite formed inside a localized closed system containing supersaturated solution exhibits growth spirals. The textural and morphological diversities of the bauxite, abundance of well-ordered bauxite minerals and the absence of any iron/silica phase along with limited occurrence of primary kaolinite together suggest that the deposit has attained a high degree of bauxitisation in weathering environment at surface temperature.     

Novel transformation-based response prediction of shear building using interval neural network

Present paper uses powerful technique of interval neural network (INN) to simulate and estimate structural response of multi-storey shear buildings subject to earthquake motion. The INN is first trained for a real earthquake data, viz., the ground acceleration as input and the numerically generated responses of different floors of multi-storey buildings as output. Till date, no model exists to handle positive and negative data in the INN. As such here, the bipolar data in [ ?1, 1] are converted first to unipolar form, i.e., to [0, 1] by means of a novel transformation for the first time to handle the above training patterns in normalized form. Once the training is done, again the unipolar data are converted back to its bipolar form by using the inverse transformation. The trained INN architecture is then used to simulate and test the structural response of different floors for various intensity earthquake data and it is found that the predicted responses given by INN model are good for practical purposes.