Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Bacterial transformation using micro-shock waves

Shock waves are one of the most competent mechanisms of energy dissipation observed in nature. We have developed a novel device to generate controlled micro-shock waves using an explosive-coated polymer tube. In this study, we harnessed these controlled micro-shock waves to develop a unique bacterial transformation method. The conditions were optimized for the maximum transformation efficiency in Escherichia coli. The maximum transformation efficiency was obtained when we used a 30 cm length polymer tube, 100 mu m thick metal foil, 200 mM CaCl(2), 1 ng/mu l plasmid DNA concentration, and 1 x 10(9) cell density. The highest transformation efficiency achieved (1 x 10(-5) transformants/cell) was at least 10 times greater than the previously reported ultrasound-mediated transformation (1 x 10(-6) transformants/cell). This method was also successfully employed for the efficient and reproducible transformation of Pseudomonas aeruginosa and Salmonella typhimurium. This novel method of transformation was shown to be as efficient as electroporation with the added advantage of better recovery of cells, reduced cost (40 times cheaper than a commercial electroporator), and growth phase independent transformation. (C) 2011 Elsevier Inc. All rights reserved.

Surface chemical and adsorption studies using Thiobacillus ferrooxidans with reference to bacterial adhesion to sulfide minerals

Adhesion of Thiobacillus ferrooxidans to pyrite and chalcopyrite in relation to its importance in bioleaching and bioflotation has been studied. Electrokinetic studies as well as FT-IR spectra suggest that the surface chemistry of Thiobacillus ferrooxidans depends on bacterial growth conditions. Sulfur-,Pyrite- and chalcopyrite-grown Thiobacillus ferrooxidans were found to be relatively more hydrophobic. The altered surface chemistry of Thiobacillus ferrooxidans was due to secretion of newer and specific proteinaceous compounds. The adsorption density corresponds to a monolayer coverage in a horizontal orientation of the cells. The xanthate flotation of pyrite in presence of Thiobacillus ferrooxidans is strongly depressed where as the cells have insignificant effect on chalcopyrite flotation. This study demonstrate that: (a)Thiobacillus ferrooxidans cells can be used for selective flotation of chalcopyrite from pyrite and importantly at natural pH values. (b)Sulfur-grown cells exhibits higher leaching kinetics than ferrous ion-grown cells.

Features of a unique intronless cluster of class I small heat shock protein genes in tandem with box C/D snoRNA genes on chromosome 6 in tomato (Solanum lycopersicum)

Physical clustering of genes has been shown in plants; however, little is known about gene clusters that have different functions, particularly those expressed in the tomato fruit. A class I 17.6 small heat shock protein (Sl17.6 shsp) gene was cloned and used as a probe to screen a tomato (Solanum lycopersicum) genomic library. An 8.3-kb genomic fragment was isolated and its DNA sequence determined. Analysis of the genomic fragment identified intronless open reading frames of three class I shsp genes (Sl17.6, Sl20.0, and Sl20.1), the Sl17.6 gene flanked by Sl20.1 and Sl20.0, with complete 5' and 3' UTRs. Upstream of the Sl20.0 shsp, and within the shsp gene cluster, resides a box C/D snoRNA cluster made of SlsnoR12.1 and SlU24a. Characteristic C and D, and C' and D', boxes are conserved in SlsnoR12.1 and SlU24a while the upstream flanking region of SlsnoR12.1 carries TATA box 1, homol-E and homol-D box-like cis sequences, TM6 promoter, and an uncharacterized tomato EST. Molecular phylogenetic analysis revealed that this particular arrangement of shsps is conserved in tomato genome but is distinct from other species. The intronless genomic sequence is decorated with cis elements previously shown to be responsive to cues from plant hormones, dehydration, cold, heat, and MYC/MYB and WRKY71 transcription factors. Chromosomal mapping localized the tomato genomic sequence on the short arm of chromosome 6 in the introgression line (IL) 6-3. Quantitative polymerase chain reaction analysis of gene cluster members revealed differential expression during ripening of tomato fruit, and relatively different abundances in other plant parts.

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.