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Search Results: 1 - 10 of 304791 matches for " G K Parshetti "
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Biodegradation of Crystal Violet by Agrobacterium radiobacter

G K Parshetti,S G Parshetti,A A Telke,D C Kalyani,R A Doong,P Govindwar,

环境科学学报(英文版) , 2011,
Abstract:
Adaline and FIS Techniques for Fault Identification in HV Transmission Line  [PDF]
K. Elango, G. Geetha
Circuits and Systems (CS) , 2016, DOI: 10.4236/cs.2016.710271
Abstract: This paper is to identify and classify the various types of shunt and line faults in transmission line. The faults may be an insulation failure, lightning or accidental faulty operation. In a transmission line protection important factor is identifying a fault because if any error occurs in finding fault may leads to abnormal operation of the protection system. So either a disturbance or steady state variation is called power quality variation. The proposed test system is modeled based on the neural network and fuzzy algorithm. The online symmetrical components are extracted by this above algorithm. The fuzzy is used to separate the oscillating components and average components. Here input for the fuzzy is trained by using neural network. It is based on current samples and very effective in fault classifier using rule base. This method is very much suitable for online implementation.
Oscillation and Asymptotic Behaviour of Solutions of Nonlinear Two-Dimensional Neutral Delay Difference Systems  [PDF]
K. Thangavelu, G. Saraswathi
Journal of Applied Mathematics and Physics (JAMP) , 2017, DOI: 10.4236/jamp.2017.56104
Abstract: This paper deals with the some oscillation criteria for the two-dimensional neutral delay difference system of the form \"\" . Examples illustrating the results are inserted.
Finding the Efficient Frontier for a Mixed Integer Portfolio Choice Problem Using a Multiobjective Algorithm  [PDF]
K. P. ANAGNOSTOPOULOS, G. MAMANIS
iBusiness (IB) , 2009, DOI: 10.4236/ib.2009.12013
Abstract: We propose a computational procedure to find the efficient frontier for the standard Markowitz mean-variance model with discrete variables. The integer constraints limit on the one hand the portfolio to contain a predetermined number of assets and, on the other hand, the proportion of the portfolio held in a given asset. We adapt the multiobjective algorithm NSGA for solving the problem. The algorithm ranks the solutions of each generation in layers based on Pareto non-domination. We have applied the procedure in sixty assets of ATHEX. We have also compared the algorithm with a single genetic algorithm. The computational results indicate that the procedure is promising for this class of problems.
Identification of Influential Sea Surface Temperature Locations and Predicting Streamflow for Six Months Using Bayesian Machine Learning Regression  [PDF]
N. K. Shrestha, G. Urroz
Journal of Water Resource and Protection (JWARP) , 2015, DOI: 10.4236/jwarp.2015.73016
Abstract: Sea surface temperature (SST) has significant influence in the hydrological cycle and affects the discharge in the stream. SST is an atmospheric circulation indicator which provides the predictive information about the hydrologic variability in the region around the world. Use of right location of SST for a given location of stream gage can capture the effect of oceanic-atmospheric interaction, improving the predictive ability of the model. This study aims on identifying the best locations of SST at the selected stream gage in the state of Utah that spatially covers the state from south to north, and use them for next six-month streamflow volume predictions. The data-driven model derived from the statistical learning theory was used in this study. Using an appropriate location of SST together with local climatic conditions and state of basin, an accurate and reliable streamflow was predicted for next six months. Influence of Pacific Ocean SST was observed to be stronger than that of Atlantic Ocean SST in the state of Utah. The SST of North Pacific developed the best model in most of the selected stream gages. Each model was ensured to be robust by the bootstrap analysis. The long-term streamflow prediction is important for water resource planning and management in the river basin scale and is a key step for successful water resource management in arid regions.
Experimental Investigations on Fluoride Removal from Water Using Nanoalumina-Carbon Nanotubes Blend  [PDF]
K. Smitha, Santosh G. Thampi
Journal of Water Resource and Protection (JWARP) , 2017, DOI: 10.4236/jwarp.2017.97050
Abstract: Fluoride is the most abundant, highly electronegative and geogenic contaminant in groundwater worldwide. Among the water quality parameters, the fluoride ion is unique in that it is beneficial to health if its concentration in water is within a threshold value (1.0 - 1.5 mg/l) and is detrimental (>2 ppm) if present in excess (WHO, 2006). High fluoride levels in drinking water has become a critical health hazard of this century as it induces intense impact on human health including skeletal and dental fluorosis. The objective of this study was to evaluate the potential of nanoalumina-carbon nanotubes blend as a sorbent for the removal of excess fluoride from water. Batch studies were conducted to assess the influence of various operational parameters viz. pH, temperature, agitation time, adsorbent dosage and presence of interfering ions. From the studies, it was clear that the rate of adsorption was initially rapid and attains equilibrium gradually in about 100 min. The presence of interfering ions such as chlorides and sulphates has very little effect on fluoride removal by nanoalumina-carbon nanotubes blend. Analysis of the equilibrium data fitted the Langmuir and Freundlich isotherms very well. The results of the study appear to be quite promising in the sense that they demonstrate the capability of nanoalumina-carbon nanotubes blend for removing fluorides from drinking water.
Characterization of In-Plane Mechanical Properties of Laminated Hybrid Composites  [PDF]
K. G. Satish, B. Siddeswarappa, K. Mohamed Kaleemulla
Journal of Minerals and Materials Characterization and Engineering (JMMCE) , 2010, DOI: 10.4236/jmmce.2010.92009
Abstract: An experimental investigation was conducted to study the effect of hybrid composite specimen subjected to in-plane tensile and compressive loading. The laminated specimens in accordance with ASTM standards were fabricated using steel and nylon bi-directional mesh as reinforcements and polyester as the binder. The various volume fractions and fiber orientations were used in which the percentage of polyester (40%) was maintained constant. From the investigations it is revealed that, the specimens with higher percentage of steel sustain greater loads & also the strengths are superior in case of 0/900 oriented specimens. A relationship between the tensile/compressive strength, fiber content and orientation has been established.
Experimental Investigation of Reversible Modulation of Biological Fluid Crystallogenesis  [PDF]
Andrew K. Martusevich, Alexander G. Samodelkin, Lida K. Kovaleva
Journal of Biomedical Science and Engineering (JBiSE) , 2016, DOI: 10.4236/jbise.2016.91001
Abstract: Free and initiated by 0.9% sodium chloride solution yielding of crystals was studied in the urine of the 35 healthy people. The metabolites—sodium lactate and sodium pyruvate—were superinduced into the dryable biological system in the increasing physiological concentrations. Regularities of the neogenic mixtures’ crystallization were revealed according to the metabolites dose. We showed the antagonistic tendencies in the parameters’ change at the increasing concentrations of the different metabolites—substratum of the enzyme’s opposite reactions—relying on the comparative estimation of the crystalloscopical and tezigraphical indices’ dynamics. Our own data and literature information brought us to a new conception of the biofluid’s stability as a crystallogenetic solution. According to it any substance injected into a dehydrated biological system proves to be a stabilizing or destabilizing agent which leads to a significant reorganization of the biocrystallogenesis. This phenomenon may cause a development of a management strategy of the biological crystallogenesis processes.
Matrix Metalloproteinases and Tissue Inhibitors of Matrix Metalloproteinases as Plasma Indicators of Bovine Cystic Ovarian Disease  [PDF]
K. F. Hentze, K. G. Younger, A. R. Menino
Natural Science (NS) , 2018, DOI: 10.4236/ns.2018.108030
Abstract:

Cystic ovarian disease (COD) is one of the main causes of infertility in dairy cattle and has a high economic impact on farmers. COD is caused by an endocrine imbalance within the hypothalamic-pituitary-ovarian axis preventing the mature Graafian follicle from ovulating. The cause at the molecular level is not well understood. However, the matrix metalloproteinases (MMP) and plasminogen activator/plasmin families of extracellular (ECM) matrix-degrading proteinases are involved in Graafian follicle breakdown and oocyte release in the ovulation process. Our research investigated the possibility of using plasma concentrations of MMP-2 and -9 and their natural tissue inhibitors (TIMP) -1 and -2 as prognostic indicators of COD. Plasma samples from cystic and non-cystic dairy cows were analyzed using ELISA. Although plasma concentrations of MMP-2 and -9 were greater and TIMP-2 was lower in non-cystic compared to cystic cows, no significant differences were observed in MMP-2 and -9 and TIMP-1 and -2 due to cyst status. However, the TIMP-1:MMP-9 and TIMP-2:MMP-2 molar ratios were greater, (P = 0.099) and (P = 0.038), respectively, in cystic compared to non-cystic cows, suggesting a proteolytic insufficiency in cows with COD that may be a contributing factor to the anovulatory pathology. These data may provide the groundwork for future research and development of tools for dairy farmers to selectively choose replacement heifers less likely to develop COD.

Modeling and Simulation of Two-Staged Separation Process for an Onshore Early Production Facility  [PDF]
Ojo Ademola, J. G. Akpa, K. K. Dagde
Advances in Chemical Engineering and Science (ACES) , 2019, DOI: 10.4236/aces.2019.92010
Abstract: Early Production Facilities are makeshift process deployment that ensures that marginal oilfield operators make revenues from their new discoveries with little cash outlay and limited investment risks. Authors have in past simulated a gas process facility using Hysys without particularly developing mathematical models for the key equipment. There also has been modeling of phase separation dynamics and process simulation but still without models for equipment. We basically developed models for the critical process equipment for early production, sized the equipment with data from a marginal field in the Niger delta region of Nigeria and then ran a dynamic simulation with the sized equipment. The important elements of the deployment are two-phase process vessel, 3-phase process vessel; knock-out drum, produced water treatment unit. Mathematical models were developed and adapted with Mathlab for the equipment sizing whilst ASPEN PLUS was used for simulating the process. Process data retrieved from a marginal field in Nigeria was used as input to quantify the equipment models. Sized equipment was deployed in Hysys V8.8 for a steady and dynamic state. The system simulation was comprised of a two-phase process vessel followed by a 3-phase process vessel [1]. The unwanted gas was sent to knock out drum for removal of entrained liquid droplets before flaring (this was because the volume of gas processed is deemed uneconomical) and produced water to treatment unit for removing droplets of oil before disposal. Gas, oil and water were fed into the first stage separator (2-phase) at 132918.34 Ibmole/hr, 7622.95 Ibmole/hr and 1082.74 Ibmole/hr respectively. The operating pressures of the first and second vessels were at 850 psi and 150 psi respectively. The 2-phase vessel
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