In vitro study of lipid peroxidation and free radical scavenging activity of cow urine

This study represents a comprehensive analysis and scientific validation of our ancient knowledge about the ethnopharmacological aspects of cow urine by measuring the lipid peroxidation, radical scavenging, and level of reduced glutathione and catalase activity. Graded doses of cow urine were administered orally to experimentally treated rats. Results of liver and plasma from experimentally treated rats indicated that cow urine reduced the levels of thiobarbituric acid reactive substance significantly in all the treatments (P < 0.01). In vitro experiments with the liver of control and experimentally treated rats were also carried out against cumene hydroperoxide-induced lipid peroxidation. On LCMS analysis, the antioxidant component of cow urine was identified as uric acid (m/z 169.07). The results demonstrate that the cow urine-mediated induction of antioxidant level controls oxidative damage, even after minimal processing, and thus is indicative of its potential as a viable substitute of synthetic antioxidants.     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Recent Developments in Integrated Solar Combined Cycle Power Plants

Global concern for depleting fossil fuel reserves have been compelling for evolving power generation options using renewable energy sources. The solar energy happens to be a potential source for running the power plants among renewable energy sources. Integrated Solar Combined Cycle(ISCC) power plants have gained popularity among the thermal power plants. Traditional ISCC power plants use Direct Steam Generation(DSG) approach. However, with the DSG method, the ISCC plant’s overall thermal efficiency does not increase significantly due to variations in the availability of solar energy. Thermal Energy Storage(TES) systems when integrated into the solar cycle can address such issues related to energy efficiency, process flexibility, reducing intermittency during non-solar hours. This review work focuses and discusses the developments in various components of the ISCC system including its major cycles and related parameters. The main focus is on CSP technologies, Heat Transfer Fluid(HTF), and Phase Change Material(PCM) used for thermal energy storage. Further, study includes heat enhancement methods with HTF and latent heat storage system. This study will be beneficial to the power plant professionals intending to modify the solar-based Combined Cycle Power Plant(CCPP) and to retrofit the existing Natural Gas Combined Cycle(NGCC) plant with the advanced solar cycle.     

Parametric analysis of solar energy conversion system using parabolic concentrator and thermopile

The present work aims at improving the design of an energy conversion system operating on solar radiation harvesting and converting it into electricity with the help of thermopiles. Here, the major focus is upon the design parameters and overall performance enhancement of the proposed energy conversion systems. The system consists of a parabolic concentrator to focus all the incident radiations available on the aluminium box containing thermopiles. A mathematical model is proposed for this system and different ways of minimising various possible heat losses are discussed. Furthermore, by maximising the heat flux in a thermoelectric generator, arrangement of the integrated system is analysed. The results obtained for the concentrator, thermoelectric generator and overall system efficiencies at varying energy losses and found that as the losses are decreased, efficiencies increased. At the losses around 40% of the total energy received, an overall efficiency as high as 12% with a considerably less bulky system.     

Influence of metal contaminants on oxidation stability of Jatropha biodiesel

According to proposed National Mission on biodiesel in India, we have undertaken studies on stability of biodiesel from tree borne non-edible oil seeds Jatropha. European biodiesel standard EN-14214 calls for determining oxidation stability at 110 °C with a minimum induction time of 6 h by the Rancimat method (EN-14112). Neat Jatropha biodiesel (JBD) exhibited oxidation stability of 3.95 h and research was conducted to investigate influence of presence of transition metals, likely to be present in the metallurgy of storage tanks and barrels, on oxidation stability of Jatropha methyl ester. It was found that influence of metal was detrimental to oxidation stability and catalytic. Even small concentrations of metal contaminants showed nearly same influence on oxidation stability as large amounts. Copper showed strongest detrimental and catalytic effect. The dependence of the oxidation stability on the type of metal showed that long-term storage tests in different types of metal containers for examining the influence of container material on oxidation stability of biodiesel may be replaced by significantly faster Rancimat test serving as an accelerated storage test.     

Effect of Metal Contaminants and Antioxidants on the Oxidation Stability of the Methyl Ester of Pongamia

According to the proposed National Mission on biodiesel in India, we have undertaken studies on the stability of biodiesel from tree-borne non-edible oil seeds like Pongamia pinnata. Neat Pongamia methyl ester (PoME) exhibited an oxidation stability (OS) of 2.54 h and research was conducted to investigate the effect of the presence of transition metals likely to be present in the metallurgy of storage tanks and barrels, on the OS of PoME. It was found that the influence of metal was detrimental to OS and was catalytic, as even small concentrations of metal contaminants showed nearly the same influence on OS as large amounts. Copper showed the strongest detrimental and catalytic effect on OS. The OS of metal-contaminated PoME was found to increase with an increase in the dosage of antioxidant but the dosage required for copper-contaminated PoME became approximately four times than required for neat PoME. The dependence of the OS on the type of metal showed that long-term storage tests in different types of metal containers for examining the influence of container material on OS of biodiesel may be replaced by the significantly faster Rancimat test serving as an accelerated storage test.     

Parametric Evaluation of Heat Recovery Steam Generator (HRSG)

Thermal efficiency of a combined cycle power plant depends strongly on a heat recovery steam generator (HRSG), which is the link between the gas turbine‐based topping cycle and steam turbine‐based bottoming cycle. This work is based upon the design of physical parameters of a HRSG. In this article, the physical parameters of a HRSG have been considered to study their implications on HRSG design by comparing the existing plant design with an optimized plant design. Thermodynamic analysis of HRSG for the two designs gives important outcomes which are useful for power plant designers. © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res; Published online in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21106     

Distributed Temporal Data Prediction Model for Wireless Sensor Network

Sensor networks are critical for building smart environments for monitoring various physical and environmental conditions. Several automated tasks involving continuous and critical practically becomes infeasible for humans to perform with precision. Therefore, wireless sensor networks have emerged as the next-generation technology to permeate the technological upgradations into our daily activities. Such intelligent networks, embedded with sensing expertise, however, are severely energy-constrained. Sensor networks have to process and transmit large volumes of data from sensors to sink or base station, requiring a lot of energy consumption. Since energy is a critical resource in the sensor network to drive all its basic functioning, hence, it needs to be efficiently utilized for elongating network lifetime. This makes energy conservation primarily significant in sensor network design, especially at the sensor node level. Our research proposes an On-balance volume indicator-based Data Prediction (ODP) model for predicting the temperature in the sensor network. Our proposed model can be used to predict temperature with a permissible error of tolerance. This helps in reducing excessive power consumption expended in redundant transmissions, thereby increasing the network lifetime. The proposed data prediction model is compared with existing benchmark time series prediction models, namely Linear Regression (LR) and Auto-Regressive Integrated Moving Average (ARIMA). Experimental outcomes endorsed that our proposed prediction model outperformed the existing counterparts in terms of prediction accuracy and reduction in the number of transmissions in clustered architecture.

     

Community structure of planktons and macrozoobenthos,with special reference to pollution in the River Khan (India)

This paper presents an assessment of water quality of the River Khan, which passes through Indore and is subjected to sewage and industrial pollution. The analysis of various pollution parameters showed an increase when the sewage and industrial channels joined the river. Changes in the biotic communities, such as phytoplankton, zooplankton and macrozoobenthos, have been explained numerically with the help of a diversity index and showed a decrease in diversity values with an increase in pollution, along with a correlation with physico‐chemical aspects. Genera tolerant to various degree of pollution has also been identified. The extend of pollution by certain heavy metals such as Cu, Zn, Ni, Cr, Cd and Hg has also been studied.     

Antimicrobial activity of nanosilver-coated socks fabrics against foot pathogens

Nanotechnology can be used in engineering-desired textile attributes, such as fabric softness and durability in fibres, yarns and fabrics. Nanocoating the surface of socks is one approach to the production of highly active surfaces with UV blocking, antimicrobial and self-cleaning properties. Synthesis of silver nanoparticles in this project was carried out chemically by wet reduction method (Ag-chem) and biologically by using neem (Azadirachta indica) leaves (Ag-neem). The formation of silver nanoparticles was monitored by UV–visible spectroscopy, which revealed the surface plasmon resonance peak at 420?nm for Ag-chem and 430?nm for Ag-neem, and transmission electron microscopy, which showed nanoparticles of various shapes and sizes (～5–50?nm). Quantification of the prepared silver nanoparticles was done by atomic absorption spectroscopy, which revealed 0.044?M Ag⁺ and 0.042?M Ag⁺ ions in Ag-chem and Ag-neem, respectively. Coating of the socks fabrics (nylon and cotton) was carried out by exposing these fabrics to the prepared nanoparticle solutions on a gyratory shaker overnight. Antimicrobial activity of the Ag-chem and Ag-neem was carried out by performing minimum inhibitory concentration (MIC) and disc diffusion test against Sarcina lutea, an odour-producing organism, Klebsiella pnuemoniae, Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus and Candida albicans, organisms causing foot infections. P. aeruginosa and S. lutea were found to be most sensitive to either of the above preparations. Ag-chem was found to be more effective than Ag-neem. Nylon and cotton socks fabrics were coated with each of the above preparations. The antibacterial efficacy of the nanosilver-finished fabrics was checked by zone inhibition test, antibacterial test and wash fastness test. In both cases, coated nylon fabrics showed better antimicrobial activity than coated cotton fabrics. S. lutea and K. pneumoniae showed greater zones of inhibition than the other test organisms. Nylon fabric coated with Ag-chem and Ag-neem gave maximum reduction in viable count of all test organisms as compared to cotton fabrics. Higher reduction in the viable count of all test organisms was observed with Ag-chem-coated nylon fabrics. Thus, coating of the nylon socks fabric with silver nanoparticles can be used as an effective way to combat foot-borne pathogens and thereby reducing discomforts like foot odour, perspiration, complications due to diabetes, athlete’s foot, etc.