Anxiolytic interventions of extracts and pure compounds from Adenanthera pavonina and Peltophorum pterocarpum leaves to treat acute anxiety and depression symptoms in mice.

Anxiolytic effect of ethanol, hexane extracts and pure compounds ß- sito sterol glucoside and bergenin isolated from Adenanthera pavonina AP (Fabaceae) and Peltophorum pterocarpum PP (Fabaceae) leaves were monitored in this study. Mice were treated with dose of 125mg/kg body weight of ethanol and hexane leaves extracts of both tested plants while, 5mg/kg body weight of ß-sito sterol glucoside and 25mg/kg body weight of bergenin. The effect was monitored by hole board test, forced swimming test, open field apparatus and stationary rod test. Results from neuropharmacological effects revealed that ethanol extract of AP leaves and hexane extract of PP leaves had significant anxiolytic (forced swimming test) exploratory (head dip and open field test) and neuro activator activity (stationary rod test) at tested dose. The greatest anti-depressant and anxiolytic effect was found in ethanol extract of AP leaves when compared to all treated drugs. A part from memory enhancing effects, diazepam treated mice also exhibited anxiolytic and antidepressant effects and found comparable with ethanol extract of AP. These findings may clarify the impact of ethanol, hexane extracts and pure substances ß-sitosterol glucoside and bergenin at tested concentrations, as well as their potential to treat the Parkinson's and related disorders as an alternative therapy.

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers.

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

Application of 0-1 test for chaos on forward converter to study the nonlinear dynamics.

DC-DC converters has significant role in the applied power electronic systems, distributed power systems, computers, home appliances and communication equipment. A converter must remain within the specified range of operation. The main goal of this paper is to discuss the nonlinear behavior of forward converter and highlighted the application of the 0-1 test by applying it on the forward converter. As forward converter may contains electronic components, which cause instability in the system. So, it is necessary to understand its behavior when specifications of components are changed. To study chaotic behavior, 0-1 test will be applied on the forward converter, which is a novel technique outperform in unearthing the subtle chaotic behavior in deterministic dynamical systems. The forward converter goes from period-1, period-2, period-4 and finally become chaotic when the load resistance is varied. This variation in the behavior of the forward converter are analysis through 0-1 test for chaos. Moreover, time series plot, phase portrait and Bifurcation diagram for forward converter is also drawn for the validation of results obtained from 0-1 test. Test algorithm is applied via MATLAB and simulation of forward converter via MultiSim by varying its load resistance.

Sustainable Solutions for Advanced Energy Management System of Campus Microgrids: Model Opportunities and Future Challenges.

Distributed generation connected with AC, DC, or hybrid loads and energy storage systems is known as a microgrid. Campus microgrids are an important load type. A university campus microgrids, usually, contains distributed generation resources, energy storage, and electric vehicles. The main aim of the microgrid is to provide sustainable, economical energy, and a reliable system. The advanced energy management system (AEMS) provides a smooth energy flow to the microgrid. Over the last few years, many studies were carried out to review various aspects such as energy sustainability, demand response strategies, control systems, energy management systems with different types of optimization techniques that are used to optimize the microgrid system. In this paper, a comprehensive review of the energy management system of campus microgrids is presented. In this survey, the existing literature review of different objective functions, renewable energy resources and solution tools are also reviewed. Furthermore, the research directions and related issues to be considered in future microgrid scheduling studies are also presented.

Optical and chemical methods of metal ash analysis and tin recovery.

'Metal ash' presents a waste disposal problem in most of the developing countries as the industries employ obsolete technologies. In this paper we describe analysis of tin ash, zinc ash and aluminium ash by means of optical methods, such as X-ray diffraction (XRD), inductively coupled plasma mass spectrometry (ICP-MS), electron probe micro analysis (EPMA), scanning electron microscopy (SEM) and chemical methods. The results of tin ash obtained by XRD method matched well with the cassiterite, a naturally occurring mineral of tin. ICP-MS studies reveal the presence of a large number of tracer metals, which may cause pollution by tertiary dispersion and this aspect is discussed. Conversely, the data generated by chemical methods are limited. However, the methods are simple and cost-effective. Then, they can easily be adopted by low-budget industries. Simple and cost-effective process to recover tin from tin ash is described. It is based on heating tin ash with sodium cyanide to about 900 degrees C to separate tin component from the metal ash. The process recovers good quality tin and offers a very high yield. The process can be scaled up to small pilot plant.

Selective recovery of gold from secondary sources by Dowex-50-X8 sorbed Rhodamine-B resin.

Gold recovery from spent materials has been performed on modified cation-exchange resin. The modified resin was prepared by sorption of Rhodamine-B on Dowex-50-X8 (Na+) resin at pH 2.0. Distribution coefficients of gold ion have been determined with 15% (w/v) potassium chloride in different concentrations of hydrochloric acid. On the basis of Kd values quantitative separation of gold from secondary sources has been achieved. Gold(III) has been selectively separated from gold containing spent materials by column chromatography using modified ionexchange resin and the recovery was more than 92% compared to conventional cyanidation process. The results of the proposed method are suggestive of its applicability to other gold containing secondary sources.