Government College

About: Government College is a based out in . It is known for research contribution in the topics: Population & Ring (chemistry). The organization has 4481 authors who have published 5986 publications receiving 57398 citations.

Enhanced Magneto-optical and Photocatalytic Properties of Ferromagnetic Mg1−yNiyFe2O4 (0.0 ≤ y ≤ 1.0) Spinel Nano-ferrites

Abstract: Spinel Mg1−yNiyFe2O4 (0.0 ≤ y ≤ 1.0) ferrite magnetic nanoparticles (MNPs) were synthesized by a sol-gel combustion method using urea as the reducing agent/fuel. Powder X-ray diffraction (XRD) analysis revealed the formation of cubic spinel Mg1−yNiyFe2O4 powders and confirmed pure crystalline phase and the average crystalline size of 21.25 to 17.04 nm. Functional group bonds between metal and oxygen (M–O) were confirmed by Fourier transform infrared (FT-IR) spectra. The microstructure of the powders was recorded by high-resolution scanning electron microscope (HR-SEM) and confirmed the particle-like surface morphology with smaller agglomeration, due to the magnetic interaction of the particles. Energy-dispersive X-ray (EDX) results showed the composition of the expected elements and confirmed the phase purity of the products. Vibrating sample magnetometer (VSM) technique recorded at room temperature was used to analyse the magnetic properties of the samples and the hysteresis loops showed the ferromagnetic behaviour. Moreover, the samples Mg1−yNiyFe2O4 NPs were tested for the photocatalytic degradation (PCD) of methylene blue (MB) dye and the sample y = 0.6 showed maximum degradation efficiency (96.83 %), due to the smaller particle size with higher surface area than other compositions. Furthermore, spinel Mg1−yNiyFe2O4 nano-photocatalysts can be reused several times without change of its catalytic activity.

Punica granatum peel extracts: HPLC fractionation and LC MS analysis to quest compounds having activity against multidrug resistant bacteria

Abstract: Medicinal plants are rich source of traditional herbal medicine around the globe. Most of the plant’s therapeutic properties are due to the presence of secondary bioactive compounds. The present study analyzed the High Pressure Liquid Chromatography (HPLC) fractions of Puncia granatum (peel) extracts (aqueous, chloroform, ethanol and hexane) against multidrug resistant bacterial pathogens (Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa and Staphylococcus aureus). All the fractions having antibacterial activity was processed for bioactive compounds identification using LC MS/MS analysis. Among total HPLC fractions (n = 30), 4 HPLC fractions of P. granatum (peel) showed potential activity against MDR pathogens. Fraction 1 (F1) and fraction 4 (F4) collected from aqueous extract showed maximum activity against P. aeruginosa. Fraction 2 (F2) of hexane showed antibacterial activity against three pathogens, while ethanol F4 exhibited antibacterial activity against A. baumannii. The active fractions were processed for LC MS/MS analysis to identify bioactive compounds. Valoneic acid dilactone (aqueous F1 and F4), Hexoside (ethanol F4) and Coumaric acid (hexane F2) were identified as bioactive compounds in HPLC fractions. Puncia granatum peel extracts HPLC fractions exhibited potential inhibitory activity against MDR bacterial human pathogens. Several bioactive compounds were identified from the HPLC fractions. Further characterization of these compounds may be helpful to conclude it as therapeutic lead molecules against MDR pathogens.

Prediction of Stock Market Price using Hybrid of Wavelet Transform and Artificial Neural Network

Abstract: Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System.