A novel analytical method is developed for the determination of azithromycin. The method utilizes continuous flow injection analysis to enhance the chemiluminescence system of luminol, H2O2, and Cr(III). The method demonstrated a linear dynamic range of 0.001–100 mmol L-1 with a high correlation coefficient (r) of 0.9978, and 0.001–150 mmol L-1 with a correlation coefficient (r) of 0.9769 for the chemiluminescence emission versus azithromycin concentration. The limit of detection (L.O.D.) of the method was found to be 18.725 ng.50 µL−1 based on the stepwise dilution method for the lowest concentration within the linear dynamic range of the calibration graph. The relative standard deviation (R.S.D. %) for n = 6 was less than 1.2%

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

In this work, composite materials were prepared by mixing different concentrations of ferrites with polyacrylonitrile (PAN) polymer. Using the electrospinning technique, these composites were deposited on a p-type silicon wafer. The prepared samples demonstrated nanofibers in both pure PAN polymers and their composites with ferrite. Prior to examining the humidity sensing effectiveness with a percentage of relative humidity at a frequency of 10 kHz, based on ambient temperature and a relative humidity range of 50–100%, the composite nanofibers demonstrated stronger humidity sensing compared to the pure PAN nanofibers, which demonstrated a powerful resistance response. More precisely, the PAN@ferrite nanocomposite showed a broad adsorption

A low-cost, effective green porous composite was prepared and used for the sorption of Zn+2 ions from aqueous solutions. The effect of contact time, sorbent dosage, initial ion concentration, solution pH, and temperature on the sorption of Zn+2 ions was investigated. The results showed higher removal efficiency of Zn+2 ions (83%), which was achieved at a contact time of 120 minutes, a normal pH of 6.4, and a temperature of 313 K. The Langmuir isotherm model successfully described the experimental results of the sorption process with a theoretical maximum sorption capacity of 4.819 mg/g. The kinetics of sorption showed that the pseudo-second-order model fitted the experimental results better than the pseudo-first-order model. The int