Research topic: (The Epistemological Foundations for Comparison of Religions by al-Amiri)

The research sought to study the topic with: a descriptive methodology by investigating the components of al-Amiri's approach to the interfaith comparison. And analytical, by showing the applied perception of an objective model in the comparison of religions to answer two questions: What are the cognitive foundations of al-Amiri? And what is his approach to establishing an objective comparison between religions?

The research started by introducing Abu al-Hassan al-Amiri, and then presented four topics: An introduction to al-Amiri's efforts in the interfaith comparison, his knowledge foundations, an applied model

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

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%

Background: Extracorporeal Shock wave lithotripsy (ESWL) is widely used in treating patients with ureteralstones because it is effective, safe, and noninvasive. Based on factors such as size and the location of stones,there is a significant variation in the overall stone-free rate (SFR).Aim of the study: To evaluate the effect of ureteral wall thickness (UWT), stone attenuation, the time fromfirst attack of pain till first session of ESWL and stone/ rib density on the outcome of SWL in the treatmentof upper ureteral stones (UUS).Patient and methods: A prospective study when 127 patients with radio-opaque UUS ranging from 7 to 20mm and treated by ESWL were included in this study. The effect of (stone/ 12th rib) density by KUB, ureter

In present project, new Schiff base of 4, 4'- (((1E, 1'E)-1,4-.phenylenebis- (methane-ylylidene))-bis-(azane-ylylidene)) bis-(5-(4-chlorophenyl) -4H -1,2,4-triazole-3-thione) (L3) has been synthesized by condensation of 4-amino-5-(4-chlorophenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione with benzene-1,4-dicarboxaldehyde. The new asymmetrical Schiff base (L3) used as a ligand to synthesize a new complex with Co(II), Ni(II), Cu(II), Pd(II), and Pt(IV) metal ions by 1:2 (Metal: ligand) ratio. New ligand and their complexes have been exanimated and Confirmed by Fourier-transform infrared (FT-IR), Ultraviolet-visible (UV-visible), Proton nuclear magnetic resonance (1HNMR), carbon13 nuclear magnetic resonance (13CNMR), carbon-hydrogen nitrogen sulf

       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