This paper examines the most important historical knowledge and knowledge of the Arabs in the pre-Islamic era based on the Holy Quran, which is the oldest and most reliable Arab Islamic sources recorded at all, as a major source of this study, to ensure a great interest in history, especially the news of the prophets, and the positions of their nations Some of them, as well as some of the news of the old Arab people such as Aad and Thamud and the owners of the elephant and others, and some of which are repeated in several Quranic fence and various methods of presentation, cited as a lesson and exhortation to the Arab opponents of Islam and a reminder of what befell the peoples of the world B relict of doom to discredit them the apostles

Recently emerging pandemic SARS CoV-2 conquered our world since December 2019. Continuous efforts have been done to find out effective immunization and precise treatment stetratigies A way from therapeutic options that were tried in SARS CoV-2, an increased attention is directed to predict natural products and mainly phytochemicals as collaborative measures for this crisis. In this review, most of the mentioned compounds specially flavonoids (biacalin, hesperidin, quercetin, luteolin,, and phenolic (resveratrol, curcumin, and theaflavin) exert their effect through interfering with the action of one or more of this proteins (spike protein, papain like protease, 3 chymotrypsin like cysteine protease, and RNA dependent RNA 

This work is licensed under a Creative Commons Attribution 4.0 International License. Abstract This study examines the working capital management

The purpose of this study is to underline the progression and development of research regarding oxygen-containing heterocycles as well as the contribution that some oxygen-containing heterocycles have made as anticancer medicines. A series of publications about the antitumor effects of derivatives of heterocyclic compounds containing an oxygen atom, such as furan, benzofuran, oxazole, benzoxazole, and oxadiazole, were evaluated, and their anticancer activities showed encouraging results when compared to those of established standard treatments.

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob