KE Sharquie, HM Al-Hamamy, AA Noaimi, IA Al-Shawi, Journal of the Saudi Society of Dermatology & Dermatologic Surgery, 2011 - Cited by 9

Wellbore instability and sand production onset modeling are very affected by Sonic Shear Wave Time (SSW). In any field, SSW is not available for all wells due to the high cost of measuring. Many authors developed empirical correlations using information from selected worldwide fields for SSW prediction. Recently, researchers have used different Artificial Intelligence methods for estimating SSW. Three existing empirical correlations of Carroll, Freund, and Brocher are used to estimate SSW in this paper, while a fourth new empirical correlation is established. For comparing with the empirical correlation results, another study's Artificial Neural Network (ANN) was used. The same data t

In this paper a stirred-bed performed of the copper catalyzed synthesis of ethylchlorosilanes from silicon and ethyl chloride was described. A Si-catalyst mixture prepared by reaction of CuCl and Si was employed. The compositions of products were mainly ethyltrichlorosilane, diethyldichlorosilane, and ethyldichlorosilane and mainly depended on the extent of Cu in the mixture and the reaction temperature. A promoting effect on the extent of adsorption was observed on the addition of certain additives. The kinetic data revealed the direct depended of the reaction rate on C2H5Cl pressure.

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi

The depletion of petroleum reserves and increasing environmental concerns have driven the development of eco-friendly asphalt binders. This research investigates the performance of natural asphalt (NA) modified with waste engine oil (WEO) as a sustainable alternative to conventional petroleum asphalt (PA). The study examines NA modified with 10%, 20%, and 30% WEO by the weight of asphalt to identify an optimal blend ratio that enhances the binder’s flexibility and workability while maintaining high-temperature stability. Comprehensive testing was conducted, including penetration, softening point, viscosity, ductility, multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), energy-dispersive X-ray spectroscopy (EDX), F

This study aimed at identifying how children express the emerging coronavirus in general and according to their age groups (4-13 years) by analyzing 91 of their drawings published online, using the descriptive content analytical approach. The results showed that children's artistic expression of the virus came according to the concepts and ideas they carried about the virus for the age groups of (4-7 years) and (7-9 years), while it came according to visual perception for age groups (9-11 years), and from (11-13) years. Also, most children were aware about the presence of the virus and its widespread around the world, but (99%) of them do not realize the seriousness of the virus. It was confirmed that between (25-34%) of children were su