The ability of using aluminum filings which is locally solid waste was tested as a mono media in gravity rapid filter. The present study was conducted to evaluate the effect of variation of influent water turbidity (10, 20and 30 NTU); flow rate(30, 40, and 60 l/hr) and bed height (30and60)cm on the performance of aluminum filings filter media for 5 hours run time and compare it with the conventional sand filter. The results indicated that aluminum filings filter showed better performance than sand filter in the removal of turbidity and in the reduction of head loss. Results showed that the statistical model developed by the multiple linear regression was proved to be
valid, and it could be used to predict head loss in aluminum filings

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Fetal growth restriction is a significant contributor to fetal morbidity and mortality. In addition, there are heightened maternal risks associated with surgical operations and their accompanying dangers. Monitoring fetal development is a crucial objective of prenatal care and effective methods for early diagnosis of Fetal growth restriction, allowing prompt management and timely intervention to improve the outcomes. Screening for Fetal growth restriction can be achieved via many modalities; it can be medical, biochemical, or radiological. Some recommended combining more than one for better outcomes. Currently, there is inconsistency about the best method of Fetal growth restriction screening.   In this review, a comprehensive

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with

The mucilage from the seeds of Lallemantia royleana family Labiatae was extracted and subjected to preformulation study for evaluation of its suitability for use as suspending agent. Furosemide suspensions were prepared using (1.5% w/v) of the extracted Lallemantia royleana mucilage, (1.5% w/v) chitosan and (0.35% w/v) xanthan gum. The mucilage was white in color and the average yield of dried mucilage obtained from L.royleana nutlets was 14 % w/w of the seeds used. It is sparingly soluble in water but swells in contact with it, giving a highly viscous solution. It is slightly acidic to neutral. It was found that the extracted natural mucilage of Lallemantia royleana exhibited a higher viscosity profil

This research studies the effect of addition of some nanoparticles
(MgO, CuO) and grain size (30,40nm) on some physical properties
(impact strength, hardness and thermal conductivity) for a matrix
blend of epoxy resin with SBR rubber. Hand –Lay up method was
used to prepare the samples. All samples were immersed in water for
9 weeks.
The Results showed decreased in the values of impact strength and
hardness but increased the coefficient of thermal conductivity.

This study deals with the subject of art criticism by using Erwin Panofsky's theory to analyze a few Saudi artists' works. The study aims to identify Panofsky's theory and provide criticism of some Saudi artworks using it. The importance of the study is that it enriches the field of art criticism in the Kingdom of Saudi Arabia and helps critics and artists in using Panofsky’s theory to analyze artworks.
The study sample consists of six artworks produced in 2021 by six contemporary Saudi artists. In the theoretical section, the study dealt with several topics; first, is art criticism, the second part presents Panofsky’s theory with its three stages, the final part deals with the beginning of Saudi art until present time and its