This research explored the performance of steel fiber concrete-filled stainless-steel tube columns stiffened with embedded carbon steel T-sections with various steel fiber ratios under biaxial bending conditions. A numerical parametric analysis was adopted, using finite element modeling with Abaqus CAE/2021 to evaluate the effects of the fiber ratio (ranging from 0% to 1.5%) on the load-bearing capacity and deflection behavior of columns. In addition, the compressive strength of concrete ranged between 45 and 65 MPa. An increase in the fiber ratio led to a substantial improvement in the ultimate load-bearing capacity (up to 24%), a reduction in deflection (of approximately 49%), and an improvement in column ductility, which were obt

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

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

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

This study focused on benthic algae (epipelic and attached algae on concrete lining stream) in Bani-Hassan stream in Holly Karbala, Iraq. The qualitative and quantitative studies of benthic algae were done by collecting 240 samples from five sites in the study area for the period from December 2012 to November 2013. Also, the environmental variables of the stream were examined in term of temporary and spatial. The results showed that the stream was alkaline, hard, oligohaline and a well aerated. The total nitrogen to the total phosphorus (TN: TP) ratio indicates nitrogen limitation. 129 species of benthic algae belonging to 57 genera were identified. Bacillariophyceae (diatoms) was the predominant taxon (95 species) followed by Chlorophyce

Persistence of antibiotics in the aquatic environment has raised concerns regarding their potential influence on potable water quality and human health. This study analyzes the presence of antibiotics in potable water from two treatment plants in Baghdad City. The collected samples were separated using a solid-phase extraction method with hydrophilic-lipophilic balance (HLB) cartridge before being analyzed. The detected antibiotics in the raw and finished drinking water were analyzed and assessed using high-performance liquid chromatography (HPLC), with fluorometric detector and UV detector. The results confirmed that different antibiotics including fluoroquinolones andB-lactams were detected in the raw an

The present study aimed to assess the antibacterial activity of peanut (Arachis hypogaea L.) skin extracts. The phytochemical analysis of the peanut skin extracts was investigated, the result showed a strong presence of flavonoids, phenols, alkaloids and tannins in methanol and ethyl acetate extracts. Antibiotic susceptibility of the bacterial isolates was performed on seven antibiotics represented by Amikacin, Tetracycline, Ciprofloxacin, Chloramphenicol, Ticarcillin, Cefotaxime and Gentamicin by disc diffusion method. The antibiogram for studied isolates revealed high level resistance of A. baumannii to all of the antibiotics under test except amikacin, while Staph. aurous was resistance to Chloramphenicol and Cefotxime and sensitive to A