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

Micro-perforated panel (MPP) absorber is increasingly gaining popularity as an alternative sound absorber in buildings compared to the well-known synthetic porous materials. A single MPP has a typical feature of a Helmholtz resonator with a high amplitude of absorption but a narrow absorption frequency bandwidth. To improve the bandwidth, a single MPP can be cascaded with another single MPP to form a double-layer MPP. This paper proposes the introduction of inhomogeneous perforation in the double-layer MPP system (DL-iMPP) to enhance the absorption bandwidth of a double-layer MPP. Mathematical models are proposed using the equivalent electrical circuit model and are validated with experiments with good agreement. It is revealed that the DL-

The present study experimentally and numerically investigated the impact behavior of composite reinforced concrete (RC) beams with the pultruded I-GFRP and I-steel beams. Eight specimens of two groups were cast in different configurations. The first group consisted of four specimens and was tested under static load to provide reference results for the second group. The four specimens in the second group were tested first under impact loading and then static loading to determine the residual static strengths of the impacted specimens. The test variables considered the type of encased I-section (steel and GFRP), presence of shear connectors, and drop height during impact tests. A mass of 42.5 kg was dropped on the top surface at the m

.

The reactive yellow azo dye (λmax = 420 nm) is widely utilized for textile coloring due to its low-cost stability and tolerance properties. Treatment of dye-containing wastewater by traditional methods is usually inadequate because of its resistance to biological and chemical degradation. From this research, the continuous reactor of an advanced oxidation method supported the use of H₂O₂/TiO₂/UV to remove the coloration of the reactive yellow dye from the discharge. At constant best conditions obtained from the batch reactor tests pH=7, H₂O₂ dosage = 400 mg/l and TiO₂=25mg/l , the aqueous solutions were tested in the continuous reactor at different dye concentration and d

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