The main objective of the present research is to conduct a thorough investigation into the impact of construction joints on the structural performance of reinforced concrete deep beams. This study involves a series of experimental tests and the use of advanced numerical analysis techniques to gain a deeper understanding of the behavior of these beams in the presence of construction joints. The experimental component incorporates analysis findings from both previous and current research. Specifically, six reinforced concrete deep beam specimens featuring horizontal and inclined construction joints were utilized as simply being supported with two-point loading. The test findings indicate that the presence of a horizontal construction

The mechanical properties and microstructure of hot-rolled steel are critical in determining its performance in industrial applications, particularly when exposed to elevated temperatures. This study examines the effects of varying temperatures and soaking times on these properties through a series of controlled experiments. The primary objective was to optimize the key response parameters, including tensile strength, yield strength, and elongation, by analyzing the influence of temperature and time. A full factorial design approach was used, applying the desirability function theory to explore all possible combinations and identify optimal processing conditions. The experimental results showed that the soaking time played a critica

The influence of SDBS, SLS, SLES, and SS as drag reducing agents on flow of Iraqi crude oil in pipelines was investigated in the present work. The effect of additive type, additive concentration, pipe diameter, solution flow rate, and the presence of elbows on the percentage of drag reduction (%Dr) and the amount of flow increases (%FI) was addressed. The maximum drag reduction was 55% obtained at 250 ppm SDBS surfactant flowing in straight pipes of 0.0508 m I.D. The dimensional analysis was used for grouping the significant quantities into dimensionless groups to reduce the number of variables. The results showed good agreement between the observed drag reduction percent values and the predicted ones with high value of the co

Nisoldipine (NSD) is a dihydropyridine class of calcium channel blockers used for hypertension treatment, it belongs to class II BCS (low solubility with high permeability), its absolute bioavailability is only 5% due to presystemic metabolism in the gut wall. It is also a substrate for a CYP3A and P-gp. Bilosomes are lipid bilayer vesicles incorporating bile salts in their walls to prevent degredation by GIT bile salts. The aim of this study is to prepare nisoldipine bilosomes as vesicular carrier and assess the effect of different formulation variables such as type of surfactant, amount of cholesterol, surfactant and sonication time on particle size, entrapment efficiency and poly dispersity index of the prepared bilos

Eco-friendly materials are increasingly used in civil engineering to support sustainable development. Conventional concrete relies heavily on Ordinary Portland Cement (OPC), the production of which contributes significantly to the carbon dioxide (CO₂) emissions. Ground Granulated Blast Furnace Slag (GGBFS) and fly ash can partially replace OPC, thereby reducing the environmental impact. This study investigates the effect of basalt fiber incorporation on the mechanical properties of geopolymer lightweight concrete. The concrete mixtures consisted of fly ash, slag, pumice aggregate, sand, and an alkaline activator prepared by combining sodium hydroxide and sodium silicate. The mix design included an activator-to-binder ratio of 0.45

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

The problem of the paper focused on the role of the learning organization in the crisis management strategy, and the extent of the actual interest in both the learning organization and the crisis management and aimed at diagnosing and analyzing that and surrounding questions. The Statistical Package for the Social Sciences (SPSS) program was used to calculate the results and the correlation coefficient between the two main variables. The methodology was descriptive and analytical. The case study was followed by a questionnaire that was distributed to a sample of 31 teachers. The paper adopted a seven-dimensional model of systemic thinking that encourages questioning, empowerment, provision of advanced technologies, and strategic lea