Glass Fiber Reinforced Polymer (GFRP) beams have gained attention due to their promising mechanical properties and potential for structural applications. Combining GFRP core and encasing materials creates a composite beam with superior mechanical properties. This paper describes the testing encased GFRP beams as composite Reinforced Concrete (RC) beams under low-velocity impact load. Theoretical analysis was used with practical results to simulate the tested beams' behavior and predict the generated energies during the impact loading. The impact response was investigated using repeated drops of 42.5 kg falling mass from various heights. An analysis was performed using accelerometer readings to calculate the generalized inertial load

Background: Traditional teaching methods of cardiopulmonary resuscitation (CPR) are not effective for most learners today. They may lead to lack of retention of survival skills and poor outcomes. Various methods are adopted to provide optimal, effective, and attractive teaching methods. Application (app)-based teaching can be used as an alternative way for learners to develop their knowledge and skills. Despite the large number of professional and nonprofessional trainee members, the high quality of CPR is still not fulfilled. Technology-based learning can prove to be an effective way to teach medical subjects such as pediatric cardiac resuscitation, which require an optimal teaching environ

Due to the advantages over other metallic materials, such as superior corrosion resistance, excellent biocompatibility, and favorable mechanical properties, titanium, its alloys and related composites, are frequently utilized in biomedical applications, particularly in orthopedics and dentistry. This work focuses on developing novel titanium-titanium diboride (TiB2; ceramic material) composites for dental implants where TiB2 additions were estimated to be 9 wt.%. In a steel mold, Ti-TiB2 composites were fabricated using a powder metallurgy technique and sintered for five hours at 1200 °C. Microstructural and chemical properties were analyzed by energy dispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM), and X-ra

A many risk challenge in (settings hospital) are multi- bacteria are antibiotic-resistant. Some type strains that ability adhesion surface-attached bio-film census. Fifteen MRSA isolates were considered as high biofilm producers Moreover all MRSA isolates; M3, M5, M7 and M11 produced biofilms but the thickest biofilm seen M7strain. The MIC values of N. sativa oil against clinical isolates of MRSA were between (0.25, 0.5, 0.75, 1.0) μg/ml While MRSAcin (50, 75, 100, 125) µg\ ml. All biofilms treated with MRSAcin and Nigella sativa developed a presence of live cells after cultured on plate agar with inhibition zone between MIC (18 – 15) and (14- 11)mm respectively.Yet, results showed that MRSA supernatant developed a inhibitory ef

In this research, experimental and numerical studies were carried out to investigate the performance of encased glass-fiber-reinforced polymer (GFRP) beams under fire. The test specimens were divided into two peer groups to be tested under the effect of ambient and elevated temperatures. The first group was statically tested to investigate the monotonic behavior of the specimens. The second group was exposed to fire loading first and then statically tested to explore the residual behavior of the burned specimens. Adding shear connectors and web stiffeners to the GFRP beam was the main parameter in this investigation. Moreover, service loads were applied to the tested beams during the fire. Utilizing shear connectors, web stiffeners,

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