Saad Aldoihi

Ingénierie Système Dirigée par L'utilisabilité des Produits: La convivialité est vitale pour chaque système fonctionnel, en particulier lorsque le système fonctionnel est étroitement lié à la sécurité et aux besoins des personnes. Le diagnostic radiologique est un outil essentiel pour identifier les maladies et les tumeurs. Par conséquent, son utilité est incontestable malgré le risque de rayonnement. En général, les appareils médicaux suivent une tendance, tout comme les produits électroniques grand public, où ils deviennent plus compliqués et les performances doublent tous les deux ans. Par conséquent, les dispositifs médicaux nécessitent une équipe multidisciplinaire pour la conception et le développement. Par conséquent, avec autant de commandes et de systèmes à gérer, les charges cognitives des utilisateurs sont mises au défi d'exécuter efficacement les commandes. En fait, les pratiques actuelles de radiologie sont confrontées à une pression extrême sur les ressources disponibles et aux exigences multidimensionnelles. Les techniciens sont au centre d'un effort constant pour une productivité et une optimisation optimales avec un minimum de ressources possibles. Dans les dispositifs médicaux, la charge de travail cognitive jouera davantage un rôle car elle utilise une technologie de pointe, est extrêmement coûteuse et nécessite une grande spécialisation pour fonctionner. Ainsi, des méthodes efficaces et économiques sont nécessaires pour évaluer l'expérience utilisateur. L'évaluation heuristique est une méthode efficace pour identifier les principaux problèmes de convivialité et mettre en évidence les problèmes rencontrés par les utilisateurs. L'évaluation heuristique a été adaptée pour identifier l'utilité d'une tomodensitométrie sur les utilisateurs saoudiens et pour déterminer si l'utilisation de la tomodensitométrie peut entraîner un effort physique et mental. Par la suite, une enquête approfondie a été menée pour identifier les principaux facteurs contribuant à l'utilisation opérationnelle du scanner

This research introduces an innovative self-sensing hybrid composite material, skillfully integrating Kevlar and carbon fibers within a cohesive matrix. The study’s primary objective is to substantially improve the mechanical strength and decrease the weight of fiber-reinforced polymer (FRP) composites. By strategically employing carbon fibers as conductive channels and Kevlar fibers as an insulating barrier, this study innovatively embeds a capacitive sensing functionality within the composite matrix. This sensor is intricately designed for the real-time, continuous assessment of strain and the preemptive detection of structural integrity issues. The electromechanical attributes of the newly developed composite are meticulously evaluated using sophisticated scanning electron microscopy (SEM) and tensile testing methodologies. These evaluations disclose the material’s outstanding ability to detect preliminary damage. Results reveal a direct relationship between the composite’s axial stress responses and its sensitivity coefficient, quantified at an approximate value of 1.7. Additionally, the material demonstrates a consistent linear behavior under lateral strains until reaching its failure point, highlighted by remarkably high gauge factors of around -19. The integrated capacitive sensor exhibits exceptional sensitivity, providing invaluable insights into the material’s stress-strain dynamics and substantially enhancing its applicability in real-time structural health monitoring (SHM) settings.

The demand for fiber-reinforced polymers (FRPs) has significantly increased in various industries due to their attributes, including low weight, high strength, corrosion resistance, and cost-efficiency. Nevertheless, FRPs, such as glass and Kevlar fiber composites, exhibit anisotropic properties and relatively low interlaminar strength, rendering them susceptible to undetected damage. The integration of real-time damage detection processes can effectively mitigate this issue. This paper introduces a novel method for fabricating embedded capacitive sensors within FRPs using a coating technique. The study encompasses two types of fibers, namely glass and Kevlar fiber/epoxy composites. The physical vapor deposition (PVD) technique is employed to coat bundle fibers with conductive material, thus creating embedded electrodes. The results demonstrate the uniform distribution of nanoparticles of gold (Au) along the fibers using PVD, resulting in a favorable resistance of approximately ≈100 Ω. Two sensor configurations are explored: axial and lateral embedding of the coated yarn (electrodes) to investigate the influence of load direction on the coating yarn. Axial-sensor configuration specimens undergo tensile testing, showcasing a linear response to axial loads with average sensitivities of 1 for glass and 1.5 for Kevlar fiber/epoxy composites. Additionally, onset damage is detected in both types of fiber composites, occurring before final fracture, with average stress at the turning point measuring 208 MPa for glass and 144 MPa for Kevlar. The lateral-sensor configuration for glass fiber-reinforced polymer (GFRP) exhibits good linearity towards strain until failure, with average gauge factors of 0.25 and −2.44 in the x and y axes, respectively.

This review thoroughly investigates the mechanical recycling of carbon fiber-reinforced polymer composites (CFRPCs), a critical area for sustainable material management. With CFRPC widely used in high-performance areas like aerospace, transportation, and energy, developing effective recycling methods is essential for tackling environmental and economic issues. Mechanical recycling stands out for its low energy consumption and minimal environmental impact. This paper reviews current mechanical recycling techniques, highlighting their benefits in terms of energy efficiency and material recovery, but also points out their challenges, such as the degradation of mechanical properties due to fiber damage and difficulties in achieving strong interfacial adhesion in recycled composites. A novel part of this review is the use of finite element analysis (FEA) to predict the behavior of recycled CFRPCs, showing the potential of recycled fibers to preserve structural integrity and performance. This review also emphasizes the need for more research to develop standardized mechanical recycling protocols for CFRPCs that enhance material properties, optimize recycling processes, and assess environmental impacts thoroughly. By combining experimental and numerical studies, this review identifies knowledge gaps and suggests future research directions. It aims to advance the development of sustainable, efficient, and economically viable CFRPC recycling methods. The insights from this review could significantly benefit the circular economy by reducing waste and enabling the reuse of valuable carbon fibers in new composite materials.

This paper discusses the use of Kevlar-carbon hybrid composites in the aerospace industry for structural and non-structural components. By combining the high tensile strength and impact resistance of Kevlar fibers with the high stiffness and dimensional stability of carbon fibers, a material with enhanced mechanical properties can be achieved. However, the combination of two materials with different properties can cause delamination between layers, making real-time monitoring of structural integrity important. This study investigates the feasibility of embedding sensors into Kevlar-carbon hybrid composites and presents the basic characterization using a mechanical tensile test. It been shown the embedded sensor has a linear response toward the axial stress and the damage can be detected at the turning point where the capacitance begins to decrease.

System modeling is essential to the growth and comprehension of artificial intelligence (AI). It offers a way to describe, examine, and improve sophisticated AI systems. The significance of system modeling in AI is explored in this paper, along with examples of its use in robotics, machine learning, and natural language processing. We also discuss the difficulties and possible directions in system modeling for AI, highlighting its potential to further improve the robustness and capabilities of AI systems.

Nowadays, the demand for glass fibre-reinforced polymers (GFRPs) has increased in the industry owing to their low weight, high strength, corrosion resistance and low cost compared with other fibre-reinforced polymer composites. However, GFRP is anisotropic material with low interlaminar strength where the damage can occur without warning. Integrating a real-time damage detection process can mitigate this problem. Therefore, this paper presents the initial fabrication of an embedded capacitive sensor into the GFRP by using conductive electrodes inbetween its layers. To form the sensing electrodes, glass fibre yarns were coated with conductive material and braided into the fibregalss woven fabric. Two coating methods were considered to form embedded electrodes in this work which include aerosol spray coatings that were carbon based and gold-based physical vapour deposition, (PVD). It has been shown that spray coating has a weak bond and the carbon particles disperse during the molding process. In the PVD technique the nanoparticle (Au) distributed uniformly along the fibres and has a good resistance (≈100Ω). The capacitive sensor based on gold coating was exaimined using a three point bending test which demonstrate linear response toward the flexural load with a sensitivity of 25.1 fF/N.

Owing to disparities in the intensity of the breakouts, state and federal regulations, accessible means, cultural elements, and social consciousness, the global reaction to the COVID-19 pandemic has been varied. The COVID-19 pandemic, on the other hand, has had an impact on all parts of society, notably efforts to combat antimicrobial resistance (AMR). The pandemic has highlighted that a greater burden on medical infrastructure can contribute to higher, often unnecessary antibiotic usage and a de-prioritization of antimicrobial stewardship and surveillance (AMS). The focus of this research is to see if there is a growth in antibiotic resistance during the covid-19 pandemic in the King Salman Hospital in Riyadh, Saudi Arabia, and to investigate the subcomponent that leads to antibiotic resistance. This is a comprehensive review of patients hospitalized at the King Salman Hospital in Riyadh, Saudi Arabia, who were admitted to the Intensive Care Unit (ICU) during the first wave of the covid-19 pandemic, which occurred between March and August 2020. An analysis of the case dataset was performed to determine the rise in antibiotic resistance and relate it to resistant cases before the pandemic (September 2019 to February 2020). Before and throughout the pandemic, fifteen kinds of bacteria were found, with K. pneumonia being the most prevalent bacteria (49; 30.6%), and Ac. Baum/haem being the most removable bacteria during the pandemic (74; 37.3%). Cephalosporin antibiotics, in notably cefotaxime and ceftazidime (100%), cefazolin (96.3%), ceftriaxone (96%), cefuroxime and ceftazidime (95%), cefotaxime ((94.7%). These antibiotics also had the same amount of resistance during the pandemic. In pre-covid-19 and during covid-19, these findings were congruent with the penicillin antibiotics class, ampicillin, and piperacillin (96.3% and 92.1%), accordingly. It is presently uncertain if COVID-19 patients would develop new or growing antibiotic resistance in locations with low historical prevalence, but this should be investigated in retrospective and future clinical and microbiology research.

Usability is essential to every functioning system, especially when the system is intertwined with people’s safety and needs. Radiological diagnosis is a critical tool to identify diseases and tumors. Therefore, its usefulness is beyond question, despite its radiation risk. Many operational difficulties related to computed tomography (CT) scanning come in the form of nontechnical requirements. Hence, there is a need to examine the technical requirements that suit its context of use. This paper examines the barriers and enablers of CT scan usability within its operational context of use. In total, 11 CT scan technicians and one radiology doctor were interviewed to constitute the primary data of this paper. The study reveals that system engineering is critical to improving the current condition. The results show cases wherein the CT scan table jammed due to the weight of the patient.