The advancement of VR technology through the increase in its processing power and decrease in its cost and form factor induced the research and market interest away from the gaming industry and towards education and training. In this paper, we argue and present evidence from vast research that VR is an excellent tool in engineering education. Through our review, we deduced that VR has positive cognitive and pedagogical benefits in engineering education, which ultimately improves the students’ understanding of the subjects, performance and grades, and education experience. In addition, the benefits extend to the university/institution in terms of reduced liability, infrastructure, and cost through the use of VR as a replacement to physical laboratories. There are added benefits of equal educational experience for the students with special needs as well as distance learning students who have no access to physical labs. Furthermore, recent reviews identified that VR applications for education currently lack learning theories and objectives integration in their design. Hence, we have selected the constructivist and variation learning theories as they are currently successfully implemented in engineering education, and strong evidence shows suitability of implementation in VR for education.

This paper presents a novel soft-switching boost DC-DC converter, which uses an edge-resonant switch capacitor based on the pulse width modulation PWM technique. These converters have high gain voltage due to coupled inductors, which work as a transformer, while the boost converter works as a resonant inductor. Upon turning on, the studied soft switching circuit works at zero-current soft switching (ZCS), and upon turning off, it works at zero-voltage soft switching (ZVS) while using active semiconductor switches. High efficiency and low losses are obtained while using soft switching and auxiliary edge resonance to get a high step-up voltage ratio. A prototype model is implemented in the Power Electronics Laboratory, Assiut University, Egypt. Seventy-two-panel PV modules of 250 W each were used to simulate and execute the setup to examine the proposed boost converter.

This study presents an AI-driven energy management system (EMS) for a hybrid electric flying car, integrating multiple power sources—including solid-state batteries, Li-ion batteries, fuel cells, solar panels, and wind turbines—to optimize power distribution across various flight phases. The proposed EMS dynamically adjusts power allocation during takeoff, cruise, landing, and ground operations, ensuring optimal energy utilization while minimizing losses. A MATLAB-based simulation framework is developed to evaluate key performance metrics, including power demand, state of charge (SOC), system efficiency, and energy recovery through regenerative braking. The findings show that by optimizing renewable energy collecting, minimizing battery depletion, and dynamically controlling power sources, AI-based predictive control dramatically improves energy efficiency. While carbon footprint assessment emphasizes the environmental advantages of using renewable energy sources, SOC analysis demonstrates that regenerative braking prolongs battery life and lowers overall energy use. AI-optimized energy distribution also lowers overall operating costs while increasing reliability, according to life-cycle cost assessment (LCA), which assesses the economic sustainability of important components. Sensitivity analysis under sensor noise and environmental disturbances further validates system robustness, demonstrating that efficiency remains above 84% even under adverse conditions. These findings suggest that AI-enhanced hybrid propulsion can significantly improve the sustainability, economic feasibility, and real-world performance of future flying car systems, paving the way for intelligent, low-emission aerial transportation.

Date palm seeds (DPS), a plentiful agricultural waste in Arab countries, present a promising, sustainable, economical reinforcement polymer composite material. This review addresses critical gaps in understanding innovative procedures to improve the mechanical and sustainability properties of DPS-reinforced polymer composites. While the promise of DPS is highlighted by recent research, it frequently lacks a comprehensive analysis of the long-term durability, optimum processing conditions, and environmental influence. In this review, the influence of filler loading, particle size, nanomaterial additions, treatment conditions, and polymer matrix selection on composite mechanical properties is investigated particular attention is given to innovative strategies containing Ultrasonic-assisted alkaline pretreatment followed by silane coupling agents addition, this technique enhances composite adhesion by combining a chemical modification and a more effective alkaline treatment, which leads to significant enhancements in tensile strength and biodegradability. Furthermore, the mechanical characterization and potential applications of these materials were investigated, including automotive, construction, packaging, and biomedical industries, and strategies for optimizing recycling processes to minimize environmental impact. By uniting recent advancements and recognizing main knowledge gaps, this review will provide a pathway for future research focused on maximizing the efficient utilization of DPS in advanced polymer composites, thus contributing to eliminating reliance on synthetic fibers and endorsing a circular economy.