Sci

This study examines the functional degradation of crystalline silicon photovoltaic cells after 17 years of field exposure in the Adrar Desert, Algeria. Harsh thermal, radiative, and mechanical conditions accelerate aging, affecting electrical performance and structural stability. Monocrystalline silicon cells were extracted and analyzed by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Raman spectroscopy, electrical resistivity measurements, and vibrating sample magnetometry (VSM). SEM revealed microcracks, delamination, and corrosion products. EDS showed Ag, Si, O, and C signals, while Raman indicated silicon features and signatures consistent with encapsulant (EVA) degradation. The temperature-dependent resistivity displayed a dual behavior with a minimum near ~72 °C, above which resistivity increased, consistent with a transition in the dominant transport mechanisms. VSM measurements showed an overall diamagnetic response with a weak hysteresis loop suggestive of defect-related contributions. The observed aging is primarily associated with oxidation, metal migration, and encapsulant degradation. These findings motivate more robust materials and interfaces for desert climates, alongside improved thermal management and active monitoring.

Background: CrossFit^® is a high-intensity functional training modality with increasing popularity, yet limited evidence describes the general profile of its practitioners. Objective: To characterize CrossFit^® athletes based on their training habits, injury prevalence, and nutritional supplement use, with specific consideration given to sex and age. Methods: An online questionnaire was completed by 358 practitioners (182 women; mean age 35.6 ± 9.1 years) from various Spanish regions. Descriptive and comparative analyses (χ² and ANOVA; p < 0.05) were conducted for training patterns, injury history, and supplement consumption. Results: Over half of the sample had practiced CrossFit^® for more than three years, typically training 3–4 days per week in one-hour sessions. Participants primarily reported social and health-related motivations and identified as non-competitive. Overall, 42.2% experienced at least one CrossFit^®-related injury, most frequently affecting the shoulder (15.6%) and lumbar spine (10.1%), largely attributed to repetitive overload. Supplement use was widespread (81.8%), with creatine (60.3%) and protein (49.4%) being the most commonly consumed. Conclusions: CrossFit^® practitioners train consistently, value the social environment, and show an injury pattern similar to that of other strength-based disciplines. Supplement consumption is highly prevalent across groups. Coaches and health professionals should prioritize injury-prevention strategies, promote safe load progression, and guide responsible supplement use.

This study aims to enhance the security of unmanned aerial vehicles (UAVs) within the Internet of Drones (IoD) ecosystem by detecting and preventing Denial-of-Service (DoS) attacks. We introduce DroneDefender, a web-based intrusion detection system (IDS) that employs machine learning (ML) techniques to identify anomalous network traffic patterns associated with DoS attacks. The system is evaluated using the CIC-IDS 2018 dataset and utilizes the Random Forest algorithm, optimized with the SMOTEENN technique to tackle dataset imbalance. Our results demonstrate that DroneDefender significantly outperforms traditional IDS solutions, achieving an impressive detection accuracy of 99.93%. Key improvements include reduced latency, enhanced scalability, and a user-friendly graphical interface for network administrators. The innovative aspect of this research lies in the development of an ML-driven, web-based IDS specifically designed for IoD environments. This system provides a reliable, adaptable, and highly accurate method for safeguarding drone operations against evolving cyber threats, thereby bolstering the security and resilience of UAV applications in critical sectors such as emergency services, delivery, and surveillance.