Sabha University is a public university in the southern city of Sabha, Libya, with campuses in Sabha, Awbari, Murzuq, Brak, and Ghat.The beginning of Sebha University was in 1976 when the Faculty of Education had been founded as a branch of the University of Tripoli and nucleus for Sebha University later. Sebha University was founded as an independent university in 1983 and included in the beginning both of Faculties of Education and Sciences. Then the Faculties of Medicine, Agriculture, Science of Engineering, Technology, Economics and Accounting were added to Sebha University. So the number of university faculties reached to nineteen faculties located in various areas in the South.
This study synthesizes 280 peer-reviewed studies (2002–2025) into a deployment-directed “sensor-to-governance” blueprint for AI/ML-enabled hazardous-waste risk detection. Publication activity shifted from early exploratory work (2002–2014) to sustained scaling after 2017, with stable output since 2022. China and the United States dominate publication volume, while wider Global South participation strengthens the case for privacy-preserving collaboration. Evidence clusters into eight innovation streams spanning multimodal sensing, geospatial intelligence, edge analytics, knowledge-graph governance, smart sensing materials, exposure analytics, circularity intelligence, and digital-twin integration. Performance gains become decision-relevant only when uncertainty calibration, drift auditing, and cost-aware risk mapping are co-optimized. Transformative potential lies in converting fragmented detections into auditable, privacy-preserving risk intelligence linked to intervention choices and equity-relevant exposure endpoints. However, sparse ground truth, domain shift, cybersecurity barriers, and the sustainability cost of continuous monitoring remain binding constraints. Practical deployment remains limited by sparse labels and external validation. Priority gaps include leakage-resistant splits, ground truth scarcity, sensor interoperability, and cybersecurity safeguards for deployed monitoring networks.
Ni(II) and Cu(II) mixed-ligand complexes derived from 2-aminothiophenol and thiourea were synthesized by sequential ethanolic reflux and evaluated as corrosion inhibitors for steel in 0.5M HCl. The products were characterized using elemental analysis, molar conductivity, FT-IR, UV–Vis spectroscopy, mass spectrometry, Cu(II) ESR, thermogravimetric analysis, and density functional theory calculations. The recalculated elemental composition, spectroscopic data, reported qualitative magnetic behavior, and DFT coordination-core models are collectively consistent with four-coordinate structures, with square-planar environments proposed for both metal centers. The Ni(II) product is reported as diamagnetic, consistent with a low-spin d⁸ configuration, whereas Cu(II) is paramagnetic; for Cu(II), g∥ = 2.22 and g⊥ = 2.07 are consistent with a predominantly d(x²−y²) ground state. Because no single-crystal X-ray structure and no quantitative magnetic-susceptibility/µeff dataset are available, the geometries are treated as spectroscopically supported structural models rather than definitive crystallographic assignments. Gravimetric measurements at 298 and 308K showed concentration-dependent inhibition, with the Cu(II) complex reaching a maximum reported inhibition efficiency of 97.8%. Langmuir analysis of the reported surface-coverage data gave R² values of 0.9914–0.9988 and ΔG°ads values of approximately −23.4 to −27.3kJmol⁻¹, indicating spontaneous adsorption with mixed physical/electrostatic and more specific interfacial contributions. DFT-based FMO, MEP, NCI-RDG, ELF, and LOL analyses describe differences in the intrinsic electronic structures of the complexes, while no direct Fe-surface adsorption is inferred in the absence of explicit surface calculations.
Pea (Pisum sativum L.) is an important legume crop due to its high nutritional value and its role in improving soil fertility through biological nitrogen fixation. This experiment was conducted to evaluate the effect of different combinations of nano and mineral NPK fertilizers on growth and yield of pea under dry conditions in Al-Ruqaybah, Libya, during the 2025–2026 growing season. The experiment was arranged in a Randomized Complete Block Design with three replicates and included six treatments, control, 100% nano NPK, 100% mineral NPK, 50% nano NPK + 50% mineral NPK, 25% nano NPK + 75% mineral NPK and 75% nano NPK + 25% mineral NPK. The results showed that the highest seed yield (3.72 kg/plot), biological yield (24.42 kg/plot), and straw yield (20.70 kg/plot) were obtained with the 75% nano NPK + 25% mineral NPK treatment. It also produced the highest 100-seed weight (35 g), lowest values occurred in the control. Although the harvest index was higher in the control, fertilization significantly increased overall biomass and yield components. These findings suggest that partial substitution of mineral NPK fertilizers with nano NPK is recommended to improve nutrient use efficiency and enhance pea productivity under arid conditions. Moreover, optimizing the ratio between nano and mineral fertilizers is strongly recommended to achieve maximum yield performance in pea cultivation under dry environmental conditions. Keywords: Pea, Nano NPK, Mineral NPK, Yield, Arid conditions
Optimization of Energy Flow Management (EFM) has become a critical area of research and development in recent years due to the increasing demand for efficient and sustainable energy use. To meet the emerging challenges in energy flow management, there is a need for methods to improve energy management and reliability. One such method is Vehicle-to-Grid (V2G) communication. Given the importance of this issue, this study investigated a hybrid grid-connected system to provide energy to a residential building in Sebha, Libya. The system was designed using $380920 \mathrm{kWh} / \mathrm{y}$ of Photovoltaics (PVs), 169650 $\mathrm{kWh} / \mathrm{y}$ of Wind Turbines (WTs), $42903 \mathrm{kWh} / \mathrm{y}$ of Battery storage (BT), and $28250.39 \mathrm{kWh} / \mathrm{y}$ of Electric Vehicles (EVs). The total investment in this system amounted to ${\$}$ 791,732, and the system’s combined Renewable Energy Sources (RES) generate 621723.39 $\mathrm{kWh} / \mathrm{y}$, which meets about 64.88% of the energy needs. Moreover, there is a surplus energy of ${\$}$17715.96 $\mathrm{kWh} / \mathrm{y}$, indicating that there is no deficit in meeting energy demand. The study aimed to optimize the system components to reduce the Levelized Cost of Electricity (LCOE), reduce the risk of Loss of Power Supply Probability (LPSP), and increase the Renewable Energy Fraction (REF). The energy management strategies were implemented through Search Algorithms (SA) such as the Cuckoo Search Algorithm (CSA) and Particle Swarm Optimization (PSO) using Matrix Laboratory (MATLAB) for system configuration. The results showed the optimal performance of cuckoo search algorithm due to the algorithm’s speed in sizing the system and its lack of mathematical complexity, with values as follows: 0.0914 ${\$}$/kWh, 0.1245 %, and 0.6488% for cost of electricity, loss of power supply probability, and renewable energy fraction, respectively.
The study aimed to determine the level of psychological quality of life among students of faculties of physical education, assess their sports performance in physical, skill, and cognitive aspects, and identify the differences in psychological quality of life according to demographic variables such as gender, academic level, age, type of practice, and years of practice. The study adopted the descriptive method. The study population consisted of students from faculties and departments of Physical Education and Sports Sciences at Libyan universities (Sabratha University – Faculty of Physical Education Al-Jamil, Department of Physical Education, Faculty of Arts and Education – Sabratha, and Al-Zawiya University) during the academic year 2024/2025. A sample of 100 male and female students from different academic levels in the faculties of physical education at Libyan universities was selected using the stratified random sampling method to ensure balanced representation of gender (male–female) and academic levels (first–second–third–fourth year. The researcher used the Psychological Well-Being Scale developed by Ryff (1995) and adapted into Arabic by Ibrahim (2019), which includes six main dimensions and consists of 35 items in a five-point Likert scale format. A Sports Performance Scale consisting of 30 items distributed across three dimensions was also used. Its validity was confirmed by a panel of experts in sports psychology, and its reliability coefficient was 0.87. The results revealed that overall psychological quality of life was positively and significantly correlated with both motor skills and academic achievement, but showed no significant relationship with physical fitness or overall sports performance (weak and non-significant negative correlations). Significant differences were found in favor of males in autonomy and motor skills, while no significant differences were observed in the remaining dimensions or overall sports performance. Significant differences were also found across all dimensions of psychological quality of life, as well as in fitness, skills, and academic achievement, according to age variable. In addition, significant differences were observed depending on years of practice, in all dimensions of psychological quality of life, motor skills, and overall sports performance.
