Zarqa University

Dual-band millimeter-wave (mmWave) heterogeneous networks (HetNets) operating at 28 and 73 GHz offer high capacity, but interference-coupled SINR, subchannel reuse, and per-base-station power constraints make transmit-power allocation nonconvex. This paper proposes Weight-Driven Particle Swarm Optimization (WD-PSO), which uses a recursively decaying weight to jointly regulate particle momentum and attraction strength. WD-PSO is evaluated against five PSO variants, Equal Power Allocation, Random Feasible Search, and Differential Evolution (DE) over 30 paired Monte Carlo realizations. Results show that WD-PSO achieves final throughput comparable to the strongest PSO variants while consistently outperforming the additional baselines. Across K=5 and K=10, WD-PSO improves throughput over DE by 4.48–6.10%. Its mean throughput varies by less than 0.8% across a 5000-fold range of the initialization coefficient α. Scalability analysis further shows near-linear runtime growth with population size and iteration count, supporting a practical balance between solution quality, robustness, and computational cost.

BACKGROUND AND OBJECTIVES: The global move toward sustainability, supported by national plans like Saudi Vision 2030, requires the combination of new technologies with environmental planning. The analysis of big data has the potential to enhance environmental performance; however, the precise methods by which it contributes to establishing a green competitive advantage remain largely unclear. This is especially true for the renewable energy sector during changing market conditions. The study objectives were to explain the connections between big data analytics and green competitive advantage. This study investigates the direct impact of big data analytics on achieving a green competitive advantage, while also exploring the contributions of green supply chain management and green human resource management in this relationship. It also tests how market turbulence changes these relationships within Saudi Arabia's renewable energy industry. METHODS: A quantitative method was used. Data were collected via questionnaire given to 420 employees working in Saudi renewable energy companies. The conceptual framework for the study is established utilizing the resource-based view in conjunction with capabilities theory. Partial least squares structural equation modeling was applied to assess direct, mediating, and moderating effects simultaneously. FINDING: The analysis indicate that big data analytics have a substantial positive direct influence on competitive green advantage, while also enhancing both green human resource management and green supply chain management. Green human resource management significantly mediates the relationship between big data analytics and green competitive advantage, whereas green supply chain management exhibits a negative mediating effect, suggesting implementation-related challenges in the studied context. Furthermore, market turbulence strengthens the relationship between green supply chain management and green competitive advantage, while weakening the relationship between green human resource management and green competitive advantage. CONCLUSION: The findings confirm that big data analytics represents a critical organizational capability for achieving a competitive green advantage; however, its effectiveness depends on the nature of internal green management practices and external market conditions. The study highlights the importance of aligning digital investments with robust green human resource strategies while carefully reassessing green supply chain initiatives. These insights provide practical guidance for managers and policymakers seeking to leverage data-driven technologies to support sustainability objectives in line with Saudi Vision 2030.

Accurate estimation of energy requirements for biomass pyrolysis is essential for designing cost‑efficient and sustainable thermochemical conversion systems. This study addresses the challenge of predicting pyrolysis energy requirement by integrating comprehensive feedstock compositional data with process operational parameters, analyzed through a machine learning (ML)‑based framework. A curated dataset of 633 experimentally validated records from peer‑reviewed publications was compiled, encompassing elemental composition (C, H, N, S, O, and ash content), biochemical composition (protein, lipid, and carbohydrate), and operational parameters. Models were trained and validated using a 9:1 split with five-fold cross‑validation to ensure robust generalization. Eight algorithms, including decision tree, adaptive boosting (AdaBoost), random forest, K‑nearest neighbors (KNN), ensemble learning, convolutional neural network (CNN), support vector regression (SVR), and multilayer perceptron (MLP), were optimized via hyperparameter tuning and evaluated through the coefficient of determination (R2), mean squared error (EMS), and average absolute relative error (EAAR). Results demonstrated that AdaBoost and random forest achieved superior generalization on unseen data (test R2≥0.893 and test EAAR≤7.18

Cisplatin is a potent chemotherapeutic agent that causes renal injury. d-limonene, a natural monoterpene, may confer renal protection. This study evaluated whether d-limonene pretreatment could mitigate cisplatin-induced nephrotoxicity. Wistar rats were divided into groups receiving vehicle, d-limonene (50 or 100 mg/kg), cisplatin (7.5 mg/kg, i.p.), or cisplatin plus d-limonene (50 or 100 mg/kg). d-limonene was administered orally once daily for 14 days, starting 7 days before and continuing 7 days after the single cisplatin injection on day 7, thus acting as both pre- and post-treatment (peri-cisplatin regimen). Cisplatin induced nephrotoxicity, shown by elevated serum urea, creatinine, uric acid, cystatin C, urine KIM-1, albumin/creatinine, and renal oxidative stress markers. It downregulated Nrf2/HO-1 and upregulated NADPH oxidase and NLRP3 inflammasome, with tubular damage histologically. d-limonene pretreatment dose-dependently improved renal dysfunction, oxidative stress, and suppressed NADPH oxidase, NLRP3, and IL-1β expression. High-dose d-limonene normalized most parameters, improved kidney histology, and renal somatic index. d-limonene mitigates cisplatin-induced nephrotoxicity with associated antioxidant and anti-inflammatory effects. The protective effect of d-limonene on cisplatin-induced nephrotoxicity through targeting oxidative stress (Nrf-2/HO-1 pathway) and inflammation (NLRP3 inflammasome)

In this paper, we introduce and investigate the notion of soft co-compact spaces as a natural generalization of classical co-compact spaces within the framework of soft topology. The aim of this new concept is to provide researchers with a flexible structure through which advanced topological properties-such as generalized compactness, separation axioms, and continuity-can be studied in the context of soft sets. We begin by presenting the formal definition of a soft co-compact space and demonstrating how it extends the classical idea of co-compactness to parameterized environments. Several fundamental properties of this new class are established, and we show that soft co-compact spaces form a distinct category that is not reducible to previously known soft topological constructs. We further explore the interaction between soft co-compactness and various soft separation axioms, thereby revealing new characterizations and criteria that govern their relationships. Motivated by these findings, we introduce associated soft operators-such as soft co-compact interior and soft co-compact closure-and describe their behaviors and structural roles. The developed framework opens a new approach in soft topology, allowing for refined analysis of soft continuity, decomposition theorems, and transitions between different soft topological settings.