This paper presents a novel hybrid maximum power point tracking strategy for photovoltaic systems operating under stochastic weather conditions and load variations with partial measurements. The proposed approach integrates three complementary computational methods: Newton-Raphson-based nonlinear optimization for dynamic adaptive reference generation, model predictive control for computing the optimal duty cycle, and an extended Kalman filter to estimate all system states, including photovoltaic voltage, inductor current, and output voltage. Uncertainties in weather conditions and load are modeled as stochastic variations in solar irradiance, temperature, and equivalent load resistance, including abrupt changes due to load switching and cloud transients. These effects are represented using an improved Ornstein-Uhlenbeck jump-diffusion process, capturing both continuous fluctuations and sudden disturbances. Simulation results demonstrate high power extraction, with a tracking efficiency of 99.98%. A root mean square error of 0.0463 V is obtained for tracking the maximum power point voltage by comparing the reference voltage with the actual photovoltaic voltage, and 0.1198 V for voltage estimation by comparing the true and estimated values. Comparative results further show that the proposed approach outperforms classical methods in terms of tracking accuracy, convergence speed, and reduction of steady-state oscillations. Furthermore, statistical evaluation via Monte Carlo sampling confirms the robustness of the proposed strategy under varying operating conditions.
The widespread discharge of synthetic dyes into aquatic environments poses a significant threat to both ecological balance and human well-being, underscoring the urgent need for effective and sustainable treatment approaches. A composite material combining CoAl2O4 and CoS was prepared to remove Brilliant Green (BG). The new CoAl2O4@CoS photocatalyst was obtained through a hydrothermal synthesis. The structural and physicochemical properties of these materials were examined by XRD, FTIR, XPS, SEM-EDS, and UV–Vis DRS, which confirmed their crystalline phases, surface composition, oxidation states, and optical characteristics. Photocatalytic tests under simulated sunlight demonstrated that the 1 g/L of CoAl2O4@CoS composite could completely degrade a 20 mg/L BG solution within 70 min. Its apparent rate constant (k = 0.0434 min−1) was significantly higher than those of CoAl2O4 (k = 0.0059 min−1) and CoS (k = 0.0091 min−1). Radical scavenging experiments indicated that hydroxyl radicals (•OH) and superoxide anions (O₂•-) played dominant roles in the degradation reaction. The composite retained its efficiency after five consecutive cycles, confirming its stability and reusability. Density functional theory (DFT) calculations supported the experimental results, showing favorable band positions and orbital hybridization responsible for the enhanced sunlight response. Overall, these findings demonstrate that CoAl2O4@CoS is a promising photocatalyst for solar-assisted wastewater purification.
Accurate extraction of photovoltaic model parameters is essential for optimal system design and performance prediction. This study introduces a hybrid methodology combining analytical techniques with the Blood-Sucking Leech Optimizer to estimate the five parameters of the single-diode model and the seven parameters of the double-diode model. The analytical stage provides reliable initial estimates for the photo-generated current, reverse saturation current, and shunt resistance using three characteristic points on the I–V curve, while the Blood-Sucking Leech Optimizer optimizes the remaining nonlinear parameters including the ideality factor and series resistance. By decoupling the analytical and optimization phases, the hybrid approach reduces the dimensionality of the search space, enhancing convergence speed and numerical stability. Experimental validation was performed under a wide range of irradiance and temperature conditions using the RTC France solar cell, Photowatt PWP201, PVM-752 GaAs, and STP6-120/36 PV module, demonstrating robust performance, low root mean square error values, and high fidelity in reproducing experimental I–V characteristics. The proposed Analytical Blood-Sucking Leech Optimizer achieved superior accuracy with minimum RMSE values of 7.81 × 10−4, 2.15 × 10−3, 3.15 × 10−4, and 1.53 × 10−2 respectively, outperforming conventional Blood-Sucking Leech Optimizer, Genetic Algorithm based on non-uniform mutation, Golf Optimization Algorithm, and Ant Lion Optimizer. Statistical analysis confirmed improved robustness, faster convergence, and lower computational cost. These results demonstrate that Analytical Blood-Sucking Leech Optimizer provides an accurate, stable, and practical solution for photovoltaic parameter extraction and photovoltaic system modeling.
Integrated Pest and Pollinator Management (IPPM) represents a transformative agroecological framework that reconciles pest suppression with pollinator conservation. This global scoping review maps the evolution, thematic structure, and knowledge gaps in IPPM research. Using the PRISMA-ScR approach, 185 peer-reviewed studies (1959–February 2026) retrieved from Scopus were analyzed and complemented by keyword co-occurrence mapping. Four major thematic clusters emerged: (i) IPPM tools and strategies (physical, biological, chemical and habitat-based control), (ii) pollinator-mediated services and disservices, (iii) trade-offs in IPPM and pollinators health, and (iv) socio-ecological knowledge for IPPM adoption. The field remains nascent: no empirical trials have yet tested IPPM as a unified on-farm framework. Research is geographically skewed—84 of studies originated in the United States—while Africa, despite its rapid growth in berry exports, remains unrepresented. Honey bees dominate experimental designs (16 records on pollinators’ health), whereas wild and solitary bees, often more efficient berry pollinators, are rarely considered. Evidence indicates that pollinator-friendly pest management can simultaneously enhance crop health indicators—including fruit set, uniformity, and marketability—but adoption is hindered by inconsistent field efficacy, economic trade-offs, and limited policy support. Future IPPM research should expand to underrepresented regions, diversify pollinator taxa, integrate landscape and climate dimensions, and quantify socio-economic returns. Advancing IPPM is pivotal to achieving multiple UN Sustainable Development Goals—ensuring food security (SDG 2), sustainable production (SDG 12), climate resilience (SDG 13), and biodiversity conservation (SDG 15).
This study examined the potential of stress-tolerant phosphate-solubilizing bacteria (PSB) to enhance drought and salinity tolerance of Vicia faba (Faba bean) and Pisum sativum (Pea). The assessment focused on soil parameters, morphological, physiological, and biochemical responses of the host plants, to clarify the mechanisms underlying PSB-mediated stress reduction under extreme conditions. After screening, DNA sequence analysis of 16 S rRNA genes of five isolates revealed that they belong to Pseudomonas spp and Bacillus spp. The five PSB isolates that exhibit plant growth-promoting properties were used as a consortium in three different treatments BC1 (3 isolates), BC2 (3 isolates), and BC3 (5 isolates). The results show that, under drought and salt stress conditions, grain number per plant increased in BC3 inoculated seeds, followed by BC1 for both tested crops, compared to untreated plants. Moreover, BC3 is the best performing, showing the highest seed protein content (83.22
