Desertification poses a significant environmental challenge in arid and semi-arid regions, driven by a complex interaction of natural and human-induced factors that accelerate land degradation, jeopardize biodiversity, and impede socio-economic advancement. The Gabès region in southeastern Tunisia exemplifies this vulnerability, characterized by an arid climate with low annual precipitation ( 200 mm/year) and high potential evapotranspiration ( 2300 mm/year). The area is marked by extensive irrigated agriculture and intensive groundwater extraction, which exacerbates desertification processes. In this study, we applied a modified Mediterranean Desertification and Land Use (MEDALUS) model integrated with Geographical Information Systems (GIS) to assess the environmental sensitivity to desertification within the region. The analysis focused on four key indicators: Soil Quality Index (SQI), Groundwater Quality Index (GWQI), Vegetation Quality Index (VQI), and Climate Quality Index (CQI). We collected and analyzed soil and groundwater samples for various physicochemical properties, including electrical conductivity, pH, organic matter content, sodium adsorption ratio, and salinity. Additionally, remote sensing data and land cover maps were utilized to evaluate vegetation parameters. Each index was weighted and synthesized to derive the Desertification Sensitivity Index (DSI), which enabled a spatial representation of desertification risk across the landscape. Findings revealed that approximately 44
Hyperlipidemia is a major global risk factor for cardiovascular disease, underscoring the need for safe, multi-target preventive strategies. In this study, two novel dietary supplements were developed by blending freeze-dried aqueous extracts of chamomile (CDS) or thyme (TDS) with linseed oil (1:1, w/w) and evaluated for their phytochemical composition, antioxidant capacity, and hypolipidemic efficacy. Total phenolics, total flavonoids, fatty acid composition, volatile constituents, and individual phenolic profiles were characterized, while antioxidant activity was assessed using DPPH & centerdot; radical-scavenging and FRAP assays. Hypolipidemic activity was investigated in a Triton X-100-induced hyperlipidemia rat model through an assessment of plasma lipid parameters, oxidative stress and inflammatory markers, and liver and kidney function indices, supported by hepatic histopathology. Molecular docking was performed to explore the interactions of major bioactive compounds with AMP-activated protein kinase (AMPK) and HMG-CoA reductase. Both CDS and TDS exhibited strong antioxidant activity and high polyphenol content, with kaempferol and chlorogenic acid identified as the predominant phenolics in CDS and TDS, respectively. beta-Farnesene and carvacrol were the main volatile constituents. In vivo, both formulations significantly reduced total cholesterol, triglycerides, LDL-C, lipid peroxidation markers, and TNF-alpha, while increasing HDL-C and improving cardiac risk indices, with more pronounced effects observed for TDS. Histopathological analyses confirmed marked hepatoprotection, particularly in the TDS-treated group. Docking analyses identified ellagic acid as the strongest dual binder to both AMPK and HMG-CoA reductase. Overall, these findings demonstrate that chamomile-linseed and thyme-linseed formulations exert synergistic, multi-target antioxidant and hypolipidemic effects, supporting their potential as nutraceutical strategies for the early prevention and management of hyperlipidemia and cardiometabolic risk.
This study quantitatively evaluates the performance of CMIP6-based neural emulators and machine learning regressors in forecasting district-level maximum surface air temperature (SAT) across Telangana during the pre-monsoon season (March–May, 1985–2014). Daily SAT data from 27 CMIP6 models and IMD Ensemble observations were harmonized through interpolation, bias correction, and outlier filtering. Three emulator configurations, Multi-Model Mean (MMM_Group-20), Shallow ANN (sANN_Group-20), and Deep ANN (dANN_Group-20), were benchmarked against CMIP6 Ensembles using statistical diagnostics, Taylor metrics, and spatial bias mapping. Results revealed persistent warm biases (+ 1.5 to + 3.5 °C), compressed confidence intervals (0.02–0.05 °C vs. 0.04–0.12 °C in Ensembles), and smoothing of extremes. Taylor analysis indicated RMSE values of 5.09–5.17 °C and correlation coefficients of 0.24–0.36, reflecting ensemble-like performance but limited improvements over physics-based models. District-level bias maps highlighted northern hotspots (Mancherial, Adilabad, Jagtial), while reliability scoring identified MMM_Group-20 as the most consistent, achieving scores above 0.85 and peaking at 1.240 under Gradient Boosting. Ensemble regressors such as XGBoost and Random Forest delivered the highest mean reliability ( 0.465), whereas Linear Regression underperformed in anomaly-dense districts. Overall, the findings demonstrate that neural emulators provide scalable and stable forecasting frameworks but exaggerate heat profiles and compress uncertainty ranges. Post-hoc bias correction and interpretability tools, including SHAP-based feature attribution, are essential to enhance realism. By linking emulator biases to structural constraints and large-scale drivers such as ENSO, this framework advances AI-driven climate diagnostics and supports agro-climatic decision-making in monsoon-affected regions. This study evaluates CMIP6-based neural emulators and machine learning regressors for predicting district-level maximum surface air temperature (SAT) across Telangana during the pre-monsoon season (March–May, 1985–2014). Daily SAT from 27 CMIP6 models, combined with IMD Ensemble observations, was harmonized through interpolation, bias correction, and outlier filtering. Three emulator strategies were tested: Multi-Model Mean (MMM_Group_20), shallow ANN (sANN_Group_20), and deep ANN (dANN_Group_20), trained via mean squared error minimization and benchmarked against CMIP6 Ensembles. Diagnostics revealed persistent warm biases (+ 1.5 to + 3.5 °C), narrower confidence intervals (0.02–0.05 °C vs. 0.04–0.12 °C in Ensembles), and smoothing of extremes. Taylor diagrams showed ensemble-like performance but limited gains over physics-based models, with RMSE 5.1 °C and correlations 0.24–0.36. District-level bias maps highlighted hotspots in northern Telangana (Mancherial, Adilabad, Jagtial). Reliability scoring of five regressors confirmed emulator robustness: ensemble methods (XGBoost, Random Forest) achieved mean reliability 0.465, Gradient Boosting peaked at 1.240, while Linear Regression underperformed in anomaly-prone districts.Findings suggest neural emulators provide scalable, stable forecasting frameworks but exaggerate heat profiles and compress uncertainty, requiring post-hoc bias correction and interpretability tools. Linking emulator biases to large-scale drivers like ENSO strengthens AI-driven climate diagnostics and supports agro-climatic decision-making in monsoon-sensitive regions. Neural emulators overestimate SAT by 1.5–2.5 °C, with strong biases in Mancherial and Jagtial. sANN and dANN show tight confidence ( 0.02–0.05 °C), masking extremes and limiting uncertainty realism. MMM_Group-20 delivers superior district reliability, especially in low-bias zones like Gadwal and Nagarkurnool. XGBoost and Random Forest excel in bias-sensitive districts, showing strong generalization and robustness. sANN and dANN show limited incremental benefit, stressing need for SHAP attribution and spatial tuning for realism.
This study develops and analyzes an eco-epidemiological predator–prey model describing the interaction between copepods and Atlantic horse mackerel (Trachurus trachurus) along the Moroccan coast. The predator population is stratified into susceptible and infected subclasses, with nonlinear prey refuge mechanisms and selective harvesting incorporated for each group. Predation follows a Holling type I functional response for each class, while disease transmission occurs through contact between susceptible and infected predators. Rigorous mathematical analysis establishes the existence, uniqueness, positivity, and boundedness of solutions, guaranteeing the biological well-posedness of the system. Stability analysis demonstrates that the disease-free equilibrium is globally asymptotically stable whenever R_0 < 1 , while a transcritical bifurcation at R_0=1 gives rise to a stable endemic equilibrium. Disease persistence or elimination is shown to be critically governed by the transmission coefficient, prey refuge intensity, and harvesting pressures applied to each predator class. Hopf bifurcation analysis further reveals the emergence of sustained oscillatory dynamics under certain parameter regimes. To account for environmental stochasticity, the deterministic framework is extended to a stochastic differential equation system, for which the existence of a unique global positive solution and sufficient conditions for disease extinction are rigorously derived. Numerical simulations corroborate the theoretical results, demonstrating that harvesting strategies, prey refuge mechanisms, and stochastic perturbations collectively govern the long-term dynamics and resilience of marine predator–prey systems. The proposed harvesting and refuge strategies contribute to reducing infection prevalence while maintaining ecological balance, providing insights for sustainable and climate-resilient fisheries management under environmental variability. This graphical abstract summarizes the structure, analytical framework, and key findings of a deterministic–stochastic eco-epidemiological predator–prey model incorporating harvesting and refuge strategies, applied to marine populations along the Moroccan Mediterranean coast. The deterministic component analyzes the existence and stability of equilibria, including the disease-free equilibrium P_6 , and characterizes infection dynamics through the basic reproduction number R_0 and bifurcation analysis. Sensitivity and contour plots illustrate the influence of biological parameters and harvesting efforts on predator and prey densities. The stochastic extension introduces environmental noise via Itô stochastic differential equations, ensuring the existence and positivity of solutions while identifying noise-induced extinction and persistence conditions. Numerical simulations highlight the impact of environmental fluctuations on infected and susceptible predator populations. The graphical abstract emphasizes that optimized harvesting and refuge strategies contribute to ecosystem resilience and long-term population persistence, even under stochastic environmental perturbations. These results provide useful insights for sustainable marine resource management and eco-epidemiological modeling. A deterministic–stochastic eco-epidemiological predator–prey model is developed for marine ecosystems. The basic reproduction number and bifurcation analysis determine infection persistence and stability regimes. Stability analysis of equilibria reveals disease-free and endemic system behavior. Environmental noise is incorporated via Itô stochastic differential equations to evaluate extinction and persistence. Numerical simulations demonstrate that optimized harvesting and refuge strategies improve longterm ecosystem resilience.
This study presents a comprehensive life-cycle assessment and techno-economic analysis of a 270.665 kWdc solar photovoltaic (PV) system installed at Aswaq Al Salam in Jordan. Using OpenLCA with the Ecoinvent database, greenhouse gas emissions were evaluated across major components including solar panels, steel structures, cables, and inverters. Results show that the total system impact is 232,367.84 kg CO2-eq over its 25-year lifetime, with the majority (74.6%) originating from solar panel manufacturing due to an energy-intensive silicon purification process. When compared to grid electricity, which would have produced 7,915,520.10 kg CO2 over the same period, the PV system achieves a 97.06% reduction in emissions, corresponding to only 15.59 g CO2/kWh. An off-grid alternative with a 3 MWh battery storage was also assessed, yielding a higher footprint of 431,929.63 kg CO2-eq, primarily due to emissions from lithium-ion battery production. In addition to the environmental assessment, a discounted techno-economic evaluation was performed. The total investment cost was estimated at USD169,200, yielding first-year savings of USD92,891. When accounting for discounting, operation and maintenance costs, and electricity price escalation, the system achieves a net present value of approximately USD979,950 over 25 years and a levelized cost of electricity of USD0.024/kWh, significantly lower than the prevailing grid electricity tariff. The simple payback period remains short at 1.82 years, confirming both the short-term attractiveness and long-term economic robustness of the grid-connected PV system.
