Search Results (36,584)

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

The sustainable valorization of food waste is essential for advancing the circular bioeconomy and reducing the environmental impacts of organic waste disposal. This study presents an integrated approach combining hydrothermal carbonization (HTC) and anaerobic digestion (AD) to recover renewable energy and valuable resources from food waste. The process was simulated in Aspen Plus^® version 14.1 using thermochemical and biochemical reaction models to evaluate the effects of feed moisture (60–85%) and HTC temperature (180–280 °C) on performance. Integration of HTC and AD increased overall energy recovery by 26–38% compared to standalone AD, with a feed moisture of 85%, organic loading of 4 kg VS m⁻³ d⁻¹, and mesophilic/thermophilic temperatures of 35 and 55 °C. Improvements resulted from higher methane yield (0.42 m³ CH₄ kg⁻¹ VS) from HTC liquor and energy-rich hydrochar (25–29 MJ kg⁻¹). The techno-economic assessment indicated a net energy ratio of 2.3, an Internal Rate of Return (IRR) of 18.6%, and a 4.8-year payback period, confirming economic viability. Sensitivity analysis highlighted energy prices and feedstock costs as key drivers, while Monte Carlo simulation demonstrated stability under ±20% uncertainty. Optimal conditions (HTC at 220 °C, 65% moisture, and 100 kg h⁻¹ solid loading) significantly enhanced profitability and carbon efficiency. Overall, the integrated HTC–AD process offers a technically, economically, and environmentally sustainable route for converting food waste into renewable energy and biochar, supporting circular bioeconomy and net-zero energy goals. Full article

Plating on Plastics (PoP) requires specific surface pre-treatment steps to enable metallization. The conventional PoP industry utilizes hexavalent chromium (toxic, carcinogenic) and palladium (critical raw material) for surface etching and activation, respectively, raising significant health, environmental, and economic concerns. This work is based on a new Cr⁶⁺-free and Pd-free PoP technology that uses piranha (H₂O₂-H₂SO₄) solutions for surface etching, nickel salts for activation, and NaBH₄ for reduction, ultimately forming metallic nucleation sites for downstream electroless plating and electroplating. A comprehensive modeling approach was developed to simulate and predict unit operation performance (reaction kinetics and yields) and material properties (contact angle and adhesion) across processing stages of the new technology. State-of-the-art and data-driven modeling revealed the combinatorial relationships among process performance, the achieved properties and the different settings of process operating conditions. The results also highlighted capabilities for tuning all processes over a range of conditions, reaching desired product specifications (adhesion and thickness). The models were constructed as a Decision Support Tool (DST) serving economic, environmental, safety and Safe and Sustainable by Design (SSbD) objectives. The DST can be used through a user-friendly interface that enables the insertion of user-defined inputs and monitoring of optimization results. Full article

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe. Full article

Background/Objectives: The question addressed in the current work is to develop a simulation of a pharmaceutical process (DNA encapsulation within lipid nanoparticles using a microfluidic micromixer) which will be of utility to the end users (laboratory-scale formulation development). The simulation and the microfluidic approach also address sustainability issues, such as reducing the environmental impact of the process itself, and reducing the need for physical testing. The paper details the implementation and validation, taking into account key performance indicators and control parameters. Methods: The main method applied for simulation development is a novel multi-agent approach to incorporate stochastic probabilistic behavior, combined with theoretical definitions from the process experts and relevant literature, and data/results from laboratory-scale experiments with different parameter configurations. Results: The simulation was implemented as a representation of the real physical process, reproducing the relationships between process parameters (flow rates) and experimental key performance indicators (capsule diameter, poly dispersion index, encapsulation efficiency). The simulation results demonstrated a general agreement with the empirical results and provided useful predictive insights for the laboratory experiments. Conclusions: The simulation has potential as a support tool for laboratory experiments to reduce physical testing and indicate the most promising configurations on which to focus, with potential savings in time, resources and other costs. Full article

Green supply chain management (GSCM) has become essential for organizations seeking to balance environmental sustainability, regulatory compliance, and economic resilience. However, selecting appropriate green supply chain strategies constitutes a complex multicriteria decision-making (MCDM) problem due to diverse sustainability practices, conflicting objectives, dynamic market conditions, and significant uncertainty in expert evaluations. To address these challenges, this study proposes an intelligent multicriteria group decision-making (MCGDM) framework to assess 15 GSCM strategies across 15 environmental, operational, economic, and regulatory criteria. The framework employs complex fractional orthopair fuzzy sets (C_FOFS) to model uncertainty, expert hesitation, and complex-valued judgments. Expert weights are determined using the analytic hierarchy process (AHP), while criteria weights are derived objectively through the entropy method. A modified technique for order preference by similarity to the ideal solution (TOPSIS) is applied to obtain a robust ranking of alternatives. Evaluations from five multidisciplinary experts ensure practical relevance and validity. The results indicate enhanced uncertainty modeling, improved ranking stability, and greater interpretability compared with conventional fuzzy and deterministic approaches. The proposed framework provides a transparent and effective decision support tool for strategic GSCM planning. Full article

Aquaculture marks the transition from the simple activity of harvesting aquatic animal resources, carried out through the catching practices of fishing, to the farming of aquatic organisms in fresh, brackish and sea waters, carried out through human intervention aimed at increasing production. To date, research is proceeding towards expanding the range of species that can be farmed, improving the number and quality of products, and reducing the environmental impact of aquaculture activities; these efforts are supported by the improvement of our knowledge of the biology of the relevant species, the significant updating/upgrading of the rearing technologies, and the increasing awareness of the importance of water quality in optimising farming conditions. While necessarily dependent on market demand, aquaculture needs to fully leverage its environmental potential; and the relationship between aquaculture and the environment requires a system of production that combines eco-compatibility and eco-sustainability. Here, we report and analyse insights and perspectives in eco-sustainable aquaculture, spanning from sustainability and innovation processes in aquaculture to antibiotic control and aquaculture ecosystem services, in the context of the United Nations Sustainable Development Goals. Full article

A water company’s water supply network is responsible for distributing good-quality water in quantities that meet customer needs, ensuring proper operation of the water supply network to ensure adequate pressure at the receiving points, efficiently repairing faults, and planning and executing maintenance, modernization, and expansion work. Managing a water supply network is a complex and complex process. A crucial challenge in water company management is detecting and locating hidden water leaks in the water supply network. Leak location in water distribution networks is a key challenge for utilities, as undetected leaks lead to water losses, increased energy consumption, and reduced service reliability. With the development of cyber-physical systems (CPSs), the integration of physical infrastructure with real-time digital monitoring has enabled more adaptive and responsive water operations. Data-driven decision-making in CPS in the water industry leverages classification and regression trees (C&RTs) to analyze real-time sensor data—such as pressure, flow, and consumption—to classify system states and predict potential faults. By transforming operational data into interpretable decision rules, C&RTs enable automated and timely maintenance actions that improve reliability, reduce water loss, and support intelligent infrastructure management. The aim of this study is to develop and evaluate AI-based optimization methods to enhance sustainability, efficiency, and resilience in the water industry by enabling autonomous, data-driven decision-making within CPSs, supporting smart industry practices, and addressing practical challenges associated with the actual implementation of smart water management solutions using simple solutions such as C&RTs. The accuracy of the best classifier was 86.15%. Further research will focus on using other types of decision trees that will improve classification accuracy. Full article

Methane steam reforming (MSR) is the most widely used industrial process for hydrogen production. However, catalyst deactivation, carbon emissions, and energy inefficiencies limit its sustainable performance. Therefore, improving catalyst selection and optimizing operating conditions are essential for efficient hydrogen generation. This study proposes an artificial intelligence-driven framework to optimize catalyst–condition combinations in MSR systems. The framework integrates Hybrid Golden Beetle Optimization (HGBO), VIKOR-based multi-criteria decision making, and Convolutional Long Short-Term Memory (ConvLSTM) modeling. HGBO explores the solution space and generates Pareto-optimal combinations of catalysts and operating conditions. These solutions are then ranked using the VIKOR method. The ranking considers hydrogen yield, methane conversion, energy efficiency, CO₂ emissions, and catalyst lifetime. Economic feasibility is also included in the decision process. ConvLSTM modeling captures spatiotemporal relationships in catalyst and process data and predicts catalyst degradation under different operating conditions. The framework is evaluated using 620 experimentally reported MSR cases collected from the published literature within industrial ranges of 600–1200 °C, 1–40 bar, and H₂O/CH₄ ratios of 1–6. The optimized configurations achieve hydrogen yields up to 98.5%, energy efficiency approaching 99%, and reduced CO₂ emissions of about 0.85 kg h⁻¹. The results provide practical guidance for catalyst selection and process optimization in industrial hydrogen production systems. Full article

Global demand for sustainable protein has intensified amid environmental, public health, and ethical concerns surrounding conventional animal agriculture. Edible mushrooms have emerged as promising next-generation protein sources, delivering 19–35% protein (dry weight) with complete essential amino acid profiles and digestibility rates of 60–80%. Beyond protein, mushrooms provide bioactive compounds, including β-glucans, ergothioneine, phenolic acids, and vitamin D₂, supporting immunomodulatory, antioxidant, and anti-inflammatory functions. Enzymatically derived bioactive peptides further demonstrate antihypertensive and antimicrobial activity. This review systematically examines mushroom protein properties, processing technologies, and product performance across three application categories: meat analogs, functional snacks, and beverages. Advanced processing technologies including high-moisture extrusion, ultrasonic-assisted extraction, and microencapsulation have improved bioactive preservation and digestibility. From an environmental perspective, mushroom cultivation requires 85–90% less water and land than animal agriculture, with 80% fewer greenhouse gas emissions. However, critical gaps remain: extraction efficiency varies 3-fold across studies, only 15–23% of commercial products are supported by clinical trials, and techno-economic analyses are largely absent. Standardized processing protocols, large-scale clinical validation, and harmonized quality standards are essential to establish mushrooms as viable, commercially scalable protein alternatives. Full article

Waste-to-energy technology plays a crucial role in advancing the circular economy framework, a strategy that contributes to achieving the United Nations Sustainable Development Goals on responsible consumption and production, as well as the provision of affordable and clean energy. Hydrothermal co-liquefaction has emerged as a promising technology for addressing waste material challenges by converting them into valuable biofuels. This review focuses on biomass feedstock classification and provides an overview of hydrothermal co-liquefaction for sustainable waste management and improved energy production. Moreover, the article provides details on integrating other waste treatment methods with hydrothermal liquefaction to promote the circular economy. Research publications from 2015 to 2025 were obtained from Web of Science and Scopus to identify research trends and output across countries and map out future research directions. The retrieved data from Web of Science was analysed for mapping research, keyword occurrence, and network analysis using VOSviewer software. The study highlighted that waste treatment techniques not only mitigate environmental pollution but also provide a sustainable pathway for energy production and contribute to global carbon neutrality. The review shows that biocrude yield varies with blending ratio because of differences in the biochemical composition of feedstocks, which affect reaction pathways and lead to synergistic or antagonistic interactions during co-processing. Therefore, careful selection of biomass feedstock is essential to achieve optimal results. Full article

Patients leave millions of medical reviews annually, providing critical data for quality management. However, manual processing is infeasible, and existing systems fail to distinguish medical from organizational problems—a distinction essential for complaint routing. The consequences of misrouting are significant: clinical issues may go unaddressed when medical complaints reach administrative staff, while systemic service problems remain unresolved when organizational complaints reach medical directors. We developed a hybrid approach combining expert annotation with Large Language Models (LLMs). Fifteen prompt iterations on 1500 reviews with expert validation (modified Cohen’s kappa (κ_mod), which weights errors hierarchically, reached 0.745) preceded the LLM annotation of 15,000 mixed-sentiment and positive reviews. These were combined with 7417 expert-annotated negative reviews to form a corpus of 22,417 reviews. Eight architectures, ranging from Logistic Regression to a BERT + TF-IDF + LightGBM ensemble, were compared using both standard metrics and domain-specific practical metrics tailored to complaint routing. The best model, scaled to 4.3 million Russian-language reviews from the Prodoctorov.ru platform, achieved 92.9% Practical Accuracy—the proportion of reviews classified without critical medical–organizational misclassification errors (M ↔ O)—compared to 68.0% standard accuracy, which treats all errors equally. Critical errors were reduced to 1.4%, yielding 144,000 more correctly processed complaints than traditional methods (TF-IDF + Logistic Regression). Analysis of the scaled data revealed the following: 46.1% M (medical), 21.0% O (organizational), and 32.9% C (combined) reviews; medical ratings were highest (4.75 vs. 4.59 for organizational, p < 0.001); combined reviews were longest (802 characters); zero-star reviews comprised 3.8% of feedback, with organizational complaints dominating (38.2%) among extreme negatives; and average ratings rose by 1.24 points over 14 years. This hybrid approach yields expert-comparable corpora, automates 93% of feedback processing, ensures correct complaint routing, and contributes to healthcare sustainability by reducing administrative burden, accelerating resolution, and enabling data-driven quality management without proportional increases in human resources. All analyses were conducted on Russian-language patient reviews. Full article

To address the challenge of depleting traditional feed resources, this study aimed to biovalorize sweet potato waste (SPW), a major byproduct of the Japanese shochu industry, into a high-value functional animal feed. An innovative two-stage solid-state fermentation (SSF) was employed, featuring an initial aerobic stage with Aspergillus oryzae for substrate degradation, followed by an anaerobic stage with Lactobacillus plantarum for nutritional enhancement. To optimize this complex, multi-variable process, the predictive performance of Artificial Neural Network (ANN) and Random Forest (RF) machine learning models was compared based on an augmented experimental dataset (N = 80). To ensure statistical robustness and prevent data leakage, a repeated k-fold cross-validation strategy was implemented. The RF model demonstrated significantly superior accuracy and reliability than the ANN model, particularly in predicting the primary metric, crude protein (R² = 0.61 ± 0.04 vs. R² = 0.12 ± 0.15). Subsequently, the validated RF model was integrated with a Constrained Differential Evolution (CDE) algorithm for global parameter optimization. The optimized process was predicted to yield a final product with a crude protein content of 25.0%, alongside significant increases of 114.1% in total amino acids and 123.9% in essential amino acids. These projections were experimentally validated in vitro, confirming the model’s accuracy with a relative error of less than 5%. Furthermore, comprehensive biochemical assays demonstrated a massive degradation of anti-nutritional factors and significant enhancements in total phenolic content and antioxidant activity. This study provides a scientifically validated, data-driven framework for the valorization of SPW. It confirms the superior efficacy of ensemble learning methods for optimizing complex bioprocesses with limited data, offering a contribution to the development of a circular bioeconomy and sustainable feed resources. Full article

Over two years, a randomized complete block field trial tested deficit irrigation [I: 70% ETc; NI] and ammonium nitrate [N₀, N₆₀, N₁₂₀; 0, 60, 120 kg N ha⁻¹] application in two northern Greece winegrape vineyards of cv. ‘Xinomavro’ (XM) and cv. ‘Cabernet Sauvignon’ (CS). Leaf-blade δ¹⁵N was measured at berry set, bunch closure, veraison, and technological maturity; berry-juice (must) δ¹⁵N at technological maturity and dormant cane δ¹⁵N in winter were also determined. In the first year, δ¹⁵N was additionally measured in petioles, unripe berries, trunks, and roots, along with arbuscular mycorrhizal fungal (AMF) colonization of fine roots. Fertilization increased δ¹⁵N in leaf blades and canes, whereas berry-juice δ¹⁵N responded weakly and inconsistently. Irrigation marginally lowered cane δ¹⁵N; cane δ¹⁵N varied between years, and berry-juice δ¹⁵N showed the highest variability across treatments. At berry set, intravine discrimination was evident: young berries and leaf blades were enriched, while fine roots and woody tissues were depleted. Root δ¹⁵N responses differed between cultivars and depended on AMF colonization in XM. Leaf and cane δ¹⁵N were positively related to vine N status, yield, and pruning weight but negatively to agronomic N-use efficiency indices. These findings indicate that δ¹⁵N serves as an integrative proxy of N cycling processes and fertilizer-use efficiency in vineyards, with potential implications for the assessment and optimization of sustainable vineyard management practices in the context of climate change. Full article

Nucleoside diphosphate kinase (NDPK) is a ubiquitous enzyme that maintains cellular nucleotide balance by catalyzing the transfer of phosphate groups between nucleoside diphosphates and triphosphates. Although the evolutionary conservation of NDPK is well established, several aspects of its diversification and functional adaptation remain unclear. The central question of this work is how NDPK evolved across plant species, focusing on the Solanaceae family and how its evolutionary history relates to the diversification of its cellular functions. Phylogenetic and molecular dating analyses showed that the division between NDPK groups 1 and 2 predates the divergence of plants and animals, whereas plant-specific NDPK types (I–IV) originated early in streptophyte evolution. Solanaceae species retain a conserved set of NDPK genes, including a type III isoform with features consistent with mitochondrial targeting. Functional assays in isolated potato tuber mitochondria revealed high NDPK activity in the intermembrane space, sustaining ADP supply to oxidative phosphorylation. Activation of mitochondrial NDPK induced a phosphorylative respiratory state, which partially dissipated the mitochondrial membrane potential and significantly reduced reactive oxygen species (ROS) production. GDP and UDP were preferentially phosphorylated, conferring a stronger antioxidant effect than other nucleotides. Consistently, the mitochondrial isoform StNDPK3 was upregulated during tuber development. Together, our results demonstrate that NDPKs are evolutionarily conserved yet functionally diversified enzymes in plants and identify mitochondrial NDPK as a key modulator of mitochondrial redox homeostasis. By linking nucleotide metabolism to Δψ_m control and ROS suppression, this study highlights a previously underappreciated antioxidant mechanism that integrates mitochondrial energy metabolism with developmental and stress-related processes in plants. Full article