Search Results (750)

As demand for rare earth elements (REEs) rises and environmental concerns about the extraction of primary resources grow, biological methods for removing these elements have gained significant attention as eco-friendly alternatives. This study assessed the ability of the green macroalga Ulva lactuca to remove europium (Eu) from aqueous solutions, evaluated the cellular partition of this element and investigated the toxicological effects of Eu exposure on its biochemical performance. U. lactuca was exposed to variable concentrations of Eu (ranging from 0.5 to 50 mg/L), and the amount of Eu in both the solution and algal biomass was analyzed after 72 h. The results showed that U. lactuca successfully removed 85 to 95% of Eu at low exposure concentrations (0.5–5.0 mg/L), with removal efficiencies of 75% and 47% at 10 and 50 mg/L, respectively. Europium accumulated in algal biomass in a concentration-dependent manner, reaching up to 22 mg/g dry weight (DW) at 50 mg/L. The distribution of Eu between extracellular and intracellular fractions of U. lactuca demonstrated that at higher concentrations (5.0–50 mg/L), 93–97% of Eu remained bound to the extracellular fraction, whereas intracellular uptake accounted for approximately 20% at the lowest concentration (0.5 mg/L). Biochemical analyses showed significant modulation of antioxidant defenses. Superoxide dismutase activity increased at 10 and 50 mg/L, while catalase and glutathione peroxidase activities were enhanced at lower concentrations (0.5–1.0 mg/L) and inhibited at higher exposures. Lipid peroxidation levels remained similar to controls at most concentrations, with no evidence of severe membrane damage except at the highest Eu level. Overall, the results demonstrate that U. lactuca is an efficient and resilient biological system for Eu removal, combining high sorption capacity with controlled biochemical responses. These findings highlight its potential application in environmentally sustainable remediation strategies for REE-contaminated waters, while also providing insights into Eu toxicity and cellular partitioning mechanisms in marine macroalgae. Full article

Background: Artificial intelligence (AI) in urban and multimodal transport has demonstrated strong potential; however, real-world deployment remains constrained by limited governance-ready design, fragmented data ecosystems, and single-objective optimization practices. The resulting problem is that agencies lack a reproducible, deployment-ready architecture that links data fusion, multi-objective optimization, and electrification constraints into daily multimodal operational decision making. Methods: This study presents a systematic review and synthesis of 145 peer-reviewed studies on network control, green routing, digital twins, and electric-bus scheduling, conducted in accordance with PRISMA 2020 using predefined inclusion and exclusion criteria. Based on these findings, a deployment-oriented operational AI framework is developed. Results: The proposed architecture comprises five interoperable layers—data ingestion, streaming analytics, optimization services, decision evaluation, and governance monitoring—supporting scalability, reproducibility, and transparency. Rather than producing a single optimal solution, the framework provides decision-ready trade-offs across service quality, cost efficiency, and sustainability while accounting for uncertainty, reliability, and electrification constraints. The approach is solver-agnostic, supporting evolutionary and learning-based techniques. Conclusions: A Thessaloniki-based multimodal case study demonstrates how reproducible AI workflows can connect real-time data streams, optimization, and institutional decision making for continuous multimodal transport management under operational constraints. Full article

Soybean root rot caused by Rhizoctonia solani is a yield-limiting disease in Northeast China, particularly under continuous monoculture and cool climatic conditions. Despite its agronomic impact, the epidemiology and fungicide resistance profile of the pathogen remain inadequately characterized. In this study, a comprehensive survey conducted in Heilongjiang Province yielded 990 pathogenic isolates belonging to 11 fungal species. Among them, 55 strains were identified as R. solani based on combined morphological and molecular analyses. These isolates induced typical symptoms of root and stem browning with constriction. Pathogenicity tests on 30 R. solani isolates indicated that 83.3% were highly pathogenic. The pathogen exhibited a distinct geographic distribution, with the highest percentage of pathogen isolation recorded in Jiamusi (26.6%), which accounted for 61.8% of all R. solani isolates. In vitro fungicide sensitivity assays demonstrated that fludioxonil and prochloraz were highly effective (EC₅₀ < 0.0050 µg·mL⁻¹), whereas resistance was observed to tebuconazole, difenoconazole, pyraclostrobin, and carbendazim. Pot experiments confirmed that fludioxonil seed treatment (15 g a.i./100 kg seeds) provided superior control efficacy (63.07%) compared to prochloraz (46.85%). These findings establish R. solani as a dominant causal agent of soybean root rot in the region and support the prioritized use of fludioxonil for sustainable disease management. By elucidating the pathogenicity, distribution, and resistance patterns of R. solani, this study provides critical insights for controlling soybean root rot in cold-climate production systems and facilitates the development of targeted management strategies. Full article

Water wastage management (WWM) in deep-level mines remains a critical challenge, as wastage increases operational costs and threatens sustainability. This study presents a systematic state-of-the-art review of WWM in deep-level mines. Relevant literature was critically assessed to establish current practices, identify limitations, and explore emerging solutions. Five principal approaches were identified: leak detection and repair, pressure control with fixed schedules, network optimisation, accountability measures, and smart management. While each provides benefits, significant challenges persist. Particularly, current pressure control techniques, essential for limiting leakage, rely on static demand profiles that cannot accommodate the stochastic nature of service water demand, often resulting in over- or under-supply. Smart management systems, which have proven effective for managing stochastic utilities in other industries, present a promising alternative. Enabling technologies such as sensors, automated valves, and tracking systems are already widely deployed in mining, underscoring the technical feasibility of such systems. However, no studies have yet examined their development for WWM in deep-level mines. This study recommends a framework for smart water management tailored to mining conditions and highlights three opportunities: developing real-time demand approximation methods, leveraging occupancy data for demand estimation, and integrating these models with mine water supply control infrastructure for implementation and evaluation. Full article

The study explores how consumer buying behavior is influenced by artificial intelligence (AI) personalization, with a specific focus on responsible and sustainability-aligned digital marketing. Using an explanatory sequential mixed-methods design, the study analyzes a quantitative survey and qualitative interviews. Results show that purchase behavior is strongly affected by exposure to AI messages—especially when recommendations are relevant, timely, and emotionally appealing—and by trust in AI, while perceived lack of trust inhibits purchasing. Qualitative findings underscore affective responses alongside ethical concerns, perceived transparency, and perceived control over data. Overall, the study shows that effective personalization depends not only on algorithmic sophistication but also on users’ sense of relevance and autonomy and on ethical data governance. The conclusions highlight sustainability-consistent implications for marketers: increase data transparency, segment customers by privacy sensitivity, and adopt accountable, consent-based personalization to build durable trust and loyalty. Future research should examine longitudinal effects and cultural differences, acknowledging limits of small purposive qualitative samples for generalization and exploring how consumer trust, ethical perceptions, and responses to AI personalization evolve over time. Full article

The gradual integration of driverless vehicles into urban traffic systems is expected to affect both operational performance and environmental outcomes, particularly during the mixed-automation phase of urban traffic systems, in which human-driven and driverless vehicles coexist. However, existing studies have rarely examined this phase through jointly accounting for behavioral heterogeneity among human drivers and varying levels of driverless vehicle penetration in signalized urban networks. This study addresses this gap through a behavior-based microscopic traffic simulation framework that explicitly incorporates different human driving styles together with driverless vehicles across penetration levels ranging from 0% to 100%. Network- and link-level indicators, including delay, queue length, fuel consumption, and emissions, are evaluated under coordinated signal control conditions. The results reveal a nonlinear relationship between the automation level and traffic performance. While changes remain limited at low and moderate penetration levels, more pronounced improvements emerge beyond a critical threshold of approximately 75% driverless vehicle penetration. At this level, network-wide average delay reductions of about 3–5% are observed, accompanied by consistent decreases in fuel consumption and emissions. By highlighting how behavioral interactions shape the effectiveness of automation, the findings provide practical insights for traffic engineers and urban planners, supporting the design and evaluation of signalized urban arterials under mixed traffic conditions while contributing to environmental sustainability and sustainable urban mobility through improved traffic efficiency and stability. Full article

In today’s volatile digital environments, conventional data governance practices fail to adequately address the dynamic, context-sensitive, and risk-hazardous nature of data use. This paper introduces DRADG (Dynamic Risk-Adaptive Data Governance), a new paradigm that unites risk-aware decision-making with adaptive data governance mechanisms to enhance resilience, compliance, and trust in complex data environments. Drawing on the convergence of existing data governance models, best practice risk management (DAMA-DMBOK, NIST, and ISO 31000), and real-world enterprise experience, this framework provides a modular, expandable approach to dynamically aligning governance strategy with evolving contextual factors and threats in data management. The contribution is in the form of a multi-layered paradigm combining static policy with dynamic risk indicator through application of data sensitivity categorization, contextual risk scoring, and use of feedback loops to continuously adapt. The technical contribution is in the governance-risk matrix formulated, mapping data lifecycle stages (acquisition, storage, use, sharing, and archival) to corresponding risk mitigation mechanisms. This is embedded through a semi-automated rules-based engine capable of modifying governance controls based on predetermined thresholds and evolving data contexts. Validation was obtained through simulation-based training in cross-border data sharing, regulatory adherence, and cloud-based data management. Findings indicate that DRADG enhances governance responsiveness, reduces exposure to compliance risks, and provides a basis for sustainable data accountability. The research concludes by providing guidelines for implementation and avenues for future research in AI-driven governance automation and policy learning. DRADG sets a precedent for imbuing intelligence and responsiveness at the heart of data governance operations of modern-day digital enterprises. Full article

Growing pressure on water resources and mineral fertilizer use calls for innovative and resource-efficient agri-food systems. Aquaponics, integrating aquaculture and hydroponics, represents a promising approach for sustainable greenhouse production. This study, aiming to explore alternative water and nutrient sources for greenhouse tomato production without compromising plant adaptability or yield, evaluated the co-cultivation of grape tomato and rainbow trout in a vertical decoupled aquaponic system under controlled greenhouse conditions. Two aquaponic nutrient strategies were tested: unmodified aquaponic water (AP) and complemented aquaponic water (CAP), with conventional hydroponics (HP) as a control, in a Deep Water Culture hydroponic system. Plant performance was assessed through marketable yield and physiological parameters, while system performance was evaluated using combined-biomass Energy Use Efficiency (EUE), Freshwater Use Efficiency (fWUE) and Nitrogen Use Efficiency (NUE), accounting for both plant and fish production. CAP significantly improved tomato yield (9.86 kg m⁻²) compared to AP (2.40 kg m⁻²), although it remained lower than HP (12.14 kg m⁻²). Fresh WUE was comparable between CAP and HP (9.22 vs. 9.24 g L⁻¹), demonstrating effective water reuse. In contrast, EUE and NUE were lower in CAP, reflecting the additional energy demand of the recirculating aquaculture system and nutrient limitations of fish wastewater. These results highlight aquaponics as a water-efficient production system while emphasizing that optimized nutrient management and energy strategies are critical for improving its overall sustainability and performance. Full article

Local energy systems (LESs) are becoming larger and more heterogeneous as distributed energy resources, electrified loads, and active prosumers proliferate, increasing the need for reliable coordination of operation, markets, and community governance. This Perspective synthesizes recent literature to map how artificial intelligence (AI) supports forecasting and situational awareness, optimization, and real-time control of distributed assets, and community-oriented markets and engagement, while arguing that adoption is limited by system-level credibility rather than model accuracy alone. The analysis highlights interlocking deployment barriers, such as governance-integrated explainability, distributional equity, privacy and data governance, robustness under non-stationarity, and the computational footprint of AI. Building on this diagnosis, the paper proposes principles-as-constraints for sustainable, trustworthy LES AI and a deployment-oriented validation and reporting framework. It recommends evaluating LES AI with deployment-ready evidence, including stress testing under shift and rare events, calibrated uncertainty, constraint-violation and safe-fallback behavior, distributional impact metrics, audit-ready documentation, edge feasibility, and transparent energy/carbon accounting. Progress should be judged by measurable system benefits delivered under verifiable safeguards. Full article

Schistosomiasis remains a major neglected tropical disease globally and presents particular challenges for countries transitioning from control to elimination. Saudi Arabia represents a unique epidemiological setting, having shifted from historical endemic transmission to very low reported incidence, yet long-term national analyses remain limited. A retrospective longitudinal analysis of national schistosomiasis surveillance data from 2002 to 2024 was conducted to evaluate temporal trends, clinical subtypes, regional distribution, and demographic characteristics. Joinpoint regression was used to identify significant changes in temporal trends, and autoregressive integrated moving average (ARIMA) models were applied to forecast national and regional trajectories. National incidence declined markedly from 5.5 per 100,000 in 2002 to 0.12 per 100,000 in 2024, with a notable change around 2010, followed by sustained low-level incidence. Intestinal schistosomiasis accounted for most cases, with increasing concentration among adult non-Saudi males and near-elimination among children. Regionally, cases were confined to a limited number of western and southwestern regions, particularly Ta’if, Al Baha, Jazan, and Madinah. Forecasting analyses indicated continued low-level detection without evidence of national resurgence. These findings demonstrate a transition to an elimination-maintenance phase and highlight the need for sustained surveillance in historically endemic regions and mobile populations. Full article

A comfortable, reliable, safe and environmentally friendly high-speed train journey that saves time and offers an unforgettable experience for passengers is not a dream. Passengers can enjoy panoramic views, delicious cuisine and use their mobile devices without restrictions. High-speed trains, powered by environmentally friendly methods, are a sustainable form of transport, reducing harmful emissions. Integrating intelligent control and management into railway networks has the capacity to increase efficiency and improve reliability and safety, as well as reduce development and maintenance costs. Future intelligent railway network architectures are expected to focus on integrated, multi-layered systems that deeply embed artificial intelligence (AI), the Internet of Things (IoT) and advanced communication technologies (5G/6G) to ensure intelligent operation, improved reliability and increased safety. Distributed intelligent control in railways refers to an advanced approach in which decision-making capabilities are distributed across network components (trains, stations, track sections, control centers) rather than being concentrated in a single central location. The recent advances in AI in railways are associated with numerous scientific papers that enable intelligent traffic management, automatic train control, and predictive maintenance, with each of the proposed intelligent solutions being evaluated in terms of key performance indicators such as latency, reliability, and accuracy. This study focuses on how different intelligent solutions in railways can be implemented in network components based on the requirements for real-time control, near-real-time control, and non-real-time operation. The analysis of related works is focused on the proposed intelligent railway frameworks and architectures. The description of typical use cases for implementing intelligent control aims to summarize latency requirements and the possible distribution of control logic between network components, taking into account time constraints. The considered use case of automatic train protection aims to evaluate the added latency of communication. The requirements for the nodes that host and execute the control logic are identified. Full article

This work presents the design, modeling, and experimental validation of an innovative, highly autonomous, and economically viable photovoltaic solar cooker, integrating a robust battery storage system. The system combines 1200 Wp photovoltaic panels, a control block with DC/DC power converters and digital control for intelligent energy management, and a thermally insulated heating plate equipped with two resistors. The objective of the system is to reduce dependence on conventional fuels while overcoming the limitations of existing solar cookers, particularly insufficient cooking temperatures, the need for continuous solar orientation, and significant thermal losses. The optimization of thermal insulation using a ceramic fiber and glass wool configuration significantly reduces heat losses and increases the thermal efficiency to 64%, nearly double that of the non-insulated case (34%). This improvement enables cooking temperatures of 100–122 °C, heating element surface temperatures of 185–464 °C, and fast cooking times ranging from 20 to 58 min, depending on the prepared dish. Thermal modeling takes into account sheet metal, strengths, and food. The experimental results show excellent agreement between simulation and measurements (deviation < 5%), and high converter efficiencies (84–97%). The integration of the batteries guarantees an autonomy of 6 to 12 days and a very low depth of discharge (1–3%), allowing continuous cooking even without direct solar radiation. Crucially, the techno-economic analysis confirmed the system’s strong market competitiveness. Despite an Initial Investment Cost (CAPEX) of USD 1141.2, the high performance and low operational expenditure lead to a highly favorable Return on Investment (ROI) of only 4.31 years. Compared to existing conventional and solar cookers, the developed system offers superior energy efficiency and optimized cooking times, and demonstrates rapid profitability. This makes it a sustainable, reliable, and energy-efficient home solution, representing a major technological leap for domestic cooking in rural areas. Full article

The water yield (WY) service is a critical ecosystem service in arid regions, and understanding its spatiotemporal heterogeneity and controls is important for sustainable watershed management. Annual water yield (WY) in the Aksu River Basin (ARB), China, from 2000 to 2020 was simulated using the InVEST model, with validation against observed runoff (NSE = 0.840, R² = 0.846, RMSE = 1.787). The results revealed a decline in WY from 66.49 mm in 2000 to 43.15 mm in 2015, while retaining a clear north–south gradient, with higher values in the north. Areas showing decreasing and increasing trends accounted for 45.34% and 3.14% of the basin, respectively. WY exhibited strong spatial autocorrelation (global Moran’s I = 0.912–0.941), with high-value clusters in the north and low-value clusters in the south. GeoDetector identified precipitation, temperature, and potential evapotranspiration as key drivers (q = 0.889, 0.880, and 0.832, respectively), with precipitation-related interactions generally exceeding 0.9, indicating enhanced explanatory power through multi-factor coupling. After variable screening and collinearity control, MGWR revealed spatially varying effects of drivers and significant spatial non-stationarity. Overall, despite the declining trend, WY in the ARB maintained a relatively stable spatial structure, with its heterogeneity primarily driven by the coupling of climatic forcing and topographic constraints, providing a scientific basis for zonal water resource management in arid river basins. Full article

Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, which poses significant challenges for integration into industrial production processes. Three-dimensional printing technology provides a cost-effective alternative to traditional fabrication methods, offering particular benefits for the development of low-cost designs for detecting liquid properties. In this work, we present a sensor system for assessing biofuel solutions. The presented device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery and resolution. This system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and $1.48\times {10}^{-2}$ mPa·s for viscosity, and 0.0485% and $1.9\times {10}^{-4}$ g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan and accelerate data acquisition. The resulting system is compact, precise and applicable to diverse industrial liquids. Full article

The contemporary agricultural market is profoundly volatile, where agricultural prices are based on a complex supply chain, climatic irregularity or unscheduled market demand. Prices of crops need to be predicted in a reliable and timely manner for farmers, policy-makers and other stakeholders to take evidence-based decisions ultimately for the benefit towards sustainable agriculture and economic sustainability. Objective: The objective of this study is to develop and evaluate a comprehensive machine learning model for predicting agricultural prices incorporating logistic, economic and environmental considerations. It is the desire to make agriculture more profitable by building simple and accurate forecasting models. Methods: An assorted dataset was collected, which covers major factors to constitute the dataset of temperature, rainfall, fertiliser use, pest and disease attack level, cost of transportation, market demand-supply ratio and regional competitiveness. The data was subjected to pre-processing and feature extraction for quality control/quality assurance. Several machine learning models (Linear Regression, Support Vector Machines, AdaBoost, Random Forest, and XGBoost) were trained and evaluated using performance metrics such as R2 score, Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results: Out of the model approaches that were analysed, predictive performance was superior for XGBoost (with an R2 Score of 0.94, RMSE of 12.8 and MAE of 8.6). To generate accurate predictions, the ability to account for complex non-linear relationships between market and environmental information was necessary. Conclusions: The forecast model of the XGBoost-based prediction system is reliable, of low complexity and widely applicable to large-scale real-time forecasting of agricultural monitoring. The model substantially reduces the uncertainty of price forecasting, and does so by including multivariate environmental and economic aspects that permit more profitable management practices in a schedule for future sustainable agriculture. Full article