Search Results (10,771)

Amid the global transition to carbon neutrality, tidal current energy has become a strategic sustainable energy resource due to its high predictability, power density, and environmental compatibility. Horizontal-axis turbines show great potential for marine energy harvesting, yet the large-scale commercialization of tidal turbines is severely hindered by complex wake dynamics and the lack of reliable, efficient prediction tools for out-of-distribution (OOD) operating conditions. Traditional high-fidelity CFD methods are computationally prohibitive for engineering optimization, while conventional data-driven surrogate models suffer from poor extrapolation performance, extrapolation collapse near training parameter boundaries, and the absence of uncertainty quantification. To address these bottlenecks, this study focuses on the OOD extrapolation of wake flow prediction across tip speed ratio (TSR) distributions for a single horizontal-axis tidal turbine. A CFD-generated spatiotemporal benchmark dataset is constructed for comparative OOD evaluation across various TSR conditions with 9504 total samples. A novel physics-constrained Fourier neural operator framework named TSR-FNO is proposed to improve OOD generalization. The model integrates TSR–Lipschitz regularization to suppress extrapolation collapse and Monte Carlo Dropout to provide reliable uncertainty estimation. Extensive experiments demonstrate that the proposed method effectively reduces prediction error in unseen TSR regimes, mitigates performance degradation in far-field extrapolation, and produces well-calibrated uncertainty estimates consistent with actual prediction confidence. This work provides a data-driven surrogate modeling strategy for fast and reliable wake prediction on a common CFD-generated benchmark, supporting the efficient design, array layout optimization, and engineering deployment of tidal current energy systems. Full article

In sensor-centric fields like healthcare, environmental monitoring, and industry, image classification is key to turning visual sensor data into actionable insights. Sensor-generated dynamic streaming data poses significant challenges for traditional static image classification models due to the continuous emergence of new categories, distribution shifts, and limited edge storage. With sensor streaming data facing label scarcity and high annotation costs, semi-supervised continual learning is essential, leveraging unlabeled data for incremental learning and reducing reliance on costly annotations. However, current semi-supervised continual learning methods rely on labeled data to generate pseudo-labels, leading to confirmation and relational biases. To mitigate these dual biases, we propose a Bias Calibration method based on nearest-neighbor semi-supervised continual learning, which integrates and adapts Confidence-Enhanced Learning (originally introduced for static datasets) and Guided Contrastive Learning. Specifically, the Confidence-Enhanced Learning aims to reduce competition among similar classes and penalizes low-confidence predictions, thereby generating high-confidence pseudo-labels for unlabeled data and mitigating confirmation bias. Guided Contrastive Learning constructs a pseudo-label graph and a feature representation graph, using the pseudo-label graph to optimize the feature representation graph, thereby improving class discrimination and reducing feature bias. Experiments on CIFAR-10, CIFAR-100, and ImageNet-100 show that our method significantly outperforms existing approaches, enhancing classification performance with partial labeling. Full article

This study explores the impact of renewable energy consumption on environmental quality in ten OECD economies over the period 1990–2024, aiming to assess its contribution as a structural driver of decarbonization in advanced economies. Given the presence of strong cross-sectional dependence and heterogeneous country dynamics, the analysis employs second-generation panel econometric techniques. Stationarity is assessed using the CIPS unit root test. Long-run relationships are examined using the Westerlund error-correction-based cointegration approach. Long-run elasticities are estimated using the Common Correlated Effects Mean Group (CCE-MG) and Augmented Mean Group (AMG) estimators. Short-run dynamics are analyzed within a panel error-correction framework. The results confirm the existence of a stable long-run equilibrium relationship among the variables. Renewable energy consumption is associated with a negative effect on CO₂ emissions, with the CCE-MG estimate indicating that a 1% increase in renewable energy reduces emissions by approximately 0.067%, although the long-run statistical significance remains marginal. In the short run, renewable energy is also associated with lower emissions, indicating both structural and immediate mitigation dynamics. By contrast, energy consumption and financial development increase emissions, while economic growth does not exhibit a robust long-run effect, providing no support for the Environmental Kuznets Curve hypothesis. The error-correction term confirms rapid convergence toward long-run equilibrium. Robustness analysis using carbon intensity as an alternative environmental indicator yields consistent findings. In sum, the results suggest that renewable energy expansion should be complemented by energy efficiency policies and the reorientation of financial systems toward green investments to achieve effective decarbonization. From a policy perspective, coordinated strategies integrating renewable deployment, efficiency improvements, and sustainable finance are essential for achieving long-term environmental sustainability in OECD economies. Full article

Background: Older adults are particularly vulnerable to heat related illness due to impaired thermoregulatory responses. Heat acclimation (HA) strategies can mitigate the negative impacts of high environmental temperatures on physiological and perceptual responses. Whilst active HA strategies may prove problematic for older adults, passive approaches such as hot water immersion (HWI) may be more feasible. Methods: This study investigated the effects of four consecutive days of HWI on physiological and perceptual markers in individuals aged over 65 years during moderate exercise. Nine healthy, recreationally active participants (76 ± 5 years) completed two 30 min cycling bouts at 75–80% age predicted HR_max pre- and post-four days of HWI at 40 °C. Measures of average HR, gastrointestinal temperature, skin temperature, thermal sensation, thermal comfort, rate of perceived exertion, power output, and distance covered were recorded during both exercise bouts. Results: Results showed a significant increase in exercise capacity as measured by power output (p < 0.05, 7.45 W) post-intervention, despite no change in ratings of perceived exertion, and reductions in average heart rate (112 ± 3 vs. 109 ± 4 bpm). There were no alterations in gastrointestinal or skin temperature, and ratings of thermal comfort and sensation remained unchanged post-intervention. Conclusions: These preliminary findings provide important new evidence that four days of passive HWI may be a practical and effective method of inducing physiological adaptations in older individuals, which may be of use in interventions to mitigate the negative impact of high environmental temperatures in this population. Full article

Using density functional theory (DFT) and the Gaussian 09 program, the study calculated Gibbs free energy to understand how easily each NP can transform. Results showed that only 2,6-dinitrophenol (2,6-DNP) and 2-chloro-6-nitrophenol (2-Cl-6-NP) had Gibbs free energies above 0 kJ/mol. The study also evaluated the toxicity of the NPs, leading to the identification of trinitrophenol (TNP), 2-chloro-4-nitrophenol (2-Cl-4-NP), and 2-nitrophenol (2-NP) with the highest risk scores. In the present study, binding energies were used only as comparative indicators of enzyme–substrate interaction favorability within a screening framework, rather than direct measures of catalytic degradation efficiency. The enzyme 1,2-dioxygenase from Acinetobacter baylyi ADP1 showed strong degradation effects on catechol, with significant binding energies for 2-NP, 2-Cl-4-NP, and TNP. The PS-AOP changed the degradation environment, which reduced enzymatic efficiency. The study also modified specific amino acids in enzymes to improve their performance. For example, the enzyme 1DLT-6 had a degradation increase of nearly 27% compared to the reference enzyme. Finally, we tried to measure the impact of different forces on the breakdown of nitrophenols by enzymes. We used a two-dimensional amino acid map based on enzyme–ligand interactions and a visualization of non-covalent interactions. Our findings show that van der Waals forces and electrostatic forces are the main factors affecting how well the material breaks down. From a sustainability perspective, the study highlights a promising strategy for mitigating secondary pollution, improving the environmental compatibility of PS-AOP-based remediation, and supporting safer and more sustainable restoration of petroleum hydrocarbon-contaminated soil and groundwater. These findings help strengthen the theoretical basis for developing greener post-oxidation remediation pathways. Full article

This article addresses the regeneration of extractive landscapes through the case study of the abandoned quarry system of Cutrofiano in the Salento region of Southern Italy, positioning the quarry as a critical interface between geology, architecture, and contemporary environmental challenges. The study aims to redefine the quarry landscape not as a residual void, but as a potential ecological and cultural infrastructure. The research adopts an interdisciplinary methodology combining geomorphological and geotechnical surveys, historical and cartographic analysis, spatial interpretation, and a multi-criteria assessment framework to identify vulnerabilities and transformation potentials. The results include a strategic masterplan articulated into three integrated interventions: the conversion of the open-pit quarry into a flood-control basin for hydrogeological risk mitigation and sustainable water management; the transformation of the quarry floor into an energy park; and the design of cultural spaces for public use and territorial enhancement. These strategies demonstrate the feasibility of reconciling environmental safety, renewable energy production, and heritage valorization within a single morphological logic. The study concludes that the quarry can be reinterpreted as a regenerative landscape model, offering transferable tools for Mediterranean contexts characterized by similar geological and socio-economic conditions. Full article

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article

A systematic elucidation of soil ammonia (NH₃) volatilization (SAV) and the underlying drivers is imperative for evaluating NH₃ pollution mitigation strategies and advancing sustainable agricultural practices. Currently, no scientific consensus has been established on the effects of maize–soybean intercropping on SAV across varying nitrogen (N) application rates. A consecutive field experiment was conducted over a 2-year period from 2024 to 2025 with a split-plot design. The experiment comprised three cropping systems (maize monocropping (MM), soybean monocropping (MS), and maize–soybean intercropping (IMS)) and three N application rates (no N application (NN), 20% reduced N application (20%RN), and conventional N application (ConN)). The results demonstrated that N application markedly increased SAV. Accumulative SAV was 4.94–6.01 kg ha⁻¹ under NN treatment, whereas it was 8.21–27.89 kg ha⁻¹ under ConN treatment, 7.25–21.52 kg ha⁻¹ under 20%RN treatment. Under ConN treatment, the accumulative SAV in IMS was 21.34 kg·ha⁻¹ and 27.89 kg·ha⁻¹ in 2024 and 2025, respectively, which were significantly higher than those in MM by 16.80% and 13.33%. Under 20% RN treatment, the accumulative SAV in IMS was 15.46 kg·ha⁻¹ and 19.24 kg·ha⁻¹ in 2024 and 2025, respectively, which were lower than those in MM by 3.07% and 10.59%. SAV was positively correlated with soil ammonium N concentration. Moreover, within an appropriate range, SAV increased in response to rising soil water content and temperature. Collectively, maize–soybean intercropping integrated with a 20% nitrogen reduction mitigated environmental risks associated with reactive nitrogen losses. This system constitutes a stable yield, resource-efficient, and ecologically sustainable cropping practice. Full article

Intensive aquaculture induces severe environmental stress and disease susceptibility in Pacific white shrimp (Litopenaeus vannamei). Cannabidiol (CBD) offers significant potential as a bioactive stress-mitigating additive. This study evaluated the effects of dietary CBD supplementation (0, 10, 20, 40, and 80 mg/kg) on the growth, intestinal microecology, and stress tolerance of juvenile L. vannamei over an 8-week feeding trial, followed by a combined chronic ammonia and acute hypoxia challenge. Moderate CBD supplementation (10–40 mg/kg) significantly promoted growth, minimized feed conversion ratios, and enriched muscle eicosapentaenoic (EPA) and docosahexaenoic acids (DHA). Furthermore, CBD restructured the intestinal microbiota by suppressing opportunistic pathogens and enriching beneficial taxa. Under combined stress, moderate CBD prolonged the median lethal time (LT₅₀) by up-regulating hypoxia-inducible factor 1-alpha (hif-1α) and heat shock protein 70 (hsp70) transcription and boosting systemic antioxidant capacity to neutralize lipid peroxidation. Conversely, the highest dose (80 mg/kg) induced metabolic exhaustion and hepatopancreatic toxicity, evidenced by drastically elevated serum transaminases and diminished stress tolerance. Conclusively, dietary CBD exerts a classic biphasic effect in L. vannamei. Inclusion at 10–40 mg/kg safely promotes the best comprehensive effects on growth, immune homeostasis, and environmental resilience within the concentration range tested in this study, whereas excessive administration provokes severe metabolic burden, highlighting the critical need for strict dosage regulation. Full article

Welfare assessment for the endangered red panda (Ailurus fulgens) in captivity requires systematic behaviour monitoring, yet traditional direct observation is often limited by observer subjectivity and time constraints. This study evaluates the feasibility of employing machine learning (ML) to automate behavioural monitoring of a red panda in a complex, mixed-species enclosure at Aalborg Zoo, Denmark. Using video data from cameras in the enclosure of the red panda, and the ML model LabGym for animal detection and behavioural categorisation, models were trained to analyse activity patterns of the red panda. The results demonstrate that, while the behaviour categorizer is a promising tool with high classification confidence, the overall system effectiveness is currently limited by the object detector’s performance in a naturalistic environment. Challenges such as environmental obstructions (e.g., rocks, foliage, and trees) and the animal’s camouflage contributed to a significant amount of unclassified time, which may affect the overall assessment of behavioural distribution. We conclude that, while ML holds potential for non-invasive behaviour monitoring, its application in complex zoo settings requires improved detection capabilities to be fully reliable. Future iterations of this system could be enhanced by complementing standard object detection with pose estimation frameworks. Implementing alternative labelling strategies or background subtraction methods could additionally mitigate the detection challenges posed by environmental obstruction. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a critical global public health challenge driven by inappropriate and excessive antimicrobial use (AMU) across human, animal, and environmental sectors. Method: This narrative review synthesizes recent evidence on antimicrobial utilization and resistance patterns. A structured search of PubMed, Scopus, and Web of Science was conducted for studies published between 2015 and 2025. Eligible sources included surveillance reports, registry-based analyses, and clinical studies. Data were qualitatively analyzed to identify key trends and regional variations. Result: Marked geographical variation in AMR was observed. Carbapenem resistance in Escherichia coli remains low globally (2–3%) but is higher in Southeast Asia (17–18%) and India (~40%). Klebsiella pneumoniae shows consistently high resistance (>40% globally; ~54% in India), while Pseudomonas aeruginosa exhibits stable resistance levels (35–45%). Resistance prevalence increases from primary to tertiary care settings, reflecting greater antimicrobial exposure. Vulnerable populations—including pediatric, elderly, pregnant, and immunocompromised patients—face higher risks of antimicrobial exposure and adverse outcomes, including nephrotoxicity, hepatotoxicity, and microbiome disruption. WHO AWaRe data indicate a global shift toward increased use of Watch-category antibiotics. Stewardship interventions, such as audit and feedback, prescribing restrictions, rapid diagnostics, and decision support systems, effectively reduce inappropriate AMU. Conclusions: Integrated, data-driven antimicrobial stewardship and robust surveillance systems are essential to mitigate the global burden of AMR. Full article

Degraded groundwater quality, characterized by elevated salinity and nitrate concentrations, poses significant public health concerns, particularly for vulnerable populations such as children. High content of nitrate in drinking water may lead to non-carcinogenic health risks, highlighting the urgent need for sustainable groundwater management strategies to protect both human health and environmental integrity. This study assesses the suitability of groundwater resources in the Regueb Basin for irrigation and drinking purposes, with particular attention paid to nitrate contamination. The Irrigation Water Quality Index (IWQI) indicates considerable spatial variability in groundwater quality, with values varying between 15.86 and 89.55 and a median of 41.69, reflecting differing levels of suitability for irrigation across the basin. Similarly, the Drinking Water Quality Index (DWQI) ranges from 149.16 to 982.42, with a median value of 445.71, suggesting significant concerns regarding groundwater suitability for drinking purposes. The health risk assessment (HHRA) based on the Nitrate Pollution Index (NPI) and the nitrate hazard quotient (HQ__nitrate) reveal substantial risks to human health. NPI values vary between 0.45 and 5.5, with a median of 1.65 indicating varying levels of nitrate pollution. The HQ__nitrate results show that all groundwater samples (100%) pose health risks for children (HQ > 1). For women, 75.61% of HQ values exceed the safe threshold, affecting approximately 80% of the study area, whereas for men, 48.48% of HQ values exceed 1, impacting about 36.67% of the area. Overall, these findings highlight the urgent need for effective groundwater management strategies to mitigate nitrate contamination and ensure the safe and sustainable use of the groundwater resources in the Regueb Basin. Full article

Photovoltaic (PV) arrays frequently operate under non-uniform environmental conditions, including partial shading, dust accumulation, and temperature differences across the array. These factors introduce an electrical mismatch among PV modules, considerably reducing overall power output. This study proposes a self-healing reconfiguration strategy that mitigates mismatch losses by dynamically redistributing PV modules across array strings based on irradiance levels. The main goal is to balance the current generation among strings and demonstrate performance improvements within scenarios characterised by highly uneven irradiance patterns under non-uniform operating conditions. The effectiveness of the proposed method is evaluated through simulations conducted using MATLAB R2025b (MathWorks, Natick, MA, USA) under several environmental scenarios. Deterministic shading patterns—including row shading, column shading, diagonal shading, and irregular dust distributions—are first analysed to investigate the behaviour of the PV array under regulated conditions. In addition, a statistical analysis of 100 randomly generated irradiance scenarios is carried out to assess the method’s robustness. Finally, realistic desert-dust patterns representative of environmental conditions in Saudi Arabia are used to evaluate the practical usefulness of the proposed approach. Simulation findings show that the self-healing reconfiguration strategy reduces mismatch effects and improves current balance within the PV array, enabling operation closer to the optimal power point under non-uniform irradiance conditions. These results indicate that the proposed method boosts current balance among PV strings and increases power extraction under strongly non-uniform irradiance scenarios. Full article

Understanding the depth and severity of corrosion is vital for evaluating the long-term durability and economic performance of Zn-based structures. In this study, a machine learning (ML) framework was applied to forecast the corrosion depth of zinc under varying environmental circumstances. A dataset consisting of 300 samples compiled from previously published atmospheric corrosion studies under various environmental conditions was used to develop and evaluate the machine learning models. Seven ML algorithms were developed by integrating different environmental constraints such as temperature, time of wetness (TOW), SO₂ concentration, Cl⁻ concentration, and exposure time as input parameters. The models were trained using cross-validation and hyperparameter optimization to ensure robust predictive performance and minimize overfitting. The Random Forest (RF) model confirmed superior predictive performance with an R² of 96.4% and RMSE of 0.642 µm among all used models. The predictive ability of the optimized RF model was further confirmed using five new environmental systems, attaining excellent agreement with predicted values (R² = 97.9%, RMSE = 0.87 µm). Model interpretability analysis using SHAP (SHapley Additive exPlanations) discovered that exposure time and SO₂ concentration are the most significant parameters leading zinc corrosion behaviour. The developed ML framework provides interpretable insights into the influence of environmental parameters on atmospheric zinc corrosion behaviour and provides a reliable tool for forecasting corrosion depth. These findings highlight the potential of ML approaches to support corrosion mitigation strategies and accelerate materials design by reducing reliance on conventional trial-and-error experimentation. Full article

Reconciling hydropower development with aquatic biodiversity conservation is a central challenge for sustainable river management worldwide. Cascade dam configurations, in which multiple impoundments are arranged in series along a single channel, impose longitudinal environmental gradients that restructure biological communities across trophic levels. Whether the resulting multi-trophic responses are independently driven by shared abiotic gradients (environmental filtering) or mechanistically coupled through direct food-web interactions (trophic cascading) remains unresolved. We surveyed phytoplankton, zooplankton, and benthic macroinvertebrates simultaneously at seven stations along a 430 km gradient downstream of Danjiangkou Dam in the Hanjiang River, the largest tributary of the Yangtze River and the source of China’s South-to-North Water Diversion Middle Route, over eight seasonal campaigns (2015–2017). Variance partitioning, piecewise structural equation modeling, Mantel tests, and co-occurrence network analysis were applied to partition environmental and trophic pathways. Environmental filtering dominated community restructuring at all three trophic levels, while the biotic proxy for direct trophic interactions explained less than 0.4% of community variation, consistent with weak detectable trophic coupling at seasonal resolution. Distance from Danjiangkou Dam shaped downstream transparency and turbidity gradients that mediated trophic-level-specific responses along distinct environmental axes (pH and water temperature for phytoplankton, conductivity for zooplankton, and transparency for benthic macroinvertebrates). Benthic macroinvertebrates were systematically decoupled from the pelagic analytical framework, absent from the cross-trophic co-occurrence network and structured more by spatial configuration than by water-column variables. Hub species in the network were associated with downstream mineralized conditions, confirming that network architecture reflects shared environmental preferences rather than biotic interactions. These findings support a management shift from single-dam mitigation toward cascade-scale coordination of environmental flow regimes, sediment connectivity, and substrate restoration as integrated strategies for sustaining multi-trophic biodiversity in regulated rivers. Full article