Search Results (51,873)

Hyper-arid marine ecosystems, characterized by extreme environmental conditions, are experiencing intensified stress from the synergistic effects of climate change and pollution. This review synthesizes current knowledge on these interactions in Qatar’s coastal waters, serving as a model system for the Arabian Gulf. We document significant accumulations of heavy metals, petroleum hydrocarbons, microplastics, and emerging contaminants near urban and industrial zones. The region’s rapid warming, hypersalinity, and restricted circulation amplify pollutant toxicity through mechanisms such as increased bioavailability, oxidative stress, and impaired physiological responses. These synergies elevate mortality in sensitive species by 50–100% compared to single stressors, push organisms beyond their physiological limits, and trigger biodiversity loss. As an example, given a baseline of around USD 148 million, a 30% decrease in exploitable fish biomass might result in an annual loss of approximately USD 45 million in the value of Qatar’s fisheries and aquaculture industry. Despite growing evidence, critical gaps persist in understanding mixture toxicity under Gulf-specific extremes, endocrine and neurobehavioral endpoints, and quantitative ecosystem service valuations. We conclude by highlighting emerging solutions, including IoT-based monitoring, AI-driven forecasting, and nature-based remediation, as pathways to enhance resilience under accelerating environmental change. These findings have important implications for marine ecosystem sustainability, food security, and sustainable coastal management in Qatar and other hyper-arid regions. This synthesis establishes Qatar’s coastal ecosystem as a global model for understanding climate–pollution feedback in hyper-arid seas. Full article

Rolling bearings represent one of the core functional components of rotating machinery, with their application scope continuously expanding into various sectors of modern social production and life, making the research on fault diagnosis of rolling bearings increasingly significant. Effective vibration feature extraction and improved classification models are crucial to achieving accurate and automated fault diagnosis of rolling bearings. We proposed a fault diagnosis approach based on a Swin Transformer–Improved ResNet module. In the data preprocessing stage, the frequency-domain features and time-domain multi-scale features of fault signals are extracted using FFT and VMD methods, respectively. And then, dual-channel feature extraction is employed using both the Swin Transformer and Improved ResNet module, followed by feature fusion through an ECA module, thereby enhancing diagnostic accuracy and model robustness. The architecture retains shallow-level feature details while incorporating global contextual information, improving feature representation and detection precision. Extensive experiments were carried out on data collected from an SEU bearing dataset, including model validation, ablation analysis, comparative evaluation and simulated noise testing. An average classification accuracy of 99.41% was achieved by the proposed model under uniform experimental conditions, as evidenced by the obtained experimental results, outperforming other models by at least 0.96%. Even under severe noise interference with a signal-to-noise ratio of -4, the model maintained an average accuracy of 91.92%, exceeding that of noise-resistant counterparts. Moreover, generalization experiments on the CWRU bearing dataset under varying load conditions revealed an average fault diagnosis accuracy exceeding 98%, confirming the model’s strong cross-domain adaptability. Full article

Methane (CH₄) is the second most important greenhouse gas after carbon dioxide (CO₂), and its atmospheric concentration is on the rise. Soil CH₄ consumption (=absorption) capacity is declining due to reduced forests and green spaces, as well as other environmental factors and anaerobic stresses. Environmental and stand structure parameters were cross-referenced with publicly available international ecosystem databases, such as FLUXNET, ICOS, NEON, AmeriFlux, the TRY plant trait database and the Oak Ridge FACE site. Searches were conducted using keywords such as region, water level, and stand density. The data indicate that under high-CO₂ conditions, the increase of forest canopy density leads to increased litter accumulation on the forest floor and reduced sunlight penetration, creating anaerobic conditions. This can cause forests to shift from CH₄ consumption to CH₄ release. Based on these findings, we discussed methods to maintain and enhance the CH₄-absorbing capacity of forest soils. This can be achieved through management practices that improve environmental conditions and increase soil fauna’s activity, such as those associated with thinning operations in overmature forest stands across various regions. This ecological manipulation through thinning practices promotes ground-level temperature increases and the activities of soil fauna, as well as maintaining aerobic conditions near the soil surface. Full article

The sustainable valorization of marine biowaste, particularly shrimp residues, has emerged as a promising strategy to develop eco-friendly agricultural inputs that enhance crop productivity and reduce environmental impacts. This study investigated the effects of a biotechnologically processed fermented shrimp-waste (Parapenaeus longirostris) formulation as a biostimulant on the growth, physiological performance, and development of a local mays variety (Zea mays L., DKC 744) under controlled pot conditions. The experiment evaluated root, foliar, and combined applications of the biostimulant at three concentrations (5%, 10%, and 15%) over a 90-day vegetative cycle. Morphological parameters, including stem height, leaf number, leaf mass, and root biomass, were measured at regular intervals, while chlorophyll a and b contents were assessed to evaluate photosynthetic efficiency. The results indicated that all biostimulant treatments significantly enhanced mays growth. Root-applied biostimulants primarily stimulated root biomass by up to 764.0 ± 66.8 g at the 10% concentration, whereas foliar applications improved above-ground traits, including stem elongation and leaf formation, reaching maximum heights of 200.0 ± 1.9 cm and 17.0 ± 0.4 leaves under intermediate concentrations. Combined root and foliar applications produced the highest stem height (240.0 ± 5.6 cm), leaf number (19.0 ± 0.0), leaf mass (1034.0 ± 11.1 g), and chlorophyll content (2.44 ± 0.9 for chlorophyll a) at 10–15% concentrations. The results also revealed that moderate concentrations generally provided the most balanced stimulation, suggesting the presence of an optimal dose threshold. This study demonstrated the comparative effectiveness of root, foliar, and combined applications of a fermented shrimp-waste biostimulant and identified an optimal concentration. However, its limitations lie in the use of controlled pot conditions and a single crop variety, which restrict the extrapolation of results to field-scale applications and diverse agroecological environments. Therefore, more research is needed to explore the action mechanisms of the studied biostimulant and elicitors, mainly the interaction between biocompounds and the treated plant. Full article

High-resolution vertical profile observations of ocean environmental parameters are essential for investigating mesoscale ocean dynamic phenomena, such as internal waves, mesoscale eddies, and oceanic fronts. At present, vertical profile measurement in marine surveys mainly relies on shipborne winches to deploy and recover marine sensors, which entails high labor costs and considerable energy consumption. Unmanned observation platforms integrated with winch systems enable automatic sensor deployment and recovery, offering a viable approach to cutting observation costs. Nevertheless, inadequate energy supply remains a critical bottleneck restricting the large-scale popularization and application of such equipment. Accordingly, the development of high-efficiency winch systems tailored for unmanned autonomous observation platforms is of great engineering significance for facilitating long-term, continuous, and low-energy marine profile observation. This paper proposes a novel energy-saving winch with an embedded three-stage parallel nested energy storage structure for unmanned marine observation platforms. During operation, the coil spring energy storage system is charged during cable payout, and the stored elastic potential energy is released to assist motor driving in the cable retraction process. This auxiliary driving mode reduces motor power demand and improves the overall energy utilization efficiency of the platform. Experimental results demonstrate that, neglecting ocean current resistance, the proposed winch reduces energy consumption by 5% during cable payout and 21% during cable retraction. The overall energy consumption is decreased by 13% throughout a complete vertical profile measurement cycle. Under constrained and fixed energy supply conditions, this technology substantially enhances the sampling capability of unmanned marine platforms for ocean environmental monitoring. It further improves operational efficiency and extends continuous service time, providing key technical support for revealing ocean dynamic evolution and clarifying the formation and driving mechanisms of marine environmental phenomena. Full article

Pomegranate peel is a food industry waste rich in polyphenols. To date, its effect in alleviating fatigue remains unclear. This study aimed to characterize the chemical composition of pomegranate peel polyphenols (PPPs), evaluate its antioxidant and anti-fatigue capacities, and investigate the underlying mechanism. In the current study, twenty main compounds, primarily flavonoids, phenolic acids, and anthocyanins, were identified from PPPs using LC-MS/MS. In H₂O₂-induced HepG2 cells, PPPs promoted cellular repair and reduced the production of intracellular malondialdehyde (MDA) and reactive oxygen species (ROS) via enhancing the activity of antioxidant enzymes (SOD, CAT, and GSH-Px). In the endurance swimming-induced fatigue mice model, PPPs prolonged mice exhaustion times, reduced accumulation of fatigue-related metabolites (BUN, LA, BA, LDH and CK), and alleviated liver and muscle tissue damage. Mechanistically, PPPs mitigated oxidative stress via activation of the Keap1/Nrf2 pathway, leading to increased expression of hemeoxygenase-1 (HO-1) and NAD(P)H quinone oxidoreductase 1 (NQO1). Furthermore, PPPs stimulated energy metabolism by activating the AMPK/PGC-1α/PPAR-α pathway, promoting mitochondrial biogenesis, enhancing glycogen storage, increasing ATPase activity (Na⁺-K⁺-ATPase, Ca²⁺-Mg²⁺-ATPase, and T-ATPase) and accelerating lipid β-oxidation. These findings suggest that PPPs is a promising anti-fatigue supplement and could be further utilized in the nutritional industry. Full article

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

The in-service assessment of marine propulsion engines requires more than nominal rating comparison because operating severity is shaped by propeller demand, resistance growth, air-path response, and thermal state. This study develops a quantitative benchmarking method for the regime-dependent performance assessment of a low-speed two-stroke Wärtsilä 6RT-flex58T-D engine installed on a 31,000 DWT multi-purpose container vessel. The method integrates certified sea-trial measurements, endurance-test records, manufacturer load-diagram constraints, and a 15% service-margin projection within one reference framework. Three representative regimes are evaluated: a measured light-running baseline (SR1), a measured thermally stabilised sustained regime (SR2), and a projected heavy-running regime derived from the baseline using a 15% sea-margin assumption (R2). Comparison is performed using indicators of operating-point position, shaft torque, propeller-law consistency, selected air-path and thermal variables, load-diagram proximity, and corrected specific fuel oil consumption where available. The SR1 baseline followed the fitted propeller law with deviations not exceeding 1.18%, confirming a coherent light-running reference. In SR2, corrected SFOC decreased from 174.4 to 172.0 g/kWh, while the exhaust temperature before turbine increased from 359 °C to 435 °C, and the corresponding thermal margin decreased from 156 °C to 80 °C. Under the +15% service-margin projection, the required shaft power at the 100% trial point increased from 12,046.0 to 13,852.9 kW, exceeding the 13,560 kW installation MCR by 2.2%, with corresponding 15% increases in torque and BMEP. These results demonstrate that measured baseline operation, sustained-load severity, and projected heavy-running demand can be distinguished quantitatively within one installation-specific load-diagram-based benchmarking framework. Full article

UAV-based infrared target detection presents inherent challenges, including low signal-to-noise ratios, texture degradation, and severe scale variations. To address these issues, we propose DFS-YOLO, an approach based on dynamic feature collaboration and efficient state-space modeling. We introduce a Dynamic Range-Calibrated Area Attention (DRCAA) module in the backbone to stabilize feature activations under strong thermal clutter. Within the neck architecture, an Efficient Attentional Scale-Sequence Fusion (EASF) strategy reduces cross-scale semantic misalignment and ensures precise spatial coherence. Additionally, an EfficientViM-based state-space module captures global contextual dependencies while maintaining linear computational complexity. Finally, the Content-Guided Triple-Attention Fusion (CGTAFusion) module maximizes feature discriminability by calibrating fusion representations across the channel, spatial, and pixel dimensions. Extensive experiments on the HIT-UAV and IRSTD-1k benchmarks validate the efficacy of the DFS-YOLO framework. Compared to the baseline YOLOv12, DFS-YOLO’s performance has been significantly improved, increasing mAP@50 and mAP@50-95 by 10.16% and 7.55% on HIT-UAV, and by 1.84% and 3.18% on IRSTD-1k, respectively. These quantitative gains establish DFS-YOLO as a highly robust and state-of-the-art solution for complex infrared aerial surveillance. Full article

Under the combined impacts of climate change and intensified human activities, harmful algal blooms (HABs) have occurred with increasing frequency in China’s coastal waters, posing growing risks to marine ecosystems and regional sustainability. Chlorophyll-a concentration (Chl-a), a key indicator of phytoplankton biomass, plays a crucial role in HAB monitoring and early warning. This study integrates satellite remote sensing data from 2000 to 2004, 2011 to 2013, and 2023 to 2024 with in situ measurements and environmental variables (e.g., dissolved oxygen) to investigate Chl-a dynamics in the East China Sea. The results indicate pronounced spatiotemporal heterogeneity across the region. Spectral features were represented using band-ratio methods and the BRG model, followed by variable selection based on the Bayesian Information Criterion (BIC) to determine the optimal band combinations for model training. Six mainstream machine learning models were evaluated, and the Backpropagation Neural Network (BP) was selected as the baseline model due to its superior performance. To further improve model robustness and global optimization capability, the Artificial Lemming Algorithm (ALA) was employed to optimize the BP network, resulting in the ALA-BP inversion model. The optimized model achieved correlation coefficients of 0.933 on the test set and 0.940 on the independent validation set, outperforming the other models. The proposed model was further applied to the 2024 algal bloom event in the East China Sea, successfully capturing the spatiotemporal variations of Chl-a. This study provides an effective retrieval framework for Chl-a in optically complex coastal waters and demonstrates its applicability in HAB monitoring. Full article

Coastal regions played a key role in the emergence of Early Neolithic cultures. Fluctuating sea levels shaped prehistoric human migration, settlement patterns, and adaptation strategies. The lower reaches of the Min River in Fujian were a major centre of activity. During the Middle to Late Neolithic, marine communities such as the Keqiutou (6500–5500 cal. a BP) and Tanshishan (5500–4300 cal. a BP) cultures flourished. However, the scarcity of earlier remains has limited understanding of Early Neolithic life before 8000 cal. a BP. We dated stratigraphic layers at the newly excavated Niutoushan site using radiocarbon dating and optically stimulated luminescence (OSL). OSL results indicate the site’s Neolithic culture layer between 9.3 ± 0.7 ka and 8.1 ± 0.5 ka, with radiocarbon dates clustering around 8300–7000 cal. a BP. Based on the younger bounds of the dating results and kernel density estimation, the Neolithic remains at the site are dated to approximately 8000–7000 cal. a BP, identifying Niutoushan as one of the earliest Neolithic sites in the region. Combined with sea-level reconstructions, the findings suggest that the rapid Early Holocene sea-level rise drove human migration along China’s eastern coast before 8000 cal. a BP. The Niutoushan culture was influenced by Neolithic cultures from northern coastal regions and potentially by those located to its south across the exposed Taiwan Strait from the Last Glacial Maximum to the Early Holocene. This points to complex interactions among Early Neolithic cultures in both northern and southern coastal China, warranting further investigation for validation. Full article

Underwater soft robots offer many potential applications, including exploration, search, and rescue missions. Notably, these recently developed underwater soft robots present a safer and more adaptable alternative to rigid robots currently in use. Their flexible and deformable bodies enable them to easily adapt to challenging underwater environments and interact with diverse aquatic creatures and structures. In this paper, we present a soft buoyancy gripper that can manage buoyancy and adjust its position in the water without relying on external mechanisms. Modulating the volume of internal fluid can function both as a gripper and adjust buoyancy as needed. When buoyancy is reduced and fluid volume is minimized, the gripper can securely grasp objects, while increased fluid volume and buoyancy allow for delicate object placement. During experiments, the gripper successfully grasped and released multiple objects. When an extra channel was added, the crawling motion was achieved. The buoyancy control system demonstrates versatility and adaptability, offering the possibility of safe underwater exploration and research. Its ability to operate without harming marine environments or organisms makes it suitable for underwater research. Full article

Intensive salmon farming is associated with high mortality rates, highlighting the need for new welfare indicators that can detect adverse conditions earlier and less invasively than many current approaches. Existing animal-based indicators used in the industry typically depend on subjective scoring and provide information mostly after welfare problems have already developed, thereby raising questions about their efficacy. Examples include emaciation, wounds, or scale loss, etc. Preliminary data and ongoing investigation suggest that melanin-based skin pigmentation may change dynamically with stress and condition in salmonid fishes. In this study, we present a semi-automated methodology for assessing changes in the grayscale intensity of melanin-based skin spots within the operculum region of adult Atlantic salmon (Salmo salar) kept in seawater. The pipeline combines computer vision models to detect the operculum, segment individual spots, and extract grayscale-based features for spot-level analysis over time. The method was applied to out-of-water images collected before and after exposure to a confinement episode. The results showed an overall shift in grayscale intensity from black to pigmentation fading after the challenge, although responses varied among individuals. These findings indicate that the proposed methodology can detect temporal changes in opercular melanin-based spots under applied experimental conditions. We therefore present this work as proof of principle for using computer vision to quantify changes in melanin-based skin spots as a potentially useful, non-invasive indicator of stress and welfare in Atlantic Salmon. Full article

This study evaluated the effects of dietary phospholipid (PL) source and supplementation level on growth performance, antioxidant capacity, and lipid metabolism in Atlantic salmon (Salmo salar) fry. A 56-day feeding trial was conducted using a basal diet containing 1.76% PL and six experimental diets with an additional 1.5%, 3.0%, or 4.5% PL provided by soybean lecithin (SL) or krill oil phospholipids (KOP). Dietary supplementation with 3.0–4.5% SL and 1.5–4.5% KOP significantly improved growth performance, whereas feed conversion ratio was significantly reduced in the 3.0–4.5% SL and 3.0% KOP groups (p < 0.05). At equivalent inclusion levels, no significant differences were observed between SL and KOP in growth performance parameters (p > 0.05). PL supplementation also reduced whole-body lipid deposition and enhanced visceral lipase activity in all groups except the 1.5% SL group, while antioxidant capacity was improved in all PL-supplemented groups (p < 0.05). SL had no significant effect on whole-body fatty acid composition (p > 0.05), whereas moderate to high levels of KOP significantly altered the fatty acid profile, characterized by reduced monounsaturated fatty acids and n-6 polyunsaturated fatty acids, along with increased eicosapentaenoic acid (EPA) levels (p < 0.05). Transcriptomic analysis indicated that PL supplementation affected hepatic lipid metabolism, with both PL sources downregulating apoa2-like, while KOP induced stronger hepatic transcriptional responses related to lipid utilization and innate immune signaling than SL (p_adj < 0.05). However, gut microbiota analysis revealed no significant differences in the relative abundances of the dominant phyla or in α- and β-diversity among the control, 3.0% KOP, and 4.5% SL groups (p > 0.05). Overall, dietary PL supplementation promoted growth, improved antioxidant capacity, enhanced lipid metabolism, and reduced lipid deposition in Atlantic salmon fry, with KOP exerting stronger effects than SL on fatty acid composition and hepatic gene expression. Full article

Polychlorinated biphenyls (PCBs) remain a major concern in marine ecosystems, where bioaccumulation in fish occurs as complex congener mixtures whose dynamics challenge conventional indicator approaches. This study develops and evaluates a data-driven framework for refining mixture-based indicators of PCB contamination by integrating ensemble machine learning with explainable artificial intelligence. Focusing on PCB-138 as a target indicator of cumulative PCB burden, we analyse concentrations of 24 organochlorines together with biological covariates in four Mediterranean edible pelagic fish species (sardine, anchovy, horse mackerel, and chub mackerel). Comparative evaluation of indicator performance shows that alternative congener combinations, including i4 PCBs (-138, -153, -170, -180), i6 PCBs (-138, -153, -170, -180, -118, -123), and mixtures incorporating DDD and DDE, more effectively represent total PCB burden than traditional indicator groups. Clustering identifies two distinct bioaccumulation settings, characterized by high-concentration coherent congener effects and low-concentration heterogeneous responses, demonstrating that indicator performance depends on concentration range and mixture context. The study illustrates how interpretable machine learning approaches can serve as formal tools for indicator evaluation and optimisation, strengthening long-term monitoring and management of legacy contaminants in marine ecosystems, particularly under conditions of persistent exposure and renewed inputs from sediment remobilization and riverine transport. Full article