Search Results (6,437)

Background: Metabolic syndrome is associated with early impairments in cellular bioenergetics that are not fully captured by conventional body composition measures. Molecular hydrogen, produced endogenously through gut microbial fermentation and measurable in breath, has been implicated in redox and mitochondrial regulation. Whether breath hydrogen relates to preservation of intracellular, metabolically active tissue in metabolic syndrome remains unclear. Objectives: To examine the association between breath hydrogen concentration and an integrated cellular bioenergetic phenotype derived from intracellular body composition indices in sedentary adults with metabolic syndrome. Methods: Twenty-eight sedentary, middle-aged adults (51.2 ± 7.9 years, 19 females) with metabolic syndrome underwent fasting breath hydrogen assessment and multifrequency bioelectrical impedance analysis. A composite cellular bioenergetic phenotype was derived using principal component analysis of body cell mass, intracellular water, total body potassium, and glycogen. Associations between breath hydrogen and the composite phenotype were evaluated using Spearman correlation with bootstrapped confidence intervals, Theil-Sen regression, and Bayesian linear regression adjusted for age, sex, and waist circumference. Sensitivity analyses included fat-free mass. Results: A single principal component explained 98.6% of the variance across intracellular variables, indicating a highly coherent cellular bioenergetic phenotype. Breath hydrogen concentration was positively associated with this phenotype (ρ = 0.43, p = 0.021; BCa 95% CI 0.07–0.70). Theil-Sen regression confirmed a robust positive association (β = 0.017 per ppm hydrogen; 95% CI 0.002–0.046). Bayesian models showed posterior distributions centered on positive effect sizes, independent of central adiposity. In contrast, the association with fat-free mass alone was borderline. Conclusions: Breath hydrogen concentration reflects an integrated intracellular bioenergetic phenotype in sedentary adults with metabolic syndrome, tracking cellular quality rather than lean mass quantity. Breath hydrogen may serve as a non-invasive biomarker of cellular bioenergetic integrity and a potential tool for phenotype-guided metabolic interventions. Full article

This study aims to explore the effects of micro-polluted river water on nitrogen and microbial communities of paddy field soil under different irrigation modes. The experiment was conducted in a water-saving park in Nanjing. By establishing three water quality conditions—clean water, micro-polluted river water, and alternating irrigation—and two moisture conditions—flood irrigation and controlled irrigation—this study investigates the effects of different irrigation patterns on soil nitrogen and microbial communities. The results indicate that, under flood irrigation, the input of micro-polluted river water can effectively alleviate NH₄⁺-N loss during the heading stages of rice growth by 49.3%. Moisture conditions are the primary factor influencing microbial community structure. Although the input of micro-polluted river water reduces community stability, rotation irrigation can increase microbial abundance and enhance network complexity, thereby enhancing the system’s resilience. Redundancy analysis shows that soil moisture, pH, and ion content are the key environmental factors driving microbial distribution. The clean and polluted water rotation irrigation model performs best in maintaining soil nitrogen and microbial health. Rotation irrigation promotes the enrichment of key functional groups, such as Actinobacteria, effectively increasing rice yield. This study provides a theoretical basis for promoting sustainable agricultural production through water resource management. Full article

As large-scale underground storage infrastructure, deep tunnels exhibit distinct water quality dynamics driven by ground temperature gradients. Currently, there is limited investigation into water quality modeling for deep tunnel systems. Unraveling the correlation between temperature–depth gradients and water quality evolution is crucial for the operation and management of such systems. In this study, field experiments were carried out in the Qianhai–Nanshan Deep Tunnel to investigate complex water quality behavior, leading to the development of chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N) models that incorporate temporal variation, temperature, and burial depth. Results indicate that temperature is the dominant factor influencing water quality in deep tunnel storage. Increased ground temperature promotes the degradation and mass transport of pollutants within the tunnel system. Owing to temperature–depth effects, the deeply buried Qianhai tunnel significantly reduces river discharge pollution after water storage, with COD and NH₃–N removal rates reaching 74.9% and 26.8%, respectively. Temperature-controlled experiments showed that COD and NH₃–N reduction rates varied between 60–94% and 10–30% across a temperature range of 20–34 °C. The proposed model was validated against experimental data, achieving Nash–Sutcliffe efficiency coefficients of 0.7–0.8. This study provides a methodological foundation for simulating complex aquatic environments and offers a decision-support tool for optimizing the operational strategies of deep tunnel systems. However, the model’s current generalization capability is constrained by the limited experimental conditions (20–34 °C, 12 days) and the lack of experimental replicates, which should be systematically addressed in future studies. Full article

Eucalyptus plantations are expanding rapidly in southern China, delivering economic benefits but also posing ecological risks, which creates a pressing need for precise, municipal-scale monitoring. Mapping eucalyptus with sub-meter resolution imagery, however, is confronted by two main challenges: (1) the pronounced multi-scale heterogeneity of fragmented stands, and (2) the difficulty in achieving precise boundary delineation due to shadowed and complex canopy edges. To address these, this study makes two primary contributions. First, we present the Eucalyptus Semantic Segmentation Dataset (ESSD)—a high-quality, pixel-level annotated dataset that includes geographic coordinates to support reproducible research. Second, we propose SDCNet, a task-specific deep learning network optimized for eucalyptus mapping. SDCNet incorporates a redesigned SD-ASPP module that leverages Deep Over-parameterized Convolution (DO-Conv) to capture multi-scale features, alongside a novel Coordinated Self-Attention Mechanism (CSAM) to enhance the accuracy of canopy boundary detection. Ablation studies confirm the effectiveness of each component. In benchmark tests against seven state-of-the-art semantic segmentation models, SDCNet achieves superior performance, obtaining a per-class Intersection over Union (IoU) of 88.83% and an F1-score of 93.81% for eucalyptus—an improvement of +2.24% in IoU and +1.71% in F1-score over the strongest baseline. Applied to Nanning City, SDCNet produces the first 0.3 m resolution eucalyptus distribution map for the region. This map reveals a critical finding: within the watershed of the Xiyunjiang Reservoir—Nanning’s primary drinking water source—eucalyptus plantations cover more than 50% of the forested area. This result provides the first quantitative, high-resolution evidence of potential hydrological risk at a municipal scale. Our work establishes an integrated framework that bridges advanced remote sensing with actionable forest governance, offering scientifically grounded support for ecological risk assessment and sustainable land-use policy. Full article

Coal-mining subsidence lakes are an expanding artificial wetland type in China, yet the relationships between waterbird diversity components and water-quality and landscape gradients remain unclear. We conducted monthly point-count surveys from January to December 2025 at 28 subsidence lakes in Huaibei, Anhui, China (lake area: 0.01–1.05 km²), and used generalized linear mixed models (GLMMs) to test relationships between waterbird diversity and water quality, lake morphology, landscape composition, and anthropogenic disturbance. Associations differed among diversity components. Species richness was positively associated with surrounding cropland and built-up area, whereas total abundance was positively associated with total nitrogen but negatively associated with total phosphorus, indicating that nutrient-related associations were not uniform across water-quality variables. Both Shannon and Margalef diversity were positively associated with surrounding cropland and also showed positive, context-dependent associations with built-up area. These findings suggest that different components of waterbird diversity were associated with different environmental gradients, with landscape context more strongly associated with richness and diversity indices, whereas water-quality gradients were more strongly associated with abundance. Conserving waterbird diversity in subsidence lakes therefore requires attention not only to nutrient conditions within lakes, but also to the surrounding wetland–farmland landscape context. Full article

Mold infection by Aspergillus and Penicillium spp. in Sithong sweet tamarind (Tamarindus indica L.) during commercial postharvest storage poses quality and food safety risks. However, the current visual detection method, which involves randomly cracking open the pods, is both destructive and laborious. The integration of near-infrared spectroscopy (NIRS) with artificial neural networks (ANN) enables rapid and non-destructive detection while capturing non-linear biochemical–spectral relationships, offering advantages over conventional destructive and linear analytical methods. It was tested as a mold classifier in sweet tamarind pods preserved in commercial ambient conditions (25 °C, 60% relative humidity) for five weeks. Six hundred pods were examined weekly using interactance spectroscopy (800–2500 nm) with six measurement points per pod and four spectral preprocessing methods. The ANN outperformed partial least squares discriminant analysis (PLS-DA) across all storage weeks, peaking at Week 2 with standard normal variate (SNV) preprocessing (prediction accuracy: 85.00%; sensitivity: 0.84; specificity: 0.86; F1-score: 0.85). Advanced tissue degeneration caused spectral heterogeneity, which decreased performance at Week 4 (prediction accuracy: 71.82–76.36%). Principal component loadings identified mold-induced water redistribution and carbohydrate depletion wavelengths at 938, 975–980, and 1035 nm. Week-adaptive calibration is essential for implementation because of the large difference between week-specific model accuracy (up to 85%) and overall storage model accuracy (63.53%). These findings provide a mechanistic underpinning for smaller wavelength-selective sensors and temporally adaptive mold screening systems in commercial tamarind storage. Full article

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

Satellite remote sensing has become essential for water quality assessment across inland and coastal environments, with rapid improvements in recent years. Significant advances have been made in detecting optically active parameters (such as chlorophyll-a, suspended matter, and turbidity), showing consistently strong performance across multiple studies. Specifically, the median validation performance (R²) derived from the quantitative synthesis indicates R² = 0.82 for chlorophyll-a (interquartile range—IQR: 0.75–0.90), R² = 0.80 for total suspended matter (IQR: 0.78–0.85), and R² = 0.88 for turbidity (IQR: 0.85–0.90). Conversely, the retrieval of optically inactive parameters (such as nutrients like total phosphorus and total nitrogen) remains more context dependent. It exhibits moderate, more variable results, with median R² = 0.68 (IQR: 0.64–0.74) for total phosphorus and R² = 0.75 (IQR: 0.70–0.80) for total nitrogen. These findings clearly illustrate the varying success of retrievals of optically active and inactive parameters and underscore the inherent difficulties of indirect estimation methods. However, high reported accuracy has yet to translate into transferable, uncertainty-informed, and operational monitoring systems. This gap stems from structural issues in validation design, physics integration, uncertainty management, and multi-sensor compatibility rather than data limitations alone. We present a PRISMA-guided, distribution-aware quantitative synthesis of 152 peer-reviewed studies (1980–2025), based on a systematic search protocol, to evaluate satellite-based retrievals of both optically active and inactive parameters. Instead of simply averaging performance, we analyse the empirical distributions of validation metrics, considering the validation protocol, sensor type, parameter category, degree of physics integration, and uncertainty quantification. The synthesis demonstrates that validation strategy often influences reported results more than the algorithm class itself, with accuracy inflated under non-independent cross-validation methods and notable variability between studies concealed by mean-based reports. Across four decades, four persistent structural challenges remain: limited transferability across sites and sensors beyond calibration areas; weak or implicit physical integration in many data-driven models; lack of or inconsistency in uncertainty quantification; and fragmented multi-sensor harmonisation that restricts operational scalability. To address these issues, we introduce two evidence-based coding frameworks: a physics-integration taxonomy (P0–P4) and an uncertainty-quantification hierarchy (U0–U4). Applying these frameworks shows that most studies remain focused on low-to-moderate levels of physics integration and primarily consider uncertainty at the prediction stage, with limited attention to upstream sources throughout the observation and inference process. Building on this structured synthesis, we propose a transferable, physics-informed, and uncertainty-aware conceptual framework that links model architecture, validation robustness, and probabilistic uncertainty to well-founded design principles. By shifting satellite water quality modelling from isolated algorithm demonstrations towards integrated, evidence-based system design, this study promotes scalable, decision-grade environmental monitoring amid the accelerating impacts of climate change. Full article

Elucidating the trade-offs and synergistic relationships between different ecosystem services is essential to optimize the benefits of ecosystem services and ensure their proper management for human well-being and ecosystem health. However, previous studies have focused only on quantitative analysis based on statistical relationships to explore ecosystem service trade-offs and synergistic relationships as a whole; additionally, some of them lack scientific expression of spatial and temporal differences within regions. Therefore, here, we explored the trade-offs and synergies among ecosystem services in the Taihang Mountains region and conducted ecological service zoning based on the findings to support ecological conservation and high-quality development in the Taihang Mountains and North China Plain. We employed yield spatialization, the InVEST model, and ArcGIS kernel density analysis to assess the interactions among ecosystem services: provisioning (food supply), regulating (water yield and carbon density), supporting (soil retention and habitat quality), and cultural services (leisure and recreation) in the study area. Linear Pearson correlation coefficients and non-linear bagplots were utilized to analyze the interrelationships among these services. Based on the bagplot results, the geographic patterns of ecosystem service trade-offs/synergies and the distribution of dominant services were identified. The results revealed considerable trade-offs between food supply and both regulating and supporting services, with most of the latter exhibiting synergistic relationships with one another. In contrast, leisure and recreation services showed a neutral relationship with other services. Among ecosystem services, carbon density services demonstrated the highest synergistic effects, whereas food supply services exhibited the most conflicts. The various ecosystem trade-off/synergy zones and dominant service distributions generated through bagplot mappings may optimize management methods for multiple ecosystem services. Overall, these findings provide significant insights for improving ecological service zoning and natural resource management. Full article

This study aimed to evaluate the effects of adding sludge generated in water treatment plants on the composting of sewage sludge, urban organic waste, and agroindustrial waste. Four treatments were conducted with different proportions of water treatment plant sludge (WTS). Four treatments were conducted with 0%, 10%, 20%, and 30% proportions of WTS. The different proportions allowed for the evaluation of the effects of WTS addition on composting. The study was carried out in composting reactors. Kinetic models were applied to study the degradation of organic matter. Physicochemical and microbiological parameters were analyzed. During the process, temperature variation and basal respiration exhibited similar patterns. Principal component analysis showed that the 30WTS (32.2% water treatment sludge) treatment presented higher values of cation exchange capacity (CEC)/total organic carbon (TOC) ratio (3.83), and germination index (94.35%), and lower values of TOC (23.67%) and C/N (carbon/nitrogen) ratio (14.45). The composts produced in all treatments complied with Brazilian regulations for the environmental and agronomic quality of organic composts. It was concluded that the inclusion of up to 30% of WTS in composting did not negatively affect the composting process and did not compromise the environmental or agronomic quality of the final organic composts. Full article

Storm-driven turbidity is a major water-quality concern in urban watersheds, reflecting the mobilization and transport of fine sediment during runoff events. This study examines how seasonal storm characteristics influence turbidity and associated sediment transport responses in the Middle Fork of Beargrass Creek, Louisville, Kentucky, over a two-year period. Forty-one erosive storm events were identified and characterized using high-resolution rainfall data to capture storm magnitude and structure. Study objectives were to: (1) quantify event-scale turbidity responses to erosive storms, (2) compare upstream and downstream turbidity behavior to assess spatial variability, (3) evaluate seasonal variation in these relationships, and (4) assess implications for sediment-focused best management practice (BMP) design. Event-based regression models related downstream turbidity to lagged upstream turbidity and downstream erosivity. Turbidity ratios and turbidity–discharge hysteresis characterized spatial and temporal sediment transport dynamics. Results showed that winter and spring storms exhibited longer durations, stronger upstream–downstream turbidity coupling, and more stable lag relationships, indicating integrated sediment transport. Short-duration, high-intensity summer storms produced elevated turbidity ratios, pronounced clockwise hysteresis, and greater model sensitivity, consistent with localized sediment mobilization. Findings support seasonally adaptive BMP strategies, with volume-reduction approaches most effective during winter–spring and source control measures critical during summer-fall. Full article

The chili pepper (Capsicum annuum) is among the most widely consumed vegetables worldwide, valued for its sensory and nutritional properties. Nevertheless, it is highly vulnerable to deterioration due to its elevated moisture content. Effective preservation strategies, such as the addition of salt combined with drying, are therefore crucial to maintaining quality and extending shelf life. This study employed a modified Reaction Engineering Approach (REA) to model the drying kinetics and temperature behavior of chili paste under continuous and intermittent conductive hydro-drying conditions. Thirty experiments were conducted considering various salt concentrations (0, 7.5 and 15 g salt/100 g paste), water temperatures in the hydro-dryer, and heating intermittency through on/off cycles. The modified REA model accurately predicted both moisture and temperature profiles, with determination coefficients of 0.9463 and 0.8820, respectively. In addition to direct validation with the complete dataset, cross-validation between cayenne and jalapeño varieties demonstrated the ability of the model to generalize across different formulations and structural characteristics. These results confirm the robustness of the proposed framework and its suitability as a predictive tool for heterogeneous food matrices. Direct and cross-validation confirmed strong predictive performance across all operating conditions and both chili varieties, supporting the use of the modified REA model as a robust tool for representing coupled moisture–temperature dynamics in conductive hydro-drying of semi-solid matrices. Overall, the model provides a reliable platform for analyzing, designing, optimizing, and controlling hydro-drying processes in semi-solid foods, supporting the development of more efficient and sustainable preservation strategies. Full article

This study systematically evaluates the regulatory effects of multi-reservoir water diversion on ecological risk thresholds in the upper Yangtze River. Taking multiple reservoirs in the upper basin as the research object, a system dynamics model was developed to simulate reservoir operation, water level regulation, ecological water diversion, and diversion capacity enhancement. Key indicators included upstream ecological risk thresholds, ecohydrological risk levels, habitat ecological risk levels, and water environment ecological risk levels. Five scenarios were designed: S0 (baseline), S1 (enhanced ecological compensation), S2 (industrial coordination and optimization), S3 (economic synergy promotion), and S4 (comprehensive regulation and optimization). These scenarios were used to assess the combined effects of different diversion strategies on ecological risk control. Results indicate the following: (1) All scenarios reduce ecological risks to some extent, but the degree of effectiveness differs. (2) The overall ranking is S4 > S1 > S3 > S2 > S0, demonstrating that comprehensive regulation optimization is most effective in mitigating ecohydrological risks, improving habitat quality, and enhancing water environment security. (3) S1 is particularly effective in reducing ecohydrological risks and is suitable as an emergency safeguard during dry seasons, though less effective than S4 in habitat and water quality improvements. (4) S3 supports economic–ecological synergy but remains less effective than S1 and S4. (5) S2 primarily enhances industrial–ecological coordination with limited contribution to overall risk control. (6) S0 yields minimal improvement under existing operational conditions, failing to meet ecosystem safety thresholds. Overall, the findings highlight that in multi-reservoir joint diversion contexts, a composite strategy centered on comprehensive regulation optimization, supplemented by ecological compensation and economic synergy, should be prioritized to achieve systematic ecological risk reduction and ensure long-term watershed ecological security. Full article

Recycled concrete aggregate (RC A) is considered a sustainable material; however, its porosity and interfacial properties are poor due to adhering mortar. This study investigates the influence of synergistic surface treatments in terms of improving RCA quality and the resulting compressive strength of recycled aggregate concrete (RAC). A machine learning (ML) model was also developed to predict the compressive strength of recycled aggregate concrete (RAC) with different surface treatments, not just untreated RCA. In this study, three different RCA surface treatments were investigated. In this regard, acetic acid, silica fume, and sodium silicate treatments were combined. The properties of concrete and fresh concrete were investigated using slump and compressive tests at 28 and 90 days. The performance of various ML models, incorporating Gradient Boosting, Random Forest, XGBoost, and Extra Trees, was investigated. The performance of different models was also evaluated using R², MAE, and RMSE. SHAP analysis was used to evaluate the performance of different models. It has been observed that the use of surface treatment leads to lower water absorption values and higher interfacial bonding, as well as substantial improvements in compressive strength. Specifically, the use of acetic acid and silica fume for treating RCA produced compressive strengths similar to those achieved from natural aggregates at lower costs. XGBoost has the highest accuracy among all models. The R² value of XGBoost was 0.909. The SHAP analysis indicates that cement and curing age are the main features. RCA treatment parameters are considered modifiers. A user-friendly online tool was created to estimate compressive strength using different types of RCA treatment. The RCA treatment with sodium silicate and silica fume performed best in terms of embodied carbon among the treated mixes; it was deemed the best alternative from an environmental standpoint. Full article