Search Results (14,628)

Monitoring thermal effluent is critical for assessing aquatic ecosystem health, yet traditional satellite remote sensing and in situ point measurements often fail to capture fine-scale thermal dynamics in narrow streams and complex coastal areas due to spatiotemporal resolution limitations. This study establishes a high-precision surface water temperature mapping protocol using a low-cost Unmanned Aerial Vehicle (UAV) equipped with an uncooled thermal infrared sensor (FLIR Vue Pro R) to overcome these observational gaps. We investigated two distinct hydrological environments—an inland stream and a coastal sea—to provide initial evidence for the applicability of an in situ-based linear regression calibration model across contrasting aquatic settings. The initial uncalibrated radiometric temperatures exhibited significant bias errors reaching up to 9.2 °C in the stream and 9.4 °C in the coastal area, primarily driven by atmospheric attenuation and environmental factors. However, the proposed calibration method dramatically reduced these discrepancies, achieving Root Mean Square Errors (RMSE) of 0.43 °C and 0.42 °C, respectively, with high determination coefficients (R² > 0.87). The derived high-resolution thermal maps successfully visualized the detailed diffusion patterns of thermal plumes, revealing a steep temperature gradient of approximately 13 °C in the stream discharge zone and a distinct 5 °C elevation in the coastal effluent area relative to the ambient water. These findings demonstrate that UAV-based thermal remote sensing, when coupled with a rigorous radiometric calibration strategy, can serve as a cost-effective and reliable tool for environmental monitoring, bridging the critical scale gap between local point measurements and regional satellite observations. Full article

Frameworks for water resources management lack alignment with the action arenas where stakeholders have influence. Decision processes without stakeholder involvement across the sequence of problem-solving in multiple situations may be ineffective. By aligning stakeholder roles with situational contexts, water management can be improved. This paper identifies stakeholder roles for archetypes of management and an improved way to classify them. Systems analysis and the IAD framework provided a framework to organize stakeholders and context, and case studies were used to explain them. The analysis showed patterns among the cases, and seven categories of stakeholders with logical involvement emerged. Four categories stood out: interest groups, who represent people and causes; officials with governance and management responsibilities; stakeholders, who are involved due to the context; and bystanders, like researchers. Stakeholder roles can include dominators, brokers, and communicators, as well as those with less involvement. Dominators facilitate coordination, synthesize viewpoints, and exert pressure for compliance. Brokers work to coordinate collective actions, and communicators work as risk mediators. Stakeholder roles should be identified at the front end of the process to solve complex water problems. Full article

Gypsum-based fire protection relies on thermally activated dehydration, where chemically bound water is released and evaporated, thereby providing an endothermic heat sink that delays heat penetration through assemblies. In parallel, inorganic hydrated salts are increasingly used as flame-retardant additives in gypsum-based systems to enhance heat absorption over specific temperature ranges. Fire simulation tools and performance-based fire engineering approaches require reliable kinetic data and reaction enthalpies that can be implemented as coupled thermal–chemical source terms. However, additive-specific kinetic datasets remain limited, particularly under restricted vapor exchange conditions representative of porous construction materials. This work investigates the thermal decomposition behavior and dehydration kinetics of Aluminum Trihydrate (Al(OH)₃, ATH), Magnesium Hydroxide (Mg(OH)₂, MDH), Calcium Aluminate Sulfate (3CaO·Al₂O₃·3CaSO₄·32H₂O, CAS), and Magnesium Sulfate Heptahydrate (MgSO₄·7H₂O, ESM) with emphasis on vapor-restricted conditions representative of confined porous systems. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (2, 10, and 20 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. The thermal analysis indicates that ATH and MDH exhibit predominantly single-step dehydration behavior, while ESM shows a complex multi-step mechanism. Although CAS presents a single dominant thermal peak in the DSC signal, the isoconversional analysis reveals a multi-stage reaction behavior, demonstrating that peak-based interpretation alone may be insufficient for such systems. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches in accordance with the recommendations of the Kinetics Committee of the International Confederation for Thermal Analysis and Calorimetry (ICTAC). All reactions were consistently described using the Avrami–Erofeev model as an effective phenomenological representation of the conversion behavior. The extracted kinetic triplets were validated through numerical simulations, showing good agreement with experimental conversion and reaction rate data. The resulting kinetic parameters and dehydration enthalpies provide a physically consistent dataset for the description of dehydration processes under restricted vapor exchange. These results support the development of thermochemical models for gypsum-based systems; however, their transferability to full-scale assemblies remains subject to validation under coupled heat- and mass-transfer conditions. 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

In response to the complex erosion environment caused by periodic water level fluctuations, dry–wet cycles, and long-term water flow scouring on the Yangtze River bank, three typical soil-fixing and bank-protecting plants, Cynodon dactylon, Carex breviculmis, and Digitaria sanguinalis, which can adapt to both aquatic and terrestrial conditions, were selected for planting experiments. Tests on root–soil composite shear strength, disintegration, and water flow scouring were conducted to investigate the effects of different bank-protecting plants on bank stabilization. The results show that: 1. The root systems of the three plants significantly enhance the soil shear strength at various soil depths, but the reinforcing effect decreases with increasing soil depth. The cohesion strength of the root–soil composites ranks as Carex breviculmis > Digitaria sanguinalis > Cynodon dactylon, with maximum increases of 54.83 kPa, 20.66 kPa, and 6.5 kPa, respectively, equivalent to 3.16, 1.82, and 1.26 times that of bare soil. 2. Under dry–wet cycling, the water stability of the root–soil composites is significantly higher than that of bare soil. The disintegration residual rate of Cynodon dactylon and Digitaria sanguinalis decreased from 81.76% to 38.23% and from 80.18% to 34.34%, respectively, whereas Carex breviculmis showed only a slight decrease from 80.41% to 75.1%. Carex breviculmis exhibits the strongest stability and is least affected by dry–wet cycles, while the water stability of Cynodon dactylon and Digitaria sanguinalis declines noticeably with increasing cycle numbers. The plants’ ability to improve soil water stability ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis. 3. The enhancement of bank erosion resistance is mainly attributed to the formation of a root-reinforced network, which strengthens the soil through root–soil interlocking and anchorage, thereby increasing resistance to flow-induced shear stress and reducing particle detachment under hydraulic action. The bank erosion resistance index ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis, and decreasing with increasing runoff velocity. Compared to bare soil slopes, the maximum enhancement effects on bank erosion resistance are 75.1%, 63.3%, and 54.2% respectively. Full article

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

Air flotation is widely used in wastewater treatment for the removal of emulsified oils and suspended solids. The complex flow disturbances generated during the flotation process play a critical role in determining separation efficiency. This study employs the volume-of-fluid (VOF) method within the OpenFOAM framework to simulate the aggregation and rising behavior of microbubbles (40–100 μm) and oil droplets under various perturbation conditions. The effects of different airflow disturbance patterns on the flotation dynamics of oil–gas compounds are systematically investigated. Results show that negative pulsation promotes the rising of bubble–oil aggregates, whereas positive pulsation hinders their coalescence and upward motion. Furthermore, recirculation vortices induced by surface disturbances increase the residence time of oil–gas compounds in the water column, thereby affecting overall separation performance. The findings demonstrate that introducing vertical upward flow and bilateral oblique upward airflow can enhance flotation efficiency. This work provides insights into optimizing airflow configurations for improved oil removal in wastewater treatment applications. 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

The Dengying Formation gas reservoir in the Penglai gas field, located in the central Sichuan Basin, exhibits substantial resource potential and promising development prospects. This reservoir is characterized by well-developed fractures and dissolution cavities, strong heterogeneity, complex gas–water relationships, and widespread edge–bottom water. During production, edge–bottom water is prone to channeling and intrusion through high-permeability pathways, which severely constrains well productivity and overall gas recovery. To address these challenges, this study takes a fractured-vuggy carbonate edge–bottom water gas reservoir as an example. By integrating large-scale physical simulation with cross-scale numerical simulation, a rational production allocation method suitable for strongly heterogeneous gas reservoirs has been developed. The research results indicate that: (1) Large-scale physical simulation experiments demonstrate that for fractured-vuggy bottom water gas reservoirs, implementing rate reduction and pressure control after water breakthrough can effectively suppress water invasion and coning, extend the stable production period, and increase the recovery factor by approximately 16%; (2) Based on the dynamic characteristics of water invasion, key similarity criteria including the Bond number, capillary number, gravity–viscous force ratio, and geometric–temporal similarity ratio were selected to establish a scientific parameter design method for cross-scale numerical simulation; (3) By considering factors such as reservoir type and aquifer energy, single-well mechanistic models were used to determine appropriate production rates for individual wells, enabling rapid optimization of production allocation plans. This provides crucial guidance for efficient gas well development and surface facility planning. Full article

The safe and efficient operation of deep-sea towing systems is heavily governed by the highly nonlinear dynamic interaction between the flexible towing cable and complex seabed topographies. While existing studies accurately predict cable dynamics in mid-water or over flat seabeds, the transient responses—such as local stress concentrations and extreme tension fluctuations—induced by discontinuous topographies (e.g., stepped or 3D irregular seabeds) remain inadequately quantified. In this study, we develop an advanced 3D dynamic numerical model combining the lumped-mass finite element formulation with a modified non-linear penalty-based seabed-contact mechanics algorithm. This framework systematically evaluates the tension distribution, bending curvature, and spatial configuration shifts in the cable during the touchdown and detachment phases across inclined, stepped, and 3D seabeds. Quantitative validation against established benchmarks demonstrates robust accuracy. Results indicate that steeper seabed inclinations linearly reduce detachment time but exponentially amplify initial contact tension. Over-stepped terrains, “point-to-line” transient collisions trigger sudden tension spikes exceeding steady-state values by up to 45%. Furthermore, 3D irregular seabeds induce severe multi-directional spatial deformations, precipitating destructive whiplash effects at high towing speeds (e.g., V > 2.2 m/s). These findings provide critical physical insights and a quantitative reference for optimizing tugboat maneuvering strategies and designing fatigue-resistant cables in complex sub-sea environments. Full article

Urban underground sewage pipelines often suffer from defects such as cracks, irregular joint misalignment, and stratified sedimentation blockages, which may lead to pipeline bursts, sewage overflow, and water pollution. Timely detection of abnormal defects in sewage pipelines is critical to ensuring public health and environmental sustainability. Vision-based sewage pipeline defect detection plays a crucial role in modern urban wastewater treatment systems. However, it still faces challenges such as limited feature extraction capabilities, insufficient multi-scale defect characterization, and poor positioning stability when dealing with low-contrast images and in environments with severe background interference. To address this issue, this study proposes an enhanced SAW-YOLOv8l model that integrates RT-DETR (real-time detection Transformer) with CNN (convolutional neural network) architecture. First, a C2f_SCA module improves the long-distance feature extraction capability and localization precision. Second, an AIFI-PRBN module enhances global feature correlation through attention-mechanism-based intra-scale feature interaction and reduces computational complexity using lightweight techniques. Finally, an adaptive dynamic weighted loss function based on Wise-IoU (weighted intersection over union) further improves training convergence and robustness by balancing the gradient distribution of samples. Experiments on a mixed dataset comprising Sewer-ML and industrial images demonstrate that the SAW-YOLOv8l model achieved mAP@0.5 of 86.2% and precision of 84.4%, which were improvements of 2.4% and 6.6% respectively over the baseline model, significantly enhancing the detection performance of abnormal defects in sewage pipelines. Full article

Hyperspectral remote sensing provides essential biochemical and structural information for crop disease monitoring, yet its application to cotton Verticillium wilt has largely focused on single-period evaluations or multi-temporal classifications. Such approaches overlook the progressive nature of this vascular disease, whose pigment, water, and mesophyll responses evolve over time, making temporal hyperspectral information critical for reliable severity estimation but still insufficiently utilized. To overcome this limitation, we conducted daily time-series observations on cotton leaves and collected 2895 hyperspectral reflectance measurements and 770 high-resolution RGB images together with disease severity records, generating a temporally dense spectral-severity dataset spanning symptom-free to severe stages. Five categories of disease-related vegetation indices were derived and organized into 5-day spectral–temporal slices. Based on these features, we introduce a dual-branch Transformer-TCN model that integrates global temporal dependencies captured by self-attention with local temporal variations resolved by dilated causal convolutions for severity inversion. The model delivers the strongest performance with an R² of 0.8813, exceeding multiple single and hybrid time-series alternatives by 0.0446–0.1407 in R², equivalent to a relative improvement of 5.33–19.00%. Temporal spectral features also outperform their non-temporal counterparts, highlighting that disease progression dynamics captured by time-series spectra are critical for reliable severity retrieval. Feature contribution analysis indicates that the blue red index BRI provides the highest contribution, consistent with the single-index time-series modelling results. Photosynthesis- and water-related indices provide secondary but complementary support. Collectively, our results demonstrate that the dual-branch Transformer-TCN model can capture complex spectral–temporal relationships between cotton Verticillium wilt and disease severity, providing methodological support for crop disease monitoring and evaluation. Full article

This paper presents a theoretical approach based on the FSDT to study the acoustic vibration performance of rib-stiffened PVC foam sandwich structures with reinforcing and weakening phases when submerged in water. The complex core layer with reinforcing and weakening phases is homogenized to an equivalent orthotropic layer. Building upon this framework, the governing equations of motion for rib-stiffened PVC foam sandwich structures under the boundary conditions of a simply supported type are derived, incorporating the coupling interaction between the reinforcing ribs and the sandwich plates. Considering the influence of the underwater environment, with the Helmholtz equation governing the continuity of the acoustic pressure field and the Euler equation regulating the fluid–structure interaction interface continuity, the Navier method is subsequently employed to solve for the natural frequencies and acoustic vibration responses. For the purpose of verifying the proposed approach, the predicted results are contrasted with both the literature-derived data and numerical simulation results. Finally, parametric research is further conducted to explore the effect of the parameters of the rib and core layers on the underwater acoustic vibration characteristics. The conclusions drawn from this study can provide meaningful guidance for engineering design and optimization of such rib-stiffened sandwich structures, incorporating both reinforcing and weakening phases in underwater engineering applications. Full article

Despite the undisputable ecosystem importance of honeybees, human activities have a substantial impact on their health. Since foraging is directly linked to a wide range of crops and bee-attracting flowers, plant protection products are at the forefront of chemical scrutiny, along with contamination of pollen, nectar, beehive components and water by other xenobiotics. In this study, a non-targeted Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) screening was applied to 25 honeybee samples collected after reported death incidents in Greece. This approach led to the tentative annotation of over 50 compounds across various chemical classes, including pesticides, PFAS candidates not included in the EFSA “PFAS-4”, pharmaceuticals, antibiotics, industrial chemicals, and natural product constituents. In parallel, targeted pesticide residue analysis using liquid and gas chromatography coupled to tandem mass spectrometry (LC-MS/MS and GC-MS/MS) was performed, covering more than 250 active substances and providing direct quantitative results, revealing 11 active substances in concentrations ranging from <limit of quantification (LOQ) to 0.95 mg/kg, overlapping substantially with the HRMS detection. Overall, this study does not allow concrete causal attribution of mortality to specific chemicals; however, it documents complex co-occurrence patterns (pesticides together with other xenobiotics and plant bioactives), not excluding sublethal and mixture-toxicity effects. Quantified pesticide concentrations were below acute LD50-based thresholds, yet selected samples combined neonicotinoid/pyrethroid/fungicide signatures and other contaminants, supporting the need for mixture-toxicity frameworks and effect-based follow-ups. Full article