Search Results (398)

This study evaluated a cost-effective UAV-based multi-sensor approach for characterizing river corridor conditions during and after moderate floods in two mountain river corridors in Northern Utah, USA: the Logan River and Blacksmith Fork River. These study reaches included urban, rural, and agricultural areas, hydraulic structures and bridges, and fish passage structures. A DJI Matrice 300 UAV was used with two separate payloads: an AgEagle Altum-PT multispectral camera and an R3 Pro V2 two-return LiDAR system. The workflow included UAV flight planning and data collection, post-processing of the multi-spectral and LiDAR sensor data, spatial resolution and accuracy assessment, and interpretation of the resultant data. The multi-spectral post-processing produced pansharpened orthomosaics with a spatial resolution of 0.0432 m, while the UAV LiDAR produced DSM/DTM products at 0.05 m resolution. LiDAR accuracy assessment showed vertical RMSE values of approximately 0.0602 m for the Blacksmith Fork and 0.0782 m for the Logan River. The results showed that multispectral imagery and 2-band LiDAR provided a cost-effective means for detailed remote sensing with each sensor providing complementary information for flood and river corridor assessment. Multispectral imagery supported interpretation of flood extent, vegetation condition, relative turbidity, and thermal patterns, while LiDAR captured terrain and surface features such as banks, levees, floodplain surfaces, channel modifications, and structures. The integrated datasets supported maximum flood extent mapping and flood-level estimation. These datasets can support reach-scale hydraulic modeling, catchment hydrology, river corridor ecology, floodplain conditions, and real-time monitoring of floods, in addition to quantification of flood hazards or post-flood impacts for municipalities and insurers. Full article

Drainage solution reuse in soilless strawberry production can reduce nutrient-rich discharge, but adoption requires microbial control, hydraulic reliability, and manageable energy demand. This field study evaluated a photovoltaic (PV) assisted ultraviolet-C (UV-C) treatment loop for substrate derived drainage solution in a 132 m² three-tier natural light greenhouse producing ‘Solhyang’ strawberry in Sokcho-si, Republic of Korea. The system used an 11.25 kWp vertical windbreak-type PV facility and a 650 W treatment loop comprising a 250 W low-pressure mercury UV-C reactor, a 350 W pump, and a 50 W controller. The loop operated for 2.5 h day⁻¹, processed 3.25 m³ day⁻¹ as cumulative reactor throughput, and consumed 1.625 kWh day⁻¹, equal to 4.22% of the measured daily PV alternating current (AC) output (38.5 kWh day⁻¹). The drainage solution had low ultraviolet transmittance at 254 nm (UVT₂₅₄; 25–50%) and moderate turbidity (5–30 NTU), conditions that can attenuate UV radiation and shield microorganisms. Across six post fruit set sampling events, the mean log₁₀ reductions were 1.15 ± 0.09 for culturable molds/fungal propagules and 1.64 ± 0.09 for culturable aerobic bacteria; paired tests on log₁₀ transformed counts were significant (p < 0.001). Total coliform bacteria were not detected after treatment, corresponding to a detection limit-based lower-bound reduction of ≥2.69 ± 0.17 log₁₀. Apparent fluence values were treated as engineering estimates rather than validated delivered dose. The results support UV-C sanitation as a preliminary enabling step for drainage solution reuse, while biodosimetry, untreated circulation controls, multi-stage seasonal sampling, full-season recirculation, and crop response validation remain necessary. Full article

Retrieving the hydrophysical properties of water columns from acoustic backscatter signals is crucial for obtaining continuous and nonintrusive observations in estuarine environments. Utilizing the pulse coherent technology in the Acoustic Doppler Velocimeter (ADV), this study presents a methodology to estimate smoothed, low-frequency practical salinity in estuarine waters by integrating synchronized temperature and pressure datasets. Laboratory calibration experiments demonstrate that the Medwin formula achieves the highest inversion accuracy across a salinity range of 0 to 35 PSU, although data dispersion increases when salinity drops below 15 PSU. This methodology was further validated using field data, where acoustic intensity profiles were derived through multi-probe spatial averaging and quadratic polynomial fitting. Field application results show that a 300 min moving average filter extracts the lower frequency salt intrusion trend from turbulent noise, allowing the framework to track tidal-scale salinity variations with a low pass trend precision of ±3.07 PSU relative to reference instruments, whereas the raw unfiltered inversion exhibits a root-mean-square error (RMSE) of 5.68 PSU. The inversion performance is sensitive to ambient dynamics: the lowest error deviations occur within a moderate environmental window characterized by current velocities of 0.10–0.58 m/s and turbidities of 109.7–208.0 NTU. In contrast, the uncertainty increases during periods with higher velocities (up to 0.83 m/s) and severe turbidities (up to 278.1 NTU) or during slack water periods with current velocities below 0.10 m/s where the acoustic backscatter drops below 90 dB. These findings quantitatively define the environmental constraints for acoustic salinity estimations, providing a low-cost and non-intrusive methodological framework for recovering low-frequency salinity trends in dynamic estuaries. Full article

This study investigated density-driven mixing and stratified flow dynamics in Paldang Reservoir, a river-type reservoir formed at the confluence of the South Han River, North Han River, and Gyeongan Stream in South Korea. High-resolution field observations were conducted under varying hydrologic and hydraulic conditions using an Acoustic Doppler Current Profiler (ADCP) and multi-parameter water quality sensors (EXO2). Spatial distributions of flow velocity, water temperature, and electrical conductivity (EC) were analyzed to evaluate tributary interaction and mixing behavior within the reservoir. Distinct spatial mixing structures associated with tributary inflow heterogeneity and hydraulic operation conditions were identified. During flood-season conditions, highly turbid and high-conductivity inflow from the South Han River propagated beneath the North Han River inflow, generating density-driven lower-layer intrusion near the confluence region. Under intermittent discharge conditions at the Cheongpyeong Dam, unstable upper- and lower-layer separation structures and localized reverse-flow behavior developed. In contrast, continuous discharge conditions promoted stable tributary propagation and persistent stratified mixing structures. Case-based Richardson number (Ri) estimates further indicated localized shear-driven mixing at low-Ri inflow sections and relatively stable stratification at high-Ri sections, providing quantitative support for the observed spatial heterogeneity in density-driven mixing. Overall, spatial mixing in Paldang Reservoir was governed by tributary density contrasts and further shaped by hydraulic operation conditions. These findings improve understanding of density-driven mixing processes in river-type reservoirs under varying hydraulic conditions. Full article

Understanding turbidity in coastal systems is essential to ensure the sustainable management of these ecosystems, which are increasingly under pressure from natural factors and human activities. Thus, this study aims to develop a local Sentinel-2-based turbidity model for the Aveiro lagoon (Portugal) by combining Sentinel-2 records with in situ measurements. A field campaign synchronized with a Sentinel-2 overpass was conducted across the lagoon channels on 28 May 2025, to capture spatial variability by measuring near-surface turbidity and Secchi depth, for correspondence with the spectral records of satellite. Remote Sensing Reflectance (Rrs) and turbidity were derived using various algorithms integrated within the ACOLITE software (v20250114.0). Additionally, new turbidity models were developed and empirically adjusted based on the Rrs data, with their performance quantified through the coefficient of determination (R²) and Root Mean Square Error (RMSE). The results showed that the existing algorithms are not directly suitable for the Aveiro lagoon, as they underestimate the highest turbidity values. The ratio between 665 and 560 nm bands (RG_ratio) proved to be the most suitable spectral index, performing best in estimating turbidity (R² = 0.822 and RMSE = 1.77 NTU). This study highlights the importance of locally calibrated models over standard ACOLITE algorithms for turbidity retrieval in shallow coastal lagoons, while emphasizing that the proposed model was calibrated for the tidal, wind, and river discharge conditions sampled during the campaign and has not yet been independently validated. Full article

In cage aquaculture, precise estimation of fish biomass is critically important for determining appropriate feeding strategies and evaluating production capacity. Currently, prevailing fish counting approaches heavily rely on acoustic or optical technologies. However, the accuracy and reliability of the obtained data are largely compromised by factors such as fish occlusion and water turbidity in practical cage farming environments. To address this limitation, this study proposed a novel method for estimating fish population size deduced from dynamic feeding information, based on the model integrated environmental and biological factors, feed intake and biomass. A 10-week feeding experiment was carried out to collect multidimensional data including feed intake, growth parameters, and environmental variables to construct a dataset correlating feeding amount with primary influential factors. Herein a bioenergetics-informed radial basis function neural network, optimized via particle swarm optimization (BE-PSO-RBF), was developed based on those empirical data. Model validation using 47 independent test samples showed that the hybrid model achieved a mean absolute error (MAE) of 26.82, a root mean square error (RMSE) of 35.62, and a mean absolute percentage error (MAPE) of 4.14%, confirming its robust generalization performance. These findings suggest that feed-intake-based population estimation may provide a feasible complementary approach for fish population assessment under cage farming conditions similar to those investigated in this study. Full article

Bacterial density, cell morphology, and carbon stock (C stock) were quantified in seven Brazilian reservoirs (Serra da Mesa, Manso, Itumbiara, Corumbá, Furnas, Mascarenhas de Moraes, and Luis Carlos Barreto) to evaluate spatial and seasonal patterns in these tropical freshwater systems. Subsurface water samples were collected before, during, and after the rainy season. Bacterial density, cell volume, elongation, and biomass were determined using epifluorescence microscopy, and bacterial C stock was estimated from biomass integrated over the 0.5 m sampling depth. C stock varied among reservoirs and sampling periods, with the highest values consistently observed in the largest reservoir (Serra da Mesa, 1.9·10⁻⁵ g C). Although bacterial densities showed limited temporal variation, biomass peaked before the rainy season. Density and biomass were negatively correlated with water transparency and positively correlated with turbidity, suggesting that particle-associated organic and inorganic matter influences bacterial biomass accumulation. These findings highlight how environmental conditions shape bacterial biomass and carbon storage in tropical reservoirs, contributing to a broader understanding of microbial carbon pools in these ecosystems. Full article

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km² in 2020 and dropping to 614.04 km² in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R² of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data. Full article

Background/Objectives: Protein misfolding and amyloid fibril formation underlie several degenerative diseases, including Alzheimer’s disease and Parkinson’s disease. Alpha-1 antitrypsin (A1AT), a serpin protein, is particularly prone to misfolding, with polymerization and aggregation implicated in alpha-1 antitrypsin deficiency and associated hepatic and pulmonary disorders. In this study, we examined the structural changes in A1AT induced by the fluorinated alcohol, trifluoroethanol (TFE), and assessed the inhibitory effects of two natural polyphenols, amentoflavone (AMF) and theaflavin (TF), on aggregation and fibril formation. Methods: A library of selected phytocompounds was virtually screened against the crystal structure of A1AT (PDB 3NE4) using AutoDock Vina to elucidate their binding affinity towards it. Based on binding affinities, two compounds, AMF and TF, were selected for further studies. Protein aggregation was induced with TFE, and the protective effects of AMF and TF were evaluated using protease inhibitory activity, intrinsic fluorescence, turbidity, Rayleigh scattering, ANS fluorescence, and ThT fluorescence assays. Furthermore, 100 ns molecular dynamics simulation and MM-PBSA calculations were performed to assess the stability and binding interactions of the A1AT–ligand complexes. Results: Pre-treatment of A1AT with AMF or TF significantly inhibited TFE-induced aggregation in a dose-dependent manner, with AMF being consistently more effective. ThT fluorescence analysis revealed a ~60–65% decrease in aggregate formation upon treatment with polyphenols, with IC₅₀ values estimated at ~40 µM for AMF and ~50 µM for TF, both of which are statistically significant. Molecular docking and 100 ns molecular dynamics simulation also revealed stable A1AT–polyphenol interactions, with AMF exhibiting greater binding affinity and greater attenuation of solvent-induced conformational perturbation. Conclusions: Collectively, our findings show that TFE causes A1AT misfolding via a molten globule-like intermediate, resulting in fibril formation at 30–40% TFE, and natural polyphenols AMF and TF inhibited aggregation in a concentration-dependent manner. These observations suggest the potential of AMF and TF as lead scaffolds for anti-aggregation strategies, as modulators of amyloidogenic processes. Full article

Submarine groundwater discharge (SGD) is an important pathway for water and solute exchange between coastal aquifers and the ocean, but its spatial detection remains challenging because field methods have limited coverage and remotely sensed anomalies may also reflect other coastal processes. This study developed a hydrodynamically constrained remote sensing framework for SGD identification by integrating optical and thermal indicators with hydrogeological constraints. Sentinel-2 imagery was used to derive the Normalized Difference Chlorophyll Index (NDCI) and Normalized Difference Turbidity Index (NDTI), while Landsat thermal data were used to quantify seasonal sea surface temperature variability using the 90th–10th percentile amplitude. These indicators were combined in a K-means clustering framework, and the classification results were further constrained using year-specific maximum offshore distances estimated from groundwater level observations with a Dupuit–Glover-based scaling approach and hydraulic time-lag correction. Applied to the north shore of Long Island, New York, the framework identified coherent nearshore SGD patches that were broadly consistent with field observation locations and showed both temporally persistent discharge zones and interannual variability in spatial extent. These results indicate that incorporating physically based constraints can improve the robustness and interpretability of remote sensing-based SGD detection. Full article

Subsea Christmas trees are often deployed in turbid coastal waters or seabed environments. During manipulator operations on Christmas tree panels, conventional optical servoing is severely limited by rapid electromagnetic attenuation and strong scattering from suspended particles, resulting in reduced visibility. Forward-looking sonar (FLS) provides stable imaging, but its unique imaging geometry and low resolution make direct 6D pose estimation challenging. To address this issue, this paper proposes a 6D object pose estimation method for FLS images, in which conventional optical control-point-based pose estimation is restructured to resolve the mismatch between optical-centric network assumptions and acoustic imaging characteristics, and is further integrated with acoustic projection-based pose inversion. First, to address the limited diversity of target appearances and the scarcity of training data, we construct an FLS imaging model based on primary truncation for image simulation, providing data for model pretraining. Second, a multi-task acoustic control-point detection network, Acoustic-Yolo6D, is designed to mitigate localization degradation caused by heavy speckle noise, low boundary contrast, and resolution variations associated with polar-coordinate imaging, through heatmap regression, auxiliary object segmentation, and explicit range-bearing positional encoding. An Acoustic-n-Point (AnP) model is then used to recover the target 6D pose. Finally, simulation and water-tank experiments on the socket target verify the feasibility and robustness of the proposed method under limited-data conditions. The method achieves a 3.1 cm mean translation error, a 10.88° mean orientation error, and 52 FPS in real underwater acoustic environments. Full article

A recent two-layer theory for long-runout turbidity currents explains sustained bypass by allowing a dense lower layer to exchange mass with a more dilute upper layer while avoiding rapid over-thickening. Here, a morphodynamic extension is developed that couples suspended load and bed exchange while treating the two-layer hydrodynamics as a prescribed background. A suspended-sediment balance with bed exchange and Exner’s equation are written on that background, the depositional state variable $B=E_s/\left(rC\right)$ is introduced, and an exact nonlinear evolution equation for B is derived within the prescribed-background setting. In the weak-exchange limit this equation reduces to an algebraic onset criterion, thereby identifying the regime in which the simpler threshold is valid. Applied to an Amazon-like local-normal-flow reconstruction, the model shows that finite exchange shifts depositional onset upstream relative to the weak-exchange estimate. Background-fidelity checks, grid-refinement tests and closure/inlet sensitivities are reported to delimit the quantitative use of the reduced application. The framework is therefore best interpreted as a coupled reduced theory for suspended load and bed exchange on a prescribed two-layer bypass background rather than a fully hydro-morphodynamic closure. Full article

It is well known that broadband solar irradiance is measured with a much higher spatial density than ultraviolet (UV) solar irradiance. Building on this, this study proposes a new model for estimating clear-sky global UV solar irradiance based on broadband radiometric measurements. The model is developed for the UV spectral range from 0.280 to 0.400 μm. The originality of the model lies in its innovative structure, empirically derived equations, and minimal input requirements, limited to global solar irradiance and atmospheric turbidity. Preliminary results demonstrate that the proposed model achieves a stable and well-balanced trade-off between simplicity and accuracy. A notable advantage of the model is its reliance on minimal inputs, enabling application over large geographical areas. Full article

To address the difficulty of directly sensing in-tank sedimentation states during sludge discharge in horizontal-flow sedimentation tanks (HSTs), this study proposes a soft-sensing framework for bottom-sludge thickness in drinking water treatment plants. This framework is designed to overcome the limited capacity of effluent-turbidity-based indicators for fine-grained discharge control and the impracticality of applying computational fluid dynamics (CFD) to real-time state estimation. The framework integrates Supervisory Control and Data Acquisition (SCADA) operational data, ultrasonic sludge–water interface measurements, and CFD-derived hydraulic priors. To incorporate hydrodynamic knowledge of sediment-particle transport, three fusion paradigms are developed: parameter transfer, representation fusion, and knowledge distillation, injecting physical priors into the parameter space, latent representation space, and supervision-constraint space, respectively. Performance is evaluated using pointwise accuracy (PA), curvature consistency error (CCE), and mass-conservation error (MCE). Experiments on a real-world HST dataset show that, across the six predictors examined, the three paradigms reduced PA, CCE, and MCE by 30.7%, 16.0%, and 56.3% on average relative to the same predictors trained without prior fusion. Under the in-distribution setting, the Attention predictor combined with parameter transfer attained the lowest PA (0.026) and the lowest MCE (1.052) among the eighteen paradigm–predictor combinations evaluated. Under the out-of-distribution setting with extended sedimentation duration, knowledge distillation attained the lowest values on all three metrics across zero-shot, 4-shot, and 6-shot adaptation; in the zero-shot setting, its PA, CCE, and MCE were 33.3%, 50.9%, and 33.8% lower than those of the second-best paradigm. These results demonstrate, within the experimental scope of this study, a methodological foundation for state-informed sludge-discharge scheduling in HSTs. Full article

This study evaluates the pH-dependent ozonation of 2,6-dichloro-1,4-benzoquinone to optimize sustainable oxidation strategies for water treatment. Experiments were conducted over a wide pH range under controlled temperature and ozone dosage. DCBQ was fully degraded within minutes following first-order kinetics, regardless of pH. Acidic to neutral systems experienced a progressive pH decrease due to the formation of oxygenated transformation products, whereas strongly alkaline conditions remained stable due to buffering effects. Aromaticity removal followed a second-order kinetic and increased with pH, reflecting enhanced aromatic ring cleavage under alkaline conditions. Color was rapidly eliminated for all tested pH values, while turbidity remained low at pH ≤ 10 but increased under extreme alkalinity due to colloidal aggregation. While previous studies have examined the influence of pH on ozone reaction pathways, its combined effect on ozonation performance and gas–liquid mass transfer remains largely unexplored. Dissolved ozone measurements enabled estimation of the gas–liquid mass transfer coefficient, which decreased linearly with increasing pH, revealing a direct coupling between pH-controlled ozone reactivity and transfer efficiency. Overall, pH 9–10 was identified as the optimal operational range, balancing effective aromaticity removal, ozone stability, and minimal turbidity, thus providing practical strategies for the treatment of chlorinated quinones in water. Full article