Search Results (634)

Laser pulses experience significant temporal broadening in underwater environments due to strong turbulence and scattering effects. As water turbidity increases, the likelihood of multiple scattering events rises, further intensifying pulse broadening and thereby degrading the ranging accuracy of underwater single-photon LiDAR systems. Accurate characterization of the return pulse shape is crucial for precise distance extraction, typically achieved via cross-correlation with the system’s Instrument Response Function (IRF). Conventional models often fail to accurately characterize the distinctive asymmetric shape of underwater LiDAR returns, which feature a rapid rise and a slow decay. To address this limitation, this paper proposes a Modified Biexponential Distribution (MBD) model, specifically designed to capture both the sharp leading edge and the gradual trailing decay of the pulses. This model enables a more accurate representation of the broadened pulse, effectively mitigating the ranging error induced by scattering. Experimental validation demonstrates that, at an attenuation length of 6.9, the Depth Absolute Error (DAE) is reduced from 3.82 cm to 3.15 cm (a 17.54% improvement), while the probability of achieving a DAE below 3.82 cm increases from 49.70% to 74.83%. These results confirm the effectiveness and robustness of the proposed model in enhancing the ranging accuracy of underwater photon-counting LiDAR systems. Furthermore, this study provides a model-driven analytical basis for improving underwater photon detection and bathymetric performance in turbid conditions. Full article

Livestock health knowledge graphs are essential for decision-making and reasoning in animal husbandry, yet existing knowledge is scattered across unstructured literature and encoded in narrowly scoped, species-specific models, resulting in semantic fragmentation and limited reusability. To address these issues, we proposed FARM (Four-dimensional Automated-Reasoning Multi-agent), a zero-shot multi-agent framework used for constructing multi-species livestock health knowledge graphs. FARM is grounded in a Four-Dimension Livestock Health Framework encompassing Rearing Environment, Physiological Status, Feed & Water Inputs, and Production Performance, and employs a unified ontology strategy that integrates cross-species general labels with species-specific constraints to achieve semantic alignment. The framework orchestrates five specialized agents—Coordination, Entity Extraction, Ontology Normalization, Relation Extraction, and Knowledge Fusion—to automate the construction process. Experiments on 2478 expertly annotated text samples demonstrate that FARM achieves an entity-level F1 score of 0.8070 (IoU ≥ 0.5), surpassing the strongest baseline by 0.1627. Moreover, it attains a corrected entity label accuracy of 90.44% and an F1 score of 0.9277 in relation existence identification, outperforming the baseline by 0.1114. Validation on 500 image samples further confirms its capability in multimodal evidence fusion. The resulting knowledge graph contains 29,064 entities and 26,662 triples, providing a reusable foundation for zero-shot extraction and unified cross-species semantic modeling. Full article

Emerging landslides and severe floods highlight the urgent need to analyse and support predictive models and early warning systems. Soil moisture is a crucial parameter and it can now be determined from space with a resolution of a few tens of meters, potentially leading to the continuous global monitoring of landslide risk. We address this issue by determining the volumetric water content (VWC) of a testbed in Southern Italy (bare soil with significant flood and landslide hazard) through the comparison of two different satellite observations on the same day. In the first observation (Sentinel-1 mission of the European Space Agency, C-band Synthetic Aperture Radar (SAR)), the back-scattered radar signal is used to determine the VWC from the dielectric constant in the microwave range, using a time-series approach to calibrate the algorithm. In the second observation (hyperspectral PRISMA mission of the Italian Space Agency), the short-wave infrared (SWIR) reflectance spectra are used to calculate the VWC from the spectral weight of a vibrational absorption line of liquid water (wavelengths 1800–1950 nm). As the main result, we obtained a Pearson’s correlation coefficient of $0.4$ between the VWC values measured with the two techniques and a separate ground-truth confirmation of absolute VWC values in the range of 0.10–0.30 within $\pm 0.05$. This overlap validates that both SAR and hyperspectral data can be well calibrated and mapped with 30 m ground resolution, given the absence of artifacts or anomalies in this particular testbed (e.g., vegetation canopy or cloud presence). If hyperspectral data in the SWIR range become more broadly available in the future, our systematic procedure to synchronise these two technologies in both space and time can be further adapted to cross-validate the global high-resolution soil moisture dataset. Ultimately, multi-mission data integration could lead to quasi-real-time hydrogeological risk monitoring from space. Full article

Methane emissions from rice paddies account for over 11% of global atmospheric CH₄, making water management practices such as Alternate Wetting and Drying (AWD) critical for climate change mitigation. Remote sensing offers an objective approach to monitoring AWD implementation and improving greenhouse gas estimation accuracy. This study investigates the backscattering mechanisms of L-band SAR for inundation/non-inundation classification in paddy fields using full-polarimetric ALOS-2 PALSAR-2 data. Field surveys and satellite observations were conducted in Ryugasaki (Ibaraki) and Sekikawa (Niigata), Japan, collecting 1360 ground samples during the 2024 growing season. Freeman–Durden decomposition was applied, and relationships with plant height and water level were analyzed. The results indicate that plant height strongly influences backscatter, with backscattering contributions from the surface decreasing beyond 70 cm, reducing classification accuracy. Random forest models can classify inundated and non-inundated fields with up to 88% accuracy when plant height is below 70 cm. However, when using this method, it is necessary to know the plant height. Volume scattering proved robust to incidence angle and observation direction, suggesting its potential for phenological monitoring. These findings highlight the effectiveness of L-band SAR for water management monitoring and the need for integrating crop height estimation and regional adaptation to enhance classification performance. Full article

In this study, a hybrid sensor based on a defective square-truncated patch antenna (STPA) and a frequency-selective surface (FSS) was analyzed numerically and experimentally for different glucose–distilled water solutions. Here, an FSS was employed to enhance the sensitivity of the hybrid sensor. The sensing principle relies on monitoring variations in the loss tangent ($tan\delta$) and relative permittivity (${\epsilon }_r$) caused by different glucose concentrations applied to the sample under test (SUT). An open-ended coaxial probe was used to measure the complex permittivity of the solutions, which was then fitted to the Debye relaxation model. The simulated and experimental results of the novel sensor showed good agreement in a glucose concentration monitoring application. The sensor spanned the glucose range from 0 mg/dL to 5000 mg/dL, exhibiting a sensitivity of 55.44 kHz/mgdL⁻¹ and a figure of merit (FOM) of 6.23 × ${10}^{-4}$ (1/mgdL⁻¹) in the experiments and 53.60 kHz/mgdL⁻¹ and 1.71 × ${10}^{-4}$ (1/mgdL⁻¹) FOM in the simulations. When solutions with different concentrations were tested in the SUT, the resonance frequency of the antenna ($f_0$, in GHz) changed. To further characterize the sensor response, the relationship between the glucose concentration (C, in mg/dL) and $f_0$ was examined. A regression-based prediction model was constructed to map the measured scattering parameters to the glucose concentration, yielding a coefficient of determination ($R^2$) of 0.976. The high sensitivity, compact size, and compatibility with planar fabrication suggest that the proposed hybrid sensor has the potential to contribute to the development of non-invasive glucose-monitoring systems. Full article

As offshore wind farms expand into deeper waters, the safe transportation of large jacket foundations presents a significant engineering challenge. This study utilizes the SESAM 2022 software suite, based on three-dimensional potential flow theory, to conduct a coupled numerical simulation and parametric analysis of a barge-jacket system. Finite element models of the barge and jacket are established, with mesh convergence verified. The influences of key parameters including wave frequencies (0.4–1.6 rad/s), wave directions (0–180°), forward speeds (0–8 knots) and jacket arrangement (vertical/horizontal) on the six degrees of freedom (6-DOF) dynamic responses of the coupled system are systematically investigated. Based on the observed response characteristics, optimized transportation configurations and practical engineering recommendations are proposed. The findings consolidate previous scattered parametric results into a single, repeatable SESAM-based benchmark data set, offering a reference against which future nonlinear or time-domain models can be validated. Furthermore, this work establishes a systematic parametric basis and offers practical guidance for assessing the safety of offshore wind turbine (OWT) foundation transportation in deep-water environments. Full article

Quantitative precipitation estimation (QPE) from radar reflectivity is fundamental for weather nowcasting and water resource management. Conventional Z-R relationship formulas, derived from Rayleigh scattering theory, rely heavily on empirical parameter fitting, which limits the estimation accuracy and generalization across different precipitation regimes. Recent deep learning (DL)-based QPE methods can capture the complex nonlinear relationships between radar reflectivity and rainfall. However, most of them overlook fundamental physical constraints, resulting in reduced robustness and interpretability. To address these issues, this paper proposes FusionQPE, a novel Physics-Constrained DL framework that integrates an adaptive Z-R formula. Specifically, FusionQPE employs a Dense convolutional neural network (DenseNet) backbone to extract multi-scale spatial features from radar echoes, while a modified squeeze-and-excitation (SE) network adaptively learns the parameters of the Z-R relationship. The final rainfall estimate is obtained through a linear combination of outputs from both the DenseNet backbone and the adaptive Z-R branch, where the trained linear weight and Z-R parameters provide interpretable insights into the model’s physical reasoning. Moreover, a physical-based constraint derived from the Z-R branch output is incorporated into the loss function to further strengthen physical consistency. Comprehensive experiments on real radar and rain gauge observations from Guangzhou, China, demonstrate that FusionQPE consistently outperforms both traditional and state-of-the-art DL-based QPE models across multiple evaluation metrics. The ablation and interpretability analysis further confirms that the adaptive Z-R branch improves both the physical consistency and credibility of the model’s precipitation estimation. Full article

Nanoemulsions (NEs) offer a promising strategy for delivering lipophilic cannabidiol (CBD) to protect skin from particulate matter (PM)-induced damage. In this study, CBD-loaded oil-in-water NEs based on Brij^® O10 (polyoxyethylene (10) oleyl ether) and olive oil were prepared by the phase inversion temperature (PIT) method and characterized. A 20% w/w Brij^® O10 formulation (B20) remained clear and stable for 30 days. CBD solubility was markedly enhanced in Brij^® O10 micelles and further increased in NEs, exceeding theoretical predictions and indicating synergistic solubilization in the oil–surfactant system. CBD incorporation lowered the PIT and induced nonlinear changes in droplet size with oil content. All formulations exhibited nanoscale droplets by dynamic light scattering and transmission electron microscopy, moderately low zeta potentials consistent with nonionic steric stabilization, and maintained physical stability despite increased turbidity at higher oil levels. In a full-thickness human ex vivo skin model exposed to PM, both blank and CBD-loaded NEs reduced interleukin-6 (IL-6) and matrix metalloproteinase-1 (MMP-1) in PM-exposed skin, with CBD-loaded NEs providing additional reductions and uniquely restoring procollagen type I C-peptide (PIP) relative to their blanks. Overall, PIT-based CBD NEs enhance CBD solubilization and protect human ex vivo skin from PM-induced inflammation and extracellular matrix degradation. Full article

The study focuses on modeling the remote sensing reflectance (R_rs) for optically complex waters based on the absorption (a), scattering (b), and backscattering (b_b) coefficients measured at selected wavelengths (420 nm, 488 nm, 555 nm, and 620 nm). R_rs was calculated using both Morel’s proxy and Monte Carlo (MC) simulations. A comparison of the R_rs values obtained from the proxy and the MC simulations allowed us to determine the proxy factor (k). The results evidenced that this proxy parameter increases with wavelength. The findings demonstrate that R_rs can be computed from inherent optical properties (IOPs) using radiative transfer modeling, providing light independent reflectance estimates, unlike direct in situ R_rs measurements, which are affected by instantaneous lightening conditions. Full article

Coastal Trapped Waves (CTWs) represent an important class of mesoscale fluctuations in nearshore shelf regions and play a crucial role in modulating coastal circulation. The South China Sea (SCS), the largest semi-enclosed marginal sea in the western Pacific Ocean, features a continental shelf approximately 200 km wide. During summer, the SCS is frequently impacted by typhoons, which often trigger significant CTWs. This study investigates the characteristics of CTWs generated by Typhoon ‘Soudelor’ (No. 1513) in the northwestern SCS, based on current observations and numerical model simulations. Under the influence of Soudelor, CTWs characterized by elevated water levels nearshore and depressed water levels offshore were initially generated by wind-induced Ekman transport in the Taiwan Strait. These waves subsequently propagated southwestward along the coastline with phase velocities ranging from 7.2 to 18.3 m/s. Model results indicate that the CTW influenced current fields up to 160 km offshore, with a maximum CTW-induced current velocity exceeding 0.7 m/s. The vertical structure of the CTW-induced current field exhibited a barotropic characteristic. The influence of CTWs on current fields diminished with propagation distance, accompanied by a reduction in the induced current velocity. This attenuation was particularly pronounced between Xiamen (XM) and Shanwei (SW). Sensitivity experiments further revealed that the slowed propagation phase velocity of CTWs between XM and SW was attributable to strong reflection, scattering, and nonlinear effects caused by the abrupt topographic changes of the Taiwan Bank. Full article

Synthetic Aperture Radar (SAR) has been widely applied for rice monitoring, especially in cloud-prone areas, due to its ability to penetrate clouds. However, only limited methods were developed to monitor separately irrigated rice and rainfed rice ecosystems. This study demonstrated the use of multi-temporal polarimetric dual-polarization (dual-pol) SAR (Sentinel-1B and ALOS PALSAR-2) data to monitor and discriminate the irrigated and favorable rainfed rice ecosystems in the province of Iloilo, Philippines. Key polarimetric parameters derived from H–A–α and model-based dual-pol decomposition were analyzed to characterize the rice phenology of both ecosystems. Segmented regression was performed to detect breakpoints corresponding to changes in rice phenology within each ecosystem and used to identify the parameters to use for classification. Based on the results, Sentinel-1B polarimetric parameters (entropy, anisotropy, and alpha) can capture the phenological dynamics, whereas ALOS2 polarimetric parameters were more sensitive to water conditions, as reflected in span and volume scattering. Furthermore, irrigated rice exhibited more stable and predictable scattering patterns than favorable rainfed rice. Using the Random Forest classifier, various combinations of backscatter and polarimetric parameters from Sentinel-1B and ALOS2 were tested to discriminate between the two ecosystems. The highest classification accuracy (81.81% overall accuracy; Kappa = 0.6345) was achieved using the combined backscatter (S1B VH, ALOS2 HH, and HV) and polarimetric parameters from both sensors. The results demonstrated that polarimetric parameters effectively capture phenological stages and associated scattering mechanisms, with the integration of Sentinel-1B and ALOS2 data improving the discrimination of irrigated and favorable rainfed rice systems. Full article

By combining the Maxwell–Garnett mixing rule, Mie scattering, and the four-stream discrete ordinates adding method, a snow albedo model with explicit consideration of water content was constructed, and the influence of snow water content on snow albedo simulation was systematically analyzed. The results indicate that liquid water content is the key factor contributing to significant changes in albedo in the near-infrared band. The albedo of snow with small particle sizes is more sensitive to water content. The water content in the surface layer of snow has a more pronounced effect on reducing albedo. The actual measurement cases at the stations on the Tibetan Plateau, Xinjiang, and Northeast China show that the model established here provides a good simulation of albedo accuracy, with a bias of −0.0069 and a Root Mean Square Error (RMSE) of 0.0583 compared to the observations. This indicates that the model has a strong ability to express physical mechanisms and performs stably in complex environments, thereby demonstrating good regional applicability. This model can also be applied to wet snow containing impurities in the future. Full article

Moisture content is one of the key indicators for evaluating the quality of apricots. When moisture levels fluctuate over an excessively wide range, scattering effects and absorption characteristics interfere with each other, making it difficult for a single model to achieve accurate predictions across the entire range. This study investigates precision modeling methods applicable to different moisture intervals based on spectral morphological features. By extracting the spectral morphological features of the water-sensitive regions (peak and valley) and conducting Pearson correlation analysis, the spectral morphological feature parameters with relatively strong correlations were selected, and they were combined with the characteristic bands to construct a segmented model for water content intervals. The results indicate that spectral morphological features of apricots within the 25–40% and 40–55% moisture range exhibit a certain correlation with moisture content. A weak correlation is observed in the 55–70% moisture range. After preliminary fusion modeling of spectral morphological features and characteristic bands for apricots across different moisture ranges, further analysis revealed that moisture content models based on valley morphology features and characteristic bands outperformed those based on peak morphology features and characteristic bands, demonstrating superior representational capability. By establishing a fusion model based on the spectral morphological parameters selected through Pearson’s method and the characteristic bands, the detection accuracy and model stability in the 25–70% moisture content range have been effectively improved. Among all the models covering different moisture content ranges, the model for the 55–70% moisture content range has the best prediction effect. The correlation coefficient of its prediction set reaches 0.8723, and the Ratio of Performance to Interquartile Range (RPIQ) is 2.5220, indicating that this range is the most suitable for establishing a high-precision quantitative moisture content detection model. This research effectively solved the problem of spectral response distortion caused by wide variations in moisture content and improved the prediction accuracy of the moisture content detection model for apricots. Full article

This study investigates the formation, physicochemical properties, and applicability of surfactant-free microemulsions (SFMEs) as nanoreactors for the synthesis of silicon dioxide nanoparticles. Surfactant-free systems offer a promising and environmentally benign alternative to traditional microemulsions in which particle formation is governed by surfactants, yet their structural behavior and synthesis mechanisms remain insufficiently understood. A ternary system composed of water, ethanol, and n-pentanol was selected as a model, and its structural organization was analyzed through electrical conductivity, surface tension, and dynamic light scattering (DLS) measurements. The results revealed a broad single-phase region, indicating high miscibility of the components and the formation of dynamically connected polar domains. Electrical conductivity data suggested gradual reorganization of the internal structure without a distinct percolation threshold, while surface tension analysis and the corresponding Gibbs free energies of aggregation (ΔG°) reflected a weaker thermodynamic driving force for aggregation compared to systems containing longer-chain alcohols. DLS measurements confirmed the presence of fluctuating aggregates with hydrodynamic radii between 30 and 85 nm, consistent with literature values for surfactant-free systems. Based on these findings, silica nanoparticles were synthesized within selected compositions of the single-phase region. The resulting particles exhibited predominantly spherical morphology and variable dispersity, reflecting the moderate structural stability of the underlying microemulsion. The synthesized silica nanoparticles typically ranged from approximately 0.9 to 1.2 μm in diameter, reflecting the structural characteristics of the selected SFME compositions. Overall, the results demonstrate that water/ethanol/n-pentanol SFMEs provide new insights into surfactant-free aggregation processes and offer a sustainable route for the synthesis of inorganic nanoparticles. Full article

Offshore tubular pile systems in earthquake-active marine regions face risks from vertical seismic excitation, water dynamics, and pile–soil interactions. Thus, an analytical solution for offshore tubular piles considering multi-physical field coupling (the mutual interactions between seawater, tubular pile, and surrounding soil) is developed to investigate their dynamic responses under vertical seismic loading. Firstly, the dynamic response of the tubular pile system is decomposed into free-field and scattered-field components. The governing equations for water, soil, and tubular piles (one-dimensional and three-dimensional tubular pile models) are established, with the strict enforcement of boundary conditions such as displacement continuity and stress equilibrium. Then, analytical solutions for both one-dimensional and three-dimensional tubular pile models are derived. The proposed framework is validated by comparison with existing literature solutions, confirming its rationality. Subsequently, parametric analyses are conducted to explore the influences of key factors. The results indicate that it is essential to consider the coupled effects of vertical earthquakes and water–pile–soil interaction in the design of offshore tubular piles, as neglecting multi-field coupling or adopting oversimplified models can lead to inaccurate predictions of dynamic responses. Full article