Search Results (80,196)

Soil organic carbon (SOC) is a crucial indicator of soil quality and carbon cycling. While remote sensing and machine learning enable regional scale SOC prediction, most studies rely on vegetation indices (VIs) derived from bare-soil periods, potentially neglecting vegetation–soil interactions during crop growth. Given the bidirectional relationship between SOC and crop growth, we hypothesized that using crop-lush period VIs (VIs_lush) instead of bare-soil period VIs (VIs_bare) would increase the inversion accuracy. To test this hypothesis, we chose the cropland area in Liaoning Province as the study area and developed three modeling strategies (MS-1: VIs_lush + other features; MS-2: VIs_bare + other features; and MS-3: without VIs) using Landsat 8 imagery, topographic and precipitation data, and ensemble learning models (XGBoost, RF, and AdaBoost), with SHapley Additive exPlanations (SHAP) analysis for variable interpretation. We found that (1) all models achieved their highest performance under MS-1, with XGBoost outperforming the others across all modeling strategies; (2) for XGBoost, MS-1 yielded the highest inversion accuracy (R² = 0.84, RMSE = 2.22 g·kg⁻¹, RPD = 2.49, and RPIQ = 3.25); compared with MS-2, MS-1 reduced the RMSE by 0.31 g·kg⁻¹, increased R² from 0.77 to 0.84, and reduced the RPD by 0.31 and the RPIQ by 0.40, and compared with MS-3, MS-1 reduced the RMSE by 0.41 g·kg⁻¹, increased R² from 0.79 to 0.84, and reduced the RPD by 0.39 and the RPIQ by 0.51; (3) based on the SHAP analysis of the three modeling strategies, it is considered that precipitation, terrain and terrain analysis results are important indicators for SOC content inversion, and it is confirmed that VIs_lush contributed more than VIs_bare, supporting the rationale of using lush-period imagery; and (4) Liaoning Province exhibited distinct SOC spatial patterns (mean: 13.08 g·kg⁻¹), with values ranging from 2.19 g·kg⁻¹ (sandy central–western area) to 33.86 g·kg⁻¹ (eastern mountains/coast). This study demonstrates that integrating growth stage-specific VIs with ensemble learning can significantly enhance regional-scale SOC prediction. Full article

Electrochemical impedance spectroscopy (EIS) is a highly versatile electrochemical technique capable of discretizing each electrochemical parameter in complex systems by employing a broad frequency spectrum. When EIS is employed in (bio)sensing applications, the electrochemical parameters are usually fitted into a relatively limited equivalent circuit model regardless of the system at hand. This work thoroughly discusses the meaning of each physical parameter in the Randles circuit, the most common equivalent circuit to model (bio)sensing systems based on EIS transduction. Additionally, it pinpoints the most suitable modifications to the Randles circuit for modern-day electrodes, where coatings of non-biological and/or biological materials can radically impact the measured impedance compared to that of unmodified electrodes. The discussion is supported by simulations that clearly exhibit the effect of each examined parameter, providing guidance for experimentalists to improve the accuracy of their work. Full article

Three-dimensional (3D) ocean physical fields are essential for understanding ocean dynamics, but reconstructing them solely from sea-surface remote sensing remains challenging. We present 3DV-Unet, an end-to-end deep learning framework that reconstructs eddy-resolving three-dimensional essential ocean variables (temperature, salinity, and currents) from multi-source satellite data. The model employs a 3D Vision Transformer bottleneck to capture cross-depth and cross-variable dependencies, ensuring physically consistent reconstruction. Trained on 2011–2019 reanalysis and satellite data, 3DV-Unet achieves RMSEs of ~0.30 °C for temperature, 0.11 psu for salinity, and 0.05 m/s for currents, with all R² values above 0.93. Error analyses further indicate higher reconstruction errors in dynamically complex regions such as the Kuroshio Extension, while spectral analysis indicates good agreement at 100 km+ but systematic deviation in the 20–100 km band. Independent validation against 6113 Argo profiles confirms its ability to reproduce realistic vertical thermohaline structures. Moreover, the reconstructed 3D fields capture mesoscale eddy structures and their life cycle, offering a valuable basis for investigating ocean circulation, energy transport, and regional variability. These results demonstrate the potential of end-to-end volumetric deep learning for advancing high-resolution 3D ocean reconstruction and supporting physical oceanography and climate studies. Full article

Maritime ship detection faces challenges due to complex object poses, variable target scales, and background interference. This paper introduces YOLO-PFA, a novel SAR ship detection model that integrates multi-scale feature fusion and dynamic alignment. By leveraging the Bidirectional Feature Pyramid Network (BiFPN), YOLO-PFA enhances cross-scale weighted feature fusion, improving detection of objects of varying sizes. The C2f-Partial Feature Aggregation (C2f-PFA) module aggregates raw and processed features, enhancing feature extraction efficiency. Furthermore, the Dynamic Alignment Detection Head (DADH) optimizes classification and regression feature interaction, enabling dynamic collaboration. Experimental results on the iVision-MRSSD dataset demonstrate YOLO-PFA’s superiority, achieving an mAP@0.5 of 95%, outperforming YOLOv11 by 1.2% and YOLOv12 by 2.8%. This paper contributes significantly to automated maritime target detection. Full article

As the demand for marine resource development escalates, underwater robots have gained prominence as a technological alternative to human participation in deep-sea exploration, resource assessments, and other intricate tasks, underscoring their academic and engineering importance. Traditional underwater robots, however, typically exhibit limited resilience to environmental disturbances and are readily obstructed or interfered with by aquatic vegetation, sediments, and other physical impediments. This paper examines the biological locomotion mechanisms of black ghostfish, which utilize undulatory fins and flapping wings, and presents a coupled undulatory-flapping propulsion strategy to facilitate rapid movement and precise posture adjustment in underwater robots. A multimodal undulatory-flapping bio-inspired underwater robotic platform is proposed, with a systematic explanation of its structure and motion principles. Additionally, kinematic and dynamic models for coordinated propulsion with multiple actuators are developed, and the robot’s performance under various driving modes is evaluated using computational fluid dynamics simulations. The simulation outcomes confirm the viability of the developed dynamic model. A prototype was constructed, and a PID-based control algorithm was developed to assess the robot’s performance in linear movement, turning, and other behaviors in both undulatory fin and flapping modes. Experimental findings indicate that the robot, functioning in undulatory fin propulsion mode at a frequency of 2.5 Hz, attains a velocity of 0.35 m/s, while maintaining attitude angle fluctuation errors within ±5°. In the flapping propulsion mode, precise posture modifications can be executed. These results validate the feasibility of the proposed multimodal bio-inspired underwater robot design and provide a new approach for the development of high-performance, autonomous bio-inspired underwater robots. Full article

Significant uncertainties persist across different Leaf Area Index (LAI) products due to multiple factors; therefore, the accuracy assessment of the global LAI products is an indispensable step before their application. In this study, comprehensive validation of multi-scale LAI products including Sentinel-2, Landsat-8/9, and MCD15A3H was implemented utilizing fine-resolution LAI maps which were based on UAV images and field-measured LAI data. The validation results demonstrated a consistent, systematic underestimation across all the LAI products within the study area, the RMSE of these products ranged from 0.56 to 1.63, and the coarse-resolution MCD15A3H LAI product demonstrated highest accuracy (RMSE = 0.56, R² = 0.69). The Sentinel-2 products exhibited intermediate accuracy among all those products (RMSE: 1.16–1.36). The Landsat-8/9 LAI product showed markedly lower accuracy relative to Sentinel-2; its RMSE (1.63) exceeded that of Sentinel-2 10 m LAI and 20 m LAI by 40.52% and 21.64%, respectively. In addition, all these LAI products showed consistent seasonal variation patterns with the reference LAI maps. Moreover, Sentinel-2 10 m LAI products showed serious underestimation for all vegetation types, with forests providing the highest RMSE = 0.89. This study serves as a valuable reference for the application of multi-scale LAI products in heterogeneous terrain and provides directions for the improvement of fine-resolution LAI retrieval algorithms. Full article

The outburst of Zonag Lake in the permafrost region of the Qinghai-Tibet Plateau (QTP) has significantly altered the local environment, particularly affecting surface conditions and permafrost dynamics. By employing remote sensing and GIS tools, this study analyzed the spatial and temporal variations in surface environmental changes (surface temperature, vegetation, and dryness) within the Zonag–Salt Lake basin. The results indicate that the outburst caused higher surface temperatures and reduced vegetation cover around Zonag Lake. Analysis using the Temperature–Vegetation Dryness Index (TVDI) reveals higher dryness levels in downstream areas, especially from Kusai Lake to Salt Lake, compared to the upstream Zonag Lake. Temporal trends from 2000 to 2023 show a decrease in average Land Surface Temperature (LST) and an increase in the Normalized Difference Vegetation Index (NDVI). Geographical centroid shifts in environmental indices demonstrate migration patterns influenced by seasonal climate changes and the outburst event. Desertification around Zonag Lake accelerates permafrost development, while the wetting environment around Salt Lake promotes permafrost degradation. The Zonag Lake region is also an ecologically significant area, serving as a key calving ground for the Tibetan antelope (Pantholops hodgsonii), a nationally protected species. Thus, the environmental changes revealed in this study carry important implications for biodiversity conservation on the Tibetan Plateau. These findings highlight the profound impact of the Zonag Lake outburst on the surface environment and permafrost dynamics in the region, providing critical insights for understanding environmental responses to lake outbursts in high-altitude regions. Full article

Diketopiperazines (DKPs) are biologically important cyclic dipeptides widespread in nature, associated primarily with microorganisms. This is the case for the 2,5-DKP derivative cyclo(L-Leu-L-Pro) (cLP), also known as gancidin W or PPDHMP, identified from a variety of bacteria and fungi, and occasionally found in food products. The present review retraces the discovery of cLP, its identification in living species, its chemical syntheses, and its biochemical properties. In bacteria, cLP is often associated with other DKPs to serve as a defense element against other microorganisms and/or as a regulator of bacterial growth. cLP plays a role in quorum-sensing and functions as an anticariogenic and antifungal agent. The antimicrobial mechanism of action and molecular targets of cLP are evoked. The interest in cLP for combatting certain parasitic diseases, such as malaria, and cancers is discussed. The capacity of cLP to interact with CD151 and to down-regulate the expression of this tetraspanin can be exploited to reduce tumor dissemination and metastases. The review sheds light on the pharmacology and specific properties of cyclo(L-Leu-L-Pro), which can be useful for the development of a novel therapeutic approach for different human pathologies. It is also of interest to help define the bioactivity and mechanisms of action of closely related DKP-based natural products. Full article

This study investigates the use of wheat straw ash (WSA) as a sustainable supplementary cementitious material, focusing on its mechanical performance optimization and environmental implications. WSA (ASTM C618, Class F), produced via controlled calcination at 700 °C, was used to replace cement at 2.5, 5, 7.5, 10% by mass. The optimal performance was observed at 5% substitution, achieving a 90-day compressive strength of 48.42 MPa (+4.7%) and a 28-day flexural strength of 7.93 MPa (+6.6%). To contextualize these findings, a multi-technique analytical approach was employed, including scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FT-IR), and ultrasonic pulse velocity (UPV). These methods confirmed that WSA enhances portlandite consumption through pozzolanic reactivity and improves matrix densification via secondary C-S-H gel formation. Additionally, satellite (Sentinel-5P) and ground-based measurements during a severe stubble fire event in Diyarbakir (20–24 June 2024) documented a fourfold increase in PM₁₀ concentrations (157 μg/m³ compared to the June average of ≈35 μg/m³), alongside 23% and 41% rises in NO₂ and SO₂ levels, respectively. These findings demonstrate that wheat straw ash utilization can mitigate agricultural waste burning, improve air quality, and reduce the carbon footprint of cement production. The study highlights WSA’s potential as a high-performance, eco-efficient construction material aligned with circular economy principles. Full article

The detection of internal solitary waves (ISWs) in the ocean using Synthetic Aperture Radar (SAR) images is important for the safety of marine engineering structures. Based on 4120 Sentinel SAR images obtained from 2014 to 2024, an ISW dataset covering the Andaman Sea (AS), the South China Sea (SCS), the Sulu Sea (SS), and the Celebes Sea (CS) is constructed, and a deep learning dataset containing 3495 detection samples and 2476 segmentation samples is also established. Based on the YOLOv8 lightweight model, combined with an anti-interference strategy, a multi-size block detection strategy, and a post-processing repair module, an ISW detection method is proposed. This method reduces the false detection rate by 44.20 percentage points in terms of anti-interference performance. In terms of repair performance, the repair rate reaches 85.2%, and the error connection rate is less than 3.1%. The detection results of applying this method to Sentinel images in multiple sea areas show that there are significant regional differences in ISW activities in different sea areas: in the AS, ISW activities peak in the dry season of March and are mainly concentrated in the eastern and southern regions; the western part of the SS and the southern part of the CS are also the core areas of ISW activities. From the perspective of temporal characteristics, the SS maintains a relatively high ISW activity level throughout the dry season, while the CS exhibits more complex seasonal dynamic features. The lightweight detection method proposed in this study has good applicability and can provide support for marine disaster prevention work. Full article

Chlorophyll-a is a key indicator characterizing the health of marine ecosystems. This study aimed to assess eutrophication risk by investigating the spatio-temporal evolution of chlorophyll-a in the South China Sea (SCS). Based on MODIS-Aqua remote sensing data from 2003 to 2024, five spatial interpolation methods were compared, and Ordinary Kriging was selected as the optimal method (r = 0.96) for reconstructing the chlorophyll-a distribution. The findings indicate that chlorophyll-a is higher in winter and autumn than in summer and spring, with significant enrichment observed near coastal areas. Concentrations decrease with increasing distance from the shore. The Mekong River estuary consistently exhibits high values, while the concentration in the SCS Basin remains persistently low. Furthermore, the spatial extent where chlorophyll concentrations exceed the bloom threshold was evaluated to highlight potential eutrophication risk. These results provide a scientific basis for understanding the response mechanism of the SCS ecosystem to climate change and have important implications for regional marine environmental management and ecological conservation. Full article

The hydrogen economy stands at the forefront of the global energy transition, and additive manufacturing (AM) is increasingly recognized as a critical enabler of this transformation. AM offers unique capabilities for improving the performance and durability of hydrogen energy components through rapid prototyping, topology optimization, functional integration of cooling channels, and the fabrication of intricate, hierarchical, structured pores with precisely controlled connectivity. These features facilitate efficient heat and mass transfer, thereby improving hydrogen production, storage, and utilization efficiency. Furthermore, AM’s multi-material and functionally graded printing capability holds promise for producing components with tailored properties to mitigate hydrogen embrittlement, significantly extending operational lifespan. Collectively, these advances suggest that AM could lower manufacturing costs for hydrogen-related systems while improving performance and reliability. However, the current literature provides limited evidence on the integrated techno-economic advantages of AM in hydrogen applications, posing a significant barrier to large-scale industrial adoption. At present, the technological readiness level (TRL) of AM-based hydrogen components is estimated to be 4–5, reflecting laboratory-scale progress but underscoring the need for further development, validation and industrial-scale demonstration before commercialization can be realized. Full article

Most biomarkers exhibit abnormal expression in more than one disease, making conventional single-biomarker detection strategies prone to false-negative results. Detecting multiple biomarkers associated with a single disease can therefore substantially improve diagnostic accuracy. Accordingly, recent research has focused on precise multiplex detection, leading to the development of sensors employing various readout methods, including electrochemical, fluorescence, Raman, and colorimetric approaches. This review focuses on optical sensing applications, such as fluorescence, Raman spectroscopy, and colorimetry, which offer rapid and straightforward detection and are well suited for point-of-care testing (POCT). These optical sensors exploit nanoscale phenomena derived from the intrinsic properties of nanomaterials, including metal-enhanced fluorescence (MEF), Förster resonance energy transfer (FRET), and surface-enhanced Raman scattering (SERS), which can be tailored through modifications in material type and structure. We summarize the types and properties of commonly used nanomaterials, including plasmonic and carbon-based nanoparticles, and provide a comprehensive overview of recent advances in multiplex biomarker detection. Furthermore, we address the potential of these nanosensors for clinical translation and POCT applications, highlighting their relevance for next-generation disease diagnostic platforms. Full article

This study investigates the sensitivity of an urban parameterization scheme of the Weather Research and Forecasting model (WRF). The model sensitivity is tested during the period April–May 2020 over the greater Paris region. The parent domain covers Europe with a 12 km horizontal resolution, with a nested one covering the greater Paris region with a 3 km horizontal resolution. A multi-layer urban scheme called Building Effect Parameterization coupled with the Building Energy Model (BEP-BEM) was applied in two simulations: (1) BEP-BEM Paris, with urban options tailored for the Paris region, which were derived from Earth Observation data, and (2) BEP-BEM Europe, which uses an updated urban parameter table with an estimated average profile for European cities. These two simulations were compared with observations and a WRF simulation using a simple urban parameterization (BULK approach). BULK and multi-layer urban scheme experiments present a similar general error for April, underestimating temperature, while the BEP-BEM runs overestimate temperature for May. The simulation with the advanced tailored urban parameterization over Paris appears to have the best overall performance in this 2-month period. Full article

This work proposes a novel polarization-maintaining hollow-core anti-resonant fiber structure characterized by high birefringence and low transmission loss. To address the inherent trade-off between birefringence and confinement loss, a Pareto-front-based multi-objective optimization algorithm is introduced into the geometrical design of the ARF. The optimal fiber design achieves a birefringence exceeding $1\times {10}^{-4}$ and a confinement loss of approximately 1 dB/m at the telecommunication wavelength of 1.55 $\mathsf{\mu }$m. In particular, the asymmetric wall thickness configuration further improves the trade-off, enabling confinement loss as low as 0.15 dB/m while maintaining birefringence on the order of $1\times {10}^{-4}$. This approach significantly reduces computational cost and exhibits strong potential for applications in polarization-maintaining communications, precision sensing, and high-power laser delivery. Full article