Search Results (385)

The precise characterization of surface rupture zones and associated co-seismic displacement fields from large earthquakes provides critical insights into seismic rupture mechanisms, earthquake dynamics, and hazard assessments. Stereo-photogrammetric digital elevation models (DEMs), produced from high-resolution satellite stereo imagery, offer reliable global datasets that are suitable for the detailed extraction and quantification of vertical co-seismic displacements. In this study, we utilized pre- and post-event WorldView-2 stereo images of the 2024 Ms7.1 Wushi earthquake in Xinjiang to generate DEMs with a spatial resolution of 0.5 m and corresponding terrain point clouds with an average density of approximately 4 points/m². Subsequently, we applied the Iterative Closest Point (ICP) algorithm to perform differencing analysis on these datasets. Special care was taken to reduce influences from terrain changes such as vegetation growth and anthropogenic structures. Ultimately, by maintaining sufficient spatial detail, we obtained a three-dimensional co-seismic displacement field with a resolution of 15 m within grid cells measuring 30 m near the fault trace. The results indicate a clear vertical displacement distribution pattern along the causative sinistral–thrust fault, exhibiting alternating uplift and subsidence zones that follow a characteristic “high-in-center and low-at-ends” profile, along with localized peak displacement clusters. Vertical displacements range from approximately 0.2 to 1.4 m, with a maximum displacement of ~1.46 m located in the piedmont region north of the Qialemati River, near the transition between alluvial fan deposits and bedrock. Horizontal displacement components in the east-west and north-south directions are negligible, consistent with focal mechanism solutions and surface rupture observations from field investigations. The successful extraction of this high-resolution vertical displacement field validates the efficacy of satellite-based high-resolution stereo-imaging methods for overcoming the limitations of GNSS and InSAR techniques in characterizing near-field surface displacements associated with earthquake ruptures. Moreover, this dataset provides robust constraints for investigating fault-slip mechanisms within near-surface geological contexts. Full article

Topographic slope is a key parameter for characterizing landscape geomorphology. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) offers high-resolution along-track slopes based on the ground profiles generated by dense signal photons. However, the across-track slopes are typically derived using the ground photon geolocations from the weak-beam and strong-beam pair, limiting the retrieval accuracy and losing valid results over rugged terrains. The goal of this study is to propose a new method to derive the across-track slope merely using single-track photon data of a strong beam based on the theoretical formula of the received signal pulse width. Based on the ICESat-2 photon data over the Walker Lake area, the specific purposes are to (1) extract the along-track slope and surface roughness from the signal photon data on the ground; (2) generate an elevation frequency histogram (EFH) and calculate its root mean square (RMS) width; and (3) derive the across-track slope from the RMS width of the EFH and evaluate the retrieval accuracy against the across-track slope from the ICESat-2 product and plane fitting method. The results show that the mean absolute error (MAE) obtained by our method is 11.45°, which is comparable to the ICESat-2 method (11.61°) and the plane fitting method (12.51°). Our method produces the least invalid data proportion of ~2.5%, significantly outperforming both the plane fitting method (10.29%) and the ICESat-2 method (32.32%). Specifically, when the reference across-track slope exceeds 30°, our method can consistently yield the optimal across-track slopes, where the absolute median, inter quartile range, and whisker range of the across-track slope residuals have reductions greater than 4.44°, 1.31°, and 0.10°, respectively. Overall, our method is well-suited for the across-track slope estimation over rugged terrains and can provide higher-precision, higher-resolution, and more valid across-track slopes. Full article

High-resolution range profile (HRRP) recognition serves as a foundational task in radar automatic target recognition (RATR), enabling robust classification under all-day and all-weather conditions. However, existing approaches often struggle to simultaneously capture the multi-scale spatial dependencies and global spectral relationships inherent in HRRP signals, limiting their effectiveness in complex scenarios. To address these limitations, we propose a novel multi-scale domain perception network tailored for HRRP-based target recognition, called MSDP-Net. MSDP-Net introduces a hybrid spatial–spectral representation learning strategy through a multiple-domain perception HRRP (DP-HRRP) encoder, which integrates multi-head convolutions to extract spatial features across diverse receptive fields, and frequency-aware filtering to enhance critical spectral components. To further enhance feature fusion, we design a hierarchical scale fusion (HSF) branch that employs stacked semantically enhanced scale fusion (SESF) blocks to progressively aggregate information from fine to coarse scales in a bottom-up manner. This architecture enables MSDP-Net to effectively model complex scattering patterns and aspect-dependent variations. Extensive experiments on both simulated and measured datasets demonstrate the superiority of MSDP-Net, achieving 80.75% accuracy on the simulated dataset and 94.42% on the measured dataset, highlighting its robustness and practical applicability. Full article

Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial resolution quad-polarization (quad-pol) SAR data at five frequencies, including the Ka-, X-, C-, S-, and L-band. A preliminary “vegetation–soil” parameter estimation model based on the multi-frequency SAR data was established. Theoretical penetration depths of the multi-frequency SAR data were analyzed using the Dobson empirical model and the Hallikainen modified model. On this basis, a water cloud model (WCM) constrained by multi-polarization weighted and penetration depth weighted parameters was used to analyze the estimation accuracy of the multi-layer and profile SM (0–50 cm depth) under different vegetation types (grassland, farmland, and woodland). Overall, the estimation error (root mean square error, RMSE) of the surface SM (0–5 cm depth) ranged from 0.058 cm³/cm³ to 0.079 cm³/cm³, and increased with radar frequency. For multi-layer and profile SM (3 cm, 5 cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm depth), the RMSE ranged from 0.040 cm³/cm³ to 0.069 cm³/cm³. Finally, a multi-input multi-output regression model (Gaussian process regression) was used to simultaneously estimate the multi-layer and profile SM. For surface SM, the overall RMSE was approximately 0.040 cm³/cm³. For multi-layer and profile SM, the overall RMSE ranged from 0.031 cm³/cm³ to 0.064 cm³/cm³. The estimation accuracy achieved by coupling the multi-source data (multi-frequency SAR data, multispectral data, and soil parameters) was superior to that obtained using the SAR data alone. The optimal SM penetration depth varied across different vegetation cover types, generally falling within the range of 10–30 cm, which holds true for both the scattering model and the regression model. This study provides methodological guidance for the development of multi-layer and profile SM estimation models based on the multi-frequency SAR data. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Background: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. Methods: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. Results: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. Conclusions: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

The formation and evolution of ice in the Yellow River represent complex dynamic processes. To elucidate the structural characteristics of ice crystals and their governing mechanisms in the Inner Mongolia reach, this investigation utilized high-resolution Sentinel-2 satellite imagery to systematically monitor spatiotemporal variations in open-water formations across diverse channel morphologies throughout the ice regime period. Systematic ice sampling was conducted across diverse channel morphologies of the Yellow River to quantify critical parameters, including crystalline structure characteristics, equivalent diameter distributions, density variations, and sediment content profiles. The results indicate the transformation of open water resulting from various river configurations during the freezing season exhibits distinct characteristics, which are significantly influenced by temperature variations. Ice crystal characterization exhibits that the crystalline structure predominantly manifests as two primary forms: columnar and granular ice formations, with their distribution varying systematically across different channel configurations. Ice crystal morphology exhibits heterogeneity in both form and dimensional characteristics. Columnar ice consistently exhibits larger equivalent diameters compared to granular ice formations. A progressive enhancement in the equivalent diameter of crystals is observed along the vertical axis corresponding to the thickness of the ice during the growth process. The ranges of variation in ice crystal size, ice density, and mud content within ice exhibit differences contingent upon the specific crystal structures present. Observational studies and comparative analyses of ice samples from the Inner Mongolia reach of the Yellow River reveal that channel morphology, ambient thermal conditions, and hydrodynamic parameters are the primary determinants governing the variability in ice microstructure and its associated physical characteristics. This investigation provides fundamental scientific insights and quantitative data that advance our understanding of river ice microstructural characteristics. Full article

Soil degradation has been accelerating globally due to climate change, which threatens food production, biodiversity, and ecosystem balance. Traditional soil restoration strategies are often expensive, slow, or unsustainable in the long term. In this context, cyanobacteria have emerged as promising biotechnological alternatives, being the only prokaryotes capable of performing oxygenic photosynthesis. Moreover, they can capture atmospheric carbon and nitrogen, release exopolysaccharides (EPSs) that stabilize the soil, and facilitate the development of biological soil crusts (biocrusts). In recent years, the convergence of multi-omics tools, such as metagenomics, metatranscriptomics, and metabolomics, has advanced our understanding of cyanobacterial dynamics, their metabolic potential, and symbiotic interactions with microbial consortia, as exemplified by the cyanosphere of Microcoleus vaginatus. In addition, recent advances in bioinformatics have enabled high-resolution taxonomic and functional profiling of environmental samples, facilitating the identification and prediction of resilient microorganisms suited to challenging degraded soils. These tools also allow for the prediction of biosynthetic gene clusters and the detection of prophages or cyanophages within microbiomes, offering a novel approach to enhance carbon sequestration in dry and nutrient-poor soils. This review synthesizes the latest findings and proposes a roadmap for the translation of molecular-level knowledge into scalable biotechnological strategies for soil restoration. We discuss approaches ranging from the use of native biocrust strains to the exploration of cyanophages with the potential to enhance cyanobacterial photosynthetic activity. By bridging ecological functions with cutting-edge omics technologies, this study highlights the critical role of cyanobacteria as a nature-based solution for climate-smart soil management in degraded and arid ecosystems. Full article

Cyanobacterial blooms are becoming increasingly frequent and intense worldwide, often dominated by Microcystis aeruginosa, a species capable of producing a wide array of bioactive metabolites beyond microcystins. This study evaluates the ecotoxicological potential of a non-microcystin-producing strain, M. aeruginosa CCIBt3106, using acute immobilization assays with three microcrustacean species: Daphnia similis, Artemia salina, and Parhyale hawaiensis. Biomass was extracted using solvents of varying polarity, and selected extracts (aqueous and 50% methanol) were further fractionated and analyzed via high-resolution liquid chromatography–tandem mass spectrometry (HR-LC-MS/MS). Significant toxicity was observed in D. similis and P. hawaiensis, with EC₅₀ values ranging from 660 to 940 µg mL⁻¹. Metabolomic profiling revealed the presence of chemically diverse metabolite classes, including peptides, polyketides, and fatty acyls, with putative annotations linked to known bioactivities. These findings demonstrate that cyanobacterial strains lacking microcystins can still produce complex metabolite mixtures capable of inducing species-specific toxic effects under environmentally relevant exposure levels. Overall, the results highlight the need to expand ecotoxicological assessments and monitoring frameworks to include non-microcystin cyanobacterial metabolites and strains in water quality management. Full article

Recently, terahertz (THz) radar has been widely researched for its high-resolution in space target imaging. Due to the high rendezvous speed and the short wavelength of THz radar, the traditional stop-and-go model, along with its supporting algorithms, is not applicable. Therefore, a method that jointly compensates the intra- and inter- pulse errors of space targets’ echo is proposed. The algorithm includes the following steps: firstly, a coarse estimation of targets’ translational velocity at part of pulses is conducted through Fractional Fourier transform (FrFT). Then, the improved least square fitting (ILSF) is employed to parameterize the velocity–time dependency of the target. Furthermore, the concept of synthetic waveform entropy (SWE) of a one-dimensional range profile is put forward as the accuracy metric of envelope alignment. Finally, with SWE serving as the fitness function, the Grey Wolf Optimizer (GWO) algorithm is used to search for optimal estimated translation parameters. After several iterations, a fine-grained estimation of target motion parameters is achieved, while simultaneously accomplishing precise joint compensation for intra-pulse and inter-pulse errors. The validity of the proposed method is verified by numerical simulation, electromagnetic calculation data, and field-measured data. Full article

High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. Our approach generates graph representations dynamically through multi-head self attention (MSA) mechanisms that adapt to target-specific scattering characteristics, integrated with a specialized meta-learning framework employing layer-wise learning rates. Experiments demonstrate state-of-the-art performance in 1-shot (82.3%), 5-shot (91.8%), and 20-shot (94.7%) settings, with enhanced noise robustness (68.7% accuracy at 0 dB SNR). Our hybrid graph mechanism combines physical priors with learned relationships, significantly outperforming conventional methods in challenging scenarios. Full article

Raspberry (Rubus idaeus L.) is a high-value horticultural crop recognized for its significant economic importance and exceptional nutritional profile. We analyzed 76 raspberry accessions (wild and cultivar) using simple sequence repeat (SSR) and sequence-related amplified polymorphism (SRAP) markers, and we established a standardized SRAP system for this species. Genetic similarity differed markedly between markers: SSR values spanned 0.47–0.98 (mean = 0.73), compared to the narrower range of 0.52–0.97 (mean = 0.75) for SRAP. Cultivar accessions exhibited higher intra-group homogeneity than wild accessions, and northeastern wild accessions showed more stable similarity metrics than Guizhou wild accessions. In hierarchical clustering, the resolution varied depending on the labeling marker. The cluster analysis by SSR markers identified two main clusters and further partitioned them into three clusters. In contrast, the SRAP system revealed two primary clusters, which subsequently diverged into five subclusters. SSR markers effectively captured population-level differentiation, whereas SRAP markers enabled precise discrimination of cultivars and ecotypes through non-coding region polymorphisms. Phylogenetic analyses confirmed closer genetic affinity between northeastern wild and cultivated accessions, which diverged significantly from Guizhou. This dual-marker approach revealed complementary information: SSR markers were used to survey genome-wide diversity, while SRAP markers were used to detect structural variations. Their integrated application enhances germplasm characterization efficiency and provides practical strategies for raspberry conservation and molecular breeding. Full article

Marine atmospheric boundary-layer height (MABLH) is crucial for ocean heat, momentum, and substance transfer, affecting ocean circulation, climate, and ecosystems. Due to the unique geographical location of the South China Sea (SCS), coupled with its complex atmospheric environment and sparse ground-based observation stations, accurately determining the MABLH remains challenging. Coherent Doppler wind lidar (CDWL), as a laser-based active remote sensing technology, provides high-resolution wind profiling by transmitting pulsed laser beams and analyzing backscattered signals from atmospheric aerosols. In this study, we developed a stacking optimal ensemble model (SOEM) to estimate MABLH in the vicinity of the site by integrating CDWL measurements from a representative SCS site with ERA5 (fifth-generation reanalysis dataset from the European Centre for Medium-Range Weather Forecasts) data from December 2019 to May 2021. Based on the categorization of the total cloud cover data into weather conditions such as clear/slightly cloudy, cloudy/transitional, and overcast/rainy, the SOEM demonstrates enhanced performance with an average mean absolute percentage error of 3.7%, significantly lower than the planetary boundary-layer-height products of ERA5. The SOEM outperformed random forest, extreme gradient boosting, and histogram-based gradient boosting models, achieving a robustness coefficient ($R^2$) of 0.95 and the lowest mean absolute error of 32 m under the clear/slightly cloudy condition. The validation conducted in the coastal city of Qingdao further confirmed the superiority of the SOEM in resolving meteorological heterogeneity. The predictions of the SOEM aligned well with CDWL observations during Typhoon Sinlaku (2020), capturing dynamic disturbances in MABLH. Overall, the SOEM provides a precise approach for estimating convective boundary-layer height, supporting marine meteorology, onshore wind power, and coastal protection applications. Full article

We report new medium-resolution spectroscopy covering the wavelength range from 0.6 to 2.4 $\mathsf{\mu }$m, as well as multi-epoch, multi-wavelength photometry, of the Class I high-mass embedded young stellar object Mol 12 (IRAS 05373+2349). It is embedded ($A_V\simeq 12$) in the centre of a dense core at a distance of 1.59 kpc from the Sun and has a total luminosity of $1.74\times {10}^3{L}_{\odot }$. The spectra show a large number of permitted atomic emission lines, mostly for Fe, H, C, N, and Ca, that originate in the inner zones of a very active protoplanetary disc and no photospheric absorption lines. Conspicuously, the He I line at 1.0830 $\mathsf{\mu }$m displays a complex P-Cygni profile. Also, the first overtone CO emission band-heads at 2.3 $\mathsf{\mu }$m are seen in emission. From the strengths of the principal emission lines, we determined the accretion rate and luminosity to be $\dot{M}\sim {10}^{-5}{M}_{\odot }$ y⁻¹ and $L_{\mathrm{acc}}\sim {10}^3{L}_{\odot }$, respectively. Decade-long light curves show a series of irregular brightness dips of more than four magnitudes in r, becoming shallower as the wavelength increases and disappearing at $\lambda >3\mathsf{\mu }$m. The colour–magnitude diagrams suggest the occurrence of a series of eclipses caused by the passage of small dust cloudlets in front of the star, producing more than 10 magnitudes of extra extinction. Full article