Search Results (325)

Approach channels are affected by sedimentation and scour, resulting in continuous changes in underwater topography. Such processes tend to generate shallow spots and inadequate navigable dimensions, posing safety hazards that undermine both waterway resilience and navigation capacity. To address these issues, this paper proposes a multi-level grid-based spatiotemporal indexing method for bathymetric data, aiming to support resilience-oriented management by improving the effectiveness of bathymetric data management. First, a channel-segment-section partitioning strategy is designed to construct hierarchical progressive grids for the efficient organization of massive bathymetric data. Second, a multi-dimensional spatiotemporal integrated query method is developed to meet diverse analytical and retrieval requirements. Third, a digital depth model (DDM) construction method is introduced that integrates boundary-constrained terrain reconstruction with efficient mesh optimization, enabling underwater terrain representation that adapts to the elongated and irregular morphology of approach channels. The contribution of this work lies not in proposing new individual algorithms but in the tailored integration of these techniques to address the specific challenges of approach-channel bathymetric data. Experimental results demonstrate that the proposed method achieves high construction efficiency across different storage and query schemes. The method enhances the retrieval and analytical capabilities of bathymetric data in representative application scenarios, such as shallow spot identification, critical section analysis, dredging analysis, and erosion–deposition evolution. Consequently, these improvements provide technical support for resilience-oriented channel management and ensure navigational safety. Full article

Glacial lakes are sensitive indicators of climate-driven cryospheric change, and their accurate mapping provides fundamental spatial information for water-resource assessment and glacial lake outburst flood (GLOF) hazard assessment. In southeastern Tibet, automatic extraction remains difficult because glacial lakes are small and easily confused with snow, mountain shadows, dark bedrock, riverine wetlands, and non-glacial water bodies. In this study, we integrate optical bands and water indices from Sentinel-2, topographic information derived from a digital elevation model, and radar backscatter from Sentinel-1 into a nine-channel multimodal dataset, and develop an improved YOLO11-seg model that combines spatial-to-depth downsampling, multi-scale attention, long-range context modeling, and content-aware upsampling to enhance small-lake detection, background suppression, and boundary delineation. Compared with U-Net, DeepLabV3+, YOLOv8-seg, YOLO11-seg, YOLO12-seg, and YOLO26-seg, the proposed model achieved the highest F1-Score of 0.9205 and an mAP50(M) of 0.9331, while its F1-Score and IoU on the independent test set reached 0.9209 and 0.8533, respectively. Using remote sensing imagery acquired in 2024, the model extracted 4766 glacial lakes in southeastern Tibet, covering 428.13 km²; 80.84% of these lakes were smaller than 0.10 km². The results demonstrate an effective and reproducible framework for automatic glacial lake mapping in complex alpine environments. Full article

Self-supervised monocular depth estimation is challenging at night because adverse illumination degrades the visual cues required for correspondence estimation and depth inference. Although appearance compensation and domain transfer can mitigate nighttime visual variations, reliable depth recovery under such conditions still depends on exploiting incomplete local structural cues and uncertain scene context. To address this challenge, we propose the Dual-Path Interaction and Dual-Attention Fusion Network (DIDAF-Depth), which improves nighttime depth recovery through coordinated convolutional neural network (CNN)–Transformer interaction, attentional feature fusion, and structure-preserving reconstruction. Specifically, we design the Transformer-CNN Vertical Interaction Fusion (TC-VIF) encoder to perform bidirectional cross-layer exchange, allowing local structural cues and global scene context to complement and progressively refine one another during feature extraction. We further develop the Dual-Coupled Attentional Fusion Module (DCAFM) to model spatial and channel interdependencies and selectively integrate complementary local and global information into a unified representation for depth decoding. Building on DCAFM’s unified representation, we construct the Edge-aware Densely Cascaded Multi-scale Network (EDCMN) to propagate features across scales, reinforce weak boundaries during upsampling, and preserve structural continuity in predicted depth maps. Experiments on the nighttime subsets of the Oxford RobotCar and nuScenes datasets indicate that DIDAF-Depth provides strong and consistent performance under the adopted evaluation protocols, supporting the effectiveness of the proposed framework. Full article

Average daily gain (ADG) is a core quantitative trait determining the economic benefits of Ashidan yak, a polled new breed adapted to cold barn feeding on the Qinghai–Tibet Plateau. Unraveling its complex genetic architecture is crucial for early molecular breeding selection. In this study, high-depth whole-genome resequencing (WGS) data from 474 Ashidan yaks were used to conduct combined evaluation of genome-wide association study (GWAS) and genomic selection (GS). During GWAS analysis, sex and measurement batch were included as fixed effects, while birth weight and principal components (PC1–PC3) were incorporated as covariates. Multi-model association analysis using GLM, MLM, and FarmCPU was performed on 3.36 million LD-pruned SNPs. The genomic inflation factors (λ ≈ 1.0) for MLM and FarmCPU confirmed effective elimination of population stratification. A total of 11 genome-wide significant SNP loci and 7 key candidate genes including PDE10A, RAD51B, BCAS3 and KCNH8 were identified via the FarmCPU model. Functional enrichment analysis indicated that these gene clusters are significantly involved in cAMP signaling pathway, regulation of ion channel activity, as well as extracellular matrix remodeling of blood vessels and skeletal muscle cells. For genomic selection, a single-trait GBLUP model was constructed using 22.87 million high-density raw SNPs to fully capture polygenic minor effects. Moderately high narrow-sense genomic heritability of ADG was estimated at h² = 0.3233 (p < 0.05). The average independent prediction accuracy across the 10-fold cross-validation reached an average of R = 0.14 ± 0.07. To evaluate marker prioritized genomic evaluation without data leakage, a strict 10-fold cross-validation scheme was implemented, where the top 1% high-priority variant set (~228,000 SNPs) was screened independently within each training fold. The resulting unbiased prediction accuracy reached R = 0.1328 ± 0.1566 (with an average RMSE of 0.3793 ± 0.0066 and a regression slope of 0.4183 ± 0.5055). Comparing this with the unselected whole-genome baseline (R = 0.14 ± 0.07) indicates that naive marker selection based solely on GBLUP effect size in small reference cohorts is influenced by sampling variance, highlighting the need to integrate multi-omics functional annotations for future custom breeding array development. This study provides quantitative insights into the polygenic architecture of ADG in yaks, offering baseline data for genomic selection and custom array development for indigenous livestock on the Qinghai–Tibet Plateau. Full article

This study presents a multidisciplinary investigation of the fluvial evolution of the northern meander of the Calore River at Cellarulo locality, near Benevento (southern Italy). The research integrates archaeological evidence, geological and geomorphological data, historical cartography, remote sensing imagery and geophysical surveys in a Geographic Information System (GIS) environment. Multi-temporal analysis of historical maps, aerial photographs and satellite images from 1824 to 2022 allowed the reconstruction of channel migration patterns and the identification of abandoned meanders and paleochannel traces. Stratigraphic data derived from boreholes revealed the presence of a channel of the Calore River dated at least in the Bronze Age (3900 years ago), abandoned in the nineteenth century. Geoelectrical investigations provided detailed information on subsurface resistivity anomalies, highlighting the presence of buried structures and possible ancient anthropogenic features located at shallow depths between 1 and 1.5 m. The combined interpretation of geomorphological, archaeological and geophysical data demonstrates significant data on the unveiling of an ancient river channel and its abandonment during the last 150 years, suggesting a strong interaction between natural fluvial dynamics and human occupation. The results confirm the effectiveness of an integrated multidisciplinary approach for reconstructing fluvial landscape evolution and for identifying buried archaeological and geomorphological features in complex floodplain environments. Full article

The Qinghai–Tibet Plateau, with an elevation of 4000 m above sea level, is rich in biological resources, and all the native creatures are exposed to extremely inhospitable environmental challenges. However, the detailed adaptation mechanisms of the native organisms to such an environment are still unclear. In this study, in-depth high-throughput RNA-Seq sequencing and metabolome data were deployed to depict the transcription and metabolism landscape of pikas living at 4630 and 2600 m altitude to elucidate the multilayer adaptation mechanisms of pikas to the environment of the Qinghai–Tibet Plateau. We identified significant variations in transcription and metabolism between pikas from 4630 and 2600 m. Various genes functioned in inflammatory-related pathways, including inflammatory mediator regulation of TRP channels and GnRH/HIF-1/p53/TNF signaling pathways, which were more active in pikas from 4630 m. Immune-response-related genes were overall downregulated in high-altitude pikas (e.g., genes for pro-inflammatory mediator synthesis and immune cell recruitment); this may represent a potential adaptive immunomodulatory mode in response to hypoxic stress. The expression of genes involved in cardiac muscle contraction and oxidative phosphorylation pathways was lower in pikas from 4630 m, leading to advantages for pikas in obtaining sufficient oxygen. Pikas from 4630 m have a more active metabolism relevant to amino acids and lipids, which provides energy for pikas to adapt to a high-altitude environment. Metabolome results reach a consensus with metabolic analysis from the transcriptome that pikas from 4630 m contain high levels of various amino acids and lipids. Overall, our study reveals multi-layer adaptive signatures in the left ventricle of plateau pikas at both transcriptional and metabolic levels, advancing our understanding of how Ochotona curzoniae adapts to high-altitude hypoxia on the Qinghai–Xizang Plateau. Full article

The collapse pressure equivalent density is a key parameter for determining the safe drilling fluid density window and evaluating wellbore stability. To address the limited number of drilled wells, the lack of continuous geomechanical labels, and the complex relationship between seismic responses and collapse pressure, this study proposes a collapse pressure prediction method based on virtual-well constraints and deep sequence learning. Virtual-well samples were constructed from the acoustic impedance distributions of drilled wells, synthetic seismic records were generated through actual seismic wavelet extraction and reflection-coefficient convolution, and collapse-pressure equivalent-density labels were calculated using a rock-mechanics model, thereby forming a large-scale training dataset. Random Forest, Polynomial Regression, Support Vector Regression, and CNN-MultiLSTM models were compared. The CNN-MultiLSTM framework was further optimized in terms of input range, feature channels, and sequence structure, resulting in a whole-well sequence model combining multichannel inputs with residual dilated convolutions. The optimized model achieved an MAE of 0.00262 g/cm³, an RMSE of 0.00352 g/cm³, a MAPE of 0.241%, and an R² of 0.9774 on the validation set. In independent validation using an actual well not involved in training, the model achieved an MAE of 0.0182 g/cm³, an RMSE of 0.0278 g/cm³, a MAPE of 1.640%, and an R² of 0.7356 within the primary target interval of 7900.000–7994.100 m. The predicted profile reproduced the main depth-dependent variation of the calculated collapse-pressure equivalent density, although larger deviations occurred in locally abrupt intervals. Analysis outside the primary target interval further showed that the model could respond to high-collapse-pressure anomalies. Overall, integrating virtual-well constraints, rock-mechanics-based labeling, and whole-well sequence learning provides a feasible approach for collapse-pressure prediction in undrilled areas and drilling fluid density design, while further multi-well validation is required to assess cross-well generalization. Full article

Hyperspectral image classification is a core task in remote sensing image analysis and understanding. Existing Transformer-based methods have achieved excellent performance but are limited by the quadratic computational complexity of the self-attention mechanism, while the high-dimensional redundancy of hyperspectral data and the difficulty in deeply integrating spatial–spectral features also restrict further performance improvement. To address these issues, we introduce the Mamba architecture based on state-space models into hyperspectral image classification and propose the DFMamba model. The main innovations include (1) constructing a Hyperspectral Spatial Attention Embed (HSAE) to achieve efficient channel compression and feature extraction via adaptive grouped convolution, depth-wise separable convolution, and spatial attention; (2) proposing a spatial–spectral dual-branch collaborative modeling mechanism, EnhancedBothMamba, which separately models global dependencies in the spatial and spectral branches and integrates their outputs through softmax-normalized learnable global weights together with a learnable residual scaling factor; and (3) building an improved classification head, ClsHead, with a multi-scale branch fusion strategy to fully exploit local and global feature information. The experimental results on four standard hyperspectral datasets demonstrate that DFMamba achieves overall accuracy (OA) of 97.41% on the Pavia University dataset, 92.25% on the HanChuan dataset, 95.12% on the HongHu dataset, and 94.98% on the Houston dataset. Under the adopted evaluation protocol, DFMamba obtains higher mean OA than MambaHSI and the other compared methods while retaining favorable computational efficiency. Full article

Dam-break floods pose severe threats to downstream urban areas due to their sudden onset, rapid propagation, and potentially catastrophic socioeconomic consequences. This study develops a multi-indicator fuzzy comprehensive evaluation framework for urban flood risk assessment under dam-break scenarios. The framework integrates hydrodynamic simulation outputs with geographic information system-based spatial analysis to characterize spatial variations in flood risk. It incorporates flood hazard factors (inundation depth, arrival time, and inundation duration) derived from a coupled one-dimensional/two-dimensional hydrodynamic model and socioeconomic vulnerability factors (population density and road network density) derived from spatial statistics. Indicator weights were determined using a combined Analytic Hierarchy Process and entropy-weight method, and risk levels were obtained through membership-function calculation and spatial overlay analysis. The framework was applied to the Dongpu and Dafangying reservoirs in Hefei, China, under a scenario of simultaneous dam failure during a probable maximum flood. Results show that, compared with hazard-only assessment, the comprehensive evaluation substantially reduced the extent of high-risk areas and altered their spatial distribution. Very high-risk zones were concentrated along the upstream main channel, where high-velocity floodwaters coincide with dense population and economic activity. Flood arrival time and population density were identified as the most influential indicators in the proposed risk assessment framework. The proposed framework provides a more comprehensive and spatially refined tool for urban dam-break flood risk management and emergency decision-making. Full article

Suspended sediment concentration (SSC) is a critical proxy for coastal water quality, geomorphological evolution, and biogeochemical cycles. In shallow macro-tidal embayments like Sanmen Bay (SMB), China, surface SSC exhibits highly dynamic spatiotemporal variations driven by intense, multi-scale tidal forcing. Using Sentinel-2 MSI imagery processed with the ACOLITE Dark Spectrum Fitting (DSF) algorithm, this study reconstructs the spatial distribution of surface SSC across the embayment. We then introduce the normalized Suspended Sediment Concentration Asymmetry Index ($A_{ssc}$) to quantitatively diagnose asymmetrical sediment responses across spring–neap and flood–ebb cycles. The results reveal a spatially divergent, dual-control mechanism governing sediment transport across the embayment’s hydro-geomorphic gradients. Quantitative trend-surface fittings and stratified regressions demonstrate that net sediment transport in deep bedrock channels is primarily governed by tidal pumping. Conversely, sediment dynamics on intertidal mudflats and shallow subtidal shoals are modulated by geomorphic resistance, exhibiting high morphodynamic sensitivity to minute water depth variations. By bridging process-based estuarine tidal theory with discrete satellite observations, this reproducible framework transforms multi-temporal remote sensing snapshots into spatially continuous diagnostics, providing a practical decision-support paradigm for coastal engineering and ecosystem management in dynamically analogous macro-tidal environments. Full article

We demonstrate an integrated eight-wavelength high-power laser source on a 4H-silicon carbide (SiC)-based optical integration platform. Eight discrete Fabry–Perot laser diodes emitting at 445 nm, 637 nm, 789 nm, 806 nm, 846 nm, 978 nm, 1316 nm, and 1552 nm are integrated on a single SiC chip, each delivering ≥100 mW continuous-wave output power. A complete fabrication process is developed, including lift-off metallization (Ni/Ti/Pt/Au), surface hydrophilic activation bonding, and multi-step blade dicing to form SiC waveguides with a width of 500 μm and a thickness defined by the ~510 μm dicing depth, matching the output aperture of the multimode laser diodes. The resulting waveguides exhibit a facet misorientation of <1° and an approximate facet mean surface roughness of ~2 nm. The laser diodes are directly butted against the waveguide facets for edge coupling, and fixed using In₅₂Sn₄₈ solder bonding with pulse temperature control. Under controlled temperature, all eight channels operate stably with measured peak wavelengths matching the design targets. This work provides a scalable and practical solution for multi-wavelength, high-power on-chip light source integration on the SiC platform, addressing critical thermal and integration challenges for dense wavelength division multiplexing. Full article

Traditional carvings, such as stone and wooden carvings, are important material carriers of intangible cultural heritage craftsmanship. High-quality three-dimensional (3D) digital replicas of these carvings provide essential support for their preservation, inheritance, interpretation, and dissemination. Owing to their intricate geometries, fine surface details, diverse materials, and complex acquisition backgrounds, high-fidelity 3D reconstruction of traditional carvings remains a challenging issue. In this study, we propose an object-centric 3D Gaussian Splatting (3DGS) framework for traditional craft carving reconstruction. Built upon the baseline 3DGS model, the proposed framework leverages the advanced segmentation capability of Segment Anything Model 2 (SAM-2) to extract foreground masks and generate alpha-channel inputs, enabling the reconstruction process to focus on the target carving. In addition, depth priors are used to guide local densification in regions with insufficient Gaussian coverage, providing auxiliary support for weakly textured or locally blurred carving details. Experiments were conducted on a self-built image dataset of stone and wooden carvings collected from Hui’an County, Quanzhou, Fujian Province, China. The experimental results show that the proposed object-centric strategy effectively preserves the original visual textures and local geometric features of traditional carvings while improving rendering efficiency. Furthermore, the optimized 3D Gaussian models are exported as lightweight digital assets and integrated into Unreal Engine 5, enabling multi-perspective visualization and interactive virtual exhibition in a contextualized digital environment. These results suggest that the proposed workflow is more suitable for producing compact object-level Gaussian assets for carving exhibition, while the depth-guided module mainly improves local details in weakly textured or shallow-relief regions. Full article

Intricate natural wood textures and diversified defect morphologies hinder high-precision recognition of visible surface defects on sawn lumber. Six common types of surface defects exist on sawn lumber, including dry knots, edge knots, small knots, sound knots, wavy defects, and splits. Among these defect types, edge knots, small knots, wavy defects, and splits bring great difficulties to detection due to their tiny areas, slender geometric outlines and indistinct boundaries. To accurately identify the above defects, a customized You Only Look Once version 8 medium (YOLOv8m)-based framework was developed for lumber surface inspection. First, the Cross-Stage Partial Bottleneck with Two Convolutions embedded with Efficient Channel Attention (C2f-ECA) and Space-to-Depth Convolution (SPD-Conv) are introduced into the backbone to enhance channel-wise feature representation and preserve fine spatial details during downsampling, while C2f with Deformable Convolution (C2f-DCN) is embedded in the deep feature extraction branch to improve the geometric modeling of irregular defects. Second, a C2f-DCN with Exponential Moving Average Attention (C2f-DCN-EMA) module and dynamic upsampling (DySample) are integrated in the feature-fusion stage to refine multi-scale features and reconstruct local edges. Third, Scaled Intersection over Union (SIoU) loss is used to improve bounding-box regression for defects with extreme aspect ratios. Experiments show that the proposed model achieves 91.8% mean Average Precision at IoU 0.5 (mAP@50) and 69.3% mean Average Precision across IoU thresholds of 0.5–0.95 (mAP@50-95), exceeding the YOLOv8m baseline by 1.0 and 1.5 percentage points, respectively. Full article

Coastal lagoon Salt Ponds are highly valuable wetland systems where traditional salt production coexists with ecosystems of significant ecological importance, often characterized by high environmental sensitivity. In data-scarce coastal settings, particularly those located near river channels and drainage networks, assessing multiple environmental hazards remains a major challenge. This study proposes a simplified and transferable methodological framework for multi-hazard assessment in coastal Salt Pond environments (DAFFLE; Data Acquisition Fluvial Flooding and Liquefaction Evaluation), with particular emphasis on areas where field data are limited and fluvial processes and seismic effects may interact. The approach integrates three main components: first, improved terrain modelling using global elevation datasets and ICESat-2 laser altimetry data to better represent very flat coastal areas; second, flood hazard simulation by modelling water depths under different flood scenarios to map potential inundation; third, liquefaction susceptibility is assessed using surficial geological data and key geomorphological parameters, producing simplified probabilistic hazard maps informed by existing seismic hazard datasets or scenario-based assumptions. The proposed framework provides a scalable and practical tool for first-order multi-hazard assessment in vulnerable coastal Salt Pond environments. It supports comparative hazard analyses and decision-making in regions where detailed site-specific data and extensive field investigations are not available, offering a consistent baseline for coastal lagoon Salt Pond risk evaluation and management. Full article

With the increasing detection of thyroid nodules in ultrasound screening, accurate nodule segmentation has become important for computer-aided assessment of clinically relevant features, such as contour regularity, aspect ratio, and margin sharpness. Although ultrasound is widely used as a first-line imaging modality in clinical practice, thyroid nodule segmentation remains challenging because of low tissue contrast, speckle-blurred boundaries, and large variations in nodule size and morphology. To address these challenges, we propose TEM-Net, a Tri-Channel Edge-Aware Multi-Scale Network for thyroid nodule segmentation. TEM-Net constructs a tri-channel representation from the raw ultrasound image, including the original grayscale image, a contrast-enhanced image, and a gradient-magnitude map, to highlight weak intensity differences and boundary-related cues in low-contrast ultrasound images. An Edge-Guided Feature Amplification (EGFA) module is introduced before the first down-sampling operation to emphasize boundary responses before spatial resolution is reduced. In addition, a Multi-Focus Cross-Scale Attention Refinement (MF-CAR) module is embedded into skip connections, combining dilated depth-wise convolutions with channel–spatial attention to improve the fusion of local boundary details and broader contextual information. Across three random seeds, TEM-Net achieves mean Dice scores of 0.8822 and 0.9066 and mean IoU scores of 0.7893 and 0.8291 on TN3K and DDTI, respectively, showing competitive performance compared with representative segmentation methods. Full article