Search Results (6,161)

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation. Full article

At present, pedestrian navigation systems using smartphones have become common in daily activities. For their ubiquitous, accurate, and reliable services, map information collection is essential for constructing comprehensive spatial databases. Previously, we have developed a map information collection tool to extract building information using Google Maps, optical character recognition (OCR), geolocation, and web scraping with smartphones. However, indoor navigation often suffers from inaccurate localization due to degraded GPS signals inside buildings and Simultaneous Localization and Mapping (SLAM) estimation errors, causing position errors and confusing augmented reality (AR) guidance. In this paper, we present an improved map information collection tool to address this problem. It captures 360° panoramic images to build 3D models, apply photogrammetry-based mesh reconstruction to correct geometry, and georeference point clouds to refine latitude–longitude coordinates. For evaluations, experiments in various indoor scenarios were conducted. The results demonstrate that the proposed method effectively mitigates positional errors with an average drift correction of 3.15 m, calculated via the Haversine formula. Geometric validation using point cloud analysis showed high registration accuracy, which translated to a 100% task completion rate and an average navigation time of 124.5 s among participants. Furthermore, usability testing using the System Usability Scale (SUS) yielded an average score of 96.5, categorizing the user interface as ’Best Imaginable’. These quantitative findings substantiate that the integration of 360° imaging and photogrammetric correction significantly enhances navigation reliability and user satisfaction compared with previous sensor fusion approaches. Full article

Adaptive optics (AO) aims to counteract wavefront distortions caused by atmospheric turbulence and inherent system errors. Aberration recovery accuracy and computational speed play crucial roles in its correction capability. To address the issues of slow wavefront aberration detection speed and low measurement accuracy in current wavefront sensorless adaptive optics, this paper proposes a wavefront correction method based on the EfficientNetV2-S model. The method utilizes paired focal plane and defocused plane intensity images to directly extract intensity features and reconstruct phase information in a non-iterative manner. This approach enables the direct prediction of wavefront Zernike coefficients from the measured intensity images, specifically for orders 3 to 35, significantly enhancing the real-time correction capability of the AO system. Simulation results show that the root mean square error (RMSE) of the predicted Zernike coefficients for D/r₀ values of 5, 10, and 15 are 0.038λ, 0.071λ, and 0.111λ, respectively, outperforming conventional convolutional neural network (CNN), ResNet50/101 and ConvNeXt-T models. The experimental results demonstrate that the EfficientNetV2-S model maintains good wavefront reconstruction and prediction capabilities at D/r₀ = 5 and 10, highlighting its high precision and robust wavefront prediction ability. Compared to traditional iterative algorithms, the proposed method offers advantages such as high precision, fast computation, no need for iteration, and avoidance of local minima in processing wavefront aberrations. Full article

Structural health monitoring of steel buildings requires accurate detection and localization of bolt loosening, a critical yet challenging task due to complex joint geometries and varying environmental conditions. We propose an intelligent framework that integrates an improved YOLOv9 model with multi-view image fusion to address this problem. The method constructs a comprehensive dataset with multi-angle images under diverse lighting, occlusion, and loosening conditions, annotated with multi-task labels for precise training. The YOLOv9 backbone is enhanced with attention mechanisms to focus on key bolt features, while an angle-aware detection head regresses both bounding boxes and rotation angles, enabling loosening state determination through a threshold-based criterion. Furthermore, the framework unifies camera coordinate systems and employs epipolar geometry to fuse 2D detections from multiple views, reconstructing 3D bolt positions and orientations for precise localization. The proposed method achieves robust performance in detecting loosening angles and spatially localizing bolts, offering a practical solution for real-world structural inspections. Its significance lies in the integration of advanced deep learning with multi-view geometry, providing a scalable and automated approach to enhance safety and maintenance efficiency in steel structures. Full article

Accurate depth perception is vital for autonomous driving and roadside monitoring. Traditional stereo vision methods are cost-effective but often fail under challenging conditions such as low texture, reflections, or complex lighting. This work presents a perception pipeline built around FoundationStereo, a Transformer-based stereo depth estimation model. At low resolutions, FoundationStereo achieves real-time performance (up to 26 FPS) on embedded platforms like NVIDIA Jetson AGX Orin with TensorRT acceleration and power-of-two input sizes, enabling deployment in roadside cameras and in-vehicle systems. For Full HD stereo pairs, the same model delivers dense and precise environmental scans, complementing LiDAR while maintaining a high level of accuracy. YOLO11 object detection and segmentation is deployed in parallel for object extraction. Detected objects are removed from depth maps generated by FoundationStereo prior to point cloud generation, producing cleaner 3D reconstructions of the environment. This approach demonstrates that advanced stereo networks can operate efficiently on embedded hardware. Rather than replacing LiDAR or radar, it complements existing sensors by providing dense depth maps in situations where other sensors may be limited. By improving depth completeness, robustness, and enabling filtered point clouds, the proposed system supports safer navigation, collision avoidance, and scalable roadside infrastructure scanning for autonomous mobility. Full article

Digital twins (DTs) are increasingly adopted to enhance the monitoring, management, and planning of urban infrastructure. While DT development for buildings is well established, applications to urban road networks remain limited, particularly in integrating heterogeneous geospatial datasets into semantically rich, multi-scale representations. This study presents a methodology for developing a BIM-based DT of urban roads by integrating geospatial data from Mobile Mapping System (MMS) surveys with semantic information from municipal geodatabases. The approach follows a multi-modal (point clouds, imagery, vector data), multi-scale and multi-level framework, where ‘multi-level’ refers to modeling at different scopes—from a city-wide level, offering a generalized representation of the entire road network, to asset-level detail, capturing parametric BIM elements for individual road segments or specific components such as road sign and road marker, lamp posts and traffic light. MMS-derived LiDAR point clouds allow accurate 3D reconstruction of road surfaces, curbs, and ancillary infrastructure, while municipal geodatabases enrich the model with thematic layers including pavement condition, road classification, and street furniture. The resulting DT framework supports multi-scale visualization, asset management, and predictive maintenance. By combining geometric precision with semantic richness, the proposed methodology delivers an interoperable and scalable framework for sustainable urban road management, providing a foundation for AI-ready applications such as automated defect detection, traffic simulation, and predictive maintenance planning. The resulting DT achieved a geometric accuracy of ±3 cm and integrated more than 45 km of urban road network, enabling multi-scale analyses and AI-ready data fusion. Full article

Most unpaved roads are subjectively classified as Class D roads. However, significant variations exist across different sites and environments (e.g., mining areas). A major challenge in the engineering field is how to quickly correct the Power Spectral Density (PSD) of the unpaved road in question using existing equipment and limited sensors. To address this issue, this study combines real-vehicle test data with a suspension dynamics simulation model. It employs time-domain reconstruction via Inverse Fast Fourier Transform (IFFT) and wavelet processing methods to construct an optimized model that fuses time-frequency domain features. With the help of a surrogate optimization method, the model achieves the best approximation of the actual road surface, corrects the PSD parameters of the unpaved road, and provides a reliable input basis for vehicle dynamics simulation, fatigue life prediction, and performance evaluation. Full article

Liver fibrosis is a significant consequence of severe liver injury resulting from viral hepatitis, alcohol, and metabolic dysfunction. Progressive fibrosis and ultimate cirrhosis are leading causes of morbidity and mortality worldwide, generally irreversible and poorly targeted by current therapies. Traditional in vitro models and animal models mostly fail to fully recapitulate human multicellular crosstalk, extracellular matrix (ECM) remodeling, and the chronic, immune modulated nature of the disease. Recent advances in three-dimensional (3D) cell culture models including organoids, spheroids, bioprinted constructs, and organ-on-a-chip systems are advantageous for reconstructing cellular diversity and mechanical microenvironments to understand pathophysiology and aid in drug discovery. Emerging multi-organ models are capable of incorporating microbiome derived cues and using multi-omics readouts and imaging-enabled mechanistic dissection for more predictive anti-fibrotic screening. These technologies align well with the recent Modernization 3.0 regulation and New Approach Methodologies by the Food and Drug Administration (FDA) and recent EU Pharmaceutical Reform. This review summarizes the pathophysiology of liver fibrosis, the current landscape of 3D human liver models, and examines how microbiome interfaces modulate fibrogenesis. Full article

Coastal waters worldwide are increasingly threatened by excessive nutrient inputs, a key driver of eutrophication. Dissolved inorganic nitrogen (DIN) serves as a vital indicator for assessing the eutrophic status of nearshore marine environments, underscoring the necessity for precise monitoring to ensure effective protection and restoration of marine ecosystems. To address the current limitations in DIN retrieval methods, this study builds on MODIS satellite imagery data and introduces a novel one-dimensional convolutional neural network (1D-CNN) model synergistically co-optimized by the Bald Eagle Search (BES) and Bayesian Optimization (BO) algorithms. The proposed BES-BO-CNN framework was applied to the retrieval of DIN concentrations in the coastal waters of Shandong Province from 2015 to 2024. Based on the retrieval results, we further investigated the spatiotemporal evolution patterns and dominant environmental drivers. The findings demonstrated that (1) the BES-BO-CNN model substantially outperforms conventional approaches, with the coefficient of determination (R²) reaching 0.81; (2) the ten-year reconstruction reveals distinct land–sea gradient patterns and seasonal variations in DIN concentrations, with the Yellow River Estuary persistently exhibiting elevated levels due to terrestrial inputs; (3) correlation analysis indicated that DIN is significantly negatively correlated with sea surface temperature but positively correlated with sea level pressure. In summary, the proposed BES-BO-CNN framework, via the synergistic optimization of multiple algorithms, enables high-precision DIN monitoring, thus providing scientific support for integrated land–sea management and targeted control of nitrogen pollution in coastal waters. Full article

Early recognition of vascular compromise is essential for reconstructive flap survival. In human surgery, local glucose monitoring is widely used as an objective indicator of perfusion, but its application in veterinary patients is still limited. This report describes postoperative glucose measurement as a simple and minimally invasive method for evaluating flap viability in two dogs. This report describes two prospectively observed clinical cases in which local glucose measurement was applied as an adjunctive monitoring tool during postoperative flap management. Local glucose values were measured with a handheld glucometer at predefined flap and control sites. Serial readings were compared with daily assessments of flap color, temperature, turgor, and wound integrity. A previously suggested threshold of 60–62 mg/dL was used as a reference for potential perfusion compromise. In Case 1, a phalangeal fillet flap showed a brief glucose decline on postoperative days 2–3, followed by normalization and uneventful healing. In Case 2, which underwent advancement flap reconstruction after wound dehiscence, glucose values remained persistently below 60 mg/dL and preceded visible ischemia and distal necrosis. Local glucose monitoring provided rapid and clinically meaningful information about flap perfusion. Transient decreases reflected reversible postoperative congestion, whereas persistent hypoglycemia indicated progressive ischemia. These findings support the use of glucose monitoring as an adjunct in small-animal reconstructive surgery. Full article

Digital technologies have improved the visualization of anatomical structures for veterinary education and clinical practice. In this study, a detailed three-dimensional anatomical model of the equine palmar metacarpal region was generated using E12-based epoxy sheet plastination combined with digital reconstruction in Amira^® V5.6 software. Serial cross-sections of the metacarpal region provided high-resolution visualization of bones, tendons, ligaments, nerves, vessels, fasciae, and synovial structures, with minimal shrinkage or deformation, ensuring improved anatomical accuracy. These sections were digitized, aligned, and manually segmented to accurately delineate anatomical boundaries, particularly in areas of low contrast. The resulting three-dimensional model represents the topographical relationships of key structures, including palmar nerves and vessels, the palmar fascia with the metacarpal flexor retinaculum (MFR), and the common synovial sheath (Vag. synovialis communis mm. flexorum, CSS). The model allows rotation and selective visualization of individual structures, facilitating examination from multiple perspectives. This combined plastination–digital approach provides an accurate anatomical reference with value for veterinary anatomy education, clinical training, surgical planning, and research on equine musculoskeletal disorders. Full article

Two-dimensional (2D) materials open up exciting possibilities for the study of ion transport behavior for green energy. Here, a simple and effective strategy to fabricate high-conductivity nanofluidic channels based on exfoliated montmorillonite (MTM) nanosheets is proposed. The resource-rich and low-cost layered MTM was first exfoliated into monolayer nanosheets using Exolit OP 550. Subsequently, the MTM nanosheets with Exolit OP 550 were assembled into 2D nanofluidic devices by the layer-by-layer self-assembly method. The results show that Exolit OP 550 exfoliates different types of layered MTM into monolayer nanosheets with uniform contrast and integrity. The reconstructed Na-MTM nanofluidic device has the highest ionic conductance. The ionic conductivity of the Na-MTM 2D nanofluidic device was effectively improved after Li⁺ modification with a higher charge density. After further optimizing the content of Exolit OP 550, the ion conductivity of the MTM nanofluidic device reached 4.66 × 10⁻⁴ S cm⁻¹, which is 55.3% higher than the highest known value among the same nanofluidic devices. Interestingly, this nanofluidic device exhibited a very high sensitivity in detecting water evaporation, which can reach 10⁻¹² S s⁻¹ in resolution. This economically viable strategy may advance the study of low-dimensional ion transport properties in new energy coatings and the design of evaporation detectors. Full article

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum. Full article

“Placoderm” and sarcopterygian fishes dominated Devonian waters. Following the end-Devonian crisis, actinopterygians rapidly became major contributors to vertebrate diversity. This transition constitutes the first major diversification event of actinopterygians. Here, we investigate the morphological diversification of Devonian and Carboniferous actinopterygians by quantifying disparity using two-dimensional (2D) geometric morphometrics, which estimates disparity from continuous data and brings geometric information related to the shape changes in several morphological features. In total, 13 landmarks and 203 semi-landmarks were digitized on the body shape reconstructions of 84 species, and 18 landmarks and 50 semi-landmarks were digitized on the reconstructions of the lateral view of the skulls of 86 species. When compared to variations in taxonomic diversity over time, the pattern of body shape variations is congruent, reaching a maximum during the Viséan, but the pattern of skull disparity is not entirely congruent, presenting a first increase during the Late Devonian. Changes in body shape are associated with locomotory properties, while changes in skull shape are associated with functional properties of the feeding apparatus. This pattern strongly suggests the diversification of actinopterygians to be driven by divergence in trophic strategies. This evolutionary radiation seems to be the result of an adaptive response to new ecological opportunities, triggered by big environmental changes in mid-Paleozoic oceans. Full article

Background: The efficacy of real-time fMRI neurofeedback (NFB) depends critically on how feedback is presented and perceived by the participant. Although various visual feedback designs are used in practice, there is limited evidence on the impact of modality on learning and performance. We conducted a feasibility study to compare the effectiveness of different feedback modalities, and to evaluate the technical performance of NFB across two scanner field strengths. Methods: In a single-session study, nine healthy adults (6 men, 3 women) voluntarily adapted the activation level of the primary sensorimotor cortex (SMC) to reach three predefined activation levels. We contrasted a continuous, signal-proportional feedback (cFB; a thermometer-style bar) with an affect-based, categorical feedback (aFB; a smiling face). A no-feedback transfer condition (noFB) was included to probe regulation based on internal representations alone. To assess technical feasibility, three participants were scanned at 7T and six at 3T. Results: Participants achieved successful regulation in 44.4% of trials overall (cFB 46.9%, aFB 43.8%, noFB 42.6%). Overall success rates did not differ significantly between modalities and field strengths when averaged across the session; given the small feasibility sample, this null result is inconclusive and does not establish equivalence. Learning effects were modality-specific. Only cFB showed a significant within-session improvement (+14.8 percentage points from RUN1 to RUN2; p = 0.031; d_z = 0.94), whereas aFB and noFB showed no evidence of learning. Exploratory whole-brain contrasts (uncorrected) suggested increased recruitment of ipsilateral motor regions during noFB. The real-time pipeline demonstrated robust technical performance: transfer/reconstruction latency averaged 497.8 ms and workstation processing averaged 296.8 ms (≈795 ms end-to-end), with rare stochastic outliers occurring predominantly during 7T sessions. Conclusions: In this single-session motor rt-fMRI NFB paradigm, continuous signal-proportional feedback supported rapid within-session learning, whereas affect-based categorical cues did not yield comparable learning benefits. Stable low-latency operation was achievable at both 3T and 7T. Larger, balanced studies are warranted to confirm modality-by-learning effects and to better characterize transfer to feedback-free self-regulation. Full article