Search Results (91)

The high-fidelity 3D digitization of small, detailed cultural heritage objects, such as Oracle Bones, presents significant challenges for which existing reconstruction workflows are often inadequate. Methods based on Structure-from-Motion (SfM) often lack the geometric density required to capture fine inscription details, while Light Detection and Ranging and RGB-Depth approaches may introduce high data overhead and unstable color mapping. Recent specialized studies have utilized multi-shading-based techniques to extract such hidden surface textures, yet integrating these results into a cohesive mesh remains difficult. To address these limitations, we propose a digitization framework specifically designed for object-level archaeological artifacts. Our method combines semi-automatic alignment with ICP-based refinement for robust camera pose estimation, reducing misalignment issues associated with feature-only registration. Furthermore, we employ an efficient mesh-based representation with vertex-level coloring, enabling detailed geometry and consistent texturing while maintaining compact storage requirements. Our contributions include: (1) a high-quality mesh reconstruction framework that preserves fine inscription geometry; (2) a hybrid camera pose estimation strategy that improves alignment robustness; and (3) an integrated hardware-assisted workflow tailored for digitizing small archaeological artifacts under controlled acquisition conditions. Experimental results on physical Oracle Bone artifacts demonstrate that the proposed method achieves a mean geometric reconstruction error of approximately 0.075 mm with a Hausdorff distance of 1 mm. These results demonstrate the effectiveness of the proposed workflow for digitization of oracle bone artifacts. Full article

Building Information Modeling (BIM) three-dimensional (3D) objects undergo repeated conversion and reconstruction processes for cross-platform utilization, during which geometric information loss, topological distortion, and semantic omission frequently occur, leading to fundamental limitations in accurate shape reconstruction and semantic-based functional reuse. The academic objective of this study is to overcome these limitations by proposing a three-stage sequential cross-platform reconstruction framework, consisting of semantic-vertex-based functional utilization, semantic-vertex-based invariant triangle mesh reconstruction, and semantic-vertex-based functional reuse, and to experimentally validate its effectiveness. To this end, an FBX–JSON dual-pipeline-based data management architecture is introduced to process visual geometric data and non-visual semantic metadata in parallel, thereby ensuring platform independence and data consistency. Experimental validation was conducted using IFC-based BIM objects generated in Autodesk Revit and triangle mesh models processed in Blender, at both the object and project levels. Quantitative evaluation was performed using geometric identity preservation, mesh completeness, semantic vertex restoration accuracy, and functional retention rate as the core performance indicators. The results reveal that the primary cause of mesh failure during platform transformation is face normal inconsistency, which can be stably resolved through auxiliary remeshing, thereby ensuring robust mesh reconstruction. Although the experiments were limited to round-trip transfers between Blender and Unity, the results experimentally verify the effectiveness of the proposed three-stage reconstruction framework and dual-pipeline data architecture, while also demonstrating their strong potential for generalization to broader cross-platform BIM environments. Full article

Accurate extraction of building vector contours from high-resolution remote sensing images is a fundamental task for urban mapping and geographic information systems. However, existing approaches often suffer from blurred boundaries and geometric distortions when dealing with buildings of complex shapes, limiting the accuracy and usability of the extracted building footprints. To address these challenges, this paper proposes a multi-level building contour refinement framework based on morphological perception. The proposed framework integrates a three-stage contour optimization strategy, including principal direction extraction, morphology-based contour reconstruction, and geometry-aware regularization, to progressively refine complex building contours under geometric constraints. In addition, a multi-dimensional contour complexity model and an adaptive threshold optimization network are introduced to dynamically adjust refinement parameters according to contour complexity. Experimental results on the WHU-Mix dataset demonstrate that the proposed method outperforms state-of-the-art approaches, achieving 87.52%, 77.43%, and 87.35% in boundary F1, vertex F1, and mIoU, respectively. These results indicate that the proposed framework provides an effective and robust solution for high-precision building vector contour extraction in complex remote sensing scenarios. Full article

In recent decades, considerable effort has been devoted to developing higher-order schemes for the discretization of convective terms that are both stable and reliable. In this work, the central idea is that the approximation should be made to reflect the physics of pure convection: the transported quantity is advected along streamlines, and information is propagated only in the upwind direction, i.e., the transported property is determined by previous values along the streamline but not by downstream values. In the proposed approach, streamlines on the computational mesh are represented by discrete streamlines, and the method is called the Discrete Streamline Method (DStreaM). A discrete streamline is constructed as a narrow triangle with one vertex at the node where the approximation is sought and two vertices at upstream neighbouring nodes. Discrete streamlines are oriented according to the local flow direction, in a manner similar to skew-upwind schemes, so that consistency with pure convection is ensured for DStreaM. The method is conservative only for uniform meshes with a constant velocity field; for general meshes and non-uniform velocity fields, it is non-conservative, and a non-zero local conservation error remains. The performance of DStreaM is assessed on the following standard test problems: convection of a step profile, a double-step profile, a sinusoidal profile, and the Smith–Hutton problem. DStreaM solutions are compared with those obtained using the first-order upwind scheme and second-order total variation diminishing (TVD) schemes with Minmod, QUICK, and SUPERBEE limiters. Across these benchmarks, high-resolution solution profiles and $L_1/L_2$ error levels comparable to those of the considered TVD schemes are produced by DStreaM. In the DStreaM construction, only local node coordinates and mesh connectivity are used; in this work, implementation is performed on both uniform Cartesian meshes and unstructured triangular meshes generated by a Delaunay triangulation. Representative results are reported with a focus on accuracy, iterative convergence, and conservation limitations. Full article

The spinal angle is an important indicator of body balance. It is important to restore the 3D shape of the human body and estimate the spine center line. Existing multi-image-based body restoration methods require expensive equipment and complex procedures, and single image-based body restoration methods struggle to accurately estimate internal structures such as the spine center line due to occlusion and viewpoint limitation. This study proposes a method to compensate for the shortcomings of the multi-image-based method and to overcome the limitations of the single-image method. We propose a 3D body posture analysis system that integrates depth images from four directions to restore a 3D human model and automatically estimate the spine center line. Through hierarchical matching of global and fine registration, restoration to noise and occlusion is performed. In addition, adaptive vertex reduction is applied to maintain the resolution and shape reliability of the mesh, and the accuracy and stability of spinal angle estimation are simultaneously secured using the level of detail (LOD) ensemble. The proposed method achieves high-precision 3D spine registration estimation without relying on training data or complex neural network models, and the verification confirms the improvement in matching quality. Full article

Cervical spine (c-spine) injuries are a prominent concern in sporting activities, and dynamic axial (i.e., head-first) impacts are associated with a high risk of c-spine trauma. This methodology study implanted pressure sensors in post-mortem human subject (PMHS) cervical intervertebral discs (CIVDs) to assess biomechanical response and disc pressure changes during dynamic injurious axial impacts. Two fresh frozen male head–neck PMHS (cephalus with complete c-spine) were instrumented with miniature pressure sensors (Model 060S, Precision Measurement Company, Ann Arbor, MI, USA) at three CIVD levels (upper, middle, and lower c-spine). Experiments replicated the Nightingale et al. studies, simulating a rigid unconstrained head vertex (0°) axial impact. PMHS were raised to a drop height of 0.53 m to reach the desired impact velocity of ~3.2 m/s and were allowed to drop vertically. Results showed characteristic c-spine deformations/buckling motion patterns and marked CIVD pressure differences between CIVD levels. The more cranial (C2–C4) and caudal (C6–T1) CIVD exhibited greater and more comparable pressure values than those of the mid-spine (C4–C6), and the pressure in upper/lower levels was at least ~four to six times higher than that of the middle. This study establishes the feasibility and assesses the potential of CIVD pressure as a biomechanical metric for assessing injurious axial loading and contributes a novel experimental framework for future injury tolerance research and model validation. Full article

Approximate query on knowledge graphs (KGs) is an important and common task in real-world applications, where the goal is to return more results on KGs that match the query criteria. Previous approximate query methods have focused on static KGs. However, many KGs in real-world applications are dynamic and evolve over time. In this paper, we consider approximate queries in temporal knowledge graphs (TKGs) that may have specific timestamps in the predicates. We propose a Two-Level Approximate Query method (TLAQ) for temporal knowledge graphs based on the two-level embedding of vertex and graph. Specifically, we first improve the eigenmatrix of the GCN to enhance the embedding representation. On this basis, TLAQ defines relational reliability and attributive confidence at the vertex level. Then, we unify the encoding format of timestamps at the graph level to further strengthen the embedding model. Finally, we demonstrate the effectiveness of our proposed approach through a comprehensive experiment. Full article

High-resolution LiDAR-derived three-dimensional (3D) digital outcrop models are crucial for detailed geological analysis. However, their massive data volumes often exceed the rendering and memory capacities of standard computer systems, posing significant visualization challenges. Although Level of Detail (LOD) techniques are well-established in Geographic Information Systems (GISs) and computer graphics, they still require customized design to address the unique characteristics of geological outcrops. This paper presents an automated method for constructing and visualizing LOD models specifically tailored to high-resolution LiDAR outcrops. The workflow begins with segmenting the single-body model based on texture coverage, followed by building an adaptive LOD tile pyramid for each segment using a pseudo-quadtree approach. The proposed LOD construction method incorporates several innovative components: segmentation based on texture coverage, an adaptive LOD tile pyramid using a pseudo-quadtree, and a feature-preserving mesh simplification algorithm that includes vertex sharpness constraint and boundary freezing strategy to maintain critical geological features. For visualization, a dynamic multi-scale loading and rendering mechanism is implemented using an LOD index with the OpenSceneGraph (OSG) engine. The results demonstrate that the proposed method effectively addresses the bottleneck of rendering massive outcrop models. The models loading time and average memory usage were reduced by more than 90%, while the average display frame rate reached around 60 FPS. It enables smooth, interactive visualization and provides a robust foundation for multi-scale geological interpretation. Full article

In this work, we calculate the Branching Ratio (BR) of the process ${\rho }^0\to {\pi }^0{\pi }^0\gamma$ at tree level within the theoretical framework of with resonances, including the odd intrinsic parity sector. Owing to the nature of an effective field theory, the BR depends on three unknown couplings of the model: $d_4$ and the combination $c_{57}=c_5+c_7$. To constrain these unknown couplings, we use the experimental BR of this decay and explore the possibility of reducing the number of unknown couplings through the on-shell condition at one vertex. Our result suggests that off-shell conditions must be applied to both vertices, leading to a space parameter for the pair ($c_{57},d_4$). Full article

Background: During postoperative recovery, patients and their caregivers often lack crucial information, leading to numerous repetitive inquiries that burden healthcare providers. Traditional discharge materials, including paper handouts and patient portals, are often static, overwhelming, or underutilized, leading to patient overwhelm and contributing to unnecessary ER visits and overall healthcare overutilization. Conversational chatbots offer a solution, but Natural Language Processing (NLP) systems are often inflexible and limited in understanding, while powerful Large Language Models (LLMs) are prone to generating “hallucinations”. Objective: To combine the deterministic framework of traditional NLP with the probabilistic capabilities of LLMs, we developed the AI Virtual Assistant (AIVA) Platform. This system utilizes a retrieval-augmented generation (RAG) architecture, integrating Gemini 2.0 Flash with a medically verified knowledge base via Google Vertex AI, to safely deliver dynamic, patient-facing postoperative guidance grounded in validated clinical content. Methods: The AIVA Platform was evaluated through 750 simulated patient interactions derived from 250 unique postoperative queries across 20 high-frequency recovery domains. Three blinded physician reviewers assessed formal system performance, evaluating classification metrics (accuracy, precision, recall, F1-score), relevance (SSI Index), completeness, and consistency (5-point Likert scale). Safety guardrails were tested with 120 out-of-scope queries and 30 emergency escalation scenarios. Additionally, groundedness, fluency, and readability were assessed using automated LLM metrics. Results: The system achieved 98.4% classification accuracy (precision 1.0, recall 0.98, F1-score 0.9899). Physician reviews showed high completeness (4.83/5), consistency (4.49/5), and relevance (SSI Index 2.68/3). Safety guardrails successfully identified 100% of out-of-scope and escalation scenarios. Groundedness evaluations demonstrated strong context precision (0.951), recall (0.910), and faithfulness (0.956), with 95.6% verification agreement. While fluency and semantic alignment were high (BERTScore F1 0.9013, ROUGE-1 0.8377), readability was 11th-grade level (Flesch–Kincaid 46.34). Conclusion: The simulated testing demonstrated strong technical accuracy, safety, and clinical relevance in simulated postoperative care. Its architecture effectively balances flexibility and safety, addressing key limitations of standalone NLP and LLMs. While readability remains a challenge, these findings establish a solid foundation, demonstrating readiness for clinical trials and real-world testing within surgical care pathways. Full article

Background: Androgenetic alopecia, also known as male pattern baldness (MPB), is a common hair loss disorder among middle-aged men. MPB shares similar risk factors with prostate cancer (PrCa), including advancing age, family history, and sex hormones. Several studies have examined the associations between MPB and PrCa; however, the evidence remains unclear. We carried out an updated meta-analysis of epidemiological studies that examined the relationship between age at onset and patterns of MPB (either frontal, vertex, or both) and their associations with risks of total and aggressive PrCa. Methods: A literature search was performed using PubMed and Web of Science databases for epidemiological studies published between 1 January 2000 and 31 December 2024 that examined the associations between MPB and PrCa. From each eligible study, relevant data were extracted on study design and population, sample size, prevalence of MPB at various ages, and their association with PrCa. Pooled relative risks (RR) and corresponding 95% confidence intervals (CI) were calculated using the Der-Simonian and Laird random-effects models. Heterogeneity across studies was assessed by I² statistics, while the quality of studies was evaluated using the Newcastle–Ottawa Scale. Results: A total of 19 observational studies, including 17,810 cases and 146,806 controls/non-cases, were analyzed. The prevalence of MPB increased from 5% to 65% with aging and varied across the studies. Both frontal and vertex MPB were associated with a pooled RR of 1.08 (95% CI 1.02–1.14) for total PrCa, but there was no association with frontal-only MPB. Younger-onset MPB (<40 years old) was also associated with an RR = 1.13 (95% CI 0.96–1.31) for PrCa, although results were not statistically significant. Vertex-only MPB was associated with more aggressive PrCa (pooled RR = 1.14; 95% CI 1.02–1.25); however, there was substantial heterogeneity in the definition of aggressive PrCa across the studies. Conclusions: Men with both frontal and vertex MPB have a modestly elevated risk of PrCa. However, most studies were conducted in Caucasian men and they did not evaluate effect modifications by genetic variations in androgen metabolism pathway genes or changes in serum levels of androgens with aging. Large prospective cohort studies with more accurate longitudinal assessment of hair loss patterns are needed to better understand the complex relationship between genetic susceptibility, endogenous hormones, MPB, and subsequent risk of PrCa. Full article

In this study, we present LizAI XT, an AI-powered platform designed to automate the structuring, anonymization, and semantic integration of large-scale healthcare data from diverse sources, into one comprehensive table or any designated forms, based on diseases, clinical variables, and/or other defined parameters, beyond the creation of a dashboard or visualization. We evaluate the platform’s performance on a cluster of 4x NVIDIA A30 GPU 24GB, with 16 diseases—from deathly cancer and COPD, to conventional ones—ear infections, including a total 16,000 patients, ∼115,000 medical files, and ∼800 clinical variables. LizAI XT structures data from thousands of files into sets of variables for each disease in one file, achieving > 95.0% overall accuracy, while providing exceptional outputs in complicated cases of cancers (99.1%), COPD (98.89%), and asthma (98.12%), without model-overfitting. Data retrieval is sub-second for a variable per patient with a minimal GPU power, which can significantly be improved on more powerful GPUs. LizAI XT uniquely enables fully client-controlled data, complying with strict data security and privacy regulations per region/nation. Our advances complement the existing EMR/EHR, AWS HealthLake, and Google Vertex AI platforms, for healthcare data management and AI development, with large-scalability and expansion at any levels of HMOs, clinics, pharma, and government. Full article

Plasma-facing components (PFCs) are among the most critical functional components in a nuclear fusion device. Their reliability and durability under high heat loads are directly tied to the safe operation and lifetime of the fusion device. Under cyclic high thermal loads, accumulated plastic strain can lead to material property degradation. Furthermore, severe temperature gradients generate alternating tensile and compressive stresses within the material, resulting in the initiation and propagation of microcracks, ultimately causing structural failure of the PFCs. This study focuses on the issues of thermal stress concentration and plastic strain accumulation at the tungsten (W)/copper (Cu) joint interface and proposes an optimized design scheme based on a laminated tungsten structure. Using a combined approach of finite element simulation and theoretical analysis, the effects of tungsten layer thickness and interface geometry on the thermomechanical performance of the PFC joint were systematically investigated. The results indicate that reducing the thickness of tungsten sheet can significantly decrease the interfacial stress level. As the tungsten sheet thickness is reduced from the millimeter scale to the micrometer scale, the thermal mismatch at the W/Cu interface is reduced, thereby leading to a notable reduction in normal stress along the axial direction. In particular, when the thickness falls below 10 μm, the axial normal stress approaches zero, and the equivalent stress at the interface is effectively mitigated. Further research indicates that optimizing the flat W/Cu interface into an arc-shaped interface can alter the location of stress concentration. When the ascending distance of the Cu exceeds 600 μm, the stress concentration at the interface vertex is essentially eliminated. However, an excessively ascending distance of the Cu can exacerbate plastic deformation in the copper layer. By optimizing the extended distance of the Cu, a balance between stress relief and plastic strain accumulation can be effectively achieved. Full article

Myelin is essential for efficient neural signaling and can be quantitatively evaluated using the T1-weighted/T2-weighted (T1w/T2w) ratio as a proxy for regional myelin content. Myelin covariance networks (MCNs) reflect correlated myelin patterns across brain regions, enabling the investigation of topological organization. However, a vertex-level map of myelin covariance gradients and their cognitive associations remains underexplored. The objective of this study was to construct and characterize vertex-level MCNs, identify their principal gradients, map their higher-order topological landscape, and determine their associations with cognitive functions and other multimodal cortical features. We conducted a cross-sectional, secondary analysis of publicly available data from the Human Connectome Project (HCP). The dataset included T1w/T2w MRI data from 1096 healthy adult participants (age 22–37). All original data collection and sharing procedures were approved by the Washington University institutional review board. Our procedures involved (1) constructing a vertex-wise MCN from T1w/T2w ratio data; (2) applying gradient analysis to identify principal organizational axes; (3) calculating network connectivity strength; (4) performing cognitive meta-analysis using Neurosynth; and (5) using graphlet analysis to assess higher-order topology. Our results show that the primary myelin gradient (Gradient 1) spans from sensory-motor to association cortices, strongly associates with connectivity strength (r = 0.66), and shows a functional dissociation between affective processing and sensorimotor domains. Furthermore, Gradient 2, as well as the positive and full connectivity strength, showed robust correlations with fractional anisotropy (FA), a DTI metric reflecting white matter microstructure. Our higher-order analysis also revealed that negative and positive myelin covariance connections exhibited distinct topologies. Negative connections were dominated by star-like graphlet structures, while positive connections were dominated by path-like and triangular structures. This systematic vertex-level investigation offers novel insights into the organizational principles of cortical myelin, linking gray matter myelin patterns to white matter integrity, and providing a valuable reference for neuropsychological research and the potential identification of biomarkers for neurological disorders. Full article

Due to the challenges in obtaining full-body point clouds and the time-consuming registration of parametric body models, we propose an end-to-end Front Point Cloud Parametric Body Regression Network (FPCR-Net). This network directly regresses the pose and shape parameters of a parametric body model from a single front point cloud of the human body. The network first predicts the label probabilities of corresponding body parts and the back point cloud from the input front point cloud. Then, it extracts equivariant features from both the front and predicted back point clouds, which are concatenated into global point cloud equivariant features. For pose prediction, part-level equivariant feature aggregation is performed using the predicted part label probabilities, and the rotations of each joint in the parametric body model are predicted via a self-attention layer. Shape prediction is achieved by applying mean pooling to part-invariant features and estimating the shape parameters using a self-attention mechanism. Experimental results, both qualitative and quantitative, demonstrate that our method achieves comparable accuracy in reconstructing body models from front point clouds when compared to implicit representation-based methods. Moreover, compared to previous regression-based methods, vertex and joint position errors are reduced by 43.2% and 45.0%, respectively, relative to the baseline. Full article