Search Results (10,240)

This study presents the development of a computational tool designed to help automate decision-making in excavation and foundation construction in rock, aiming to minimize risks to adjacent historical structures in an urban context. The tool uses a graphical interface and focuses on estimating the propagation of vibrations generated by these construction processes. A working methodology has been proposed, and a computational tool has been developed to predict the feasibility and safety of specific construction techniques in different areas of study. Using field-collected data, a three-dimensional survey of adjacent buildings is conducted in a 3D CAD model, converting the continuous terrain into a discrete point mesh. This mesh enables the tracing of vibrational wave trajectories from their origin to potentially affected structures. The tool then calculates the peak particle velocities (PPV) at the foundations of these structures. By comparing these PPV values with predefined thresholds—selected from different excavation procedures with heavy equipment—excavation zones where equipment can be safely used are visually represented using a color-coded scheme. To validate the applicability of the proposed method and developed approach, the tool was tested on a case study: The Rehabilitation Project of the Cervantes Theater in Segovia, promoted by the Ministry of Transport, Mobility, and Urban Agenda. This project is currently halted due to damage sustained by adjacent buildings during the excavation process. Full article

This study presents a comprehensive investigation of the electrical, structural, and electrochemical properties of graphite flake (GF)-reinforced epoxy composites for energy storage applications. Epoxy/GF composites with filler loadings of 10, 30, 50, 70, and 80% wt. were fabricated to evaluate the effect of graphite concentration on conductivity, charge storage, and structural integrity. Impedance spectroscopy demonstrated that quantum-mechanical tunneling, consistent with fluctuation-induced tunneling transport, predominates charge transfer over a wide temperature range, ensuring strong electrical performance. The results show that at 10–30% wt.% GFs, incomplete conductive networks and limited electron and ion transport reduce electrochemical performance. At 50–70% wt.% GFs, the composites exhibited the highest specific capacitance and excellent cyclic stability due to the formation of well-connected three-dimensional conductive networks with sufficient porosity for efficient ion diffusion and charge transport. At filler loadings above 70 wt.%, graphite agglomeration, pore blockage, and microstructural defects were observed, resulting in reduced conductivity and capacitance. SEM, FTIR, and XRD analyses confirmed optimal chemical and morphological interactions at moderate filler contents, highlighting structural degradation at excessive loadings. These results indicate that an optimal graphite content of 50–70% by weight balances conductive pathways, mechanical stability, and electrolyte accessibility, providing a blueprint for designing epoxy/graphite composites that are robust and efficient for next-generation energy storage devices. Full article

In order to solve the problems of insufficient medical image feature extraction, high classification accuracy, and computational complexity in automatic diagnosis of skin lesions in the edge computing environment, this paper proposes a real-time pseudo-multimodal low-delay diagnosis framework, SCGViT, based on a vision transformer. The framework is constructed around three functional objectives: mitigating data imbalance through generative modeling, capturing diverse representations via multi-dimensional perception, and optimizing feature fusion through adaptive refinement. Firstly, using Class-Conditioned Generative Adversarial Networks (CGANs) simulates manifolds of minority class samples in latent space, achieving preliminary balance of data distribution. Secondly, a branch feature-extraction path is constructed to simulate inversion (INV) and infrared (IR) modes in the original visual primary color mode (RGB), in order to achieve multi-dimensional perception. Finally, a cross-attention mechanism is combined for cross-branch feature aggregation, and a channel-attention mechanism (squeeze and excitation) is embedded for secondary refinement of the mixed global local features to enhance the representation ability of key pathological regions by integrating complementary structural and contrast information. The experimental results on the HAM10000 dataset showed that the F1 score reached 0.973, the inference speed reached 304.439 FPS, the parameter count was only 0.524 M, and the computational complexity was only 0.866 G FLOPs, achieving a balance between high accuracy and light weight. Full article

Treacher Collins syndrome (TCS) is a rare disorder within the group of mandibulofacial dysostoses, occurring in 1 in 50,000 live births. It is characterized by anomalies in the maxillary, mandibular, and stapes bones, among others. TCS is caused by pathogenic variants in the TCOF1, POLR1D, POLR1C, and POLR1B genes with autosomal dominant or recessive inheritance patterns. Genetic data from Latin American populations remain scarce. Eleven patients from three different families were recruited. Whole-exome sequencing (WES) was performed on the probands to identify genetic variants, followed by Sanger sequencing for variant validation and familial segregation analysis. Finally, three-dimensional protein structures of wild-type and mutant proteins were predicted. In Family 1, a heterozygous pathogenic splice-site variant in the TCOF1 gene, c.4345 + 1 G > A, was identified and inherited from her mother. In Family 2, a heterozygous pathogenic variant in the TCOF1 gene, c.226_227insC (p.R77fs), was identified and inherited from the paternal lineage. In Family 3, a heterozygous pathogenic POLR1D variant, c.290_291delAG (p.G99fs), was identified among multiple affected relatives; direct parent-of-origin could not be established due to unavailability of one parent, but segregation supports autosomal dominant transmission across three generations. All findings were validated by Sanger sequencing. Our findings highlight the utility of WES for the molecular diagnosis of TCS and underscore the importance of including underrepresented populations in genetic studies to improve diagnosis, genetic counseling, and perinatal planning in at-risk pregnancies. Full article

Autonomous in-space assembly using a free-flying robot can lead to residual vibrations and positioning errors of the target modules during the grasping process. This places stringent demands on end-effectors, which must tolerate large misalignments while maintaining high positioning accuracy. In this regard, this paper presents a novel self-centring mechanism, which consists of two self-centring fingers mounted on the end-effector and a double V-groove mechanism attached to the target module. The proposed compact structural design passively corrects substantial parallel offsets and angular misalignments between the end-effector and the module. A multi-point contact model consistent with this mechanism is then developed using the virtual sphere layer method to describe the self-centring process. This model incorporates a normal contact force model and a three-dimensional bristle frictional force model to characterise the multi-point bouncing contact behaviours during the self-centring process. Numerical simulations and experimental tests involving the grasping of a module with a single robotic arm confirm that the self-centring mechanism effectively eliminates initial misalignments, achieving sub-millimetre positioning accuracy. The measured parallel offsets and contact forces align closely with numerical predictions, with minor discrepancies attributed to environmental noise and vibrations from the elastic bungees in the gravity compensation system. Finally, the self-centring mechanism is applied to grasp two modules with a dual-arm robot in the Space Proximity Operations Test facility. The centroid displacements of the robot closely match the simulation results, further validating the accuracy of the proposed multi-point contact model. Full article

This research investigates the dynamic response of three-dimensional concrete beams with coated aggregates subjected to transient wave propagation induced by piezoelectric actuators (PZT-lead zirconate titanate). The aim was to assess the effects of coated aggregates on concrete’s damping behavior and wave attenuation. The effect of various coating materials (epoxy and rubber) and replacement levels ranging from 5% to 25% by volume of the natural coarse aggregates on wave attenuation and energy dissipation were investigated through Finite Element Modeling (FEM) using Abaqus/CAE 6.14-1 software. Moreover, the effect of coating thickness is investigated for thicknesses ranging from 1.0 mm to 3.5 mm. The findings show that the replacement level, coating thickness, and coating material have a major effect on damping characteristics of concrete. It was also observed that rubber-coated aggregates with a 3.0 mm coating thickness (20% replacement level) exhibited an optimal damping ratio of 6.15%, representing a 29.5% increase, and offer enhanced energy dissipation, better damping performance, and the ability to alter wave travel paths, all of which could be advantageous for Structural Health Monitoring (SHM) applications. In line with this, the damping ratio of concrete beam models with epoxy- and rubber-coated aggregates, embedded with PZT material, was significantly higher (by approximately 1% to 18%) compared to that of the concrete model without PZT materials. Additionally, the findings showed that the concrete’s damping properties were greatly impacted by the interaction between PZT materials and coated aggregates. All things considered, the dynamic response and damping performance of concrete with coated-aggregate surface properties were successfully assessed using PZT-based simulations. Full article

Additive manufacturing is increasingly used in construction, yet reliable quality assurance for three-dimensional-printed concrete elements remains a major challenge. Existing digital defect-detection methods, particularly voxel-based and mesh-based approaches, are often evaluated separately, which limits understanding of their relative capabilities for construction-scale inspection. This study establishes a controlled comparison of the two representations using identical scan-to-design data, consistent preprocessing, and unified defect thresholding. A voxel pipeline employing signed distance fields and a three-dimensional convolutional neural network, and a mesh pipeline using triangular surface reconstruction, geometric surface descriptors, and MeshCNN, were applied to structured-light scans of printed clay wall segments containing intentional voids, material buildup, and layer-height inconsistencies. Across common performance metrics, the voxel-based method achieved a recall of 95% for spatially coherent, volumetric-consistent void-related anomalies inferred from surface geometry, reflecting improved aggregation of distributed deviations, while the mesh-based method attained a mean surface defect localization error of 0.32 mm with a substantially lower computational cost in runtime and memory. These results clarify representation-dependent trade-offs and provide guidance for selecting appropriate inspection pipelines in extrusion-based construction. The findings establish a controlled, construction-oriented comparative framework for digital defect detection and support more efficient, reliable, and scalable quality-assurance workflows for sustainable additive manufacturing. Full article

As a critical aerospace structural material, the fatigue crack propagation behavior and fatigue life of the nickel-based GH4169 superalloy are directly related to the service safety of engineering components. The crack closure effect is one of the key factors influencing the fatigue life of metallic materials. At present, the finite element method (FEM) is widely used to investigate fatigue crack propagation in metals. However, the commercial software ABAQUS 2021b employs the conventional Paris law for crack growth simulation, which neglects the influence of crack closure. In addition, ABAQUS cannot simultaneously perform fatigue life prediction and crack path prediction within a single numerical model. To overcome these limitations, the bi-prism-based single-lens (BSL) three-dimensional digital image correlation (3D DIC) technique was employed to experimentally investigate the crack closure behavior during fatigue crack propagation in GH4169 compact tension (CT) specimens. A new parameter, termed the crack opening ratio (COR), was introduced to quantitatively characterize the crack closure effect. Furthermore, a self-developed plugin was implemented on the ABAQUS platform through secondary development, enabling the numerical model to incorporate the influence of crack closure during fatigue crack propagation. The plugin automatically records the crack tip coordinates at each propagation step, calculates the stress intensity factors near the crack tip, and predicts the corresponding fatigue life, thereby integrating crack path prediction and fatigue life prediction within a unified framework. The results demonstrate that the COR effectively characterizes the crack closure effect in the numerical model, and the predicted fatigue life agrees with experimental results within an 11% deviation once the crack reaches a certain length. Full article

In the post-pandemic era, student mental health challenges have emerged as a critical issue in higher education. However, conventional assessment approaches often treat at-risk populations as a monolithic entity, thereby limiting intervention effectiveness. This study proposes a novel computational framework that integrates explainable artificial intelligence (XAI) with unsupervised learning to decode the latent heterogeneity of psychological risk mechanisms. We developed a “predict-explain-discover” pipeline leveraging TreeSHAP and Gaussian Mixture Models to identify distinct risk subtypes based on a 2556-dimensional feature space encompassing lexical, linguistic, and affective indicators. Our approach identified three theoretically-grounded subtypes: academically-driven (28.46%), socio-emotional (43.85%), and internal regulatory (27.69%) risks. Sensitivity analysis using top-20 core features further validated the structural stability of these mechanisms, proving that the subtypes are anchored in the model’s primary decision drivers rather than high-dimensional noise. The framework demonstrates how black-box classifiers can be transformed into diagnostic tools, bridging the gap between predictive accuracy and mechanistic understanding. Our findings align with the Research Domain Criteria (RDoC) and establish a foundation for precision interventions targeting specific risk drivers. This work advances computational mental health research through methodological innovations in mechanism-based subtyping and practical strategies for personalized student support. Full article

Focusing on the construction of a 58-m-diameter double-layer steel space frame dome at the Han Culture Museum Assembly Hall, this study addresses scheme selection and safety control challenges in staggered jacking of large-span spatial structures. A three-dimensional finite element model in MIDAS Gen simulated the three-stage jacking process to compare three temporary support layouts. Numerical evaluation metrics included maximum vertical displacements, peak internal forces, the proportion of members undergoing stress state transitions, and spatio-temporal evolution of stress concentrations. Scheme B demonstrated superior performance, reducing peak vertical displacement by 44% under critical conditions, lowering peak stresses, and enabling more uniform internal force redistribution—effectively mitigating tension–compression cycling and buckling risks. Crucially, only nodal displacements and support elevations were monitored in situ using a 3D system based on magnetic prisms and total stations; no strain or force measurements were conducted due to practical constraints during construction. Monitoring data show good agreement with simulated displacements and support elevations under Scheme B, validating the model’s deformation response. However, localized deviations—including a 29 mm deflection discrepancy and elevation errors up to 28 mm—reveal the influence of uneven boundary conditions, with potential implications for long-term structural behavior. The findings confirm that numerical predictions of deformation are reliable, while internal forces remain unvalidated by field data. The integrated approach of “scheme comparison–construction simulation–full-process displacement monitoring” proves effective for safety control and decision-making in complex jacking operations, offering a transferable framework for similar large-span double-layer space frame projects. Full article

In the context of three-dimensional (3D) point cloud modeling for pillar-type insulators during the “post-production–pre-use” phase, current methodologies encounter challenges in achieving a balance between cost-effectiveness, comprehensive coverage, and high precision. This study introduces a novel 3D point cloud modeling approach that utilizes multi-view two-dimensional (2D) LiDAR technology. This method employs three 2D LiDAR sensors positioned at 120° intervals to conduct layer-by-layer scanning, thereby capturing the surface point cloud data of insulators from various heights and perspectives. This approach effectively mitigates the impact of occlusion and facilitates comprehensive 360° data acquisition. Based on this foundation, the skirt structure characteristics of pillar-type insulators were extracted, and a point cloud registration and stitching algorithm, grounded in structural constraints, was developed to facilitate a high-precision 3D reconstruction. The experimental findings indicate that the proposed approach in this study demonstrates substantial improvements in modeling accuracy compared with the baseline methods. In repeated experiments, the proposed method in this study showed an average distance error with a mean (${\mu }_{\mathrm{MDE}}$) ± standard deviation ($\sigma$) of 1.15 ± 0.07, while the root mean square error had a mean (${\mu }_{\mathrm{RMS}}$) ± standard deviation ($\sigma$) of 1.26 ± 0.11. This method offers several advantages, including a straightforward structure, low system cost, and excellent point cloud continuity (1 mm). The maximum measurement error for the disc diameter was 2.986 mm, which satisfies the engineering application requirement of ±5 mm, thereby confirming the feasibility and practical utility of the method in the 3D modeling of pillar-type insulators. Full article

Background/Objectives: Accurate three-dimensional (3D) facial scanning is increasingly important in digital dentistry for diagnosis, treatment planning, and virtual patient creation. Multiple facial scanning technologies are available; however, their metric reliability varies depending on acquisition principles and anatomical orientation. This study aimed to evaluate the trueness, orientation-dependent performance (vertical midline versus horizontal facial measurements), and scanning time of four facial scanning technologies using calibrated manual anthropometry as the reference standard. Methods: Thirty dentate adult participants received adhesive fiducial markers on five predefined facial landmarks. Four linear facial distances were measured clinically using a digital caliper and compared with corresponding measurements obtained from standardized 3D facial scans. Digital measurements were extracted following uniform metric normalization. Inter-examiner reliability, measurement trueness, orientation-related differences, and scanning time were analyzed. Results: Inter-examiner reliability was excellent for both clinical and digital measurements (ICC > 0.93). All facial scanning technologies significantly overestimated manual distances (p < 0.001). The structured-light scanning system showed the smallest deviations (typically <1 mm) and the highest overall accuracy, followed by the depth-fusion system, while photogrammetry-based and NeRF-based approaches demonstrated larger errors, frequently exceeding 2–3 mm. Horizontal facial distances consistently showed greater deviations than vertical midline measurements across all systems. Scanning time differed significantly between technologies, with passive image-based approaches being the fastest and NeRF-based acquisition requiring the longest capture time. Conclusions: Active structured-light facial scanning demonstrated the highest trueness for linear facial anthropometry, whereas passive photogrammetry and NeRF-based approaches showed lower metric trueness and are currently more suitable for educational applications. Full article

Aimed at the elevation ambiguity problem in array synthetic aperture radar (SAR) three-dimensional imaging, this paper proposes a novel ambiguity-resolving method based on wavefront modulation. By introducing measured plasma lens modulation phases and constructing an array SAR signal echo model incorporating wavefront modulation, the method effectively overcomes the physical size limitations of traditional array antennas. Theoretical analysis demonstrates that wavefront modulation significantly reduces the grating lobe level of the array pattern, equivalently increasing the number of array channels and thereby shortening the shortest baseline length, which enhances the system’s maximum unambiguous height. At the signal processing level, an observation equation based on compressed sensing is established, and target reconstruction is achieved using the Orthogonal Matching Pursuit (OMP) algorithm. Monte Carlo simulation results indicate that under the same signal-to-noise ratio conditions, when the observation range is extended to twice the theoretical maximum unambiguous height, the proposed method maintains a reconstruction success rate of over 95%, whereas the traditional method’s reconstruction success rate drops rapidly below 40% once the maximum unambiguous range is exceeded. This study also investigates the 3D reconstruction of spatial point targets and a rectangular building, with the analysis of their theoretical ambiguous positions confirming the method’s effectiveness in suppressing ambiguous targets in the vicinity of spatial point targets as well as in front of and behind the structure. This study provides a new technical approach to overcoming antenna size constraints on airborne platforms, with significant application value in fields such as digital elevation model construction and urban 3D imaging. Full article

Osteoarthritis (OA) is a prevalent degenerative disease of the musculoskeletal system worldwide. Self-assembled hydrogels, as a novel drug delivery system, have demonstrated significant advantages in the treatment of OA. Through non-covalent interactions such as hydrogen bonding, hydrophobic interactions, electrostatic interactions, and π-π stacking, these hydrogels spontaneously form a three-dimensional network structure under physiological conditions without the need for chemical crosslinking agents, offering excellent biocompatibility, injectability, and controllable degradation properties. This system enables in -situ gelation within the joint, minimally invasive injection, sustained and controlled drug release, and intelligent responsive release. It is suitable for various delivery forms, including single-drug targeted delivery, exosome-based composite synergistic delivery, and microenvironment-responsive precise delivery, effectively inhibiting inflammation and promoting cartilage repair. Despite facing challenges in clinical translation, such as consistency in large-scale production, long-term safety evaluation, and regulatory standards, continued optimization in material design and preparation processes holds promise for self-assembled hydrogels to become a key platform for precise and minimally invasive OA treatment, offering new solutions for joint disease therapy. Full article

Traditional fluid network theory often underestimates pressure losses in complex pipe-bundle systems operating under vortex-dominated flow conditions, with deviations exceeding 20% in many cases. To address this limitation, this study proposes a vortex-based correction method. Three-dimensional simulations were performed on a multidirectional parallel pipe bundle to analyze vortex formation and to quantify the effects of fluid properties (viscosity and inlet velocity) and structural parameters (branch diameter, manifold cross-sectional ratio, and manifold arrangement) on pressure loss. To account for vortex-induced energy dissipation that is overlooked by conventional one-dimensional network models, an additional vortex-induced loss coefficient, α, is introduced to modify the pressure-loss formulation. Results indicate that higher viscosity, larger branch diameter, a higher manifold cross-sectional ratio, and a co-flow arrangement improve flow uniformity and prediction accuracy. Conversely, higher inlet velocities and counter-flow arrangements intensify vortex effects and increase prediction deviations. Least-squares fitting indicates that α ranges from 1.15 to 1.37. Implementation of the proposed correction reduces pressure-loss prediction errors to within 5%, demonstrating the method’s effectiveness and extending the applicability of fluid network theory to vortex-dominated flows. Full article