Search Results (3,483)

[d=LE]Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human–automation interaction. Recent studies show that transition performance depends not only on takeover timing or response speed but also on traffic complexity, driver readiness, automation limitations, trust calibration, and situational-awareness recovery. As in-vehicle interaction evolves toward conversational and agentic AI assistance, takeover support also becomes a problem of governing how natural-language AI systems communicate with the driver under uncertainty.Transitions of control between automated driving systems and human drivers remain safety-relevant and cognitively demanding moments in human-automation interaction. Recent studies suggest that transition performance should not be assessed only through takeover timing or response speed since control resumption quality also depends on traffic complexity, driver readiness, automation limitations, and situational awareness recovery. [d=LE]This paper proposes a digital-twin-mediated framework for human-aware takeover support in automated driving. In this framework, the companion AI is treated as an assumed LLM-based in-vehicle conversational or agentic assistant used as an advisory interaction component. The contribution is defined at the architectural level: human, vehicle, and context/road digital twins provide structured semantic state abstractions through a semantic state interface exposing confidence, freshness, provenance, and consistency metadata, while a trustworthy companion AI (TCAI) layer grounds, constrains, validates, and governs companion AI output proposals before HMI delivery.This paper motivates and defines a trustworthy companion AI (TCAI) layer for human-aware transition support in automated driving. The TCAI is conceived as a bounded, supervised, and explainable advisory agent that supports the driver without entering the safety-critical vehicle-control loop. It reasons over structured semantic state abstractions derived from a human digital twin, a vehicle digital twin, and a context/road digital twin, exposing driver readiness, automation capability, and contextual urgency in a form that supports traceable, uncertainty-aware, and degradation-aware assistance. [d=LE]Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, conversational assistance, and human assistance systems (HASs), the framework coordinates advisory interaction across vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The TCAI layer combines bounded reasoning, human-factor-derived guardrails, state-consistency management, dynamic explanation-depth control, trust-dynamics modeling, graded watchdog veto handling, mandatory access-control assumptions, and deterministic fallback. Safety-critical vehicle-control and minimum risk condition (MRC) functions remain assigned to the deterministic vehicle-control stack, while the authorized output path of the TCAI layer is validated HMI delivery.Building on the research on driver-state monitoring, adaptive HMI, trust calibration, explainability, and conversational assistance, we propose a conceptual architecture in which the TCAI coordinates multimodal assistance across different interaction conditions, including vigilance support, contextual explanation, trust-calibrating communication, and directive handover guidance. The companion does not actuate the vehicle; its outputs are constrained by runtime governance, policy enforcement, and deterministic fallback mechanisms. [d=LE]The paper concludes with a validation agenda and technical roadmap covering planned transitions, urgent handovers, degraded or adversarial conditions, temporal fusion of driver-state evidence, phase-sensitive HMI policies, trust-calibration trajectories, driver veto and partial-disabling mechanisms, and staged simulator-to-vehicle evaluation. Although motivated by SAE Level 3 automation, the framework may also inform fallback-related Level 4 scenarios in which human and automated agency must be managed under uncertainty.The paper concludes with a research roadmap for validating the proposed architecture under planned transitions, urgent handovers, and degraded or adversarial conditions. Although motivated by SAE Level 3 automation, the approach may also inform fallback-related Level 4 scenarios. Full article

Following the PRISMA-ScR framework for scoping reviews, we systematically searched five databases (Scopus, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science) using a Boolean query combining real-time processing, 3D reconstruction, and deformation modelling terms. From 86 records identified, 56 peer-reviewed publications (2009–2025) were retained after two-stage screening and organized into a unified taxonomy covering sensing modalities (RGB-D, LiDAR, tactile), reconstruction pipelines (volumetric fusion, NRSfM, neural radiance fields), and deformation models (FEM, PBD, mass-spring, GNN-based surrogates, differentiable simulators). Of the 56 included works, 60% were published between 2022 and 2025, confirming the field’s rapid growth. Neural and implicit representations account for 20% of contributions, FEM-based methods for 16%, and hybrid or application-specific pipelines for 21%. Four systemic gaps emerge: the absence of a unified physics-aware benchmark; unresolved speed–accuracy trade-offs (PBD achieves >30 FPS on desktop GPUs for 10³–10⁴ vertex meshes but lacks mapping to physical material constants (Young’s modulus, Poisson’s ratio), limiting material fidelity; full-order FEM ensures physically consistent stress–strain behavior but runs at only 1–10 FPS without order reduction; reduced-order FEM recovers interactive rates for low-frequency deformation modes); fragile handling of occlusions and multi-object contact; and limited end-to-end integration of sensing and simulation. The findings support the presentation of a research roadmap centered on model order reduction, differentiable physics, multimodal sensing fusion, and standardized evaluation protocols, with implications for robust digital twins of deformable environments. Full article

Accurate transfer of implant position is essential for implant-supported prosthodontic workflows. This in vitro study compared the trueness and precision of five intraoral scanners in single crown, three-unit fixed partial denture, and full-arch implant-supported scanning scenarios using root mean square (RMS) deviation analysis. Two maxillary resin models, representing partially dentulous and fully edentulous conditions, were fabricated through a CAD/CAM and 3D-printing workflow with implant analogs and scan bodies. Reference datasets were obtained with an InEos X5 desktop scanner, and each intraoral scanner was used to perform 10 scans per scenario. After standardized scenario-specific trimming, datasets were analyzed in Geomagic Control X. Statistical analysis included two-way analysis of variance and follow-up one-way analysis of variance with Tukey post hoc comparisons using Bonferroni-adjusted thresholds. Trueness was affected by scanner type (p < 0.001) and scenario (p < 0.001), without interaction (p = 0.096). Precision was affected by scanner type (p = 0.012), scenario (p = 0.004), and their interaction (p < 0.001). iTero Lumina and Helios 600 showed lower trueness deviations, whereas Trios 5 showed greater deviations, especially in full-arch scans. Scanner selection and scan extent should therefore be considered when interpreting surface-based RMS accuracy in implant-supported digital scans. Full article

This study presents a comprehensive symmetry-breaking analysis framework for a twelve-legged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neuro-fuzzy inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels (20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid machine learning framework is proposed, applying ANFIS to maximum stress (R² = 0.817) and safety factor (R² = 0.936) predictions, while reserving FEA tables for bimodal outputs. The ANFIS surrogate achieves approximately 10⁶× speedup over FEA (262.6 μs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation. Full article

Additive manufacturing (AM), commonly known as 3D printing, has rapidly transformed modern manufacturing, creating a growing demand for engineers with both theoretical knowledge and practical skills. Despite its increasing relevance, AM is often incorporated into engineering curricula as a supplementary tool rather than a fully integrated subject, limiting students’ understanding of fundamental material–process–performance relationships. This study presents the development, implementation, and assessment of an integrated lecture–laboratory framework for AM education at the New Jersey Institute of Technology (NJIT). Two complementary courses were developed: an undergraduate course (Introduction to 3D Printing, CHE 415) and a graduate course (Additive Manufacturing and Applications, CHE 722). The curriculum integrates instruction in AM technologies, materials, and digital workflows with hands-on design challenges, team-based projects, and structured literature reviews, enabling students to engage in the complete design-to-fabrication process. Student learning outcomes were evaluated over multiple academic years using ABET-aligned assessments, grade distributions, and student self-assessments. Results demonstrate consistently high levels of student proficiency and engagement, with strong performance in design, problem-solving, and communication skills. The courses also attracted students from diverse disciplines, underscoring the interdisciplinary nature of AM education. While limitations remain in providing hands-on exposure to a broader range of AM technologies, ongoing expansion of laboratory infrastructure is expected to address these challenges. Overall, this work demonstrates that an integrated, project-based approach effectively bridges theory and practice and provides a scalable model for incorporating AM into engineering curricula. Full article

The digitization of existing building stock often depends on legacy 2D raster floor plans (scanned drawings, PDF exports, or photographs) because structured building information models are frequently unavailable for older properties. Manual measurement and visual inspection of such documents are time consuming and error prone. This paper presents an integrated deep learning pipeline that extracts semantic information from unstructured two-dimensional floor plan images of existing structures and supports preliminary compliance screening via locally deployed large language models. The pipeline employs YOLOv8 for the localization and classification of 18 architectural symbols and furniture items, and a U-Net with a ResNet34 encoder for the semantic segmentation of walls and interior room spaces. To translate pixel-level predictions into physical metrics, we implement an area calculation module based on user-defined reference scale calibration. An LLM evaluation module, deployed locally via Ollama with a retrieval-augmented generation pipeline, interprets extracted room metrics and flags potential non-compliance against referenced residential design guidelines; it is intended for the assessment of existing layouts rather than generative co-design. We expand a core dataset of 101 manually annotated source floor plans to 303 augmented instances using label-aligned geometric transformations, while reporting generalization in terms of the 101 unique source plans. On the held-out validation split (10 source plans), YOLOv8 achieves 92.3% mAP50 versus 87.2% for a Faster R-CNN reference model on the same data split (detection baselines differ in training epochs and pretraining; see Experiments); U-Net achieves 95.71% mIoU, surpassing DeepLabv3+ (93.2%) under matched segmentation training settings. The system is deployed as an interactive web application for legacy building survey and preliminary regulatory review when only two-dimensional documentation is available. Full article

Fringe projection profilometry (FPP) is widely used for 3D reconstruction, but conventional single-view FPP systems suffer from inherent occlusions and shadow regions, leading to incomplete surface recovery. In this study, we propose CVA-Net, an end-to-end deep learning framework with cross-view attention (CVA) that directly reconstructs dense depth maps from multi-view fringe patterns. CVA-Net simultaneously processes four fringe images acquired from orthogonal projection directions and leverages a CVA module to explicitly model inter-view dependencies, enabling adaptive fusion of complementary information. A 3D U-Net backbone with attention gates, atrous spatial pyramid pooling (ASPP), and an auxiliary parameter estimation branch further enhances reconstruction accuracy and structural consistency via multitask learning. To support Sim2Real network training, we build a Blender-based digital twin of a multi-view FPP system and generate a large-scale synthetic dataset with perfect ground truth. Extensive experiments on both synthetic and real-world objects demonstrate that CVA-Net significantly outperforms state-of-the-art single-view methods. With a symmetric four-view configuration and fringe period of 8, CVA-Net achieves an MAE of 0.0359 mm, an MSE of 0.0379 mm² and an RMSE of 0.1947 mm, reducing the MAE, MSE, and RMSE by 32.8%, 54.1%, and 32.2%, respectively, compared to the best single-view competitor. Ablation studies validate the contribution of each architectural component, while real-system experiments demonstrate the feasibility of transferring a network trained purely on synthetic data to practical FPP measurements without domain adaptation. Although further improvements are required to enhance reconstruction accuracy under real imaging conditions, the proposed framework provides an effective initial step toward bridging the gap between digital-twin-based training and real-world multi-view FPP applications. CVA-Net provides a robust, occlusion-aware solution for multi-view FPP reconstruction. Full article

In the context of an increasingly complex and dynamic digital ecosystem, accurately assessing the value of Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises is crucial for making effective decisions. Traditional valuation methods struggle to effectively address issues such as the high R&D expenditures and significant operational risks associated with these enterprises. This study proposes an interpretable intelligent decision-support framework for valuing SRDI enterprises listed on the Beijing Stock Exchange (BSE), constructing a multidimensional indicator system that encompasses solvency, profitability, and R&D capabilities. Feature importance screening using the XGBoost algorithm was conducted to identify key indicators as input variables for a backpropagation (BP) neural network. Concurrently, the Particle Swarm Optimization (PSO) algorithm was applied to the neural network to optimize initial weights and thresholds, thereby modeling nonlinear valuation relationships. Empirical analysis of 770 SRDI firms listed on the Beijing Stock Exchange from 2020 to 2024 indicates that the XGBoost-PSO-BPNN model achieved a coefficient of determination of 0.8083 on the test set, outperforming traditional linear models and benchmark models such as single-tree models. SHAP explainability analysis further reveals that current asset turnover, return on assets, and equity concentration are the primary value drivers. This study employs various clustering methods to further classify enterprises into three categories and proposes recommendations for differentiated regulatory policies, providing intelligent decision support for enterprises operating within complex digital ecosystems. Full article

Background: Advances in additive manufacturing and CAD/CAM technologies have expanded the use of 3D-printed orthodontic models in digital aligner workflows. Intraoral scanners (IOS) are critical for accurately capturing attachment geometries and dental morphology during these workflows. However, comparative evidence regarding IOS accuracy in models with complex orthodontic structures remains limited. Therefore, this study aimed to compare the trueness and precision of five IOS using 3D-printed orthodontic models with attachments. Methods: In this in vitro study, thirty independent single-arch 3D-printed models (either maxillary or mandibular) with orthodontic attachments were scanned twice with each IOS. The Smart Optics Vinyl laboratory scanner served as the reference scanner. Scans were aligned and superimposed in CloudCompare, and root mean square (RMS) deviation values were calculated to evaluate accuracy. Nonparametric Kruskal–Wallis and Dunn tests were applied (α = 0.05). Results: Significant differences were found among scanners for both trueness and precision (p < 0.001). Primescan, TRIOS 3, and iTero element 5D demonstrated comparable trueness (p > 0.05) and outperformed Rapideye MI-1000 (p < 0.001). iTero element 2 plus showed slightly lower accuracy but remained clinically acceptable. Primescan achieved the highest precision, significantly exceeding iTero element 2 plus, iTero element 5D, and Rapideye MI-1000 (p < 0.01). TRIOS 3 also exhibited excellent repeatability, comparable to Primescan (p = 1.000). Conclusions: All intraoral scanners, except Rapideye MI-1000, demonstrated accuracy levels generally considered clinically acceptable for digital orthodontic and additive manufacturing workflows. Primescan, TRIOS 3, and iTero element 5D exhibited similarly high trueness, while Primescan showed the most consistent precision. The ability of these scanners to reproduce fine anatomical details may improve the reliability of 3D-printed orthodontic models and in-office aligner production workflows. Full article

Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. Full article

Preserving cultural heritage sites requires an interoperable digital framework capable of integrating heterogeneous spatial data and supporting immersive interaction for inspection and management. This study investigates the integration of multiple heritage data representations—including IFC-based Heritage Building Information Modeling (HBIM), terrestrial and UAV LiDAR point clouds, and 3D Gaussian Splatting reconstructions—into a unified digital management environment for the East Hall (Aula Timur) heritage site within the Bandung Institute of Technology (ITB) campus. A semantic–spatial interoperability workflow is proposed to harmonize BIM, point cloud, and landscape-scale data within a common georeferenced context, supported by a CityGML-based base map of the surrounding site. An immersive virtual environment was implemented using a head-mounted display to enable walkthrough-based inspection and damage annotation. All datasets were georeferenced within a unified coordinate system, allowing spatial registration between digital objects and the physical heritage site. The results demonstrate that multi-source heritage datasets can be integrated with high geometric accuracy, achieving TLS registration errors of approximately 2 mm and georeferencing residuals within 11.1 cm (horizontal) and 0.95 cm (vertical), while preserving semantic information and ensuring spatial coherence across HBIM, GIS, and immersive environments. The system is implemented in VR, with an architecture designed to support future MR-based on-site annotation and visualization. The proposed framework establishes a foundation for future heritage digital twin deployments and supports informed conservation decisions. Full article

The documentation and reconstruction of fragile underwater organic artifacts remain among the most challenging tasks in digital heritage practice. This study presents a conservation-first, contact-minimizing protocol applied to a rare Byzantine leather bag recovered from the commercial port of Rhodes, Greece. Due to its incomplete preservation and structural instability, exclusively non-invasive methodologies were employed. High-resolution close-range photogrammetry and structured-light 3D scanning were integrated to capture both micro-topographic detail and metrically stable geometry. Quantitative deviation analysis (nearest-neighbor cloud-to-mesh distances) indicated that most geometric differences remain below 0.5 mm. The resulting models were processed through controlled mesh optimization, UV remapping, and conservation-oriented digital completion workflows. In addition, radiance field visualization techniques such as Gaussian Splatting were explored as complementary visualization approaches for incomplete geometries. These methods were evaluated primarily in terms of visual continuity and interpretative support rather than as reconstruction tools. The study demonstrates that the integration of photogrammetry, structured-light scanning, and Gaussian Splatting can significantly enhance the documentation and visualization of fragile underwater organic heritage. At the same time, it highlights the necessity of methodological transparency and ethical framing when incorporating probabilistic reconstructions into conservation workflows. Full article

The industrial Internet of Things (IoT) allows traditional electromechanical systems to be connected to digital monitoring and control platforms, especially when field devices use industrial protocols that must be integrated into web services without modifying their main operation. This work implements an IoT architecture based on the Open Systems Interconnection (OSI) model to interconnect two Variable Frequency Drives (VFDs) through a LOGO! Programmable Logic Controller (LOGO! PLC), a Human–Machine Interface (HMI), a ZLAN5143D gateway, Node-RED, Message Queuing Telemetry Transport (MQTT), and Adafruit IO. The communication integrates RS485/Modbus RTU at the field level and Modbus TCP/IP over Ethernet at the upper network level using the gateway as the protocol conversion element. The validation was performed through Modbus Poll, variable acquisition, MQTT publication, and web visualization. The results show local communication response, acquisition of frequency, voltage, current, and revolutions per minute (RPM), together with remote control of start, stop, frequency setpoint, and rotation direction. The architecture is presented as a modular solution for electromechanical applications with IoT projection. Full article

Stone arch bridges represent culturally significant heritage assets that exhibit distinct regional characteristics. At present digital preservation largely attends to geometric modeling and typically neglects the identification and conformance of core culture genes. This oversight has resulted in a disconnect between technological application and core heritage values, a prevalent issue globally. To address this, this study employs cultural gene theory to formulate a systematic framework for investigating the architectural cultural genes of stone arch bridges from the three dimensions: material–morphological, technical–behavioral, and cultural–symbolic. This study takes the Hongji Bridge in Handan as an example and uses literature research and 3D laser scanning and UAV oblique photogrammetry and qualitative extraction and visual presentation of the architectural genetic characteristics of stone arch bridges. This study identifies 11 core genetic indicators from the dimensions of genetic architecture, inheritance, and evolution, for the architectural cultural genes for the Chinese stone arch bridges The Zhaozhou Bridge (China) and Serranos Bridge (Europe)’s cross-cultural comparative analyses are adopted to validate the generalizability of the framework and the genetic uniqueness of the Chinese stone arch bridge. This research introduces a gene-based model of digital conservancy that fosters the transition of heritage preservation from technology-driven to value-driven. Full article

Conventional rock blasting produces large rock masses that do not fully meet engineering construction requirements. Therefore, mechanical crushing technology is necessary to reduce these masses into crushed stone of a specific particle size. Consequently, enhancing the comprehensive utilisation rate of excavated materials and exploring new application avenues has become critical. Initial crushing experiments were conducted on limestone of varying strengths. Based on the measured parameters, simulation experiments were performed to analyse the accuracy of crushing particles of different strengths. Cube specimens confirmed that the created crushing model accurately reflects the actual crushing behaviour of particles with different strengths. A Structure Sensor 3D scanner was used to scan representative shapes of rock particles. Software processing yielded the true three-dimensional apparent morphology of the rock material. Combined with physical crushing tests and simulation experiments, this confirmed that the developed crushing model accurately reflects the actual crushing behaviour of rock particles when their true morphology is considered. The research findings demonstrate that the digital crushing model can accurately depict the crushing process and particle size distribution of rock materials with different lithological characteristics and true morphology. Full article