Search Results (3,350)

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

Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies. Full article

Urban Digital Twins (UDTs) represent a recent application of Digital Twins (DTs), with the objective of replicating cities and providing a framework for urban planning. The utilization of UDTs provides a structured approach for the modeling and analysis of urban environments, incorporating a range of geospatial data presented in both two-dimensional (2D) and three-dimensional (3D) formats. This article details the process of processing, modeling, and integrating urban geospatial data into a Digital Twin. Two integrations for end-user platforms were demonstrated using a Geographic Information System (GIS) and an Unreal Engine (UE5) game platform. GIS-based dashboard systems provide professionals with the tools necessary to monitor, analyze, and create scenarios, thereby promoting collaboration between authorities and citizens. Game engines have the potential to play a pivotal role in the development of future UDTs by facilitating the creation of immersive experiences that aid users in comprehending their environment and promoting citizen engagement. Full article

This paper presents a comparative analysis of time parameters involved in the preparation phase of machining processes of the taper tap, contrasting traditional and modern approaches. The study examines the time required for the creation of a technological process sheet using traditional methods, and the time necessary for 3D modeling and CNC machine programming using modern CAD/CAM software Fusion 360 software (version v2.0.21286). Both approaches are based on the same workshop drawing so initial input data is consistent. The modern approach utilizes the Fusion 360 software (version v2.0.21286) for the creation of a 3D model and CNC machine programming. The traditional method relies on manual interpretation of the workshop drawing and handwritten technological process sheet that contains information about machining operations. Time consumption for each phase (technological planning in the traditional method and digital modeling and programming in the modern method) is measured and compared. The study aims to determine which approach demonstrates higher practical efficiency in specific production contexts and conditions. The scientific contribution of this work lies in providing quantifiable insights into the differences between traditional and modern production preparation methods, thereby supporting decision-making processes in the selection of optimal machining preparation strategies. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. 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

Increasing urbanisation and the intensification of environmental and climate challenges require a review of governance models and tools supporting urban and territorial planning. The Twin Transition concept (green and digital) requires the integration of advanced monitoring and simulation systems. In this context, Digital Twins (DTs) have evolved from static virtual replicas to dynamic urban intelligence systems. Thanks to the integration of IoT sensors and artificial intelligence algorithms, DT enables the transition from a descriptive to a prescriptive approach, supporting climate uncertainty management and real-time territorial governance. The ability to integrate multi-source data and provide high-resolution site-specific representations makes these tools strategic for planning, resource management, and the assessment of urban and peri-urban resilience. The contribution comparatively analyses different digital twin frameworks, with particular attention to their applicability in highly complex environmental contexts, such as the city of Taranto. As a Site of National Interest, Taranto requires models capable of integrating industrial pollutant monitoring with urban regeneration and biodiversity protection strategies. The study assesses the potential of DT as predictive models to support governance for more sustainable, adaptive, and resilient cities. Full article

Background/Objectives: Facial and intraoral parameters are important guidelines in prosthetic planning and rehabilitation. This study aimed to analyze and determine the relationship between facial parameters and measurements on the upper anterior teeth using digital photography of the participants. Methods: This cross-sectional observational study included 82 student participants. Digital images (front facial and dental view) were taken of each participant, and then standardized images were used to measure facial and dental parameters. Results: The width of the maxillary anterior teeth and facial parameters were greater in males than in females, except for the medial canthus of the eye, which was slightly larger in females. A significant positive correlation was found between all facial parameters and the widths of the central and lateral incisors, as well as their combined sum. The strongest correlation was observed between the lateral canthus of the eye and the total width of the maxillary anterior teeth (r = 0.546; p < 0.001). In regression analysis, it was shown that the bizygomatic width had a statistically significant contribution to the prediction of the central incisor width (p = 0.045). It was also shown that the intraoral parameters, such as the height of the interdental papilla and interpapillary angle, are shape-dependent. Interincisal angles between the central incisors in all participants are significantly lower (p < 0.05) than the angles between incisal edges in other anterior teeth. Conclusions: Facial parameters cannot be used independently to predict dental parameters; nevertheless, when integrated with basic esthetic principles, they provide complementary information relevant to analytical procedures in restorative and prosthetic dentistry. Full article

Sustainable tourism has become a priority as environmental pressures on the hospitality sector intensify. Despite increasing promotion of green hotels, a persistent gap remains between pro-environmental intentions and actual booking behavior. Prior applications of the Theory of Planned Behavior (TPB) largely focus on developed economies and offer limited insight into how digital platforms, organizational credibility, and information-seeking behavior shape green hotel decisions in emerging tourism markets. To address this gap, this study extends TPB by integrating social media marketing, environmental knowledge, organizational green practices awareness, self-image in environmental protection, and consumer information-seeking behavior. Survey data from 538 foreign tourists staying in hotels in Turkey were analyzed using Partial Least Squares Structural Equation Modeling. The findings indicate that awareness of organizational green practices is the strongest predictor of consumer attitude, followed by self-image, social media marketing, and environmental knowledge. Consumer attitude, subjective norms, and perceived behavioral control shape purchase intention, while purchase behavior is driven by intention, perceived behavioral control, and information-seeking behavior. Notably, information-seeking behavior exerts a direct and mediating effect on purchase behavior but does not moderate the intention–behavior relationship, indicating a post-intentional verification role. 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

Cultural tourism (CT) has emerged as a critical driver of destination competitiveness; however, stakeholders struggle to balance heritage preservation, sustainable growth, and visitor management. Current decision making often lacks the practical information required to assess the multi-dimensional impacts of CT and to align strategies with sustainability goals. This paper presents a user-centred digital decision support system (DSS) developed under the European project IMPACTOUR. The methodological contribution is a procedure that uncovers links among strategies, actions, and performance indicators, conditioned on destination characteristics, by leveraging hierarchical multi-criteria analysis to weight sustainability domains. Co-designed with stakeholders, it integrates social and technological components and uses triangulated data to prioritise strategies and evaluate impacts. The visual interface offers a smart dashboard that supports strategic decision making and displays related key performance indicators, enabling stakeholders to monitor outcomes against predefined sustainability objectives. Pilot implementations in several European regions demonstrate the tool’s efficacy in fostering data-driven planning to achieve a balanced approach between tourism and liveability. While the system is scalable, its current limits include regional specificity and data availability. Future work will incorporate AI-driven predictive analytics and adapt the DSS for application in non-European contexts, providing a replicable framework for advancing sustainable tourism policies in culturally rich destinations. Full article

Artificial intelligence (AI) is rapidly reshaping gastrointestinal (GI) surgery by enhancing decision-making, intraoperative performance, and postoperative management. The integration of AI-driven systems is enabling more precise, data-informed, and personalized surgical interventions. This review provides a state-of-the-art overview of AI applications in GI surgery, organized into four key domains: surgical simulation, surgical computer vision, surgical data science, and surgical robot autonomy. A comprehensive narrative review of the literature was conducted, identifying relevant studies of technological developments in this field. In the domain of surgical simulation, AI enables virtual surgical planning and patient-specific digital twins for training and preoperative strategy. Surgical computer vision leverages AI to improve intraoperative scene understanding, anatomical segmentation, and workflow recognition. Surgical data science translates multimodal surgical data into predictive analytics and real-time decision support, enhancing safety and efficiency. Finally, surgical robot autonomy explores the progressive integration of AI for intelligent assistance and autonomous functions to augment human performance in minimally invasive and robotic procedures. Surgical AI has demonstrated significant potential across different domains, fostering precision, reproducibility, and personalization in GI surgery. Nevertheless, challenges remain in data quality, model generalizability, ethical governance, and clinical validation. Continued interdisciplinary collaboration will be crucial to translating AI from promising prototypes to routine, safe, and equitable surgical practice. 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

Sustainable virtualization is essential for enterprises seeking to reduce energy use, increase resource efficiency, and connect IT operations with global sustainability goals. This study describes a hybrid decision-support framework that uses the ISO/IEC 25010 quality characteristics and sustainability factors to evaluate virtualization technologies using FAHP, RST, and TOPSIS. To obtain robust FAHP weights in uncertain situations, expert linguistic assessments are converted into fuzzy pairwise comparisons. RST is then used to determine the most important sustainability criteria, thereby improving interpretability while minimizing model complexity. TOPSIS compares virtualization platforms to the best sustainability solution. Empirical validation involved five domain experts, eight criteria, and four virtualization platforms. Performance efficiency, reliability, and security are the main criteria, with lightweight, resource-efficient hypervisors scoring highest in sustainability factors. To implement the framework, a lightweight web-based decision-support dashboard was developed. The dashboard allows real-time FAHP computation, RST reduct extraction, TOPSIS ranking visualization, and automatic sustainability reporting. The proposed technique provides a clear, replicable, and functional tool for sustainability-focused virtualization decisions. It helps IT administrators link digital infrastructure planning with the SDG-driven green IT objectives. Full article

The sustainable development of cultural heritage heavily relies on visitors’ sustainable practices, with education serving as the key to regulating visitor behavior and promoting their engagement in sustainable tourism. However, the mechanisms linking education and sustainable tourism remain unclear in the virtual context. This research aims to determine the potential of digital cultural heritage learning outcomes in supporting sustainable tourism behaviors (environmental, cultural, economic) among visitors. This study integrates the Generic Learning Outcomes (GLOs) with the Theory of Planned Behavior (TPB), collecting 642 valid samples and employing PLS-SEM analysis. Research findings indicate that knowledge and understanding (KU), skills (S), attitudes and values (AV), enjoyment, inspiration, and creativity (EIC), and activity, behavior, and progression (ABP) positively influence sustainable tourism practices. Cost perception (CP), however, weakens the conversion from intention to actual behavior. This provides empirical support for the development of digital cultural heritage projects and the sustainable management of heritage sites. Full article