Search Results (3,341)

Accurate 3D documentation of large and complex structures is essential for long-term stability assessment, structural monitoring, and conservation planning, particularly for heritage sites exposed to environmental and anthropogenic threats. This study develops an integrated workflow combining Terrestrial Laser Scanning (TLS), Global Navigation Satellite System (GNSS), and Total Station geodetic control for large-scale, high-precision documentation. The approach was implemented at the Saqqara archaeological zone, a UNESCO World Heritage Site facing significant deterioration risks, to document four major pyramids: Djoser, Unas, Teti, and Userkaf. More than 2.1 billion georeferenced points were acquired from 16 scan positions with sub-centimeter registration errors and overall geometric accuracy better than ±1 cm. From these datasets, detailed mesh models, orthoimages, Digital Elevation Models (DEMs), contour maps, and 2D plans were derived. These enabled quantitative analyses of height loss and volumetric change, indicating severe structural degradation in Unas (~53%), Teti (~66%), and Userkaf (~63%), as well as localized deformations such as 4.2 cm displacement at Teti’s south flank. The degradation results from environmental factors and anthropogenic influences. Beyond this case study, the workflow proves that integrated TLS documentation can be applied to large and complex structures, supporting deformation monitoring, stability assessment, and digital twin development. Full article

Digital elevation models (DEMs) are foundational for critical tasks such as flood inundation simulation, disaster risk assessment, and ecosystem monitoring in coastal zones, yet their vertical accuracy is significantly compromised by complex terrain and surface characteristics. This study quantitatively decomposes the vertical errors of three 30 m global DEMs (COP30, NASADEM, and AW3D30) across the subtropical coastal region of Southeast China using ICESat-2 ATL08 data as a reference. By integrating an eXtreme Gradient Boosting (XGBoost) model with SHapley Additive exPlanations (SHAP), we successfully decoupled systematic biases from random noise. The results show that NASADEM achieved the lowest RMSE (7.775 m), followed by COP30 and AW3D30. While the Terrain Ruggedness Index (TRI) and categorically encoded Land Cover were identified as the universally dominant error drivers across all datasets, explainable analysis revealed distinct secondary mechanisms: X-band COP30 is notably susceptible to canopy height, exhibiting significant positive bias in forests exceeding 15 m; C-band NASADEM shows a systematic bias related to topographic position, typically overestimating ridges and underestimating valleys; and optical AW3D30 is significantly affected by stereo-matching errors. Furthermore, the analysis quantified a systematic error component of ~40%. These findings provide a data-driven basis for DEM selection and highlight that accuracy improvements should prioritize vegetation removal for radar DEMs and enhanced stereo-matching for optical models. Full article

Background/Objectives: Resin 3D printing is widely used to fabricate orthodontic diagnostic models, but the practical performance of commercial desktop workflows compared to dental-certified workflows is still debated. This study compared the dimensional accuracy and 7-day stability of maxillary orthodontic models printed from the same master STL file using a dental-certified workflow versus two commercial desktop workflows. Methods: An ISO 20896-1:2019-based reference cast with four 6 mm calibration spheres was used to generate a master STL file. Fifteen models were printed (n = 5 per workflow) using Primeprint™ (dental-certified workflow) and two commercial desktop printers (Anycubic Photon Mono M5s; Phrozen Sonic Mighty 14K REVO). The models were digitized at baseline (T0, ≤48 h) and after 7 days (T7) using a laboratory scanner. Surface superimposition in CloudCompare^® calculated the RMS (root mean square) surface deviation and mean signed deviation, and two calibrated operators performed independent extractions. Results: The mean RMS deviations were <0.10 mm for all workflows at both time points. No between-workflow differences were detected at T0 (H = 2.000; p = 0.368) or T7 (H = 1.520; p = 0.468), no within-workflow T0–T7 changes were significant (all p > 0.05), and the inter-operator agreement was excellent (ICC 0.991–0.999). Conclusions: Under the tested workflows, dental-certified and commercial desktop resin printing produced orthodontic models with a comparable global surface accuracy and short-term dimensional stability. Full article

Background: As Taiwan transitions into a super-aging society, the government has launched “Long-term Care (LTC) 3.0,” a policy initiative that marks a strategic shift from service expansion to integrated quality verification, digital oversight, and social resilience. This transition demands a robust quality verification mechanism. Ensuring perceptual consistency between service providers and external evaluators is critical for systemic fairness and sustainable service quality. Objective: This study utilized a two-dimensional gap analysis to examine the discrepancy in service quality benchmarks between home-based LTC center managers and assessment committee members, identifying critical divergence zones for institutional improvement. Methods: A cross-sectional evaluative study was conducted, involving center managers (evaluatees, n = 50) and external experts (evaluators, n = 28). The data were collected via a structured instrument covering 20 consensus benchmarks. Results: Significant perceptual gaps were identified across all dimensions (p < 0.001), with “Professional Care Quality” exhibiting the largest effect size (Cohen’s d > 1.5). Benchmarks with low external scores but high internal ratings were categorized into the “Overestimation (Management Blind Spot)” quadrant, signaling a systemic overestimation bias in administrative and clinical risk management. Conclusions: This study provides empirical evidence for the refinement of LTC 3.0 assessment systems. The results offer a strategic roadmap for policymakers to enhance organizational resilience by transitioning from subjective self-perception to objective, data-driven quality management through the two-dimensional gap model. Full article

Three-dimensional (3D) epicuticular wax coverage on plant surfaces contributes to multifunctional surface properties, such as enhanced water repellence, reduced pathogen adherence, modified optical properties, and reduced insect adhesion. The diversity in wax projection morphology, size, abundance, and spatial arrangement among plant species results in a broad spectrum of anti-adhesive effects, reflecting both phylogenetic history and ecological function. This study presents a numerical model consisting of 3D tubular-shaped structures randomly deposited on a substrate and forming a highly porous layer. The simulations based on this model demonstrate a strong reduction in adhesion to the contacting insect adhesive pad. It is found that a structure formed by sufficiently long tubes, where the length is enough to support the tubes in space and build a porous 3D structure with a very low density, at relatively weak attraction to the underlying substrate, leads to the weakest adhesion. The model is constructed on the basis of our recent works combining discrete and continuous approaches in biological modeling. It mainly exploits the technique of the movable digital automata, allowing modeling of numerous numerically elastic cylinders that can be moved in 3D space, elastically collide with one another and with boundaries, and build self-consistent surface structures, which can be used to mimic nano- or microscale surface coverages of real plants. Full article

With the increasing penetration of converter-based devices, harmonic distortion has become a major challenge for power quality monitoring in large-scale power systems. This study presents a systematic review of methods for modeling harmonic sources and their applicability to real-time monitoring of power distribution systems. The review was conducted following PRISMA guidelines, considering literature published between 2000 and 2026. Searches were performed across Scopus, IEEE Xplore, Web of Science, ScienceDirect, and MDPI using predefined keywords. A total of 128 peer-reviewed journal articles were included. Potential sources of bias were qualitatively assessed, including selection, retrieval, and classification bias; however, residual bias may still arise from database selection, keyword design, and study classification. A structured comparative framework is introduced, based on a six-dimension coverage scoring scheme and maturity analysis, enabling consistent evaluation across both methodological and deployment aspects. The robustness of this framework was evaluated using leave-one-out and perturbation analyses, indicating low variability in coverage scores and stable rankings across both corpora. A taxonomy of harmonic source modeling approaches is proposed. Comparative synthesis indicates that measurement-based approaches, particularly those leveraging distribution-level PMUs, show strong potential for real-time monitoring. Key challenges include D-PMU placement, data integration, and computational scalability. Future work should focus on physics-informed AI and digital twin-based monitoring. Full article

The accuracy of Digital Elevation Models (DEMs) plays a crucial role in determining their reliability for geoscientific and engineering applications. Next-generation distributed interferometric synthetic aperture radar (SAR) constellations, such as the PIESAT-1 wheel constellation with its “one primary, three secondary” setup, provide a novel method for efficiently acquiring high-precision DEMs. However, a comprehensive and systematic performance evaluation of DEMs derived from such an innovative constellation is lacking, particularly in the context of comparative studies under complex terrain conditions. This study uses PIESAT-1 SAR imagery to generate a 10 m resolution DEM through multi-baseline interferometric processing. The ICESat-2 ATL08 dataset serves as the reference baseline, and mainstream products, including ZY-3, GLO-30, TanDEM-X DEM, and AW3D30, are incorporated for a multidimensional vertical accuracy evaluation, considering land cover, slope, aspect, and topographic profiles. The results indicate that, in three representative mountainous regions, the PIESAT-1 DEM achieves optimal overall accuracy (RMSE = 3.25 m). Furthermore, in regions with significant radar geometric distortions, such as south-facing slopes, vegetation-covered areas, and regions with noticeable anthropogenic topographic changes, the PIESAT-1 DEM demonstrates superior stability and information capture capabilities relative to conventional single- or dual-baseline SAR systems. This study validates the technological potential of the PIESAT-1 wheel constellation in enhancing DEM accuracy and terrain adaptability, and provides insights for the scientific selection of high-resolution topographic data and the design of future spaceborne interferometric missions. Full article

This study analyzes the long-run relationships between digitalization, investment, innovation, and economic growth in connection with the energy transition in the SPRING-F group (Spain, Poland, Romania, Italy, the Netherlands, Germany, and France) using annual data for the period of 2000–2024. The analysis starts from the premise that digitalization affects economic performance not only directly, but also through structural transmission mechanisms linked to investment and the energy transition. To capture these dynamics, this study employs three complementary panel ARDL models. The first model explains economic growth (GDP per capita) as a function of digitalization, capital accumulation, R&D expenditure, renewable energy consumption, trade openness, and foreign direct investment. The second model estimates gross capital formation (GCF) in order to assess the investment transmission channel. The third model explains renewable energy consumption (RNEC) in order to capture the sustainability dimension. The results show that trade openness and capital accumulation are the strongest long-run drivers of economic growth in the SPRING-F group. Internet use, R&D expenditure, and FDI also display positive long-run associations with GDP per capita, whereas fixed broadband subscriptions and renewable energy consumption enter the growth equation with negative coefficients, suggesting that digital infrastructure and the green transition do not automatically generate immediate growth gains. The GCF model confirms that investment acts as an important transmission mechanism, especially through the robust GDP–GCF linkage. The RNEC model indicates that the energy transition is positively associated with investment, innovation, and trade openness, while GDP and digital infrastructure remain negatively associated with the renewable energy share. Overall, the findings point to a conditional and nonlinear relationship between growth, digitalization, investment, and sustainability, with the sustainability channel remaining more specification-sensitive than the growth and investment equations. The long-run results for the GDP equation should also be interpreted with additional caution, given the comparatively weaker cointegration evidence for Model 1. Full article

Background/Objectives: Digital health ecosystems increasingly integrate content, behavioral interventions, and commercial offerings across multiple platforms. While design consistency is established as critical for trust in commercial contexts, its associations with health behavior change and objective health outcomes remain underexplored. This study examined how cross-platform design consistency and aesthetic experience are associated with behavioral adoption through psychological pathways and investigated relationships between design-driven adoption and objective health outcomes. Methods: A convergent mixed-method design comprised five integrated studies: systematic content analysis of short-form videos (N = 200), expert evaluation and user testing (N = 33), a cross-sectional survey (N = 186), semi-structured interviews (N = 15), and a 3-month longitudinal health outcome analysis (N = 143). Structural equation modeling tested pathways from design features through psychological mediators and COM-B components (capability, opportunity, motivation) to behavioral adoption and health outcomes. Results: Design consistency was significantly associated with trust (β = 0.52), perceived value (β = 0.68), and reduced perceived risk (β = −0.41; all p < 0.001). Aesthetic experience predicted emotional resonance (β = 0.71, p < 0.001) and moderated design–trust associations. COM-B components mediated 75% of the intention-to-adoption pathway (total indirect effect = 0.51, p < 0.001). High-adoption users showed clinically meaningful improvements in weight (−2.8 kg, d = 0.89), HbA1c (−0.7%, d = 0.65), fasting glucose (−0.9 mmol/L, d = 0.72), and LDL-C (−0.4 mmol/L, d = 0.51) over three months. Conclusions: Within a single, influencer-centered Chinese digital health ecosystem, design consistency and aesthetic experience were significantly associated with trust, psychological readiness, and behavioral adoption. These findings are observational; randomized controlled trials and multi-site replication are required to establish causal mechanisms and assess generalizability. Full article

With the continued development of spatial information technologies, Digital Surface Models (DSMs) have become fundamental data products for urban planning, virtual reality, geographic information systems, and digital-earth applications. Neural Radiance Fields (NeRFs) have achieved remarkable success in multi-view 3D reconstruction in computer vision. Still, their application to DSM generation from satellite imagery remains challenging because of differences in imaging geometry, complex surface structure, and varying illumination conditions. To address these issues, this paper proposes a Shading and Geometric Constraint (SGC) method tailored to satellite photogrammetry and designed to integrate with existing NeRF-based frameworks such as Sat-NeRF and EO-NeRF. First, a physical imaging model based on Lambertian reflectance and spherical harmonics is introduced to represent the complex illumination variations in satellite images. Synthetic images generated by this model provide auxiliary supervision that improves robustness to illumination inconsistency. Second, inspired by classical shading-based refinement methods, we introduce a bilateral edge-preserving geometric constraint. Unlike standard smoothness terms, this constraint uses photometric discrepancies to weight geometric smoothing, thereby preserving sharp building boundaries while smoothing flat surfaces. We integrate the method into two state-of-the-art baselines, Sat-NeRF and EO-NeRF. EO-NeRF+SGC achieves up to a 57.93% reduction in elevation MAE relative to EO-NeRF, which is the largest relative MAE reduction reported in this study. The method also recovers finer structural details and sharper edges than recently published NeRF-based DSM reconstruction methods. Full article

Building Information Modeling (BIM) has changed the landscape of the architectural, engineering, and construction (AEC) industry in recent decades. However, BIM is not well researched in most developing countries; in particular, few studies have addressed its adoption in Malawi. A non-probability, purposive sampling approach was adopted. A total of 143 questionnaires were completed. This research reveals that, while construction experts are aware of BIM, the level of uptake remains quite low. Architects in Malawi are the most knowledgeable, followed by land surveyors and then engineers. This research shows that most experts in Malawi are at level 1 of BIM usage, which is the first stage of BIM adoption and is characterized by the use of 3D models and output representation. Furthermore, the study results have shown that the Malawian AEC sector is currently succeeding at the modelling stage of maturity but is stalled by lack of collaborative frameworks, such as Integrated Project Delivery (IPD). Therefore, unless the industry shifts toward a unified Common Data Environment (CDE), advanced capabilities like clash detection will remain underutilized and disconnected from broader project success metrics. Statistical analysis has shown that the correlation analysis demonstrates a strong link (r = 0.75) between Integrated Project Delivery (IPD) and high BIM maturity, whereas traditional Design-Bid-Build methods show a critical misalignment with digital workflows. The study identifies high software costs and a lack of national standards as the primary barriers to adoption. Therefore, there is a need for robust sensitization to the benefits of BIM and training to improve its uptake in the context of Malawi’s construction industry. In order to advance Malawi’s BIM maturity, the research recommends a strategic shift toward integrated procurement models, the establishment of national BIM mandates, and the modernization of technical education to bridge the existing knowledge gap. Full article

This paper investigates the quality of mobile network coverage along the Riga–Tukums railway corridor with a focus on the performance of 4G and 5G technologies. Ensuring reliable mobile connectivity along suburban railway corridors remains a significant technical challenge due to mixed forest–urban propagation conditions, macro-cell-dominated LTE infrastructure, mobility-induced channel variability, and fluctuating passenger density. Unlike high-speed railway environments that are extensively studied in dedicated 5G-R scenarios, suburban railway systems often rely on existing macro-cell deployments, where coverage continuity, signal quality stability, and capacity constraints must be addressed simultaneously. This study presents a measurement-based evaluation of 4G and 5G radio performance along the Riga–Tukums railway corridor under real operational conditions (50–90 km/h). Classical propagation models (Okumura–Hata and COST231-Hata) are quantitatively validated using MAE and RMSE metrics, followed by correlation analysis between RSSNR and QoS indicators. A theoretical Doppler sensitivity assessment (80–200 km/h) is conducted to evaluate mobility robustness across LTE and 5G frequency bands. Mobility transition regions and handover-related time windows are geometrically estimated, and passenger density-based capacity modeling is applied to assess throughput degradation under peak occupancy scenarios. Based on these results, a multi-layer network planning strategy integrating 700 MHz macro coverage, 1700 MHz capacity enhancement, and 3500 MHz 5G NR deployment is proposed. The optimization strategy resulted in an estimated 22–28% increase in stable service coverage in previously weak-signal zones and demonstrated that propagation model deviations remain within ranges comparable to recent railway studies (≈15–25 dB RMSE). These findings provide a structured framework for suburban railway communication optimization and support the gradual modernization of railway infrastructure toward FRMCS-ready architectures. The study illustrates the applicability of modern modelling tools for assessing and improving mobile communication systems and contributes to the broader development of digital infrastructure within Latvia’s transport sector. Full article

Accurate modeling of outdoor wireless propagation in dense urban environments is essential for smart city connectivity. Deterministic ray-tracing techniques provide high-fidelity multipath insight; however they suffer from high computational cost and limited scalability in large 3D environments. This work proposes a hybrid framework combining MATLAB-based (MATLAB 2024b 24.2.0.2773142, 64-bit, 22 October 2024) ray tracing and Machine Learning for scalable Wi-Fi 7 channel analysis. A large dataset is generated over a realistic university campus across multiple frequency bands, transmit powers, and reflection/diffraction configurations. Several regression models are evaluated, with emphasis on transformer-based architectures. The FT-Transformer achieves a Mean Absolute Error (MAE) of $3.49$ dB, RMSE of $5.36$ dB, and an $R^2$ of $99.63\%$ for validation, reducing computation time from months of simulation to seconds at inference. The framework enables accurate and efficient surrogate modeling for network planning and digital twin applications. Full article

The rapid advancement of digital and mobile technologies has reshaped the educational landscape, fostering the adoption of interactive and learner-centered methodologies. Among these, immersive technologies such as Augmented Reality (AR), when coupled with next-generation wireless communication systems, hold the potential to revolutionize knowledge acquisition and student engagement. In this paper, we present the design and development of an AR-based educational tool specifically oriented to teaching concepts of fifth-generation (5G) mobile networks. The tool provides a real-time interactive visualization of 3D network components on mobile devices, enabling learners to explore 5G NSA/SA architectures in an accessible manner with real-world environments through mobile devices and their integrated cameras. The application was developed using Blender for 3D modeling and Unity as the rendering engine, incorporating the Vuforia SDK for marker-based AR tracking, and it was deployed on the Android operating system. Unlike traditional static approaches, the proposed solution enables learners to explore complex network architectures and key functionalities of 5G in an interactive and accessible manner. To assess its perceived effectiveness, quantitative surveys were conducted with both university and high school students, focusing on usability, engagement, and perceived learning outcomes. Results indicate that the tool is user-friendly, enhances motivation, and supports conceptual understanding as perceived by participants of 5G technologies. These findings highlight the potential of AR, supported by advanced wireless networks, as a pedagogical strategy to improve STEM education and foster technological literacy in the era of digital transformation. Full article

Physical inactivity remains a major modifiable risk factor for non-communicable diseases and continues to exhibit marked socioeconomic and gender disparities in Latin America. Identifying robust and interpretable predictors of inactivity in nationally representative datasets is essential for informing public health strategies. This study compared a survey-weighted logistic regression model and an explainable machine learning approach (XGBoost) to predict physical inactivity among Chilean adults using data from the 2024 National Physical Activity and Sports Survey (ENAFyD; n = 5248). Models were evaluated on a stratified held-out test set (n = 1050) using weighted and unweighted area under the ROC curve (AUC), Brier scores, and calibration curves. Survey-weighted logistic regression achieved a weighted AUC of 0.801, while XGBoost achieved 0.797, demonstrating comparable discrimination. XGBoost showed marginally lower Brier scores, indicating slightly improved probabilistic calibration. Low socioeconomic status, female sex, lower monthly physical activity expenditure, limited facility access, and lower engagement with digital resources were consistently associated with higher inactivity risk. SHAP-style contribution analysis provided additional insight into feature-level influence within the machine learning framework. Overall, both approaches demonstrated similar predictive capacity, supporting the complementary use of classical regression and explainable machine learning for population-level physical inactivity research. Full article