Search Results (3,138)

Electrospinning is a widely used technique for fabricating nanomaterials with tailored morphology and functional properties. This study investigates how two fundamental process parameters—applied voltage and needle tip-to-collector distance—affect the spatial geometry and deposited mass of electrospun nanofiber mats in a top-down vertical electrospinning setup using a 10% (w/v) PVA solution prepared in deionized water. To support this hypothesis, both experimental measurements and 3D geometric modeling were performed to evaluate the area, perimeter, and deposited mass under different parameter combinations. Digital image analysis and cross-sectional reconstruction were applied to model nanofiber deposition. Regression and ANOVA analyses reveal that the tip-to-collector distance has a statistically significant impact on both area and perimeter of the electrospun nanofiber mat, while the applied voltage in the tested range (15–20 kV) has no significant effect. Interestingly, the total deposited mass shows no clear dependence on either parameter, likely due to startup irregularities or solution droplets. Full article

Characterizing tight reservoirs is challenging due to the complex pore structure and strong heterogeneity at various scales. Current digital rock physics often struggles to reconcile high-resolution imaging with representative sample sizes, and 3D digital cores are frequently used primarily as visualization tools rather than predictive, computable platforms. Thus, a clear methodological gap persists: high-resolution models typically lack macroscopic geological features, while existing 3D digital models are seldom leveraged for quantitative, predictive analysis. This study, based on a full-diameter core sample of a single lithology (gray-black shale), aims to bridge this gap by developing an integrated workflow to construct a high-fidelity, computable 3D model that connects the micro–nano to the macroscopic scale. The core was scanned using high-resolution X-ray computed tomography (CT) at 0.4 μm resolution. The raw CT images were processed through a dedicated pipeline to mitigate artifacts and noise, followed by segmentation using Otsu’s algorithm and region-growing techniques in Avizo 9.0 to isolate minerals, pores, and the matrix. The segmented model was converted into an unstructured tetrahedral finite element mesh within ANSYS 2024 Workbench, with quality control (aspect ratio ≤ 3; skewness ≤ 0.4), enabling mechanical property assignment and simulation. The digital core model was rigorously validated against physical laboratory measurements, showing excellent agreement with relative errors below 5% for key properties, including porosity (4.52% vs. 4.615%), permeability (0.0186 mD vs. 0.0192 mD), and elastic modulus (38.2 GPa vs. 39.5 GPa). Pore network analysis quantified the poor connectivity of the tight reservoir, revealing an average coordination number of 2.8 and a pore throat radius distribution of 0.05–0.32 μm. The presented workflow successfully creates a quantitatively validated “digital twin” of a full-diameter core. It provides a tangible solution to the scale-representativeness trade-off and transitions digital core analysis from a visualization tool to a computable platform for predicting key reservoir properties, such as permeability and elastic modulus, through numerical simulation, offering a robust technical means for the accurate evaluation of tight reservoirs. Full article

The development of digital twins for railway freight vehicles represents a key step toward more efficient, data-driven maintenance and safety assessment. This study focuses on the creation of a digital twin of the T3000 articulated freight wagon, one of the most widespread intermodal transport solutions in Europe. Despite its relevance, the dynamic behavior of this vehicle type has been scarcely investigated so far in scientific literature. A dedicated onboard measurement layout was defined to enable comprehensive monitoring of vehicle dynamics and the interactions between adjacent wagons within the train. The experimental setup integrates inertial sensors and a 3D vision system, allowing for detailed analysis of both rigid-body and vibrational responses under real operating conditions. A high-fidelity multibody model of the articulated wagon was developed and tuned using the acquired data, achieving optimal agreement with experimental measurements in both straight and curved track segments. The resulting model constitutes a reliable and scalable digital twin of the T3000 wagon, suitable for predictive simulations and virtual testing. Future developments will focus on a deeper investigation of the buffer interaction through an additional experimental campaign, further extending the digital twin’s capability to represent the full dynamic behavior of articulated freight trains. Full article

Salmonella, a major global foodborne pathogen, is a leading cause of salmonellosis. Quantitative detection of Salmonella provides a scientific basis for establishing microbiological criteria and conducting risk assessments. The plate count method remains the primary approach for bacterial quantification, whereas the most probable number (MPN) method is commonly used for detecting low levels of bacterial contamination. However, both methods are time-consuming and labor-intensive. Validated digital polymerase chain reaction (dPCR) techniques are emerging as promising alternatives because they enable rapid, absolute quantification with high specificity and sensitivity. Herein, we developed a novel droplet dPCR (ddPCR) assay for identifying and quantifying Salmonella using invA as the target. The assay demonstrated high specificity and sensitivity, with a limit of quantification of 1.1 × 10² colony-forming units/mL in meat samples. Furthermore, the log₁₀ values obtained via ddPCR and plate counting exhibited a strong linear relationship (R² > 0.99). Mathematical modeling of growth kinetics further confirmed a high correlation between plate count and ddPCR measurements (Pearson correlation coefficient: 0.996; calculated bias factor: 0.88). Collectively, these results indicate that ddPCR is a viable alternative to the MPN method and represents a powerful tool for the quantitative risk assessment of food safety. Full article

Future lunar south pole missions face dual challenges of highly variable illumination and rugged terrain that directly constrain rover mobility and energy sustainability. To address these issues, this study proposes a dynamic illumination-constrained spatio-temporal A* (DIC3D-A*) path-planning algorithm that jointly optimizes terrain safety and illumination continuity in polar environments. Using high-resolution digital elevation model data from the Lunar Reconnaissance Orbiter Laser Altimeter, a 1300 m × 1300 m terrain model with 5 m/pixel spatial resolution was constructed. Hourly solar visibility for November–December 2026 was computed based on planetary ephemerides to generate a dynamic illumination dataset. The algorithm integrates slope, distance, and illumination into a unified heuristic cost function, performing a time-dependent search in a 3D spatiotemporal state space. Simulation results show that, compared with conventional A* algorithms considering only terrain or distance, the DIC3D-A* algorithm improves CSDV by 106.1% and 115.1%, respectively. Moreover, relative to illumination-based A* algorithms, it reduces the average terrain roughness index by 17.2%, while achieving shorter path length and faster computation than both the Rapidly-exploring Random Tree Star and Deep Q-Network baselines. These results demonstrate that dynamic illumination is the dominant environmental factor affecting lunar polar rover traversal and that DIC3D-A* provides an efficient, energy-aware framework for illumination-adaptive navigation in upcoming missions such as Chang’E-7. Full article

Script-based computer-aided design tools offer accessible and customizable environments, but their broader adoption is limited by the cognitive and computational difficulty of describing curved, irregular, or free-form geometries through code. This study addresses this challenge by contributing a unified, open-source framework that enables concept-to-model transformation through 2D point-based representations. Unlike previous ad hoc methods, this framework systematically integrates an interactive point-cloud modeling layer with modular systems for curve construction, point generation, transformation, sequencing, and formatting, together with script-based rendering functions. This framework allows users to generate geometrically valid models without navigating the heavy geometric calculations, strict syntax requirements, and debugging demands typical of script-based workflows. Structured case studies demonstrate the underlying workflow across mechanical, artistic, and handcrafted forms, contributing empirical evidence of its applicability to diverse tasks ranging from mechanical component modeling to cultural heritage digitization and reverse engineering. Comparative analysis demonstrates that the framework reduces user-facing code volume by over 97% compared to traditional scripting and provides a lightweight, noise-free alternative to traditional hardware-based reverse engineering by allowing users to define clean geometry from the outset. The findings confirm that the framework generates fabrication-ready outputs—including volumetric models and vector representations—suitable for various manufacturing contexts. All systems and rendering functions are made publicly available, enabling the entire pipeline to be performed using free tools. By establishing a practical and reproducible basis for point-based modeling, this study contributes to the advancement of computational design practice and supports the wider adoption of script-based design workflows. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

After nearly two decades of developments in Historic/Heritage Building Information Modeling (HBIM), the field has reached a stage of maturity that calls for a critical reassessment of its evolution, achievements, and remaining challenges. Digital representation has become a central component of contemporary heritage conservation, enabling advanced methods for analysis, management, and communication. This review examines the maturation of HBIM as a comprehensive framework that integrates extended reality (XR), artificial intelligence (AI), machine learning (ML), semantic segmentation and Digital Twin (DT). Six major research domains that have shaped recent progress are outlined: (1) the application of HBIM to restoration and conservation workflows; (2) the expansion of public engagement through XR, virtual museums, and serious games; (3) the stratigraphic documentation of building archaeology, historical phases, and material decay; (4) data-exchange mechanisms and interoperability with open formats and Common Data Environments (CDEs); (5) strategies for modeling geometric and semantic complexity using traditional, applied, and AI-driven approaches; and (6) the emergence of heritage DT as dynamic, semantically enriched systems integrating real-time and lifecycle data. A comparative assessment of international case studies and bibliometric trends (2015–2025) illustrates how HBIM is transforming proactive and data-informed conservation practice. The review concludes by identifying persistent gaps and outlining strategic directions for the next phase of research and implementation. Full article

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance cultural-heritage experience through context-aware, gamified exploration. A quasi-experimental field study with 254 participants across three age groups examined the system’s impact on cultural-heritage experience, knowledge acquisition, and real-world engagement. Results demonstrate substantial knowledge gains, with a mean increase of 7.74 points (SD = 4.37) and a large effect size (Cohen’s d = 1.77), supporting the effectiveness of LBAR in supporting tangible and intangible heritage understanding. Behavioral log data reveal clear age-related engagement patterns: older participants (41–51) showed declining mission completion rates and reduced interaction times at later points of interest, which may reflect increased cognitive and physical demands during extended AR navigation under real-world conditions. These findings underscore the potential of location-based AR to enhance cultural-heritage experience in real-world settings while highlighting the importance of age-adaptive interaction and route-design strategies. The study contributes a replicable model for integrating digital tourism, embodied AR experience, and community-based heritage preservation. Full article

This study aims to establish an integrated landscape planning model and explore its applicability through the convergence of digital twin technology. The primary goal is to address the fragmented implementation of landscape policies and to provide a systematic framework that enhances efficiency and visualization in the planning process. To this end, text-mining analysis was conducted to extract relevant laws, statutory plans, and project data, thereby identifying key factors for model construction. The resulting model integrates conservation-oriented and recreation-oriented modules, presenting a practical approach for landscape management. Furthermore, by utilizing Blender 3D and OpenStreetMap, this study demonstrates the process through which a digital twin visualizes and simulates the spatial characteristics of the actual target site, thereby validating its utility in decision-making and stakeholder communication. The results indicate that the landscape planning model was reconfigured and integrated into 6 detailed implementation measures and 41 specific indicators. Moreover, the model visually linked 36 laws and approximately 70 plans and projects. Ultimately, the study confirms that the proposed approach provides a dynamic, data-driven platform for sustainable landscape management. Full article

In response to the growing interest in sustainable and material-efficient architectural solutions, this study focuses on innovative applications of engineered timber in lightweight structural systems. It investigates the material optimization and structural performance of engineered timber thin-shell structures through an integrated parametric design approach. The study compares three prefabricated, panelized building systems, gridshell, segmented full-plate shell, and ribbed shell, to evaluate their efficiency in terms of material intensity, stiffness, and geometric behavior. Using Rhinoceros and Grasshopper environments with Karamba3D, Kiwi3D, and Kangaroo plugins, a comprehensive parametric workflow was developed that integrates geometric modeling, structural analysis, and material evaluation. The results show that segmented ribbed shell and two segmented gridshell variants offer up to 70% reduction in material usage compared with full-plate segmented timber shells, with hybrid timber shells achieving the best balance between stiffness and mass, offering functional advantages (roofing without additional load). These findings highlight the potential of parametric and computational design methods to enhance both the environmental efficiency (LCA) and digital fabrication readiness of timber-based architecture. The study contributes to the ongoing development of computational timber architecture, emphasizing the role of design-to-fabrication strategies in sustainable construction and the digital transformation of architectural practice. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

The entrepreneurial ecosystem theory posits that regional development emerges from synergistic interactions among entrepreneurs, institutions, and markets. This study positions entrepreneurial vitality as the core catalyst synchronizing digital inclusive finance (DIF) with rural revitalization—two systems often advancing in isolation, leading to unbalanced rural development. Using a coupling coordination degree model and provincial panel data from China (2011–2020), we demonstrate that entrepreneurial vitality significantly strengthens DIF–rural revitalization coupling coordination, following a nonlinear threshold pattern. Coordination gains accelerate only after vitality passes empirically identified critical levels, explaining persistent regional disparities in coupling coordination. Furthermore, the vitality–coordination link is moderated by technological infrastructure, organizational electronic commerce (e-commerce) engagement, and regional economic development, as outlined by the Technology–Organization–Environment framework. Framing DIF as an e-commerce-related ICT input, this paper advances the entrepreneurial ecosystem, e-commerce, and ICT-for-development (ICT4D) literature by revealing the threshold-driven nature of resource coordination in rural contexts. The findings offer a contextualized framework for catalyzing balanced and inclusive rural development in emerging economies. Full article

The use of geological models for mine production scheduling, planning, and design is a common aspect of current digital mine construction. Establishing a mapping relationship from digital geological resources to mining process simulation and then to risk early warning, enabling real-time interaction between digital models and physical mines, is an essential component of mining digital twins and an important direction for future development. This study is based on a non-ferrous metal mine and involves the development of data interaction functionality between 3Dmine (enterprise edition) and 3DEC7.0 software. This enables data mapping between geological models and numerical models, as well as real-time 3D visualization of risk points in the geological model. The main research findings are as follows: (1) Based on UAV photogrammetry and geological exploration data, a refined 3D geological model incorporating the surface, subsidence zones, goaf groups, and roadway systems was constructed using 3Dmine. The mine numerical model was then generated through 3Dmine-3DEC coupling technology. (2) A 3DEC-3Dmine data interaction interface based on Python was developed. Intelligent extraction and format conversion of mechanical parameters, such as stress and displacement, were achieved through secondary development, and a multi-software collaboration platform was built using an SQL database. A three-dimensional visual characterization script for risk points was developed. (3) Based on the strength–stress ratio and the nearest distance attribute assignment method, the three-dimensional visualization of blocks with different risk levels in 3Dmine is realized. (4) When the adjacent mine rooms are excavated in turn, the range of grade II risk area will be obviously expanded and a more serious grade III risk area will appear. The research findings offer a direction for the future development of mining digital twin technology, as well as technical support and theoretical guidance for analyzing and predicting safety risks during the mining process. Full article

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article