Search Results (436)

Gamification increasingly influences learning experiences, cognitive engagement, and behavioral performance in digital learning, cognitive training, and health intervention contexts. However, the mechanisms underlying cognitive factors, along with related research hotspots and evolutionary trends, have not been adequately synthesized. Using the Web of Science Core Collection, this study analyzes 813 publications on gamification and cognitive factors published between 2012 and 2024. Using a bibliometric method, CiteSpace was utilized to analyze publication trends, collaborations, keyword co-occurrence, cluster structures, burst terms, cited references, and knowledge-map visualizations. The cluster analysis produced 10 interrelated themes: “flipped classroom,” “active learning,” “continuance intention,” “dementia,” “executive function,” “cognitive control training,” “computational thinking,” “cognitive training,” “cognitive load” and “user experience”. Potential future directions suggested by the bibliometric patterns include: (1) expanding gamification across educational contexts; (2) refining gamification theory models that focus on cognitive processes by examining user experience and cognitive load as potential mechanisms that link gamification design features to outcomes such as motivation, self-efficacy, task performance, and continuance intention; (3) promoting applications in cognitive training, cognitive impairment intervention, and digital health; (4) optimizing experimental design, data collection, interdisciplinary collaboration, and personalized design; and (5) clarifying how gamification shapes cognitive processes such as attention allocation, cognitive load regulation, problem solving, executive function, and computational thinking. This study does not aim to establish causal effects; rather, it uses bibliometric evidence to reveal the developmental trajectory, thematic structure, and emerging directions of research on gamification and cognitive factors. Full article

Selective harvesting requires tree-level decisions that balance operational productivity, stand development, and sustainable forest management, yet current AI and sensing studies often remain fragmented across remote sensing, machine perception, optimization, and forestry domains. This review synthesizes research on AI-assisted selective harvesting and smart forest management, with emphasis on multimodal sensing, decision support, and harvester-oriented deployment. A PRISMA-informed scoping review was conducted using Scopus, Web of Science Core Collection, IEEE Xplore, ScienceDirect, and SpringerLink, resulting in 82 studies retained for qualitative synthesis. The reviewed literature was organized into eight thematic groups covering smart forestry, harvesting operations, RGB-based perception, LiDAR and point-cloud processing, multimodal fusion, edge deployment, decision-support systems, and forecasting-oriented digital forestry. The analysis shows that RGB imaging provides semantic tree recognition, LiDAR enables spatial localization and structural assessment, and decision-support methods can translate tree-level observations into transparent cut/keep recommendations. However, integrated harvester-mounted systems remain underdeveloped, particularly regarding real-time RGB–LiDAR fusion, operator-facing recommendations, and forecasting integration. This review proposes a reference architecture for human-in-the-loop AI-assisted selective harvesting and identifies future research priorities for field validation and smart forest-management integration. Full article

Computational fluid dynamics provides detailed spatial distributions of physical fields in biomass boiler combustion, but the computational cost of each simulation limits its application in parametric studies and near-real-time workflows. This work investigates whether a Graph Neural Operator (GNO) can serve as a fast surrogate model that maps boiler operating parameters to six coupled CFD field distributions simultaneously. The reference case is a 10 kW wood-pellet boiler with internal flue gas recirculation (FGR), described and experimentally validated in an earlier publication by the authors. CFD data were collected on the symmetry plane of the combustion chamber for 80 operating points defined by the thermal load ratio (P/P₀) and the excess air ratio λ. A GNO surrogate was trained on 64 cases to predict temperature, velocity magnitude, static pressure, and mole fractions of CO, O₂, and CO₂ at each node of an unstructured spatial graph. On a held-out validation set of 16 operating cases, the model achieved R² values of 0.988 for temperature, 0.919 for velocity magnitude, 0.982 for pressure, 0.999 for CO, 0.992 for O₂, and 0.985 for CO₂. After training, each prediction is generated in a single forward pass, providing a computationally efficient approximation compared to the full CFD solver. A dedicated generalisation study on independent off-grid CFD cases confirmed that the surrogate interpolates within the parameter domain with essentially no loss of accuracy and degrades only moderately when extrapolated towards a higher thermal load and leaner mixtures. The results demonstrate that a baseline GNO surrogate can capture the spatial structure of coupled thermo-fluid and species fields in a realistic combustion geometry within the investigated parameter range and suggest applicability to digital-twin-oriented workflows where repeated parametric queries of boiler operation are required. Full article

Traditional wood anatomy provides the gold standard for timber identification, yet its reliance on centralized laboratory infrastructure severely limits its efficacy during real-time field inspections. This study addresses a critical research question: How can physical xylotheque resources, national timber extraction registries, and edge-computing computer vision be integrated into a cohesive framework to enable robust, forensic-level wood identification at field control stations? To resolve this, we implemented a three-tier methodology: first, we audited historical records from Ecuador’s Forest Administration System (SAF) encompassing 129 commercial timber species; second, we conducted a gap analysis using the Wood Anatomy Laboratory and Xylotheque (LAMX) repository (510 cataloged samples, 2267 histological preparations) to secure botanically validated references; and third, we leveraged a curated database of high-resolution digital cross-section captures (4900 images) to evaluate CNN architectures via k-fold cross-validation and a standard 70/15/15% training/validation/testing split. Benchmarking demonstrated that the lightweight MobileNetV2 architecture achieved a global accuracy of 94.04% and an F1-score of 0.976. External field validation conducted across commercial timber yards in Ibarra confirmed an offline inference latency of just 145 ms on mid-range Android devices, proving the framework’s operational transparency and low-cost scalability. Furthermore, Explainable AI analysis using Class Activation Maps (Grad-CAM) provided visual evidence indicating that the neural network targeted diagnostic xylotomic features (vessel distribution and axial parenchyma), minimizing reliance on external environmental noise. In conclusion, this study demonstrates that hybridizing physical taxonomic reference collections with targeted edge AI models provides a scalable, transparent, and low-cost solution that successfully bridges academic research and active forest law enforcement in tropical regions. Full article

The rapid digitization of large source collections in the humanities over the last three decades has comprehensively transformed the discipline. The accessibility of primary sources has improved drastically, the pre-processing of research data has been revolutionized in some areas, and new transdisciplinary approaches have emerged and become possible. However, while digital and computational historians have produced extensive reflection on these developments, the theoretical grounding of this transformation has not been fully integrated into mainstream historical methodology: most critically, the concept of ‘information’, central to computer science and computational methods, has not yet been systematically received as a technical category within the discipline’s methodological canon. In this contribution, we employ a concept from Science and Technology Studies—Bruno Latour’s ‘circulating reference’—to analyze and render describable the processes of historical research within a digitized research environment. Through three case studies—AI-supported segmentation of Habsburg cadastral maps (1817–1861), computational analysis of the Hof- und Staatsschematismus (1702–1918), and the datafication of the Munich Special Court archive inventory (1933–1945, 1975–1977)—we demonstrate how and at which specific points historical research benefits from this framework, and what new insights it enables. Full article

Databases serve as critical repositories of digital evidence in criminal investigations, and the recoverability of deleted data is a key determinant of forensic success. Microsoft SQL Server, one of the most widely deployed relational database management systems, has been the subject of multiple forensic studies examining how deleted records persist in physical database files across different acquisition methods. A previous study established a reference baseline using SQL Server 2008 and 2017, demonstrating that the Database Shrink operation causes version-specific and method-specific behavior: under logical collection with Shrink applied in SQL Server 2017, unallocated deleted data becomes fully initialized, rendering recovery impossible—a pattern not observed in SQL Server 2008 or under physical collection in either version. With the release of SQL Server 2025, the most significant architectural update to the platform in a decade, it remained unknown whether these forensic behaviors persist in the latest version. This study replicates the experimental design of in a controlled SQL Server 2025 environment, applying the same deletion scenario (DELETE command without conditions), the same two acquisition methods (logical and physical collection), and the same Shrink condition. The results demonstrate that SQL Server 2025 does not reproduce the version-specific initialization behavior observed in SQL Server 2017: across all four experimental conditions, deleted data residue in unallocated page space remains recoverable, indicating a fundamental change in the interaction between the Shrink operation and the logical collection mechanism. This recoverability is a double-edged property: while it benefits forensic investigators by preserving deleted evidence, it simultaneously represents a data-sanitization risk from a security and privacy standpoint, as deleted records are not reliably erased. These findings provide updated forensic guidance for digital investigators operating in contemporary SQL Server environments. Specifically, the results inform acquisition-method selection in real-world investigations where a suspect may have deleted records and where only a logical backup (.bak) is available to investigators. Full article

Forging is a key manufacturing route for high-performance structural components, but its process design, quality prediction, and adaptive control still rely heavily on empirical rules, offline simulations, and fragmented production data. This review examines intelligent forging from the perspective of mechanism–data–knowledge fusion, with emphasis on forging-specific process chains, real alloy systems, model validation, and industrial maturity. To improve methodological traceability, a structured literature search was conducted using Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, and Google Scholar, covering studies published from 1996 to 2026. The screened literature was organized around process perception, mechanism-based modeling, data-driven learning, hybrid modeling, knowledge representation, digital twins, online prediction, and adaptive regulation. Representative cases are discussed for closed-die forging, open-die/large forging, multistage forging, radial forging, and forging of aluminum alloys, titanium alloys, steels, and Ni-based superalloys. Particular attention is given to how specific models are validated, including independent experiments, finite-element benchmarks, industrial datasets, new geometries, sensor noise, and cross-material or cross-equipment transfer. The review further distinguishes consolidated technologies, such as FEM-based process simulation and die/preform optimization, from methods still under validation, including hybrid digital twins, sensor-updated models, and adaptive control. Large-model-assisted forging is considered a prospective direction mainly for information retrieval, case recovery, diagnostic support, and engineer-supervised recommendation rather than unsupervised real-time control. This review provides a more process-specific and critically assessed reference for developing explainable, validated, and deployable intelligent forging systems. Full article

The digital documentation of existing buildings is particularly important when original construction drawings or reliable as-built records are unavailable. This study evaluates the feasibility and selected dimensional consistency of a locally registered Scan-to-BIM workflow integrating unmanned aerial vehicle (UAV) photogrammetry for exterior documentation and smartphone LiDAR for interior data capture. A two-storey reinforced-concrete building with unavailable original project documentation was selected as a single case study. Exterior images were acquired using a DJI Mavic 3E (DJI, Shenzhen, China), while interior spaces were scanned using an iPhone 16 Pro Max (Apple Inc., Cupertino, CA, USA) and Polycam v5.1.5 in LiDAR mode. The UAV images were processed in Agisoft Metashape Professional 2.2.0 to generate the exterior photogrammetric point cloud, and the smartphone LiDAR data were organised with this dataset in Autodesk ReCap Pro 2025. Both point clouds were then used as geometric references for creating a geometry-oriented as-is BIM model in Autodesk Revit 2025. To evaluate selected dimensional consistency, 32 independent field measurements collected using a steel tape measure and a laser distance meter were compared with corresponding BIM-derived dimensions. The dimensional comparison yielded a mean absolute error (MAE) of 29.56 mm, a root mean square error (RMSE) of 31.21 mm, a maximum absolute error (MaxAE) of 46.00 mm, and a mean signed error (MSE) of +29.56 mm. These results indicate centimetre-level dimensional consistency for the selected validation dimensions, with a small systematic positive offset in the BIM-derived dimensions. The workflow can support preliminary geometric documentation and general as-is BIM for a small existing building, but it does not demonstrate survey-grade georeferencing, full registration accuracy, modelling reproducibility, or general applicability without further testing. Full article

Large-scale location-based virtual reality (LBE VR) has become an important form of immersive cultural tourism, but its role in supporting sustained value transformation remains insufficiently understood. In this study, “sustained value transformation” refers to the extension, reinterpretation, and circulation of cultural, educational, social, and engagement-related value across physical venues, embodied virtual narratives, and digital platforms. Rather than assessing economic performance, environmental impact, or long-term operational viability, this study focuses on the cultural and social circulation of experiential value. It examines how physical venues, embodied virtual narratives, and digital platforms jointly mediate visitor experience in LBE VR-based cultural tourism. It compares representative LBE VR projects in museums and heritage institutions, emerging public cultural spaces, and commercial venues in China. A total of 10,862 project-related textual items and 464 visual samples were collected from Xiaohongshu and Douyin and analyzed through comparative content and visual analyses. The findings show that visitor choices are shaped by both the spirit of place in physical venues and platform-visible experience labels. In museums and heritage institutions, institutional knowledge authority and embodied narrative depth help visitors recognize interactive educational value. In emerging public cultural spaces, the intertwining of historical narratives and commercial operations produces more ambiguous experience labels. In commercial venues, platform discussions focus more strongly on value-for-money judgment, sensory stimulation, product quality, and service experience. The study argues that sustained value transformation in LBE VR-based cultural tourism cannot rely solely on platform traffic. Instead, it depends on collaboration among cultural institutions, tourism enterprises, platform content creators, educational actors, and community stakeholders to preserve cultural distinctiveness, improve experience quality, and extend cultural and social value beyond the immediate on-site experience. Full article

This study uses data of A-share listed companies in Shanghai and Shenzhen from 2020 to 2024. We manually collect green procurement lists from official government procurement websites and match them with firm samples. Employing the two-way fixed effects model and the Bootstrap method, this paper empirically examines the impact of green public procurement on corporate carbon reduction. The results show that green public procurement significantly improves firms’ carbon reduction performance. Mechanism analysis indicates that AI adoption and government green subsidies further strengthen this effect. Heterogeneity tests reveal that the impact is more pronounced for state-owned enterprises, high-tech firms and enterprises in regions with advanced digital economies. Accordingly, we propose suggestions including strengthening the driving role of green procurement, promoting coordination between green procurement and digital technology, optimising the allocation of green funds, and implementing targeted differentiated incentives. This research helps clarify the internal mechanism of green public procurement on carbon emission reduction performance and provides references for improving relevant practices in carbon emission reduction. Full article

Introduction: Studying the structure and dynamics of living communities is essential from both ecological and wildlife management perspectives. Objective: The main objective of this study was to analyze the fish community structure inhabiting different river sections across several basins in the Mediterranean area. The data collected here contributed to: (i) creating a regional and national reference inventory to assess ichthyological biodiversity; (ii) generating digital cartographic information on species distribution and potential habitats; and (iii) providing scientific data to update national legal protection for governments. Methodology: Fish assemblages were monitored using electrofishing, which ensures reproducible data and long-term comparability. The study period extended until autumn 2025, with intensive sampling at 30 sites across major water bodies in the Valencian Community and selected rivers in Mijares, Turia, Jucar and Palancia basins. Results: The results reveal notable ichthyological richness in the studied basins (Turia, Júcar, Palancia, Mijares), with 12 native species identified. Cyprinidae and Leuciscidae were the most representative families, both in species number and spatial distribution, consistent with their dominance in Mediterranean river systems. Areas with the highest species richness corresponded to the middle and lower river sections and to ecologically valuable coastal wetlands. However, the study also detected 10 invasive alien species, representing 45% of the total fish fauna recorded. This high proportion reflects the significant ecological alteration affecting rivers and wetlands in these basins and underscores the urgent need for management actions to limit the spread of invasive species and reduce their impact on native biodiversity. The most widespread IAS were the bleak (A. alburnus), mainly in the Júcar basin, and the mosquitofish (G. holbrooki), predominantly in coastal wetlands. Conclusions: This study contributes directly to updating the Atlas of Ichthyofauna of the Valencian Community, providing a robust and current information base to support environmental decision-making at regional and national levels. The findings highlight the importance of strengthening proactive conservation measures, particularly in areas where biodiversity is most vulnerable. Full article

Smart buildings require intelligent and scalable solutions to monitor environmental conditions and manage increasingly complex data streams generated by distributed sensing infrastructures. In this context, the paper presents an edge-enabled Digital Twin framework for smart office environments, integrating real-time data acquisition, distributed intelligence, and machine learning-based analytics. The framework adopts a multi-layer architecture composed of a sensor layer, a cloud-edge intelligence layer, and an interaction layer, aligned with Digital Twin reference models. By enabling low-latency processing at the edge and supporting continuous model lifecycle management through Machine Learning Operations (MLOps) practices, the proposed approach overcomes key limitations of traditional cloud-centric solutions. Autoencoder-based models are deployed across the cloud-edge continuum to perform real-time anomaly detection on time-series sensor data. A prototype has been implemented in a real smart office environment, where heterogeneous environmental data are continuously collected and processed. Experimental results demonstrate effective end-to-end data flow, stable long-term operation, and reliable anomaly detection with low-latency response. The system enables real-time monitoring and data-driven analysis of environmental conditions, improving situational awareness and supporting operational decision-making. These findings confirm the effectiveness of integrating Digital Twin technologies with edge AI and MLOps principles for scalable and efficient smart building monitoring systems. Full article

Digital twin technology is becoming a core enabler for the intelligent transformation of civil engineering. This review adopts an integrated mixed-method design that combines a reproducible bibliometric protocol with structured content analysis to connect macro-level knowledge evolution with domain-specific engineering implementation. Based on the Web of Science Core Collection, the study analyzes publication trends, collaboration patterns, highly cited studies, keyword co-occurrence, network centrality, and citation bursts, and then reviews application status and technical pathways across five thematic areas: intelligent construction, bridge engineering, tunnel engineering, smart water conservancy, and other infrastructure. Key findings include: rapid growth in publication volume after 2021, three dominant keyword clusters (model/system construction, structural health monitoring and sensing, and AI-enabled optimization/decision-making), and an evolution of research frontiers from concept introduction to engineering scenario deepening and further to three-dimensional reconstruction, knowledge fusion, and intelligent decision-making. The content analysis shows differentiated technical pathways across sub-domains and identifies data heterogeneity/interoperability as the most urgent bottleneck because it constrains model updating, cross-platform integration, and engineering-scale deployment. Future directions should focus on data standardization, hybrid modeling, platform interoperability, artificial intelligence empowerment, and full-lifecycle cross-system coordination. This review provides a quantitatively supported panoramic reference for digital twin research in civil engineering. Full article

With the expanding application of digital technologies in urban renewal, more effective ways of incorporating dispersed public experience and needs into the renewal process still require further exploration. To address this issue, this research innovatively proposes an AI-assisted renewal method for historic districts driven by urban memory, constructing a continuous methodological chain from the identification of public evaluations to problem translation, to scheme generation and feedback validation. This research integrates the concept of interessement devices from Actor-Network Theory (ANT) with generative AI technologies for case application and validation. Taking Hanzheng Street as a case study, this research extracts the public’s urban memory of the historic district from online comments and identifies renewal demands. These demands were further associated with urban image elements to clarify their spatial carriers and support the subsequent generation of scene-based renewal schemes. On this basis, AI-generated images are further used to present renewed scenarios, and public evaluations of the renewal effects are collected. The results show that urban memory of Hanzheng Street can be summarized into five themes, which were further translated into five obligatory passage points (OPPs), one core issue, and corresponding renewal demands for scene units. The renewal schemes generated through this method achieved a relatively high level of public recognition overall, with mean evaluation scores ranging from 4.10 to 4.27, an overall satisfaction mean of 4.19, and a Top-2 proportion of 82.8%. By incorporating public experience into the formation of renewal schemes, this research provides a people-oriented and effective pathway for participation and feedback in the renewal of historic districts, while also offering methodological reference for the renewal of similar historic districts. Full article

Kazakhstan has one of the highest suicide rates globally, yet little is known about how suicide-related content is structured and expressed on VKontakte, the country’s widely used social network. This study aimed to qualitatively analyze VKontakte communities associated with suicide, depression, and self-harm, with a focus on naming conventions, thematic characteristics, and potential indicators relevant for digital prevention strategies. A qualitative content analysis was conducted on 50 public VKontakte communities selected from a larger dataset of 2353 communities collected between December 2021 and March 2025. Communities were included if suicide- or self-harm-related references appeared in their names, descriptions, posts, or visual elements and if they had at least one subscriber with a probable connection to Kazakhstan. Textual and visual content was examined manually at the community level. Six naming typologies were identified: explicitly suicidal, self-harm-focused, depressive, ironic, supportive, and non-related. Community content ranged from direct expressions of suicidal ideation to aestheticized or romanticized representations of pain and death. Some communities contained material that encouraged or normalized self-harm with minimal moderation, while others combined supportive interactions with potentially harmful content. Overall, VKontakte communities linked to users from Kazakhstan represent a heterogeneous digital environment in which supportive and risk-related elements may coexist. These findings highlight challenges for automated detection and suggest that patterns of engagement with specific community types may serve as descriptive indicators for future ethically guided research. Full article