Search Results (4,630)

Yunnan Jiama (paper horse prints), a representative form of intangible cultural heritage in southwest China, is characterized by subtle inter-class differences, complex woodblock textures, and heterogeneous preservation conditions, which collectively pose significant challenges for digital preservation and automatic image classification. To address these challenges and improve the computational analysis of Jiama images, this study proposes an enhanced object detection framework based on YOLOv8 integrated with a Global Attention Mechanism (GAM), referred to as YOLOv8-GAM. In the proposed framework, the GAM module is embedded into the high-level semantic feature extraction and multi-scale feature fusion stages of YOLOv8, thereby strengthening global channel–spatial interactions and improving the representation of discriminative cultural visual features. In addition, image augmentation strategies, including brightness adjustment, salt-and-pepper noise, and Gaussian noise, are employed to simulate real-world image acquisition and degradation conditions, which enhances the robustness of the model. Experiments conducted on a manually annotated Yunnan Jiama image dataset demonstrate that the proposed model achieves a mean average precision (mAP) of 96.5% at an IoU threshold of 0.5 and 82.13% under the mAP@0.5:0.95 metric, with an F1-score of 94.0%, outperforming the baseline YOLOv8 model. These results indicate that incorporating global attention mechanisms into object detection networks can effectively enhance fine-grained classification performance for traditional folk print images, thereby providing a practical and scalable technical solution for the digital preservation and computational analysis of intangible cultural heritage. Full article

The snowpack in the European Alps is declining due to global warming, which affects both the amount of seasonal snow and the timing of accumulation and melt. As the European Alps is the largest winter tourism destination in the world by revenue, this decline in natural snow poses an existential threat to the sector. Several smaller ski areas have closed permanently since 1980, and all Alpine regions face rising costs due to an increasing reliance on snowmaking. Professional winter sports are also affected, with several canceled events in recent years due to unsuitable snow conditions. In this study, we present the first remote sensing-based assessment of long-term snow reliability for winter tourism in the European Alps. Using snowline elevation (SLE) data derived from Landsat observations from 1985 to 2024, combined with OpenStreetMap ski infrastructure data and digital elevation models, we quantified the monthly snow coverage of ski area segments across 43 Alpine basins. Theil–Sen trends and Mann–Kendall significances were calculated for the full season and for three subseasons, with quality checks applied to guarantee sufficient data coverage. The results show predominantly negative trends across all seasons, with the strongest declines occurring in the late season. In this period, 97.8% of all downhill ski areas and 99.5% of the cross-country ski areas for which a trend was derived exhibited negative trends. For the full season, the corresponding shares were 94% for downhill ski areas and 99.2% for cross-country ski areas. In addition, areas located at the geographical edges of the European Alps showed more pronounced negative trends compared with the core regions. These findings align with previous studies on the subject and highlight the ongoing shortening of natural snow seasons and thus the increased challenges for the winter tourism sector in the Alps. Full article

The heavy use of digital images across network systems has become a major concern regarding data confidentiality and unauthorized access. Conventional image encryption techniques hardly achieve high security levels efficiently, especially in real-time and resource-constrained environments. These challenges motivate the development of more robust and efficient encryption mechanisms. In this paper, a dual-chaotic image encryption framework is developed where two complementary chaotic systems are combined to effectively realize confusion and diffusion. The proposed method uses a chaotic permutation mechanism to find the pixel positions and enhanced chaotic diffusion to change the pixel values for eliminating the statistical correlations. An extended family of piecewise affine chaotic maps is designed to enhance the dynamic range and complexity of the diffusion process for strengthening the resistance capability against cryptographic attacks. Intensive experimental validations confirm that the proposed scheme well obscures the visual information and strongly reduces the pixel correlations in the encrypted images. High entropy values, uniform histogram distributions, high resistance to differential attacks, and improved robustness are further evidenced by statistical and security analyses compared to some conventional image encryption techniques. The results also show extremely low computational overheads, hence allowing for efficient implementation. The proposed encryption framework provides more security for digital image transmission and storage, and the performances are still practical. Given its robustness, efficiency, and scalability, it is equally adequate for real-time multi-media applications and secure communication systems, hence promising to offer a reliable solution for modern image protection requirements. Full article

This paper presents a human-centered, AI-driven framework for font design that reimagines typography generation as a collaborative process between humans and large language models (LLMs). Unlike conventional pixel- or vector-based approaches, our method introduces a Continuous Style Projector that maps visual features from a pre-trained ResNet encoder into the LLM’s latent space, enabling zero-shot style interpolation and fine-grained control of stroke and serif attributes. To model handwriting trajectories more effectively, we employ a Mixture Density Network (MDN) head, allowing the system to capture multi-modal stroke distributions beyond deterministic regression. Experimental results show that users can interactively explore, mix, and generate new typefaces in real time, making the system accessible for both experts and non-experts. The approach reduces reliance on commercial font licenses and supports a wide range of applications in education, design, and digital communication. Overall, this work demonstrates how LLM-based generative models can enhance creativity, personalization, and cultural expression in typography, contributing to the broader field of AI-assisted design. Full article

The accelerating climate emergency places the built environment under increasing pressure as both a major source of greenhouse gas emissions and a system highly vulnerable to climate impacts. Buildings contribute substantially to global operational energy use and embodied carbon, while much of the existing stock remains poorly adapted to changing climatic conditions. This paper examines the role of artificial intelligence (AI) in improving energy efficiency, enabling circular material flows, and enhancing resilience across the building lifecycle. Based on a structured synthesis of recent peer-reviewed literature, institutional reports, and documented case examples, the study maps AI applications in design, construction, operation, and end-of-life stages, including generative design, predictive maintenance, digital twins, and construction and demolition waste analytics. The analysis shows how AI can reduce operational energy demand, optimize material use, and support reuse and recycling strategies, while enabling new software-driven business models in the building sector. The paper argues that AI’s effectiveness depends on data availability, interoperability, regulatory alignment, and workforce capabilities, and that its benefits are maximized when integrated with circular economy strategies and supportive policy and financial frameworks. This integrated perspective highlights pathways for reducing emissions and improving the resilience of the built environment under climate stress. Full article

The rapid digital transformation of healthcare has improved clinical efficiency, patient engagement, and data accessibility, but it has also introduced significant cyber security and data privacy challenges. Healthcare IT systems increasingly rely on interconnected networks, electronic health records (EHRs), tele-medicine platforms, cloud infrastructures, and Internet of Medical Things (IoMT) devices, which collectively expand the attack surface for cyber threats. This scoping review maps and synthesizes recent evidence on cyber security risks in healthcare, including ransomware, data breaches, insider threats, and vulnerabilities in legacy systems, and examines key data privacy concerns related to patient confidentiality, regulatory compliance, and secure data governance. We also review contemporary security strategies, including encryption, multi-factor authentication, zero-trust architecture, blockchain-based approaches, AI-enabled threat detection, and compliance frameworks such as HIPAA and GDPR. Persistent challenges include integrating robust security with clinical usability, protecting resource-limited hospital environments, and managing human factors such as staff awareness and policy adherence. Overall, the findings suggest that effective healthcare cyber security requires a multi-layered defense combining technical controls, continuous monitoring, governance and regulatory alignment, and sustained organizational commitment to security culture. Future research should prioritize adaptive security models, improved standardization, and privacy-preserving analytics to protect patient data in increasingly complex healthcare ecosystems. Full article

Digital signal processing plays a central role in intelligent railway communications under high-mobility, strong-multipath, and time-varying-channel conditions. This review surveys representative techniques for multi-carrier modulation, precoding, index modulation, and chaos-inspired physical layer security and highlights their mathematical foundations. Core themes include transform-domain representations typified by time–frequency analysis, linear-algebraic formulations of precoding and equalization, combinatorial structures underlying index mapping and spectral efficiency gains, and nonlinear dynamical systems theory of chaotic encryption. The methods are compared in terms of bit error performance, peak-to-average power ratio, spectral efficiency, computational complexity, and information security, with emphasis on railway-specific deployment constraints. The synergistic application of these methods with intelligent railway transportation systems is expected to enhance the overall performance of railway transportation systems in terms of transmission efficiency, reliability, and security. It provides critical technological support for the efficient and secure operation of next-generation intelligent transportation systems. Full article

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

The exponential growth of digital data and the escalating sophistication of cyber threats have intensified the demand for secure yet computationally efficient encryption methods. Conventional algorithms (e.g., AES-based schemes) are cryptographically strong and widely deployed; however, some implementations can face performance bottlenecks in large-scale or real-time workloads. While many modern systems seed from hardware entropy sources and employ standardized cryptographic PRNGs/DRBGs, security can still be degraded in practice by weak entropy initialization, misconfiguration, or the use of non-cryptographic deterministic generators in certain environments. To address these gaps, this study introduces FileCipher. This novel file-encryption framework integrates a chaos-enhanced Cryptographically Secure Pseudorandom Number Generator (CPRNG) based on the State-Based Tent Map (SBTM). The proposed design achieves a balanced trade-off between security and efficiency through dynamic key generation, adaptive block reshaping, and structured confusion–diffusion processes. The SBTM-driven CPRNG introduces adaptive seeding and multi-key feedback, ensuring high entropy and sensitivity to initial conditions. A multi-threaded Java implementation demonstrates approximately 60% reduction in encryption time compared with AES-CBC, validating FileCipher’s scalability in parallel execution environments. Statistical evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and TestU01 confirm superior randomness with over 99% pass rates, while Avalanche Effect analysis indicates bit-change ratios near 50%, proving strong diffusion characteristics. The results highlight FileCipher’s novelty in combining nonlinear chaotic dynamics with lightweight parallel architecture, offering a robust, platform-independent solution for secure data storage and transmission. Ultimately, this paper contributes a reproducible, entropy-stable, and high-performance cryptographic mechanism that redefines the efficiency–security balance in modern encryption systems. 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

As a vital component of urban tourism, urban theme parks increasingly face experience homogenization and intensifying competition. Accordingly, the implementation of refined digital marketing and operational strategies based on visitor digital behavior has become increasingly essential. In this context, tourists’ social media sharing has become a crucial link between destination marketing and visitors’ experience construction. Within the SOBC (Stimulus–Organism–Behavior–Consequence) framework, this study examines how theme park servicescapes (S) shape sharing motivations (O), which, in turn, influence multimodal sharing intentions (B—text, image + text, video) and subsequently contribute to memorable theme park experience (C). A two-stage, mixed-method design was employed, and the study considered visitors to Beijing Universal Studios and Shanghai Disney Resort. Semi-structured interviews and grounded analysis identified five motivations: altruism, self-presentation, affective expression, hedonic motivation, and community identification. Testing was performed using a survey (N = 604), along with structural equation modeling. The findings indicate that the staff-related social environment exerts significant positive effects on all five motivations, whereas the effects of the physical environment are more selective. Motivations differentially predict modal intentions: text aligns with altruism and affective expression; image + text aligns with altruism, community identification, and self-presentation; and video aligns with self-presentation, hedonism, community identification, and affective expression. All three intentions positively affect memorable theme park experience. These results clarify how motivations map onto content forms and validate a support SOBC framework from servicescapes to memorable experience, offering actionable implications for experience design and digital marketing. 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

Post-disaster reconstruction requires instruments capable of ensuring procedural consistency, administrative transparency, and the systematic integration of geohazards, all of which are essential for safeguarding communities. This study presents the digital platform established under Italian Law 55/2019 for the reconstruction of the areas on Mt. Etna affected by the Mw 4.9 earthquake of 26 December 2018, emphasizing its innovative contribution to current international approaches to reconstruction governance. The platform standardizes the entire administrative workflow and is centered on the Parametric Form, which enables an objective calculation of eligible reconstruction grants based on damage indicators, vulnerability metrics, and parametric cost functions. A defining feature of the Etna model is the structural integration between administrative procedures and geohazard mitigation, achieved through updated hazard maps and protocols that incorporate geological, hydrogeological, and geomorphological conditions. This approach reframes reconstruction as an opportunity to reduce overall territorial vulnerability. The system also includes public monitoring tools (WebGIS and dashboards) that enhance traceability, compliance, and stakeholder engagement. Expected outcomes include shorter administrative timelines, improved interinstitutional coordination, and the potential transferability of the model to other emergency contexts. In comparison with international cases, the Etna experience represents an original integration of digitalization, parametric assessment, and site-specific hazard mitigation. Full article

The main purpose of this study was to conduct a bibliometric analysis of scientific knowledge and trends in modern finance. To this end, the analysis was based on the keywords: “finance”, “automation”, and “ESG”. The analysis aimed to provide theoretical insights into the economic and financial implications of automation and its role in achieving ESG objectives. From a methodological standpoint, bibliometric research was conducted on 21 September 2025. It involved analysing a total of 16,500 scientific articles published between 1974 and 2026 in two databases: The Web of Science Core Collection and Scopus. The Bibliometrix R 5.2.0 version tool was used to generate visualisations. Thematic mapping, three-field plotting, keyword mapping, and clustering were the main methods used to analyse the associations between finance, automation, and ESG principles. The study’s results showed an average annual increase in publications of approximately 3.80% and 2.50%, respectively, while international collaborations between researchers have become increasingly prominent in recent years. At the same time, the co-occurrence network analysis identified five key thematic clusters in the Web of Science Core Collection and three in Scopus. From a comparative perspective, these clusters highlight the most significant connections between environmental, social, and governance (ESG) performance, corporate social responsibility (CSR) impact, financial performance, economic growth, sustainable development, and the implications of the automation process. From a bibliometric point of view, this research contributes to a better understanding of the multiple digital transformations specific to the current financial framework, generating possible future research directions on the significant role of automation in financial, environmental, and social performance. Furthermore, automation is a critical component of the digital future of finance. Analysing and investigating the causal relationships between automation and Environmental, Social, and Governance (ESG) principles will necessitate new areas of study within the financial sphere. Full article

Digital chaotic systems suffer severe dynamical degradation under finite computational precision, compromising their randomness and unpredictability in security-critical applications. To address this challenge, we introduce the High-Dimensional Delayed Cyclic-Coupled Chaotic Model (HD-DCCCM), a unified framework that integrates high-dimensional coupling, delayed feedback, and time-varying parameter control. In this synergistic design, dynamic perturbations from delays and time-varying signals continuously excite the high-dimensional structure, effectively preventing the collapse into short periodic orbits typical of low-precision environments. Systematic numerical analyses confirm that the HD-DCCCM generates stable hyperchaos with significantly extended periods, consistently outperforming classical maps and representative anti-degradation methods. Moreover, the framework demonstrates strong robustness and flexibility across both homogeneous (identical maps) and heterogeneous (hybrid maps) configurations. These results position the HD-DCCCM as a general and powerful paradigm for constructing degradation-resilient chaotic systems, with broad potential for next-generation secure communications and cryptographic applications. Full article