Search Results (1,410)

This research identifies key criteria for designing mobile applications for cultural heritage tourism, aiming to provide a planned, engaging, and functional solution that enhances the visitor’s experience and strengthens the competitiveness of destinations in the absence of technological tools for promotion and management. The city of Riobamba was selected as a case study due to its significant potential for this type of tourism. The proposed method is structured around three main dimensions: interpretative, inclusive, and immersive. The methodology combines a literature review, benchmarking, and user-centered design. The findings highlight the importance of integrating heritage storytelling, structuring information by levels based on user profiles, addressing the demand for practical data, and complementing digital resources with physical signage. The model is replicable if the tourism demand is adapted to the local supply of each destination. Full article

Background: Bicycles constitute a primary means of transportation, particularly within the scope of urban micromobility. However, the use of this mode of transport is associated with the risk of traffic accidents and subsequent maxillofacial trauma. Cyclists are classified as vulnerable road users, among whom the assessment of injury patterns is a significant issue. This study aimed to identify the most common maxillofacial fractures resulting from bicycle-related traffic accidents. Methods: A retrospective analysis was conducted on the medical records of patients treated at the Clinic of Maxillofacial Surgery at the University Clinical Hospital in Poznan, who sustained maxillofacial injuries as a result of bicycle-related accidents between 2019 and 2023. Results: A total of 99 patients met the inclusion criteria. Most of the study population was males (70.7%), with a median age of 38. Accidents most frequently occurred during the summer months and on Fridays and weekends. The most common fracture site was the mandible (40.4%), with double fractures being the predominant type. Additionally, zygomatic-orbital fractures were frequently observed (30.3%). In terms of treatment, surgical intervention was predominant, and the mean duration of hospitalisation was 6 days. Only 5.1% of patients were under the influence of alcohol at the time of the incident. Furthermore, it was found that isolated mandibular fractures occurred more frequently in younger patients, whereas midface fractures of the Le Fort II and III types were more commonly observed in individuals under the influence of alcohol at the time of the event. Moreover, accidents involving alcohol consumption were associated with a higher incidence of concomitant cranio-cerebral injuries. Conclusions: Defining the profile of maxillofacial fractures resulting from bicycle accidents constitutes a clinically relevant issue. Additionally, identifying the main risk factors and developing preventive measures is of critical importance. Full article

Gamified virtual reality (VR) environments are increasingly used to enhance engagement and learning, yet most designs still adopt a one-size-fits-all approach that overlooks motivational diversity. The HEXAD framework, which classifies users into six player types, provides a promising lens for addressing this gap, but its predictive validity in immersive VR remains contested. This study investigates how HEXAD profiles shape navigation, time allocation, and engagement dynamics in an open-ended gamified VR environment. Thirty-two undergraduate participants, all regular gamers, completed the HEXAD scale before exploring a VR setting with five thematic islands without predefined tasks. System logs and screen recordings captured first island choices, sequential visit patterns, and time spent, and data were analyzed using qualitative pattern analysis alongside nonparametric statistics. The results showed significant associations between player type and initial choices, with Players favoring Game Island, Socialisers choosing Social Island, Philanthropists engaging most with Library, and Achievers and Free Spirits drawn to Experience. Kruskal–Wallis tests of exploration breadth revealed moderate effect sizes across types, though significance was limited by sample size. Three emergent strategies, Focused Explorers, Wanderers, and Strategic Switchers, captured motivational orientations beyond single metrics, while heat map visualizations highlighted clustering around Game and Experience Islands. By situating these findings within flow theory and inclusive–adaptive design principles, this study demonstrates how behavioral traces can link motivational typologies with embodied interaction. Overall, the results advance debates on HEXAD’s robustness and contribute practical pathways for developing adaptive, motivation-sensitive VR environments that support sustained engagement and inclusivity. Full article

Fast fashion is a rapidly expanding sector characterized by high production volumes, low costs, and short product lifecycles. While recent efforts have focused on improving sustainability within supply chains, consumer behavior remains a critical yet underexplored driver of environmental impacts. This study presents a web-based calculator tool designed to estimate both the carbon and plastic footprints associated with individual fast fashion consumption, with a particular focus on shopping behaviors, garment disposal, and laundry habits. Adopting a geographical perspective, the analysis explicitly considers the spatial dynamics of consumption and logistics within the urban context of Milan (Italy), a dense metropolitan area representative of high fashion activity and mobility. By incorporating user-reported travel patterns, logistics routes, and localized emission factors, the tool links consumer habits to place-specific environmental impacts. By involving over 360 users, the tool not only quantifies emissions and plastic waste (including microfibers) but also serves an educational function, raising awareness about the hidden consequences of fashion-related choices. Results reveal high variability in environmental impacts depending on user profiles and behaviors, with online shopping, frequent use of private vehicles, and improper garment disposal contributing significantly to emissions and plastic pollution. Our findings highlight the importance of integrating consumer-focused educational tools into broader sustainability strategies. The tool’s dual function as both calculator and awareness-raising platform suggests its potential value for educational and policy initiatives aimed at promoting more sustainable fashion consumption patterns. Full article

This study applies a multicriteria decision analysis to explore how experts from different backgrounds assess traditional and emerging criteria for urban cycling. A hierarchical model with 7 main criteria and 31 subcriteria was evaluated by 30 specialists from academic, technical, and user-focused groups. Using pairwise comparisons and aggregated judgments, this study reveals points of agreement and divergence among expert priorities. Safety and infrastructure were rated as the most important factors. In contrast, contextual and technological aspects, such as Multimodality, Environmental Quality, Shared Systems, and Digital Solutions, received moderate to lower weights, with differences linked to expert profiles. These results highlight how different disciplinary perspectives influence the understanding of bikeability-related factors. Conceptually, the findings support a broader view of cycling conditions that incorporates both established and emerging criteria. Methodologically, this study demonstrates the value of the Analytic Hierarchy Process (AHP) as a participatory and transparent tool to integrate diverse stakeholder opinions into a structured evaluation model. This approach can support cycling mobility planning and policymaking. Future applications may include case studies in specific cities, combining expert-based priorities with local spatial data, as well as longitudinal research to track changes in cycling conditions over time. Full article

In an era when AI systems curate increasingly fine-grained aspects of everyday media use, understanding algorithmic fatigue and resistance is essential for safeguarding user agency. Within the horizon of a more algorithmic and hyper-personalized advertising environment, knowing how people resist algorithmic advertising is of immediate importance. This research formulates and examines a structural resistance model for algorithmic advertising, combining psychological and cognitive predictors such as perceived ad fatigue (PAF), digital well-being (DWB), advertising literacy (ADL), and perceived relevance (PR). Based on a cross-sectional survey of 637 participants, the research employs Partial Least Squares Structural Equation Modeling (PLS-SEM) and mediation and multi-group analysis to uncover overall processes and group-specific resistance profiles. Findings show that DWB, ADL, and PR are strong positive predictors of resistance to persuasion, while PAF has no direct effect. PAF has significant indirect influences through both PR and ADL, with full mediation providing support for the cognitive filter function of resistance. DWB demonstrates partial mediation, indicating that it has influence both directly and through enhanced literacy and relevance attribution. Multi-group analysis also indicates that there are notable differences in terms of age, gender, education, social media consumption, ad skipping, and occurrence of digital burnout. Interestingly, younger users and those who have higher digital fatigue are more sensitive to cognitive mediators, whereas gender and education level play a moderating role in the effect of well-being and literacy on resistance pathways. The research provides theory-informed, scalable theory to enhance the knowledge of online resistance. Practical implications are outlined for policymakers, marketers, educators, and developers of digital platforms based on the extent to which psychological resilience and media literacy underpin user agency. In charting resistance contours, this article seeks to maintain the voice of the user in a world growing increasingly algorithmic. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article

Cocaine use remains a major public health concern, with rising global prevalence and a well-established profile of neurotoxicity and addictive potential. While the central nervous system has been the primary focus of cocaine research, emerging evidence indicates that cocaine also disrupts male reproductive physiology. In the testis, cocaine alters the endocrine microenvironment, induces cell-specific damage, and disrupts spermatogenesis. Cocaine also interferes with epigenetic programming in germ cells and mature sperm, potentially leading to heritable epimutations. Epidemiology data reveal that approximately two-thirds of regular cocaine users are males of reproductive age, and preclinical models have documented numerous behavioral and molecular alterations in their offspring, often linked to paternal cocaine exposure—such as increased drug resistance or vulnerability, altered anxiety-like behavior, impaired learning/memory, disrupted social behaviors, and shifts in neural circuitry and gene expression in reward-related brain regions. This review aims to integrate findings from studies that have independently examined testicular dysfunction, germline epigenetic reprogramming, and offspring outcomes, offering a unified perspective on their potential interconnections and highlighting future directions for research in the field of epigenetic inheritance. Full article

Serious games are an important tool for education and training that offers interactive and powerful experience. However, a significant challenge lays with adapting a game to meet the specific needs of each player in real-time. The present paper introduces a framework to guide the development of serious games using a phased approach. The framework introduces a level of standardization for the game elements, scenarios and data descriptions, mainly to support portability, interpretability and comprehension. This standardization is achieved through semantic annotation and it is utilized by digital twins to support self-adaptation. The proposed approach describes the game environment using ontologies and specific semantic structures, while it collects and semantically tags data during players’ interactions, including performance metrics, decision-making patterns and levels of engagement. This information is then used by a digital twin for automatically adjusting the game experience using a set of rules defined by a group of domain experts. The framework thus follows a hybrid approach, combing expert knowledge with automated adaptation actions being performed to ensure meaningful educational content delivery and flexible, real-time personalization. Real-time adaptation includes modifying the game’s level of difficulty, controlling the learning ability support and maintaining a suitable level of challenge for each player based on progress. The framework is demonstrated and evaluated using two real-word examples, the first targeting at supporting the education of children with syndromes that affect their learning abilities in close collaboration with speech therapists and the second being involved with training engineers in a poultry meat factory. Preliminary, small-scale experimentation indicated that this framework promotes personalized and dynamic user experience, with improved engagement through the adjustment of gaming elements in real-time to match each player’s unique profile, actions and achievements. Using a specially prepared questionnaire the framework was evaluated by domain experts that suggested high levels of usability and game adaptation. Comparison with similar approaches via a set of properties and features indicated the superiority of the proposed framework. Full article

Electric vehicle (EV) charging infrastructures raise significant concerns about data security and user privacy because traditional centralized authorization and billing frameworks expose sensitive information to breaches and profiling. To address these vulnerabilities, we propose a novel decentralized framework that couples a permissioned blockchain with fully homomorphic encryption (FHE). Unlike prior blockchain-only or blockchain-and-machine-learning solutions, our architecture performs all authorization and billing computations on encrypted data and records transactions immutably via smart contracts. We implemented the system on Hyperledger Fabric using the CKKS-based TenSEAL library, chosen for its efficient arithmetic on real-valued vectors, and show that homomorphic operations are executed off-chain within a secure computation layer while smart contracts handle only encrypted records. In a simulation involving 20 charging stations and up to 100 concurrent users, the proposed system achieved an average authorization latency of 610 ms, a billing computation latency of 310 ms, and transaction throughput of 102 Tx min while maintaining energy overhead below 0.14 kWh day per station. When compared to state-of-the-art blockchain-only approaches, our method reduces data exposure by 100%, increases privacy from “moderate” to “very high,” and achieves similar throughput with acceptable computational overhead. These results demonstrate that privacy-preserving EV charging is practical using present-day cryptography, paving the way for secure, scalable EV charging and billing services. Full article

This study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging virtual and augmented reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases generated using Direct Numerical Simulation (DNS) while maintaining visual fidelity and ensuring efficient rendering for user interaction. Fully immersive visualization of supersonic (Mach number 2.86) spatially developing turbulent boundary layers (SDTBLs) over strong concave and convex curvatures was achieved. The comprehensive DNS data provides insights on the transport phenomena inside turbulent boundary layers under strong deceleration or an Adverse Pressure Gradient (APG) caused by concave walls as well as strong acceleration or a Favorable Pressure Gradient (FPG) caused by convex walls under different wall thermal conditions (i.e., Cold, Adiabatic, and Hot walls). The process begins with a .vts file input from a DNS, which is visualized using ParaView software. These visualizations, representing different fluid behaviors based on a DNS with a high spatial/temporal resolution and employing millions of “numerical sensors”, are treated as individual time frames and exported in GL Transmission Format (GLTF), which is a widely used open-source file format designed for efficient transmission and loading of 3D scenes. To support the workflow, optimized Extract–Transform–Load (ETL) techniques were implemented for high-throughput data handling. Conversion of exported Graphics Library Transmission Format (GLTF) files into Graphics Library Transmission Format Binary files (typically referred to as GLB) reduced the storage by 25% and improved the load latency by 60%. This research uses Unity’s Profile Analyzer and Memory Profiler to identify performance limitations during contour rendering, focusing on the GPU and CPU efficiency. Further, immersive VR/AR analytics are achieved by connecting the processed outputs to Unity engine software and Microsoft HoloLens Gen 2 via Azure Remote Rendering cloud services, enabling real-time exploration of fluid behavior in mixed-reality environments. This pipeline constitutes a significant advancement in the scientific visualization of fluid dynamics, particularly when applied to datasets comprising hundreds of high-resolution frames. Moreover, the methodologies and insights gleaned from this approach are highly transferable, offering potential applications across various other scientific and engineering disciplines. Full article

In recent years, the rapid growth of artificial intelligence (AI) has significantly transformed higher education, particularly among Generation Z students who are more open to new technologies. Tools such as ChatGPT are increasingly being used for learning, yet empirical research on their acceptance, especially in Hungary, is limited. This study aims to explore the psychological, technological, and social factors that influence the acceptance of AI among Hungarian university students and to identify different user groups based on their attitudes. The methodological novelty lies in combining two approaches: partial least-squares structural equation modelling (PLS-SEM) and cluster analysis. The survey, based on the TAM and UTAUT models, involved 302 Hungarian students and examined six dimensions of AI acceptance: perceived usefulness, ease of use, attitude, social influence, enjoyment and behavioural intention. The PLS-SEM results show that enjoyment (β = 0.605) is the strongest predictor of the intention to use AI, followed by usefulness (β = 0.167). All other factors also had significant effects. Cluster analysis revealed four groups: AI sceptics, moderately open users, positive acceptors, and AI innovators. The findings highlight that the acceptance of AI is shaped not only by functionality but also by user experience. Educational institutions should, therefore, provide enjoyable and user-friendly AI tools and tailor support to students’ attitude profiles. Full article

Sign language avatar systems have emerged as a promising solution to bridge communication gaps where human sign language interpreters are unavailable. However, the design of these avatars often fails to account for the diversity in how users acquire and perceive sign language. This study presents a rapid review of 17 empirical studies (2020–2025) to synthesize how linguistic and cognitive variability affects sign language perception and how these findings can guide avatar development. We extracted and synthesized key constructs, participant profiles, and capture techniques relevant to avatar fidelity. This review finds that delayed exposure to sign language is consistently linked to persistent challenges in syntactic processing, classifier use, and avatar comprehension. In contrast, early-exposed signers demonstrate more robust parsing and greater tolerance of perceptual irregularities. Key perceptual features, such as smooth transitions between signs, expressive facial cues for grammatical clarity, and consistent spatial placement of referents, emerge as critical for intelligibility, particularly for late learners. These findings highlight the importance of participatory design and user-centered validation in advancing accessible, culturally responsive human–computer interaction through next-generation avatar systems. Full article

Background/Objectives: Poor classroom acoustics and inadequate digital environments in educational settings can pose an additional barrier for students, especially those with special needs, such as students with hearing difficulties. These challenges can hinder communication, academic achievement, and social inclusion. Speech-to-text captioning systems offer a promising assistive tool to support education. This study aimed to evaluate the strengths and limitations of implementing such systems in schools through a structured strategic analysis. Methods: The analysis method consisted of two phases. A SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis was performed on data from a survey compiled by an interdisciplinary team. A subsequent TOWS analysis was used to develop strategic recommendations by cross-referencing internal and external factors. Results: The analysis highlighted key strengths, including improved communication, support for inclusive practices, and adaptability to diverse learning needs. Identified weaknesses included cognitive load, synchronization delays, and variability in student profiles. Opportunities included educational innovation, access to funding programs, and interdisciplinary collaboration. Threats included inadequate classroom technology, poor acoustics, and the risks of social stigma. The analysis yielded 17 recommendations to improve the usability and customization of the tool. Conclusions: Speech-to-text captioning systems have significant potential to promote accessibility and inclusion in education. This strategic analysis provides a structured, interdisciplinary approach to strategic planning and the successful implementation of assistive technology in schools. By combining multidisciplinary expertise with structured evaluation, it identified key design, training, and policy priorities. This approach offers a replicable model for user-centered planning and the development of assistive tools and can inform wider efforts to reduce communication barriers in inclusive education. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article