Search Results (304)

Geoheritage assessment supports inventory design, geoconservation, geoeducation, public interpretation, and geotourism, yet many methods still merge intrinsic site significance with present-day conditions of access, interpretation, and management. This paper introduces the Geoheritage Evaluation and Classification Alphanumeric System (GECAS), a modular, non-compensatory framework for classifying geosites and geomorphosites through autonomous profiles rather than through a single total score. The revised framework distinguishes a minimum deployable core from standard and full applications, separates descriptive metadata from evaluative axes, and produces purpose-specific outputs for scientific significance, geoeducational suitability, public-facing visit readiness, geotourism use potential, and conservation priority. GECAS also formalises evidence quality, expert- and user-derived channels, degradation-risk gates, and the Geo-Pass communication profile. A structured comparison with established assessment approaches, a step-by-step workflow, time–effort guidance, and a secondary-data demonstrator is provided. The framework is proposed as a scalable methodological grammar that improves transparency, avoids hidden compensation among non-equivalent criteria, and supports future calibration through field trials, inter-rater testing, and comparative applications. Full article

As smart city approaches increasingly extend to public open spaces, understanding what urban park users expect from digital park applications has become a critical issue for sustainable urban management. This study examines park users’ expectations of smart park applications through an exploratory sequential mixed-methods design. In the first phase (Study I), semi-structured interviews were conducted with 32 purposively selected participants representing four user groups—parents with children, sport-oriented users, older adults, and general adults—in urban parks in Eskişehir, Türkiye. Thematic analysis identified eight user expectation themes, which were subsequently operationalized into a seven-factor quantitative structure. In the second phase (Study II), a seven-factor scale derived from the qualitative findings was administered to 374 participants. Confirmatory factor analysis demonstrated a good overall model fit, and the scale exhibited strong reliability and convergent validity. One-way ANOVA revealed significant between-group differences in six of the seven dimensions, with sport-oriented users consistently reporting higher expectations than older adults. Safety and Activity Diversity was the only dimension showing no significant group differences, indicating a universal expectation across all user profiles. Multiple regression analysis showed that Independent Functionality was the strongest predictor of use intention, followed by Centrality and Communal Function and Safety. Integration of both phases through a joint display revealed that expectations are both universal and user profile-specific, underscoring the need for user-sensitive smart park design. By linking digital park services to user expectations, well-being-oriented park design, and the sustainable use of urban green spaces, these findings contribute to the literatures on smart cities, urban green spaces, and well-being, providing an empirically informed and user-centred framework for digital park applications that may inform efforts toward healthier, more inclusive, and more sustainable urban public spaces in line with SDGs 3 and 11. Full article

Artificial intelligence (AI) is increasingly embedded in tourism platforms and services, yet continued participation often coexists with residual privacy concerns, accountability uncertainty, and uneven governance visibility. This study examines multi-actor trust in AI-enabled smart tourism governance as a problem of governance adequacy rather than trust maximization. Using the Three-Line Heuristic Framework (TLHF) and Satisficing Equilibrium (SE) as descriptive organizing tools, it asks whether trust-related outcomes can be organized around a mid-to-high adequacy region when visibility, accountability, usability, and platform choice conditions are perceived as sufficiently acceptable. TLHF organizes government-related visibility, firm-side operational adequacy, and user-side familiarity, while SE provides a descriptive governance heuristic for interpreting adequacy-oriented concentration under bounded concern. Empirically, the study uses a multi-context but unevenly distributed adult online survey sample of 1568 respondents, together with 35 semi-structured interviews as contextual qualitative material. Kernel density estimation, LOESS diagnostics, internal visual checks, and binary logit with Average Marginal Effects are used to summarize concentration patterns and marginal associations with safe platform preference. The results show that platform benefit evaluation and higher residual privacy concern after reverse scoring have the clearest positive associations with safe platform preference, while AI use breadth shows a more modest positive association and travel frequency is negatively associated. The density profiles show a recognizable mid-to-upper concentration zone, with similar visual patterns under limited resampling and split half comparisons. Within the retained adult sample, the findings highlight the relevance of visible transparency, maintainable service conditions, residual privacy concern, and low-friction usability for sustainable AI tourism governance and destination management. Full article

This article presents ELA Tutor, a generative AI-based Intelligent Tutoring System (ITS) built on a Multi-Agent System (MAS) architecture and implemented using the n8n platform to support personalized learning processes in higher education. The proposal integrates adaptive, ethical, and pedagogical components within a technology-enhanced learning environment. The architecture consists of specialized agents (pedagogical, technical, performance analysis, adaptive empathy, and ethical–pedagogical), coordinated through an intelligent decision router that distributes user queries according to their type, complexity, and learning profile. This approach enables automated information flow management and supports context-aware response generation. In addition, it facilitates integration with learning management systems such as Moodle. An exploratory qualitative study was conducted with 20 university instructors from the State Polytechnic University of Carchi (UPEC) to evaluate usability, adaptability, personalization, perceived reliability, and potential for institutional adoption. The evaluation was carried out in a controlled testing environment prior to deployment with students. Additionally, a preliminary validation was conducted with students from the Multimedia and Audiovisual Production program, who interacted with the system in a pilot context. The results indicate that ELA Tutor is perceived as easy to use and capable of providing responses that align with students’ learning processes. Instructor feedback highlights the system’s potential to extend tutoring through asynchronous interactions and supports its integration within institutional platforms. The proposal represents an initial validation of the system and identifies key areas for improvement, including content generation based on teaching guidelines and integration with academic data sources. Future work will focus on quantitative evaluation, including learning outcomes and system performance metrics, in real educational environments. Full article

The volume of daily email traffic continues to grow rapidly, creating challenges in efficiently distinguishing important from irrelevant messages. Beyond spam detection, modern email systems classify messages into categories such as promotions, social, updates, and forums, many of which are ignored or deleted without review. To address this issue, researchers have explored intelligent classification systems to predict the importance of emails, enhance user productivity, and improve organizational communication efficiency. This study proposes an email classification model that adapts to different users’ work functions and communication patterns within an organizational context. Using three-month historical real corporate anonymized email data from 9788 individuals across 12 work functions, the proposed Adaptive Semi-Personalized Email Classification Model (ASPEC) automatically retrieves each employee’s occupational profile—including job category and years of work experience—from the organization’s Human Resources (HR) system, enabling seamless personalization without manual configuration. ASPEC significantly improves email classification accuracy over the best-performing baseline of 73.50%, with incremental learning further enabling continuous adaptation to evolving data streams and achieving accuracy up to 92.57% in stable user segments. Unlike most existing email classification frameworks, which rely on static batch-learning models and lack memory-based or incremental update mechanisms, ASPEC addresses this gap by continuously adapting to evolving communication patterns without requiring full model retraining. The adoption of this incremental learning framework offers tangible benefits for organizations, including reduced manual email filtering workload, improved communication efficiency, and decreased operational burden on IT departments in managing email-related tasks and issues. Full article

Recently, numerous approaches have been proposed to improve State of Charge (SoC) prediction, demonstrating the potential of deep learning (DL) techniques for accurate battery state estimation. However, most of these methods are validated on laboratory-controlled or synthetic datasets and do not sufficiently consider real-world battery operating conditions. In practice, batteries operate under highly diverse usage patterns, environmental conditions, and user profiles, which can significantly affect SoC estimation accuracy. In this paper, we address this limitation by leveraging real-world data, which contains measurements from vehicle batteries under heterogeneous user behaviors and operating scenarios. The proposed methodology includes a data cleaning and filtering preprocessing stage, followed by an original DL framework designed to evaluate SoC estimation under different learning conditions. The framework is data driven and built upon a TimerV2-based architecture capable of capturing long-term temporal dependencies and nonlinear relationships in battery signals. Furthermore, transfer learning strategies are explored to enhance adaptability across different battery configurations and datasets for efficient knowledge transfer. Extensive experiments show that the proposed approach achieves high estimation accuracy and strong generalization performance, demonstrating its suitability for reliable real-time SoC estimation in practical battery management systems. Full article

Background: Asymptomatic intracranial atherosclerotic arterial stenosis (ICAS) is an underrecognized entity for which vascular risk-factor optimization is the primary management strategy, with no current indication for routine antiplatelet therapy or endovascular intervention for primary stroke prevention. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce major adverse cardiovascular events, including stroke, in high-risk cardiometabolic populations, but their association with outcomes in asymptomatic ICAS is yet to be evaluated. The present study aims to evaluate the association between GLP-1RA use and cerebrovascular outcomes in adults with asymptomatic ICAS. Materials and Methods: We used the TriNetX US Collaborative Network (71 healthcare organizations) to identify adults (≥18 years) with ICAS between 1 January 2016 and 31 December 2025, and excluded patients with prior cerebral infarction, intracranial hemorrhage, or cerebrovascular ischemic syndromes. Exposure was defined as initiation of any GLP-1 receptor agonist (lixisenatide, semaglutide, liraglutide, tirzepatide, dulaglutide) during the 6 months before or on the date of index ICAS diagnosis. Outcomes were assessed at 1 year, and included ischemic stroke, all-cause mortality, and a composite of ischemic stroke or mortality. Propensity-score matching (1:1) was performed, including demographics, vascular risk factors, comorbidities, antithrombotics, lipid/diabetes therapies, and cardiometabolic laboratory/physiologic measures. Results: Before matching, 1746 GLP-1RA users and 71,792 non-users met inclusion criteria; after matching, 1728 patients remained in each cohort. GLP-1RA use was associated with lower 1-year risk of ischemic stroke (4.40% vs. 6.10%; hazard ratio [HR] 0.70, 95% CI 0.52–0.95; p = 0.044), lower all-cause mortality (3.40% vs. 9.40%; HR 0.35, 95% CI 0.26–0.47; p < 0.001), and lower composite outcome risk (7.50% vs. 15.00%; HR 0.48, 95% CI 0.39–0.59; p < 0.001). Notably, these associations were observed despite matching for HbA1c, LDL cholesterol, BMI, and systolic blood pressure, suggesting potential effects beyond measured cardiometabolic risk profiles. Conclusions: In this large, propensity-matched cohort of adults with a-ICAS, GLP-1RA use was associated with lower ischemic stroke, all-cause mortality, and composite outcome at 1 year. These findings are hypothesis-generating and require further prospective studies to confirm this observation. Full article

Smart building services are increasingly realised through combinations of sensors, actuators, communication infrastructures, software platforms, analytics, and artificial intelligence-based functions. These configurations enable adaptive control, real-time monitoring, contextual automation, predictive support, user interaction, and cross-domain coordination across heating, ventilation, air conditioning, lighting, energy management, security and access control, water management, and user-centric comfort services. At the same time, the European Union Smart Readiness Indicator provides a formal basis for assessing building smartness through technical domains, service functionalities, and multidimensional impact criteria. A systematic basis for translating real-world descriptions of smart building services and their enabling technology stacks into Smart Readiness Indicator-aligned assessment inputs remains underdeveloped. A PRISMA ScR informed review was conducted to identify principal smart building service domains, synthesise their core functionalities, and reconstruct the digital technologies through which these functionalities are realised. The synthesis shows that heating, ventilation, and air conditioning and lighting provide comparatively direct translation pathways to formal Smart Readiness Indicator domains, while energy management operates mainly as a supervisory and cross-domain layer. Security and access control, water management, and several user-centric services contribute meaningfully to building smartness but often show partial or extended formal correspondence. Monitoring and control emerge as a central cross-cutting layer because many higher-order smart building capabilities are expressed through visibility, supervision, orchestration, and digital representation. Building on this review, a methodological framework is established for translating smart building services into Smart Readiness Indicator-aligned assessments. The procedure uses the smart building service instance as the unit of analysis and links service identification, functionality formulation, technology stack reconstruction, formal domain correspondence, impact profiling, maturity classification, and building-level aggregation. This enables heterogeneous service descriptions to be converted into structured readiness profiles while preserving the distinction between operational functionality, enabling technology, formal assessment correspondence, and multidimensional impact contribution. Application of the framework to the IoT Building Cloud platform shows that a substantial share of smart building capability may derive from supervisory digital infrastructure rather than from isolated end-use control alone. The resulting readiness profile is characterised by strong representation in monitoring and control, information to occupants and operators, and maintenance awareness, together with more selective contributions to indoor environmental control and limited flexibility-related capability. The proposed framework supports Smart Readiness Indicator-aligned pre-assessment, comparative analysis, design stage reasoning, and digital tool development by providing a transparent bridge between smart building service descriptions and formal assessment-oriented interpretation. Full article

Forests in Poland play a key recreational role, and the growing interest in sylvaturism requires optimized management. Despite the growing body of research on forest recreation, existing studies rarely address the role of small-scale infrastructure in shaping user preferences and its integration into spatial planning frameworks, which constitutes a research gap in this study. This study aimed to identify user preferences for small infrastructure and to develop an application-oriented, socially sensitive model for forest trail design that supports sustainable management. The research was conducted in 2021–2024 using the CAWI method on a group of 402 adult Poles. Data analysis included descriptive statistics, Pearson’s chi-square tests to assess demographic differences, and correspondence analysis to identify user preference profiles. The results not only confirmed a clear hierarchy of needs but also demonstrated that differences between user groups relate primarily to the intensity rather than the structure of preferences. A clear hierarchy of needs was confirmed, with route map boards (86.32%), educational boards (72.64%), and benches (71.14%) dominating. Based on the results, a modular design model was developed (modules: basic, comfort, accessibility, and activity), which constitutes a conceptual advancement over existing planning approaches by introducing a flexible, user-oriented framework that links social preferences with spatial decision-making. By integrating empirical social data into the planning process, the proposed framework extends current knowledge on recreation planning and provides a structured basis for adaptive forest trail design. This tool could help managers efficiently channel tourist traffic, protect ecosystems, and promote public health. Full article

Background/Objectives: Acanthamoeba keratitis (AK) is a rare but sight-threatening corneal infection. This study reviews the clinical profile, diagnostic pathways, treatment strategies, and outcomes of AK cases managed over a 34-year period. Methods: We conducted a retrospective analysis of 52 microbiologically AK cases from 1983 to 2017. Results: The mean age at presentation was 27.7 ± 9.4 years, with a female predominance (63.5%). The majority (82.7%) were contact lens users, almost exclusively soft lens wearers, with documented risk behaviors such as poor hygiene and sleeping with lenses. 44.2% were initially misdiagnosed as nonspecific microbial keratitis. Common clinical findings included epithelial defects (30.8%), ring infiltrates (44.2%), superficial infiltrates (53.8%), hypopyon (30.8%), and corneal thinning (13.5%). Diagnosis was confirmed by culture/stain in 61.5% of cases, while others required confocal microscopy or corneal biopsy. Co-infections with bacteria were noted in ~20%. Prior to referral, 82.7% of patients had received empirical topical therapy. At KKESH, all received dual anti-Acanthamoeba therapy, and 69.2% underwent surgical intervention, including tectonic PKP (46.2%) and optical PKP (19.2%). Visual acuity improved from a mean logMAR of 1.51 at presentation to 0.87 at last follow-up. Anti-Acanthamoeba therapy was discontinued in 95.9% of patients by the end of follow-up, with steroid use tapering from 61.5% at 3 months to 16.3% at final visit. Conclusions: Acanthamoeba keratitis in Saudi Arabia predominantly affects young female contact lens users and often presents with diagnostic delays and misclassification as herpetic or bacterial keratitis. Despite aggressive medical and surgical therapy, visual outcomes remain suboptimal in many cases. Full article

Customer loyalty has become a critical factor for the sustainability of fitness centers amid growing industry competition, yet limited research has examined recommendation patterns across user profiles in mid-cost facilities. This study aimed to analyze customer recommendation in a mid-cost fitness center in Spain using the Net Promoter Score (NPS) and to identify factors associated with loyalty by gender, age, membership duration, and service usage pattern. A cross-sectional observational study was conducted with 102 adult members (63.7% women) who completed a self-administered questionnaire distributed via QR code. The NPS served as the primary outcome measure, complemented by open-ended questions on perceived strengths and areas for improvement. The center achieved a high overall NPS of +66.7, with 70.6% of respondents classified as promoters and only 3.9% as detractors. Women reported significantly higher NPS scores than men (p = 0.037), whereas no significant differences emerged by age, membership duration, or service usage pattern. Qualitative analysis revealed that instructor quality, service organization, and facility management were the primary drivers of recommendation. These findings support the utility of the NPS as a practical tool for assessing customer loyalty in fitness centers and underscore the role of service quality in shaping recommendation behavior. Full article

Many people suffer from cervical disc herniation, which significantly affects the lives of individuals, causing chronic pain and functional limitations. This paper presents the development and evaluation of an AI-powered inflatable neck collar designed to provide personalized and adaptive support for individuals experiencing neck pain, particularly those with disc herniations. The system seamlessly integrates motion sensors, a robust AI model trained on a dataset of MRI scans, and a custom-designed inflatable collar. The AI model accurately detects disc herniations and segments vertebral structures, enabling real-time, targeted inflation adjustments based on the user’s unique anatomy, posture, and movements. A user-centered design approach ensures a seamless and intuitive user experience, allowing for personalized profile management, control over inflation levels, and data logging for tracking progress. Extensive simulations using 3D models and real-time data flow systems validated the effectiveness of the AI-guided system. Results demonstrated accurate detection and segmentation of disc herniations, robust real-time response, and adaptability to user needs. The proposed system, reviewed and validated by a neurosurgeon, demonstrates significant potential as a novel and effective solution for personalized treatment of neck pain, particularly in cases of disc herniation. Further development and research will focus on expanding the dataset to improve fairness and accuracy for diverse demographics and increasing the robustness and generalizability of the system. Full article

The escalating financial burden of deteriorating civil infrastructure worldwide necessitates a shift from conventional repair methods towards more durable and economically efficient long-term solutions. This paper presents a comprehensive techno-economic review of using carbon fibre-reinforced polymer (CFRP) fabrics for structural strengthening. Moving beyond a simple first-cost comparison, this review utilizes a life-cycle cost analysis (LCCA) framework to evaluate the total cost of ownership. The analysis deconstructs the complete cost profile, demonstrating that while CFRP systems have a high initial material cost, this is frequently offset by substantial savings in labour, equipment, and, critically, the indirect costs associated with reduced construction time and operational disruption. Furthermore, the inherent corrosion immunity of CFRP virtually eliminates future maintenance and repair expenditures, leading to a lower total life-cycle cost compared to traditional steel or concrete-based methods in a wide range of applications. Specifically, the conducted LCCA case study demonstrates that the CFRP alternative can reduce total life-cycle costs by nearly 25% relative to conventional steel sheet bonding, overwhelmingly driven by minimized operational downtime and related indirect costs. The value proposition is shown to be context-dependent, driven by minimizing user delay costs in bridges, mitigating catastrophic risk in seismic retrofitting, preserving cultural value in heritage structures, and maximizing revenue uptime in industrial facilities. The review also examines market dynamics, including the roles of standardization and government policy in driving adoption, and explores future trends such as inorganic matrix composites (TRM/FRCM), integrated structural health monitoring (SHM), and the push towards a circular economy. The findings conclude that a holistic, life-cycle-based economic assessment establishes CFRP strengthening as a cornerstone technology for the sustainable and resilient management of modern civil infrastructure. Full article

In the evolving landscape of next-generation wireless networks, ensuring seamless mobility and high-quality service delivery for millions of devices and end users in dynamic scenarios, where the speed of a wireless device keeps changing with time, is important. The mobility, seamless and continuous connectivity, and ultra-dense deployment of wireless networks pose a significant challenge. Seamless and successful transition of a wireless device from point A to point B in variable-speed scenarios is one of the major challenges in future networks. This paper presents a novel Deep Q-Network (DQN)-based reinforcement learning (RL) framework integrated with Software-Defined Networking (SDN) for intelligent mobility management in hybrid 5G cellular networks consisting of macro and small base stations. The proposed system architecture utilizes a SDN controller to receive real-time user measurement reports, including Reference Signal Received Power (RSRP), Signal-to-Interference Noise Ratio (SINR), and user velocity, thereby classifying user mobility into distinct subclasses and dynamically determining optimal handover parameters. Leveraging the DQN’s capability to learn adaptive strategies, the model enables seamless transitions between macro and small cells based on mobility profiles, thereby enhancing Quality of Service (QoS) metrics such as latency, throughput, and handover efficiency. Simulation results demonstrate consistent performance improvements over baseline and existing models in ultra-dense network environments, with handover success rates 10–15% higher across SINR and different speed scenarios, while maintaining a packet failure rate of 9% across different speed scenarios, allowing more users to transition during various environmental changes seamlessly. Our proposed model is compared with our previous work and Learning-based Intelligent Mobility Management (LIM2) models. Specifically, our previous work focused on adaptive handover management primarily for high-speed train scenarios using a learning-assisted approach tailored to fixed high-mobility scenarios, with a limitation to single mobility conditions. This work contributes to the field of merging SDN’s centralized control with the predictive power of RL, paving the way for more resilient and responsive mobile networks in high-mobility scenarios. The proposed approach incorporates subclass-based mobility action abstraction, joint optimization of TTT and hysteresis margin, and dynamic target cell selection using global network information available at the SDN controller. Full article

This article presents the design and development of an interactive architecture oriented toward the management of traditional vallenato, a musical genre recognized as an Intangible Cultural Heritage of Humanity by UNESCO. Architecture combines the principles of contextual awareness and the use of massive open online courses (MOOCs) to face the current challenges of preservation, dissemination, and teaching of this cultural expression, threatened by commercialization and the loss of its traditional roots. Through a modular structure, adaptive technological tools are integrated to capture, process, and use contextual information, personalizing learning experiences and strengthening the link between communities and their cultural heritage. The proposal consists of several functional layers, including context management, user profiles, educational resources, and a persistence unit, each designed to ensure the interoperability and sustainability of cultural data. In addition, the capacity of architecture to be used in other cultural contexts is highlighted, expanding its impact on different artistic manifestations and heritages worldwide. This article includes a comparative analysis with other existing models, highlighting the advantages of this solution in terms of customization and adaptability. Finally, opportunities for improvement and expansion are explored, as well as the pending challenges in the implementation of this technological tool in educational and cultural environments. Full article