Search Results (581)

Background: Immersive virtual reality (VR) may support rehabilitation and patient engagement; however, little is known about how frontline healthcare professionals perceive its clinical relevance and feasibility in post-acute community care settings. This study explored healthcare professionals’ perceptions of the potential clinical uses, usability, and implementation requirements of patient-facing immersive VR in a community hospital providing post-acute care in Singapore. Methods: Using purposive sampling, nineteen healthcare professionals from a subacute hospital in Singapore participated in individual semi-structured interviews. Participants discussed their expectations of VR, experienced approximately 10–15 min of a reminiscence-oriented virtual tour of a local (now defunct) village, using an Oculus Quest head-mounted display, and subsequently reflected on its usability and potential applications. The Unified Theory of Acceptance and Use of Technology 2 informed the interview guide and was used as a sensitising framework. Interviews were transcribed verbatim and analysed using hybrid deductive–inductive framework analysis. Results: Four themes were identified: (1) clinical value was conditional on therapeutic relevance; (2) anticipated patient acceptability depended on person–content fit and social context; (3) simple navigation did not remove physical and sensory barriers; and (4) clinical integration required a supporting service model. Conclusions: Healthcare professionals viewed immersive VR as potentially useful but not yet ready for routine clinical implementation. Adoption in post-acute care should be guided by therapeutic purpose, accessibility, and integration into existing workflows rather than technological novelty alone. Future research and development should prioritise co-designed therapeutic content, patient selection, accessibility, workflow integration, and implementation support. Full article

Background/Objectives: The integration of artificial intelligence (AI) and digital technologies into dental practice is reshaping clinical workflows, administrative processes, and, increasingly, the patient experience and the doctor–patient relationship. While prior research has documented the attitudes of clinicians and practice managers, the perspective of the patient remains comparatively underexplored. This study examined how dental patients perceive AI integration and digital tools across the dental care pathway, together with the associated implications for data security, cost, and the human dimension of care. Methods: A cross-sectional, questionnaire-based study was conducted among 200 dental patients in Bucharest, Romania, and the surrounding region. The instrument assessed perceived difficulty and availability regarding digital technology, current use of digital tools, demographic and educational characteristics (age, gender, practice environment, educational level), and two attitudinal dimensions, namely digital prudence and concern for technological sustainability, across five subdomains of the dental care pathway: scheduling, diagnosis, treatment planning, feedback, and follow-up (dispensarization). Responses were analyzed using non-parametric tests and exploratory principal component analysis with internal-consistency validation. Results: Patients expressed moderate-to-high interest in AI support during the diagnostic (median = 3.3, IQR = 2.7–3.9) and feedback (median = 3.11, IQR = 2.78–3.67) stages and the lowest interest in scheduling (median = 2.7, IQR = 2.0–3.3). A marked level of digital prudence was observed (median = 3.24, IQR = 2.82–3.61), reflecting concerns about data security, automation, and a possible weakening of the clinician–patient bond. Younger and academically educated patients reported lower perceived difficulty, higher availability, and greater current use of digital tools (all p ≤ 0.001); counterintuitively, the same patients scored significantly higher on digital prudence (Spearman’s ρ = −0.260, p < 0.001). Greater familiarity with digital tools was therefore accompanied by a more critical awareness of their informational risks rather than by uncritical acceptance. Conclusions: Dental patients approach AI through a dual lens of openness and informed caution, welcoming efficiency gains in the clinical and continuity-of-care stages while voicing measured concerns about data security, affordability, and the preservation of human contact. To interpret this profile, we propose two conceptual contributions: a mapping of patient needs onto Maslow’s hierarchy in the context of AI-mediated care and the Informational VUCA framework, which characterizes the volatility, uncertainty, complexity, and ambiguity that patients face when navigating AI-generated information. The findings point to a clear practical agenda of transparent communication, robust data governance, and education strategies adapted to patients’ educational and demographic profiles, so that AI-enhanced workflows strengthen rather than erode the doctor–patient relationship. Full article

Climate change perception among youth has gained increasing scholarly attention; however, immigrant youth remain underrepresented in empirical research despite navigating climate change impacts across multiple sociocultural and geopolitical contexts. This study investigates (1) immigrant youths’ beliefs about climate change, (2) how climate risks are perceived across space and time, and (3) how they construct their climate change beliefs and associated risks. Drawing on Social Constructivism, and Risk Perception Theory, the study employs a qualitative descriptive design using eight focus group discussions with 41 immigrant youth aged 14–18 in Calgary, Canada. Reflexive thematic analysis reveals that immigrant youth widely recognise climate change as real and intensifying, with beliefs shaped through lived and observed environmental experiences, social interaction, family narratives, and transnational comparisons rather than abstract scientific reasoning alone. Climate risk perception emerges as complex and relational. Participants associated climate change with impacts on health, wellbeing, food security, and everyday life; however, they often perceived its most severe consequences as temporally and spatially distant. This produces a notable “belief–risk gap,” where acceptance of climate change does not translate into a strong sense of personal vulnerability or urgency. Risk perceptions are mediated by everyday experiences, institutional trust in the host country, and comparisons with other global crises. The findings highlight the critical role of transnational experiences in shaping climate understanding and advance an integrated interpretation linking socially constructed climate knowledge with perceptions of risk and psychological distance, with implications for climate communication and public health communication strategies for immigrant youth. Full article

Integrating data science into undergraduate teaching in the United States requires educators to navigate disciplinary boundaries, integrating knowledge in ways that reshape teaching practices and disciplines themselves. While existing models of teaching and learning effectively describe expertise within single disciplines, they do not account for interdisciplinary knowledge movement or the specific challenges of bringing data science into established curricula. The Apian Model addresses this gap using bee behavior as a metaphor: instructors (bees) gather insights (nectar) from diverse curricular domains (flowers), transform them into learning experiences (honey), and disseminate them within and beyond their disciplines (hives). Through incorporation, communication, pollination, and hive splitting dynamics, this model explains how teaching evolves from individual learning to broader disciplinary change, processes especially relevant for navigating the complexity and interdisciplinarity of data science education. The model was developed with data science education in higher education in mind, but future research may establish its broader utility for interdisciplinary education more generally, particularly as a complement to existing models designed for single-discipline instruction. Full article

Uropathogenic Escherichia coli (UPEC) remains the principal driver of urinary tract infections (UTIs), utilizing sophisticated immunoevasive strategies that consistently outmaneuver host defenses and conventional clinical management. This review synthesizes current evidence highlighting the development of recurrent UTIs (rUTIs). Particular emphasis is given to the transition of UPEC into intracellular bacterial communities (IBCs) and quiescent intracellular reservoirs (QIRs)—survival phenomena primarily characterized across specialized mammalian and cellular models. By highlighting the translational evidence gaps surrounding these intracellular sanctuaries, and the challenge of directly extrapolating these model-derived dynamics to human recurrent UTIs (rUTIs), alongside the role of extra-urinary niches, we provide a comprehensive view of the mechanisms driving recurrence. A critical assessment of the UPEC virulence arsenal, involving molecular mechanisms that regulate fimbrial phase variation resulting in evading host immune surveillance, to specialized adhesins, like FimH, which tightly bind uroplakin receptors, triggering host actin rearrangements via rho-GTPase signaling and facilitating bacterial internalization, is also addressed. Additionally, we integrate the roles of cytotoxic necrotizing factor-1 (CNF1) and hemolysin A (HlyA) in promoting cytosolic escape, host cell survival, and long-term persistence. Finally, this review presents a multidisciplinary framework evaluating next-generation non-antibiotic strategies, including bacteriophage therapy, vaccines, phytotherapy, probiotics, and estrogen therapy. By critically assessing their efficacy and clinical maturity across a spectrum of preclinical models and human trials, we evaluate the potential to navigate translational limitations to effectively disrupt the infection cycle. Full article

The mission planning problem of using ground-fixed communication base stations to guide Unmanned Aerial Vehicle (UAV) swarms back under communication denial is addressed. The core challenge is to optimally match limited resources with massive UAV demands under constraints such as time windows, base station exclusivity, and relay continuity, which we formulate as an NP-hard combinatorial optimization problem. We first build a static model maximizing comprehensive benefits, incorporating base station heterogeneity and a super-linear congestion penalty for load balancing. We then extend it to a rolling horizon dynamic framework. Through task state partitioning and frozen resource inheritance, this extension decomposes the long-term optimization into sequential finite-horizon subproblems, enabling online decisions as UAV information is gradually revealed. To solve these models, we propose CMSA-MSWOA, which integrates elite opposition-based learning and Lévy flights to navigate the fragmented feasible solution space. Simulation results show 100% guidance coverage across scales from 100 to 500 UAVs in static scenarios, with the benefit advantage over the best benchmark growing from 10.0% to 65.5% as scale increases. In dynamic scenarios, the rolling framework satisfies all constraints and achieves full coverage. While our framework performs robustly in simulations, the current evaluation assumes idealized communication conditions; validation under more complex interference and external testing remains future work. Overall, our model and algorithm offer a useful simulation-based closed-loop framework for resource scheduling in denial environments, providing a foundation for further validation under more realistic field conditions. Full article

Quality control data for construction materials is frequently exchanged as heterogeneous documents, limiting traceability and making element-level retrieval slow and error-prone. This study develops and assesses a digital methodology that integrates laboratory test results with BIM-referenced assets and delivers the integrated information via interactive 3D-enabled dashboards. The methodology comprises three stages, starting with the acquisition of data from laboratory deliverables and 3D models, followed by data standardisation and relational structuring in the software Power BI Desktop (version 2.157.879.0, Microsoft Corporation, Redmond, WA, USA), and finally the publishing of generated dashboards embedded in a web application environment. The methodology is assessed through a real case study of a railway infrastructure asset, showing how laboratory records can be accessed and interpreted within a 3D model context, while preserving stakeholder-specific visibility through access control. The proposed approach supports element-level navigation of quality control and provides a practical pathway for laboratories to centralise, filter, and communicate test results without embedding full datasets into the BIM environment. Full article

Background: Rural and resource-constrained settings face major barriers to timely cancer screening, diagnosis, and treatment due to limited specialist availability and centralised service-delivery models. In the Eastern Cape, a largely rural province with a constrained oncology workforce, a decentralised–centralised hybrid model was introduced to improve access to cancer care. Nelson Mandela Academic Hospital serves as the central referral hub within this model. This study evaluates the implementation process and impact of this decentralised cancer care model on service utilisation, access, and continuity of care. Methods: A quantitative quasi-experimental pre–post implementation and quality improvement evaluation was conducted using retrospectively collected routine service utilisation and programme data from April 2023 to February 2025. The study assessed the impact of a decentralised oncology care model on access, service integration, and utilisation outcomes. Data from facility registers and district health information systems were managed using Microsoft Excel and analysed using Stata and IBM SPSS Statistics. Descriptive statistics, correlation analysis, and linear regression were used to compare pre- and post-implementation changes in patient volumes, screening coverage, referral completion, workforce capacity, gender distribution, and service uptake. The intervention decentralised screening, diagnosis, follow-up, and patient navigation services to district and satellite facilities while centralising specialised oncology care at referral centres to improve accessibility, efficiency, and continuity of care. Results: Cancer patient attendance increased substantially over the study period, from 355 patients in April 2023 to a peak of 1039 in April 2024, with a sustained upward trend (B = 18.03, p = 0.005), reflecting an average monthly increase of 18 patients. Female patients accounted for most visits, while male attendance showed a significant increasing trend (B = 8.03, p < 0.001). Service integration improved, with strong positive correlations between new patient registrations, follow-up care, palliative services, and inpatient admissions, indicating an expanding continuum of care. Breast and cervical cancers contributed the highest service burden, while cervical and lung cancers showed significant upward trends. Seasonal variation in attendance was observed, particularly during festive periods. From an implementation perspective, screening coverage for priority cancers increased by 18%, while 732,349 individuals were reached through community awareness initiatives. Access improved substantially, evidenced by a reduction of 56,400 km in cumulative patient travel distance over one year. Workforce capacity was strengthened through the training of 517 healthcare workers, and 1943 patients received structured navigation support. Referral efficiency and continuity of care improved, although persistent bottlenecks were observed in diagnostic and referral pathways. Conclusions: The decentralised–centralised oncology care model demonstrated improved cancer service utilisation, access, and continuity of care in a rural, resource-limited setting. However, increasing patient volumes and interconnected service demands place additional pressure on health system capacity. Sustained investment in workforce development, screening—particularly for cervical cancer—and system efficiency is required. This model provides a scalable and context-appropriate framework for strengthening oncology services in similar low-resource settings. Full article

Autonomous field robots are increasingly used in precision agriculture for monitoring, phenotyping, spraying, and site-specific intervention, yet reliable autonomy remains difficult under vegetation occlusion, uneven terrain, variable illumination, and intermittent communications. This review provides a deployment-oriented synthesis of navigation and sensor-fusion methods for agricultural robots, with emphasis on what is practical under field conditions rather than only in laboratory settings. The literature was examined through a structured narrative-review workflow using Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and related citation tracking, with primary emphasis on studies published between 2015 and 2025. We compare global, local, and hybrid planning methods; motion-control strategies such as PID, Pure Pursuit, and MPC; and localization pipelines that combine GNSS, IMU, LiDAR, cameras, odometry, and SLAM or Kalman-family fusion. Beyond algorithm summaries, the review links method selection to agricultural deployment constraints, including GNSS degradation, dynamic obstacles, compute limits, ROS 2 integration, time synchronization, and coordinate-frame management. The synthesis shows that no single stack is optimal across all crop systems: lightweight GNSS/IMU-based solutions remain attractive in structured open fields, whereas orchards, vineyards, and other occluded environments benefit more from tighter multi-sensor fusion and SLAM-supported localization. Finally, the review distills design guidance for sensing, planning, validation, and digital-twin-supported testing, and identifies research gaps related to robustness, benchmarking, safety, and scalable on-farm deployment. Full article

Tourism plays an important role in the global economy, contributing massively to the gross domestic product (GDP) and creating numerous jobs. The introduction of emerging technologies can accelerate the sector’s growth through personalization, improved user experience and better operational efficiency. The present work investigates the impact of visible light communications (VLC) in the tourism and hospitality industry based on the analysis of the recent literature published in the last decade, with the scope of improving tourist experience, operational efficiency and sustainability. Additionally, this work aims to critically evaluate the advantages and disadvantages of implementing VLC technology in the tourism and hospitality industry. For these purposes, this study presents a narrative review of the recent academic literature. The findings indicate that VLC technology can be used in a wide range of tourism-related applications, including contactless hotel services, indoor positioning and navigation, secure communications, accessibility solutions for visually impaired individuals and energy-efficient lighting infrastructure. In addition, this review demonstrates VLC’s potential to support the development of smart and sustainable tourism destinations through the integration of user-centered communication and illumination infrastructure. Finally, this work also identifies several challenges that affect large-scale deployment, including implementation costs, line-of-sight dependency and ongoing standardization issues. Full article

Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services. Full article

Digital platforms for cultural heritage are increasingly expected to document the geometric and material properties of sites. Furthermore, such platforms are increasingly expected to preserve and communicate the intangible practices and community knowledge that give these places cultural significance. In this context, this paper presents a hybrid virtual reality platform for the documentation, communication, and dissemination of both tangible and intangible heritage in underground wine landscapes. The framework integrates 360° panoramic imagery, 360° videos, lightweight object visualisations, and georeferenced 3D point clouds within a unified interface adapted to different device capabilities. The system’s key contributions include seasonal navigation, participatory recording of community practices, multiscale representation of artefacts and architecture, and a guided narrative system that improves orientation for non-expert users. The platform has been validated through the underground wineries of Baltanás (Spain), thereby demonstrating a novel approach that brings together metric documentation and immersive storytelling. This enhances accessibility, facilitates heritage interpretation, and ensures the digital preservation of living cultural practices in complex heritage settings with broad public dissemination potential. Full article

High-precision frequency standards underpinning signal synchronization and data consistency are essential for satellites. Navigation satellites use atomic clocks as time and frequency standards; however, limitations in size, weight, and cost constrain their performance. Geostationary Earth orbit (GEO) communication satellites typically rely on onboard oscillators as references, whose performance is generally poor. Moreover, satellite–ground links can be used for frequency synchronization between GEO satellites and ground stations. However, phase drift in onboard oscillators restricts improvements in synchronization precision. This paper introduces a frequency synchronization method based on a beacon transponder that enables ground stations to calculate and eliminate satellite oscillator phase drift. This design achieves high-precision frequency synchronization through real-time carrier-phase compensation and replicates the ground station’s frequency standard onboard. Ground-based experiments validated the method using a beacon transponder prototype in motion. The standard deviation of the phase synchronization error between the replicated clock and ground-station frequency standard was 2.97 ps over 24 h. The stability was better than 2.61 × 10⁻¹² and 9.98 × 10⁻¹⁶ for averaging times of 1 s and 10,000 s, respectively. These results demonstrate this method’s potential for high-precision frequency synchronization between satellites and ground stations, crucial for establishing an integrated space–air–ground frequency synchronization network. Full article

In this publication we investigate in depth the design, architecture and implementation of a bio and cultural guiding system with augmented capabilities. Our platform will be able to provide a flexible and user-friendly application for smart mobile devices with Android OS, which will be able to provide to the user the ability to discover nearby places of interest such as museums, archeological sites, religious sites, natural beauty sites, etc. In addition, our application is able to offer users an enhanced, comprehensive and augmented tour experience, based on visual and audio smartphone services, using asynchronous and real-time user–server communication mechanisms, thus eliminating the need for a human tour guide operator in cases of a remote area, guiding service cost or unavailable time slot. The proposed platform integrates visual overlays, audio narration, Geolocation services, and cloud-based data management to enable users to explore cultural, historical, and natural points of interest without the need for a human tour guide. A modular, layered system architecture is developed, combining Firebase Realtime Database, RESTful web services, OpenStreetMap-based navigation, and Android-native components to ensure scalability, flexibility, and real-time responsiveness. UML modeling, including class and sequence diagrams, is employed to formally describe system structure and behavior, while a mathematical data model validates the consistency of the underlying database schema. The implementation demonstrates how AR guiding systems can enhance spatial understanding, accessibility, and user engagement while supporting sustainable tourism practices and efficient destination management. The results indicate that the proposed solution is technically feasible, user-centered, and adaptable to diverse bio-cultural contexts, contributing to the advancement of intelligent, inclusive, and sustainable digital tourism platforms. Full article

The treatment of osteoarthritis (OA), a leading cause of disability worldwide, is increasingly integrating nutritional strategies that appear to offer the opportunity to significantly improve the quality of care. Patients are also aware of this and often seek clarification and advice from OA professionals. However, it is still too early to formulate definitive recommendations, as the scientific evidence is still heterogeneous. This can create a gap between patients’ need for clear guidance and the necessarily prudent communication of professionals. As a result, some patients may make decisions on their own, forgoing the opportunity to receive nutritional care personalised according to their dietary habits and disease severity. This commentary presents an expert interpretation of the currently available evidence and proposes a practical framework to support clinical discussions with patients. It is not intended as a formal clinical practice guideline but aims to help OA professionals navigate conversations about the role that nutritional strategies may play in disease management. Full article