Search Results (347)

The rapid digitization of library resources has intensified the need for robust digital-rights management (DRM) mechanisms to safeguard copyright, control access, and preserve user privacy. Conventional DRM approaches are often centralized, prone to single-point-of-failure, and are limited in transparency and interoperability. To address these challenges, this article puts forward a decentralized DRM framework for library systems by leveraging blockchain technology and decentralized DRM-key mechanisms. An integrative review of the available research literature provides an analysis of current blockchain-based DRM library systems, their limitations, and associated challenges. To address these issues, a controlled experiment is set up to implement and evaluate a possible solution. In the proposed model, digital content is encrypted and stored in the Inter-Planetary File System (IPFS), while blockchain smart contracts manage the generation, distribution, and validation of DRM-keys that regulate user-access rights. This approach ensures immutability, transparency, and fine-grained access control without reliance on centralized authorities. Security is enhanced through cryptographic techniques for authentication. The model not only mitigates issues of piracy, unauthorized redistribution, and vendor lock-in, but also provides a scalable and interoperable solution for modern digital libraries. The findings demonstrate how blockchain-enabled DRM-keys can enhance trust, accountability, and efficiency through the development of secure, decentralized, and user-centric digital library systems, which will be of interest to practitioners charged with library IT technology management and to researchers in the wider field of blockchain applications in organizations. Full article

Cultural tourism (CT) has emerged as a critical driver of destination competitiveness; however, stakeholders struggle to balance heritage preservation, sustainable growth, and visitor management. Current decision making often lacks the practical information required to assess the multi-dimensional impacts of CT and to align strategies with sustainability goals. This paper presents a user-centred digital decision support system (DSS) developed under the European project IMPACTOUR. The methodological contribution is a procedure that uncovers links among strategies, actions, and performance indicators, conditioned on destination characteristics, by leveraging hierarchical multi-criteria analysis to weight sustainability domains. Co-designed with stakeholders, it integrates social and technological components and uses triangulated data to prioritise strategies and evaluate impacts. The visual interface offers a smart dashboard that supports strategic decision making and displays related key performance indicators, enabling stakeholders to monitor outcomes against predefined sustainability objectives. Pilot implementations in several European regions demonstrate the tool’s efficacy in fostering data-driven planning to achieve a balanced approach between tourism and liveability. While the system is scalable, its current limits include regional specificity and data availability. Future work will incorporate AI-driven predictive analytics and adapt the DSS for application in non-European contexts, providing a replicable framework for advancing sustainable tourism policies in culturally rich destinations. Full article

Background and Objectives: Renin-angiotensin system inhibitors (RASIs) are recommended to preserve residual kidney function (RKF) in patients on peritoneal dialysis (PD); however, evidence of benefit is inconsistent. This study evaluated the effect of RASI on RKF decline among patients undergoing PD. Materials and Methods: We conducted a retrospective cohort study among PD patients at a large metropolitan dialysis centre in Australia. RKF was assessed using residual Kt/V and urine volume from PD adequacy tests. Time zero was PD initiation. RASI exposure was modelled as a time-dependent variable to avoid immortal-time bias. Linear mixed-effects models were fitted for each outcome, including random intercepts and slopes for time (years since PD start) with unstructured covariance. Fixed effects included time, RASI(t), time × RASI(t), age, sex, baseline RKF, PD modality, PD infection episodes, loop diuretic use, and comorbidities. Results: Of 307 PD patients, 231 met the inclusion criteria; 111 (48.1%) received RASI. RASI users were younger than non-users [65 years (IQR 56–74) vs. 72 years (IQR 61–77); p = 0.014]. Residual Kt/V declined by 0.26 units/year; RASI exposure showed no significant effect on urine volume trajectory and a borderline slower Kt/V decline (interaction β = +0.038, p = 0.069). Hospitalisation and PD-related infection rates were similar between groups. Conclusions: RASI therapy was not associated with meaningful RKF preservation in PD patients in this cohort. While earlier studies suggested renoprotective effects of RASI while on PD, our findings align with recent evidence of mixed efficacy. Larger prospective trials are needed to clarify the role of RASI in maintaining RKF and improving long-term outcomes in PD. Full article

Background: Truck drivers constitute a high-risk occupational group due to irregular schedules, prolonged sedentary work, fatigue, and limited access to healthcare, contributing to adverse physical and mental health outcomes. Although mobile health (mHealth) tools offer potential to support driver health, sustained engagement remains a persistent challenge. Objectives: This systematic review aimed to identify behavioural, technological, and contextual determinants influencing truck drivers’ compliance, retention, and long-term engagement with digital health interventions. Methods: Following the PRISMA 2020 guidelines, six eligible studies were identified and thematically synthesised across technology acceptance, behaviour change, and persuasive system design perspectives. Results: Across studies, sustained engagement was facilitated by self-monitoring, real-time feedback, goal-setting, coaching support, and simple, flexible system design. In contrast, technological complexity, high interaction demands, limited digital literacy, privacy concerns, misalignment with irregular schedules, and fatigue consistently undermined engagement and retention. Autonomy, trust, and voluntary participation emerged as cross-cutting determinants supporting continued use. Based on the synthesis, an integrative framework was developed to explain how behavioural, technological, and contextual factors interact to shape truck drivers’ compliance, engagement, and retention with mHealth. Despite generally moderate to high study quality, the evidence base remains fragmented and dominated by short-term evaluations. Conclusions: The findings highlight the importance of context-sensitive, user-centred design to support effective digital health interventions in the trucking sector. Full article

Conversational agents are transforming digital interactions across various domains, including healthcare, education, and customer service, thanks to advances in large language models (LLMs). As these systems become more autonomous and ubiquitous, understanding what constitutes high-quality interaction from a user perspective is increasingly critical. Despite growing empirical research, the field lacks a unified framework for defining, measuring, and designing user-perceived interaction quality in human–artificial intelligence (AI) dialogue. Here, we present an integrative review of 125 empirical studies published between 2017 and 2025, spanning text-, voice-, and LLM-powered systems. Our synthesis identifies three consistent layers of user judgment: a pragmatic core (usability, task effectiveness, and conversational competence), a social–affective layer (social presence, warmth, and synchronicity), and an accountability and inclusion layer (transparency, accessibility, and fairness). These insights are formalised into a four-layer interpretive framework—Capacity, Alignment, Levers, and Outcomes—operationalised via a Capacity × Alignment matrix that maps distinct success and failure regimes. It also identifies design levers such as anthropomorphism, role framing, and onboarding strategies. The framework consolidates constructs, positions inclusion and accountability as central to quality, and offers actionable guidance for evaluation and design. This research redefines interaction quality as a dialogic construct, shifting the focus from system performance to co-orchestrated, user-centred dialogue quality. Full article

Accurate measurement of wall shear stress (WSS) is essential for both fundamental and applied fluid dynamics, where it governs boundary-layer behavior, drag generation, and the performance of flow-control systems. Yet, existing WSS sensing methods remain limited by low spatial resolution, complex instrumentation, or the need for user-dependent calibration. This work introduces a method based on artificial intelligence (AI) and Oil-Film Interferometry, referred to as AI-OFI, that transforms a classical optical technique into an automated and sensor-like platform for local WSS detection. The method combines the non-intrusive precision of Oil-Film Interferometry with modern deep-learning tools to achieve fast and fully autonomous data interpretation. Interference patterns generated by a thinning oil film are first segmented in real time using a YOLO-based object detection network and subsequently analyzed through a modified VGG16 regression model to estimate the local film thickness and the corresponding WSS. A smart interrogation-window selection algorithm, based on 2D Fourier analysis, ensures robust fringe detection under varying illumination and oil distribution conditions. The AI-OFI system was validated in the high-Reynolds-number Long Pipe Facility at the Centre for International Cooperation in Long Pipe Experiments (CICLoPE), showing excellent agreement with reference pressure-drop measurements and conventional OFI, with an average deviation below 5%. The proposed framework enables reliable, real-time, and operator-independent wall shear stress sensing, representing a significant step toward next-generation optical sensors for aerodynamic and industrial flow applications. Full article

Generative Artificial Intelligence (GenAI) models produce increasingly sophisticated outputs, yet their underlying mechanisms remain opaque. To clarify how explainability is conceptualized and implemented in GenAI research, this two-stage review systematically examined 261 articles retrieved from six major databases. After removing duplicates and applying predefined inclusion criteria, 63 articles were retained for full analysis. In the first stage, an umbrella review synthesized insights from 18 review papers to identify prevailing frameworks, strategies, and conceptual challenges surrounding explainability in GenAI. In the second stage, an empirical review analyzed 45 primary studies to assess how explainability is operationalized, evaluated, and applied in practice. Across both stages, findings reveal fragmented approaches, a lack of standardized evaluation frameworks, and persistent challenges, including limited generalizability, interpretability–performance trade-offs, and high computational costs. The review concludes by outlining future research directions aimed at developing user-centric, regulation-aware explainability methods tailored to the unique architectures and application contexts of GenAI. By consolidating theoretical and empirical evidence, this study establishes a comprehensive foundation for advancing transparent, interpretable, and trustworthy GenAI systems. Full article

Urban air mobility solutions such as drone taxi services are increasingly viewed as a promising response to congestion, sustainability, and smart-city mobility challenges. However, the large-scale adoption of such services depends on users’ perceptions of service experience, trust, and readiness to engage with emerging technologies. This study investigates the determinants of sustainable adoption of drone taxi services in the United Arab Emirates (UAE) by examining technology readiness and service experience factors, interpreted through conceptual alignment with the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A structured questionnaire was administered to potential users, capturing perceptions related to optimism, innovation readiness, efficiency, control, privacy, insecurity, discomfort, inefficiency, and perceived operational risk, along with behavioral intention to adopt drone taxi services. Measurement reliability and validity were rigorously assessed using Cronbach’s alpha, composite reliability, average variance extracted (AVE), and the heterotrait–monotrait (HTMT) criterion. The validated latent construct scores were subsequently used to estimate a structural regression model examining the relative influence of each factor on adoption intention. The results indicate that privacy assurance and perceived control exert the strongest influence on behavioral intention, followed by optimism and innovation readiness, while negative readiness factors such as discomfort, insecurity, inefficiency, and perceived chaos demonstrate negligible effects. These findings suggest that in technologically progressive contexts such as the UAE, adoption intentions are primarily shaped by trust-building and empowerment-oriented perceptions rather than deterrence-based concerns. By positioning technology readiness and service experience constructs within established TAM and UTAUT theoretical perspectives, this study contributes a context-sensitive understanding of adoption drivers for emerging urban air mobility services. The findings offer practical insights for policy makers and service providers seeking to design user-centric, trustworthy, and sustainable drone taxi systems. Full article

With the advancement of digital technology, serious games have become an essential tool for disseminating and educating individuals about cultural heritage. However, systematic empirical research remains limited with respect to how visual elements influence users’ cognitive and emotional engagement through interactive behaviors. Using the “Macau Historic Centre Science Popularization System” as a case study, this mixed-methods study investigates the mechanisms by which visual elements affect user experience and learning outcomes in digital interactive environments. Eye-tracking data, behavioral logs, questionnaires, and semi-structured interviews from 30 participants were collected to examine the impact of visual elements on cognitive resource allocation and emotional engagement. The results indicate that the game intervention significantly enhanced participants’ retention and comprehension of cultural knowledge. Eye-tracking data showed that props, text boxes, historic buildings, and the architectural light and shadow shows (as incentive feedback elements) had the highest total fixation duration (TFD) and fixation count (FC). Active-interaction visual elements showed a stronger association with emotional arousal and were more likely to elicit high-arousal experiences than passive-interaction elements. The FC of architectural light and shadow shows a positive correlation with positive emotions, immersion, and a sense of accomplishment. Interview findings revealed users’ subjective experiences regarding visual design and narrative immersion. This study proposes an integrated analytical framework linking “visual elements–interaction behaviors–cognition–emotion.” By combining eye-tracking and information dynamics analysis, it enables multidimensional measurement of users’ cognitive processes and emotional responses, providing empirical evidence to inform visual design, interaction mechanisms, and incentive strategies in serious games for cultural heritage. Full article

This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management. Full article

This study explores how artificial intelligence can promote accessibility and inclusiveness in digital culinary environments. Centred on the Receitas +Power platform, the research adopts an exploratory, multidimensional case study design integrating qualitative and quantitative analyses. The investigation addresses three research questions concerning (i) user empowerment beyond recommendation systems, (ii) accessibility best practices across disability types, and (iii) the effectiveness of AI-enabled inclusive solutions. The system was developed following user-centred design principles and WCAG 2.2 standards, combining generative AI modules for recipe creation with accessibility features such as voice interaction and adaptive navigation. The evaluation, conducted with 87 participants, employed the System Usability Scale complemented by thematic qualitative feedback. Results indicate excellent usability (M = 80.6), high reliability (Cronbach’s α = 0.798–0.849), and moderate positive correlations between usability and accessibility dimensions (r = 0.45–0.55). Participants highlighted the platform’s personalisation, clarity, and inclusivity, confirming that accessibility enhances rather than restricts user experience. The findings provide empirical evidence that AI-driven adaptability, when grounded in universal design principles, offers an effective and ethically sound pathway toward digital inclusion. Receitas +Power thus advances the field of inclusive digital gastronomy and presents a replicable framework for human–AI co-creation in accessible web technologies. Full article

Digital transformation in government services represents a strategic shift that leverages digital technologies to enhance efficiency, accessibility, convenience, and user-centricity. In the wake of the COVID-19 pandemic, many governments accelerated the digitisation of services to support remote access and social distancing. Governments typically progress from digitisation (converting physical processes into digital formats) to digitalisation (automating service delivery and improving process efficiency), and ultimately to full digital transformation, where services are completed instantly and entirely online. However, varying levels of maturity across countries influence service outcomes differently, and indicators related to service quality, convenience, and security remain underexamined, particularly in developing contexts. This study addresses these gaps by examining Kuwait’s progress along the digitalisation–digital transformation continuum. It investigates current trends and user preferences in the use of digital government services based on empirical quantitative data collected from users in Kuwait. Specifically, the research objectives are fourfold: (i) to identify crucial outcome metrics for the success of digital government services, (ii) to assess user evaluations of these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and empirically test a model for evaluating digital transformation success. Drawing on established Information Systems’ (ISs’) success perspectives, a customised conceptual model incorporating six outcome metrics in three domains—service-related (user satisfaction, service quality), convenience-related (accessibility, ease of use), and security-related (perceived security, perceived trust)—was developed. A survey of 378 users of digital government services in Kuwait was conducted to compare perceptions across service types using independent-samples t-tests and linear regression analyses. The study found that users primarily accessed government services through smartphones and dedicated applications, highlighting the importance of mobile optimisation, and showed a clear preference for real-time, fully automated services over those requiring extended approval processes. The results indicate that digital transformation services significantly outperform digitalisation services across five outcome metrics—satisfaction, service quality, accessibility, ease of use, and perceived security—while trust remains consistent across both. These findings underscore the importance of advancing comprehensive digital transformation to enhance public service delivery. Practical recommendations are provided to support Kuwait’s digital government strategy. Given the purposive sampling and cross-sectional, comparative design, the findings should be interpreted with caution, and future studies are encouraged to apply probability-based sampling and more advanced multivariate techniques (e.g., structural equation modelling) to validate and extend the proposed model. Full article

Honeybee products (HBPs), including honey, bee pollen, bee bread, royal jelly, propolis, beeswax, and bee brood, are increasingly used in food, nutraceutical, and cosmetic contexts. Because of their natural origin, HBPs can provoke allergic reactions ranging from localised dermatitis to life-threatening, systemic anaphylaxis. As the use of bee products for health purposes grows in apitherapy (a branch of alternative medicine), raising public awareness of their potential risks is essential. This narrative review synthesises the clinical manifestations of HBP allergy, culprit allergens present in each product, immunological mechanisms, diagnostic approaches, at-risk populations, and knowledge gaps. The analysis of the available literature suggests that, although relatively rarely, HPB may trigger allergic reactions, including anaphylactic shock. The sensitisation mechanism may be associated with both primary sensitisation and cross-reactivity and can be classified into type I (IgE-mediated) and type IV (T-cell-mediated). However, bee bread appears less allergenic than other HBPs, potentially due to lactic fermentation that can degrade allergenic proteins. Severe reactions following intake of bee bread have not been reported to date. Management of HBP allergic reactions centres on avoiding the products, educating about the risks, and providing more precise product labelling, specifying the allergen content. Individuals with atopy and beekeepers are at heightened risk of developing anaphylaxis; therefore, they should be particularly aware of the potential dangerous consequences of HPB use. Further research is needed to clarify the mechanisms of HBP allergies and improve safety for all users. Full article

Video-centric applications have seen significant growth in recent years with HTTP Adaptive Streaming (HAS) becoming a widely adopted method for video delivery. Recently, low-latency (LL) adaptive bitrate (ABR) algorithms have recently been proposed to reduce the end-to-end delay in HTTP adaptive streaming. This study investigates whether low-latency adaptive bitrate (LL-ABR) algorithms, in their effort to reduce delay, also compromise video quality. To this end, this study presents both objective and subjective evaluation of user experience with traditional DASH and low-latency ABR algorithms. The study employs crowdsourcing to evaluate user-perceived video quality in low-latency MPEG-DASH streaming, with a particular focus on the impact of short segment durations. We also investigate the extent to which quantitative QoE (Quality of Experience) metrics correspond to the subjective evaluation results. Results show that the Dynamic algorithm outperforms the low-latency algorithms, achieving higher stability and perceptual quality. Among low-latency methods, Low-on-Latency (LOL+) demonstrates superior QoE compared to Learn2Adapt-LowLatency (L2A-LL), which tends to sacrifice visual consistency for latency gains. The findings emphasize the importance of integrating subjective evaluation into the design of ABR algorithms and highlight the need for user-centric and perceptually aware optimization strategies in low-latency streaming systems. Our results show that the subjective scores do not always align with objective performance metrics. The viewers are found to be sensitive to complex or high-motion content, where maintaining a consistent user experience becomes challenging despite favorable objective performance metrics. Full article

This review investigates the application of Explainable Artificial Intelligence (XAI) in biomedical informatics, encompassing domains such as medical imaging, genomics, and electronic health records. Through a systematic analysis of 43 peer-reviewed articles, we examine current trends, as well as the strengths and limitations of methodologies currently used in real-world healthcare settings. Our findings highlight a growing interest in XAI, particularly in medical imaging, yet reveal persistent challenges in clinical adoption, including issues of trust, interpretability, and integration into decision-making workflows. We identify critical gaps in existing approaches and underscore the need for more robust, human-centred, and intrinsically interpretable models, with only 44% of the papers studied proposing human-centred validations. Furthermore, we argue that fairness and accountability, which are key to the acceptance of AI in clinical practice, can be supported by the use of post hoc tools for identifying potential biases but ultimately require the implementation of complementary fairness-aware or causal approaches alongside evaluation frameworks that prioritise clinical relevance and user trust. This review provides a foundation for advancing XAI research on the development of more transparent, equitable, and clinically meaningful AI systems for use in healthcare. Full article