Search Results (522)

Over the past decade, developments in artificial intelligence (AI) have sparked a new wave of debate and research across nearly all areas of life, including education. In English as a Foreign Language (EFL) education, AI-based technologies are also widely adopted to support learners and instructors. This trend has led to numerous studies focused on understanding AI’s role in identifying potential opportunities and challenges. This study offers a systematic review of relevant research, highlighting the benefits and obstacles of AI use in the Saudi EFL context. About 60 peer-reviewed articles were selected following PRISMA guidelines. The findings reveal multiple opportunities for AI integration in Saudi Arabia, such as improved language skills, personalized learning experiences, increased self-regulated learning, boosted motivation and confidence among learners, expanded learning opportunities, and support for pedagogy and institutional performance. Major challenges include biased and inaccurate data, students’ overdependence on technology, ethical concerns, and a lack of technological skills among users. The study also suggests future directions, including localizing AI tools, conducting long-term impact studies, providing faculty and student training, and establishing ethical guidelines within institutions. Full article

Inadequate management of municipal solid waste represents a critical challenge for the sustainability of modern cities, characterized by low citizen participation rates due to the lack of direct incentives. Unlike existing approaches that isolate hardware classification or fleet monitoring, this article presents RENOVA as a socio-technical closed-loop system based on the Internet of Things (IoT) and artificial intelligence (AI). This system integrates an IoT-enabled smart bin, a gamified mobile application for citizens, and an administrative web panel for merchant redemption, all interconnected via a REST API. The system employs computer vision through the GPT-4o (OpenAI, San Francisco, CA, USA) multimodal model for the automatic classification of recyclable materials (PET plastic and Aluminum) and integrates a gamified rewards program to incentivize citizen participation. The methodology follows an applied technological development approach under the agile Scrum framework. Prototype validation demonstrated successful real-time communication between the IoT device and the cloud platform, achieving classification accuracy exceeding 95% under controlled conditions. A diagnostic survey applied to a convenience sample of 51 participants revealed that 94.1% accepted the proposed gamification model, while user experience evaluation (n = 74; consisting primarily of university-affiliated individuals aged 15–24) yielded a mean overall satisfaction score of 4.77/5.0 (SD = 0.48), with 79.7% of participants assigning the maximum rating. These findings reflect stated user acceptance and behavioral intention under prototype conditions rather than observed long-term behavioral change, and should not be generalized to broader urban populations without further validation. The proposed solution directly contributes to Sustainable Development Goals 11 (Sustainable Cities) and 12 (Responsible Consumption), suggesting a potentially scalable framework. Full article

High-fidelity motion cueing in driving simulators is essential for delivering a realistic and immersive user experience. However, the trade-off between motion accuracy and computational efficiency often hinders achieving this. Fixed-horizon Model Predictive Control (MPC)-based Motion Cueing Algorithm (MCA) frameworks frequently struggle to adapt to rapid dynamic changes in vehicle behaviour, resulting in suboptimal simulator responses. Their reliance on worst-case horizon tuning can result in inefficient platform usage and increased computational load, limiting computational efficiency and practical deployment. This study presents an adaptive MPC-based MCA designed to enhance the fidelity of motion platforms used in vehicle dynamic simulations. The proposed method dynamically adjusts the MPC prediction horizon to improve overall simulation performance while minimising motion sensation error. Within the simulation environment, the prediction horizon is adaptively updated at each simulated control step according to recent tracking-performance metrics, enabling responsiveness to varying vehicle dynamic models and driving scenarios. The system was developed and implemented using Python and MATLAB environments, with Long Short-Term Memory (LSTM) networks employed to enhance the adaptability and precision of prediction horizon adjustments. Due to safety constraints, the proposed framework was evaluated exclusively within a simulation environment and compared against both classical MPC-based MCA and RL MPC-based MCA. Experimental results demonstrate that the proposed adaptive framework improves workspace utilisation and substantially reduces computational load compared with the classical and RL-based MPC-based MCA approaches, while maintaining competitive motion cueing tracking performance. The adaptive system effectively enhances linear displacement (LD), ensuring better alignment of motion cues with platform constraints. While minor trade-offs were observed in root mean square error (RMSE) and correlation coefficients (CCs) for sensed angular velocity (SAV) and sensed specific force (SSF), the framework improves workspace utilisation and computational efficiency while maintaining competitive motion cueing performance. Furthermore, the adaptive LSTM-MPC framework substantially reduces computational load, achieving approximately 44.26 times faster execution compared with the classical MPC-based MCA and approximately 30.03 times faster execution compared with the RL MPC-based MCA. These findings highlight the potential of integrating deep learning (DL) with MPC to optimise the trade-off between motion cueing performance, platform utilisation, and computational efficiency in driving simulators. Full article

This study presents a Systematic Literature Review (SLR) of gamified platforms for cybersecurity education and training, with a particular focus on environments that incorporate attack–defence simulation. The review examines how the literature addresses gamification, User eXperience (UX), didactics, platform design, and sustainability-related deployment conditions across higher education as the primary reference context, while also considering adjacent applied training contexts when they provide transferable evidence for platform design, deployment, or evaluation, including online learning. We analysed 172 studies published between 1 January 2015 and 8 March 2026. The findings show that the field combines authentic hands-on practice with challenge-based learning, feedback-rich progression, and diverse technical formats, including cyber range environments, serious games, CTF-oriented platforms, cloud-based infrastructures, and modular training systems. The review also indicates that the educational value of these approaches depends on the alignment between pedagogical structure, interaction design, and technical architecture, as well as on safe experimentation, adaptability, governance, and long-term maintainability. The review contributes an evidence-based taxonomy and configurational synthesis of recurrent design patterns across UX, didactics, gamification, architecture, and sustainability, and it identifies implications for future empirical research and for the design of sustainable, learner-centred cybersecurity teaching platforms. Full article

Large language models (LLMs) have significantly enhanced the fluency, consistency, and adaptability of social bots, raising new concerns about their ability to integrate into online communities. However, community integration requires more than text generation alone. Social bots often lack a systematic understanding of community culture, struggle to maintain consistency between persona settings and posting behavior, and have difficulty identifying users with higher interaction potential. To address these challenges, this paper proposes HSEF-CI, a human-like social bot enhancement framework for community integration. The framework constructs community profiles from target communities, reshapes bot identities and long-term memory, adopts a staged text generation workflow, and selects interaction targets via homophily-based matching. Experiments on multiple English-speaking communities show that the framework lowers detectability across several detectors, improves social bots’ ability to integrate into target communities, and increases users’ willingness to interact. These findings highlight the importance of jointly modeling community profiles, identity reshaping, adaptive text generation, and target selection in studying LLM-driven social bots for community integration. The proposed framework also helps reveal how social bots adapt to and integrate into online communities, and provides an empirical baseline for the future development of detectors targeting community integration behaviors. Full article

Powder coatings applied to medium-density fiberboard (MDF) substrates have attracted increasing attention due to their low volatile organic compound (VOC) emissions and high material utilization efficiency. The review synthesizes the interdisciplinary literature from coating engineering, CMF design, and furniture design. However, existing studies often focus on individual coating properties and lack a systematic framework integrating color, material, and finish (CMF). Therefore, this review examines the design and application of MDF powder coatings from a CMF perspective, focusing on the relationships between coating engineering parameters and user-oriented perceptual requirements. Within this framework, color performance is associated with pigment dispersion and particle size distribution; the material dimension is governed by low-temperature curing kinetics and substrate properties, and the finish dimension is shaped by surface texturing and functional additives. The review also discusses current limitations, including the trade-off between low-temperature curing reactivity and storage stability, the influence of nano-additives on surface quality, and the recyclability challenges of powder-coated MDF. Future research should focus on industrial scalability, lifecycle sustainability, and long-term durability of MDF powder coating systems. This review provides a CMF-oriented framework for linking user experience requirements with coating engineering strategies, which is of great importance for the development of customized home furnishing. Full article

Conventional P300-based brain–computer interfaces (BCIs) commonly rely on two-dimensional (2D) visual flashing, which may induce visual fatigue and limit immersion, thereby restricting long-term usability and system performance. To address these limitations, this study proposes an immersive P300-BCI framework integrating a three-dimensional morphological stimulation paradigm, termed 3D-Morph, with self-adaptive Bayesian linear discriminant analysis (SA-BLDA). Instead of using color or luminance flickering, the proposed paradigm employs dynamic 2D-to-3D morphological transformations of virtual objects in a virtual reality environment to enhance target-related event-related potentials while preserving visual immersion. SA-BLDA further adjusts the number of stimulation rounds according to classification confidence to balance accuracy and interaction efficiency. Experiments with 24 participants showed that the proposed system outperformed the conventional 2D paradigm. In offline analysis, the proposed method achieved an average classification accuracy of 94.17% and an information transfer rate (ITR) of 25.50 bits/min, significantly outperforming the 2D paradigm (87.29% accuracy, 22.75 bits/min ITR, both $p<0.001$, Cohen’s $d\ge 1.22$). In online experiments, the 3D-Morph paradigm achieved an average accuracy of 91.46% and an ITR of 37.23 bits/min, compared with 83.96% and 28.74 bits/min for the conventional 2D paradigm (both $p<0.01$, Cohen’s $d\ge 1.14$). The average response time was reduced by 0.46 s ($p<0.01$, Cohen’s $d=0.78$), and the processing time per stimulation round (PT) of SA-BLDA was significantly reduced from $48.54\pm 10.47$ ms in the 2D paradigm to $26.40\pm 9.41$ ms in the 3D-Morph paradigm ($p<0.01$, Cohen’s $d=2.34$), corresponding to a 45.61% reduction in computational time per round. NASA-TLX evaluations indicated a significantly lower subjective workload across all dimensions (all $p<0.05$, Cohen’s $d\ge 0.76$). These results demonstrate that combining 3D-Morph stimulation with SA-BLDA can significantly improve classification performance, interaction efficiency, and user experience, providing a feasible framework for immersive and practical P300-BCI applications. Full article

With the increasing integration of artificial intelligence into e-commerce platforms, trust in algorithmic decision-making has become a critical issue. Recommender systems significantly shape consumer choices and influence visibility within digital marketplaces, yet remain largely opaque. This study aims to bridge the gap between algorithmic accuracy and perceived trustworthiness by conducting a bibliometric and topic modeling analysis of 163 peer-reviewed publications (2012–2025). Results indicate a paradigmatic shift from usability-focused approaches toward governance-aware frameworks encompassing fairness, explainability, and accountability. To capture this transformation, the Acceptance Triangle model is introduced, conceptualising algorithmic acceptability across three interdependent layers: trust calibration at the interface level, exposure fairness at the platform level, and accountability mechanisms at the institutional level. The model is further operationalised through the Trust UX Playbook—nine managerial design levers with associated key performance indicators—and a Composite Acceptability Score integrating accuracy, fairness, and complaint reduction. The findings suggest that trust alone may be insufficient for understanding long-term acceptability in e-commerce recommender systems. Instead, the alignment between user experience, market equity, and governance legitimacy is interpreted as an analytically useful condition for conceptualising algorithmic acceptability. This research contributes a structured framework for assessing and designing acceptable recommender systems, offering actionable guidance for designers, decision-makers, and regulatory stakeholders seeking to improve algorithmic transparency, fairness, and accountability in online commerce. 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

Heritage-led urban redevelopment is increasingly adopted to advance cultural continuity and social vitality; however, its long-term sustainability is often compromised due to the absence of user-oriented assessment methods. Conventional Post-Occupancy Evaluation (POE) approaches are limited in their ability to capture experiential, social, and participatory dimensions of architectural and urban spaces. This study examines the potential of Virtual Reality (VR) as a participatory POE tool for sustainable heritage redevelopment through the case study of Souq Al Muharraq in Bahrain. A convergent mixed-method approach is employed, integrating immersive VR 360-degree walkthroughs, structured questionnaires, qualitative semi-structured interviews, and expert evaluation. The findings reveal significant discrepancies between design intentions and lived experience, specifically in thermal comfort, circulation, social usability, and informal spatial practices. The study demonstrates that VR supports a user-centered and experiential approach aligned with Sustainable Development Goals (SDGs) 9, 11, and 16. It further proposes a sustainable and cost-efficient framework for architecture and urban projects’ evaluation by enabling early and post-user-centered evaluation of projects to reduce costly revisions and the creation of inclusive, adaptive, and resilient architecture and urban spaces. Full article

Purpose: Access to assistive technology (AT) is a fundamental human right and a critical component of Universal Health Coverage (UHC). In the Middle East and North Africa (MENA) region, the scarcity of trained professionals remains a significant barrier to AT service provision. This study evaluates the effectiveness and perceived usefulness of the Assistive Technology Training Program (ATTP), a specialized continuing education initiative designed to build workforce capacity among rehabilitation and healthcare professionals. Methods: A convergent mixed methods design was used to analyze quantitative pre/post-test scores and qualitative focus group open-ended responses. Quantitative data were gathered from 386 participants across 11 MENA countries using a pre- and post-test assessment of AT knowledge. Qualitative utility and participant satisfaction were assessed through a 5-point Likert scale survey evaluating content relevance, trainer expertise, and facilities. Association tests (ANOVA and t-tests) were conducted to identify factors influencing knowledge gain. Results: Participants demonstrated a statistically significant improvement in AT knowledge, with the overall mean score increasing from 3.67 ± 1.13 to 7.50 ± 1.25 (p < 0.001). High levels of satisfaction were reported, with 92% of participants rating the training as “Very Good” or “Excellent” regarding its relevance to clinical needs. Association tests revealed that professional background (p < 0.001), employment status (p = 0.0017), level of education (p = 0.011), and prior training experience (p = 0.026) were significant factors in the magnitude of improvement, although all subgroups achieved significant learning gains. Qualitative thematic analysis per the focus group discussions using the WHO-GATE 5 P framework identified three major themes: (1) Structural Challenges: Issues with Products and Provision point toward a need for better infrastructure and localized supply chains. (2) Human Capital: Personnel barriers emphasize that training shouldn’t just be for professionals, but should extend to caregivers as well. (3) Systemic and Social Change: Policy and People focus on the “soft” side of AT moving toward user-involved guidelines and fighting social stigma to ensure rights are upheld. Conclusions: The ATTP is an impactful educational intervention that significantly enhances the foundational competencies of healthcare professionals in the MENA region. By addressing knowledge gaps and fostering practical skills, the program serves as a preliminary model that demonstrates potential for building regional capacity and supporting the United Nations’ Sustainable Development Goal (SDG) #3 related to health and wellbeing and SDG #4 related to quality education and lifelong learning opportunities for all. Further research is required to evaluate its long-term scalability and clinical impact. Full article

Background: A physically active lifestyle is crucial for long-term cardiovascular health; however, access to supervised exercise programs for children and adolescents with congenital heart disease (CHD) remains limited. Although prior digital exercise interventions for this population have demonstrated safety and feasibility, adherence has often been low. Mobile health approaches integrating gamification may enhance motivation and engagement, particularly among young “digital natives.” FOOTLOOSE is an app-based home exercise program developed specifically for children and adolescents with CHD. This study aimed to evaluate user experience, usability, and perceived impact using a multimethod approach. Methods: Children and adolescents aged 10–18 years with simple, moderate, or complex CHD were recruited between July and December 2025 mainly during routine outpatient visits at the TUM Klinikum Deutsches Herzzentrum. Participants used the FOOTLOOSE app in their daily lives over a two-week period. Evaluation included semi-structured qualitative interviews and standardized questionnaires assessing physical activity self-efficacy, enjoyment of physical activity (PACES-S), user experience (UEQ), and health-related quality of life (KINDL^®). Interviews were conducted digitally, transcribed verbatim, and analyzed using qualitative content analysis according to Kuckartz until thematic saturation was reached. Results: A total of 22 participants (mean age 13.4 ± 2.3 years; 54.5% female) were included. Overall, the FOOTLOOSE app was perceived positively, with participants highlighting enjoyment, intuitive usability, and personalized workout creation. Participants contributed diverse and creative suggestions for further app development, particularly regarding more advanced gamification features (e.g., games or rankings). Most participants reported self-perceived increase in physical activity during the intervention period (n = 15). UEQ scores (mean ± SD) were as follows: attractiveness (1.3 ± 0.8), perspicuity (1.7 ± 1.1), efficiency (1.2 ± 0.9), dependability (1.4 ± 0.7), stimulation (1.0 ± 1.1), and novelty (0.6 ± 1.0). Conclusions: This study demonstrates the feasibility and user acceptance of a gamified, app-based home exercise program for children and adolescents with CHD. User-centered feedback highlights important directions for iterative refinement, particularly regarding age-appropriate and engaging gamification elements. These findings provide a foundation for future studies evaluating long-term engagement and effectiveness in larger samples. Full article

Urban waterfronts are vital public spaces that contribute to urban sustainability by providing residents with opportunities for recreation, social interaction, and nature experiences. Understanding user perceptions in these environments is essential for evidence-based design. Taking Taiyuan Fenhe Park in China as a case with local residents as respondents, this study investigated how objective audiovisual characteristics are associated with tourist loyalty through perceptual dimensions, while also examining interactive associations between visual and auditory elements. Data were collected at 539 spatial samples spaced at five-minute walking intervals. Methods included on-site acoustic measurements, panoramic imaging, computer-based visual and auditory quantification, and questionnaire surveys, yielding a total of 1768 valid responses. Visual features were quantified using semantic segmentation, object detection, and depth prediction, whereas the auditory environment was characterized by physical acoustic and psychoacoustic indicators. Three perceptual dimensions—environmental restorativeness (ERS), spatial vitality (SVS), and environmental controllability (ECS)—were extracted and tested as mediators within the stimulus–organism–response (S-O-R) framework. Results indicated that ERS, SVS, and ECS function as three parallel mediating constructs in the statistical model, with SVS showing the strongest statistical association with tourist loyalty. In addition, fluctuation strength exhibited a significant direct effect on tourist loyalty independent of these three perceptual dimensions. A total of 17 significant audiovisual interactions were identified, revealing both synergistic and antagonistic effects. These findings contribute to theoretical frameworks of multisensory integration and provide practical guidance for sustainable waterfront design. Specifically, zoning strategies and carefully selected audiovisual combinations are relevant to enhanced user experience and may contribute to long-term social well-being. Full article

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

Household rainwater tanks (HRWTs) have re-emerged globally as a decentralised strategy to address water scarcity, climate variability, and increasing urban water demand. In several jurisdictions, including New South Wales, Australia, rainwater tanks have been chosen to meet the mandatory potable water reduction target in new residential developments for nearly two decades; however, growing evidence indicates persistent underutilisation and variable performance in practice. Despite their recognised benefits in reducing potable water demand, mitigating stormwater runoff, and enhancing urban resilience, the global HRWT research landscape remains fragmented across disciplinary and thematic boundaries. This paper presents a multifaceted review, defined here as an approach that synthesises multiple perspectives on the topic. It integrates systematic mapping of peer-reviewed literature with a critical thematic analysis across four dominant research domains: technological and design innovation, policy and governance frameworks, environmental performance, and social–behavioural dimensions. The findings reveal a strong research focus on technical optimisation, while policy effectiveness, environmental trade-offs, and household-level behavioural factors receive comparatively uneven attention. Regulatory and incentive-based instruments are shown to produce inconsistent outcomes, shaped by local institutional capacity to design, implement, enforce, and sustain programs, as well as by climatic context and household acceptance. Environmental assessments identify both benefits and burdens, including energy use, treatment requirements, and operational complexity. Social and behavioural studies indicate growing acceptance of household rainwater tank (HRWT) systems. However, financial constraints, local conditions, and ongoing maintenance demands continue to influence adoption and performance. A key insight from this review is the limited attention given to households’ lived experiences, particularly how users adopt, adapt, operate, and maintain HRWT systems over time. This gap constrains progress across technical, policy, environmental, and social dimensions and risks cycles of early policy uptake followed by stagnation. The review highlights the need to integrate household perspectives into future research, policy design, and industry practice to improve system performance, user experience, and the long-term contribution of HRWTs to sustainable urban water management. Full article