Search Results (350)

Aircraft digital twins are increasingly used for diagnostics, prognostics, and predictive maintenance, but their role as lifecycle-oriented, multi-stakeholder decision-support ecosystems remains insufficiently developed. This paper addresses this gap by proposing a conceptual systems-engineering framework for an aircraft digital twin ecosystem supporting sustainable aviation transport management. The framework integrates physics-based, data-driven, hybrid, probabilistic, and federated modelling approaches and includes a three-layer ecosystem model, formal mathematical representation of aircraft and digital twin lifecycle evolution, federated model updating, lifecycle decision-support scenarios, reference architecture, validation and trustworthiness principles, and a five-level maturity model. Representative aviation industrial cases are used to interpret the framework. The analysis shows that current industrial practice already contains elements of predictive maintenance, fleet analytics, engine health monitoring, and cloud-enabled MRO optimization, but full aircraft-level lifecycle governance, sustainability trade-off analysis, federated validation, and multi-stakeholder decision orchestration remain underdeveloped. The proposed framework positions aircraft digital twins as asset-level instruments for lifecycle planning, coordinated governance, and sustainability-oriented decision support. Full article

Artificial intelligence (AI) and digital platforms are increasingly influencing how universities manage experiential learning, interdisciplinary collaboration, and innovation-oriented educational activities. Challenge-based capstone and graduation projects play an important role in this context because they connect technical learning with teamwork, stakeholder engagement, project management, and applied innovation. However, many universities still rely on fragmented and highly manual coordination processes, which can limit scalability, transparency, and effective alignment between project requirements and participant capabilities. This study presents Smart Innovation Hub, an AI-enabled information system developed to support challenge-based innovation and capstone-project coordination in higher education. The platform brings together challenge intake, participant profiling, AI-supported recommendations, mentor coordination, workflow governance, and human review within a shared educational innovation environment. The system operationalizes an Innovation Bridge ecosystem model that connects students, faculty mentors, research centers, and external partners through a data-supported coordination framework. A Design Science Research (DSR) methodology guided the development and pilot evaluation of the platform within a public university environment. The pilot evaluation relied on several evidence sources, including platform logs, coordinator records, stakeholder surveys, milestone documentation, and partner feedback collected during implementation activities. Early pilot observations suggested an approximate 60% reduction in average team-formation cycle time, together with positive stakeholder perceptions regarding workflow usability and recommendation quality. These findings should be interpreted as preliminary implementation indicators within a single-institution pilot environment. The study contributes an AI-enabled educational innovation ecosystem architecture, a hybrid semantic-structured recommendation framework for challenge-based coordination, and a structured workflow model that integrates explainability and human oversight into educational innovation management. The findings further suggest that AI-enabled information systems may improve the transparency and coordination of challenge-based innovation workflows while preserving institutional governance and human decision-making. Full article

Climate change intensifies hydrological variability and threatens agricultural water security. This review synthesizes literature on agricultural water system resilience under climate change through a structured critical narrative approach informed by PRISMA/SALSA reporting principles. We examine four linked domains: resilience concepts and indicators, assessment methods under uncertainty, climate impact and vulnerability evidence, and adaptation/governance pathways. The synthesis indicates a broad shift from engineering-centered water-supply approaches toward socio-ecological resilience frameworks that combine infrastructure, ecosystem processes, farmer behavior, and institutions. Methodologically, deterministic optimization is increasingly complemented by stochastic, robust, integrated-assessment, remote-sensing, and machine-learning approaches, although data requirements, uncertainty propagation, and interpretability remain important constraints. Evidence suggests that crop water demand and irrigation requirements may increase substantially under high-emission scenarios, with acute risks in arid and semi-arid regions. Effective adaptation is unlikely to rely on single technologies alone; precision irrigation, nature-based solutions, climate services, and infrastructure investments require complementary demand-side rules, water accounting, equity safeguards, and participatory governance to avoid maladaptation such as the irrigation-efficiency rebound effect. We identify priority research needs in transparent review protocols, uncertainty quantification, cross-scale governance, farmer decision-making, digital inclusion, and monitoring systems. The review provides a moderated conceptual framework and policy-oriented research agenda for strengthening agricultural water resilience. Full article

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services. Full article

While higher education institutions strive for academic excellence, they also bear the responsibility of caring for and ensuring the sustainable well-being of their students. After the COVID-19 pandemic, these institutions have transitioned to hybrid and digital education models and have begun to experience the opportunities and threats of digital learning ecosystems. With the introduction of AI technology, this transformation has taken on a new dimension: while students benefit from the flexibility, instant feedback, and personalized learning offered by AI tools, they have also begun to experience new challenges, including cognitive overload, digital fatigue, and social isolation. In this context, the aim of this research is to assess students’ overall psychological well-being and to provide a support system that promotes sustainable well-being by anticipating potential psychological strain and recommending necessary precautions. Accordingly, the purpose of this study, drawing on Self-Determination Theory and Conservation of Resources Theory, is to examine the direct effects of an AI-enabled student support system on sustainable well-being and academic engagement, as well as its indirect effects through self-efficacy and academic resilience. Data will be collected from undergraduate students from a public university in Istanbul. Data will be analyzed in the R statistical environment. We expect that academic resilience, and self-efficacy will mediate the relationship between an AI-enabled student support system and sustainable well-being. At the end of the study, we propose a conceptual model that can be tested empirically by further research. Managerial and further research directions, as well as limitations, are also discussed. Full article

This systematic review examines research published between 2021 and 2025 on generative AI and chatbot use in secondary education across nine countries in Southern Europe and the Balkans: Greece, Italy, Spain, Portugal, Malta, Serbia, Croatia, Bulgaria, and Romania. Drawing on studies from IEEE Xplore, the ACM Digital Library, Google Scholar, and arXiv, this review synthesizes evidence on instructional uses, reported learning outcomes, teacher readiness, governance, and language-localization constraints. Across the region, the literature shows rapid experimentation in writing, language learning, programming, and project-based learning but limited long-term evaluation and weak cross-country comparability. Teacher interest is high, yet institutional guidance, assessment frameworks, and local-language resources remain uneven. This review argues that the next phase of adoption should move from isolated classroom experimentation to system-level implementation built around teacher AI literacy, transparent assessment, and context-sensitive design for smaller linguistic ecosystems. Full article

Against the backdrop of the digital transformation in engineering education, this study developed and implemented a Learning Analytics (LA)-driven and Artificial Intelligence (AI)-supported blended learning model to address structural challenges in the “Combustion” course, including highly abstract theories, experimental safety risks, and compressed instructional hours. Moving beyond mere technical stacking, the model establishes a closed-loop data ecosystem that integrates “pre-class adaptive diagnosis, in-class contextualized internalization, and post-class personalized transfer,” while deeply embedding engineering ethics and sustainability issues related to carbon neutrality. A one-semester quasi-experimental study (Experimental N = 60, Control N = 60) was conducted, utilizing a triangulated assessment of final exam scores, platform-based behavioral trajectories, and semi-structured interviews. The results showed that the experimental group achieved significantly higher final assessment scores than the control group (82.4 ± 5.7 vs. 73.2 ± 6.9), with normality tests supporting the use of parametric analysis and Analysis of Covariance (ANCOVA) indicating a significant instructional effect after controlling for Grade Point Average (GPA) and pre-test scores. Furthermore, behavioral analysis confirms that the LA mechanism significantly enhances students’ self-regulated learning and engagement by increasing the visibility of the learning process. This study provides an evidence-based reform paradigm for engineering curricula to achieve the synergistic cultivation of knowledge acquisition, competency development, and value alignment within constrained instructional timeframes. Full article

This paper examines the intersection between digital learning environments and multilingual education policies, with a focus on the linguistic integration of migrant students in Europe. It explores how technology, particularly mobile-assisted learning, artificial intelligence, and immersive tools, can strengthen language acquisition and promote social inclusion. Drawing on European and Greek policy frameworks, the study shows how digital pedagogies operationalize multilingualism as both an educational objective and a social justice priority. Based on a qualitative review of contemporary research and institutional reports, the findings indicate that digitally enhanced learning environments act as catalysts for equity, intercultural dialogue, and active participation when supported by coherent pedagogical design. The paper concludes by outlining policy recommendations for the development of multilingual digital ecosystems that align technological innovation with democratic, inclusive, and human-centred education. Overall, the analysis highlights that technology-mediated multilingualism can effectively reinforce participation, inclusion, and linguistic integration when embedded within robust policy structures and sound pedagogical practice. Full article

Soil erosion is a global ecological and environmental issue that severely degrades terrestrial ecosystems. A range of soil and water conservation measures, notably the construction of check dams in gullies, have been widely implemented to mitigate soil erosion and sustain agricultural productivity. In this study, Ningxia province in China was selected as the study area. High-resolution Google Earth imagery and digital elevation model (DEM) data were integrated with three representative deep learning semantic segmentation models—FCN, U-Net, and DeepLab v3+—to achieve automatic extraction and spatial distribution analysis of engineered check dams. Model performance was quantified using overall accuracy (OA), F1-score, and mean intersection over union (mIoU), among other metrics. The results demonstrated that U-Net outperformed FCN and DeepLab v3+ across all evaluation metrics. On the test dataset, U-Net’s F1-score exceeded those of FCN and DeepLab v3+ by 3.89% and 7.08%, while mIoU increased by 2.17% and 6.57%, demonstrating superior boundary delineation. Based on the precise area extraction by U-Net, a piecewise empirical equation was subsequently developed to relate predicted silted land area to actual sediment volume, achieving R² values of 0.92 for small dams and 0.96 for large dams. Spatial distribution analysis revealed that check dams are predominantly concentrated in the southern mountainous and hilly-gully regions, moderately distributed in the central areas, and relatively sparse in the northern plains. Overall, this study demonstrates the feasibility and effectiveness of deep learning-based semantic segmentation for automated check dam mapping and sediment volume estimation. Full article

This article presents a sustainable, system-level approach to personalized practical learning in digital education environments based on tightly integrating formal models of practical tasks and artificial intelligence technologies. The authors resolve the limitations of current methods in e-learning personalization—such as lack of scalability, insufficient adaptability, and unreliable automation—by introducing an improved application which uses Belief–Desire–Intention (BDI) multi-agent system with adaptive orchestration and domain-specific language of formal practical task specification in the framework of an AI assistant, based on service-oriented architecture (SOA). The proposed approach provides automation for the entire lifecycle of practical tasks, encompassing generation, parameterization, and deployment of a virtual run-time environment and result verification for correctness, reproducibility, and academic integrity. Experimental tests demonstrate that combining a large language model (LLM) with dynamic verification significantly outperforms traditional purely generative approaches in terms of reliability, scalability, and reduction in instructor workload, as well as contributing to more effective task performance by students in practice-oriented learning scenarios. The study concludes that the synergistic integration of formal control mechanisms and AI-driven adaptivity offers a robust foundation for building sustainable smart environments for digital learning ecosystems. Full article

The rapid integration of artificial intelligence (AI) accelerates the need for continuous skill acquisition. Consequently, this increases the importance of lifelong learning while raising fundamental questions about pedagogical interaction and human social values. To remain competitive, individuals must constantly acquire new skills and enhance existing ones. The aim of the article is to evaluate the stability of individual social value systems and the role of pedagogical interaction in lifelong learning during AI integration. The study uses a quantitative survey (N = 160) with a retrospective self-assessment model based on Schwartz’s Theory of Basic Human Values. The study processed data in IBM SPSS using non-parametric tests (Wilcoxon signed-rank, Kruskal–Wallis, Kendall’s rank correlation) to analyze how digital skills and sociodemographics influence technology perception. Findings reveal core value systems remain statistically stable; AI integration causes no internal value conflict. Digital skill level, rather than age, is the most significant factor in AI perception. While participants highly rate AI’s potential to customize learning, they express concerns regarding technological dependence. In the lifelong learning ecosystem, AI is viewed as a didactic tool rather than an educator replacement, as technology cannot provide essential social interaction and emotional support. Finally, higher education fosters a critical attitude toward AI’s ethical risks. Full article

As the “fifth major factor of production,” data plays a crucial role in fostering China’s tourism industry, advancing high-quality economic development, and gaining competitive market advantages. Serving as institutional infrastructure for data factor rights confirmation, pricing, trading, and value conversion, data trading platforms are central to the market-based allocation of data factors. The efficient flow and value realization of data elements have paved the way for the rapid development of digital tourism; new forms of digital tourism represent a profound transformation of the industry resulting from integration and innovation with other sectors. Based on the platform ecosystem theory, we select the panel data of 297 Chinese cities from 2012 to 2024 and innovatively use the Double/Debiased Machine Learning (DDML) model to empirically test the impact of data trading platforms on the new forms of digital tourism and its mechanisms. It is found that the construction of data trading platforms effectively empowers the development of new forms of digital tourism, and this conclusion still holds after a series of robustness tests, such as changing the sample split ratio, replacing the machine learning algorithm, and the instrumental variables method. Mechanism analysis indicates that data trading platforms significantly promote new forms of digital tourism through dual pathways of talent agglomeration and technological innovation, an effect further strengthened by increased government support. Heterogeneity analysis found that the empowerment effect is more significant in cities with lower resource endowment and common administrative level and historical cities, which can be effectively transformed into an employment support effect. Spatial effect analysis reveals that the establishment of data trading platforms exerts a positive pull effect on new forms of tourism in surrounding cities within a 30 km core zone. However, this effect gradually weakens with increasing distance, turning into a significant negative siphon effect beyond 60 km. The findings provide theoretical basis and empirical support for regionally differentiated digital tourism development policies. Full article

Despite the growing use of artificial intelligence (AI) in science education, little is known about the motivational value of AI-supported digital tools in upper-primary ecosystem science learning. This quasi-experimental study examined whether participation in a teacher-guided digital ecosystems unit integrating AI-supported elements and interactive non-AI tools was associated with sixth-grade students’ ecosystem achievement, interest in science, attitudes toward science, and science self-efficacy. Four sixth-grade classes in an Israeli elementary school (123 students) participated. The experimental group completed six 45-min lessons; the control group studied the same content without the AI-supported components and integrated digital sequence. Students completed pretest and posttest questionnaires and an ecosystem achievement test; the experimental group also completed a satisfaction questionnaire. Semi-structured interviews were conducted with 10 students from the experimental group. Baseline-adjusted analyses indicated higher post-intervention achievement and motivational outcomes in the experimental group. Boys reported higher interest and self-efficacy than girls, and mothers’ education was positively associated with interest and attitudes. Within the experimental group, satisfaction was positively related to all motivational outcomes and significantly predicted self-efficacy. Interview themes highlighted visualization, feedback, collaboration, and occasional cognitive and technical challenges. Overall, this teacher-guided instructional package was associated with more favorable outcomes under classroom conditions in schools. Full article

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

This paper presents FMECA-VR-0.1 Prototype, a Maintenance 4.0-oriented immersive Virtual Reality (VR)-based training platform that integrates tools in a digital and virtual environment with Failure Modes, Effects, and Criticality Analysis (FMECA) and the Qualitative Risk Criticality Matrix (QRCM) to enhance reliability-oriented maintenance training in the wind energy sector. The methodological framework is aligned with the Maintenance Management Model (MMM) developed by INGEMAN. It is applied to a VESTAS V100–2.0 MW wind turbine operating at the Valle de los Vientos Wind Farm in northern Chile. The study includes the definition of the operational context, subsystem-level criticality assessment, and a detailed FMECA of the blade subsystem, which are integrated as analytical layers within the immersive VR environment. The proposed platform enables users to visualize critical components, analyze physical failure modes, understand associated consequences, and review preventive and corrective maintenance strategies in an interactive 3D scenario. Preliminary qualitative feedback suggests potential improvements in user engagement and conceptual understanding; however, no formal experimental validation has been conducted at this stage. The FMECA-VR-0.1 prototype demonstrates a feasible path for incorporating risk-based engineering logic into immersive training ecosystems. It establishes the foundation for future developments involving digital twins, real-time monitoring data, multi-subsystem modeling, and quantitative assessment of learning performance. Full article