Search Results (4,171)

Decommissioned airfields are increasingly recognized as strategic sites for ecological regeneration, climate adaptation, and the creation of new public spaces. However, research on their transformation has predominantly focused on the environmental performance of Nature-based Solutions (NBS), often overlooking the role of inherited spatial morphology in structuring regeneration processes and outcomes. This paper proposes an AI-assisted, morphology-based proto-ontological framework for analyzing and designing post-airfield architecture. The framework was developed through the inductive and comparative analysis of a corpus of 32 urban post-airfield regeneration projects, from which recurrent inherited morphologies, transformation actions, spatial devices, and NBS were identified and structured into a relational sequence. The framework was then applied to two contrasting case studies: Maurice Rose Airfield Park (Frankfurt) and Xuhui Runway Park (Shanghai); these were selected for their different transformation logics. The results show that similar airfield morphologies can generate markedly different climatic, ecological, social, and memory-related outcomes depending on how they are transformed and linked to NBS. The study demonstrates that inherited airfield morphologies are not passive remnants but operative spatial structures, and that NBS should be understood as spatially embedded and form-generating design components. The proposed proto-ontology offers a transferable analytical model and a basis for future computational and generative design applications. Full article

Background/Objectives: This review explores the role of artificial intelligence (AI), particularly with deep learning and machine learning, in the detection and classification of papilledema using retinal fundus imaging. Methods: The study synthesizes historical, technical, and clinical insights, comparing AI-based diagnostic accuracy with conventional methods. Results: Our findings demonstrate that AI systems, especially convolutional neural networks (CNNs), offer sensitivity and specificity comparable to, or even surpassing, expert-level fundoscopy. Conclusions: These results suggest significant implications for early diagnosis, triage, and telemedicine integration in ophthalmic care. Full article

This narrative review examines how big data analytics supports geospatial decision-making in smart cities through the combined roles of spatial data foundations, GeoAI methods, and urban digital twins. Methodologically, the article follows a structured narrative and critical review design rather than a PRISMA-based systematic review, bibliometric analysis, or meta-analysis. The paper responds to fragmentation across GIScience, smart-city studies, urban analytics, geospatial data engineering, and digital twin research, where related contributions often remain technically rich but weakly integrated from a decision-oriented perspective. Rather than treating geospatial decision-making as an extension of GIS or as a general expression of data-driven governance, the review frames it as a layered socio-technical process through which heterogeneous urban data are transformed into decision-relevant knowledge. The analysis first clarifies the conceptual evolution from GIS to spatial decision support and urban governance, and then examines the spatial data sources, integration problems, and representational limits that shape smart-city evidence. It also reviews GeoAI and geospatial analytics methods, including spatial statistics, machine learning, spatiotemporal forecasting, graph-based modeling, optimization, and explainable GeoAI. Urban digital twins are then analyzed as decision infrastructures that connect sensing, data integration, synchronization, semantic modeling, simulation, visualization, user interaction, and feedback into planning or operations. The review further maps these capabilities across mobility, land use, utilities, risk management, environmental resilience, public health, and cross-domain decision contexts. Overall, the paper argues that the value of smart-city geoinformation systems depends not on data abundance or model sophistication alone, but on their capacity to support interpretable, accountable, and context-sensitive urban decisions. Full article

This study examines the development and pedagogical impact of an AI-based pedagogical agent designed for modern e-learning environments. The research addresses a key challenge in digital education: the lack of personalization and immediate feedback in traditional e-learning systems. AI-driven agents “support and motivate learners through instructional interaction” and provide adaptive, data-driven learning experiences that surpass the limitations of rule-based systems. The study begins with a systematic literature review following PRISMA 2020, analyzing 46 publications from 2020 to 2025 to identify current AI architectures, pedagogical roles, and the empirical evidence of learning impact. The findings highlight the growing use of machine learning, deep learning, multimodal analytics, and large language models in educational agents. These systems perform roles such as tutor, coach, evaluator, dialogue partner, and consultant, offering cognitive, metacognitive, emotional, and analytical support. Modern agents “continuously monitor user interaction, analyze engagement, and adapt learning content”, enabling highly personalized learning pathways. The study also presents the design of a multimodal pedagogical agent capable of explanation, task generation, diagnostics, and adaptive feedback. Experimental results with students (n = 20) show improved performance, reduced errors, and higher engagement when learning with the agent. Overall, the research demonstrates that AI-based pedagogical agents enhance learning effectiveness and support autonomous learning in higher education. Full article

Purpose: This study investigates the mediating role of internal marketing (IM) in the relationship between artificial intelligence (AI) applications and quality of work life (QWL). Methodology: A quantitative cross-sectional research design was employed. Data were collected via self-administered questionnaires from a sample of 418 employees across service ministries in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) as the analytical instrument. Findings: The results reveal that the direct association between AI applications and QWL was not statistically significant. However, a significant indirect relationship was established, indicating that the effect operates entirely through IM. Specifically, AI applications are positively associated with IM practices, which in turn strongly predict higher QWL in the tested model. Originality/Contributions: The study advances current literature by empirically validating IM as the critical organizational mechanism required to translate AI deployment into employee well-being within public-sector institutions. Practical Implications: Decision-makers must couple AI adoption with targeted IM strategies—such as continuous training, job empowerment, and effective internal communication—to ensure a sustainable, human-centered digital transformation. Full article

Recent advances in generative Artificial Intelligence (AI) have rapidly transformed research and practice across the Cultural Heritage domain. While several studies have investigated AI applications in documentation, analysis and dissemination, a focused and critical assessment of generative AI within 3D virtual reconstruction workflows is still lacking. This paper presents a systematic review of the literature addressing the use of generative AI in 3D heritage virtual reconstructions, with particular attention to methodological implications, scientific reliability and ethical challenges. A large-scale bibliographic analysis covering publications from 2015 to 2024 was conducted using OpenAlex, complemented by targeted manual searches. From an initial corpus of over 8700 papers on 3D heritage reconstruction, only 13 directly addressed generative AI-driven reconstruction processes. The analysis highlights a significant gap between the rapid technological development of AI-based tools and their cautious, often problematic, adoption in virtual reconstruction practices. Results reveal recurring issues related to terminological ambiguity, opacity of reconstruction processes, evaluation metrics focused on visual plausibility rather than scientific transparency and the risk of interpretative bias. The paper argues that current AI-driven approaches tend to privilege speed and aesthetic outcomes over heuristic, source-based reconstruction workflows. Finally, future research directions are discussed, emphasizing the potential role of AI as an evaluative and analytical support tool rather than a fully autonomous reconstruction agent, in alignment with established charters and principles of virtual archaeology. Full article

The digital transformation of agrifood systems demands an integrated infrastructure to ensure traceability, trust, and intelligent decision-making across complex and heterogeneous value chains. METROFOOD-IT, a large-scale national research infrastructure in food metrology aligned with the ESFRI METROFOOD-RI, addresses these challenges by combining advanced experimental facilities with a comprehensive digital ecosystem. This paper focuses on the IT kernel of METROFOOD-IT and presents an integrated architectural model that brings together four key technological paradigms: data acquisition through Internet of Things (IoT) and laboratory infrastructures, an Open Data Platform for interoperability and sharing, blockchain-based notarization for integrity and provenance, and Artificial Intelligence (AI) for knowledge extraction and decision support. Rather than describing these components in isolation, the paper abstracts from their implementation within the Italian National Recovery and Resilience Plan (NRRP) project METROFOOD-IT to distill a coherent and reusable architectural pattern in which data management, trust enforcement, and intelligent analytics are tightly coupled. Five explicit design principles are identified and articulated: federated data with centralized metadata, selective on-chain anchoring, user-unobtrusive trust infrastructure, explainability as a first-class architectural concern, and machine learning as the backbone of decision-making. Two empirical case studies—one centered on explainable AI for hyperspectral crop nitrogen assessment and the other on IoT-driven sustainable agriculture monitoring secured by distributed ledger technology—serve a dual role: they motivate and shape the architectural pattern, and they exemplify the operational regimes the resulting design supports. A reference deployment on the Ethereum Sepolia public test network, grounded on an IBM Power E1050 and IBM Storage Scale enterprise substrate, provides quantitative evidence for the proposed hybrid on-chain/off-chain pattern with streaming hash-only notarization. The architecture illustrates how research infrastructures can evolve into integrated digital platforms that enable transparent, verifiable, and scalable agrifood systems, and offers a foundation for generalizable design principles in data-intensive and trust-sensitive settings. Full article

The gut microbiota is recognized as a programmable metabolic organ that governs systemic homeostasis. Recent advances (2023–2025) have pivoted Type 2 Diabetes Mellitus (T2DM) research from a host-centric perspective toward a failure of bidirectional host–microbe metabolic flux. This review evaluates the molecular mechanisms underpinning this shift, focusing on microbial metabolite signaling, virome-mediated modulation, and the emergence of drug–microbiome interactions as critical therapeutic variables. We highlight the transformative role of AI-guided mapping and digital twin simulations in modeling high-resolution metabolic flux and predicting the stability of engineered microbial consortia. By integrating meta-transcriptomics and epigenomics, we characterize the functional plasticity of the microbiome under therapeutic stress. We argue that framing the microbiota as a programmable infrastructure—integrated with AI analytics and metabolic engineering—enables adaptive, real-time interventions. This synthesis offers a blueprint for transitioning from correlative observations toward precision microbiome engineering to achieve sustained metabolic resilience. Full article

Biomass valorization plays a vital role in achieving carbon neutrality and circular economy frameworks. Owing to its carbon-rich structure, biomass represents a promising feedstock to produce bio-based hydrocarbons via biological and thermochemical pathways. While biological conversion routes have been extensively studied, their deployment at commercial scale is constrained by high capital costs and low product yields. In contrast, thermochemical conversion technologies are increasingly being explored as viable large-scale biomass valorization routes. This review presents a comprehensive assessment of thermochemical pathways, with particular emphasis on hydrothermal liquefaction (HTL). The review identifies hydrothermal liquefaction (HTL) as a strategically advantageous route for wet and heterogeneous biomass valorization, due to simultaneous yields of liquid biocrude, and solid hydrochar. The review emphasizes the application of biocrude upgradation processes like hydrodeoxygenation under biphasic solvent systems using sulfided NiMo and CoMo catalysts. Further, the review also establishes hydrochar as a tunable functional material rather than a mere byproduct for applications in fields of energy production, soil amendment, and heterogeneous catalysis. The review article examines technology readiness levels of different biomass valorization techniques, and suggests that while combustion, anaerobic digestion, torrefaction, and transesterification are commercially mature, HTL and carbon capture utilization and storage (CCUS)-integrated fuel synthesis pathways remain at intermediate readiness. Additionally, the review carries out an in-depth study on artificial intelligence and machine learning (AI and ML) applications in biomass valorization, where it observes that Tree-based ensemble models, particularly Random Forest and XGBoost, show strong performance for several HTL prediction tasks, while Gaussian Process Regression and neural network–Bayesian optimization approaches provide additional advantages for uncertainty estimation and process-level optimization. Finally, the future research opportunities in biomass valorization and AI/ML application in HTL-process optimization have been identified for improving the bio-based fuel production techniques. Full article

The increased use of technology-assisted distance counseling practices is one result of COVID’s impact on behavioral health, including in counselor education and the delivery of supervision. First, technology-assisted distance supervision needed for “real time” communication grew. Furthermore, there is an emergence of artificial intelligence (AI) technologies that have the potential to contribute to aspects of supervision; however, current evidence remains emerging, context-dependent, and at times mixed, warranting cautious interpretation of their effectiveness. The article offers an overview of using AI in clinical supervision, examines the benefits and potential concerns of AI from different perspectives, and considers the significance of using AI in counseling supervision. The role of AI is discussed as applied to counseling supervision including the use of AI tools, such as chatbots and reasoning AI, to detect and track sessions, note behavioral and emotional cues, aid/monitor communication and feedback, while also attending to ethical and legal consideration for its use. The article will report a range of benefits for supervisors and trainees using AI—for example, by enhancing data-driven supervision decisions, analyzing feedback trends, providing more efficient administrative monitoring, flexible/remote support, skill development, and promoting ethical decisions and self-reflection. Special attention is given to the challenges of using AI in supervision, including risks of undervaluing intuition and qualitative insights, potential for algorithms to reinforce systemic biases, risks of replacing human interaction, as well as non-compliance with HIPAA, FERPA, and ethical guidelines in data storage and privacy. The article will discuss privacy concerns, depersonalized feedback, and increased judgment-driven anxiety despite needed empathy when using AI as a tool for clinical supervision. Recommendations will also be offered for effective, ethical integration of AI in counseling supervision. Full article

Efficient and financially sustainable social protection systems are essential under conditions of economic instability and increasing social demand. However, traditional administrative models are often characterized by high operational costs, procedural complexity, and delayed benefit delivery. This study examines the role of digitalization, process automation, and AI-driven administrative solutions in reducing administrative expenses while enhancing the sustainability and resilience of social protection systems. An integrated Automation Index is developed using standardized proxy indicators that reflect reductions in operational and transaction costs associated with digital and automated technologies. To assess future trajectories of administrative expenses, scenario-based modelling is applied under three digital transformation paths—baseline, moderate, and intensive. Administrative efficiency is estimated using a translog Stochastic Frontier Analysis (SFA) framework. The results indicate that digitalization and automation significantly reduce administrative costs only when supported by favorable institutional conditions, including decentralized governance, effective inter-agency coordination, and clearly regulated administrative procedures. Under the intensive digital transformation scenario, administrative expenses decline substantially relative to the baseline, while system responsiveness and beneficiary coverage improve. In contrast, weak institutional environments limit the efficiency gains of technological solutions. The study concludes that AI agents and automated systems should be viewed not as substitutes for human decision-making but as tools for optimizing administrative architectures. This transition from resource-intensive to technology-intensive models is particularly important for developing countries seeking sustainable social protection under constrained fiscal conditions. Full article

This study examines the role of artificial intelligence (AI) in marine insurance within the Moroccan maritime sector. Drawing on Dynamic Capabilities Theory, the study investigates the relationships among AI Adoption, Risk Assessment Accuracy, Fraud Detection Capability, Claim Processing Efficiency, and Customer Trust, while also examining the mediating role of these operational capabilities. A quantitative survey was conducted among maritime and insurance professionals operating within the Tangier Med and Casablanca port ecosystems, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that AI Adoption is positively associated with Risk Assessment Accuracy, Fraud Detection Capability, and Claim Processing Efficiency. These operational capabilities are also positively associated with Customer Trust and function as significant mediating pathways between AI Adoption and stakeholder confidence. The study contributes to the emerging literature on AI applications in marine insurance by providing empirical evidence from an emerging maritime economy and offers theoretical and practical implications for insurers, maritime operators, and policymakers. Full article

The integration of Artificial Intelligence (AI) into smart classrooms (SCs) has accelerated in recent years, fostering new forms of interaction, personalization, and data-driven educational decision-making. Despite this growing interest, the literature remains conceptually fragmented, particularly regarding how AI is integrated across the technological, pedagogical, and environmental dimensions of SCs. This systematic literature review aims to provide a structured synthesis of how AI is integrated into SC contexts, their main functions, their relation to these three dimensions, and the teacher’s role in the system. Following PRISMA guidelines, peer-reviewed studies published between 2021 and 2026 were selected from Web of Science and Scopus, yielding a final corpus of 29 studies. The findings showed that AI integration is mostly concentrated in the technological dimension. The pedagogical dimension is linked to personalization, active learning, formative assessment, and instructional adaptation, while the environmental dimension is less developed. Teachers remain central actors who integrate technological tools, interpret the generated data, and mediate pedagogical decisions. Overall, AI-supported SCs are not only defined by technology but also by pedagogical use and teacher mediation. Full article

Smart Mobility plays a key role in Smart Cities, given its ability to support the rollout of intelligent transport systems, allowing for more sustainable urban transportation and greater interoperability across diverse mobility modes. Furthermore, Smart Mobility is essential to maximize the quality of life for the community while advancing principles of sustainability, economic development, technological innovation, and collaborative governance. Real-Time Traffic Management (RTTM) emerges as a vital technology for optimizing traffic management in Smart Mobility. Using the PRISMA framework, the proposed systematic literature review examines 165 peer-reviewed publications related to RTTM research work published between 2019 and 2025. This review identified eleven application domains, with Urban Traffic Management Systems (36.97%) and Artificial Intelligence (AI) and Predictive Analytics (12.73%) representing the most prominent areas. A retrospective analysis of the literature on control architecture used in closed-loop feedback systems indicates that most studies (89%) have adopted a more dynamic control model, while 7.8% adopted a Digital Twin (DT)-based approach. However, several implementation barriers persist, including limited integration of online optimization and learning loops into RTTM systems, gaps in performance comparisons between simulation and reality, scalability issues due to heterogeneous environments, inconsistent data quality caused by various sensor types, and difficulties integrating sensors into a control system. In addition, this paper proposes a taxonomy of RTTM applications and control architectures, while outlining key practical barriers to implementation and charting future research directions for advancing Smart Mobility through robust RTTM. Full article

The existing literature highlights that artificial intelligence (AI) creates both hope and threat perceptions among managers and workers, particularly due to concerns about potential job losses and the negative effect on continued professional development. Employee trust in AI-based systems varies depending on their features and performance. Furthermore, regardless of the performance of such systems, some individuals are inherently opposed to AI, a phenomenon known as AI aversion. In this study, an Integrative AI Adoption Framework is developed, drawing upon principles from established theories, including the technology acceptance model, behavioral decision theory, and emotion-based frameworks, to assess how perceived usefulness and perceived ease of use, along with perceived threat, trust, and AI aversion, influence human resources (HR) professionals’ attitudes and behavioral intentions to use AI-based recruitment systems. In doing so, the study conceptualizes AI-based recruitment as a socio-technical system in which a technical subsystem (the system’s instrumental and AI-specific properties) and a social subsystem (the affective and trust-related responses of HR professionals) must be jointly considered to explain adoption. The model was tested using the partial least squares structural equation modeling (PLS-SEM) approach through survey-based data collected from 242 HR professionals. The study’s findings indicate that attitude plays an important role in shaping behavioral intention, and perceived usefulness is a key driver of attitude. AI aversion negatively influences attitudes, while trust has a twofold effect of reducing AI aversion and positively influencing attitude. Additionally, perceived threat significantly increases AI aversion, which is driven by concerns over job replacement and personal development. Full article