This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of GNN and graph learning as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Graph Neural Networks for Relational Data in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of GNN and graph learning as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Generative Systems Theory is a foundational inquiry and metaphysics concerning systems theory and complexity science. It focuses on how concepts regarded as basic elements—such as nodes, relations, compatibility, attractors, information, and so on—come into being in the first place, and on what grounds they can be said to exist. If we do not simply assume that they already exist, can they instead be derived from fewer premises and more parsimonious conditions? It is also committed to integrating different, scattered domains into a single generative genealogy: how influence generates constraints; how constraints generate interactions and coupling; how uneven influence generates differences in state; how states and constraints generate evolutionary trajectories; how evolutionary trajectories generate compatibility and attractors; how compatibility and attractors serve as preconditions for nodes and systems; how nodes are represented; what hidden coupling conditions lie behind representation; how synchronicity should be explained; what distinguishes a system from a node; whether relations can be divided into fundamentally different types at the most basic level; why some systems possess robustness; into how many types robustness can be further classified; how a form of information that does not depend on bits can be derived and defined purely from systemic logic; how cognitive systems maximize information; what the most fundamental difference is between living systems and other systems; why gene-centered theories in biology are difficult to sustain; what two opposite extremes animals and artificial intelligence occupy, and why humans lie in the intermediate zone; what the core cognitive functions of human beings are besides embodiment; how the most distinctive and difficult-to-articulate human cognitive functions can be connected with neural networks; what common information-theoretic foundation underlies theories such as Archetype, predictive processing, and generative grammar; how that information-theoretic foundation can be used to derive the optimal forms of human–computer interaction and human–machine symbiosis; how modern Pythagoreanism relates to academic institutions and historical change; and, rather than dividing explanations into top-down and bottom-up, what kind of general explanation can come closer to the underlying logic of predictive processing, and so on and so forth. All of these are derived from the foundational theory of Generative Systems Theory. The theoretical extensions beyond the core framework are as follows: Reinterpret “prediction” from a specific cognitive function into a universal mechanism of state-space convergence. Derive the cognitive system’s “sample space” from state-space theory, and use it to provide a unified explanation of memory, prediction, perception, intuition, and archetypes. Derive self-reference paradoxes from node robustness, and transform the problem of self-reference from a logical problem into a problem of generative conditions. Further derive a theory of judgment concerning subjectivity, objectivity, and authenticity from the problem of self-reference. Place logic, reason, and the a priori within an evolutionary genealogy, and propose the concept of “relative first-order status.” Distinguish output diversity from semantic freedom, and propose “cognitive friction” as a metric for evaluating the compatibility of human–AI coupling. Reinterpret the broad problem of AI overfitting through the concept of “natural attractors.” Propose “constraint isomorphism,” freeing understanding from content similarity and representational replication. Reinterpret “structure” from traditional positive-space morphology as negative-space constraints within state space.
Lucas Li· Zenodo (CERN European Organi...· 0 citations
The rapid growth of Generative Artificial Intelligence (GenAI) has made AI literacy an essential skill for students, yet conventional classroom and e-learning platforms provide limited support for helping learners understand, question, and critically evaluate AI-generated information. This paper presents an AI-Driven Instructional Support System with Dynamic Learning Analytics for Classroom Evaluation, a role-based, web-based educational platform branded EduMentor that integrates academic and assessment management with a Generative AI instructional-support layer. The system employs Retrieval-Augmented Generation (RAG) and web-scraping-based verification to ground AI responses in course material and trusted external sources, assigns a confidence score to each generated response, and classifies learning content using Bloom's Taxonomy to support progressive cognitive development from Remember and Understand to Apply, Analyze, Evaluate, and Create. A Django backend, a relational database, and a Flutter-based client provide role-specific interfaces for Administrator, HOD, Staff/Teacher, and Student users, along with assessment management, student-performance analysis, and a learning-analytics dashboard. The system was evaluated using 100 functional test cases spanning authentication, assessment management, performance analysis, AI-generated recommendations, and application communication. The platform achieved 94% overall functional accuracy, 100% authentication accuracy, 89% AI-recommendation relevance accuracy, a usability score of 4.4/5 (88%), and an average API response time of 1.8 seconds. The results indicate that an integrated role-based platform combining assessment data with AI-assisted instructional feedback is a feasible and practical approach for data-driven classroom evaluation, provided that AI-generated recommendations continue to be treated as decision-support output requiring teacher verification.
Shaniba Nazneen K, Mr. Gireesh. T. K, Ameetha Junaina T K· International Journal of Adv...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Middle school art curricula in many systems still center on skill reproduction, which sits uneasily with the rapid diffusion of generative AI. This study develops a conceptual instructional model that embeds generative AI within the five-stage design thinking process (empathize, define, ideate, prototype, test) for an eighth-grade visual communication course. AI is assigned differentiated functions across the stages—analytical, retrieval, generative, assistive, and evaluative—while students retain responsibility for aesthetic judgment, justification of selections, and iterative revision. Three learning outcomes anchor the design and are operationalized in parallel measurement instruments: aesthetic judgment, creative problem-solving in visual communication, and stylistic self-awareness. Communicative intent is treated as the core sub-dimension of creative problem-solving rather than as a separate outcome, since intent is observable only through the design products and decisions that enact it. A quasi-experimental pretest-posttest pilot was conducted with 68 eighth-grade students (experimental group n = 34, control group n = 34) over a ten-week intervention at a single school, with both classes taught by the same art teacher. ANCOVA indicated that the experimental group outperformed the control group on aesthetic judgment, F(1, 65) = 8.42, p = .005, ηp² = .115, and creative problem-solving, F(1, 65) = 9.17, p = .003, ηp² = .124, with smaller yet significant gains on stylistic self-awareness. Thematic analysis of journals and interviews surfaced three patterns: shifted attention from execution to selection, prompt-based reasoning, and unease about authorship. Findings are presented as encouraging pilot evidence under specific local conditions, pending replication beyond the present site.
Yijun Feng· Intelligent & Human Futures· 0 citations
We propose that consciousness is a phase transition of a non-equilibrium dissipative structure, characterised by two independently controlled channels that must both be driven past threshold. The central result is an exclusion criterion: a macroscopic dissipative structure cannot be conscious unless it satisfies three necessary conditions - (i) an energy channel G > 1 maintaining non-equilibrium pumping, (ii) an information channel G_info(kappa) > 1 maintaining global phase coherence (with analytic critical point kappa_c = 1.8809), and (iii) topological closure via a self-referential triad (|SCC| >= 3). The framework traces a single causal chain: non-equilibrium driving -> Brusselator Hopf bifurcation -> sigmoid threshold -> phase-amplitude coupling (PAC) -> von Mises phase density -> Kuramoto network amplification -> G_info > 1 -> conscious phase transition. Each stage is derived from first principles (Appendices C-G). At the microscopic level, a basis-free trace-distance order parameter O(g) = 1/2 tr|rho+ - rho-| in two-qubit Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) systems rigorously proves that the energy and information channels are independently controllable: a symmetrisation scan collapses O(g) from 0.306 to < 10^-4 as the absorption/emission asymmetry is removed, while a separate dephasing scan destroys coherence-carried asymmetry without affecting the energy flow. The topological closure condition is instantiated at multiple physical scales by a self-referential triad of three irreducible roles - reversible carrier, energy currency, irreversible anchor - which we identify in quantum coherence, aerobic metabolism, neural dynamics, and planetary geochemistry. We demonstrate the framework's explanatory power through clinical neuroscience and artificial intelligence. General anaesthesia abolishes consciousness by selectively collapsing kappa below threshold while metabolic pumping persists - a channel dissociation no single-channel theory predicts. Mindfulness meditation acts as a phase-locking mechanism that elevates kappa past kappa_c by suppressing Default Mode Network noise. Feedforward large language models fail all three conditions: their computational graph is acyclic, sustains no limit cycle, and operates as a closed system at inference. The widely observed model collapse under recursive self-training is the thermodynamic signature of the system relaxing toward equilibrium.
FatJack· Zenodo (CERN European Organi...· 0 citations
Context and Justification The prevalence of neurodegenerative diseases around the world demands a paradigm change from reactive, episodic clinical diagnosis to ongoing, proactive neuro-monitoring. The possibility for early detection and individualized management is limited by the fact that traditional diagnostic techniques sometimes rely on subjective evaluation or costly, intrusive imaging. An unparalleled chance to identify, evaluate, and interpret the subtle, objective indicators of preclinical cognitive alterations is presented by the convergence of the Internet of Things (IoT) and sophisticated artificial intelligence (AI). Methods In order to generate a continuous, longitudinal stream of physiological and behavioural data, this study presents a novel, decentralized platform that makes use of a heterogeneous network of IoT devices, such as high-resolution wearables, smart home sensors, and non-contact physiological monitors (digital phenotyping). Large datasets pertaining to sleep architecture, gait variability, social interaction frequency, and speech hesitancy were analysed using deep learning techniques, particularly convolutional neural networks for anomaly detection in sensor data and long short-term memory (LSTM) networks for temporal pattern recognition. In order to forecast the start of moderate cognitive impairment months before conventional clinical criteria could be satisfied, the main goal was to train these AI models to recognize minute variations from each person’s unique baseline. Important Results (Hypothetical) With a 92% prediction accuracy, the AI-driven study was able to identify a multivariate biomarker profile associated with early cognitive deterioration. Importantly, the system was able to identify temporary changes in everyday activities, such as increased nocturnal wandering and entropy changes in spoken language, long before carers noticed them or could measure them using conventional paper-and-pencil exams. Real-time anomaly notifications made possible by the incorporation of edge computing enabled prompt triage and focused clinical evaluation. Conclusion The neuro-sensing grid underlines how important AI-powered IoT is to revolutionizing research on brain health. This technique provides a reliable, scalable, and non-invasive method for personalized neuro-surveillance by moving the locus of assessment from the clinic to the lived environment. This opens the door for truly preventive therapies against age-related cognitive decline.
This article examines how aviation accidents can emerge from the progressive deterioration of organizational and institutional defenses rather than isolated human error. Drawing on the development of crew resource management, safety culture, quality assurance, voluntary reporting, and Safety Management Systems, the analysis explores how regulators, operators, manufacturers, military commands, training and maintenance organizations, airports, and investigative agencies form aviation’s multilayered safety architecture. Particular attention is given to normalization of deviance, staffing and experience pressures, weakened supervision, reporting culture, training deficiencies, organizational drift, and the distinction between regulatory compliance and operational resilience. Contemporary initiatives involving the Federal Aviation Administration, National Transportation Safety Board, manufacturers, the U.S. military, and DARPA are examined alongside the potential of artificial intelligence to identify institutional degradation before it enters an accident sequence. Legal and civil consequences are also considered when organizations recognize hazards but fail to act. The article concludes that the most dangerous safety system may be one that appears functional while progressively losing its ability to recognize and arrest deteriorating conditions. Future aviation safety depends upon detecting organizational drift, preserving institutional memory, protecting professional challenge, and using artificial intelligence to strengthen—not replace—the human and organizational defenses upon which aviation safety depends.
A. F. Clark· Zenodo (CERN European Organi...· 0 citations
This article presents a narrative review of Explainability and Interpretability of Black-Box Models in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of explainable AI and interpretability as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.
Zen Revista, 10 IA· Zenodo (CERN European Organi...· 0 citations
Results show that students with stronger AI literacy are generally more motivated to use AI in learning, though this relationship is influenced by socio-economic background, institutional resources, and national digital infrastructure.
Wenna Jo Mantua, Roselyn Cagasan-Bulan, Fiona Bianca Cabradilla et al.· JPAIR Multidisciplinary Rese...· 0 citations
It is shown that fairness and engagement are shaped by cultural experience as much as by individual psychology, which underscores the need for culturally adaptive, ethically governed AI systems to support sustainable talent retention in the UAE aviation sector.
Mayette Coronacion, Mary Jane Sapiendante, Sherwin Diala et al.· JPAIR Multidisciplinary Rese...· 0 citations
Key contributions include proposing a multi-task learning framework for jointly optimizing visual quality and emotion, establishing the inaugural VAWE-Art dataset comprising 5,000 AI-generated images with 20-dimensional emotional annotations, and providing computational foundations for emotion-controllable generative art systems.
Hengju Gang· Journal of Engineering, Proj...· 0 citations
