Search Results (629)

UAV accident investigation is essential for safeguarding the fast-growing low-altitude airspace. While near-daily incidents are reported, they were rarely analyzed in depth as current inquiries remain expert-dependent and time-consuming. Because most jurisdictions mandate formal reporting only for serious injury or substantial property damage, a large proportion of minor occurrences receive no systematic investigation, resulting in persistent data gaps and hindering proactive risk management. This study explores the potential of using large language models (LLMs) to expedite UAV accident investigations by extracting human-factor insights from unstructured narrative incident reports. Despite their promise, the off-the-shelf LLMs still struggle with domain-specific reasoning in the UAV context. To address this, we developed a human factors analysis and classification system (HFACS)-guided analytical framework, which blends structured prompting with lightweight post-processing. This framework systematically guides the model through a two-stage procedure to infer operators’ unsafe acts, their latent preconditions, and the associated organizational influences and regulatory risk factors. A HFACS-labelled UAV accident corpus comprising 200 abnormal event reports with 3600 coded instances has been compiled to support evaluation. Across seven LLMs and 18 HFACS categories, macro-F1 ranged 0.58–0.76; our best configuration achieved macro-F1 0.76 (precision 0.71, recall 0.82), with representative category accuracies > 93%. Comparative assessments indicate that the prompted LLM can match, and in certain tasks surpass, human experts. The findings highlight the promise of automated human factor analysis for conducting rapid and systematic UAV accident investigations. Full article

The study of fractals has a long history in mathematics and signal analysis, providing formal tools to describe self-similar structures and scale-invariant phenomena. In recent years, cognitive science has developed a set of powerful theoretical and experimental tools capable of probing the representations that enable humans to extend hierarchical structures beyond given input and to generate fractal-like patterns across multiple domains, including language, music, vision, and action. These paradigms target recursive hierarchical embedding (RHE), a generative capacity that supports the production and recognition of self-similar structures at multiple scales. This article reviews the theoretical framework of RHE, surveys empirical methods for measuring it across behavioral and neural domains, and highlights their potential for cross-domain comparisons and developmental research. It also examines applications in linguistic, musical, visual, and motor domains, summarizing key findings and their theoretical implications. Despite these advances, the computational and biological mechanisms underlying RHE remain poorly understood. Addressing this gap will require linking cognitive models with algorithmic architectures and leveraging the large-scale behavioral and neuroimaging datasets generated by these paradigms for fractal analyses. Integrating theory, empirical tools, and computational modelling offers a roadmap for uncovering the mechanisms that give rise to recursive generativity in the human mind. Full article

In Georgia (Sakartvelo), a program promoting bilingual education in preschool institutions was formally adopted in 2020. It aligns with the objectives of the 2021–2030 State Strategy for Civic Equality and Integration Plan, which envisions a comprehensive reform of bilingual education across Georgia’s regions. Any reform requires research and evaluation to measure how effectively it is being implemented and whether the intended outcomes have been achieved. The bilingual education initiative pursues a dual objective: to preserve the native languages of minority communities while ensuring effective acquisition of the state language. This dual mandate is intrinsically linked to state language policy and constitutes a sensitive issue for local communities, parents, and preschool administrators, thereby necessitating a careful and nuanced approach. The present study analyzed the readiness of the social environment to support the implementation of bilingual education programs at the preschool level in the regions of Georgia in which ethnic minorities live side by side. Research was carried out in two ethnically diverse regions—Kvemo Kartli and Samtskhe–Javakheti. The author conducted individual and group interviews, and the elicited data were analyzed with the help of content and thematic analyses. This study examines key attributes of the ongoing preschool reform to identify factors that facilitate the effective implementation of early bilingual education initiatives. The findings highlight both commonalities and regional variations in parental attitudes toward the bilingual education reform. Full article

This paper introduces a hypergraph-based formalism for modeling kinematic and dynamic structures in robotics, addressing limitations of the existing formats such as Unified Robot Description Format (URDF), MuJoCo-XML, and Simulation Description Format (SDF). Our method represents mechanical constraints and connections as hyperedges, enabling the native description of multi-joint closures, tendon-driven actuation, and multi-physics coupling. We present a tensor-based representation derived via star-expansion, implemented in the Hypergraph Model Cognition Framework (HyMeKo) language. Comparative experiments show a substantial reduction in model verbosity compared to URDF while retaining expressiveness for large-language model integration. The approach is demonstrated on simple robotic arms and a quarter vehicle model, with derived state-space equations. This work suggests that hypergraph-based models can provide a modular, compact, and semantically rich alternative for the next-generation simulation and design workflows. The introduced formalism reaches 50% reduction compared to URDF descriptions and 20% reduction compared to MuJoCo-XML descriptions. Full article

Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents a novel system that combines a patient-centred chatbot with a deep learning framework to support early diagnosis, symptom triage, and health education. The system integrates convolutional neural networks, class activation mapping, and natural language processing within a conversational interface. Five deep learning models were evaluated (CNN, DenseNet121, DenseNet169, DenseNet201, and InceptionV3) using two balanced public datasets. Model performance was assessed using accuracy, sensitivity, specificity, diagnostic odds ratio (DOR), and Cohen’s Kappa. InceptionV3 consistently outperformed the other models across these metrics, achieving the highest diagnostic accuracy (77.6%) and DOR (20.67), and was selected as the core engine of the chatbot’s diagnostic module. The deployed chatbot provides real-time image assessments and personalised conversational support via multilingual web and mobile platforms. By combining automated image interpretation with interactive guidance, the system promotes timely consultation and informed decision-making. It offers a prototype for a chatbot, which is scalable and serves as a low-cost solution for underserved populations and demonstrates strong potential for integration into digital health pathways. Importantly, the system is not intended to function as a formal screening tool or replace clinical diagnosis; rather, it provides preliminary guidance to encourage early medical consultation and informed health decisions. Full article

Smart contracts are central to blockchain ecosystems, yet their development remains technically demanding, error-prone, and tied to platform-specific programming languages. This paper introduces SCEditor-Web, a web-based modeling environment that combines model-driven engineering (MDE) with generative artificial intelligence (Gen-AI) to simplify contract design and code generation. Developers specify the structural and behavioral aspects of smart contracts through a domain-specific visual language grounded in a formal metamodel. The resulting contract model is exported as structured JSON and transformed into executable, platform-specific code using large language models (LLMs) guided by a tailored prompt engineering process. A prototype implementation was evaluated on Solidity contracts as a proof of concept, using representative use cases. Experiments with state-of-the-art LLMs assessed the generated contracts for compilability, semantic alignment with the contract model, and overall code quality. Results indicate that the visual-to-code workflow reduces manual effort, mitigates common programming errors, and supports developers with varying levels of expertise. The contributions include an abstract smart contract metamodel, a structured prompt generation pipeline, and a web-based platform that bridges high-level modeling with practical multi-language code synthesis. Together, these elements advance the integration of MDE and LLMs, demonstrating a step toward more accessible and reliable smart contract engineering. Full article

Programmable Logic Controllers (PLCs), widely used in industrial automation, are often programmed in IEC 61131-3 Structured Text (ST), which is prone to subtle logic vulnerabilities. Traditional tools like static analysis and fuzzing struggle with the complexity and domain-specific semantics of ST. This work explores Large Language Models (LLMs) for PLC vulnerability detection, supported by both theoretical insights and empirical validation. Theoretically, we prove that control flow features carry the most vulnerability-relevant information, establish a feature informativeness hierarchy, and derive sample complexity bounds. We also propose an optimal synthetic data mixing strategy to improve learning with limited supervision. Empirically, we build a dataset combining real-world and synthetic ST code with five vulnerability types. We fine-tune open-source LLMs (CodeLlama, Qwen2.5-Coder, Starcoder2) using LoRA, demonstrating significant gains in binary and multi-class classification. The results confirm our theoretical predictions and highlight the promise of LLMs for PLC security. Our work provides a principled and practical foundation for LLM-based analysis of cyber-physical systems, emphasizing the role of domain knowledge, efficient adaptation, and formal guarantees. Full article

This article examines how Ukrainian migrants and war refugees in Finland perceive and use Suržyk, a cluster of intermediate varieties between Ukrainian and Russian, as a transitional stage facilitating the shift from Russian-dominant to Ukrainian-dominant speech. Drawing on 1615 survey responses collected between November 2022 and January 2023, the study reveals that 42 respondents view Suržyk as a bridge that supports the gradual acquisition of standard Ukrainian. Qualitative content analysis of open-ended responses shows repeated references to Suržyk as a “stepping stone”, “temporary means” or “bridge”, highlighting its role in maintaining intelligibility and fluency for speakers who are not confident in standard Ukrainian. Although some respondents acknowledge the stigma associated with mixed speech, they also stress Suržyk’s practical advantages in contexts shaped by the 2022 full-scale war and heightened purist discourses. Speakers report pressure to adhere to purist language norms in formal settings, whereas in informal spaces, they consider Suržyk a natural outcome of bilingual backgrounds. These findings illuminate the interplay between language ideologies, sociopolitical dynamics, and individual agency, suggesting that for many Ukrainians in Finland, Suržyk serves as a temporary yet functional means to align with Ukrainian identity under rapidly changing circumstances. Full article

Translating knowledge into formal representation for the purpose of building an expert system is a daunting task for domain experts and requires information technology (IT) competence and software developer support. The availability of open and robust expert system shells is a way to solve this task. A new architecture of a rule-based expert system combining the digital twin paradigm and a capability-driven approach is presented in this study. The aim of the architecture is to provide a user-friendly framework for domain experts to build upon without the need to delve into technical aspects. To support this architecture, an open-source no-coding shell RecGen has been developed (Python and Django framework). RecGen was validated on a use case of an expert system for providing recommendations to reduce plate waste in schools. In addition, the article presents experiments with large language models (LLMs) by implementing a question-answering functionality in an attempt to improve the user experience while working with large expert system knowledge bases. A mean classification accuracy of 74.1% was achieved experimentally using the injection method with language prefixes. The ablation test was applied in order to investigate the effect of augmentation, injection, a linear layer size, and lowercase text on LLM accuracy. However, the analysis of the results showed that clustering algorithms would be a more suitable solution for future improvements of the expert system shell RecGen. Full article

Generative AI is reshaping k–12 learning as a multi-agent system in which goals, activities, and roles co-evolve across formal and informal environments. Following PRISMA and appraising quality with MMAT, we synthesize 84 peer-reviewed empirical studies (2020–2025) involving learners aged 3–18. Using Biggs’s 3P model as a systems lens and embedding CIMO logic, we code learning objectives, activity designs, AI role paradigms, and outcomes. Seven recurring objectives emerge (language/literacy; STEM; creativity; socioemotional skills; feedback literacy and self-regulation; motivation; AI literacy). Five dominant activity patterns are identified: dialogic tutoring and formative feedback, generative iterative co-creation, project-based problem-solving, simulation/game-based learning, and assessment support. Across studies, AI roles shift from AI-directed to AI-supported/empowered, re-allocating agency among students, teachers, and caregivers via feedback loops. Reported outcomes span three categories—epistemic, practice, and affective/identity—with opportunities of deeper knowledge, improved practice, and stronger engagement, and risks of hallucinations, reduced originality, over-reliance, motivational loss, and ethical concerns. We propose a goal–activity–role alignment heuristic for instructional design, plus safeguards around teacher professional development, feedback literacy, and ethics. We call for longitudinal and cross-cultural research to evaluate the impacts of GenAI in k–12. Full article

Recurrence of the original glomerular disease (GN) poses a significant threat to kidney transplant function and longevity. The probability and severity of this recurrence vary, with C3 glomerulopathy and certain forms of FSGS exhibiting particularly high rates. Kidney transplant GN recurrence risk hinges on the characteristics of the initial GN, recipient/donor genetics, recipient age, donor type, end-stage kidney disease (ESRD) progression rate, and proteinuria levels. Standard immunosuppression has limited efficacy in preventing primary disease recurrence; however, agent selection and induction therapy can influence the risk for specific GNs. Diagnosing recurrent GN involves a comprehensive approach, including clinical evaluation, laboratory tests (such as proteinuria, hematuria, and specific biomarkers like anti-PLA2R for membranous nephropathy or complement for C3G), and, critically, an allograft biopsy analyzed with light, immunofluorescence, and electron microscopy. Treatment strategies are evolving towards targeted therapies, such as rituximab for antibody-mediated GN and complement inhibitors for C3G, moving away from broad immunosuppression. This narrative literature review provides practical monitoring algorithms for post-transplant settings, synthesizing information on the incidence, predictors, diagnostic strategies, and therapeutic options for various glomerular disease subtypes. The methodology involved searching MEDLINE, Embase, and Cochrane databases from 1996 to 2025, prioritizing systematic reviews, cohort studies, registries, and interventional reports. Eligibility criteria included adult transplant recipients and English-language reports on recurrent glomerular disease outcomes, excluding most single-patient case reports. Limitations include potential selection bias, omission of relevant studies, and the absence of a formal risk-of-bias assessment or meta-analysis. The evidence base is heterogeneous, with inconsistent outcome reporting and scarce randomized controlled trials. Future efforts should focus on developing predictive biomarkers, standardizing diagnostic and response criteria, conducting multicenter prospective cohorts and pragmatic trials, and creating shared registries with harmonized data. Full article

Despite increased interest in embodied learning, the role of sensorimotor activity in shaping students’ probabilistic reasoning remains underexplored. This design-based study examines how high school students develop key probabilistic concepts, including sample space, certainty, and event probability, through whole-body movement activities situated in an authentic classroom setting. Grounded in embodied cognition theory, we introduce a two-axis interpretive framework. One axis spans sensorimotor exploration and formal reasoning, drawing from established continuums in the literature. The second axis, derived inductively from our analysis, contrasts engagement with distraction, foregrounding the affective and attentional dimensions of embodied participation. Students engaged in structured yet open-ended movement sequences that elicited intuitive insights. This approach, epitomized by one student’s spontaneous question, “Can I use my leg too?”, captures the agentive and improvisational character of the embodied learning environment. Through five analyzed classroom episodes, we trace how students shifted between bodily exploration and formalization, often through nonlinear trajectories shaped by play, uncertainty, and emotionally driven reflection. While moments of insight emerged organically, they were also fragile, as they were affected by ambiguity and the difficulty in translating physical actions into mathematical language. Our findings underscore the pedagogical potential of embodied design for probabilistic learning while also highlighting the need for responsive teaching that balances structure with improvisation and supports affective integration throughout the learning process. Full article

Clinical guidelines, which represent the normative process models for healthcare organizations, are typically available in a textual, unstructured form. This issue hampers the application of classical conformance-checking algorithms to the medical domain, which take in input of a formalized and computer-interpretable description of the process. In this paper, (i) we propose overcoming this problem by taking advantage of a Large Language Model (LLM), in order to extract normative rules from textual guidelines; (ii) we then check and quantify the conformance of the patient event log with respect to such rules. Additionally, (iii) we adopt the approach as a means for evaluating the quality of the models mined by different process discovery algorithms from the event log, by comparing their conformance to the rules. We have tested our work in the domain of stroke. As regards conformance checking, we have proved the compliance of four Northern Italy hospitals to a general rule for diagnosis timing and to two rules that refer to thrombolysis treatment, and have identified some issues related to other rules, which involve the availability of magnetic resonance instruments. As regards process model discovery evaluation, we have assessed the superiority of Heuristic Miner with respect to other mining algorithms on our dataset. It is worth noting that the easy extraction of rules in our LLM-assisted approach would make it quickly applicable to other fields as well. Full article

The intersection of Artificial Intelligence (AI) and education is revolutionizing learning and teaching in this digital era, with Generative AI and large language models (LLMs) providing even greater possibilities for the future. The digital transformation of language education demands innovative approaches that combine pedagogical rigor with explainable AI (XAI) principles, particularly for low-resource languages. This paper presents a novel methodology that integrates Business Process Model and Notation (BPMN) with Multi-Agent Systems (MAS) to create transparent, workflow-driven language tutors. Our approach uniquely embeds XAI through three mechanisms: (1) BPMN’s visual formalism that makes agent decision-making auditable, (2) Retrieval-Augmented Generation (RAG) with verifiable knowledge provenance from textbooks of the National Institute of Languages of Luxembourg, and (3) human-in-the-loop validation of both content and pedagogical sequencing. To ensure realism in learner interaction, we integrate speech-to-text and text-to-speech technologies, creating an immersive, human-like learning environment. The system simulates intelligent tutoring through agents’ collaboration and dynamic adaptation to learner progress. We demonstrate this framework through a Luxembourgish language learning platform where specialized agents (Conversational, Reading, Listening, QA, and Grammar) operate within BPMN-modeled workflows. The system achieves high response faithfulness (0.82) and relevance (0.85) according to RAGA metrics, while speech integration using Whisper STT and Coqui TTS enables immersive practice. Evaluation with learners showed 85.8% satisfaction with contextual responses and 71.4% engagement rates, confirming the effectiveness of our process-driven approach. This work advances AI-powered language education by showing how formal process modeling can create pedagogically coherent and explainable tutoring systems. The architecture’s modularity supports extension to other low-resource languages while maintaining the transparency critical for educational trust. Future work will expand curriculum coverage and develop teacher-facing dashboards to further improve explainability. Full article

This article critically examines the evolution of EU migration policy discourse from 1989 to 2024, highlighting the shift from overt securitization to a more humanitarian and managerial framing, which still retains some securitization elements. By analyzing key policy documents, including the Hague and Stockholm Programmes, the Global Approach to Migration and Mobility (GAMM), and the 2024 Pact on Migration and Asylum, this paper demonstrates how migration has been increasingly framed as a technical and economic issue while still maintaining exclusionary logics. Although humanitarian language has softened, policy goals remain focused on containment, selective inclusion, and externalizing responsibility. The second part of the article explores the religious aspect of EU migration policy, arguing that, despite the formal secularism of EU institutions, religious identity, particularly Islam, is implicitly intertwined with discourses of risk, cultural incompatibility, and integration. Drawing on Peter Berger’s theory of pluralism, the paper highlights a fundamental tension between the EU’s normative claims to diversity and its implicit preference for secular Christian frameworks. The analysis examines pathways for integrating religious consultation into EU governance and its potential to address the persistent marginalization of religion as a factor in inclusion and political agency. By linking migration discourse to the often-overlooked role of religion, this article calls for a more coherent, pluralist-informed EU strategy for migration and integration. Full article