Search Results (141)

Knowledge graph completion via link prediction is critical for intelligent equipment maintenance systems to support accurate fault diagnosis and maintenance decision making. However, existing approaches struggle to simultaneously capture local structural dependencies and perform effective multi-hop reasoning due to limited receptive fields or inefficient path exploration mechanisms. Traditional path-based methods implicitly assume path symmetry, treating all reasoning chains equally without considering their task-specific relevance. To address this issue, we propose a Graph Attention Network (GAT)-guided semantic path reasoning framework that breaks this symmetry through attention-driven asymmetric weighting, integrating local structural encoding with global multi-hop inference. The key innovation lies in a target-guided biased path sampling strategy, which transforms GAT attention weights into probabilistic transition biases, enabling adaptive exploration of high-quality semantic paths relevant to specific prediction targets. GATs learn importance-aware local representations, which guide biased random walks to efficiently sample task-relevant reasoning paths. The sampled paths are encoded and aggregated to form global semantic context representations, which are then fused with local embeddings through a gating mechanism for final link prediction. Experimental evaluations on FB15k-237, WN18RR, and a real-world equipment maintenance knowledge graph demonstrate that the proposed method consistently outperforms state-of-the-art baselines, achieving an MRR of 0.614 on the maintenance dataset and 0.485 on WN18RR. Further analysis shows that the learned path attention weights provide interpretable asymmetric reasoning evidence, enhancing transparency for safety-critical maintenance applications. Full article

Background: Undernutrition among infants and young children in South Asia remains a major public health concern, contributing to high rates of morbidity and mortality. While quantitative systematic reviews have identified various risk factors for undernutrition, no review has focused on qualitative studies. This study aims to review published literature on family member and healthcare provider perceptions about influences on undernutrition among infants and young children in South Asia. Methods: We searched for qualitative research articles published from 2000 to 2026 in the PubMed, Scopus and CINAHL databases, and used the Critical Appraisal Skills Program (CASP) tool to assess the quality of selected articles. Selected articles were analyzed thematically. The PROSPERO registration number is CRD42022385382. Results: After screening 201 research articles, 19 articles were selected for inclusion in this review. Perceived influences of undernutrition among children were categorized into individual, socio-cultural, economic, environmental and system factors. Interconnected influences included maternal illness, single motherhood, mothers’ knowledge and awareness, convenience of providing low-quality ready-made and junk food, spiritual beliefs and superstition, violence against women, financial constraints in a context of rising food prices and seasonal impacts on food production, and physical accessibility of healthcare services. Conclusions: This review emphasizes the complex interplay of influences on undernutrition among young children in South Asia. Potential interventions must be culturally tailored and gender-sensitive, with key strategies including nutrition education, community-based support, maternal health improvements, and policies addressing food insecurity and healthcare accessibility. Full article

Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits their adoption in regulated healthcare environments. To address these challenges, this paper proposes a Privacy-by-Design (PbD) multi-agent architecture that enables consent-aware, auditable, and privacy-preserving AI-assisted support for caregivers of children with ASD. The effectiveness of the proposed architecture was evaluated using two datasets: one focusing on clinically grounded autism-related knowledge and another reflecting naturalistic caregiver observation language. System performance was assessed using a Retrieval-Augmented Generation Assessment (RAGAs)-based framework with a Large Language Model (LLM)-as-a-Judge approach implemented via a locally deployed Llama 3 8B model. The system achieved answer relevancy scores of 0.767 for the clinical dataset and 0.750 for the observational dataset, with corresponding Recall@K values of 0.400 and 0.742, respectively. Context precision ranged from 0.599 to 0.631, and no harmful content was detected. Overall, the proposed architecture demonstrates secure caregiver–specialist collaboration through consent-aware routing, anonymised data storage, and controlled data reconstruction, providing a regulation-aligned design option for privacy-preserving AI integration in assisted care platforms. Full article

General-purpose large language models excel at open-domain question answering, but in railway operation and maintenance (O&M) scenarios they still suffer from hallucinated knowledge and poor domain adaptation. In practice, railway O&M knowledge mainly arises from two heterogeneous sources: spatio-temporal data such as train trajectories, which are organized along the spatial layout of railway lines, and domain documents such as operating rules, which exhibit varying degrees of structural regularity. Traditional retrieval-augmented generation (RAG) systems usually flatten these multi-source data into a single unstructured text space and perform global retrieval in one embedding space, which easily introduces noisy context and makes it difficult to precisely target knowledge for specific lines, sections, or equipment states. To overcome these limitations, we propose CARAG, a context-aware RAG framework tailored to railway O&M data. CARAG treats domain documents and spatial data as a unified knowledge substrate and builds a spatial knowledge graph with concept and instance levels. On top of this knowledge graph, a GraphReAct-based multi-turn interaction mechanism guides the LLM to reason and act over the concept knowledge graph, dynamically navigating to spatially and semantically relevant candidate regions, within which vector retrieval and instance-level graph retrieval are performed. Experiments show that CARAG significantly outperforms baseline RAG methods on RAGAS metrics, confirming the effectiveness of structure-guided multi-step reasoning for question answering over multi-source heterogeneous railway O&M data. Full article

Honey fraud represents a persistent and analytically challenging form of food adulteration, driven by globalised supply chains, strong economic incentives and asymmetries in regulatory oversight and analytical capacity. Conventional physicochemical, spectroscopic and isotopic methods provide legally robust tools for routine control, yet increasingly struggle to detect sophisticated adulteration strategies that are compositionally optimised to mimic authentic honey profiles. These challenges are amplified in a global context, where heterogeneous enforcement landscapes and fragmented analytical infrastructures create exploitable vulnerabilities across international trade networks. This narrative review synthesises current knowledge on honey fraud typologies and critically evaluates established analytical approaches alongside emerging omics-based authentication strategies, including genomics, metabolomics, proteomics and microbiome profiling. Omics-based approaches extend authenticity assessment beyond single-marker paradigms by capturing multidimensional biological and compositional signatures, thereby improving sensitivity to subtle and system-aware fraud (i.e., adulteration strategies that adapt to prevailing analytical detection methods and regulatory thresholds) strategies. To maintain evidentiary clarity, this review explicitly distinguishes between analytically demonstrated vulnerabilities, technically feasible adulteration scenarios and fraud practices documented in regulatory or enforcement contexts. Advanced technology-driven strategies are therefore discussed as potential system-level risks rather than confirmed large-scale honey fraud cases. This differentiation not only safeguards evidentiary precision but also highlights the structural limits of purely analytical solutions. Beyond analytical performance, honey authentication is framed as a systemic challenge embedded in global food systems. This review highlights the need for integrated, data-driven and scalable authentication frameworks that align analytical innovation with reference harmonisation, governance structures and international regulatory cooperation to support resilient and globally robust honey authenticity control. Full article

The global shift toward sustainable food packaging has renewed interest in bio- and plant-derived materials as alternatives to conventional plastics. Leaf-based packaging, a long-standing practice in many regions, represents a low-technology and culturally embedded option that is gaining attention, particularly in low- and middle-income countries. Despite this interest, evidence on its functional suitability, safety, regulatory alignment, and real-world adoption remains scattered and uneven. This systematic review synthesises current knowledge on leaf-based food packaging to determine where, how, and under what conditions it may be viable. Following PRISMA-ScR 2020 guidelines, peer-reviewed studies published between 1997 and 2025 were identified from major scientific databases and assessed using study-type-appropriate quality appraisal tools. Evidence was organised through a thematic framework addressing consumer awareness and willingness to pay, practical adoption and cultural patterns, economic trade-offs, and functional co-benefits alongside microbial and toxicological risks within existing regulatory and end-of-life systems. Comparative analysis considered differences between low- and middle-income and high-income contexts. The findings show that leaf-based packaging is most suitable for short shelf-life and low-risk foods, especially within traditional food service settings. Adoption is encouraged by cultural familiarity and environmental perceptions but limited by performance variability, hygiene concerns, compliance requirements, and infrastructure constraints. Scalability remains restricted by cost-effectiveness and compatibility with formal packaging and waste systems. Leaf-based materials should therefore be viewed as a context-specific sustainability option rather than a universal replacement for plastics, requiring targeted and risk-informed integration into appropriate food systems. Full article

As part of our technology-enhanced learning (TEL) strategy, we developed a field-based augmented reality (AR) learning system for biodiversity education among senior elementary school students. Using a 2D illustration style to present the appearance of the species and a situational interactive design, the AR app focused on common wild animals in Taiwan. They also gained insight into wild animal species in outdoor settings, gained knowledge about the phenomenon of roadkill and the rescue of wild animals, and promoted their awareness of ecological conservation. Using the design-based research (DBR) method, we integrated user-oriented design processes and iteratively modified the system functions and interface through expert review and field usability testing. During this activity, 26 senior elementary school students were recruited to participate in an interactive AR game designed for a single player. As part of the learning content, students must collect images of species, recognize roadkill, and learn about wildlife rescue. To evaluate the effect of the activity on knowledge learning and the app’s usability, data were collected through pre- and post-test paper tests, questionnaires, and so on. Based on the research results, this system can significantly enhance students’ learning interests and contextual understanding of biodiversity topics as an effective technology-assisted learning tool. Students reported high levels of immersion and learning motivation, and the teachers agreed that it promoted inquiry-based and independent learning. The results of this study contribute to the field of educational and environmental education. Consequently, context-aware AR tools may enhance students’ situational learning experience and environmental literacy. In addition, it provides a practical design reference for future AR educational applications, demonstrating that gamification and outdoor learning can enhance the learning outcomes of traditional science education. Full article

This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems. Full article

The sustainable management of marine resources requires accurate knowledge of fishing activity patterns and their interaction with coastal infrastructure. Intelligent Transportation Systems (ITS) are increasingly applied in the maritime domain, where data-driven approaches enhance safety, efficiency, and sustainability. In this context, Automatic Identification System (AIS) data provide valuable insights into vessel behavior and fisheries management. This study employs the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify fishing grounds, and a density map-based approach to recognize port locations. By integrating AIS data with machine learning techniques, the study detects and analyzes fishing vessel activities, providing deeper insights into behaviors such as fishing ground visit times, durations, and transitions between fishing grounds and ports. A case study in the Aalesund area of Norway demonstrates that DBSCAN effectively reveals fishing activity patterns relevant to regulatory oversight and spatial planning, while density mapping accurately identifies fishing ports. The findings highlight the potential of AIS-based analytics and clustering methods within maritime ITS frameworks to enhance situational awareness, support compliance with fisheries regulations, and contribute to sustainable marine resource management. Using 2023 AIS data from the Aalesund region, 6 recurrent fishing grounds and 15 port locations are identified, and size-stratified visit frequency and residence-time distributions are quantified together with monthly seasonality in ground usage. Full article

The expansion of Internet of Things (IoT) devices in domestic smart homes has created new conveniences but also significant security risks. Insecure firmware, weak authentication and weak encryption leave households exposed to privacy breaches, data leakage and systemic attacks. Although research has addressed several challenges, contributions remain fragmented and difficult for non-technical users to apply. This work addresses the following research question: How can a theoretical framework be developed to enable automated vulnerability scanning and prioritisation for non-technical users in domestic IoT environments? A Systematic Literature Review of 40 peer-reviewed studies, conducted under PRISMA 2020 guidelines, identified four structural gaps: dispersed vulnerability knowledge, fragmented scanning approaches, over-reliance on technical severity in prioritisation and weak protocol standardisation. The paper introduces a four-module framework: a Vulnerability Knowledge Base, an Automated Scanning Engine, a Context-Aware Prioritisation Module and a Standardisation and Interoperability Layer. The framework advances knowledge by integrating previously siloed approaches into a layered and iterative artefact tailored to households. While limited to conceptual evaluation, the framework establishes a foundation for future work in prototype development, household usability studies and empirical validation. By addressing fragmented evidence with a coherent and adaptive design, the study contributes to both academic understanding and practical resilience, offering a pathway toward more secure and trustworthy domestic IoT ecosystems. Full article

Pear cultivation in Greece, with an annual production of approximately 81,000 tonnes, constitutes a significant segment of the national fruit industry, particularly in Northern regions such as Macedonia and Thessaly. Despite ranking 8th in the EU in terms of pear production, Greece’s cultivated area is slightly declining, and adoption of smart agriculture technologies (SAT) remains limited. In this context, the present study investigates the preferences, patterns, and barriers of SAT adoption within the Greek pear sector, aiming to lay the groundwork for more effective digital transformation in the agri-food domain. Using a structured interview-based survey, data were collected from 30 pear growers, revealing critical insights into the technological landscape of the sector. A central challenge that emerged was the insufficient internet connectivity in rural farming areas, highlighting the urgent need for improved digital infrastructure to support SAT deployment. Furthermore, the study emphasizes the importance of targeted education and awareness programs to bridge the digital knowledge gap among pear farmers. An especially notable finding concerns the role of the chosen tree training system in influencing SAT uptake: more than 50% of adopters utilize the palmette training system, suggesting a strong correlation between orchard design and technological readiness. Among the SAT categories, Data Analytics and Farm Management Software were the most widely adopted, a trend partly driven by attractive governmental subsidies of €30 per hectare. Importantly, all respondents who had implemented SAT (100%) reported a measurable increase in farm income, reinforcing the technologies’ impact on productivity and profitability. Foremost among the challenges encountered is the deficit in technical knowledge and training. In conclusion, this study offers a comprehensive overview of Greek pear producers’ perceptions, challenges, and emerging opportunities related to smart agriculture. Full article

The Wua-Lai silvercraft community in Chiang Mai is experiencing a widening disconnect with younger visitors, raising concerns about the erosion of intangible cultural heritage. This study evaluates “Silver Craft Journey,” a location-based augmented reality (LBAR) system designed to revitalize cultural engagement and enhance cultural-heritage experience through context-aware, gamified exploration. A quasi-experimental field study with 254 participants across three age groups examined the system’s impact on cultural-heritage experience, knowledge acquisition, and real-world engagement. Results demonstrate substantial knowledge gains, with a mean increase of 7.74 points (SD = 4.37) and a large effect size (Cohen’s d = 1.77), supporting the effectiveness of LBAR in supporting tangible and intangible heritage understanding. Behavioral log data reveal clear age-related engagement patterns: older participants (41–51) showed declining mission completion rates and reduced interaction times at later points of interest, which may reflect increased cognitive and physical demands during extended AR navigation under real-world conditions. These findings underscore the potential of location-based AR to enhance cultural-heritage experience in real-world settings while highlighting the importance of age-adaptive interaction and route-design strategies. The study contributes a replicable model for integrating digital tourism, embodied AR experience, and community-based heritage preservation. Full article

Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article

Background: Adult females with Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) are frequently underdiagnosed due to gender bias, overlapping symptoms, and limited awareness among healthcare professionals. The scarcity of research on this subject—particularly in the UK context—underscores the need for further investigation. Accordingly, the aim of this study was to explore the lived experiences of adult females receiving a late diagnosis of ASD and/or ADHD and to identify key barriers within the UK diagnostic pathway. This study addresses a critical knowledge gap by examining the factors contributing to delayed diagnosis within the United Kingdom. Study Design and Method: The study employed a qualitative approach, utilising an anonymous online questionnaire survey comprising nine open-ended questions. Responses were obtained from 52 UK-based females aged 35–65 years who had either received or were awaiting a diagnosis of ASD and/or ADHD. Data were analysed thematically within a constructivist framework. Findings: The analysis revealed three overarching themes: (i) limited understanding and lack of empathy among healthcare professionals, (ii) insufficient post-diagnostic support, with most participants reporting no follow-up care, and (iii) a complex, protracted diagnostic process, often involving waiting periods exceeding three years. Gender bias and frequent misdiagnosis were recurrent issues, contributing to significant psychological distress. These findings underscore the need for systemic reforms and align closely with gaps identified in the existing literature. Conclusions: The findings emphasise the urgent need for gender-sensitive diagnostic frameworks, enhanced professional training, and a person-centred approach to care. Key recommendations include shortening diagnostic waiting times, strengthening healthcare professionals’ knowledge base, and ensuring equitable and consistent post-diagnostic support. Full article

Artificial Intelligence (AI) systems typically inherit biases from their training data, leading to discriminatory outcomes that undermine equity and inclusion. This issue is particularly significant when popular Generative AI (GAI) applications are used in educational contexts. To respond to this challenge, the study evaluates the effectiveness of dialogic reflection-based training for educators in identifying and mitigating biases in AI. Furthermore, it considers how these sessions contribute to the advancement of algorithmic justice and inclusive practices. A key component of the proposed training methodology involved equipping educators with the skills to design inclusive prompts—specific instructions or queries aimed at minimizing bias in AI outputs. This approach not only raised awareness of algorithmic inequities but also provided practical strategies for educators to actively contribute to fairer AI systems. A qualitative analysis of the course’s Moodle forum interactions was conducted with 102 university professors and graduate students from diverse regions of the Dominican Republic. Participants engaged in interactive activities, debates, and practical exercises addressing AI bias, algorithmic justice, and ethical implications. Responses were analyzed using Atlas.ti across five categories: participation quality, bias identification strategies, ethical responsibility, social impact, and equity proposals. The training methodology emphasized collaborative learning through real case analyses and the co-construction of knowledge. The study contributes a hypothesis-driven model linking dialogic reflection, bias awareness, and inclusive teaching, offering a replicable framework for ethical AI integration in higher education. Full article