Search Results (10,942)

This study examines the geostrategic role of Hungarian-language higher education institutions beyond Hungary’s border. These institutions not only fulfil an educational function but also play a role in preserving identity and geopolitics in the national policy of the Hungarian state. This research is based on a narrative review of the literature, which analyses the demographic situation of Hungarians living beyond the borders and the tools used to support higher education by synthesising domestic and international literature, statistical data, and forecasts. The results highlight that Hungarian-language higher education plays a key role in preserving ethnocultural identity and increasing the chances of success in the homeland, but also faces constraints such as labour market disadvantages resulting from a lack of state language skills. This study concludes that, in order to ensure the sustainability of Hungarian higher education beyond the border, it is necessary to strike a balance between identity preservation and integration, thereby promoting geopolitical stability and cultural cohesion with the majority society. Full article

Multi-focus image fusion (MFIF) is an image-processing method that aims to generate fully focused images by integrating source images from different focal planes. However, the defocus spread effect (DSE) often leads to blurred or jagged focus/defocus boundaries in fused images, which affects the quality of the image. To address this issue, this paper proposes a novel model that embeds the Kolmogorov–Arnold network with convolutional layers in parallel within the U-Net architecture (KCUNet). This model keeps the spatial dimensions of the feature map constant to maintain high-resolution details while progressively increasing the number of channels to capture multi-level features at the encoding stage. In addition, KCUNet incorporates a content-guided attention mechanism to enhance edge information processing, which is crucial for DSE reduction and edge preservation. The model’s performance is optimized through a hybrid loss function that evaluates in several aspects, including edge alignment, mask prediction, and image quality. Finally, comparative evaluations against 15 state-of-the-art methods demonstrate KCUNet’s superior performance in both qualitative and quantitative analyses. Full article

Ensuring the structural integrity of reinforced concrete (RC) components in nuclear facilities exposed to extreme conditions is essential for safe decommissioning. This study investigates the impact of high-temperature exposure on RC pedestal structures supported by steel ring-shaped shear connectors—critical elements for maintaining vertical and lateral load paths in containment systems. Scaled-down cyclic loading tests were performed on pedestal specimens with and without prior thermal exposure, simulating post-accident conditions observed at a damaged nuclear power plant. Experimental results show that thermal degradation significantly reduces lateral stiffness, with failure mechanisms concentrating at the interface between the concrete and the embedded steel skirt. Complementary finite element analyses, incorporating temperature-dependent material degradation, highlight the crucial role of load redistribution to steel components when concrete strength is compromised. Parametric studies reveal that while geometric variations in the inner skirt have limited influence, thermal history is the dominant factor affecting vertical capacity. Notably, even with substantial section loss in the concrete, the steel inner skirt maintained considerable load-bearing capacity. This study establishes a validated analytical framework for assessing structural performance under extreme conditions, offering critical insights for risk evaluation and retrofit strategies in the context of nuclear facility decommissioning. Full article

Background/Objectives: As an essential polyunsaturated fatty acid, linoleic acid (LA) plays an important role in maintaining the integrity of cellular membranes, modulating inflammatory responses, and mediating intracellular signaling. This review explores the structure, properties, and nutritional significance of LA and its bioactive derivatives, with particular attention to sustainable production methods and their potential applications. Methods: A comprehensive review of the recent literature was conducted, emphasizing the use of green synthesis techniques, such as enzyme-catalyzed biocatalysis and microbiological transformations, in order to obtain LA-derived nutraceuticals. Analyses were conducted on the key aspects related to food industry applications, regulatory frameworks, and emerging market trends. Results: Through green synthesis strategies, LA derivatives with antioxidant, anti-inflammatory, and antimicrobial properties have been developed. There is potential for these compounds to be incorporated into health-oriented food products. In spite of this, challenges remain regarding their stability and bioavailability. Furthermore, there are inconsistencies in international regulatory standards which prevent these compounds from being widely adopted. Conclusions: The development of functional and sustainable food products based on linoleic acid derivatives obtained using ecological methods offers significant potential. Research is required to optimize production processes, enhance compound stability, and clinically validate health effects. The integration of the market and the safety of consumers will be supported by addressing regulatory harmonization. Full article

Structural Health Monitoring (SHM) techniques are crucial for evaluating the condition of structures, enabling early maintenance interventions, and monitoring factors that could compromise structural integrity. Modal analysis studies the dynamic response of structures when subjected to vibrations, evaluating natural frequencies and vibration modes. This study focuses on detecting and comparing the natural frequencies of a 3-DoF structure under various excitation scenarios, including ambient vibration (in healthy and damaged conditions), two types of transient excitation, and three harmonic excitation variations. Signal processing techniques, specifically Power Spectral Density (PSD) and Continuous Wavelet Transform (CWT), were employed. Each method provides valuable insights into frequency and time-frequency domain analysis. Under ambient vibration excitation, the damaged condition exhibits spectral differences in amplitude and frequency compared to the undamaged state. For the transient excitations, the scalogram images reveal localized energetic differences in frequency components over time, whereas PSD alone cannot observe these behaviors. For the harmonic excitations, PSD provides higher spectral resolution, while CWT adds insight into temporal energy evolution near resonance bands. This study discusses how these analyses provide sensitive features for damage detection applications, as well as the influence of different excitation types on the natural frequencies of the structure. Full article

Plant growth monitoring is a complex and challenging task, which depends on a variety of environmental variables, such as temperature, humidity, nutrient availability, and solar radiation. Advances in optical sensors have significantly enhanced data collection on plant growth. These developments enable the optimization of agricultural practices and crop management through the integration of artificial vision techniques. Despite advances in the application of these technologies, limitations and challenges persist. This review aims to analyze the current state-of-the-art methodologies for using artificial vision and optical sensors in plant growth assessment. The systematic review was conducted following the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Relevant studies were analyzed from the Scopus and Web of Science databases. The main findings indicate that data collection in agricultural environments is challenging. This is due to the variability of climatic conditions, the heterogeneity of crops, and the difficulty in obtaining accurately and homogeneously labeled datasets. Additionally, the integration of artificial vision models and advanced sensors would enable the assessment of plant responses to these environmental factors. The advantages and limitations were examined, as well as proposed research areas to further contribute to the improvement and expansion of these emerging technologies for plant growth assessment. Finally, a relevant research line focuses on evaluating AI-based models on low-power embedded platforms to develop accessible and efficient decision-making solutions in both agricultural and urban environments. This systematic review was registered in the Open Science Framework (OSF). Full article

Background: Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), are genetically complex and often linked to structural genomic variations such as copy number variants (CNVs). Current diagnostic strategies face challenges in interpreting the clinical significance of such variants. Methods: We developed a customized, gene-oriented chromosomal microarray (CMA) targeting 6026 genes relevant to neurodevelopment, aiming to improve diagnostic yield and candidate gene prioritization. A total of 39 patients with unexplained developmental delay, intellectual disability, and/or ASD were analyzed using this custom platform. Systems biology approaches were employed for downstream interpretation, including protein–protein interaction networks, centrality measures, and tissue-specific functional module analysis. Results: Pathogenic or likely pathogenic CNVs were identified in 31% of cases (9/29). Network analyses revealed candidate genes with key topological properties, including central “hubs” (e.g., NPEPPS, PSMG1, DOCK8) and regulatory “bottlenecks” (e.g., SLC15A4, GLT1D1, TMEM132C). Tissue- and cell-type-specific network modeling demonstrated widespread gene involvement in both prenatal and postnatal developmental modules, with glial and astrocytic networks showing notable enrichment. Several novel CNV regions with high pathogenic potential were identified and linked to neurodevelopmental phenotypes in individual patient cases. Conclusions: Customized CMA offers enhanced detection of clinically relevant CNVs and provides a framework for prioritizing novel candidate genes based on biological network integration. This approach improves diagnostic accuracy in NDDs and identifies new targets for future functional and translational studies, highlighting the importance of glial involvement and immune-related pathways in neurodevelopmental pathology. Full article

Enteric diseases represent one of the main causes of morbidity and mortality in poultry production, especially in turkeys (Meleagris gallopavo), significantly affecting the profitability of the sector. Turkey enteric complex (PEC) is a multifactorial syndrome characterized by diarrhea, stunting, poor feed conversion, and increased mortality in young turkeys. Its aetiologia includes multiple avian enteric viruses, including astrovirus, rotavirus, reovirus, parvovirus, adenovirus, and coronavirus, which can act singly or in co-infection, increasing clinical severity. This study performs a systematic review of the literature on these viruses and a meta-analysis of their prevalence in different regions of the world. Phylogenetic analyses were used to assess the genetic diversity of the main viruses and their geographical distribution. The results show a wide regional and genetic variability, which underlines the need for continuous epidemiological surveillance. Health and production implications are discussed, proposing control strategies based on biosecurity, targeted vaccination, and optimized nutrition. These findings highlight the importance of integrated management to mitigate the impact of CSF in poultry. Full article

The application of artificial intelligence has significantly advanced interactive storytelling. However, current research has predominantly concentrated on the content generation capabilities of AI, primarily following a one-way ‘input-direct generation’ model. This has led to limited practicality in AI story writing, mainly due to the absence of investigations into user-driven creative processes. Consequently, users often perceive AI-generated suggestions as unhelpful and unsatisfactory. This study introduces a novel creative tool named Story Forge, which incorporates a card-based interactive narrative approach. By utilizing interactive story element cards, the tool facilitates the integration of narrative components with artificial intelligence-generated content to establish an interactive story writing framework. To evaluate the efficacy of Story Forge, two tests were conducted with a focus on user engagement, decision-making, narrative outcomes, the replay value of meta-narratives, and their impact on the users’ emotions and self-reflection. In the comparative assessment, the participants were randomly assigned to either the experimental group or the control group, in which they would use either a web-based AI story tool or Story Forge for story creation. Statistical analyses, including independent-sample t-tests, p-values, and effect size calculation (Cohen’s d), were employed to validate the effectiveness of the framework design. The findings suggest that Story Forge enhances users’ intuitive creativity, real-time story development, and emotional expression while empowering their creative autonomy. Full article

This paper examines the intersection of global climate governance, carbon markets, and Indigenous Peoples’ rights under the United Nations Framework Convention on Climate Change. It critically analyses how Indigenous Peoples have contested the Article 6 market mechanisms of the Paris Agreement at the height of their negotiation during COP25 and COP26 by drawing attention to their role in perpetuating “carbon colonialism,” thereby revealing deeper power dynamics in global climate governance. Utilising a political ecology framework, this study explores these power dynamics at play during the climate negotiations, focusing on the instrumental, structural, and discursive forms of power that enable or limit Indigenous participation. Through a qualitative case study approach, the research reveals that while Indigenous Peoples have successfully used discursive strategies to challenge market-based solutions, their influence remains limited due to entrenched structural and instrumental power imbalances within the UNFCCC process. This study highlights the need for equitable policies that integrate human rights safeguards and prioritise Indigenous-led, non-market-based approaches to ecological restoration. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected. Full article

Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article

Applying language models (LMs) and generative artificial intelligence (GenAI) to the study of Ancient Greek offers promising opportunities. However, it faces substantial challenges due to the language’s morphological complexity and lack of annotated resources. Despite growing interest, no systematic overview of existing research currently exists. To address this gap, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology. Twenty-seven peer-reviewed studies were identified and analyzed, focusing on application areas such as machine translation, morphological analysis, named entity recognition (NER), and emotion detection. The review reveals six key findings, highlighting both the technical advances and persistent limitations, particularly the scarcity of large, domain-specific corpora and the need for better integration into educational contexts. Future developments should focus on building richer resources and tailoring models to the unique features of Ancient Greek, thereby fully realizing the potential of these technologies in both research and teaching. Full article

Background/Objectives: Executive function (EF) difficulties are increasingly recognized as closely linked to ADHD, particularly when assessed via rating scales. Methods: The present study investigated the nature of these associations, using the Conners 3 Rating Scales to assess ADHD symptoms and the Executive Function Questionnaire (EFQU) to assess EF impairments, in a sample of 1068 children (40.8% males, 38.8% females) aged 7–14 years (M = 10.7, SD = 1.74). Results: Both parent and teacher ratings revealed strong correlations, particularly between inattentive symptoms and EF difficulties, across multiple executive domains. To examine whether these associations stemmed from construct or phrasing overlap, exploratory and confirmatory factor analyses were conducted. The results demonstrate that the Conners 3 and the EFQU capture distinct latent dimensions of functioning, with virtually no overlap in item content. Conclusions: The strength and consistency of the associations between these latent factors support the interpretation that, although conceptually distinct, ADHD symptoms and EF impairments are empirically intertwined in everyday functioning, as consistently reported by both parents and teachers. Interestingly, teachers provided more integrated views of behavior, while parents tended to distinguish ADHD and EF traits more clearly. These findings underscore the importance of multi-informant assessment and contextual variability in understanding children’s functioning. Full article