Search Results (413)

Teachers’ readiness in bilingual early childhood education is increasingly recognized as a multidimensional construct shaped by both professional and language-related factors. However, existing research has typically examined these factors separately, with limited evidence on how they combine across teacher groups, particularly in minority-language contexts. This study examined teachers’ readiness to deliver state-language instruction to dual language learners (DLLs) in Hungarian-medium kindergartens in Slovakia. A total of 313 kindergarten teachers participated in the study. Data were collected through a survey assessing multiple dimensions of readiness. Principal component analysis and confirmatory factor analysis supported a six-factor model comprising professional preparation, teacher competencies, challenge management, instructional aids use, professional needs, and Slovak language use outside kindergarten. Latent profile analysis identified three readiness profiles (low, moderate, and high), reflecting differences in overall preparedness. Background characteristics, particularly age, teaching experience, and language-related factors, were significantly associated with higher readiness. Teachers who used Slovak more frequently in everyday contexts showed higher readiness. Mediation analysis indicated that language proficiency and preferred language use did not mediate the relationship between teaching experience and teachers’ readiness, but functioned as independent predictors. These findings highlight the joint importance of professional and language-related factors in shaping teachers’ readiness and offer implications for teacher education and policy in bilingual early childhood settings. Full article

Background/Objectives: Echocardiography reports are essential diagnostic tools, but their complexity and specialized English terminology frequently hinder comprehension for non-specialists and patients. This study addresses these accessibility gaps by developing a resource-efficient large language model (LLM) system designed to translate and summarize English echocardiography results into Traditional Chinese. Methods: To overcome significant hardware constraints, we utilized Quantized Low-Rank Adapter (QLoRA) techniques and the Unsloth acceleration framework to fine-tune LLaMA-3.2-1B and LLaMA-3.2-3B-Instruct models on a single mid-tier GPU. The system employs a dual-stage inference architecture: the first stage provides technical medical translation for clinicians, while the second stage generates simplified, patient-centric educational summaries to enhance health literacy. Results: Evaluation across multiple metrics, including BLEU, ROUGE, METEOR, and Perplexity, demonstrated that the LLaMA-3.2-3B-Instruct model with the AdamW 8-bit optimizer achieved the most stable validation performance, excelling in semantic coherence and structural consistency. A preliminary qualitative error analysis conducted in the Discussion section further identified clinical nuances, such as terminology simplification and minor hallucinations, underscoring the critical necessity of a Human-in-the-Loop verification procedure. Conclusions: These findings validate the feasibility of deploying cutting-edge medical AI in resource-limited clinical environments. While the results reflect validation-only performance on a specialized dataset, the platform offers a scalable foundation for enhancing clinical decision support and health literacy through accessible, automated medical text processing. Full article

The proliferation of fake news across social media, headlines, and news articles poses major challenges for automated detection, particularly in multilingual and cross-media settings affected by data imbalance. We propose a fake news detection framework based on LLM-driven, feature-guided text augmentation. The method generates realistic synthetic samples across languages, media types, and text granularities while preserving meaning and stylistic coherence. Experiments with classical and transformer-based models (Random Forest, Logistic Regression, BERT, XLM-R) across social media, headlines, and multilingual news datasets show consistent improvements in performance. For inherently balanced datasets (e.g., social media), synthetic augmentation yields negligible but stable performance changes. Across imbalanced scenarios, synthetic augmentation substantially improves minority-class recall and F1-score (e.g., fake news recall from 0.57 to 0.86), while preserving majority-class performance, leading to more balanced and reliable classifiers, whereas oversampling significantly degrades results due to overfitting on duplicated language patterns. Overall, a hybrid semantic- and style-based model proves to be the most robust strategy, outperforming oversampling and matching or exceeding baseline performance across datasets. Full article

This study analyses NoViolet Bulawayo’s We Need New Names through the framework of intersectional feminism, a concept introduced by Kimberlé Crenshaw that examines how multiple identities, such as race, gender, and class, intersect to shape distinct experiences of marginalization. Bulawayo’s narrative, centred on the protagonist Darling, reveals the complex social forces she encounters as she navigates cultural and geographic transitions. Through a blend of English and Shona, the text reflects cultural duality and the tensions of migration, including acculturation and displacement. The episodic structure mirrors the fragmentation inherent in Darling’s African upbringing and her transcontinental journey. The analysis situates the novel alongside contemporary works such as Chimamanda Ngozi Adichie’s Americanah and Yaa Gyasi’s Homegoing, highlighting shared thematic concerns with identity, oppression, and the migrant experience. Ultimately, the study argues that Bulawayo’s representation of intersecting identities enriches the novel’s engagement with gender, race, class, and the transformative potential of language in articulating minority experiences. Full article

This study provides a systematic review of research hotspots and trends in the field of parent–child reading, covering the period from 2005 to 2024, based on data retrieved from the China National Knowledge Infrastructure (CNKI) and the Web of Science (WOS). The results indicate that both Chinese- and English-context research on parent–child reading focus on the family literacy environment, the impact of parent–child reading on child development, social support systems, and educational equity. Chinese research places greater emphasis on family reading, family–kindergarten collaboration, and father involvement. This research mainly examines parental guidance strategies and pays particular attention to current practices, especially in rural areas. It highlights the role of fathers in reading, with picture books being the most commonly used reading materials. In contrast, English-context research focuses more on language development and early literacy, with particular emphasis on the development of children’s literacy skills and school readiness. Greater attention is also given to multicultural and minority groups, the role of mothers in reading is more frequently emphasized, and the reading materials are predominantly storybooks and wordless books. Research in both Chinese and English contexts reveals that parent–child reading interactions serve as a channel for the transmission of cultural values, leading to distinct developmental priorities for children. These differences profoundly reflect the systematic influence of sociocultural logics on parental reading behaviors and related research. This analysis provides an empirical foundation for future international collaboration in cross-cultural research. Full article

Word sense induction (WSI) aims to automatically discover the different senses of a word from contextual usage without predefined sense inventories. However, existing distributional clustering methods often suffer from dominant-sense bias and struggle to correctly identify minority senses. In this paper, we propose a definition-anchored reclassification framework for WSI that leverages large language models (LLMs) to generate explicit sense descriptions and refine cluster assignments. Unlike purely distributional approaches, our method integrates semantic definitions into the induction process. Our method improves instance-level alignment by introducing a trade-off with global structural consistency, as it shifts the decision process from geometric clustering to definition-based semantic matching. Experiments on the SemEval-2010 and SemEval-2013 datasets demonstrate that the proposed method consistently outperforms traditional clustering baselines and existing WSI systems across both structural metrics (NMI and V-measure) and instance-level metrics (F-B³ and Fuzzy-F-B³). In particular, our approach effectively mitigates dominant-sense bias and improves the recovery of minority senses by preserving them as distinct clusters while correctly assigning their instances. These results suggest that explicit semantic representations generated by LLMs provide a promising direction for addressing long-standing challenges in unsupervised word sense induction. Furthermore, unlike purely distributional clustering approaches, our method explicitly introduces LLM-generated semantic definitions as anchors, enabling more robust mitigation of dominant-sense bias and improved recall of minority senses. Full article

The rapid growth of Arabic social media content requires the development of accurate and efficient methods for sentiment analysis. We propose a resource-efficient multi-task learning (MTL) framework for modern standard Arabic (MSA). The model uses a shared AraBERT encoder to jointly predict emotion, polarity, and intention. We integrate knowledge distillation (KD) from a large teacher model, self-distillation (SD) using model self-ensembling, and adversarial training (AT) as a regularization strategy. Experiments conducted on an annotated corpus of MSA tweets demonstrate that all distilled models outperform a fine-tuned multi-task baseline, and the combined KD+SD+AT configuration achieves competitive results. For instance, KD alone raised Macro F1 for emotion from 0.83 to 0.88 and for intention from 0.67 to 0.72. KD+SD+AT achieved the best intention F1 (0.76) and the highest polarity F1 (0.90). Notably, F1-scores for several minority classes show consistent improvement, particularly under KD and combined configurations. Paired t-tests confirm that several improvements, especially those obtained with KD and KD+SD+AT, are statistically significant ($p<0.05$). Our results indicate that distillation, combined with adversarial regularization, enables the development of smaller and more efficient Arabic sentiment models while maintaining competitive accuracy. These findings address a gap in Arabic multi-task sentiment analysis and provide a scalable, resource-efficient framework, along with empirical insights for distillation in Arabic language models. Full article

Background/Objectives: Although many studies have examined the determinants and management of dental anxiety (DA), its broader placement as a potential bottleneck along oral–systemic health pathways, from the determinants of DA to consequences through dental avoidance, oral outcomes, psychosocial impacts, and possible systemic health outcomes, has not been mapped across the review literature. This review aimed to conceptually map how existing DA reviews are distributed across this pathway, whether this broad framing changed across 5-year periods, and how systemic health outcomes were framed. Methods: We conducted a conceptual mapping review of DA-focused review articles published between 2005 and 2025. PubMed and Scopus were searched for English-language narrative, systematic, scoping and umbrella reviews and meta-analyses addressing the determinants or consequences of DA. One reviewer screened records, extracted review characteristics, and classified each review into predefined domains using binary framed/not framed coding rules. A structured AI-assisted prompt was used only to support full-text evaluation across domains; all final coding decisions were made by the reviewer. Results: The search identified 851 records; after removing 426 duplicates, 425 unique records were screened, and 39 reviews met the inclusion criteria. Framing concentrated on environmental and psychological determinants and on the pathway from DA to avoidance and poor oral health, whereas broader consequences, including shame, OHRQoL, and systemic health outcomes, were less consistently framed. Across 5-year periods, the broad pattern of framing remained relatively stable. Systemic health outcomes were framed in only a minority of reviews. Conclusions: Future research should test hypothesized pathways from DA to broader health consequences using clearly specified bridge mechanisms and appropriate temporal designs. Full article

A significant amount of recent scientific literature emphasizes the importance of mother tongue education, as minority languages continue to be underrepresented in formal schooling. While some progress has been made in integrating these languages into curricula, the situation varies widely across different regions. Ideally, populations would achieve proficiency in multiple languages, yet in practice, this phenomenon is relatively rare. This article examines the status of minority language education across five regions of the post-USSR. The analysis is conducted according to specific principles, with attention to demographic patterns, economic conditions, legislative frameworks, national and regional educational policy documents, and the types and outcomes of programs involving minority languages. Methodologically, the study employs a comparative qualitative approach, combining document analysis, secondary data review, and the synthesis of existing case studies. By applying these methods, the research seeks to identify correlations between the presence of minority languages in the public sphere and their incorporation into educational programs. Findings indicate that active use of minority languages in everyday life and public domains provides the strongest motivation for sustained investment in education. At the same time, the introduction of modern educational technologies offers promising opportunities to achieve more positive results in the future. Full article

Speech sound disorder (SSD) is common in childhood and can persist, adversely affecting language, literacy, and social functioning. Yet consonant error patterns in school-age children, particularly in non-English-speaking populations, remain insufficiently characterized. Short-term memory (STM) supports phonological processing and speech learning, but its relationship with SSD severity in school-age children is not well established. This study profiles consonant errors and short-term memory in school-age Chinese children with SSD and examines short-term memory correlates and predictors of disorder severity to inform targeted interventions. A total of 142 Mandarin-speaking school-age children with SSD were recruited. For the short-term memory analyses, we randomly selected 70 children with SSD and recruited 70 typically developing controls. Speech was assessed using a word-level picture-naming task to derive consonant accuracy and characterize error types/patterns, and short-term memory was measured with the WISC-IV Digit Span (forward and backward). Substitutions predominated for most consonants, and individual phonemes often exhibited co-occurring error patterns. In addition, school-age children with SSD showed significantly poorer short-term memory than typically developing peers across multiple indices. Notably, backward digit span was positively associated with consonant accuracy and remained an independent predictor of consonant accuracy. These results advance our understanding of the mechanisms underlying SSD and provide an evidence-based rationale for future interventions that combine speech-focused therapy with cognitive training to enhance clinical outcomes. Full article

Major Depressive Disorder (MDD) is a significant global health burden that contributes to disability and reduced quality of life. Its impact extends beyond individuals, placing emotional, social, and economic strain on families and healthcare systems worldwide. Despite its prevalence, MDD remains widely misunderstood, with limited mental health literacy and persistent stigma often preventing individuals from seeking help. This research explored the prediction of MDD utilising social media data via Natural Language Processing (NLP), Machine Learning (ML), and explainable Machine Learning (xML) techniques. The research aimed at identifying depressive indicators on X (formerly Twitter) and developing interpretable models for depression risk detection. The study’s methodology followed the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework to ensure a systematic approach to data analysis. Data was collected via X’s API and processed using regex-based noise removal, normalisation, tokenisation, and lemmatisation. Symptoms were mapped to DSM-5-TR criteria at the post-level, with user-level MDD risk assessed based on symptom persistence over a two-week period. Risk levels were classified as No Risk, Monitor, and High Risk to facilitate early intervention. Six ML models were trained and tested, while the Synthetic Minority Over-sampling Technique (SMOTE) was applied to mitigate class imbalance. The dataset was partitioned into training and testing sets using an 80:20 split. ML models were evaluated, and the Extreme Gradient Boosting model outperformed the others. Extreme Gradient Boosting achieved an accuracy of 0.979, F1-score of 0.970, and ROC-AUC of 0.996, surpassing benchmark results reported in prior studies. Explainability techniques, such as LIME and tree-based feature importance, enhance model transparency and clinical interpretability. Depressed mood consistently emerged as the highest-weighted predictor across different models. The findings highlight the value of aligning ML models with validated diagnostic frameworks to improve trustworthiness and reduce false positives. Future research can expand beyond text-based analysis by incorporating multimodal features to broaden diagnostic depth. Full article

Bridge structures are considered complex and significant. Accordingly, the knowledge of the engineering domain of bridge construction and related specialist areas is multidimensional and highly specific. Sometimes this knowledge is explicitly documented in standards, technical regulations, or information sheets. At other times, it resides implicitly in the expertise of the specialists involved. Ontologies are used to structure and formalize such domain knowledge, but creating them is resource-intensive and requires specialized expertise. Large language models (LLMs) offer one way to automate ontology creation through their natural language processing capabilities. This article examines LLMs’ ability to generate ontologies in the specialized field of structural non-destructive testing (NDT) in bridge construction. Four different LLM-based approaches are employed. The results are compared with a previously created human-generated ontology and subsequently evaluated by external experts. Experts rate the human-developed SODIA ontology highest, with an average score of 3.44 out of 5 points. Only the ChatGPT 4.0-created ontology performed similarly well, with a score of 3.3 out of 5.00. All other LLM-based ontologies with ratings below 3.0 are of minor quality. These results underscore the potential and constraints of using LLMs to structure and formalize engineering domain knowledge into ontologies. Full article

Background/Objectives: The use of artificial intelligence (AI) to elicit health advice is a rapidly developing phenomenon that could dramatically change healthcare delivery, including in the field of homeopathy. However, the potential costs and benefits of this shift are largely unknown. Methods: Researchers studied whether there was a difference between homeopathy guidance provided by large language model (LLM) AI chatbots and live practitioners for acute illnesses. This study used practitioner notes from 100 cases to elicit remedy recommendations from four free, publicly accessible AI chatbots. The results were compared against live practitioners’ initial remedy recommendations across different AI platforms and a purpose-built (non-LLM) homeopathic remedy finder, and subsequent queries on the same AI platforms using the same input. Results: AI chatbots regularly provided medical disclaimers, including recommendations to seek medical care, and provided remedy recommendations that were sometimes consistent with a live practitioner’s initial recommendation. In the 100 cases compared, the initial practitioner-recommended remedy was included among the AI chatbots’ recommendations in 36.5% (N = 100) of the cases on average, and was the top recommendation in 20.8% (n = 100) of the cases. In a small minority of cases (6%, where N = 100), all four AI chatbots agreed with the practitioner’s initial recommendation, and in a slightly larger minority (10% where N = 100), all four AI chatbots agreed on a remedy that was at odds with the practitioner’s initial recommendation, indicating potential areas for further investigation. Conclusions: AI chatbot remedy recommendations were not routinely consistent with a live practitioner’s initial recommendation or across AI platforms. Results were not even routinely consistent when the same case notes were entered multiple times on the same platform or when challenged by a researcher. Full article

Objectives: This scoping review synthesized the available evidence on bedside ultrasonography used to confirm short-term nasogastric tube (NGT) placement in adults. Methods: The review followed JBI Collaboration methodology. Searches were conducted in CINAHL, Embase, LILACS, PubMed, and Scopus, as well as in gray literature sources (Google Scholar and ProQuest Dissertation & Thesis Global). Primary studies and clinical guidelines addressing bedside ultrasonography for short-term NGT placement in adults (≥18 years) were eligible, with no limits on language or publication year. Data were extracted and narratively summarized with the I-AIM framework (Indication, Acquisition, Interpretation, and Decision-Making). Results: Twenty-nine studies met the inclusion criteria. Most were single-center observational studies performed in intensive care units or emergency departments. Ultrasound was primarily used for confirmation prior to enteral nutrition initiation, while gastric decompression was less frequently reported. Acquisition protocols varied, although supine positioning, convex abdominal probes, and linear cervical probes were most commonly described. The gastric antrum and esophagus were the principal anatomical landmarks, with interpretation based on direct tube visualization and dynamic fogging; color Doppler was occasionally used. Radiography remained the reference standard in most studies, and only a minority initiated feeding based solely on ultrasound findings. Reported facilitators included bedside feasibility, absence of radiation exposure, and timeliness. Barriers included operator dependency, limited visualization in patients with obesity or gas interposition, protocol heterogeneity, and the limited methodological robustness of available studies. Conclusions: Current evidence suggests that ultrasonography may represent a feasible, radiation-free bedside approach for confirmation of NGT placement. Evidence from selected studies suggests that, with structured training, healthcare professionals may achieve diagnostic accuracy in specific clinical settings, although further robust multicenter investigations are needed to confirm these findings. Full article

Background: Zygomatic implants (ZIs) were initially pioneered by Brånemark to rehabilitate patients suffering from destructive diseases through original surgical technique (OST). Subsequently, other techniques were proposed, such as the zygomatic anatomy-guided approach (ZAGA). This umbrella review was conceived to quantify and critically characterize the spectrum of complications associated with different techniques of ZI placement. Methods: Systematic reviews, encompassing both those with and without meta-analysis, focusing on the complications associate with ZIs and published only in the English language were systematically sought. A systematic literature search was performed through MEDLINE/Pubmed, Scopus, BioMed Central, and the Cochrane Library, and the PROSPERO register. Results: A total of 11 articles were included. The latter documented the spectrum of complications associated with ZIs, ranging from minor morbidities such as sinusitis, hematoma, and soft tissue complications up to severe adverse events such as orbital penetration and diplopia. Conclusions: The use of described ZI OST and ZAGA in cases of severe maxillary resorption is associated with a high implant survival rate and a low incidence of surgical complications. However, complications, the most common of which were sinusitis and peri-implant soft tissue infection, may be underestimated due to the heterogeneity of the studies included. Full article