Search Results (52)

Augustine of Hippo (354–430), one of the most influential theologians of Late Antiquity, spent nearly a decade in the Manichaean sect before becoming a central figure in the shaping of Western “orthodox” Christianity. While his major works such as the Confessiones and De civitate Dei have been extensively studied for their treatment of Manichaeism, the vast collection of his ca. 300 preserved letters (Epistulae) remains an understudied source for understanding this aspect of his intellectual and theological development. This article addresses that gap by proposing a methodology to identify both anti- and crypto-Manichaean themes in his letters. Drawing on phenomenological openness, hermeneutical perspective, and close reading, the study also incorporates genuine Manichaean sources and anti-Manichaean polemics to contextualise Augustine’s rhetorical strategies. The Epistulae, unpolished and situated in specific communicative contexts, offer a unique view of Augustine’s doctrinal positioning after his conversion. Traces of his Manichaean past re-emerge in vocabulary, argumentation, and theological emphasis. This is exemplified in Epistula 137 to Volusianus (411–412), which, without naming the sect, covertly critiques key Manichaean doctrines such as Docetism and materialism. These critiques align with extant Manichaean sources and may reflect Augustine’s awareness of latent Manichaean influence in Christian communities. By bringing the Epistulae into the broader discussion of Augustine’s anti-Manichaean engagement, this study highlights their value as a window into his theological evolution and pastoral strategy in a religiously contested environment. Full article

Background: Antinuclear antibodies (ANAs) serve as crucial biomarkers for diagnosing systemic autoimmune diseases; however, their interpretation can be complex and may not always correlate with clinical symptoms. Methods: A comprehensive narrative review was conducted to evaluate the peer-reviewed literature published between 1961 and 2025. Databases, including PubMed and Scopus, were searched using combinations of controlled vocabulary and free-text terms relating to antinuclear antibodies and their clinical significance. The objective was to gather and synthesize information regarding the diagnostic utility and interpretation of ANA testing in routine medical practice. Discussion: The indirect immunofluorescence assay (IIF) on HEp-2 cells is established as the gold standard for detecting ANAs, facilitating the classification of various fluorescent patterns. While a positive ANA test can suggest autoimmune disorders, the presence and titre must be interpreted alongside clinical findings, as low titres often lack diagnostic significance. Findings indicate that titres higher than 1:160 may provide greater specificity in differentiating true positives from false positives in healthy individuals. The study also emphasizes the relevance of fluorescence patterns, with specific patterns linked to particular diseases, although many do not have strong clinical correlations. Moreover, certain autoantibodies demonstrate high specificity for diseases like systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD). Ultimately, while ANA testing is invaluable for diagnosing connective tissue diseases, healthcare providers must consider its limitations to avoid misdiagnosis and unnecessary treatment. Conclusions: ANA testing is a valuable tool in the diagnosis of connective tissue diseases, but its interpretation must be approached with caution. Clinical context remains crucial when evaluating ANA results to avoid misdiagnosis and overtreatment. This review is about the diagnostic aspects and clinical consequences of ANA testing, as well as highlighting both the diagnostic benefits and the potential limitations of this procedure in everyday clinical practice. The review fills a gap in the literature by integrating the diagnostic and clinical aspects of ANA testing, with a focus on real-world interpretation challenges. Full article

Large-language-model (LLM) APIs demonstrate impressive reasoning capabilities, but their size, cost, and closed weights limit the deployment of knowledge-aware AI within biomedical research groups. At the other extreme, standard attention-based neural language models (SANLMs)—including encoder–decoder architectures such as Transformers, Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks—are computationally inexpensive. However, their capacity for semantic reasoning in noisy, open-vocabulary knowledge bases (KBs) remains unquantified. Therefore, we investigate whether compact SANLMs can (i) reason over hybrid OpenIE-derived KBs that integrate commonsense, general-purpose, and non-communicable-disease (NCD) literature; (ii) operate effectively on commodity GPUs; and (iii) exhibit semantic coherence as assessed through manual linguistic inspection. To this end, we constructed four training KBs by integrating ConceptNet (600k triples), a 39k-triple general-purpose OpenIE set, and an 18.6k-triple OpenNCDKB extracted from 1200 PubMed abstracts. Encoder–decoder GRU, LSTM, and Transformer models (1–2 blocks) were trained to predict the object phrase given the subject + predicate. Beyond token-level cross-entropy, we introduced the Meaning-based Selectional-Preference Test (MSPT): for each withheld triple, we masked the object, generated a candidate, and measured its surplus cosine similarity over a random baseline using word embeddings, with significance assessed via a one-sided t-test. Hyperparameter sensitivity (311 GRU/168 LSTM runs) was analyzed, and qualitative frame–role diagnostics completed the evaluation. Our results showed that all SANLMs learned effectively from the point of view of the cross entropy loss. In addition, our MSPT provided meaningful semantic insights: for the GRUs (256-dim, 2048-unit, 1-layer): mean similarity $\left({\mu }_{sts}\right)$ of 0.641 to the ground truth vs. 0.542 to the random baseline (gap 12.1%; $p<{10}^{-180}$). For the 1-block Transformer: ${\mu }_{sts}=0.551$ vs. $0.511$ (gap 4%; $p<{10}^{-25}$). While Transformers minimized loss and accuracy variance, GRUs captured finer selectional preferences. Both architectures trained within <24 GB GPU VRAM and produced linguistically acceptable, albeit over-generalized, biomedical assertions. Due to their observed performance, LSTM results were designated as baseline models for comparison. Therefore, properly tuned SANLMs can achieve statistically robust semantic reasoning over noisy, domain-specific KBs without reliance on massive LLMs. Their interpretability, minimal hardware footprint, and open weights promote equitable AI research, opening new avenues for automated NCD knowledge synthesis, surveillance, and decision support. Full article

This study reviews various data quality frameworks that have some form of regulatory backing. The aim is to identify how these frameworks define, measure, and apply data quality dimensions. This review identified generalisable frameworks, such as TDQM, ISO 8000, and ISO 25012, and specialised frameworks, such as IMF’s DQAF, BCBS 239, WHO’s DQA, and ALCOA+. A standardised data quality model was employed to map the dimensions of the data from each framework to a common vocabulary. This mapping enabled a gap analysis that highlights the presence or absence of specific data quality dimensions across the examined frameworks. The analysis revealed that core data quality dimensions such as “accuracy”, “completeness”, “consistency”, and “timeliness” are equally and well represented across all frameworks. In contrast, dimensions such as “semantics” and “quantity” were found to be overlooked by most frameworks, despite their growing impact for data practitioners as tools such as knowledge graphs become more common. Frameworks tailored to specific domains were also found to include fewer overall data quality dimensions but contained dimensions that were absent from more general frameworks, highlighting the need for a standardised approach that incorporates both established and emerging data quality dimensions. This work condenses information on commonly used and regulation-backed data quality frameworks, allowing practitioners to develop tools and applications to apply these frameworks that are compliant with standards and regulations. The bibliometric analysis from this review emphasises the importance of adopting a comprehensive quality framework to enhance governance, ensure regulatory compliance, and improve decision-making processes in data-rich environments. Full article

This study explores the effectiveness of Technology-Assisted Vocabulary Learning (TAVL) using student-created video learning materials within a tertiary-level English as a Foreign Language (EFL) flipped classroom. By leveraging the flipped classroom model, which allocates classroom time for interactive activities and shifts instructional content delivery outside of class, the research investigates how student-produced videos can enhance vocabulary acquisition and retention. Conducted with 47 university students from a Translation and Translation Studies course, the study aims to fill a gap in empirical evidence regarding this innovative approach. Quantitative analysis revealed that students who created and utilized videos (Group 1) showed the highest improvement in vocabulary scores, followed by those who only used the videos (Group 2), with the control group relying on traditional teacher-led methods showing the least improvement. Qualitative feedback highlighted that video creators experienced deeper engagement and better vocabulary retention, while users appreciated the videos’ visual and auditory elements but faced challenges with vocabulary overload. The findings suggest that incorporating student-created videos into the curriculum fosters a dynamic and collaborative learning environment, offering practical implications for enhancing vocabulary instruction through technology-enhanced pedagogical practices. Future research should focus on optimizing video production processes and integrating these methods with traditional teaching for comprehensive vocabulary learning. Full article

One major difference between more and less successful readers involves their access to complex texts and the conceptual knowledge and extensive vocabulary presented in them. The current research looks at four variations of scaffolded reading instruction on the reading achievement of struggling third-grade readers using informational texts. Social studies texts were selected using Lexile levels to determine the selections’ difficulty; Guided Reading levels were also used when available. Twenty-four students from three different classrooms at a Title One school in the Midwest participated in one of four groups (Wide Reading with instructional level texts, Wide Reading with grade level texts, Repeated Reading with instructional level texts, and Repeated Reading with grade level texts). An analysis of the pretest indicated that the initial ability levels of the four groups were equivalent. This research demonstrated overall gains for the third-grade readers in terms of reading ability while expanding their access to vocabulary and conceptual knowledge, two of the factors underlying the achievement gap. Full article

Research has provided evidence of the protective characteristics of extended family for U.S. emerging adults, but no research has specifically explored cousin relationships. The current study fills this gap by analyzing qualitative data collected from 192 U.S. 18–29-year-old adults (M age = 25.6 years). As this topic is relatively unexplored, examining qualitative data provides scope and vocabulary for further exploration. Participants completed an online survey asking them to describe interactions with extended family, identifying 561 cousins (M age = 28.2 years). A thematic analysis based on the process defined by Braun and Clark generated four primary themes: (1) emerging adults feel varying degrees of closeness and distance with their cousins, (2) relational maintenance with cousins is both planned and incidental, (3) family membership provides resources, and (4) cousins share the same generational position. These results describe important characteristics of the cousin relationship, including moments of unexpected closeness and shared experience of family. The findings also highlight the relevance of sharing a similar life stage within the same family system. Practitioners can utilize findings to help clients identify extended family members that can be tapped for bonding and support. Full article

Eco-literacy, the ability to understand and apply ecological knowledge to foster sustainable living, is increasingly recognized as an essential educational objective. Traditionally linked to disciplines such as science and social studies, eco-literacy is now gaining traction in English language teaching (ELT) to align language instruction with global sustainability goals. While previous research has highlighted the importance of sustainability in education, there is a scarcity of studies on how eco-literacy can be effectively integrated into language learning. To address this gap, this study aims to explore ELT students’ perceptions of the impact of eco-literacy-focused writing activities on their language development and awareness of environmental issues. Employing a mixed-methods design, data were collected through pre-and post-tests on changes in students’ perceptions of eco-literacy-focused writing activities. Semi-structured interviews with a subset of participants offered qualitative insights into their experiences. The findings revealed significant changes in students’ perceptions, particularly in their understanding of environmental vocabulary and critical thinking related to eco-literacy. Writing tasks and discussions on ecological themes enhanced their engagement and understanding of global sustainability issues. However, challenges such as limited prior knowledge of environmental topics and the need for further training were identified. This study contributes to the growing body of literature on sustainable language education by demonstrating the pivotal role of integrating eco-literacy into ELT classrooms. It also provides practical recommendations for teacher training programs to develop pedagogical strategies promoting linguistic and environmental learning outcomes, ultimately preparing educators to address global issues in diverse teaching contexts. Full article

Vocabulary instruction is pivotal to literacy development. While extensive research has been conducted, few studies have examined how research translates into practice and how theory informs it. This study addresses this gap by identifying theories guiding vocabulary instruction in leading practitioner-oriented journals in the field, assessing trends and practices, and examining shifts over the past decade. The systematic review critically extends the scope of existing research in two directions. First, it focuses on English learners (ELs), a growing K-12 population globally. Second, literacy theories were utilized to identify the frameworks that guided vocabulary instruction for ELs. The results reveal the predominance of instructional practices guided by schema and psycholinguistic theories, followed by social constructivism and sociocultural theories. Furthermore, the findings underscore the importance of adapting vocabulary instructional practices to meet the developmental needs of ELs at different grade levels. Full article

In addition to being influenced by structural factors like socioeconomic background (SES), children’s achievements are also influenced by psychological factors like the parental mindset. Mindsets may thus be a key to tempering the income–vocabulary gap in emerging bilingual children’s L1 and L2 vocabulary. This study compared how SES and the parental mindset connect with parental values on the importance of English, home literacy practices, and children’s Chinese and English vocabulary. We assessed 271 (M_age = 60.78 months) children’s Chinese and English vocabulary, and their parents completed a home literacy environment questionnaire. Path analysis showed that parental growth mindset does not vary with their SES. In addition, parents’ mindsets have a unique contribution to children’s outcomes through parents’ beliefs and the home literacy environment. Full article

Automatic speech recognition (ASR) systems often struggle to recognize speech from individuals with dysarthria, a speech disorder with neuromuscular causes, with accuracy declining further for unseen speakers and content. Achieving robustness for such situations requires ASR systems to address speaker-independent and vocabulary-mismatched scenarios, minimizing user adaptation effort. This study focuses on comprehensive training strategies and methods to tackle these challenges, leveraging the transformer-based Wav2Vec2.0 model. Unlike prior research, which often focuses on limited datasets, we systematically explore training data selection strategies across diverse source types (languages, canonical vs. dysarthric, and generic vs. in-domain) in a speaker-independent setting. For the under-explored vocabulary-mismatched scenarios, we evaluate conventional methods, identify their limitations, and propose a solution that uses phonological features as intermediate representations for phone recognition to address these gaps. Experimental results demonstrate that this approach enhances recognition across dysarthric datasets in both speaker-independent and vocabulary-mismatched settings. By integrating advanced transfer learning techniques with the innovative use of phonological features, this study addresses key challenges for dysarthric speech recognition, setting a new benchmark for robustness and adaptability in the field. Full article

The use of different generative AIs such as OpenAI’s ChatGPT, Microsoft Copilot, or Google’s Gemini has been implemented and studied in different aspects of language education. However, exploring how the combination of teacher-generated feedback and AI-generated feedback influences student revision practices in EFL academic writing remains largely unexplored. To fill in the gap, this preliminary study investigates the impact of two forms of feedback, including teacher-generated feedback and AI-generated feedback, as well as the orders in which the two types of feedback have been executed, that is, teacher-generated feedback before AI-generated feedback (TGF-AIGF) or AI-generated feedback before teacher-generated feedback (AIGF-TGF), on EFL students’ writing revision practices in a 15-week course with fourteen Vietnamese undergraduates. Using Gemini as an AI-generated feedback tool, the study analyzed student revisions in four essays, focusing on local (grammar and vocabulary) and global (content and organization) aspects. Findings revealed that AI-generated feedback consistently resulted in higher revision frequencies compared to teacher-generated feedback alone, as it provided specific, actionable, and comprehensive suggestions. The integration of teacher- and AI-generated feedback yielded the highest revision frequencies, demonstrating complementary strengths, including AI-generated feedback that addressed surface-level issues, while teacher-generated feedback focused on higher-order concerns. Although no statistically significant differences were found between the two orders in which the two types of feedback have been executed, the AIGF-TGF order showed a slightly greater quantity of revisions made by students, allowing AI-generated feedback to scaffold surface-level revisions before teacher-generated feedback addressed global issues. These results highlight the potential of combining AI- and teacher-generated feedback to enhance writing revisions and provide pedagogical insights for integrating AI tools into academic writing courses. Full article

Over the last ten years, continuous attention has been paid to the use of digital games in vocabulary learning. Their effectiveness and availability have been widely discussed. However, the experiences of language learners and the underlying patterns of their engagement while using digital games for vocabulary learning remain underexplored. In order to fill this significant gap, this study aimed to examine the influential mechanism of English as a Foreign Language (EFL) learners’ flow experiences in digital game-based vocabulary learning (DGBVL). The sample consisted of 306 Chinese EFL learners who had DGBVL app usage experience, and data collection was based on a DGBVL flow experience instrument employed through an online platform. Structural equation modeling (SEM) was employed to assess the reliability and validation of the existing scale for various DGBVL apps. A multi-group analysis was then conducted, revealing that the influential mechanism was a process in which the effects of antecedents on outcomes could be mediated by flow experiences. In addition, the role of usage frequency was also explored, and three paths were found to differ across three usage frequency levels (i.e., seldom, sometimes, and always): the effect of balance of skill and challenge on enjoyment, the effect of enjoyment on satisfaction, and the effect of perceived learning on satisfaction. These findings provide new insights for the influential mechanism of flow experiences and will assist EFL learners in optimizing their learning outcomes in digital game-based vocabulary learning. Full article

This study addresses the integration of Spanish for Specific Purposes in Agriculture (SSPA) into the curricula of English-speaking veterinary and animal sciences students at three collaborating universities (Texas Tech University School of Veterinary Medicine, North Carolina State University, and Tarleton State University), to bridge communication gaps in agricultural settings. We designed and implemented three SSPA courses focused on key areas such as animal health, welfare, and food safety, using insights from industry professionals. The courses incorporated contextualized language instruction, including vocabulary, grammar, and practical communication scenarios relevant to the field. Teaching materials including interactive resources and metacognitive strategies were developed to enhance learning and engagement. The results showed that while the courses improved students’ communication skills, challenges such as technical issues and balancing academic commitments required adjustments to course delivery. Significant differences in performance were observed between institutions, with a considerable impact on the course completion rates and final test scores. The study concludes that SSPA courses significantly enhance the ability of veterinary and animal sciences students to effectively communicate with the Hispanic workforce, providing a model for similar language integration efforts in specialized fields. Full article

This paper proposes a robust methodology for integrating process-specific data and domain expert knowledge into linked knowledge graphs. These graphs utilize an ontology that provides a standardized vocabulary for material science and facilitates the creation of semantic models for various processes along the digital process chain. A generic template for structuring processes is proposed, simplifying subsequent data retrieval. The templates of specific processes are designed collaboratively by domain and ontology experts, aided by a proposed interview template that bridges the knowledge gap. Following the digitalization of material data through semantic modeling, machine-readable data with contextual metadata is stored in a graph database, which can be efficiently queried using the SPARQL language, enabling seamless integration into data pipelines. To demonstrate this approach, a knowledge graph is developed to represent the process chain of AlSi10Mg objects manufactured via permanent mold casting, capturing their complete history from the initial manufacturing step to final non-destructive testing and mechanical characterization. This methodology enhances data interoperability and accessibility while providing context-rich data for training AI models, potentially accelerating new knowledge discovery in material science. Full article