Search Results (66)

This study examines lexical and syntactic convergence between Dai Lue and Chinese in the multilingual environment of Sipsongpanna, employing an apparent-time approach across three generational cohorts (N = 90, balanced gender). Through mixed-methods analysis (structured questionnaires and semi-structured interviews), significant diachronic variation was observed. Younger speakers exhibited pronounced convergence, adopting Chinese-derived syntactic patterns (e.g., prenominal quantifiers and preverbal adjunct phrases) and borrowing Chinese lexical elements (e.g., an adverb sɛn⁵⁵ ‘first’ ← Chinese 先 xiān, and a superlative marker tsui³⁵ ‘most/best’ ← Chinese 最 zuì). Middle-aged speakers use transitional hybrid structures, while older speakers more consistently maintain native Dai Lue features. The results conform with Labov’s age-grading model in contact linguistics and refine Thomason’s borrowing hierarchy by revealing two factors: First, the prestige of the Chinese language drives convergence among youth. Second, syntactic compatibility with Chinese is mediated not merely by language structure, but by discourse-pragmatic needs, functional load redistribution, and the social indexicality of borrowed structures. This underscores the interplay between sociolinguistic motivations and structural-adaptive constraints in language change. The findings provide critical insights into language contact mechanisms among ethnic minorities of China, with implications for sociolinguistic theory, language revitalization efforts, and bilingual education policy implementation in linguistically diverse communities. Full article

Background/Objectives: The functional architecture connecting speech production and reading comprehension remains unclear in multilinguals. This study investigated the cross-modal interaction between these systems in trilinguals to resolve the debate between Age of Acquisition (AoA) and usage frequency. Methods: We recruited 144 Uyghur (L1)–Chinese (L2)–English (L3) trilinguals, a population uniquely dissociating acquisition order from social dominance. Participants completed a production-to-comprehension priming paradigm, naming pictures in one language before performing a lexical decision task on translated words. Data were analyzed using linear mixed-effects models. Results: Significant cross-language priming confirmed an integrated lexicon, yet a fundamental asymmetry emerged. The top-down influence of production was governed by AoA; earlier-acquired languages (specifically L1) generated more effective priming signals than L2. Conversely, the bottom-up efficiency of recognition was driven by social usage frequency; the socially dominant L2 was the most receptive target, surpassing the heritage L1. Conclusions: The trilingual lexicon operates via “Two Rules”: a history-driven production system (AoA) and an environment-driven recognition system (Social Usage). This asymmetrical baseline challenges simple bilingual extensions and clarifies the dynamics of multilingual language control. Full article

Nonverbal tests assess cognitive ability in multicultural and multilingual children, but language-based instructions disadvantage non-proficient children. This is a growing concern worldwide due to the increasing number of multilingual classrooms. The tablet-based FLUX (Fluid Intelligence Luxembourg) test was developed within a highly multicultural and multilingual educational context to offer not only nonverbal test content but also language-fair animated video instructions. A total of 703 third graders (M_age = 8.85, SD = 0.66; 48.8% females, 51.1% males, 0.1% with no gender specified) were included in the standardisation sample and were assessed with tasks measuring figural fluid intelligence, quantitative fluid intelligence, visual processing and short-term memory. The test proved sufficiently reliable (FLUX Full-scale: McDonald’s Omega = 0.94; split-half = 0.95). Test fairness was ensured by analysing each item for Differential Item Functioning (DIF) on children’s background characteristics (language spoken at home, socioeconomic status, gender). Its factorial structure was confirmed using Confirmatory Factor Analysis (CFA). Further validity evidence was provided by determining its concurrent and criterion-related validity (correlations with a test of cognitive ability and educational achievement scores). Research implications and future prospects in promoting equal opportunities in a heterogeneous multilingual educational context are discussed. Full article

This scoping review examines how workplace language proficiency and corporate language policies function as dimensions of human capital, shaping employee mobility and organisational outcomes in multilingual contexts. Drawing on 12 empirical studies (2010–2025), supplemented by one influential review work used for context, the review integrates two analytical lenses: (1) language ceilings and walls, which capture invisible barriers to vertical and horizontal mobility, and (2) the Language Needs Analysis (LANA) framework, which categorises language demands at the individual, organisational, and operational levels. Findings indicate that language proficiency and inclusive language policies act as strategic resources that enhance employability, cross-border collaboration, and knowledge transfer. Conversely, rigid monolingual policies often reproduce inequalities and limit career progression. The review highlights the role of language-sensitive HRM in developing sustainable talent pipelines, advancing diversity and inclusion, and strengthening workforce resilience. Methodologically, this study applies PRISMA-ScR guidelines to ensure transparency and rigour, while offering a framework for future research at the intersection of human capital theory, language policy, and global HRM. By reframing communicative competence as career capital, the review underscores the need to integrate language training and policy design into broader human capital development strategies. Full article

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

This paper addresses the challenge of segmenting consumer behavior in contexts characterized by both numerical regularities and semantic variability. Traditional models, such as RFM-based segmentation, capture the transactional dimension but neglect the implicit meanings expressed through product descriptions, reviews, and linguistic diversity. To overcome this gap, we propose a hybrid clustering algorithm that integrates numerical and semantic distances within a unified scalar framework. The central element is a scalar objective function that combines Euclidean distance in the RFM space with cosine dissimilarity in the semantic embedding space. A continuous parameter λ regulates the relative influence of each component, allowing the model to adapt granularity and balance interpretability across heterogeneous data. Optimization is performed through a dual strategy: gradient descent ensures convergence in the numerical subspace, while genetic operators enable a broader exploration of semantic structures. This combination supports both computational stability and semantic coherence. The method is validated on a large-scale multilingual dataset of transactional records, covering five culturally distinct markets. Results indicate systematic improvements over classical approaches, with higher Silhouette scores, lower Davies–Bouldin values, and stronger intra-cluster semantic consistency. Beyond numerical performance, the proposed framework produces intelligible and culturally adaptable clusters, confirming its relevance for personalized decision-making. The contribution lies in advancing a scalarized formulation and hybrid optimization strategy with wide applicability in scenarios where numerical and textual signals must be analyzed jointly. Full article

This research addresses the critical need for language preservation among the Higaonon indigenous community in Mindanao, Philippines, through the development of a culturally responsive mobile dictionary application. The Higaonon language faces significant endangerment due to generational language shift, limited documentation, and a scarcity of educational materials. Employing user-centered design principles and participatory lexicography, this study involved collaboration with tribal elders, educators, and youth to document and digitize Higaonon vocabulary across ten culturally significant semantic domains. Each Higaonon lexeme was translated into English, Filipino, and Cebuano to enhance comprehension across linguistic groups. The resulting mobile application incorporates multilingual search capabilities, offline access, phonetic transcriptions, example sentences, and culturally relevant design elements. An evaluation conducted with 30 participants (15 Higaonon and 15 non-Higaonon speakers) revealed high satisfaction ratings across functionality (4.81/5.0), usability (4.63/5.0), and performance (4.73/5.0). Offline accessibility emerged as the most valued feature (4.93/5.0), while comparative analysis identified meaningful differences in user experience between native and non-native speakers, with Higaonon users providing more critical assessments particularly regarding font readability and performance optimization. The application demonstrates how community-driven technological interventions can support indigenous language revitalization while respecting cultural integrity, intellectual property rights, and addressing practical community needs. This research establishes a framework for ethical indigenous language documentation that prioritizes community self-determination and provides empirical evidence that culturally responsive digital technologies can effectively preserve endangered languages while serving as repositories for cultural knowledge embedded within linguistic systems. Full article

The development of software applications using multiple programming languages has increased in recent years, as it allows the selection of the most suitable language and runtime for each component of the system and the integration of third-party libraries. However, this practice involves complexity and error proneness, due to the absence of an adequate system for the interoperability of multiple programming languages. Developers are compelled to resort to workarounds, such as library reimplementation or language-specific wrappers, which are often dependent on C as the common denominator for interoperability. These challenges render the use of multiple programming languages a burdensome and demanding task that necessitates highly skilled developers for implementation, debugging, and maintenance, and raise doubts about the benefits of interoperability. To overcome these challenges, we propose MetaFFI, introducing a fully in-process, plugin-oriented, runtime-independent architecture based on a minimal C abstraction layer. It provides deep binding without relying on a shared object model, virtual machine bytecode, or manual glue code. This architecture is scalable (O(n) integration for n languages) and supports true polymorphic function and object invocation across languages. MetaFFI is based on leveraging FFI and embedding mechanisms, which minimize restrictions on language selection while still enabling full-duplex binding and deep integration. This is achieved by exploiting the less restrictive shallow binding mechanisms (e.g., Foreign Function Interface) to offer deep binding features (e.g., object creation, methods, fields). MetaFFI provides a runtime-independent framework to load and xcall (Cross-Call) foreign entities (e.g., getters, functions, objects). MetaFFI uses Common Data Types (CDTs) to pass parameters and return values, including objects and complex types, and even cross-language callbacks and dynamic calling conventions for optimization. The indirect interoperability approach of MetaFFI has the significant advantage of requiring only $2n$ mechanisms to support n languages, compared to direct interoperability approaches that need $n^2$ mechanisms. We developed and tested a proof of concept tool interoperating three languages (Go, Python, and Java), on Windows and Ubuntu. To evaluate the approach and the tool, we conducted a user study, with promising results. The MetaFFI framework is available as open source software, including its full source code and installers, to facilitate adoption and collaboration across academic and industrial communities. Full article

Public libraries serve as vital community hubs that foster engagement, empowerment, and education, particularly for vulnerable populations, including refugee children and families. This study examines how Oklahoma’s public libraries contribute to refugee resilience and identifies challenges they face in providing these essential services. Using a qualitative method approach, including 20 semi-structured interviews with library staff, questionnaire surveys, and observations conducted across three Oklahoma library systems (Metropolitan, Pioneer, and Tulsa City-County) the study explored programs, services, and strategies that support refugee adaptation and integration. Findings reveal that libraries excel in three key areas: cognitive services (language literacy, digital access, educational resources), socio-cultural services (community building, cultural exchange), and physiological services (safe spaces, welcoming environments). These services contribute to building human, social, and economic capital, with human capital consistently ranked as most crucial for refugee resilience. However, libraries face significant challenges, with language barriers, program gaps, and outreach limitations being the most prevalent obstacles. Additional barriers include facility constraints, transportation difficulties, resource limitations, and privacy concerns. The study proposes nine comprehensive guidelines for creating sustainable pathways to refugee resilience through enhanced library services, emphasizing proactive community engagement, staff training, multilingual resources, advocacy, strategic partnerships, tailored programming, transportation solutions, cultural competence, and welcoming environments. This study contributes to understanding how public libraries can function as inclusive institutions that support refugee children’s successful integration and development in their new communities. Full article

Since the early 2000s, Malaysian Chinese independent cinema has garnered international recognition, with James Lee emerging as one of its most influential figures. Distinct from many of his contemporaries, Lee’s films feature a unique sound design that plays a pivotal role in articulating cultural identity. This study, grounded in in-depth interviews with the director, investigates how sound aesthetics function as a vital medium for cultural expression. In the postcolonial context of Malaysia, sound is revealed not merely as a narrative device but as a complex tool of cultural translation. Lee’s creative practice exemplifies what this study terms a “sound-driven non-conscious cultural expression”, wherein surreal sound treatments and multilingual environments construct an aesthetic that is both locally rooted and transnational in scope. By drawing upon sound theory and theories of cultural identity, this research uncovers the significance of sound aesthetics in multicultural contexts, offering new perspectives for film and cultural studies alike. Full article

With the exponential growth of textual data, traditional topic modeling methods based on static analysis demonstrate limited effectiveness in tracking the dynamics of thematic content. This research aims to develop a method for quantifying the dynamics of topics within text corpora using a thematic signal (TS) function that accounts for temporal changes and semantic relationships. The proposed method combines associative tokens with original lexical units to reduce thematic entropy and information noise. Approaches employed include topic modeling (LDA), vector representations of texts (TF-IDF, Word2Vec), and time series analysis. The method was tested on a corpus of news texts (5000 documents). Results demonstrated robust identification of semantically meaningful thematic clusters. An inverse relationship was observed between the level of thematic significance and semantic diversity, confirming a reduction in entropy using the proposed method. This approach allows for quantifying topic dynamics, filtering noise, and determining the optimal number of clusters. Future applications include analyzing multilingual data and integration with neural network models. The method shows potential for monitoring information flows and predicting thematic trends. Full article

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article

Language-in-education policies often serve hidden political and economic agendas, and thus language policy research must examine policies beyond official state discourse. This article critically analyzes Morocco’s Language Alternation Policy (LAP), introduced in 2019, using the historical–structural approach. It examines the broader historical context and structural factors that shape the adoption and implementation of LAP. While the official policy discourse frames LAP as an egalitarian reform aimed at promoting balanced multilingualism by alternating instructional media in science education, its de facto implementation reveals a stark contradiction. The ideological underpinnings of LAP are the resurgence of French as the exclusive medium of instruction in science and technology classrooms. This policy undercuts a decades-long Arabization of science and the promotion of the Amazigh language, as well as denying Moroccans the potential advantages of learning English. The disparity between official policy discourse and implementation reveals the influence of France’s neocolonial agenda, exercised through Francophonie, international clientelism, and financial patronage. Through implementing LAP to align with France’s interests in Morocco, French-trained political actors undermine the country’s decolonization efforts and preserve the long-standing socioeconomic privileges of the francophone elite. We analyze how LAP functions ideologically to resolidify France’s cultural and linguistic hegemony and reinforce pre- and post-independence linguistic and social inequalities. Full article

In the rapidly evolving field of e-commerce, precise and efficient attribute extraction from product descriptions is crucial for enhancing search functionality, improving customer experience, and streamlining the listing process for sellers. This study proposes a large language model (LLM)-based approach for automated attribute extraction on Trendyol’s e-commerce platform. For comparison purposes, a deep learning (DL) model is also developed, leveraging a transformer-based architecture to efficiently identify explicit attributes. In contrast, the LLM, built on the Mistral architecture, demonstrates superior contextual understanding, enabling the extraction of both explicit and implicit attributes from unstructured text. The models are evaluated on an extensive dataset derived from Trendyol’s Turkish-language product catalog, using performance metrics such as precision, recall, and F1-score. Results indicate that the proposed LLM outperforms the DL model across most metrics, demonstrating superiority not only in direct single-model comparisons but also in average performance across all evaluated categories. This advantage is particularly evident in handling complex linguistic structures and diverse product descriptions. The system has been integrated into Trendyol’s platform with a scalable backend infrastructure, employing Kubernetes and Nvidia Triton Inference Server for efficient bulk processing and real-time attribute suggestions during the product listing process. This study not only advances attribute extraction for Turkish-language e-commerce but also provides a scalable and efficient NLP-based solution applicable to large-scale marketplaces. The findings offer critical insights into the trade-offs between accuracy and computational efficiency in large-scale multilingual NLP applications, contributing to the broader field of automated product classification and information retrieval in e-commerce ecosystems. Full article

Background: Mobile gaming applications are increasingly marketed as cognitive training tools for older adults, yet their scientific validity and accessibility remain uncertain. This scoping review evaluates their effectiveness and inclusivity. Methods: A systematic search of the Apple App Store and Google Play Store identified 227 applications, with 14 meeting inclusion criteria. Apps were assessed for scientific validity, theoretical foundation, accessibility, cognitive targeting, user engagement, and monetization models. Results: While all 14 apps claimed cognitive benefits, only one cited empirical research. None included baseline cognitive assessments or progress tracking. Accessibility was limited—eight apps had visual accommodations, but none provided auditory support. Six apps were English-only, restricting linguistic inclusivity. Monetization varied, with eight requiring in-app purchases or subscriptions, posing financial barriers. Conclusions: This review highlights critical gaps in the current cognitive gaming application market for older adults. Despite their popularity, cognitive training apps for older adults lack scientific validation and accessibility, limiting their effectiveness as cognitive interventions. Developers should integrate evidence-based training, adaptive assessments, and inclusive accessibility features such as voice guidance and multilingual support. Future research should prioritize longitudinal studies to assess real-world efficacy, refine interventions targeting memory, executive function, and processing speed, and enhance inclusive design for diverse aging populations. Full article