Search Results (2,199)

Personal names carry cultural and linguistic identity, yet most African countries lack large-scale, structured name datasets suitable for natural language processing (NLP) research and computational social science. We present CongoNames, the first large-scale corpus of personal names from the Democratic Republic of the Congo (DRC), derived from publicly released national secondary-school examination palmarès (result lists) 8,053,983 published annually by the DRC Ministry of Education. The corpus comprises name records spanning 16 examination years (2008–2023) across 12 provinces and 304 sub-provincial regions, each enriched with a reported sex marker (M/F) and regional provenance metadata. We describe a fully deterministic, layered processing pipeline (bronze–silver–gold architecture) that converts raw protable document format (PDF) documents into structured comma-separated values (CSV) datasets without manual annotation or machine-learning-based inference. The dataset is validated against school-level census counts extracted from the same source PDFs, yielding extraction error rates below 2% for all years except 2023 (7.81%, flagged due to a layout change). Descriptive analyses document name length and token-count distributions, character-level n-gram profiles, provincial diversity indices, and inter-provincial name-inventory overlap, collectively establishing the dual linguistic origin—locally rooted Bantu components and Christian/French-origin components—that characterize modern Congolese naming practice. The dataset, processing code, and documentation are released openly to support research in African natural language processing (NLP), onomastics, and computational social science. Full article

Electromagnetic wave absorbing materials are important for electromagnetic protection, radar stealth, wireless communication, and advanced electronic systems. However, traditional design methods mainly rely on repeated experiments and full-wave simulations, which are time-consuming and inefficient when dealing with complex compositions, microstructures, and multilayer structures. Machine learning provides a new route to accelerate the design of high-performance absorbers by learning the relationship among material composition, structure, electromagnetic parameters, and absorption performance. This review summarizes recent progress in machine-learning-empowered electromagnetic wave absorbing materials. First, the basic physical principles of electromagnetic wave absorption are introduced, including reflection loss, impedance matching, attenuation, and physical limits such as the Rozanov and Snoek limits. Then, typical machine learning models are discussed, including classical machine learning, deep learning, generative models, physics-informed models, large language models, and artificial-intelligence (AI) Agents. Their applications are further summarized from forward property prediction, high-throughput screening, inverse design, electromagnetic parameter decoupling, physics-informed modeling, explainability, multi-objective optimization, and data augmentation. Finally, the main challenges and future directions are discussed, including data standardization, physics-guided learning, foundation models, autonomous laboratories, and engineering-scale validation. This review shows that machine learning is changing absorber research from experience-driven trial-and-error to data-driven and knowledge-driven design, and provides a useful reference for developing next-generation electromagnetic wave absorbing materials. Full article

Large language models (LLMs) are changing how students approach problem solving, code interpretation, and software development. Successful task completion with AI assistance, however, does not necessarily indicate conceptual understanding of underlying programming principles, making it difficult to determine how programming skills develop over time. This study examines whether students’ critical engagement, collaborative learning practices, and exploratory use of LLMs are associated with self-reported programming competence, coding practices, and longer-term knowledge retention. Survey data from 189 students with varying levels of LLM use in educational and coding-related contexts were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings suggest that students perceive LLMs as more educationally valuable when they actively question, reinterpret, and adapt AI-generated responses rather than accept them as final answers. Students who interacted more reflectively with AI outputs reported stronger perceived competence and more deliberate attention to code organization, readability, and maintainability. Collaborative use corresponded to broader development of programming abilities, whereas creative experimentation was more closely related to stylistic refinement and perceived benefits for longer-term retention. Active engagement with AI-generated material may therefore promote analytical reasoning and deeper conceptual involvement instead of merely accelerating code production. By moving beyond technology adoption and productivity-oriented perspectives, the study highlights the role of learner agency in shaping LLM-supported programming education. Full article

Background: Effective nurse–patient communication is fundamental to quality care delivery, yet language barriers pose significant challenges in multicultural healthcare environments. In Saudi Arabia’s diverse healthcare landscape, nurses frequently encounter patients who do not speak Arabic, potentially compromising care quality and patient safety. Objective: To explore multilevel factors influencing communication between Saudi nurses and non-Arabic-speaking patients, using Bronfenbrenner’s ecological systems theory as a conceptual framework. Design: A qualitative descriptive study employing semi-structured interviews analyzed through reflexive thematic analysis. Setting: Four healthcare facilities (two governmental and two private hospitals) across Saudi Arabia. Participants: Eighteen Saudi registered nurses with experience caring for non-Arabic-speaking patients, recruited through purposive sampling. Methods: Semi-structured interviews (n = 18) were conducted in Arabic or English between November 2025 and February 2026. Data were analyzed using Braun and Clarke’s reflexive thematic analysis, organized within Bronfenbrenner’s ecological levels. Collaborative reflexive coding and member-checking with six participants supported analytical rigor. Results: Five main themes emerged: (1) Individual-level competencies and preparedness (microsystem), (2) Interpersonal dynamics and cultural sensitivity (microsystem), (3) Unit-level resources and organizational support (mesosystem), (4) Institutional policies and language services (exosystem), and (5) Healthcare system and societal influences (macrosystem). Participants identified language proficiency gaps, cultural misunderstandings, inadequate interpreter services, and systemic barriers as primary challenges affecting communication quality. Conclusions: Communication between Saudi nurses and non-Arabic-speaking patients is influenced by complex, interconnected factors across multiple ecological levels. Interventions should address individual competency development, organizational support systems, and policy-level changes to ensure equitable, safe, and effective communication for all patients. Full article

Long-term analyses of year changes in preschoolers’ cognitive and affective development are limited. This field-based study examined yearly trends in a multimedia-assisted, theme-based English and Chinese program in South Korean daycare centers (2022–2024). Participants included 112, 120, and 124 children aged 3–5 annually. Changes were analyzed for cognitive (vocabulary/sentence comprehension and expression) and affective (learning interest, confidence, and motivation) domains. Group differences based on duration of enrollment (three years vs. less than three years) were examined among five-year-old children. Significant cognitive gains occurred for both languages across all three years. Affective development was significant for both languages in 2022 and 2023; however, in 2024, significant positive changes persisted for English across all subdomains, while for Chinese, only confidence improved. Correlation patterns varied by year and language; notably, English motivation was significantly associated with selected cognitive domains in the third year. Furthermore, group comparisons indicated that children enrolled for three years demonstrated better performance in both English and Chinese than those enrolled for less than three years, with statistically significant differences particularly evident in sentence expression. These findings underscore the value of developmentally appropriate and engaging learning contexts that foster both the cognitive and affective dimensions of early learning. Full article

Deforestation remains a crucial Anthropocene challenge, driving biodiversity loss, carbon emissions, and socio-ecological disruption. Despite extensive study, the long-term structure, thematic evolution, and collaborative patterns of deforestation research remain insufficiently synthesized. This bibliometric analysis examines 5091 publications from WoS and Scopus (1974–2025), using RStudio (version 4.5.2 (31 October 2025)), VOSViewer (version 1.6.20), and Excel to analyze publication trends, citation patterns, thematic clusters, and collaboration networks. Results show rapid growth after 2000, with citation peaks in 2010 and 2020. Major thematic clusters include deforestation, climate change, agriculture, governance, REDD+, and remote sensing. Environmental Research Letters is the most influential journal; Fearnside, P., is the leading author, and the UC system is a top institution. The USA and Brazil lead nationally, with the Amazon, Congo Basin, and Southeast Asia as primary geographic foci, reflecting persistent North–South collaboration dynamics. Limitations include reliance on English-language publications and title-only search criteria, which may underrepresent non-Anglophone research. Future research should expand to multiple languages, incorporate gray literature, and examine the policy impacts of deforestation-free supply chain regulations, such as the EUDR. This review underscores deforestation science as a growing, multidisciplinary field that requires the integration of social and ecological sciences, AI, and geospatial tools, alongside stronger research-policy linkages and enhanced capacity in forest-affected regions. Full article

Building Information Modeling (BIM) usually represents a building as a static snapshot of the model’s state. Dynamic extensions, such as Internet of Things(IoT)-enabled sensing or immersive visualization, already exist, but the underlying data model remains state-based. The Industry Foundation Classes (IFC) standard does not define a formal mechanism that would link the same physical element across successive phases of a building’s life cycle. Design, construction, and operation are recorded in separate IFC files, and the same element is assigned different Globally Unique Identifiers (GUIDs) in each. The result is fragmentation of the element’s identity, loss of the history of property changes, and the inability to formulate cross-phase queries. This paper proposes the BIM-Phase ontology based on the fundamental Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) ontology, which solves this problem by introducing a distinction between a building element as an endurant and its life cycle phases as perdurants. The ontology comprises nine classes, six object relations, and six axioms expressed in Web Ontology Language 2 Description Logic (OWL 2 DL). Phase properties and relations are represented using a reification pattern, which maintains full compatibility with the expressiveness of OWL 2 DL. The ontology was validated using an example of a single-family residential building developed in Autodesk Revit. Three structural elements (external wall, floor slab, and column) were tracked across three phases of the life cycle. Eight competency questions covering scalar, constitutional, and mereological changes were defined and mapped to ontology constructs, confirming that the BIM-Phase enables the recording of changes and the formulation of cross-phase queries that are impossible in classic IFC. All eight questions were answered correctly on the published knowledge graph, and the HermiT reasoner confirmed the logical consistency of the model. The findings show that preserving element identity across phases requires only a minimal ontological layer on top of existing standards. We recommend introducing persistent, phase-independent identifiers of building elements alongside IFC GUIDs, as this single change enables full lifecycle change tracking. Full article

Accurate dairy cow behavior recognition is important for health monitoring, welfare assessment, and early warning in smart livestock farming. However, recognizing fine-grained behaviors such as feeding, drinking, and rumination remains difficult in real barns because of occlusion, complex backgrounds, subtle motion changes, and class imbalance. This study proposes a behavior recognition method that integrates temporal modeling and behavioral priors. The Contrastive Language–Image Pre-training (CLIP) visual encoder is used as the feature extraction backbone, while two temporal adapters are introduced to model dynamic information across consecutive video frames. Dairy cow behavior recognition is further decoupled into posture recognition and action recognition, and a behavioral prior loss is designed to softly constrain unlikely posture–action combinations, such as lying with feeding or lying with drinking. On the test set, the proposed method achieves a five-class accuracy of 75.45%, a five-class Macro-F1 of 0.7246, and an Action Macro-F1 of 0.7605, outperforming the CLIP baseline and several representative video recognition models. These results indicate that the proposed method can support non-contact monitoring of key dairy cow behaviors for practical barn management. Full article

Blockchain is a fast-changing field that is highly useful in such areas as finance, supply chain management, voting systems, and healthcare. As a consequence, software developers are increasingly creating Blockchain-Based Applications (BBAs) and Smart Contracts (SCs). However, the development of BBAs has been associated with various problems, especially in the process of updating and debugging such systems with a high degree of reliability. This is due to the immutability of deployed SCs. In this paper, we conduct an in-depth analysis of 61 published BBA articles between 2017 and 2025 to identify some causes of these challenges. Our results indicate that there is inadequate adaptation of the Software Development Life Cycle (SDLC) for BBAs. In particular, few BBA projects—only 32% of the reviewed projects—address the analysis phase, and only 29% deal with the design phase, frequently ignoring formal modeling methods. Based on these observations, we propose a new, context-adaptive methodology that facilitates BBA developers passing through the requirements, analysis, design, and implementation processes. Formal modeling techniques—such as Use Case Maps (UCMs), Finite State Machines (FSMs), and extended Unified Modeling Language (UML) class and sequence diagrams—are used within the methodology to document BBA structural and behavioral features and maintain complete traceability between requirements and implementation. In order to overcome the blockchain-specific drawbacks of traditional UML, we present formal stereotype extensions of UML class diagrams, where a four-compartment structure is introduced to differentiate state variables, functions, events, and access modifiers on SCs. We also provide analogous extensions to UML sequence diagrams using differentiated arrow notations to distinguish between function calls and event emissions to support accurate modeling of decentralized transaction flows. These extensions are described with a rationale and are formally defined and justified by mapping rules. Our methodology is justified by two case studies that prove its applicability in different fields of blockchain. The initial case study thus designs and executes a system of a halal chicken meat supply chain on Ethereum, showing the complete traceability of requirements that are based on UCM-based requirements and FSM-generated algorithms to implement SCs. The second case study applies the methodology to a decentralized Electronic Health Record (EHR) management system, and it shows coverage and completeness modeling. The methodology was evaluated through two case studies using a structured questionnaire and quantitative metrics, including traceability accuracy, reduction-in-error indicators, SC defect and gas-analysis results, modeling overhead measurements, and static security analysis with Slither. It is also evaluated based on a group of seven literature-based qualitative evaluation criteria that include workflow expressiveness, reusability, technical concept coverage, intelligibility, completeness, tool support, and blockchain limitation modeling. Full article

Background/Objectives: Large language models (LLMs) are increasingly used by the public to seek health information, yet their accuracy in addressing common vaccine myths remains unclear. Sycophantic LLM behavior, where models align with rather than correct user-stated beliefs, poses specific risks in health contexts. Methods: We conducted an exploratory multi-vendor evaluation of three LLMs (GPT-5, Gemini 2.5 Flash, Claude Sonnet 4) using officially curated vaccination myths from Germany’s public health institution and two realistic user framings (curious skeptic, convinced believer). All model responses were independently evaluated by two blinded medical experts for misconception addressal (binary criterion applied to the response text), scientific accuracy, and communication clarity (5-point Likert scales). Additionally, blinded marketing experts ranked models for lay communication clarity. Flesch Reading Ease scores were computed for all outputs. Results: Across all myths, framings, and models (66 response items), both medical raters judged that all responses refuted the targeted misconception; no response affirmed or ignored a myth, including under the adversarial convinced believer framing. Scientific accuracy and clarity ratings were high and tightly clustered (median 4.0–4.5), with no combined score below 3 and substantial inter-rater agreement. Marketing experts independently ranked Gemini 2.5 Flash and GPT-5 highest for lay clarity. Readability analysis revealed generally low accessibility, particularly for the convinced believer framing and for Claude Sonnet 4 outputs. Conclusions: Our findings suggest that general-purpose LLMs can produce scientifically accurate, on-topic rebuttals to widely documented vaccine myths under realistic default conditions, although linguistic complexity and framing-sensitive style may limit accessibility. Whether such outputs change beliefs or behavior in hesitant individuals was not tested. With readability optimization, these outputs could serve as building blocks for myth-debunking tools, given prospective evaluation with behavioral endpoints. Full article

The unprecedented rate of technological advances, accelerated industry disruptions and social and environmental sustainability crises require very different business organizations from the traditional paradigm. The main research question for this paper is: What change (paradigm shift) is needed for South African business organizations to be future-fit? The paper introduces an integrated, transdisciplinary paradigmatic model of an emerging, progressive future business organization in South Africa, as mostly influenced by Western futurists, and proposes an understanding of the paradigm shift required in our socially constructed reality for such organizations to emerge. A multi-method methodology, based on complexity theory and a transdisciplinary approach, was developed and applied. The researcher’s conceptualization of a ‘paradigm’, focusing on language-based representations, is explicated as a theoretical foundation. Textual analyses, including corpus linguistics, of practitioner-focused literature were used to elicit concept maps (or domain models) of the shared, societal-level mental models of a South African business organization for two periods: (1) the Traditional Business Organization, and (2) a Progressive Future Business Organization. The outcomes were compared using a novel qualitative method, resulting in a proposed set of societal-level ontological shifts needed for a progressive organizational future. The study shows a paradigm shift to complexity and social responsibility, and the need for transdisciplinarity to reflect complex, integrated organizational realities. Full article

Artificial intelligence is rapidly changing education. However, many learning chatbots are still reactive tools, which respond to arbitrary questions without leading learners through a meaningful pedagogical journey. This article presents a deterministic state-machine orchestrator coupled with a local large language model and a knowledge-graph-framed tutoring strategy for personalized education. The proposed virtual tutoring agent is designed to combine the flexibility of conversational AI with the reliability of explicit instructional states, key performance indicator (KPI) tracking, learner profiling, and controlled transitions between explanation, practice, feedback, assessment, and remediation. The system is not meant to replace the teacher, but rather to act as a teaching co-pilot that provides ongoing feedback, personalized learning paths, accessibility, and safer deployment by processing data locally. The study also presents a compact interview-based evaluation framework and statistical analysis of user perceptions across interactivity, individuality, proactivity, security, accessibility, gamification, and global preference for educational agents over classical chatbots. The findings show that learners appreciate personalized and interactive support and that proactivity is the key feature that distinguishes an educational agent from a regular chatbot. With this article we argue that deterministic orchestration can help make AI tutoring more transparent, controllable, and ethically fit for real learning contexts. Finally, it discusses privacy, educational value, limitations and future improvements to be made before the large-scale adoption of such systems. Full article

Product recognition in e-commerce live streaming is hindered by rapid viewpoint changes, occlusions, motion blur, and inconsistencies between visual and spoken information. Existing approaches typically focus on individual components such as detection, OCR, or speech recognition, which limits their effectiveness in end-to-end structured product understanding. To address this problem, we propose an integrated framework that combines task-oriented keyframe selection with multimodal semantic fusion. The framework first uses D-FINE to localize product regions and then selects informative frames through two complementary strategies. Strategy A considers both detection confidence and Laplacian-based sharpness, while Strategy B combines detection confidence with a learned quality component estimated by an EfficientNetV2-M regression model. OCR, visual-semantic recognition, and ASR are then applied to extract complementary evidence, and a Qwen3.5-27B large language model is used to structure and fuse multimodal evidence into standardized product outputs, including brand, product name, and category. Experiments on an in-house e-commerce livestreaming dataset demonstrate substantial gains over a last-frame baseline. Strategy B achieves the best overall result, improving the Perfect Match Rate from 0.609 to 0.775 and the Semantic Similarity from 0.697 to 0.802. Ablation studies further show that the full multimodal framework consistently outperforms unimodal and dual-modality variants under both frame selection strategies. In addition, Top-K analysis indicates that single-frame inference provides a practical balance between OCR evidence completeness and efficiency. Efficiency analysis shows that the per-video API monetary cost remains low under the pricing configuration used in this study, while API latency is mainly limited by Qwen3.5-27B LLM calls for evidence structuring and final fusion. Overall, the proposed framework offers an effective and extensible solution for structured product recognition in complex live-streaming scenarios. Full article

Background/Objectives: Borderline personality disorder (BPD) is a severe mental disorder characterised by emotion dysregulation, impulsivity and interpersonal hypersensitivity. Its prevalence ranges from 0.5% to 6.4%. Eye tracking and pupillometry provide objective indices of social attention and inhibitory control, but the BPD literature using these techniques has not been systematically reviewed. The aim of this work was to synthesise the empirical evidence on visuo-attentional and pupillary alterations in BPD. Methods: Following the PRISMA 2020 statement, Web of Science, Scopus and PubMed were searched up to 13 March 2026, with no date or language restrictions. Search terms combined borderline personality disorder and eye-tracking constructs. Two reviewers independently screened records with complete inter-rater agreement at the title-and-abstract stage (Cohen’s κ = 1.00); two generative artificial-intelligence assistants (ChatGPT, NotebookLM) were additionally consulted as a non-systematic plausibility check and returned no eligible studies beyond the database search. Risk of bias was appraised with the framework appropriate to each study design (RoB 2 for randomised trials and Newcastle–Ottawa Scale logic for observational studies, with ROBINS-I held in reserve for non-randomised intervention designs). Results: Seventeen studies met the inclusion criteria, with sample sizes ranging from 19 to 164 participants and predominantly adult female samples. Designs included antisaccade and oculomotor tasks, free-viewing, dot-probe, affective priming and pharmacological challenge. Four findings recurred across studies. First, patients with BPD showed an early reflexive vigilance to the eye region of emotional and neutral faces, followed by reduced time on positive stimuli during longer presentations. Second, self-reported impulsivity was elevated, but laboratory inhibition was largely preserved; the deficits that did emerge were limited to preparatory control and were greater in patients with comorbid ADHD or under induced negative affect. Third, autonomic dysregulation was indexed by lower heart-rate variability and a larger baseline pupil size; in a single longitudinal study, pupillary reactivity was prospectively associated with subsequent symptom change. Finally, intranasal oxytocin reduced amygdala-driven vigilance. Conclusions: Eye-tracking and pupillometric measures appear to capture meaningful aspects of the BPD clinical picture. The two-stage profile of early vigilance followed by reduced sustained engagement is most parsimoniously described as a vigilance–avoidance pattern, which is compatible with, but not uniquely explained by, the hypersensitivity hypothesis of emotion dysregulation. Because thirteen of the seventeen studies recruited women only, these conclusions apply primarily to adult women with BPD. Methodological heterogeneity, the predominance of female samples and the scarcity of longitudinal data justify the need for standardised protocols, transdiagnostic comparisons and the inclusion of male and gender-diverse populations in future research. Full article

This paper examines how heritage Russian reflects morphosyntactic dynamics in baseline Russian, drawing on three diachronically spaced corpora collected in 2001, 2010, and 2022 in Germany. It identifies recurrent patterns of variation in heritage Russian and assesses how they correspond to domains of instability already attested in the baseline system. The study assumes that transfer, variation, and convergence in heritage Russian are shaped not only by its reduced use in favor of the societal language but also by internal trigger mechanisms analogous to those operating in baseline Russian, while at the same time exhibiting specific variation patterns arising from multilingual variation typical of communication among heritage speakers. The analysis shows that the most prominent domains that consistently emerge as unstable across the corpora are nominal number and case, shifts in nominal and verbal government, and calquing of syntactic constructions. In this respect, heritage Russian mirrors baseline tendencies while also exhibiting contact-enhanced effects. For instance, GEN/ACC competition and the extension of animate object marking occur in both baseline and heritage Russian, whereas the overgeneralization of inanimate object marking appears predominantly in heritage Russian. Similarly, the loss of an instrumental predicative in favor of the nominative and fluctuations in prepositional government reflect baseline instability amplified by German influence. Syntactic variation—including borrowing of connectors, loss of baseline intonation patterns, and pro-drop in impersonal constructions—further illustrates the interaction of internal instability and contact effects, whereby contact-affected syntactic patterns are not attested in baseline Russian. The calquing of German possessive constructions and the shift from prepositionless to prepositional government patterns can be interpreted as instances of contact-induced leveling of Russian typological features, accompanied by simplification where baseline structures diverge from dominant-language patterns. Interpreted as part of a broader shift from synthetic to analytic government, these developments align with both contact-induced tendencies and historically attested changes in Slavic languages. The identified patterns of variation in heritage Russian correspond to domains of instability in baseline Russian, showing that the loci of variation are largely analogous, while their concrete manifestations are partly different. In comparison with baseline Russian, heritage Russian exhibits both analogous and specific—non-canonical—variants, reflecting unstable areas of the baseline in a multilingual setting and becoming particularly visible in subsequent generations of the migrant community. Within a broader typological context, the documented variation trends align with sensitive language domains observed in other contact situations, confirming their systematic character across diverse minoritized contexts. Full article