Search Results (329)

Background: Q&A corpora generated through university student–AI mental health support tools may reveal how physical activity (PA) and psychosocial themes are represented in support-oriented text. However, the absence of individual-level demographic metadata and the pooling of prompt and response fields limit attribution of any expression to a particular speaker, and the corpus describes a specific student population rather than a general or clinical one. Objective: This exploratory study described PA-, sport-, physical education (PE)-, body-, lifestyle-, and emotion-related patterns in a large corpus of university student–AI mental health exchanges collected through an institutional counselling platform. Methods: This study analysed 209,715 paired prompt–response records as combined exchange-level units using a BERTopic-based computational text-mining workflow. The full corpus was used for the main 18-topic model and overlapping dictionary analyses. After secondary data-quality filtering, 178,062 eligible exchanges formed the sampling frame from which a systematic sample of 10,000 exchanges was drawn for a separate complementary BERTopic and scenario-mapping analysis. The workflow used Qdrant/bge-small-zh-v1.5 embeddings, NFKC normalisation, an archived stop-word list, UMAP (n_neighbors = 15, n_components = 5, min_dist = 0.0, cosine metric, seed = 42), HDBSCAN (min_cluster_size = 300, min_samples = 10, Euclidean metric, EOM), c-TF-IDF topic representations, overlapping dictionary screens, and stability testing across seeds 42, 52, and 62. Results: A student/school/family-context lexical screen matched 83,215 exchanges (39.68%), and a broad PA/body/lifestyle screen matched 82,464 exchanges (39.32%). These overlapping indicators describe topical co-occurrence and do not establish PA behaviour or which party to the exchange produced a given term. Eighteen corpus-level themes were retained. In the 10,000-exchange analysis, 13.11% of exchanges matched a narrow movement-related expression screen, with the highest within-topic rate in the sample topic labelled emotional outburst and relaxation regulation (51.09%). Conclusions: The findings describe exchange-level lexical and topic patterns in student–AI interactions rather than actual PA behaviour, intervention delivery, clinical efficacy, or population prevalence, and they do not identify which party introduced the language. The mapping to autonomy, competence, relatedness, and emotional regulation is a post hoc interpretive lens, offered as a hypothesis to inform future, prospectively validated design work in PE and digital mental health support rather than as a demonstrated result. Full article

Back-end-of-line-compatible oxide-semiconductor field-effect transistors (OSFETs) enable vertically stackable two-transistor zero-capacitor (2T0C) dynamic random-access memory (DRAM) cells, offering high densities while maintaining low write energy. The capacitorless 2T0C cell intrinsically benefits from decoupled read/write paths and a sub-fF storage-node (SN) capacitance $C_{\mathrm{SN}}$, reducing the stored charge to the sub-fC level. However, the control-line energy overhead introduced by the independent write word-line (WWL) activation can erode this advantage. The minimum write energy is ultimately bounded not by $C_{\mathrm{SN}}$ alone but also by the drive capability and leakage of the write transistor ${\mathrm{T}}_{\mathrm{W}}$ and the WWL and write bit-line (WBL) voltage configuration. A systematic co-optimization methodology addressing these coupled constraints has yet to be established. This work is built on the insight that the write-optimization target should be the stored-charge margin $\Delta Q_{\mathrm{SN}}$ rather than the storage-node voltage $V_{\mathrm{SN}}$ because $\Delta Q_{\mathrm{SN}}$ remains nearly constant across all phases and directly governs readout distinguishability. The methodology combines a coupled analysis of threshold voltage ($V_{\mathrm{th}}$), SS, and ${\mu }_{\mathrm{FE}}$ of ${\mathrm{T}}_{\mathrm{W}}$ with the adoption of $\Delta Q_{\mathrm{SN}}$ as the read-margin metric. On this basis, the write-overdrive margin, the WBL voltage, and $V_{\mathrm{th}}$ of ${\mathrm{T}}_{\mathrm{W}}$ are sequentially optimized. The three-step procedure reduces $\Delta V_{\mathrm{WWL}}$ from 3.0 V to 2.10 V while satisfying the 2 ns design-level write-time constraint, the 0.54 fC read-margin criterion, and the 1 ks retention benchmark. Under the assumptions of the present case study, the optimized 2T0C array yields a 2.5× reduction in array-level write-related energy relative to the unoptimized baseline and a 3.0× reduction relative to the LPDDR6 1T1C array reference. Full article

The language used to describe and discuss mental ill-health continues to evolve and improve, driven by advocacy and commitment to reducing the harmful effects of stigma. However, from a lived experience perspective, room for improvement remains. This project aimed to explore the use of language and labels within the mental health sector and its impact on people with lived experience of mental ill-health. Interviews were conducted with mental health consumers and/or carers (n = 15), and co-researchers with lived experience of mental ill-health contributed researcher positionality to the research. Six themes were generated using reflexive thematic analysis; they accented how participants expressed complex and nuanced experiences of language and labels, and the overlapping themes highlight the ‘blurry edges’ when people with lived experience discuss language, with the individual at the centre. Responses reflected the nature of inclusive and exclusive language in the medical system and the community. Central to experiences of exclusion were times when people felt dismissed and misunderstood due to the language and labels others use and how the words are delivered. These negative experiences reflect the influence of stigma. Positive language provides validation, understanding and a sense of hope. Health professionals may have opportunities to apply more personalised communication approaches, which could improve outcomes for the people they support. Full article

Existing research on sentiment classification primarily focuses on textual contextual semantics while neglecting word co-occurrence and syntactic structure information. It also emphasizes semantic analysis of sequential structures but overlooks non-linear structural semantics. To address these limitations, we propose the DGCNCF (Dual-Graph Convolutional Network with Context Fusion) model for sentiment classification. The model employs a BiLSTM with an attention mechanism to extract textual contextual information. Subsequently, a text co-occurrence graph and a syntactic dependency graph are constructed separately. Meanwhile, contextual information is fused with node information within these graphs to obtain graph embedding representations of the text. Then, the model performs representation learning on the two graphs using GCN, thereby capturing the global word co-occurrence features and global syntactic dependency structure characteristics of the text. By integrating textual contextual information, word co-occurrence information, and syntactic dependency structure information, the model captures text features from semantic, lexical, and syntactic perspectives, thereby mitigating the limitations of relying on single-dimensional features. The validity of the model is verified on two public datasets, and the experimental results demonstrate that the model achieves effective sentiment classification performance. Full article

Mentoring has become a central mechanism of teacher support and professional learning, while the conditions of teaching have been reshaped by socio-political uncertainty, teacher shortages, digitalisation, increasingly diverse student needs, and the COVID-19 pandemic itself. This study aimed to map the conceptual structure and temporal evolution of research on teacher mentoring and professional learning. A longitudinal bibliometric and science-mapping analysis was conducted on 2843 peer-reviewed articles indexed in the Scopus database and published between 2010 and 2025, using co-occurrence (co-word) analysis of author and indexed keywords in VOSviewer 1.6.20, complemented by network, overlay and density visualisations. The final co-word map comprised 123 keyword elements organised into four clusters, with 3312 links and a total link strength of 17,024. The central node was ‘mentoring’ (532 occurrences; 121 links; total link strength = 2212; average publication year = 2019.40). Four thematic lines structured the discourse: (1) professionalisation in the wider sense, including health education mentoring; (2) initial teacher education and the induction of new teachers; (3) higher education and digitally mediated environments of professional learning; and (4) bridging concepts of collaboration and professional identity formation. The findings indicate that, within the literature mapped, mentoring functions as a relational, context-sensitive form of support connecting several arenas of professional learning. This study contributes a longitudinal conceptual map of the field; the results describe the intellectual structure of the literature and do not constitute evidence of the effectiveness of mentoring practices. Full article

Green finance has become a key component of sustainability transitions by supporting the alignment of financial systems with environmental and climate-related objectives. As discussions on sustainable investments and climate finance increasingly take place in digital environments, analyzing large-scale user-generated content has become important for understanding online discussions and emerging discourse patterns. This study investigates green finance discourse in the digital public sphere using a multi-method Natural Language Processing (NLP) framework. A dataset of 23,452 posts collected from the platform X was analyzed using TF-IDF-based word frequency analysis, FinBERT-based sentiment analysis, Latent Dirichlet Allocation (LDA) topic modelling, and keyword co-occurrence network analysis. The results indicate that green finance discourse is dominated by positive (44.61%) and neutral (45.85%) sentiment, suggesting a generally favorable and institutionalized public perception. Topic modelling identified three dominant themes: climate finance and sustainability transition, digital finance and speculative investment narratives, energy investments and financial infrastructure. Network analysis revealed that sustainability, climate, energy, investment, and finance constitute the core conceptual structure of discourse. These findings demonstrate the effectiveness of NLP-based approaches for analyzing large-scale digital discussions and provide insights for policymakers, financial institutions, and organizations seeking to better understand online discussions surrounding green finance and support more effective sustainability communication strategies. Full article

Nuclear power technology plays a vital role in the global low-carbon energy transition. Horizon scanning and research frontier detection of nuclear power technology help accurately grasp the development trend, thereby optimizing its strategic layout. However, the quantitative studies on the development trend of nuclear power technology over the last decade are limited. A data-driven, integrated framework for quantitative evaluation is proposed in the study, integrating descriptive statistics, co-word analysis, network analysis, and coupling analysis to reveal research advances, the conceptual structure, international cooperation, the research frontier, and the research evolution of nuclear power technology. The results indicate a slow growth trend in research on nuclear power technology. The research’s conceptual structure is primarily focused on radioactivity from nuclear power accidents. Germany and the United States are the most important in the international cooperation network (with Degree Centrality of 0.675 and 0.663, respectively). Furthermore, seven research frontiers in global nuclear power technology are identified, with a focus on radioactivity related to nuclear power plant accidents. In addition, the research evolution findings suggest that we should strengthen frontier exploration in the fields of ocean diffusion of radionuclides (ODR), migration of nuclear contamination (MNC), and radioactivity control and immobilization (RCI). Full article

The proliferation of generative artificial intelligence (GenAI) has precipitated an urgent, global reassessment of how higher education evaluates critical thinking, creative agency, and academic integrity. However, scholarly and institutional responses remain fragmented across cultural contexts, impeding the development of robust, flexible, and discipline-adaptable assessment frameworks. Responding to the imperative to move beyond the traditional standardized assessment paradigm, this study conducts a comparative discourse analysis of 5368 academic articles in Anglophone/Western scholarly discourse (Web of Science, WoS) and Chinese (CNKI). Using LDA topic modeling and Word2Vec-enhanced semantic analysis, the study identifies two divergent orientations: an Anglophone/Western discourse that frames AI as an instrument for cognitive augmentation, efficiency optimization, and functional human–AI collaboration; and a Chinese discourse that emphasizes epistemic sovereignty, the reconstruction of creative subjectivity, and systemic institutional rebuilding against technological alienation. These pathways are mapped onto a tripartite framework of Tools, Creative Subjectivity, and Organizational Ecosystems. The findings demonstrate that AI integration is culturally embedded rather than technically determined, carrying profound implications for assessment validity, academic integrity policy, and equitable access to AI-enhanced learning. The study synthesizes these insights into a culturally responsive assessment framework that redirects evaluation from standardized, product-centric outputs toward process-oriented, transparent, and ethically governed human–AI co-authorship. By centering critical autonomy, AI literacy, and epistemological diversity, this framework offers actionable strategies for inclusive assessment redesign, institutional policy development, and sustainable competency cultivation in the GenAI era. Full article

The Behavioral Finance (BF) has undergone significant developments due to the transformative influence of Environmental, Social and Governance (ESG) practices. BF and Sustainable Investment (SI) are closely intertwined domains, both of which bring into line with the broader framework of ESG. Integrating BF into the field of SI expands the understanding of how psychological biases, emotional factors, and cognitive constraints influence investors decisions connected to sustainability focused assets. Despite their growing relevance, the existing literature lacks a comprehensive review that provides holistic reviewing of research integrating into these areas. To address this gap, we provide an overview of BF and SI research in Socially Responsible Investments (SRI). Using both co-citation and bibliometric-coupling analysis, we infer the thematic structure of key words of BF and SI for a period of 20 years starting from 2004 to September 2025. Additionally using performance analysis and co-occurrence analysis, we highlighted trends and research directions regarding BF and SI. Further, seven thematic clusters and coupling networks were also identified which are offering to the researchers a structured foundation to explore emerging trends and consolidate knowledge within the BF and SI field. This Bibliometric study aids in recognizing the emerging topics for research in the domain of BF and SI. Full article

Social mobility is a central indicator of socioeconomic development. It indicates the improvement of an individual’s socioeconomic position across generations. Recently, welfare policies, education, and redistribution schemes have received increasing attention from the academic community as they affect social mobility outcomes. Despite the growing volume of literature, there is an inadequate linkage between research on social mobility and social policy. This study uses a bibliometric analysis of 389 Scopus-indexed articles to examine research on social mobility and social policy from 1990 to 2025. The findings highlight the relationship between the impacts of policy interventions on social mobility. Performance analysis and science mapping are used, which provide insight into publication trends and leading contributors and reveal the intellectual and conceptual structures of the research field. Studies are concentrated in developed economies such as the United States and the United Kingdom. Further, in the science mapping analysis, co-word analysis is followed by bibliographic coupling, which reveals emerging trends and promising themes. The study provides a comprehensive synthesis of the conceptual and intellectual evolution of social mobility research, offers insights for policymakers and highlights the future direction of interdisciplinary research. Full article

Psychological capital (PsyCap), encompassing hope, self-efficacy, resilience, and optimism, has established itself as a key psychological resource for individuals. However, research in this field remains fragmented, which limits a comprehensive understanding of its role in the psychological mechanisms underlying entrepreneurial behavior, particularly in terms of motivation, coping with stress, and resilience in the face of uncertainty. This study aims to examine and organize the intellectual landscape of PsyCap. A scoping review of 215 articles indexed in Scopus and Web of Science over nearly two decades was conducted in accordance with PRISMA-ScR, using a co-thematic analysis based on text mining techniques. The results reveal a three-phase evolution of the field (emergence, growth, and maturity), built around individual functioning, entrepreneurial cognitions and attitudes, and psychosocial resources. The analysis also highlights unequal access to and use of PsyCap across contexts, as well as differences related to the specific characteristics of the populations studied, shedding light on underexplored groups such as women, refugees, rural and social entrepreneurs, migrants, and entrepreneurs with disabilities. These findings contribute to advancing knowledge in entrepreneurial psychology and offer a detailed analysis of future research avenues, including emerging research questions, methodological approaches, and theoretical interdisciplinary perspectives. Full article

This bibliometric study examined 179 Scopus-indexed publications on the physical and mechanical properties of medium-density fiberboard (MDF) published between 2016 and 2025. BiblioMagika^® was used for performance analysis, and Biblioshiny was used for keyword co-occurrence, thematic mapping, and thematic evolution. The papers identified as the cohort for analysis had received 2830 citations in total, with an average of 15.81 citations per paper, and an average h-index of 30. The European Journal of Wood and Wood Products and BioResources were the most productive sources. Three distinct categories were identified through keyword mapping among the studies reviewed: (1) advanced composites and reinforcement, (2) adhesive and emission-related studies, and (3) circular-material strategies. Thematic evolution showed a trend away from traditional resin-performance topics toward broader sustainability-related themes, particularly bio-based adhesives and recycling-related topics. Overall, this review provides a quantitative overview of publication patterns, influential sources, and thematic development in MDF research. It also provides direction for future MDF research, focusing on durability, large-scale feasibility, life-cycle assessments, and practical implementation. Full article

Background: Hospice refers to specialised end-of-life care that supports patients and families, making it an important area for studying how language shapes experiences and expectations of care. This study compares hospice discourse on websites in Hong Kong and the United Kingdom, analysing how NLP-based sentiment and interpersonal features, such as personal pronouns and conjunctions, shape logical relations, structure information, and express emotion in patient narratives. Methods: Using a mixed approach that integrates sentiment analysis with Systemic Functional Linguistics (SFL), and taxonomy of conjunctions in particular, this study draws on a 52,086-word corpus from 40 hospice websites (20 from each region). The corpus analytical tool AntConc was used to identify co-occurrence, interpret log-likelihood, and perform concordance analysis. Results: The findings reveal significant differences in the digital delivery of hospice care across regions. According to our data, UK websites tend to express a wider range of personal emotions and frequently use concessive conjunctions when discussing sensitive palliative care topics. In contrast, Hong Kong websites tend to use more additive and causal conjunctions, projecting a stronger focus on institutional care. For example, Hong Kong texts tend to use formal, service-oriented connections such as “we + offer”, reflecting a more informational communicative style. However, both regions frequently use personal pronouns such as “you” and “we” to convey positive sentiment and demonstrate empathy towards patients and their caregivers. Conclusion: These patterns appear to be used strategically by hospice providers to build trust, signal alignment, and strengthen relationships tailored to each region. Lastly, this study makes an original contribution by combining computational and functional linguistic approaches to develop a systematic method for examining culturally shaped digital communication in end-of-life contexts, thereby enriching the field of healthcare discourse analysis. Full article

Zoological parks increasingly operate as sustainability-oriented institutions that integrate biodiversity conservation, environmental education, animal welfare, and community engagement. In parallel with these evolving roles, digital and interactive technologies have emerged as key instruments for supporting sustainable visitor engagement and conservation communication. This study provides a systematic review and conceptual mapping of the literature by combining a PRISMA-based systematic literature review with bibliometric co-word analysis. The bibliometric results reveal four thematic clusters: (1) mobile and visitor-oriented digital technologies, (2) immersive augmented reality (AR) or virtual reality (VR) based solutions, (3) animal–computer interaction and welfare-focused technologies, and (4) traditional conservation and education research. While digital technologies demonstrate measurable short-term effects on engagement, empathy, and knowledge retention, their long-term sustainability impact and systemic integration remain limited. To address this gap, the study introduces three theoretical contributions: the concept of the zoo-based digital learning ecology, the identification of the digital fragmentation problem, and the Integrated Digital Zoo Ecosystem (IDZE) model. The proposed framework conceptualizes digital visitor experience, animal welfare technologies, and conservation communication as interdependent subsystems within a unified sustainability-oriented ecosystem. This study provides a conceptual foundation for future sustainability-driven digital innovation in zoological parks. Full article

The Mediterranean Sea is both a global biodiversity hotspot and the world’s most heavily invaded marine region, where non-indigenous species arrivals are accelerating under intensifying shipping, Suez Canal traffic, aquaculture, and climate warming. Yet, despite rapidly growing research activity, a comprehensive synthesis of the scientific literature on Mediterranean marine invasions has been lacking. This study provides the first Mediterranean-wide combined bibliometric and topic-modeling analysis of invasive marine species research, using 3521 unique documents retrieved from Scopus and Web of Science. We quantify temporal growth in publications and citations, map the conceptual structure of the field through co-citation, co-word, and topic modeling, and reveal pronounced regional and thematic biases. Latent Dirichlet Allocation resolves 13 coherent topics, dominated by first records of non-native species, invasive macroalgae, alien species diversity, and ecological impacts, with strong signals for Lessepsian migration and climate-driven range shifts, particularly in the Eastern Mediterranean. Spatial and thematic analyses reveal pronounced regional biases, with invasion hotspots in the Aegean and Levantine seas contrasted by comparatively sparse coverage of western and central sub-basins, and notable gaps in predictive modeling and socioeconomic assessments. The results underscore the need to rebalance effort toward under-studied regions and themes, while leveraging existing collaboration networks and methodological advances to support MSFD (Marine Strategy Framework Directive) implementation, International Maritime Organization (IMO) instruments, and broader ecosystem-based management. The reproducible framework presented here offers a baseline for periodically tracking research evolution and guiding adaptive, transboundary governance of Mediterranean marine bio-invasions. Full article