Search Results (411)

The study presents a comprehensive approach to reviewing achievements in sustainable finance, identifying research trends and directions, key problem areas, the geographical origins of publications, keyword clusters, and the research methods applied by scholars in this field. The analysis is based on a dataset of 9218 publications indexed in the Web of Science and Scopus databases covering the period 2012–2025. The methodological framework combines bibliometric analysis, text mining, and topic modelling techniques, including the Latent Dirichlet Allocation (LDA) method, supported by visualization tools. These methods enable the identification of publication dynamics, the geographic distribution of research activity, thematic clusters, and the dominant research methods used in the literature. The results indicate a significant increase in academic interest in sustainable finance, particularly after 2018. The main research trends identified include green finance, climate finance, renewable energy financing, ESG investing, sustainability reporting, and the development of green financial instruments. The analysis also highlights the growing role of innovation and financial institutions in supporting sustainability transitions. In addition, the study identifies emerging research areas such as blue finance and green banking, providing a broader perspective on the evolving structure of sustainable finance research. To the best of our knowledge, this is the only review paper that approaches sustainable finance in such a comprehensive manner, particularly by incorporating the emerging concept of blue finance. Full article

Background/Objectives: The global health landscape is currently confronted with dual challenges from both infectious diseases and chronic conditions. Medical–preventive integration has emerged as a pivotal strategy to address these issues, aiming to create a comprehensive, closed-loop framework that spans disease prevention, treatment, rehabilitation, and healthcare, ultimately improving population health outcomes. In the Chinese context, existing policies remain fragmented, scattered across various healthcare-related regulations, and lack systematic and comprehensive analysis. This policy fragmentation may impede the creation of synergistic effects essential for the effective implementation of integrated healthcare strategies. Methods: This study adopted a mixed-methods approach to analyze 85 national policies: a three-stage coding process identified 1088 policy nodes, and a three-dimensional framework (policy instruments (X) × full-cycle health service (Y) × integration stages (Z)) was applied to uncover systemic imbalances. Social network analysis and Latent Dirichlet Allocation (LDA) topic modeling were utilized to map interagency collaboration patterns and thematic shifts, which were visualized using Gephi and Sankey. Results: The analysis revealed that policy instruments are predominantly supply-side (45.04%) and environmental-side (40.35%), with demand-side instruments (14.61%) being notably underutilized, particularly in health financing. Rehabilitation services, representing just 8.27% of the policy focus, were identified as a significant gap in the comprehensive health service cycle. While 44.58% of the instruments facilitated collaboration of medical and preventive services, integration of medical–preventive management stagnated at 25.28%, reflecting institutional inertia that impedes the redistribution of cross-sector resources. Agency collaboration evolved from a siloed approach (2015–2018) to a networked structure (2019–2021) and transitioned to centralized governance post-2022. Thematic shifts in policy discourse moved from a “Healthy China” focus toward pandemic-driven disease surveillance, culminating in the recent development of smart health ecosystems. Conclusions: China’s policies for medical–preventive integration demonstrate notable structural imbalances, particularly in the economic instruments related to health financing and the private-sector participation in healthcare. These imbalances may impede the effective allocation of healthcare resources and hinder the seamless transition toward integrated care. Future policy efforts should focus on optimizing the structure of policy instruments, addressing gaps in the full lifecycle of health services, advancing integration reforms, and promoting the transformation of the healthcare system through enhanced collaborative governance among key stakeholders. Full article

Vaccine hesitancy—which can be defined as a delay in acceptance or the refusal to get vaccinated—has substantially increased over the past decade. This study introduces a computational and qualitative approach designed to efficiently classify stance and uncover narratives in social media discourse without relying on extensive manual annotation. Using 298,356 COVID-19 vaccine-related X posts geolocated to South Carolina (June 2021–May 2022), zero-shot and few-shot learning with instruction-tuned large language models (Mistral-7B, Meta-Llama-3.1, and DeepSeek-7B) was applied for stance detection while Latent Dirichlet Allocation (LDA) was used for topic modeling. The topic modeling identified five dominant themes in vaccine hesitant conversations: skepticism of vaccine efficacy, comparative framing, scientific justification, disapproval of regulations, and distrust. Temporal analysis revealed that skepticism peaked during late 2021, coinciding with booster campaigns and mandate debates. These findings suggest that vaccine hesitancy is influenced through complex rhetorical strategies rather than misinformation alone. These underlying narratives often frame skepticism as rational and evidence-based, using scientific language and statistical reasoning to challenge the effectiveness of vaccines. Full article

Non-consumptive wildlife tourism serves as a vital vehicle for promoting ecological conservation and nature education. Understanding visitors’ perceptions of wildlife behavior and how these perceptions translate into long-term pro-environmental behavioral intentions is crucial for balancing visitor recreational experiences with ecological management in nature reserves. This study developed a hybrid analytical method integrating Latent Dirichlet Allocation (LDA) and Structural Equation Modeling (SEM). Based on 62,557 online reviews and 351 questionnaires collected from 33 macaque tourism sites in China, we identified three dimensions of perceived macaque behavior: food-driven approach (FDA), co-presence experience (CPE), and natural habitat-based behavior (NHB). SEM results revealed that all three dimensions significantly influenced Perceived Ecological Value (PEV) and Positive Emotional Arousal (PEA). NHB and FDA exert a stronger influence on PEV, while CPE primarily drives PEA. Furthermore, both PEV and PEA significantly promote PEBI, with PEV having a stronger effect. These findings indicate that PEBI formation relies more heavily on understanding ecological significance than on immediate positive emotions alone. These findings refine the “experience-to-conservation support” mechanism and suggest that managers should optimize ecological interpretation and regulate food interactions to foster sustainable wildlife tourism. Full article

Age-friendly interaction design remains challenging in intelligent furniture applications, where complex interfaces and unreliable control may hinder middle-to-older adults’ safe operation and long-term acceptance. This study proposes a data-driven framework to support the design and evaluation of an age-friendly smart bed application by linking large-scale requirement mining, requirement-to-design translation, and controlled usability validation. First, large-scale e-commerce reviews (n = 20,174) were analyzed using LDA to identify dominant unmet needs. Key pain points clustered around interaction usability, information interpretability, and voice control reliability. Based on these findings, we developed an age-friendly smart bed app prototype. A within-subject usability study with 27 middle-to-older adults (55–65 years) compared the proposed system against a representative commercial reference application under standardized task protocols. The results showed significantly improved perceived usability, with a higher System Usability Scale (SUS) score (94.91 vs. 79.63, p < 0.001) and higher recommendation intention measured by overall NPS (59.52% vs. 34.39%). Beyond system-level usability improvement, this study makes three key contributions: (1) a reproducible ‘demand mining → design translation → empirical validation’ framework that bridges the methodological gaps in current intelligent furniture research; (2) operationalized mapping from LDA-derived user pain points to concrete multimodal interaction modules; and (3) empirical evidence from comparative usability testing with explicitly profiled middle-to-older adults (55–65 years) against a commercial reference system. Full article

This study examines public perceptions and discourse on climate change using artificial intelligence (AI)-based analysis of social media data, with implications for climate adaptation and sustainability policy. A dataset of 29,576 posts from the X platform (December 2025) was analyzed through an integrated framework combining text mining, TF-IDF-based word analysis, deep learning-based sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modelling. The findings reveal that climate change discourse is predominantly characterized by negative sentiment, reflecting high levels of concern, perceived risk, and urgency, while positive content emphasizes awareness, solutions, and collective action. Topic modelling identifies three main themes: skepticism shaped by daily weather experiences, scientific and policy-oriented climate debates, and discussions on carbon emissions and human impact. These results demonstrate that social media serves not only as a space for emotional expression but also as a dynamic platform for information exchange and public opinion formation. From an adaptation perspective, AI-driven social media analytics provide valuable insights into public risk perception, misinformation patterns, and knowledge gaps, supporting evidence-based climate communication and policy development. Full article

This study examines how immersive narrative resources, whether technological–sensory, narrative–structural, or contextual, are deployed in contemporary blockbuster cinema and to what extent audiences recognize and value them in their evaluations. Using Avatar: Fire and Ash as a case study, the research follows a sequential mixed-methods design. In the first phase, a qualitative film analysis identifies eight types of cognitive immersion, drawing on established theoretical frameworks of narrative immersion. The second phase is quantitative and involves the computational analysis of 1133 valid reviews from Internet Movie Database (IMDb) through Natural Language Processing (NLP) techniques, including n-gram frequency analysis, Latent Dirichlet Allocation (LDA) topic modeling with 3 topics after perplexity minimization, and sentiment polarity analysis. The LDA model reveals three discursive clusters, experiential and emotional, technical and comparative, and critical, with the latter concentrated mostly in low-rated reviews. Text sentiment and numeric ratings show a moderate positive correlation (r = 0.53, p < 0.001), pointing to a general but imperfect alignment between the two modes of evaluation. Markers of content fatigue (nothing new, predictable, boring) appear in 25.1% of the reviews, yet a third of those are still rated 8 or higher. When cross-tabulating the immersion categories with audience language, phenomenological and affective dimensions such as Emotional Engagement (59.8%) and Haptic/Sensory Experience (59.1%) emerge as the most frequently discussed, while cinematographic techniques like Bracketing (2.6%) are barely mentioned. Taken together, the findings suggest that the franchise sustains its appeal through a form of embodied sensory engagement that operates largely independent of narrative novelty. Full article

With the in-depth advancement of the “Digital China” initiative, policies and research discourses related to the digital economy have continued evolved, making it necessary to systematically examine their stage-specific characteristics and underlying logic from a long-term perspective. Accordingly, this study adopts information society theory as the analytical framework and selects the annual series of reports on China’s digital economy development published by the China Academy of Information and Communications Technology (CAICT) from 2015 to 2024 as the research corpus. Using text mining techniques and Latent Dirichlet Allocation (LDA) topic modeling, this paper conducts a longitudinal examination of the stage-wise systemic evolution of key topics in China’s digital economy development. The findings indicate that over the past decade, the topic structure of China’s digital economy has followed a clear evolutionary trajectory, progressing from “informatization-driven development” to “platform expansion,” and subsequently to “data factors and institutional governance.” In the early stage, the focus was on information infrastructure development and industrial integration; the middle stage shifted toward the platform economy and enterprise growth; more recently, the emphasis has increasingly been placed on the construction of data factor markets and the improvement of governance frameworks. This process of topic evolution not only reflects changes in the practical forms of the digital economy but also reveals the ongoing adjustment of the state’s cognitive framework and governance logic regarding digital economy development. These findings provide empirical evidence for understanding the systemic evolution of China’s digital economy over time. By identifying the stage-specific pathways of China’s digital economy, this study extends the application of information society theory within this context and provides new empirical evidence for understanding the evolutionary logic underlying high-quality digital economy development. Full article

Driving behavior primitives serve as fundamental building blocks for modeling and semantically interpreting time-series driving behavior. Extracting behavior primitives is challenging due to the high dimensionality and complex interdependencies among behavioral variables, as well as the rich temporal dynamics of real-world driving maneuvers. This paper proposes an unsupervised two-stage framework that optimizes time-series segmentation and segment clustering to yield interpretable and context-aware behavior primitives. First, a Hierarchical Bayesian Model-based Agglomerative Sequence Segmentation (H-BMASS) method is introduced that decouples longitudinal and lateral driving behaviors and performs hierarchical segmentation. This design mitigates under-segmentation by ensuring that change points reflect genuine behavioral transitions. Second, to cluster driving segments of varying durations into a finite set of primitive types, an Integrating Numerical and Trend Discretization Latent Dirichlet Allocation (INT-LDA) model is developed. The model combines variables’ temporal trend discretization with numerical discretization to create symbolic representations of driving data, thereby preserving the essential time dependency of driving behavior and improving segment clustering accuracy. Evaluated on naturalistic driving data collected from a high-fidelity simulator, the proposed framework identifies five distinct behavior primitives with clear physical interpretations. The resulting primitives provide a compact, semantically rich representation of driving behavior, facilitating driver modeling, decision prediction, and scenario-based testing for autonomous vehicles. Full article

Text summarization involves analyzing large amounts of text, selecting the salient text features, and arranging them coherently. The graph-based TextRank and statistical topic modeling are unsupervised approaches for generating an extractive synopsis. Deep learning models are supervised, data-driven, and pre-trained on a huge corpus of data, making a significant contribution to automatic text summarization systems. Despite grammatical correctness and coherence, deep learning-based summarization systems are prone to factual inconsistency. This has hindered the applicability of transformer-based summarizers, particularly in critical domains where misleading summarization systems can lead to severe consequences due to their significant social impact. This work proposes an ingenious hybrid hierarchical approach that combines unsupervised approaches, such as the TextRank algorithm and Latent Dirichlet Allocation (LDA)-based summaries, with contemporary transformer-based language models. When validated on three benchmark summarization datasets, empirical results prove that our hybrid hierarchical transformer-based approach mitigates the factual inconsistency problem inherent in abstractive summarization. The improved summary consistency score of the abstractive summaries generated with our multilevel hybrid approach, in comparison to the fine-tuned baseline transformer-based language models, increases trust in transformer-based summarizers. Full article

Artificial intelligence (AI) systems and tools are increasingly reshaping educational practices. This study examines perspectives shared in student-focused online communities on AI’s impact on education, comparing those of computer science (CS) and business students through an analysis of Reddit posts. Using natural language processing (NLP), sentiment analysis, and Latent Dirichlet Allocation (LDA) topic modeling, we analyzed 1108 posts collected from six subreddits. Results reveal distinct thematic focuses: CS students emphasize technical aspects, including programming efficiency, coding assistance, and concerns about job displacement, while business students focus on decision-making enhancement, financial analysis applications, and operational efficiency. Sentiment analysis indicates that the Business/Finance-oriented corpus is slightly more positive than the CS-oriented corpus (51.9% vs. 50.1% positive). The CS-oriented corpus also contains a higher proportion of negative posts (36.0% vs. 33.2%). These differences reflect discipline-specific epistemological frameworks shaping AI perception. The findings provide educators with guidelines for developing tailored AI integration strategies that address discipline-specific concerns and opportunities. This study contributes to understanding how academic background influences perceptions of AI in education, offering insights for curriculum design and policy development. Full article

Virtual reality (VR) has been increasingly adopted as a digital tool in rehabilitation for balance training, coordination improvement, and motor recovery, yet the literature remains dispersed across clinical rehabilitation, exercise-based interventions, and broader motor-related applications. This fragmentation makes it difficult to determine how the field has evolved and where research emphasis has shifted. This study mapped the research landscape and thematic evolution of VR for balance, coordination, and motor rehabilitation using bibliometric analysis and topic modeling. A total of 1258 articles indexed in the Web of Science Core Collection from 2011 to 2025 were analyzed. Only English language articles and reviews relevant to VR-based balance, coordination, or motor rehabilitation research were included, yielding a final dataset of 1258 publications. CiteSpace and VOSviewer were used to examine keyword co-occurrence, clustering patterns, and temporal trends, while Latent Dirichlet Allocation (LDA) was applied to identify latent themes and their temporal dynamics. The field has moved beyond early feasibility testing toward a more differentiated landscape shaped by distinct clinical targets, population groups, and training purposes. Seven recurring themes were identified, including vestibular rehabilitation and immersive training, post-stroke upper-limb rehabilitation, efficacy and adverse-effect assessment, balance and gait training interventions, evidence synthesis and review-based evaluation, elderly exercise and cognitive interventions, and skill-oriented virtual task training with recent expansion toward broader population groups and task-specific applications beyond traditional rehabilitation settings. VR research on balance, coordination, and motor rehabilitation has evolved into a more thematically differentiated field rather than remaining a single rehabilitation-oriented domain. By combining bibliometric mapping with topic modeling, this study clarifies where evidence is concentrated and which thematic directions are gaining visibility, providing a clearer basis for future evidence synthesis and more comparable intervention reporting. Full article

In the context of the aesthetic economy and the rapid development of digital intelligence, product design is increasingly required to address not only functional performance but also users’ emotional needs. However, due to the ambiguity and subjectivity of perceptual requirements, it remains difficult to accurately translate user emotions into specific design solutions. To address this challenge, this study proposes an integrated Kansei Engineering–machine learning framework for optimizing product design. First, user perceptual data are collected through questionnaires and interviews, and key perceptual imagery words are extracted using the Latent Dirichlet Allocation (LDA) model and factor analysis. Then, product design elements are systematically decomposed, and their relative importance is determined using the fuzzy analytic hierarchy process (FAHP). Based on this, a mapping relationship between perceptual imagery and design elements is established. Subsequently, the XGBoost model is employed to predict and optimize design element combinations. The optimized design schemes are further generated using AIGC technology and validated through eye-tracking experiments and subjective evaluations.The results show that the proposed method achieves high predictive accuracy (R² = 0.87) and significantly improves the emotional expression of product design. This study contributes to the integration of Kansei Engineering and machine learning by providing a data-driven approach for emotional design optimization, offering theoretical, practical, and strategic guidance for intelligent product design in industrial contexts. Full article

This article introduces the CoLiRa (Computational Literature Review & Analysis) framework, a novel integration of established computational algorithms designed to quantitatively analyze and map the evolution of scientific fields. Employing a human-in-the-loop epistemological approach, CoLiRa combines the scalability of automated algorithms with the semantic coherence of expert-driven qualitative research. The multi-stage pipeline incorporates Latent Dirichlet Allocation (LDA) for thematic discovery, cluster analysis (K-Means and Multidimensional Scaling) for conceptual mapping, and Ordinary Least Squares (OLS) regression to monitor temporal trends. Algorithmic outputs are structurally validated by domain experts using quantitative metrics. The framework’s end-to-end capabilities are demonstrated through a proof-of-concept case study on the semantic enrichment of tabular data, encompassing studies up to 2024 that utilize Semantic Web ontologies, Linked Data, and knowledge graphs. The analysis identifies three core research topics and finds no statistically significant linear trends, suggesting thematic coexistence. This work provides a validated, hybrid computational approach for conducting robust literature reviews and mapping research trajectories. Full article

Spam consists of unsolicited messages, and the posting of such irrelevant messages often presents significant challenges in technical forums. Two particular challenges are the dynamic nature of spamming tactics and the inadequacy of adaptable spam databases for automated classifiers. Our work addresses the need for a robust spam classification solution that can be seamlessly integrated with database, SQL, and APEX applications. We developed a labeled spam database by asking experts to categorize 1916 posts as spam or regular posts to ensure accurate classification and then created an SVM-based spam classification model that achieves an average validation accuracy of 90%. Our research enhances the current understanding of spam in technical forums and represents a solution for embedding spam classifiers into widely used platforms with an accuracy of 98.1%. Furthermore, we explore the incorporation of generative topics into our approach by integrating generative topic modeling techniques, such as latent Dirichlet allocation. In our work, the spam classifier is dynamically updated to account for emerging spam patterns and topics based on a generative approach that improves the robustness of the classifier against new spamming tactics and enables nuanced, context-aware filtering of messages. In addition, our experiments highlight the potential of text SVM classifiers for real-time applications through the fine-tuning of text features. Full article