Search Results (540)

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

Against the backdrop of a multi-tiered governance system and increasingly institutionalized norms, China’s historical and cultural preservation policies have long emphasized institutional standardization and hierarchical uniformity. Local policy texts are typically viewed as localized replicas of central institutional logic, overlooking internal variations and differences in information structure. Accordingly, this study examines the Regulations on the Protection of Historical and Cultural Cities, Towns, and Villages issued by 13 provincial-level administrative regions in China. It conceptualizes provincial regulatory texts as institutionalized policy information systems, constructs a cross-regional corpus, and develops a comparative information structure analytical framework based on the Latent Dirichlet Allocation (LDA) topic model. This study operationalizes LDA-derived topic-weight distributions into a comparative analytical framework that captures structural prominence, dispersion, concentration, and priority hierarchy in provincial policy texts. The findings reveal that provincial-level historical and cultural preservation regulations in China exhibit a highly institutionalized information backbone, centered on administrative procedures, legal norms, and macro-level planning controls, and demonstrate significant institutional similarity across provinces. However, within this unified institutional framework, provinces exhibit structural differences in the distribution of thematic weights, information prioritization, and internal textual sequencing, resulting in multiple distinguishable information organization patterns. Consequently, this study highlights the coexistence of formal institutional uniformity and structural differentiation in provincial regulatory texts, providing a more precise basis for understanding variation in local policy expression within China’s historical and cultural governance field. Full article

The Marshall–Olkin family of distributions has gained increasing attention in fields such as reliability engineering, survival analysis, financial risk modeling, and actuarial science because of its flexibility in modeling dependence among events and its wide range of extensions. Despite its growing relevance, a systematic understanding of how research on Marshall–Olkin models has evolved over time is still limited. This study addresses this gap by combining bibliometric techniques with topic modeling to analyze the structure and evolution of the scientific literature on Marshall–Olkin models. The analysis includes all 266 peer-reviewed publications on Marshall–Olkin models indexed in Scopus between 1981 and 2025. Bibliometric techniques (including heatmaps, clustering analyses, and temporal visualizations) are used to characterize publication patterns, source relationships, and thematic evolution. In addition, Latent Dirichlet Allocation (LDA) uncovered 27 topics and examined their prevalence across journals and time periods. The results reveal five main clusters of publication sources and three temporal groupings derived from hierarchical clustering of topic distributions, reflecting the thematic progression of the field. Overall, the findings highlight both the persistence of core research themes and the emergence of new applications, particularly in areas such as Bayesian competing risks, censoring models, and parameter estimation in Weibull-based frameworks. This study provides a systematic and data-driven perspective on the intellectual evolution of Marshall–Olkin research, helping scholars identify emerging trends and potential directions for future work. Full article

Water governance faces growing challenges from climate change, pollution, and increasing demand, rendering policy evolution a critical research focus. This study analyzes the evolutionary characteristics of China’s national water resources governance policies from 1988 to 2025 through an integrated quantitative textual analysis. Based on 154 authoritative policy documents, the study employs Latent Dirichlet Allocation topic modeling, semantic network analysis, and a tripartite policy instrument coding scheme (command-and-control, market-based, and public participation instruments). The results reveal three key findings: a significant shift in policy attention from early administrative control toward system-oriented governance emphasizing watershed/ecological protection, conservation, and technology; a persistently imbalanced instrument mix with command-and-control tools remaining dominant, despite gradual diversification after 2000; and a three-stage evolutionary trajectory from administrative framework building (1988–1999), through comprehensive management and diversification (2000–2015), to collaborative innovation and basin/ecology integration (2016–2025). This study contributes a long-term empirical perspective on water policy evolution in an emerging economy, demonstrates an integrated textual-analytic approach, and provides actionable insights for optimizing policy mixes through strengthened incentive compatibility, substantive participation mechanisms, and coherent governance-aligned instrument portfolios. Full article

This study maps competency requirements for two representative frontline marketing roles in China’s new energy vehicle (NEV) sector, NEV sales consultants and livestreaming hosts, and examines their alignment with current vocational education standards. Using a market-oriented, data-driven design, recruitment texts were collected from Zhaopin across more than 20 major Chinese cities. Latent Dirichlet Allocation (LDA) identified competency themes, which were then organized into work-process task domains and visualized as position–task–competency mappings. Mapping these demand-side requirements to national teaching standards reveals relatively strong alignment for sales in market insight and sales strategy, but also gaps in omni-channel lead operations, customer experience management, and operational coordination; livestreaming roles show systematic gaps across the entire work process, particularly in on-air control, customer conversion process design, and data-driven optimization. Building on the identified gaps, the study proposes a position–task–competency-to-curriculum translation pathway to support modular updates in NEV marketing talent development within vocational education and training. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

Promoting the commercialization of agricultural science and technology (S&T) achievements is a critical pathway toward achieving agricultural sustainability and a key governance challenge in advancing global food security and the Sustainable Development Goals (SDGs). However, China faces a structural paradox: despite sustained expansion of policy supply, the performance gains in technology commercialization remain limited. To uncover the underlying causes, this study integrates Latent Dirichlet Allocation (LDA) topic modeling with the Policy Modeling Consistency (PMC) index to conduct a systematic analysis of 82 central-level policy documents issued between 2015 and 2025. The findings reveal that policy attention is heavily concentrated on upstream R&D support, while insufficient emphasis is placed on downstream “last-mile” enablers—such as diffusion services, risk-sharing mechanisms, and intermediary capacity building. Moreover, many policies exhibit structural deficiencies in temporal specificity and multi-actor coordination, which hinder the formation of closed-loop implementation chains. The results suggest that policy structural inconsistency may be a key mechanism constraining policy effectiveness. By adopting a dual analytical lens of “attention allocation–structural design,” this study provides empirical evidence for optimizing policy formulation and enhancing institutional efficacy in agricultural S&T commercialization. Full article

Cyber aggression and abuse (CAA) has become a major interdisciplinary research area spanning psychology, communication, public health, and computer science. Existing reviews have largely focused on detection methods and model performance, offering limited insight into how CAA research themes have evolved over time at the field level. This study addresses this gap by, to the best of our knowledge, applying Latent Dirichlet Allocation (LDA) to 2309 Web of Science–indexed publications with English-language abstracts published between 2000 and 2024, providing a large-scale, longitudinal, and multi-level analysis of the literature. The model identifies 29 latent topics, which are organized using the User–Activity–Content (UAC) framework to link psychosocial research, platform-mediated behaviors, and computational detection approaches. Temporal analysis reveals a clear methodological transition: early dominance of survey-based and psychosocial themes gradually declines in relative prominence, while computational topics related to machine learning, deep learning, and pre-trained language models exhibit sustained growth, particularly after 2010. A Hot–Cold topic classification further distinguishes emerging, stable, and declining research directions. Journal-level, disciplinary, and geographic analyses reveal systematic differentiation across venues and regions, with complementary emphases on psychosocial and computational approaches. These findings provide a structured, field-level perspective on the evolution of CAA research and offer practical value for researchers, funding agencies, journal editors, and publishers by identifying dominant, emerging, and declining themes that can inform research prioritization, editorial planning, and strategic investment. Full article

With the advancement of digital technology and Industry 4.0, artificial intelligence (AI) is gradually embedded in human resource management and has become an important digital foundation to support the sustainable transformation of enterprises. However, the research in the manufacturing context, particularly through the challenge perspective at different levels, remains fragmented. This work represents a systematic review of 347 articles from Scopus and Web of Science from 2000 to 2025 and employs a dual-method analysis strategy embracing metrics and in-depth coding on 100 core publications. Excel, Bibliometrix, CiteSpace, Latent Dirichlet Allocation (LDA), and VOSviewer were utilized for quantitative analysis, while open–axial–selective coding of the Grounded theory approach was applied to generate qualitative results. The findings revealed six key challenges in integrating AI-HRM within manufacturing and six approaches to solve the identified issues. The Challenge–Approach Matching Matrix was constructed, illustrating the suitability of different pathways for addressing specific challenges. Analysis of thematic evolution in AI-HRM research resulted in the identification of three distinctive phases and demonstrated a consistent shift from technology-centric approaches towards human–machine collaboration. The primary contribution of this research lies in proposing a Multi-Level Embedded Framework providing a complex view of AI-HRM in a manufacturing sector at micro, meso, and macro levels. The absence of sustainable HR transformation through AI integration was identified as the critical challenge at the macro level. This research provides theoretical and practical implications for designing the sustainable HRM system based on ESG principles and favors the United Nations Sustainable Development Goals 9 and 12. Full article

The transition to environmentally sustainable healthcare is gaining urgency, yet public discourse shaping this shift remains underexamined. This study employs natural language processing (NLP) to analyze 15,976 English-language tweets (2006–2024) related to sustainable healthcare. Using Latent Dirichlet Allocation (LDA), eight dominant topics were identified, including eco-friendly access, net-zero implementation, climate impact, emissions, cost and waste, education, infrastructure, and green technologies. Sentiment analysis (VADER) of 9433 tweets showed 59.1% positive, 31.1% neutral, and 9.8% negative sentiment, with AI and technology topics receiving the highest positivity (73.5%) and climate-related topics the most negativity. Thematic analysis of 800 tweets revealed six cross-cutting themes, including healthcare’s environmental responsibility, co-benefits for health, urgency of climate action, and optimism in technological solutions. These findings offer a nuanced understanding of public perceptions, informing targeted strategies and communication for healthcare sustainability. The study also demonstrates the value of mixed-method NLP in examining enablers and barriers to health system transformation. Full article

Against the backdrop of global aging, whether the diverse content of elderly care policies can be systematically analyzed and interpreted through a unified theoretical framework remains an open question in gerontology. This study analyzes 2508 elderly care policy documents issued in China from 2000 to 2025 to assess the alignment degree between elderly care policies and the Integrated Theory of Social Gerontology, utilizing Latent Dirichlet Allocation (LDA) topic modeling and a text content alignment degree model constructed based on a normalized co-occurrence algorithm. The findings reveal that all 11 topics extracted from Chinese elderly care policies correspond to the six dimensions of the Integrated Theory of Social Gerontology, with an overall alignment coefficient of 0.689, indicating moderate alignment. This threshold is defined based on domain-specific benchmarks: alignment coefficients ≥0.75 are classified as ‘high alignment’, 0.5–0.74 as ‘moderate alignment’, and <0.5 as ‘low alignment’, consistent with quantitative standards for policy-theory alignment research in gerontology. Four dimensions (public support systems, individual physical/psychological health, economic security, family-cultural traditions) show high alignment, while two (social stratification, historical context) exhibit low alignment, reflecting significant policy coverage asymmetries. Methodologically, this study develops a replicable policy theory alignment model, filling gaps in integrated gerontology policy analytical tools. Empirically, it provides the first large-scale longitudinal analysis of Chinese elderly care policies, illuminating policy design’s theoretical foundations and gaps in structural/historical dimension coverage. Full article

Social media platforms serve as critical channels for autism support groups to communicate and seek assistance. This study employed Latent Dirichlet Allocation (LDA) topic modeling to analyze discourse patterns within the Autism Bar on Baidu Tieba, a major Chinese social media. A dataset of 14,151 posts was collected through web crawling, with 12,667 posts retained after preprocessing. The analysis revealed two key findings: (1) The discourse among autism support communities on Baidu Tieba focuses on four central themes: intervention and therapy, early educational journey, early symptom detection and family interaction, and access to educational resources and community support. (2) Sociocultural factors exert a significant influence on autism-related discourse, particularly in shaping societal attitudes toward individuals with autism and the formation of support networks. Traditional Chinese cultural values, such as collectivism and familial centrality, impact the behavioral patterns and decision-making processes of families with autistic children. This study has demonstrated the unique needs and challenges faced by the autism support community, while also informing strategies to promote social media platforms as spaces for support and information exchange. The findings have practical implications for designing targeted interventions and support mechanisms for individuals with autism and their families. Full article