Search Results (155)

Claims of AI sovereignty are increasingly invoked but operational control remains uneven. Claims to control are made through national models, sovereign clouds, data localization mandates, and procurement rules; however, whether such claims translate into demonstrable control over how AI systems are run, inspected, and contested remains poorly understood. This paper introduces credible sovereignty, the gap between declared and demonstrable control in deployment, as a conceptual lens for analyzing AI governance to examine how this gap is opened and closed across infrastructure, data, and model supply chains. Using a PRISMA-guided social-science corpus and machine learning-based BERTopic modeling, validated through topic diversity and topic separation diagnostics and triangulated through close reading, the analysis identifies four governance logics through which sovereignty is contested: data infrastructure and legitimacy frameworks; techno-bloc diplomacy and infrastructure politics; European regulatory sovereignty; and community-driven sovereignty in the Global South. Across these logics, sovereignty is enacted less through national capabilities than through proxy mechanisms—certification regimes, procurement clauses, cloud governance, and deployment architectures—each carrying trade-offs between autonomy, dependence, and accountability. Rereading the corpus through an Antecedents–Decisions–Outcomes lens yields a testable research agenda: antecedents that push actors toward sovereignty seeking; design and governance choices that translate ambition into implementation; and outcomes—resilience, inclusion, accountability—against which sovereign AI programs should be assessed. This paper reframes sovereignty as a layered operational capability rather than a discursive claim and links computational synthesis to a normative construct that applies across jurisdictions and scales. Full article

Emerging technology topic identification is an important research task in the field of scientific and technological intelligence. To achieve a more comprehensive identification of emerging technology topics, this study proposes a multi-source data fusion framework that integrates three types of data sources: academic publications, patent data, and data from the GitHub open-source platform. In addition, an evaluation indicator system is constructed from four dimensions: growth, novelty, continuity, and impact. During the identification process, the BERTopic topic modeling approach is employed to uncover latent topics within the data, while the entropy weight method is applied for objective weighting, ultimately enabling the identification of emerging technology topics. The results indicate that the identified emerging technology topics include, but are not limited to, large language model-driven intelligent interaction, embodied intelligence perception, context memory management, and multimodal generation. Among the data sources, GitHub data provide earlier signals of technological evolution. Incorporating open-source platform data into the framework can effectively alleviate the lagging issues associated with traditional data sources. The proposed framework provides a more comprehensive research perspective for emerging technology topic identification. Full article

The rapid growth of organisational textual data has increased the value of Natural Language Processing (NLP) and semantic analytics for strategic decision support. However, many employees still lack the knowledge and skills needed to evaluate AI-generated information critically. This study develops an explainable Management Information Systems (MIS) framework that integrates NLP-based semantic analytics with PLS-SEM to examine the relationship between AI illiteracy and strategic decision quality. A convergent mixed-methods design with sequential analytical integration was used with a valid sample of 200 knowledge workers from public organisations in Saudi Arabia across six industries. The sample included employees from Saudi Arabia, Egypt, Jordan, Sudan, Syria, India, and the Philippines. Quantitative data were analysed using PLS-SEM, while textual data were analysed using Sentence-BERT, BERTopic, semantic network analysis, and Aspect-Based Sentiment Analysis. The results showed that higher AI illiteracy was negatively associated with strategic decision quality and positively associated with automation bias, uncritical trust in AI, and cognitive offloading. Digital proficiency and AI governance awareness weakened the negative association between AI illiteracy and decision quality, while functional-background differences were examined through multigroup analysis. The semantic analysis identified six themes: AI competency, decision trust, AI governance, decision support, organisational learning, and risk awareness. Sentiment analysis showed positive views of productivity and decision support, together with concerns about algorithmic bias, explainability, transparency, and AI governance. The study contributes an integrated human–AI decision vulnerability framework in which semantic evidence complements structural modelling and provides a clearer understanding of AI-related competency, reliance, governance, and decision-support issues. Full article

IT help desks at large organizations face a high volume of recurrent, well-documented user requests that nevertheless require human-written replies, creating a persistent staff workload that is repetitive in content but non-trivial in tone and procedural correctness. We present FroLineR, short for Front-Line Response, a system that drafts the initial staff reply to such tickets in the login and account-activation category and integrates into a human-in-the-loop ticketing workflow on a Romanian-language ticketing platform. The generator is an unmodified instruct model augmented with retrieval from a small set of hand-curated guide documents, using a Romanian system prompt refined over several rounds of staff review. To evaluate and refine the prompt without manual labeling, we cluster the first user message of every historical thread with both BERTopic and Semantic Signal Separation ($S^3$), score configurations along coherence and lexical-diversity axes, and extract a 200-message evaluation set from the winning model. Prompt convergence was certified by several rounds of manual review by support staff. The production system is quantized to Q4_K_M GGUF, served through llama-cpp-python behind a small Flask API, and deployed with GPU offloading on the target server, reducing end-to-end per-answer latency from approximately 830 s on the server’s CPU to roughly 61 s once layers are offloaded to the GPU, with no observable degradation in answer quality. Full article

High-density school design has become an important challenge in rapidly urbanizing cities, where land scarcity, increasing educational demand, and evolving pedagogical models generate multiple and interrelated design constraints. Although a wealth of design experience has accumulated through the execution of numerous school planning projects, this knowledge remains fragmented across architectural publications and project narratives. Existing studies have primarily focused on evaluating built environments or individual cases, and limited attention has been paid to how dispersed architectural design reasoning can be systematically extracted, organized, and represented. This study was conducted to explore how AI-assisted semantic modeling can support the extraction and organization of architectural design knowledge from large-scale design discourse through a human–AI collaborative interpretation framework. Using a corpus of 330 documents reporting school design in Shenzhen published between 2017 and 2024, the proposed framework integrates BERTopic-based semantic modeling, scenario–strategy coding, network analysis, and document-based architectural interpretation to establish a continuous workflow from architectural discourse to structured knowledge representation and spatial interpretation. The results reveal a density-conditioned knowledge structure consisting of six interconnected design agendas, 25 recurrent design scenarios, 44 original design strategies, 17 core strategies, four strategy clusters, and four document-supported spatial response patterns. The findings demonstrate that high-density school design knowledge is organized through recurring problem–strategy relationships rather than isolated project solutions. Through human–AI collaborative interpretation, fragmented design narratives are transformed into hierarchical representations linking design concerns, scenarios, strategies, and spatial organizations. The aim of the proposed framework is not to automate architectural decision-making or generate implementation-ready design solutions but to provide a methodological foundation for AI-assisted architectural knowledge retrieval, knowledge representation, and future multimodal design intelligence systems. Full article

As tourism shifts from sightseeing to experience-oriented consumption, understanding how tourist perception differs across heterogeneous destination types and spatial scales remains challenging. Using 23,439 Volunteered Geographic Information (VGI) reviews archived for six destinations in Shangrao, China, this study compares mountain-type and rural comprehensive destination products. These are operational dominant-function categories rather than mutually exclusive geomorphological classes. The archive supports fine-grained sentiment, topic, and semantic-network analyses; annual temporal comparisons use the full 23,439-review corpus covering 2019–2025, whereas a separate subset of reviews posted from 1 August 2022 with official IP labels, aggregated into 2022–2024 province–year observations, supports Pooled Ordinary Least Squares (Pooled OLS) estimation. A hybrid lexicon–XLM-RoBERTa workflow, BERTopic, semantic co-occurrence analysis, and Pooled OLS are integrated in a cross-scale framework. Static results reveal shared strengths and weaknesses—high scenery and overall-experience evaluations but low price evaluations—alongside type-specific structures: mountain reviews concentrate on natural scenery, climbing effort, and accessibility, whereas rural reviews span village landscapes, cultural activities, accommodation, and nighttime experiences. Temporally, mountain demand retains a stable scenic core while accessibility concerns become more salient; rural demand shifts from traditional agricultural landscapes toward nighttime performances and other experience-oriented products. Cross-scale regressions identify destination- and dimension-specific correlates rather than causal drivers: urbanization is positively associated with several rural evaluations, while ecological contrast, climatic difference, and competing scenic resources are associated with more critical assessments in selected dimensions. The findings show that perception differences arise from the interaction of destination product structures and origin-region contexts, supporting differentiated accessibility management for mountain destinations and balanced product innovation, service improvement, and commercialization control for rural destinations. Full article

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

Social commerce platforms increasingly serve as ecosystem orchestrators, coordinating technological infrastructures, financial systems, governance mechanisms, and algorithmic resource allocation. While prior research has predominantly focused on consumer behavior, limited attention has been given to merchant vulnerabilities arising from platform dependency. This study investigates operational vulnerabilities within the TikTok Shop ecosystem using 8993 negative merchant reviews collected from the Google Play Store. BERTopic, a transformer-based contextual topic modeling approach, was employed to identify latent vulnerability themes embedded in merchant complaint narratives. The results revealed 28 interpretable topics, which were aggregated into six higher-order vulnerability dimensions: Infrastructural Instability, Ecosystem Integration Vulnerability, Financial Vulnerability, Algorithmic Dependency, Coordination Breakdown, and Governance Asymmetry. Among these dimensions, Infrastructural Instability (13.59%) and Ecosystem Integration Vulnerability (13.44%) emerged as the most prominent sources of merchant dissatisfaction. The findings indicate that merchant vulnerability extends beyond isolated operational issues and is embedded within interconnected technological, financial, governance, algorithmic, and coordination structures. This study contributes to platform ecosystem research by providing empirical evidence and a structured interpretation of how platform-controlled resource orchestration may be associated with merchant vulnerability alongside value creation, while highlighting ecosystem integration as a significant source of risk during platform transformation. Full article

Social Q&A platforms produce short, noisy, and highly diverse user questions, making coarse topic labels insufficient for accurate information organization. This study proposes a dynamic seed topic construction and multi-demand topic identification framework for health science popularization questions on the Zhihu platform. Using 3529 cleaned questions derived from 2011 to 2024, the framework combines BERTopic clustering, LLM-based topic naming, and a TOP-K cross-filtering update strategy that integrates semantic similarity and frequency. The LLM then performs topic identification, optimization, and assignment, and a co-occurrence network analyzes topic associations. The method generates 26 initial seed topics, which expand to 51 topic instances and are subsequently optimized to 36 topics. Compared with LDA, BERTopic, and LLM-based baselines, our framework achieved the best topic coherence and the lowest topic similarity while maintaining good topic diversity. Human evaluation by 17 volunteers on 30 questions showed that the model performed at least as well as user-assigned tags across relevance, comprehensiveness, clarity, accuracy, and readability, with clearer advantages in relevance and accuracy. These results suggest that the proposed framework can identify fine-grained, interpretable, and strongly associated information-demand topics for social Q&A platforms. Full article

Although esports-themed hotels have rapidly emerged as a technology-driven segment of smart hospitality, empirical research on their customer experience and satisfaction formation remains limited. Drawing on schema theory and expectancy violations theory (EVT), this study adopts a mixed-methods approach to examine the thematic attributes of esports-themed hotel experiences and their effects on customer satisfaction. Study 1 applied BERTopic and aspect-based sentiment analysis (ABSA) to 225,318 online reviews of 1601 esports-themed hotels. The results show that customer experience encompasses not only conventional hotel attributes, such as service, environment, and cleanliness, but also esports-specific attributes centered on gaming facilities, technological configurations, and esports-related social activities. ABSA results further reveal generally positive sentiment across most attributes, with service attitude and cleanliness receiving the strongest approval, whereas room facilities and comfort were the main sources of dissatisfaction. Study 2 employed hierarchical linear modeling to further examine the effects of six experience-related sentiments on customer satisfaction. The results indicate that all six sentiment dimensions significantly influence satisfaction and that hotel type moderates these relationships. Compared with professional esports-themed hotels (PETHs), the effects of experience-related sentiments on satisfaction are stronger in non-professional esports-themed hotels (NPETHs). This study extends schema theory and EVT to the esports-themed hotel context and offers practical implications for customer experience design and service management in this emerging hospitality segment. Full article

Quantitative portfolio optimization has accelerated sharply since 2018, with deep learning and reinforcement learning agents now competing with the mean–variance framework that defined six decades of research. Existing narrative reviews struggle to track this expansion. We screen 832 documents from Scopus and Web of Science under PRISMA 2020 and retain 589 unique articles spanning 2003–2025. Applying BERTopic with SPECTER scientific embeddings, UMAP and HDBSCAN, we identify five coherent topics with a mean coherence of 0.864: classical mean–variance (T0; n = 270), deep reinforcement learning (T1; n = 116), machine learning return forecasting (T2; n = 87), covariance estimation and robust optimization (T3; n = 52)—and metaheuristics (T4; n = 56). A rank-weighted similarity analysis, designed to neutralise the c-TF-IDF collinearity artefact, shows that deep reinforcement learning is the most isolated paradigm. The two methodological families bifurcate over time: AI/deep learning approaches grow from 3.6% of annual output before 2018 to 40.2% afterwards, while classical methods retain volume but lose share. We synthesise the empirical practices of each family along five dimensions critical to applied finance and identify three under-explored integration frontiers. Full article

As electric vehicles become widespread, home EV chargers have emerged as a key interface between household energy use and daily mobility. However, their design optimization remains insufficiently informed by large-scale user feedback. This study develops a review-driven, interpretable machine learning framework to identify design priorities for home EV chargers. Of the 26,763 reviews collected from the JD e-commerce platform, 23,893 were retained after cleaning. BERTopic extracted raw topics, which were consolidated into ten design dimensions through independent coding, inter-coder agreement assessment, and consensus adjudication. A structured large language model protocol then transformed the reviews into evidence-constrained, aspect-level semantic proxy variables representing evaluative direction and intensity. Coding reliability was evaluated against dual-coder annotations, while a matched absence-as-zero specification examined sensitivity to the treatment of unmentioned dimensions. Platform ratings were subsequently introduced as the prediction target, and repeated data partitions and cross-model SHAP comparisons were used to assess partition- and model-level stability. Charging Performance, Operational Stability, Perceived Product Quality, and Operational Convenience and Portability consistently ranked as the most important factors associated with platform-rated satisfaction. In contrast, Installation Friendliness and After-sales Service showed asymmetric attribution patterns characterized by stronger low-value penalties than high-value gains. The framework supports translating online review evidence into product-level design priorities, while emphasizing that SHAP identifies predictive associations rather than causal effects. Full article

Advances in artificial intelligence (AI) are revolutionizing agriculture through applications in crop monitoring, precision agriculture, automation, and environmental management. With the rapid development of AI in agriculture, there is an increasing need for extensive evaluation to identify emerging trends and potential future directions. This paper provides a descriptive bibliometric and thematic synthesis of 31,452 publications on the application of artificial intelligence in agriculture in Scopus from 2020 to 2025. The trends and structure of topics in the area are analyzed using BERTopic topic modeling alongside thematic synthesis, temporal trend analysis, centrality-density mapping, and evidence synthesis. Six higher-order themes were identified in the analysis, with crop and production intelligence being the most common. Despite the current progress, the topic of crop-related applications of computer vision remains dominant in the research landscape. However, applications in livestock, socio-technical systems, sustainability, and advanced distributed artificial intelligence fall at the periphery of the landscape. Temporal and structural analyses reveal a concentrated research landscape dominated by production-oriented applications, while interdisciplinary and emerging AI domains remain relatively fragmented. The paper also emphasizes the importance of combining transformer-based topic modeling with thematic and structural analysis in multidisciplinary fields. The results have revealed important insights into the direction AI technology is taking in the agricultural sector, underscoring the need for interoperable, explainable, sustainable, and farmer-centered systems for agricultural development. Full article

Multistakeholder partnerships (MSPs) are central to the 2030 Agenda for Sustainable Development, yet the UN Partnership Platform suffers from an extreme validation asymmetry: fewer than 5% of registered projects undergo independent verification. This study examines whether discourse patterns distinguish validated from non-validated MSPs. Applying BERTopic neural topic modeling to 3807 project descriptions in the GENESIS WP4 Database, we identify three substantive thematic clusters, spanning climate, sanitation, and health; marine and fisheries; and sustainable textiles, alongside a combined language–artifact cluster excluded from thematic interpretation. The target topic count was fixed to ensure reproducibility after an initial automatic-selection step proved unstable across runs; a multi-seed check confirms stable topic counts with moderate assignment-level agreement (mean Adjusted Rand Index = 0.63). We introduce the Validated-Discourse Similarity Index (VDSI), a leave-one-out cosine similarity measure comparing each project’s textual embedding to a centroid of validated MSPs shown to be more homogeneous than random samples of non-validated projects (p = 0.002). VDSI analysis indicates that 3538 of the 3807 projects (92.9%) exhibit Inconsistent Non-Validated language resembling validated MSPs despite lacking independent verification, though raw semantic similarity alone only modestly discriminates validation status (AUC = 0.686), indicating a real but partial signal rather than a proxy for validation. Partner count is the strongest structural discriminator of validated MSPs (r = −0.389, p < 0.001), remaining significant after adjusting for SDG scope, description length, duration, topic, and language (adjusted OR = 1.38 per SD, p < 0.001), and consistent across six SDG-level subgroups. These findings extend SDG-washing scholarship to the UN multilateral voluntary commitment ecosystem and offer a provisional, discourse-informed basis for partnership evaluation. Full article

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a densely populated, highly connected coastal urban region that is exposed to typhoons, extreme rainfall, flooding, storm surges, heat-related risks, and public health emergencies. Existing studies have clarified many aspects of hazard exposure and spatial vulnerability, but evidence on how residents receive warnings, interpret risks, prepare, evacuate, and contribute to community resilience remains dispersed across disciplines and jurisdictions. This study maps the intellectual structure and thematic evolution of research on residents’ responses to urban disaster risks in the GBA. Following a PRISMA 2020-aligned identification and screening process, 144 records from the Web of Science Core Collection were manually screened. Bibliometric analysis, keyword co-occurrence analysis, a region–hazard matrix, and a BERTopic-inspired topic modelling procedure were then used to identify publication trends, disciplinary sources, spatial hazard patterns, and latent themes. The results show accelerated growth after 2020 and identify six interrelated themes: risk perception and preparedness; evacuation and shelter accessibility; urban flooding vulnerability; typhoon, storm surge, and coastal community risk; community resilience and climate adaptation governance; and health emergency response and psychosocial resilience. The review synthesises these findings into a heuristic framework linking risk information, cognitive appraisal, response action, and community resilience. The GBA is treated as an analytically informative, rather than statistically representative, case of a high-density, coastal, and multi-jurisdictional urban region. The findings suggest that resident-centred resilience planning should connect trusted and actionable warnings with inclusive digital communication, accessible protective resources, and cross-boundary coordination for compound hazards. Full article