Search Results (1,543)

Wastewater treatment systems are under increasing pressure to improve nitrogen removal while reducing carbon emissions, yet the deployment of anaerobic ammonium oxidation (anammox) remains constrained by uncertainty about technical readiness, operational robustness, nitrous oxide (N₂O) emissions, life-cycle carbon performance, monitoring capacity, and transferability across wastewater contexts. This study uses dynamic topic modelling and trend assessment of 998 publications from 2001 to 2025 to synthesize deployment-relevant evidence for anammox-based wastewater treatment. The results indicate that the field has shifted from reactor start-up and process-parameter optimization toward microbial regulation, mainstream process integration, coupled nitrogen-removal strategies, and intelligent control. Building on these topic-evolution patterns and reported engineering evidence, this study provides an evidence-based qualitative appraisal of deployment-readiness signals and evidence gaps, distinguishing comparatively mature side-stream applications from mainstream systems that still require monitored demonstrations, transparent N₂O accounting, life-cycle assessment, and locally validated operating data. The study argues that anammox should be evaluated as a technology with potential but conditional carbon-saving benefits: its potential carbon-saving benefits depend on operational evidence specific to each application stage, carbon-accounting credibility, and implementation capacity, rather than assuming that research activity alone justifies broad deployment. Full article

Early prediction of epileptic seizures remains an active research area in scalp electroencephalography (EEG) analysis. Current methods focused on this topic often rely on handcrafted features that insufficiently capture the multiscale nonlinear dynamics of preictal activity, process EEG channels independently without modeling brain connectivity, or demand computationally expensive manual hyperparameter tuning. To address these limitations, this study presents a framework integrating multifractal analysis (MFA), graph neural networks (GNNs), and a differential evolution algorithm (DEA) to classify non-overlapping one-minute EEG segments as preictal (within 60 min before onset) or reference (interictal) states. Five complementary MFA techniques extract nonlinear descriptors from each segment, which serve as node attributes in a graph of the 21 EEG channels. Unlike conventional approaches, this graph explicitly encodes neuroanatomical proximity to capture spatial brain dynamics and inter-channel topological dependencies. This graph-based representation allows the GNN to learn from the relational structure of the brain network, capturing spatiotemporal interactions critical for early prediction, while the DEA systematically optimizes the GNN architecture, eliminating subjective manual tuning. Under a segment-level validation protocol on the CHB-MIT database, the framework achieved 96.38% accuracy, 94.36% sensitivity, 98.41% specificity, and an AUC-ROC of 99.37%. These results demonstrate the effectiveness of combining multifractal descriptors, graph-based processing, and evolutionary optimization for segment-level discrimination. Nevertheless, patient-wise validation remains essential for clinical translation, and this work is consequently presented as a proof-of-concept demonstration. Full article

This study conceptualizes RE100 not merely as a voluntary corporate campaign, but as an evolving socio-technical system that intersects with South Korea’s path-dependent energy regime. Applying BERTopic to a comprehensive corpus of 21,901 raw news articles (reduced to 13,507 analytically relevant articles after noise filtering; January 2019–October 2025), we trace the systemic evolution and multi-layered governance of RE100. Grounded in social constructionism and institutional systems theory, the research reveals how the global sustainability norm becomes structurally embedded in South Korea’s industrial and economic systems through a partially sequential evolutionary process of externalization, objectivation, and internalization. BERTopic identified 39 analytically meaningful topics, subsequently organized into four interpretive clusters: (1) local government-centered construction, (2) spatial reconfiguration and industrial-policy formation, (3) corporate-led market institutionalization, and (4) ESG-driven corporate governance. Collectively, these clusters demonstrate that RE100 has evolved from a peripheral international initiative into a multilayered governance norm embedded across local governance, industrial infrastructure, corporate strategy, and ESG systems. The findings hold theoretical and policy implications for energy-transition governance and climate communication. This progression is recursive rather than strictly linear: corporate-level internalization (Cluster 4) is conceptually linked to, and may interact with, regional and industrial-policy externalization (Clusters 1–2), consistent with a non-linear reading of institutionalization in which corporate site-selection criteria shape local-government infrastructure competition. Cluster 1’s prominence reflects local governments’ discursive visibility rather than substantive policy authority. Notably, RE100’s “normalization” denotes routinized corporate compliance practices, not political consensus on its legitimacy relative to alternatives such as carbon-free 100% or nuclear power. Full article

Legal systems evolve continuously in response to legislative reforms, emerging judicial interpretations, and shifting societal expectations, making it increasingly difficult to anticipate changes in legal precedent using conventional analytical methods. This study introduces an explainable artificial intelligence framework for predictive jurisprudence that captures the temporal evolution of legal reasoning by jointly modeling semantic, structural, and causal relationships within judicial decisions. The proposed framework integrates neural temporal graph networks to learn evolving citation dependencies, dynamic topic modeling to characterize changes in legal doctrines over time, and causal-inference techniques to distinguish genuine jurisprudential influence from spurious associations. To enhance transparency, the predictive process is complemented by GNNExplainer, enabling the identification of the legal principles, precedents, and citation patterns that most strongly influence model predictions. The framework is evaluated using the Free Law Project and LePaRD benchmark datasets and demonstrates superior performance over existing approaches in detecting causal judicial influences and accurately quantifying precedent evolution. Its practical applicability and interpretability are further validated through expert legal assessment and historical backtesting against documented jurisprudential shifts. The experimental findings demonstrate that integrating explainable machine learning with causal legal analytics provides reliable early indicators of doctrinal change, offering valuable decision-support capabilities in legal environments. Full article

Green taxation has evolved from a fiscal instrument for correcting environmental externalities into a strategic policy mechanism for promoting sustainable development and supporting the transition toward low-carbon economies. As research on this topic has expanded, the literature has become increasingly fragmented, characterized by diverse research streams, limited interdisciplinary integration, and an incomplete understanding of its intellectual and conceptual development. This study provides a comprehensive bibliometric analysis of green taxation research to examine its scientific evolution, map its knowledge structure, and identify emerging research frontiers and knowledge gaps. Conceptually, the literature on green taxation extends beyond conventional Pigouvian foundations to encompass ecological, institutional, and broader heterodox perspectives. The analysis is based on 2952 publications indexed in the Scopus database between 1990 and 2025. Biblioshiny (Bibliometrix in R) and VOSviewer were employed to examine publication trends, co-citation networks, keyword co-occurrence, and international scientific collaboration. The results reveal sustained growth in scientific production, reaching its highest level in 2024, and a heterogeneous research landscape structured around major themes, including the double dividend, innovation for sustainable development, environmental policy related to pollution and welfare, circular economy and sustainability transitions, as well as green investment linked to technological change. Scientific output remains highly concentrated in a limited number of countries, with China emerging as the leading contributor, while international collaboration remains comparatively limited. The analysis also highlights significant geographical disparities, particularly the underrepresentation of Africa and the MENA region, and reveals that the field remains only partially integrated despite its rapid expansion. These findings provide an integrated understanding of the evolution of green taxation research and identify key priorities for future empirical and comparative studies to support more effective and inclusive environmental fiscal policies. Full article

Driven by the escalating worldwide consumption of animal-sourced commodities, the livestock industry faces intensified constraints regarding its ecological consequences, encompassing large-scale deforestation, elevated greenhouse gas emissions, progressive soil deterioration, and the suboptimal management of hydrological resources. In response to these challenges, the concept of sustainable livestock husbandry integrates production practices with environmental management strategies designed to conserve biodiversity, optimize resource use, and reduce emissions. The objective of this review is to emphasize the significance of sustainable cervid farming as an integral element of contemporary food supply chains, comprehensively addressing its environmental, production, and nutritional dimensions. A comprehensive assessment of current scholarly articles from Scopus, Web of Science, and Google Scholar was performed to explore topics concerning eco-conscious livestock practices, non-conventional farming models, organic rearing, animal well-being, and the evolution of deer breeding. Particular attention was paid to the role of permanent grasslands in venison production, soil protection, carbon sequestration, and biodiversity preservation. The nutritional value of red deer and fallow deer meat and its significance in sustainable food systems are also outlined. As indicated by the analysis of available research findings, extensive cervid farming based on the use of permanent grasslands and local feed resources can reduce environmental pressures through support of landscape conservation, preservation of ecosystem functions, and efficient utilization of biomass that is inedible for humans. Venison is shown to be a valuable source of high-quality protein, minerals, and essential fatty acids, which meets growing consumer demands for high-quality food. The literature review has confirmed that properly managed cervid farming can be an important element of sustainable food production systems combining production goals with environmental protection and animal welfare. It also emphasizes the need for further research into the environmental, economic, and social aspects of this branch of animal production. Full article

Background/Objectives: Nursing research on children with disabilities spans diverse disability groups, care settings, and areas of practice; however, its development and structure have not been comprehensively examined. This study aimed to examine publication trends, major research themes, temporal development, and collaboration patterns. Methods: The Web of Science Core Collection was searched on 29 June 2026 without publication-year restrictions. English-language articles and reviews indexed in Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) were included, yielding 1915 publications from 1977 to 2026. VOSviewer and Biblioshiny were used to analyze publication trends, keyword co-occurrence, thematic structure and evolution, trend topics, and country and institutional collaboration. Results: Research output increased substantially, particularly during the last decade. Across the study period, the United States had the highest publication output and co-authorship connectivity, followed by England, Canada, and Australia. Nursing, children, autism spectrum disorder, intellectual disability, and cerebral palsy were among the largest nodes in the keyword network. The thematic map positioned the autism–pediatrics–developmental disability cluster slightly within the motor themes quadrant, while the disability–children with disabilities–qualitative research, adolescents–communication–transition, and children–nursing–intellectual disability clusters were positioned among the basic themes. Thematic evolution showed both continuity and diversification, with autism, nursing, children, and intellectual disability represented across multiple periods, while quality of life, education, and mental health were represented in the most recent period. Trend topic analysis further showed that well-being, implementation, pediatric nursing, anxiety, and mental health were among the topics with more recent median publication years. Conclusions: Nursing research on children with disabilities has expanded and diversified, with recurring disability-specific topics alongside more recent topics related to psychosocial issues, pediatric nursing, and implementation. Future research could build on the thematic patterns identified in this study through systematic reviews and primary nursing research, while bibliometric studies incorporating additional databases could provide a more comprehensive view of the field. Full article

Background: Artificial intelligence (AI) is increasingly being investigated in perioperative medicine for risk stratification, complication prediction, patient safety, and clinical decision support. This study characterized the research landscape, intellectual structure, and thematic evolution of AI-based perioperative risk prediction in anesthesiology. Methods: Publications indexed in the Web of Science Core Collection between 2018 and 3 June 2026 were analyzed using Bibliometrix and Biblioshiny. Scientific productivity, citations, collaboration networks, keyword co-occurrence, thematic evolution, and Reference Publication Year Spectroscopy were evaluated. Partial 2026 data were excluded from the compound annual growth-rate calculation. Results: A total of 152 publications were included. Scientific production increased substantially, with a compound annual growth rate of 77.72% between 2018 and 2025. The United States and China led publication output, while the United States had the highest total citation count. Citation and thematic analyses identified perioperative outcome prediction, predictive hemodynamic monitoring, and explainable AI as important components of the field’s intellectual structure. Research attention increasingly shifted from methodological machine learning development toward clinically oriented topics, including mortality, postoperative complications, delirium, postoperative nausea and vomiting, and perioperative risk stratification. Emerging themes included explainable AI, large language models, natural language processing, and clinical decision-support systems. Conclusions: Research attention has increasingly shifted from exploratory model development toward clinically oriented risk-stratification and decision-support applications. Emerging directions emphasize interpretability, multimodal data integration, and individualized risk assessment. These bibliometric patterns reflect changes in research emphasis but do not demonstrate clinical adoption or effectiveness. Full article

Game Theory provides a foundation for multi-agent systems, reinforcement learning, mechanism design, and adversarial learning within AI and ML. Nevertheless, few comprehensive studies map the structure and branches of this cross-disciplinary field. The current research attempts to fill this gap by providing a combined bibliometric and semantic study of 6974 records obtained from Scopus and Web of Science during the years 1972 to 2025. This research is among the first to implement traditional bibliometrics integrated with LDA (Latent Dirichlet Allocation) topic modeling through R (version 4.5.1) to map research networks and surface latent research themes across domains such as multi-agent reinforcement learning and adversarial learning. The major contribution is the development of an evolutionary model that identifies the Game Theory’s paradigmatic evolution in the context of AI and ML from 1972 to 2025. This research also provided the characteristics that describe the shift from the rational equilibrium paradigms to intelligent systems that learn, collaborate, and devise strategies autonomously. Full article

The rapid convergence of artificial intelligence, machine learning, and financial and risk analytics has produced a large and fragmented body of scientific literature, making it difficult to identify its thematic structure and evolutionary trends. This study addresses that gap by proposing a bibliometric-computational framework that integrates Latent Dirichlet Allocation (LDA) and the HJ-Biplot to map research at the intersection of artificial intelligence and data-driven methods with financial and risk analytics. Bibliographic records were retrieved from Scopus and Web of Science, yielding a corpus of 20,213 documents published between 1999 and 2025 from 4640 sources. After preprocessing, LDA was applied to extract latent topics, and the HJ-Biplot was used to represent the relationships among topics, years, countries, and scientific sources within a multivariate framework. The number of topics was selected through a grid search over every integer value of K between 5 and 40, using probabilistic topic coherence together with manual inspection of topic separability. The analysis identified 36 latent topics (K = 36) spanning portfolio optimization, risk assessment, fintech, and deep learning-based forecasting, and shows a rapid post-2010 expansion in the topics concerned with artificial intelligence and predictive analytics, with output concentrated in China, the United States, and India. Increasing, declining, and fluctuating topics were distinguished by the sign and statistical significance of a linear trend in annual topic prevalence. By combining probabilistic topic modeling with multivariate biplot representation, this study offers a more granular and data-driven alternative to conventional bibliometric counting methods, providing a replicable framework for tracking the evolution of interdisciplinary research fields. The design is descriptive: it characterizes the structure of the indexed literature and does not measure financial practice or establish the causes of the patterns reported. Full article

Social media has become an important platform for the public to obtain information, express emotions, seek help, and participate in collaborative responses during major natural disasters. However, existing studies have mainly focused on the intensity of disaster-related discussions, emotional evolution, or patterns of information dissemination, while systematic examination remains limited regarding how public risk perception is associated with specific behavioral responses and whether these associations vary across disaster stages, degrees of regional impact, and user types. Using the stimulus–organism–response (SOR) framework as an organizing heuristic, this study examines the 2023 Beijing–Tianjin–Hebei extreme rainfall event. A total of 31,102 Sina Weibo posts were collected. By integrating LDA topic modeling, risk-perception dictionary matching, a BERT-based semantic robustness check, and chi-square tests, this study analyzes descriptive associations between public risk perception and behavioral responses. The results show that Weibo discussions evolved across disaster stages, with topics shifting from meteorological warnings and disaster reports to mutual aid, rescue operations, material support, and post-disaster reflection. Public risk perception shifted from information-oriented to relationship-oriented cognition, while behavioral responses transitioned from information attention to emergency mutual aid, sustained support, and reflective expression. Further analysis indicates that uncertainty perception was mainly associated with information attention; susceptibility perception co-occurred with help-seeking and emotional support; positive trust perception was strongly associated with emotional support; and negative trust perception co-occurred with questioning, accountability, and reflective suggestions. Because the negative-trust classifier performed substantially less well than the other dimensions, findings involving negative trust are treated as exploratory. Heterogeneity analyses show that observed perception–behavior associations varied across disaster stage, regional impact, and user type. These findings deepen understanding of social-media discourse during disasters while supporting cautious, stage-, region-, and group-sensitive risk communication. Full article

Agroforestry integrates environmental, climatic, productive, and socioeconomic dimensions. The position of rural livelihoods within its scientific structure remains still characterized. This study evaluated the temporal evolution, conceptual organization, domain representation, and geographical and economic distribution of global agroforestry research using 5711 Scopus-indexed journal articles published between 1979 and 2025. Scientific production increased markedly, with 4133 articles published during 2013 to 2025, accounting for 72.37% of the corpus. Thematic evolution showed a shift from early agronomic topics toward biodiversity, ecosystem services, carbon sequestration, and climate change. Multiple Correspondence Analysis of harmonized Author Keywords identified five clusters, with the first two dimensions explaining 39.76% of total inertia. Livelihood-related terms were embedded within the central agroforestry and sustainability cluster under the analytical configuration applied, and this pattern was maintained after excluding corpus-defining search-anchor terms. Production, silvopastoral, and agronomic management had the highest proportional contribution (57.92%), whereas environmental and biodiversity (43.88%), livelihoods and socioeconomic (43.81%), and climate, carbon and soil dimensions (42.50%) occurred at comparable proportions. Fractional affiliation analysis indicated that all domains occurred across regions and income groups, with livelihoods and socioeconomic research showing larger fractional contributions from Africa and lower-middle-income countries. Full article

Decision-making under uncertainty, risk, and ambiguity is a central topic in behavioral sciences, yet the cognitive and neural mechanisms through which individuals construct subjective value remain fragmented across disciplines. Neuroeconomics has emerged as an interdisciplinary field integrating psychology, economics, and neuroscience to explain how subjective value guides judgment and choice under uncertain conditions. However, the conceptual foundations of subjective value and its role across different forms of uncertainty remain insufficiently integrated. This study provides the first comprehensive review of subjective value research by combining a systematic literature review with bibliometric analysis. We first trace the theoretical evolution of judgment and decision-making under uncertainty, emphasizing the development of subjective value as a core construct linking cognitive evaluation, affective processing, and behavioral choice. We then review major approaches to measuring subjective value and examine their contributions to understanding individual differences in decision-making. Using VOSviewer, we identify the intellectual structure, major research themes, and emerging trends in the field. The findings reveal two complementary knowledge foundations: theories of judgment and decision-making under uncertainty and the neural mechanisms underlying subjective value representation. Three dominant research themes emerge: (1) cognitive and affective processes underlying subjective value construction during uncertainty-related judgment; (2) neural representations of subjective value across risky and ambiguous decision contexts; and (3) the development of integrative cross-domain models that connect psychological, economic, and neuroscientific perspectives on decision-making. Based on these findings, we propose future research directions that emphasize distinguishing risk, ambiguity, and uncertainty at both behavioral and neural levels, integrating contextual and individual-difference factors into subjective value models, and extending neuroeconomic research beyond laboratory paradigms to real-world decision environments. By synthesizing theoretical and empirical advances across disciplines, this review offers an integrative framework for understanding how subjective value shapes human judgment and decision-making under uncertainty. Full article

Climate change has intensified heat stress in poultry production systems, raising concerns about productivity, animal welfare, and system sustainability. This study aimed to analyze the evolution, structure, and emerging trends of scientific research on climate change and heat stress in industrial poultry production using a bibliometric approach. Records were retrieved from the Scopus and Web of Science Core Collection databases. After deduplication and eligibility screening, 342 documents published between 1974 and 2025 were retained. Bibliometric analyses were performed using Bibliometrix/Biblioshiny in RStudio, including publication dynamics, international collaboration, author co-citation, conceptual structure, thematic evolution, trend topics, and keyword co-occurrence. Scientific production increased markedly in recent years, with an annual growth rate of 8.36% and international co-authorship in 22.51% of the publications. China and the United States were the leading contributors. At the same time, the international research structure showed interconnected collaboration networks and complementary intellectual communities focused on physiological and neuroimmune responses, cellular and oxidative mechanisms, and nutritional and management-based mitigation. Heat stress was the main conceptual hub, connecting thermoregulation, oxidative stress, immunity, welfare, productive performance, and product quality. Heat tolerance, thermotolerance, chronic heat stress, and genetic adaptation showed recent or developing bibliometric visibility, although their structural and temporal patterns differed across analyses. However, heat waves, long-term resilience, validation under commercial conditions, climate-vulnerable regions, environmental engineering, and digital monitoring exhibited lower bibliometric representation and network centrality within the analyzed corpus. Future research should combine nutritional, physiological, genetic, environmental, and precision-monitoring approaches to support economically viable and climate-resilient poultry production. 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