Search Results (1,825)

Selenium (Se) is a beneficial element involved in plant growth, metabolism, stress adaptation, and crop quality improvement, but its effects are strongly influenced by chemical form, application dose, plant species, growth stage, and environmental conditions. To integrate mechanistic understanding with global research trends, this study combines a concise mini-review with a bibliometric analysis of Se research in plants from 2000 to 2025. The mini-review summarizes Se speciation and bioavailability in the soil–plant–microbe system, root uptake and long-distance transport, metabolic assimilation and detoxification, physiological regulation, stress tolerance, biofortification, and nano-Se applications. Bibliographic data were retrieved from the Web of Science Core Collection and analyzed using CiteSpace, VOSviewer, and Scimago Graphica. A total of 3451 valid publications were identified, showing a sustained increase in annual output, especially after 2018. The field has expanded from early studies on Se speciation, uptake, assimilation, and antioxidant responses toward broader themes involving crop biofortification, molecular regulation, stress physiology, foliar application, nano-Se applications, green synthesis, and phytoremediation. Overall, plant Se research has evolved into an interdisciplinary field linking mechanistic studies with safe agricultural application. Future work should emphasize standardized experimental frameworks, causal mechanism validation, precise biofortification, field-based evaluation, and safety assessment of emerging Se-based technologies. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Mass–balance discrepancies exist between estimated land-based inputs and observed marine plastic inventories. While current global mass–balance models predominantly treat the open ocean as a passive terminal sink, they overlook the rapid expansion of offshore and deep-sea industrial frontiers. This review identifies offshore and deep-sea activities as active, in situ emission nodes of microplastics (MPs). Through a bibliometric analysis and numerical descriptions of studies, we document that direct offshore emissions are underrepresented in the current literature. By synthesizing these limited quantitative data, preliminary metrics indicate localized MP enrichment signals and elevated biological exposure near specific offshore infrastructures. Furthermore, plastics released directly into the marine environment bypass terrestrial weathering, undergoing distinct multiscale aging pathways governed by the complex interplay of wave-induced physical fragmentation bounded by critical size thresholds, UV-driven chemical photo-oxidation, and biological interactions. We conclude that refining global plastic budgets supports moving toward an integrated ocean-industrial framework. However, the synthesis remains constrained by data scarcity and high methodological heterogeneity across different environmental matrices. Future strategies must prioritize standardized in situ flux quantification and the incorporation of MP emission risks into offshore Environmental Impact Assessments. Full article

This bibliometric study maps olive (Olea europaea L.) yield prediction research as a coherent scientific domain for the first time. A Scopus query (27 February 2026) yielded 84 peer-reviewed articles (2002–2025), from which co-authorship network analysis, Bradford’s and Lotka’s Laws, Latent Dirichlet Allocation topic modelling (LDA), and OLS regression on citation counts were applied. Publication output increased nearly fourfold across three periods: 1.7 articles yr⁻¹ (2002–2014), 4.4 yr⁻¹ (2015–2019), and 6.7 yr⁻¹ (2020–2025). The 84 articles involve 382 authors, 61 journals, and 1551 citations (H-index = 22). Network analysis reveals a concentrated Spanish–Italian co-authorship axis. OLS regression (adj. R² = 0.267) identifies article age and abstract length as the only significant citation predictors, consistent with cumulative exposure time and study scope as structural drivers. Term-frequency screening against 18 a priori concepts finds that transfer learning, federated learning, hyperspectral imaging, digital twins, and SHAP-based explainability are absent or marginal. The field is producing more papers than ever on a narrowing methodological base geographically concentrated in the Mediterranean basin. Priority gaps—explainable AI, multi-region datasets, sensor-fusion pipelines, and federated data infrastructure—align directly with European Farm to Fork and Horizon Europe objectives. Full article

This review examines the scientific basis and technological development of natural zeolites, focusing particularly on deposits and research from Bulgaria and Turkey. It traces the transformation of zeolites from conventional industrial minerals into advanced, high-performance functional materials. A stepwise methodological framework was employed to conduct a bibliometric assessment of research and review articles published between 2015 and 2025, primarily focusing on contributions from researchers in Bulgaria and Turkey. In parallel, the study evaluates historical industrial data and the foundational literature. Beyond its regional focus, the bibliometric analysis reveals that Turkey ranks among the world’s leading contributors to zeolite research after Iran, China, and Indonesia, while Bulgaria maintains significant presence within the global network of active researchers in the field. The findings suggest that zeolite science in the region is expanding rapidly and dynamically, with current research increasingly focused on modifying, functionalizing, and diversifying zeolitic materials for a wide range of scientific and technological applications, including wastewater treatment, environmental remediation, construction materials production, agriculture, animal husbandry, and healthcare enhancement. The growing demand for effective adsorbents, therapeutic agents, and nutritional supplements, coupled with the critical need to reduce manufacturing costs, serves as a primary driver for accelerated zeolite research. Full article

Saudi Arabia’s audit market faces three governance challenges that existing frameworks may not fully address. These challenges concern a potential regulatory gap around autonomous AI accountability, a trust dimension that standard technology-adoption models may not fully capture, and limited mechanisms for independently verified ESG assurance under Vision 2030. This study adopts a conceptual design approach within the design science research tradition and proposes the CMA Agentic AI Platform as a practical response to these challenges. The platform comprises two segments. Segment 1 deploys autonomous drone swarms to verify corporate assets across four audit tasks—asset valuation, ESG compliance, anomaly detection and construction progress—using deep learning, thermal imaging and social-media cross-referencing. Segment 2 continuously monitors discretionary accruals and uses objective earnings-management data to inform auditor assignment and rotation decisions. This approach replaces subjective reputational assessments with transparent, quantifiable governance criteria. The platform is governed through the Triadic Agentic Framework, which extends classical agency theory by distributing authority across the Principal, the Human Agent and the AI Agent. The framework also operationalises Trust Expectancy as the primary adoption condition. The evidence base draws on two complementary streams: a PRISMA-guided systematic review and bibliometric analysis of thirty-nine peer-reviewed studies, and a documentary analysis of four national agentic-AI regulatory frameworks (SDAIA, MDDI/IMDA, NIST and ICO). The study contributes the concept of Algorithmic Accountability as a distinct governance domain, the Triadic Agentic Framework as an operational architecture for autonomous regulatory monitoring, and a reframing of the UTAUT trust construct for agentic-AI adoption in mature professional contexts. The platform converts theoretical governance into a regulatory architecture with direct implications for concentrated capital market regulators. Full article

Google Earth Engine (GEE) has become a central platform for planetary-scale geospatial analysis, but its rapid evolution in the last few years is not yet reflected in the existing review literature. Earlier reviews mainly describe the platform’s architecture and its initial application domains, whereas a structured bibliometric and thematic overview of the post-2022 phase of GEE is still lacking. In this more recent phase, the platform has introduced foundation models, satellite embeddings, and native links to cloud databases. Drawing on a structured bibliometric analysis of 5591 Scopus and Web of Science indexed documents published between 2011 and 2025, the results reveal sustained long-term growth, with annual publications increasing from 3 records in 2011 to 1371 records in 2025, corresponding to a compound annual growth rate (CAGR) of 54.88%, indicating a shift from exploratory testing of the platform to more operational use. Logistic growth modelling ($R^2=0.991$) suggests that GEE research is transitioning from rapid expansion towards a scientific maturity phase, where the platform increasingly functions as a normalized analytical infrastructure embedded within broader cloud-native geospatial ecosystems. The full 2011–2025 corpus is used to establish long-term bibliometric trajectories, whereas the thematic synthesis focuses on the post-2022 transition towards Geospatial Artificial Intelligence(GeoAI), satellite embeddings, and cloud-database interoperability. The review examines how new satellite embedding datasets and BigQuery integrations help close the gap between raster-centric Earth observation (EO) workflows and tabular data science. We summarise methodological changes from traditional pixel-based classifiers to multimodal fusion approaches that combine Synthetic Aperture Radar (SAR), Global Ecosystem Dynamics Investigation (GEDI), and optical sensors, and we discuss how GEE’s highly integrated ecosystem influences reproducibility and the risk of vendor lock-in. Finally, we propose a roadmap for the ongoing transition of GEE towards GeoAI, offering researchers and policymakers a transparent and reproducible framework for deploying the platform in high-impact environmental governance. Full article

Background: Exogenous nucleotides are bioactive compounds involved in nucleic acid synthesis, cellular metabolism, intestinal function, immune regulation, and related physiological processes. Owing to their potential roles in supporting growth, gut health, immune function, metabolic regulation, and physiological resilience, they have attracted increasing attention as functional dietary supplements and feed additives. However, the global research landscape of exogenous nucleotides has not been systematically characterized. This study aimed to map the development of this field and identify its major contributors, knowledge structures, application domains, and emerging research hotspots. Methods: Global literature on exogenous nucleotides published between 2000 and 2025 was retrieved from the Web of Science Core Collection. After screening and data standardization, 710 records were analyzed using VOSviewer, CiteSpace, and R-based visualization tools. The bibliometric analysis included publication output, country and institutional collaboration, keyword co-occurrence, co-cited references and journals, and citation burst detection. Results: A total of 710 publications were included. Annual publication output showed an overall upward trend, with marked growth after 2017. China and the United States were the leading contributors, while the Chinese Academy of Sciences and Peking University were among the most productive institutions. Keyword and co-citation analyses identified three major research themes: basic molecular mechanisms, physiological and health-related effects, and practical applications. Aquaculture and animal nutrition represented the most prominent application areas, with studies mainly focusing on growth performance, feed utilization, intestinal morphology, immune responses, oxidative stress resistance, and disease resistance. In human nutrition, research was mainly related to infant nutrition, intestinal and immune health, nutritional recovery, metabolic regulation, and healthy aging. Burst detection indicated a shift in research attention from early topics such as human milk and receptors to intestinal morphology and, more recently, nicotinamide mononucleotide and molecular activation. Conclusions: Research on exogenous nucleotides has expanded rapidly and is moving toward more mechanism-oriented and more diverse applications. Current evidence suggests that exogenous nucleotides have potential value as functional dietary supplements and feed additives, particularly in aquaculture, animal nutrition, infant nutrition, gut and immune health, metabolic regulation, and healthy aging. Future studies should further clarify compound-specific mechanisms, effective dose ranges, bioavailability, long-term safety, and population- or species-specific benefits to support their evidence-based application in functional foods, dietary supplements, infant formula, clinical nutrition, and functional feed products. Full article

This study examines the extent to which Chile’s 18 state universities contribute to sustainability and the 2030 Agenda, with a specific focus on the 17 Sustainable Development Goals (SDGs). To this end, scientific publications, technological developments, innovation initiatives, and funded research projects carried out between 2022 and 2023 were analyzed using a combination of bibliometric analysis and document review. Data were collected from Scopus, Web of Science, and national databases, and classified using a structured keyword strategy aligned with each SDG. A PRISMA-inspired screening and selection workflow was employed to ensure consistency and transparency in the selection of the results. The analysis reveals a clear institutional focus on SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities), which together account for the majority of outputs analyzed. In contrast, SDG 14 (Life Below Water) and SDG 15 (Life on Land) exhibit comparatively lower levels of representation. Differences were also observed among universities and across geographical macro-zones. The integrated analysis revealed important thematic asymmetries, territorial specialization patterns, and differentiated institutional sustainability profiles across the Chilean public university system. These findings highlight both the strengths and the current gaps in institutional alignment with the SDGs. The paper concludes by proposing concrete measures to improve coordination and information systems with the aim of reinforcing the strategic role of public universities in advancing sustainable development at both the national and regional levels. Full article

Urban street lighting remains a significant source of energy consumption in cities, largely due to static operation and limited responsiveness to real-time conditions. This inefficiency increases operational costs and environmental impact, especially in rapidly urbanizing regions. To address this issue, this study investigates IoT-enabled smart street lighting as an adaptive and data-driven solution within smart city frameworks. The work focuses on the growing body of research in this domain and examines its evolution, technical structure, and emerging environmental role. The study aims to provide a structured synthesis that connects research trends with system-level design, while highlighting the transition from energy-focused systems to multifunctional urban platforms. A bibliometric-driven and thematic review approach is adopted. A dataset of 151 publications was analyzed using Bibliometrix and Biblioshiny tools to extract trends, collaboration patterns, and research themes. This analysis is complemented by a qualitative evaluation of system architectures, sensing technologies, communication models, and control strategies. The findings indicate a sustained annual growth rate of 14.87% and a highly collaborative research landscape, with an average of 3.97 authors per study. The results also reveal that energy efficiency remains the dominant focus, while environmental integration is emerging but still underrepresented. The study further identifies key gaps related to scalability, sensor reliability, and the lack of standardized evaluation metrics. The outcomes provide a comprehensive roadmap for future research and support the development of scalable, intelligent, and sustainable lighting systems. The proposed insights are applicable to urban environments globally, particularly in regions seeking cost-effective and energy-efficient infrastructure solutions. Full article

Background: The Vehicle Routing Problem (VRP) is foundational to logistics optimization, yet its alignment with the Triple Bottom Line (TBL) and UN Sustainable Development Goals (SDGs) remains fragmented. This study conducts a strategic bibliometric audit of 301 peer–reviewed publications (1992–2025) to quantify the evolutionary progression and thematic maturity of sustainable routing research. Methods: A four–stage scientometric framework was employed, utilizing Scopus–based data retrieval, longitudinal mapping, and Python 3.14–driven text mining to visualize keyword co–occurrence networks, author collaborations, and regional research clusters. Results: Findings reveal a pronounced “Sustainability Asymmetry,” where 51.5% of studies prioritize economic efficiency, while only 2.6% address the social pillar. Additionally, social sustainability remains an “isolated island” with minimal cross–citation to the research core. Geographic analysis identifies a heavy concentration in China, the USA, and Western Europe, uncovering a critical North–South—collaboration gap. Conclusions: The study proves that while environmental themes reached maturity between 2018 and 2022, social indicators exhibit a significant maturity lag. This quantified social deficit, centered on the neglect of SDG 3 and SDG 10, mandates a fundamental paradigm shift toward a geographically inclusive and socially conscious research agenda to ensure global logistical equity. Full article

Global climate change is causing an increase in extreme rainfall events, which impacts the risk of hydrometeorological disasters. To support disaster mitigation and early warning systems, accurate and reliable rainfall predictions are required. Although ensemble forecasting is widely used to model atmospheric uncertainty, raw ensemble results often exhibit insufficient bias and dispersion. Therefore, post-processing techniques are needed to improve the quality of probabilistic predictions. The most commonly used calibration method is Bayesian Model Averaging (BMA). This study conducted a scoping review of peer-reviewed papers on ensemble forecast calibration using BMA, based on the PRISMA-ScR framework. Furthermore, this study presents a comprehensive bibliometric analysis involving co-authorship networks of productive authors and bibliometric maps with clustered terms. A total of 35 relevant articles were identified from 49 screened publications. The bibliometric analysis revealed that “ensemble forecasting” and “Gaussian distribution” are the most dominant terms in the research network, indicating that Gaussian-based approaches remain more widely used in ensemble forecast calibration studies. In contrast, studies explicitly applying Gamma-based approaches are still relatively limited despite their relevance for modeling asymmetric rainfall data. The results obtained in this study highlight the importance of developing and integrating more appropriate probability distributions, such as those within the Extreme Value Theory framework, into BMA models. These findings suggest that the selection of appropriate probabilistic distributions in BMA-based calibration frameworks plays an important role in improving forecast reliability and the representation of uncertainty in rainfall prediction. Furthermore, the development of more suitable probability distributions, including Extreme Value Theory (EVT)-based distributions, has strong potential to enhance probabilistic calibration performance for asymmetric rainfall data. This approach is expected to improve the accuracy and reliability of extreme rainfall predictions. The findings of this study provide an important contribution to the development of early warning systems for hydrometeorological disasters and support the achievement of Sustainable Development Goals (SDGs). Full article

This study examines the positioning of biblical narratives within international relations scholarship, with particular emphasis on their function as cultural resources shaping identity, geopolitical discourse, and soft power dynamics. Although religion has gained increasing recognition within international relations, the extent to which scriptural narratives are systematically integrated into analytical frameworks remains insufficiently defined. To address this issue, the study employs a mixed-methods research design that combines a systematic literature review with bibliometric analysis. Bibliographic data were retrieved from the Scopus and Web of Science databases through a structured query linking biblical terminology to diplomacy, geopolitics, and religion–politics interactions, and were analyzed using the Bibliometrix package in R. The analysis draws on two datasets comprising 135 publications from Scopus and 88 from Web of Science, spanning 1989 to 2026. The findings indicate that scholarship examining biblical narratives in international relations is moderately developed and interdisciplinary, yet remains fragmented, with geopolitical themes predominating. Biblical narratives are consistently present but are primarily embedded within broader analytical categories such as identity, discourse, and legitimacy, rather than being treated as central variables. The results further suggest that religious content is often incorporated in indirect or implicit forms, reflecting a broader tendency to approach religion as a contextual rather than a constitutive element. Overall, the findings indicate that biblical narratives function primarily as interpretive and symbolic frameworks in international relations, while their analytical potential remains only partially developed, underscoring the need for more systematic integration of cultural and religious analysis in the study of global politics. Full article

Smart tourism destinations, embedded by the internet and information and communication technologies, have been improving tourists’ experiences and connectivity. However, Destination Management Organisations (DMOs) still lack knowledge of how digital technologies can enhance their role and bring greater competitive advantage to destinations. In this sense, this study aims to develop an integrated smart tourism destination management ecosystem model that clarifies the relationships between digital technologies, managerial functions, benefits and implementation barriers within the broader smart city context. The study adopts a mixed-review design, combining bibliometric analysis and a systematic literature review. Bibliometric mapping was conducted using VOSviewer to analyse co-occurrence networks, thematic clusters and research trends. At the same time, the systematic review, with a systems thinking approach, enabled an in-depth qualitative examination of technological applications, managerial roles and governance implications. Data was gathered from 29 Scopus-indexed articles. The analysis identifies key benefits, including enhanced visitor experiences, improved decision-making and increased destination competitiveness, alongside persistent barriers related to governance, digital literacy, interoperability and cybersecurity. Based on these findings, the study proposes a conceptual ecosystem model that illustrates how DMOs can orchestrate digital technologies to support smart, sustainable and adaptive destination management. This research contributes to the smart tourism and smart cities literature by integrating bibliometric insights with a systems thinking perspective to develop a holistic destination management ecosystem model. Unlike prior reviews that address technologies or outcomes in isolation, this study offers a structured and actionable framework that advances theoretical understanding of smart tourism destinations while providing practical guidance for DMOs engaged in digital transformation. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article