Search Results (3,030)

The demand for advanced detection methods and accurate staging remains a global challenge in cancer diagnosis. Even though traditional deep learning models in medical imaging achieve high precision, they suffer from limited explainability and multimodal reasoning due to their black-box nature, thereby limiting their clinical applicability. To address this gap, recent research has increasingly explored multimodal approaches that integrate visual and textual clinical data to enhance diagnostic accuracy and interpretability. This study presents a bibliometric analysis of 408 publications from 2021 to 2025, collected from Web of Science and Scopus, using VOSviewer and R-Bibliometrix to map citation networks, co-authorship, and keyword co-occurrences. The results reveal a rapid growth from 1 publication in 2021 to 269 in 2025, with significant contributions from leading countries and institutions. Thematic analysis indicates a shift from conventional convolutional approaches toward transformer-based and self-supervised methods, alongside increasing attention to multimodal learning in cancer imaging tasks such as breast, lung, and brain cancer analysis. Overall, this study provides a structured overview of the evolving research landscape, highlighting key trends, emerging themes, and research gaps to inform future developments in multimodal artificial intelligence for cancer diagnosis. Full article

Maritime traffic complexity (MTC) is a term that has gained increased importance in the last decade in the maritime safety domain. It is a concept for understanding navigational safety and operational challenges in congested maritime environments. Although research interest in MTC has grown, it is a concept that remains fragmented, with various interpretations of definitions, indicators, and modeling approaches present in the literature. This study presents a comprehensive literature review and bibliometric analysis to synthesize the current state of research on MTC as a scientific construct and clarify its conceptual foundations from an analytical perspective. In accordance with PRISMA guidelines and systematic literature review (SLR) methodology, relevant studies were identified and screened across major scientific databases. A detailed analysis was conducted on 40 scientific publications. The findings indicate that most existing MTC models rely mainly on Automatic Identification System (AIS) data and corresponding derived metrics. MTC is primarily assessed through geometric vessel–vessel interactions, relative motion parameters, and collision-risk indicators. Bibliometric analysis demonstrates a rapid increase in scientific interest in this topic since 2015, with research concentrated in several leading journals. The study identifies a significant methodological limitation in current frameworks, which often overlook the heterogeneity of marine traffic, environmental conditions, vessel reliability, and human factors. Therefore, this study highlights the need for a more comprehensive MTC evaluation framework that incorporates operational, geographical constraint-based, environmental, and behavioral variables alongside traditional AIS-based metrics. Full article

Recent geopolitical events have led to an increased research focus on the experiences of female refugees. As careers play a crucial role in socio-economic integration, this study aims to examine the scope and characteristics of research findings on the careers of refugee women in host countries. Following the general research questions for bibliometric analysis, the major trends and intellectual structures of the research field of women refugees’ careers were identified. Four hundred and fifty-three articles selected from the Web of Science database (search by title, abstract, and keywords) for the period 2000–2023 were analyzed using VOSviewer (1.6.20). The results show that key challenges faced by forcibly displaced women include mental health disorders, language barriers, discrimination, downward career mobility, and pressure of traditional gender roles. The research reveals that critical enablers for female refugees’ workforce participation and economic independence are language training, culturally sensitive healthcare, and access to childcare. Simultaneously, empowerment strategies, including entrepreneurship and participation in professional networks, are proved to foster resilience and create pathways for successful career steps. Full article

This review provides a comprehensive bibliometric analysis of the literature on the integration of remote sensing data and machine learning or deep learning algorithms in precision agriculture. The analysis covers 1056 publications, included in the Web of Science Core Collection, and identifies the temporal patterns of research, the most frequently used algorithms, the prominent remote sensing technologies, and the geographical distribution of research output. Increased research output during the period of 2013–2025 is attributed to the availability of high-level computing, satellites, and UAV imagery. The earlier studies in machine learning primarily involved the use of the Random Forest and Support Vector Machine algorithms, whereas in the past few years, deep learning, and especially Convolutional Neural Networks, have become more dominant. The most widely used data sources in remote sensing are the imagery from UAVs and the Sentinel satellite missions. The evaluation revealed that most of the geographical research activity was centered in the United States and China, but there is a trend of increasing research activity in most of the other developed countries. Research in Africa and South America remains particularly underdeveloped. Considering the rapid development of research, data fusion of optical and radar satellite imagery, UAV imagery, weather and soil datasets are expected to further improve the representation of agricultural systems. Full article

Quantum computing challenges the long-term security assumptions of blockchain systems that rely on classical public-key cryptography, motivating the adoption of post-quantum cryptography and quantum key distribution (QKD). This review maps research fronts at the intersection of blockchain and quantum-safe security, linking threat assumptions to post-quantum mechanisms, blockchain layers, and QKD positioning. Records were retrieved from Scopus and Web of Science using a two-block query and filtered through a PRISMA-guided workflow for bibliometric mapping. The final corpus comprises 648 journal articles and shows accelerated publication growth after 2023, with scientific production concentrated in a small set of leading countries. Keyword structures indicate that IoT-centric deployments dominate the semantic backbone, where authentication and intelligent methods co-occur with blockchain security primitives, while post-quantum and privacy-preserving constructs form a cohesive technical stream. QKD appears as a distinct but more specialized theme, typically discussed at the system level and shaped by infrastructure and scalability constraints. Overall, the literature is moving from conceptual risk articulation toward engineering integration; however, progress is limited by inconsistent reporting of threat models, post-quantum parameter sets, and ledger-level cost trade-offs, highlighting the need for auditable and reproducible evaluation. Full article

Precast concrete (PC) component production scheduling is essential to the efficiency and reliability of industrialized construction. Although intelligent algorithms have been widely applied in this field, the relationships among research evolution, collaboration patterns, and industrial applicability remain insufficiently understood. To address this issue, this study presents a bibliometric review of 1272 publications indexed in the Web of Science Core Collection from 1990 to 2025. CiteSpace was employed to analyze publication trends, collaboration networks, co-citation structures, keyword co-occurrence, and burst terms. On this basis, a technology adaptability evaluation framework was developed to assess the alignment between algorithmic advances and industrial implementation in terms of dynamic adaptability, verification completeness, and technological generation gap. The results indicate that the field has evolved through four broad stages, from early static optimization to multi-objective coordination, digital twin-enabled dynamic scheduling, and emerging human-centric intelligent autonomous systems. The analysis also shows an increasing convergence of operations research, computer science, and civil engineering. However, a gap remains between academic output and industrial application. Specifically, 32% of the retrieved studies focused on genetic algorithms, whereas only 6% reported full-process industrial validation. In addition, Gen 4.0-related studies showed a technological generation gap of 82.5%, indicating that many frontier technologies have not yet reached broad industrial implementation. The collaboration network further reveals a “high-output, low-synergy” pattern, in which major publishing countries contribute substantially to the literature but exhibit limited cross-institutional integration. This study provides a structured overview of the development of PC component production scheduling research and highlights future directions for digital twin integration, human–robot collaboration, and cross-sector validation platforms. Full article

The proliferation of marine data presents both an opportunity for ocean governance and a challenge, contributing to fragmentation across disciplines, institutions, and sectors. Marine Spatial Data Infrastructure (MSDI) stands out as a major framework for integrating marine information. However, an integrated synthesis that combines quantitative mapping of publication patterns with qualitative analysis of thematic evolution remains absent. This study employs a two-step approach combining systematic review and bibliometric analysis of Scopus-indexed literature (2000–2024). Based on a focused corpus of 20 publications rigorously screened for explicit MSDI relevance, we examine publication trends, collaboration patterns, thematic structures, and evolutionary trajectories. Results indicate accelerating scholarly interest in MSDI, with European institutions contributing 75% of the analysed publications. Policy frameworks such as the INSPIRE Directive (Infrastructure for Spatial Information in the European Community) and the Marine Strategy Framework Directive (MSFD) emerge as key drivers of research activity. Temporal analysis of this corpus suggests a tentative five-phase evolution in MSDI research: (1) foundational technical standardisation, (2) governance model implementation, (3) semantic interoperability enhancement, (4) policy integration, and (5) advanced applications incorporating FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles and Artificial Intelligence (AI). These phases, derived from systematic coding of thematic focus across publications, represent observed patterns within the analysed literature rather than definitive stages. This paper concludes that MSDI is moving toward a more socio-technical approach that requires the consideration of a technical-focused tool in present-day ocean governance. Future work should combine semantic AI, decentralised architectures, polycentric governance models, and impact assessment frameworks to align MSDI development with the objectives of equity, inclusion, and sustainability. Full article

As global urbanization accelerates, biophilic urbanism has emerged as a key nature-based strategy for enhancing public health. While plants are critical active agents for psychological restoration, the specific pathways through which vegetation characteristics influence human–environment interactions remain fragmented. This knowledge gap hinders the evidence-based translation of biophilic principles into actionable urban design and governance. This study conducts a systematic bibliometric analysis of 443 peer-reviewed articles (2000–2025) at the intersection of restorative landscapes, urban settings, and plant-based interventions retrieved from the Web of Science Core Collection. Employing multiple visualization tools (VOSviewer, bibliometrix, and CiteSpace), we map publication trends, international collaborations, and thematic evolution. The results demonstrate a significant shift in the field, moving beyond the validation of foundational restorative theories (e.g., ART and SRT) to a more precise, implementation-oriented framework. This shift is characterized by the operationalization of vegetation attributes as controllable design variables, increasingly relating biophilic principles to broader nature-based solutions (NbS) agendas and evidence-informed urban governance. Thematic clustering analysis identified three core knowledge domains: (1) the role of plants as active exposure agents and behavioral mediators in psychological restoration; (2) the impact of specific plant characteristics—such as canopy structure, species diversity, and seasonal variation—on therapeutic outcomes; and (3) the integration of urban green spaces into broader governance frameworks to promote health equity and inclusive well-being. Our analysis highlights that plant-based interventions are evolving from aesthetic ornaments into precision design levers for fostering human–nature interactions. This study provides a science-based foundation for developing practical design guidelines and policy frameworks, shifting biophilic urbanism toward a robust governance strategy for creating equitable, restorative, and resilient cities. Full article

The agricultural sector is undergoing a profound digital transformation driven by artificial intelligence, the Internet of Things, remote sensing, robotics, blockchain, and edge computing, which are being integrated into crop monitoring, irrigation management, disease detection, and supply chain transparency systems. This study employs systematic evidence mapping to characterize the applications of emerging digital technologies in sustainable agriculture; it delineates technological trajectories, areas of application, implementation gaps, and opportunities for improvement. Adhering to the PRISMA 2020 reporting protocol, 101 peer-reviewed articles indexed in Scopus and Web of Science (2020–2025) were identified, screened, and subjected to integrated thematic and bibliometric synthesis, using RStudio Version: 2026.01.1+403 and VOSviewer 1.6.20 for data mining on keywords and technological evolution patterns. Results show that deep learning and computer vision models achieved diagnostic accuracies of 90–99%, smart irrigation systems reduced water consumption by 10–30%, predictive yield models frequently reported R² values above 0.80, and greenhouse automation reduced energy consumption by approximately 20–30%. Blockchain-based architectures improved traceability and secure data transmission by 15–20%, while remote sensing integration enhanced spatial estimation accuracy up to R² = 0.92. The findings demonstrate a measurable transition toward data-driven, resource-efficient agricultural ecosystems supported by validated digital architectures. However, interoperability limitations, lack of standardized performance metrics, scalability challenges, and uneven geographical implementation—identified in nearly 40% of studies—highlight the need for harmonized evaluation frameworks, cross-platform integration standards, and long-term field validation to ensure sustainable and scalable digital transformation. Full article

Ecological restoration—whether active or passive—includes forest development, forest rehabilitation, and a range of other activities that contribute to ecosystem services. To provide a formal framework, we hypothesized how does reforestation (through different forestry practices) affect the conservation of soil functionality? That is, how does reforestation/afforestation/forest restoration improve soil quality? And, specifically, how do they improve physical properties (such as structural stability, infiltration) and chemical properties (such as acidity, electrical conductivity)? For this purpose, we conducted a bibliometric analysis review of the peer-reviewed scientific literature and research reports of numerous articles in order to compile a large database of forest restoration studies, with an emphasis on the Mediterranean region. The final focus was to obtain conclusions about how it affects soil quality. Overall, our examination confirms that deforestation drives a decline in soil carbon and nitrogen, subsequently impairing microbial activity. Consequently, forest removal frequently leads to accelerated erosion, nutrient depletion, and compaction. In contrast, reforestation acts as a critical intervention, stabilizing soil structure, reestablishing fertility, and enhancing soil quality overall. Additionally, three case studies are synthetically presented concerning the short-, medium-, and long-term results of forest restoration projects carried out mainly in central and northern Spain. These cases corroborate the significant role of forest restoration in the control and enhancement of ecosystem services, particularly in relation to soil improvement, the enhancement of hydrological regulation processes within watersheds (runoff, infiltration, erosion), landscape amelioration, and the socio-economic aspects of rural environments. Ultimately, forest restoration is established as a necessary and essential practice in ecological restoration efforts to counteract the impacts of anthropogenic activities. Full article

The objectives were: (i) mapping of bibliometric characteristics of publications related to sheep and goats internationally, (ii) comparison of publications related to each animal species, and (iii) comparison of characteristics in a 55-year long timespan from 1970 to 2024. The Web of Science was used with the following search terms: [sheep OR ovine OR Ovis aries] or [goat* OR caprine OR Capra hircus].account, and 165,052 papers related to sheep and 67,637 papers related to goats were considered. There was a progressive increase in papers published annually, with a higher proportion of papers related to goats published recently. Most papers were published in Small Ruminant Research (2.2% and 4.4% of papers related to sheep and goats, respectively), the journal with most published papers for 21 (sheep) and 29 (goats) years. Most papers originated from the United States of America, the country with most published papers for 52 (sheep) and 41 (goats) years. Most published papers related to sheep or goats were classified in the Dairy and animal sciences topics-meso. The two predominant topics-micro were Ruminant nutrition and Livestock reproduction for published papers related to sheep and papers related to goats. Overall, 31.7% and 34.9% of papers related to sheep and goats, respectively, were published under open access, with a progressive increase yearly. On average, papers related to sheep had received 0.93 citations annually and papers related to goats 0.73 citations annually. Full article

This article presents a systematic bibliometric analysis on academic research into Artificial Intelligence (AI) applications in Architectural Conceptual Design (ACD). Based on a curated selection of publications indexed in the Web of Science (WoS) and Scopus databases between 2010 and 2025, this article shows a study that maps the intellectual evolution, thematic composition, and methodological trends of the field. By using the software tool VOSviewer, this study generates a series of knowledge graphs, including Keyword Co-Occurrence and International Collaboration Networks. The findings from this study reveal a rapid acceleration in AI-related research focused on the conceptual design stage, highlighting its transformative potential for architectural practice. Through a critical analysis of bibliometric results, this study identifies dominant research emphases, emerging directions, and persistent frictions between academic approaches and industry adoption. This review article contributes to the theoretical consolidation of AI applications in ACD and provides a structured foundation for future ACD-related research and practice. Full article

Remote-sensing inversion of water-quality parameters is a critical interdisciplinary field, integrating remote-sensing technology, environmental science, and water resources management, providing key technical support for precise water resources monitoring and ecological governance. To address the lack of comprehensive systematic reviews in this field, this study conducted a bibliometric-based narrative review, selecting 2812 valid English studies published during 1980–2026 from the Web of Science Core Collection (WOSCC) and performing in-depth knowledge mapping analysis via CiteSpace software. The results showed that global research in this field has gone through three stages: initial exploration (1980–2000), slow growth (2001–2015), and rapid explosion (2016–2026). China ranks first in publication volume worldwide, with a collaborative research pattern dominated by core institutions, including the Chinese Academy of Sciences, Wuhan University, and the National Aeronautics and Space Administration (NASA). The core research hotspots focus on multi-source data fusion, AI-driven inversion-model optimization, and the research shift from coastal to inland water bodies. Current research faces three key challenges: poor adaptability of multi-source data-fusion technologies to water-quality monitoring, inadequate integration of geospatial and thematic factors in inversion models, and an insufficient systematic approach of inland-water-body research. Accordingly, future research should focus on advancing remote-sensing data-fusion methods, further optimizing water-quality inversion models, and strengthening inland-water-body studies. This study clarifies the field’s development context and research characteristics, providing valuable references for subsequent academic exploration and practical applications in water resources management. Full article

Urban socio-economic resilience to climate change has emerged as a central research theme as cities increasingly confront interconnected environmental, economic, and social risks. Despite the rapidly expanding body of literature, the conceptual boundaries, thematic evolution, and analytical priorities of this field remain fragmented across disciplines, and no prior study has systematically mapped the socio-economic dimension of urban resilience through a combined bibliometric and thematic analysis over a multi-decadal horizon. This study addresses that gap by providing a systematic review of global research on urban socio-economic resilience to climate change, integrating bibliometric and thematic analyses of peer-reviewed publications from 1990 to 2025. Following the PRISMA 2020 guidelines, records were retrieved from the Web of Science Core Collection and subjected to a multi-stage screening procedure that combined automated relevance scoring with mandatory manual validation of the socio-economic dimension, resulting in a final dataset of 5076 publications. The analysis examines conceptual interpretations of socio-economic resilience, dominant climate hazards affecting urban systems, methodological approaches and assessment indicators, adaptation strategies and governance responses, and emerging research gaps. The results reveal a marked acceleration of scientific output after 2015, driven by the Paris Agreement and the IPCC Special Report on Global Warming of 1.5 °C (2018). The bibliometric network analyses identify adaptation, vulnerability, flooding, and sustainability transitions as the core thematic clusters. The findings trace a paradigmatic trajectory from equilibrist recovery frameworks toward transformative, socio-economically grounded resilience models and reveal persistent gaps in the operationalization of governance, equity measurement, and geographic representation. By synthesizing three-and-a-half decades of scholarship, this review clarifies the intellectual structure of the field and proposes four specific post-2026 research pathways that emphasize longitudinal cross-city comparisons, mixed-methods assessments, sector-specific compound hazard analyses, and governance mechanism studies. Full article

The renewable energy transition is a central component of global strategies to mitigate climate change and achieve sustainable development. However, the large-scale integration of renewable energy sources introduces significant challenges related to variability, system complexity, and operational efficiency. In recent years, artificial intelligence (AI) has emerged as a promising enabler for addressing these challenges through advanced data-driven forecasting, optimization, and decision-support capabilities. This study presents a systematic bibliometric and thematic review of peer-reviewed research on AI applications in the renewable energy transition published between 2015 and 2025, and was conducted following the PRISMA framework. Using the Scopus database, a total of 595 journal articles were analyzed through bibliometric performance indicators, network analysis, and thematic synthesis. The results reveal a rapidly growing and highly collaborative research field, characterized by strong international co-authorship and increasing methodological diversity. Early research predominantly focused on prediction and forecasting tasks, while more recent studies emphasize system-level optimization, energy management, and integrative AI applications across renewable technologies. The review further highlights key research trends, conceptual framing, and methodological orientations shaping the field. By consolidating dispersed literature and mapping its evolution, this study provides a structured overview that supports future research, policy development, and practical implementation of AI-enabled solutions for a sustainable energy transition. Full article