Search Results (1,340)

The high costs and time-consuming nature of space exploration missions are among the major barriers to studying deep space. The lack of samples and limited information make such studies challenging, highlighting the need for innovative solutions, including advanced data-mining techniques and tools such as geochemical modeling, as strategies for overcoming challenges in data scarcity. Geochemical modeling is a powerful tool for understanding the processes that govern the composition and distribution of elements and compounds in a system. In cosmology, space geochemical modeling could support cosmochemistry by simulating the evolution of the atmospheres, crusts, and interiors of astronomical objects and predicting the geochemical conditions of their surfaces or subsurfaces. This study uniquely focuses on the geochemical modeling of celestial bodies beyond Mars, fills a significant gap in the literature, and provides a vision of what has been done by analyzing, categorizing, and providing the critical points of these research objectives, exploring geochemical modeling aspects, and outcomes. To systematically trace the intellectual structure of this field, this study follows the PRISMA guidelines for systematic reviews. It includes a structured screening process that uses bibliographic methods to identify relevant studies. To this end, we developed the Custom Bibliometric Analyses Toolkit (CBAT), which includes modules for keyword extraction, targeted thematic mapping, and visual network representation. This toolkit enables the precise identification and analysis of relevant studies, providing a robust methodological framework for future research. Europa, Titan, and Enceladus are among the most studied celestial bodies, with spectrometry and thermodynamic models as the most prevalent methods, supported by tools such as FREZCHEM, PHREEQC, and CHNOSZ. By exploring geochemical modeling solutions, our systematic review serves to inform future exploration of distant celestial bodies and assist in ambitious questions such as habitability and the potential for extraterrestrial life in the outer solar system. Full article

The convergence of digital innovation and sustainability imperatives is transforming the architecture of agri-food systems, signaling not just a technological upgrade, but a reorganization of how food production, distribution, and governance are approached. This study presents a comprehensive bibliometric mapping of global research on sustainable and digital agri-food systems between 2004 and 2025, based on data from the Web of Science Core Collection and analyzed using the Bibliometrix within RStudio (Version: 2024.12.1+563). Through co-word analysis, bibliographic coupling, and temporal trend exploration, the study identified a marked surge in scholarly activity after 2020, driven by the alignment of digital innovation with major policy frameworks such as the European Green Deal and the Farm-to-Fork Strategy. Findings highlight Europe—particularly Italy, the Netherlands, and France—as the leading knowledge hub, demonstrating both institutional capacity and policy responsiveness. Thematic clusters revealed four dominant trajectories in recent research: digital governance, blockchain and traceability, circular economy integration, and ESG-based performance frameworks. These directions suggest a transition from narrow efficiency-centered approaches to more comprehensive, ethically informed, and technologically integrated agri-food systems. The study frames digitalization as both a technical infrastructure and a socio-organizational driver that reshapes transparency, accountability, and coordination within food value chains. It also outlines strategic entry points for improving interoperability, bridging digital divides, and advancing collaborative governance models across the agri-food sector. In addition to its empirical findings, the article contributes methodologically by positioning bibliometric analysis as a valuable tool for tracking major conceptual and structural shifts within food system research. In conclusion, digital transformation in agri-food systems is not merely about technological enhancement—it is a fundamental restructuring of processes, relationships, and governance mechanisms that define how food systems operate in an era of innovation, complexity, and sustainability challenges. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article

Heat stress is a major management challenge in dairy cows, leading to lower dry matter intake (DMI), reduced milk yield (MY), and changes in milk composition. A practical nutritional strategy is supplementing diets with live yeast (Saccharomyces cerevisiae) during hot periods. This meta-analysis evaluated the effects of live-yeast supplementation on DMI, MY, and milk components in heat-stressed dairy cows. Bibliographic searches for the period 2000 to 2025 were conducted. Based on the inclusion and exclusion criteria, 14 studies were selected. For each outcome, effect sizes were reported as the standardized mean difference (SMD) and the raw mean difference. Analyses were conducted using a random-effects model, and heterogeneity was assessed using the Q test and the I² statistic. Based on the SMD, live-yeast supplementation of the diets of heat-stressed dairy cows increased both DMI (p = 0.003) and MY (p < 0.001). Milk fat percentage (MFP; p = 0.104) and milk lactose percentage (MLP; p = 0.530) were not significantly affected, whereas milk protein percentage (MPP) tended to increase (p = 0.078). In contrast, milk fat yield, milk protein yield (MPY), and milk lactose yield (MLY) all increased significantly (p ≤ 0.001). Heterogeneity was significant only for DMI, MPY, and MLY. Overall, based on the available evidence, supplementing heat-stressed dairy cows with live yeast can improve DMI and MY, and increase milk component yields, without materially altering component percentages. Full article

Background and aim: Artificial intelligence (AI) is gaining increasing relevance in orthopaedic surgery, particularly in prosthetic surgery, due to its ability to support preoperative planning through advanced imaging analysis, implant size prediction, and outcome forecasting. However, recent literature shows considerable variability in employed models, evaluated outcomes, and clinical applicability. The objective of this scoping review is to map AI applications in preoperative planning for orthopaedic arthroplasties and to assess their impact on radiographic and clinical outcomes, also discussing key ethical and medicolegal implications within both Italian and international contexts. Materials and methods: A literature review was conducted following scoping review methodology. The bibliographic search (10 September 2025) was performed in PubMed and Scopus using the query “preoperative planning WITH artificial intelligence AND prosthesis orthopaedic surgery AND outcomes”, restricted to the years 2020–2025, English-language studies, and research focused specifically on real-world AI techniques applied to preoperative planning in prosthetic surgery, reporting radiographic and/or clinical outcomes related to planning. Exclusion criteria included intra/postoperative studies, non-orthopaedic applications, robotic surgery, studies lacking clinical outcomes, case reports, and articles without full-text availability. After PRISMA screening and selection, 42 primary studies were included. Results: Of the 42 studies included, 20 focused on the hip, 19 on the knee, and 3 on the shoulder. Available evidence indicates that AI may improve templating accuracy and prosthetic component positioning, with more robust results in hip and knee arthroplasty, while applications in shoulder arthroplasty remain emerging. Nonetheless, important methodological limitations persist, including algorithm heterogeneity. Discussion: Overall, the findings suggest a promising role for AI in preoperative planning; however, the heterogeneity and variable quality of the evidence call for caution in interpretation and highlight the need for more rigorous prospective research. These considerations also carry relevant medicolegal implications, as the reliability and standardisation of AI-based tools represent essential prerequisites for their safe and conscious integration within diverse regulatory frameworks. Conclusions: AI appears to be a promising tool in the preoperative planning of orthopaedic arthroplasties, although further clinical validation and methodological standardisation are required. The evidence gathered also provides a useful foundation for addressing the associated medicolegal and regulatory implications, particularly in light of evolving Italian and European regulations and their differences from U.S. models. Full article

Background/Objectives: Diseases affecting the craniofacial skeleton are normally associated with disturbances in the regulation of cellular differentiation, the development of bone structures, and changes in bone density and ossification. Thus, the objective of this integrative review is to evaluate the published scientific literature from the last 8 years concerning the impact of genetics on some craniofacial dysplasias. Our aim covers the identification of oral cavity alterations to those dysplasias, through the most common orofacial manifestations. Three dysplasias were selected to be part of this integrative review: cleidocranial dysplasia, ectodermal dysplasia and Apert syndrome. Methods: For this purpose, a bibliographic search was performed in the PubMed, ScienceDirect, Web of Science and Google Scholar databases with several keywords combined with each other. The research question of this review was as follows: “What is the impact of genetic factors on the development of craniofacial dysplasias and associated oral malformations?”. Results: After selecting the articles through the application of inclusion and exclusion criteria, 11 articles were selected for this review. Conclusions: Genetics plays a crucial role in craniofacial dysplasias and subsequent oral malformations. The main conclusion was that mutations in different genes can lead to identical phenotypes, while mutations in the same gene can present slight phenotypic differences depending on where they occur. In the future, it would be important to conduct studies with larger samples and control groups that include genetic testing to allow for a more comprehensive study on the impact of genetics on craniofacial dysplasias. Full article

Despite the growing recognition of Artificial Intelligence (AI)’s potential for global education, the literature lacks strategic analyses on how to maximize personalized learning and ensure equitable access within the vast and diverse Indian educational system. The objective of this study is to analyze this strategic integration of AI into the Indian educational system, focusing on maximizing personalized learning and ensuring equitable access across diverse socioeconomic contexts, while evaluating current initiatives and the relevance of reporting guidelines, such as the use of Self-Sovereign Identity (SSI). The methodology employed bibliographic and documentary research, alongside the analysis of governmental and sectoral policies. The results indicate that the sustainable implementation of AI is critically dependent on the mitigation of algorithmic bias and the rigorous assurance of data privacy. In conclusion, to balance technological innovation with human-centered pedagogical approaches, maintaining the educator’s fundamental role and fostering collaboration among stakeholders for responsible governance are essential. Full article

The rise in antimicrobial resistance (AMR) among the most clinically significant bacteria presents a global threat. The coexistence of resistance mechanisms to both carbapenems and colistin is particularly concerning, as these are last-line treatments, specifically reserved for the most challenging infections caused by clinically multidrug-resistant Enterobacterales. Natural aquatic environments have become environmental reservoirs for the transmission of AMR, particularly concerning mechanisms against these two types of critically important drugs. The crucial role of environmental settings as a driving force for the spread and evolution of AMR associated with these drugs is underestimated, and scientific knowledge on this topic is limited. This review aims to fill an important gap in the scientific literature and comprehensively consolidate the available data on carbapenem- and colistin-associated AMR in the aquatic environment. This study provides a comprehensive synthesis of the current knowledge by integrating bibliographic data with a detailed genomic analysis of 278 bacterial genomes sourced from natural waters. It explores the distribution of carbapenemase and mobile colistin resistance (mcr) genes, identifying their hosts, geographical spread, and complex gene–plasmid–host associations. This review distinguishes two critical host groups for genes that provide resistance to last-resort drugs, Enterobacterales and autochthonous aquatic microbiota, highlighting both confirmed and potential interactions between them. Crucially, genomic analysis highlights the alarming co-occurrence of carbapenem and colistin resistance in single cells and on single plasmids, contributing to the spread of multidrug resistance phenotypes. These findings clearly indicate that aquatic environments are not merely passive recipients but active, evolving hubs for high-risk AMR determinants. Future research should focus on the interplay between allochthonous vectors and autochthonous microbiota to better understand the long-term stabilization of carbapenemase and mcr genes. Such efforts, combined with advanced sequencing technologies, are essential to ensure that carbapenems and colistin remain viable treatment options in clinical settings. Full article

The use of instruments for recording neurophysiological metrics is increasingly prevalent in natural settings, driven by advances in portability, reduced invasiveness, and the advantages these technologies offer for objective assessment of cognitive processes. A key application is the investigation of cognitive processes underlying human learning. In this context, the use of electroencephalography (EEG) in educational settings has grown in recent years. This systematic review examines how EEG has been utilized over the past decade to study teaching and learning processes in higher education. Its objectives were to identify the purposes for which EEG has been employed, its integration with other instruments, the cognitive processes analyzed, and the main findings reported. Bibliographic sources included Scopus and Web of Science databases. Results indicate that EEG has been primarily used to investigate attention, memory, and cognitive load, often combined with other neurophysiological and behavioral instruments. This integration provides objective insights into learning while highlighting methodological limitations and the need for standardized approaches. Despite variations in study designs, devices, and analytical methods, EEG demonstrates substantial potential for exploring cognitive processes in both face-to-face and virtual learning environments. In conclusion, EEG is an effective tool for investigating learning-related cognitive processes in higher education, providing objective data to guide future research, and the development of innovative educational programs. This review offers a comprehensive overview of current research, supports the design of evidence-based educational strategies, and lays the groundwork for future studies aiming to enhance learning outcomes through neurophysiological methods. Full article

The aim of this study is to systematically capture, synthesize, and evaluate current research trends related to Automated Multiple-Choice Question Generation as they emerge within the broader landscape of natural language processing (NLP) and large language model (LLM)-based educational and assessment research. A systematic search and selection process was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, using predefined inclusion and exclusion criteria. A total of 240 eligible publications indexed in the Scopus database were identified and analyzed. To provide a comprehensive overview of this evolving research landscape, a bibliometric analysis was performed utilizing performance analysis and scientific mapping methods, supported by the Bibliometrix (version 4.2.2) R package and VOSviewer (version 1.6.19) software. The findings of the performance analysis indicate a steady upward trend in publications and citations, with significant contributions from leading academic institutions—primarily from the United States—and a strong presence in high quality academic journals. Scientific mapping through co-authorship analysis reveals that, despite the increasing research activity, there remains a need for enhanced collaborative efforts. Bibliographic coupling organizes the analyzed literature into seven thematic clusters, highlighting the main research axes and their diachronic evolution. Furthermore, co-word analysis identifies emerging research trends and underexplored directions, indicating substantial opportunities for future investigation. To the best of our knowledge, this study represents the first systematic bibliometric analysis that examines Automated Multiple-Choice Question Generation research within the context of the broader LLM-driven educational assessment literature. By mapping the relevant scientific production and identifying research gaps and future directions, this work contributes to a more coherent understanding of the field and supports the ongoing development of research at the intersection of generative AI and educational assessment. Full article

Recent studies in the Romanian Western Carpathians have revealed increasing socio-demographic fragility in rural areas and small towns, driven by depopulation, population aging, and declining living standards. These trends stem from the legacy of forced collectivization and industrialization (1950–1990) and the post-1990 transition, which triggered extensive out-migration and the erosion of local socio-economic structures. This study examines the fragility of human communities in the Trascău Mountains in order to evaluate spatial, demographic, and economic recovery dynamics and to assess settlement vulnerability as a major obstacle to sustainable regional development. Fragility was measured using indicators of population density and change, age structure, accessibility, and socio-demographic dynamics, based on comparative data for the interval of 1977–2021. These variables were integrated into a composite development index (Id), derived from twelve indicators covering demography, economy, infrastructure, and living standards, enabling the hierarchical classification of settlements by degree of vulnerability. The methodological framework combines empirical and analytical methods, statistical, cartographic, bibliographic, and field-based analyses within evolutionary, structural–functional, and typological perspectives. The results identify the main drivers of decline, quantify their impacts, and outline development prospects and policy directions for reducing territorial disparities. Overall, fragile settlements emerge as critical pressure points that undermine sustainability, intensify regional instability, and increase risks related to migration and social cohesion. Full article

To maintain power system stability and supply quality when integrating doubly fed induction generator (DFIG)-based wind energy conversion systems (DFIG-WECSs), regulators regularly update grid codes specifying low voltage ride-through (LVRT) requirements. This paper presents a systematic literature review (SLR) on the use of STATCOMs to enhance LVRT capability in DFIG-WECSs. Objectives included a structured literature search, bibliographic analysis, thematic synthesis, trend identification, and proposing future research directions. A PRISMA-based methodology guided the review, utilising PRISMA 2020 for Abstracts in the development of the abstract. The final search was conducted on Scopus (31 March 2025). Eligible studies were primary research in English (2009–2014) where STATCOM was central to LVRT enhancement; exclusions included non-English studies, duplicates, reviews, and studies without a STATCOM focus. Quality was assessed using an adapted Critical Appraisal Skills Programme (CASP) tool. No automation or machine learning tools were used. Thirty-eight studies met the criteria and were synthesised under four themes: operational contexts, STATCOM-based schemes, control strategies, and optimisation techniques. Unlike prior reviews, this study critically evaluates merits, limitations, and practical challenges. Trend analysis shows evolution from hardware-based fault survival strategies to advanced optimisation and coordinated control schemes, emphasising holistic grid stability and renewable integration. Identified gaps include cyber-physical security, techno-economic assessments, and multi-objective optimisation. Actionable research directions are proposed. By combining technical evaluation with systematic trend analysis, this review clarifies the state of STATCOM-assisted LVRT strategies and outlines pathways for future innovation in DFIG-WECS integration. Full article

The agricultural sector is predominantly male, with approximately 30% of farms in the EU operated by women. The European Union Rural Pact, the Agri-Food Pact for Skills, and the Common Agricultural Policy have catalysed an increase in agricultural 4.0 research, with the role of women emerging as a subfield of sustainable agriculture. The primary objective of this paper is to evaluate the current literature on women’s roles in smart agriculture, examining the advantages of their participation as a digitally competent workforce that could catalyse improvements in productivity and resilience in rural areas and promote women’s empowerment. A bibliometric study was conducted utilising the Scopus database to fulfil the research objective. This led to the incorporation of 253 articles into the sample. The records were examined using performance analysis and bibliographic coupling (science mapping), facilitated by Biblioshiny 5.0 and VOSviewer 1.6.20 software. The primary findings elucidate essential concepts, predominant study themes, and the temporal progression of the research domain. The identification of numerous women’s role and socio-economic constraints affecting women, which are overlooked in the creation and implementations of technology advancements. Additionally, a research agenda was developed, alongside practical implications for managers and policymakers, to aid the formulation of inclusive agriculture 4.0 projects. Full article

The global proliferation of plastics and their degradation into microplastics (<5 mm) have created a pervasive environmental crisis with severe ecological and human health consequences. Despite the exponential growth in microplastic research over the past decade, standardized protocols are still lacking. The absence of consistent sampling, analysis, and reporting methods limits data comparability, interoperability, and harmonization across studies. This study conducted a systematic bibliographic review of 355 peer-reviewed articles published between 2010 and 2022 that investigated microplastics in freshwater as well as marine water and sediment environments. The goal was to evaluate methodological consistency, sampling instruments, measurement units, reported characteristics, and data-sharing practices to identify pathways toward harmonized and FAIR (Findable, Accessible, Interoperable, and Reusable) microplastic data. Results show that 80.6% of studies focused on marine environments, 18% on freshwater, and 1.4% on both. This highlights persistent data gaps in freshwater systems, which function as key transport pathways for plastics to the ocean. Most studies targeted water (59%) rather than sediment (41%) and were mostly based on single-time sampling, limiting long-term analyses. Surface layers (<1 m) were predominantly sampled, while deeper layers remain understudied. Nets, particularly Manta, neuston, and plankton nets were the dominant tools for water sampling, whereas grabs, corers, and metallic receptacles were used for sediments. However, variations in mesh size and sampling depth introduce substantial biases in particle size recovery and reduce comparability across studies. The most common units were counts/volume for water and counts/g dry weight for sediments, but more than ten unit expressions were identified, complicating conversions. Only 35% of studies reported all four key microplastic characteristics (color, polymer type, shape, and size), and less than 20% made datasets publicly available. To advance harmonization, we recommend the adoption of consistent measurement units, mandatory reporting of key metadata, and wider implementation of open data practices aligned with the FAIR principles. These insights provide a foundation for developing robust monitoring strategies and evidence-based management frameworks. This is especially important for freshwater systems, where data remain scarce, and policy intervention is urgently needed. Full article

Background/Objectives: Telemedicine has emerged as a pivotal approach to improving access to diabetic retinopathy (DR) screening, diagnosis, management, and monitoring. Over the past two decades, rapid advancements in digital imaging, mobile health technologies, and artificial intelligence have substantially expanded the role of teleophthalmology in DR, resulting in a large volume of pertinent publications. This study aimed to provide a scientific overview of telemedicine applied to DR through bibliometric analysis. Methods: A search of the Web of Science Core Collection was conducted on 15 November 2025 to identify English-language original research and review articles regarding telemedicine for DR. Bibliographic data from relevant publications were extracted and underwent quantitative analysis and visualization using the tools Bibliometrix and VOSviewer. Results: A total of 515 articles published between 1998 and 2025 were included in our analysis. During this period, the research field of telemedicine for DR exhibited an annual growth rate of 13.14%, with publication activity markedly increasing after 2010 and peaking in 2020–2021. Based on the number of publications, United States, China, and Australia were the most productive countries, while Telemedicine and e-Health, Journal of Telemedicine and Telecare, and British Journal of Ophthalmology were the most relevant journals in the field. Keyword co-occurrence analysis revealed three major thematic clusters within the broader topic of telemedicine and DR, namely, public health-oriented work, telehealth service models, and applications of artificial intelligence technologies. Conclusions: The role of telemedicine in DR detection and care represents an expanding multidisciplinary field of research supported by contributions from multiple authors and institutions worldwide. As technological capabilities continue to evolve, ongoing innovation and cross-domain collaboration could further advance the applications of teleophthalmology for DR, promoting more accessible, efficient, and equitable identification and management of this condition. Full article