Search Results (1,780)

The increasing demand for high-quality geospatial visualizations in scientific publications has highlighted the need for accessible and standardized tools that support reproducible research. Researchers from various disciplines—often without expertise in Geographic Information Systems (GIS)—frequently require a map figure to locate their study area. This paper presents the Study Area Map Generator, a web-based application developed using Shiny for Python, designed to automate the creation of country- and city-level study area maps. The tool integrates geospatial data processing, cartographic rendering, and user-friendly customization features within a browser-based interface. It enables users—regardless of GIS proficiency—to generate publication-ready maps with customizable titles, basemaps, and inset views. A usability survey involving 92 participants from diverse professional and geographic-based backgrounds revealed high levels of satisfaction, ease of use, and perceived usefulness, with no significant differences across GIS experience levels. The application has already been adopted in academic and policy contexts, particularly in low-resource settings, demonstrating its potential to democratize access to cartographic tools. By aligning with open science principles and supporting reproducible workflows, the Study Area Map Generator contributes to more equitable and efficient scientific communication. The application is freely available online. Future developments include support for subnational units, thematic overlays, multilingual interfaces, and enhanced export options. Full article

Nepal is highly susceptible to natural hazards, including earthquakes, flooding, and landslides, all of which may occur independently or in combination. Climate change is projected to increase the frequency and intensity of these natural hazards, posing growing risks to Nepal’s infrastructure and development. To the authors’ knowledge, the majority of existing geohazard research in Nepal is typically limited to single hazards or localised areas. To address this gap, MiMapper was developed as a cloud-based, open-access multi-hazard mapping tool covering the full national extent. Built on Google Earth Engine and using only open-source spatial datasets, MiMapper applies an Analytical Hierarchy Process (AHP) to generate hazard indices for earthquakes, floods, and landslides. These indices are combined into an aggregated hazard layer and presented in an interactive, user-friendly web map that requires no prior GIS expertise. MiMapper uses a standardised hazard categorisation system for all layers, providing pixel-based scores for each layer between 0 (Very Low) and 1 (Very High). The modal and mean hazard categories for aggregated hazard in Nepal were Low (47.66% of pixels) and Medium (45.61% of pixels), respectively, but there was high spatial variability in hazard categories depending on hazard type. The validation of MiMapper’s flooding and landslide layers showed an accuracy of 0.412 and 0.668, sensitivity of 0.637 and 0.898, and precision of 0.116 and 0.627, respectively. These validation results show strong overall performance for landslide prediction, whilst broad-scale exposure patterns are predicted for flooding but may lack the resolution or sensitivity to fully represent real-world flood events. Consequently, MiMapper is a useful tool to support initial hazard screening by professionals in urban planning, infrastructure development, disaster management, and research. It can contribute to a Level 1 Integrated Geohazard Assessment as part of the evaluation for improving the resilience of hydropower schemes to the impacts of climate change. MiMapper also offers potential as a teaching tool for exploring hazard processes in data-limited, high-relief environments such as Nepal. Full article

As cities confront rising populations and mounting environmental pressures, waste is rapidly transforming from a logistical liability into a strategic economic resource. In this article, we investigate the evolving nexus between waste and urban economic systems by analyzing over 2000 scientific publications sourced from Web of Science and Scopus. Using advanced semantic embedding and network analysis, we identify seven major research communities at the intersection of digital innovation, circular economy, and smart urban infrastructure. Through PageRank-based influence mapping, we highlight key contributions that shape each thematic cluster—ranging from AI-powered waste classification to blockchain-enabled traceability and IoT-driven logistics. Our results reveal a dynamic and interdisciplinary research landscape where waste valorisation is not only a sustainability imperative but also a driver of urban economic renewal. This study offers both a conceptual map and a methodological framework for understanding how cities can embed intelligence, efficiency, and circularity into waste systems as part of a broader transition to regenerative, data-informed urban economies. Full article

Background: Bioelectrical impedance analysis (BIA) is a widely used field technique for assessing body composition in football. However, its reliance on population-specific regression equations limits its accuracy. Objective: This scoping review aimed to map the scientific literature on BIA applications in professional and semi-professional football, highlighting uses, limitations, and research opportunities. Methods: A comprehensive search was conducted in the scientific databases PubMed, EMBASE, Web of Science, and SPORTDiscus. Identified studies involved the use of BIA in professional and semi-professional football players (≥16 years) in the context of routine training and competition. Results: From 14,624 records, 39 studies met the inclusion criteria and were included. Three main applications were identified: (1) quantitative body composition assessment, (2) qualitative/semi-quantitative analysis (e.g., bioelectrical impedance vector analysis (BIVA)), and (3) muscle health and injury monitoring. Seven specific research areas emerged, including hydration monitoring, cross-method validation of body composition analyses, development of predictive models, sport phenotype identification, tracking training adaptations, performance/load assessment via phase angle, and localized BIA for injury diagnosis and recovery. Conclusions: While quantitative BIA estimates may lack individual-level precision, raw parameter analyses may offer valuable insights into hydration, cellular integrity, and muscle injury status, yet further research is needed to fully realize these applications. Full article

Lung-protective ventilation and other experimental conditions raise arterial carbon dioxide tension (PaCO₂) and alter pH. Short-term benefits are reported in non-infectious settings, whereas infection and/or prolonged exposure are typically harmful. This scoping review systematically maps immune-mediated effects of hypercapnia on innate immunity and alveolar epithelial repair. Scoping review per Levac et al. and PRISMA Extension for Scoping Reviews (Open Science Framework protocol: 10.17605/OSF.IO/WV85T; post hoc). We searched original preclinical studies (in vivo/in vitro) in PubMed, Web of Science, ScienceDirect, Cochrane Reviews, and SciELO (2008–2023). PaCO₂ (mmHg) was prioritized; %Fraction of inspired Carbon Dioxide (%FiCO₂) was recorded when PaCO₂ was unavailable; pH was classified as buffered/unbuffered. Data were organized by context, PaCO₂, and exposure duration; synthesis used heat maps (0–120 h) and a narrative description for >120 h. Mechanistic axes extracted the following: NF-κB (canonical/non-canonical), Bcl-2/Bcl-xL–Beclin-1/autophagy, AMPK/PKA/CaMKKβ/ERK1/2 and ENaC/Na,K-ATPase trafficking, Wnt/β-catenin in AT2 cells, and miR-183/IDH2/ATP. Thirty-five studies met the inclusion criteria. In non-infectious models, a “protective window” emerged, with moderate PaCO₂ and brief exposure (65–95 mmHg; ≤4–6 h), featuring NF-κB attenuation and preserved epithelial ion transport. In infectious models and/or with prolonged exposure or higher PaCO₂, harmful signals predominated: reduced phagocytosis/autophagy (Bcl-2/Bcl-xL–Beclin-1 axis), AMPK/PKA/ERK1/2-mediated internalization of ENaC/Na,K-ATPase, depressed β-catenin signaling in AT2 cells, impaired alveolar fluid clearance, and increased bacterial burden. Chronic exposures (>120 h) reinforced injury. Hypercapnia is a context-, dose-, time-, and pH-dependent double-edged modulator. The safe window is narrow; standardized, parallel reporting of PaCO₂ and pH—with explicit comparisons of buffered vs. unbuffered hypercapnia—is essential to guide clinical translation. Full article

The recent growth of generative artificial intelligence (AI) has brought new possibilities and revolutionary applications in many fields. It has also, however, created important ethical and security issues, especially with the abusive use of deepfakes, which are artificial media that can propagate very realistic but false information. This paper provides an extensive bibliometric, statistical, and trend analysis of deepfake research in the age of generative AI. Utilizing the Web of Science (WoS) database for the years 2014–2024, the research identifies key authors, influential publications, collaboration networks, and leading institutions. Biblioshiny (Bibliometrix R package, University of Naples Federico II, Naples, Italy) and VOSviewer (version 1.6.20, Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) are utilized in the research for mapping the science production, theme development, and geographical distribution. The cutoff point of ten keyword frequencies by occurrence was applied to the data for relevance. This study aims to provide a comprehensive snapshot of the research status, identify gaps in the knowledge, and direct upcoming studies in the creation, detection, and mitigation of deepfakes. The study is intended to help researchers, developers, and policymakers understand the trajectory and impact of deepfake technology, supporting innovation and governance strategies. The findings highlight a strong average annual growth rate of 61.94% in publications between 2014 and 2024, with China, the United States, and India as leading contributors, IEEE Access among the most influential sources, and three dominant clusters emerging around disinformation, generative models, and detection methods. Full article

As a global staple ensuring food security, maize incurs 15–20% annual yield loss from pests/diseases. Conventional manual detection is inefficient (>7.5 h/ha) and subjective, while existing YOLO models suffer from >8% missed detections of small targets (e.g., corn armyworm larva) in complex fields due to feature loss and poor multi-scale fusion. We propose YOLO-FDLU, a YOLO11s-based framework: LAD (Light Attention-Downsampling)-Conv preserves small-target features; C3k2_DDC (DilatedReparam–DilatedReparam–Conv) enhances cross-scale fusion; Detect_FCFQ (Feature-Corner Fusion and Quality Estimation) optimizes bounding box localization; UIoU (Unified-IoU) loss reduces high-IoU regression bias. Evaluated on a 25,419-sample dataset (6 categories, 3 public sources + 1200 compliant web images), it achieves 91.12% Precision, 92.70% mAP@0.5, 78.5% mAP@0.5–0.95, and 20.2 GFLOPs/15.3 MB. It outperforms YOLOv5-s to YOLO12-s, supporting precision maize pest/disease monitoring. Full article

Background: The aim of this bibliometric study was to evaluate publication trends in the most frequently cited clinical trials on the treatment of gingival recession, taking into account the augmentation materials used. Methods: A Web of Science search was performed among articles published by 30 September 2024. Two independent reviewers evaluated year of publication, journal, authorship country of authors, collaborative relationship, keywords, and the main domains. Results: The top one hundred most-cited clinical trials were published in the span of 26 years from 1993 to 2019, and the total citation counts varied from 44 to 284 (83.69 citations per paper). There was correlation between the time of publication and the number of citations. The articles were authored by 333 researchers representing twenty-two countries. Italy contributed the highest number of articles (n = 36), followed by the USA (n = 28) and Brazil (n = 17). International collaborations were predominantly observed between Italy, the USA, and Switzerland. The type of graft was the most cited field of research (34), followed by guided tissue regeneration (17) and enamel matrix derivative (13). Conclusions: The country that produced the highest number publications among the 100 most-cited clinical trials on gingival recession treatment was Italy. The use of connective tissue graft (CTG) and coronally advanced flap (CAF) was the most prominent trend. Future work should combine bibliometric mapping with critical quality appraisal and explore whether citation trends align with best available evidence. Full article

Smart tourism destinations, shaped by the integration of tourism and information technology, have become a central theme in international academic research. This study employs bibliometric methods using CiteSpace to conduct co-authorship, co-citation, keyword co-occurrence, and burst analyses, with the aim of mapping the knowledge structure and research evolution of the field. Drawing on 232 articles from the Web of Science Core Collection (2013–2025), the results reveal a shift from technology-centered approaches toward themes of visitor experience, collaborative governance, and sustainable development. The Universitat d’Alacant (Spain) and The Hong Kong Polytechnic University (China) have emerged as leading research hubs, with Ivars-Baidal and colleagues as major contributors. Foundational studies by Buhalis and Gretzel continue to shape the domain. Keyword trends highlight increasing attention to technological efficiency and sustainable ethics. Overall, the study traces the developmental trajectory of smart tourism destinations, proposes a systematic knowledge framework, and identifies future directions for theoretical integration and methodological innovation. The findings provide both conceptual insights for academic research and strategic guidance for destination governance and policy. Full article

Discrimination between normal (fresh/non-frozen) and frozen-thawed beef is crucial for ensuring food safety. This paper proposed a novel, non-destructive and real-time you only look once for normal and frozen-thawed beef discrimination (YOLO-NF) model using deep learning techniques. The simple, parameter-free attention module (SimAM) and the squeeze and excitation (SE) attention mechanism were introduced to enhance the model’s performance. A total of 1200 beef samples were used, with their images captured by a charge-coupled device (CCD) camera. In the model development, specifically, the training set comprised 3888 images after data augmentation, while the validation set and test set each included 216 original images. Experimental results on the test set showed that the YOLO-NF model achieved precision, recall, F1-Score and mean average precision (mAP) of 95.5%, 95.2%, 95.3% and 98.6%, respectively, significantly outperforming YOLOv7, YOLOv5 and YOLOv8 models. Additionally, gradient-weighted class activation mapping (Grad-CAM) was adopted to interpret the model’s decision basis. Moreover, the model was deployed on the web interface for user convenience, and the discrimination time on the local server was 0.94 s per image, satisfying the requirements for real-time processing. This study provides a promising technique for high-performance and rapid meat quality assessment in food safety monitoring systems. Full article

Given the construction industry’s significant contribution of approximately 39% of global CO₂ emissions, implementing effective carbon reduction strategies is becoming increasingly critical. In this context, Internet of Things (IoT) technologies present promising solutions for monitoring and reducing emissions. However, there is a lack of comprehensive understanding regarding specific IoT applications, implementation barriers, and opportunities for carbon reduction in construction practices. This study investigates the role of IoT in reducing carbon emissions in the construction industry. Following PRISMA guidelines, this study analyzed bibliometric data from Scopus and Web of Science databases using VOSviewer for science mapping visualization. Content analysis was conducted on 17 carefully selected articles to identify key research topics and applications. The analysis identified four mainstream application areas: (1) IoT-based smart monitoring systems for carbon emissions, (2) energy efficiency and management applications, (3) sustainable construction implementation frameworks, and (4) smart cities and other built environment applications. Key findings highlight growing research interest in IoT applications for sustainable construction, with China, the United States, and the United Kingdom leading collaborative efforts. Despite demonstrated carbon reduction potential, significant implementation barriers exist, including technical limitations, organizational resistance, skill gaps, and economic constraints. Key opportunities include Artificial Intelligence (AI) integration, Building information modeling (BIM)-IoT synergies, energy prosumer models, and standardization frameworks. This study provides the first focused review of IoT applications specifically targeting carbon reduction in construction, highlighting a critical technology-practice gap where organizational factors frequently outweigh technological barriers. A proposed socio-technical integration framework in this study bridges technical and organizational elements to overcome adoption barriers. Full article

Machine learning (ML) is reshaping how environmental chemicals are monitored and how their hazards are evaluated for human health. Here, we mapped this landscape by analyzing 3150 peer-reviewed articles (1985–2025) from the Web of Science Core Collection. Co-citation, co-occurrence, and temporal trend analyses in VOSviewer and R reveal an exponential publication surge from 2015, dominated by environmental science journals, with China and the United States leading in output. Eight thematic clusters emerged, centered on ML model development, water quality prediction, quantitative structure–activity applications, and per-/polyfluoroalkyl substances, with XGBoost and random forests as the most cited algorithms. A distinct risk assessment cluster indicates migration of these tools toward dose–response and regulatory applications, yet keyword frequencies show a 4:1 bias toward environmental endpoints over human health endpoints. Emerging topics include climate change, microplastics, and digital soil mapping, while lignin, arsenic, and phthalates appear as fast-growing but understudied chemicals. Our findings expose gaps in chemical coverage and health integration. We recommend expanding the substance portfolio, systematically coupling ML outputs with human health data, adopting explainable artificial intelligence workflows, and fostering international collaboration to translate ML advances into actionable chemical risk assessments. Full article

Background/Objectives: Acute myocardial infarction (MI) is a major cause of death worldwide, and it imposes a heavy burden on health care systems. Although diagnostic methods have improved, detecting the disease early and accurately is still difficult. Recently, AI has demonstrated increasing capability in improving ECG-based MI detection. From this perspective, this scoping review aimed to systematically map and evaluate AI applications for detecting MI through ECG data. Methods: A systematic search was performed in Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, and Cochrane Central. The search covered publications from 2015 to 9 October 2024; non-English articles were included if a reliable translation was available. Studies that used AI to diagnose MI via ECG were eligible, and studies that used other diagnostic modalities were excluded. The review was performed per the PRISMA extension for scoping reviews (PRISMA-ScR) to ensure transparent and methodological reporting. Of a total of 7189 articles, 220 were selected for inclusion. Data extraction included parameters such as first author, year, country, AI model type, algorithm, ECG data type, accuracy, and AUC to ensure all relevant information was captured. Results: Publications began in 2015 with a peak in 2022. Most studies used 12-lead ECGs; the Physikalisch-Technische Bundesanstalt database and other public and single-center datasets were the most common sources. Convolutional neural networks and support vector machines predominated. While many reports described high apparent performance, these estimates frequently came from relatively small, single-source datasets and validation strategies prone to optimism. Cross-validation was reported in 57% of studies, whereas 36% did not specify their split method, and several noted that accuracy declined under inter-patient or external validation, indicating limited generalizability. Accordingly, headline figures (sometimes ≥99% for accuracy, sensitivity, or specificity) should be interpreted in light of dataset size, case mix, and validation design, with risks of spectrum/selection bias, overfitting, and potential data leakage when patient-level independence is not enforced. Conclusions: AI-based approaches for MI detection using ECGs have grown quickly. Diagnostic performance is limited by dataset and validation issues. Variability in reporting, datasets, and validation strategies have been noted, and standardization is needed. Future work should address clinical integration, explainability, and algorithmic fairness for safe and equitable deployment. Full article

This study examines the global mobility of researchers in the smart city domain from 2000 to 2020, using inter-country and intercity affiliation data from the Web of Science. Employing network analysis and spatial econometric models, the paper maps the structural reconfiguration of scientific labor circulation. The results show that the international mobility network is dense yet asymmetric, dominated by a small set of high-frequency corridors such as China–United States, which intensified markedly over the two decades. While early networks were fragmented and polycentric, the later period reveals a multipolar configuration with significant growth in South–South and intra-European exchanges. At the city level, Beijing, Shanghai, Wuhan, and Nanjing emerged as central nodes, reflecting the consolidation of East Asian hubs within the global knowledge system. Mesoscale community detection highlights the coexistence of territorially embedded ecosystems and transregional corridors sustained by thematic and reputational affinities. Growth decomposition indicates that high-income countries benefit from both talent retention and international inflows, while upper-middle-income countries rely heavily on inbound mobility. Spatial regression and quantile models confirm that economic growth and baseline scientific visibility remain robust drivers of urban smart city performance. In contrast, mobility effects are context-dependent and heterogeneous across city positions. Together, these findings demonstrate that researcher mobility is not only a vector of knowledge exchange but also a mechanism that reinforces spatial hierarchies and reshapes the geography of global smart city innovation. Full article

High-Intensity Functional Training (HIFT) is a training method that has garnered increasing attention due to the rise in hybrid competitions such as CrossFit or Hyrox, a race format combining strength and endurance tasks in a fixed structure. Therefore, an integrative approach is needed to help us understand which physiological capacities this training method enhances. Objectives: This scoping review aimed to map the current scientific literature related to HIFT, with a particular focus on physiological and psychobiological determinants of performance in hybrid competition contexts. Methods: Following the methodological framework of Arksey and O’Malley and the PRISMA-ScR guidelines, a systematic search was conducted in Web of Science, Scopus, and PubMed. Thirty-nine studies published between 2015 and 2025 were included. Results: HIFT was found to improve key physical attributes such as aerobic capacity, muscular strength, anaerobic power, and fatigue tolerance. Increases in VO₂max ranging from 8% to 15% and strength gains of 10% to 20% in major lifts were commonly reported. Improvements in local muscular endurance, power output, and recovery capacity were also observed. The physiological benefits appeared more pronounced in trained individuals, especially those with greater resistance training volume. In addition, psychobiological responses, including perceived exertion, cognitive control, and motivation, were explored in several studies, with more experienced athletes showing higher fatigue tolerance and better performance consistency under stress. Conclusions: HIFT enhances essential physical attributes applicable to hybrid events. The findings support the use of HIFT as a foundational method for training athletes involved in demanding multi-domain fitness settings, without attributing these benefits specifically to any single competitive event. Full article