F1000Research最新文献

{"title":"The distribution of groundwater uranium in Chintamani village, Karnataka, India.","authors":"Sadashiva Rampur, Mahesh Kumar V K, Pavan R Pelli, Senjuti Sarkar, Samayeta Pramanik, Upama Majumder, Shravanthi S, Bhavana Meenakshi T, Srinidhi G Santhanakrishnan, Rushi Pendem, Tanushree Ghosh, Deepesh Nagarajan","doi":"10.12688/f1000research.162525.3","DOIUrl":"10.12688/f1000research.162525.3","url":null,"abstract":"<p><strong>Background: </strong>Chintamani village, Chikkaballapura district, Karnataka, India was found to possess high aquifer uranium concentrations. Geologically, Chintamani village is located on bedrock that is rich in elements like potassium (K) that naturally contain high levels of radioactive elements, such as uranium and thorium, due to the presence of alkali-feldspar granites and gneisses. Aquifer depletion has caused the concentration of these elements in groundwater to increase over time, posing a potential health hazard to the residents of Chintamani village.</p><p><strong>Methods: </strong>Here, we report the sampling of groundwater from 12 borewells located in Chintamani village in between the period of August 2024 to December 2024. We observed groundwater uranium concentrations of 0.018 ppm to 8.64 ppm. Data for borewell depth, the quantity of total dissolved solids (TDS), and the elemental composition of TDS is also reported. We observed a statistically significant spatial distribution of uranium concentrations in Chintamani village. Borewells possessing the highest observed concentrations of uranium were clustered towards the northwestern region of the village.</p><p><strong>Conclusions: </strong>This dataset is expected to serve as a resource for guiding potential remediation efforts in these locations.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"321"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409252/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145014169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in sleep dentistry research in Asia: A bibliometric analysis.","authors":"Faizul Hasan, Mokh Sujarwadi, Lia Taurussia Yuliana, Ervina Restiwulan Winoto, An'nisaa Chusida, Hendrik Setia Budi","doi":"10.12688/f1000research.164414.2","DOIUrl":"10.12688/f1000research.164414.2","url":null,"abstract":"<p><strong>Background: </strong>Sleep-related conditions such as obstructive sleep apnea and bruxism significantly affect both oral and systemic health, posing substantial public health challenges. Growing scholarly interest in sleep dentistry reflects an emerging effort to address these conditions through multidisciplinary research. This study employs bibliometric analysis to examine emerging themes, collaborative patterns, influential authors, and research trajectories related to sleep dentistry in Asia.</p><p><strong>Methods: </strong>A comprehensive search was conducted using the Scopus database to identify relevant publications from inception through April 2025. Bibliometric techniques were applied to analyze co-authorship networks, annual publication trends, institutional and international collaborations, keyword co-occurrence, and citation metrics. VOSviewer and the Bibliometrix package in R were utilized for data visualization and network mapping.</p><p><strong>Results: </strong>The analysis included 1,237 publications. China was the leading contributor, followed by the United States and India. The United Kingdom exhibited the highest ratio of Multiple Country Publications, followed by Australia and Canada. Tehran University of Medical Sciences emerged as the most productive institution, followed by the All India Institute of Medical Sciences and Shahid Beheshti University of Medical Sciences. Co-authorship analysis revealed six distinct collaborative clusters, with a total of 5,828 scholars contributing to the field.</p><p><strong>Conclusion: </strong>A substantial and growing body of research on sleep dentistry has emerged in Asia. The bibliometric findings highlight influential contributors, international cooperation, and key research themes particularly obstructive sleep apnea and bruxism underscoring the value of bibliometric methods in shaping responses to this pressing regional public health concern.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"489"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12355164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144872126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of mutations in the partial sequences of the surface and polymerase genes of hepatitis B virus associated with immune escape and drug resistance in HIV-infected patients.","authors":"Lorato Modise, Nomathamsanqa Sithebe, Hazel Mufhandu","doi":"10.12688/f1000research.132498.4","DOIUrl":"10.12688/f1000research.132498.4","url":null,"abstract":"<p><strong>Background: </strong>Co-infection of hepatitis B virus (HBV) and human immunodeficiency virus (HIV) has an impact on high HBV replication and progression to liver cancer. These may lead to cross-resistance of drugs due to natural mutations or therapeutic pressure. These require continuous monitoring of HBV variants for better diagnosis and treatment strategies.</p><p><strong>Methods: </strong>Convenience sampling was used to collect fifty archival sera from Inkosi Albert Luthuli Central Hospital. Sera were subjected to HBsAg screening using ELISA, DNA extraction, PCR amplification, Sanger sequencing, genotype prediction and mutation analysis.</p><p><strong>Results: </strong>Of the 50 samples, 86% (43/50) were HBsAg positive; 82% (41/50) PCR positive with 92% (38/41) sequenced and only 26 sequences were subjected to molecular characterization. The HBV sequences showed similarity to genotype A (73% [19/26]), genotypes G (5% [3/26]) and genotype C (15% [4/26]). Prevalence of the mutations in the surface region was (47% [18/38]); including diagnostic failure (K122R and T143S) and immune escape mutations (P127T, G145R, S207N, Y200T, E164D, Y206H and L209V). The mutations in the RT were at (36% [14/38]) with drug resistance mutations (DRM) at (50% [7/14]). Mutations showed resistance to lamivudine (LMV) at (35% [5/14]), telbivudine (LdT) at (29% [4/14]), (14% [2/14]) for entecavir (ETV) and (21% [3/14]) for adefovir (ADV). One sample had a combination of L180M, M204V, S202K, and M250I mutations.</p><p><strong>Conclusions: </strong>Our findings highlight the prevalence of HBV genotype A in HIV-infected patients in South Africa. The study provides evidence of mutations linked to immune evasion and drug resistance; this infers that these mutations may have clinical implications for the diagnosis and treatment of HBV in HBV/HIV co-infected individuals. Further <i>in vitro</i> studies must be conducted to explore the impact of the identified mutation on the surface protein expression during diagnosis; phenotype impact of the mutant virus towards the antiviral drugs.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"12 ","pages":"1232"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12426594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research trends in mobile payment adoption: Research trends and agenda.","authors":"Alejandro Valencia-Arias, Jesus Alberto Jimenez Garcia, Gustavo Moreno-López, Aaron José Alberto Oré León, Lucia Palacios-Moya, Jackeline Valencia, Martha Benjumea-Arias","doi":"10.12688/f1000research.159551.2","DOIUrl":"10.12688/f1000research.159551.2","url":null,"abstract":"<p><p>This study explores the adoption of mobile payments as a research field to understand the technological, socioeconomic, and cultural factors that influence the understanding of the users' experience and satisfaction and attract more users. It is vital to examine the research trends in the adoption of mobile payments from a systematic literature review using PRISMA 2020 to select 63 documents from Scopus and WOS. Among the results, the most used models for this analysis are extended TAM and extended UTAUT. The most representative variables in the studies are social influence, security perception, risk perception, trust and perceived usefulness. It is concluded that the future research agenda should focus on topics such as biometric authentication, payment flexibility and contactless payments. In addition, from the business perspective, there is a focus on designing innovative interfaces that are more intuitive for users.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"358"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145000029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalised and Collaborative Learning Experience (PCLE) Framework for AI-driven Learning Management System (LMS).","authors":"Claireta Tang Weiling, Lew Sook Ling, Ooi Shih Yin","doi":"10.12688/f1000research.166248.1","DOIUrl":"10.12688/f1000research.166248.1","url":null,"abstract":"<p><strong>Background: </strong>Understanding student engagement and academic performance is crucial in AI-driven e-learning environments. Many learning management systems (LMS) lack effective collaborative course recommendation strategies, limiting support for personalised learning experiences.</p><p><strong>Methods: </strong>This study developed and evaluated collaborative filtering and machine learning models to generate course recommendations. Machine learning models such as K-Nearest Neighbours (KNN), Singular Value Decomposition (SVD), and Neural Collaborative Filtering (NCF) were applied. Two education-related datasets from Kaggle were used. The first contains 100,000 course reviews from Coursera, and the second dataset includes 209,000 course details and comments from Udemy. Data preprocessing was conducted to clean and structure both datasets. The model effectiveness was evaluated using Mean Absolute Error (MAE), Hit Rate (HR), and Average Reciprocal Hit Ranking (ARHR).</p><p><strong>Results: </strong>K-Nearest Neighbours showed the highest performance on the Coursera dataset, while Singular Value Decomposition and Neural Collaborative Filtering maintained stable predictive accuracy across both datasets. The findings indicate that dataset characteristics influenced model performance. K-Nearest Neighbours worked effectively with structured and consistent data, while Singular Value Decomposition and Neural Collaborative Filtering produced consistent outcomes across diverse datasets.</p><p><strong>Conclusions: </strong>This study contributes to e-learning research by demonstrating the potential of collaborative filtering and machine learning in enhancing course recommendations and promoting engagement in the learning management system. Limitations include the use of two datasets and a limited set of machine learning models. Future work aims to integrate learning styles and evaluate the framework across more diverse educational contexts to support adaptive and collaborative learning.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"809"},"PeriodicalIF":0.0,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12432497/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review the role of oxygen-delivering nanobubbles in stroke therapy: A novel approach.","authors":"Hamzah Hamzah, Suryanti Suryanti, Idris Adewale Ahmed, Bambang Pujo Semedi, Abdullah Machin, Aditya Tri Hernowo","doi":"10.12688/f1000research.162742.3","DOIUrl":"10.12688/f1000research.162742.3","url":null,"abstract":"<p><p>Stroke remains a leading cause of mortality and long-term disability worldwide, necessitating innovative therapeutic strategies. The advent of nanotechnology, particularly oxygen-delivering nanobubbles (ODNBs), has introduced a promising avenue for enhancing stroke therapy. ODNBs have demonstrated the ability to improve oxygen delivery, enhance therapeutic efficacy, and provide diagnostic advantages through imaging contrast enhancement. However, challenges such as toxicity, off-target effects, and regulatory hurdles must be addressed before clinical translation. This review synthesizes the latest findings on ODNBs in stroke therapy, highlights their key benefits and challenges, and explores future applications, including gene therapy and brain tissue regeneration. By addressing these aspects, this review aims to provide a comprehensive understanding of the potential of ODNBs in revolutionizing stroke treatment.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"406"},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145000085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Security inequalities in Non-EU European Countries: A Cross-National Comparative Assessment Using an Integrated MCDM-Machine Learning Approach.","authors":"Mijahed Nasser Aljober, Adel A Nasser, Abed Saif Ahmed Alghawli, Amani A K Elsayed","doi":"10.12688/f1000research.163662.2","DOIUrl":"10.12688/f1000research.163662.2","url":null,"abstract":"<p><strong>Objectives: </strong>In an increasingly interconnected world, the effectiveness of health security (HeS) is pivotal in shaping informed health policies and enhancing public health outcomes. This study aims to analyses HeS in 27 non-EU European countries, identifying key priorities and trends, benchmarking against African and Eastern Mediterranean regions (EMR), and ranking and clustering health security performance to inform targeted interventions.</p><p><strong>Methods: </strong>Utilizing 2019, 2021, and aggregated 2017-2021 data from six Global Health Security Index indicators, this study applied an integrated Entropy-CoCoSo-K-means framework. The Entropy method was employed to identify health security (HeS) priorities and trends in Non-EU countries, enabling cross-regional comparisons with African and EMR regions to highlight priority shifts and disparities. The Entropy-CoCoSo (Combined Compromise Solution) model generated dynamic rankings, while K-means clustering categorized countries into five risk clusters (high to dangerous). This integration facilitated cross-national dynamic rankings and cluster analyses, informing targeted interventions across Non-EU countries.</p><p><strong>Results: </strong>Entropy analysis reveals that detection and reporting emerged as the most critical indicator (weight: 0.388), reflecting disparities in surveillance. The risk environment remains minimally influential (0.067), highlighting consistent vulnerabilities to external threats. Compliance with norms shows a sharp rise (0.091 → 0.123), indicating emerging regulatory gaps or uneven adherence to health standards post-2019. Cross-regional comparisons highlighted a focus on detection and reporting in non-EU countries versus an emphasis on prevention in Africa and healthcare infrastructure prioritization in the EMR. Ranking and clustering revealed stark disparities: Armenia, Norway, and the UK consistently ranked \"High,\" In contrast, Andorra, Monaco, San Marino, and Tajikistan (Cluster 5: \"Dangerous\") exhibited systemic weaknesses.</p><p><strong>Conclusion: </strong>This study underscores the need for tailored policies to address non-EU Europe's evolving HeS challenges. Harmonizing surveillance systems, scaling preventive measures, and bridging compliance gaps are critical. Regional collaboration and resource reallocation to low-performing nations are essential to mitigate disparities.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"462"},"PeriodicalIF":0.0,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12423625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomy support in higher education: a key strategy for the well-being of university students.","authors":"David Pineda, Jose Eduardo Lozano-Jimenez, Juan Antonio Moreno-Murcia","doi":"10.12688/f1000research.144803.2","DOIUrl":"10.12688/f1000research.144803.2","url":null,"abstract":"<p><strong>Introduction: </strong>Amid changing social dynamics, the world of higher education faces, among other challenges, the growing impact on the mental health of students. In this scenario, the Self-Determination Theory (SDT) highlights the important role of autonomy support as it generates positive effects on students' motivation and well-being.</p><p><strong>Methods: </strong>The present study tests the predictive capacity of the teacher's interpersonal style of autonomy support in a higher education institution, in relation to the satisfaction of basic psychological needs, autonomous motivation and depressive symptoms. A sample composed of 356 Spanish university students of which 237 were male (66.57%) from different grades and courses, aged between 17 and 57 years ( <i>M</i> = 20.83; <i>SD</i> = 3.44), from middle socioeconomic strata, was used, and selected through purposive sampling.</p><p><strong>Results: </strong>After the analysis of structural equations, the results showed that the teacher's interpersonal style of autonomy support positively predicted: the satisfaction of basic psychological needs, and autonomous motivation; but negatively depressive symptoms.</p><p><strong>Conclusions: </strong>The model describes the possible importance of promoting the teacher's interpersonal style of autonomy support in higher education as a protective factor for well-being and mental health. These findings highlight the importance of motivational strategies that higher education teachers must implement to promote student motivation and well-being.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"13 ","pages":"839"},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12498519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Internal and external risk factors analysis on pilot precondition in Indonesia.","authors":"Inne Yuliawati, Budi Sampurna, Tjhin Wiguna, Imam Subekti, Aria Kekalih, Widura Imam Mustopo, Hervita Diatri, Wawan Mulyawan","doi":"10.12688/f1000research.163883.3","DOIUrl":"10.12688/f1000research.163883.3","url":null,"abstract":"<p><strong>Background: </strong>Pilot errors and preconditions are major concerns that affected by multiple factors physiologically, psychologically and psychosocially. This study aimed to analyse the correlation between the internal and external risk factors, and pilot preconditions in Indonesia.</p><p><strong>Methods: </strong>A cross-sectional study design with purposive sampling, directed to male pilots who had flight duty in the past seven days, underwent medical examination at the Aviation Medical Center, Jakarta, August 12-16, 2024. The data were collected through a self-report questionnaire, Trail Making Test A and B, laboratory tests (plasma lipid, fasting blood glucose), and physical measurements (height, weight, waist circumference, and blood pressure). The independent variables divided into internal factors (Age, Burnout, Metabolic Syndrome parameters); and external factors (Flight Time, Duty Time, Unscheduled Flight Duty, Number of Sectors, Sleep Duration). The dependent variables were pilot preconditions based on the Human Factor Analysis and Classification System (HFACS).</p><p><strong>Results: </strong>A total of 122 participants participated and 28.7% had an Unscheduled Flight Duty in the last 30 days. Significant correlations were found between Adverse Mental State and HDL-Cholesterol (95%CI=1.52-5.80), Adverse Physiological State and Burnout [Personal (95%CI=0.005-0,04), Work-Related (95%CI=0.009-0.042)], Physical Mental Limitation and Fasting Blood Glucose (95%CI=(-0.479)-(-0.071)), Number of Sectors (95%CI=0.022-3.001). For Personal Readiness (PR), significant correlation was found between PR-Psychological Demand and Flight-Time One Year (95%CI=(0.000-0.001), Sleep Duration (95%CI=(-0.137)-(-0.013)), Waist Circumference (95%CI=(-0.014)-(-0.002)), PR-Social Support and Sleep Duration (95%IK=0.018-0.207), Client-Related Burnout (95%IK=(-0.011)-(-0.002)).</p><p><strong>Conclusions: </strong>The internal factors that correlated with pilot preconditions in Indonesia were Waist Circumference, HDL-Cholesterol, Fasting Blood Glucose, Personal Burnout, Work, and Client-related burnout. External factors that correlated with pilot preconditions were the Number of Sectors, Flight-Time One Year and Sleep duration. These findings emphasize the need to address physical and mental health aspects of pilots to enhance aviation safety.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"14 ","pages":"458"},"PeriodicalIF":0.0,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12219539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in Smart Restaurant Research: Bibliometric Review and Research Agenda.","authors":"Alejandro Valencia-Arias, Sebastián Cardona-Acevedo, Ezequiel Martínez Rojas, Juana Ramírez Dávila, Paula Rodriguez-Correa, Lucia Palacios-Moya, Renata Teodori de la Puente, Erica Agudelo-Ceballos, Martha Benjumea-Arias","doi":"10.12688/f1000research.158066.4","DOIUrl":"10.12688/f1000research.158066.4","url":null,"abstract":"<p><strong>Background: </strong>The automation of processes and services has transformed various industries, including the restaurant sector. Technologies such as the Internet of Things (IoT), machine learning, Radio Frequency Identification (RFID), and big data have been increasingly adopted to enhance service delivery, improve user experiences, and enable data traceability. By collecting user feedback and analyzing sentiments, these technologies facilitate decision-making and offer predictive insights into future food preferences. This study aims to explore current research trends in intelligent restaurants, focusing on technological applications that improve service and decision-making.</p><p><strong>Methods: </strong>A bibliometric analysis was conducted in accordance with the PRISMA-2020 guidelines. A total of 94 academic documents were reviewed from the Scopus and Web of Science databases, focusing on publications related to intelligent restaurant systems, particularly involving IoT and automation technologies.</p><p><strong>Results: </strong>The analysis revealed that the United States, India, and China have contributed the most to the field, with a particular emphasis on China's implementation of IoT architecture and robotics in restaurant settings. Chinese restaurant innovations, particularly in robotics, are among the most frequently cited in the literature. The study identifies these countries as leading the research in the intelligent restaurant domain.</p><p><strong>Conclusions: </strong>Technologies such as IoT, machine learning, RFID, and big data are driving advancements in restaurant automation, enhancing service efficiency and user experience. The United States, India, and China are leading research in this area, with China standing out for its application of robotics and IoT in restaurants. This research provides a foundation for future studies aimed at improving predictive models for food selection and service optimization.</p>","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"13 ","pages":"1505"},"PeriodicalIF":0.0,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12413613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145014172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}