Bibliometric analysis of childhood-onset systemic lupus erythematosus from 2000 to 2022.

BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic, multisystem autoimmune disorder. When SLE occurs in individuals under the age of 18, it is referred to as childhood-onset SLE (cSLE). Currently, there is a dearth of bibliometric research pertaining to cSLE. METHOD: Relevant studies in the field of cSLE from 2000 to 2022 were screened from the Web of Science Core Collection (WoSCC). CiteSpace and VOSviewer software were used to visualize the annual publications, countries, institutions, authors, journals, keywords, and references, after which the authors conducted the scientific analysis. RESULTS: A total of 2857 articles were included in this study, and the number of articles published in the past 20 years showed an overall upwards trend. The most prolific countries are the United States, China, and Brazil; however, the United States, Canada, and the United Kingdom are clearly superior in terms of literary influence, and there is more cooperation between them and their institutions. LUPUS (n = 389) contributed the most to the variance. Brunner, HI's contribution in the field of cSLE is outstanding. The words related to 'lupus nephritis' and 'antibodies' are important words reflected in the keyword network diagram. The keywords included 'evidence-based recommendation', 'validation', 'diagnosis' and 'adult' from 2019, and 'continuous bursts' to the present. CONCLUSION: This study examined the research status of cSLE patients, discussed and analysed the research hotspots and trends in this field, and provided a reference for further research in this field to promote the development of cSLE research.

Research trends and hotspots of sleep disorder and cancer: a bibliometric analysis via VOSviewer and CiteSpace.

PURPOSE: The purpose of this research is to further understand the research status and summarize the research hotspots of sleep disorder and cancer, so as to provide insights into future researches. METHODS: In this research, the publications pertaining to sleep disorders and cancer from 1992 to 2022 was retrieved from SCIE and SSCI databases in the Web of Science Core Collection. The subject, journal, country/regions, institutions, author, and citations of publications were descriptively analyzed and visual analysis. RESULTS: From 1992 to December 2022, a total of 732 relevant literatures were retrieved from WOS SCIE and SSCI databases, the number of publications showed an increasing trend year by year. These articles were published in 252 journals, and the three most productive journals included Supportive Care in Cancer (80 publications), Psycho-oncology (32 publications), and Journal of Pain and Symptom Management (32 publications). The three most productive countries included the USA (367 publications, 50.1%), China (133 publications, 18.2%), and Canada (97 publications, 13.25%), with total citations of 12,684, 1866, and 5263. The three latest hot keywords in this field were sleep duration, validity, and inflammation. CONCLUSION: The USA, China, and Canada produced a lot of literature in the research field of sleep disorders and cancer, and had relatively great academic influence from 1992 to 2022. Researchers could pay more attention to the published in journals such as Journal of Clinical Oncology, Sleep, and Supportive Care in Cancer to timely grasp the latest progress and expand the breadth and depth in this area. Looking at the history of tumor and sleep disorder research in the past 20 years, the clinical treatment of sleep disorder caused by tumor and the direct bidirectional mechanism of the two may be a new focus of future research.

Global Trends and Frontier in Research on Pancreatic Alpha Cells: A Bibliometric Analysis from 2013 to 2023.

PURPOSE: Over the past 20 years, much of the research on diabetes has focused on pancreatic beta cells. In the last 10 years, interest in the important role of pancreatic alpha cells in the pathogenesis of diabetes, which had previously received little attention, has grown. We aimed to summarize and visualize the hotspot and development trends of pancreatic alpha cells through bibliometric analysis and to provide research direction and future ideas for the treatment of diabetes and other islet-related diseases. METHODS: We used two scientometric software packages (CiteSpace 6.1.R6 and VOSviewer1.6.18) to visualize the information and connection of countries, institutions, authors, and keywords in this field. RESULTS: A total of 532 publications, published in 752 institutions in 46 countries and regions, were included in this analysis. The United States showed the highest output, accounting for 39.3% of the total number of published papers. The most active institution was Vanderbilt University, and the authors with highest productivity came from Ulster University. In recent years, research hotspots have concentrated on transdifferentiation, gene expression, and GLP-1 regulatory function. Visualization analysis shows that research hotspots mainly focus on clinical diseases as well as physiological and pathological mechanisms and related biochemical indicators. CONCLUSIONS: This study provides a review and summary of the literature on pancreatic alpha cells through bibliometric and visual methods and shows research hotspot and development trends, which can guide future directions for research.

A bibliometric and visual analysis of the research status and hotspots of seborrheic dermatitis based on web of science.

BACKGROUND: Seborrheic dermatitis (SD) is a common chronic inflammatory skin disease. In recent years, significant progress has been made in the field of SD, but there has been no bibliometric research yet. This study aims to use bibliometric methods to analyze the current research status and hot topics of SD, to understand further the research trends and future development prospects in this field. METHODS: Retrieve core literature on SD from the Web of Science database and conduct a detailed analysis using CiteSpace and VOSviewer software based on factors such as publication volume, countries (regions), research institutions, journals, authors, highly-cited papers, and keywords. RESULTS: From 1996 to 2024, a total of 1436 publications were included in the bibliometric analysis. The number of publications has shown an increasing trend year by year. The USA is the leading country in this field of research. The University of California System is the primary research institution. The International Journal of Dermatology is the journal with the highest number of publications. The author Yang Won Lee has the highest number of publications, while the article "Seborrheic Dermatitis" (2004) by Gupta, A.K. has been cited the most. "Seborrheic dermatitis" is the most frequently occurring keyword. The main research hotspots and frontiers in SD are as follows: (1) The relationship between SD and other skin diseases is a popular research topic; (2) Malassezia and inflammation are current research hotspots in SD; and (3) Focusing on antifungal and anti-inflammatory treatments for SD is the current frontier direction in this field. CONCLUSION: This study is a summary of the current status and hot trends of SD research, which helps clinical doctors and researchers quickly understand the insights and valuable information of SD research and provides reference for clinical decision-making and finding future research directions.

Global hotspots and trends in Acanthosis nigricans research: A bibliometric and visualized analysis.

BACKGROUND: Acanthosis nigricans (AN) is a common skin disease characterized by clear pigmented patches on the folds of the skin. However, the AN research field lacks scientific and comprehensive bibliometric analysis. This article aims to use bibliometric methods to summarize and visualize the distribution patterns, research hotspots, and development trends of AN literature. METHODS: Literature from 1900 to 2024 was retrieved from the Web of Science Core Collection database through AN's thematic search. Using CiteSpace, VOSviewer, and Excel 2019, conduct a comprehensive analysis of the number of publications, countries/regions, institutions, authors, journals, highly cited literature, keywords, and so on, and sort out the hotspots and directions of AN. RESULTS: From 1900 to 2024, 1675 publications were included in the bibliometric analysis, showing a steady annual increase in the number of publications. The United States leads in this research field, with the University of Texas System being a key research institution. The Journal of Clinical Endocrinology & Metabolism has the highest number of published papers. The most cited article is "Syndromes of Insulin Resistance and Acanthosis Nigricans: Insulin-Receptor Disorders in Man" (1976). The author Taylor, SI, has published the most papers. "Acanthosis nigricans" is the most frequently occurring keyword. The main research hotspots and frontier areas in AN research are as follows: (1) The relationship between AN and other diseases is a popular research topic; (2) The connection between AN and insulin resistance (IR) is a current research focus; (3) Treatment of AN, with an emphasis on addressing the underlying causes and improving local skin conditions, represents the cutting edge of this field. CONCLUSION: This study summarizes the research trends and hotspots in the field of AN, offering valuable information and insights for scholars focused on AN scientific research, and providing a reference for future research directions.

Big data research in nursing: A bibliometric exploration of themes and publications.

AIMS: To comprehend the current research hotspots and emerging trends in big data research within the global nursing domain. DESIGN: Bibliometric analysis. METHODS: The quality articles for analysis indexed by the science core collection were obtained from the Web of Science database as of February 10, 2023.The descriptive, visual analysis and text mining were realized by CiteSpace and VOSviewer. RESULTS: The research on big data in the nursing field has experienced steady growth over the past decade. A total of 45 core authors and 17 core journals around the world have contributed to this field. The author's keyword analysis has revealed five distinct clusters of research focus. These encompass machine/deep learning and artificial intelligence, natural language processing, big data analytics and data science, IoT and cloud computing, and the development of prediction models through data mining. Furthermore, a comparative examination was conducted with data spanning from 1980 to 2016, and an extended analysis was performed covering the years from 1980 to 2019. This bibliometric mapping comparison allowed for the identification of prevailing research trends and the pinpointing of potential future research hotspots within the field. CONCLUSIONS: The fusion of data mining and nursing research has steadily advanced and become more refined over time. Technologically, it has expanded from initial natural language processing to encompass machine learning, deep learning, artificial intelligence, and data mining approach that amalgamates multiple technologies. Professionally, it has progressed from addressing patient safety and pressure ulcers to encompassing chronic diseases, critical care, emergency response, community and nursing home settings, and specific diseases (Cardiovascular diseases, diabetes, stroke, etc.). The convergence of IoT, cloud computing, fog computing, and big data processing has opened new avenues for research in geriatric nursing management and community care. However, a global imbalance exists in utilizing big data in nursing research, emphasizing the need to enhance data science literacy among clinical staff worldwide to advance this field. CLINICAL RELEVANCE: This study focused on the thematic trends and evolution of research on the big data in nursing research. Moreover, this study may contribute to the understanding of researchers, journals, and countries around the world and generate the possible collaborations of them to promote the development of big data in nursing science.

MoOx-Based Colorimetric Sensor for Ultraviolet Visualization.

Due to the depletion of the global ozone layer and the presence of ozone holes, humans are increasingly exposed to threats from solar ultraviolet radiation. Therefore, researching and developing a highly selective, sensitive, simple, and fast ultraviolet sensor is of significant importance for personal protection. In recent years, new nanomaterials have shown good application prospects in the research of ultraviolet sensors. MoOx nanostructures were prepared by a hydrothermal method. The experimental results show that, compared to traditional photochromic compounds, the new MoOx nanostructures exhibit high uniqueness, high selectivity, and excellent stability, and can perform rapid and accurate detection under full-band light. The beam sensor can not only detect through traditional electrical signal output, but also amplify, display, and analyze the beam through visualization and visual analysis, further improving the reliability and practicality of its application.

Optimization of Bear Oil Extraction Process and Hair Growth Activity.

According to ancient Chinese books, bear grease has the effects of strengthening muscles and bones, which is beneficial for weakness, but there is relatively little research on it. Thus, the extraction of it is beneficial for compensating for research in this area. In this study, a uniform experimental design method was used to optimize the extraction process of bear grease by enzymatic hydrolysis extraction, and the extraction rate can reach 81.89% under optimized extraction conditions. Furthermore, the components of bear grease obtained by this study were analyzed by GC-MS, and the results showed that ursolic oil was rich in unsaturated fatty acids (67.51%), which was higher than that of the traditional method (66.92%). The composition of bear grease extracted by the enzymatic method was also better than that extracted by the traditional method. In addition, bear grease obtained in this study had the obvious activity of promoting hair growth. The length, weight, and number of hair follicles in the depilation area of mice in the high-dose group were significantly different from those in the blank group (p < 0.01). This study optimized the extraction process of bear grease and conducted a preliminary analysis of its fatty acid composition, which is expected to provide some reference for the development of the medicinal value of bear grease.

[Application strategy of artificial intelligence technology in feed-based development of medicinal and edible homologous traditional Chinese medicine(TCM)resources in era of the "antibiotic ban"].

In the context of the "antibiotic ban" era, the feed conversion of medicinal and edible traditional Chinese medicine(TCM) resources is a research hotspot in the field of antibiotic alternatives development. How to develop feed products that are beneficial to agriculture and livestock while ensuring nutrient balance and precision using medicinal and edible TCM resources as raw materials has become a challenge. Artificial intelligence(AI) technology has unique advantages in feed production and improving the efficiency of intelligent breeding. If AI technology is applied to the feed development of medicinal and edible TCM resources, it is possible to realize feeding and antibiotic-replacement value while ensuring precise nutrition. In order to better apply AI technology in the field of feed development of medicinal and edible TCM resources, this article used CiteSpace software to carry out literature visualization analysis and found that AI technology had a good application in the field of feed formulation optimization in recent years. However, there is still a gap in the research on the intelligent utilization of medicinal and edible TCM resources. Nonetheless, it is feasible for AI technology to be applied to the feed conversion of medicinal and edible TCM resources. Therefore, this article proposed for the first time an intelligent formulation system framework for feed materials derived from medicinal and edible TCM resources to provide new ideas for research in the field of feed development of medicinal and edible TCM resources and the research on the development of antibiotic alternatives. At the same time, it can pave the way for a new green industry chain for contemporary animal husbandry and the TCM industry.

[Development of green pesticides from traditional Chinese medicines based on artificial intelligence and compatibility of traditional Chinese medicines].

The food security of China as a big agricultural country is attracting increasing attention. With the progress in the traditional Chinese medicine industry, Chinese medicinal materials and their preparations have been gradually developed as agents for disease prevention and with antimicrobial and insecticidal functions in agriculture. Promoting pesticide innovation by interdisciplinary integration has become the trend in pesticide research globally. Considering the increasingly important roles of green pesticides from traditional Chinese medicines and artificial intelligence in pest target prediction, this paper proposed an innovative green control strategy in line with the concepts of ecological sustainable development and food security protection. CiteSpace was used for visual analysis of the publications. The results showed that artificial intelligence had been extensively applied in the pesticide field in recent years. This paper explores the application and development of biopesticides for the first time, with focus on the plant-derived pesticides. The thought of traditional Chinese medicine compatibility can be employed to creat a new promosing field: pesticides from traditional Chinese medicine. Moreover, artificial intelligence can be employed to build the formulation system of pesticides from traditional Chinese medicines and the target prediction system of diseases and pests. This study provides new ideas for the future development and market application of biopesticides, aiming to provide more healthy and safe agricultural products for human beings, promote the innovation and development of green pesticides in China, and protect the sustainable development of the environment and ecosystem. This may be the research hotspot and competition point for the green development of the pesticide industry chain in the future.

A Bibliometric and Visual Analysis of the Current Status and Trends of Forensic Mixed Stain Research.

OBJECTIVES: To explore the context and hotspot changes of forensic mixed stain research through bibliometric approach. METHODS: The literature of forensic mixed stain included in the core collection of Web of Science database from 2011 to 2022 were collected as the study object, and the annual publication number, countrie (region), institution, journal, keywords, etc. were bibliometrically and visually analyzed using the R-based Bibliometrix 1.1.6 package and VOSviewer 1.6.18 software. RESULTS: A total of 732 articles on forensic mixed stain were included from 2011 to 2022, with the annual number of articles published and the annual citation frequency showing a steady increase year by year. Among the 59 countries (regions) with the most published articles, the United States ranked first with 246 articles, followed by China with 153 articles. The literature came from 104 journals, and the total number of articles published in the top 10 journals was 633. FORENSIC SCI INT GENET ranked first with 307 articles. Visual analysis using VOSviewer software showed that keywords could be divided into four research clusters, namely the genetic marker development group (blue), the mixed stain typing analysis theory group (red), the sequencing analysis group (yellow), and the case sample research group (green). It can be divided into four development stages in terms of different time periods: early development (2011-2013), middle development (2014-2016), rapid development (2017-2020) and latest development (2021-2022). CONCLUSIONS: The number of publications by domestic and foreign scholars in the study of mixed stain in forensic science is showing a relatively stable trend. Machine learning, next generation sequencing and other research have been the hottest topics that have attracted the most attention in recent years, which is expected to further develop the theory of mixed stain typing and sequencing analysis in forensic mixed stain research.

[Bibliometric and visual analysis of pneumoconiosis based on Cite Space].

Objective: Through the bibliometrics analysis and visual analysis of Chinese and English literature related to pneumoconiosis through CiteSpace, to understand the research situation, research trend and hotspots of pneumoconiosis, so as to provide reference for further research. Methods: In August 2022, CNKI (China National Knowledge Infrastructure) data baseand Web of Science core collection database were used as data sources for literature retrieval. Cite Space.5.8.R3c software was used to analyze the cooperation between authors and institutions, keyword co-occurrence analysis, keyword clustering analysis and keyword emergence analysis. Results: A total of 4726 Chinese literature and 2490 English literature related to pneumoconiosis were included; The annual publication volume of Chinese literature shows a fluctuating downward trend, while the annual publication volume of English literature shows a fluctuating upward trend. The Institute of Labor Health and Occupational Disease of the Chinese Academy of Preventive Medical Sciences and the Institute of Occupational Health and Poisoning Control of the Chinese Center for Disease Control and Prevention have the highest publication volume (55 articles) in the institutional cooperation network; The National Institute for Occupational Safety and Health (NIOSH) in the United States has the highest publication volume (153 articles) in the institutional collaboration network. The results of keyword co-occurrence, clustering, and prominence analysis show that Chinese literature focuses more on clinical research on pneumoconiosis, while English literature focuses more on experimental research related to the pathogenesis of pneumoconiosis. Conclusion: In the related field of pneumoconiosis research, the experimental research and clinical research on the pathogenesis are the main research hotspots.

The visualization of autism: Filming children at the Maudsley Hospital, London, 1957-8.

This article examines three films made during the 1950s by Elwyn James Anthony at the psychotic clinic for children at the Maudsley Hospital that marked an important transition in the purpose and practice of visual documentation in a clinical setting: film as a research tool was transitioning from the recording of external signs as indicators of internal subjective states, to the capture of the visual flow of communication between subjects. It is a shift that had a particular impact on the emergent classification of autism, a modality not yet properly separated from the broader term of psychosis, as a non-relational condition whose visual capture demonstrated a void of inter-human communicational exchange. Film was significant not only as a recording apparatus, but as a method of cutting and crafting sequences of movements into brief repetitive motifs. The filmed behaviour of children remained opaque to interpretation, a 'finding' that facilitated the modelling of an emergent autism as subjects who were isolated, alienated and automaton-like, inhabiting a separate temporality. The article situates this 'second', affectless autism, within a broader context of post-war research into gestures as a language of the body, developed largely through an intellectual network of German émigré psychoanalysts who had fled to the US and UK in the 1930s.

Knowledge map and hotspot analysis in climate resilience infrastructure (CRI) from 1997 to 2022 through scientometric analysis.

The global consensus is to reduce greenhouse gas emissions and actively respond to climate change (CC). Global warming has irreversibly altered the Earth's ecosystems. Unpredictable extreme weather events caused by CC are posing new risks to urban infrastructure. Infrastructure is one of the primary guarantees to maintain the stable operation of the city. Therefore, it is imperative to strengthen the climate resilience of infrastructure to avoid the loss of life and property caused by climate risks. This paper uses CiteSpace to analyze data in the field of climate resilience infrastructure (CRI) over the past 25 years. We find that global CRI research has transitioned through three stages. According to the geographic spatial distribution map drawn by ArcGIS, it can be found that developed countries account for a relatively large number of documents. The research institution is dominated by institutions of higher learning, with limited cooperation between institutions and loose organizational collaboration. CRI is composed of multi-disciplinary collaborative development, from a single discipline of environmental ecology or water resources to a research field integrating engineering, meteorology, sustainability, and energy. Urban resilience and Nature-based solutions are research hotspots. Small Island Developing States are major objects in the future. The research emphasis has shifted from addressing the multiple problems caused by CC to increasing the climate resilience of infrastructure to enhance the resistance of urban systems. Renewable energy and climate models are applied to infrastructure construction. In general, CRI is a effective measure that can help reduce environmental pollution, carbon emissions, and global climate regulation. In addition, we suggest taking cities as pilot projects in the future, increasing CRI projects and providing policy guidance for urban planning and construction.

High-impact papers in the field of anesthesiology: a 10-year cross-sectional study.

PURPOSE: This study was performed to evaluate trends in and provide future direction for anesthesiology education, research, and clinical practice. METHODS: We collected high-impact papers, ranking in the top 10% in the field of anesthesiology and published from 2011 to 2020, by the InCites tool based on the Web of Science Core Collection. We analyzed the trends, locations, distribution of subject categories, research organizations, collaborative networks, and subject terms of these papers. RESULTS: A total of 4,685 high-impact papers were included for analysis. The number of high-impact papers increased from 462 in 2011 to 520 in 2020. The paper with the highest value of category normalized citation impact (115.95) was published in Anesthesia and Analgesia in 2018. High-impact papers were mainly distributed in the subject categories of "Anesthesiology," "Clinical Neurology," "Neurosciences," and "Medicine General Internal." They were primarily cited in "Anesthesiology," "Clinical Neurology," "Neurosciences," "Medicine General Internal," and "Surgery." Most of these high-impact papers came from the USA, UK, Canada, Germany, and Australia. The most productive institutions were the League of European Research Universities, Harvard University, University of Toronto, University of London, University of California System, and University Health Network Toronto. Research collaboration circles have been formed in the USA, UK, and Canada. Subject-term analysis indicated postoperative analgesia, chronic pain, and perioperative complications were high-interest topics, and COVID-19 became a new hot topic in 2020. CONCLUSIONS: The current study provides a historical view of high-impact papers in anesthesiology in the past ten years. High-impact papers were mostly from the USA. Postoperative analgesia, chronic pain, and perioperative complications have been hot topics, and COVID-19 became a new topic in 2020. These findings provide references for education, research, and clinical practice in the field of anesthesiology.

RéSUMé: OBJECTIF: Cette étude a été réalisée pour évaluer les tendances et fournir une orientation future à l'enseignement, la recherche et la pratique clinique en anesthésiologie. MéTHODE: Nous avons colligé des articles à fort impact, classés dans le top 10 % dans le domaine de l'anesthésiologie et publiés de 2011 à 2020, par l'outil InCites basé sur la Web of Science Core Collection. Nous avons analysé les tendances, les emplacements, la répartition des catégories de sujets, les organismes de recherche, les réseaux de collaboration et les termes des sujets de ces articles. RéSULTATS: Au total, 4685 articles à fort impact ont été inclus pour analyse. Le nombre de manuscrits à fort impact est passé de 462 en 2011 à 520 en 2020. L'article ayant la valeur la plus élevée de l'impact normalisé des citations de catégorie (CNCI) (115,95) a été publié dans la revue Anesthesia and Analgesia en 2018. Les articles à fort impact ont été principalement distribués dans les catégories de thèmes « Anesthésiologie ¼, « Neurologie clinique ¼, « Neurosciences ¼ et « Médecine générale interne ¼. Ils ont été principalement cités dans les catégories « Anesthésiologie ¼, « Neurologie clinique ¼, « Neurosciences ¼, « Médecine générale interne ¼ et « Chirurgie ¼. La plupart de ces articles à fort impact provenaient des États-Unis, du Royaume-Uni, du Canada, d'Allemagne et d'Australie. Les établissements les plus productifs étaient la League of European Research Universities, l'Université Harvard, l'Université de Toronto, l'Université de Londres, l'Université de Californie System et le University Health Network de Toronto. Des cercles de collaboration en recherche ont été formés aux États-Unis, au Royaume-Uni et au Canada. L'analyse des termes indiquait que l'analgésie postopératoire, la douleur chronique et les complications périopératoires étaient des sujets suscitant un fort intérêt, et la COVID-19 est devenue un nouveau sujet brûlant en 2020. CONCLUSION: La présente étude propose une vue historique des articles à fort impact en anesthésiologie au cours des dix dernières années. Les manuscrits à fort impact provenaient principalement des États-Unis. L'analgésie postopératoire, la douleur chronique et les complications périopératoires ont été des sujets d'actualité, et la COVID-19 est devenue un nouveau sujet en 2020. Ces résultats fournissent des références pour la formation, la recherche et la pratique clinique dans le domaine de l'anesthésiologie.

Perceptions of the COVID-19 pandemic as demonstrated in drawings of Swedish children aged 4-6 years.

AIM: To explore the perceptions of the COVID-19 pandemic, as expressed in the drawings of Swedish children aged 4-6 years. METHODS: We analysed 91 drawings with associated descriptions submitted to the Swedish Archive of Children's Art from April 2020 to February 2021. RESULTS: The visual and text analysis resulted in three themes. First, illustrating the virus and embodying the danger showed that young children understood the invisible enemy, combining realistic elements of the virus particle with fantasies of the 'virus monster' that needs to be fought off. Second, life has changed for the worse describes the profound effect the pandemic had on young children's lives also in Sweden, with comparatively mild restrictions and no school closures. Third, the virus spreads across the world, affects people and infects their bodies conveys young children's understanding of the pandemic as an existential challenge affecting the whole world. Children displayed surprisingly high levels of health literacy related to COVID-19, including contagion routes and the importance of hand hygiene. CONCLUSION: The drawings of Swedish children aged 4-6 years demonstrated the profound, practical and emotional effects of the pandemic on their lives. Drawings also illuminated young children's level of health literacy regarding COVID-19.

Real-time threat assessment based on hidden Markov models.

An essential factor toward ensuring the security of individuals and critical infrastructures is the timely detection of potentially threatening situations. To this end, especially in the law enforcement context, the availability of effective and efficient threat assessment mechanisms for identifying and eventually preventing crime- and terrorism-related threatening situations is of utmost importance. Toward this direction, this work proposes a hidden Markov model-based threat assessment framework for effectively and efficiently assessing threats in specific situations, such as public events. Specifically, a probabilistic approach is adopted to estimate the threat level of a situation at each point in time. The proposed approach also permits the reflection of the dynamic evolution of a threat over time by considering that the estimation of the threat level at a given time is affected by past observations. This estimation of the dynamic evolution of the threat is very useful, since it can support the decisions by security personnel regarding the taking of precautionary measures in case the threat level seems to adopt an upward trajectory, even before it reaches the highest level. In addition, its probabilistic basis allows for taking into account noisy data. The applicability of the proposed framework is showcased in a use case that focuses on the identification of potential threats in public events on the basis of evidence obtained from the automatic visual analysis of the footage of surveillance cameras.

The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis.

BACKGROUND: This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. METHOD: From Web of Science, we searched for articles published between 2020 and 2022 using the terms online education, medical education and COVID-19, ended up with 2555 eligible papers, and the articles published between 2010 and 2019 using the terms online education, medical education and COVID-19, and we ended up with 4313 eligible papers. RESULTS: Before the COVID-19 outbreak, Medical students and care were the most frequent keywords and the most cited author was BRENT THOMA with 18 times. The United States is the country with the greatest involvement and research impact in the field of online medical education. The most cited journal is ACAD MED with 1326 citations. After the COVID-19 outbreak, a surge in the number of research results in related fields, and ANXIETY and four secondary keywords were identified. In addition, the concentration of authors of these publications in the USA and China is a strong indication that local epidemics and communication technologies have influenced the development of online medical education research. Regarding the centrality of research institutions, the most influential co-author network is Harvard Medical School in the United States; and regarding the centrality of references, the most representative journal to which it belongs is VACCINE. CONCLUSION: This study found that hey information such as keywords, major institutions and authors, and countries differ in the papers before and after the COVID-19 outbreak. The novel coronavirus outbreak had a significant impact on the online education aspect. For non-medical and medical students, the pandemic has led to home isolation, making it difficult to offer face-to-face classes such as laboratory operations. Students have lost urgency and control over the specifics of face-to-face instruction, which has reduced the quality of teaching. Therefore, we should improve our education model according to the actual situation to ensure the quality of teaching while taking into account the physical and psychological health of students.

Assessing Virtual Reality Spaces for Elders Using Image-Based Sentiment Analysis and Stress Level Detection.

Seniors, in order to be able to fight loneliness, need to communicate with other people and be engaged in activities to keep their minds active to increase their social capital. There is an intensified interest in the development of social virtual reality environments, either by commerce or by academia, to address the problem of social isolation of older people. Due to the vulnerability of the social group involved in this field of research, the need for the application of evaluation methods regarding the proposed VR environments becomes even more important. The range of techniques that can be exploited in this field is constantly expanding, with visual sentiment analysis being a characteristic example. In this study, we introduce the use of image-based sentiment analysis and behavioural analysis as a technique to assess a social VR space for elders and present some promising preliminary results.

Data Fusion for Cross-Domain Real-Time Object Detection on the Edge.

We investigate an edge-computing scenario for robot control, where two similar neural networks are running on one computational node. We test the feasibility of using a single object-detection model (YOLOv5) with the benefit of reduced computational resources against the potentially more accurate independent and specialized models. Our results show that using one single convolutional neural network (for object detection and hand-gesture classification) instead of two separate ones can reduce resource usage by almost 50%. For many classes, we observed an increase in accuracy when using the model trained with more labels. For small datasets (a few hundred instances per label), we found that it is advisable to add labels with many instances from another dataset to increase detection accuracy.