Global Advances and Frontiers of Phytochemicals in Tumor Research: A Bibliometric Study (2010-2023)

Objectives: Analysis of advances, hotspots and frontiers of tumor-related phytochemicals by scientifc bibliometric methods during 2012-2023. Background: Natural phytochemicals are abundantly found in nature and have a wide range of biological activities. Phytochem-icals have been shown to provide both curative and preventive benefts on many chronic diseases such as cancers. Tumor research on phyto-chemicals is one of the felds with the greatest potential for expansion in the world. However, there is still much to explore about the action mechanism of phytochemicals, the efcacy and safety of application in vivo, and the value of clinical practice. Methods: Atotal of 6523 arti-cles on tumor-related phytochemicals were identifed from the Web of Science Core Collection (WOSCC) database for research on tumor-re-lated phytochemicals. The bibliometric analysis was carried out using CiteSpace and the R package “Bibliometrix”. Results: The analysis includes 6523 publications from 144 nations or regions, with China leading the way. The number of annual publications increased rapidly from 2012 to 2022 and reached a maximum in 2022. China published the most articles, followed by India and the United States. There is a wide range of collaborations between countries, with Saudi Arabia and Egypt being the closest partners. LI Y has produced the most research outputs, yet Prof. Liu RH has received the most local citations. Although MOLECULES has the most articles, FOOD CHEMISTRY is the journal with the highest H-index. The main topics include phytochemical mechanisms and clinical applications in carcinogenesis and devel-opment. “Secondary metabolite”, “green synthesis”, “functional food”, and “degradation” all exhibit signifcant citation burstness between 2019-2023. Conclusions: This study is the frst to apply bibliometrics to examine the development of phytochemicals in oncology research over the period 2010-2023, which gives researchers a brief overview of advances, hotspots, and potential future trends in the feld

Structural analysis and evolutionary exploration based on the research topic network of a field: a case in high-frequency trading

This study aims to systematically analyze the distribution dynamics of research topics and uncover the development state of the research in the specific field, which will provide a practical reference for developing professional subject knowledge services in the era of big data. The research topic network is constructed and analyzed using methods and tools of scientometrics. Basic statistics on network characteristics are performed to reveal the research status. Community detection, node ordering, and other steps are conducted to generate the evolutionary alluvial diagram. Then, relevant results are analyzed to explore the knowledge structure of the specific field and evolutionary context of research topics. Visualization analysis on the network structure of the latest period is executed to distinguish related concepts and predict the research trends. Taking high-frequency trading (HFT) as a case, this study achieves diversified scientometrics analysis of the research topic network and multi-dimensional evolution exploration of the relevant research topics in the specific field, which obtaining some knowledge insights. (1) Six major topics in HFT: liquidity & market microstructure, market efficiency, financial market, incomplete market, cointegration & price discovery, and event study. (2) The research focus about markets gradually transferred from international to emerging, meanwhile continuous attention to volatility/risk related issues. (3) The emphasis will change from theory to practice, technologies (big data, etc.) and theories (behavioral finance, etc.) will have more interaction with HFT. An effective research idea is proposed to reveal the knowledge structure of field and analyze the evolutionary context of research topics, which demonstrating the knowledge insights

Bibliometric Analysis on Recent Advances and Development of Microcontroller Application in The Postharvest System

Postharvest is a vital stage in agricultural production which is prone to causing losses due to improper implementation. Using a microcontroller that allows automation and increased precision in the postharvest process will likely reduce costs and potential losses. This research conducted a bibliometric study on applying microcontrollers in postharvest systems in Scopus-indexed publications from 2003 to 2022. The aim was to reveal microcontroller developments, evaluate current research topics, and discuss future challenges facing microcontroller applications in postharvest systems. First, this paper presents a bibliometric review of the role of microcontrollers in postharvest. Second, co-citation, coupling, and cluster analysis methods were used to analyze collaboration networks, and VOSviewer was used to visualize these networks. Third, Biblioshiny was used to analyze thematic trends of microcontroller applications. Finally, the paper discusses the challenges of using microcontrollers and provides suggestions for overcoming them. The results show that institutions from China and Italy lead research production in this field, with globally popular studies focusing primarily on fruit, digital storage, moisture determination, and cost. In addition, the thematic evolution of keywords indicating response time, cost, and design reliability issues have become basic and emerging topics in microcontroller application research for postharvest systems in recent years.Postharvest is a vital stage in agricultural production which is prone to causing losses due to improper implementation. Using a microcontroller that allows automation and increased precision in the postharvest process will likely reduce costs and potential losses. This research conducted a bibliometric study on applying microcontrollers in postharvest systems in Scopus-indexed publications from 2003 to 2022. The aim was to reveal microcontroller developments, evaluate current research topics, and discuss future challenges facing microcontroller applications in postharvest systems. First, this paper presents a bibliometric review of the role of microcontrollers in postharvest. Second, co-citation, coupling, and cluster analysis methods were used to analyze collaboration networks, and VOSviewer was used to visualize these networks. Third, Biblioshiny was used to analyze thematic trends of microcontroller applications. Finally, the paper discusses the challenges of using microcontrollers and provides suggestions for overcoming them. The results show that institutions from China and Italy lead research production in this field, with globally popular studies focusing primarily on fruit, digital storage, moisture determination, and cost. In addition, the thematic evolution of keywords indicating response time, cost, and design reliability issues have become basic and emerging topics in microcontroller application research for postharvest systems in recent years

Visual analytics of location-based social networks for decision support

Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds

Mapping the knowledge domains of emerging advanced technologies in the management of prefabricated construction

Emerging advanced technologies (EAT) have been regarded as significant technological innovations which can greatly improve the transforming construction industry. Given that research on EAT related to the management of prefabricated construction (MPC) has not yet been conducted, various researchers require a state-of-the-art summary of EAT research and implementation in the MPC field. The purpose of this paper is to provide a systematic literature review by analysing the selected 526 related publications in peer-reviewed leading journals during 2009–2020. Through a thorough review of selected papers from the state-of-the-art academic journals in the construction industry, EAT is recognised as the key area affecting the development of the MPC discipline. This study has value in offering original insights to summarise the advanced status quo of this field, helping subsequent researchers gain an in-depth understanding of the underlying structure of this field and allowing them to continue future research directions

Examining the Trend of Literature on Classification Modelling: A Bibliometric Approach

This paper analyses and reports various types of published works related to classification or discriminant modelling. This paper adopted a bibliometric analysis based on the data obtained from the Scopus online database on 27th July 2019. Based on the ‘keywords’ search results, it yielded 2775 valid documents for further analysis. For data visualisation purposes, we employed VOSviewer. This paper reports the results using standard bibliometric indicators, particularly on the growth rate of publications, research productivity, analysis of the authors and citations. The outcomes revealed that there is an increased growth rate of classification literature over the years since 1968. A total of 2473 (89.12%) documents were from journals (n=1439; 51.86%) and conference proceedings (n=1034; 37.26%) contributed as the top publications in this classification topic. Meanwhile, 2578 (92.9%) documents are multi-authored with an average collaboration index of 3.34 authors per article. However, this classification research field found that the famous numbers of authors’ collaboration in a document are two (with n=758; 27.32%), three (n=752; 27.10%) and four (n=560; 20.18%) respectively. An analysis by country, China with 1146 (41.30%) published documents thus is ranked first in productivity. With respect to the frequency of citations, Bauer and Kohavi (1999)’s article emerged as the most cited article through 1414 total citations with an average of 70.7 citations per year. Overall, the increasing number of works on classification topics indicates a growing awareness of its importance and specific requirements in this research field