Important Keywords Research Articles

Visual analysis of coal mine safety using CiteSpace V

AbstractTo intuitively and systematically grasp the development status and trend of safety evaluation research in China's coal mining operations, we consulted relevant literature in the fields of “coal mine” and “safety evaluation” collected by China Knowledge Network, and deployed CiteSpace V software to summarize and analyze research pertaining to safety evaluation in China's coal mines from three aspects: research institutions, authors, and hot keywords. With respect to research institutions, the results show that although many researchers have conducted in‐depth evaluations of coal mine safety, most of these studies were independent, and two‐way information exchange and cooperation between research institutions remains scarce. With respect to authors, most cooperation between authors has been limited to team members, and relatively stable research teams have been formed. However, most of these research teams are independent. With respect to hot keywords, the trends of coal mine safety evaluation studies exhibit continuous change, with an overall increase in richness. According to the frequency burst times of important keywords, “entropy weight method” and “mining with pressure” are expected to remain important keywords in future evaluations of coal mine safety in China.

Digital Transformation of Indian Healthcare: A Bibliometric Analysis Based on Telemedicine

India's healthcare system faces challenges due to a shortage of hospitals, doctors, and facilities. The integration of telemedicine into healthcare makes it possible to spread medical expertise to remote areas and improve access to health services. Thus, this study's objective is to ascertain and assess the scientific literature on telemedicine in the Indian context by conducting a bibliometric analysis to identify the publication's trends, important and trending keywords, authors, sources, and most cited publications. Another goal is to determine the conceptual, intellectual, and social structure of the relevant literature. This research conducts a bibliometric analysis of telemedicine literature in India using Scopus data from 2013 to 2022. Among the findings, Kumar A. stands out as the most prolific author, and the “All-India Institute of Medical Science” is the leading institution. The “Journal of Medical Systems” is the most influential source. Trending keywords in 2022 include psychiatry, technology, and attitude. Collaboratively, India and the USA published the highest number of articles. Our study provides a detailed overview of telemedicine advancements in India's healthcare sector, offering valuable insights for academics, policymakers, and practitioners. The findings validate telemedicine's progress in India's healthcare and suggest future studies expand to other countries.

Knowledge graph analysis of research on nurses' psychological resilience.

In recent years, a surge in literature on psychological nurse resilience, largely driven by the COVID-19 pandemic, has prompted the need for a comprehensive understanding of the current state and emerging trends through reliable methodologies. The purpose of this study was to analyzes the research on nurses' psychological resilience through bibliometrics to understand the current situation, foundation, and hotspot of this research field. The Web of Science core collection database was used as the search source, and CiteSpace analysis software was employed to conduct bibliometric analysis on authors, countries, institutions, keywords, and references of nurse psychological resilience-related literature published from the establishment of the database to July 11, 2023. A total of 1060 articles were included in the final analysis. The study of nurses' psychological resilience had been highly popular and had formed a new and important research basis in recent years. China and the United States led in the number of publications and centrality respectively, with Monash Univ and Curtin Univ as top institutions in the number of publications and centrality respectively. The authors with the highest number of publications and the most frequently cited were Rees and Connor Km respectively. The most frequently cited article was Factors Associated with Mental Health Outcomes Among Health Care Workers Exposed to Coronavirus Disease 2019 published by Lai, JB, etc. Important key keywords included mental health, resilience, stress, health, outbreak, acute respiratory syndrome, etc. The research topics in this field mainly focused on 4 aspects, including nurses' mental health, post-traumatic stress disorder, job burnout and job satisfaction, and intervention research on psychological resilience. The results of bibliometric analysis provide direct support for future scholars to explore and determine the research direction, hot spots, and find authoritative authors and institutions. At the moment, nurses' psychological resilience research has established a new foundation, primarily focusing on COVID-19-related topics. Given the potential prolonged coexistence of COVID-19 and other diseases, the main research focus remains innovating and validating effective psychological resilience intervention strategies for nurses' overall well-being.

Adjusted TextRank for keyword extraction in petrochemical project correspondence documents

A large petrochemical construction project is typically executed by multiple parties, all bound by contract agreement. During the execution phase, issues and problems may arise because the work details are not clearly specified in the contractual agreement. These issues are formally communicated and documented through written correspondence letters. By identifying important keywords within these formal letters, a comprehensive narrative of the project, including its associated issues, can be identified and analyzed. In this research, we introduce an adjusted TextRank algorithm that integrates external features from the Indonesian FastText language model and term frequency-inverse document frequency (TF-IDF) scores to identify important keywords within a dataset of correspondence letters of petrochemical projects. This enhancement involves refining phrase detection, semantic relationship estimation between words, and part-of-speech (POS) identification for words or phrases. Our results show that the proposed adjustments result in improved evaluation scores compared to the baseline standard TextRank and standard TF-IDF, respectively by 24.1% and 25% in terms of F-1 scores.

STEAM Education with Gamification: A Bibliometric Analysis

STEAM education with gamification, a method that integrates game elements into the learning process, has proven to be a powerful tool. It not only encompasses various forms of gamified education but also has the potential to captivate and inspire students, breathing life into the course material. This study aims to present a comprehensive summary of the research carried out in the domain of STEAM education, with a particular emphasis on gamification. The focus will be on analyzing studies published in the past seven years. A meticulous bibliometric analysis was conducted to investigate the patterns in the published literature on STEAM education with gamification from 2017 to 2023. The relevant documents were retrieved by using keywords related to steam and gamification in the title, abstract, and keywords of the documents. Thus, 34 documents were acquired from the Scopus database for bibliometric analysis. The review analyzes the rate of publication growth, identifies the papers with the highest number of citations, determines the primary sources of these articles, evaluates the productivity of authors, examines the leading countries contributing to the field, and identifies the prominent subject areas within the research domain. Thailand has the highest output level in terms of publications and citations, as inferred from the results of our analysis. The Ceur Workshop Proceedings are widely acknowledged as the foremost scholarly resource in their subject. We have identified the most important keywords related to gamified STEAM education by conducting keyword analysis. Factorial Analysis provides a visual summary of the complex relationships between various concepts related to educational technology.

Mapping the field of sensory marketing: a comprehensive bibliometric analysis

Purpose The objective of this review article is to meticulously examine and critically evaluate the present body of literature on sensory marketing. The study aims to explore this domain’s latest trends and prospects using diverse metrics such as annual publication trends, authorship patterns, most cited publications, sources analysis, co-citation patterns, keyword analysis and thematic analysis. Design/methodology/approach In the study, bibliometric analysis is used to assess the different advancements in the field. To perform performance analysis, science mapping and network analysis, VOSviewer, Bibloshiny and MS Excel are used. Additionally, the Scopus database is used to extract literature related to the subject area. Findings The most cited author of the domain is Biswas D, while the most prolific source of the domain is Plos One, additionally “An integrative review of sensory marketing: Engaging the senses to affect perception, judgment, and behavior” is stated as the most cited publication in the domain. Moreover, experiential marketing is reported as the most important keyword of the domain. Additionally, other findings reported are stated in the main body of the research study. Practical implications Practical implications of the study extended to marketing practitioners, future researchers and various stakeholders interested in the subject area. Originality/value What stands this research paper apart from other studies is the comprehensive approach to the evaluation of literature using VOSviewer, Bibloshiny and MS Excel and in-depth evaluation of the domain using performance analysis, science mapping, network analysis and thematic analysis.

Marxism and Class-Based Analysis in the Body of Knowledge: The Scientometric Analysis and Research Agenda

This study provides a comprehensive scientometric analysis to examine the incorporation and impact of Marxist theory and class-based analysis within the broader academic knowledge base. By utilizing Scientometric data from renowned scientific databases covering the last seventy years, we can pinpoint important patterns, notable authors, essential publications, subject groups, and relevant keywords relating to Marxism and class analysis. These findings show increasing interest in the topic, especially after the 2000s. This analysis emphasizes the importance of theoretical study and the working class as topics that often appear in recent research. Additionally, we analyze the distribution of research output and country collaborations, observing substantial clustering in the Americas and the United Kingdom. Despite increasing interest in this topic, our analysis reveals gaps in existing research, namely in the practical implementation and integration of different disciplines. Based on these observations, we suggest a study plan that prioritizes the need for more detailed empirical investigation, examination of social class dynamics in non-Western settings, and the incorporation of Marxist analysis into current topics such as globalization and the working class. This agenda aims to increase the significance and practicality of class-based analysis in understanding contemporary socio-economic phenomena.

Bibliometrics Analysis and Thematic Mapping of Global Literature on Human Papillomavirus (HPV)

Background: Since human papillomavirus (HPV) infection is known as one of the main causes of cervical cancer, medical researchers pay significant attention to this issue2. The current study applies the bibliometric method to identify the trends and advancements of research on HPV worldwide. Methods: This descriptive study was conducted using a bibliometric approach. All HPV-related scientific publications in the Web of Science database until 2022, August 30 were included in the statistical population. The Bibliometrix package of the R programming language was used to analyze the data. Results: In the field of HPV, a total of 45,657 scientific publications were retrieved, the majority of which were articles. The countries with the highest publications were the USA (45,954 document), China (9977), and UK (6652). The words “Infection,” “Cancer,” and “Women” are the most important keywords of scientific publications on HPV. “Cancer,” “infection,” and “women” are the more developed and essential in HPV subject area. “Protein,” “gene-expression,” and “human-keratinocytes” are in this quadrant. “DNA,” “squamous-cell carcinoma,” and “head” keywords are basic themes in the HPV subject area. “Human-papillomavirus vaccination,” “knowledge,” and “attitude” are in this quadrant. Conclusion: The study suggests the need to enhance the focus on global and public health aspects within HPV research to encourage greater participation from various countries in international research endeavors.

Evolution of blockchain accounting literature from the perspective of CiteSpace (2013–2023)

Against the backdrop of the Industrial Revolution 4.0, the advantages of blockchain technology in traceability, transparency, safety improvement, and efficiency improvement have made it possible to reduce the work of accounting personnel by 50 %, thus saving billions of dollars for global companies by combining this technology with accounting. However, the blockchain technology associated with accounting is in the experimental stage and has several problems to be solved including limited data processing capacity, information confidentiality, and regulatory difficulties. This innovation and progress in science and technology has provided more abundant, efficient, and professional technical support for the research of blockchain accounting documents. Among these advances, CiteSpace software has promoted the development of blockchain and accounting in the direction of visualization, comprehensiveness, security, and relevance. In this study, we used the knowledge map drawn by CiteSpace to search the core Blockchain Accounting database from 2013 to 2023 on the Web of Science (WoS). We obtained 1414 documents measured according to co-citation analysis, log-likelihood ratio (LLR) network clustering, co-occurrence keywords, and emergent time zone diagram method. We analyzed and summarized the important documents, research keywords, key research fields, and knowledge evolution related to "blockchain accounting" by network, literature integration, and popular research topics. We found that adopting blockchain technology in accounting information systems is expected to improve recordkeeping and reporting. Blockchain, as an innovative technology, provides a tamper-proof, traceable, and shareable platform for accounting information by using a distributed ledger system. By implementing blockchain, artificial intelligence can improve safety, transparency, and accuracy, and also may completely change the way we manage financial records. With its ability to improve overall efficiency and reduce errors, blockchain technology may change our familiar accounting methods. In addition, blockchain technology, intelligent contract, artificial intelligence, the Internet, information systems, and supply chain are the most important keywords, while blockchain technology, intelligent contract, and artificial intelligence are important components of blockchain accounting knowledge system. This research provided an important opportunity to advance the understanding of the crucial contribution of blockchain to the accounting field.

A Bibliometric Overview of Fund Managers' Bias: Research Contributions and Influence

The study aims to comprehensively examine the behavioral biases of fund managers by conducting a bibliometric analysis of research papers published during the years 2011–2022 from the Scopus database based on the keywords searched for behavioral biases of fund managers. One hundred and thirty-five articles have been chosen after careful review. This article explains the most cited articles, top authors, leading countries, prolific journals, and important keywords. This study has identified 10 different types of behavioral biases which are summarized in this article. In this review article, we only considered the journal articles excluding conference publications, editorials, and book chapters. This article is based on the existing literature on behavioral biases in investment decision-making processes. This study will be helpful for researchers and academicians to understand the impact of behavioral biases on investment decisions and to reduce it. Finally, this research will provide a roadmap for future research.

Suicidal Ideation Detection and Influential Keyword Extraction from Twitter using Deep Learning (SID)

INTRODUCTION: This paper focuses on building a text analytics-based solution to help the suicide prevention communities to detect suicidal signals from text data collected from online platform and take action to prevent the tragedy. OBJECTIVES: The objective of the paper is to build a suicide ideation detection (SID) model that can classify text as suicidal or non-suicidal and a keyword extractor to extracted influential keywords that are possible suicide risk factors from the suicidal text. METHODS: This paper proposed an attention-based Bi-LSTM model. An attention layer can assist the deep learning model to capture influential keywords of the model classifying decisions and hence reflects the important keywords from text which highly related to suicide risk factors or reason of suicide ideation that can be extracted from text. RESULTS: Bi-LSTM with Word2Vec embedding have the highest F1-score of 0.95. Yet, attention-based Bi-LSTM with word2vec embedding that has 0.94 F1-score can produce better accuracy when dealing with new and unseen data as it has a good fit learning curve. CONCLUSION: The absence of a systematic approach to validate and examine the keyword extracted by the attention mechanism and RAKE algorithm is a gap that needed to be resolved. The future work of this paper can focus on both systematic and standard approach for validating the accuracy of the keywords.

Understanding Tibetan Exile: a review of literature and bibliometric analysis (1960-2021)

ABSTRACT This research aims to use VOSviewer to undertake bibliometric analysis to map the primary areas of Tibetan Exile literature to create a taxonomy of topic subareas and a research plan on this issue. Two hundred eighty-one papers published between 1960 and 2021, which were extracted from the Scopus database, have been examined to evaluate their interconnection, clusters, and citations to build a text-based map. The study highlights the most cited document, essential journals publishing on the issue, important keywords, prominent authors, and discussions on Tibetan refugees; also classifying the subthemes and the leading author’s name. The findings will guide researchers interested in contemporary Tibetan studies. This categorization table of subthemes explored in the last 61 years comprises the following subjects: identity & human rights, immigration, government, media, health, international politics, Dalai Lama, nationhood, preservation of culture, resettlement, and a new generation of Tibetans.

Circular economy in agro food supply chain: Bibliometric and network analysis

AbstractRecently, there has been growing interest among scholars and industry professionals in understanding how agro‐food supply chains can shift from a linear to a circular model. This involves managing waste, recovering resources, and adopting sustainable practices. However, knowledge on circular economies in agro‐food supply chains is scattered, with limited comprehensive studies available. To address this gap, our study conducts a bibliometric and network analysis of 364 documents from the Scopus database. We identify influential authors, contributing countries and journals, and most cited documents. Additionally, we use network analysis to visualize connections between authors and keywords, providing insights into emerging trends and critical research areas. We also uncover important keywords and themes, laying a foundation for future research directions. Overall, our study offers a comprehensive overview, serving as a basis for further exploration in this field.

Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasakidisease: A bibliometric analysis (1997-2023).

Kawasaki disease (KD) is an autoimmune disease with cardiovascular disease as its main complication, mainly affecting children under 5 years old. KD treatment has made tremendous progress in recent years, but intravenous immunoglobulin (IVIG) resistance remains a major dilemma. Bibliometric analysis had not been used previously to summarize and analyze publications related to IVIG resistance in KD. This study aimed to provide an overview of the knowledge framework and research hotspots in this field through bibliometrics, and provide references for future basic and clinical research. Through bibliometric analysis of relevant literature published on the Web of Science Core Collection (WoSCC) database between 1997 and 2023, we investigated the cooccurrence and collaboration relationships among countries, institutions, journals, and authors and summarized key research topics and hotspots. Following screening, a total of 364 publications were downloaded, comprising 328 articles and 36 reviews. The number of articles on IVIG resistance increased year on year and the top three most productive countries were China, Japan, and the United States. Frontiers in Pediatrics had the most published articles, and theJournal of Pediatricshad the most citations. IVIG resistance had been studied by 1889 authors, of whom Kuo Ho Chang had published the most papers. Research in the field was focused on risk factors, therapy (atorvastatin, tumor necrosis factor-alpha inhibitors), pathogenesis (gene expression), and similar diseases (multisystem inflammatory syndrome in children, MIS-C). "Treatment," "risk factor," and "prediction" were important keywords, providing a valuable reference for scholars studying this field. We suggest that, in the future, more active international collaborations are carried out to study the pathogenesis of IVIG insensitivity, using high-throughput sequencing technology. We also recommend that machine learning techniques are applied to explore the predictive variables of IVIG resistance.

Beyond Extractive Methods – Navigating the landscape of Abstractive Summarization Methods

Today, millions of data are generated every hour, which highlights the need for summarizing all this data accurately and efficiently. Doing such a task manually is tedious. This welcomes the need for automatic summarizing techniques. Generating precise and concise summaries of long text data is a necessity. Automatic summarization includes two primary techniques- Extractive and Abstractive Summarization. Extractive Summarization uses important sentences and keywords to construct the summary whereas abstractive summarization understands the text and generates a summary. The encoder-decoder architecture is generally used for abstractive summarization. This study briefs about various transformer architectures, including T5, BART, and Pegasus. Furthermore, a comparative analysis of these models on the same data is presented and the result of the same is compared on scores with the manually generated summaries- ROUGE1, ROUGE2, and ROUGEL. The purpose of this study is to understand the advancement of abstractive text summarization models as well as to understand the strategies and their usefulness.

언어네트워크분석에 따른 가톨릭교육의 국내 연구동향

[Objective] In this study, we conducted an analysis of Catholic education research trends by utilizing multiple academic paper search engines. Through keyword searches such as ‘Catholic,’ ‘education,’ ‘Catholic education,’ and ‘faith education,’ we identified important keywords, centrality, and performed CONCOR analysis among the papers retrieved. This enabled us to gain insights into the research trends in Catholic education. Departing from conventional literature reviews, it aimed to explore the evolution of research in Catholic education, emphasizing the interrelationships among keywords through an objective and scientific method of keyword network analysis.
 [Contents] The analysis revealed that the centrality of major keywords in academic journal articles related to Catholic education included terms such as ‘Catholic Church,’ ‘education,’ ‘catechesis,’ ‘faith,’ and ‘Christ.’ In the TF-IDF analysis indicating keyword importance, terms like ‘religious education,’ ‘youth,’ ‘Korea,’ ‘theology,’ and ‘character’ emerged at the forefront. Finally, the CONCOR analysis related to Catholic education research defined groups such as ‘history and direction of catechesis,’ ‘ethical education of educators and schools,’ ‘holistic education in Catholic schools,’ and ‘practical faith education.’ [Conclusion] Through this study, analyzing the trends in Catholic education is expected to broaden the comprehensive understanding of Catholic education from an integrated perspective. Additionally, it offers suggestions for future directions in Catholic education research through this comprehensive analysis.

The extended post spinal surgery syndrome (EPSS)

Pain can occur after any spinal surgery. Despite this, there are other many signs and symptoms of neurological deficits that can occur in patients with varying severity. Our aim is to find some of the main neurological deficits that can occur after any spinal surgery. We searched the literature based on some of the important keywords like neurological deficits after spine surgery, foot drops, cauda equina syndrome, epidural hematoma, and nerve and dural injury. Based on this we analyzed the most important and widely read articles. The problems associated with spine surgery have been published in the literature but are much more than the failed back surgery syndrome and cause more discomfort to patients with varying degrees of neurological deficits. We have coined a new term “Extended Post-spinal Surgery Syndrome (EPSS)” for these conditions. We propose this to include the other complications after lumbar surgery including nerve injury, dural injury, cauda equina syndrome and epidural haematoma.

An Extractive-and-Abstractive Framework for Source Code Summarization

(Source) Code summarization aims to automatically generate summaries/comments for given code snippets in the form of natural language. Such summaries play a key role in helping developers understand and maintain source code. Existing code summarization techniques can be categorized into extractive methods and abstractive methods . The extractive methods extract a subset of important statements and keywords from the code snippet using retrieval techniques and generate a summary that preserves factual details in important statements and keywords. However, such a subset may miss identifier or entity naming, and consequently, the naturalness of the generated summary is usually poor. The abstractive methods can generate human-written-like summaries leveraging encoder-decoder models. However, the generated summaries often miss important factual details. To generate human-written-like summaries with preserved factual details, we propose a novel extractive-and-abstractive framework. The extractive module in the framework performs the task of extractive code summarization, which takes in the code snippet and predicts important statements containing key factual details. The abstractive module in the framework performs the task of abstractive code summarization, which takes in the code snippet and important statements in parallel and generates a succinct and human-written-like natural language summary. We evaluate the effectiveness of our technique, called EACS, by conducting extensive experiments on three datasets involving six programming languages. Experimental results show that EACS significantly outperforms state-of-the-art techniques for all three widely used metrics, including BLEU, METEOR, and ROUGH-L. In addition, the human evaluation demonstrates that the summaries generated by EACS have higher naturalness and informativeness and are more relevant to given code snippets.

Vernonia amygdalina: a comprehensive review of the nutritional makeup, traditional medicinal use, and pharmacology of isolated phytochemicals and compounds

Vernonia amygdalina is a perennial shrub that belongs to the family Asteraceae. The herb is an indigenous African plant that grows in most parts of sub-Saharan Africa. It is probably the most used medicinal plant in the genus Vernonia. Previous studies on the traditional medicinal value, nutritional composition, classes of phytochemical or compound isolation, and evaluation of their pharmacology activity are numerous. This provokes us to review and provide up-to-date evidence-based information on the study plant. A systematic online search using the databases of Google Scholar, PubMed, Science Direct, Wiley, Elsevier and Sci-Hub was carefully applied, using some important key words to get appropriate information. The leafy part of Vernonia amygdalina contributes greatly to the nutritional requirements for human health and to food security since it contains enough concentrations of proximate composition, minerals, and vitamins. The plant parts are used in traditional medicine for many human and animal healthcare purposes, including diarrhea, diabetes, wound healing, tonsillitis, evil eye, retained placenta, headache, eye disease, intestinal parasite, bloating, hepatitis, toothache, anthrax, malaria, urine retention, gastritis, stomach disorders, and snake bites. The chemical analysis revealed the presence of flavonoids, alkaloids, saponins, tannins, triterpenoids, sesquiterpene lactones, steroids, cardiac glycosides, oxalates, phytates, cyanogenic glycosides, and phenols. Additionally, various compounds such as vernolide, luteolin, vernodalol, vernoamyoside A, vernoamyoside B, isorhamnetin, glucuronolactone, and 1-Heneicosenol O-β-D-glucopyranoside were isolated. Some of the isolated compounds pharmacological activity was evaluated against some diseases and showed antioxidant, antibacterial, antifungal, antihelmintic, anticancer, and anti-inflammatory potencies. Thus, the review provides comprehensive information about ethnomedicinal value, nutritional composition, isolated classes of phytochemicals, and compounds, including an evaluation of the pharmacological activity of the isolated compounds of Vernonia amygdalina. A review with this much information could be extremely valuable for future research on developing innovative nutraceutical products.

HISTORY OF IDEALS OF HUMAN BEAUTY

This article tells about the role of a teacher in leading teenagers to self-consciousness. Some interesting and important key-words for teenagers are given in the article. The tasks and intellectual activities for teenagers are described here full.