Occurrence Of Keywords Research Articles

“Digital Humanities-2023” in Graz live: ideas, methods and pumpkin seed oil

The article presents the observations of a live participant of the “Digital Humanities -2023”, held in Graz, Austria in the summer of 2023. The “Digital Humanities” Congress is held annually by the Alliance of Digital Humanities Organizations (ADHO). Digital Humanities (DH) are at the intersection of computing or digital technologies and the disciplines of the humanities. It involves the development and use of digital resources and methods in the humanities, as well as the analysis of their application. DH scholarship means collaborative, transdisciplinary, and computationally engaged research, teaching, and publishing. The annual ADHO Conference on Digital Humanities is the central and largest event of the international Digital Humanities community and brings together scholars from around the world, providing them with a unique opportunity to exchange ideas and research results, and to promote future collaboration. Particular attention is paid to historical issues presented at the congress, both in workshops and at section meetings and poster presentations. Statistical observations are presented, and a frequency analysis of the occurrence of keywords is carried out. The main conclusion of the study is the observed trend towards an increase in historical reports, posters and workshops. A noticeable increase in interest in historical sources, historical databases and geographic information systems, and virtual reconstruction of the past using a wide range of digital humanities research tools is noticeable (in comparison with the programs and publications of past Digital Humanities congresses). The 2023 conference theme, “Collaboration as an Opportunity,” is about the transdisciplinary and transnational collaboration, showing how increased cross-national collaboration—across continents and geopolitical locations—can transform regional knowledge hubs into international networks of excellence in research for the benefit of the global digital humanities community. Historians have firmly taken their place in contemporary digital humanities research.

Analysis of Department of Home Economics Education Curriculum of College of Education through Keyword Network Analysis

본 연구는 우리나라 사범대학의 가정교육과 교육과정과 382개의 교수요목에 포함된 내용을 키워드 네트워크 방식을 통해 특성을 파악하고 세부 분야별 상관성을 분석하였다. 총 11개 대학의 가정교육과 교육과정과 382개의 교수요목을 분석하기 위해 Krword 프로그램을 활용하여 키워드 출현 빈도를 분석하고, 키워드 간의 연결 정도와 다양한 중심성 척도를 시각화하여 분석한 결과는 다음과 같다. 첫째, 전체 교수요목을 분석한 결과는 가족, 중등학교, 의복, 식품, 소비자, 디자인 등 다양한 분야를 나타내는 키워드들이 골고루 나타났으며 ‘방법’, ‘실제’, ‘변화’, ‘원리’ 등과 같은 교수법 및 수업 방식에 관한 키워드가 높은 연결 정도와 중심성 지표를 나타냈다. 둘째, 세부 분야별 분석에서 영역별 핵심키워드가 나타났으며, 키워드들의 주제는 학문기반의 핵심키워드를 반영하고 있었다. 본 연구는 가정교육과 교육과정에서 미래지향적이고 융합적인 교육내용을 다룰 수 있는 교육과정으로 전환하는 것에 기여하는데 의의가 있다.The purpose of this study was to identify the characteristics of the contents included in the curriculum and 382 syllabi of the department of home economics education of College of Education in Korea and analyze the correlation by detailed area through the keyword network method. In order to analyze the home economics education curriculum and 382 syllabuses of a total of 11 universities, the frequency of keyword occurrence was analyzed using the KrKwic program, also the degree of connection between keywords and various centrality scales were calculated and visualized. The results of this study were as follows. First, as a result of analyzing the entire syllabi, keywords representing various fields such as family, secondary school, clothing, food, consumer, and design appeared evenly, and keywords related to teaching methods such as ‘method’, ‘practice’, ‘change’, and ‘principle’ were appeared. Those keywords showed high degree of connection and centrality. Second, in the detailed sectoral analysis, core keywords for each area appeared, and each subject were found to reflect the core keywords of the academic base. This study contributes to the conversion of curriculum of the department of home economics education to future-oriented and convergent curriculum.

Systematic bibliometric and visualized analysis of research hotspots and trends in attention-deficit hyperactivity disorder neuroimaging.

This study focused on the research hotspots and development trends of the neuroimaging of attention deficit hyperactivity disorder (ADHD) in the past thirty years. The Web of Science database was searched for articles about ADHD neuroimaging from January 1992 to September 2022. CiteSpace was used to analyze the co-occurrence of keywords in literature, partnerships between authors, institutions, and countries, the sudden occurrence of keywords, clustering of keywords over time, and analysis of references, cited authors, and cited journals. 2,621 articles were included. More and more articles have been published every year in the last years. These articles mainly come from 435 institutions and 65 countries/regions led by the United States. King's College London had the highest number of publications. The study identified 634 authors, among which Buitelaar, J. K. published the largest number of articles and Castellanos, F. X. was co-cited most often. The most productive and cited journal was Biological psychiatry. In recent years, burst keywords were resting-state fMRI, machine learning, functional connectivity, and networks. And a timeline chart of the cluster of keywords showed that "children" had the longest time span. Increased attention has been paid to ADHD neuroimaging. This work might assist researchers to identify new insight on potential collaborators and cooperative institutions, hot topics, and research directions.

Feature engineering from medical notes: A case study of dementia detection

Background and objectivesMedical notes are narratives that describe the health of the patient in free text format. These notes can be more informative than structured data such as the history of medications or disease conditions. They are routinely collected and can be used to evaluate the patient's risk for developing chronic diseases such as dementia. This study investigates different methodologies for transforming routine care notes into dementia risk classifiers and evaluates the generalizability of these classifiers to new patients and new health care institutions. MethodsThe notes collected over the relevant history of the patient are lengthy. In this study, TF-ICF is used to select keywords with the highest discriminative ability between at risk dementia patients and healthy controls. The medical notes are then summarized in the form of occurrences of the selected keywords. Two different encodings of the summary are compared. The first encoding consists of the average of the vector embedding of each keyword occurrence as produced by the BERT or Clinical BERT pre-trained language models. The second encoding aggregates the keywords according to UMLS concepts and uses each concept as an exposure variable. For both encodings, misspellings of the selected keywords are also considered in an effort to improve the predictive performance of the classifiers. A neural network is developed over the first encoding and a gradient boosted trees model is applied to the second encoding. Patients from a single health care institution are used to develop all the classifiers which are then evaluated on held-out patients from the same health care institution as well as test patients from two other health care institutions. ResultsThe results indicate that it is possible to identify patients at risk for dementia one year ahead of the onset of the disease using medical notes with an AUC of 75% when a gradient boosted trees model is used in conjunction with exposure variables derived from UMLS concepts. However, this performance is not maintained with an embedded feature space and when the classifier is applied to patients from other health care institutions. Moreover, an analysis of the top predictors of the gradient boosted trees model indicates that different features inform the classification depending on whether or not spelling variants of the keywords are included. ConclusionThe present study demonstrates that medical notes can enable risk prediction models for complex chronic diseases such as dementia. However, additional research efforts are needed to improve the generalizability of these models. These efforts should take into consideration the length and localization of the medical notes; the availability of sufficient training data for each disease condition; and the variabilities resulting from different feature engineering techniques.

Global research on digital divide during the past two decades: a bibliometric study of Web of Science indexed literature

Purpose This study aims to examine the research output on digital divide from 2001 to 2020 and measure the qualitative and quantitative growth of literature during the stated period by using required bibliometric measures for identifying the types of documents, yearly growth, country productivity, citation network of collaborative countries, authorship pattern, top authors, cocitation networks and assorted facets. Design/methodology/approach Web of Science database was used to retrieve the required data for this study. Keeping the objectives of this study in mind, the keyword “Digital Divide” was used as the search term. Moreover, the retrieved data were limited from the year 2001 to 2020 for two decades. A total of 5,518 publications were filtered and focused for subsequent facet-wise analysis and interpretation. Required bibliometric indicators like types of documents, yearly growth, authorship pattern, degree of collaboration (DC), country productivity, h-index and citation impact were used to study various dimensions of publication trends. VOSviewer software was used to visualize the authorship network, bibliographic coupling and keyword occurrences. Findings This study finds a total of 5,518 publications on the topic digital divide contributed by 14,277 authors from 130 countries across the world published through 2,843 source titles in 13 global languages during the past two decades (2001–2020). The annual growth of publications (AGP) on the topic digital divide shows 38.43% AGP globally. Journal articles have been identified as the preferred type of document with 73.11% of the literature. The DC indicates a healthy trend of collaborative research with a mean value of 0.70. The USA is the table topper with the contribution of 1,933(35.03%) publications and 77 h-index and James J., from Tilburg University, The Netherlands, is identified as top amongst the most productive authors with the highest number of 34 publications (h-index 14). Research limitations/implications This study restricts its scope on research productivity to the theme “digital divide” regarding authorship pattern, DC, most productive authors, most productive countries, most published sources and other key facets. This study exclusively refers to the Web of Science database in retrieving the required data. Moreover, this study takes global research into account with no geographical or language limitations and comprehends literature on digital divide for two decades ranging from the years 2001 to 2020. Practical implications Teachers and research scholars interested in bibliometric studies can benefit from insights into the scholarly documents published on the topic digital divide from 2001 to 2020. Originality/value This study yields some interesting findings on published literature on the digital divide during the past two decades relating to the most striking contributions, highly cited journals, the most prolific authors, country productivity, keyword cooccurrence and assorted parameters.

Analysis of News Regarding on Shared Housing Using Text Mining Techniques

This study was conducted to explore the development direction of shared housing as a sustainable housing product by exploring the perception of shared housing and related words through Big data analysis and examining the correlation between keywords. Data was collected using Big Kinds, a news search service, and sentiment analysis, text mining analysis, N-gram and network analysis, and CONCOR analysis were performed using TEXTOM and UCINET, which are Big data analyses programs. As a result of the analysis. First, shared housing began to be introduced as a new housing type in late 2010 and began to be supplied in earnest in 2013. Second, the sensibility for shared housing has a liking and interest in the new space, but there is also a sense of rejection of living together. Third, as a result of keyword frequency analysis, the frequency of occurrence of related keywords indicating the residence of shared housing such as youth and single-person households and keywords indicating the characteristics of youth housing policy projects was high. Fourth, CONCOR analysis found significant four cluster characteristics: ‘Share house’, ‘Co-living’, ‘Remodeling social housing’, and ‘Young people rental housing’.

Sustainable intensification of agriculture as a tool to promote food security: A bibliometric analysis

Sustainable intensification (SI) of agriculture is required to satisfy the growing populations' nutritional needs, and therefore food security while limiting negative environmental impacts. The study aims to investigate the global scientific output of sustainable intensification research from 2010 to 20 August 2021. The data was retrieved from the Web of Science (WoS) Core Collection and was analyzed using a bibliometric method and VOS viewer to determine the most productive countries and organizations by collaboration analysis, including the keywords to analyze the research hotspots and trends, and the most cited publications in the field. From the 1,610 studies published in the theme of sustainable agriculture by 6,346 authors belonging to 1,981 organizations and 115 countries, the study found an increased number of publications and citations in 2020, with 293 publications and 10,275 citations. The United States ranked highest in countries collaborating with the most publications in the field. The occurrence of keywords like “food security”, “climate change”, “agriculture”, “ecosystem services”, “conservation agriculture”, “Sub-Sahara Africa”, “Africa”, “biodiversity”, and “maize” in both author and all keywords (author and index) reveal the significance of sustainable intensification in Africa, as a solution to food insecurity under climate change conditions. The availability of funding agencies from big economies explains the growing interest by developing countries in the SI of agriculture research due to the growing population, food insecurity, and access to limited land for farming.

키워드 출현 빈도 분석과 CONCOR 기법을 이용한 ICT 교육 동향 분석

본 연구는 기계학습의 키워드 출현 빈도 분석과 CONCOR(CONvergence of iteration CORrealtion) 기법을 통한 ICT 교육에 대한 흐름을 탐색한다. 2018년부터 현재까지의 등재지 이상의 논문을 ‘ICT 교육’의 키워드로 구글 스칼라에서 304개 검색하였고, 체계적 문헌 리뷰 절차에 따라 ICT 교육과 관련이 높은 60편의 논문을 선정하면서, 논문의 제목과 요약을 중심으로 키워드를 추출하였다. 단어 빈도 및 지표 데이터는 자연어 처리의 TF-IDF를 통한 빈도 분석, 동시 출현 빈도의 단 어를 분석하여 출현 빈도가 높은 49개의 중심어를 추출하였다. 관계의 정도는 단어 간의 연결 구조와 연결 정도 중심성을 분 석하여 검증하였고, CONCOR 분석을 통해 유사성을 가진 단어들로 구성된 군집을 도출하였다. 분석 결과 첫째, ‘교육’, ‘연 구’, ‘결과’, ‘활용’, ‘분석’이 주요 키워드로 분석되었다. 둘째, 교육을 키워드로 N-GRAM 네트워크 그래프를 진행한 결과 ‘교 육과정’, ‘활용’이 가장 높은 단어의 관계로 나타났다. 셋째, 교육을 키워드로 군집분석을 한 결과, ‘교육과정’, ‘프로그래밍’, ‘학생’, ‘향상’, ‘정보’의 5개 군이 형성되었다. 이러한 연구 결과를 바탕으로 ICT 교육 동향의 분석 및 트렌드 파악을 토대로 ICT 교육에 필요한 실질적인 연구를 수행할 수 있을 것이다.

ReqGen: Keywords-Driven Software Requirements Generation

Software requirements specification is undoubtedly critical for the whole software life-cycle. Currently, writing software requirements specifications primarily depends on human work. Although massive studies have been proposed to speed up the process via proposing advanced elicitation and analysis techniques, it is still a time-consuming and error-prone task, which needs to take domain knowledge and business information into consideration. In this paper, we propose an approach, named ReqGen, which can provide further assistance by automatically generating natural language requirements specifications based on certain given keywords. Specifically, ReqGen consists of three critical steps. First, keywords-oriented knowledge is selected from the domain ontology and is injected into the basic Unified pre-trained Language Model (UniLM) for domain fine-tuning. Second, a copy mechanism is integrated to ensure the occurrence of keywords in the generated statements. Finally, a requirements-syntax-constrained decoding is designed to close the semantic and syntax distance between the candidate and reference specifications. Experiments on two public datasets from different groups and domains show that ReqGen outperforms six popular natural language generation approaches with respect to the hard constraint of keywords’ (phrases’) inclusion, BLEU, ROUGE, and syntax compliance. We believe that ReqGen can promote the efficiency and intelligence of specifying software requirements.

A critical review of HR analytics: visualization and bibliometric analysis approach

PurposeThis study aims to analyze the existing literature in human resource analytics and highlights the future research agenda and trends in the same context. It deals with evaluating regional distribution, identifying key authors, publications, journals and keyword occurrences while examining current literature.Design/methodology/approachA total of 127 articles exported from the Scopus database were systematically analyzed using bibliometric analysis through VOSviewer, including performance analysis and science mapping of the literature studied.FindingsThis research postulates the inconsistency between the number of publications and citations received by an author. There was an increase in collaborative research over the years. Human Resource Management Review was regarded as the most influential journal with maximum citation. Maximum publications came from Asian countries. The study revealed that the author with maximum citation were mostly the first authors of the most cited documents.Practical implicationsThis research may be beneficial for both researchers and human resource (HR) practitioners because it identifies the research gaps and research needs in the HR analytics domain. Besides, this study recognizes the patterns in HR analytics literature that helps researchers better understand the subject area.Originality/valueThis research incorporates bibliometric analysis for analyzing HR analytics literature to establish a more exhaustive and systematic understanding of the research area. This research contributes to the existing body of literature and assists fellow researchers in future studies.

A Bibliometric Analysis of Artificial Intelligence-Based Stock Market Prediction

The primary purpose of this study is to conduct a scientometrics analysis of stock market forecasts based on artificial intelligence. This research examined 1,301 publications that were published between January 2002 and June 2022. We investigated 183 journal articles among 1,329 papers. In addition to entering the keywords into Scopus, a comprehensive dataset of relevant research papers was compiled. These papers discussed the optimization of investment portfolios, artificial intelligence-based stock market forecasts, investor emotions, and market monitoring. We found the most prolific documents by affiliation, the most prolific author, the most cited papers, nations, institutions, co-authorship maps, inter-country co-authorship maps, and keywords occurrences in this study. Co-authorship analysis network maps and keyword occurrence linkages are generated using the VOS-viewer software. According to our findings, it is evident from the review that the body of literature is becoming more specific and extensive. Primarily, neural networks, support vector machines, and neuro-fuzzy systems are employed to predict the future price of a stock market index based on the composite index's historical prices. Artificial intelligence techniques are able to consider challenges facing financial systems when forecasting time series. Our findings provide actionable guidance on how artificial intelligence can be used to predict stock market movements for market participants, including traders, investors, and financial institutions.

Peta hubungan topik penelitian skripsi IPB University lulusan tahun 2020

Introduction. Agricultural progress cannot be separated from research activities carried out by researchers and students. It is suspected that the thesis research topics in IPB undergraduate theses are thought to be related to one another. This study aims to determine the relationship of the research topics of the IPB undergraduate theses in 2020.
 Research methods. This research is a mixed method research. The data collected by purposive sampling technic with a 2020 limit. The data was retieved from IPB repocitory.
 Data analysis. The data were tabulated and analyzed using the VOSviewer to generate maps relationships between keywords.
 Results and Discussion. Co-word analysis of controlled keywords found 181 keywords. Co-word analysis with a limitation of three keyword occurrences found 120 related keywords grouped into 10 clusters. Co-word analysis of the keywords formed by the author found 10,943 keywords. The ten occurrence limit found 81 keywords in 11 clusters. Similar research topics were found in at least two faculties.
 Conclusion. The research topics of IPB's thesis were spread into 120 topics with 10 clusters having close relationships. It was found that there were similarities in research topics between one faculty and another.

Bibliometric Analysis of Research on the Main Genes Involved in Meat Tenderness.

Tenderness is one of the main characteristics of meat because it determines its price and acceptability. This is the first bibliometric study on the trend of research on the role of genes in meat tenderness. A total of 175 original and English-language articles published up to 2021 were retrieved from Scopus. The bibliometric analysis was carried out with VOSviewer (version 1.6.18, Eck and Waltman, Leiden, Netherlands) and complemented with the Analyze search results service from Scopus. Erroneous and duplicate data were eliminated, and incomplete information was added to standardize the results. Scientific production was evaluated by means of quantity, quality and structure indicators. As a first glance, 8.816% of authors have published more than 50% of papers mainly related to genes encoding the calpain (CAPN)-calpastatin (CAST) system and single nucleotide polymorphisms (SNPs). Among other findings, a strong link was found between the contribution of the main countries (led by the United States with) and their institutions (led by the USDA Agricultural Research Service with) to their gross domestic product. Most studies on the topic are published in the Journal of Animal Science, and other journals with high impact according to the number of citations and different metrics. Finally, when evaluating the most cited articles, the occurrence and association of the main keywords, it was confirmed that research is focused on the role of CAPN and CAST genes and of SNPs in beef tenderness. The change in science was emphasized; although marker-assisted selection is still used, genes have an infinitesimal effect on complex traits. Therefore, since about 2010, new research groups adopted genomic selection to evaluate dense panels of SNPs and better explain genetic variation in meat tenderness.

The study of students' discussion process in a teacher-developed problem-solving learning design: Transcript-based lesson analysis of a social studies lesson

PurposeThis study aims to clarify the process of students' discussion in a problem-solving lesson developed autonomously by a teacher. It is expected to contribute to the accumulating and theorizing of teachers' professional development in school-based curriculum studies.Design/methodology/approachA transcript-based lesson analysis (TBLA) was conducted using quantitative and qualitative methods. The data were from a third-year junior high school social studies lesson in which whole-class discussions took place. The frequency of the appearance of keywords in the utterances was represented in the form of cumulative relative frequency graphs, and a diagram of the students' discussion process was drawn based on them.FindingsThe diagram confirmed the frequent occurrence, disappearance and transmission of keywords in the process of the lesson. The interpretation of the lesson transcript revealed that the students listened carefully to others' utterances, shared important keywords and examined the ideas critically. They were supported in identifying the connections between their interests by the teacher. The students' interactions and teacher's interventions were facilitated by the integrated curriculum design of teaching and learning.Originality/valueThis study verifies that students' collaborative and critical learning takes place in a lesson developed by a teacher based on the practiced curriculum. The findings of this study suggest that lesson study (LS) focusing on the words in utterances provide clues for evaluating the practiced curriculum and reveal the teacher's professional development.

Exploring the last 50 years of Indian research on the impact of coal mining using bibliometric analysis with an overview of water-related impacts.

This paper throws light on the bibliometric review of the impact of coal mining in India over the past 50years, emphasizing environmental, especially water-related impacts. The data were refined from the Web of Science database and analyzed in a bibliometric map visualization software tool, VOSviewer, to grasp the research focus, status quo and analyze the trend and direction of the work being carried out in this area. The methodology was covered in three phases: search and document selection, software and data extraction, and analysis of results and trends. The study results indicated that (i) the publication has increased in the past two decades (2001-2021) with a steep increase in the period from 2010 to 2021 with 74.68% article types documents and a mere 7.74% review documents. (ii) In India, the significant contribution is made by the Indian Institute of Technology (Indian School of Mines), Dhanbad with Department of Science and Technology as a primary funding agency. (iii) The bibliometric map of co-occurrence of author keywords showed that keywords relating to the "pollution" (connected to air, water) from "Jharia coalfield" have highest occurrences in the relevant published works of literature and topics like "reclamation," "mine spoil," and application of approaches like "remote sensing and GIS" have lower linkage strengths in general. (iv) The result of the co-citation network study has marked the most significant authors and the highly cited sources of the database revealing Ghose M.K. and Singh A.K. as among the most cited authors with citations more than 150 in the field of our interest. (v) The trend of publication in the research area of Water Resources showed a significant increase after 2015. The keyword occurrence map reveals that water quality studies have been extensively studied, but quantifications of the coal mining-induced changes in water regimes at river basin scales are absent.

Food Waste and Circular Economy: Challenges and Opportunities

The world’s population is expected to grow at an increasing rate, leading to increased food consumption and waste production. Even though food waste represents one of the most challenging economic and environmental issues of the 21st century, it also provides a vast array of valuable resources. To address the challenge, this study uses resource recovery from food waste to close the supply chain loop, which is the cornerstone of a circular economy. By applying the bibliometric review technique, trends and patterns in food waste and circular economy were studied. The analysis of frequent keywords in the field provided insights into further research directions. A Boolean search of the keywords in the Scopus database resulted in 288 articles, published between 2015 and 2021. Further screening of titles, keywords, and abstracts resulted in 155 journal articles. Bibliometric coupling, including authors’ co-citation data, co-occurrence, and the occurrence of keywords, was graphically mapped using VOSviewer software. From the analysis of the publications, eight broad themes emerged: (1) anaerobic digestion of food waste for circular economy creation; (2) food waste systems and life cycle assessments for circular economy; (3) bio-based circular economy approaches; (4) consumer behavior and attitudes toward circular economies; (5) food supply chains and food waste in a circular economy; (6) material flow analysis and sustainability; (7) challenges, policies, and practices to achieve circularity; and (8) circular economy and patterns of consumption. Based on the eight themes, we emphasize an urgent need to promote the collaboration of governments, the private sector, educational institutions, and researchers, who should combine efforts to promote, integrate and accelerate acceptance of circularity, which will potentially mitigate greenhouse emissions associated with food loss and waste. We also highlight an opportunity to encourage consumer acceptance of upcycled food in the food waste hierarchy. In addition, we deduce that there is a need to quantify food waste and emissions of greenhouse gases due to this waste along the food value chain; this is important as it is one pathway of examining the ‘food leaks’ along the food supply chain. This can then inform optimal strategies targeting specific areas of the food supply chain experiencing food leaks. Lastly, food wastage affects the entire globe; however, future studies and funding need to be channeled towards investigating the possibility of implementing circularity in developing countries.

Utilizing graphene oxide in cementitious composites: A systematic review

Graphene oxide (GO) is a 2D nanoparticle with dimensions less than 100 nm and acts as nano reinforcement in cementitious composites as a filling, crack-arresting agent, and nuclei for cement hydration. GO can be utilized to improve the microstructure and mechanical properties in a variety of construction and building technology applications. Through a coherent, comprehensive mixed reviewing method, this study seeks to evaluate the research efforts on utilizing GO in cementitious composites and map the research viewpoints from available literature (bibliometric analysis and systematic review). A total of 1437 publications were screened in which 59 articles were selected upon relevancy. Bibliometric mapping is used for keyword occurrence and the tested properties of cementitious composites containing GO. The review finds that the advantage of GO as a reinforcement in cement composite is that it is more effective in controlling crack development and propagation at the nano scale level in contrast to conventional reinforcement. The use of GO in cementitious composites offers many interesting paths and provides a foundation for further optimization in concrete, mortar, engineered cementitious composites, and cement paste. On the other hand, this use has a negative influence on various features of each type of reviewed material. More research is needed to gain a better knowledge of the long-term effects of the mechanical properties and durability of GO-based cementitious composites to promote their wider use in diverse applications in the construction sector.

Evidence to Inform Resilience Policy in the SADC: Current Limitations and Future Research Areas

The Southern African Development Community (SADC) and its member countries are potentially vulnerable to external disturbances, including environmental, economic, and social shocks. Regional policy emphasizes interventions to ensure long-term growth and development in the face of potential disturbance. Current emphasis is placed on mitigating the impact of climate change, including the creation of the SADC Climate Change Strategy and Action Plan. However, the need for long-term adaptation and associated policy is evident to reduce regional vulnerabilities. Research is an important source of evidence to support policymaking, with specific importance to institutions in developing regions such as the SADC and during times of global change and disruption. SADC development policy related to resilience outcomes ought to be informed by research. This paper investigates the extent to which existing research supports regional resilience policymaking. Objectives include investigating available research on resilience in the SADC, identifying potential limitations, and delineating areas of future research to be considered by researchers that contribute to knowledge and evidence creation. A bibliometric review and selective content review of existing research were utilized. Relevant publications for the aforesaid analysis were delineated using the source concepts “resilience”, “adaptation”, “adaptability”, and “Southern African Development Community” (or “SADC”). Using the SCOPUS database, bibliometric data of 65 publications were imported into the VOSviewer application (v.1.6.17). Keyword occurrences and network and overlay visualizations were applied to identify the research themes underlying current research. The findings, which were supported by the selective content review, indicate that existing research focuses primarily on environmental and social disturbances, while the applicable regional planning scale and development policy are considered to a limited extent. Areas of future research ought to create evidence that is thematically relevant to policy areas and applicable to policy interventions, which necessitates increased research on economic disturbances, a broadening of existing themes to the appropriate regional planning scale, and consideration of explicit regional development objectives and policy. Transdisciplinarity ought to be central to future research on the diverse disturbances facing the region, while researchers ought to leverage knowledge-creation opportunities catalyzed by SDG implementation.

Bibliometric Analysis and Challenges in Biofuel Production using Petroplants

A bibliometric analysis based on PubMed database were carried out to provide insights into research activities and tendencies, challenges of global biodiesel from 1997-2020.The document type and language,characteristics of publication output, keywords and most cited articles were emphasized. The biofuel production faces many challenges which include land availability, high cost, plant health and availability of labour. Data visualization using VOS viewer clearly stated that annual output of related scientific articles increased steadily. On analyzing the keywords, network visualization showed 100 lines with biofuel most related to biotechnology field.The most ideal keyword occurrence were visualized for bio-oil followed by co- digestion of biological matter are seen for density visualization. Six clustering of keywords were noted with links 20,6701 among keywords. The relevance score calculated using VOS viewer software generated 2.35 as score for Biodiesel engine and 2.25 for Organic loading rate. The analysis of review papers from pubMed clearly indicated that biofuels showed direct link to biotechnology field, plant oils and ethanol production.

Methods of Multi-Criteria Analysis in Technology Selection and Technology Assessment: A Systematic Literature Review

Abstract Technology assessment and selection problems have gained importance in recent decades as the used technology often determines the enterprises’ competitive advantage. Due to the extensive catalogue of criteria that should be considered and, on the other hand, the extensive catalogue of available technologies and solutions, the decision-making process of choosing a technology becomes a significant challenge for organisations and individuals. This study aims to identify the main research directions and trends in the scientific literature on applying multi-criteria analysis (MCA) in the context of technology assessment and/or technology selection. The author conducted a bibliometric analysis of publications indexed in the Web of Science and Scopus databases. The methodology of this study also included identifying the most productive authors, countries, organisations, and journals and analysing the occurrence and co-occurrence of terms. Final analyses included 380 publications retrieved from the Scopus database and 311 documents retrieved from the Web of Science repository. The analysis of the occurrence of terms and keywords allowed distinguishing two main research directions in using MCA methods in assessing and selecting industrial and health and medicine-related technologies. Some sub-areas have also been distinguished within these two areas: energy and renewable energy technologies, waste management, biomedical and medical technologies, and drug production technologies.