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This study is aimed to quantitatively classify the publications about the concept of tribalism and to reveal the reflections of the relevant concept in the literature. A systematic literature review was conducted with bibliometric analysis. The study examined 334 articles published between 2003 and 2023 that used the terms "tribalism", "tribal", or "tribes." The articles were exported from Web of Science database in November 2023. The data set of the study was analyzed using the Bibliometix package in the R Studio program. In this context, content analysis and citation network analysis were performed on the data. As a result of the analysis, it was found that the number of articles published on tribalism has fluctuated over the years. Brand tribalism, culture and customer tribes are the most frequently used keywords along with tribalism. A diffraction occurred in 2016 and the words "community", "brand community", "consumer tribes" and "brand" came to the fore. In the last five years, tribalism, brand, brand tribalism and tribal marketing were the most trending topics. It was found that the most frequently cited articles regarding the concept of tribalism are mainly theoretical articles. [ABSTRACT FROM AUTHOR]

The genus amaranth is comprised of 60–70 species. This climate hardy plant is grown as a vegetable as well as cereal crop in many areas of the world. Current study multivariate analysis was used to characterize the exotic vegetable amaranth genotypes. The experiment was conducted in the research area of Muhammad Nawaz Shareef University of Agriculture, Multan under RCBD design with three replications of 50 exotic amaranth genotype. 22 different variables were investigated for the principal component and cluster analysis using different software packages in R studio. According to the results of principal component analysis the first five PCs had eigenvalues greater than one and they explained the 84.59% diversity in exotic vegetable amaranth accessions. First two principal components explained 57.31% of variation. Traits that loaded highly for first PC include moisture (-0.892), carbohydrates (0.938), energy (0.920), stomatal conductance (0.803), net photosynthesis (0.795) and dry weight (0.770). The traits which impacted the second PC highly include protein (0.692), fat (-0.571), nitrogen (0.692), calcium (0.545), phosphorus (0.908), zinc (0.960), iron (0.826) and potassium (0.872). For future breeding program the selection based on these results will be effective for crop enhancement drive in exotic vegetable amaranth genotypes. Hierarchical clustering was done and dendrogram split the 50 genotypes into 5 groups using silhouette method. The pairwise clustering revealed that intra cluster distance is smaller than inter cluster distance. The maximum inter cluster distance was observed between cluster I and II (9.88); between cluster I and III (9.47); between cluster II and IV (9.27). Clinical trial number Clinical trial number not applicable. [ABSTRACT FROM AUTHOR]

Purpose: Experiential learning is crucial in education, as it offers hands-on, practical experiences that enable individuals to develop their skills and knowledge more engagingly and interactively. In recent years, experiential learning has become a significant aspect of education. To provide academic scholars with a thorough roadmap for further investigation, this study aims to provide useful insights into the bibliometric and content analysis of experiential learning, including keywords, well-known authors, publications, nations and topics. Design/methodology/approach: This research does a rigorous bibliometric analysis to give a thorough and visually instructional assessment of the evolution and advancement of the literature on experiential learning. Its fast development between 1976 and 2022 is meticulously tracked in the research. By using VOSviewer and Biblioshiny tools, the present study presents a concise overview of 507 records retrieved from the Scopus database using the keyword "Experiential Learning", following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis protocol. Deeper text mining was done using Python libraries "Pandas" and "Natural Language Toolkit" and regular expressions. Findings: The findings reveal a surge in the number of publications on experiential learning and provide insights, particularly using the theory, context, characteristics, methodology analysis, supporting researchers and practitioners to understand learning better and provide perspectives for future research. Descriptive bibliometric analysis showed that most contributions are from the USA, the UK and Canada. In-depth content analysis revealed five clusters: developments in learning, management education, engineering curricula, organisational learning and knowledge management and entrepreneurship education. The keyword co-occurrence analysis enabled linkages between relevant fields of study and significant research domains. The most commonly used theories were: experiential learning theory, social learning theory, relational coordination theory, empowerment theory, feedback learning theory, effectuation theory and human capital theory. Originality/value: This study uses information from the Scopus database to do a bibliometric analysis of experiential learning from 1976 to 2022. This study serves as a valuable resource for researchers in the field, helping them to position their work more explicitly within the existing literature and highlighting potential areas for future research. It does this by thoroughly analysing the literature on experiential learning using bibliometrics. [ABSTRACT FROM AUTHOR]

Abstract The genus amaranth is comprised of 60–70 species. This climate hardy plant is grown as a vegetable as well as cereal crop in many areas of the world. Current study multivariate analysis was used to characterize the exotic vegetable amaranth genotypes. The experiment was conducted in the research area of Muhammad Nawaz Shareef University of Agriculture, Multan under RCBD design with three replications of 50 exotic amaranth genotype. 22 different variables were investigated for the principal component and cluster analysis using different software packages in R studio. According to the results of principal component analysis the first five PCs had eigenvalues greater than one and they explained the 84.59% diversity in exotic vegetable amaranth accessions. First two principal components explained 57.31% of variation. Traits that loaded highly for first PC include moisture (-0.892), carbohydrates (0.938), energy (0.920), stomatal conductance (0.803), net photosynthesis (0.795) and dry weight (0.770). The traits which impacted the second PC highly include protein (0.692), fat (-0.571), nitrogen (0.692), calcium (0.545), phosphorus (0.908), zinc (0.960), iron (0.826) and potassium (0.872). For future breeding program the selection based on these results will be effective for crop enhancement drive in exotic vegetable amaranth genotypes. Hierarchical clustering was done and dendrogram split the 50 genotypes into 5 groups using silhouette method. The pairwise clustering revealed that intra cluster distance is smaller than inter cluster distance. The maximum inter cluster distance was observed between cluster I and II (9.88); between cluster I and III (9.47); between cluster II and IV (9.27). Clinical trial number Clinical trial number not applicable.

This paper presents a bibliometric analysis examining investment patterns in diverse economies and their relationship with global financial transformations. The study spans from 1964 to 2024, utilising data from the Web of Science (WoS) and analysed with R Studio and CiteSpace to identify trends and shifts in academic focus on investment strategies. A dataset of 1595 articles from 554 sources was processed, revealing key trends and shifts in investment patterns across different regions. The research highlights a notable rise in academic interest since 2015, with an 8.46% annual growth rate in publications. Methodologically, thematic evolution and network visualisation techniques were used to track collaboration patterns and emerging topics. For instance, 40.94% of the articles involved international co-authorship, emphasising global interest in investment dynamics. The study reveals critical insights into foreign direct investment (FDI) trends, particularly in developing and transitional economies, and how various global financial events influence investment behaviours. The average citation rate of 2.7 per article further underscores the significance of this field. The analysis confirms that investment patterns are crucial to understanding global economic transformations, providing a foundation for policymakers to craft economic strategies aimed at promoting balanced and sustained investment growth.

The study deals with the important question of how educational reforms affect healthcare competitiveness, especially when it comes to medical education. The primary study goal is to investigate the connection between improvements to the educational system and the calibre of medical care. To provide a thorough examination of trends over previous ten years, the study focusses on a dataset of 2,646 articles published between 2013 and 2023, taken from the WoS and Scopus databases. The necessity to record current and pertinent advancements in medical education and healthcare justifies the selection of this time frame. The CiteSpace and Biblioshiny programs were applied to conduct a bibliometric analysis, enabling the identification and visualisation of significant patterns and trends in the literature. The research hypothesis was verified by the analysis, which showed a strong association between educational changes and increases in healthcare quality. According to the report, health literacy, health disparities, and medical education are the most important topics. Improving healthcare competitiveness depends on these areas. Policymakers, educators, and healthcare administrators can benefit from the research findings because they offer evidence-based perspectives that can direct future medical education reforms to enhance healthcare outcomes and system competitiveness. The results also point to potential prospects for future investigation, especially when evaluating the long-term effects of educational reforms on international healthcare systems.

The project aimed to conduct an up-to-date and comprehensive bibliometric analysis of robotic surgery to provide a detailed and holistic understanding of the field. Three strategies were employed in the data analysis i.e. search terms were explored in (A) the title, abstract, and keywords and (B) only in the title of the documents. In 3rd part we analyzed the top 100 most cited papers. Vosviewer and R Studio were utilized for detailed bibliometric and network analyses. Strategy one identified 38,469 publications, and strategy two identified 6451 publications from 2001 to 2023. The top authors, universities, countries, sponsors, and sources based on the number of publications were identified for both strategies. The top 100 most cited papers were analyzed, providing the annual number of publications and various citation metrics. Top authors (by number of publications, total citations, h-index, g-index, and m-index), universities, and countries within these highly cited papers, along with their co-authorship networks and dynamics, were examined. Co-words analysis of the top 100 most cited papers revealed the primary focus of these documents across 25 categories. This comprehensive bibliometric analysis of robotic surgery highlighted significant contributions and collaborations in the field, emphasizing the importance of global and collaborative efforts in advancing robotic surgery research. [ABSTRACT FROM AUTHOR]

This study aims to examine, ascertain, and measure the extent of research conducted on real-time surveillance video analytics (RTSVA) and uncover the patterns, advancements, and progression in the SCOPUS database. An electronic search methodology was employed to identify the most relevant scholarly papers. Each analytical tool was analyzed using the R program code and VOS Viewer software. Multiple factors were explored, including co-authorship, co-citation, conceptual structure, co-word occurrence, trend themes analysis, thematic map, and visualization analysis. It is observed the study includes data from 653 sources, such as journals, books, and conference proceedings. There are 1041 documents, with an annual growth rate of 21%. IEEE Access has the highest impact, and Multimedia Tools and Applications has the second highest source impact. The most significant author among the research group is Chen Y, with a fractionalization value of 6.59 (27 articles), followed by Wang J (15 articles) with a value of 3.80. The keywords related to the video analytics domain along with its frequency of occurrence, are "Edge computing," which has appeared 158 times, and video surveillance has appeared 95 times. The Thematic map is divided into four quadrants: Niche, Motors, Emerging, and Basic themes. Traffic surveillance, blockchain, and COVID-19 are under Niche themes. It is also necessary to explore the sociological and ethical implications linked to using video surveillance systems. This study has the potential to aid both novice and experienced researchers in identifying novel research domains, suitable sources, and opportunities for collaboration. The results pertaining to relational and evaluate methodologies could be helpful to the new researchers. [ABSTRACT FROM AUTHOR]

This study uses bibliometric analysis to systematically review 894 scientific publications on green intellectual capital from 2008 to 2024. The study is based on Scopus database and analyzed by keyword, author, country, citations, publications, universities and journals. The findings reveal that research interest has grown dramatically since the notion of green intellectual capital was originally introduced in 2008, particularly following the COVID-19 outbreak. Malaysian author Yusliza Mohd Yusoff, as well as Spanish authors Bartolome Marco-Lajara and Eduardo Sanchez-Garcia, have written numerous high-quality works in this topic, making them prolific authors. Furthermore, Malaysian and Chinese institutions studying green intellectual capital have a big number of papers and a diverse range of academic influence. China has contributed the most funding for green intellectual capital initiatives. According to keyword co-occurrence analysis, green innovation, environmental performance, and sustainable development are the three main issues of green intellectual capital research. This study also discovers that countries with more green intellectual capital projects receive higher financing support for these initiatives. [ABSTRACT FROM AUTHOR]

This paper presents a bibliometric analysis examining investment patterns in diverse economies and their relationship with global financial transformations. The study spans from 1964 to 2024, utilising data from the Web of Science (WoS) and analysed with R Studio and CiteSpace to identify trends and shifts in academic focus on investment strategies. A dataset of 1595 articles from 554 sources was processed, revealing key trends and shifts in investment patterns across different regions. The research highlights a notable rise in academic interest since 2015, with an 8.46% annual growth rate in publications. Methodologically, thematic evolution and network visualisation techniques were used to track collaboration patterns and emerging topics. For instance, 40.94% of the articles involved international coauthorship, emphasising global interest in investment dynamics. The study reveals critical insights into foreign direct investment (FDI) trends, particularly in developing and transitional economies, and how various global financial events influence investment behaviours. The average citation rate of 2.7 per article further underscores the significance of this field. The analysis confirms that investment patterns are crucial to understanding global economic transformations, providing a foundation for policymakers to craft economic strategies aimed at promoting balanced and sustained investment growth. [ABSTRACT FROM AUTHOR]

The study deals with the important question of how educational reforms affect healthcare competitiveness, especially when it comes to medical education. The primary study goal is to investigate the connection between improvements to the educational system and the calibre of medical care. To provide a thorough examination of trends over previous ten years, the study focusses on a dataset of 2,646 articles published between 2013 and 2023, taken from the WoS and Scopus databases. The necessity to record current and pertinent advancements in medical education and healthcare justifies the selection of this time frame. The CiteSpace and Biblioshiny programs were applied to conduct a bibliometric analysis, enabling the identification and visualisation of significant patterns and trends in the literature. The research hypothesis was verified by the analysis, which showed a strong association between educational changes and increases in healthcare quality. According to the report, health literacy, health disparities, and medical education are the most important topics. Improving healthcare competitiveness depends on these areas. Policymakers, educators, and healthcare administrators can benefit from the research findings because they offer evidence-based perspectives that can direct future medical education reforms to enhance healthcare outcomes and system competitiveness. The results also point to potential prospects for future investigation, especially when evaluating the long-term effects of educational reforms on international healthcare systems. [ABSTRACT FROM AUTHOR]

The concepts of Society 5.0 (S5.0) and Industry 5.0 (I5.0) have emerged in recent years as part of the digital transformation landscape, influenced by the advent of Industry 4.0. S5.0 represents a smart society approach rooted in digital transformation, originating in Japan, while I5.0 stems from European studies. This study explores the evolving research landscape surrounding S5.0 and I5.0 by conducting a bibliometric analysis using theWeb of Science database, thereby illuminating potential avenues for future studies. The analysis includes studies in which both concepts are keywords, revealing an increasing trend in awareness, scholarly output, and citation counts over time. Notably, Japan and China emerged as prominent contributors, with Nahavandi S. (2019) identified as the most cited author. Engineering emerges as the researched field in connection with these concepts. Furthermore, it becomes evident that S5.0 and I5.0 are closely linked to terms such as artificial intelligence, digital transformation, Industry 4.0, internet of things, sustainability, machine learning, and Economy 5.0/Education 5.0. It is anticipated that these concepts will assume a more comprehensive role in the business landscape, driven by technological advancements and social developments in the coming years. [ABSTRACT FROM AUTHOR]

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi's Vision 2030. [ABSTRACT FROM AUTHOR]

This study aimed to give a thorough overview of how to analyze and visualize creative and critical thinking trends in science education research using the bibliometrics technique. This study is based on 319 papers that were gathered and indexed by Scopus between the years 2002 and 2022 that dealt with critical and creative thinking in scientific education. For bibliometric analysis, we employed R Studio. There is an upsurge in studies on critical and creative thinking tendencies, and some nations have recently investigated critical and creative thinking in science education. Additionally, there is a growing tendency in education that emphasizes the connection between critical and creative thinking in the subject of study covered in this study. Considering the findings of this bibliometric analysis, it thus offers a summary and opportunities for additional research. [ABSTRACT FROM AUTHOR]

Recently, there has been an emerging trend to analyse time series data and utilise sophisticated tools for optimally fitting time series models. To date, Malaysian industrial accident data is underutilised and lacks informative records. Thus, this paper aims to investigate the Malaysian accident database and further evaluate the optimal forecasting models in accident prediction. The model's input was based on available data from the Department of Occupational Safety and Health, Malaysia (DOSH), from 2018 until 2021, with 80% of the dataset to train the models and the remaining 20% for validation. The negative binomial and Poisson distribution prediction showed a mean absolute percentage error (MAPE) of 33% and 51%, respectively. It indicated that the negative binomial performed better than the Poisson distribution in accident frequency prediction. The available time series accident data were gathered for four years, and stationarity was checked in R Studio software for the Augmented Dickey-Fuller test. The lowest Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and other error values were used to justify the best model, which was the ARIMA(2,0,2)(2,0,0)(12) model. The ARIMA models were considered after the data showed autocorrelation. The MAPE for both ARIMA in R and manual time series were 40% and 49%, respectively. Therefore, the accident prediction by using R Studio would outperform the manually negative binomial and Poisson distribution. Based on the findings, industrial safety practitioners should report accidents to DOSH truthfully in the era of digitalisation. It could enable future data-driven accident predictions to be carried out. [ABSTRACT FROM AUTHOR]

Social, economic and political developments in the globalising world have necessitated a re-examination of the concept of democracy. The concept of organisational democracy is a process that expresses the inclusion of individuals in the management processes of organisations and allows freedom of expression in organisations. In this study, it is aimed to reveal at which stage the concept of organisational democracy is included in the international literature. In this context, it is desired to evaluate the development stages of the concept in terms of literature and to set an example for future studies by filling the gaps in the literature. In line with the main purpose, 99 publications related to the concept of “organisational democracy” between 1990 and 2023 in the Scopus database were analysed using R Studio and VOSviewer softwares. The analyses were evaluated by considering the studies conducted in the fields of “Social Sciences”, “Business, Management and Accounting” between 1990 and 2023. The concept of organisational democracy is often associated with concepts such as participation, hierarchy, employee participation organizational culture, organizational commitment and communication. As a result, it has been determined that organisational democracy plays a role in the satisfaction of internal stakeholders. [ABSTRACT FROM AUTHOR]

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Perguruan tinggi sangat memperhatikan penerimaan mahasiswa baru sebagai indikator kemajuan dan pertumbuhan institusi. Namun, fluktuasi jumlah penerimaan mahasiswa baru dapat menjadi kendala dalam perencanaan dan pengambilan keputusan. Penelitian ini bertujuan untuk menerapkan metode Autoregressive Integrated Moving Average (ARIMA) dalam menghitung prediksi jumlah mahasiswa baru pada Universitas Papua. Oleh karena itu, penelitian ini menggunakan metode ARIMA untuk meramalkan jumlah mahasiswa baru tahun 2023 dengan memanfaatkan data historis dari tahun 2017 hingga 2022. Hasil analisis menggunakan R Studio menunjukkan bahwa model terbaik adalah ARIMA (11,0,12) dengan nilai error terendah. Penelitian ini memberikan manfaat bagi peneliti dalam memahami metode peramalan menggunakan ARIMA dan juga memberikan informasi yang berguna bagi Universitas Papua dalam perencanaan strategis untuk meningkatkan kualitas dan minat calon mahasiswa baru. Universities pay great attention to the acceptance of new students as an indicator of the progress and growth of the institution. However, fluctuations in the number of new student admissions can be an obstacle in planning and decision making. This study aims to apply the Autoregressive Integrated Moving Average (ARIMA) method in calculating predictions for the number of new students at the University of Papua. Therefore, this study uses the ARIMA method to predict the number of new students in 2023 by utilizing historical data from 2017 to 2022. The results of the analysis using R Studio show that the best model is ARIMA (11,0,12) with the lowest error value. This research provides benefits for researchers in understanding forecasting methods using ARIMA and also provides useful information for the University of Papua in strategic planning to improve the quality and interest of prospective new students.

Social, economic and political developments in the globalising world have necessitated a re-examination of the concept of democracy. The concept of organisational democracy is a process that expresses the inclusion of individuals in the management processes of organisations and allows freedom of expression in organisations. In this study, it is aimed to reveal at which stage the concept of organisational democracy is included in the international literature. In this context, it is desired to evaluate the development stages of the concept in terms of literature and to set an example for future studies by filling the gaps in the literature. In line with the main purpose, 99 publications related to the concept of “organisational democracy” between 1990 and 2023 in the Scopus database were analysed using R Studio and VOSviewer softwares. The analyses were evaluated by considering the studies conducted in the fields of “Social Sciences”, “Business, Management and Accounting” between 1990 and 2023. The concept of organisational democracy is often associated with concepts such as participation, hierarchy, employee participation organizational culture, organizational commitment and communication. As a result, it has been determined that organisational democracy plays a role in the satisfaction of internal stakeholders.

Tribal women entrepreneurship has gained significant attention in recent years as a means to empower women from indigenous communities and foster sustainable development within their societies. However, with the recognition of their unique skills, knowledge, and cultural heritage, tribal women are increasingly embracing entrepreneurship as a pathway to economic self-sufficiency and empowerment. Our research analysed bibliometric data from 2002 to 2023 to see how the field of study on tribal women entrepreneurs has evolved. This study looked at a wide range of factors, including publication details, authorship details, country of origin, and author-related keywords. The R studio Bibliometrix programme was used to make the visuals. The annual output of publications increased by 9.82% after the COVID-19 pandemic, and there was a notable increase in the number of publications on tribal women entrepreneurship. Possible causes include supportive technological development, a thriving environment for new businesses, and government initiatives. India and the United States have produced the most comprehensive studies of entrepreneurship followed by the United Kingdom, but researchers from all over the world have worked together on similar projects. This study has shown that there has been a significant increase in entrepreneurial research activity, as well as demonstrating the breadth and interdisciplinary character of this research. However, more work needs to be carried out to bring together scholars from different fields to examine how different organisations have impacted the study of tribal women entrepreneurs. [ABSTRACT FROM AUTHOR]

This document is a bibliometric analysis of research on employee turnover. The study aims to analyze the scope of research on employee turnover and track its growth and development using the Scopus database. The researchers analyzed 1948 articles from 1957 to 2023 using various methods, including co-word analysis and thematic mapping. The findings revealed significant progress in the study of employee turnover and identified key components related to turnover behavior. The research provides insights for future scholars and has the potential to contribute to the understanding of employee turnover and its causes. [Extracted from the article]

This meta-analysis study investigates the effect of computer-based tests on motivation. The random effect model is the basis for calculating the summary effect, and it is found that the 30 articles obtained through the PRISMA method, which consist of four parts, namely identification, screening, eligibility, and included, can be generalized. Data analysis was performed using R studio and OpenMEE. Based on the research results, the p-value <.05 was obtained, so it was concluded that using a computer-based test significantly affected motivation. In the moderator analysis, it was found that computer-based tests were most effective for intrinsic motivation for the motivation variable, university students for the participants variable, and the country of Iran for the region variable. Evaluation of publication bias using the funnel plot and regression method shows no publication bias, proving that the 30 articles analyzed represent all existing studies on computer-based tests. [ABSTRACT FROM AUTHOR]

The imperative demands placed on modern business management necessitate the exploration of novel approaches to adapt, innovate, and ensure effectiveness and efficiency. The integration of art into business management aligns with this rationale, indicating a trend towards art's incorporation into various managerial dimensions. As a discipline primarily focused on understanding managerial practices, management science has been investigating the utilization of art in the workplace since the early 2000s. The subject of art in the business environment has garnered various names and significant attention as a major trend. Through bibliometric analysis, we can characterize the evolution of scientific production and map the prevailing ideas in this field. By examining a sample of 69 articles retrieved from the Scopus database, this study lays the foundation for structuring art-based managerial thinking, while acknowledging certain limitations that may guide future research endeavors. [ABSTRACT FROM AUTHOR]

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Debates about how best to support students' transition from school to university have re-emerged periodically since at least the 1970s. This paper focuses upon one aspect of this transition: how to develop the quantitative skills students acquire at school throughout the first year of their degree. We report on an attempt to inject pedagogic innovation into the teaching of quantitative methods to first-year geography undergraduates at a large Russell Group university in the UK. More specifically, we report on moving to a pedagogic approach of student-centred, inquiry-based learning, which uses quantitative methods to investigate the issue of air pollution. We explore whether "statistical anxiety" is still a common experience of undergraduate geographers and the extent to which pedagogical innovation can help to alleviate this. Although the focus is on UK Geography, the paper has wider relevance to anywhere geographical research methods are taught. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is expanding rapidly and can potentially be a huge data source. This study aimed to close the knowledge gap about possible IoT applications in health care. English-language academic publications from the Web of Science (WoS) were used for the present study. Bibliometric networks were used to analyze pertinent publications to examine the connections between authors, nations, and affiliations. Papers on digital health care have been published in more than 1195 sources, among which the most popular was the Journal of Medical Internet Research. Six top institutions were from the Netherlands, making the Vrije Universiteit Amsterdam the most productive institution after Australia and England. This study provides an overview of IoT-related research conducted in digital health care, helping academic researchers, policymakers, and practitioners to better understand the development of digital health care research. [ABSTRACT FROM AUTHOR]

La presente investigación realiza un recorrido por el concepto de confiabilidad como una de las propiedades psicométricas fundamentales en la Teoría Clásica de los Test. Se desarrolla el concepto y cuáles son sus diferentes aplicaciones prácticas para indagar el grado de confiabilidad de un instru-mento de medición. Se centra el estudio en el cálculo de la consistencia interna a partir del alfa y el omega como los coeficientes más utilizados y la importancia de calcularlos mediante la utilización de matrices de correlaciones policoricas (MCP). Como objetivo principal se presenta una guía en español para el cálculo de coeficientes ordinales de confiabilidad al utilizar el programa R/RStudio. Se brinda un ejemplo a nivel empírico que da cuenta la relevancia de calcular este tipo de coeficientes para el cálculo de la confiabilidad de un instrumento. Al emplear una muestra de 266 adultos entre 18 y 63 años (M = 31.91, DE = 11.50), se administró la versión adaptada a argentina del Inventario de Ansiedad de Beck y el Cuestionario de Regulación Emocional. De esta manera, se exponen coefi-cientes para estimar la confiabilidad de los instrumentos que dan cuenta de sus ventajas y desventa-jas, al realizar el cálculo mediante MCP, matriz de correlaciones de Pearson y matriz de covarianzas de Pearson. A partir de los resultados, se evidencia que el cálculo mediante MCP proporcionó grados de confiabilidades mayores respecto al cálculo mediante las otras dos matrices. Se espera que el pre-sente documento sea de importancia para investigadores no familiarizados con R.

Data mining is a data analysis process using software to find certain patterns or rules in a large amount of data, which is expected to provide knowledge to support decisions. However, missing value in data mining often leads to a loss of information. The purpose of this study is to improve the performance of data classification with missing values, precisely and accurately. The test method is carried out using the Car Evaluation dataset from the UCI Machine Learning Repository. RStudio and RapidMiner tools were used for testing the algorithm. This study will result in a data analysis of the tested parameters to measure the performance of the algorithm. Using test variations: performance at C5.0, C4.5, and k-NN at 0% missing rate, performance at C5.0, C4.5, and k-NN at 5–50% missing rate, performance at C5.0 + k-NNI, C4.5 + k-NNI, and k-NN + k-NNI classifier at 5–50% missing rate, and performance at C5.0 + CMI, C4.5 + CMI, and k-NN + CMI classifier at 5–50% missing rate, The results show that C5.0 with k-NNI produces better classification accuracy than other tested imputation and classification algorithms. For example, with 35% of the dataset missing, this method obtains 93.40% validation accuracy and 92% test accuracy. C5.0 with k-NNI also offers fast processing times compared with other methods. [ABSTRACT FROM AUTHOR]

Maritime freight has gained popularity among researchers and practitioners due to its cost efficiency and eco-friendly nature. It was initially developed for cargo transfer, but its widespread adoption has made it the backbone of global economy. Despite its favourable nature, some of its serious negative effects have attracted the attention of researchers and scholars. Therefore, the present study reviews the extensive literature available on maritime freight logistics, and evaluates the existing access distance between sustainability practices and maritime freight logistics. A systematic three-stage review process including review planning, review conduct and evaluation is followed in this study. VOSviewer and the R language are used to evaluate relevant issues and changes in the literature. Thereafter, the content analysis highlights the major themes of the subject. This study underscores the impact of innovative technologies discovered to make maritime freight sustainable and also examines maritime freight transport in terms of three pillars of sustainability. The result has implications for policymakers to facilitate the smooth implementation of sustainable practices in maritime freight transportation. [ABSTRACT FROM AUTHOR]

Combined sewer overflows (CSOs) are a source of microbial contamination of drinking water intakes located downstream from their discharge. To safeguard the quality of the source water, it is essential to evaluate the risk levels associated with these municipal structures. This study compares two risk assessment approaches to test their applicability for assessing the risk of CSOs to drinking water intakes in a highly urbanized watershed. The first approach was based on a deterministic equation that combines the characteristics of an overflow structure allowing the risk to be rated as very low, low, medium, high, or very high. The second probabilistic risk assessment approach yielded findings that are probabilistically distributed across the five levels of risk. This approach was developed by constructing a novel Bayesian network to probabilistically link the different factors defining the exposure of water intakes to the hazards of CSOs. The comparison between the results of these two approaches highlighted the importance of simultaneously considering many scenarios for assessing the risk of contamination of source waters. It was possible to use the Bayesian network rather than the deterministic equation, which only supports one scenario at a time. It was also shown that the deterministic approach often overestimated risk levels for CSO outfalls close to the water intake. This occurred because the assessment process emphasized the distance factor between the discharge point and the water intake, while neglecting other crucial characteristics of the overflow, such as duration and frequency. In particular, the deterministic approach tended to underestimate risk for CSOs associated with low overflow frequencies as it did not support scenarios of overflow duration, unlike the probabilistic approach. The validation and sensitivity analysis of the Bayesian model revealed that the population residing in the CSO's drainage basin, along with the frequency and duration of the overflows, exerted the greatest influence on the resulting risk levels. These factors outweighed other variables utilized in the risk assessment, including vulnerability of the drinking water intake, the type of overflow recorder, pipe diameter, and variables defining the exposure of the water intake to the discharge. In the context of implementing action plans, the Bayesian network is estimated as a cost-effective technique as it prioritized overflow structures needing special attention in a highly urbanized watershed, where the same CSOs were deterministically rated as having the same risk level. The results also demonstrated the effectiveness of the Bayesian model in addressing data gaps faced by water managers and stakeholders. The Bayesian model proved capable of assessing risks with uncertainties for CSOs, even with limited input data available. These findings can assist managers in identifying problematic structures by considering various scenarios, unlike the deterministic approach, which left almost half (n = 42) of the study site's overflow structures unassessed due to data limitations.

Remote sensing techniques based on soil spectral characteristics are the key to future land management; however, they still require field measurement and an agrochemical laboratory for the calibration of the soil property model. Visible and near-infrared diffuse reflectance spectroscopy has proven to be a rapid and effective method. This study aimed to assess the suitability of multispectral data acquired with the agricultural digital camera in determining soil properties. This 3.2-Mpx camera captures images in three spectral bands – green, red and near-infrared. First, the reference data were collected, which consist of 151 samples that were later examined in the laboratory to specify the granulometric composition and to quantify some chemical elements. Second, additional soil properties such as cation exchange capacity, organic carbon and soil pH were measured. Finally, the agricultural digital camera photograph was taken for every soil sample. Reflectance values in three available spectra bands were used to calculate the spectra indices. The relationships between the collected data were calculated using the independent validation regression model such as Cubist and cross-validation model like partial least square in R Studio. Additionally, different types of data normalisation multiplicative scatter correction, standard normal variate, min–max normalisation, conversion into absorbance] were used. The results proved that the agricultural digital camera is suitable for soil property assessment of sand and silt, pH, K, Cu, Pb, Mn, F, cation exchange capacity and organic carbon content. Coefficient of determination varied from 0.563 (for K) to 0.986 (for soil organic carbon). Higher values were obtained with the Cubist regression model than with partial least squares.

This study is motivated by the actual conditions for the drinking water supply system in Patumbak District which has been served by PDAM Tirta Deli as the manager of drinking water in Deli Serdang Regency, and partially served by PDAM Tirtanadi, but the service coverage is still low at 30.33%. The purpose of this research is to study the Feasibility of Economic and Financial Investment in the development of drinking water supply systems. The data processing technique in this study is to use a monte carlo simulation technique. This Monte Carlo simulation is run through an application program, namely the R Studio program which will estimate the profit of a SPAM company for the next 20 years.. Based on the results of calculations and analysis, it can be concluded that from the analysis of economic and financial aspects the results are: NPV IDR 12,659,105,099 (> 0), IRR 14.58% (> 12%), Net B/C Ratio 2.796 (> 1). Results The average NPV value based on the Monte Carlo simulation results with R Studio is IDR 1,060,115,259 and from the analysis of the financial aspects the results are: The NPV value is IDR 25,994,551 (> 0), IRR 9.48 % (> 9.20 %), Net B/C Ratio 1.03 (> 1), PP 9th year, BEP 0. So it can be concluded that SPAM Development Investment IKK Patumbak in Deli Serdang Regency is said to be FEATURED economically and financially. [ABSTRACT FROM AUTHOR]

This research forecasts the gasoline price in U.S. and analyzes its managerial implications by means of both univariate and multivariate time series forecasting models. Gasoline price forecast is among the most difficulty time series variables during its importance to the economy and extremely volatile nature. The average regular gasoline price in U.S. reached $5.06 per gallon in the month of June 2022, as opposed to $3.41 at the beginning of 2022, a 48% increase. While gasoline prices had been rising over the first half of 2022 due to supply chain disruptions as a result of global Covid 19 lockdowns and the Russia invasion of Ukraine since February 24, 2022, they then surprisingly came down in the second half of 2022 to $3.85 in November 2022, which has caught many consumers and business organizations offguard. Both univariate time series forecasting models, such as exponential smoothing and autoregressive integrated moving average, and multivariate time series forecasting models, such as time series regression models, are used in this research with the data for the period January 2002 through November 2022. We find that the time series regression model with trend, season, GDP, CPI, and crude oil price turns out the be the best forecasting model both on the training data and the testing data, even with the testing data containing significant turning points. Managerial implications and future research directions are also discussed. [ABSTRACT FROM AUTHOR]

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Mass spectrometry imaging (MSI) has emerged as a rapidly expanding field in the MS community. The analysis of large spectral data is further complicated by the added spatial dimension of MSI. A plethora of resources exist for expert users to begin parsing MSI data in R, but there is a critical lack of guidance for absolute beginners. This tutorial is designed to serve as a one-stop guide to start using R with MSI data and describe the possibilities that data science can bring to MSI analysis.

Ranked set sampling is an approach to data collection originally combines simple random sampling with the field investigator's professional knowledge and judgment to pick places to collect samples. Alternatively, field screening measurements can replace professional judgment when appropriate and analysis that continues to stimulate substantial methodological research. The use of ranked set sampling increases the chance that the collected samples will yield representative measurements. This results in better estimates of the mean as well as improved performance of many statistical procedures. Moreover, ranked set sampling can be more cost-efficient than simple random sampling because fewer samples need to be collected and measured. The use of professional judgment in the process of selecting sampling locations is a powerful incentive to use ranked set sampling. This paper is devoted to the study, we introduce an approach to the mean estimators in ranked set sampling. The amount of information carried by the auxiliary variable is measured with the on populations and samples and to use this information in the estimator, the basic ratio and the generalized exponential ratio estimators are as an improved form of a difference cum exponential ratio type estimator under the ranked set sampling in order to estimate the population mean ... of study variate Y using single auxiliary variable X. The expressions for the mean squared error of propose estimator under ranked set sampling is derived and theoretical comparisons are made with competing estimators. We show that the proposed estimator has a lower mean square error than the existing estimators. In addition, these theoretical results are supported with the aid of some real data sets using R studio. Therefore, Under RSS architecture, a better difference cum exponential ratio type estimator has been suggested. The estimator's mathematical form has been developed, and its efficiency requirements have been developed in relation to various already-existing estimators from the literature. By imputing various values for the constants used in the creation of our proposed estimator, we also provide several specific situations of our estimator. [ABSTRACT FROM AUTHOR]

The high Himalayas in northern India are an essential source of climate generation and maintenance over the entire northern belt of the Indian subcontinent. It also affects extreme weather phenomena such as western disturbances in the region during winter. The work presented here describes the trends in 117-year precipitation changes and their impact on the western Himalayas and suggests some possible explanations in the context of changing rainfall patterns. Under the investigation, the forecasting efficiency and the prediction pattern of artificial neural network (ANN) and seasonal autoregressive integrated moving average (SARIMA) models for rainfall series in the western Himalayan states of India have been assessed. The results revealed significant changes in the monthly, seasonal, and annual rainfall series data for the three states of the Western Himalayan regions from the years 1900 to 2017. The study also concludes that the nonlinear autoregressive neural network (NARNN) models can be used to forecast the western Himalayan region data series well. According to the result interpretation, the highest rainfall may be estimated in August, 1632.63 mm (2023), whereas the lowest rainfall can be obtained in October (0.43 mm) during 2023. The model predicted a gradual decrease in annual rainfall trends in Uttarakhand and Himachal Pradesh from 2018 to 2023 despite heavy rainfall prediction in the monsoon season, whereas Jammu and Kashmir increase in annual rainfall has been predicted from 2018 to 2023. Possible explanations for the change in precipitation over the western Himalayas have also been proposed and explained. [ABSTRACT FROM AUTHOR]

The current study established a research mapping of the vaccine design using bibliometric indicators and network visualization. For an analysis of the result, the study retrieved a total of 5379 documents from Scopus from 1983 to 2021. The study used the VOS Viewer and the RStudio tools for data visualization. The findings revealed that there has been significant growth in literature on vaccine design in the last two decades; in the last ten years, the year with the most publications were 2020, with 477 publications, and the highest had a total of 14,145 citations. D.R. Burton was ranked as the most prolific author, with 86 publications and 18,449 total citations and was observed as the most frequently published author in the domain. The National Institute of Health (NIH) was the most productive organization in the domain, with 266 publications. The document entitled "Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae" received a total of 1398 citations, and was the most cited document in the field of vaccine design. In network visualization, an analysis of the co-occurrence of keywords showed that "vaccine" and "vaccine design" occurred the most, which was 761 and 335 times, respectively. The study also observed that there were five clusters of author collaboration with a maximum of 18 authors and a minimum of two authors. The findings of the study will aid scholarly coalitions in the domains of medicine and health, information science and bibliometric professionals to carry out further research in the area of vaccine design. [ABSTRACT FROM AUTHOR]

Objetivo: Esta investigación presenta un análisis estadístico de la radiación solar en la ciudad de Cúcuta, con el objetivo de brindar una descripción detallada de su variabilidad entre los años 2005 y 2015. Esta información representa una herramienta evaluativa en el estudio del potencial solar de la región para el diseño de sistemas fotovoltaicos, partiendo de la necesidad de mejorar la relación costo/beneficio de esta tecnología, y así incrementar su penetración en la matriz eléctrica colombiana. Metodología: Se realizó un estudio de tres bases de datos climatológicas con información horaria, seleccionando aquella con la mayor cantidad de datos disponibles. Por medio del software R Studio, se ejecutaron dos tipos de métodos estadísticos: análisis de la varianza de un solo factor (ANOVA) y test de Bonferroni. A partir de esto, se obtuvieron gráficas que representan el resumen estadístico de los valores de radiación solar en la última década. Resultados: El análisis ANOVA arrojó un valor p de 6,28x10-7, indicando que existe una diferencia estadísticamente significativa de la media muestral entre los diferentes años de estudio. Asimismo, se identificaron los años y meses con mayor desviación y las posibles causas de la variabilidad de este parámetro. Conclusiones: A pesar de tener un comportamiento estable, la radiación de la ciudad de Cúcuta requiere de un análisis muy específico para su uso en aplicaciones que necesiten una alta sensibilidad en el manejo de esta información, ya que hay variaciones estadísticamente significativas que se pueden presentar para su uso. Financiamiento: Universidad Francisco de Paula Santander.

Background: There have been no researches assessing the research trends of the application of artificial intelligence in glioma researches with bibliometric methods. Purpose: The aim of the study is to assess the research trends of the application of artificial intelligence in glioma researches with bibliometric analysis. Methods: Documents were retrieved from web of science between 1996 and 2022. The bibliometrix package from Rstudio was applied for data analysis and plotting. Results: A total of 1081 documents were retrieved from web of science between 1996 and 2022. The annual growth rate was 30.47%. The top 5 most productive countries were the USA, China, Germany, France, and UK. The USA and China have the strongest international cooperative link. Machine learning, deep learning, radiomics, and radiogenomics have been the key words and trend topics. "Neuro-Oncology", "Frontiers in Oncology", and "Cancers" have been the top 3 most relevant journals. The top 3 most relevant institutions were University of Pennsylvania, Capital Medical University, and Fudan University. Conclusions: With the growth of publications concerning the application of artificial intelligence in glioma researches, bibliometric analysis help researchers to get access to the international academic collaborations and trend topics in the research field. [ABSTRACT FROM AUTHOR]

Numerous studies have highlighted the utility of glycan microarray analysis for the elucidation of protein-glycan interactions. However, most current glycan microarray studies analyze glycan binding protein (GBP)-glycan interactions at a single protein concentration. While this approach provides useful information related to a GBP’s overall binding capabilities, extrapolation of true glycan binding preferences using this method fails to account for printing variations or other factors that may confound relative binding. To overcome this limitation, we examined glycan array binding of three galectins over a range of concentrations to allow for a more complete assessment of binding preferences. This approach produced a richer data set than single concentration analysis and provided more accurate identification of true glycan binding preferences. However, while this approach can be highly informative, currently available data analysis approaches make it impractical to perform binding isotherms for each glycan present on currently available platforms following GBP evaluation. To overcome this limitation, we developed a method to directly optimize the efficiency of assessing association constants following multi-GBP concentration glycan array analysis. To this end, we developed programs that automatically analyze raw array data (kdMining) to generate output graphics (kaPlotting) following array analysis at multiple doses. These automatic programing methods reduced processing time from 32.8 h to 1.67 min. Taken together, these results demonstrate an effective approach to glycan array analysis that provides improved detail and efficiency when compared to previous methods.

The COVID-19 pandemic has pushed the world towards a digital era and affected the environment significantly. The present study uses a bibliometric approach to provide a comprehensive overview based on existing literature related to COVID-19 and E-learning and its environmental consequences, particularly from the year 2020–2022. In addition to the terrible impact of the pandemic on the world, environmental advantages have also been noticed. The findings show that the use of E-learning reduces the consumption of paper and prevents the cutting of trees which makes the environment more sustainable. The optimum use of technology leads to the conservation of the environment. Second, E-learning ensures developing and less developing countries to provide education at lower levels or remote areas of the society. The findings also suggest that governments and educational institutions should upgrade technology and digital tools in order to enhance E-learning education. Additionally, reviewing 1807 published articles extracted from the SCOPUS database, enrich literature related to COVID-19, E-learning, and the environment. This study also represents a graphical visualization of the bibliometric analysis using VOSviewer and R studio software. A coupling map and three-field plot also have been used for directions for future research.

Background: Enterprises' decision-making could be facilitated by properly creating or choosing and implementing demand forecasting systems. Currently, there are more and more advanced forecasting algorithms based on sophisticated technologies such as artificial neural networks and machine learning. The following research paper focuses on a case study of an automotive manufacturer. The main research aim is to propose the proper demand forecasting tool and show the prospects for implementing the mentioned solution. Methods: The research paper contains the statistical analysis of a chosen time series referring to the demanded quantity of the manufactured products. To create forecasts, models based on the following forecasting algorithms were created: ARIMA, ELM (Extreme Learning Machine), and NNAR (Neural Network Autoregressive). All algorithms are based on the R programming language. All algorithms are run in the same time series where the training and testing periods were established. Results: According to the forecasts ex-post errors and FVA (forecasts value-added) analysis, the best fitting algorithm is the algorithm based on ELM. It yields the most accurate predictions. All other models fail to add value to the forecast. Specifically, the ARIMA models damage the forecast dramatically. Such significant magnitudes of negative FVA values indicate that choosing not to forecast and plan based on the sales of the same period of the previous year is a better choice. However, in the case of the ELM model, the forecasts can be worth the time, finance, and human resources put into preparing them. Conclusion: The increased accuracy of ELM forecasts can contribute to optimizing the process of reaching consensus forecasts. While unconstrained statistical forecasts tend to be overridden, not only to produce constrained forecasts incorporating various variables such as calendar events, promotional activities, supply capacity, and operational abilities, they are also overridden by planners to reflect their foreseeing of demand. The proposed solution could also be easily implemented in the resource planning process to improve it. The proposition of the resource planning process supported by the proposed forecasting system is also shown in the following paper using a BPMN 2.0 (Business Process Modelling Notation 2.0) map. [ABSTRACT FROM AUTHOR]

The global economy can be regarded as a complex global adaptive system, which adapts and evolves according to the environment and the behavior of the agents existing on the market. A topic that is topical and can even affect the welfare of a country is the field of cryptocurrencies. Today, the most well-known phenomenon by people in this field is the emergence of bitcoin. Cryptocurrencies or virtual currencies are an emanation of the financial crisis that started in 2008, a crisis that had led to a decline in confidence of traditional bank. In this paper, we discussed the creation of possible speculative bubbles, we presented the virtual currencies, we applied techniques of multidimensional analysis of the data for their analysis, and we evaluated the effects that the appearance of the cryptocurrencies had on the cybernetic economic system in Romania. Also, the paper deals with a section on identifying risks in the field of cryptocurrencies. In the last part of this paper, we focus our attention on the viability of these coins and their future prospects.

Muscle sympathetic nerve activity (MSNA) can be acquired from humans using the technique of microneurography. The resulting integrated neurogram displays pulse-synchronous bursts of sympathetic activity, which undergoes processing for standard MSNA metrics including burst frequency, height, area, incidence, total activity, and latency. The procedure for detecting bursts of MSNA and calculating burst metrics is tedious and differs widely among laboratories worldwide. We sought to develop an open-source, cross-platform web application that provides a standardized approach for burst identification and a tool to increase research reproducibility for those measuring MSNA. We compared the performance of this web application against a manual scoring approach under conditions of rest, chemoreflex activation (n = 9, 20-min isocapnic hypoxia), and metaboreflex activation (n = 13, 2-min isometric handgrip exercise and 4-min postexercise circulatory occlusion). The intraclass correlation coefficient (ICC) indicated good to strong agreement between scoring approaches for burst frequency (ICC = 0.92–0.99), incidence (ICC = 0.94–0.99), height (ICC = 0.76–0.88), total activity (ICC = 0.85–0.99), and latency (ICC = 0.97–0.99). Agreement with burst area was poor to moderate (ICC = 0.04–0.67) but changes in burst area were similar with chemoreflex and metaboreflex activation. Scoring using the web application was highly efficient and provided data visualization tools that expedited data processing and the analysis of MSNA. We recommend the open-source web application be adopted by the community for the analysis of MSNA. NEW & NOTEWORTHY The basic analysis of muscle sympathetic nerve activity (MSNA) requires the identification of pulse-synchronous bursts from the integrated neurogram before standard MSNA metrics can be quantified. This process is a time-consuming task requiring an experienced microneurographer to visually identify and manually label bursts. We developed an open-source, cross-platform application permitting a standardized approach for sympathetic burst identification and present the performance of this application against a manual scorer under basal conditions and during sympathoexcitatory stresses. [ABSTRACT FROM AUTHOR]