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This study employs a comprehensive bibliometric analysis, adhering to the PRISMA protocol, to systematically review and map global academic trends in neuroimaging tools for neuromarketing research. Utilising data from the Scopus database spanning January 2007 to July 2023, 104 documents were subjected to analysis, revealing a discernible upward trajectory in publications. The findings revealed that the United States emerges as the predominant contributor, with 19 papers, while influential authors such as Balconi, M., and the most-cited article, "The Neural Mechanisms Underlying the Influence of Pavlovian Cues on Human Decision Making," signify pivotal contributions to the field. A keyword analysis reveals the prominence of key themes, including "emotion," "attention," and "advertising," offering valuable theoretical insights into the field of neuromarketing research. The journal Frontiers in Human Neuroscience is identified as the most productive, with 11 papers published. This comprehensive bibliometric analysis offers insights into the current landscape of neuroimaging tools in neuromarketing, as well as providing a foundation for future research directions. The implications of these findings extend to theoretical advancements, which provide guidance to researchers in refining frameworks and offering insights for strategic decision-making in the use of neuroscientific approaches for effective marketing strategies.

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 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 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]

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.

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]

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]

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.

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.

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]

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]

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]

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.

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]

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 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]

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.

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.

Today, the importance of reproducible and transparent research has been increased along with the use of large data sets in a multinational environment. Researchers using intermedia agenda-setting theory have also affected this trend by conducting studies using big data and social media data. The Rozelle-Campbell formula is widely used by such researchers to investigate media effects and their direction. “R” is a general purpose statistical software package used in many fields of research. It is licensed for a free and an open-source software. There are many researchers have released applications for general use in “R” after they have been introduced into the literature. The authors have written this informative paper as a reference text for users of R in communication studies. Our primary purpose is to provide users an easy way to learn how to perform using R and R Studio analysis to use in intermedia agenda-setting, without having to navigate through the extensive, and excessive documentation. Another purpose of this study is to simplify and to reduce analyzing steps in complex cross-lagged correlations (synchronous correlations, cross-lagged correlations and autocorrelations) and Rozelle-Campbell Baseline analysis. On the other hand, neither do we claim to provide the most convenient solution for the researchers. We have tried to provide a simple approach which is easily understandable for those with an understanding of statistics. We anticipate that this tool “R” will be particularly useful for intermediate users, who need or want to conduct their studies when retrieving from the big data. In addition, this article will encourage researchers to improve their analytic abilities, by providing a brief manual and tested examples. Following the existing literature, this study re-tests the results of previous four intermedia agenda-settings studies, which had been previously analyzed by SPSS Software Program or Microsoft Excel. The present study’s data analysis 100% has matched with original studies. The results suggest that researchers can use this R Studio scripts to conduct their intermedia agenda-setting analysis.

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]

In this data article, we present the dataset from the RNA-Seq analysis of subcutaneous adipose tissue collected from 5 healthy normal weight women (NW, age 37 ± 6.7 years, BMI 24.3 ± 0.9 kg/m2) and 5 obese women (OBF, age 41 ± 12.5 years, BMI 38.2 ± 4.6 kg/m2). Raw data obtained from Illumina NextSeq 500 sequencer were processed through BlueBee® Genomics Platform while differential expression analysis was performed with the DESeq2 R package and deposited in the GEO public repository with GSE166047 as accession number. Specifically, 20 samples divided between NW (control), OBF (obese women), OBM (obese male) and OBT2D (obese women with diabetes) are deposited in the GSE166047. We hereby describe only 10 samples (5 healthy normal weight women reported as NW and 5 obese women reported as OBF) because we refer to the data published in the article “Transcriptional characterization of Subcutaneous Adipose Tissue in obesity affected women highlights metabolic dysfunction and implications for lncRNAs” (DOI: 10.1016/j.ygeno.2021.09.014). Pathways analyses were performed on g:Profiler, Enrichr, ClueGO and GSEA to gain biological insights on gene expression. Raw data reported in GEO database along with detailed methods description reported in this data article could be reused for comparisons with other datasets on the topic to obtain transcriptional differences in a wider co-hort. Moreover, detailed pathways analysis along with cross-referenced data with other datasets will allow to identify novel dysregulated pathways and genes responsible for this regulation. The biological interpretation of this dataset, along with related in vitro experiments, is reported by Rey et al., in Genomics (DOI: 10.1016/j.ygeno.2021.09.014).

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]

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.

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.

This paper aims to conduct a bibliographic analysis on the published papers related to anthropogenic deposits. Based on data recovered from the Scopus database, a bibliometric analysis was performed using the softwares R Studio and Vosviwer. As a result, there was an increase in the number of articles published since the year 2006. The Russian Federation is the country that most publishes works in this field of research. On the other hand, the United States of America is the country that most presents papers considered relevant. A number of 445 authors published papers related to this topic, as first author or co-author, being the journal International Multidisciplinary Survey of Scientific Geoconference, with the largest number of publications. The most frequent keywords in the articles were mapped and divided into four groups: Archeology; Geology and Soils; Urban Deposits; Mining, areas of prominence in this field of research.

Copyright of Turkish Review of Communication Studies / Türkiye İletişim Araştırmaları Dergisi is the property of Marmara University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article is aimed at insurance markets research for 31 countries based on insurance indicators such as insurance den-sity and insurance penetration and providing hierarchical clustering analysis using RStudio. The clustering is done with k-means and Ward methods and the authors tried to interpret the clustering analysis and prove the reasons why coun-tries refer to the specific cluster and explain further development opportunities for each cluster. The authors suggested the hypothesis that Russia should be in one cluster with Eastern European counties and the hypothesis was rejected as a result of the research. The analysis revealed that 31 countries should be divided into 5 clusters and Russia is referred to the cluster with Turkey and Mexico that have very similar insurance systems and share common problems in insurance industry development. The authors infer that insurance instruments that are used by members of one cluster can poten-tially be adopted by other members within the cluster. The calculations in R are included in the paper and the research can be repeated by students in classes with insurance industry data as well as in other sectors and with different data.

Copyright of Anuario do Instituto de Geociencias is the property of Universidade Federal do Rio de Janeiro, Instituto de Geociencias and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)