For more than three decades, higher education has attracted growing interest from scholars, students, and academic institutions worldwide. This paper aims to analyze the literature review of quality of higher education, using the bibliometric analysis adapted from VOSviewer software to examine the data of 500 studies published in the Web of Science from 2000 to 2018 related to this topic. The results were presented and discussed with the following approaches: keywords, authors, references (research papers), research work, countries, and research institutions. The study found that bibliometric analysis is fundamental in detailing the theoretical literature and developing an integrated theoretical framework on quality of higher education. This review provides reference points for entry into this interdisciplinary field.

The purpose of this paper is to discuss death cases on the World, exacerbated investor fears, uncertainties, and increased volatility of crude oil prices in financial markets. The reaction absorbed the epidemic gradually until January 22. Still, the market situation changed soon with a sharp drop in prices, and prices slowly recovered after that until June 14. The data of this research using an econometric model, the ARDL (Autoregressive Distributed Lag), according to the Gets methodology, using daily data, January 22 –June 14, 2020. Our ARDL shows, the death ratio has a significant negative effect on oil price dynamics. However, the death ratio has an indirect impact on volatility in Crude Oil prices. The findings show that the death toll of COVID-19 has a significant impact on oil prices in Saudi Arabia (KSA). However, the preliminary results mainly influence by the situation reported in the USA. When we assess the case outside the USA, and we see the positive effect of the COVID-19 death figures on oil prices, therefore, stress the amplification of death-related risks to the financial market and the real economy, caused by increased, policy-induced economic uncertainty in the United States.

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within “e-learning in higher education.” E-learning as a concept has been widely used in the academic and professional communities and has been approved as an educational approach during COVID-19. This article starts with a literature review of e-learning. Diverse subjects have appeared on the topic of e-learning, which is indicative of the dynamic and multidisciplinary nature of the field. These include analyses of the most influential authors, of models and networks for bibliometric analysis, and progress towards the current research within the most critical areas. A bibliometric review analyzes data of 602 studies published (2020–2021) in the Web of Science (WoS) database to fully understand this field. The data were examined using VOSviewer, CiteSpace, and KnowledgeMatrix Plus to extract networks and bibliometric indicators about keywords, authors, organizations, and countries. The study concluded with several results within higher education. Many converging words or sub-fields of e-learning in higher education included distance learning, distance learning, interactive learning, online learning, virtual learning, computer-based learning, digital learning, and blended learning (hybrid learning). This research is mainly focused on pedagogical techniques, particularly e-learning and collaborative learning, but these are not the only trends developing in this area. The sub-fields of artificial intelligence, machine learning, and deep learning constitute new research directions for e-learning in light of COVID-19 and are suggestive of new approaches for further analysis.

This article opens up a new field of research in Light of COVID-19 Artificial Intelligence, mainly explaining this binding domain's current trends and knowledge fields.        The bibliometric analysis was performed to present new research trends in Artificial Intelligence in light of COVID-19. The data of 1635 studies published in Web of Science were analyzed during the last two years (2020-2021) using three software CiteSpace, VOSviewer, and KnowledgeMatrix Plus.       The findings suggest that there are twelve research clusters in this topic (emerging industry, cross-sectional survey study, emerging technologies, joint position paper, colony predation algorithm, medical worker, deep learning, covid-19 risk prediction, future smart connected communities, supply chain resilience, virtual screening, and k-12 students). The United States, People's Republic of China, the United Kingdom, India, Saudi Arabia, Italy, Australia, Spain, South Korea, and Canada are the most intriguing countries that investigated this issue during COVID-19, so this study reveals the latest policy trends in Artificial intelligence using bibliometric analysis

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