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The aim of the study is to analyze viruses using parameters obtained from distributions of nucleotide sequences in the viral RNA. Seeking for the input data homogeneity, we analyze single-stranded RNA viruses only. Two approaches are used to obtain the nucleotide sequences; In the first one, chunks of equal length (four nucleotides) are considered. In the second approach, the whole RNA genome is divided into parts by adenine or the most frequent nucleotide as a "space". Rank-frequency distributions are studied in both cases. The defined nucleotide sequences are signs comparable to a certain extent to syllables or words as seen from the nature of their rank-frequency distributions. Within the first approach, the Pólya and the negative hypergeometric distribution yield the best fit. [https://www.selleckchem.com/products/tertiapin-q.html selleck] For the distributions obtained within the second approach, we have calculated a set of parameters, including entropy, mean sequence length, and its dispersion. The calculated parameters became the basis for the classification of viruses. We observed that proximity of viruses on planes spanned on various pairs of parameters corresponds to related species. In certain cases, such a proximity is observed for unrelated species as well calling thus for the expansion of the set of parameters used in the classification. We also observed that the fifth most frequent nucleotide sequences obtained within the second approach are of different nature in case of human coronaviruses (different nucleotides for MERS, SARS-CoV, and SARS-CoV-2 versus identical nucleotides for four other coronaviruses). We expect that our findings will be useful as a supplementary tool in the classification of diseases caused by RNA viruses with respect to severity and contagiousness.In this study, cluster hypergraphs are introduced to generalize the concept of hypergraphs, where cluster nodes are allowed. Few related terms and properties on cluster hypergraphs are discussed. Some operations, including the Cartesian product, union, intersection, etc., are studied. Different types of matrix representations and isomorphism are also proposed on cluster hypergraphs. The notion of an effective degree for nodes is introduced to capture the group/ cluster effects. At last, the area of applications and future directions with conclusions is deployed.COVID-19 was recognized as a pandemic in the United States in March 2020. Since the emergence, research has explored conditions associated with the illness; however, racial disparities remain underexplored. The purpose of this paper is to explore disparities in conditions associated with an increased severity risk of COVID-19 including race, personal factors, healthcare accessibility, and affordability. Using data from the 2018 National Health Interview Survey (NHIS), univariate and multivariate analysis were performed. More Non-Hispanic (NH) Blacks (61.1%) and NH Whites (61.2%) had conditions associated with increased severity risk of COVID-19 compared to Hispanics (47.1%) (p  less then  .001). Racial differences revealed a higher proportion of NH Blacks with increased severity risk of COVID-19 were female (p  less then  .001), not married (p  less then  .001), not employed for wages (p  less then  .001), had accessibility issues with transportation (p  less then  .001), and had affordability issues with paying for medicine (p  less then  .001). A higher proportion of Hispanic persons had a health place change (p = .020), had accessibility issues (e.g. telephone (p  less then  .001), longer wait times (p  less then  .001), closed facility (p = .038)) and had affordability issue with worrying about pay (p  less then  .001). Significant predictors that were positively associated with increased severity risk of COVID-19 for all racial/ethnic groups were being NH Black, older age, having appointment issues, and affordability issues with medicine. Differences in magnitude across racial group dynamics were observed. Racial disparities exist in conditions associated with increased severity risk of COVID-19. As future policies and interventions are developed, it is important to consider differentials across racial group dynamics.Documents specifying a national mathematics curriculum for grades K-12 have recently been written in Israel. We focus on the calculus component for the highest of three matriculation bound levels, and specifically on the influence of research on this component. In addition to issues of content, we identify three principles that have led the writing team, namely, the manner in which sample tasks in the curriculum document incorporate fundamental mathematical ideas and mathematical Reasoning, the Impact expected from connecting mathematics to its role in everyday life and science, and the Cultivation of fertile intellectual ground from which new concepts may emerge naturally. We demonstrate how these principles are implemented in the unit on integration. We show that mathematics education research, though not mentioned explicitly, has had a profound and pervasive influence on the content and principles of the curriculum document, from the design of entire units down to the formulation of sample tasks.In June, 2019, Japan submitted its mid-century strategy to the United Nations Framework Convention on Climate Change and pledged 80% emissions cuts by 2050. The strategy has not gone through a systematic analysis, however. The present study, Stanford Energy Modeling Forum (EMF) 35 Japan Model Intercomparison project (JMIP), employs five energy-economic and integrated assessment models to evaluate the nationally determined contribution and mid-century strategy of Japan. EMF 35 JMIP conducts a suite of sensitivity analyses on dimensions including emissions constraints, technology availability, and demand projections. The results confirm that Japan needs to deploy all of its mitigation strategies at a substantial scale, including energy efficiency, electricity decarbonization, and end-use electrification. Moreover, they suggest that with the absence of structural changes in the economy, heavy industries will be one of the hardest to decarbonize. Partitioning of the sum of squares based on a two-way analysis of variance (ANOVA) reconfirms that mitigation strategies, such as energy efficiency and electrification, are fairly robust across models and scenarios, but that the cost metrics are uncertain.