Educational governance in a pandemic scenario: mapping of public actions in the state of Amazonas

To analyze the design of educational governance for Basic Education that materialized in the first year of the Covid-19 pandemic in Amazonas, the article resumes and analyzes the decision-making of the public power on the municipal and state education systems of the capital and state between March 2020 and February 2021. Based on the Sociology of Public Action and a qualitative approach, the study that serves as the basis for this article – developed by researchers in the field of Education at the Federal University of Amazonas, based on an integrative review of the specialized literature, mapping and analysis of the actions of the education departments of Manaus and Amazonas – reconstructed the consolidated paths in the educational field in one of the epicenters of the pandemic in the country. The data reveal the complex network of public and private agents that engender the governance investigated here, the tensions between the comings and goings around the resumption of face-to-face classes, as well as alternative teaching models for the pandemic scenario and the deepening of public-private partnerships. © 2023, Ensaio. All Rights Reserved.

Educational Governance in One of the Epicenters of the Pandemic

The pandemic scenario is analyzed in this article focusing on the educational field and on the transformations and challenges faced by Basic Education schools. With the objective of analyzing the pendular tensions between suspension and return of face-to-face classes in the state of Amazonas in the first months of the Covid-19 pandemic, the article retraces educational governance in the first half of 2020. From the Sociology of Public Action, we demonstrate: the main consequences of the pandemic on education;the complex web of educational governance in times of a pandemic;and the struggles for hegemony from the movements between suspension and return of classes in schools in Amazonas.

Applications of sars-COV-2 sequencing data

Monitoring and understanding pathogen epidemiology and evolution is crucial, especially during an outbreak. COVID-19 pandemic, caused by the newly identified virus SARS-CoV-2, is one of the current major challenges that require the best scientific and technological means. Secondary to sequencing, one of the most basic steps in the study of the many virus strains is sequence alignment. In this work, the focus was on the search and selection of a Biopython algorithm of global pairwise sequence alignment, its implementation and analysis of effectiveness and complexity. To apply this algorithm, which provides the level of similarity between sequences, it was used SARS-CoV-2 genomic sequences of samples collected in 10 countries to analyse its similarity with the reference sequence (NC-045512). There were obtained very high similarities between all sequences and it was observed a decrease of similarity over time in most of the countries included in this study. These results are concordant with recent ones that are publicly shared. In this context, the algorithm performed effectively and its low complexity will not limit its application in a bigger volume of data. © Proceedings of the 14th IADIS International Conference Computer Graphics, Visualization, Computer Vision and Image Processing 2020, CGVCVIP 2020 and Proceedings of the 5th IADIS International Conference Big Data Analytics, Data Mining and Computational Intelligence 2020, BigDaCI 2020 and Proceedings of the 9th IADIS International Conference Theory and Practice in Modern Computing 2020, TPMC 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.