Online Learning, Library Access, and Bookcase Insecurity: A German Case Study

During the “Bookshelves in the Age of the COVID-19 Pandemic” conference, a round table of past and present Book Studies students from the University of Münster, Germany, reflected on how the pandemic has affected their access to books and bookshelves. Building on these conversations, this chapter explores the issues of student life during the pandemic and bookcase insecurity in Germany. Drawing from secondary sources in addition to interviews with students and librarians and an original survey sent out to students at the English Department of the University of Münster, we examine the impact of the pandemic on students vis-à-vis the conflation of public and private spaces and the ensuing “bookcase insecurity,” as well as the effect of restricted access to libraries and university spaces on students, with a special focus on the experiences of students with disabilities. These conversations ultimately open up to the broader questions of what accessibility means in an increasingly digital age and which barriers must be crossed to make resources and online learning available to all students. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mutation Prediction for Coronaviruses Using Genome Sequence and Recurrent Neural Networks

The study of viruses and their genetics has been an opportunity as well as a challenge for the scientific community. The recent ongoing SARSCov2 (Severe Acute Respiratory Syndrome) pandemic proved the unpreparedness for these situations. Not only the countermeasures for the effect caused by virus need to be tackled but the mutation taking place in the very genome of the virus is needed to be kept in check frequently. One major way to find out more information about such pathogens is by extracting the genetic data of such viruses. Though genetic data of viruses have been cultured and stored as well as isolated in form of their genome sequences, there is still limited methods on what new viruses can be generated in future due to mutation. This research proposes a deep learning model to predict the genome sequences of the SARS-Cov2 virus using only the previous viruses of the coronaviridae family with the help of RNN-LSTM (Recurrent Neural Network-Long ShortTerm Memory) and RNN-GRU (Gated Recurrent Unit) so that in the future, several counter measures can be taken by predicting possible changes in the genome with the help of existing mutations in the virus. After the process of testing the model, the F1-recall came out to be more than 0.95. The mutation detection???s accuracy of both the models come out about 98.5% which shows the capability of the recurrent neural network to predict future changes in the genome of virus.