Between Minimum Living Income and Basic Income: disputes over the government of poverty in Spain during the Great Interruption

In this article, the discourses around wealth redistribution policies in Spain based on the case of disputes around the Minimum Living Income during the Great Interruption ???the period of health and economic crisis from March to September 2020??? are analyzed. To do this, the most meaningful moments in the evolution of the public debate on the policy are synthesized and analyzed: the outbreak of the pandemic in March, the approval of the Minimum Living Income in June, and the first phase of implementation of the policy until September of 2020. The analysis of political, media and activist discourses shows the disputes in the ways of tackling poverty, on the one hand, with the initial openings of the political imagination to innovative policies ???such as the Universal Basic Income or the Quarantine Income??? or the normalization of an approach to the expansion of social and economic rights, and on the other hand, the transversal affirmation of the work ethic, the narratives of stigmatization of people without income, the modes of control, bureaucratization and government of the poverty, and the technocratic justification of the policy as an improvement of the minimum income system of the regions. Despite the continuities and discontinuities in the discursive frameworks, it is concluded that the disputes analyzed around the Minimum Vital Income express some of the social tensions derived from the trends of growing inequality since the 2008 which have not been reversed to date.

An Ensemble Machine Learning Model to Investigate the Screening System for Identification of Potential Patients with COVID-19 in Sudan

This study aims at designing an ensemble Machine Learning Model to serve as a screening system to predict the potential of COVID-19 infection. according to specific parameters, it considers an online survey filled by 5966 participants from Khartoum City since Khartoum was under quarantine. Major statistical approaches were implemented as data cleaning, performing feature selection using Random Forest algorithm to elect the proper features, and finally, building the model on two parts: the first one used K-mode clustering algorithm whereas the second utilized Support Vector Classifier (SVC). The features included symptoms, age, underlying conditions, geographical location, the period of the symptoms, close contact with someone who has confirmed a case of coronavirus, and the number of deaths among the family members. The results indicated that the overall accuracy of the K-mode Part was 71 %;however, the sensitivity to predict cases as negative was 77%, while the accuracy of SVC Part was 76 %. The identity between predictions of the two Parts was 79%. The work concluded that the symptoms in the proposed Screen system - considering the highest weight- appeared as following: Fatigue, Headache, Fever, Gastrointestinal Disorders, Anosmia, Dry Cough, Short of Breath, and Chest Pain, respectively. © 2021 IEEE.