ABSTRACT
The Sars-Cov2 virus has caused the worst health emergency of the last decade. Furthermore, new strains make the fight against COVID-19 appear far from over. The virus causes a severe acute respiratory syndrome that can lead to death. Effective identification of lung damage by chest radiography using deep learning methods could be advantageous for imaging physicians in differentiating people who need to be admitted to an intensive care unit (ICU) from people that don’t require medical attention, to avoid the collapse of health systems. This article describes the development of a deep learning model to classify and assess lung injuries with a protocol for lung injury quantification. The model is based on U-Net segmentation and injury classification according to the RALE score system. Kaggle platform was used to obtain the chest radiography dataset and MATLAB to generate the mask dataset for training. Finally, each lung is divided in 4 quadrants for lesion quantification. An accuracy of 92.86% was obtained in the segmentation process and 100% in the process of classifying levels of lung lesions. © 2022, Springer Nature Switzerland AG.
ABSTRACT
We report data from an online experiment which allows us to study how generosity changed over a 6-day period during the initial explosive growth of the COVID-19 pandemic in Andalusia, Spain, while the country was under a strict lockdown. Participants (n = 969) could donate a fraction of a 100 prize to an unknown charity. Our data are particularly rich in the age distribution and we complement them with daily public information about COVID-19-related deaths, infections and hospital admissions. We find correlational evidence that donations decreased in the period under study, particularly among older individuals. Our analysis of the mechanisms behind the detected decrease in generosity suggests that expectations about others' behaviour, perceived mortality risk and (alarming) information play a key-but independent-role for behavioural adaptation. These results indicate that social behaviour is quickly adjusted in response to the pandemic environment, possibly reflecting some form of selective prosociality.
ABSTRACT
We evaluate the impact of the COVID-19 pandemic on the volume and quality of firms' daily usage of remote (video) meeting technologies. While per-firm daily meeting volume (minutes, number of meetings, and total participants) increase significantly (between 15\% and 48\%), the average meeting is more crowded (+15\%), shorter (-30\%, or 10 minutes) and of significantly poorer (video/audio) quality (-59\%). Firms in the service sector experience the most notable increases in volume usage, while effects on the duration, size and quality of meetings is experienced by firms in all industries. © 2021. All Rights Reserved.