Teaching Industry 4.0 Related Topics During the COVID-19 Restrictions - An Experience Report

The COVID-19 pandemic has imposed numerous restrictions on face-to-face meetings, and one of the most impacted activities was engineering education. Most teaching professionals have adopted diverse solutions based on distance learning techniques. However, practical experiments that require laboratories could not be performed in most cases. One of the subjects studied in modern engineering that has been impacted is related to industrial automation systems. In April 2020, amidst tighter restrictions on social contact, we started cooperating with a local company to train our students on topics of interest to the corporation. Our research group was responsible for dealing with Industry 4.0 and its associated technologies. Over the past two years, we have conducted this interaction with the industry under the pandemic's constraints and obtained very interesting results. In this report, on an innovative experience in engineering education, we will describe our project's syllabus, how the relationship was between students and teachers through online tools, and mainly how we managed to ensure that students had access to experiments close to the industrial reality. © 2022 IEEE.

The Impact of Reduced Working Hours and Furlough Policies on Workers' Mental Health at the Onset of COVID-19 Pandemic: A Longitudinal Study

Although reduced working time and furlough policy initiatives are widely regarded as important for economic and business reasons, little is known about their impacts on workers' mental health at the onset of COVID-19 pandemic. Using data from the UK Household Longitudinal Panel Study data from 2018 to February 2020 and April 2020 and change score analysis, this study aims to compare mental health changes between those who worked reduced hours, were furloughed and left/lost paid work. The results suggest that at the onset of COVID-19 reduced working time and furlough can protect workers' mental health, but only for men not for women. The gender differences remain significant even after controlling for housework and childcare responsibilities at the onset of COVID-19. These results highlight the importance of distributing paid work more equitably and formulating gender-sensitive labour market policies in protection of workers' mental health.

On the explainability of hospitalization prediction on a large COVID-19 patient dataset

We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN, where combination of the data modalities (tabular and time dependent) are performed at different stages (early vs. model fusion). Despite high data unbalance, the models reach average precision 0.96-0.98 (0.75-0.85), recall 0.96-0.98 (0.74-0.85), and F1-score 0.97-0.98 (0.79-0.83) on the non-hospitalized (or hospitalized) class. Performances do not significantly drop even when selected lists of features are removed to study model adaptability to different scenarios. However, a systematic study of the SHAP feature importance values for the developed models in the different scenarios shows a large variability across models and use cases. This calls for even more complete studies on several explainability methods before their adoption in high-stakes scenarios.