Breaking Down the Lockdown: The Causal Effects of Stay-At-Home Mandates on Uncertainty and Sentiments During the COVID-19 Pandemic

We study the causal effects of lockdown measures on uncertainty and sentiment on Twitter. To this end, we exploit the quasi-experimental framework created by the first COVID-19 lockdown in a high-income economy--the unexpected Italian lockdown in February 2020. We measure changes in public sentiment using deep learning and dictionary-based methods on the text of daily tweets geolocated within and near the locked-down areas, before and after the treatment. We classify tweets into four categories--economics, health, politics, and lockdown policy--to examine how the policy affected emotions heterogeneously. Using a staggered difference-in-differences approach, we show that the lockdown did not have a significantly robust impact on economic uncertainty and sentiment. However, the policy came at the price of higher uncertainty on health and politics and more negative political sentiments. These results, which are robust to a battery of robustness tests, show that lockdowns have relevant non-health related implications

Consensus guidelines for the use and interpretation of angiogenesis assays

The formation of new blood vessels, or angiogenesis, is a complex process that plays important roles in growth and development, tissue and organ regeneration, as well as numerous pathological conditions. Angiogenesis undergoes multiple discrete steps that can be individually evaluated and quantified by a large number of bioassays. These independent assessments hold advantages but also have limitations. This article describes in vivo, ex vivo, and in vitro bioassays that are available for the evaluation of angiogenesis and highlights critical aspects that are relevant for their execution and proper interpretation. As such, this collaborative work is the first edition of consensus guidelines on angiogenesis bioassays to serve for current and future reference

GRD-Net: Generative-Reconstructive-Discriminative Anomaly Detection with Region of Interest Attention Module

Anomaly detection is nowadays increasingly used in industrial applications and processes. One of the main fields of the appliance is the visual inspection for surface anomaly detection, which aims to spot regions that deviate from regularity and consequently identify abnormal products. Defect localization is a key task that is usually achieved using a basic comparison between generated image and the original one, implementing some blob analysis or image-editing algorithms in the postprocessing step, which is very biased towards the source dataset, and they are unable to generalize. Furthermore, in industrial applications, the totality of the image is not always interesting but could be one or some regions of interest (ROIs), where only in those areas there are relevant anomalies to be spotted. For these reasons, we propose a new architecture composed by two blocks. The first block is a generative adversarial network (GAN), based on a residual autoencoder (ResAE), to perform reconstruction and denoising processes, while the second block produces image segmentation, spotting defects. This method learns from a dataset composed of good products and generated synthetic defects. The discriminative network is trained using a ROI for each image contained in the training dataset. The network will learn in which area anomalies are relevant. This approach guarantees the reduction of using preprocessing algorithms, formerly developed with blob analysis and image-editing procedures. To test our model, we used challenging MVTec anomaly detection datasets and an industrial large dataset of pharmaceutical BFS strips of vials. This set constitutes a more realistic use case of the aforementioned network

Why is my job so stressful?: characteristics, processes and models of stress work

This chapter deals with the issue of job stress in relation to employee health, well‐being and performance. The chapter starts with an outline of job stress as a societal problem, illustrating current trends in society, the nature of work, and job stress. It continues with a discussion of the main perspectives on job stress, including bad and good stress, and of the potential role of individual differences in the job stress process. Next, an integrative process model of job stress is presented that will pave the way for a profound discussion of four prominent theoretical models on job stress: (1) the Demand‐Control‐Support Model, (2) the Effort‐Reward Imbalance Model, (3) the Job‐Demands Resources Model, and (4) the Demand‐Induced Strain Compensation Recovery Model. Using the insights gained through these models, the chapter ends by explaining how a stressful working situation can be transferred into ‘healthy work’

How does work fit with my life? The relation between flexible work arrangements, work-life balance and recovery from work

The right balance between work, personal life and daily recovery is an important determinant of employees’ well-being, health and experience of stress. One of the most important things in many people's lives is their relationship with their family. Work and home life are central aspects of most employees' lives, although there are also other significant aspects of the work-life balance, such as leisure, political and social engagements. The interaction between work and home can be negative, for example, when the time spent working leaves too little time for private life; or it can be positive, for example, when a stimulating job enhances one's general mood and life satisfaction. This chapter reviews the concepts of work-life balance, discusses recovery from work, and presents two key components of flexible work - time flexibility and place flexibility. The review focuses on the antecedents of these concepts and their consequences in terms of health, well-being and job-related outcomes