Scenario design for macrofinancial stress testing

This paper provides a novel empirical approach to scenario design for selecting a stress scenario for international macrofinancial variables. The scenario design framework is composed of several building blocks. First, multiple scenarios on the risk factors are generated by simulating a multi-country large Bayesian vector autoregression. Second, we take the perspective of a representative investor who aims to select a severe-yet-plausible scenario for a set of systematic risk factors following a factor-investing strategy. Moreover, we compare the stress scenarios selected under different approaches to measure plausibility (the Mahalanobis distance and entropy pooling under subjective views with a clear economic narrative). Finally, we compare our scenario design approach with a historical scenario approach in terms of its ability to select a stress scenario in the run-up to a rare adverse event such as the Covid-19 pandemic. We give evidence that our framework is suitable for the selection of a proper forward-looking severe-yet-plausible macrofinancial stress scenario.

Scenario for Analysing Student Interactions and Orchestration Load in Collaborative and Hybrid Learning Environments

Educational environments have been affected by the COVID-19 pandemic and have evolved to support classes, which involve in some cases synchronous hybrid learning environments. These environments enable students attend classes online and on-site simultaneously. Synchronous hybrid environments provide a greater flexibility for students but, in contrast, are likely to increase teachers’ orchestration load and decrease interactions between students, especially between those online and those on-site. This study proposes a scenario to explore the factors affecting the orchestration load and the student interactions in collaborative and synchronous hybrid learning environments. The scenario involves the use of a collaborative learning flow pattern (jigsaw) and the technologies that will enable the data collection to understand such factors affecting to orchestration load and interaction. The outcomes from the implementation of this scenario will provide useful insights to further understand the benefits and limitations of synchronous hybrid learning environments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A practice of anesthesia scenario design for emergency cesarean section in patients with COVID-19 infection based on the role of standard patient.

The new coronavirus (COVID-19) has been characterized as a world pandemic by WHO since March 11, 2020. Although it is likely that COVID-19 transmission is primarily via droplets and close contact, airborne transmission and fecal-oral route remains a possibility. The medical staff working in the operating room, such as anesthesiologists, surgeons and nurses, are at high risk of exposure to virus due to closely contacting patients. The perioperative management is under great challenge while performing surgeries for patients suffering COVID-19, including emergency cesarean section, which is one of the most common surgeries under such circumstances. How to prevent medical staff from cross-infection is an issue of great concern. In this article, we give a practice of anesthesia scenario design for emergency cesarean section in a supposed standard patient suffering COVID-19, aimed to optimize the work flow and implement the protective details through simulation of a real operation scenario, which may be useful for training and clinical practice of anesthesia management for patients suffering COVID-19 or other fulminating infectious diseases.