It is widely known that the hospitality industry is rife with sexual harassment of especially female employees, yet every year hospitality programs send out thousands of students to complete internships making them vulnerable to sexual harassment, which is a violation of their Title IX rights. The purpose of this descriptive study was to be the first to survey US hospitality students to see if they experienced sexual harassment during a recently completed internship. The majority of the 297 respondents did not experience sexual harassment but a sufficient number experienced sexist and sexual hostility mostly from male managers, coworkers, and customers. The majority of respondents were not informed as to the inappropriate sexual behaviors they may encounter during their internship; over half were given no training by the internship coordinator or employer on what to do if harassed. It is clear that more needs to be done by internship coordinators and employers to protect student interns Title IX rights.

The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical “hybrid” approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.

This paper focuses on the problem of observability and observer design for linear stochastic systems. To introduce our idea, we first construct an idealistic observer. This idealistic observer is not causal as it requires perfect knowledge of the Brownian motion. However, after introducing an a posteriori method to reconstruct the variations of the Brownian motion in discrete time, we propose a realistic hybrid observer which approximates the idealistic observer. The performance of this hybrid observer can be made arbitrarily close to that of the idealistic observer. Numerical simulations illustrate the results of the paper.

The implementation of lockdowns has been a key policy to curb the spread of COVID-19 and to keep under control the number of infections. However, quantitatively predicting in advance the effects of lockdowns based on their stringency and duration is a complex task, in turn making it difficult for governments to design effective strategies to stop the disease. Leveraging a novel mathematical "hybrid" approach, we propose a new epidemic model that is able to predict the future number of active cases and deaths when lockdowns with different stringency levels or durations are enforced. The key observation is that lockdown-induced modifications of social habits may not be captured by traditional mean-field compartmental models because these models assume uniformity of social interactions among the population, which fails during lockdown. Our model is able to capture the abrupt social habit changes caused by lockdowns. The results are validated on the data of Israel and Germany by predicting past lockdowns and providing predictions in alternative lockdown scenarios (different stringency and duration). The findings show that our model can effectively support the design of lockdown strategies by stringency and duration, and quantitatively forecast the course of the epidemic during lockdown.