ABSTRACT
In sparsely occupied large industrial and commercial buildings, large-diameter ceiling fans1 (LDCFs) are commonly utilized for comfort cooling and destratification;however, a limited number of studies were conducted to guide the operation of these devices during the COVID-19 pandemic. This study conducted 223 parametrical computational-fluid-dynamics (CFD) simulations of LDCFs in the U.S. Department of Energy warehouse reference building to compare the impacts of fan operations, index-person, and worker-packing-line locations on airborne exposures to infectious aerosols under both summer and winter conditions. The steady-state airflow fields were modeled while transient exposures to particles of varying sizes (0.5–10 μm) were evaluated over an 8-h period. Both the airflow and aerosol models were validated by measurement data from the literature. It was found that it is preferable to create a breeze from LDCFs for increased airborne dilution into a sparsely occupied large warehouse, which is more similar to an outdoor scenario than a typical indoor scenario. Operation of fans at the highest feasible speed while maintaining thermal-comfort requirements consistently outperformed the other options in terms of airborne exposures. There is no substantial evidence that fan reversal is beneficial in the current large space of interest. Reversal flow direction to create upward flows at higher fan speeds generally reduced performance compared with downward flows, as there was less airflow through the fan blades at the same rotational speed. Reversing flow at lower fan speeds decreased airflow speeds and dilution in the space and, thus, increased whole-warehouse concentrations. © 2023 Elsevier Ltd
ABSTRACT
We investigated the impact of sleep and training load of Division - 1 women's basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks. With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and F2 scores of 0.94 and 0.83 for game performance and injuries, respectively, show that we can use the prediction for informative analysis in the future for coaches to make insightful decisions. Our data analysis also showed that collegiate athletes sleep less than the recommended hours (6-7 instead of 8 hours). This coupled with a long hiatus in games and training increases the risk of injury. Varied training and higher heart rate variability (due to better quality sleep) indicated a better performance, while athletes with poor sleep patterns, were more prone to injuries.