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In this contribution we expand on the critical role played by kinematic of flow in conjunction with the principle of the conversation of total mass in guiding students towards the observation skills, geometry, flow dimensions and total mass conservation in predicting the type of velocity functions as a prerequisite to the application of momentum conservation equations to determine the actual velocity profile. Adopting a practitioner-based approach, we leverage cycles of inquiry1 that are guided by the six elements of the Renaissance Foundry Model2 (herein the Foundry) to explore the role of kinematics of fluid flow as implemented in a undergraduate engineering curriculum. In particular, we identify the Challenge, review the fundamental concepts of the kinematics of the particle to formulate the Organization Tools, and identify related Resources to this challenge. Subsequently, Knowledge Acquisition will guide the understanding of important connections with the kinematic of flow, and then we will apply the Transfer of Knowledge to develop the fundamental aspects of the Prototype of Innovative Technology. This will be centered on a methodology useful to guide students in applying the concepts of kinematics of flows to obtain the fluid velocity functionality. The research will be illustrated with a case study relevant to the curriculum in chemical engineering in the context of ChE 3550, Fluid Mechanics, a core course in the chemical engineering curriculum. 

During the COVID-19 health crisis, local public officials continue to expend considerable energy encouraging citizens to comply with prevention measures in order to reduce the spread of infection. During the pandemic, mask-wearing has been accepted among health officials as a simple preventative measure; however, some local areas have been more likely to comply than others. This paper explores methods to better understand local attitudes towards mask-wearing as a tool for public health officials’ situational awareness when preparing public messaging campaigns. This exploration compares three methods to explore local attitudes: sentiment analysis, n-grams, and hashtags. We also explore hashtag co-occurrence networks as a possible starting point to begin the filtering process. The results show that while sentiment analysis is quick and easy to employ, the results offer little insight into specific local attitudes towards mask-wearing, while examining hashtags and hashtag co-occurrence networks may be used a tool for a more robust understanding of local areas when attempting to gain situational awareness.