University Members Earn NASA Connecticut Space Grant Consortium Awards
The NASA Connecticut Space Grant Consortium (CTSGC) recently announced the recipients of its Fall 2020 Call for Proposals. Award recipients include one student and two faculty from the University of Hartford.
Nathan Green ’23, a biomedical engineering major in the College of Engineering, Technology, and Architecture, was one of five students to receive a $3,000 CTSGC scholarship.
Xin Ye, assistant professor of physical therapy in the College of Education, Nursing and Health Professions, earned a faculty project grant for his research on Acute Effects of Combining Neuromuscular Electrical Stimulation and Voluntary Isometric Exercise on Neuromuscular Functions. He described his proposal as:
Aligning with the Human Exploration & Operations Mission Directorate (HEOMD) to provide countermeasures to microgravity-induced human neuromuscular deterioration, the purpose of this investigation is to examine the acute effects of combining neuromuscular electrical stimulation (NMES) and isometric exercise (ISO) on human neuromuscular functions. At least 28 healthy participants will be recruited to participate in this 4-visit experiment. The participants will undergo different exercise modalities (Control, ISO, and NEMS + ISO), and pre- and post-measurements such as strength and motor unit firing properties will be conducted to evaluate the efficacy of the combined exercise modality (NMES + ISO).
Reihaneh Jamshidi, assistant professor of mechanical engineering in the College of Engineering, Technology, and Architecture, earned a faculty travel grant to attend the 2021 Materials Research Society (MRS) meeting in Seattle, Washington. MRS is the largest meeting for materials scientists all over the world. One of the symposiums of the conference is dedicated to Artificial Intelligence and Automation for Materials Design. Jamshidi plans to present her research, "Machine Learning Prediction of Creep Rupture Behavior for Metal Alloys," which details her implementing AI and machine learning algorithms for prediction and optimization of creep resistance (creep rupture lifetime and rupture stress) in steel alloys as a function of the alloy composition, processing methods, and temperature. The results will be disseminated among experts of Artificial Intelligence for Materials, from industry, academia, and national labs.
Read more about all of the award recipients here.