Open Access. Powered by Scholars. Published by Universities.®
University of Tennessee, Knoxville
Faculty Publications and Other Works -- Mechanical, Aerospace and Biomedical Engineering
Articles 1 - 2 of 2
Full-Text Articles in Mechanical Engineering
Experimental Studies And Dynamics Modeling Analysis Of The Swimming And Diving Of Whirligig Beetles (Coleoptera: Gyrinidae), Zhonghua Xu, Scott C. Lenaghan, Benjamin E. Reese, Xinghua Jia, Mingjun Zhang
Experimental Studies And Dynamics Modeling Analysis Of The Swimming And Diving Of Whirligig Beetles (Coleoptera: Gyrinidae), Zhonghua Xu, Scott C. Lenaghan, Benjamin E. Reese, Xinghua Jia, Mingjun Zhang
Faculty Publications and Other Works -- Mechanical, Aerospace and Biomedical Engineering
Whirligig beetles (Coleoptera, Gyrinidae) can fly through the air, swiftly swim on the surface of water, and quickly dive across the air-water interface. The propulsive efficiency of the species is believed to be one of the highest measured for a thrust generating apparatus within the animal kingdom. The goals of this research were to understand the distinctive biological mechanisms that allow the beetles to swim and dive, while searching for potential bio-inspired robotics applications. Through static and dynamic measurements obtained using a combination of microscopy and high-speed imaging, parameters associated with the morphology and beating kinematics of …
A Data-Driven Predictive Approach For Drug Delivery Using Machine Learning Techniques, Yuan Yuan Li, Scott C. Lenaghan, Mingjun Zhang
A Data-Driven Predictive Approach For Drug Delivery Using Machine Learning Techniques, Yuan Yuan Li, Scott C. Lenaghan, Mingjun Zhang
Faculty Publications and Other Works -- Mechanical, Aerospace and Biomedical Engineering
In drug delivery, there is often a trade-off between effective killing of the pathogen, and harmful side effects associated with the treatment. Due to the difficulty in testing every dosing scenario experimentally, a computational approach will be helpful to assist with the prediction of effective drug delivery methods. In this paper, we have developed a data-driven predictive system, using machine learning techniques, to determine, in silico, the effectiveness of drug dosing. The system framework is scalable, autonomous, robust, and has the ability to predict the effectiveness of the current drug treatment and the subsequent drug-pathogen dynamics. The system consists …