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Mechanical Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

University of Tennessee, Knoxville

2012

Series

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 Nov 2012

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 the whirligig beetle ...


A Data-Driven Predictive Approach For Drug Delivery Using Machine Learning Techniques, Yuan Yuan Li, Scott C. Lenaghan, Mingjun Zhang Jan 2012

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 of ...