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Full-Text Articles in Engineering

Spatial Fingerprinting Of Biogenic And Anthropogenic Volatile Organic Compounds In An Arid Unsaturated Zone, Christopher T. Green, Wentai Luo, Christopher H. Conaway, Karl B. Haase, Ronald J. Baker Jan 2019

Spatial Fingerprinting Of Biogenic And Anthropogenic Volatile Organic Compounds In An Arid Unsaturated Zone, Christopher T. Green, Wentai Luo, Christopher H. Conaway, Karl B. Haase, Ronald J. Baker

Chemistry Faculty Publications and Presentations

Subsurface volatile organic compounds (VOCs) can pose risks to human and environmental health and mediate biological processes. Volatile organic compounds have both anthropogenic and biogenic origins, but the relative importance of these sources has not been explored in subsurface environments. This study synthesized 17 yr of VOC data from the Amargosa Desert Research Site in Nevada with the goal of improving understanding of spatial and temporal variations that distinguish sources of VOCs from a landfill and from ambient sources including biogenic VOCs (bVOCs). Gas samples were collected from 1999 to 2016 from an array of shallow sample points (0.5- ...


Design And Evaluation Of A Non-Contact Bed-Mounted Sensing Device For Automated In-Home Detection Of Obstructive Sleep Apnea: A Pilot Study, Clara Mosquera-Lopez, Joseph Leitschuh, John Condon, Chad C. Hagen, Uma Rajhbeharrysingh, Cody Hanks, Peter Jacobs Jan 2019

Design And Evaluation Of A Non-Contact Bed-Mounted Sensing Device For Automated In-Home Detection Of Obstructive Sleep Apnea: A Pilot Study, Clara Mosquera-Lopez, Joseph Leitschuh, John Condon, Chad C. Hagen, Uma Rajhbeharrysingh, Cody Hanks, Peter Jacobs

Electrical and Computer Engineering Faculty Publications and Presentations

We conducted a pilot study to evaluate the accuracy of a custom built non-contact pressure-sensitive device in diagnosing obstructive sleep apnea (OSA) severity as an alternative to in-laboratory polysomnography (PSG) and a Type 3 in-home sleep apnea test (HSAT). Fourteen patients completed PSG sleep studies for one night with simultaneous recording from our load-cell-based sensing device in the bed. Subjects subsequently installed pressure sensors in their bed at home and recorded signals for up to four nights. Machine learning models were optimized to classify sleep apnea severity using a standardized American Academy of Sleep Medicine (AASM) scoring of the gold ...