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Search Publications by: Mary Gregg (Fed)

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Displaying 1 - 5 of 5

Concentration of Ignitable Liquid Residue from Simulated Fire Debris by Dynamic Vapor Microextraction: Sensitivity to Instrument Settings and Debris Characteristics

July 6, 2023
Author(s)
Jennifer Berry, Mary Gregg, Amanda Koepke, Reta Newman, Kavita Jeerage
Dynamic vapor microextraction (DVME) is a potential method for the extraction and concentration of ignitable liquid (IL) residue in fire debris. This low flow rate, purge-and-trap headspace concentration method collects IL vapors onto a chilled adsorbent

Reproducibility Assessment of a Telecommunication Testbed

September 16, 2022
Author(s)
Jeanne Quimby, Jake Rezac, Mary Gregg, Michael Frey, Jason Coder, Anna Otterstetter
Telecommunication testbeds are a fundamental tool in communication research, enabling prototyping and validating new ideas. Unfortunately, these testbeds are often highly complex, costly, and challenging to operate, requiring simultaneous Open System

Dynamic vapor microextraction of ignitable liquid from casework containers

April 25, 2022
Author(s)
Jennifer Berry, Mary Gregg, Adam Friss, Amanda Koepke, Chris Suiter, Reta Newman, Megan Harries, Kavita Jeerage
Dynamic vapor microextraction (DVME) is a headspace concentration method that can be used to collect ignitable liquid (IL) from fire debris onto chilled adsorbent capillaries. Unlike passive headspace concentration onto activated carbon strips (ACSs) that

Generative Adversarial Network Performance in Low-Dimensional Settings

April 20, 2021
Author(s)
Felix M. Jimenez, Amanda Koepke, Mary Gregg, Michael R. Frey
A generative adversarial network (GAN) is an artificial neural network with a distinctive training architecture, designed to create examples that faithfully reproduce a target distribution. GANs have recently had particular success in applications