URCAF Abstracts 2019

Permanent URI for this collection

https://hdl.handle.net/10057/16104

2019 URCAF Organizing Committee

Chair: John Hammond, Liberal Arts and Sciences (Math/Social Sciences)

Shuang Gu, Engineering

Anthony May, Business

Heidi Bell, Honors College

Janice Ewing, Education

Susan Sterrett, Liberal Arts & Sciences (Humanities)

Abu Asaduzzaman , Engineering

Jessica Mirasol, University Libraries

Kelly Anderson, Health Professions

Shirlene Small, Liberal Arts and Sciences (Social Sciences)

Jessica Wewer, Student Member

Browse

Now showing 1 - 5 of 12

19th Annual Undergraduate Research and Creative Activity Forum
(Wichita State University, 2019-04-19) WSU Undergraduate Students
Implementation of artificial neural networks to classify human forearm muscle signals for individual finger movement of a robotic hand
(Wichita State University, 2019-04-19) Dowling, Anne; Desai, Jaydip M.
Artificial Neural Networks (ANNs) have been utilized in the engineering field to identify patterns from a given dataset. Powered prosthetics have specifically implemented them to detect muscle patterns; however, this has been achieved only for two patterns (open and closing). The objectives of this study were to acquire surface electromyography (sEMG) signals from human forearm muscles on fifteen participants, train Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), and Bayesian Regularization (BR) ANNs to extract eight features (six for fingers, hand close and hand open), and implement real-time control algorithms to individual finger movement of the YouBionic robotic hand using Arduino Mega 2560 microcontroller and Simulink. An Institutional Review Board approval was acquired prior to human subject testing. Each participant sat at a computer desk and performed individual finger movements while wearing a Myoband; a wireless noninvasive band of eight sEMG sensors. Results shows that the BR training algorithm outperforms the SCG and the LM training algorithms for accuracy in identifying the individual finger movements. The lowest and highest percentages in the confusion matrix were 70.4%, 89.9%, 69.2%, 89.3%, 64.3%, and 84.6% for BR, LM, and SCG training algorithms respectively. Future work includes testing these algorithms on persons with disabilities and integration of deep convolutional neural networks for higher accuracy.
The effects of mental illness and substance abuse amongst homeless veterans
(Wichita State University, 2019-04-19) Hodges, Raven; Lee, Kyoung Hag
This paper explores how mental illness and substance abuse correlate, to cause an increase in the homeless veteran population. The specific mental illnesses that will be explored is posttraumatic stress disorder (PTSD); alcohol and drug abuse are the two forms of substance abuse that will be used within the research. This paper examines several articles, that explain comorbidity and how it has a significant effect on veterans. The information gathered is combined with reports from Veteran Affairs (VA), the Annual Homeless Assessment Report (AHAR) and the U.S. Conference Mayors' Report to support the data that homelessness among the veteran populations is an area of concern for the United States government.
Exploring the antimicrobial effects of neem and cranberry in a liquid-based assay system
(Wichita State University, 2019-04-19) Oshakuade, Ricky; McDonald, J. David; Prince, Alisha; McDonald, J. David
This study was conducted to explore the antimicrobial effects of neem, an extract from an evergreen tree native to India, and cranberry in preventing the formation of biofilm on the surface of the tooth that leads to the development of dental caries or tooth decay. These naturally occurring products are ideal considering their availability, particularly in developing regions of the world that lack access to consistent dental care. The goal of this project is to explore the combined effect of two naturally-occurring antimicrobial agents to see whether they display synergy. A synergistic combination is revealed with the display of an inhibitory effect such that 1+1 > 2. Synergy was assessed using a Checkerboard Assay system, which measures the Minimum Inhibitory Concentration (MIC) of the compounds. The MIC is visualized by applying varying concentrations of neem and cranberry along with bacteria and broth in each well of a 96-well plate. The wells that appear to lack any bacterial growth indicate that they are at or above the MIC. These findings were then quantified to assess synergy using the Lowest Fractional Inhibitory Concentration Index (FIC), Mean FIC, and the Two Well Method. Results were not supportive for synergy using the Lowest FIC. Results in the Mean FIC and Two Well method were inconclusive. These results are preliminary and more experimentation will be necessary for definitive conclusions.
Female tricksters in the Old Testament
(Wichita State University, 2019-04-19) Herda, Holly; Thelle, Rannfrid I.
The trickster is a character found in stories from virtually all cultures. The trickster can be male or female, malevolent, humorous, or both. They exist on the fringes of society, being different or disenfranchised in some way, and often possess secret knowledge that enables them to get ahead. The Hebrew Bible (Old Testament) is no different. Tricksters abound, both male and female, but particularly female. So, I ask: why are female tricksters so prevalent in the Old Testament? How do they differ from the male tricksters? How do they compare to the general trickster archetype? To explore this, I survey each book of the Old Testament, cataloging every character that engages in trickery or deceit. I compare their circumstances and motives and demonstrate that while the male tricksters stick closely to the traditional trickster archetype, utilizing trickery for self-preservation, personal gain, or amusement, the female tricksters defy norms. As opposed to the men, their actions are never malevolent or humorous, but often desperate, last-ditch attempts to protect their husbands, families, and communities.

Browse

Recent Submissions