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Educating Future Engineers in the Art of Communication through Presenting

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Effective communication and engineering are not a natural pairing. The incongruence is because engineering students are focused on making, designing and analyzing. Since these are the core functions of the field there is not a direct focus on developing communication skills. This honors thesis explores the role and expectations for…

Effective communication and engineering are not a natural pairing. The incongruence is because engineering students are focused on making, designing and analyzing. Since these are the core functions of the field there is not a direct focus on developing communication skills. This honors thesis explores the role and expectations for student engineers within the undergraduate engineering education experience to present and communicate ideas. The researchers interviewed faculty about their perspective on students' abilities with respect to their presentation skills to inform the design of a workshop series of interventions intended to make engineering students better communicators.
ContributorsAlbin, Joshua Alexander (Co-author) / Brancati, Sara (Co-author) / Lande, Micah (Thesis director) / Martin, Thomas (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Software Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05

Data Analytics to Identify the Genetic Basis for Resilience to Temperature Stress in Soybeans

Description
This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range…

This paper explores the ability to predict yields of soybeans based on genetics and environmental factors. Based on the biology of soybeans, it has been shown that yields are best when soybeans grow within a certain temperature range. The event a soybean is exposed to temperature outside their accepted range is labeled as an instance of stress. Currently, there are few models that use genetic information to predict how crops may respond to stress. Using data provided by an agricultural business, a model was developed that can categorically label soybean varieties by their yield response to stress using genetic data. The model clusters varieties based on their yield production in response to stress. The clustering criteria is based on variance distribution and correlation. A logistic regression is then fitted to identify significant gene markers in varieties with minimal yield variance. Such characteristics provide a probabilistic outlook of how certain varieties will perform when planted in different regions. Given changing global climate conditions, this model demonstrates the potential of using data to efficiently develop and grow crops adjusted to climate changes.
ContributorsDean, Arlen (Co-author) / Ozcan, Ozkan (Co-author) / Travis, Daniel (Co-author) / Gel, Esma (Thesis director) / Armbruster, Dieter (Committee member) / Parry, Sam (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05