Physical Sciences and Mathematics Commons^™

Unlocking Nature’S Code: Design Principles For Cooperativity In Multi-Electron Redox Processes, Mariona Garcia Dalmases, Alexis Telford, Courtney Young

Undergraduate Research Conference

No abstract provided.

Megordle: A Wdolre Usclnbaermr, Meg Arney

Undergraduate Research Conference

Our goal was to make a dynamic word unscrambler app based on the random generation of words from a database. The original database contains 4,320 of the most common words in the English language. This project was originally designed in Visual Studio Code in java and takes input from the terminal. The project was later translated into C# and transferred into Unity to provide a visual and dynamic interface for users.

Using Macroinvertebrates As Water Quality Indicators In Angelina River, Aditi Dangi, Asa Dillon, Jolene Manlapaz, Ashley Riley, Hope Stowe

Undergraduate Research Conference

The Angelina River supports a variety of macroinvertebrates, fertilizers, and water-tolerant flora. The goal of this research was to create a complete dataset on water quality that included variables such as pH, dissolved oxygen content, nitrate levels, and temperature. The main objective was to link water contamination indicators with macroinvertebrate diversity to establish an early warning system for ecosystem health by collecting samples at two different locations of the Angelina River.

Analysis using Environmental Protection Agency (EPA) and Lehigh Environmental Initiative guidelines showed that both locations had acceptable dissolved oxygen levels and healthy nitrate levels despite the environmental fluctuation. Using …

No Generation Without Representation: Solving Ai Art Attribution With Sno-E, Sadie Campbell

Undergraduate Research Conference

Artificial intelligence has sparked a new-age debate about its ethical implications, specifically in the world of art. The Signature Neural Operative- Environment (SNO-E) is a multidisciplinary solution that draws on computer science, statistics, and art. It addresses the issue of proper attribution for Al-generated artworks through a Convolution Neural Network that adopts signature checks to cite artists whose works are sampled by Al. This novel approach ensures proper recognition and compensation for artists in the Al-generated era.