Multivariate Analysis Commons^™

A Novel Approach To Creativity Assessment: Forced Pairwise Ranking With Large Language Models, Phillip R. Gregory Jr

Master's Theses

Assessing creativity at scale remains a persistent challenge in cognitive science, as human raters are costly, slow, and often inconsistent in their judgments. This thesis introduced a novel framework for automated scientific creativity assessment using forced pairwise ranking, in which fine-tuned large language models compared response pairs and determined which was more creative. Five empirical studies were conducted using Llama-2-7B and Llama-2-13B models adapted via LoRA fine-tuning and benchmarked against human scored responses from the Scientific Creative Thinking Test. A regression baseline achieved Pearson �� = .74 on the test set, matching the human inter-rater ceiling reported in the literature. …

Chemical And Physical Characterization Of Verbenaceae Essential Oils Using Modern Analytical Methods And Chemometrics, Aleksandra N. Hilliard

Master's Theses

Essential oils have been used for generations to treat various health issues, including mitigating insomnia, reducing stress, inflammation, and preserving foods, to mention a few. Essential oils have complex chemistry, which defines the essence of the host plant. Recently, increased commercial uses of essential oils have prompted attention at the governmental level to monitor the chemical, physical, or biological activity of essential oils. This study is an attempt to add new information for consumers to the understanding of the composition of essential oils.

Modern analytical techniques including FTIR-ATR, GC-MS, LC-MS-MS, multivariate analysis statistics, and physio-chemical techniques (electrochemistry, DPPH, TRC, and …

Investigating Nekton Response To Changing Salinities In The Mississippi Sound: An Experimental And Statistical Approach, Adam Murray

Master's Theses

The Mississippi Sound provides nursery habitats for many coastal species and is recognized for its commercial fisheries. Previous freshening events linked to the Bonnet Carré Spillway, a Mississippi River flood diversion structure, proved catastrophic for oyster populations in the Mississippi Sound, but effects on mobile fish and shellfish species are not well-defined. The planned Mid-Breton Sediment Diversion (MBSD), an initiative to combat wetland loss, is also forecasted to lower salinities in the region, increasing the need to better understand responses of commercially and ecologically important species to freshening events. The objective of this study was to characterize effects of salinity …

Modeling And Solving The Outsourcing Risk Management Problem In Multi-Echelon Supply Chains, Arian A. Nahangi

Master's Theses

Worldwide globalization has made supply chains more vulnerable to risk factors, increasing the associated costs of outsourcing goods. Outsourcing is highly beneficial for any company that values building upon its core competencies, but the emergence of the COVID-19 pandemic and other crises have exposed significant vulnerabilities within supply chains. These disruptions forced a shift in the production of goods from outsourcing to domestic methods.

This paper considers a multi-echelon supply chain model with global and domestic raw material suppliers, manufacturing plants, warehouses, and markets. All levels within the supply chain network are evaluated from a holistic perspective, calculating a total …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …