Multivariate Analysis Commons^™

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 …

An Assessment Of Convergence In The Feeding Morphology Of Xiphactinus Audax And Megalops Atlanticus Using Landmark-Based Geometric Morphometrics, Edward Chase Shelburne

Master's Theses

Convergence is an evolutionary phenomenon wherein distantly related organisms independently develop features or functional adaptations to overcome similar environmental constraints. Historically, convergence among organisms has been speculated or asserted with little rigorous or quantitative investigation. More recent advancements in systematics has allowed for the detection and study of convergence in a phylogenetic context, but this does little to elucidate convergent anatomical features in extinct taxa with poorly understood evolutionary histories. The purpose of this study is to investigate one potentially convergent system—the feeding structure of Xiphactinus audax (Teleostei: Ichthyodectiformes) and Megalops atlanticus (Teleostei: Elopiformes)—using a comparative anatomical approach to assess …

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 …

The Effect Of Vegetative Structure On Nest-Burrow Selection By The Western Burrowing Owl: Comparing Traditional Methods To Photogrammetry With An Unmanned Aerial System, Dylan J. Steffen

Master's Theses

The shortgrass prairie ecoregion in the United States has been reduced to 52% of its historical extent, contributing to reduced habitat for native species. One such species is the Burrowing Owl (Athene cunicularia). The Western Burrowing Owl subspecies (A. c. hypugaea) is listed as a Species of Special Concern in nearly every western and midwestern state, including Kansas where it is designated as a Tier II Species of Greatest Conservation Need. Habitat destruction due to conversion to cropland, increasing use of pesticides, and reduction in burrowing mammal abundance are the primary threats that have led to …