Physical Sciences and Mathematics Commons^™

From Total Domination To Graph Coloring, Sawyer Isaac Osborn

Dissertations

A question involving a chess piece called a prince on the 8×8 chessboard leads to a concept in graph theory involving total domination. We say a vertex u in a graph G totally dominates a vertex v if u is adjacent to v. A subset S of the vertex set of a graph G is a total dominating set for G if every vertex in G is totally dominated by at least one vertex of S. If S is a total dominating set of G, then σ_S(v) denotes the number of …

Mgrre_Thinsections_Mgrre-101_12, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_14, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_8, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_6, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_16, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_18, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-102_5, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-102_12, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_1, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-101_11, Mgrre

Thin Sections

No abstract provided.

Quantum And Conventional Informatics Studies Of Synthesis Energetics And Defect Formation In Nitride Crystal Epitaxy, Andrew Steven Messecar

Dissertations

Machine learning is a valuable approach for the processing and analysis of complex information. By estimating relationships from recorded data, machine learning methodologies can be effective strategies for pattern recognition, enabling investigations and technological applications based thereon. The potential for improved understanding of high-dimensional data has drawn interest towards machine learning from across the sciences, including the research and development of new and improved material systems. In the context of experimental materials research, much of the reported efforts to incorporate machine learning into conventional practice have been primarily focused on either the enhanced analysis of characterization experiment data or the …

Enhancing Control Charting Schemes And Exploring New Assessment Metrics To Advance Quality Control And Cyber-Attack Detection In Manufacturing, Ahmad Al Majali

Dissertations

The increasing integration of digital technologies and industrial control systems in modern manufacturing has introduced new cybersecurity vulnerabilities within cyber–physical production environments. Malicious actors can exploit these vulnerabilities to induce subtle process deviations that degrade product quality while remaining undetected by conventional statistical monitoring tools. Such attacks can be deliberately engineered to manipulate process behavior through transient shifts that vary in magnitude, duration, and frequency. Despite extensive research on transient shifts caused by assignable causes in Statistical Process Control (SPC), limited attention has been given to intelligently designed cyber–physical attacks that exploit the structural characteristics and limitations of control charting …

Barriers To Climate Change And Sustainability Action: Nursing Education And Practice, Dawn Marie Smith

Dissertations

Climate change is one of the most pressing public health emergencies of our time and nurses can have a great impact in their current practice and in the education of future nurses (The Alliance of Nurses for Healthy Environments, n.d.; American Nurses Association, 2023; Health Care without Harm, 2025). Deaths due to rising temperatures, vector-borne illness, and food insecurity related to drought and extreme weather are on the rise (WHO, 2024). It has been estimated that globally over 250,000 additional deaths will be attributed to climate related effects between 2030 and 2050 (Watts et al., 2020; WHO, 2023).

A primary …

Mgrre_Thinsections_Mgrre-150_10, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-95_15, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-150_3, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-150_5, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-150_7, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-150_9, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-150_18, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-150_25, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-151_1, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_6, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_7, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_8, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_9, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_13, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_15, Mgrre

Thin Sections

No abstract provided.

Mgrre_Thinsections_Mgrre-152_24, Mgrre

Thin Sections

No abstract provided.