Physical Sciences and Mathematics Commons^™

Training Sensor-Agnostic Deep Learning Models For Remote Sensing: Achieving State-Of-The-Art Cloud And Cloud Shadow Identification With Omnicloudmask, Nicholas J. Wright, John M A Duncan, Nik Callow, Sally E. Thompson, Richard J. George

Natural Resources Research Articles

Deep learning models are widely used to extract features and insights from remotely sensed imagery. However, these models typically perform optimally when applied to the same sensor, resolution and imagery processing level as used during their training, and are rarely used or evaluated on out-of-domain data. This limitation results in duplication of efforts in collecting similar training datasets from different satellites to train sensor-specific models. Here, we introduce a range of techniques to train deep learning models that generalise across various sensors, resolutions, and processing levels. We applied this approach to train OmniCloudMask (OCM), a sensor-agnostic deep learning model that …

Pastoral Land Condition: Kimberley Land Condition Standards, Robert Sudmeyer, Rick Fletcher, Kathryn Ryan, Matthew Fletcher, Charles Martin, Philip Thomas, Karen Holmes, Karyn Reeves, Wayne Fletcher

Natural resources published reports

This report outlines the conceptual basis and quantitative land condition standards that define acceptable pastoral land condition for the Kimberley, Western Australia (WA), to meet the regulatory requirements of the Land Administration Act 1997 and the Soil and Land Conservation Act 1945.

The Department of Primary Industries and Regional Development (DPIRD) develops regional-level pastoral land condition standards for WA to implement the Framework for sustainable pastoral management – Revised edition, which is designed to address the Office of the Auditor General’s recommendations to improve the management of pastoral lands in WA. The application of these standards is expected to …

Yield Prediction Of Pv Solar Energy Systems And Its Application In The Energy Grid For Operational Efficiency, Pablo Bustamante

Open Access Theses & Dissertations

The generation of power from photovoltaic (PV) solar panels is influenced by a multitude of factors. These include the tilt and orientation of the solar panels, the latitude of their location, and the prevailing climate and weather conditions. Additionally, shading at specific locations, particularly if the panels are not part of a solar facility, can significantly impact their efficiency. The quality and efficiency of the panels themselves, along with the preventive maintenance of both the solar panels and associated components such as inverters and trackers, are also critical. Finally, the overall system design and installation play a vital role in …

Cyber Resiliency Framework And Mechanisms For Software Defined Tactical Networks, Anthony Castanares

Open Access Theses & Dissertations

Traditional tactical networks fail to achieve cyber resiliency for many reasons, but the most prevalent causes include flat designs, the absence of cyber detection capabilities at the lowest level, and immutable resource allocations after instantiation. These design choices allow network threats direct visibility to each device on the network, and the lack of detection allows infections to proliferate. Furthermore, tactical battlefield networks are difficult to secure because of the lack of persistent oversight by an intelligent agent that can exercise control over the network's topology or resources in real-time. However, advances in software-defined networking (SDN) provide an opportunity to address …

Optimal Accelerated Life-Testing Plans Under Progressive Type Ii First Failure Censoring Scheme, Emmanuella Duah

Open Access Theses & Dissertations

Designing optimal accelerated life testing (ALT) plans under progressive Type-II first failure censoring involves complex computational challenges, especially when trying to balance efficiency, precision, and practicality. This research introduces a novel optimization framework aimed at determining the best test configuration that minimizes estimation uncertainty within constrained experimental conditions. We developed a tailored meta-heuristic strategy based on an enhanced Variable Neighborhood Search (VNS) algorithm, which efficiently navigates the complex landscape of censoring schemes and stress allocations. Unlike conventional methods that typically focus on simpler censoring or stress-level structures, this approach simultaneously optimizes both the censoring points and sample allocations across various …

Computing Optimal Multi-Level Stress Testing Plans Using A Combined Variable Neighborhood Search Algorithm Under Progressive Type-Ii Censoring Scheme, Michael Obuobi

Open Access Theses & Dissertations

In multi-level stress life tests under Type-II progressive censoring, determining optimal allocation poses significant computational challenges due to the vast solution space. Efficient methods are essential for exploring the admissible censoring schemes effectively. This thesis introduces a novel meta-heuristic algorithm, the Combined Variable Neighborhood Search (CVNS), which computes optimal schemes at different stress levels simultaneously. Unlike methods focusing on marginal stress levels or one-step progressive censoring, this approach leverages a unified framework to ensure enhanced computational efficiency and solution quality. By integrating the components of the design parameters into a cohesive optimization process, the algorithm effectively reduces computational time while …

Computing Optimal Progressive Hybrid Censoring Schmes Using An Mcmc Type Probabilistic Approach, Irene Yemotiorkor Odoi

Open Access Theses & Dissertations

Progressive hybrid censoring schemes play a crucial role in optimizing life-testing experiments by balancing test duration and statistical efficiency. This study presents a Markov Chain Monte Carlo (MCMC)-based probabilistic approach for determining optimal progressive hybrid censoring schemes, incorporating a time-dependent component that enhances traditional progressive censoring methods.We implement our approach using three distinct probability distributions the multinomial, hypergeometric, and uniform distributions to simulate censoring schemes. The optimal censoring schemes are then identified based on three optimality criteria: A-optimality, D-optimality, and T-optimality, ensuring robust selection by minimizing estimator variance, maximizing Fisher information, and optimizing test duration, respectively..To evaluate the effectiveness of …

Clinicogenomic Insights For Prostate Cancer Progression, Kelvin Ofori-Minta

Open Access Theses & Dissertations

Prostate cancer (PrCa) remains a critical challenge in precision oncology due to several reasons including its apparent heterogenous condition, recurrence following treatment and rapid progressive forms. Therefore, identifying patients at risk of progression is essential to fast-track therapeutic decisions and improve outcomes. Despite recent advances in genomic and molecular profiling, conventional PrCa risk assessment tools heavily rely on a few clinical parameters, neglecting the prognostic potential of genomic biomarkers in the presence of clinical biomarkers. This study presents a computational pipeline to harmonize and evaluate the prognostic value of clinicogenomic profiles of patients in modelling progression free survival (PFS). PFS, …

From Land To Lagoon: The Spatiotemporal Biogeochemical Dynamics Of Arctic Coastal Erosion In Elson Lagoon, Alaska, Sasha Victoria Peterson

Open Access Theses & Dissertations

TThe Arctic is warming rapidly, driving widespread environmental change across coastal landscapes, including permafrost thaw, shoreline erosion, and shifts in sediment and carbon dynamics, pose growing challenges for predicting carbon cycling and climate feedbacks. This dissertation investigates the spatiotemporal variability of erosion, decomposition, and nearshore turbidity in Elson Lagoon, Alaska (1955–2023), to better understand how carbon, nitrogen, and sediment are mobilized, transformed, and potentially emitted as greenhouse gases in Arctic coastal systems. Chapter 2 presents a high-resolution analysis of shoreline change and erosion-driven material fluxes spanning nearly seven decades. Drawing on 795 soil data points, airborne LiDAR, and five shoreline …

Multiscale Integration Of Receptor-Ligand Dynamics Into Discrete And Continuous Tumor Growth Models With Application To Tyrosine Kinase Inhibitor Treatment, Romasa Qasim

Open Access Theses & Dissertations

The epidermal growth factor (EGF) receptor cascade plays a crucial role in the survival and proliferation of tumor cells. Tyrosine kinase inhibitors (TKIs) are a class of drugs that inhibit epidermal growth factor receptors (EGFRs), thereby preventing the downstream signal transduction. Despite their importance, models that link spatial receptor dynamics to tumor growth remain scarce. Further, TKIs act through selective mechanisms, inhibiting active, inactive, or all receptor states, which poses a challenge to traditional modeling approaches.

We propose to numerically study two mathematical models incorporating receptor-dynamics into cancer models to describe the impact of EGFR overexpression and TKIs. The first …

Enhancing Security And Resiliency In Operational Technology Environments Through Network Slicing And Federated Learning, Brian Giovanni Rodiles Delgado

Open Access Theses & Dissertations

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation.

The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, …

Machine Learning And Protein Engineering Approaches To Understanding Kinesin-5 Activity, Jason Eden Sanchez

Open Access Theses & Dissertations

Cancer is a term describing a collection of diseases that result in uncontrolled cell growth. Cancer has manifold etiologies and underlying cancers are rouge biochemical pathways involving many different proteins. In the current work, two approaches are used to enhance knowledge of kinesin-5, a potential cancer target involved in cell division. Kinesin-5 promotes cell division by cross-linking and separating microtubules in dividing cells. The first approach uses machine learning (ML) to identify small molecule inhibitors for kinesin-5. Though decades of research have uncovered classes of small-molecules which inhibit kinesin-5 in vitro and in vivo, no candidates have reached phase III …

A Sociocognitive Perspective On The Activation Of Productive Epistemic Resources In College Students' Understanding Of Chemical Equilibrium, Henk Steven Van Den Bogaard

Open Access Theses & Dissertations

Science education, particularly in higher education, has a content coverage problem: it has long been described as "a mile wide, an inch deep" in various consensus documents from the National Academies. In chemistry education, the most common general chemistry curriculum covers topics as chapters based on the popular Sienko and Plane 1960's general chemistry textbook. The limited time available in such fast-paced coverage may limit the instructor's modeling and assessment of key performances in developing a mechanistic understanding of complex chemistry phenomena. In this sociocognitive, qualitative investigation, general chemistry II students' responses to assessment prompts were used to activate cognitive …

Application Of Deep Learning On Gage R&R For Anomaly Detection, Oluwatope Richard Ojo

Electronic Theses and Dissertations

This thesis explores the application of deep learning techniques, specifically autoencoder based models, to enhance anomaly detection within Gage Repeatability and Reproducibility (Gage R&R) studies—an essential component of Measurement System Analysis (MSA) in quality engineering. Traditional Gage R&R methodologies, while effective for linear and low-dimensional data, exhibit limitations in detecting subtle, nonlinear variations in complex measurement systems. To address this challenge, an unsupervised autoencoder was developed and trained on a synthetically generated dataset comprising 2,500 voltage measurements (5V and 33V) derived using Generative Adversarial Networks (GANs) based on real-world manufacturing data measurements.

The proposed autoencoder model achieved a 95th percentile-based …

Towards Visual Inertial Navigation With Fixed Tetrahedral Targets, Joao Leonardo Silva Cotta

Theses and Dissertations

This dissertation presents a robust method for 6DoF position estimation under impaired visual conditions utilizing a minimum 4-point Perspective-n-Point (P4P) solver designed for tetrahedral targets. Using SO(3) × R 3 instead of SE(3), the method uses a Lie group-based formulation to discriminate between rotation and translation, thereby enabling computationally efficient, resource-conscious op- optimization while preserving correct geometric behavior. Designed using the contemporary C++17 library ShomerTarget, the solver is analytically formulated and assessed under pragmatic robotic conditions. Particularly in low-light and high-dynamic environments, experiments on embedded systems, UAVs, and NASA’s Astrobee show that the proposed solver attains enhanced accuracy compared to …

Effects Of Reflective Journaling And Nudges On Academic Motivation And Engagement, Lakshmi Satya Sai Veda Mahita Uppuluri

Theses and Dissertations

This study investigates the effects of reflective journaling and motivational nudges on academic motivation and engagement among college students. Grounded in Self- Determination Theory (SDT), the research examines how different interventions influence intrinsic motivation, and academic behaviors such as class attendance, participation, and preparation. The study employed a between-group experimental design with three conditions: a control group, a journaling group, and a journaling group that also received daily motivational nudges. Results showed that students in the journaling groups—particularly those who received nudges—experienced a significant increase in academic motivation. While changes in academic engagement were not significant, the effect size suggested …

Stochastic Analysis With Operational Calculus In Reliability, Hend Hamdan Al-Jahani

Theses and Dissertations

Our dissertation models several reliability systems subject to occasional random shocks of random magnitudes W1,W2,..occurring at times tau0, tau2,..In Model 1, the underlying system becomes inoperational if it wears out due to aging specified by a monotone increasing continuous function Delta identifying system's wear at any time t>=0 . The system is deemed inoperational if delta crosses a sustainability threshold at some moment T=(delta )^-1(D). The precise time T is difficult to identify making the realistic failure time randomly delayed and thus identified at some opportune observation epoch. The system can also fail due to damages by external shocks, …

Personality Trait Recognition Through Deep Neural Temporal Modeling Of Non-Verbal Behavior, Kushal Vangara

Theses and Dissertations

Personality research seeks to explain the wide range of human behaviors through stable, measurable traits. Human interactions are inherently rich and multidimensional, and analyzing behavioral data offers a promising path to uncover personality insights. The increasing convergence of psychology, computer science, and machine learning has fueled interest in computational approaches to personality assessment. Advances in sensing technologies have made it possible to capture fine-grained information about individuals’ behaviors and interactions in naturalistic and controlled environments. Automated audio-visual analysis techniques extract relevant behavioral cues, which machine learning models then interpret to infer underlying personality traits. This work provides a comprehensive overview …

How Will Artificial Intelligence Impact The Roles, Skills, And Design Processes Of Ux Professionals In The Next 5 Years?, Olivia Behan

Informatics

Artificial intelligence (AI) is changing the way we think about user experience (UX) design. This thesis explores how AI could impact the roles, skills, and design processes of UX professionals over the next five years. The research conducted consists of a mixed-methods approach, combining an online survey administered through Qualtrics to 50 survey respondents and a case study, which included individually interviewing 3 UX professionals over Zoom, to gather data and personal insights. The results show that while AI has the power and potential to make some parts of UX work faster, like research analysis, prototyping, and automating redundant tasks, …

Geometric Algebra For Field Theory In Curved Spacetime, Kevin Rhine

All Graduate Reports and Creative Projects, Fall 2023 to Present

Physics seeks to understand the universe by uncovering the fundamental laws that govern matter, energy, space, and time. At its heart lies the challenge of unification: finding a mathematical framework that consistently describes these interactions across all scales, from the subatomic to the cosmological.

This thesis explores geometric algebra, a mathematical language that unifies algebra and geometry, as a tool for advancing this understanding. By extending this framework to curved spacetimes, where gravity influences the structure of space and time, we investigate its ability to describe physical phenomena such as electromagnetism and general relativity. A notable contribution includes the geometric …

Aleci: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots, Matthew R. Lister

All Graduate Reports and Creative Projects, Fall 2023 to Present

Machine learning models can take a collection of inputs and craft an output. The mathematical formulas these models use to calculate their outputs easily become too complex or time consuming for a human to analyze. Collectively, we refer to these as black box models. Accumulated local effects plots (ALE) are a method for adding interpretability and visibility into the effects that individual variables contribute to the predictions made by black box models. The method designed by D.W. Apley calculates equally spaced point estimates of the response value to construct a graph across the range of the variable of interest. AleCI …

Jwst Reveals The Diversity Of Nuclear Obscuring Dust In Nearby Agn: Nuclear Isolation Of Miri/Mrs Data Cubes And Continuum Spectral Fitting, Omaira González-Martín, Daniel J. Díaz-González, Mariela Martínez-Paredes, Almudena Alonso-Herrero, Enrique López-Rodríguez, Begoña García-Lorenzo, Cristina Ramos Almeida, Ismael García-Bernete, Donaji Esparza-Arredondo, Sebastian F. Hoenig, Santiago García-Burillo, Chris Packham, Nancy A. Levenson, Alvaro Labiano, Miguel Pereira-Santaella, Francoise Combes, Anelise Audibert, Erin K.S. Hicks, Lulu Zhang, Enrica Bellocchi, Richard I. Davies, Laura Hermosa Muñoz, Masatoshi Imanishi, Claudio Ricci, Marko Stalevski

Faculty Publications

We investigate the capabilities of the mid-infrared instrument (MIRI) of JWST to advance our knowledge of active galactic nucleus (AGN) dust using the spectral fitting technique on an AGN collection of 21 nearby (z < 0.05) AGN (7 type-1 and 14 type-2) observations obtained with the medium resolution spectroscopy (MRS) mode. This collection includes publicly available AGN and data from the collaboration of Galactic Activity, Torus, and Outflow Survey (GATOS). We developed a tool named MRSPSFisol that decomposes MRS cubes into point-like and extended contributions. We found statistically good fits for 12 targets with current AGN dust models. The model that provides good fits (χ²/dof< 2) for these 12 targets assumes a combination of clumpy and smooth distribution of dust in a flare-disc geometry where the dust grain size is a free parameter. Still, two and one AGN statistically prefer the disc + wind and the classical clumpy torus model, respectively. However, the currently available models fail to reproduce 40 per cent of the targets, likely due to the extreme silicate features not well reproduced by the models and signatures of water-ice and aliphatic hydrocarbon absorption features in most targets. New models exploring, for instance, new chemistry, are needed to explain the complexity of AGN dust continuum emission observed by JWST.

Chemistry Education At The University Of Southern Mississippi Through Computation And Active Learning Methods, Sadie Pitre

Honors Theses

Education is one of the pillars of society. It evolves alongside scientific progress, shaping how students learn and engage with complex topics. The incorporation of new educational standards at The University of Southern Mississippi (USM) was implemented in two parts: through computational chemistry laboratory exercises for teaching laboratories and through the synthesis of organic molecules for research projects in an upper-level spectroscopy class. The goal of these projects is to improve learning of abstract and/or complex topics beyond the teaching methods traditionally utilized in the undergraduate laboratory and in the classroom.

Computational chemistry, which uses theoretical calculations to model molecular …

A Comprehensive Performance Comparison Of Machine Learning And Federated Learning For Intrusion Detection In Vehicular Ad-Hoc Networks Using Can-Bus Data, Tim Leonhardt

Honors Theses

Federated Learning (FL) is a Machine Learning (ML) approach that decentralizes training across distributed devices, eliminating the need to centralize data. Unlike traditional ML, where models are trained on aggregated data, FL sends a global model to multiple nodes for local training, with updated parameters transmitted back to the server for aggregation. This process preserves data privacy, making FL ideal for sensitive applications like cybersecurity. However, FL introduces challenges such as data heterogeneity, communication overhead, and difficulties in achieving model convergence, which can impact performance.

This study investigates a fundamental assumption in ML and FL research: that the superior performance …

Cache-Augmented Generation In Rag Pipelines: Fast And Memory-Efficient Approach To Multi-Agent Knowledge Query Systems, Yaju Gopal Shrestha

Honors Theses

This thesis presents an implementation and evaluation of Cache-Augmented Generation (CAG) for knowledge query systems, building upon the approach introduced by Chan et al. (2024). Traditional Retrieval-Augmented Generation (RAG) systems (Lewis et al., 2020) face challenges including high latency, excessive memory usage, and complex infrastructure requirements. By implementing a cache-augmented architecture that preloads relevant knowledge and eliminates real-time retrieval, our approach significantly improves response time while reducing resource requirements. The research demonstrates the effectiveness of CAG through a comprehensive implementation for The University of Southern Mississippi's chatbot system, achieving a 49.02% improvement in response time compared to traditional RAG approaches. …

Cellular Telemetry For Real-Time River Flow Velocity Data From Fluvial Acoustic Tomography Loggers, Joshua Lee Seymour

Honors Theses

Streamflow is a critical element in understanding watershed processes and the effects of land use on those processes because it is the primary medium through which water, sediment, nutrients, organic material, thermal energy, and aquatic species move. Fluvial Acoustic Tomography (FAT) systems offer accurate direct measurements of river section-averaged flow velocity by measuring reciprocal acoustic travel times between at least two acoustic nodes positioned on opposite riverbanks. However, similar to many in-situ sensors used in estuarine and riverine environments, FAT loggers require manual data retrieval, which is time-consuming, labor-intensive, and limits real-time access and increases maintenance costs. This project presents …

The Numerical Method For Finding All The Zeros Of A Function F (X) On An Interval [A,B], Franissa Simon

Honors Theses

In this study, we develop a simple mathematical method for finding all the roots of a function on a specified interval. Existing classical numerical methods, including the Bisection Method, Secant Method, and Newton Method, cannot find all the zeros of a function f (x) on an interval [a,b]. Popular numerical root solvers like Matlab’s ‘fzero’, Maple’s ‘fsolve’, and SageMath’s ‘find_root’ typically yield only a single zero. This thesis explores a proposed interval computation bisection method, a systematic approach based on the traditional Bisection Method and interval computation. Unlike traditional bisection, which relies on the intermediate value theorem, this approach uses …

Investigation Into The Effectiveness Of G-C3n4 For The Degradation Of Indigo Dye In Aqueous Solution Via Electrocatalysis, Rolando R. Barron Jr

Theses and Dissertations

Indigo dye (IUPAC 2-(3-hydroxy-1H-indol-2-yl)indol-3-one) is one of the most used dyes in the textile industry worldwide with more than 40,000 tons produced each year. It can elicit adverse physiological responses in humans including vomiting, diarrhea, and other gastrointestinal conditions, and it is toxic in large amounts. There is also evidence to suggest that indigo is a teratogen and genotoxic in aquatic life. Environmentally, the enormous amount of indigo used in the textile industry has caused rivers and waterways to run blue as well as severely impacting aquatic and plant ecosystems. The chemicals used in its synthesis are also …

Joint Modelling Of Longitudinal Egfr Trajectory And Time To Acute Kidney Injury In Lung Transplant Patients, Samiha Zakir

Theses and Dissertations

Progressive declines in estimated glomerular filtration rate (eGFR) often precede acute kidney injury (AKI), yet the relationship between eGFR trends and AKI risk remains unclear. This study investigates longitudinal eGFR changes and their association with AKI in 459 lung transplant patients followed for up to 7 years (n = 6419). We applied a piecewise linear mixed-effects model to evaluate eGFR trajectories and a Cox proportional hazards model to assess time to AKI. A joint model was used to explore the interplay between longitudinal and survival processes. Key covariates included gender, age at transplantation, antibody-mediated rejection (AMR), and pre-transplant eGFR. Males …

Hydrothermal Synthesis Of Single-Crystal Magnetic Oxides, Bhakti Patel

All Dissertations

Multifunctional materials are systems that have two or more properties of interest. Some of these features are polar axes, chirality, magnetic ions, triangular arrangements, and/or magnetically silent building blocks. Various combinations of these properties can result in chiral ferromagnetism, multiferroic magnetoelectric behavior, antiferromagnetism, and quantum spin liquids and spin ices. By tuning these structure-property relations, quantum computing, memory, and sensors may be created. Large single crystals allow for a better understanding of the magnetic behavior of these crystals since they can be oriented in a magnetic field. Inorganic single crystals have a wide range of applications but growing high-quality single …