Physical Sciences and Mathematics Commons^™

High Temperature Alteration Conditions Within Lava Tubes: An Analogue For Venus Surface Geology, Dylan Alexander Childs

Theses and Dissertations

Scientific exploration of other planetary bodies comes with many limitations, and Venus is no exception. Average surface temperatures of ∼460°C and an atmospheric pressure of ∼96 bars has severely limited the longevity of any instruments sent to the surface of Venus. Furthermore, with an atmosphere composed of ∼96.5% CO2, there are only three wavelength windows in the near infrared region that can be used to measure the mineralogical composition of the surface. Due to these constraints, our understanding of Venus’ surface mineralogy is severely limited and to date direct measurements of its geologic composition are limited to a handful of …

Optimization Of Mass Spectrometry-Based Methods For Low-Input And Spatial Proteomics, Andikan Jones Nwosu

Theses and Dissertations

Eukaryotic cells are highly heterogeneous. These cells are arranged into different compartments, carrying out separate functions and facilitating biological processes. Proteins are the effector biomolecules targeted to subcellular locations that help fulfill specific tasks in living organisms. Spatial proteomics can help unravel molecularly how protein abundance and localization are altered in cells, which is not feasible in traditional bulk-scale proteomics. To achieve this, our lab has developed a miniaturized sample processing platform called nanoPOTS, reduced separation columns' inner diameter to increase ionization efficiency and concentrate analytes for mass spectrometers and optimized data acquisition modes for increasing proteome coverage in spatial …

Increasing The Robustness Of Machine Learning By Adversarial Attacks, Gourab Mukhopadhyay

Theses and Dissertations

By perturbation or physical attacks any machine can be fooled into predicting something else other than the intended output. There are training data based on which the model is trained to predict unknown things. The objective was to create noises and shades of different levels on the images and do experiments for measuring accuracy and making the model classify the traffic signs. When it comes to adding shades to the pictures, pixels were modified for three different layers of the pictures. The experiment also shows that with the shadows getting deeper, the accuracies drop significantly. Here, some changes in pixels …

Personalized Driving Using Inverse Reinforcement Learning, Rodrigo J. Gonzalez Salinas

Theses and Dissertations

This thesis introduces an autonomous driving controller designed to replicate individual driving behaviors based on a provided demonstration. The controller employs Inverse Reinforcement Learning (IRL) to formulate the reward function associated with the provided demonstration. IRL is implemented through a dual-feedback loop system. The inner loop utilizes Q-learning, a model-free reinforcement learning technique, to optimize the Hamilton-Jacobi-Bellman (HJB) equation and derive an appropriate control solution. The outer loop leverages this derived control solution to generate parameters for the reward function, which are subsequently integrated into the HJB equation. The ultimate control policy is deduced from the final reward function obtained …

Striking At The Root: A Categorization Of Dns Clients, Tyler Dean

Theses and Dissertations

The Domain Name System (DNS) root servers have provided a useful look into the DNS and internet ecosystems for decades. We present a categorization of clients querying DNS root servers. Using two clustering algorithms on DNS traffic sampled in 2020, we can predict the structure and volume of queries originating from different types of clients. Previous research has used unsupervised techniques to better understand DNS traffic patterns, but none have, to our knowledge, considered clients beyond those driven by queries from end users. By performing clustering on IP addresses rather than on individual queries, we are able to examine the …

Strong Gelfand Pairs Of Some Finite Groups, Joseph E. Marrow

Theses and Dissertations

Strong Gelfand pairs describe a relation between a group and a subgroup, using a relation between inner products of their characters. We find all strong Gelfand pairs of the dihedral and dicyclic groups, and several of the sporadic groups. We provide some results for the strong Gelfand pairs of the affine linear groups, in addition to the exceptional classical groups $\mathrm{Sp}_4(q)$ for $q$ a power of $2$.

Documentation Of Norm Negotiation In A Secondary Mathematics Classroom, Michelle R. Bagley

Theses and Dissertations

While previous mathematics education research has shown that norms can change in mathematics classrooms, little research has closely documented the struggles, concessions, and compromises involved in the negotiation process. Negotiating norms is a complex process entailing deliberate teacher moves intended to promote norms, often met with misunderstandings or resistance from students. This thesis documented the negotiation process that occurred when a teacher attempted to require conceptual explanations from students who had not previously been accustomed to doing so. I recorded six days of instruction in a 9th grade algebra unit, transcribed the class discussions, and inferred norms "at play" for …

Rigorous Verification Of Stability Of Ideal Gas Layers, Damian Anderson

Theses and Dissertations

In this thesis we develop tools for carrying out computer assisted proof of the stability of traveling wave solutions of the spatially one-dimensional compressible Navier-Stokes equations with an ideal gas equation of state. In particular, we obtain rigorous, tight error bounds on a high-accuracy numerical approximation of the traveling wave profile for parameters corresponding to air, and we obtain rigorous representations in a neighborhood of positive and negative infinity of the solution to the first order ODE associated with linearizing the PDE equations about the traveling wave solution. We also develop supporting tools for rigorous verification of wave stability.

Design And Implementation Of Truly Random Number Generation Using Memristors For In-Memory Computing, Nick Felker

Theses and Dissertations

This paper proposes a new security module based on non-volatile memory. The module uses a memristor-based true random number generator to generate random numbers which can be used for cryptography. The module is implemented in software using a modified RISC-V instruction set architecture. The paper evaluates the performance of the module using the RISC-V simulator Gem5. The results show that the module can generate random numbers at a rate of 63 microseconds per number, which is faster than the standard C library’s random number generator. The module can also be used to scramble strings of characters and generate hashes of …

Existence Of Smooth Solutions For The Landau Equation With Hard Potentials, Shelly Ann Taylor

Theses and Dissertations

This dissertation is concerned with the Landau equation, an integro-differential equation that models the particle density of a plasma as it evolves in phase space. The main topic is the (large-data) local existence of classical solutions to the Landau equation in the case of hard potentials (γ ∈ (0, 1]). Solutions have previously been constructed by Chaturvedi [SIAM. J. Math. Anal., 55(5), 5345–5385, 2023] for initial data in an exponentially-weighted Sobolev space of order 10, but it is not a priori clear whether these solutions have more regularity than the initial data. We improve Chaturvedi’s existence result in two ways: …

Hierarchical Quantized Autoencoders: Using Hierarchical Models For Data Compression Across Multiple Domains, Armani Lorenzo Rodriguez

Theses and Dissertations

In the era of vast data processing and transmission, sending data over a channel for downstream operations is a very common occurrence. The bandwidth of this data channel acts as a limiting factor in this operation, capping the amount of data that can be sent over a time period. Therefore, in addition to pursuing advancements in networking technology, there exists a need for more efficient means of data compression. Learned compression is the application of machine learning models to the data compression problem, and in this study, we leverage the ability of neural networks to learn the underlying structure of …

Study Of Modified Salinomycin Analogs On Inhibition Of Triple Negative Breast Cancer Metastasis And Invasion, Farnaz Malekmarzban

Theses and Dissertations

Triple negative breast cancer (TNBC) is a more aggressive type of breast cancer which contains faster growth, higher metastasis rate and worse prognosis. The main challenge toward treatment of this type of cancer is being heterogeneous. Recently, Salinomycin (SAL) has been considered as a potential agent for triple negative breast cancer treatment. However, chemical modification of SAL is needed to improve SAL selectivity to cations and decrease its cytotoxicity. The purpose of this study was to evaluate the effect of chemically modified analogs of SAL on the viability, metastasis, Wnt pathway as well as angiogenesis of Triple negative breast cancer …

Application And Development Of Ceragenins In Medical Device Coatings For Clinical Settings, Elliot E. Sherren

Theses and Dissertations

Hospital-acquired infections (HAIs) pose a significant and increasing threat to global health. One primary cause of this threat is increasing antibiotic resistance. As traditional antibiotics continue to grow less effective, there is an urgent need for novel antimicrobial strategies. This work explores the potential of ceragenins, also known as cationic steroid antimicrobials (CSAs), as a promising alternative to combat HAIs. Specifically, we investigated potential roles that CSAs can play in the context of multiple medical device coatings in healthcare settings. Ceragenins are synthetic mimic of antimicrobial peptides (AMPs) which exhibit broad-spectrum antimicrobial activity against many common pathogens that have been …

A Nordhaus-Gaddum Type Problem For The Normalized Laplacian Spectrum And Graph Cheeger Constant, Adam Widtsoe Knudson

Theses and Dissertations

We will study various quantities related to connectivity of a graph. To this end, we look at Nordhaus-Gaddum type problems, which are problems where the same quantity is studied for a graph $G$ and its complement $G^c$ at the same time. For a graph $G$ on $n$ vertices with normalized Laplacian eigenvalues $0 = \lambda_1(G) \leq \lambda_2(G) \leq \cdots \leq \lambda_n(G)$ and graph complement $G^c$, we prove that \begin{equation*} \max\{\lambda_2(G),\lambda_2(G^c)\}\geq \frac{2}{n^2}. \end{equation*} We do this by way of lower bounding $\max\{i(G), i(G^c)\}$ and $\max\{h(G), h(G^c)\}$ where $i(G)$ and $h(G)$ denote the isoperimetric number and Cheeger constant of $G$, respectively. We …

Potent And Selective Small Molecule Mcl-1 Inhibitor Demonstrates Anti-Myeloma Activity And Overcomes Chemotherapy Resistance, Omar Sami Faleh Al-Odat

Theses and Dissertations

Despite a record number of clinical studies investigating various anti-myeloma treatments, the 5-year survival rate for multiple myeloma (MM) patients in the US is only 55%, and nearly all patients relapse. Poor patient outcomes demonstrate that myeloma cells are "born to survive” which means they can adapt and evolve following treatment. Thus, new therapeutic approaches to combat survival mechanisms and target treatment resistance are required. Importantly, Mcl-1, anti-apoptotic protein, is required for the development of MM and treatment resistance. This study looks at the possibility of KS18, a selective Mcl-1 inhibitor, to treat MM and overcome resistance. Our investigation demonstrates …

An Empirical Study On Detecting And Explaining Global Structural Change In Evolving Graph Using Martingale, Tarun Teja Kairamkonda

Theses and Dissertations

There is a growing interest in practical applications involving networks of interacting entities such as sensor networks, social networks, urban traffic networks, and power grids, all of which can be represented using evolving graphs. Changes in these evolving graphs can signify shifts in the behavior of interacting entities or alterations in the patterns of their interactions. Identifying and detecting these changes is crucial for addressing potential challenges or opportunities in various domains. In this study, we propose an approach for detecting structure change in evolving graphs based on the martingale change detection framework on multiple graph features extracted over time. …

The Osteological Neutral Pose Of The Neck Of An Apatosaurine Sauropod Based On Virtual And Physical Models, Colin Don Boisvert

Theses and Dissertations

An array of sauropod taxa coexisted in Late Jurassic North America. How so many giant genera coexisted without competing for food is puzzling considering their potential metabolic requirements. One aspect of this question is neck posture, which has been much contested, both in terms of the intrinsic curvature of the neck and the resultant elevation of the head. Neck curvature is characterized by the Osteological Neutral Pose (ONP), wherein the intervertebral joints are in an undeflected state. This pose is crucial to understanding neck posture. The articulated series of presacral vertebrae 2-19 of an exceptionally well-preserved apatosaurine, BYU 18531, are …

"Vampire Plastics": An Investigation Of Poly(Olefin Sulfone) Depolymerization And Its Dust Mitigation Abilities, Alexandra Kathryn Kanani Gallion Stapley

Theses and Dissertations

The ubiquity of particulate contamination requires dust mitigation techniques to provide low-scatter surfaces and edges on sensitive optical devices in space. Poly(olefin sulfone)s have been shown to photodegrade with the assistance of a photobase generator when exposed to UV light (254 nm) and heat (120 °C). These polymers may be useful for minimizing dust on optical surfaces for space applications. However, their behavior in vacuum has not been fully characterized. We synthesized poly(2-methyl-1-pentene sulfone) (PMPS) and poly(1-hexene sulfone) (PHS) with and without a photobase generator. We studied the photodegradation (172 nm or 254 nm) of thin films in vacuum. Spectroscopic …

Implementing 1.5 Millimeter Internal Diameter Columns And A Portable Capillary Column Instrument Into Monoclonal Antibody Analytical Workflows, Benjamin Libert

Theses and Dissertations

Using 1.5 mm inner diameter (i.d.) columns to bridge the gap between routinely used 2.1 mm i.d. columns and capillary bore columns allows for a sequential but significant increase in performance without the need for specialized equipment associated with very low dispersion liquid chromatography (LC) systems. These 1.5 mm i.d. columns balance an increase in sensitivity compared to 2.1 mm i.d. columns (theoretically doubling the time-domain peak area in mass sensitive detectors for the same mass load), while mitigating the efficiency losses due to extra-column dispersion effects that are commonly observed with 1.0 mm i.d. columns. Here, the use of …

Reinforcement Learning For Robotic Tasks: Analyzing And Understanding The Learning Process Using Explainable Artificial Intelligence Methods, Brian J. Campana

Theses and Dissertations

As deep reinforcement learning (RL) models gain traction across more industries, there is a growing need for reliable agent-explanation techniques to understand these models. Researchers have developed explainable artificial intelligence (XAI) methods to help understand these 'black boxes'. While these models have been tested on many supervised learning tasks, there is a lack of examination of how these well these methods can explain hard reinforcement learning problems like robotic control. The sequential nature of learning RL policies and testing episodes create fundamentally different policies over time compared to more traditional supervised learning models. In this thesis, two important questions are …

Procedural Pre-Training For Visual Recognition, Connor S. Anderson

Theses and Dissertations

Deep learning models can perform many tasks very capably, provided they are trained correctly. Usually, this requires a large amount of data. Pre-training refers to a process of creating a strong initial model by first training it on a large-scale dataset. Such a model can then be adapted to many different tasks, while only requiring a comparatively small amount of task-specific training data. Pre-training is the standard approach in most computer vision scenarios, but it's not without drawbacks. Aside from the cost and effort involved in collecting large pre-training datasets, such data may also contain unwanted biases, violations of privacy, …

Zeros Of Convex Combinations Of Elementary Families Of Harmonic Functions, Rebekah Ottinger

Theses and Dissertations

Brilleslyper et al. analyzed a one-parameter family of harmonic trinomials, and Brooks and Lee analyzed a one-parameter family of harmonic functions with poles. Each family was explored to find the relationship between the size of the parameter and the number of zeros of the harmonic function. In this thesis, we examine convex combinations of members of these families. We determine conditions under which the critical curves separating the sense-preserving and sense-reversing regions are circular. We show that the number of zeros of a convex combination can be greater than the maximum number of zeros of either part.

Hardware Acceleration Of Numerical Methods For Solving Ordinary Differential Equations, Soham Bhattacharya

Theses and Dissertations

Along with the advancement in technology, the role of hardware accelerators is increasing consistently, delivering advancements in scientific simulations and data analysis in scientific computing, signal processing tasks in communication systems, matrix operations, and neural network computations in artificial intelligence and machine learning models. On the other hand, several high-speed computer applications in this era of high-performance computing often depend on ordinary differential equations (ODEs); however, their nonlinear nature can present a challenge to obtaining analytic solutions. Consequently, numerical approaches prove effective in delivering only approximate solutions to these equations. This research discusses the implementation of a customized hardware accelerator …

Differential Elliptic Flow Analysis Of Hadrons With Different Wuark Content In Simulated Pp Collisions, Elhussein Osama

Theses and Dissertations

According to current physics theories, it is assumed that in the first microsecond after the big bang, the universe was in a state of matter called Quark-Gluon Plasma (QGP), where the fundamental consistent of matters (quarks and leptons), were highly energetic, and floating around freely. Searching for such phase of matter; as the possible earliest signatures after the big bang, and among many other interesting experimental measurements, the jet quenching and elliptic flow are the most important ones, in the heavy ion collisions at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) experiments.

The azimuthal anisotropy …

Federated Learning Based Autoencoder Ensemble System For Malware Detection On Internet Of Things Devices, Steven Edward Arroyo

Theses and Dissertations

New technologies are being introduced at a rate faster than ever before and smaller in size. Due to the size of these devices, security is often difficult to implement. The existing solution is a firewall-segmented “IoT Network” that only limits the effect of these infected devices on other parts of the network. We propose a lightweight unsupervised hybrid-cloud ensemble anomaly detection system for malware detection. We perform transfer learning using a generalized model trained on multiple IoT device sources to learn network traffic on new devices with minimal computational resources. We further extend our proposed system to utilize federated learning …

Feasibility Of Oil Palm Agroforestry System: Evaluating The Application Of Oil Palm Ash For Enhancing Cherry Tomato (Solanum Lycopersicum. Var. Nancy Rz) Production Using Brackish Water., Ime Joseph Bassey

Theses and Dissertations

Monoculture systems of oil palm production are ravaging the world’s tropical forests, causing deforestation and biodiversity loss, which contribute to climate change at an uncontrollable pace. Oil palm agroforestry systems (AFSs), one of the strategies of regenerative agriculture, have proven to reverse the unfavorable effects of the oil palm monoculture systems. A sustainable means of soil fertilization and irrigation must be explored to improve the feasibility of oil palm AFSs. This study evaluates the impact of the application of oil palm ash and brackish irrigation on cherry tomato production. The experiment was conducted in a hydroponic system using a 2*4 …

Leveraging Biological Mechanisms In Machine Learning, Kyle J. Rogers

Theses and Dissertations

This thesis integrates biologically-inspired mechanisms into machine learning to develop novel tuning algorithms, gradient abstractions for depth-wise parallelism, and an original bias neuron design. We introduce neuromodulatory tuning, which uses neurotransmitter-inspired bias adjustments to enhance transfer learning in spiking and non-spiking neural networks, significantly reducing parameter usage while maintaining performance. Additionally, we propose a novel approach that decouples the backward pass of backpropagation using layer abstractions, inspired by feedback loops in biological systems, enabling depth-wise training parallelization. We further extend neuromodulatory tuning by designing spiking bias neurons that mimic dopamine neuron mechanisms, leading to the development of volumetric tuning. This …

Investigation Of Collision Cross Sections & Time-Resolved Structural Modification Of Biomolecules, Host-Guest Systems, & Small Molecules Using Ion Mobility & Fourier Transform Ion Cyclotron Resonance Mass Spectrometry, Noah Mismash

Theses and Dissertations

This thesis explores the structures and structural changes of supramolecular host-guest systems, proteins, and other small molecules in the gas phase, utilizing a combination of computational modeling and experimental data. The primary instruments employed were a Fourier transform ion cyclotron resonance mass spectrometer (FTICR-MS) and an ion mobility mass spectrometer (IM-MS). In the IM-MS experiments, the focus was on investigating the binding behavior of cyclodextrin macrocycles—specifically α, β, and γ-cyclodextrin—with per-fluoroalkane substances (PFAS), which are pervasive environmental contaminants. This investigation involved measuring ion-neutral collision cross sections and using computational modeling to determine whether PFAS compounds bind inside or outside the …

Back To The Future: A Case For The Resurgence Of Approximation Theory For Enabling Data Driven “Intelligence”, Michael Dominic Ciocco

Theses and Dissertations

Artificial Intelligence (AI) has exploded into mainstream consciousness with commercial investments exceeding $90 billion in the last year alone. Inasmuch as consumer-facing applications such ChatGPT offer astounding access to algorithms that were hitherto restricted to academic research labs, public focus of attention on AI has created an avalanche of misinformation. The nexus of investor-driven hype, “surprising” inaccuracies in the answers provided by AI models – now anthropomorphically labeled as “hallucinations”, and impending legislation by well-meaning and concerned governments has resulted in a crisis of confidence in the science of AI. The primary driver for AI’s recent growth is the convergence …

Minimal Specialization: The Coevolution Of Network Structure And Dynamics, Annika King

Theses and Dissertations

The changing topology of a network is driven by the need to maintain or optimize network function. As this function is often related to moving quantities such as traffic, information, etc., efficiently through the network, the structure of the network and the dynamics on the network directly depend on the other. To model this interplay of network structure and dynamics we use the dynamics on the network, or the dynamical processes the network models, to influence the dynamics of the network structure, i.e., to determine where and when to modify the network structure. We model the dynamics on the network …