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Semi-Supervised Multimodal Multi-Instance Learning For Aortic Stenosis Diagnosis, Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes

Computer Science Faculty Research & Creative Works

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available Spectral Doppler imaging that contains important complementary information about pressure gradients and blood flow abnormalities associated with AS. Second, obtaining labeled data is difficult. There are often far more unlabeled echocardiogram recordings available, but these remain underutilized by existing methods. To overcome these limitations, we introduce Semi-supervised Multimodal Multiple-Instance Learning (SMMIL), …

Biological Computing From Microscopic To Macroscopic Evolution, Xiaofeng Ding

Masters Theses

A unified theory from microscopic to macroscopic DNA-based biological systems is explained in terms of the rule components used when the system size is increased. Even though the eight female rules in each ruleset are provided with an equal probability of the computation-state outcomes, the initial ensemble of the computation states will exponentially settle into one dominant rule that supports the nutrition needed for growth. The remaining seven rules form into two minority groups to provide the biological characteristics of the system's growth from a micro to macroscopic evolution. The object of this study is to prove that such a …

Lifelong Machine Learning With Adaptive Resonance Theory, Sasha Petrenko

Doctoral Dissertations

"This publication option dissertation is composed of three papers concerning the study of the problem lifelong machine learning with Adaptive Resonance Theory (ART) algorithms. Lifelong learning (L2) is a challenging machine learning paradigm that both encompasses and formalizes the fields of continual learning and incremental learning. The field is concerned with the mitigation of the phenomenon of catastrophic forgetting whereby learning agents that are faced with incrementally novel information deleteriously overwrite previous knowledge if that learning process is not regularized to counteract this consequence. ART algorithms solve this stability-plasticity dilemma by optimally assigning learning to categories or instantiating new knowledge …

Metal-Free Photoredox Catalysis For The S-Trifluoromethylation Of Thiols And Investigation Of Ph-Dependent Decontamination Of Dimethyl Chlorophosphate, A Nerve Agent Simulant, Via Hydrolysis With Azoles, Raheemat Rafiu

Doctoral Dissertations

"Organofluorine chemistry plays a critical role in synthetic chemistry due to the unique properties of fluorine. However, the S-trifluoromethylation of thiols remains underexplored, with existing methods often relying on toxic and expensive organometallic catalysts. Given the advantages of S-trifluoromethylated compounds, including enhanced pharmacological activity and improved metabolic stability, we developed a green and cost-effective method for the synthesis of S-CF3 compounds using diacetyl as the organic catalyst and Langloi's reagent as the trifluoromethylating reagent. This reaction, which proceeds under mild conditions using visible light photoredox catalysis, works across various substrate scopes, including aromatic, heteroaromatic, and aliphatic thiols.

Chemical Warfare Agents …

Applications Of Tetramethyl-Guanidine Supported Cationic Transition Metal Sites Towards C─N Bond Construction Methodologies And [3+2] Cycloaddition Reactions, Suraj Kumar Sahoo

Doctoral Dissertations

"We present a family of divalent cationic bipodal and tripodal M(II) reagents (M = Mn, Fe, Co) supported by superbasic tetramethyl-guanidinyl (TMG) residue framework with applications towards C─N bond construction methodologies. These cationic reagents possess unique characteristics since their enhanced Lewis acidity facilitates in situ elaboration of the primary nitrene-transfer product (aziridine). Indeed, further aziridine-ring opening and cycloaddition of several dipolarophiles (nitriles, alkenes, ketones) permits the three-component synthesis of valuable five-membered N-heterocycles, commonly found in pharmaceuticals and agrochemicals.

We undertake a more systematic study to unravel parameters that make certain divalent metal sites more suitable as catalysts for the in-situ …

Development Of Ligands And Metal Complexes With Weakly Coordinating Apical Elements And Their Application To Nitrene Insertion And Addition To C‒H And C=C Bonds, Meenakshi Sharma

Doctoral Dissertations

"We present a family of triangular coinage metal M₃Cl₃ complexes [M = Cu (I), Ag (I)] supported by antimony [Sb (III)], and bismuth [Bi (III)]-centered highly basic tetramethyl-guanidinyl [TMG₃trphen-E] (E = Sb, Bi) ligand frameworks with their applications as catalysts toward the construction of C‒N bond through aziridination and amination reaction methodologies.

The [(TMG₃trphen-Sb)Cu₃(µ-Cl)₃] catalyst gives better yields of aziridines with styrenes than [(TMG3trphen-Bi)Cu₃(µ-Cl)₃] with PhINTs as a nitrene source. However, the catalytic reactivity of both catalysts is enhanced by an electrophilic nitrene, PhINTces for …

Metal-Organic Thin Film Coated On Optical Fiber, Nahideh Salehifar

Doctoral Dissertations

This dissertation explores the development of metal-organic framework (MOF)-based optical fiber sensors for detecting volatile organic compounds (VOCs) at low concentrations (parts-per-billion to parts-per-million). In the first part of the study, theoretical calculations were performed using effective medium approximation (EMA) models, including Lorentz–Lorentz, Maxwell–Garnett, and Bruggeman equations, to predict the refractive index changes of MOFs upon gas adsorption. These models were applied to MOFs such as ZIF-7, ZIF-8, ZIF-90, MIL-101(Cr), and HKUST-1 to evaluate their potential for gas sensing.

In the second part of the dissertation, experimental work was conducted to validate the theoretical predictions. MOF-coated optical fibers were fabricated …

Synthesis And Analysis Of Materials For Quantum Devices, Mathew Pollard

Doctoral Dissertations

Quantum materials play a pivotal role in the advancement of next-generation technology. Superconducting quantum computing, dissipationless spintronics, or valleytronics offer promising ways forward beyond traditional chip miniaturization. Josephson Junctions (JJs) have already revolutionized quantum information and high precision detectors. Quantum systems, however, are either hard to control, produce, and/or maintain. This calls for a better understanding of microscopic properties and tuning of these quantum states.

In this work, we experimentally investigated growth methods to control the electric and magnetic properties of Topological Insulator (TI) Sb2Te3 through Cr-doping. Our results demonstrate the onset of a Magnetic Topological Insulator (MTI) and have …

Deep Learning Architecture Design For Nano-Satellite Image Super-Resolution, William Everette Symolon

Doctoral Dissertations

Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. NanoSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation, and research. They also have the potential to enhance satellite constellation resilience. However, the inherent size, weight, and power limitations of NanoSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution, which limits the utility of CubeSat images for military planning purposes and national intelligence applications. This research proposed a deep learning architecture capable of enhancing …

Selected Topics In Blockchain-Aided Iot Inference, Yiming Jiang

Doctoral Dissertations

"Blockchain has recently been considered in the Internet of Things (IoT) to secure data exchanges and storage across various engineering applications. This newly emerging blockchain-aided IoT (BIoT) network has been applied to a great deal of security-related applications. However, the BIoT still has the vulnerability that can be exploited by attackers, potentially impairing the performance of BIoT applications. Despite this, research addressing the vulnerability has been limited. This work presents four studies aimed at addressing critical gaps in current BIoT research.

The first study introduces a Time-Restricted Double-Spending Attack (TR-DSA) model for Proof-of-Work (PoW)-based blockchains, where the adversary only conducts …

Drone-Based Multimodal Sensing On Vegetations And Data Analytics For Early Detection Of Gas Leakage From Underground Pipelines, Pengfei Ma

Doctoral Dissertations

"This study explored the feasibility of using multimodal remote sensors in the early detection of natural gas leaks from underground pipelines, as vegetation situated above pipelines can discern microbial changes in soil when affected by gas and exhibit physiological stress symptoms on leaves. Three sensors (RBG, thermal, and hyperspectral cameras) were utilized to monitor vegetation stress as an indicator of gas leaks. A laboratory experiment was conducted to evaluate the workability of vegetation for gas leak detection. Regular hyperspectral imagery was collected from test vegetations to identify gas stress and distinguish it from other environmental stressors such as salinity impact, …

A Hybrid Data Processing, Computational Intelligence, And Complex Systems Modeling Approach For Describing And Predicting The Bitcoin Market, Oluwadamilare Akinpelu Omole

Doctoral Dissertations

"The Bitcoin market, like traditional financial markets, is a complex system with intricate interdependencies and nonlinear interactions among various market elements. This, coupled with the inherent uncertainty and high volatility present, makes predicting Bitcoin price movements difficult. The lack of understanding of the underlying market dynamics often results in significant losses for investors and traders. Existing studies have focused on the use of predictive models, which have not sufficiently captured the complexities of the market and cannot forecast extreme market events. This research endeavors to bridge this gap by combining data processing techniques, computational intelligence, and complex systems theory to …

Atmospheric Transport Of Radionuclides, Meteorological Data Mining & Synthesis For Site Data Supplementation, Ben Yao Sonpon

Doctoral Dissertations

"This research aligns with the NRC's vision of implementing risk-informed decision-making to support its regulatory charter. The suite of tools developed includes automated procedures for data mining and extracting meteorological (met) data from various national and regional repositories. A second data analytics tool automates the process of data fusion into a uniform format while preserving data fidelity. Autocorrelations were then established, and correlation coefficients were calculated between the Callaway Nuclear Power Plant (Callaway) and nearby off-site weather stations. The complexity of these correlations depends on factors such as physical proximity, geographic features, and topology, potentially necessitating multivariate analysis (MVA). Furthermore, …

Leveraging Deep Learning Models And Social Media Data For Enhanced Situation Awareness In Disaster Management, Ademola Abdulganiyu Adesokan

Doctoral Dissertations

"In recent years, social media has become a crucial source of real-time data for disaster management, supporting emergency responses when traditional channels like 911 are overcrowded and overwhelmed. It offers authorities valuable data for developing effective strategies, especially when swift actions are essential to save lives. However, the informal language, ambiguous meanings, and irrelevant content on social media pose challenges to accurate classification and hinder the efficient extraction of disaster-relevant information, leading to inefficiencies in emergency response efforts.

This research focuses on seven key questions: i) How can we detect, classify, and analyze hate and offensive tweet emotions during large-scale …

Investigation Of Shockwave Behavior To Inform Helmet Design Using Schlieren Imagery And Free-Field Blasts, Cody Jh Thomas

Doctoral Dissertations

"Ballistic resistance of military helmet designs is a prioritized feature to protect warfighters from traumatic brain injury. However, injuries from shock-based threats have not been optimized for warfighter protection and have resulted in the prevalence of blast-induced traumatic brain injury (bTBI). Research gaps were identified in understanding the under wash effect using free field blasts and schlieren imagery. Thus, the following were investigated: (1) overpressure changes on a warfighter’s head due to directionality from a blast, (2) shock wave dampening effects of viscoelastic materials in helmets, and (3) geometric effects of a helmet brim design in the reduction of bTBI. …

Polybenzodiazine Aerogels: All-Nitrogen Analogues Of Polybenzoxazines - Synthesis, Characterization, And Their Application In Carbon Dioxide Capture, Vaibhav Edlabadkar

Doctoral Dissertations

"The rapidly risen concentration of carbon dioxide in the atmosphere over recent years contributed to global warming and ocean acidification. Carbon capture is viewed as an essential tool from keeping atmospheric CO₂ levels from rising further. This thesis focuses on the development of new materials for high capacity and selective adsorption of CO₂. One such material was based on a newly synthesized tetrahydroquinazoline (THQ), which was designed as an all-nitrogen analogue of main-stream benzoxazine monomers. THQ solutions in DMF gelled at 100 °C via HCl-catalyzed ring opening polymerization to polybenzodiazine (PBDAZ) wet gels, which were dried in …

Vibration Modeling Of Rectangular Plates And Application To Solar Cell Dust Mitigation, Jeremiah John Rittenhouse

Doctoral Dissertations

"Many engineering structures can be modeled as rectangular plates, including solar cells. Carefully controlled plate vibration can be useful to solve an engineering problem, such as lunar dust accumulation on solar cells, which blocks light and reduces power generation. For this application, plate vibration at resonance is studied and used experimentally to eject lunar dust from a solar cell.

Piezoelectric actuator placement on the inactive side of a solar cell to induce vibration dust mitigation from the active side of the cell is presented in this work. Three solar cell prototypes were created and tested for efficacy, resulting in an …

New Topologies For High Voltage Gain Dc-Dc Power Electronic Converters – Theoretical Analysis And Experimental Validation, Saeed Habibi

Doctoral Dissertations

"This dissertation is focused on developing and analyzing new high voltage gain DC-DC power electronic converters. The proposed converters in this dissertation are suitable candidates for connecting low voltage sources such as photovoltaic (PV) panels or fuel cell (FC) stacks to a DC microgrid or DC link of an inverter. The proposed converters provide desired characteristics such as high voltage gain at a low or medium duty cycle values, low voltage stress on power semiconductors, and continuous input current. Initially, the research introduces three novel converter topologies: First, two quadratic converters based on the combination of a three-winding coupled inductor …

Photoionization Of Spin-Polarized Rydberg Atoms Out Of A Continuous Wave Optical Dipole Trap, Kevin Lee Romans

Doctoral Dissertations

"Recent research has demonstrated the need for a fully differential experiment on the photoionization of ⁶Li Rydberg atoms in a continuous wave (cw) laser field, as this would provide a new path forward for studying never before accessible atom-light interactions. Rydberg atoms are back in the focus of intense research due to their peculiar properties, which make them interesting candidates for quantum optics and information applications. In this work, we study the ionization of such Rydberg atoms, due to their interaction with a trapping laser field, by utilizing a reaction microscope (ReMi) capable of measuring photoelectron momentum distributions with …

Parallel Algorithms For Large Scale Dynamic Graph Analysis, Arindam Khanda

Doctoral Dissertations

A complex system of interacting entities in contemporary scenarios, be it biological, technological, or social, can be represented using graphs. Dynamic graphs, unlike their static counterparts, are ones in which the underlying topology changes over time. These networks act as a model for numerous systems, from transportation to social interactions, capturing the ever-evolving nature of real-world phenomena. However, the inherent temporality of these networks presents a unique set of challenges and the traditional static graph algorithms often fall short in efficiency and applicability. In our research, we delve into the complexities presented by large dynamic networks and suggest various methodologies …

Investigation Of The Transport And Plugging Behavior Of Preformed Particle Gels (Ppgs) In Fractures For Conformance Control In Carbonate Reservoirs, Abdulaziz A. Almakimi

Doctoral Dissertations

This study aims to systematically evaluate the performance of preformed particle gels (PPGs) in fractured carbonates mainly through a series of core flooding experiments. Carbonate reservoirs exhibit unique features of heterogeneity and pore geometry, while (PPGs) are known for their plugging efficiency, deformability, and dehydratable nature. The main goal of gel injection in fractured reservoirs is to place a gel pack appropriately into the fracture, diverting injected fluid to the oil-bearing matrix and reducing excessive water production. However, considerable dehydration of (PPGs) is possible at the adjacent matrix and within the fracture, which could lead to a filter-cake formation and/or …

Provenance Of The Uppermost Carboniferous–Lower Triassic Sandstones, Bogda Mountains, Nw China: Implications On The Late Paleozoic Tectonic History Of The Southern Central Asian Orogenic Belt, Sixuan Wu

Doctoral Dissertations

"The Permian-Triassic period represents a significant but insufficiently understood tectonic transition stage in the southern Central Asian Orogenic Belt (CAOB) between the Paleozoic continental amalgamation and Cenozoic orogenic reactivation. This study integrates fieldwork, petrographic, and detrital zircon geochronological data to document the trend of depositional environments, framework grain compositions, and dates of detrital zircon grains of the uppermost Carboniferous–Lower Triassic sandstones from three study areas in Bogda Mountains, the greater Turpan-Junggar basin, NW China, to decipher the spatial and temporal changes in the provenance lithology and area and reconstruct the unroofing history of the southern CAOB.

In the first part …

Power Supply Induced Jitter Analysis In High-Speed Drivers, Yifan Ding

Doctoral Dissertations

The Input/Output Buffer Information Specification (IBIS) model faces challenges in accurately simulating power-supply-induced jitter (PSIJ), particularly under nonlinear and time-varying power noise conditions with pre-driver stages. This work introduces advancements in IBIS model modification algorithms to enhance PSIJ simulation accuracy for high-speed drivers.

First, a correction coefficient-based approach was developed to adjust switching coefficients K_pu and K_pd using pre-driver DC jitter sensitivity, significantly improving model robustness under DC and AC power noise. However, its effectiveness diminished under large noise amplitudes due to coefficient shape dominance.

To address this, a simplified algorithm bypassed correction coefficient, directly correlating switching transitions with jitter …

Quantum And Classical Phase Transitions In Disordered Systems, Gaurav Ramesh Khairnar

Doctoral Dissertations

This work examines the effects of quenched disorder on classical and quantum phase transitions. There are countless examples of phase transitions in nature. Here, we study phase transitions in condensed matter with the help of simple models that focus on the essential unifying features of the problems. We are particularly interested in investigating how the presence of disorder changes the behavior of the system in the vicinity of a phase transition.

Here, we discuss three closely related problems regarding phase transitions. First we consider the behavior of highly diluted magnets with varying dilution levels in order to explain the unusual …

Development Of Catalytic Membranes And Composites For Energy Storage Devices And Nonenzymatic Biosensors, Harish Singh

Doctoral Dissertations

The excessive use of fossil fuels has led to their rapid depletion, causing an energy crisis and environmental issues. Consequently, there's a growing focus on sustainable energy conversion. Electrocatalysts are pivotal in this development, particularly for fuel cells and solar fuel generators, necessitating high-efficiency, cost-effective catalysts for large-scale adoption. Transition metal chalcogenides have emerged as promising electrocatalysts due to their high efficiency, electrochemical tunability, and abundant active sites. This work is divided in two parts. In Part A, we have investigated transition metal chalcogenide and few atom clusters FACs as electrocatalysts for oxygen evolution and reduction, highlighting their structure-property relationships …

Deep Learning And Adaptive Clustering Approaches For Flood Prediction And Efficient Sensor Placement In Missouri, Fahimeh Sharafkhani

Doctoral Dissertations

Floods represent formidable natural calamities, posing a significant threat to communities and infrastructure due to their unpredictable and often devastating consequences. The occurrence of floods is influenced by a convergence of meteorological, hydrological, and geographical factors, resulting in changes to the patterns of rising water levels. Machine learning models have emerged as favored tools in recent times for modeling water levels and enhancing the precision of flood predictions. This research employs both supervised and unsupervised machine learning models, with the main objective of improving the accuracy of flood predictions and sensor placement. Four distinct deep learning models are used to …

Measurement Method And Applications Of Transfer Function In Rf Desensitization Problem, Xiangrui Su

Doctoral Dissertations

Radio frequency (RF) desensitization issues comprise two components: noise radiation sources and the transfer function from noise sources to the victim antenna. RFI is a critical challenge in modern electronic systems, particularly in densely packed environments. This work presents a comprehensive study of RFI, addressing key aspects through three novel contributions. First, a transfer function measurement method is developed for compact metallic enclosures. This method provides a precise characterization of the electromagnetic (EM) environment within confined spaces, enabling accurate identification of interference pathways. Second, an EM emission management analysis framework is proposed, leveraging transfer functions to quantify and mitigate interference …

Topics On Ai Fairness Preferences In Kidney Transplantation, Mukund Telukunta

Doctoral Dissertations

Modern kidney transplantation incorporates artificial intelligence (AI) decision-support systems which exhibit social discrimination due to biases inherited from training data. Although researchers have proposed various group-based fairness notions to assess biases in AI, it remains uncertain which criterion is most suitable for evaluating biases in such complex healthcare systems. This dissertation explores human perception of fairness to identify the most appropriate fairness criterion for assessing AI tools in kidney transplantation, focusing on the preferences of non-expert (e.g. public, patients) stakeholders. The study examines two distinct AI systems employed in kidney transplantation: a classification model and a regression model. Human subject …

The Real-Time Detection Infrastructure Of Ligo, Virgo, And Kagra: Data Products, Current Performance, And Future Developments, Sushant Sharma Chaudhary

Doctoral Dissertations

The discovery of the binary neutron star merger event GW170817 marked the dawn of Multi-Messenger Astronomy (MMA) with Gravitational Waves (GWs). Such multi-messenger events are of immense scientific interest due to the wealth of information they provide through joint observations across different messengers. In this rapidly evolving field, prompt identification and timely distribution of alerts is critical for follow-up observations.

This work offers a comprehensive overview of the LIGO-Virgo-KAGRA (LVK) Collaboration’s low-latency analysis pipeline for GW events, covering key stages from calibration and data analysis to the issuance of public alerts. I examine the latency and accuracy of each stage …

Assessing And Comparing Geophysical Methods For Resolving Shallow Subsurface Geology, Abdullah Basaloom

Doctoral Dissertations

Geophysical methods are essential tools for investigating shallow subsurface structures, offering non-invasive means to characterize geological features and detect anomalies. Techniques such as electrical resistivity tomography (ERT), towed Transient Electromagnetic (tTEM), seismic refraction, magnetic, gravity, and magnetotelluric (MT) are commonly employed to image subsurface conditions with high resolution.

This study started with an in-depth analysis of comparing results co-located in space to assess uncertainties and resolution in the obtained resistivity models in the Kansas River Alluvial Aquifer (KRAA). Our results provided a quantitative interpretation of resistivity estimates between different geo-electrical methods (ERT, tTEM and DPEC). Following this, we provided a …