Physical Sciences and Mathematics Commons^™

Global Challenges In Accessing Mental Health Services And Addressing The Impact Of Alzheimer's Disease And Depression, Padmapriya Velupillai Meikandan

Master's Theses (2009 -)

This research project focuses on developing a quantum sensing system that can detect biomarkers associated with health disorders, like Alzheimer’s and depression. Our goal is to create a sensitive and highly selective quantum sensing device using a diamond nitrogen vacancy (NV) center. To train and test our quantum machine learning algorithms we will preprocess data from the available Human Connectome Project dataset. This dataset forms the basis of our quantum-based methods. The core of our project revolves around developing quantum machine learning algorithms that utilize techniques such as Support Vector Machines and neural networks to diagnose health disorders using data …

Integrating Drone And Satellite Imaging With Machine Learning For Green Stormwater Infrastructure Condition Assessments, Matthew Dupasquier

Master's Theses (2009 -)

Green Stormwater Infrastructure (GSI) has been increasingly utilized to improve urban stormwater management strategies. However, the performance and utility of GSI decrease over time if the infrastructure is not properly maintained. In recent history, the intrinsic operations and maintenance costs associated with the complex networks of new infrastructure have placed a burden on municipalities, ultimately prohibiting many from reaching the full extent of their stormwater management goals. One way for cities to achieve cost savings is through automated monitoring that can quickly assess the condition of GSI assets; however, existing cost-effective technologies are limited. Drones and satellites may be able …

Advanced Machine Learning Approaches For Predicting Mental Health Disorders Following Long Covid Diagnosis, Manoj Purohit

Master's Theses (2009 -)

After the global spread of COVID-19, the enduring effects of Long COVID and its health implications have emerged as a significant global issue, affecting people worldwide. The lingering symptoms post a COVID-19 infection can significantly affect individuals who had previously contracted the virus, exerting considerable influence over their mental well-being. Prolonged recuperation associated with Long COVID has been connected with the emergence of symptoms such as depression and anxiety, all of which can have adverse effects on emotional health. This project delves into an in-depth analysis of healthcare data pertaining to Long COVID from the Froedtert Health (FH) Medical System …

Modeling And Numerical Analysis Of The Cholesteric Landau-De Gennes Model, Andrew L. Hicks

LSU Doctoral Dissertations

This thesis gives an analysis of modeling and numerical issues in the Landau-de Gennes (LdG) model of nematic liquid crystals (LCs) with cholesteric effects. We derive various time-step restrictions for a (weighted) $L^2$ gradient flow scheme to be energy decreasing. Furthermore, we prove a mesh size restriction, for finite element discretizations, that is critical to avoid spurious numerical artifacts in discrete minimizers that is not well-known in the LC literature, particularly when simulating cholesteric LCs that exhibit ``twist''. Furthermore, we perform a computational exploration of the model and present several numerical simulations in 3-D, on both slab geometries and spherical …

Spectroscopy Of Atmospheres, Randika Dodangodage

Physics Theses & Dissertations

Spectroscopic methods are used to study planetary and stellar atmospheres. The information obtained from spectroscopic studies provides insight into atmospheric compositions and dynamics, which can be used to model and characterize atmospheres and climates. Laboratory-recorded absorption cross-sections are needed to interpret the recorded spectra of planets and stars. High resolution ethane, neopentane, propene, and n-butane spectra have been recorded, and absorption cross-sections have been provided for different temperatures and total pressures with different broadening gases, including hydrogen, helium, and nitrogen. The Atmospheric Chemistry Experiment (ACE) satellite orbits Earth and records spectra through solar occultation limb observations. HOCl is a chlorine …

Tools For Biomolecular Modeling And Simulation, Xin Yang

Mathematics Theses and Dissertations

Electrostatic interactions play a pivotal role in understanding biomolecular systems, influencing their structural stability and functional dynamics. The Poisson-Boltzmann (PB) equation, a prevalent implicit solvent model that treats the solvent as a continuum while describes the mobile ions using the Boltzmann distribution, has become a standard tool for detailed investigations into biomolecular electrostatics. There are two primary methodologies: grid-based finite difference or finite element methods and body-fitted boundary element methods. This dissertation focuses on developing fast and accurate PB solvers, leveraging both methodologies, to meet diverse scientific needs and overcome various obstacles in the field.

Generation, Dynamics, And Interaction Of Quartic Solitary Waves In Nonlinear Laser Systems, Sabrina Hetzel

Mathematics Theses and Dissertations

Solitons are self-reinforcing localized wave packets that have remarkable stability features that arise from the balanced competition of nonlinear and dispersive effects in the medium. Traditionally, the dominant order of dispersion has been the lowest (second), however in recent years, experimental and theoretical research has shown that high, even order dispersion may lead to novel applications. Here, the focus is on investigating the interplay of dominant quartic (fourth-order) dispersion and the self-phase modulation due to the nonlinear Kerr effect in laser systems. One big factor to consider for experimentalists working in laser systems is the effect of noise on the …

Predicting Biomolecular Properties And Interactions Using Numerical, Statistical And Machine Learning Methods, Elyssa Sliheet

Mathematics Theses and Dissertations

We investigate machine learning and electrostatic methods to predict biophysical properties of proteins, such as solvation energy and protein ligand binding affinity, for the purpose of drug discovery/development. We focus on the Poisson-Boltzmann model and various high performance computing considerations such as parallelization schemes.

Broadband Dielectric Spectroscopic Detection Of Volatile Organic Compounds With Zinc Oxide And Metal-Organic Frameworks As Solid-State Sensor Materials, Papa Kojo Amoah

Electrical & Computer Engineering Theses & Dissertations

The industrial revolution drove technological progress but also increased the release of harmful pollutants, posing significant risks to human health and the environment. Volatile organic compounds (VOCs), which have various anthropogenic and natural sources, are particularly concerning due to their impact on public health, especially in urban areas. Addressing these adverse effects requires comprehensive strategies for mitigation as traditional gas sensing techniques have limitations and there is a need for innovative approaches to VOC detection.

VOCs encompass a diverse group of chemicals with high volatility, emitted from various human activities and natural sources. These compounds play a crucial role in …

Computational Modeling And Analysis Of Facial Expressions And Gaze For Discovery Of Candidate Behavioral Biomarkers For Children And Young Adults With Autism Spectrum Disorder, Megan Anita Witherow

Electrical & Computer Engineering Theses & Dissertations

Facial expression production and perception in autism spectrum disorder (ASD) suggest the potential presence of behavioral biomarkers that may stratify individuals on the spectrum into prognostic or treatment subgroups. High-speed internet and the ease of technology have enabled remote, scalable, affordable, and timely access to medical care, such as measurements of ASDrelated behaviors in familiar environments to complement clinical observation. Machine and deep learning (DL)-based analysis of video tracking (VT) of expression production and eye tracking (ET) of expression perception may aid stratification biomarker discovery for children and young adults with ASD. However, there are open challenges in 1) facial …

Application Of The Fokker-Planck Equation For Quantifying Initial Condition Uncertainty Of Reversible Dynamic Systems, Troy S. Newhart

Mechanical & Aerospace Engineering Theses & Dissertations

Characterizing the behavior of dynamic systems requires the inclusion of initial conditions to propagate behavior forward in time. More realistic representations of system behavior quantify uncertainty about the initial conditions to assess sensitivity, reliability, and other stochastic response parameters. In many engineering applications, the uncertain initial conditions may be unknown given a desired response. This research applies the Fokker-Planck equation to reversible dynamic systems of select multi-dimensional nonlinear differential equations as a means for predicting the uncertainty about initial conditions. An alternating directions implicit numerical scheme is used to numerically solve the Fokker-Planck equation for both forward and reversed equations …

Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry

Electrical & Computer Engineering Theses & Dissertations

This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …

Trust And Contexts: A Conceptual Framework For Understanding Coastal Household Preparedness, Ogechukwu M. Agim Nwandu-Vincent

School of Public Service Theses & Dissertations

Despite research findings that show the benefits of being prepared for increasingly tumultuous natural and coastal hazard events, studies on hazard preparedness indicate that low levels of preparedness may occur in vulnerable areas due to the uncertainty around hazard risks, expected hazard onset and impact strength, as well as associated effects. Study findings indicate that trust may impact the uncertainty and complexity faced by people dealing with unfamiliar, infrequent, and complex hazards, as well as contexts such as factors such as age, gender, prior hazard experience, and homeownership.

While studies have looked at the relationship between trust and compliance (desired …

Impact Of Climate Change On Carbon And Nitrogen Balance In Zostera Marina L. (Eelgrass), Malee Jinuntuya

OES Theses and Dissertations

Seagrasses face vulnerability to both global stressors like Ocean Acidification (OA) and climate warming compounded by local stressors such as eutrophication that reduces light availability, leading to a complex dynamic of positive and negative effect on their growth and survival. Increased dissolved aqueous CO₂ (CO_2(aq)) benefits seagrasses by enhancing photosynthetic and growth rates, but it may increase nutrient demand, potentially depleting nutrient supply, especially in oligotrophic environments.

In this study, the long-term impact of CO₂ on Zostera marina L. (eelgrass) were investigated across a gradient of CO_2(aq) concentrations (55 – 2200 µM CO_{2(aq)) …}

Exploring Cation Exchange: Unveiling Its Significance In Biochar And Bioenergetics Applications, Gyanendra Kharel

Chemistry & Biochemistry Theses & Dissertations

Cation exchange, a cornerstone of soil chemistry and nutrient cycling, is a fundamental chemical process that occurs in soils, sediments, membranes, and other solid materials. It involves the interchange of positively charged ions, or cations, between a solid matrix and a surrounding solution. This process is crucial in various natural and engineered systems, leading to a range of applications across different fields.

This dissertation presents an extensive investigation into the applications of cation exchange in the fields of biochar and bioenergetics, encompassing three distinct aims. The first aim concentrates on the surface oxygenation of biochar through ozonization, aiming to achieve …

Interactive Effects Of Co2, Temperature, And Nitrate Limitation On The Growth And Physiology Of Marine Cyanobacterium Synechococcus Sp. Ccmp 1334, Alyssa K. Sharbaugh

LSU Master's Theses

The marine cyanobacterium Synechococcus sp. CCMP 1334 was grown in a continuous culture system on a 12:12 h light:dark cycle at all combinations of low and high pCO2 (400 and 1000 ppmv, respectively), nitrate availability (nitrate-limited and nutrient-replete conditions), and temperatures of 21°C, 24°C, 28°C, 32°C, and 35°C. The maximum median nutrient-replete growth rate was ~1.15 d−1 at 32 –35°C. Median growth rates at 1000 ppmv pCO2 were higher than those at 400 ppmv at all temperatures, but most of the differences were statistically insignificant. Carbon:nitrogen ratios were independent of pCO2 at a fixed relative growth rate but decreased with …

Use Of Unoccupied Aerial Vehicle (Drones) Based Remote Sensing To Model Platform Topography And Identify Human-Made Earthen Barriers In Salt Marshes, Joshua J. Ward

Masters Theses

Elevation is a foundational driver of salt marsh morphology. Elevation governs inundation and hydrological patterns, vegetation distribution, and soil health. Anthropogenic impacts at grand scales (e.g., rising sea levels) and local scales (e.g., infrastructure) have altered the elevation of the salt marsh surface, changing the topography and morphology of these ecosystems. This study establishes and assesses means to document and analyze these impacts using Unoccupied Aerial Vehicle (UAV) based remote sensing to model platform topography. This thesis’s first and primary study presents and compares methods of producing high-resolution digital terrain models (DTMs) with UAV-based Digital Aerial Photogrammetry (DAP) and Light …

Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim

Masters Theses

Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …

Sensor Analytics For Subsea Pipeline And Cable Inspection: A Review, Connor R. Vincent

LSU Master's Theses

Submarine pipelines and cables are vital for transmitting physical and digital resources across bodies of water, necessitating regular inspection to assess maintenance needs. The safety of subsea pipelines and cables is paramount for sustaining industries such as telecommunications, power transmission, water supply, waste management, and oil and gas. Incidents like those involving the Nord Stream subsea pipeline and the SEA-ME-WE 4 subsea communications cable exemplify the severe economic and environmental consequences of damage to these critical infrastructures. Existing inspection methods often fail to meet accuracy requirements, emphasizing the need for advancements in inspection technologies. This comprehensive survey covers the sensors …

An Analysis And Ontology Of Teaching Methods In Cybersecurity Education, Sarah Buckley

LSU Master's Theses

The growing cybersecurity workforce gap underscores the urgent need to address deficiencies in cybersecurity education: the current education system is not producing competent cybersecurity professionals, and current efforts are not informing the non-technical general public of basic cybersecurity practices. We argue that this gap is compounded by a fundamental disconnect between cybersecurity education literature and established education theory. Our research addresses this issue by examining the alignment of cybersecurity education literature concerning educational methods and tools with education literature.

In our research, we endeavor to bridge this gap by critically analyzing the alignment of cybersecurity education literature with education theory. …

Characterizing And Mitigating Transient Noise In Ligo Observatories For Gravitational Wave Detection, Jane Glanzer

LSU Doctoral Dissertations

The existence of gravitational waves is predicted by Albert Einstein's Theory of General Relativity. Commonly referred to as "ripples in spacetime", these waves are generated by some of the most violent and energetic processes in the universe. Despite their theoretical prediction over a century ago, it wasn't until 2015 that the Advanced LIGO (aLIGO) interferometers in Hanford, WA and Livingston, LA directly detected gravitational waves for the first time, confirming Einstein's theory and ushering in a new era of astrophysics.

Detecting gravitational waves requires incredible precision. Because of the extreme sensitivity required, it is possible for the gravitational wave data …

Investigation Of Gas Dynamics In Water And Oil-Based Muds Using Das, Dts, And Dss Measurements, Temitayo S. Adeyemi

LSU Master's Theses

Reliable prediction of gas migration velocity, void fraction, and length of gas-affected region in water and oil-based muds is essential for effective planning, control, and optimization of drilling operations. However, there is a gap in our understanding of gas behavior and dynamics in water and oil-based muds. This is a consequence of the use of experimental systems that are not representative of field-scale conditions. This study seeks to bridge the gap via the well-scale deployment of distributed fiber-optic sensors for real-time monitoring of gas behavior and dynamics in water and oil-based mud. The aforementioned parameters were estimated in real-time using …

Multi-Scale Simulations Of Dynamic Protein Structures And Interactions, Yumeng Zhang

Doctoral Dissertations

Intrinsically disordered proteins (IDPs) are functional proteins that lack stable tertiary structures in the unbound state. They frequently remain dynamic even within specific complexes and assemblies. IDPs are major components of cellular regulatory networks and have been associated with cancers, diabetes, neurodegenerative diseases, and other human diseases. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for mechanistic understanding of IDPs in biology, diseases, and therapeutics. However, accurate simulation of the heterogeneous ensembles and dynamic interactions of IDPs is extremely challenging because of both the prohibitive computational cost and demanding force field …

An Efficient Privacy-Preserving Framework For Video Analytics, Tian Zhou

Doctoral Dissertations

With the proliferation of video content from surveillance cameras, social media, and live streaming services, the need for efficient video analytics has grown immensely. In recent years, machine learning based computer vision algorithms have shown great success in various video analytic tasks. Specifically, neural network models have dominated in visual tasks such as image and video classification, object recognition, object detection, and object tracking. However, compared with classic computer vision algorithms, machine learning based methods are usually much more compute-intensive. Powerful servers are required by many state-of-the-art machine learning models. With the development of cloud computing infrastructures, people are able …

Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan

Doctoral Dissertations

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in …

Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia

Doctoral Dissertations

AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …

Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota

Doctoral Dissertations

Policy gradient methods are a class of reinforcement learning algorithms that optimize a parametric policy by maximizing an objective function that directly measures the performance of the policy. Despite being used in many high-profile applications of reinforcement learning, the conventional use of policy gradient methods in practice deviates from existing theory. This thesis presents a comprehensive mathematical analysis of policy gradient methods, uncovering misconceptions and suggesting novel solutions to improve their performance. We first demonstrate that the update rule used by most policy gradient methods does not correspond to the gradient of any objective function due to the way the …

Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed

Doctoral Dissertations

Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all' solutions are thus exceedingly …

Quantum Chaos, Integrability, And Hydrodynamics In Nonequilibrium Quantum Matter, Javier Lopez Piqueres

Doctoral Dissertations

It is well-known that the Hilbert space of a quantum many-body system grows exponentially with the number of particles in the system. Drive the system out of equilibrium so that the degrees of freedom are now dynamic and the result is an extremely complicated problem. With that comes a vast landscape of new physics, which we are just recently starting to explore. In this proposal, we study the dynam- ics of two paradigmatic classes of quantum many-body systems: quantum chaotic and integrable systems. We leverage certain tools commonly employed in equilibrium many-body physics, as well as others tailored to the …

High Resolution Mass Spectrometry As A Platform For The Analysis Of Polyoxometalates, Their Solution Phase Dynamics, And Their Biological Interactions., Daniel T. Favre

Doctoral Dissertations

Polyoxometalates (POMs) are a class of inorganic molecule of increasing interest to the inorganic, bioinorganic and catalytic communities among many others. While their prevalence in research has increased, tools and methodologies for the analysis of their fundamental characteristics still need further development. Decavanadate (V10) specifically has been postulated to have several unique properties that have not been confirmed independently. Mass spectrometry (MS) and its ability to determine the composition of solution phase species by both mass and charge is uniquely well suited to the analysis of POMs. In this work we utilized high-resolution mass spectrometry to characterize V10 in aqueous …