Physical Sciences and Mathematics Commons^™

Machine Learning Assisted Optimization For Calculation And Automated Tuning Of Antennas, Lauren Linkous

Theses and Dissertations

The Antenna Calculation and Autotuning Tool (AntennaCAT) software suite represents a significant advancement in the field of antenna design by automating the entire design, CAD, simulation, and optimization process compatible with several EM simulation software suites. It is the first comprehensive implementation of machine learning in this context. In particular, this work includes the capability to create and export structured datasets from the aforementioned EM software for iterative improvement and includes an expandable selection of optimizers.

Mapping With The Land: Co-Developing A Cumulative Impact Monitoring And Land Stewardship Framework With Sambaa K’E First Nation, Northwest Territories, Canada, Michael S. Mcphee

Theses and Dissertations (Comprehensive)

Across the Northwest Territories (NWT), Canada, Indigenous populations are striving to achieve effective environmental protection, whilst navigating complex methods, policies, and research relationships within co-management contexts. This thesis seeks to identify how differing cultural systems, environmental change, and fractured partnerships may be unified to align with the needs of the Sambaa K’e First Nation (SKFN), a remote Dehcho Dene community. Indigenous methodologies guided co-development of research questions with SKFN leadership which yielded objectives a) develop a GIS-based method to manage, organize and mobilize cultural and environmental data; b) develop a new stewardship monitoring procedure so that users can apply the …

A Data-Driven Machine Learning Approach For Electron-Molecule Ionization Cross Sections, Allison Harris, Josh Nepomuceno

Faculty publications – Physics

Despite their importance in a wide variety of applications, the estimation of ionization cross sections for large molecules continues to present challenges for both experiment and theory. Machine learning (ML) algorithms have been shown to be an effective mechanism for estimating cross section data for atomic targets and a select number of molecular targets. We present an efficient ML model for predicting ionization cross sections for a broad array of molecular targets. Our model is a 3-layer neural network that is trained using published experimental datasets. There is minimal input to the network, making it widely applicable. We show that …

Immobilization Of Thiol-Modified Horseradish Peroxidase On Gold Nanoparticles Enhances Enzyme Stability And Prevents Proteolytic Digestion, Faith E. Breausche, Annelise Somerlot, Jason Walder, Kwame Osei, Samuel Okyem, Jeremy D. Driskell

Faculty Publications – Chemistry

The specificity and efficiency of enzyme-mediated reactions have the potential to positively impact many biotechnologies; however, many enzymes are easily degraded. Immobilization on a solid support has recently been explored to improve enzyme stability. This study aims to gain insights and facilitate enzyme adsorption onto gold nanoparticles (AuNPs) to form a stable bioconjugate through the installation of thiol functional groups that alter the protein chemistry. In specific, the model enzyme, horseradish peroxidase (HRP), is thiolated via Traut’s reagent to increase the robustness and enzymatic activity of the bioconjugate. This study compares HRP and its thiolated analog (THRP) to deduce the …

Quantum Interference Enhancement Of The Spin-Dependent Thermoelectric Response, Runa X. Bennett, Joshua R. Hendrickson, Justin P. Bergfield

Faculty publications – Physics

We investigate the influence of quantum interference (QI) and broken spin-symmetry on the thermoelectric response of node-possessing junctions, finding a dramatic enhancement of the spin-thermopower (Ss), figure-of-merit (ZsT), and maximum thermodynamic efficiency (ηsmax) caused by destructive QI. Using many-body and single-particle methods, we calculate the response of 1,3-benzenedithiol and cross-conjugated molecule-based junctions subject to an applied magnetic field, finding nearly universal behavior over a range of junction parameters with Ss, ZsT, and reaching peak values of 2𝜋/ √3(𝑘/𝑒)2𝜋/3(𝑘/𝑒), 1.51, and 28% of Carnot efficiency, respectively. We also find that the …

Sequential Infiltration Synthesis Of Silicon Dioxide In Polymers With Ester Groups─Insight From In Situ Infrared Spectroscopy, Mahua Biswas, Vepa Rozyyev, Anil U. Mane, Amelia Korveziroska, Uttam Manna, Jeffrey W. Elam

Faculty publications – Physics

New strategies to synthesize nanometer-scale silicon dioxide (SiO2) patterns have drawn much attention in applications such as microelectronic and optoelectronic devices, membranes, and sensors, as we are approaching device dimensions shrinking below 10 nm. In this regard, sequential infiltration synthesis (SIS), a two-step gas-phase molecular assembly process that enables localized inorganic material growth in the targeted reactive domains of polymers, is an attractive process. In this work, we performed in situ Fourier transform infrared spectroscopy (FTIR) measurements during SiO2 SIS to investigate the reaction mechanism of trimethylaluminum (TMA) and tri(tert-pentoxy) silanol (TPS) precursors with polymers having ester functional groups (poly(methyl …

Reducing Short-Chain Pfas Levels In California Water Supplies, Manu Prabandham

Pomona Senior Theses

This thesis proposes twelve specific policies based on precedents set by prior regulation of persistent organic pollutants (such as PCBs), the costs and benefits of short-chain PFAS, technologies available to remove and destroy short-chain PFAS, and the roles and limitations of California’s regulatory institutions. The twelve policies are chosen to be politically and financially feasible, effective at removing short-chain PFAS from water supplies, and equitable towards lower-income and minority Californians, who suffer the most from the consequences of PFAS and other environmental pollutants. The new definitions, education campaigns, studies, taxes, bans, standards, testing, and filtration systems proposed are intended to …

Antibody-Driven Assembly Of Plasmonic Core–Satellites To Increase The Sensitivity Of A Sers Vertical Flow Immunoassay, Eunice Ebbah, Anthony Amissah, Jun-Hyun Kim, Jeremy D. Driskell

Faculty Publications – Chemistry

Here, we describe a SERS-based vertical flow assay as a platform technology suitable for point-of-care (POC) diagnostic testing. A capture substrate is constructed from filter paper embedded with spherical gold nanoparticles (AuNPs) and functionalized with an appropriate capture antibody. The capture substrate is loaded into a filtration device and connected to a syringe to rapidly and repeatedly pass the sample through the sensor for efficient antigen binding. The antigen is then labeled with a SERS-active detection probe. We show that only a few Raman reporter molecules, exclusively located adjacent to the plasmonic capture substrate, generate detectible signals. To maximize the …

Diastereoselective Synthesis Of The Hiv Protease Inhibitor Darunavir And Related Derivatives Via A Titanium Tetrachloride-Mediated Asymmetric Glycolate Aldol Addition Reaction, Jordan M. Witte, Emmanuel Ayim, Christopher J. Sams, Jasmine B. Service, Caitlyn C. Kant, Lillian Bambalas, Daniel Wright, Austin Carter, Kelly Moran, Isabella G. Rohrig, Gregory M. Ferrence, Shawn R. Hitchcock

Faculty Publications – Chemistry

Darunavir is a potent HIV protease inhibitor that has been established as an effective tool in the fight against the progression of HIV/AIDS in the global community. The successful application of this drug has spurred the development of derivatives wherein strategic regions (e.g., P1, P1’, P2, and P2’) of the darunavir framework have been structurally modified. An alternate route for the synthesis of darunavir and three related P1 and P1’ derivatives has been developed. This synthetic pathway involves the use of a Crimmins titanium tetrachloride-mediated oxazolidine-2-thione-guided asymmetric glycolate aldol addition reaction. The resultant aldol adduct introduces the P1 fragment of …

Attitudes And Perceptions Towards Privacy And Surveillance In Australia, Aleatha J. Shanley

Theses: Doctorates and Masters

Understanding attitudes towards privacy and surveillance technologies used to enhance security objectives is a complex, but crucial aspect for policy makers to consider. Historically, terrorism-related incidents justified the uptake of surveillance practices. More recently however, biosecurity concerns have motivated nation-states to adopt more intrusive surveillance measures. There is a growing body of literature that supports the public’s desire to maintain privacy despite fears of biological or physical threats.

This research set out to explore attitudes towards privacy and surveillance in an Australian context. Throughout the course of this endeavour, the COVID-19 pandemic emerged bringing with it a variety of track …

Delayed Coastal Inundations Caused By Ocean Dynamics Post-Hurricane Matthew, Kyungmin Park, Emanuele Di Lorenzo, Yinglong J. Zhang, Tal Ezer, Fei Yi

CCPO Publications

Post Hurricane Abnormal Water Level (PHAWL) poses a persistent inundation threat to coastal communities, yet unresolved knowledge gaps exist regarding its spatiotemporal impacts and causal mechanisms. Using a high-resolution coastal model with a set of observations, we find that the PHAWLs are up to 50 cm higher than the normal water levels for several weeks and cause delayed inundations around residential areas of the U.S. Southeast Coast (USSC). Numerical experiments reveal that while atmospheric forcing modulates the coastal PHAWLs, ocean dynamics primarily driven by the Gulf Stream control the mean component and duration of the shelf-scale PHAWLs. Because of the …

Western Boundary Current-Subtropical Continental Shelf Interactions, William B. Savidge, Dana K. Savidge, Frederico Brandini, Adam T. Greer, Eileen E. Hofmann, Moninya Roughan, Iison De Silvera, Iain M. Suthers

CCPO Publications

Western boundary currents (WBCs) adjacent to subtropical continental shelves (STCSs; between ~25° and 35° latitude; Figure 1) transport heat, nutrients, and biota poleward along the western margins of major ocean basins, interacting with the continental margins and influencing their physics and biology. Eddies and meanders along the shelf edge upwell deep, nutrient-laden water that can be advected onto the adjacent shelves with a corresponding export of particle-rich shelf water (e.g., Lee et al., 1991; Kimura et al., 1997; Campos et al., 2000; Roughan and Middleton, 2002, 2004; Lutjeharms, 2006; Savidge and Savidge, 2014). Despite their similarities, the various STCS regions …

Extensions Of The Standard Model With Improved Ultraviolet Behavior, Mikayla Anderson

Dissertations, Theses, and Masters Projects

Although general relativity and the standard model have proved incredibly consistent at all scales accessible to tests, they are not expected to accurately describe nature at all scales; we know there is new physics to be discovered at higher energy scales (shorter distances). The nonrenormalizability of gravity prohibits a predictive quantum field theory description, unless the infinite parameter space needed to absorb divergences can be constrained. An asymptotically safe theory is one in which all of the couplings in the theory run to either zero or a nonzero ultraviolet fixed point. Requiring that a coupling reach an ultraviolet fixed point …

Scheduled Contrastive Loss In Continued Transfer Learning For Software Engineering Tasks, Aaron Michael Harris

Dissertations, Theses, and Masters Projects

Considerable research has been performed with regard to using text-to-text machine learning methods to perform various software engineering tasks. At the same time, contrastive learning has shown promise in other modalities, such as computer vision-related problems, and has been explored to some extent in terms of limited software engineering tasks. We demonstrate that contrastive loss, on its own, is insufficient to surpass current baselines for these tasks; however, we note that there is a high degree of orthogonality in the results from existing and contrastive models. We show that when our contrastive method is used as an additional transfer learning …

Emergent Capabilities Of Llms For Software Engineering, Conor O'Brien

Dissertations, Theses, and Masters Projects

A growing interest for Large Language Models (LLMs) is how increasing their size might result in changes to their behavior not predictable from relatively smaller-scaled models. Analyzing these emergent capabilities is therefore crucial to understanding and developing LLMs. Yet, whether LLMs exhibit emergence, or possess emergent capabilities, is a contested question. Furthermore, most research into LLM emergence has focused on natural language processing tasks and models suited for them. We focus on investigating emergence in the context of software engineering, and recontextualize the discussion of emergence in the context of prior research. We propose a multifaceted pipeline for evaluating and …

Automated Bug Report Management To Enhance Software Development, Yang Song

Dissertations, Theses, and Masters Projects

Bug report management is crucial yet challenging process that affects the efficiency of software development process. It involves reporting, triaging, detecting duplicates, assigning, localizing, fixing bugs, and thorough verification. The high volume and variety of bug reports complicate these tasks, highlighting the need for innovative solutions to improve the process and boost development efficiency. This dissertation explores the potential of automating the bug management process to optimize the effectiveness of software development and maintenance. It focuses on three key stages of bug management: reporting, assignment, and localization, presenting four innovative solutions for these phases. First, it discusses the challenges faced …

Investigating The Latent Security And Privacy Risks In Consumer-Oriented Software Systems, Kaushal Kafle

Dissertations, Theses, and Masters Projects

Consumer-oriented software systems have become the foundation on which consumer data is collected and transported from the consumers to the data processors. They are complex, with various interconnected, heterogeneous components working together, making their security and privacy analysis challenging, and impact on the user uncertain. In this work, we first explore how security threats can arise in novel context in such systems by performing a security evaluation of data-store based smart home platforms and the overall security risks posed by the design of routines within such platforms. We analyze various components of the smart home such as the platform’s permission …

Transitions To Two-Hadron States From Quantum Chromodynamics, Felipe Ortega Gama

Dissertations, Theses, and Masters Projects

The rich spectrum of hadrons reflects the complexity of interactions between quarks and gluons confined within them. Most of these hadrons are extremely short-lived and are called resonances. Experimentally, they are observed indirectly through their effects on the energy distribution in scattering experiments. Additionally, the non-perturbative nature of Quantum Chromodynamics (QCD), which governs the dynamics of quarks and gluons, prevents the implementation of known analytical techniques for calculating transition and interaction rates between hadrons. Lattice QCD (LQCD), a numerical implementation of QCD, provides a non-perturbative approach to studying the spectrum, as long as we understand how to account for finite-volume …

Probing With Displacements For Variance Reduction And The Effectiveness Of Sketched Krylov Eigenvalue Solvers, Heather Maria Switzer

Dissertations, Theses, and Masters Projects

Scientific Computing is a multidisciplinary field that intersects Computer Science, Mathematics, and some other discipline to address complex problems utilizing computational systems. Numerical Linear Algebra (NLA), a subfield within Scientific Computing, develops and analyzes numerical algorithms for tasks involving linear operators such as matrices and their transformations. This dissertation focuses on developing kernels for two specific areas of NLA. Firstly, we explore variance reduction methods for trace approximation. In Lattice Quantum Chromodynamics (LQCD), computing the trace of a matrix inverse is crucial for investigating interactions among quarks and gluons in subatomic space. However, directly computing a matrix inverse is computationally …

Advancing Policy Insights: Opinion Data Analysis And Discourse Structuring Using Llms, Aaditya Bhatia

Graduate Thesis and Dissertation 2023-2024

The growing volume of opinion data presents a significant challenge for policymakers striving to distill public sentiment into actionable decisions. This study aims to explore the capability of large language models (LLMs) to synthesize public opinion data into coherent policy recommendations. We specifically leverage Mistral 7B and Mixtral 8x7B models for text generation and have developed an architecture to process vast amounts of unstructured information, integrate diverse viewpoints, and extract actionable insights aligned with public opinion. Using a retrospective data analysis of the Polis platform debates published by the Computational Democracy Project, this study examines multiple datasets that span local …

Enhancing Scanning Tunneling Microscopy With Automation And Machine Learning, Darian Smalley

Graduate Thesis and Dissertation 2023-2024

The scanning tunneling microscope (STM) is one of the most advanced surface science tools capable of atomic resolution imaging and atomic manipulation. Unfortunately, STM has many time-consuming bottlenecks, like probe conditioning, tip instability, and noise artificing, which causes the technique to have low experimental throughput. This dissertation describes my efforts to address these challenges through automation and machine learning. It consists of two main sections each describing four projects for a total of eight studies.

The first section details two studies on nanoscale sample fabrication and two studies on STM tip preparation. The first two studies describe the fabrication of …

Visual Experience Enhancement In Augmented Reality Displays, Qian Yang

Graduate Thesis and Dissertation 2023-2024

In the dynamic arena of display technology, augmented reality (AR) displays represent a pivotal advancement, seamlessly bridging the digital and physical worlds. This dissertation delves into the realm of AR display technologies, spotlighting the challenges and limitations of current systems, including transparent and near-eye displays, and proposes innovative solutions to enhance user experience and display performance. With a focus on overcoming issues such as diffraction-induced image blur, the trade-off between resolution and field of view (FoV) in near-eye displays, and FoV constraints in waveguide-based displays, this research introduces new evaluation methods, optimization techniques, and system designs. First, the dissertation presents …

Demystifying The Hosting Infrastructure Of The Free Content Web: A Security Perspective, Mohammed Alqadhi

Graduate Thesis and Dissertation 2023-2024

This dissertation delves into the security of free content websites, a crucial internet component that presents significant security challenges due to their susceptibility to exploitation by malicious actors. While prior research has highlighted the security disparities between free and premium content websites, it has not delved into the underlying causes. This study aims to address this gap by examining the security infrastructure of free content websites. The research commences with an analysis of the content management systems (CMSs) employed by these websites and their role. Data from 1,562 websites encompassing free and premium categories is collected to identify CMS usage …

A Comprehensive And Comparative Examination Of Healthcare Data Breaches: Assessing Security, Privacy, And Performance, Mohammed Al Kinoon

Graduate Thesis and Dissertation 2023-2024

The healthcare sector is pivotal, offering life-saving services and enhancing well-being and community life quality, especially with the transition from paper-based to digital electronic health records (EHR). While improving efficiency and patient safety, this digital shift has also made healthcare a prime target for cybercriminals. The sector's sensitive data, including personal identification information, treatment records, and SSNs, are valuable for illegal financial gains. The resultant data breaches, increased by interconnected systems, cyber threats, and insider vulnerabilities, present ongoing and complex challenges. In this dissertation, we tackle a multi-faceted examination of these challenges. We conducted a detailed analysis of healthcare data …

Machine Learning Algorithms To Study Multi-Modal Data For Computational Biology, Khandakar Tanvir Ahmed

Graduate Thesis and Dissertation 2023-2024

Advancements in high-throughput technologies have led to an exponential increase in the generation of multi-modal data in computational biology. These datasets, comprising diverse biological measurements such as genomics, transcriptomics, proteomics, metabolomics, and imaging data, offer a comprehensive view of biological systems at various levels of complexity. However, integrating and analyzing such heterogeneous data present significant challenges due to differences in data modalities, scales, and noise levels. Another challenge for multi-modal analysis is the complex interaction network that the modalities share. Understanding the intricate interplay between different biological modalities is essential for unraveling the underlying mechanisms of complex biological processes, including …

Bayesian Variable Selection With Shrinkage Priors And Generative Adversarial Networks For Fraud Detection, Amina Issoufou Anaroua

Graduate Thesis and Dissertation 2023-2024

This research paper focuses on fraud detection in the financial industry using Generative Adversarial Networks (GANs) in conjunction with Uni and Multi Variate Bayesian Model with Shrinkage Priors (BMSP). The problem addressed is the need for accurate and advanced fraud detection techniques due to the increasing sophistication of fraudulent activities. The methodology involves the implementation of GANs and the application of BMSP for variable selection to generate synthetic fraud samples for fraud detection using the augmented dataset. Experimental results demonstrate the effectiveness of the BMSP GAN approach in detecting fraud with improved performance compared to other methods. The conclusions drawn …

The Crash Consistency, Performance, And Security Of Persistent Memory Objects, Derrick Alex Greenspan

Graduate Thesis and Dissertation 2023-2024

Persistent memory (PM) is expected to augment or replace DRAM as main memory. PM combines byte-addressability with non-volatility, providing an opportunity to host byte-addressable data persistently. There are two main approaches for utilizing PM: either as memory mapped files or as persistent memory objects (PMOs). Memory mapped files require that programmers reconcile two different semantics (file system and virtual memory) for the same underlying data, and require the programmer use complicated transaction semantics to keep data crash consistent.

To solve this problem, the first part of this dissertation designs, implements, and evaluates a new PMO abstraction that addresses …

Advancements In Nuclear Magnetic Resonance, Electron Paramagnetic Resonance, Multipole Moments, And Lie Group Proprieties, Zhichen Liu

Graduate Thesis and Dissertation 2023-2024

To accurately solve the general nuclear spin state function in Nuclear Magnetic Resonance (NMR), a rotation wave approach was employed, allowing the reference frame to rotate in sync with the oscillating magnetic field. The spin state system was analogously treated as a Rubik's Cube, ensuring the diagonalization of only the time-dependent part of the state function. Although Gottfried's equation (1966) aligns with transitions between specific spin states m and m′, his second rotation contradicts the conservation of angular momentum, resulting in inaccuracies for spin states with initial phase shifts or entangled states. Contrarily, Schwinger (1937) efficiently computed the coefficients for …

Human Visual Search Performance For Close Range Detection Of Static Targets From Moving Sensor Platforms, Jennifer Hewitt

Graduate Thesis and Dissertation 2023-2024

Search models based on human perception have been developed by military researchers over the past few decades and have both military and commercial applications for sensor design and implementation. These models are created primarily for static imagery, and accurately predict task performance for systems with stationary targets and stationary sensors, if the observer is given infinite time to make targeting decisions. To account for situations where decisions must be made on a shortened time scale, the time-limited search model was developed to describe how task performance evolves with time. Recent variations of this model have been made to account for …

Brown Thrasher (Toxostoma Rufum) Habitat Preferences In Pine Barrens, Shrublands, And Agricultural Areas In New England, Alec Woolf

Antioch University Dissertations & Theses

Early successional habitat and related species are in decline in the Northeastern United States. One species of shrubland bird that is declining in the northeast is the Brown Thrasher, considered a species of conservation concern in several states such as New Hampshire, Rhode Island, Connecticut, New York, Vermont, and New Jersey. However, few studies have focused on examining Brown Thrasher habitat preferences. The purpose of this study was to evaluate Brown Thrasher associations with early successional habitat adjacent to farmland, within deciduous forests, and within pine barrens. I conducted systematic point count surveys with playback to assess thrasher occupancy and …