Mixed Methods Investigation of the Validity of the Items on the Rosenberg Self-Esteem Scale

This study explores the validity of the items on the Rosenberg Self Esteem scale. Self-esteem is the degree that someone feels favorably about themselves and how much value they personally feel they have. While other measures have been developed to measure global self-esteem, the RSES is overwhelmingly used most often. The definition of self-esteem and its role in education has been debated by educators, psychologists and educational psychologists for over 100 years. The self-esteem movement of the late 1980’s and 90’s in the United States was a campaign that began in the state of California and was widely adopted by the educational system. Understanding if the construct of self-esteem has changed since initial measurement tools were developed in 1965 is important. For example, items on the RSES may now be measuring other constructs of self. A mixed method design was utilized to examine the validity of the items on the RSES using the structure of Strong Program of Construct Validation combining qualitive and quantitative data. Qualitive data from thirty-three participants was collected via interviews using an informal interview style and research translation process called Think Aloud Protocol. Quantitative data was collected through one hundred and seventy-four surveys containing the RSES and the Self-Liking/Self-Competence Scale – Revisited. Findings showed that the items on the RSES were found to have statistical validity, but this measure may not be measuring current perceptions of self-esteem

Meta Gentrification: The Gentrification Nexus in the Advent of Corporate Landlordism

This dissertation explores the rise of corporate landlords taking hold of the Phoenix Metropolitan Area’s housing market. The dissertation explores the local and national historical settings that led way to the Phoenix Metropolitan Area becoming one of the largest markets in the country for corporate landlords. After exploring the history of the Phoenix Metropolitan Area and the rise of corporate landlordism, the dissertation creates a unique dataset that classifies millions of sales records from 2000-2020 in the Phoenix Metropolitan Area. From this dataset, spatial statistics and predictive modelling can be used to describe and predict where corporate landlords are purchasing housing units, explain how corporate landlords are exacerbating housing scarcity, and lend hand in answering who is being affected by investors commodifying houses. Corporate landlords have greatly shaped both rental and homebuying markets. Due to this, conceptualizations of displacement and gentrification appear to struggle with the broad reaching effects of corporate landlordism. Gentrification typically assumes that displacement occurs because of a wealthier, more privileged group moving into a disadvantaged or ethnic enclave. Through the creation of this granular dataset and spatial analysis, this dissertation finds that corporate landlords affect nearly all demographics evenly and have consumed nearly 25% of Phoenix’s housing units. A quarter of the Phoenix Metropolitan Area’s homes are now held by investors. Contemporary conceptions of gentrification are too myopic; with a broad-based housing crunch, it is likely that serialized displacement within a region is normalized and not only contained to ethnic enclaves. This dissertation argues that meta gentrification, the omnipresence of housing competition onset by corporate landlords, is leading to housing scarcity across entire cities

Experimental Investigation of Boundary-Layer Transition in High-Speed Two-Dimensional Boundary Layers

This dissertation focuses on Mack’s first- and second-mode instabilities in planarboundary layers. Experiments were first conducted on a flat plate in the Quiet Mach 4 Ludwieg Tube at the University of Arizona. Data showed presence of first-mode waves, but the amplitudes were too low to cause the boundary layer to transition. The frequency and wave angle of these first-mode waves agreed well with theory. No second-mode waves were detected in these experiments. The Reynolds number in this facility was too low to perform meaningful boundary-layer transition experiments. As such, experiments were then conducted on a hollow cylinder in the Mach 5 Ludwieg Tube at the University of Arizona, which has a larger maximum Reynolds number. In this conventional wind tunnel, the transition from a laminar boundary layer to a turbulent boundary layer was observed. The unit Reynolds numbers for these experiments ranged from Re′ = 6.5 × 106m−1 to 18.5 × 106m−1. The experimental data show evidence of the first and second modes in this boundary layer, as predicted by linear stability theory. The main instrumentation used in the experiments were surface pressure transducers and Z-type schlieren imaging. The pressure data showed spectral content in frequency bands where linear stability theory (LST) predicts the second mode to exist. The second mode becomes unstable at the locations predicted by LST and the experimentally measured second-mode growth rates agree well with second-mode growth rates from LST. The first-mode waves did not produce a significant signal in the pressure data, which is expected since these waves are often difficult to detect with surface pressure sensors. However, wave angle calculations performed on the pressure data in the predicted first-mode frequency range showed an oblique wave angle that was consistent with the first-mode wave angles of LST. First-mode structures were pronounced in the schlieren data and their shape and wavelength agree with LST amplitude reconstruction. The second-mode waves show some resemblance to the structures predicted by LST, but there are subtle differences as well. Nonlinear analysis of the pressure data was conducted to understand the ultimate breakdown mechanism. It appears that an interaction between low-frequency first-mode waves that starts around Rex = 2×106 is causing boundary-layer transition. The Reynolds transition number measured was approximately Rex = 3.5 × 106

AI-Powered Portable Optical Biosensors for Environmental Toxicants and Biomarkers

Detecting environmental toxicants and biomarkers is vital for protecting ecosystems and public health, yet current methods often lack portability, speed, and precision for field and point- of-care applications. AI-powered portable optical biosensors address these challenges by combining molecular detection units with compact optical devices and advanced data analytics. These systems offer high sensitivity, specificity, portability, and rapid decision-making.This dissertation focuses on developing AI-powered biosensor platforms for environmental field testing and medical diagnostics. Four platforms were designed to detect microRNAs, protein biomarkers, micro/nanoplastics (MNPs), and per- and polyfluoroalkyl substances (PFAS). The detection relies on light scattering or intensity measurements captured by compact photodetectors or smartphone cameras, analyzed using AI frameworks like computer vision and machine learning for pattern recognition, contaminant level prediction, and scalability via cloud processing. Chapter 2 outlines the targets, detection methods, and data analysis techniques across the projects. Overall, this work highlights the transformative potential of AI-powered biosensors in advancing environmental monitoring, healthcare, and industry, with future research aiming to enhance detection capabilities and expand applications

Phenotypic Profiling of Colon Tumor Organoid Monolayers Characterizes a FOLFOX-Induced, AKT-Driven Subpopulation of Chemoresistant Cells

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the US as well as the third most common cause of cancer-related death. Standard treatment includes surgical resection and adjuvant chemotherapy with multidrug regimens such as FOLFOX (Folinic Acid, 5-Fluorouracil, Oxaliplatin). However, due to high heterogeneity in colon tumors, patient response is variable and tools to predict drug response are few. Additionally, resistance, which can lead to recurrence and metastasis, is not uncommon. As such, there is a need for improved treatment options, better methods for predicting drug response, and better understanding of resistance mechanisms in CRC. Here, we introduce a host of new methodologies to phenotypically characterize CRC with the long term goals of better understanding chemotherapy resistance and creating actionable clinical tools that can model and predict individual tumors’ drug response. We have curated a set of colon tumor organoids, titled the Heterogeneity Library, that represents a variety of CRC subtypes. We used our newly designed phenotypic profiling pipeline, which leverages DAPI, pAKT, CD44v9, and H2AX, to characterize a FOLFOX-induced, PI3K/AKT-driven survival signature originally identified in our transcriptomic data. Unbiased subpopulation analysis confirms a cohort of cells with high PI3K/AKT signaling, high DNA damage, and high levels of stem cell markers across organoids. We believe that cells increasingly upregulate survival signaling, including extracellular matrix components and growth factor receptors, which all activate AKT, which in turn leads to a protective, resistant phenotype. Ultimately, we posit the observed FOLFOX dose-dependent increases in the fraction of these resistant cells across tumor types is a combination of adaptation down this proposed Resistance Trajectory in combination with a selection process whereby susceptible cells are readily eliminated, leaving the increasingly mesenchymal progenitor- and stem-like cells to expand despite FOX exposure. Finally, we endeavored to target this resistant population by treating organoid monolayers with a combination of FOLFOX and Dactolisib, the PI3K/mTOR dual inhibitor, which exhibited significant synergy across organoids. Even the most FOLFOX-resistant organoid – made up almost entirely of the previously described highly damaged, AKT-driven, stem-like resistant cells – demonstrated increased FOLFOX efficacy when also treated with Dactolisib. We hope that with further validation, the novel contributions of this dissertation can be developed into actionable tools for use from bench to bedside

Evaluating Proteinopathy and Consequences for Behavior in Animal Models of Human Neurodegenerative Disease

Neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS), Frontotemporal Dementia (FTD) and Parkinson’s Disease (PD) are devastating and incurable conditions associated with human aging that are rapidly growing in incidence and global economic burden. While these diseases of the central nervous system are complex, heterogeneous and multi-factorial in humans, experimental animal models based on the most common pathological predictors provide a framework for elucidating the cellular and molecular mechanisms leading to the progressive deterioration of behavior that largely characterizes these afflictions. Chapter 1 of this dissertation summarizes current literature outlining the characterization and pathophysiology of ALS, FTD and PD. Chapter 2 summarizes my contribution to novel work modeling FTD in fruit flies (Drosophila melanogaster) based on TDP-43 proteinopathy in mushroom body (MB) neurons. This work demonstrates a progressive thinning of MB lobes with resulting deficits in working memory, sleep and lifespan, and uncovers novel RNA targets of TDP-43 proteinopathy in MB neurons that are both unique and shared with targets identified in motor neurons within a fly model of ALS. Chapter 3 describes my most recent work in zebra finch songbirds evaluating the regional and subcellular distribution of alpha-synuclein (αsyn) neuropathy in the basal ganglia and the resulting effects on song behavior. This work establishes a novel tool, the Border Expression Ratio, for measuring locality and severity of αsyn neuropathy, revealing a positive correlation between right hemisphere pathology and a reduction in the variation of harmonic syllable duration. This work also revealed the detection of pSer129, a marker for pathologically aggregated αsyn typically enriched in Lewy bodies common to PD. Chapter 4 discusses the convergence of pathologies across neurodegenerative diseases, the limitations of these models and suggestions for future studies

Snail1 Negatively Regulates Prostate Cancer Cell Growth, and Drives Prostate Cancer Cell Invasion and Migration in the Context of Tumor Microenvironment

Most prostate cancer (PCa) deaths are attributed to cancer metastasis. Despite the high efficacy of anti androgen-receptor signaling therapy in early disease, most PCa patients will develop resistance to the treatment. Roughly 36,000 PCa deaths will be recorded for the year 2025. This represents a large unmet need and calls for a deeper understanding of PCa metastasis. In this report, I investigate the role of Epithelial-Mesenchymal-Transition (EMT) in promoting PCa metastatic potentials. Our understanding of EMT in PCa is growing thanks to the contribution of many investigators. Going forward, my research adheres to the following criteria, to better capture clinical relevance. Partial-EMT should be represented in PCa models. Manipulation of EMT should be transient. Expression of EMT drivers should be at physiological levels. Meeting the described criteria, I’ve discovered that Snail1 (an EMT driver) promotes cancer dormancy through cell proliferation suppression. My work shows Snail1 regulation of EGR1, FOXO1, p21, and cyclins A2 and B2. Snail1-dependent FOXO1 upregulation requires EGR1. Although FOXO1 has been shown to regulate p21, my data strongly suggests that the Snail1/EGR1/FOXO1 axis does not impact Snail1’s ability to regulate p21, cyclin A2 or B2. Furthermore, Snail1 promotion of PCa invasion/migration is dependent on the tumor micro-environment (TME). Snail1’s upregulation of MMP7 and Integrin-β3 is further enhanced by the TME. Herein, I discovered GPR1 as a novel target of Snail1. GPR1 is a G-protein coupled receptor, involved in metastatic progression of breast and gastric cancer. Chemerin, secreted by stromal myofibroblast, is GPR1’s primary ligand. Thus Snail1-dependent upregulation of GPR1, MMP7 and Integrin-β3 suggest the coupling of EMT and the TME to drive PCa metastatic progression. Taken together, Snail1 and its associated circuits serve as viable therapeutic targets for metastatic PCa, having demonstrated that Snail1 can drive both dormancy and invasion/migration.Release after 11/17/202

Cis Lunar Surveillance System

In an era defined by accelerated human and robotic ventures beyond low–Earth orbit and an increasing imperative to detect, track, and characterize transient near–Earth objects, this thesis presents a transformative concept: a distributed cis–lunar surveillance constellation that leverages the intrinsic dynamical pathways of the Earth–Moon three–body environment to achieve persistent, low–energy monitoring and rapid intercept capabilities. Drawing upon the rich structure of halo orbits and their associated invariant manifolds, our design embeds a network of microsatellites in carefully chosen manifold–guided trajectories, minimizing station–keeping ∆V while maximizing spatial coverage of the cis–lunar arena. The constellation’s hardware architecture integrates high–resolution optical imagers, wide–band radio–frequency transceivers, and autonomous inter–satellite ranging instruments, all orchestrated through a decentralized liaison framework that distributes processing and decision authority across the network. To navigate the complex gravitational interplay in the Circular Restricted Three–Body Problem without continuous ground intervention, we develop a passive measurement strategy that fuses star–line and inter–satellite range observations within an Unscented Kalman Filter tailored to the nonlinearities of the CR3BP, thereby achieving real–time, fault–tolerant orbit determination and guidance. We demonstrate the system’s rapid–response potential through a detailed case study: an opportunistic flyby and subsequent rendezvous with the temporary moon 2020 CD3. Beginning from a nominal halo–orbit constellation configuration, our transfer optimization—formulated as a constrained two–point boundary-value problem—yields a total ∆V of approximately 1.75km s−1, on par with dedicated singular missions yet accomplished without extensive pre–mission planning or large payload capacity. Monte Carlo simulations confirm a > 95% probability of intercept within a 1km corridor, and covariance analysis indicates sub–10m positional uncertainty at rendezvous, sufficient to support high–resolution imaging, in–situ sampling, and autonomous proximity operations. Beyond validating the concept, these results underscore manifold–enabled cis–lunar constellations as cost–effective, scalable platforms for planetary defense, resource reconnaissance, and sustained lunar exploration objectives. By uniting advanced astrodynamics, distributed sensing, and autonomous navigation, this work lays the foundation for a resilient space situational awareness infrastructure that can adapt to evolving threats and opportunities across Earth–Moon space

Fabrication and Integration Strategies for Atomically Precise Graphene Nanoribbon Field-Effect Transistors

Bottom-up synthesized, atomically precise graphene nanoribbons (GNRs) provide a powerful platform for studying electronic behavior in low-dimensional quantum systems and advancing next-generation nanoelectronic technologies. With their well-defined atomic structure, tunable bandgap, and excellent charge transport properties, GNRs are strong candidates for future low-power, high-performance electronics. Yet, despite their theoretical promise, experimental device performance remains limited. Bridging this gap requires advances in both materials synthesis and device fabrication. In this Thesis, I present our efforts to address these challenges through the development of integration strategies for GNR field-effect transistors (GNRFETs). I will first introduce a double-resist lithography process forintegrating 7- and 9-atom-wide armchair GNRs into FETs with sub-30 nm channel lengths in a local back-gate geometry. Next, I will discuss how this process was adopted to study GNR-FETs in two publications. The first paper demonstrated the long-term stability of passivated GNR devices, while the second enabled GNR integration via a sustainable, wafer-scale, and etch-free transfer method. Finally, I will describe a metal electrode transfer technique designed to enable scalable fabrication without direct metal deposition on the ribbons, aiming to reduce structural damage and improve the contact–channel interface. I will conclude by outlining the broader implications of this work, remaining challenges, and future directions toward realizing GNR-based nanoelectronics

Non-Contact Rock Strength Characterization

Understanding and classifying the compressive strength of rock, particularly the Unconfined Compressive Strength (UCS), is fundamental to rock mass classification and geotechnical design in mining, tunneling, and civil infrastructure development. Traditional methods rely on index testing and destructive laboratory testing, often requiring engineers and geologists to be exposed to rockfall on steep slopes, and producing sparse data sets due to sampling, access, and cost limitations. This dissertation investigates a novel non-contact methodology to characterize intact rock strength using image-based technologies across the electromagnetic spectrum, specifically long-wave infrared and short-wave infrared. The research was conducted in two phases: (1) a field-deployed LWIR imaging study to detect rockfall events using thermal infrared cameras; and (2) the Multi-Image Deformation Analysis System (MIDAS), which used SWIR hyperspectral imaging and machine learning to classify UCS categories from an altered porphyry granite in Arizona. The result demonstrated that thermal imaging can detect rockfall under a range of environmental conditions, from the extreme cold of winter in British Columbia to summer heat at mines in Arizona. In the laboratory, several SWIR absorption features could be correlated to the UCS strength of the granite, and a k-nearest neighbors classification could be used to classify rock strength according to the ISRM classification. While there are limits in detecting quartz and feldspar in the SWIR, the study highlights the potential for expanded spectral coverage and integration into geotechnical workflows. Additionally, a path is outlined for developing physical rock descriptions for engineering classifications using the VNIR and SWIR spectroscopy. This dissertation contributes a repeatable, non-destructive, and auditable framework for strength classification that improves safety, enhances data coverage, and supports the utilization of commercial off-the-shelf imaging technologies from exploration to mining applications. The approach has broad implications for safety and more effective site characterization in mining, civil tunneling, and critical infrastructure monitoring, particularly in altered rock masses.Release after 03/03/202