Doctor of Philosophy, Case Western Reserve University, 2019, Biology

In ecology, discerning process from pattern can be quite challenging. In plant community ecology, for example, multiple species, each with their own physiological characteristics and population dynamics, can interact in diverse ways across spatial and temporal scales. Moreover, plants can respond to multiple abiotic factors, which themselves will also vary across spatial and temporal scales. Given this complexity, both the abiotic environment itself, and the way in which species respond to it, is often simplified. This begs the question, does abiotic complexity matter? The following chapters ask when, and how, including various components of abiotic complexity will change the qualitative conclusions drawn about herbaceous layer plant communities. Previous research investigating the connection between the abiotic environment and community patterns often make at least one of three simplifications: 1) observational studies tend to consider only the mean or only the heterogeneity of abiotic conditions, even though both are known to impact plant communities. 2) Theoretical studies model survival probability as a function of abiotic conditions, even though survival depends on multiple life history events which may interact with the abiotic environment in different ways. Finally, 3) many studies assume that abiotic factors are important at large, but not small, spatial scales. Using an observational study, we show that accounting for both the mean and heterogeneity of conditions across multiple abiotic factors matters when we want to know how much variability in species richness can be explained by the abiotic environment. Using a simulation model, we show that the life stage at which species partition resources matters when we want to know how local processes will scale up to affect larger community patterns. And finally, using spatial point pattern analysis we show that both small- and large-scale structure in the abiotic environment matter when we want to understa (open full item for complete abstract)

Committee: Karen Abbott Dr. (Advisor); Robin Snyder Dr. (Committee Member); Jean Burns Dr. (Committee Member); Constance Hausman Dr. (Committee Member) Subjects: Ecology

PhD, University of Cincinnati, 2024, Arts and Sciences: Geography

Governments worldwide implemented stay-at-home orders and facility closures to limit movement and stem the spread of the global COVID-19 pandemic. These policies directly impact people's daily mobility, the racial composition of people on the move, and the number and spatial arrangement of Point-of-Interests (POIs). However, few studies have explored how to measure ambient population-based racial heterogeneity, the spatial configuration of POIs, and their effects on crime. According to Shaw and McKay's social disorganization theory, racial heterogeneity can disrupt a local community's social organization and contribute to crime and delinquency. While residents play the most critical role in defining the characteristics of a community, non-residents frequenting a community may also affect the community. The perceived racial composition will likely differ from the static residential population's makeup. Crime pattern theory suggests that POIs are the general representative of facilities and can function as crime generators and attractors, shaping criminal activities. Traditional methods focus solely on POI counts, overlooking their spatial arrangement, which can significantly impact crime patterns. Given the effect of stay-at-home orders and facility closures on perceived racial heterogeneity, human mobility, and the numbers and spatial arrangements of active POIs on crime, examining how these crime-contributing factors shape crime patterns across different COVID-19 lockdown periods is essential. However, there is a lack of studies that 1) measure perceived racial heterogeneity based on human mobility, 2) capture the spatial dimension of POIs, and 3) analyze crime patterns across different periods - before, during, and post the stay-at-home orders and facility closures. Drawing on recent big data that captures human mobility and POI locations, this dissertation proposes novel methods for measuring racial heterogeneity of the ambient population and capturing the sp (open full item for complete abstract)

Committee: Lin Liu Ph.D. (Committee Chair); Diego Cuadros Ph.D. (Committee Member); Tomasz Stepinski Ph.D. (Committee Member); Kevin Raleigh Ph.D. (Committee Member); John Eck Ph.D. (Committee Member) Subjects: Geography

MS, Kent State University, 2024, College of Arts and Sciences / Department of Earth Sciences

This study explores the effects of pore-scale sedimentary structure and grain-size heterogeneity on Darcy to non-Darcy flow characteristics, focusing on non-Darcy key parameters such as the inertial coefficient (β), Izbash exponent (θ), critical head gradient (Ic) and critical Reynolds number (Rec). By examining random, graded, and laminated sedimentary structures with varying degrees of heterogeneity, we investigate fluid flow behavior across a range of Reynolds numbers (Re). We simulate flow in different sedimentary structures from laminar to transitional turbulent flow, to understand how structural heterogeneities impact flow dynamics in porous media. Key findings reveal that both the critical head gradient (Ic) and the critical Reynolds number (Rec) exhibit a power law relationship with permeability, which serves as an index of heterogeneity across all sedimentary structures. As Re increases, the inertial coefficient rises, eventually reaching a steady state (βs). Notably, βs also exhibit a power law relationship with permeability in each sedimentary structure. These insights underline the role of sediment heterogeneity in modulating flow characteristics, particularly in non-Darcy flow regimes. This research expands the scope of non-Darcy flow studies by including diverse sedimentary domains, providing a comprehensive understanding of flow dynamics that accounts for both structural and grain-size heterogeneity. The findings are critical for improving predictions of fluid movement in subsurface environments, particularly under conditions where accurate modeling of non-Darcy flow is essential.

Committee: Kuldeep Singh (Advisor) Subjects: Geology

Master of Science, University of Akron, 0, Polymer Engineering

Soft Particle Glasses (SPGs) are jammed suspensions formed by packing soft and deformable particles above their random-close packing limit. These suspensions belong to a class of materials known as yield stress fluids, as they exhibit weak elastic solid-like properties at low strains and flow-like liquids at high strains above their yield stress limit. In these suspensions, the contact forces play a dominant role and are practically athermal. Due to the unique properties of these materials, they are widely used in industries as rheological modifiers in products like inks, pastes, drilling fluids, food products, and personal care products. These multifaceted practical applications make understanding the processing parameters, i.e., the rheological response and the dynamics of the system, of utmost importance. In our study, particle dynamic simulation is used to implement a 3-D simulation of SPGs, and a comprehensive analysis is made to understand the particles' motion and the changes in their microstructure under shear flow. These studies reveal that SPGs exhibit cage-like dynamics during motion, and the flow curve response for these materials is well-defined by the Herschel-Bulkley model. Our results indicate the presence of two distinct regimes, namely quasi-static and flow regimes. A constitutive equation is established between the macroscopic rheology and microscopic dynamics based on the constituent materials' intrinsic properties, like the compressibility of the particles and elastic modulus. A detailed analysis of the microscopic dynamics in the steady-state regime reveals the presence of heterogeneous flow in our suspensions. Analytical tools like the self-part of the van Hove function define the domain lengths of these heterogeneous flows. A qualitative analysis of these domains reveals that these heterogeneous flows exhibit localized dynamics at high shear rates and propagate as avalanches at low shear rates. Analysis of these mobile clusters quantifi (open full item for complete abstract)

Committee: Fardin Khabaz (Advisor); Kevin Cavicchi (Committee Member); Weinan Xu (Committee Chair) Subjects: Physics; Plastics

Doctor of Philosophy, The Ohio State University, 2024, Economics

My dissertation explores the interaction between firm dynamics and financial frictions and its implication for aggregate dynamics. In Chapter 1, I study the efficiency of the U.S. corporate bankruptcy laws in the aggregate economy. To do so, I develop a general equilibrium heterogeneous firms model with endogenous bankruptcy choice and private information about firms' permanent productivity levels. According to current U.S. bankruptcy law, firms can choose from two bankruptcy options: Chapter 11 reorganization or Chapter 7 liquidation. In the model, private information hinders the screening of firms' permanent productivity, and firms with low permanent productivity can be more likely to be reorganized and continue operations after filing for bankruptcy, compared to the case with perfect information. Combined with private information, Chapter 11 reorganization increases the fraction of firms with low permanent productivity. As a result, this channel reduces aggregate output by 1.9\% in the economy with fixed wage. However, the lower equilibrium wage reduces the bankruptcy rate, increases firms' production, and restores the declined aggregate output in the general equilibrium. Lastly, as a source of business cycle fluctuations, I consider uncertainty shocks that raise the volatility of firms' idiosyncratic productivity and negative TFP shocks. The volatility effect of the shock makes lenders more uncertain about firms' types and increases the reorganization rates of firms with low permanent productivity. Even though the fraction of firms with high permanent productivity declines, the overall loss of aggregate output is quantitatively small because of the higher total production of firms with low permanent productivity. In addition, the negative TFP shock has a quantitatively small difference in an economy with and without private information. This is because the TFP shock evenly reduces the profit of firms with high and low permanent productivity. As a result, the ra (open full item for complete abstract)

Committee: Aubhik Khan (Advisor); Gabriel Mihalache (Committee Member); Julia Thomas (Committee Member) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2024, Economics

This dissertation consists of two chapters that examine macroeconomic implications of heterogeneous responses of the agents in the economy. The first chapter answers the following question: Will reduced wages of young households during a recession have lasting impacts on their house purchases along the housing ladder? Previous papers studied whether reduced asset prices could benefit young households during the Great Recession, but little attention has been paid to how their housing wealth accumulation process was affected. This paper complements the literature by adding a new channel of a housing ladder along which households upgrade or downgrade their housing quality among rental units, small owner-occupied houses, and big owner-occupied houses. I answer the question by developing a quantitative overlapping-generations model with heterogeneous households in age, liquid assets, housing status, and defaultable mortgages. The housing ladder affects the composition of housing types of different age groups in the dynamic analysis. Lower interest rates facilitate house purchases by offering cheaper mortgages during a recession. However, adversely impacted wage levels and financial constraints discourage young renters from purchasing small houses by causing congestion on the housing ladder. On the other hand, small house owners face lower returns on savings, wages, and home values that obstruct their housing upgrade, hence the supply of small houses. A fall in house prices has a general equilibrium effect that relaxes the financial constraints and provides easier access to homeownership, but the income effect from suppressed wages dominates. As a result, young households with little savings and scarred wages experience a continuously low homeownership rate after a recession compared to a counterfactual economy without a recession. The second chapter studies how government fiscal policies propagate through different structures of the input-output network in a two-s (open full item for complete abstract)

Committee: Aubhik Khan (Advisor); Julia Thomas (Committee Member); Gabriel Mihalache (Committee Member) Subjects: Economics

MA, Kent State University, 2024, College of Communication and Information / School of Communication Studies

This study examines how young women's attitudes towards health are influenced by normative patterns and network heterogeneity. It assesses the role of discussion network heterogeneity in shaping health beliefs and scrutinizes the varying impacts of communication in familial and social media contexts. The study focuses on (a) understanding women's perceptions of different descriptive norms across communication contexts, (b) analyzing the influence of these perceptions on their intention to undergo gynecological examinations, and (c) investigating the effect of network heterogeneity on these perceptions and intentions.

Committee: Nichole Egbert (Advisor); Erin Hollenbaugh (Committee Member); David Silva (Committee Member) Subjects: Communication

PhD, University of Cincinnati, 2023, Arts and Sciences: Geography

Urban flooding has become a growing threat to human society worldwide. Urban flood hazards have occurred more frequently, resulting in a growing population exposed to flood hazards and increasing flood damage to human communities. Thus, there is an urgent need to develop an urban pluvial flooding risk framework that can be used to evaluate the impacts of the flood and to provide helpful guidance on flood mitigation. The first chapter is a general introduction of this dissertation. It also reviews the existing studies related to physical and social factors affecting urban pluvial flooding. The research gaps are highlighted, and the aim of this dissertation is described. There is a lack of studies on investigating the spatial heterogeneity of the risk factors of urban pluvial flooding and their interplay impacts, and discussing how these may affect the urban flood mitigation strategies. The second chapter investigates the spatial relationship between the urban pluvial flood locations and the controlling factors through a case study in the City of Cincinnati. Three controlling factors are investigated, including precipitation, impervious area and topography. Two comparisons are conducted, one is depression-based which utilizes the random sampling method and the other comparison analysis is flooded-location-based. The results show that topography and precipitation are more important in the prediction of urban pluvial flooding risk than impervious areas. There is spatial heterogeneity of the three controlling factors as well as their interplay impacts. The third chapter develops an integrated approach for assessing urban pluvial flooding risk by combining two components, physical exposure and social vulnerability at catchment level. An exposure index (EI), a social vulnerability index (SoVI) and a composite pluvial flood risk index (PFRI) are developed. The results demonstrate the spatial heterogeneity of exposure and social vulnerability of urban (open full item for complete abstract)

Committee: Xi Chen Ph.D. (Committee Chair); Steven Buchberger Ph.D. (Committee Member); Susanna Tong Ph.D. (Committee Member); Kevin Raleigh Ph.D. (Committee Member); Lin Liu Ph.D. (Committee Member) Subjects: Geography

Doctor of Philosophy, The Ohio State University, 2023, Comparative and Veterinary Medicine

Metastasis is the primary killer of patients with osteosarcoma. To effectively treat metastasis, we must first understand how tumor cells colonize and survive within the lungs. It is well established that only a small number of cells from the primary tumor go on to metastasize, but whether this is a pre-existing capability of metastatic cells or a phenotype they develop in response to a new microenvironment has yet to be established. Furthermore, osteosarcoma metastases are often chemoresistant, such that surgical resection is the only treatment option that has been shown to provide a reasonable chance of a longer term “cure”. Identifying the survival requirements of metastatic cells in the lung may allow us to target those dependencies therapeutically. Because osteosarcoma is a rare disease, there is a critical need for models that recapitulate what is seen in human patients as faithfully as possible so that clinical trials focus on therapeutics with the greatest potential for success. Interrogating disease biology and screening therapeutics in dogs holds great potential in this regard. While investigating tumor heterogeneity, we found that osteosarcoma cells undergo clonal selection when crossing the so-called metastatic bottleneck, although the degree of transcriptional heterogeneity among tumor cells remains similar to that seen in primary tumors. We also identified heterogeneous expression of glycolysis markers among tumor cells in tissue culture, primary tumors, and metastases. These findings indicated that transcriptional heterogeneity may be an intrinsic aspect of osteosarcoma, and implied that metastasis may be a cooperative event. Building on our findings of transcriptional heterogeneity and previous work evaluating subpopulations in metastases, we sought to identify factors that allow metastatic cells to survive in the lungs. We pharmacologically targeted an identified factor, MCL1, in vitro, and found that it readily killed osteosarcoma cell spheroids. (open full item for complete abstract)

Committee: Ryan Roberts (Advisor); Cheryl London (Committee Member); Estelle Cormet-Boyaka (Committee Member); Maxey Wellman (Committee Member) Subjects: Biology; Cellular Biology; Comparative; Oncology

Doctor of Philosophy, The Ohio State University, 2023, Molecular, Cellular and Developmental Biology

The number of cancer diagnoses worldwide is on the rise as populations continues to grow older. In the US, the amount of money allocated to cancer research by the National Cancer Institute increases yearly. With increasing focus towards cancer research, it is important researchers maintain perspective and to ensure that these resources are utilized efficiently. The research mission of the Stover Lab is to improve the outcomes of patients with cancer. We keep the patients in mind during the entire research process, from project conception to publication. In this dissertation, three distinct research projects undertaken during my PhD are summarized. In Chapter 2, we investigated the survivorship needs of patients with gynecological cancers. By extracting posts made on the American Cancer Society's Cancer Survivorship forums, we discovered some of the needs of cancer patients by looking at their posted conversations and concerns. We developed an analysis methodology to allow post extraction that pertain to custom themes. We showed its utility by extracting and qualitatively analyzing posts that pertain to the psychosocial aspects of survivorship. In Chapter 3, a novel image analysis-based algorithms were developed to investigate the patterns of expression of HER2 in breast cancer patients. Current treatment strategy for breast cancer is reliant on determining whether a patient is HER2 positive using a clinical immunohistochemistry stain for HER2. The criteria used by pathologists for this test is simplistic, in that it only looks at a proportion of intensely stained cells and uses a single cutoff to define a patient as HER2 positive or negative. We believe there is an opportunity to gather more information from these IHC stains and use this information to further delineate breast cancer patients based on their HER2 expression, better predicting patient outcomes. We showed a new method that quantifies the heterogeneity of HER2 expression and significantly predicted recu (open full item for complete abstract)

Committee: Daniel Stover (Advisor); Ramesh Ganju (Committee Member); Raghu Machiraju (Committee Member); Anne Strohecker (Committee Member) Subjects: Molecular Biology

Doctor of Philosophy, The Ohio State University, 2023, Economics

In these essays, I explore the importance of firm's Research & Development (R&D) on economic aggregates. I focus on the fact that firms are heterogeneous in both their current R&D decisions and how those R&D decisions change in the face of different economic conditions. This heterogeneity then feeds into the way in which economic aggregates themselves change over time. In this work, I contribute to our understanding of the impact of private R&D on those economic aggregates. In my first chapter, “Boosting Innovation or Entry: What Works Best?”, I ask how effective are government policies that aim to increase output by incentivising firm investment in Research & Development (R&D)? Could policies that do not focus on R&D boost output more? To do so, I first estimate the effect of individual firm and aggregate stocks of R&D on a firm's productivity and find that there are negative spillovers to higher aggregate R Using these results, I develop a discrete-time dynamic general equilibrium model with endogenous entry and exit in which firms choose their stocks of R&D, physical capital, and debt (subject to a collateral constraint) in the face of idiosyncratic shocks to output and research productivity. My model makes the important distinction between intangible R&D and tangible physical capital. Physical capital is easy for a firm to sell and (therefore) use as collateral for its debt. The intangible nature of R&D means that firms cannot borrow against it (as a lender could not sell it off to recoup any debts that a firm fails to pay). Hence, the collateral constraint in my model depends only on a firm's physical capital stock; a firm cannot borrow against its stock of R This means that firms might want to invest more in physical capital instead of R&D so that they can borrow more and grow faster, which provides a role for government to subsidise R&D to ameliorate these incentives. My model is the first to incorporate such a financial friction when examini (open full item for complete abstract)

Committee: Aubhik Khan (Advisor); Kyle Dempsey (Committee Member); Julia Thomas (Committee Member) Subjects: Economics

Doctor of Philosophy, Case Western Reserve University, 2023, Nutrition

Molecular heterogeneity in cancer has been recognized as one of the main drivers of disease relapse and drug resistance. In addition, effects of tumor-microenvironment have shown to contribute to the diversity as well. Consequently, cancer research has aimed at generating and utilizing inherently high-dimensional molecular datasets for the past decade to characterize tumors specifically with the development of `sequencing-by-synthesis' Next-Generation Sequencing (NGS) platforms. Large collections of high-dimensional multi-omics datasets exemplified by TCGA and PCAWG, 1) elaborate on the heterogeneity of cancer progression and 2) allow for increasingly complex models to be utilized. Respecting the black-box nature of machine-learning driven models, here we develop multiple strategies to leverage molecular information to delineate disease progression/mechanisms in Leukemia. Furthermore, we show the requirement of careful selection of strategies in noisy scRNA-Seq datasets in solid tumors and we propose an integrative model to investigate collateral drug-responses in a pan-cancer fashion. We present multiple strategies in two parts. First, we present a Bayesian Latent Class Analysis to incorporate molecular information in a large cohort of (n=2681) AML patients with heterogeneous characteristic and generate novel unsupervised clusters with clinical relevance. Furthermore, we utilize Autoencoder structure to develop distance-based, low dimensional clustering model to group MDS patients (n=3588) into 14 novel groups. This approach allowed us to extract relevant features otherwise difficult to capture with Bayesian strategies in noisy datasets. In the second part, we conduct a comprehensive benchmarking study to evaluate the vast repository of methods developed for scRNA-Seq analysis. We show, in contrast with the current practice, scRNA-Seq analysis is amenable to variation and results, specifically unsupervised clustering, is of qualitative nature rather t (open full item for complete abstract)

Committee: Jacob Scott (Advisor) Subjects: Bioinformatics

Master of Science (MS), Ohio University, 2022, Physics and Astronomy (Arts and Sciences)

Glass transitions appear across physical systems of widely varying types. Molecular liquids, polymers, and colloids have all demonstrated transition to an amorphous glassy solid when subjected to rapid cooling or compression. These materials appear frequently in nature and are useful in numerous industrial applications, including the window glass we are familiar with and many materials in the category of plastics. The macroscopic behaviors of these systems have been well documented and leveraged, but we still lack a full picture of the microscopic origins of those behaviors. Such a picture, besides its conceptual appeal, would provide a more robust framework for materials engineering, and methods developed along the way will be applicable to studies of other emergent phenomena. We thus want to investigate the physics underlying the glass transition. Specifically, we would like to quantify the dynamic heterogeneity that experiments and simulations have indicated is present. Dynamic heterogeneity refers to the presence of distinct spatial regions with collectively fast or slow dynamics, which exist at different places in the system and change over time. These heterogeneities are thought to influence the slowdown and “freezing” of the system into a glassy state. I focus here on quantifying the lifetime of the heterogeneous regions. I perform analysis on data from numerical simulations of several different systems, including a new set of molecular dynamics simulations of a Lennard-Jones variant system, at densities and temperatures approaching their glass transitions. I begin by quantifying bulk properties of each system as a function of the simulation timescale. I then compute a correlation function that comes out of previously developed theory, which provides a measure of the persistence of the heterogeneity as a function of the timescale. I observe how long that function takes to decay, and compare my results with previous attempts at measuring this quantity via othe (open full item for complete abstract)

Committee: Horacio Castillo (Advisor); Daniel Phillips (Committee Chair); Chaden Djalali (Committee Member) Subjects: Condensed Matter Physics; Materials Science; Nanoscience; Physics

PHD, Kent State University, 2022, College of Arts and Sciences / Department of Chemistry and Biochemistry

One of the primary threats to civilization is climate change due to the excessive combustion of fossil fuels. Fortunately, solar power has rapidly emerged as a green substitution for fossil fuels in recent years. Semiconductor TiO2 nanocatalysts are crucial for solar power harvesting because of their excellent photoactivity, high stability, and nontoxicity. Hence, it is critical to resolve the structure-performance correlation of TiO2 in photochemical catalysis and elucidate its mechanism. By using a fluorogenic reaction to report the reactivity of homemade TiO2 nanoparticles, we found that the chemical reactivity of TiO2 stems from its defect sites formed during the synthesis process. Specially Ti3+-containing defects are directly related to the activity of Amplex red oxidation without light irradiation. It turns out that creating extremely fluorescent resorufin is a useful method for identifying defect locations in TiO2 nanocatalysts. Furthermore, we performed single-molecule super-resolution imaging to chemically map out the reactivity distributions along commercial TiO2 nanowires. Significant inter- and intra-particle heterogeneities exist, in which clear size and reactivity correlations were observed. Chemical transformation adopts two competing pathways on TiO2 surface, resulting in positive correlations in catalytic turnovers. Finally, we examined the impacts of the defects on the photochemical reactivity of TiO2 nanowires. Our findings imply that the high Ti3+ content could promote photochemical reactivity.

Committee: Hao Shen (Advisor) Subjects: Analytical Chemistry; Chemistry

Doctor of Philosophy, The Ohio State University, 2022, Economics

In these essays, I explore how a liquid-illiquid asset choice affects economic predictions. In our daily lives, illiquid assets are represented by assets such as retirement accounts, home equity, and sometimes stock and bond holdings. While it is clear that this liquid-illiquid asset decision is vital to reproducing realistic consumption responses to fiscal stimuli,1 it is less clear if the inclusion of an illiquid asset is both necessary and sufficient for reasonable model results. In this work, I contribute to our understanding of these illiquid assets and their relative importance and distortion of our economic estimates. In my first chapter, “Fiscal Stimulus Payments & their Effect on Aggregate Consumption”, I focus on empirical analyses of the 2001 and 2008 stimulus checks which find strong effects on consumption on impact, but have been unable to identify longer-run effects. While some suggestive empirical evidence of long-run effects exists, attempts to empirically estimate effects beyond 2 quarters after a fiscal stimulus have been thwarted by data availability. This chapter finds evidence of long-term trends of these economic stimulus payments, and focuses in particular on how these trends vary across households with different levels of wealth. I find these effects within a model with a liquid-illiquid asset choice that is the first to reproduce moments from the distribution of marginal propensities to consume in the United States during a fiscal stimulus. Using this model, I show that the long-run effects of these fiscal stimuli are approximately twice that of the change in consumption on impact date. Furthermore, both in the model and empirical analysis, I show that while the consumption of hand-to-mouth households is statistically higher than that of otherwise similar households, I do not find evidence that the wealthy hand-to-mouth have similarly high MPCs either empirically or in the model. However, despite these 1In Kaplan and Violante (2014) (open full item for complete abstract)

Committee: Aubhik Khan (Advisor); Julia Thomas (Committee Member); Meta Brown (Committee Member) Subjects: Economics

Doctor of Philosophy, The Ohio State University, 2022, Economics

In these essays, I study macroeconomic responses to large recessions, in environments with heterogeneous agents. In the first chapter, "Long-Term Debt, Default Risk, and Policy Transmission during Severe Recessions", I study the implications of rollover risk on firm-level investment and aggregate dynamics. A growing empirical literature suggests that the maturity risk associated with long-term debt reduces firm-level investment, particularly during recessions. I introduce discretely maturing long-term debt into a dynamic stochastic general equilibrium model where heterogeneous firms borrow subject to default risk. My model is distinguished relative to existing long-term debt models in that it captures the rollover risk arising from uncertainty about what economic conditions will be when debt matures. Moreover, my firms actively save in a short-term financial asset to help hedge against the maturity risk associated with their debt. Nonetheless, the rollover risk associated with discretely maturing long-term debt exacerbates the debt overhang problem arising in conventional long-term debt models. Thus, firms effectively face greater financial frictions, and output is on average lower. Consequently, my model predicts a larger rise in defaults and a greater decline in endogenous aggregate productivity in its response to a financial shock. Thus, its financial recessions are both deeper and longer-lived than in conventional models. I also consider a large non-financial aggregate shock, and use my model to study the efficacy of targeted stimulus policies implemented over the U.S. 2020 recession. My findings suggest that the combined effects of the Paycheck Protection Program and the expansion of quantitative easing helped stem the rise in defaults and stimulate the subsequent economic recovery. The second chapter, "The Persistence of Recessions with Incomplete Markets and Time-Varying Risk" (joint with Aubhik Khan), studies the implications of precautiona (open full item for complete abstract)

Committee: Julia Thomas (Advisor); Kyle Dempsey (Committee Member); Aubhik Khan (Committee Member) Subjects: Economics

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2022, Biological Sciences

Bats are critically important for their control of insects but are experiencing population declines. The biggest reason for these declines is anthropogenic land use. Despite negative impacts, anthropogenic land use has variable impact on bats, with tolerance for more developed areas being species dependent and varying depending on the spatial or temporal scale. Previous studies on land use and bats lack spatial variability and are often single year. My goal was to determine how habitat factors related to human land use impact bat activity and species richness at multiple spatial scales over a period of several years. This research was conducted in the Oak Openings Region, which is a highly developed mixed-use region with high biodiversity that serves as important bat habitat. Specific objectives included determining (1) changes in bat activity and species richness over time, (2) differences in bat activity and species richness between protected and non-protected areas, (3) how factors related to human land use impact bat activity and species richness, and (4) to map current bat habitat suitability and see how it may change in the future. Calls increased each subsequent year during the 2019-2021 period, showing a trend of consistently increasing bat activity. However, during 2011-2019 bat activity significantly decreased. Protected areas had higher species richness and activity than unprotected areas. Higher activity and species richness were found in areas with greater percent upland prairie, sand barrens, and savanna and less floodplain and conifer forest and wet prairie. Activity was higher with less structural clutter at 3-6.5 m, lower understory height, taller canopy height, more canopy cover, and more structural clutter 0-3 m. Number of habitats was positively associated with bat species richness and activity along transects, but negatively associated with activity at stationary points. An opposite trend was observed for cropland. Activity and species richnes (open full item for complete abstract)

Committee: Karen Root (Advisor); Juan Bes (Other); Moira van Staaden (Committee Member); Kevin McCluney (Committee Member); Shannon Pellini (Committee Member) Subjects: Acoustics; Animals; Biology; Climate Change; Conservation; Environmental Science; Geographic Information Science; Macroecology; Wildlife Conservation; Wildlife Management

Doctor of Philosophy, The Ohio State University, 2022, Materials Science and Engineering

Electrospinning is an electrohydrodynamic process generating polymeric micro or nanofibers having immense technological benefits in biomedical, energy, and filtration applications. However, the microstructural heterogeneity inherent to electrospun materials has led to unreliable performance, fundamentally limiting the potential of this technology. While this heterogeneity is readily revealed by point-to-point comparisons (e.g., electron microscopy), full quantification remains challenging due to the extremely limited field-of-view associated with these techniques. To address this, we developed a novel technique that can characterize internal porosity gradients in thin films that reflect the large-scale microstructural heterogeneity of electrospun deposits. Accurate measurements of both as-spun depositions and the same depositions post-densification, are obtained via contact-free laser metrology. A formula was developed to enable ‘mapping' of the spatial porosity distribution by comparing both dimensions and quantifying the vertical shrinkage. The automated prototype developed generates porosity ‘maps' – each consisting of ~14,000 datapoints at a spatial resolution of ~1 mm – within a few hours, an achievement > 1,000 times faster than traditional methods such as porosimetry. Our technique also enables in situ characterization thus minimizing the risk of sample distortion or other artifacts. In addition, the technique is believed to be compatible with any other open-porous materials that can be densified. Utilizing this innovation, an extensive investigation was conducted to understand the porosity gradients found within tubular electrospun polycaprolactone (PCL), a frequently studied polymer thanks to its specific combination of biocompatibility and biodegradability. Variations in porosity values are found in many examples of electrospun PCL and can range from ~0 to 88% within the same deposition in extreme cases. These variations also exhibit signific (open full item for complete abstract)

Committee: John Lannutti (Advisor); Jinghua Li (Committee Member); Heather Powell (Committee Member); Pelagia-Iren Gouma (Committee Member) Subjects: Engineering; Materials Science; Mechanical Engineering; Mechanics; Nanoscience; Nanotechnology; Polymers

Doctor of Philosophy, The Ohio State University, 2021, Molecular, Cellular and Developmental Biology

A patient's tumor cells are heterogeneous with differing phenotypic adaptations that facilitate selection in response to changing environmental conditions. This intra-tumor heterogeneity is a major clinical challenge in the treatment of many human cancers, including osteosarcoma – the most common malignancy of the bone. Technical challenges have precluded our ability to tease out what to target: inherent genetically encoded features that facilitate survival of cancer cells across selective pressures, or acquired changes in response to environmental signals. One of the unique features of osteosarcoma is that metastases present mostly in the lung; less than 2% of patients develop lesions in other organs. While therapies that treat lung metastasis in adolescents with osteosarcoma could save more than 80% of the lives currently lost to this disease, we understand too little about tumor cell adaptations during lung colonization to move the needle on treatment options and patient outcome. My dissertation focusses on understanding the role of phenotypic and genotypic heterogeneity within tumor cells themselves during tumor growth and progression. The first set of experiments presented here combine lineage tracing and single cell transcriptomics to track clonality and phenotype during tumor cells adaptations that facilitate lung colonization. Osteosarcoma is characterized by high genomic complexity with extensive somatic copy-number aberrations. In line with this, the second set of experiments presented here combines single cell DNA sequencing with an allele- and haplotype specific somatic copy-number aberration inference algorithm to study intra-tumor genomic heterogeneity in osteosarcoma tumors and how this evolves over therapeutic time. In Chapter 1 of this dissertation, I summarize the current knowledge about topics related to my dissertation, including, an overview of osteosarcoma biology, lung as a site for metastasis, and the role of genetic and non-genetic so (open full item for complete abstract)

Committee: Ryan Roberts (Advisor); James Chen (Committee Member); Tsonwin Hai (Committee Member); Stephen Lessnick (Committee Member) Subjects: Bioinformatics; Biology; Cellular Biology; Genetics; Oncology

MS, University of Cincinnati, 2021, Arts and Sciences: Geology

A detailed understanding of mechanisms that control geological carbon dioxide (CO2) sequestration (GCS) requires simulating fully coupled thermal-hydrological-mechanical-chemical (THMC) processes in a well-characterized subsurface sedimentary architecture. I simulate CO2 transport in a multiphase multicomponent (brine-CO2) system at field-scale conditions for the CO2 injection pilot project conducted in Cranfield, Mississippi, USA. I use high-resolution geologic models to evaluate how bottom-hole pressure (BHP) in the injection well, CO2 breakthrough times at observation wells, and efficiency of trapping mechanisms (e.g., solubility trapping and snap-off trapping (i.e., CO2 entrapment in the form of immobile bubbles)) are controlled by (1) non-isothermal flow of injected CO2, (2) geochemical reactions, (3) facies-dependent capillary pressure (Pc) characteristics, (4) geomechanical effects, and (5) permeability (k) enhancement around the injection well. Model results suggest that ignoring thermal effects leads to lower BHP, shorter breakthrough times, overestimation of solubility trapping, and underestimation of snap-off trapping. If neglecting geochemical reactions, the BHP remains unchanged, but breakthrough times are overestimated, and solubility trapping and snap-off trapping are slightly underestimated. Results also demonstrate that by ignoring the Pc heterogeneity, BHP is underestimated, breakthrough times are overestimated, snap-off trapping remains almost unchanged, and solubility trapped CO2 is underestimated. Disregarding the geomechanical effects results in considerably lower BHP, earlier breakthrough times, greater solubility trapping, and smaller snap-off trapping. Further, I find that if k enhancement around the injection well is ignored, the BHP exhibits abrupt changes in the early period, breakthrough times are underestimated, snap-off trapping is slightly overestimated, and solubility trapping is underestimated. Permeability enhancement has a leading (open full item for complete abstract)

Committee: Reza Soltanian Ph.D. (Committee Chair); Seyyed Abolfazl Hosseini Ph.D. (Committee Member); Attila Kilinc Ph.D. (Committee Member); Drew McAvoy Ph.D. (Committee Member); Daniel Sturmer Ph.D. (Committee Member) Subjects: Environmental Geology