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Title: Quantum coherent transport phenomena in epitaxial halide perovskite thin films
Creator: Zhang, Liangji
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The subject of this dissertation is the experimental study of quantum coherent transport phenomena in epitaxial single-crystal halide perovskite thin films. The experiments consist of low-temperature (down to 10 mK) magnetotransport measurements at high magnetic field (up to 14 T).The recent advent of epitaxial thin film growth of inorganic halide perovskites has made it possible to investigate the quantum behavior of charge carriers in these materials in low-dimensional form. We present...
Show moreThe subject of this dissertation is the experimental study of quantum coherent transport phenomena in epitaxial single-crystal halide perovskite thin films. The experiments consist of low-temperature (down to 10 mK) magnetotransport measurements at high magnetic field (up to 14 T).The recent advent of epitaxial thin film growth of inorganic halide perovskites has made it possible to investigate the quantum behavior of charge carriers in these materials in low-dimensional form. We present results on epitaxial single-domain cesium tin iodide (CsSnI3) thin films that clearly demonstrate quantum transport in this material for the first time. The observed low-field magnetoresistance shows signatures of weak anti-localization (WAL) that reveals coherent quantum interference effects and spin-orbit coupling. A micron-scale (≈5 um) low-temperature phase coherence length for charge carriers in the system is extracted from these WAL measurements.Additionally, we present low-temperature quantum magnetotransport measurements on thin film devices made of epitaxial single-crystal CsSnBr3, which exhibit two-dimensional Mott variable range hopping (VRH) and a large negative magnetoresistance. These findings are described by the Nguyen-Spivak-Shkovskii (NSS) model for quantum interference between different directed hopping paths, and we extract the temperature-dependent hopping length of charge carriers, their localization length, and a lower bound for their phase coherence length of ~100 nm at low temperatures. These results from CsSnI3 and CsSnBr3 devices demonstrate that epitaxial halide perovskite devices are emerging as a material class for low-dimensional quantum coherent transport devices.In addition to the works that are described above, I have also been involved in several additional projects, such as experiments on low-dimensional electron systems and superconducting qubit experiments, which will not be described in this dissertation.
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Title: PLANNING FOR AUTONOMY AND ELECTRIFICATION IN FUTURE TRANSPORTATION SYSTEMS
Creator: Singh, Harprinderjot
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Autonomous vehicles (AVs) and electric vehicles (EVs) will improve safety, mobility, roadway capacity and provide efficient driving, efficient use of travel time, and reduced emissions. However, these technologies affect vehicle miles traveled (VMT), travel time, ownership cost, and electric grid network. Shared mobility systems can ameliorate the high price of these technologies. However, the shared mobility system poses additional problems such as users’ waiting time, inconvenience, and...
Show moreAutonomous vehicles (AVs) and electric vehicles (EVs) will improve safety, mobility, roadway capacity and provide efficient driving, efficient use of travel time, and reduced emissions. However, these technologies affect vehicle miles traveled (VMT), travel time, ownership cost, and electric grid network. Shared mobility systems can ameliorate the high price of these technologies. However, the shared mobility system poses additional problems such as users’ waiting time, inconvenience, and increased VMT. Further, the impact of these emerging technologies varies on different groups of users (different values of travel time (VOTT). Another hurdle to the adoption of EVs is the limited range and scarcity of charging infrastructure. A well-established network of charging infrastructure, especially the direct current fast chargers (DCFC), can alleviate this challenge. However, the widespread adoption of EVs and the growing network of DCFC stations will increase the electric energy demand affecting the electric grid stability, demand-supply imbalance, overloading, and degradation of the electric grid components. Distributed energy resources (DER) such as solar panels and energy storage systems (ESS) can support the EV demand and reduce the load on the electric grid. This study develops modeling frameworks for the optimal adoption of AVs and EVs, considering their effect on transportation systems, the environment, and the electric grid network. Further, it suggests different scenarios that would promote the adoption of these technologies and provide a sustainable and resilient system.This study proposes a multi-objective mathematical model to estimate the optimal fleet configuration in a system of private manual-driven vehicles (PMVs), private AVs (PAVs), and shared AVs (SAVs) while minimizing the purchase and operating costs, time (travel and waiting time), and emission production. SAVs can be the optimal solution with the efficient use of travel time or the purchase price below a certain relative threshold. PAVs can be the optimal solution only if the onboard amenities are improved, lifetime mileage is increased, AV technology is installed in luxurious cars, and adopted by people with high VOTT. The framework is extended to consider different combinations of EVs, AVs, and conventional human-driven vehicles in a private and shared mobility system. The metaheuristics based on genetic and simulated annealing algorithms are developed to solve the large-scale NP-hard nonlinear optimization problem. The model is implemented for the network of Ann Arbor, Michigan. The results suggest that EVs are optimal for the system due to low operating costs and zero tailpipe emissions. Shared autonomous electric vehicles (SAEVs) are the best option for users with low VOTT. Private autonomous electric vehicles (PAEVs) would favor the system if the travel time savings are at least 20% or the price of AV technology is less than one-third of the vehicle price. The study then investigates the optimum investment technology to support the rising energy demand at the DCFC stations and reduce the load on the electric grid network. The different investments include purchasing and installing various ESS (new batteries (NB), second-life batteries (SLB), flywheels), solar panels, electric grid upgrades, and the cost of buying/selling electricity from/to the electric grid. The model is implemented for the DCFC stations supporting the future needs of EV charging demand for urban trips in the major cities of Michigan in 2030. The combination of SLBs and solar panels provides maximum benefits. The total annual and electricity savings are $25,000-$165,000 and $40,000-$300,000 per city.
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Title: Teaching Presence in a Fully Online Asynchronous Undergraduate Mathematics Course and its Impact on Social and Cognitive Presence
Creator: Elmore, Robert Andrew
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The number of fully online asynchronous undergraduate mathematics courses is growing rapidly, making it imperative that the instructional choices that are chosen by instructors and their effects on students’ opportunities to learn in the online learning environment be further explored. Therefore, this research aims to understand instructors' choices when teaching an online undergraduate mathematics course, and how these decisions impact students' communication opportunities. This research...
Show moreThe number of fully online asynchronous undergraduate mathematics courses is growing rapidly, making it imperative that the instructional choices that are chosen by instructors and their effects on students’ opportunities to learn in the online learning environment be further explored. Therefore, this research aims to understand instructors' choices when teaching an online undergraduate mathematics course, and how these decisions impact students' communication opportunities. This research organized the instructors' decisions and their impacts on students using the community of inquiry framework. The three categories of the community of inquiry framework, teaching presence, social presence, and cognitive presence, were analyzed through course artifacts, an instructor interview, student interviews, student surveys, and course usage data. The primary analysis was performed using the interviews with the other data sources providing further detail and explanation. Four claims were generated while analyzing these data sources. Claim one posits that students tend to have singular preferences of the course’s direct instructional elements. Claim 2 proffers that students who chose to work with others report having positive experiences, and those who decided not to work with others report not needing help, with one exception. Claim 3 states that meaningful contact points can be created between the instructor and student using surveys and personalized mass emails; however, most describe learning mathematics in Math 101 as not making them feel a part of a learning community. And claim 4 posits that elements of the teaching presence were more likely to foster participation if they were associated with a grade. The results of this study have implications for both the research and practice communities. The current study’s results imply that—even though sizes of online mathematics classes may still grow—there are ways instructors can facilitate high levels of social processes using mass email, surveys, cooperative learning groups, and other online tools. These specific tools should be studied and evaluated for their effects on social presence and cognitive presence on the mass scale. The present study suggests four specific things that instructors should familiarize themselves with that are available today, (a) prescribe opportunities for students to communicate with each other such as having assignments that are completed in cooperative learning groups, (b) communicate with your students through personalized means (e.g., emails, surveys, and Zoom sessions), (c) use feedback from surveys to inform your future teaching practice, and (d) ensure that students observe your communication and direct instruction by tying them to elements associated with grades. Keywords: teaching presence, social presence, cognitive presence, online mathematics learning.
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Title: DO INTERFACES MATTER? A REEXAMINATION OF XBRL USING FINANCIAL STATEMENT ACQUISITION AND MARKET ACTIVITY
Creator: Anderson, James J.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Starting in 2009 the eXtensible Business Reporting Language (XBRL) standard was mandated for financial statements by the SEC. The XBRL standard was intended to encourage less-sophisticated trader disclosure processing; however, previous literature has conjectured that the standard primarily aided more-sophisticated traders’ disclosure processing. I reexamine the effect of XBRL on more- and less-sophisticated trader disclosure processing by testing whether XBRL influenced their information...
Show moreStarting in 2009 the eXtensible Business Reporting Language (XBRL) standard was mandated for financial statements by the SEC. The XBRL standard was intended to encourage less-sophisticated trader disclosure processing; however, previous literature has conjectured that the standard primarily aided more-sophisticated traders’ disclosure processing. I reexamine the effect of XBRL on more- and less-sophisticated trader disclosure processing by testing whether XBRL influenced their information acquisition and testing whether the proportional relationship between information acquisition and market activity is different for more- and less-sophisticated traders. I find the staggered implementation of XBRL is associated with a 49% (26%) increase in less (more) sophisticated trader information acquisition. Next, I find the proportional relationship between information acquisition and market activity is greater for less-sophisticated traders when compared to more-sophisticated traders. Specifically, I find information acquisition for less-sophisticated traders has a greater proportional relationship with abnormal price movement, abnormal trading volume, and abnormal bid-ask spreads. Together these findings suggest that XBRL did not provide a disproportionate information advantage to more-sophisticated traders, but rather benefited less-sophisticated traders by decreasing their information acquisition costs.
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Title: QUALITY OF LIFE IN CONTEMPORARY NEIGHBORHOOD DESIGN INITIATIVES : AN EMPIRICAL STUDY TO ASSESS QUALITY OF LIFE THROUGH SPATIAL DIMENSION IN NEW URBANIST AND LEED-ND CERTIFIED NEIGHBORHOODS
Creator: Shaaban, Amal Hamdy
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The overarching purpose of this study is to analyze qualitatively and quantitatively the spatial characteristics of two types of neighborhoods, namely New Urbanist (NU) neighborhoods and Leadership in Energy and Environmental Design (LEED) certified neighborhoods. LEED certified neighborhoods are commonly referred to as LEED for Neighborhood Development (LEED-ND). In this research study, the spatial quality of these two types of neighborhoods was examined through assessing the quality of five...
Show moreThe overarching purpose of this study is to analyze qualitatively and quantitatively the spatial characteristics of two types of neighborhoods, namely New Urbanist (NU) neighborhoods and Leadership in Energy and Environmental Design (LEED) certified neighborhoods. LEED certified neighborhoods are commonly referred to as LEED for Neighborhood Development (LEED-ND). In this research study, the spatial quality of these two types of neighborhoods was examined through assessing the quality of five dimensions of a neighborhood’s urban form, which are: safety, comfort, connectivity, place making and aesthetic quality. Three types of neighborhoods were selected and examined using two main analytical tools: neighborhood scorecard, and residents’ survey. Two neighborhoods were selected to examine NU neighborhoods in Michigan in the United States, which are Cherry Hill Village (CHV) a greenfield residential development, and Mason Run (MR) a brownfield residential development. The third neighborhood was selected to examine LEED-ND certified neighborhoods, which is Saint Luke (SL) neighborhood in Ohio in the United States. The neighborhood scorecard included a total of 150 design guidelines that yield better Quality of Life (QoL) in residential developments through urban form. The residents’ survey included a set of questions that examined the residents’ perceptions regarding the five physical characteristics necessary to yield better QoL. A total of 154 surveys were collected for data analysis that used one-way ANOVA tests, Tukey’s post-hoc tests, and multiple regression models. The major findings from the neighborhood scorecard are that CHV neighborhood provides was safer than the brownfield NU residential development and the LEED-ND certified neighborhood. Both NU neighborhoods provided more comfortable environments of their residents to live in more than the LEED-ND certified neighborhood. The greenfield NU neighborhood earned more points than the other two neighborhoods in terms of the internal and external connectivity degree of the neighborhood. In terms of fulfilling the recommended design guidelines for place making and aesthetic quality, again the Greenfield NU neighborhood had earned higher points than the other two neighborhoods. The major findings from the survey are: Survey participants living in both NU neighborhoods were more satisfied with their QoL more than the participants living in the LEED-ND certified neighborhood. Also, respondents living in NU neighborhoods perceived their neighborhoods as safer places to live in more than participants living in the LEED-ND certified neighborhood. On the other hand, respondents living in the NU brownfield development perceived their neighborhood as the least comfortable neighborhood. In terms of the internal connectivity of the neighborhood, respondents living in NU greenfield development identified the internal connectivity of their neighborhood as the highest compared to the other two neighborhoods. On the other hand, the perception of the internal connectivity perception was the lowest amongst respondents living in the LEED-ND certified neighborhood. Respondents living in the LEED-ND certified neighborhood had the highest mean in terms of their perceptions of sense of belonging. On the other hand, respondents living in the NU brownfield development had the lowest mean in terms of their perception of sense of belonging. The perception of the aesthetic quality of the neighborhood was the highest among respondents living in the NU greenfield development. On the other hand, the perception of aesthetic quality of the neighborhood was the least among respondents living in NU brownfield development.This research study concludes by suggesting recommendations to improve the principles and design guidelines of NU and LEED-ND certified neighborhoods to achieve better QoL. The recommendations suggest emphasizing certain spatial characteristics that yield better QoL in Greenfield, and brownfield residential developments.
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Title: IMPACTS OF DISTANT DRIVERS ON LANDSCAPES AND BIODIVERSITY
Creator: Hovis, Ciara Layne
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Global biodiversity is increasingly impacted by distant drivers. With societies more connected than ever before, natural resource consumption has expanded beyond administrative and political boundaries. International food trade in particular has profound impacts on land-use and socioeconomic and environmental outcomes. At the same time, global biodiversity is threatened at an unprecedented scale, with many of the causes obfuscated by complexities of distant, interacting socioecological...
Show moreGlobal biodiversity is increasingly impacted by distant drivers. With societies more connected than ever before, natural resource consumption has expanded beyond administrative and political boundaries. International food trade in particular has profound impacts on land-use and socioeconomic and environmental outcomes. At the same time, global biodiversity is threatened at an unprecedented scale, with many of the causes obfuscated by complexities of distant, interacting socioecological systems. Understanding the ultimate drivers of biodiversity change and translating them to local biodiversity outcomes is integral to addressing conservation challenges in the age of globalization. This dissertation analyzes the impacts of international trade on biodiversity in an agroecosystem undergoing land-use change driven by global markets. Chapter 1 provides background on the study region, Heilongjiang Province, and describes disruption of soybean production in the area due to changes in global trade. Chapter 2 is a systematic review of studies on distant drivers of biodiversity change. Across all taxa, harmful impacts on biodiversity were the most frequent outcome reported, with distant impacts of trade and tourism most frequently studied. In Chapter 3, satellite imagery was classified into landcover classes to create high-fidelity maps of the agriculture-dominated study landscape. By utilizing phenological, synthetic aperture radar, and vegetation/soil index data, accuracies of 91%- 80% were achieved. In Chapter 4 these landcover maps were used to calculate landscape metrics. These metrics were then used to analyze relationships between landscape structure (i.e., composition and configuration) and bird communities. Functional biodiversity indices derived from life history and morphological traits were examined in addition to taxonomic measures. Though no discernable differences between taxonomic and functional community metrics were observed, several significant relationships between landscape structure and biodiversity metrics were found. Crop diversity, natural landcover, and edge metrics, were positively correlated with bird richness. Aggregation of patches, corn area, and soybean area were negatively correlated. We also compared landscape structure and biodiversity between two regions impacted by global soybean trade. Despite the more impacted region having lower crop diversity and natural area, there was no difference in biodiversity between the two regions. The more impacted region also had more rice area, demonstrating that negative biodiversity impacts may be mitigated by rice cultivation. Chapter 5 built on the previous chapter by modeling bird occupancy to assess species-specific relationships with landscape structure. Results indicated that increased crop diversity significantly increased occupancy of birds at both the taxonomic and functional level, particularly for birds belonging to less common functional groups. Percentage of natural area was not as important as expected, while metrics related to landscape configuration had very few significant impacts on occupancy. Increases in rice area were not as detrimental to bird occupancy as increases in corn and soybean. In fact, soybean area exhibited more significant negative relationships with bird occurrence than corn, suggesting that decreases in soybean area due to global trade may have benefitted bird biodiversity in the case of a monocultural landscape. However, due to the prevalence of small-scale farming practices, the more likely outcome would be a decrease in crop diversity due to soybean fields being converted to more profitable crops (e.g., corn, rice). By linking global trade, changes in landcover/use, landscape structure, and local bird communities in the same context, the results of this dissertation highlight the need for integrated biodiversity studies that place ecosystems in the broader context of globalization.
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Title: A CASE STUDY EXPLORING HOW K-12 STUDENTS LEARN TO USE SOCIAL MEDIA FOR CIVIC GOOD
Creator: Askari, Emilia Shirin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: This case study explores what K-12 students learn from a 13-week class activity about attracting attention to civic issues on social media. This research responds to calls by scholars of civic education to expand notions of civic engagement and digital citizenship, which often have focused on urging students to protect their reputations in digital spaces. In contrast, the learning activity examined here encourages community-oriented digital citizenship, preparing students to inform and...
Show moreThis case study explores what K-12 students learn from a 13-week class activity about attracting attention to civic issues on social media. This research responds to calls by scholars of civic education to expand notions of civic engagement and digital citizenship, which often have focused on urging students to protect their reputations in digital spaces. In contrast, the learning activity examined here encourages community-oriented digital citizenship, preparing students to inform and possibly empower social change. This study is grounded in Cognitive Flexibility Theory, which focuses on learning in ill-structured domains such as public social media. Further, the study builds on the increasingly popular idea of the Fifth Estate, which posits that people acting in civic ways in public spaces can be a powerful check on government, playing a role similar to that of journalism institutions, sometimes referred to as the Fourth Estate. Data collected in this study included a pre-survey, a written reflection and post interviews with 4 students as well as artifacts such as social media posts. Students employed two main strategies to draw attention to civic issues on social media: audience-signaling and networking. Further, students learned to seek credible and diverse information using class accounts on TikTok, Instagram, and Twitter. Finally, students offered definitions of digital citizenship and shared thoughts about how schools should teach it via social media. This study fills a gap in the research literature about K-12 teaching with social media; few prior studies take advantage of social media’s affordance as a bridge between the classroom and communities outside the school. This study also illuminates learning as schools globally moved online in response to the pandemic.
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Title: Examining an Important Assumption in the Faultline Literature
Creator: Guo, Zhiya
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Group faultlines are defined as hypothetical dividing lines that split a team into subgroups based on the alignment of team members’ attributes. Prior faultline research has almost exclusively focused on the implications of between-subgroup relationships assuming that “team members form homophilous ties on either side of a faultline by associating with others in the team who have similar demographic attributes” (Ren et al., 2015, p. 390). However, this important assumption has not been tested...
Show moreGroup faultlines are defined as hypothetical dividing lines that split a team into subgroups based on the alignment of team members’ attributes. Prior faultline research has almost exclusively focused on the implications of between-subgroup relationships assuming that “team members form homophilous ties on either side of a faultline by associating with others in the team who have similar demographic attributes” (Ren et al., 2015, p. 390). However, this important assumption has not been tested. Drawing from social comparison theory and its “similarity hypothesis,” I argue that homogeneous, faultline-based subgroups may serve as a hotbed for social comparisons, and comparisons on social power can engender conflict under certain circumstances, triggering within-subgroup conflict. More specifically, consistent with the emerging research that recognizes different types of group faultlines, I outlined a) different dimensions that different faultline-based subgroups are more likely to compare and b) the downstream effects of these comparisons. Hypotheses were tested using multi-wave, round-robin data from multiple intact work teams of full-time employees. Results largely supported my predictions regarding knowledge-based subgroups but not so much for identity-based subgroups or resource-based subgroups. Implications and future directions are discussed.
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Title: PALETTEVIZ : A METHOD FOR VISUALIZATION OF HIGH-DIMENSIONAL PARETO-OPTIMAL FRONT AND ITS APPLICATIONS TO MULTI-CRITERIA DECISION MAKING AND ANALYSIS
Creator: Talukder, AKM Khaled Ahsan
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Visual representation of a many-objective Pareto-optimal front in four or more dimensional objective space requires a large number of data points. Moreover, choosing a single point from a large set even with certain preference information is problematic, as it imposes a large cognitive burden on the decision-makers. Therefore, many-objective optimization and decision-making practitioners have been interested in effective visualization methods to en- able them to filter down a large set to a...
Show moreVisual representation of a many-objective Pareto-optimal front in four or more dimensional objective space requires a large number of data points. Moreover, choosing a single point from a large set even with certain preference information is problematic, as it imposes a large cognitive burden on the decision-makers. Therefore, many-objective optimization and decision-making practitioners have been interested in effective visualization methods to en- able them to filter down a large set to a few critical points for further analysis. Most existing visualization methods are borrowed from other data analytics domains and they are too generic to be effective for many-criterion decision making. In this dissertation, we propose a visualization method, using star-coordinate and radial visualization plots, for effectively visualizing many-objective trade-off solutions. The proposed method respects some basic topological, geometric and functional decision-making properties of high-dimensional trade- off points mapped to a three-dimensional space. We call this method Palette Visualization (PaletteViz). We demonstrate the use of PaletteViz on a number of large-dimensional multi- objective optimization test problems and three real-world multi-objective problems, where one of them has 10 objective and 16 constraint functions. We also show the uses of NIMBUS and Pareto-Race concepts from canonical multi-criterion decision making and analysis literature and introduce them into PaletteViz to demonstrate the ease and advantage of the proposed method.
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Title: Dynamical Systems Analysis Using Topological Signal Processing
Creator: Myers, Audun
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Topological Signal Processing (TSP) is the study of time series data through the lens of Topological Data Analysis (TDA)—a process of analyzing data through its shape. This work focuses on developing novel TSP tools for the analysis of dynamical systems. A dynamical system is a term used to broadly refer to a system whose state changes in time. These systems are formally assumed to be a continuum of states whose values are real numbers. However, real-life measurements of these systems only...
Show moreTopological Signal Processing (TSP) is the study of time series data through the lens of Topological Data Analysis (TDA)—a process of analyzing data through its shape. This work focuses on developing novel TSP tools for the analysis of dynamical systems. A dynamical system is a term used to broadly refer to a system whose state changes in time. These systems are formally assumed to be a continuum of states whose values are real numbers. However, real-life measurements of these systems only provide finite information from which the underlying dynamics must be gleaned. This necessitates making conclusions on the continuous structure of a dynamical system using noisy finite samples or time series. The interest often lies in capturing qualitative changes in the system’s behavior known as a bifurcation through changes in the shape of the state space as one or more of the system parameters vary. Current literature on time series analysis aims to study this structure by searching for a lower-dimensional representation; however, the need for user-defined inputs, the sensitivity of these inputs to noise, and the expensive computational effort limit the usability of available knowledge especially for in-situ signal processing.This research aims to use and develop TSP tools to extract useful information about the underlying dynamical system's structure. The first research direction investigates the use of sublevel set persistence—a form of persistent homology from TDA—for signal processing with applications including parameter estimation of a damped oscillator and signal complexity measures to detect bifurcations. The second research direction applies TDA to complex networks to investigate how the topology of such complex networks corresponds to the state space structure. We show how TSP applied to complex networks can be used to detect changes in signal complexity including chaotic compared to periodic dynamics in a noise-contaminated signal. The last research direction focuses on the topological analysis of dynamical networks. A dynamical network is a graph whose vertices and edges have state values driven by a highly interconnected dynamical system. We show how zigzag persistence—a modification of persistent homology—can be used to understand the changing structure of such dynamical networks.
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Title: ASSESSING DISASTER MANAGEMENT EFFECTS ON RECOVERY OUTCOMES IN RURAL POST-DISASTER JAPAN
Creator: Ward, Kayleigh
Date: 2022
Collection: Electronic Theses & Dissertations
Description: As a country frequented by natural disasters, Japan has robust disaster management systems that can be employed quickly to mitigate human, environmental, and economic harm and losses. However, these systems tend to be most effective when handling small-scale localized disasters. In the face of the 2011 Great East Japan Earthquake which decimated the northeastern communities of the Tohoku region, Japan’s disaster management system collapsed, unable to handle such large scale and widespread...
Show moreAs a country frequented by natural disasters, Japan has robust disaster management systems that can be employed quickly to mitigate human, environmental, and economic harm and losses. However, these systems tend to be most effective when handling small-scale localized disasters. In the face of the 2011 Great East Japan Earthquake which decimated the northeastern communities of the Tohoku region, Japan’s disaster management system collapsed, unable to handle such large scale and widespread damage. In the ten years since the disaster many rural communities have contended with a variety of social and economic problems, often left unremedied despite on-going government intervention. In this context, this dissertation will explore the complex problems in Minamisanriku, Miyagi—a rural coastal community decimated by the 2011 Great East Japan Earthquake. By engaging and collaborating with organizations in this community, I assess the connections between disaster management and post-disaster recovery outcomes through various applications of social capital and power. I first investigate how historical legacies of national government policies influenced recovery outcomes in the Tohoku region and how have these processes influenced economic restructuring and social development in Minamisanriku during reconstruction. Next, I consider how governance structures within Miyagi prefecture influenced the social and economic development of Minamisanriku during reconstruction. Lastly, I look to how disaster management affects the ability of residents to handle locally-identified and in turn, how residents utilize their social capital to driver social and economic recovery. I assess several key ideas on the connections between forms and theories of social capital and how they affect long-term disaster recovery outcomes through the disaster management process. The dissertation is situated to improve our understanding of how social capital affects rural communities’ ability to respond to these troubles and to craft context specific solutions to them. It also offers a variety of policy recommendations about how to improve community-centered recovery within disaster management frameworks.
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Title: SYNTHETIC BIOLOGY APPROACHES ESTABLISH THE FOUNDATION FOR SUSTAINABLE PRODUCTION OF HIGH VALUE TERPENOIDS
Creator: Bibik, Jacob David
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Plants have become a promising platform for sustainable bioproduction of an array of natural products and specialty chemicals. Of particular interest are terpenes and the functionalized terpenoids, which represent the largest and most diverse class of natural products. These natural products are commonly used commercially as major constituents of flavorings and fragrances, oils, pigments, and pharmaceuticals, while having many other applications. Given the diversity and structural complexity...
Show morePlants have become a promising platform for sustainable bioproduction of an array of natural products and specialty chemicals. Of particular interest are terpenes and the functionalized terpenoids, which represent the largest and most diverse class of natural products. These natural products are commonly used commercially as major constituents of flavorings and fragrances, oils, pigments, and pharmaceuticals, while having many other applications. Given the diversity and structural complexity of many terpenoids, they are often expensive and difficult, if not impossible, to chemically synthesize. Engineering these biosynthetic pathways in plant hosts may provide a sustainable platform to access terpenoids for industrial production. While plants offer a sustainable production platform, metabolic engineering for chemical production has largely focused on microbial hosts, and further development of strategies and tools for plant engineering is needed. In my dissertation, I have taken multi-pronged approaches to further develop sustainable bioproduction of terpenoids in plants. First, I developed strategies to optimize, re-target, and compartmentalize production of squalene, a C30 triterpene, within plant cells to improve yields in plants. Re-targeting the final steps in squalene production, farnesyl diphosphate synthase (FDPS) and squalene synthase (SQS), from the cytosol to plastids enabled compartmentalization of biosynthesis away from competing cytosolic enzymes. I then anchored an optimized FDPS and SQS pair to the surface of cytosolic lipid droplets through fusions to the Nannochloropsis oceanica Lipid Droplet Surface Protein (NoLDSP), where squalene can be sequestered and stored. Scaffolding the pathway to the surface of lipid droplets increased yields to more than twice that of plastidial targeting. Re-targeting this lipid droplet scaffolding to plastids, produced similar squalene yields as the soluble, plastid targeted pathway, and ameliorated some of the negative effects on photosynthesis. Second, I worked to engineer poplar, a bioenergy crop which emits large amounts of the hemiterpene isoprene, with these pathways as a platform for bioproduction and adding value to a bioenergy pipeline. Transformants were successfully created for plastid targeted squalene production, producing up to 0.63mg/gFW of squalene. The lipid droplet scaffolding strategies appeared toxic during tissue regeneration, suggesting a need for tissue specific engineering of these pathways in future iterations. Third, I developed a pipeline to identify, characterize, and engineer bidirectional promoters (BDPs), which enable divergent expression of two genes and improve gene stacking in plant constructs. As seen above with poplar, plant engineering is often limited by construct size, diverse promoter availability, and expression regulation, and a BDP library enables a range of expression in more compact constructs. I identified 34 BDPs from Populus trichocarpa and Arabidopsis thaliana, characterized their activity via Nicotiana benthamiana transient expression, and engineered select BDPs to further alter activities. Combining these BDPs with previously developed terminator sequences provided further regulation of expression. These genetic tools provide an array of expression activities and enable greater gene stacking options while offering the potential for more fine tuning of expression for multiple genes in a metabolic pathway. The work performed in this dissertation provide strategies to improve production of terpenoids in plants, establish production hosts, and engineer larger, complex pathways.
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Title: UNDERSTANDING DRIVERS OF PLANT MICROBIOME IN MICHIGAN AGRICULTURE : STUDIES OF THE APPLE ROOT ZONE AND COMMON BEAN SEEDS
Creator: Bintarti, Ari Fina
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Plant-associated microbial communities are crucial for plant health and fitness, and may enhance plant tolerance to various environmental stresses. As global climate change threatens crop production and increases demands on sustainable agriculture, harnessing the plant microbiome has become one potential strategy to address these issues. Thus, it is fundamental to understand the relative contributions of both the host plant as well as the environment in shaping the plant microbiome. Moreover,...
Show morePlant-associated microbial communities are crucial for plant health and fitness, and may enhance plant tolerance to various environmental stresses. As global climate change threatens crop production and increases demands on sustainable agriculture, harnessing the plant microbiome has become one potential strategy to address these issues. Thus, it is fundamental to understand the relative contributions of both the host plant as well as the environment in shaping the plant microbiome. Moreover, the response of plant microbiomes to stress and any consequences of microbiome stress responses for the host plants are poorly understood, though this information is critical to achieve a basis of knowledge for plant microbiome engineering. My research aimed to contribute to this knowledge by investigating the factors that structure root- and seed-associated microbial communities of two valuable crops for Michigan’s agricultural economy: apple and common bean. The first chapter of my dissertation aimed to assess the biogeography of bacterial, archaeal, fungal, and nematode communities in the root zone of apple trees, and to determine their relationships with each other and their changes over natural abiotic gradients across orchards. I also assessed the influence of plant cultivar on microbiome structure in the root zone. I found that root zone microbiome community structure was strongly affected by geographic location and edaphic properties of soil. The next chapter of my dissertation investigated the variability of seed endophyte community of common bean (Phaseolus vulgaris L.). My results showed that plant-to-plant variability under controlled growth conditions exceeded within-plant variability among seeds from different pods. My study developed protocols and added insights to the growing toolkit of approaches to understand the plant-microbiome engagements that support the health of agricultural and environmental ecosystems. The last chapter assessed the responses of common bean seed endophytes to drought stress in the field across two growing locations and four genotypes of common bean. To summarize, this work advances foundational knowledge of the seed microbiome as a critical component of the plant microbiome, and in the context of two key crops for Michigan agriculture.
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Title: Supervised Dimension Reduction Techniques for High-Dimensional Data
Creator: Molho, Dylan
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The data sets arising in modern science and engineering are often extremely large, befitting the era of big data. But these data sets are not only large in the number of samples they have, they may also have a large number of features, placing each data point in a high-dimensional space.However, unique problems arise when the dimension of the data has the same or even greater order than the sample size. This scenario in statistics is known as the High Dimension, Low Sample Size problem (HDLSS...
Show moreThe data sets arising in modern science and engineering are often extremely large, befitting the era of big data. But these data sets are not only large in the number of samples they have, they may also have a large number of features, placing each data point in a high-dimensional space.However, unique problems arise when the dimension of the data has the same or even greater order than the sample size. This scenario in statistics is known as the High Dimension, Low Sample Size problem (HDLSS). In this paradigm, many standard statistical estimators are shown to perform sub-optimally and in some cases can not be computed at all. To overcome the barriers found in HDLSS scenarios, one must make additional assumptions on the data, either with explicit formulations or with implicit beliefs about the behavior of the data. The first type of research leads to structural assumptions placed on the probability model that generates the data, which allow for alterations to classical methods to yield theoretically optimal estimators for the chosen well-defined tasks. The second type of research, in contrast, makes general assumptions usually based on the the causal nature of chosen real-world data application, where the data is assumed to have dependencies between the parameters.This dissertation develops two novel algorithms that successfully operate in the paradigm of HDLSS. We first propose the Generalized Eigenvalue (GEV) estimator, a unified sparse projection regression framework for estimating generalized eigenvector problems.Unlike existing work, we reformulate a sequence of computationally intractable non-convex generalized Rayleigh quotient optimization problems into a computationally efficient simultaneous linear regression problem, padded with a sparse penalty to deal with high-dimensional predictors. We showcase the applications of our method by considering three iconic problems in statistics: the sliced inverse regression (SIR), linear discriminant analysis (LDA), and canonical correlation analysis (CCA). We show the reformulated linear regression problem is able to recover the same projection space obtained by the original generalized eigenvalue problem. Statistically, we establish the nonasymptotic error bounds for the proposed estimator in the applications of SIR and LDA, and prove these rates are minimax optimal. We present how the GEV is applied to the CCA problem, and adapt the method for a robust Huber-loss based formulation for noisy data. We test our framework on both synthetic and real datasets and demonstrate its superior performance compared with other state-of-the-art methods in high dimensional statistics. The second algorithm is the scJEGNN, a graphical neural network (GNN) tailored to the task of data integration for HDLSS single-cell sequencing data.We show that with its unique model, the GNN is able to leverage structural information of the biological data relations in order to perform a joint embedding of multiple modalities of single-cell gene expression data. The model is applied to data from the NeurIPS 2021 competition for Open Problems in Single-Cell Analysis, and we demonstrate that our model is able to outperform top teams from the joint embedding task.
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Title: PRENATAL CANNABIS EXPOSURE AMONG PREGNANT PEOPLE IN TWO MICHIGAN SAMPLES
Creator: Vanderziel, Alyssa
Date: 2022
Collection: Electronic Theses & Dissertations
Description: This dissertation will address three study aims: Aim 1 will estimate the size of a suspected causal influence of prenatal cannabis exposure on a set of inter-related birth outcomes: birth size, gestational age, 5-minute Apgar score, and neonatal intensive care unit admission. Aim 2 will investigate the degree to which morning sickness might be associated with higher odds of cannabis use. Aim 3 is to conduct a feasibility study to assess the recruitment and retention of pregnant people who...
Show moreThis dissertation will address three study aims: Aim 1 will estimate the size of a suspected causal influence of prenatal cannabis exposure on a set of inter-related birth outcomes: birth size, gestational age, 5-minute Apgar score, and neonatal intensive care unit admission. Aim 2 will investigate the degree to which morning sickness might be associated with higher odds of cannabis use. Aim 3 is to conduct a feasibility study to assess the recruitment and retention of pregnant people who regularly use cannabis, measured by willingness to participate and complete the study survey; willingness to provide urine samples; the percentage of participants who are cannabis-only users; and the percentage of pregnant people retained for the three follow-up assessments. Aims 1 and 2 use data for the Michigan Archive for Research on Child Health, a prospective cohort of pregnant people recruited from 11 sites across Michigan between 2017 and 2021. Aim 1 and Aim 2 analytic sample sizes are n= 584 and n= 826, respectively. Results of Aim 1 suggest a modest but statistically significant association between prenatal cannabis exposure and birth size z-score after model adjustment for potential confounding variables (betamodel4= -0.3; 95% CI: -0.5, -0.003). Results of Aim 2 suggest higher odds of prenatal cannabis use with increasing morning sickness severity (ORmodel4= 1.2; 95% CI: 1.1, 1.2). Sensitivity analyses indicate higher odds of using cannabis during the first trimester with increasing morning sickness severity (ORmodel4= 1.1; 95% CI: 1.01, 1.2). Similarly, findings indicate higher odds of cannabis use in the second or third trimester of pregnancy with increasing morning sickness severity (ORmodel4= 1.2; 95% CI: 1.1, 1.4). Sensitivity analyses also suggest an association between pre-pregnancy and prenatal cannabis use and morning sickness severity (betamodel4= 0.1; 95% CI: 0.003, 0.2 and betamodel4= 0.2; 95% CI: 0.1, 0.2, respectively). For Aim 3, Cannabis Legalization in Michigan-Maternal & Infant Health, a prospective feasibility study, was designed to better understand the recruitment and retention of pregnant people who regularly use cannabis. The study recruited n= 77 baseline participants of which n= 15 were prospectively followed and assessed during each trimester of pregnancy and once post-delivery. Of the participants recruited at baseline, 42% reported using cannabis during pregnancy, of which 87% were cannabis-only users (i.e., no reported polysubstance use). All prospective participants were willing to provide urine samples; the concordance between self-reported cannabis use and urinalysis was 100% in the first and second trimesters and 92% in the third trimester of pregnancy. Study retention of the prospective sample was 80%; of n= 15 first trimester participants, n= 3 were loss-to-follow-up. Of the remaining 12 participants, 83% had complete data across all four timepoints.Findings from this dissertation reveal that pregnant people are willing to participate in a study that explores the health effects of prenatal cannabis use on birth outcomes and maternal health. Larger studies are warranted to assess the association between prenatal cannabis exposure and fetal growth and development, as well as the relationship between morning sickness and cannabis use. This dissertation also detected an association between prenatal cannabis exposure and lower birth size, suggesting that pregnant people, or people contemplating pregnancy, should be cautioned against using cannabis until more studies are conducted to establish causality between prenatal cannabis use and neonatal health.
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Title: SCATTERING AMPLITUDES FOR ZZ PRODUCTION AT THE LHC AND TOP-QUARK MASS EFFECTS
Creator: Agarwal, Bakul
Date: 2022
Collection: Electronic Theses & Dissertations
Description: With the Large Hadron Collider providing experimental data with unprecedented precision, theoretical predictions must improve similarly to keep up.Among a plethora of processes being studied at the LHC, the production of a pair of vector bosons is of particular importance. Consequently, precise theoretical predictions for these processes are necessary. This thesis discusses primarily the calculation of ZZ production through gluon fusion at 2-loops with full top-quark mass dependence as well...
Show moreWith the Large Hadron Collider providing experimental data with unprecedented precision, theoretical predictions must improve similarly to keep up.Among a plethora of processes being studied at the LHC, the production of a pair of vector bosons is of particular importance. Consequently, precise theoretical predictions for these processes are necessary. This thesis discusses primarily the calculation of ZZ production through gluon fusion at 2-loops with full top-quark mass dependence as well as the technological improvements required to successfully perform the calculation. Also discussed briefly is the quark initiated production of $\gamma\gamma + \text{jet}$ at 2-loops where some of these technologies allowed to overcome prior bottlenecks in the calculation of the helicity amplitudes.The 2-loop corrections for ZZ production through massless quarks had been known; in this work, the 2-loop corrections through the massive top quark are calculated .To achieve this, a new algorithm to systematically construct linear combinations of integrals with a convergent parametric integral representation is developed. This algorithm finds linear combinations of general integrals with numerators, dots, and dimension shifts as well as integrals from subsectors.To express the amplitudes in terms of these integrals, Integration-By-Parts (IBP) reduction is performed making use of syzygies and finite field based methods.A new algorithm is employed to construct these syzygies using linear algebra. The IBP reductions for $gg\rightarrow ZZ$ are successfully performed using these techniques. Further improvements, including predetermining the structure of the coefficients in IBP reductions, are used to successfully perform the reductions for $\gamma\gamma + jet$. Multivariate partial fractioning is used to simplify the final expressions to more manageable forms and render them suitable for fast numerical evaluation.%\thispagestyle{empty}In the case of $gg\rightarrow ZZ$, due to the presence of structures beyond polylogarithms, sector decomposition is employed to numerically evaluate the finite master integrals.Evaluating the amplitudes, agreement is found with previously calculated expansions specifically in the limit of large and small top mass. Improved results are presented for scattering at intermediate energies and/or for non-central scattering angles. With this calculation, the last building block required for the calculation of the full NLO cross-section for $gg\rightarrow ZZ$ is known.
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Title: REACTIVE ION ENHANCED MAGNETRON SPUTTERING OF NITRIDE THIN FILMS
Creator: Talukder, Al-Ahsan
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Magnetron sputtering is a popular vacuum plasma coating technique used for depositing metals, dielectrics, semiconductors, alloys, and compounds onto a wide range of substrates. In this work, we present two popular types of magnetron sputtering, i.e., pulsed DC and RF magnetron sputtering, for depositing piezoelectric aluminum nitride (AlN) thin films with high Young’s modulus. The effects of important process parameters on the plasma I-V characteristics, deposition rate, and the properties...
Show moreMagnetron sputtering is a popular vacuum plasma coating technique used for depositing metals, dielectrics, semiconductors, alloys, and compounds onto a wide range of substrates. In this work, we present two popular types of magnetron sputtering, i.e., pulsed DC and RF magnetron sputtering, for depositing piezoelectric aluminum nitride (AlN) thin films with high Young’s modulus. The effects of important process parameters on the plasma I-V characteristics, deposition rate, and the properties of the deposited AlN films, are studied comprehensively. The effects of these process parameters on Young’s modulus of the deposited films are also presented. Scanning electron microscope imaging revealed a c-axis oriented columnar growth of AlN. Performance of surface acoustic devices, utilizing the AlN films deposited by magnetron sputtering, are also presented, which confirms the differences in qualities and microstructures of the pulsed DC and RF sputtered films. The RF sputtered AlN films showed a denser microstructure with smaller grains and a smoother surface than the pulsed DC sputtered films. However, the deposition rate of RF sputtering is about half of the pulsed DC sputtering process. We also present a novel ion source enhanced pulsed DC magnetron sputtering for depositing high-quality nitrogen-doped zinc telluride (ZnTe:N) thin films. This ion source enhanced magnetron sputtering provides an increased deposition rate, efficient N-doping, and improved electrical, structural, and optical properties than the traditional magnetron sputtering. Ion source enhanced deposition leads to ZnTe:N films with smaller lattice spacing and wider X-ray diffraction peak, which indicates denser films with smaller crystallites embedded in an amorphous matrix.
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Title: Towards Robust and Reliable Communication for Millimeter Wave Networks
Creator: Zarifneshat, Masoud
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The future generations of wireless networks benefit significantly from millimeter wave technology (mmW) with frequencies ranging from about 30 GHz to 300 GHz. Specifically, the fifth generation of wireless networks has already implemented the mmW technology and the capacity requirements defined in 6G will also benefit from the mmW spectrum. Despite the attractions of the mmW technology, the mmW spectrum has some inherent propagation properties that introduce challenges. The first is that free...
Show moreThe future generations of wireless networks benefit significantly from millimeter wave technology (mmW) with frequencies ranging from about 30 GHz to 300 GHz. Specifically, the fifth generation of wireless networks has already implemented the mmW technology and the capacity requirements defined in 6G will also benefit from the mmW spectrum. Despite the attractions of the mmW technology, the mmW spectrum has some inherent propagation properties that introduce challenges. The first is that free space pathloss in mmW is more severe than that in the sub 6 GHz band. To make the mmW signal travel farther, communication systems need to use phased array antennas to concentrate the signal power to a limited direction in space at each given time. Directional communication can incur high overhead on the system because it needs to probe the space for finding signal paths. To have efficient communication in the mmW spectrum, the transmitter and the receiver should align their beams on strong signal paths which is a high overhead task. The second is a low diffraction of the mmW spectrum. The low diffraction causes almost any object including the human body to easily block the mmW signal degrading the mmW link quality. Avoiding and recovering from the blockage in the mmW communications, especially in dynamic environments, is particularly challenging because of the fast changes of the mmW channel. Due to the unique characteristics of the mmW propagation, the traditional user association methods perform poorly in the mmW spectrum. Therefore, we propose user association methods that consider the inherent propagation characteristics of the mmW signal. We first propose a method that collects the history of blockage incidents throughout the network and exploits the historical blockage incidents to associate user equipment to the base station with lower blockage possibility. The simulation results show that our proposed algorithm performs better in terms of improving the quality of the links and blockage rate in the network. User association based only on one objective may deteriorate other objectives. Therefore, we formulate a biobjective optimization problem to consider two objectives of load balance and blockage possibility in the network. We conduct Lagrangian dual analysis to decrease time complexity. The results show that our solution to the biobjective optimization problem has a better outcome compared to optimizing each objective alone. After we investigate the user association problem, we further look into the problem of maintaining a robust link between a transmitter and a receiver. The directional propagation of the mmW signal creates the opportunity to exploit multipath for a robust link. The main reasons for the link quality degradation are blockage and link movement. We devise a learning-based prediction framework to classify link blockage and link movement efficiently and quickly using diffraction values for taking appropriate mitigating actions. The simulations show that the prediction framework can predict blockage with close to 90% accuracy. The prediction framework will eliminate the need for time-consuming methods to discriminate between link movement and link blockage. After detecting the reason for the link degradation, the system needs to do the beam alignment on the updated mmW signal paths. The beam alignment on the signal paths is a high overhead task. We propose using signaling in another frequency band to discover the paths surrounding a receiver working in the mmW spectrum. In this way, the receiver does not have to do an expensive beam scan in the mmW band. Our experiments with off-the-shelf devices show that we can use a non-mmW frequency band's paths to align the beams in mmW frequency. In this dissertation, we provide solutions to the fundamental problems in mmW communication. We propose a user association method that is designed for mmW networks considering challenges of mmW signal. A closed-form solution for a biobjective optimization problem to optimize both blockage and load balance of the network is also provided. Moreover, we show that we can efficiently use the out-of-band signal to exploit multipath created in mmW communication. The future research direction includes investigating the methods proposed in this dissertation to solve some of the classic problems in the wireless networks that exist in the mmW spectrum.
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Title: CLIMATIC VARIABILITY AND CHANGE IN THE MIDWESTERN UNITED STATES : IMPLICATIONS FOR NITROGEN LEACHING IN AGRICULTURAL SYSTEMS
Creator: Baule, William James
Date: 2022
Collection: Electronic Theses & Dissertations
Description: How has the background climate of the Midwestern United States changed over recent decades and how has this affected nitrate leaching? These are the core questions addressed in this dissertation, through three self-contained studies focused on different aspects of the climate-agriculture interface in the Midwestern United States. In Chapter 2, statistical methods are used to quantify the solar radiation biases present in a widely used reanalysis-based hydrometeorological dataset over space,...
Show moreHow has the background climate of the Midwestern United States changed over recent decades and how has this affected nitrate leaching? These are the core questions addressed in this dissertation, through three self-contained studies focused on different aspects of the climate-agriculture interface in the Midwestern United States. In Chapter 2, statistical methods are used to quantify the solar radiation biases present in a widely used reanalysis-based hydrometeorological dataset over space, implement statistical bias correction and interpolation to address the spatial nature of this bias, and quantify the impacts of the solar radiation bias and proposed correction on simulated maize yields and water stress. Correction of reanalysis solar radiation alone brought simulated yield and water usage more in line with simulations forced with in-situ solar radiation. Chapter 3 examines changes in precipitation, utilizing a unique approach to station screening during the period 1951-2019 over a region encompassing the Great Lakes and broader Midwestern regions, of the United States. A multiple tier procedure was utilized to identify high quality input data series from the Global Historical Climatology Network-Daily dataset. Temporal and spatial trends were analyzed for a broad range of related annual and seasonal indicators ranging from accumulated totals and frequency of threshold events to event duration and potential linkages with total precipitable water. Our analyses confirm the results of previous studies while providing unique insights to data quality and seasonality. The trends of the indicators in our study exhibited more cohesive spatial patterns and temporal similarities when compared with studies with different quality control criteria, illustrating the importance of quality control of observations in climatic studies and highlighting the complexity of the changing character of precipitation. In Chapter 4, System Approach to Land Use Sustainability, a process-based crop model was applied with gridded soil and meteorological data using a yield stability zone concept to simulate corn and soybean production in 14 Midwestern states at the sub-field scale during the 1989-2019 period. Five zones based on multi-year yield stability were simulated for each field at 30m x 30m resolution, with zones being relative to each individual field. Outputs were evaluated using a nitrogen balance approach to establish zone-specific statistical distributions of nitrate leaching across the 14 states, specifically highlighting periods with changing and highly variable precipitation. Results indicate that low stable, unstable hill tops, and unstable slope zones are associated with an outsized contribution to overall nitrate leaching and that unstable zones exhibit variable year-to-year response to weather tied to their position in the landscape. Spatial analysis of the results suggests leaching is tied to precipitation variability, water stress, and total precipitation amount. In aggregate, the chapters presented here highlight the interconnectedness of the soil-plant-atmosphere continuum to changes in hydrologic regime and sensitivity to the biases in the data used to conduct analyses, run models, and from which conclusions are drawn. The study findings shed light on the potential for improved management of agricultural fields and illustrate how process-based crop models can be useful for designing management practices to reduce environmental pollution and increase profits to producers.
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Title: EPIDEMIOLOGY, EVOLUTION, AND DIAGNOSTICS OF TUBERCULOSIS IN HUMANS AND ANIMALS
Creator: Hadi, Syeda Anum
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Tuberculosis control in animals and humans alike requires early detection of Mycobacterium tuberculosis complex as well as current knowledge about the transmission patterns of the disease in the respective populations. These two building blocks provide the foundation on which the disease control programs can build their policies to expediate control efforts. In this thesis we amalgamate molecular epidemiology, genomics, and proteomics. We studied the transmission pattern of M. tuberculosis...
Show moreTuberculosis control in animals and humans alike requires early detection of Mycobacterium tuberculosis complex as well as current knowledge about the transmission patterns of the disease in the respective populations. These two building blocks provide the foundation on which the disease control programs can build their policies to expediate control efforts. In this thesis we amalgamate molecular epidemiology, genomics, and proteomics. We studied the transmission pattern of M. tuberculosis and its evolution within a marginalized population. The patterns led to the identification of gaps in TB control policies in marginalized populations with little access to healthcare. Similarly, we studied the genomewide polymorphisms in a naturally attenuated strain - M. bovis strain Ravenel to elucidate possible mechanisms for its reduced virulence and pathogenicity. Insights gained from genome sequence analysis in conjunction with pathogenesis study for M. bovis Ravenel paved the pathway to defining the complex and multi-faceted reasons for attenuation of the oldest bacteria of the world. Next, pathogen-specific biomarkers were evaluated to assist in unambiguous disease detection across multiple host species. Discovery and validation of biomarkers work facilitated the field diagnostic applications for TB in animals and humans. This three-pronged approach developed in this study, understanding the genomic basis of attenuation, and enhanced field TB diagnostics in the animal-human interface.
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