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Title: Sex and Individual Differences in Agonistic Behavior of Spotted Hyenas (Crocuta Crocuta) : Effects on Fitness and Dominance
Creator: McCormick, S. Kevin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Agonistic behavior can be observed across all taxa arising from a common need to compete over limiting resources. Within species, individual variation of agonistic behavior can allow individuals to acquire and maintain limiting resources leading to higher reproductive success or fitness. However, what is often overlooked in studies of agonistic behavior is submissiveness, and how this aspect of agonistic behavior relates to aggressiveness. Further, historical studies of agonistic behavior...
Show moreAgonistic behavior can be observed across all taxa arising from a common need to compete over limiting resources. Within species, individual variation of agonistic behavior can allow individuals to acquire and maintain limiting resources leading to higher reproductive success or fitness. However, what is often overlooked in studies of agonistic behavior is submissiveness, and how this aspect of agonistic behavior relates to aggressiveness. Further, historical studies of agonistic behavior among social mammals are biased towards studies of male agonistic behavior, often ignoring aspects and effects of female agonistic behavior. Here, I address these knowledge gaps through a long-term study of a free living highly gregarious mammal, the spotted hyena (Crocuta crocuta). Spotted hyenas offer an excellent model system for studying variation in aggressive and submissive behavior within individuals and between sexes, as they live in complex societies formed around a female dominated, or matrilineal, hierarchy that is enforced through constant agonistic interactions. For this dissertation, I utilized 30 years’ worth of consistently recorded behavioral data collected by Dr. Kay E. Holekamp and her team from free living hyenas residing within the Masai Mara National Reserve, Keyna. Because this dissertation involved many collaborations with other scientists, I use “we” throughout this abstract to describe participation in each chapter. In Chapter 1, we describe sexually dimorphic traits within spotted hyenas that fit common mammalian patterns, as well as numerous traits that violate mammalian norms, including sex differences in agonistic behavior. In particular, adult female spotted hyenas are significantly more likely to emit unsolicited acts of aggression down the hierarchy than adult breeding males, and females do so significantly more ferociously, or intensely. For Chapter 2, we analyzed rates and intensities of unprovoked aggressive and submissive acts emitted by adult females to determine if these two behaviors were individually consistent, as well as testing the hypothesis that these two behaviors may represent separate traits within individuals. Here we found that the intensity at which females emit aggressive and submissive behaviors are consistent, and that these traits were not correlated within individuals. Further, both consistent aggressive intensity and submissive intensity were correlated to adult female fitness, such that individuals expressing high or low extremes of these behaviors had lower annual offspring survival. Then in Chapter 3, we assessed drivers of female dominance within spotted hyenas. Within this chapter we tested two hypotheses 1) that intrinsic sex differences in agonistic behavior drives female dominance and/or 2) social support facilitates female dominance in this species. Further, we assessed these hypotheses among juvenile age classes to determine if drivers of female dominance occurred prior to sexual maturity and subsequent male dispersal. We found that females are intrinsically more aggressive both as cubs and adults, and adult males more submissive whether provoked or not. Further, social support during agonistic encounters is more likely to occur when acting against a female than a male, and adult females can dominate males with or without support. In completion, my dissertation provides interesting insights to sexual and individual variation on agonistic behavior among a social mammal.
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Title: LEAN IN : THE ANTECEDENTS AND CONSEQUENCES OF FEMALE DIRECTORS’ ATTAINMENT OF POWERFUL POSITIONS ON BOARDS
Creator: Kim, Jooyoung
Date: 2022
Collection: Electronic Theses & Dissertations
Description: With the heightened societal demand for promoting egalitarian values in corporate boards, firms have devoted much effort to improving female board representation. However, recent insights have suggested that some progress made thus far may be the result of firms’ symbolic conformity to external pressure, which alludes to the possibility that female directors’ low influence and lack of integration on boards may still persist. In this dissertation, I aim to develop and test theoretical...
Show moreWith the heightened societal demand for promoting egalitarian values in corporate boards, firms have devoted much effort to improving female board representation. However, recent insights have suggested that some progress made thus far may be the result of firms’ symbolic conformity to external pressure, which alludes to the possibility that female directors’ low influence and lack of integration on boards may still persist. In this dissertation, I aim to develop and test theoretical arguments of what determines and follows female directors’ attainment of power and influence, as reflected in their assignment to major committee member or chair positions. First, I propose that female directors are at a relative disadvantage in attaining major committee positions. Specifically, although directors in general can benefit from expertise cues to be assigned to committee positions, the benefits are less for females than males. This gap can be attenuated when females are similar to board members in terms of other demographic dimensions and when the board is demographically heterogeneous. Second, I develop predictions about how female representation on board committee positions can contribute to gender diversity on boards and firm performance. I propose that greater female representation on board committees has negative relationship with a female director’s likelihood of exit, and has positive relationship with female additions to the board and firm performance. Results were drawn from a sample of S&P 1,500 companies during 2009 to 2019.
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Title: Role of circular exercise on forelimb loading and accompanying skeletal and joint adaptations
Creator: Logan, Alyssa A.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Circular exercise is used frequently in equine exercise and competition, but little is known of the impact circular diameter and gait have to the joint and bone health of the forelimb. The first study evaluated the impact of circle diameter (10-m and 15-m) and gait to the forelimb solar outputs of average surface area, vertical force, and average pressure. Nine horses exercised in a straight line and in a round pen while wearing the Tekscan Hoof SystemTM on both front hooves with a glue-on...
Show moreCircular exercise is used frequently in equine exercise and competition, but little is known of the impact circular diameter and gait have to the joint and bone health of the forelimb. The first study evaluated the impact of circle diameter (10-m and 15-m) and gait to the forelimb solar outputs of average surface area, vertical force, and average pressure. Nine horses exercised in a straight line and in a round pen while wearing the Tekscan Hoof SystemTM on both front hooves with a glue-on shoe, a method of adherence which was determined to be reliable when measurements were recorded within one session. Gait, and not circle diameter, impacted forelimb outputs, with the average loaded area of the outside hoof while circling, being greatest at the canter (P = 0.001). While exercising on both a large and small circle, the outside hoof had greater vertical force at the canter than the trot (P = 0.01). A second study utilizing calves as a model for juvenile horses allowed the determination of physiological responses to circular exercise. Calves were assigned to small circle exercise (12 m), large circle exercise (18 m), treadmill exercise, or non-exercised control treatments (n = 6). Computed tomography and biomarkers were evaluated to determine impacts to bone and joint health. The inside leg of the small circular exercise group had larger dorsopalmar external diameter than the outside (P = 0.05). The medial proximal phalanx had greater mediolateral diameter than the lateral proximal phalanx of the small circle group (P = 0.01). Cartilage glycosaminoglycan concentration was greater in the outside leg of the small circle exercise treatment than the inside leg (P = 0.03). Combined, both of these studies suggest that circular exercise diameter and gait can impact animal health and should be considered when performing circular exercise.
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Title: Causal Inference with Mendelian Randomization for Longitudinal Data
Creator: Qu, Jialin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Mendelian Randomization (MR) uses genetic variants as instrumental variables (IVs) to examinethe causal relationship between an exposure and an outcome in observational studies. When confounding factors exist, the correlation between a predictor variable and an outcome variable does not imply causation. IV regression has been a popular method to control the confounding effect for causal inference. According to Mendel’s first and second laws of inheritance, genetic variants can be considered...
Show moreMendelian Randomization (MR) uses genetic variants as instrumental variables (IVs) to examinethe causal relationship between an exposure and an outcome in observational studies. When confounding factors exist, the correlation between a predictor variable and an outcome variable does not imply causation. IV regression has been a popular method to control the confounding effect for causal inference. According to Mendel’s first and second laws of inheritance, genetic variants can be considered as valid IVs. Popular MR methods include the ratio estimator, the inverse-variance weighted estimator and the two stage estimator. However, all these methods are based on cross-sectional data. In practice, data in the observational studies can be collected over time, the so-called longitudinal data. Longitudinal data makes it possible to capture changes within subjects over time and thus offers advantages to causal modeling to establish causal relationships. However, causal inference method that can control the time-varying confounding effect is largely lacking in literature. In this dissertation, we explore MR analysis for longitudinal data by proposing different causal models and assuming different casual mechanisms. The proposed methods are strongly motivated by a real study to examine the causal relationship between hormone secretion and emotional eating disorder in teen girls. We start with a concurrent model which assumes current outcome is only affected by current exposure. Coefficients of both genetic variants (i.e., IVs) and exposure are considered as time- varying effects. We apply the quadratic inference function approach in a two-step IV regression framework and focus on statistical testing to infer causality. Through extensive simulation studies, we show that the proposed method can well protect type I error and has reasonable testing power. In Chapter 3, we generalize the concurrent model to a more complex case and propose a time lag model to investigate time delayed causal effects. In the time lag model, we assume current outcome at time ? is affected by previous exposures measured up to ? − ? time points, where the time lag △? can be determined by a rigorous model selection procedure based on data. Similar to the concurrent model, we assume the effects of genetic variants on exposure and the effects of exposure on outcome both are time-varying. We propose different tests for point-wise and simultaneous testing to assess the causal relationship. In Chapter 4, We further generalize the time lag model to the case where the cumulative effect of previous ? exposures contributes to the outcome at time ?, under a sparse functional data analysis framework. The causal relationship is examined under the functional principal component regression framework with sparse functional data. Simulation results show that the type I error is well controlled. We apply our models to the emotional eating disorder data to examine if hormone secretion during the menstrual cycle in teen girls has a causal effect on emotional eating behavior and identify interesting results. This thesis work represents the very first exploration in MR analysis with longitudinal data.
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Title: PHENYLENEDIAMINE PYRIDYL LIGANDS AND BORYL SUPPORT LIGANDS FOR ORTHO-DIRECTED IRIDIUM CATALYZED C–H BORYLATION
Creator: O'Connell, Alex C.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: With organoboron compounds being useful components in the synthesis of pharmaceuticals, agrochemicals, and materials, it is imperative to find new catalytic strategies to design an effective system capable of borylating a broad range of (hetero)arene substrates in high yields and high selectivity. Traditional iridium-catalyzed systems borylate aromatic compounds and are directed by steric factors of the substrate. These steric-directed catalysts are hypothesized to have a singly open...
Show moreWith organoboron compounds being useful components in the synthesis of pharmaceuticals, agrochemicals, and materials, it is imperative to find new catalytic strategies to design an effective system capable of borylating a broad range of (hetero)arene substrates in high yields and high selectivity. Traditional iridium-catalyzed systems borylate aromatic compounds and are directed by steric factors of the substrate. These steric-directed catalysts are hypothesized to have a singly open coordination site on the metal center where activation of the most accessible C–H bond can occur. In order to change regioselectivity from steric products to alternatives, new catalyst systems must be designed.A phenylenediamine pyridyl framework was implemented for chelate-directed C–H borylation, where an aromatic substrate undergoes borylation of the ortho C–H bond, relative to a directing group. This ligand type has been explored and shown to have three major components that influence the reactivity, selectivity, and coordination of the ligand. These parts that make up the ligand were examined using a ligand screen, NMR studies, and stoichiometric reactions. From the literature, it has been shown that double B,N-bidentate ligated catalysts work well for a broad substrate scope and produce borylated products whose substitution pattern is based on steric effects. Other variants of this system have used a single B,N-bidentate ligand to produce products borylated in the ortho-position relative to a directing group on the substrate. To improve upon these catalytic systems, experiments were performed to optimize ortho-selectivity of the originally steric-directed catalyst containing two B,N-bidentate ligands by reducing the loading of the dimer boryl ligand. In doing so, regioselectivities can be completely switched from steric products to chelate products. This modification of ligand to metal ratio greatly effects selectivity and is a unique feature to dimer boryl ligands. These phenylenediamine pyridyl ligands and boryl support ligands will be explored.
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Title: Efficient Transfer Learning for Heterogeneous Machine Learning Domains
Creator: Zhu, Zhuangdi
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Recent advances in deep machine learning hinge on a large amount of labeled data. Such heavy dependence on supervision data impedes the broader application of deep learning in more practical scenarios, where data annotation and labeling can be expensive (e.g. high-frequency trading) or even dangerous (e.g. training autonomous-driving models.) Transfer Learning (TL), equivalently referred to as knowledge transfer, is an effective strategy to confront such challenges. TL, by its definition,...
Show moreRecent advances in deep machine learning hinge on a large amount of labeled data. Such heavy dependence on supervision data impedes the broader application of deep learning in more practical scenarios, where data annotation and labeling can be expensive (e.g. high-frequency trading) or even dangerous (e.g. training autonomous-driving models.) Transfer Learning (TL), equivalently referred to as knowledge transfer, is an effective strategy to confront such challenges. TL, by its definition, distills the external knowledge from relevant domains into the target learning domain, hence requiring fewer supervision resources than learning-from-scratch. TL is beneficial for learning tasks for which the supervision data is limited or even unavailable. It is also an essential property to realize Generalized Artificial Intelligence. In this thesis, we propose sample-efficient TL approaches using limited, sometimes unreliable resources. We take a deep look into the setting of Reinforcement Learning (RL) and Supervised Learning, and derive solutions for the two domains respectively. Especially, for RL, we focus on a problem setting called imitation learning, where the supervision from the environment is either non-available or scarcely provided, and the learning agent must transfer knowledge from exterior resources, such as demonstration examples of a previously trained expert, to learn a good policy. For supervised learning, we consider a distributed machine learning scheme called Federated Learning (FL), which is a more challenging scenario than traditional machine learning, since the training data is distributed and non-sharable during the learning process. Under this distributed setting, it is imperative to enable TL among distributed learning clients to reach a satisfiable generalization performance. We prove by both theoretical support and extensive experiments that our proposed algorithms can facilitate the machine learning process with knowledge transfer to achieve higher asymptotic performance, in a principled and more efficient manner than the prior arts.
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Title: MACHINE LEARNING TOWARDS DATA WITH COMPLEX STRUCTURES
Creator: Su, Runze
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The development of sequential analysis provides a deeper understanding in the exploration of many different fields. In the application of sequential analysis, there are two main challenges: How to extract informative features from a high-dimensional noisy domain? How to model the interaction for the information flow from multiple domains? We explored the two core challenges in bio-informatics, sales forecasting and multimedia services. In biology field, a typical problem is the to evaluate...
Show moreThe development of sequential analysis provides a deeper understanding in the exploration of many different fields. In the application of sequential analysis, there are two main challenges: How to extract informative features from a high-dimensional noisy domain? How to model the interaction for the information flow from multiple domains? We explored the two core challenges in bio-informatics, sales forecasting and multimedia services. In biology field, a typical problem is the to evaluate the interaction mechanism between non-coding DNA sequences and transcription. We propose CANEE, a convolutional self-attention architecture to analyze the function of non-coding DNA sequences. Compared to other existing models, CANEE achieves a better performance in overall prediction of 919 regulatory functions with respect to receiver operating characteristics and has a significant improvement on some responses in precision recall curve with shorter training time. In sales forecasting field, we extract a unique customers’ microbehavior dependency structure from clickstream data based on a Word-to-Vector model. Then, we build a clickstream informed LSTM model to forecast the car sales over 30 days. Our model significantly outperforms the classic seasonal autoregressive integrated moving average model. Besides, we demonstrate that transfer knowledge among different car models can further improve the performance. Other applications for multi-domain sequences happens in multimedia service field, where we focus on the understanding of multiple domain modalities, we propose new principles for audio visual learning and introduce a new framework as well as its training algorithm to set sight of videos’ themes to facilitate AVC learning.
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Title: EMOTIONALLY FOCUSED THERAPY FOR COUPLES IN TAIWAN
Creator: Tseng, Chi-Fang
Date: 2022
Collection: Electronic Theses & Dissertations
Description: This is the first study to begin to explore the effectiveness and predictors of change of emotionally focused therapy for relationship satisfaction and depressive symptoms among couples in Taiwan. This one-arm pragmatic trial assessed the clinical outcomes of 17 couples using paired-samples t-tests and multilevel modeling. Paired-sample t-tests revealed no statistical differences in relationship satisfaction and depressive symptoms before and after EFT. Additionally, multilevel modeling...
Show moreThis is the first study to begin to explore the effectiveness and predictors of change of emotionally focused therapy for relationship satisfaction and depressive symptoms among couples in Taiwan. This one-arm pragmatic trial assessed the clinical outcomes of 17 couples using paired-samples t-tests and multilevel modeling. Paired-sample t-tests revealed no statistical differences in relationship satisfaction and depressive symptoms before and after EFT. Additionally, multilevel modeling indicated no change in relationship satisfaction over time. However, there was a quadratic change in depressive symptoms over the course of EFT. While the study results were unexpected, it is important to note that most couples did not receive the recommended treatment “dose” in this pragmatic trial. In addition, the small sample size limited statistical power. In addition to assessing treatment outcomes, predictors of change were also examined. Findings showed that high traditionalism at intake predicted an increase in relationship satisfaction for women. Attachment was also a significant predictor of change; men with high attachment avoidance at intake demonstrated a significant decrease in depression, and men and women with high attachment anxiety at intake also experienced a significant decrease in depression. Lastly, emotional expressivity at intake was associated with an increase in relationship satisfaction and a decrease in depression for both men and women. Our study suggested that traditionalism, attachment, and emotional expressivity are important predictors of change among couples in Taiwan who receive EFT. While more research is needed, these findings offer preliminary support for the types of partners who may be more likely to experience change after receiving EFT.
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Title: ELEMENTARY TEACHER CANDIDATES’ CONNECTIONS BETWEEN MATHEMATICS AND LITERACY AND THE CONTEXTUAL FACTORS THAT ENCOURAGE CONNECTION-MAKING
Creator: Hawley, Lisa A.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Elementary teacher candidates (TCs) must learn to teach many subject areas. Although some mathematics education researchers have framed elementary teachers’ knowledge as a deficit (i.e., lack of depth of mathematics knowledge), this dissertation considers elementary teachers’ broad knowledge as a strength. Many elementary teachers and TCs feel anxious about teaching mathematics, but more confident in teaching other subjects, such as literacy. By identifying similarities between the teaching...
Show moreElementary teacher candidates (TCs) must learn to teach many subject areas. Although some mathematics education researchers have framed elementary teachers’ knowledge as a deficit (i.e., lack of depth of mathematics knowledge), this dissertation considers elementary teachers’ broad knowledge as a strength. Many elementary teachers and TCs feel anxious about teaching mathematics, but more confident in teaching other subjects, such as literacy. By identifying similarities between the teaching and learning of two subjects, they can draw on their knowledge of teaching other subjects to teach mathematics in a conceptually oriented, inquiry-based way. This case study of a cohort of elementary TCs taking concurrent mathematics and literacy methods courses sought to learn more about their connection-making by asking two questions: (a) What connections between subject areas, if any, do elementary TCs enrolled in concurrent literacy and mathematics methods courses identify? and (b) How do the contexts in which they are learning to teach encourage or limit the opportunities to make connections across subject areas? To answer the first question, I developed a conceptual framework of types of connections between mathematics and literacy, based on the research literature. This framework includes integrated curriculum, language as a basis for learning, and similarities in teaching and learning. I generated data through participant observations of class sessions and focus group discussions and analyzed the types of connections the TCs made using my framework. They identified a variety of connections between mathematics and literacy, with the two most frequent categories being about the role of reading in learning mathematics and similarities in pedagogy. To analyze the conditions which supported their connection making, I conceptualized the two methods courses as separate, but overlapping, communities of practice, and the focus group discussions as boundary encounters between them (Wenger, 1998). The focus groups, as boundary encounters, enabled TCs to identify a larger number of boundary objects (i.e., connections), as well as make richer connections. This took place through two types of knowledge brokering: brainstorming to identify boundary objects, and collaborative brokering, in which multiple participants contributed knowledge from other courses or experiences to collectively make sense of similarities or differences across the two subjects. In addition, my participation in collaborative brokering during the second focus group discussion suggests that TCs need the support of a more experienced knowledge broker to support their connection-making in order to go beyond surface-level similarities. These findings suggest that, in order to make connections that would enhance their mathematics teaching, elementary TCs need intentionally created spaces and the support of an instructor who is familiar with the teaching and learning of more than one subject area. This has implications for the structure of elementary teacher preparation programs, as well as the background and/or professional development of mathematics teacher educators who work with elementary TCs.
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Title: EFFECTS OF PLACENTAL LISTERIA MONOCYTOGENES INFECTION ON FETAL NEURODEVELOPMENT
Creator: Lee, Kun Ho
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Maternal infection can lead to adverse pregnancy outcomes. Numerous epidemiological studies have demonstrated an association between prenatal infection and neuropsychiatric disorders, including autism spectrum disorder (ASD). Different prenatal infections are associated with distinct neurological pathologies, necessitating studies of the diversity of prenatal pathogens and their consequences. Listeria monocytogenes (Lm) is a foodborne pathogen that causes listeriosis, which typically affects...
Show moreMaternal infection can lead to adverse pregnancy outcomes. Numerous epidemiological studies have demonstrated an association between prenatal infection and neuropsychiatric disorders, including autism spectrum disorder (ASD). Different prenatal infections are associated with distinct neurological pathologies, necessitating studies of the diversity of prenatal pathogens and their consequences. Listeria monocytogenes (Lm) is a foodborne pathogen that causes listeriosis, which typically affects immunocompromised individuals, including pregnant mothers. Prenatal infection with Lm can cause detrimental pregnancy outcomes, such as miscarriages, stillbirths, preterm labor, and death in newborns. However, neurological outcomes of maternal listeriosis have not been characterized. Here, I sought to investigate whether placental infection with Lm is associated with altered neurodevelopment by using a bioluminescence strain of Lm and a murine model of pregnancy-associated listeriosis. I show that placental infection affects neurodevelopment during pregnancy and behavior in the offspring.To investigate how placental infection with Lm dysregulates fetal brain development, I performed RNA-seq on fetal brains to quantify the enrichment of genes that were associated with the infection during gestation. The findings of RNA-seq analysis illustrated that placental infection with Lm altered fetal brain transcriptome and showed sexually dichotomous gene expression profiles. I further assessed the effects of different traits, including Lm exposure, the intensity of placental infection, and sex on the fetal transcriptome using systems biology. The genes were grouped into co-expression modules. Notably, maternal infection and its intensity measured by bioluminescence imaging signal were significantly associated with specific modules, suggesting these traits are the main factors driving these transcriptional changes. Lastly, I showed that placental Listeria infection enriched ASD-associated genes. These results demonstrate that maternal listeriosis dysregulates fetal brain transcriptome during gestation. Neurodevelopment is a complex process influenced by various environmental factors during pregnancy. To examine whether prenatal infection with Lm affects cortical lamination and neural activity, I performed hematoxylin and eosin staining and immunohistochemistry. Gross anatomy of the brain structure analysis showed that placental infection with Lm affected cortical lamination in a localized manner. Furthermore, increased neural activity was observed in Lm- exposed male offspring. These results illustrate that placental infection with Lm induces morphological changes in brain tissue during neurodevelopment. Behavioral symptoms of neuropsychiatric disorders are an important component of the diagnosis. Animal behavioral assays and tools have been developed to examine animal behavior such as social interactions, anxiety, and repetitive behaviors. I examined behavior tests that resembled ASD to determine if mouse offspring born following placental infection displayed abnormal behavior. Lm-exposed offspring exhibited altered behaviors and showed sex-dependent behavioral changes. Overall, my work highlights the impact of maternal listeriosis on brain development during pregnancy and its effects on offspring’s behavior and contributes to the understanding of the spectrum of fetal neurodevelopment.
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Title: Multi-Modality Nondestructive Evaluation Techniques for Inspection of Plastic and Composite Pipeline Networks
Creator: Alzuhiri, Mohand
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The extensive adoption of plastic pipelines is a growing phenomenon in different fields of the industry, with applications that extend from municipal water and sewer systems to the water lines in nuclear reactors. The large-scale adoption is motivated by the unique features of plastics like corrosion and chemical resistance, low cost, and design flexibility. While the dielectric nature of plastic pipelines provides unique design capabilities, it also introduces new challenges for the...
Show moreThe extensive adoption of plastic pipelines is a growing phenomenon in different fields of the industry, with applications that extend from municipal water and sewer systems to the water lines in nuclear reactors. The large-scale adoption is motivated by the unique features of plastics like corrosion and chemical resistance, low cost, and design flexibility. While the dielectric nature of plastic pipelines provides unique design capabilities, it also introduces new challenges for the operators when it comes to inspecting and ensuring the integrity of these pipelines’ networks. In this study, a multi-modal approach is adopted to address the threats affecting the safety of small diameter plastic pipelines and explore possible inspection solutions for emerging materials like composites. Structured light endoscopes with RGB-D inspection capability were developed for the inspection of surface defects in small diameter pipelines with novelties a) Design and miniaturization of RGB-D structured light sensor with electronic stabilization, b) Development of an algorithm to automatically calibrate the sensor when placed in a cylindrical environment, c) Design of a single shot phase measurement SL sensor that employs the sensor movement to improve the 3D reconstruction, and d) Design a stereoscopic SL sensor for 360-degree inspection. EM-based inspection was adopted to inspect subsurface defects and classify materials around the inspected pipelines. An investigative study was performed to test the probability of detecting cold fusion in butt fusion joints by using emerging NDE techniques, and a coplanar capacitive sensor was designed for the detection of legacy crossbores in gas pipelines. Finally, a thermoacoustic imaging system was developed in this study with potential applications for the inspection of composites and medical imaging. The novelties of this work can be summarized as follows: a) Development of a simulation model to study the thermoacoustic waves generation and the effect of multiple experimental parameters on the performance of thermoacoustic imaging systems, b) Improving the signal to noise ratio of pulsed TAI imaging systems by adoption non-coherent pulse compression. In summary, this study presents a multi-modal approach for the inspection of pipeline networks by adopting optical RGB-D imaging sensors for surface inspection, EM-based sensors for subsurface inspection and classification of objects outside the pipe, and finally, a hybrid imaging method with potential applications in medical imaging and inspection of composites.
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Title: SIGNALING MECHANISMS OF PULMONARY ARTERIAL HYPERTENSION
Creator: Ji, Yajing
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Pulmonary arterial hypertension (PAH) is a severe and life-threatening disease that is characterized by elevated pulmonary blood pressure. A challenge in treating PAH is that while the current generation of therapeutics alleviate symptoms, they fail to target the underlying causes of the disease. Initially it was thought that PAH is caused by increased pulmonary vasoconstriction; it is now understood that PAH mainly results from remodeling of the pulmonary vasculature. Further...
Show morePulmonary arterial hypertension (PAH) is a severe and life-threatening disease that is characterized by elevated pulmonary blood pressure. A challenge in treating PAH is that while the current generation of therapeutics alleviate symptoms, they fail to target the underlying causes of the disease. Initially it was thought that PAH is caused by increased pulmonary vasoconstriction; it is now understood that PAH mainly results from remodeling of the pulmonary vasculature. Further characterization of the underlying mechanisms of PAH will identify newpharmacological targets to treat PAH. In this dissertation I seek to address this challenge from three distinct perspectives. In Chapter 2, I investigated the signaling network downstream of TGFβ and highlighted the MRTF/SRF pathway as potential therapeutical targets for PAH given its pivotal role regulating expression of contractile proteins in PASMCs. In Chapter 3, I aim to test whether TGFβ and the silencing of BMPR2, a member of the TGFβ family of receptors, contribute to the activation of lung fibroblasts in vitro. My results presented do not replicate the role of BMPR2 silencing found in other studies. This could be caused by the relatively short duration of BMPR2 silencing in our system. Finally, in Chapter 4, I perform a combined meta-analysis of several publicly available transcriptomic datasets of lung tissues from PAH patients. Using this approach, I identify PAH-associated signaling pathways, and chemical compounds which reverse a PAH-associated gene expression signature. My findings also suggest that while we bin PAH patients into various subtypes in the clinic, on a transcriptional level, PAH patients tend to group into distinct gene expression clusters without relying on their clinical subtype. These findings improve our understanding of PAH biology and also highlight several potential drug targets for PAH.
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Title: I. Determination of Absolute Configuration of Chiral 1,2-Diols. II. Progress Towards the Total Synthesis of Napyradiomycin A1.
Creator: Torabi Kohlbouni, Saeedeh
Date: 2022
Collection: Electronic Theses & Dissertations
Description: This dissertation focuses on two parts. The first part introduces an operationally simple and microscale method for the absolute stereochemical determination of 1,2-diols. In situ derivatization of 1,2-diols with dinaphthyl borinic acid generates the induced helicity of the two naphthyl groups, which leads to an observable ECCD spectrum. The observed P or M helicity follows a predictable trend for S and R chiral 1,2-diols, respectively.The Second chapter is the progress towards the asymmetric...
Show moreThis dissertation focuses on two parts. The first part introduces an operationally simple and microscale method for the absolute stereochemical determination of 1,2-diols. In situ derivatization of 1,2-diols with dinaphthyl borinic acid generates the induced helicity of the two naphthyl groups, which leads to an observable ECCD spectrum. The observed P or M helicity follows a predictable trend for S and R chiral 1,2-diols, respectively.The Second chapter is the progress towards the asymmetric catalytic synthesis of napyardiomycin A1. The chapter is divided to three sections. The first section is installation of chlorine chiral center at C3. This goal is achieved using cinchona chiral catalyst, and DCDMH as chloronium source. The second section is the synthesis of the -lapachone core of napyradiomycin A1, was accomplished using Diels-Alder/aromatization cascade reaction. The last section shows our effort toward the attachment of geranyl side chain.
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Title: Solving Computationally Expensive Problems Using Surrogate-Assisted Optimization : Methods and Applications
Creator: Blank, Julian
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Optimization is omnipresent in many research areas and has become a critical component across industries. However, while researchers often focus on a theoretical analysis or convergence proof of an optimization algorithm, practitioners face various other challenges in real-world applications. This thesis focuses on one of the biggest challenges when applying optimization in practice: computational expense, often caused by the necessity of calling a third-party software package. To address the...
Show moreOptimization is omnipresent in many research areas and has become a critical component across industries. However, while researchers often focus on a theoretical analysis or convergence proof of an optimization algorithm, practitioners face various other challenges in real-world applications. This thesis focuses on one of the biggest challenges when applying optimization in practice: computational expense, often caused by the necessity of calling a third-party software package. To address the time-consuming evaluation, we propose a generalizable probabilistic surrogate-assisted framework that dynamically incorporates predictions of approximation models. Besides the framework's capability of handling multiple objectives and constraints simultaneously, the novelty is its applicability to all kinds of metaheuristics. Moreover, often multiple disciplines are involved in optimization, resulting in different types of software packages utilized for performance assessment. Therefore, the resulting optimization problem typically consists of multiple independently evaluable objectives and constraints with varying computational expenses. Besides providing a taxonomy describing different ways of independent evaluation calls, this thesis also proposes a methodology to handle inexpensive constraints with expensive objective functions and a more generic concept for any type of heterogeneously expensive optimization problem. Furthermore, two case studies of real-world optimization problems from the automobile industry are discussed, a blueprint for solving optimization problems in practice is provided, and a widely-used optimization framework focusing on multi-objective optimization (founded and maintained by the author of this thesis) is presented. Altogether, this thesis shall pave the way to solve (computationally expensive) real-world optimization more efficiently and bridge the gap between theory and practice.
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Title: FIBER-OPTIC SILICON FABRY-PEROT INTERFEROMETERS FOR HIGH-SPEED ANEMOMETER AND HIGH-SENSITIVITY BOLOMETER APPLICATIONS
Creator: Uddin, Nezam
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Fiber-optic silicon Fabry-Perot interferometric temperature sensor offers the advantage of high-speed and high-resolution to characterize the ocean turbulence in oceanographic research. Compared to silica, the material that makes the optical fiber, silicon has a thermo-optic coefficient ten times higher and a thermal conductivity sixty time higher. Silicon is highly transparent in the infrared wavelength range and can be easily processed with the mature microfabrication technology. All of...
Show moreFiber-optic silicon Fabry-Perot interferometric temperature sensor offers the advantage of high-speed and high-resolution to characterize the ocean turbulence in oceanographic research. Compared to silica, the material that makes the optical fiber, silicon has a thermo-optic coefficient ten times higher and a thermal conductivity sixty time higher. Silicon is highly transparent in the infrared wavelength range and can be easily processed with the mature microfabrication technology. All of these make silicon a very attractive material for high-speed and high-resolution turbulence measurement. We attached a small silicon pillar to the end of an optical fiber to make fiber-optic Fabry-Perot interferometric sensor demodulated by a white light system for fast turbulence measurement. We studied the two modes of fiber-optic hot wire anemometer operation for turbulence measurement theoretically and experimentally. The constant temperature operation of the fiber-optic hot wire anemometer was introduced for the first time to reduce the time constant significantly. The anemometer used for demonstration is based on a silicon low-finesse Fabry-Perot interferometer (FPI) attached to the tip of a single mode fiber. Turbulent flow measurement method based on constant temperature operation offers high measuring speed, because the wire temperature is kept constant, the effect of thermal inertia of the wire is suppressed. We also investigated a new sensor structure experimentally and theoretically for the measurement of water flow with reduced directivity. This sensor consists of a laser heated silicon FPI embedded in a metal microsphere. Herein, the spherical shape of the outside metal shell gives a symmetric response to water flow direction; thus, the directivity is reduced greatly. Moreover, the water flow measurement by the hot wire fiber-optic water flow sensor based on laser heated silicon FPI need to compensate the effect of water temperature variation. We reported a technique to compensate the effect of water temperature change in the flow measurement by using another sensor which will track the temperature of the water. By using the information of the water temperature change, baseline can be defined which will provide unique wavelength change for the flow. Finally, the wavelength change corresponding to the flow speed were calibrated using the sensor pair after compensating the effect of water temperature variation. We expanded the use of silicon Fabry-Perot interferometric sensor in the measurement of plasma radiation by modifying the structure with gold coated silicon and multimode graded index fiber between the single mode fiber (SMF) and silicon. We reported the design, fabrication, and characterization of a fiber-optic bolometer (FOB) with improved noise equivalent power density (NEPD) performance and increased absorption to high energy photons by engineering the absorber of the FOB. We also have developed a multichannel fiberoptic bolometry system with five bolometers connected to each channel of the coarse wavelength division multiplexer (CWDM), a single light source of super luminescent LED (SLED) and a single I-MON 512 OEM spectrometer. Easy sensor fabrication, significantly enhanced measurement range compared to the previous high-finesse FPI bolometer system for measuring radiation are some of the advantages. Moreover, utilization of the FOB in the vacuum for radiation measurement with reduced time constant was also studied which is practically required in the fusion devices. This was done by adding a heat sink with the current FOB structure and using the deconvolution method to get better temporal resolution. Finally, the FOB with the heat sink was tested in the vacuum condition to measure the radiation using the deconvolution method. Experimental results are presented to support the idea of heat sink and deconvolution method for plasma radiation measurement.
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Title: KNOWLEDGE SPILLOVERS AND SAFE DRINKING WATER ACT COMPLIANCE
Creator: Redican, Kyle James
Date: 2022
Collection: Electronic Theses & Dissertations
Description: In the wake of the 2014 Flint Water Crisis, researchers, regulators, and utility professionals have given increased attention to understanding drivers of (CWS) Safe Drinking Water Act (SDWA) compliance by community water systems (CWSs). Most of this research has only explored system traits while ignoring the vital role of human capital, especially the operator. The status of CWS operators can vary widely between different systems. More critically, scholars have not investigated how effective...
Show moreIn the wake of the 2014 Flint Water Crisis, researchers, regulators, and utility professionals have given increased attention to understanding drivers of (CWS) Safe Drinking Water Act (SDWA) compliance by community water systems (CWSs). Most of this research has only explored system traits while ignoring the vital role of human capital, especially the operator. The status of CWS operators can vary widely between different systems. More critically, scholars have not investigated how effective external linkages between CWS operators have impacted SDWA compliance. Drawing from the theories of Organizational Learning’s inter-organizational learning, Innovation Systems’ knowledge transfers, and Agglomeration Economics’ knowledge spillovers, I hypothesized that increased interactions between CWS operators, facilitated in part by geographic proximity, would lead to more information sharing, increased CWS performance, and fewer SDWA violations. Remarkably little is known about the drivers of inter-operator interactions or whether such interactions improve SDWA compliance, and this research helped fill the data gap through a large-sample survey of CWS operators in Michigan to capture the frequency of interactions along with a range of operator and system characteristics which may explain why some operators participate in more inter-operator interactions than others. With this novel dataset, along with publicly available system and community data, this research first investigated what endogenous operator characteristics were associated with more reported inter-operator interactions. Through multiple methods on reported operator interactions, the Utility and Contract operators and operators with memberships in professional organizations appear more likely to report more interactions than Non-Affiliated operators and all operators who were not members of professional organizations. Second, based on Tobler’s first law of geography, there should be some spatial autocorrelation in the number of reported interactions, and this was tested using variogram modeling. Observed spatial autocorrelation indicated location-based differences in the number of reported interactions. Third, we used multiple methods to explore the primary research question to identify endogenous and spatial drivers of reported inter-operator interactions. Multiple models found that rural districts had a higher probability of fewer SDWA violations with increased interactions, while the urban districts had the inverse relationship. Fourth, the research incorporated CWS-specific and operator-specific variables, as the operator-specific data were not independent of the CWS observations (since some operators run multiple CWSs). I used a Generalized Linear Mixed-Model to estimate these relationships accounted for the multiple levels and found that more interactions increased the probability of SDWA compliance for certain types of operators. The broader implications of this research encourage stakeholders to pursue more inter-operator interactions as a low-cost mechanism to increase SDWA compliance. Seven avenues to increase interactions are outlined, ranging from open operator contact lists to operator focus groups to identify common problems and solutions to creating a state-level operator mentorship program to support new operators.
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Title: BUILDING STATE WILDLIFE AGENCY CAPACITY FOR EFFECTIVE PARTNERSHIPS
Creator: Cross, Megan M
Date: 2022
Collection: Electronic Theses & Dissertations
Description: State wildlife agencies (SWAs) partner with organizations of various types, on projects of various types, at what is anticipated to be an increasing rate. Inclusion of multiple and diverse stakeholders and partners is postulated to improve effectiveness of wildlife management (Anderson & Loomis, 2007; Jacobson et al., 2010). Through partnerships, actors from private, civil, and public sectors work together to reduce negative impacts from wildlife and improve access to and benefits of wildlife...
Show moreState wildlife agencies (SWAs) partner with organizations of various types, on projects of various types, at what is anticipated to be an increasing rate. Inclusion of multiple and diverse stakeholders and partners is postulated to improve effectiveness of wildlife management (Anderson & Loomis, 2007; Jacobson et al., 2010). Through partnerships, actors from private, civil, and public sectors work together to reduce negative impacts from wildlife and improve access to and benefits of wildlife resources. Although partnerships can improve the ability of SWAs to address these issues, little is known about how the perspectives of internal employees and external partners and stakeholders differ regarding factors affecting perceived success of partnerships in wildlife conservation.This dissertation addresses SWA partnerships through an examination of one prototypical SWA’s partnership arrangements. I propose a typology for categorization of SWA partnerships and apply a theory of collaborative capacity to the assessment of them. I surveyed all employees of the Michigan SWA and asked them to identify the three partners they consider most key to their work and found gaps in the frequencies of partners considered key to the work of SWA employees based on their locations in the defined typology. Additionally, the model of collaborative capacity tested varied in performance when applied to SWA employees and SWA partners. This research has implications for transparency regarding how state power is shared and considers how the disparate prevalence of various partnership arrangements may affect wildlife governance. Furthermore, my research findings may be used to improve SWA partnership arrangements and improve their alignment with governance and management-relate goals, as well as increase awareness of differences in views regarding partnership success as defined by SWA employees and partners.
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Title: Life Cycle Monitoring of Reversible Adhesive Bonded Joints using Guided Waves
Creator: Palanisamy, Rajendra Prasath
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Recent advancements in automotive, aerospace, civil and wind-energy industries have resulted in an ever-increasing demand for lightweight, cost-effective, rapidly manufactured and recyclable/reusable of structural components. Adopting composite materials is a popular solution to achieve light-weighting, however it requires complex joining methods compared to traditional mechanical fasteners. Electromagnetic targeted heating of nano-Fe3O4 reinforced thermoplastic adhesives (Reversible-Adhesive...
Show moreRecent advancements in automotive, aerospace, civil and wind-energy industries have resulted in an ever-increasing demand for lightweight, cost-effective, rapidly manufactured and recyclable/reusable of structural components. Adopting composite materials is a popular solution to achieve light-weighting, however it requires complex joining methods compared to traditional mechanical fasteners. Electromagnetic targeted heating of nano-Fe3O4 reinforced thermoplastic adhesives (Reversible-Adhesive) is an emerging technique for rapid assembly, dis-assembly, and re-assembly of bonded composite parts. Alternate magnetic field applied to the dispersed ferromagnetic nanoparticles (FMNP) within a thermoplastic adhesive results in these particles acting as nano-heaters and rapidly heating the surrounding material resulting in melting and flow of the adhesive, which upon cooling forms a structural bond. This process can be repeated and hence termed reversible adhesive. Reversible-adhesive bonded composite structures (RBCS) offer a greater advantage over thermosets or mechanical joints such as rapid processing, easy repair, quick disassembly, and possible re-usability of components. However, it is essential to accurately measure the temperature of the adhesive during processing and repair, since overheating may cause chemical degradation and underheating may introduce improper bonds. Adhesively bonded composite structures provide a more uniform stress distribution in the bond-line than riveted joints resulting in higher fatigue life. However, modeling the physics behind crack initiation and propagation inside bonded regions is challenging especially under fatigue loading. As a result, real-time in-service bond monitoring is required to ensure structural safety. In addition to monitoring the damage state, prediction of damage area and remaining useful life of the component is imperative. Thus, this research work focusses on developing a life cycle monitoring solution for RBCS using the guided wave (GW) technique. Ultrasonic guided waves were made to propagate across the bond-line of the joint by exciting and sensing them using miniature piezoelectric wafers. Analysis of dispersion relations and dynamic wave propagation were performed using finite element modeling (FEM). Fundamental longitudinal mode L_0 at 35 kHz was found optimal for bond process monitoring. Mapping between the FE-simulated transmission coefficient of L_0 and actual temperature of the thermoplastic adhesive was established using the DMA test data. Real-time guided wave measurements were used as feedback in the discrete control of the induction heater so as to provide optimal bonding and prevent adhesive degradation. The developed ultrasonic technique was successfully validated by fiber-optic temperature sensing. Results indicate that the bondlines processed with GW control offer better ultimate strength compared to uncontrolled processing.Guided wave modal and frequency sensitivity analysis for fatigue damage was performed. Based on the analysis, symmetric mode at 85 kHz was found optimal for fatigue damage detection. Further, a damage propagation model based on Paris law was developed to estimate remaining useful life in terms of the GW signal features. Finally, the remaining useful life of the lap-joint was predicted and validated experimentally. One of the major advantages of reversible adhesive is its ability to repair/heal the damage. The controlled processing technique developed earlier was used for controlled healing of fatigue damaged joints. Experimental investigation proves the healed-bond line have returned to its original strength. A holistic approach of a complete lifecycle monitoring of bonded joints was aimed at increasing the confidence in the use of bonded joints relative to mechanical fasteners, and can be easily extended to other structural applications.
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Title: High-precision and Personalized Wearable Sensing Systems for Healthcare Applications
Creator: Tu, Linlin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world...
Show moreThe cyber-physical system (CPS) has been discussed and studied extensively since 2010. It provides various solutions for monitoring the user's physical and psychological health states, enhancing the user's experience, and improving the lifestyle. A variety of mobile internet devices with built-in sensors, such as accelerators, cameras, PPG sensors, pressure sensors, and the microphone, can be leveraged to build mobile cyber-physical applications that collected sensing data from the real world, had data processed, communicated to the internet services and transformed into behavioral and physiological models. The detected results can be used as feedback to help the user understand his/her behavior, improve the lifestyle, or avoid danger. They can also be delivered to therapists to facilitate their diagnose. Designing CPS for health monitoring is challenging due to multiple factors. First of all, high estimation accuracy is necessary for health monitoring. However, some systems suffer irregular noise. For example, PPG sensors for cardiac health state monitoring are extremely vulnerable to motion noise. Second, to include human in the loop, health monitoring systems are required to be user-friendly. However, some systems involve cumbersome equipment for a long time of data collection, which is not feasible for daily monitoring. Most importantly, large-scale high-level health-related monitoring systems, such as the systems for human activity recognition, require high accuracy and communication efficiency. However, with users' raw data uploading to the server, centralized learning fails to protect users' private information and is communication-inefficient. The research introduced in this dissertation addressed the above three significant challenges in developing health-related monitoring systems. We build a lightweight system for accurate heart rate measurement during exercise, design a smart in-home breathing training system with bio-Feedback via virtual reality (VR) game, and propose federated learning via dynamic layer sharing for human activity recognition.
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