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Title: MACHINE LEARNING APPROACHES FOR PROCESSING AND DECODING ATTENTION MODULATION OF SENSORY REPRESENTATIONS FROM EEG
Creator: saba-sadiya, sari
Date: 2023
Collection: Electronic Theses & Dissertations
Description: This thesis presents novel machine learning algorithms that achieve state-of-the-art performance on a variety of electroencephalography (EEG) tasks, including decoding, classification, and unsupervised / semi-supervised artifact detection and correction. These algorithms are then used within the scope of an EEG experiment that explores how attention to multiple items modulates sensory representations. Using a signal detection paradigm, we demonstrate that attending to multiple items impacts...
Show moreThis thesis presents novel machine learning algorithms that achieve state-of-the-art performance on a variety of electroencephalography (EEG) tasks, including decoding, classification, and unsupervised / semi-supervised artifact detection and correction. These algorithms are then used within the scope of an EEG experiment that explores how attention to multiple items modulates sensory representations. Using a signal detection paradigm, we demonstrate that attending to multiple items impacts the sensitivity of our participants, causing a sharp increase in false-alarm rates and only slightly decreasing hit-rate. We conclude that our behavioral and EEG decoding results contradict simultaneous attention guidance by multiple items (the multiple item template hypothesis).
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Title: DEVELOPMENT OF 3D BIOACTIVE AND ANTIBACTERIAL SILICATE-BASED SCAFFOLDS FOR BONE TISSUE REGENERATION IN LOAD-BEARING APPLICATIONS
Creator: Marsh, Adam Christoph
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Current gold-standard approaches to addressing the needs of bone defects in load-bearing applications entail the use of either autographs or allographs. Both solutions, however, are imperfect as both autographs and allographs carry the risk of additional trauma, threat of disease transmission, and potential donor rejection respectively. Porous 3D scaffolds are attractive alternatives, illuminating a potential path towards achieving the ideal scaffold for targeting bone tissue regeneration in...
Show moreCurrent gold-standard approaches to addressing the needs of bone defects in load-bearing applications entail the use of either autographs or allographs. Both solutions, however, are imperfect as both autographs and allographs carry the risk of additional trauma, threat of disease transmission, and potential donor rejection respectively. Porous 3D scaffolds are attractive alternatives, illuminating a potential path towards achieving the ideal scaffold for targeting bone tissue regeneration in load-bearing applications, usurping autographs to become the new gold-standard. To unlock the full healing potential of 3D scaffolds, such scaffolds must be multifunctional such that (1) their mechanical performance meets the requisite requirements as dictated by the mechanical performance characteristics of interest for native bone tissue, (2) they stimulate the necessary biological responses for bone tissue regeneration, and (3) they exhibit antibacterial characteristics to combat the threat of infection. To date, no reports document 3D scaffolds exhibiting all three performance characteristics. The aim of this dissertation, therefore, is to deliver 3D scaffolds that are mechanically competent, possess and exhibit inherent and advanced antibacterial characteristics, and are successful at providing the needed biological characteristics for bone tissue regeneration. To achieve this, this dissertation implements a multidisciplinary approach, utilizing comprehensive structural characterization across a wide range of scales to elucidate process – performance relationships to execute scientifically driven modifications to engineer and deliver a 3D scaffold to successfully target bone tissue regeneration in load-bearing applications. A silver-doped bioactive glass-ceramic (Ag-BG) composition was selected as the material for scaffold synthesis due to its inherent and attractive antibacterial and biological performance characteristics. Two fundamentally different processing approaches were utilized for synthesizing Ag-BG scaffolds: the polymer foam replication technique and fused filament fabrication (FFF). The Ag-BG scaffolds studied herein were found to exhibit advanced antibacterial performance characteristics against methicillin-resistant Staphylococcus aureus (MRSA), a common pathogen implicated in osteomyelitis development, able to combat MRSA both in planktonic and biofilm forms. Ag-BG scaffolds demonstrated the ability to form an apatite-like layer when immersed in simulated body fluid (SBF), an indicator that Ag-BG scaffolds will induce the necessary mineralization for bone tissue regeneration, in addition to exhibiting attractive cell viability, proliferation, and differentiation characteristics when studied in vitro. The mechanical performance of Ag-BG scaffolds reported herein saw progressive improvements in each iteration of Ag-BG scaffold synthesis, achieving desirable mechanical competency and reliability as a result of the multidisciplinary approach formulated. In addition to the exploration of developing 3D antibacterial and biological silicate-based scaffolds capable of targeting bone tissue regeneration in load-bearing applications, foundational work towards the development of class II hybrid scaffolds comprised of gelatin methacryloyl (GelMA) and Ag-BG for targeting softer tissue regeneration. The novel syntheses applied to the successful molecular coupling of GelMA and Ag-BG presents an attractive class II hydrogel showing great promise as a compatible ink for 3D bioprinting cell-laden scaffolds capable of targeting tissue regeneration of more sophisticated systems.
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Title: Molecular epidemiology, pangenomic diversity, and comparative genomics of Campylobacter jejuni
Creator: Rodrigues, Jose Alexandre
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Campylobacter jejuni, the leading cause of bacterial gastroenteritis in the United States, is often resistant to commonly used antibiotics and has been classified as a serious threat to public health. Through this work, we sought to evaluate infection trends, quantify resistance frequencies, identify epidemiological factors associated with infection, and use whole-genome sequencing (WGS) as well as comparative phylogenomic and pangenomic approaches to understand circulating C. jejuni...
Show moreCampylobacter jejuni, the leading cause of bacterial gastroenteritis in the United States, is often resistant to commonly used antibiotics and has been classified as a serious threat to public health. Through this work, we sought to evaluate infection trends, quantify resistance frequencies, identify epidemiological factors associated with infection, and use whole-genome sequencing (WGS) as well as comparative phylogenomic and pangenomic approaches to understand circulating C. jejuni populations in Michigan. C. jejuni isolates (n=214) were collected from patients via an active surveillance system at four metropolitan hospitals in Michigan between 2011 and 2014. Among the 214 C. jejuni isolates, 135 (63.1%) were resistant to at least one antibiotic. Resistance was observed for all nine antibiotics tested yielding 11 distinct resistance phenotypes. Tetracycline resistance predominated (n=120; 56.1%) followed by resistance to ciprofloxacin (n= 49; 22.9%), which increased from 15.6% in 2011 to 25.0% in 2014. Notably, patients with ciprofloxacin resistant infections were more likely to report traveling in the past month (Odds Ratio (OR): 3.0; 95% confidence interval (CI): 1.37, 6.68) and international travel (OR: 9.8; 95% CI: 3.69, 26.09). To further characterize these strains, we used WGS to examine the pangenome and investigate the genomic epidemiology of this set of C. jejuni strains recovered from Michigan patients. Among the 214 strains evaluated, 83 unique multilocus sequence types (STs) were identified that were classified as belonging to 19 previously defined clonal complexes (CCs). Core-gene phylogenetic reconstruction based on 615 genes identified three clades, with Clade I comprising six subclades (IA-IF) and predominating (83.2%) among the strains. Because specific cattle-associated STs, such as ST-982, predominated among strains from Michigan patients, we also examined a collection of 72 C. jejuni strains from cattle recovered during an overlapping time period by WGS. Several phylogenetic analyses demonstrated that most cattle strains clustered separately within the phylogeny, but a subset clustered together with human strains. Hence, we used high quality single nucleotide polymorphism (hqSNP) profiling to more comprehensively examine those cattle and human strains that clustered together to evaluate the likelihood of interspecies transmission. Notably, this method distinguished highly related strains and identified clusters comprising strains from both humans and cattle. For instance, 88 SNPs separated a cattle and human strain that were previously classified as ST-8, while the human and cattle derived ST-982 strains differed by >200 SNP differences. These findings demonstrate that highly similar strains were circulating among Michigan patients and cattle during the same time period and highlight the potential for interspecies transmission and diversification within each host. In all, the data presented illustrate that WGS and pangenomic analyses are important tools for enhancing our understanding of the distribution, dissemination, and evolution of specific pathogen populations. Combined with more traditional phenotypic and genotypic approaches, these tools can guide the development of public health prevention and mitigation strategies for C. jejuni and other foodborne pathogens.
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Title: DEVELOPING LIGNIN-BASED EPOXY AND POLYURETHANE RESINS
Creator: Nikafshar, Saeid
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Lignin, the most abundant natural aromatic polymer, is currently produced as by-product during biorefinery and chemical pulping processes. Lignin is rich in hydroxyl functional groups (both phenolic and aliphatic OH), making it an excellent raw material for synthesizing epoxy and polyurethane resins. However, there are several challenges in utilizing unmodified lignins as feedstock for product development, including high polydispersity/heterogeneity, low reactivity, poor accessibility of...
Show moreLignin, the most abundant natural aromatic polymer, is currently produced as by-product during biorefinery and chemical pulping processes. Lignin is rich in hydroxyl functional groups (both phenolic and aliphatic OH), making it an excellent raw material for synthesizing epoxy and polyurethane resins. However, there are several challenges in utilizing unmodified lignins as feedstock for product development, including high polydispersity/heterogeneity, low reactivity, poor accessibility of hydroxyl groups for reaction with co-monomers low solubility in common organic solvents, and dark color. There are significant variations in lignin characteristics, depending on the source of biomass and isolation methods. Therefore, in-depth lignin characterization is needed to provide the basic knowledge of the structural, chemical, and thermal properties to facilitate lignin valorization.This study was focused on lignin characterization and development of lignin-based epoxy and polyurethane resins. First, a wide range of lignin samples was fully characterized by measuring their ash contents, elemental analyses, hydroxyl contents, chemical structures, molar mass distributions, and thermal properties. Next, a novel method was developed to measure the reactivity of thirteen different unmodified lignins toward biobased epichlorohydrin (ECH). A partial least square regression (PLS-R) model (with 92 % fitting accuracy and 90 % prediction ability) was created to study the correlation between lignin properties and epoxy content. The results showed that lignins with higher phenolic hydroxyl contents and lower molecular weights were more suitable for replacing 100 % of toxic bisphenol A (BPA) in the formulation of resin precursors. Additionally, two epoxidized lignin samples (with the highest epoxy contents) were cured using a biobased hardener (Cardolite from cashew nutshell), showed comparable thermomechanical performances and thermal stabilities to a petroleum-based epoxy system. Biobased waterborne polyurethane resins (PUDs) were also developed by entirely replacing the petroleum-based polyol and the internal emulsifier with either alkaline pre-extraction lignins or enzymatic hydrolysis lignins as well as tartaric acid (a biobased diacid). The formulated resins had zero VOC (volatile organic compound), which was achieved by replacing toxic n-methyl-2-pyrrolidone (NMP) with cyrene (a biobased solvent). To further improve the mechanical properties of our biobased PUD resins, 20 wt.% of lignin was substituted with low hydroxyl value soy-polyol, which increased their tensile strength and elongation at break to 87% and 68% of a commercial PUD resin. The results of this study demonstrated that it is imperative to fully characterize lignin and choose the right lignin for each specific application. This approach enabled us to entirely replace petroleum-based raw materials (BPA and polyol) with lignin and formulate biobased epoxy and polyurethane resins.
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Title: ASSESSING THE DETERMINANTS THAT PARSE THE LYASE AND MUTASE ACTIVITIES OF A PLANT AMINOMUTASE, AND DEVELOPING A REGIOSELECTIVE COUPLING REACTION FOR A TRIALKYL PYRAZINE.
Creator: Attanayake, Gayanthi Kumari
Date: 2021
Collection: Electronic Theses & Dissertations
Description: This dissertation is mainly contributed with two main projects. The first project is based on aminomutase enzyme. Recent discovery of MIO-dependent aminomutases on the biosynthetic pathways of biologically active, medicinal compounds in plants and microorganisms raises interest in further understanding how they catalyze β-amino acid building blocks. A tyrosine aminomutase isolated from Japanese rice, Oryza sativa (OsTAM), converts α-tyrosine to β-tyrosine (75%) and makes an acrylate, p...
Show moreThis dissertation is mainly contributed with two main projects. The first project is based on aminomutase enzyme. Recent discovery of MIO-dependent aminomutases on the biosynthetic pathways of biologically active, medicinal compounds in plants and microorganisms raises interest in further understanding how they catalyze β-amino acid building blocks. A tyrosine aminomutase isolated from Japanese rice, Oryza sativa (OsTAM), converts α-tyrosine to β-tyrosine (75%) and makes an acrylate, p-coumarate (25%), as a by-product. OsTAM is the first TAM to have slight phenylalanine aminomutase (PAM) activity (3%). This may not be surprising since the active sites of OsTAM and TcPAM from Taxus plants differ by only two residues (Y125 and N446 of OsTAM compared to C107 and K427 of TcPAM, respectively) positioned similarly near the aryl ring of their substrates. We anticipated by changing key active site residues of OsTAM to nonpolar side chains found in TcPAM would improve the binding of substituted phenylalanine substrates. Another feature of MIO-aminomutases, highlighted in a previous study,1 is a hinge-gate inner loop that opens and closes the entry to the active site. We changed hydrophilic for more hydrophobic residues within the OsTAM loop like in TcPAM to make it function as a more efficient PAM and expected the mutants to produce a greater proportion of the β-amino acid over acrylate compared to that made by wild-type OsTAM. Our data suggested that a combination of active site mutants and loop mutants generally increased the turnover of OsTAM for para-substituted substrates over the other meta- and ortho- regioisomers to their corresponding cinnamates, and not to the β-amino acids, as the major products. These findings suggest that while active site residues may be involved primarily in creating broad substrate selectivity, their role along with that of the inner loop to parse the reaction toward β-amino acids remains elusive.The second project mainly focused on regioselective synthesis of ethyl dimethyl pyrazine. Alkylpyrazines are important heterocyclic compounds used as flavorants in the food and beverage industries. This study developed a regioselective synthesis of 2-ethyl-3,5-dimethylpyrazine (235-EDMP) over its 3-ethyl-2,5-dimethyl isomer (325-EDMP). Our first attempts explored how steric direct the coupling orientations between diamines to diketones to access 235-EDMP. Also, various physical parameters of the reaction conditions were changed, such as reduced temperature, the order-of-addition of reactants, and supplementation with chiral zeolites (Montmorillonite phyllosilicates) to template the orientation of the coupling partners to direct the regiochemistry of the reaction. Each reaction trial resulted in 50:50 mixtures of the ethyl dimethylpyrazine regioisomers. An alternative approach was explored to direct the regioselectivity of the reactions; acyloins (α-hydroxy ketone) replaced the diketone as the electrophilic coupling reactant used in the previous trial experiments. The hydroxy ketone reactants were made biocatalytically with pyruvate decarboxylase (E.C. 4.1.1.1). The coupling reaction between 2-hydroxypentan-3-one and propane-1,2-diamine resulted in the desired 235-EDMP at >70% (~77 mg total) relative to 30% 325-EDMP in the product mixture. The 3-hydroxypentan-2-one acyloin congener bio catalyzed and reacted with propane-1,2-diamine as proof of principle to make 325-EDMP (~60% relative abundance, ~57 mg) over the 235-EDMP. These results hinted toward a mechanism directed by the hydroxy ketone electrophilicity and the sterics at each nucleophilic center of the diamine.
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Title: Safe Control Design for Uncertain Systems
Creator: Marvi, Zahra
Date: 2021
Collection: Electronic Theses & Dissertations
Description: This dissertation investigates the problem of safe control design for systems under model and environmental uncertainty. Reinforcement learning (RL) provides an interactive learning framework in which the optimal controller is sequentially derived based on instantaneous reward. Although powerful, safety consideration is a barrier to the wide deployment of RL algorithms in practice. To overcome this problem, we proposed an iterative safe off-policy RL algorithm. The cost function that encodes...
Show moreThis dissertation investigates the problem of safe control design for systems under model and environmental uncertainty. Reinforcement learning (RL) provides an interactive learning framework in which the optimal controller is sequentially derived based on instantaneous reward. Although powerful, safety consideration is a barrier to the wide deployment of RL algorithms in practice. To overcome this problem, we proposed an iterative safe off-policy RL algorithm. The cost function that encodes the designer's objectives is augmented with a control barrier function (CBF) to ensure safety and optimality. The proposed formulation provides a look-ahead and proactive safety planning, in which the safety is planned and optimized along with the performance to minimize the intervention with the optimal controller. Extensive safety and stability analysis is provided and the proposed method is implemented using the off-policy algorithm without requiring complete knowledge about the system dynamics. This line of research is then further extended to have a safety and stability guarantee even during the data collection and exploration phases in which random noisy inputs are applied to the system. However, satisfying the safety of actions when little is known about the system dynamics is a daunting challenge. We present a novel RL scheme that ensures the safety and stability of the linear systems during the exploration and exploitation phases. This is obtained by having a concurrent model learning and control, in which an efficient learning scheme is employed to prescribe the learning behavior. This characteristic is then employed to apply only safe and stabilizing controllers to the system. First, the prescribed errors are employed in a novel adaptive robustified control barrier function (AR-CBF) which guarantees that the states of the system remain in the safe set even when the learning is incomplete. Therefore, the noisy input in the exploratory data collection phase and the optimal controller in the exploitation phase are minimally altered such that the AR-CBF criterion is satisfied and, therefore, safety is guaranteed in both phases. It is shown that under the proposed prescribed RL framework, the model learning error is a vanishing perturbation to the original system. Therefore, a stability guarantee is also provided even in the exploration when noisy random inputs are applied to the system. A learning-enabled barrier-certified safe controllers for systems that operate in a shared and uncertain environment is then presented. A safety-aware loss function is defined and minimized to learn the uncertain and unknown behavior of external agents that affect the safety of the system. The loss function is defined based on safe set error, instead of the system model error, and is minimized for both current samples as well as past samples stored in the memory to assure a fast and generalizable learning algorithm for approximating the safe set. The proposed model learning and CBF are then integrated together to form a learning-enabled zeroing CBF (L-ZCBF), which employs the approximated trajectory information of the external agents provided by the learned model but shrinks the safety boundary in case of an imminent safety violation using instantaneous sensory observations. It is shown that the proposed L-ZCBF assures the safety guarantees during learning and even in the face of inaccurate or simplified approximation of external agents, which is crucial in highly interactive environments. Finally, the cooperative capability of agents in a multi-agent environment is investigated for the sake of safety guarantee. CBFs and information-gap theory are integrated to have robust safe controllers for multi-agent systems with different levels of measurement accuracy. A cooperative framework for the construction of CBFs for every two agents is employed to maximize the horizon of uncertainty under which the safety of the overall system is satisfied. The information-gap theory is leveraged to determine the contribution and share of each agent in the construction of CBFs. This results in the highest possible robustness against measurement uncertainty. By employing the proposed approach in constructing CBF, a higher horizon of uncertainty can be safely tolerated and even the failure of one agent in gathering accurate local data can be compensated by cooperation between agents. The effectiveness of the proposed methods is extensively examined in simulation results.
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Title: CHARACTERIZATION AND APPLICATION OF THE SURFACE CHARGE-INDUCED LONG-RANGE ORGANIZATION IN ROOM TEMPERATURE IONIC LIQUIDS
Creator: Wang, Yufeng
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Room temperature ionic liquids (RTILs) are salts characterized by a melting point below room temperature. RTILs have a wide range of applications, in areas ranging from supercapacitor energy storage to sequestration of toxic gas phase species and use as reusable solvents for selected organic reactions. All these applications stem from their unique physical and chemical properties, which remain understood to a limited extent. Among the issues of greatest importance is the extent to which RTILs...
Show moreRoom temperature ionic liquids (RTILs) are salts characterized by a melting point below room temperature. RTILs have a wide range of applications, in areas ranging from supercapacitor energy storage to sequestration of toxic gas phase species and use as reusable solvents for selected organic reactions. All these applications stem from their unique physical and chemical properties, which remain understood to a limited extent. Among the issues of greatest importance is the extent to which RTILs exist as dissociated ionic species, and the length scales over the organizations are seen to exist in them. Our group have reported previously on the existence of a surface charge-induced free charge density gradient in RTILs with a characteristic persistence length of ca. 50 μm. The existence of such a long-range organization in fluid medium is unusual. The overall goal of this work is to achieve a deeper understanding of this phenomenon, thereby providing an opportunity to better understand the local and long-range organization in RTILs and broad their potential applications which benefit from gaining such knowledge.The induced free charge density gradient (ρf) is probed by measuring the fluorescence anisotropy decay of a trace-level charged chromophore in the RTIL as a function of distance from the indium-doped tin oxide (ITO) support surface. In chapter 2, we characterize the structure-dependence of this charge-induced organization as a function of the RTIL constituent identity, and use these data to evaluate the magnitude of the induced free charge density gradient. The magnitude of this gradient is found to depend on the chemical structures of the cationic and anionic constituents of the RTIL used. In chapter 3, we characterize ρf in three different pyrrolidinium RTILs and two imidazolium RTILs, which aims to expand on prior results (chapter 2) on the chemical structure-dependence of ρf. Our measurements demonstrate that the magnitude of ρf depend on the alkyl chain length of RTIL cation. ρf is larger in the RTIL with longer cation alkyl chain. This dependence has been revealed in both pyrrolidinium and imidazolium ionic liquids. In chapter 4, we report on the existence of a surface charge-induced gradient in the RTIL refractive index (n) and evaluate the relationship between the gradient in n and ρf. Because ρf is uniaxial, the induced change in n is manifested as an induced birefringence. We characterize the ρf -dependent n of the RTIL with an apparatus that uses the RTIL as a lens. ρf is controlled by the surface charge density (σs) of the RTIL support. The far-field image of light passed through the RTIL lens as a function of σs is used to measure charge-induced changes in n of the RTIL. We demonstrate a significant modulation of the n with modest changes in σs of the RTIL support. This report places the relationship between ρf and RTIL dielectric response on a quantitative footing and suggests the utility of RTILs for electro-optic applications. In chapter 5, We report on the dependence of surface charge-induced birefringence in room temperature ionic liquids (RTILs) with different cation constituents. The induced birefringence is related to ρf in the RTIL. We find that in all cases the induced birefringence is proportional to the σs and, that the change in n nearest the ITO surface can be on the order of 30%. Our findings indicate that the induced birefringence depends more sensitively on the cation aliphatic substituent length than on the identity of the charge-carrying headgroup.
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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: GENOMIC APPLICATIONS TO PLANT BIOLOGY
Creator: Hoopes, Genevieve
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The study of the total nuclear DNA content of an organism, i.e., the genome, is a relatively new field and has evolved as sequencing technology and its output has changed. A shift from model species to ecological and crop species occurred as sequencing costs decreased and the technology became more broadly accessible, enabling new discoveries in genome biology as increasingly diverse species and populations were profiled. Here, a genome assembly and several transcriptional studies in multiple...
Show moreThe study of the total nuclear DNA content of an organism, i.e., the genome, is a relatively new field and has evolved as sequencing technology and its output has changed. A shift from model species to ecological and crop species occurred as sequencing costs decreased and the technology became more broadly accessible, enabling new discoveries in genome biology as increasingly diverse species and populations were profiled. Here, a genome assembly and several transcriptional studies in multiple non-model plant species provided new knowledge of molecular pathways and gene content. Over 157 Mb of the genome of the medicinal plant species Calotropis gigantea (L.) W.T.Aiton was sequenced, de novo assembled and annotated using Next Generation Sequencing technologies. The resulting assembly represents 92% of the genic space and provides a resource for discovery of the enzymes involved in biosynthesis of the anticancer metabolite, cardenolide. An updated gene expression atlas for 79 developmental maize (Zea mays L., 1753) tissues and five abiotic/biotic stress treatments was developed, revealing 4,154 organ-specific and 7,704 stress-induced differentially expressed (DE) genes. Presence-absence variants (PAVs) were enriched for organ-specific and stress-induced DE genes, tended to be lowly expressed, and had few co-expression network connections, suggesting that PAVs function in environmental adaptation and are on an evolutionary path to pseudogenization. The Maize Genomics Resource (http://maize.plantbiology.msu.edu/) was developed to view and data-mine these resources. Through profiling global gene expression over time in potato (Solanum tuberosum L.) leaf and tuber tissue, the first circadian rhythmic gene expression profiles of the below-ground heterotrophic tuber tissue were generated. The tuber displayed a longer circadian period, a delayed phase, and a lower amplitude compared to leaf tissue. Over 500 genes were differentially phased between the leaf and tuber, and many carbohydrate metabolism enzymes are under both diurnal and circadian regulation, reflecting the importance of the circadian clock for tuber bulking. Most core circadian clock genes do not display circadian rhythmic gene expression in the leaf or tuber, yet robust transcriptional and gene expression circadian rhythms are present.
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Title: DATA DRIVEN BASED ESTIMATION AND CONTROL FOR AUTOMOTIVE SYSTEMS
Creator: Tang, Jian
Date: 2022
Collection: Electronic Theses & Dissertations
Description: This dissertation focuses on predicting the system responses and using them to improve the automotive system performance based on the data-driven based algorithms. Two applications included are multivariable borderline knock prediction and control and tire-road friction coefficient estimation. Internal combustion engines are core components of traditional and hybrid passenger vehicles and also widely used for off road applications. When the combustion is limited by the engine knock, it is...
Show moreThis dissertation focuses on predicting the system responses and using them to improve the automotive system performance based on the data-driven based algorithms. Two applications included are multivariable borderline knock prediction and control and tire-road friction coefficient estimation. Internal combustion engines are core components of traditional and hybrid passenger vehicles and also widely used for off road applications. When the combustion is limited by the engine knock, it is desired to operate it as close to its borderline knock limit as possible to optimize combustion efficiency. Traditionally, this limit is detected by sweeping tests of related control parameters, which is expensive and time-consuming; and also, the detected borderline knock limit often is relatively conservative. When more advanced control parameters (subsystems) are added, these sweeping tests lead to tremendous higher test cost. An intelligent and efficient way to predict borderline knock without detailed knowledge of combustion dynamics is proposed. This supervised-learning based Bayesian optimization method is assisted by a surrogate model trained based on the system statistic properties. A two-control-parameter (spark timing and intake valve timing) case is demonstrated for optimizing two competing objectives (knock intensity (KI) and fuel economy). A complete borderline knock control structure is proposed and divided into three parts. The first part is about offline training with necessary modifications of the Bayesian optimization algorithm. Engine tests are conducted under two different operational conditions to obtain knock borderline limit, indicating the proposed algorithm is able to reduce required experimental budget (cost and time) significantly. The predicted mean Pareto front and its variance can be used to find the optimum control parameters at borderline knock limit for the best fuel economy possible. Smooth response surfaces of surrogate models can also be used as the initial model to be updated in real-time. The second part is an online updating process, based on the offline-trained surrogate model, using modified likelihood ratio controller. Principal component analysis indicated that spark timing is the most sensitive factor affecting the Pareto front. A two-buffer design was proposed to update the surrogate model under different rates so that both short-term compensation for environment changes and long-term for slow engine aging effect are covered. Both simulation and engine test results indicate that the proposed control strategy is able to update the machine-learned surrogate models in real-time, which outperforms the conventional knock control strategy and offline-trained knock limit, and especially reduces the conservativeness of borderline knock control significantly. Finally, to reduce cycle-to-cycle combustion variations, a real-time cycle-wised knock compensation scheme is developed based on the measured exhaust temperature when the engine is operated close to its knock borderline. To make model-based control possible, ?-Markov COVER (COVariance Equivalent Realization) system identification was used to obtain a linearized engine exhaust temperature model from change of spark timing to associated variations of exhaust temperature and knock intensity (KI). Accordingly, a Linear–Quadratic–Gaussian (LQG) controller is designed to minimizing the KI fluctuations based on change (?) of exhaust temperature. For the entire control architecture, results of three test scenarios indicated that the spark timing can be further advanced while maintaining the same knock intensity level due to reduced knock combustion variations. For the vehicle dynamics research, estimation of tire-road friction coefficient is very important due to new active safety control systems, especially for autonomous vehicles that rely on the accurate estimation of road surface conditions to find vehicle operational boundary and achieve the best performance possible. Several cause- and effect-based methods were proposed with their own limitations. A new evaluation criterion associated with slip-ratio is found based on CarSim simulation data on different road conditions; and strong correlation between proposed criterion and tire-road friction under different road surface conditions is observed. Note that the data-driven based method proposed in this dissertation only utilizes the statistic information from existing production vehicle sensors without increasing hardware cost. A computational cheap black-box model of proposed criterion and tire-road friction can be obtained and augmented with the existing dual-Kalman filter estimation algorithm, which improves tire-road friction estimation.
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Title: Total synthesis of pyrrole-alkaloid-like natural products and analogues
Creator: Hubbell, Grace E.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The pyrrole-alkaloid family of natural products represents a wide range of biological activities, making the synthesis of these types of scaffolds a worthy endeavor. Of particular interest to our lab is the inhibitory activity of some of these natural products towards the human 20S proteasome, a validated target for the treatment of specific cancer including multiple myeloma and mantle cell lymphoma. With this in mind, the synthesis of scaffolds which bear structural similarity to these...
Show moreThe pyrrole-alkaloid family of natural products represents a wide range of biological activities, making the synthesis of these types of scaffolds a worthy endeavor. Of particular interest to our lab is the inhibitory activity of some of these natural products towards the human 20S proteasome, a validated target for the treatment of specific cancer including multiple myeloma and mantle cell lymphoma. With this in mind, the synthesis of scaffolds which bear structural similarity to these natural products was endeavored. Herein, the synthesis of pyrrole-alkaloid-like scaffolds is represented in several approaches: small molecule design of bromoindolophakellstatins, methodology development, and total synthesis. The development of a novel Rh(III)-catalyzed C-H activation/annulation between 2-imidazolones and N-pivaloyloxybenzohydroxamates is reported, which facilitates access to urea-fused tetrahydroisoquinolone scaffolds which are reminiscent of members of the pyrrole-alkaloid family. Efforts towards the syntheses of bromoindolophakellstatin small molecules is also described. Lastly, route development towards the total syntheses of nagelamide M and the ugibohlin natural products and the particular challenges associated with these approaches are discussed.
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Title: UNDERSTANDING CAREGIVER PERCEPTIONS ON SCHOOL PARTNERSHIPS
Creator: Rice, Darreth R.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Caregivers play an integral role in a child’s academic development, including their literacy development (Compton-Lilly et al., 2019; Cunningham, 2021; Edwards, 2004; Edwards, 2016; Smith, 2020; Volk, 2021). One way caregivers have supported their children’s literacy development is by assisting them with school activities at home. While some caregivers are willing to assist, schools must recognize that not all caregivers know what specific activities support the development of literacy skills...
Show moreCaregivers play an integral role in a child’s academic development, including their literacy development (Compton-Lilly et al., 2019; Cunningham, 2021; Edwards, 2004; Edwards, 2016; Smith, 2020; Volk, 2021). One way caregivers have supported their children’s literacy development is by assisting them with school activities at home. While some caregivers are willing to assist, schools must recognize that not all caregivers know what specific activities support the development of literacy skills. This partnership between home and school becomes especially important when Michigan schools are in the midst of a state-wide literacy policy aimed at improving students’ achievement levels on the state standardized assessments (Weyer, 2018). This study examined the perspective of the caregiver on this partnership during the implementation of a state-wide literacy policy. Overall, this study sought to understand the degree to which current school outreach to caregivers was aligned to current research on caregiver engagement. The study used the caregivers’ own words (Lumby, 2007), whenever possible. This study further investigated caregivers’ perceptions of the school’s outreach during the 2021-22 school year. Additionally, the study sought to discover caregivers’ desires for future partnerships with schools to continue to support their child in early literacy development. Lastly, this study analyzed the availability of resources, as reported by caregivers and found on school websites, as well as how aligned those resources were to current research on caregiver engagement. To undertake this endeavor, this qualitative study utilized an online survey focused on four distinct areas within a state undergoing a state-wide literacy policy. The four areas were chosen for their diversity in race, ethnicity, location, religions practices, population of immigrants, and population of migrant season farm workers. Following the survey, a subset of the caregivers were interviewed. After the interviews, a review of early literacy materials was conducted using the school’s websites. The review included at least one elementary school from each of the focal areas in the survey and interviews. Additionally, twenty-nine other schools, chosen through random interval sampling, were included in the review. The findings of this study shed light on the partial alignment between the current research and the school outreach to caregivers, specifically in relation to literacy activities focused on student literacy development. Using the frameworks of parental involvement (Epstein et al., 2019), intentionality (Edwards, 2016), and efficacy (Bandura, 1977), this study discovered resources offered to caregivers do not always align with intentionally. Current communication methods do not align with parental involvement framework’s tenet of two-way communication. Lastly, the supports offered to caregivers do not always favor efficacious behavior in caregivers. Implications for this work have wide-reaching opportunities for change in the culture of both policymaking and education. Policymakers can use these findings to understand the importance of including the voice of all policy actors. Teacher educators can view these findings to ensure they teach their teacher candidates how to communicate with caregivers. This includes having difficult conversations. School leadership can support current teachers with stronger engagement with caregivers by using the findings in this study and learning to listen to the caregivers and their concerns. Lastly, caregivers can also learn they are their child’s advocate, and they will have to do their part to work with the child.
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Title: Depression Detection in Social Media via Differential Text Embedding
Creator: alfadhli, Norah
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Deep learning models have shown promising results for depression detection using social media data (i.e., Twitter), but the difficulties of maintaining explainability and few-shot adaptation of models for new problems remain an open challenge. Another challenging aspect of the problem of depression detection in social media is the fact that the number of instances belonging to the depressed class are in a minority when compared to the number of instances belonging to the non-depressed class....
Show moreDeep learning models have shown promising results for depression detection using social media data (i.e., Twitter), but the difficulties of maintaining explainability and few-shot adaptation of models for new problems remain an open challenge. Another challenging aspect of the problem of depression detection in social media is the fact that the number of instances belonging to the depressed class are in a minority when compared to the number of instances belonging to the non-depressed class. This, especially, makes it harder for supervised machine learning algorithms to learn and predict depressed class instances.In this study, we proposed a simple solution to this problem by generating \textit{differential embeddings} using the Sentence BERT transformer architecture. More specifically, we proposed a few-shot model that can leverage state-of-the-art (SOTA) representation learning techniques and used it in supervised and unsupervised tasks. We constructed a small set of dysfunctional thought patterns in the embedding space, i.e., a set of clinically-backed depression symptoms. We then used SBERT embedding vectors to measure the similarities between different tweets and anchor points as a distance in the vector space, or fed them directly into the machine learning model. We assessed the capability of our approach on two different datasets. We trained supervised and unsupervised models using different approaches that were derived from Sentence-BERT and the anchor points. Results show that the proposed solution improved SBERT in both supervised and unsupervised tasks.
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Title: EVOLUTION OF FIRE INDUCED RESTRAINT FORCES AND THEIR EFFECT ON THE FIRE RESPONSE OF PRESTRESSED CONCRETE BEAMS
Creator: KUMAR, PUNEET
Date: 2023
Collection: Electronic Theses & Dissertations
Description: Precast prestressed concrete (PC) construction provides numerous advantages over traditional reinforced concrete (RC) construction, in terms of speed of construction, better quality control, cost-effectiveness, better space utilization, and optimized production. Owing to these advantages, the use of PC construction in the built environment has increased significantly in recent decades. While the structural behavior of PC members is well understood at ambient temperatures, there is a lack of...
Show morePrecast prestressed concrete (PC) construction provides numerous advantages over traditional reinforced concrete (RC) construction, in terms of speed of construction, better quality control, cost-effectiveness, better space utilization, and optimized production. Owing to these advantages, the use of PC construction in the built environment has increased significantly in recent decades. While the structural behavior of PC members is well understood at ambient temperatures, there is a lack of understanding on the evolution of fire induced restraint forces in PC beams and their effect on the fire resistance of PC beams. Further, the fire resistance of PC members is currently evaluated using prescriptive design approaches which do not account for all critical factors governing the fire response of PC beams, including realistic restraint conditions, and therefore, current fire resistance provisions may not provide realistic predictions of fire performance. Therefore, a detailed experimental and numerical study is conducted to evaluate the evolution of fire induced restraint forces and to quantify their effect on the fire response of PC beams. Fire resistance tests were conducted on four PC beams under restrained and unrestrained end conditions. Test variables included fire exposure, restraint conditions, load level, and concrete strength. The fire response of the beams was traced throughout the fire exposure duration by measuring sectional temperatures, beam deflections, and fire induced restraint forces. All four beams were designed as per current building code recommendations to have a fire resistance of 4 hours, however, all four beams attained failure within 2 hours of fire exposure. A numerical model was developed for tracing the fire response of PC beams under specified fire, loading, and restraint conditions. The model accounts for critical factors governing the fire response of PC beams including fire-induced restraint forces, cracking and crushing of concrete, spalling, material and geometric non-linearity, and geometry of the beam. For modeling fire-induced restraint forces a new efficient spring idealization framework for connections is implemented. Also, the cracking and crushing of concrete is captured by developing a new modified adaptive temperature-dependent failure envelope. The developed numerical model was validated by comparing response predictions from the model with measured data in fire tests. Results from these comparisons show that the model can capture the fire response of PC beams with reasonable accuracy in both thermal and structural domains. The validated numerical model is applied to carry out a series of parametric studies on the effect of fire-induced restraint forces on the response of PC beams. The effect of cross-sectional shape, support conditions, the gap in connection, level of prestress, and concrete cover thickness on the evolution of fire induced restraint forces is studied for PC and equivalent RC beams. Results from parametric studies show that current prescriptive codes and standards may over-predict fire resistance of PC beams by as high as 100%, PC beams develop 5% to 20% lower restraint forces than equivalent RC beams, and for PC beams with gaps of more than 50 mm experience minimal restraint forces. Also, the fire-induced restraint forces can be either beneficial or detrimental and can significantly alter the fire response of the PC beam, and therefore, should be included in the design process. Based on the results from the fire tests and parametric studies, simplified recommendations are proposed for evaluating the fire resistance of PC beams.
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Title: Filling in the Gaps : Modeling the Role of Groundwater in Lake Erie’s Nutrient Budget
Creator: Lanier, Alexis Ann
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Lake Erie is a hotspot for large harmful algal blooms, which damage human health, degrade natural habitats, and impair industries reliant on the lake. The Maumee River watershed, the largest in the Great Lakes, often acts as a major driver for these blooms, as it is the largest contributor of nutrients to the lake, mainly attributed to intense agricultural activity. Consequently, surficial transport of phosphorus and nitrogen within the Maumee River watershed has been extensively studied....
Show moreLake Erie is a hotspot for large harmful algal blooms, which damage human health, degrade natural habitats, and impair industries reliant on the lake. The Maumee River watershed, the largest in the Great Lakes, often acts as a major driver for these blooms, as it is the largest contributor of nutrients to the lake, mainly attributed to intense agricultural activity. Consequently, surficial transport of phosphorus and nitrogen within the Maumee River watershed has been extensively studied. However, there has been very little research into the role of groundwater here, especially groundwater modeling studies. Here, I evaluate the literature that has explored nutrient transport to Lake Erie, with a focus on the Maumee River watershed, and examine groundwater nutrient transport. This knowledge will inform nutrient management decisions, especially those regarding future and legacy nutrient loads. In Chapter 1, I review the current state of literature on hydrologic nutrient modeling in the Lake Erie Basin. I highlight common themes in the literature and detail prominent gaps. Specifically, I focus on the role of groundwater in nutrient modeling studies within the Maumee River watershed and recommend future directions for research. In Chapter 2, I create a spatially explicit, process-based groundwater model of the Maumee River watershed. This model allows me to quantify the contributions of groundwater in the context of total basin loading. I then quantify the role of legacy nutrient accumulation by reducing input loads in a projected future scenario. This research completes the nutrient budget by highlighting ‘hidden’ groundwater nutrient loads and informs the timescale of subsurface nutrient management.
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Title: SAFETY PERFORMANCE OF MEDIAN U-TURN INTERSECTIONS
Creator: Kay, Jonathan James
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The use of alternative intersection designs can provide both safety and operational benefits for road users at potentially lower costs when implemented in the appropriate setting. The Federal Highway Administration has previously recognized a subset of alternative intersections designs broadly referred to as “reduced left-turn conflict intersections” as a proven safety countermeasure that have been shown to decrease the risk of potentially severe crash types by reducing conflict points...
Show moreThe use of alternative intersection designs can provide both safety and operational benefits for road users at potentially lower costs when implemented in the appropriate setting. The Federal Highway Administration has previously recognized a subset of alternative intersections designs broadly referred to as “reduced left-turn conflict intersections” as a proven safety countermeasure that have been shown to decrease the risk of potentially severe crash types by reducing conflict points through the use of indirect left-turn movements. Median U-turn intersections (also referred to as “Michigan lefts” or “boulevard turnarounds”) are one such alterative design that accommodates indirect left-turn movements via directional U-turn crossovers located within the median along one or both of the intersecting roadways. Michigan has long been a pioneer in the implementation of median U-turns along urban and suburban divided boulevards, with initial installations dating back several decades. Additionally, various indirect left-turn configurations have been implemented along rural highways and frontage roads for urban freeways.While prior work has consistently demonstrated that median U-turn intersection designs represent an effective countermeasure that can improve operational performance and reduce the frequency of severe crash types when implemented in the appropriate context, much of the extant research is outdated and several important areas of investigation remain unexplored. This includes defining the appropriate crash influence area, the impacts of pre-conversion characteristics, impacts to pedestrian and bicycle collisions, and evaluating crashes pre/post conversion (e.g., longitudinal panel data) compared to a purely cross-sectional evaluation. To address these and other knowledge gaps, research was performed to quantify the safety performance characteristics and develop analytical tools related to the utilization of median U-turn intersections. Historical traffic crash data were collected for signalized and unsignalized intersections in Michigan where left-turns are accommodated by a median U-turn design. To allow for comparison of the performance between the median U-turn and traditional designs, data were also collected for a sample of reference intersections (divided and undivided) where conventional direct left-turn movements were maintained. A novel approach was developed to define the safety performance influence area of a median U-turn intersection, which subsequently improved the method of identifying and collecting target crash data. Utilizing the traffic crash data, a series of analyses were performed to identify the differences between conventional and median U-turn intersections, and to also identify the differences in safety performance between various median U-turn design characteristics. The analyses compared crash rates, types, severity distributions, and severe injury collision patterns, and included development of series of safety performance functions and crash modification factors. The results were then generalized into a series of recommendations for roadway agencies considering future implementation of median U-turn intersections, including specific design recommendations intended to improve safety performance for all road users. Ultimately, it was concluded that median U-turn designs represent an effective safety countermeasure to target the reduction of severe crash types for both unsignalized and signalized intersections. While there are some potential tradeoffs with respect to non-injury crash frequencies for specific pre-conversion configurations, the use of these indirect left-turn intersection designs is consistent with the Safe System approach adopted by the United States Department of Transportation within the National Roadway Safety Strategy. Unsignalized median U-turn intersections offer superior fatal and injury crash performance compared to conventional unsignalized intersections. The removal of the crossing conflict points at unsignalized median U-turn designs (which include a closed median at the intersection) essentially eliminates the pattern of severe head on left-turn and angle collisions occurring within conventional intersections. However, it is important to recognize that non-injury crashes were shown to increase when converting a conventional unsignalized intersection to a median U-turn at locations with an existing median on the major roadway. Signalized median U-turn intersections offer superior safety performance for both injury and non-injury crashes compared to conventional signalized intersections along undivided roadways. However, the comparison of median U-turns locations to conventional divided signalized intersections was limited by a lack of reference sites with comparable traffic volumes. Annual average frequencies of severe pedestrian and bicycle crashes were similar between the signalized median U-turn and conventional undivided sites. Finally, several design features of signalized median U-turn intersections were identified as having a significant impact on safety performance, including the distance to crossovers from the main intersection, the length of weaving areas, the number of signalized crossovers, and the number of storage lanes.
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Title: “THE DAM DOMINATED THE WATER” : SOCIAL-ECOLOGICAL IMPACTS AND ENERGY INJUSTICES ASSOCIATED WITH DAM DEVELOPMENT IN THE GLOBAL SOUTH.
Creator: Castro Diaz, Laura del Pilar
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Countries in the Global South favor hydropower because it is a low-carbon and sustainable energy source that can satisfy their energy needs and allow them to meet anticipated increases in energy demand. However, the construction of hydroelectric dams increases social and environmental inequities across multiple scales. In this dissertation, I explore the social-ecological impacts and energy injustices generated by large-scale hydroelectric dams in the Global South, focusing on dams in the...
Show moreCountries in the Global South favor hydropower because it is a low-carbon and sustainable energy source that can satisfy their energy needs and allow them to meet anticipated increases in energy demand. However, the construction of hydroelectric dams increases social and environmental inequities across multiple scales. In this dissertation, I explore the social-ecological impacts and energy injustices generated by large-scale hydroelectric dams in the Global South, focusing on dams in the Brazilian Amazon. In chapter one, I conduct a meta-analysis and fuzzy-set qualitative comparative analysis (fsQCA) to understand the changes in local livelihoods in 33 hydroelectric dam projects built in the Global South. I found that natural, social, human, and financial capital are negatively impacted, whereas physical capital is positively impacted. The findings showed a relationship between lack of participation in decision-making and negative impacts on people’s capital. I also found that mega-dams negatively impact people’s capital regardless of the energy security status of a nation. In chapter two, I examine how the construction of the Madeira hydroelectric complex in Brazil (the Jirau and San Antônio dams) has impacted the adaptive capacity of local communities in terms of food and energy security. I find that the adaptive capacity of local communities has been significantly reduced, which limits the opportunities of these communities to adapt to future climatic and anthropogenic shocks. Food security has been significantly affected and that the energy supply in the communities is unreliable. Despite living near two large hydroelectric dams, many still lack electricity access and depend on diesel generators. In chapters three and four, I conduct a longitudinal qualitative case study of data collected in a community downstream from the Belo Monte hydroelectric dam. Data were collected at three points: during the late stage of construction (2016) and early operation (2017, 2019). Chapter three explores the multidimensional and multitemporal energy injustices experienced by this community. In this chapter, I use the distributional, procedural, recognition, restorative, and capabilities energy justice tenets to understand how local actors experience different injustices and how these interact over time. I found that these injustices are intertwined, causing and perpetuating the new and established structural injustices these communities have faced. In chapter four, I study, from a social-ecological resilience approach, the responses of individuals and households towards the effects of the construction of the Belo Monte dam. I show how individual and household responses to hydropower development occur along the spectrum from absorptive/coping to adaptation to transformation. These responses differ by gender and household characteristics. The dissertation shows how an energy source portrayed as a solution for achieving energy transition generates immense social-ecological impacts and multidimensional and multitemporal energy injustices perpetuating structural inequities. As energy demand and the need for a clean energy transition are increasing, we must find energy systems that look beyond just low carbon emissions to those that also address energy injustices, and provide fair and equitable processes that consider gender, ethnicity, race, and class.
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Title: INVARIANT REPRESENTATION LEARNING VIA FUNCTIONS IN REPRODUCING KERNEL HILBERT SPACES
Creator: Sadeghi, Bashir
Date: 2023
Collection: Electronic Theses & Dissertations
Description: Many applications of representation learning, such as privacy preservation and algorithmic fairness, desire explicit control over some unwanted information being discarded. This goal is formulated as satisfying two objectives: maximizing utility for predicting a target attribute while simultaneously being invariant (independent) to a known sensitive attribute (like gender or race). Solutions to invariant representation learning (IRepL) problems lead to a trade-off between utility and...
Show moreMany applications of representation learning, such as privacy preservation and algorithmic fairness, desire explicit control over some unwanted information being discarded. This goal is formulated as satisfying two objectives: maximizing utility for predicting a target attribute while simultaneously being invariant (independent) to a known sensitive attribute (like gender or race). Solutions to invariant representation learning (IRepL) problems lead to a trade-off between utility and invariance when they are competing. Most existing works are empirical and implicitly look for single or multiple points on the utility-invariance trade-off. They do not explicitly seek to characterize the entire trade-off front optimally and do not provide invariance and convergence guarantees. In this thesis, we address the shortcoming mentioned above by considering simple linear modeling and building upon them. As a first step, we derive a closed-form solution for the global optima of the underlying linear IRepL optimization problem. In further development, we consider neural network-based encoders, where we model the utility of the target task and the invariance to the sensitive attribute via kernelized ridge regressors. This setting leads to a stable iterative optimization scheme toward global/local optima(s). However, such a setting cannot guarantee universal invariance.This drawback motivated us to further study the case where the invariance measure is modeled universally via functions in some reproducing kernel Hilbert spaces (RKHS)s. By modeling the encoder and target networks via functions in some RKHS, too, we derive a closed formula for a near-optimal trade-off, corresponding optimal representation dimensionality, and the associated encoder(s). Our findings have an immediate application to fairness in terms of demographic parity.
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Title: Nonlinear Extensions to New Causality and a NARMAX Model Selection Algorithm for Causality Analysis
Creator: da Cunha Nariyoshi, Pedro
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Although the concept of causality is intuitive, an universally accepted objective measure to quantify causal relationships does not exist. In complex systems where the internal mechanism is not well understood, it is helpful to estimate how different parts of the system are related. In the context of time-series data, Granger Causality (GC) has long been used as a way to quantify such relationships, having been successfully been applied in fields as diverse as econometrics and neurology....
Show moreAlthough the concept of causality is intuitive, an universally accepted objective measure to quantify causal relationships does not exist. In complex systems where the internal mechanism is not well understood, it is helpful to estimate how different parts of the system are related. In the context of time-series data, Granger Causality (GC) has long been used as a way to quantify such relationships, having been successfully been applied in fields as diverse as econometrics and neurology. Multiple Granger-like and extensions to GC have also been proposed. A recent measure developed to address limitations of GC, New Causality (NC), offers several advantages over GC, such as normalization and better proportionality with respect to internal mechanisms. However, NC is limited in scope by its seminal definition being based on parametric linear models. In this work, a critical analysis of NC is presented, NC is extended to a wide range of nonlinear models and finally, enhancements to a method of estimating nonlinear models for use with NC are reported.A critical analysis is conducted to study the relationship between NC values and model estimation errors. It is shown that NC is much more sensitive to overfitting in comparison to GC. Although the variance of NC estimates is reduced by applying regularization techniques, NC estimates are also prone to bias. In this work, diverse case-studies are presented showing the behavior of NC estimation in the presence of regularization. A mathematical study of the sources of bias in the estimates is given.For systems that cannot be modeled well by linear models, the seminal definition of NC performs poorly. This works gives examples in which nonlinear observation models cause NC values obtained with the seminal definition to behave contrary to intuitive expectations. A nonlinear extension of NC to all linear-in-parameters models is then developed and shown to address these limitations. The extension reduces to the seminal definition of NC for linear models and offers a flexible weighting mechanism to distribute contributions among nonlinear terms. The nonlinear extension is applied to a range of synthetic data and real EEG data with promising results.The sensitivity of NC to parameter estimation errors demands that special care be taken when using NC with nonlinear models. As a complement to nonlinear NC, enhancements to a algorithm for nonlinear parametric model estimation are presented. The algorithm combines a genetic search element for regressor selection with a set-theoretic optimal bounded ellipsoid algorithm for parameter estimation. The enhancements to the genetic search make use of sparsity and information theoretic measures to reduce the computational cost of the algorithm. Significant reductions are shown and direction for further improvements of the algorithm are given. The main contributions of this work are providing a method for estimating causal relationships between signals using nonlinear estimated models, and a framework for estimating the relationships using an enhanced algorithm for model structure search and parameter estimation.
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Title: Heterogeneous Thalamic Reticular Nucleus Neurons and Their Functional Role in Thalamocortical Processing
Creator: Harding-Jackson, Laura
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The thalamic reticular nucleus (TRN) is an integral regulator of information flow between the thalamus and cortex. The TRN receives synaptic inputs from both cortical and thalamic regions and based upon this information it selectively inhibits thalamic activity. TRN neurons produce action potentials in two distinct modes: a fast, transient burst discharge from a hyperpolarized state, and a prolonged, tonic discharge from a relatively depolarized state. While previous studies have...
Show moreThe thalamic reticular nucleus (TRN) is an integral regulator of information flow between the thalamus and cortex. The TRN receives synaptic inputs from both cortical and thalamic regions and based upon this information it selectively inhibits thalamic activity. TRN neurons produce action potentials in two distinct modes: a fast, transient burst discharge from a hyperpolarized state, and a prolonged, tonic discharge from a relatively depolarized state. While previous studies have characterized burst discharge as a transient high frequency discharge (> 250 Hz), these electrophysiological studies reveal a highly variable range of burst frequencies (4- 342 Hz). In these studies, I aim to discover the mechanisms underlying these highly variable burst frequencies, as well as their functional role in thalamocortical processing.In chapter two, I found that bursts from TRN neurons with relatively higher frequency discharge (>100 Hz) contain more action potentials per burst. These neurons also have higher input resistances, broader action potentials, higher action potential thresholds, and larger somas. The amplitude of the T-type calcium channel-mediated low-threshold spike, which underlies the burst discharge, is positively correlated with both the burst discharge frequency and the number of action potentials per burst. I next investigated whether small conductance calcium-activated potassium channels (SK channels) could mediate the differences in burst firing rate and action potential number. Blocking SK channels increased the frequency and duration of the burst but did not increase the amplitude of the underlying T-type calcium current. Prior studies suggest that T-type calcium channels are distributed along the dendrites in TRN neurons with high frequency burst discharge. In chapter three, I examine the distribution of dendritic calcium activity within the lower frequency bursting neurons. While the calcium signal was lower in these neurons all along the dendrites, the calcium signal was evenly distributed across proximal, intermediate, and distal dendritic regions. Investigation of SK channel activity revealed significant location-specific effects. In lower frequency bursting neurons, SK channels had the greatest influence at proximal and distal locations. In higher frequency bursting neurons, SK channels had the greatest influence at proximal and intermediate dendritic locations. Heterogeneous TRN burst discharge frequencies may represent a diverse cell population with unique dendritic ion channel composition and distribution. These results may improve our understanding of the mechanisms of TRN neuron afferent synaptic integration as well as modulation of thalamocortical inhibition. In chapter four I investigate whether intrinsic properties of TRN neurons are altered in the Fmr1-KO mouse model of Fragile X Syndrome (FXS). Individuals with FXS experience a variety of comorbidities that could involve TRN function, such as altered sensory perceptions, sleep disorders, and epilepsy. Analysis of intrinsic cellular properties revealed no differences in TRN neuron properties. Further investigation of synaptic plasticity, which is an abnormal finding in several other brain regions in FXS, also revealed no pathology. These findings suggest that TRN dysfunction does not contribute to FXS pathology.
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