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Title: LOW-DOSE CARDIAC SPECT USING POST-FILTERING, DEEP LEARNING, AND MOTION CORRECTION
Creator: Song, Chao
Date: 2019
Description: Single photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery...
Show moreSingle photon emission computed tomography (SPECT) is an important technique in use today for the detection and evaluation of coronary artery diseases. The image quality in cardiac SPECT can be adversely affected by cardiac motion and respiratory motion, both of which can lead to motion blur and non-uniform heart wall. In this thesis, we mainly investigate imaging de-noising algorithms and motion correction methods for improving the image quality in cardiac SPECT on both standard dose and reduced dose.First, we investigate a spatiotemporal post-processing approach based on a non-local means (NLM) filter for suppressing the noise in cardiac-gated SPECT images. Since in recent years low-dose studies have gained increased attention in cardiac SPECT owing to its potential radiation risk, to further improve the image quality on reduced dose, we investigate a novel de-noising method for low-dose cardiac-gated SPECT by using a three dimensional residual convolutional neural network (CNN). Furthermore, to reduce the negative effect of respiratory-binned acquisitions and assess the benefit of this approach in both standard dose and reduced dose using simulated acquisitions. Inspired by the success in respiratory correction, we investigate the potential benefit of cardiac motion correction for improving the detectability of perfusion defects. Finally, to combine the benefit of above two types of motion correction, dual-gated data acquisitions are implemented, wherein the acquired list-mode data are further binned into a number of intervals within cardiac and respiratory cycle according to the electrocardiography (ECG) signal and amplitude of the respiratory motion.
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Title: Predictive energy efficient control framework for connected and automated vehicles in heterogeneous traffic environments
Creator: Vellamattathil Baby, Tinu
Date: 2023
Description: Within the automotive industry, there is a significant emphasis on enhancing fuel efficiency and mobility, and reducing emissions. In this...
Show moreWithin the automotive industry, there is a significant emphasis on enhancing fuel efficiency and mobility, and reducing emissions. In this context, connected and automated vehicles (CAVs) represent a significant advancement, as they can optimize their acceleration pattern to improve their fuel efficiency. However, when CAVs coexist with human-driven vehicles (HDVs) on the road, suboptimal conditions arise, which adversely affect the performance of CAVs. This research analyzes the automation capabilities of production vehicles to identify scenarios where their performance is suboptimal, and proposes a merge-aware modification of adaptive cruise control (ACC) method for highway merging situations. The proposed algorithm addresses the issue of sudden gap and velocity changes in relation to the preceding vehicle, thereby reducing substantial braking during merging events, resulting in improved energy efficiency. This research also presents a data-driven model for predicting the velocity and position of the preceding vehicle, as well as a robust model predictive control (MPC) strategy that optimizes fuel consumption while considering prediction inaccuracies. Another focus of this research is a novel suggestion-based control framework in interactive mixed traffic environments leveraging the emerging connectivity between vehicles and with infrastructure. It is based on MPC to optimize the fuel efficiency of CAVs in heterogeneous or mixed traffic environments (i.e., including both CAVs and HDVs). In this suggestion-based control framework, the CAVs are considered to provide non-binding velocity and lane change suggestions to the HDVs to follow to improve the fuel efficiency of both the CAVs and the HDVs. To achieve this, the host CAV must devise its own fuel-efficient control solution and determine the recommendations to convey to its preceding HDV. It is assumed that the CAVs can communicate with the HDVs via Vehicle to Vehicle (V2V) communication, while the Signal Phase and Timing (SPaT) information is accessed via Vehicle-to- Infrastructure (V2I) communication. These velocity suggestions remain constant for a predefined period, allowing the driver to adjust their speed accordingly. It is also considered that the suggestions are non binding, i.e., a driver can choose not to follow the suggested velocity. For this control framework to function, we present a velocity prediction model based on experimental data that captures the response of a HDV to different suggested velocities, and a robust approach to ensure collision avoidance. The velocity prediction’s accuracy is also validated with the experimental data (on a table-top drive simulator), and the results are presented. In cases of low CAV penetration, a CAV needs to provide suggestions to multiple surrounding HDVs and incorporating the suggestions to all the HDVs as decision variables to the optimal control problem can be computationally expensive. Hence, a suggestion-based hierarchical energy efficient control framework is also proposed in which a CAV takes into account the interactive nature of the environment by jointly planning its own trajectory and evaluating the suggestions to the surrounding HDVs. Joint planning requires solving the problem in joint state- and action-space, and this research develops a Monte Carlo Tree Search (MCTS)-based trajectory planning approach for the CAV. Since the joint action- and state-space grows exponentially with the number of agents and can be computationally expensive, an adaptive action-space is proposed through pruning the action-space of each agent so that the actions resulting in unsafe trajectories are eliminated. The trajectory planning approach is followed by a low-level model predictive control (MPC)-based motion controller, which aims at tracking the reference trajectory in an optimal fashion. Simulation studies demonstrate the proposed control strategy’s efficacy compared to existing baseline methods.
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Title: Parking Demand Forecasting Using Asymmetric Discrete Choice Models with Applications
Creator: Zhang, Ji
Date: 2023
Description: Using discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The...
Show moreUsing discrete choice models to forecast travelers parking location choice has been a branch of parking demand research for many years. The most used discrete choice models have fairly simple mathematical expressions, such as the probit and logit models. The application of simple models helps release the computational burdens brought by parameter estimation tasks in practice, but the cost is the unwanted properties of classic models such as the “symmetry property” that we argue is often undesirable in many fields. To some extent, the symmetry property of related models limits the shape of curves that makes the model fitting less flexible technically. This study addresses the following question: “Can discrete choice models with asymmetry property outperform classic models with symmetry property in forecasting travelers’ parking location choices?” The contributions of this study include: (1) providing a new perspective of using asymmetric discrete choice models to explain and forecast individual’s parking location choice; and (2) completing the travel demand forecasting process from choices of the destination zone centroid to the parking location, enabling parking choice forecasting. This provides a generalized framework to calibrate and validate asymmetric discrete choice models with the field observed parking facility-specific arrival profile data integrated into a large-scale, high-fidelity regional travel demand model. Further, an experimental study is conducted to compare the performance of the proposed asymmetric discrete choice models in the parking demand forecasting framework. The results suggest that asymmetric discrete choice models for individual’s parking choice modeling outperform the symmetric discrete choice models such as the logit models owing largely to their flexibility of parameter fitting and training using the available dataset.
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Title: Large-Signal Transient Stability and Control of Inverter-Based Resources
Creator: Wang, Duo
Date: 2024
Description: Renewable generation, including solar photovoltaic (PV) systems, type 3 and 4 wind turbine generation systems (WTG), battery energy storage...
Show moreRenewable generation, including solar photovoltaic (PV) systems, type 3 and 4 wind turbine generation systems (WTG), battery energy storage systems (BESS), as well as high voltage direct current (HVDC) and flexible alternating current (FACT) transmission system devices with increasing penetration level are being connected to the bulk power systems (BPS) via power electronic (PE) converters as the interface, referred to as the inverter-based resources (IBRs) on the transmission and sub-transmission levels or distributed energy resources (DERs) located on the distribution level. The IBR is almost entirely defined by the control algorithms and found to be more prone to experiencing large disturbances due to the lack of the conventional synchronous machine (SM) intrinsic synchronous characteristics and mechanical inertia, as well as being in smaller capacity sizes. Thus, these reasons motivate this dissertation to study the large-signal transient stability and control of IBRs for reliable grid integration and rapid grid transformation. For large-signal stability analysis methods, Lyapunov-based methods are the fundamental theory used to characterize the stability issues with analytical solutions, although other non-Lyapunov methods could also be very helpful. A main difficulty hindering the widespread adoption of the Lyapunov stability analysis method is the difficulty of finding the proper Lyapunov function candidate for a higher dimensional nonlinear system. The Port-Hamiltonian (PH) nonlinear control theory is explored in this dissertation as a promising theoretical framework solution addressing this challenging issue. A PH-based tracking and robust control method is proposed to facilitate the practical application of the PH framework in IBR controls. In addition, considering the typical grid-forming (GFM) IBR control with a first-order low pass filter (LPF) block is usually involved with control saturation function for protection purposes under abnormal operating conditions with anti-windup issue in practical implementation, a PH-based bounded LPF (PH-BLPF) control is proposed to incorporate this in the large-signal PH interconnection modeling framework while preserving the robust tracking Lyapunov stability with improved transient dynamic performance and stability margin.Moreover, specific real-world transient synchronization stability issues, such as the grid voltage large fault disturbance case, are studied. In addition, to meet the recent emerging IBR grid code requirements, such as the current magnitude limitation, grid support function, and fault recovery capability of GFM-VSCs, a virtual impedance-based current-limiting GFM control with enhanced transient stability and grid support is proposed.
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Title: Two Essays on Mergers and Acquisitions
Creator: Xu, Yang
Date: 2024
Description: This dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we...
Show moreThis dissertation is composed of two self-contained chapters that both relate to mergers and acquisitions (M&A). In the first essay, we examine the Delaware (DE) reincorporation effect on firms’ post-IPO behaviors on mergers and acquisitions. We find that firms’ DE reincorporation decisions enhance the likelihood of engaging in M&A as targets. However, as a tradeoff, DE reincorporated firms get lower takeover valuations compared to stay-at-home-state firms, and the acquisition of reincorporated firms is less likely to be successful. Our second essay aims to explore the role of the options market in price discovery for M&A. We find that the predictive power of the changes in implied volatility of the target firm stock for the takeover outcome is statistically and economically significant. The risk arbitrage portfolios incorporating filters derived from the options on stocks of the target firms generate annualized risk-adjusted abnormal returns between 2.6% and 5%, depending on the portfolio weighting method, the threshold of filters for the implied volatility change, and the asset pricing models applied for abnormal returns. The results are robust to different empirical setups and are not explained by traditional factors.
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Title: Heterogeneous Workloads Study towards Large-scale Interconnect Network Simulation
Creator: Wang, Xin
Date: 2023
Description: High-bandwidth, low-latency interconnect networks play a key role in the design of modern high- performance computing (HPC) systems. The ever...
Show moreHigh-bandwidth, low-latency interconnect networks play a key role in the design of modern high- performance computing (HPC) systems. The ever-increasing need for higher bandwidth and higher message rate has driven the design of low-diameter interconnect topologies like variants of dragonfly. As these hierarchical networks become increasingly dominant, interference caused by resource sharing can lead to significant network congestion and performance variability. Meanwhile, with the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. However, little work has been conducted to understand performance implications of co-running heterogeneous workloads on large-scale dragonfly systems. There is a greater need to study how different interconnect technologies affect workload performance, and how conventional scientific applications interact with emerging big data applications at the underlying interconnect level. In this work, we firstly present a comparative analysis exploring the communication interference for traditional HPC applications by analyzing the trade-off between localizing communication and balancing network traffic. We conduct trace-based simulations for applications with different communication patterns, using multiple job placement policies and routing mechanisms. Then we develop a scalable workload manager that provides an automatic framework to facilitate hybrid workload simulation. We investigate various hybrid workloads and navigate various application-system configurations for a deeper understanding of performance implications of a diverse mix of workloads on current and future supercomputers. Finally, we propose a scalable framework, Union+, that enables simulation of communication and I/O simultaneously. By combining different levels of abstraction, Union+ is able to efficiently co-model the communication and I/O traffic on HPC systems that equipped with flash-based storage. We conduct experiments with different system configurations, showing how Union+ can help system designers to assess the usefulness of future technologies in next-generation HPC machines.
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Title: Empowering Visually Impaired Individuals With Holistic Assistance Using Real-Time Spatial Awareness System
Creator: Yu, Xinrui
Date: 2024
Description: The integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired...
Show moreThe integration of artificial intelligence (AI) into daily life opens unprecedented avenues for enhancing the experiences of visually impaired individuals, offering them greater autonomy and quality of life. This thesis introduces a Visually Impaired Spatial Awareness (VISA) system designed to assist visually impaired individuals holistically through a structured approach. At the foundational level, the VISA system incorporates several key technologies to interpret the surroundings and assist in basic navigation tasks. It utilizes Augmented Reality (AR) markers to facilitate recognition of places and aid in navigation, employs neural network models for advanced object detection and tracking, and leverages depth information for accurate object localization. Progressing to the intermediate level, the VISA system integrates the data obtained from object detection and depth sensing to assist in more complex navigational tasks such as obstacle avoidance and pathfinding toward a desired destination. At the advanced level, the VISA system synthesizes the capabilities developed at the foundational and intermediate levels to enhance the spatial awareness of visually impaired users, allowing them to undertake complex tasks, such as navigating complex environments and locating specific items. The VISA system also emphasizes efficient human-machine interaction, incorporating text-to-speech and speech-to-text technologies to facilitate natural and intuitive communication between the user and the system. The VISA system's performance was evaluated in different environments simulating real-world scenarios. The experimental results show that the user can interact with our system intuitively with minimal effort, and affirm that the VISA system can effectively assist the visually impaired user in locating and reaching for objects, navigating indoors, identifying merchandise, and recognizing both handwritten and printed texts.
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Title: LOCAL VISCOELASTIC PROPERTIES OF SOFT ANISOTROPIC FIBROUS TISSUE
Creator: Gallo, Nicolas Remy
Date: 2020
Description: The current aging population, with more than 80 million "baby boomers", will present a steep medical challenge for our society in a...
Show moreThe current aging population, with more than 80 million "baby boomers", will present a steep medical challenge for our society in a foreseeable future. Half of the adults over 85 years old are predicted to be diagnosed with Alzheimer's disease by 2050. With healthcare cost reaching over 700 billion dollars in the United States, early detection of Alzheimer's disease (AD) and other co-existing neurodegenerative diseases is crucial to improve the recovery odds in patients and to decrease individual care cost. This work seeks to tackle this problem by proposing a novel computational framework toward improving the measurement of shear visco-elastic properties of brain white matter (WM), which vary with age. These measurements practically represent the effective (average) response of many cells and are typically obtained by using rheology or elastography. Although the former is direct, the latter requires the solution of an inverse problem based on a priori mechanical tissue model. The mechanical anisotropy of WM has previously not been fully explored although many inconsistencies have been reported in brain MRE experiments. To account for these inconsistencies a transversely isotropic constitutive model for the brain WM is proposed to interpret prior experiments involving 7 young and 4 older healthy men. By employing a novel inversion scheme, we report the local variation of the effective transverse and axial shear moduli in two well aligned WM structures (corpus callosum: CC; and cortical spinal tract: CST) for both the young and old cohort of healthy subjects part of the study. This work reports statistically significant changes in local regional variation of the transverse modulus across the CC for the young cohort. In the older cohort, the trend was similar yet not statistically significant. A novel candidate biomarker, the shear anisotropy metric, defined as the ratio of the transverse and axial shear moduli, found statistically significant local regional variation across the CC but not in the CST. Healthy aging was observed to decrease both transverse and axial in both CC and CST, although the variation was significant only for the CC. Finally, in an effort to understand the cause of effective transverse mechanical properties variation in WM with aging, the connection between effective and intrinsic contribution of WM cellular constituents is established. The intrinsic mechanical contributions of axons and glial matrix are separated by fitting the estimates of the effective shear moduli to a microscopic composite fiber model of myelinated axons embedded in the glial matrix. This work provides a method to establish a baseline for healthy brain mechanical properties thus promising to increase the specificity of MRE toward early diagnosis of neurodegenerative diseases. Additional oscillating disc rheology experiments with decellularized porcine myocardium, and the fabrication of a stable heterogeneous phantom matching the mechanical, diffusional and electrical properties of the WM provide foundational knowledge for due development and validation of MRE methodologies employed in other tissues.
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Title: Laser Powder Bed Fusion Of Cost-Effective Non-Spherical Ti-6Al-4V Powder
Creator: Asherloo, Mohammadreza
Date: 2023
Description: This comprehensive research delves into the intricate dynamics of Laser Powder Bed Fusion (L-PBF) of Ti-6Al-4V powders, emphasizing the...
Show moreThis comprehensive research delves into the intricate dynamics of Laser Powder Bed Fusion (L-PBF) of Ti-6Al-4V powders, emphasizing the potential of non-spherical, hydride-dehydride (HDH) powders as a cost-efficient alternative to traditional spherical powders. The study systematically explores the interplay between powder morphology, granulometry, and various post-processing treatments in shaping the resultant microstructure, porosity, and mechanical properties of L-PBF fabricated Ti-6Al-4V components.Initial investigations focused on the flowability, packing density, and resultant density of L-PBF parts using HDH powders with varying size distributions. Through meticulous optimization of laser parameters, parts with a relative density exceeding 99.5% were achieved, even at production rates 1.5–2 times higher than conventional LPBF processes. Dynamic synchrotron X-ray imaging provided insights into laser-powder interactions, revealing key mechanisms of porosity formation associated with HDH powders. Further microstructural examinations highlighted the formation of columnar β grains with acicular α/α′ phases in the as-built condition. Mechanical tests, including fatigue assessments under fully-reversed tension-compression conditions, revealed the critical role of surface roughness in fatigue performance. Notably, mechanical grinding significantly improved fatigue strength, especially in the high cycle fatigue region, by eliminating surface micro-notches. X-ray diffraction analyses further elucidated the stress and micro-strain profiles, offering insights into the material's deformation mechanisms. A pivotal discovery was the presence of α/α′ on prior β/β grain boundaries, challenging the prevailing notion that high cooling rates in L-PBF preclude β/β grain boundary variant selection. Electron backscatter diffraction and synchrotron X-ray imaging illuminated the role of powder characteristics in locally modulating cooling rates, leading to β/β grain boundary α′ lath growth. Lastly, the research underscored the multifaceted interdependencies among contouring, powder granulometry, Hot Isostatic Pressing (HIP), and mechanical surface treatments. A pronounced increase in sub-surface porosities was identified when contouring was combined with fine powder granulometry. However, post-HIP treatments induced a phase transformation from martensitic α′ to a basket-weave α+β microstructure, enhancing the material's fatigue resistance to levels comparable to wrought Ti-6Al-4V. In summation, this doctoral research offers a holistic understanding of the L-PBF process for Ti-6Al-4V, emphasizing the viability of non-spherical HDH powders and providing a roadmap for parameter optimization, defect minimization, and mechanical property enhancement in L-PBF-fabricated Ti-6Al-4V structures.
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Title: Case Study: A Comparison of Pedagogical Content Knowledge Between Coaches and Coaches/Mentees
Creator: Barone, Ana MargaritaSalinas
Date: 2024
Description: This multiple case study dissertation aimed to examine one of the domains of pedagogical content knowledge, knowledge of content and students,...
Show moreThis multiple case study dissertation aimed to examine one of the domains of pedagogical content knowledge, knowledge of content and students, between different types of elementary coaches and between coach and their respective collaborating teachers. It also investigated the impact a coaches’ background experiences have on the dynamic between coaches and teachers and the perceptions' teacher have on the effectiveness of coaching. The theoretical framework used in this qualitative study was Ball, Thames, and Phelps’ (2008) definition of PCK. Data was collected from six coaches–four instructional coaches and two math coaches–and eleven k-5th grade teachers. Data collection involved a survey, LMT assessment, and semi-structured interviews, and a thematic analysis method was conducted. The findings from the cross-case analysis resulted in ten themes, with the majority having multiple categories. One finding to one of the research questions was that there were no differences in knowledge of content and students between mathematics coaches and general instructional coaches, but other areas to further investigate emerged. Another finding was that coaches were either within the same capacity as their respective teachers or had extra knowledge of content and students. Although the majority of the coaches’ knowledge of content and students was at a higher level according to their LMT score, it does not necessarily mean that coaches are working with teachers in improving knowledge of content and students. In addition, more research is recommended in creating a pedagogical content knowledge instrument that is specific for coaches.
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Title: Characterization of Radiation Damage Effects in High-Energy Neutrino Target Graphite using Low-Energy Ions
Creator: Burleigh, Abraham C.
Date: 2023
Description: Exposure of graphite targets to high intensity proton beams at neutrino production facilities causes changes in the target material that can...
Show moreExposure of graphite targets to high intensity proton beams at neutrino production facilities causes changes in the target material that can result in a shortened operation lifetime. The dominant factors in this process are currently thought to be mechanical in nature resulting primarily from microstructural effects that lead to thermal and structural changes in bulk material properties. As currently planned beam facilities with increased proton energy and intensity begin to come online it will be important to thoroughly understand these processes, and ideally to be able to predict the effects of new beam designs on target properties. Direct analysis of targets exposed to existing high-energy proton beams is complicated by several factors, such as very limited access to proton beam facilities, high associated costs, irradiation times on the order of months, and the resulting radioactivity of irradiated samples that requires special facilities for post-irradiation examination. Much of the existing literature concerning irradiation damage in graphite has been focused on the needs of the nuclear engineering community, however high-energy proton targets operate in a much different environment. In comparison to graphite irradiated in a nuclear reactor, graphite used in proton beam targets receives a higher dose rate, have greater gas production, and experience short irradiation pulses as opposed to continuous irradiation. Low-energy ion irradiation offers a method of inducing similar levels of radiation damage to high-energy protons while avoiding many of the difficulties and limitations associated with high-energy proton beams and the corresponding activated specimen testing. My research described in this thesis focused on investigating how low-energy ion irradiation could be used to induce the same or similar types of microstructural alteration and mechanical property degradation as that seen in high-energy neutrino production target graphites by varying damage levels and irradiation temperatures prior to post-irradiation characterization.
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Title: Nanopore sensing for environmental and biomarker analysis
Creator: Arora, Pearl
Date: 2024
Description: Nanopore stochastic sensing is a powerful analytical tool for detecting target molecules through a nanoscale pore. The analyte and electrolyte...
Show moreNanopore stochastic sensing is a powerful analytical tool for detecting target molecules through a nanoscale pore. The analyte and electrolyte ions are subjected to a voltage bias which drives them to translocate through the nanopore, resulting in disruptions in the ionic current. These disruptions are translated to blockage events which can serve as a signature of the analyte. Owing to its unique features of single-molecule and label-free sensing, nanopore technique has been exploited in a wide array of applications such as detection of metal ions, proteins, DNA, microRNA, toxic agents etc. In this dissertation, projects showcasing nanopore’s sensing capability of different biomarkers and in the detection of a wide range of target molecules based on non-covalent interactions are presented. Particularly in the first two projects, nanopore detection of ferric ions relevant to environmental regulation as well as a biomarker for human health and a miRNA-based biomarker for oral cancer and oral related diseases are summarized. Ferric ions, which are benign if present in balanced quantities but can be toxic otherwise, are detected by using an engineered multifunctional nanopore and a chelating organophosphonic acid ligand. The chelate complex formed after ferric ions bind to ligand gives significantly different event signatures than the free ligand in the solution enabling ferric ion detection. Even in the presence of interfering ions, the ferric ions could be recognized easily because of the conformational changes brought in the nanopore lumen by the interaction of the interfering metal ions with the His-tags of the nanopore which in turn resulted in variations in the characteristics of blocking events. In the second project, miR31, an oral cancer biomarker, is selectively detected with the help of an engineered nanopore, and a DNA based probe. Several probes with variations in length, composition and position of the overhangs or probes with no overhangs were compared and studied as the probes play a crucial role in capturing the target of interest with high specificity. Our strategically designed probe emerged as the most effective in capturing the target even in presence of large background from human saliva samples and enhanced the sensitivity of the system. In the first two projects, nanopores are utilized for selective and specific detection of certain target molecules. However, in order to analyze diverse range of analytes, numerous sensing systems have to be constructed which can be a time-consuming and challenging task. To circumvent this limitation, in the third project, diverse recognition sites based on various non-covalent interactions are incorporated into the α-hemolysin protein pore to achieve detection of not just a single analyte but broad category of molecules such as cations, anions, aromatic and hydrophobic compounds.
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Title: Investigation of Electrochemical Properties and Fabrication of Lithium- and Sodium-ion Batteries
Creator: Chen, Changlong
Date: 2023
Description: Since the successful commercialization of Li-ion battery, the opportunity in creating a sustainable world with evenly-distributed energy...
Show moreSince the successful commercialization of Li-ion battery, the opportunity in creating a sustainable world with evenly-distributed energy supply and less environmental concerns has been significantly increased. This triggered tremendous efforts from both academy and industry in building better Li-ion batteries. Along the research and development over past 30 years, the performance of current Li-ion batteries has met some basic needs in our daily life, such as powering electronic devices and electric vehicles for a short time, while superior capabilities, like extended operating life, stable function under extreme circumstances, is always pursued. Under the pressure from these ever-growing demands, the corresponding Li-ion battery production is faced with a lot of new challenges. Regarding the battery production, the present Li-ion battery manufacturing heavily relies on the use of certain repo-toxic solvent, N-methyl-2-pyrrolidone (NMP), which arouses safety concerns to human health. In the pursuit of a higher energy density, silicon anode, bearing ten times the gravimetric capacity of commercially-dominating graphite anode, is intensively studied as the anode material for next-generation Li-ion batteries. However, its degradation mechanism is not completely revealed yet, which makes the methods of effective optimizations hard to be developed. In terms of the cost control, Na-ion batteries have been revisited and have received extra attention in the past decade owing to the abundance in raw materials and the high compatibility with state-of-art Li-ion industry while blank space in understanding primary electrochemical properties, such as impedance signals, has not been totally filled. This will also cause the misunderstandings in such interpretation and, thereby, postpone the pace of relevant advancement. Targeting these proposed issues, this thesis provides a series of feasible solutions via careful investigation and rational analysis with the aid of various advanced (non)electrochemical techniques, which offers a few unique perspectives in studying Li- and Na-ion batteries, and further facilitates the following research and development in the corresponding communities.
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Title: Quantification of Imaging Markers at Different MRI Contrast Weightings, Vasculature, and Across Field Strengths
Creator: Nguyen, Vivian S.
Date: 2024
Description: Quantitative MRI measures physical characteristics of tissue, which creates a set scale with units that allows longitudinal monitoring and...
Show moreQuantitative MRI measures physical characteristics of tissue, which creates a set scale with units that allows longitudinal monitoring and cross-patient and cross-center studies. It enables earlier detection of disease, complements biopsy, and provides a clear numeric scale for differentiation of disease states. However, quantitative MRI acquisitions and post-processing are not trivial, which makes it hard to implement the clinical setting. This along with the variability in clinically used acquisitions and post-processing techniques leads to difficulty in establishing reliable, consistent, and accurate quantitative information. There is a critical need for rigorous validation of quantitative imaging biomarkers, both for current and novel quantitative imaging techniques. This dissertation seeks to both validate current quantitative MR imaging techniques and develop new ones in the heart and brain by: 1) examining the data variability and the loss in tag fidelity that occurs when quantitative cardiac tagging is incorrectly run post-Gadolinium injection; 2) quantifying the negative impact of unexpected relaxometric behavior observed in low field MR imaging for low inversion times during T1 mapping; 3) validating retrospectively calculated T1 as a biomarker for Multiple Sclerosis progression; 4) and prototyping an oxygen extraction fraction (OEF) mapping technique for the purpose of stroke prediction and establishment of a numeric scale for tissue health for stroke patients.Assessment of pre-Gadolinium and post-Gadolinium cardiac tag quality showed that post-Gadolinium tags are less saturated (p = 0.012) and have a wider range of saturation, contrast, and sharpness. This results in a loss of information in the late cardiac cycle and impeding quantification of myocardial function.Investigation of 64mT T1 mapping revealed unique relaxometric behavior in that at low inversion times (<250 ms), the signal response curve displayed an increase in signal intensity or a plateau in signal intensity dependent on T1 relaxation time. Inclusion of this increase or plateau in signal intensity negatively impacted T1 fitting algorithms, leading to their failure or incorrectly calculated T1 values. The maximum peak signal intensity before the null point was found to be 210 ms, which impacts current low field T1 mapping protocols which use an initial inversion time of 80-110 ms.Validation of retrospectively calculated T1 as a biomarker in Multiple Sclerosis revealed that T1 of normal appearing brain tissue correlates with measures of Multiple Sclerosis progression (EDSS, BPF, and disease duration) with normal appearing white matter T1 correlating with BPF (r = -0.49, p = 0.0018); putamen T1 correlating with EDSS (r = 0.48, p = 2.40e-03), with BPF (r = 0.69, p = 2.04e-06), and disease duration (r = -0.37; p = 0.02); and globus pallidus T1 correlating with disease duration (r = -0.42; p = 0.0093). Lesion T1 is reflective of MS severity whereas MTR is not.Finally, development of an oxygen extraction fraction (OEF) mapping technique showed that application of independent component analysis (ICA) to cardiac gated spiral-trajectory phase images yielded components that feature stenosis features observed in magnitude images. These ICA components form the basis of OEF mapping from phase images. This dissertation presents four studies that seek to improve either current quantitative MR imaging protocols in the heart, or to develop and validate new quantitative MR imaging techniques in the brain for the purpose of monitoring disease progression or predicting disease.
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Title: Utilizing Concurrent Data Accesses for Data-Driven and AI Applications
Creator: Lu, Xiaoyang
Date: 2024
Description: In the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical,...
Show moreIn the evolving landscape of data-driven and AI applications, the imperative for reducing data access delay has never been more critical, especially as these applications increasingly underpin modern daily life. Traditionally, architectural optimizations in computing systems have concentrated on data locality, utilizing temporal and spatial locality to enhance data access performance by maximizing data and data block reuse. However, as poor locality is a common characteristic of data-driven and AI applications, utilizing data access concurrency emerges as a promising avenue to optimize the performance of evolving data-driven and AI application workloads.This dissertation advocates utilizing concurrent data accesses to enhance performance in data-driven and AI applications, addressing a significant research gap in the integration of data concurrency for performance improvement. It introduces a suite of innovative case studies, including a prefetching framework that dynamically adjusts aggressiveness based on data concurrency, a cache partitioning framework that balances application demands with concurrency, a concurrency-aware cache management framework to reduce costly cache misses, a holistic cache management framework that considers both data locality and concurrency to fine-tune decisions, and an accelerator design for sparse matrix multiplication that optimizes adaptive execution flow and incorporates concurrency-aware cache optimizations.Our comprehensive evaluations demonstrate that the implemented concurrency-aware frameworks significantly enhance the performance of data-driven and AI applications by leveraging data access concurrency.Specifically, our prefetch framework boosts performance by 17.3%, our cache partitioning framework surpasses locality-based approaches by 15.5%, and our cache management framework achieves a 10.3% performance increase over prior works. Furthermore, our holistic cache management framework enhances performance further, achieving a 13.7% speedup. Additionally, our sparse matrix multiplication accelerator outperforms existing accelerators by a factor of 2.1.As optimizing data locality in data-driven and AI applications becomes increasingly challenging, this dissertation demonstrates that utilizing concurrency can still yield significant performance enhancements, offering new insights and actionable examples for the field. This dissertation not only bridges the identified research gap but also establishes a foundation for further exploration of the full potential of concurrency in data-driven and AI applications and architectures, aiming at fulfilling the evolving performance demands of modern and future computing systems.
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Title: Resolvent analysis of turbulent flows: Extensions, improvements and applications
Creator: Lopez-Doriga Costales, Barbara
Date: 2024
Description: This thesis presents several advances in both physics-based and data-driven modeling of turbulent fluid flows. In particular, the present...
Show moreThis thesis presents several advances in both physics-based and data-driven modeling of turbulent fluid flows. In particular, the present thesis focuses on resolvent analysis, a physics-based framework that identifies the coherent structures that are most amplified by the Navier-Stokes equations when they are linearized about a known turbulent mean flow via a singular value decomposition (SVD) of a discretized operator. This method has proven to effectively capture energetically-relevant features observed in various flows. However, it has some shortcomings that the present work intends to alleviate. First, the original formulation of resolvent analysis is restricted to statistically-stationary or time-periodic mean flows. To expand the applicability of this framework, this thesis presents a spatiotemporal variant of resolvent analysis that is able to account for time-varying systems. Moreover, sparsity (which manifests in localization) is also incorporated to the analysis through the addition of an l1-norm penalization term to the optimization associated with the SVD. This allows for the identification of energetically-relevant coherent structures that correspond to spatio-temporally localized amplification mechanisms, for flows with either a time-varying or stationary mean. The high computational cost associated with the discretization and analysis of a large discretized of the mean-linearized Navier-Stokes operator represents the second drawback of resolvent analysis. As a second contribution, this thesis provides an analytic form of resolvent analysis for planar flows based on wavepacket pseudomode theory, avoiding the numerical computations required in the original framework. The third contribution focuses on the characterization of the energetically-dominant coherent structures that arise in turbulent flow traveling through straight ducts with square and rectangular cross-sections. First, resolvent analysis is applied to predict the coherent structures that arise in this flow, and to study the sensitivity of this methodology to the secondary mean flow components that display a distinct pattern near the duct corners. Next, a data-driven causality analysis is performed to understand the physical mechanisms involved in the evolution of coherent structures near the duct corners. To do this, a nonlinear Granger causality analysis method is developed and applied to proper orthogonal decomposition coefficients of direct numerical simulation data, revealing that the structures associated with the secondary velocity components are behind the formation and translation of the near-wall and near-corner streamwise structures. A general discussion and future prospects are discussed at the end of this thesis.
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Title: Health and Well-Being Benefits of Different Types of Urban Green Spaces (UGS): A Cross-Sectional Study of Communities in Chicago, U.S.
Creator: Kang, Liwen
Date: 2023
Description: There are three main interrelated areas of focus in this doctoral research related tourban green spaces (UGS): general well-being, mental and...
Show moreThere are three main interrelated areas of focus in this doctoral research related tourban green spaces (UGS): general well-being, mental and physical health. In this study, these three different health aspects were analyzed separately. The data of these three health outcomes were collected from the Healthy Chicago Survey (HCS), an annual telephone survey that interviewed adults in Chicago, U.S., based on the randomly selected addresses.Urban green spaces have been associated with better health and well-being. Theyprovide sites for physical activity, buffer air and noise pollution, and alleviate thermal discomfort. Urban green spaces also promote social interaction and increase social cohesion. However, research is limited on the health benefits of different types of UGS exposure. This research aimed to reveal the associations between the provision of different UGS types and urban residents’ general, mental, and physical health in Chicago, the third-largest city in the U.S.Urban green spaces data were collected from the National Land Cover Database(NLCD), the Meter-Scale Urban Land Cover (MULC), and the Chicago Park District (CPD). Different types of UGS were obtained, namely 1) the percent tree canopy cover (TCC) from the first database; 2) the percentage of trees and the percentage of grass from the second database; and 3) the number of parks, park areas, percentage of park areas from the third database. Using hierarchical and logistic regression models that controlled for a range of confounding factors (age, gender, race, education level, employment status, and poverty level), this study assessed which type of UGS affects general well-being, mental health, and physical health, respectively. The results indicated that increased park area was significantly associated with better perceived general health; higher percent of TCC was significantly associated with a lower level of psychological distress (PD); and increased percentage of park areas and increased number of parks were associated with lower odds of being obese. Two micro-scaled on-site observations were conducted in the Avalon Park community and the Loop community to analyze some other UGS characteristics besides quantity and availability. Other characteristics of UGS, such as quality of facilities, attractiveness, and maintenance, are suggested to be taken into consideration for future studies. The study highlights that different UGS types have various impacts on general, mental, and physical health of urban residents. By providing scientific evidence, this study may help policymakers, urban planners, landscape architects, and other related professionals to make informed decisions on maximizing the health benefits of UGS and to achieve social equity. The findings of this study may be applied to other metropolitan cities.
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Title: Efficacy and Mechanisms of Power Ultrasound-Based Hurdle Technology for Reduction of Pathogens in Fresh Produce
Creator: Zhou, Xinyi
Date: 2023
Description: Minimally processed produce is frequently contaminated with foodborne bacterial pathogens. Power ultrasound is a non-thermal and cost...
Show moreMinimally processed produce is frequently contaminated with foodborne bacterial pathogens. Power ultrasound is a non-thermal and cost-effective technology that can be combined with other chemical sanitization methods. This study investigated the reduction of Listeria monocytogenes and Salmonella Newport on grape tomato, romaine lettuce, and spinach washed with water, chlorine, or peroxyacetic acid alone or in combination with 25 or 40 kHz power ultrasound for 1, 2, or 5 min. Produce items were inoculated with selected pathogens at approximately 10 log CFU/g, air dried for 2 h, and then treated. Combined treatment of ultrasound and sanitizers resulted in 1.44-3.99 log CFU/g reduction of L. monocytogenes and 1.35-3.62 log CFU/g reduction of S. Newport on washed produce items, with significantly higher reductions observed on grape tomato. Synergistic effects were achieved with the combined treatment of power ultrasound coupled with the chemical sanitizers when compared to the single treatments. An additional 0.48-1.40 log CFU/g reduction of S. Newport was obtained with the combined treatment on grape tomato. In general, no significant differences (p<0.05) were observed in pathogen reductions between the selected ultrasound frequencies, sanitizers, or treatment lengths. Results from this study suggest that incorporation of power ultrasound to current treatment can enhance bacterial pathogen reduction on fresh produce surface, but cannot completely eliminate bacterial pathogens. Transcriptomic study revealed significant (|Log2 fold change|<1 and false discovery rate < 0.05) transcriptional changes in L. monocytogenes LS810 in response to the 2 min power ultrasound treatment. The up-regulation of genes encoding TPI, LLO, and PTS indicates increased energy requirements, enhanced virulence, and demand for sugar sources in bacteria. On the other hand, the down-regulation of genes involved in cyclic dimeric GMP hydrolysis and transcriptional regulation suggests a modulation of intracellular signaling, cellular processes, and metabolisms to enhance survival and recovery. The GO and KEGG analysis demonstrated defense mechanisms against ultrasound stress more comprehensively. L. monocytogenes adjusts its metabolism, repairs cell membranes, and conserves energy for survival. These findings enhance our understanding of its adaptation to environmental stress. Results of this study can be used as a start point for optimizing the efficacy of ultrasound-based hurdle treatments for fresh produce disinfection.
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Title: Stigma, depression, and help-seeking: Experiences of parents/caregivers of children with mental health challenges
Creator: Serchuk, Marisa D.
Date: 2023
Description: The impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The...
Show moreThe impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The current investigation examines how specific types of stigma experienced by parents/caregivers (N=250) of children with mental health challenges are related to symptoms of depression and attitudes towards help-seeking. Results found that higher levels of public stigma, self-stigma, and vicarious stigma were associated with higher levels of depression and were differentially associated with attitudes towards help-seeking. Findings from this investigation add to the small body of literature examining stigma experienced by parents/caregivers of children with mental health challenges.
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Title: Development of Granular Jamming Soft Robots from Boundary Constrained to Interconnected Systems
Creator: Tanaka, Koki
Date: 2023
Description: This dissertation provides a detailed study on the conceptualization, creation, and optimization of a unique, interconnected soft robot system...
Show moreThis dissertation provides a detailed study on the conceptualization, creation, and optimization of a unique, interconnected soft robot system. It introduces a flexible assembly of locomotive robotic modules interconnected by an envelope, capable of granular jamming. In doing so, it highlights the practical capabilities of these interconnected modules to adapt and function cohesively as a single robot system.As a precursor to the primary investigation, the study initially presents the development and experimental validation of a boundary constrained mobile soft robot. This design leverages granular jamming for locomotion and object grasping, thereby laying a robust foundation for the subsequent exploration of complex soft robotic systems.The cornerstone of this study is the development of an interconnected soft robot system, where locomotive robotic modules, primarily composed of an elastic material, are bound together by a flexible envelope designed for granular jamming. The robotic modules, fundamentally constructed from an elastic material, incorporate origami-inspired artificial muscle actuators. These actuators, with their semi-soft characteristics, complement the inherent flexibility of the modules and play a significant role in facilitating module propulsion. Although the design incorporates a traditional rigid power source, as opposed to a fully soft robot system, the integration of a pneumatic power method into the system successfully reduces the mechanical intricacy and unwieldiness typically associated with rigid mechanisms.This research further probes into the diverse applications of this interconnected soft robot system. Its ability to shape-shift and maintain these forms during locomotion exemplifies a robust control strategy for the system that may undergo substantial deformation, proving instrumental in dynamic environments. The study demonstrates a methodology for object manipulation and obstacle avoidance that does not rely heavily on precise control and sensing. Instead, it utilizes the inherent compliance of the soft robot system. In a notable departure from previous studies, the system also exhibits a unique capability for ascending and traversing inclined surfaces.Additionally, the study dives into the optimization of the interconnected robot system via a physics-based simulation and genetic algorithm. This approach results in an assortment of optimized configurations that excel in object grasping tasks of various shapes, thereby laying a robust groundwork for the progression of soft robotics in the future.In conclusion, this investigation reveals groundbreaking insights into the field of soft robotics through the successful design and optimization of an interconnected soft robot system. Its standout performances in deformation, manipulation, and navigation tasks set it apart. This work serves to significantly enhance the adaptability and functionality of future robotic systems, pushing the edge of what is possible across a diverse range of sectors. By portraying a significant step towards a future where robots can dynamically adapt to their environments and efficiently accomplish complex tasks, this dissertation exemplifies a transformative stride in the field.
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