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Title: Simulation and Experimental Testing of High-Gradient Dielectric Disk Accelerating Cavities
Creator: Weatherly, Sarah K.
Date: 2022
Description: Structure-based wakefield acceleration can be accomplished using either Collinear Wakefield Acceleration (CWA) where the drive beam and the...
Show moreStructure-based wakefield acceleration can be accomplished using either Collinear Wakefield Acceleration (CWA) where the drive beam and the witness beam are located on the same beamline or Two Beam Acceleration (TBA) where the RF power generated by the drive beam is extracted and transferred to the witness beam line. A Dielectric Disk Accelerator (DDA) is an accelerating structure that is utilized by TBA that uses dielectric disks to improve the structure's shunt impedance and accelerate the witness beam. Dielectric based accelerators studied in this thesis are X-Band structures (have a working frequency between 8 and 12 GHz) that can use any pulse length but in this study utilize short (<20 ns) traveling wave pulses. Short pulse lengths are used to decrease breakdown probability and allow for a large gradient. DDAs have a higher group velocity and a larger shunt impedance compared to traditional metallic accelerating structures while maintaining a large accelerating gradient. DDAs are a strong candidate for use in the Argonne Wakefield Accelerator’s 500 MeV Demonstrator. Recent experimental results of a clamped single cell structure demonstrated a >100 MV/m accelerating gradient with no evidence of breakdown in the RF volume. Additional structures, including a brazed single cell model and a multicell structure, have been designed and are now being fabricated for high power testing.
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Title: ESTIMATING PM2.5 INFILTRATION FACTORS FROM REAL-TIME OPTICAL PARTICLE COUNTERS DEPLOYED IN CHICAGO HOMES BEFORE AND AFTER MECHANICAL VENTILATION RETROFITS
Creator: Wang, Mingyu
Date: 2021
Description: PM2.5 are fine inhalable particles that are 2.5 micrometers or smaller in size. Indoor PM2.5 consists of outdoor PM2.5 (ambient PM2.5) that is...
Show morePM2.5 are fine inhalable particles that are 2.5 micrometers or smaller in size. Indoor PM2.5 consists of outdoor PM2.5 (ambient PM2.5) that is infiltrated into the indoor environment and indoor generated PM2.5 (non-ambient PM2.5). As people spend nearly 90% of their lifetimes indoors, with most of that time in their homes, PM2.5 exposure in homes results in severe health effects such as asthma. One strategy increasingly being used to dilute air pollutants generated indoors and improve indoor air quality (IAQ) in homes is the introduction of mechanical ventilation systems. However, mechanical ventilation systems also have the potential to introduce more ambient PM2.5 than relying on infiltration alone, although limited data exist to demonstrate the magnitude of impacts in occupied homes. The objective of this paper is to estimate the infiltration factor (Finf) of PM2.5 before and after installing mechanical ventilation systems in a subset of occupied homes. The data source utilized comes from the Breathe Easy Project, a more than 2-year-long study conducted in 40 existing homes in Chicago, IL aiming to explore the effects of three different types of mechanical ventilation system retrofits on IAQ and asthma. An automated algorithm was developed to remove indoor PM2.5 peaks in time-series data collected from optical particle counters deployed inside and outside of each home. The Finf was estimated using the resulting indoor/outdoor ratio with indoor peaks removed. Before mechanical ventilation retrofits, the weekly median Finf was 0.29 (summer median = 0.41, fall median = 0.26, winter median = 0.29, spring median = 0.30); after mechanical ventilation retrofits, the median Finf was 0.34 (winter median= 0.28, spring median = 0.45, summer median = 0.54, fall median = 0.20). Differences in Finf between pre- and post-intervention periods were not statistically significant (p = 0.23 from Wilcoxon signed rank tests). The median PM2.5 infiltration factor increased ~22% (from 0.27 to 0.33) with the installation of balanced ventilation systems with energy recovery ventilators (ERV), although differences were not statistically significant (Wilcoxon signed rank p = 0.35). The median PM2.5 infiltration factor decreased ~4% (from 0.28 to 0.27) after installing intermittent CFIS systems, which intermittently supply ventilation air through the existing central air handling units and associated filters (which were upgraded to a minimum of MERV 10 in all CFIS homes), although differences were not statistically significant (Wilcoxon signed rank p = 0.24). The median PM2.5 infiltration factor increased ~26% (from 0.35 to 0.44) with the installation of continuous exhaust-only systems, and differences were significant (Wilcoxon signed rank p = 0.04). These results suggest that the filtration mechanisms used on the CFIS and balanced systems were adequate for maintaining similar distributions of Finf values pre- and post-interventions whereas the increased delivery of outdoor air via the building envelope by exhaust-only systems significantly increased Finf following retrofits.
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Title: A New Control and Decision Support Framework To Avoid Fast-Evolving System Collapse and Cascading Failure
Creator: Guha, Bikiran
Date: 2022
Description: The modern power system is a vast and incredibly complex network with a very large number of equipment operating round the clock to reliably...
Show moreThe modern power system is a vast and incredibly complex network with a very large number of equipment operating round the clock to reliably transport electricity from generators to consumers. However, factors such as aging and faulty equipment, extreme and unpredictable weather, cyber attacks and increasing amounts of unpredictable renewable generation have made it increasingly vulnerable to cascading failure and wide-area collapse. Therefore, a lot of work has been done over the years on cascading failure vulnerability analysis and mitigation. However, to the best of our knowledge, the existing literature on this topic focus on preventive analysis and mitigation, mostly from a planning perspective. There is a lack of decision support schemes which can take real-time preventive action when the system becomes vulnerable to cascading failure, while taking into account the various dynamics and uncertainties involved in these types of failures. The only defense under these situations are pre-designed emergency control schemes. However, they are only effective against known vulnerabilities and can make matters worse if not accurately designed and calibrated.This research work has proposed a novel wide-area monitoring protection and control (N-WAMPAC-20) framework designed to make decisions in real-time to assess the vulnerabilities of the system (when a disturbance happens) and to implement mitigation actions, if necessary. The main contributions of this dissertation focus on the disturbance monitoring, real-time control and decision making aspects of this framework. The proposed framework has been divided into two major parts: an offline part and an online part. The offline part continuously runs extreme contingency analysis in the background (using combined dynamics and protection simulators) to generate elements which can assess system vulnerabilities and suggest suitable mitigation actions, if necessary. In this regard, a novel load shedding adjustment scheme is also proposed, which has been shown to be effective against a variety of fast-evolving cascading failure scenarios. The online part consists of real-time disturbance monitoring and decision-making components. The disturbance monitoring component focuses on real-time fault detection and location. If a fault has been identified and located, the real-time decision making component determines the vulnerability of the system, by consulting with the elements designed offline. If vulnerabilities are identified, targeted mitigation actions are implemented. The design and applicability of a prototype of N-WAMPAC-20 has been presented using a case of voltage collapse and a case of wide-area loss of synchronization on a synthetic model of the Texas grid.
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Title: Distinctive Categorization Deficits in Repeated Sorting of Common Household Objects in Hoarding Disorder
Creator: Hamilton, Catharine Elizabeth
Date: 2022
Description: The present study examines sorting techniques and deficits among individuals with hoarding disorder (n = 34) compared to age- and gender...
Show moreThe present study examines sorting techniques and deficits among individuals with hoarding disorder (n = 34) compared to age- and gender-matched adults (n = 35) in the general population. Performance was compared on the Booklet Category Test (BCT), selected other neuropsychological measures, and an ecologically valid sorting task designed for the study to model the Delis-Kaplan Executive Function System (D-KEFS) Sorting subtest but with common household objects as stimuli. Contrary to predictions, individuals with hoarding disorder did not perform significantly worse than controls on the BCT or the sorting task designed for the present study. Also contrary to predictions, the hoarding group performed significantly better when initiating their own sorts of the objects than when tasked with naming categories grouped by the researcher. These findings are discussed as well as exploratory analyses suggesting participants with hoarding put forth more mental effort sorting the household objects (shoes and mail). They provided significantly more individual responses on the task with significantly more description errors. IQ and performance on other selected neuropsychological measures were not significantly different between groups. These findings provide preliminary evidence there may be specific types of real-life sorting difficulties associated with hoarding disorder that are subtle and beyond what existing neuropsychological tests can measure. Given that current CBT treatments for hoarding presuppose a certain level of competency in sorting (e.g., recognizing and naming different categories of household items to complete a personal organizing plan), it is important to clarify potential sorting and categorization deficits in this group as one possible avenue to help improve treatment response among individuals struggling with hoarding disorder.
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Title: Machine Learning On Graphs
Creator: He, Jia
Date: 2022
Description: Deep learning has revolutionized many machine learning tasks in recent years.Successful applications range from computer vision, natural...
Show moreDeep learning has revolutionized many machine learning tasks in recent years.Successful applications range from computer vision, natural language processing to speech recognition, etc. The success is partially due to the availability of large amounts of data and fast growing computing resources (i.e., GPU and TPU), and partially due to the recent advances in deep learning technology. Neural networks, in particular, have been successfully used to process regular data such as images and videos. However, for many applications with graph-structured data, due to the irregular structure of graphs, many powerful operations in deep learning can not be readily applied. In recent years, there is a growing interest in extending deep learning to graphs. We first propose graph convolutional networks (GCNs) for the task of classification or regression on time-varying graph signals, where the signal at each vertex is given as a time series. An important element of the GCN design is filter design. We consider filtering signals in either the vertex (spatial) domain, or the frequency (spectral) domain. Two basic architectures are proposed. In the spatial GCN architecture, the GCN uses a graph shift operator as the basic building block to incorporate the underlying graph structure into the convolution layer. The spatial filter directly utilizes the graph connectivity information. It defines the filter to be a polynomial in the graph shift operator to obtain the convolved features that aggregate neighborhood information of each node. In the spectral GCN architecture, a frequency filter is used instead. A graph Fourier transform operator or a graph wavelet transform operator first transforms the raw graph signal to the spectral domain, then the spectral GCN uses the coe"cients from the graph Fourier transform or graph wavelet transform to compute the convolved features. The spectral filter is defined using the graph’s spectral parameters. There are additional challenges to process time-varying graph signals as the signal value at each vertex changes over time. The GCNs are designed to recognize di↵erent spatiotemporal patterns from high-dimensional data defined on a graph. The proposed models have been tested on simulation data and real data for graph signal classification and regression. For the classification problem, we consider the power line outage identification problem using simulation data. The experiment results show that the proposed models can successfully classify abnormal signal patterns and identify the outage location. For the regression problem, we use the New York city bike-sharing demand dataset to predict the station-level hourly demand. The prediction accuracy is superior to other models. We next study graph neural network (GNN) models, which have been widely used for learning graph-structured data. Due to the permutation-invariant requirement of graph learning tasks, a basic element in graph neural networks is the invariant and equivariant linear layers. Previous work by Maron et al. (2019) provided a maximal collection of invariant and equivariant linear layers and a simple deep neural network model, called k-IGN, for graph data defined on k-tuples of nodes. It is shown that the expressive power of k-IGN is equivalent to k-Weisfeiler-Lehman (WL) algorithm in graph isomorphism tests. However, the dimension of the invariant layer and equivariant layer is the k-th and 2k-th bell numbers, respectively. Such high complexity makes it computationally infeasible for k-IGNs with k > 3. We show that a much smaller dimension for the linear layers is su"cient to achieve the same expressive power. We provide two sets of orthogonal bases for the linear layers, each with only 3(2k & 1) & k basis elements. Based on these linear layers, we develop neural network models GNN-a and GNN-b, and show that for the graph data defined on k-tuples of data, GNN-a and GNN-b achieve the expressive power of the k-WL algorithm and the (k + 1)-WL algorithm in graph isomorphism tests, respectively. In molecular prediction tasks on benchmark datasets, we demonstrate that low-order neural network models consisting of the proposed linear layers achieve better performance than other neural network models. In particular, order-2 GNN-b and order-3 GNN-a both have 3-WL expressive power, but use a much smaller basis and hence much less computation time than known neural network models. Finally, we study generative neural network models for graphs. Generative models are often used in semi-supervised learning or unsupervised learning. We address two types of generative tasks. In the first task, we try to generate a component of a large graph, such as predicting if a link exists between a pair of selected nodes, or predicting the label of a selected node/edge. The encoder embeds the input graph to a latent vector space via vertex embedding, and the decoder uses the vertex embedding to compute the probability of a link or node label. In the second task, we try to generate an entire graph. The encoder embeds each input graph to a point in the latent space. This is called graph embedding. The generative model then generates a graph from a sampled point in the latent space. Di↵erent from the previous work, we use the proposed equivariant and invariant layers in the inference model for all tasks. The inference model is used to learn vertex/graph embeddings and the generative model is used to learn the generative distributions. Experiments on benchmark datasets have been performed for a range of tasks, including link prediction, node classification, and molecule generation. Experiment results show that the high expressive power of the inference model directly improves latent space embedding, and hence the generated samples.
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$X-Ray Diffraction Studies of Activation and Relaxation In Fast and Slow Rat Skeletal Muscle$

Title: X-Ray Diffraction Studies of Activation and Relaxation In Fast and Slow Rat Skeletal Muscle
Creator: Gong, Henry M.
Date: 2022
Description: The contractile properties of fast-twitch and slow-twitch skeletal muscles are primarily determined by the myosin isoform content and...
Show moreThe contractile properties of fast-twitch and slow-twitch skeletal muscles are primarily determined by the myosin isoform content and modulated by a variety of sarcomere proteins. X-ray diffraction studies of regulatory mechanisms in muscle contraction have focused predominately on fast- or mixed-fiber muscle with slow muscle being much less studied. Here, we used time-resolved x-ray diffraction to investigate the dynamic behavior of the myofilament proteins in relatively pure slow fiber rat soleus (SOL) and pure fast fiber rat extensor digitorum longus (EDL) muscle during twitch and tetanic contractions at optimal lengths (Lo), 95% Lo, and 90% Lo. Before the delivery of stimulation, reduction in muscle length led to decrease in passive tension. The x-ray reflections upon reduction in length showed no transition in the myosin heads from ordered OFF state, where heads are held close to the thick filament backbone, to disordered ON states, where heads are free to bind to thin filament, in both muscles. When stimulation was delivered to both muscles for twitch contractions at Lo, x-ray signatures indicating the transition of myosin heads to ON states were observed in EDL but not in soleus muscle. During tetanic contractions, changes in the disposition of myosin heads as active tension develops is a cooperative process in EDL muscle whereas in soleus muscle this relationship is less cooperative. Moreover, this high cooperativity was maintained in EDL at all lengths tested here, but cooperativity decreased upon reduction in lengths in soleus. The observed reduced extensibility of the thick filaments in soleus muscles as compared to EDL muscles indicate a molecular basis for this behavior. These data indicate that for the EDL thick filament activation is a cooperative strain-induced mechano-sensing mechanism, whereas for the soleus thick filament xiii activation has a more graded response. Lastly, x-ray data collected at different lengths demonstrated that the effect of length on soleus is more pronounce compared to EDL, particularly noticeable in the thick filament during relaxation phase after stimulation ceased. These observations indicate that soleus is more length-dependent than EDL. These different approaches to thick filament regulation in fast- and slow-twitch muscles may be designed to allow for short duration, strong contractions versus sustained finely controlled contractions, respectively.
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Title: Pressure Feedback Control on a UCAS Model in Random Gusts
Creator: He, Xiaowei
Date: 2021
Description: This research focuses on efficient active flow control (AFC) of the aerodynamic loads on a generic tailless delta wing in various flow/flight...
Show moreThis research focuses on efficient active flow control (AFC) of the aerodynamic loads on a generic tailless delta wing in various flow/flight conditions, such as, flying through atmosphere gusts, fast pitching, and other rapid maneuvers that would cause the aircraft to experience unsteady aerodynamic effects. A feedback control scheme that uses the surface pressure measurements to estimate the actual aerodynamic loads that act on the aircraft is put forward, with the hypothesis that a pressure surrogate can replace the inertia-based sensors to provide the controller with faster and/or more accurate feedback signals of the real-time aerodynamic load. The control performance of the AFC actuation and conventional elevons were evaluated. Results showed that the AFC with a momentum coefficient input of 2% was equivalent to 27-deg elevon deflection in terms of roll moment change and the control derivative of the AFC is at least doubled than that of the elevons.Streamwise and cross-flow gusts were simulated in the Andrew Fejer Unsteady Wind Tunnel at IIT. A spectral feedback approach was tested by generating the horizontal velocity components of the von Karman and the Dryden turbulence spectra. The velocity components in the test section were controlled temporally and spatially to generate transverse cross-flow gusts with designated wavelengths and frequencies. Sparse surface pressure measurements on the aircraft surface were used to develop lower-order models to estimate the instantaneous aerodynamic loads using the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm. The pressure-based models acted as surrogates of the aerodynamic loads to provide feedback signals to the closed-loop controller to alleviate the gust effects on the wing. The control results showed that the pressure feedback scheme was sufficient to provide feedback signals to the controller to reduce the roll moment fluctuations caused by the dynamic perturbations down to 20% comparing to 30% to 50% in previous studies.
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Title: Fault Detection and Localization in Flying Capacitor Multilevel Converters
Creator: Hekmati, Parham
Date: 2021
Description: This dissertation addresses fault detection, fault localization, and recovery in different topologies of the flying capacitor multilevel...
Show moreThis dissertation addresses fault detection, fault localization, and recovery in different topologies of the flying capacitor multilevel converters to guarantee the safe post-fault operation of the system and maintain load supply. There are multiple contributions of this dissertation, including techniques for device open-circuit fault (OCF) detection in stacked multicell converters (SMCs), a windows detector circuit to track the output terminal voltage levels and current directions, a fast and straightforward active power device OC fault detection and localization technique for the family of flying capacitor multilevel converters (FCMCs), a model-based open circuit fault detection and localization technique for the Buck-FCMC, a new estimator for tracking the voltage of flying capacitors, and fault detection and localization for interleaved converters. Each of these contributions is summarized below.The first contribution of this dissertation proposes a fast and straightforward technique for power device OCF detection in SMCs. The fault detection concept only needs to sense the converter's output terminal voltage and current. The sensed output terminal voltage is compared to a predicted one to detect and localize the OCF. A front-end routing circuit is then added to the SMC to maintain the operation of the converter post fault. The second contribution proposes a window detector circuit to track the output terminal voltage levels and current directions. The window detector circuit detects output terminal voltage level and current direction instead of requiring high sample rates and interrupt loops in the controller.The third contribution proposes a fast and straightforward active power device OCF detection and localization technique for the family of FCMCs, including DC to DC FCMCs, single or multi-phase H-bridge FCMCs, and cascaded H-bridge multilevel converters. This technique only needs to sense voltage and direction of current at the output terminals of the converters to detect and localize the fault. The method compares the measured and the expected terminal voltage while considering the commanded switch states and the terminal current direction. As switches transition to different states, healthy switches are excluded from the set of possible faulty switches until only one faulty switch remains. Coordination of the asynchronous operation of FPGA, DSP, and sensors is addressed for practical implementation. The fourth contribution is a model-based OCF detection and localization technique for the Buck-FCMC using model predictive control. In this technique, state-space equations of the system are developed. Comparison of the measured output inductor with the predicted one from the state-space model is used for the OCF detection and localization. This technique can potentially be used for other converters of the FCMC family. The fifth contribution is a new estimator for tracking the voltage of flying capacitors as the internal states of the FCMC. Using the proposed flying capacitor voltage estimator reduces the number of required sensors compared to the conventional model-based methods. At the same time, the overall technique's robustness to dynamic changes, including startup and load changes, is maintained. The last contribution is open and short circuit switch fault detection and localization for interleaved converters using the harmonic analysis of the output terminal parameters. With this method, monitoring electrical parameters of each leg of the interleaves converters is no longer required for fault detection and localization purposes.
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Title: ROBUST AND EXPLAINABLE RESULTS UTILIZING NEW METHODS AND NON-LINEAR MODELS
Creator: Onallah, Amir
Date: 2022
Description: This research focuses on robustness and explainability of new methods, and nonlinear analysis compared to traditional methods and linear...
Show moreThis research focuses on robustness and explainability of new methods, and nonlinear analysis compared to traditional methods and linear analysis. Further, it demonstrates that making assumptions, reducing the data, or simplifying the problem results in negative effect on the outcomes. This study utilizes the U.S. Patent Inventor database and the Medical Innovation dataset. Initially, we employ time-series models to enhance the quality of the results for event history analysis (EHA), add insights, and infer meanings, explanations, and conclusions. Then, we introduce newer algorithms of machine learning and machine learning with a time-to-event element to offer more robust methods than previous papers and reach optimal solutions by removing assumptions or simplifications of the problem, combine all data that encompasses the maximum knowledge, and provide nonlinear analysis.
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Title: How Does Self-Stigma Influence Functionality in People with Serious Mental Illness? A Multiple Mediation Model of "Why-Try" Effect, Coping Resources, and Personal Recovery
Creator: Qin, Sang
Date: 2022
Description: People with serious mental illness (SMI) face self-stigma effects that often undermine their functionality. Functionality herein refers to a...
Show morePeople with serious mental illness (SMI) face self-stigma effects that often undermine their functionality. Functionality herein refers to a person's execution of tasks (i.e., activities) and engagement in life situations (i.e., participation). This study used a path model to examine three mediating factors between self-stigma and functionality: The "why-try" effect, coping resources, and personal recovery. Specifically, the “why-try” effect was viewed as an extension of self-stigma harm that occurred when people suffered from a loss of self-esteem and self-efficacy. Coping resources were conceptualized as individuals’ strengths and the support they had to overcome negative stigma outcomes, particularly stigma stress. Endorsement of personal recovery, namely pursuing self-defined life goals despite illness—had a buffering effect reducing self-stigma. These three mediators were examined simultaneously using an archival dataset. Due to poor internal consistency, coping resources were eventually removed from the model; the subsequent, revised model achieved a good model fit. Results showed that people with SMI experiencing self-stigma were found to have an enhanced "why-try" effect as well as reduced personal recovery, leading to a decline in functionality. Implications of the results and future research directions are discussed.
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Title: Decreasing Body Dissatisfaction in Male College Athletes: A Pilot Study of the Male Athlete Body Project
Creator: Perelman, Hayley
Date: 2020
Description: Body dissatisfaction is associated with marked distress and often precipitates disordered eating symptomology. Body dissatisfaction in male...
Show moreBody dissatisfaction is associated with marked distress and often precipitates disordered eating symptomology. Body dissatisfaction in male athletes is an important area to explore, as research in this field often focuses on eating disorders in female athletes. The current body of literature regarding male college athletes suggests that they experience pressures associated with both societal muscular ideals and sport performance. While there is a clear association between drive for muscularity and body dissatisfaction in college male athletes, no study to date has evaluated the efficacy of a body dissatisfaction intervention for this population. Therefore, the present study sought to investigate the efficacy and feasibility of a pilot intervention program that targeted body dissatisfaction in male college athletes. Participants were randomized into an adapted version of the Female Athlete Body Project (i.e., the Male Athlete Body Project) or an assessment-only control condition. A total of 79 male college athletes (39 in treatment condition) completed this study for a retention rate of 84.9%. Participants in the experimental group attended three 80-minute group sessions once a week for three weeks. All participants completed measures of body dissatisfaction, internalization of the body ideal, drive for muscularity, negative affect, and sport confidence at three time points: baseline, post-treatment (three weeks after baseline for the control condition), and one-month follow-up. Hierarchical Linear Modeling was used to assess differences between conditions across time. Participation in the MABP improved men’s satisfaction with specific body parts, drive for muscularity, and body-ideal internalization at post-treatment. Men in the MABP also reported improvements in appearance evaluation and overweight preoccupation at post-treatment and one-month follow-up, and in negative affect at one-month follow-up only. Improvements in drive for muscularity were retained at one-month follow-up. This study provides preliminary evidence for the feasibility and efficacy of the Male Athlete Body Project.
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Title: Population dynamics and pathogens of the western bean cutworm (Striacosta albicosta)
Creator: Bunn, Dakota C.
Date: 2022
Description: Understanding an herbivorous pest’s population dynamics is necessary to ensure proper integrated pest management strategies are being...
Show moreUnderstanding an herbivorous pest’s population dynamics is necessary to ensure proper integrated pest management strategies are being developed and used. The western bean cutworm is a pest of both corn and dry beans that is understudied and difficult to manage due to its nocturnal lifestyle, adaptation to current management techniques and a general lack of understanding regarding its population structure. Our studies focused on the effects of host plant and pathogens on western bean cutworm population structure and found that mainly adults which developed on corn are contributing to the next generation of western bean cutworm in Michigan, making corn and dry beans unsuitable as co-refuges in insecticide resistance management strategies.We also found a 100% prevalence of the Nosema sp. in the adult population of western bean cutworm in Michigan. This prevalence, when paired with the consistent crop damage caused annually by the western bean cutworm, which confirms an abundance of cutworms are present, suggests the infection is slow acting and non-lethal to its host. Following sequencing, assembly, and annotation of the Nosema sp. genome, we were unable to provide a reason for the Nosema sp.’s low virulence, however, we were able to confirm the presence of all 6 polar tube proteins. Upon further examination of the Nosema sp. genome we were able to determine that it is a true Nosema with genome size of ~9.57 Mbp (~20% of which are transposable elements), which is within the typical range for this genus.
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Title: ESTIMATES OF AIR EXCHANGE RATES THROUGH THE USE OF TOTAL VOLATILE ORGANIC COMPOUND DECAY MEASUREMENTS
Creator: Bradley, Christopher
Date: 2021
Description: Indoor air exchange rates are commonly used to assess the overall fitness of a building and assess its performance. More recently, air...
Show moreIndoor air exchange rates are commonly used to assess the overall fitness of a building and assess its performance. More recently, air exchange has become a concern due to the COVD-19 pandemic, requiring replacement air to ensure safety; especially so considering that humans spend much of their time indoors. Building science has focused on air exchange to quantify needs for thermal loads, balancing the overall tightness of a building with the amount of energy consumed. Moreover, guidelines have been created by several different organizations to maintain adequate ventilation to remove indoor air pollution, replacing it with clean outdoor air. Research focuses on how to maintain a comfortable and safe quality of indoor air while balancing the needs of the energy crisis.When installed with proper HVAC systems, air exchange rates can be set to a recommended value based upon the conditions of the environment. Buildings without mechanical ventilation face another issue, mainly that they only rely on natural ventilation and the infiltration rate. Temperature differences between the indoor and outdoor environment and the condition of wind speed and direction create pressure differences across the building envelope, influencing the infiltration rate, which can change the amount of air exchange in buildings with natural or mechanical ventilation. Currently, air exchange rates are commonly measured using tracer gases. More frequently used gases have included perfluorocarbon, sulfur hexafluoride, and carbon dioxide, though none of these have proven to be ideal tracers. Alongside this, cost and burden on the participants of these studies often limit the amount of measurements made. Numerous studies have been conducted on how to model the air exchange rate by the changes in concentrations, but accuracy depends on the amount of information available. Other attempts have been made to characterize buildings by their infiltration rate to make estimations, but other questions have arisen about the accuracy of these methods. Due to their ubiquity in indoor environments, volatile organic compounds have been suggested as a plausible tracer gas for measuring air exchange rates. The plausibility of this method raises questions, such as their behavior within the indoor environment, their ability to be measured and the cost to measure concentrations, and the analytical requirements to characterize the rates of removal as air exchange rates. However, due to the rapid increase of available technology in low cost, lightweight, high-resolution sensors, this novel method of using VOCs, especially indicators of total VOCs (TVOCs), may prove fruitful in measuring air exchange within specific microenvironments. Analysis of time-series TVOC concentration measurements taken from a study conducted in multiple residences was conducted to investigate the feasibility of using these measurements, and especially naturally occurring elevation and decay periods, as a proxy for calculating air exchange rates. Though the removal rates of these compounds fell within the range of typical air exchange rates for residential spaces, the results of this analysis suggest the method has potential but with limitations, including the unknown behavior of the individual compounds comprising TVOC measurements within the space, proximity and mixing effects, and potentially invalid comparisons to air exchange rates given from a LBLX model rather than simultaneous tracer gas tests. Future work should explore simultaneous use of TVOC measurements alongside conventional tracer gas testing to further explore the potential utility of such methods.
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Title: Efficiency of Carbon Fiber Composite Structural Systems for Tall Buildings: A Parametric Simulation Based Framework for Finite Element Analysis
Creator: Khairnar, Piyush Rajendra
Date: 2022
Description: The rate of global urban migration has increased drastically over the last century. With increasing population, the need for dense urban...
Show moreThe rate of global urban migration has increased drastically over the last century. With increasing population, the need for dense urban habitats is growing. Tall buildings are at the forefront of this growth and changing skyline of different cities around the globe is evident. The Structural system is an important and critical component of any tall building. Structural material can significantly impact the performance of a structural system as well as the way it is constructed. Carbon composite is known for its high strength and stiffness, also it is a lightweight structural material. Current industrial techniques allow for manufacturing of structural components made of carbon composite to be used in building structures. Carbon composite as a structural material shows potential to be used in tall buildings where strength and stiffness requirements are a key parameter.This research focuses on applications of Carbon Composite, also known as Carbon Fiber Reinforced Polymer (CFRP), as a structural material for tall buildings. The research aims to study the properties of carbon composite as a structural material and to explore its application in the structural system for tall buildings. Mechanical properties of CFRP such as strength, stiffness, etc. are studied with available literature to assess the potential of the material to be used in the design of structural system for tall buildings. Manufacturing processes along with fabrication methods are also studied to investigate the constructability using CFRP. The research draws attention on the issues of connectivity within CFRP structural components as well as performance of CFRP as a structural material in tall building structural systems. Computer based simulations are utilized to develop digital models and analyze the performance of the material in structural systems of tall buildings. Current applications of the material in building and construction sector are addressed in the literature review. This research evaluates the performance of the structural systems for tall buildings using carbon composite as the primary structural material. Connection level simulations presented in this research provide insights on the significance of fiber orientation in the fabrication of structures. Other challenges in the widespread use of CFRP material in tall buildings are addressed in the research but focus of the research is on the structural applications of the material in tall buildings. The research provides information about the use of CFRP as a structural material in tall buildings. The results of this study offer significant insights about the issues of connectivity and constructability related to use of CFRP in tall buildings. This research also provides a parametric framework for architects and designers to evaluate and study the performance of a structural materials to be used in tall building structural systems using finite element analysis.
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Title: Evaluating the Impact of Residential Indoor Air Quality and Ventilation and Filtration Interventions on Adult Asthma-Related Health Outcomes in Chicago, IL
Creator: Kang, Insung
Date: 2022
Description: Human exposure to a variety of airborne pollutants is associated with various adverse health effects, ranging from respiratory symptoms to...
Show moreHuman exposure to a variety of airborne pollutants is associated with various adverse health effects, ranging from respiratory symptoms to exacerbation of chronic diseases to cardiovascular disease and cancer. While most of our knowledge of the adverse impacts of air pollution comes from studies utilizing outdoor air pollutants as surrogates for exposure, people spend most of their time indoors, especially at home, where pollutant concentrations are often higher than outdoors. And within homes, mechanical ventilation systems and filtration are increasingly recommended to provide fresh air for ventilation and dilute indoor pollutant sources. There are a variety of ventilation system types that can be used for home retrofits; however, there is limited information on how they affect indoor air quality (IAQ) from both indoor and outdoor sources and how they influence occupant health and well-being. Therefore, to fill some of these knowledge gaps, this research aims to evaluate the effects of indoor air quality broadly, as well as interventions with three common types of residential mechanical ventilation system retrofits (i.e., continuous exhaust-only, intermittent fan-integrated supply, and continuous balanced systems with energy recovery ventilators), on asthma-related health outcomes in a cohort of adults in Chicago, IL. The key findings of this dissertation indicate that exposures to indoor NO2 and PM, higher indoor temperature, and mold/dampness were associated with poorer asthma control. The home ventilation and air filtration interventions, regardless of ventilation system type, significantly improved asthma control of the study population (~4% increase in ACT score; p < 0.001), and led to reductions in indoor concentrations of formaldehyde (HCHO) (-19.5 ppb; -63%; p < 0.001), carbon dioxide (CO2) (-120 ppm; -15%; p < 0.001), nitrogen dioxide (NO2) (-1.8 ppb; -3%; p = 0.035), and particulate matter (PM), including PM1 (-4.9 µg/m3; -43%; p = 0.001), PM2.5 (-4.9 µg/m3; -39%; p = 0.003), and PM10 (-6.2 µg/m3; -41%; p = 0.003). Additionally, asthma control was significantly improved in all subgroups: participants who received both ventilation and filtration interventions (~6% increase in ACT score; p < 0.001); continuous exhaust-only systems (~3% increase in ACT score; p = 0.033); intermittent central-fan-integrated-supply (CFIS) systems (~3% increase in ACT score; p = 0.018); and continuous balanced systems with an energy recovery ventilator (ERV) (~7% increase in ACT score; p < 0.001). Indoor CO2 concentrations were significantly reduced in homes with continuous ventilation systems, including exhaust-only (-165 ppm, -20%; p = 0.005) and balanced ERV systems (-186 ppm, -23%; p = 0.004), while indoor particulate matter (PM1, PM2.5, and PM10) concentrations were significantly reduced in homes with ventilation systems with filtration upgrades, including CFIS (PM1: -5.3 µg/m3, -46%; PM2.5: -5.0 µg/m3, -39%; and PM10: -6.2 µg/m3, -41%; all p < 0.05) and balanced ERV systems (PM1: -7.5 µg/m3, -59%; PM2.5: -8.3 µg/m3, -58%; and PM10: -10.4 µg/m3, -61%; all p < 0.05). Last, results of a cost-benefit analysis (CBA) of the three types of mechanical ventilation systems over an assumed 10-year life span, which predicted impacts on mortality and asthma outcomes based on measured impacts on two indoor pollutants – PM2.5 and NO2 – relative to initial and operational costs, as well as filtration upgrade costs, suggest that the intermittent CFIS system with improved MERV 10 filtration was the most beneficial approach, with the central benefit-cost ratio (BCR) of 6.0, followed by the continuous balanced ERV system (central BCR = 3.7) and exhaust-only system (central BCR = 3.2). This dissertation provides the first known empirical data in the U.S. on asthma outcomes associated with different types of mechanical ventilation systems that have highly varying impacts on indoor pollutant concentrations of both indoor and outdoor origin and environmental conditions. Results are also expected to provide much-needed guidance to homeowners, contractors, builders, and agencies on the advantages and disadvantages of different types of residential mechanical ventilation systems.
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Title: Intelligent Battery Switching Module for Hybrid Electric Aircraft
Creator: Kamal, Ahmad
Date: 2022
Description: The growth in world economics, tourism and international cooperation has resulted in significant growth of civil aviation industry. This...
Show moreThe growth in world economics, tourism and international cooperation has resulted in significant growth of civil aviation industry. This growing number of fossil fuel reliant aircrafts will significantly increase waste gas emissions with detrimental impact on the environment. The system efficiency of the aircraft must be substantially improved to reduce the fuel burn and thus waste gas emissions. Therefore, the aircraft industry is pushing towards higher electrification of future aircrafts to increase system efficiency, reduce fuel burn and to lower emissions as well as operational costs. The more electric aircraft (MEA) design concept, commercially realized by Boeing 787 and Airbus A380, increases system efficiency by replacing the mechanical, pneumatic, and hydraulic systems with electrical systems. However, global regulation authorities demand further reduction in waste gas emissions and fuel burn. To meet these stringent demands, the aircraft industry is exploring hybrid electric aircrafts which can significantly reduce fuel burn by electrifying the propulsion train of the aircraft. This higher penetration of electrical energy in the aircraft warrants smart short-circuit protection with ultrafast response time. However, current hybrid aircrafts still use outdated mechanical and thermal short-circuit protection which have historically proven to cause numerous tragedies. Solid-state power controller (SSPC) is an alternate solution which uses semiconductor devices to offer faster response. However, the main drawbacks of SSPCs are their need for active cooling due to higher conduction loss and the use of foldback current limiting approach to limit the inrush current of DC-link capacitor of the powertrain. The foldback current limiting approach degrades the power semiconductor devices used due to excessive heat loss by driving the device near the safe operating area (SOA) limits of the device. This thesis presents a 750V/250A intelligent Li-ion battery switching module (BSM) for hybrid electric aircraft propulsion application. The BSM uses commercially available 1200 V SiC JFET power modules with ultra-low RDSON in parallel to achieve sub-mΩ total on-resistance, comparable to the incumbent mechanical contactor solution. This allows the total nominal conduction power loss of the BSM to be less than merely 23 W, permitting maintenance-free passive cooling. In contrast to the incumbent contactor solution, the BSM has ultrafast response (µs-level) to a fault condition. Which, in conjunction with the reduced fault current stress, significantly improves the operation lifetime of the entire system. The BSM incorporates various intelligent features by implementing a tri-mode operation concept, which allows to pre-charge the DC-link capacitor with a limited charging current in PWM mode. To mitigate single-point failures, several design redundancy measures are implemented to ensure reliability and safety for the aircraft. Design considerations of the circuit and physical design of the BSM are discussed in detail including the design of the custom laminated busbar and thermal analysis. Furthermore, the inherent uncontrolled oscillation phenomenon of the JFET cascode structure is explored and addressed. Finally, the experimental results obtained from the built and tested prototype of the BSM are reported.
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Title: Examining Associations Between Discrimination, Social Cohesion, and Health among White and POC LGBT Chicagoans
Creator: Kannout, Lynn
Date: 2022
Description: Consistent with the minority stress perspective, lesbian/gay, bisexual, and transgender (LGBT) individuals on average report worse health than...
Show moreConsistent with the minority stress perspective, lesbian/gay, bisexual, and transgender (LGBT) individuals on average report worse health than heterosexual individuals in several domains, e.g., general health, mental health, physical health, and health care access. Intersectionality-based research shows that LGBT-POC are, on average, at even greater risk for adverse health outcomes compared to their White LGBT counterparts. Discrimination and social cohesion may be two mechanisms underlying these between- and within-group disparities, given their broader relations to health and their relatively high frequency within marginalized populations. This study used data from the Chicago Department of Public Health to examine broad health differences between LGBT White and LGBT-POC individuals, and to test specific mediations models in which social cohesion mediated links between discrimination and health. LGBT-POC reported experiencing worse general health, lower access to health care, more experiences of discrimination, and lower feelings of social cohesion than did White LGBT individuals. No mediation effects emerged, however there was a direct effect of experiencing discrimination on mental health distress. Further, discrimination exposure related inversely to feelings of social cohesion. Study strengths, limitations, and implications are discussed.
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Title: Predictive Energy Management of Connected Hybrid Electric Vehicles in the Presence of Uncertainty
Creator: Sotoudeh, Seyedeh Mahsa
Date: 2022
Description: Energy efficiency improvements brought by electrification of the powertrain in Hybrid Electric Vehicles (HEVs) highly depend on their...
Show moreEnergy efficiency improvements brought by electrification of the powertrain in Hybrid Electric Vehicles (HEVs) highly depend on their powertrain Energy Management Strategy (EMS) that determines optimal power allocation between powertrain components.Eco-driving based EMS seeks further energy efficiency improvements through optimizing vehicle's driving cycle (velocity and hence torque demand), in addition to the powertrain's EMS. A novel hierarchical EMS is developed in this thesis for connected human-driven HEVs and then extended to automated HEVs that effectively addresses some of the major challenges of the energy management problem. At its high-level, a computationally-tractable Pseudospectral Optimal Controller (PSOC) with discounted cost is employed to approximately solve the powertrain's energy management problem over driving cycle previews of the entire trip. The high-level's approximate solution is then used as a reference by the low-level tube-based Model Predictive Controller (MPC) that solves the problem over higher-quality, short-horizon driving cycles in a real-time applicable fashion. For human-driven HEVs, a Long Short-Term Memory (LSTM) neural network predicts the human driver's velocity profile over low-level's short horizons. A velocity optimizer is added to the low-level for automated HEVs that optimizes the vehicle's driving cycle by effectively utilizing regenerative braking capability of the HEV. At the low-level, the tube-based MPC controller solves the powertrain's energy management problem over either predicted (human-driven HEV) or optimized (automated HEV) driving cycles by accounting for driving cycle's uncertainty, due to uncertain future information, and hence ensures robust constraints satisfaction. A novel cost-to-go approximation method is developed that uses the optimal costate trajectories obtained from the high-level PSOC controller to generate terminal costs for the low-level controller. This improves suboptimality of the short-horizon solutions and ensures charge balance constraint satisfaction at the end of the trip without having to impose conservative constraints. A novel learning-based framework is also proposed to jointly optimize the automated HEV's driving cycle and its powertrain's power split. A Deep Neural Network (DNN)-based MPC controller is developed for the low-level that jointly optimizes the HEV's driving cycle and powertrain energy management in a real-time applicable manner. To ensure constraints satisfaction, a novel Quadratic Programming (QP)-based projection of the DNN-based approximate control laws is proposed that can be efficiently solved in real-time. Simulation results over standard and real-world driving cycles demonstrate efficacy of the proposed control frameworks in terms of suboptimality (fuel efficiency) improvement, potential real-time applicability, and constraints (especially charge balance constraint) satisfaction in the presence of driving cycle uncertainty.
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Title: Thermal Effects in Fluid Dynamics
Creator: Sulzbach, Jan-Eric
Date: 2021
Description: In this thesis we propose a mathematical framework modeling non-isothermal fluids.The framework is based on a coupling between non-equilibrium...
Show moreIn this thesis we propose a mathematical framework modeling non-isothermal fluids.The framework is based on a coupling between non-equilibrium thermodynamics and an energetic variational approach for the mechanical parts of the system. From this general model we derive and analyze three separate systems.The first application is the Brinkman-Fourier model. This is related to the ideal gas system, where the pressure and internal energy depend linearly on the product of density and temperature. This is a subsystem to the general Navier-Stokes-Fourier system. We prove the existence of local-in-time weak solutions via compensated compactness arguments.The next model we study is a non-isothermal diffusion system involving chemical reactions. For a system close to chemical equilibrium we show the well-posedness of classical solution using a fixed-point argument involving theory of homogeneous Besov spaces.The third application of the general theory is for another general diffusion system with a Cahn-Hilliard energy. In this framework, we study in detail how the temperature can affect the system on different scales, leading to different models. For the analysis, we focus on one case and show the well-posedness of classical solutions. The proof relies on methods from the theory of Besov spaces and paradifferential calculus.
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Title: Technological Consciousness in Midwestern American Farming: From Party Lines to Autonomous Tractors
Creator: Sziron, Mónika
Date: 2022
Description: This dissertation is primarily concerned with understanding the current conceptions, perceptions, and ethical concerns of artificial...
Show moreThis dissertation is primarily concerned with understanding the current conceptions, perceptions, and ethical concerns of artificial intelligence in Midwestern agriculture. Using the theory of technological consciousness as a backdrop for understanding the relationship between Midwestern agriculture and technology, in chapter two this dissertation first provides a narrative review of major technological developments throughout history in Midwestern farming and how the human experience in farming is influenced by technology throughout history. This history provides context for the current state of Midwestern agriculture, which is now increasingly entangled with artificial intelligence. The theory behind artificial intelligence ethics and general trends in artificial intelligence are discussed in chapter three. To understand present conceptions, perceptions, and ethical concerns of artificial intelligence for Midwestern farmers, a pilot survey was dispersed to farmers and pilot media content analysis was conducted on Midwestern agriculture publications. The results from this pilot survey and pilot media content analysis are discussed in chapter four. Chapter five delves into theory and how the human experience with technology has evolved over time and its effects on the human experience today. This chapter also provides theoretical insights for the future of farming with artificial intelligence. The dissertation concludes with reviewing the ethical concerns relating to artificial intelligence in agriculture for Midwestern farmers, provides recommendations for developers of agriculture technology, and highlights the new partnership between farmers and computer scientists and how this partnership will lead the way in the future of Midwestern farming.
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