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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: Resolvent Analysis of Turbulent Flow over Compliant Surfaces: Optimization Methods and Stability Considerations.
Creator: Lapanderie, Kilian Pierre Lucien
Date: 2024
Description: This thesis delves into the manipulation of turbulence properties through innovative compliant surface designs. Turbulence, known for its...
Show moreThis thesis delves into the manipulation of turbulence properties through innovative compliant surface designs. Turbulence, known for its unpredictable fluid movements, presents substantial challenges across engineering disciplines, particularly in optimizing system efficiency and minimizing energy losses. This research explores the potential of compliant surfaces to control and mitigate the adverse effects of turbulent flow, thereby enhancing the performance and reliability of engineering systems.Employing the resolvent analysis method, this work investigates the interaction between turbulent flows and surfaces capable of dynamic adaptation. The study evaluates the impact of these surfaces on turbulence suppression through the application of both space-dependent and independent compliance models, where the compliance model is characterised by an admittance, which represents the relationship between the instantaneous surface pressure and surface velocity. This approach allows for a nuanced understanding of how different surface properties can influence the behavior of turbulent flows.A significant contribution of this thesis is the comprehensive stability analysis conducted to assess the implications of compliant surfaces on the linear stability of the dynamical system. By examining the eigenvalues of the mean-linearized system, the research identifies the conditions under which compliant surfaces may induce or mitigate instabilities within turbulent flows. This analysis is pivotal in developing compliant surface designs that not only reduce turbulence-induced energy losses but also ensure the stability of the flow, a critical consideration for practical engineering applications.The findings of this thesis offer valuable insights into the role of surface compliance in turbulence control, paving the way for further research and the development of advanced engineering solutions. Through a detailed investigation of the interactions between compliant surfaces and turbulent flows, this work contributes to the broader field of fluid dynamics and underscores the potential of innovative surface designs in achieving more efficient and sustainable engineering systems.
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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: Utility of a Low-Coverage Genome Assembly for Discovery of Genes Associated with Pyrethroid Resistance in Smicronyx Fulvus LeConte
Creator: Markiv, Paulina Patrycja
Date: 2023
Description: Red sunflower seed weevil (RSSW) is a major insect pest of cultivated and wild common sunflowers in the Great Plains of North America. The...
Show moreRed sunflower seed weevil (RSSW) is a major insect pest of cultivated and wild common sunflowers in the Great Plains of North America. The extent of the sunflower damage due to RSSW infestation is too great for the natural sunflower defense mechanisms to protect the agriculture industry from losses. Pyrethroids are the only type of insecticide designated for the control of RSSW; however, instances of pyrethroid insecticide ineffectiveness against RSSW have been annually reported to entomologists at South Dakota State University since 2017. The biological bases of insecticide resistance are unknown but common mechanisms associated with pyrethroid resistance include general detoxification mechanism driven by cytochrome P450s (CYP450s) as well as mutations in the pyrethroid target, voltage-gated sodium channels (VGSCs). The goal of this study was to determine if the computational analysis of a low-coverage genome assembly is sufficient to identify and characterize genes associated with insecticide resistance which could contribute to pest control research efforts. By using a low-coverage genome assembly, RNA-Seq data, and bioinformatic tools, 40 complete and 33 partial gene models coding for CYP450 as well as a partial gene model coding for VGSC have been identified in the genome of RSSW. Twenty-seven mutation sites, previously associated with the pyrethroid resistance in other insects, have been identified in the VGSC gene of RSSW. The low-coverage genome proved to be a sufficient resource for preliminary studies of gene identification which could bring significant knowledge to subsequent research focusing on insecticide resistance and pest control.
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Title: LOW-COVERAGE GENOMES AS AN EFFECTIVE AND ECONOMICAL APPROACH FOR LEPIDOPTERAN MICROSATELLITE ISOLATION
Creator: Liang, Huijia
Date: 2020
Description: This study aimed to verify that whether a low-coverage genome can work as an effective approach to isolate Lepidopteran microsatellites. As...
Show moreThis study aimed to verify that whether a low-coverage genome can work as an effective approach to isolate Lepidopteran microsatellites. As microsatellites are useful tool to study population genetics, and there are many Lepidopteran agriculture pests which can cause huge economic damages every year, additionally, Lepidoptera have abundant similar flanking sequences making it difficult to develop reliable microsatellites. However, there are not enough published genomes of Lepidoptera species. If low-coverage Lepidopteran genomes can be used to isolate reliable microsatellites, the low-coverage genomes would be an effective and economical approach for microsatellites isolation, because low-coverage genome sequencing is much cheaper and less time-consuming than the published genome sequencing.
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Title: DEVELOPMENT AND APPLICATION OF A NATIONALLY REPRESENTATIVE MODEL SET TO PREDICT THE IMPACTS OF CLIMATE CHANGE ON ENERGY CONSUMPTION AND INDOOR AIR QUALITY (IAQ) IN U.S. RESIDENCES
Creator: Fazli, Torkan
Date: 2020
Description: Americans spend most of their time inside residences where they are exposed to a number of pollutants of both indoor and outdoor origin....
Show moreAmericans spend most of their time inside residences where they are exposed to a number of pollutants of both indoor and outdoor origin. Residential buildings also account for over 20% of total primary energy consumption in the U.S. and a similar proportion of greenhouse gas emissions. Moreover, climate change is expected to affect building energy use and indoor air quality (IAQ) through both building design (i.e., via our societal responses to climate change) and building operation (i.e., via changing meteorological and ambient air quality conditions). The overarching objectives of this work are to develop a set of combined building energy and indoor air mass balance models that are generally representative of both the current (i.e., ~2010s) and future (i.e., ~2050s) U.S. residential building stock and to apply them using both current and future climate scenarios to estimate the impacts of climate change and climate change policies on building energy use, IAQ, and the prevalence of chronic health hazards in U.S. homes. The developed model set includes over 4000 individual building models with detailed characteristics of both building operation and indoor pollutant physics/chemistry, and is linked to a disability-adjusted life years (DALYs) approach for estimating chronic health outcomes associated with indoor pollutant exposure. The future building stock model incorporates a combination of predicted changes in future meteorological conditions, ambient air quality, the U.S. housing stock, and population demographics. Using the model set, we estimate the total site and source energy consumption for space conditioning in U.S. residences is predicted to decrease by ~37% and ~20% by mid-century (~2050s) compared to 2012, respectively, driven by decreases in heating energy use across the building stock that are larger than coincident increases in cooling energy use in warmer climates. Indoor concentrations of most pollutants of ambient origin are expected to decrease, driven by predicted reductions in ambient concentrations due to tighter emissions controls, with one notable exception of ozone, which is expected to increase in future climate scenarios. This work provides the first known estimates of the potential magnitude of impacts of expected climate changes on building energy use, IAQ, and the prevalence of chronic health hazards in U.S. homes.
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Title: Colored Pencil Drawings, undated
Description: Untitled colored pencil drawings by Mary Henry, date unknown. Inscription on verso: "William Winter Comments, PO Box 817, Sausalito"
Collection: Mary Dill Henry Papers, 1913-2021

Title: Computational Genomics of Human-Infecting Microsporidia Species from the Genus Encephalitozoon
Creator: Mascarenhas dos Santos, Anne Caroline
Date: 2023
Description: Microsporidia are obligate intracellular pathogens classified as category B priority pathogens by the National Institute of Allergy and...
Show moreMicrosporidia are obligate intracellular pathogens classified as category B priority pathogens by the National Institute of Allergy and Infectious Diseases (NIAID), a division of the National Institutes of Health (NIH). Microsporidian species from the genus Encephalitozoon infect humans and can cause encephalitis, keratoconjunctivitis or enteric diseases in both immunocompromised and immunocompetent individuals. The main treatment for disseminated microsporidiosis available in the United States is albendazole, an anthelmintic benzimidazole that is also used to treat fungal infections, but species from the Encephalitozoonidae have already shown signs of resistance against this drug. The Encephalitozoonidae harbors highly specialized pathogens with the smallest known eukaryote genomes, with Encephalitozoon cuniculi featuring a genome of only 2.9 Mbp and coding for a proteome of roughly 2,000 proteins. Pathogens are in an everlasting race to quicken their adaptation pace against host defenses. This adaptation is often driven by gene duplication, recombination and/or mutation, and due to the potentially disruptive nature of duplication and recombination processes, many of these evolutions in pathogens are taking place outside conserved genomic loci. As such, genes involved in virulence and drug resistance in pathogens are often localized in the (sub)telomeres rather than in chromosome cores. The small and streamlined nature of microsporidian genomes makes them excellent candidates to investigate the adaptation of pathogens to host defenses, the evolution of their virulence, and the development of their resistance to drugs from a genomic perspective. However, microsporidian genomes are highly divergent at the DNA sequence level and the ones that have been sequenced so far are incomplete and are lacking the telomeres. This high level of sequence divergence hinders standard sequence homology-based functional annotations, blurring our understanding of what these organisms are capable of from a metabolic perspective. The gap in our knowledge of what is encoded in the microsporidia telomeres could lead to an underestimation of their pathogenic capabilities. Therefore, deciphering the functions of unknown proteins in microsporidia genomes and unraveling the content of their telomeres is important to fully assess their potential for adaptability to host defenses and predisposition to drug resistance. Likewise, a better understanding of the genetic diversity in microsporidia will help assess the extent by which host-pathogen interactions are shaping the adaptation of these parasites to humans. As observed in the COVID-19 pandemic, genetic diversity can influence the speed at which pathogens adapt to host defenses and thus can pose a big challenge to disease control. The development of strategies for controlling microsporidiosis outbreaks will likely benefit from the work performed in this thesis. As part of my PhD work, I investigated the virulence and host-adaptation capabilities of human-infecting microsporidia species from the genus Encephalitozoon with computational genomic approaches. This work included: 1) using structural homology to infer the functions of unknown proteins from the microsporidia proteome; 2) sequencing the complete genomes from telomere-to-telomere of three distinct Encephalitozoon spp. (E. cuniculi, E. hellem and E. intestinalis) to determine the genetic makeup of their telomeres and better understand the extent of their diversity; and 3) assessing the intraspecies genetic diversity that exists between Encephalitozoon species.
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Title: Eating disorder support group utilization: Associations with psychological health and eating disorder psychopathology among support group attendees
Creator: Murray, Matthew F.
Date: 2023
Description: Individuals with eating disorders (EDs) report psychosocial impairments that may persist beyond ED symptom remission, suggesting a need to...
Show moreIndividuals with eating disorders (EDs) report psychosocial impairments that may persist beyond ED symptom remission, suggesting a need to examine ED treatment-adjunctive services that foster psychosocial health. One promising resource is support groups, as evidence across medical and psychiatric illnesses shows associations between group utilization and wellbeing. However, virtually no literature has examined ED-specific support groups and psychosocial health, and it is also unknown how use of supportive services relates to ED symptoms. The present study examined associations between past-month ED support group attendance and participation frequency, psychosocial health indices, and ED symptoms. A total of 215 participants who attended weekly virtual clinician-moderated ED support groups completed measures of psychosocial health, internalized stigma of mental illness, psychosocial impairment from an ED, specific types of social support elicited in group, and ED psychopathology. Adjusting for past-month ED treatment, Benjamini-Hochberg-corrected partial correlation analyses indicated that more frequent attendance was negatively related to body dissatisfaction, purging, excessive exercise, and negative attitudes toward obesity, and positively related to social support. More frequent verbal and chat participation were positively related to emotional and informational support and social companionship. Chat participation was additionally negatively related to excessive exercise and negative attitudes toward obesity. Results suggest that utilizing and participating in clinician-moderated ED support groups could provide an outlet for ED symptom management and solicitation of social support. Findings highlight areas for further consideration in the delivery of and future research on ED support groups.
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Title: Optimization methods and machine learning model for improved projection of energy market dynamics
Creator: Saafi, Mohamed Ali
Date: 2023
Description: Since signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon...
Show moreSince signing the legally binding Paris agreement, governments have been striving to fulfill the decarbonization mission. To reduce carbon emissions from the transportation sector, countries around the world have created a well-defined new energy vehicle development strategy that is further expanding into hydrogen vehicle technologies. In this study, we develop the Transportation Energy Analysis Model (TEAM) to investigate the impact of the CO2 emissions policies on the future of the automotive industries. On the demand side, TEAM models the consumer choice considering the impacts of technology cost, energy cost, refueling/charging availability, consumer travel pattern. On the supply side, the module simulates the technology supply by the auto-industry with the objective of maximizing industry profit under the constraints of government policies. Therefore, we apply different optimization methods to guarantee reaching the optimal automotive industry response each year up to 2050. From developing an upgraded differential evolution algorithm, to applying response surface methodology to simply the objective function, the goal is to enhance the optimization performance and efficiency compared to adopting the standard genetic algorithm. Moreover, we investigate TEAM’s robustness by applying a sensitivity analysis to find the key parameters of the model. Finally based on the key sensitive parameters that drive the automotive industry, we develop a neural network to learn the market penetration model and predict the market shares in a competitive time by bypassing the total cost of ownership analysis and profit optimization. The central motivating hypothesis of this thesis is that modern optimization and modeling methods can be applied to obtain a computationally-efficient, industry-relevant model to predict optimal market sales shares for light-duty vehicle technologies. In fact, developing a robust market penetration model that is optimized using sophisticated methods is a crucial tool to automotive companies, as it quantifies consumer’s behavior and delivers the optimal way to maximize their profits by highlighting the vehicles technologies that they could invest in. In this work, we prove that TEAM reaches the global solution to optimize not only the industry profits but also the alternative fuels optimized blends such as synthetic fuels. The time complexity of the model has been substantially improved to decrease from hours using the genetic algorithm, to minutes using differential evolution, to milliseconds using neural network.
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Title: Factor Analysis of the Neurobehavioral Symptom Inventory in Veterans with Posttraumatic Stress Disorder
Creator: Scimeca, Lauren
Date: 2020
Description: The Neurobehavioral Symptom Inventory (NSI) is a widely used measure of postconcussive symptoms in veteran populations. Previous psychometric...
Show moreThe Neurobehavioral Symptom Inventory (NSI) is a widely used measure of postconcussive symptoms in veteran populations. Previous psychometric studies used samples of veterans with mild Traumatic Brain Injury (mTBI) and high rates of comorbid Posttraumatic Stress Disorder (PTSD). The present study aims to determine the best-fitting factor structure of the NSI in veterans with PTSD and to evaluate the relationship between the best-fitting factor structure and the symptom clusters of PTSD. A confirmatory factor analysis (CFA) found that 4-factors had the best overall fit in veterans with PTSD. Correlational analyses found high rates of correspondence between the cognitive and affective factors of the NSI and the alterations in cognition and mood and hyperarousal symptom clusters of PTSD. The analyses reveal that symptoms of the NSI cluster in the same way in a sample of veterans with PTSD as they do in veterans with mTBI, suggesting that lingering postconcussive symptoms in veterans with PTSD are better characterized as non-specific generalized health symptoms on the NSI.
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Title: PTSD Symptoms as a Potential Link Between Military Sexual Assault and Disordered Eating
Creator: Sandhu, Danielle
Date: 2020
Description: Despite increasing rates of sexual assault in the military and high rates of disordered eating and posttraumatic stress disorder (PTSD) among...
Show moreDespite increasing rates of sexual assault in the military and high rates of disordered eating and posttraumatic stress disorder (PTSD) among veterans, little is known about how these constructs are related. The present study examined whether PTSD symptoms mediate the relation between military sexual assault and disordered eating among female veterans. Prolific Academic was used to recruit 98 United States female veterans as participants for the study. Participants completed an online questionnaire of self-report measures assessing demographic characteristics, military sexual assault, PTSD symptoms, and disordered eating. Mediational analyses were conducted using the PROCESS v3 macro in IBM SPSS Statistics. Within the sample, 61% of female veterans reported being sexually assaulted while serving in the military. Military sexual assault was associated with higher levels of PTSD symptoms and disordered eating. Findings did not support the hypothesis that PTSD symptoms would mediate the relation between military sexual assault and disordered eating among women veterans. Given the heterogeneous nature of disordered eating, post-hoc mediational analyses were conducted to examine specific facets of eating pathology. Results indicated that PTSD symptoms fully mediated the relation between military sexual assault and bulimia and food preoccupation. Awareness of these psychopathological sequelae following military sexual assault may improve screening and intervention efforts at Veteran Affairs (VA) medical centers. The present study highlights the importance of future longitudinal studies that can establish temporal precedence in order to better understand the pathways leading to disordered eating in female veterans.
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Title: Advertisement for Saturday Morning Children's Club at Chicago School of Design, 1944
Date: 1944
Description: Advertisement for Saturday Morning Children's Club, a multidisciplinary children's art workshop offered by the Chicago School of Design,...
Show moreAdvertisement for Saturday Morning Children's Club, a multidisciplinary children's art workshop offered by the Chicago School of Design, featuring artwork by two local children. The date listed is uncertain, but inferred from the pencil notation on recto.
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Collection: Institute of Design records, 1937-ca. 1962

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: BAYESIAN MOMENTUM STRATEGY OF EXCHANGE RATES
Creator: Lee, Namhoon
Date: 2011-11, 2011-12
Description: A disagreement has existed between the foreign currency trading community and academic researchers relating to the time series properties of...
Show moreA disagreement has existed between the foreign currency trading community and academic researchers relating to the time series properties of exchange rates. Traders typically view exchange rates as strongly trending prices and suggest that simple rules, based solely on past prices, have generated predictable profits with acceptable risk over most of the floating-rate period. However, many surveys presenting controversial results. This research identifies the non-linear trend momentum in monthly exchange rate and examines the profitability of momentum trading model within exchange rate returns in the context of Bayesian econometrics. A development of Bayesian momentum trading strategy based on trend component of the spot exchange rate is established. First, parameters of momentum model for each main currency are estimated. The momentum is defined as a simple nonlinear function of return series and the model is designed to estimate the expected conditional mean and associated conditional volatilities simultaneously. The empirical results reported several notable confirmation and findings; first, predictability of momentum model with Bayesian approach show better accuracy than model with maximum likelihood estimation or moving average rule in terms of directionality and model fitting. Second, parameters are restricted to be same across the currencies with the assumption that currencies share some degree of commonality within the system. The result confirms that the restricted model work as well as the unrestricted model within the currency model in terms of model fitting and directional accuracy. Third, principal component analysis is used to analyze the exchange rate movements. PCA found that the first principal component shows parallel shift of all currencies and second principal component tilt shift where high yield currencies move down and low yield currencies move up. Fourth, the parameter estimates from the models are used for portfolio allocation ix applying Bayesian Principal Component(PC) GARCH(1,1) model and the portfolio performance is compared with the performance with classical maximum likelihood approach and other benchmarks. The results show that the Bayesian PC-GARCH(1,1) performs better than classical PC-GARCH(1,1) in terms of Sharpe ratio, Value at Risk, Expected shortfall, maximum drawdown and other statistical criteria. Sixth, the GARCH parameter space is found to be non-symmetric confirming that maximum likelihood estimation would have over or under estimated the parameter causing misspecifying the model parameters. The result from this research confirms simple nonlinear momentum model combined with Bayesian approach can be a good forecasting tool, and restricted model can simplify the complexity of parameter space of exchange rate movement. In addition, by correctly detecting the parameter space, Bayesian approach outperforms the classical maximum likelihood approach. Keywords : Bayesian framework, Momentum, Moving Average rules, Carry trade strategy, Mean-variance Optimization, Trading strategy, Metropolis-Hastings Algorithm, Gibbs Sampler
Ph.D. in Management Science, December 2011
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Title: IMPROVEMENT OF VERTICAL-AXIS WIND TURBINE PERFORMANCE VIA TURBINE COUPLING
Creator: Mehrpooya, Payam
Date: 2014, 2014-07
Description: While vertical-axis wind turbines (VAWTs) have a simpler design than the horizontal-axis wind turbines, their development has been hindered...
Show moreWhile vertical-axis wind turbines (VAWTs) have a simpler design than the horizontal-axis wind turbines, their development has been hindered due to their unsteady aerodynamics and complex flow field. In this thesis, a parameterized study is conducted to simulate a baseline VAWT using STAR-CCM+, a commercial finite volume code. A hybrid grid scheme, with structured prism layer mesh at the surface of the blades, is used to properly resolve the turbulent boundary layers on the blades. The flow was highly unsteady due to the rotating geometries. Thus, a sliding mesh technique is implemented at the interface of rotating and stationary zones. The dominant factors limiting the performance of the VAWTs are investigated for a range of moderate tip speed ratios, by visualizing the flow field and modeling the individual blade aerodynamics. The VAWT aerodynamics is shown to be dominated by the dynamic stall, at low tip speed ratios, and by the blade-wake interactions and the wake blockage effects, at high tip speed ratios. The concept of turbine coupling is used to improve the performance of the VAWTs by their internal aerodynamic interactions. Two counter-rotating turbines are placed in close proximity, and simulated over the same range of tip speed ratios as before, and for a set of different spacing between them. The effects of spacing and the tip speed ratio on their overall power output and their wake recovery characteristics are then investigated. A cluster of turbines with spacing equal to 1.50 turbine diameters and tip speed ratio of three is shown to have the quickest wake recovery and highest power enhancement, increasing the turbine average power coefficient by 22%.
M.S. in Mechanical and Aerospace Engineering, July 2014
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Title: INFORMATION OF MARKET EFFICIENCY, VOLATILITY, VOLUME, AND TREND FROM LIMIT ORDER BOOK
Creator: LI, SHOUHAO
Date: 2019
Description: This study mainly focuses on a series of topics within high frequency data of aprivate limit order book from NASDAQ. The interest of our...
Show moreThis study mainly focuses on a series of topics within high frequency data of aprivate limit order book from NASDAQ. The interest of our research first comes from thefamous classical theory “Efficient Market Hypothesis (EMH)”. Given the existence of aseparate market in which high frequency traders compete together under today’senvironment, we show that this market is quite adaptive rather than efficient since thestatistical quantity measuring market efficiency will have fluctuating values in differenttime point, confirmed in our study. Then we explore the linkage between high frequency cancelling activity and marketshort-term volatility (quote volatility). The findings for this topic until now are rather notconclusive yet. In our design, we first use Grange Causality test. It turns out realizedvolatility and cancelling activity granger cause each other, and cancelling activitycontributes tremendously to volatility forecasting. Then we fit our data in a generic ofARCH models to establish the predictability of realized volatility by cancellinginformation. Finally, we take advantage of the cancelling activity to predict real timetrading. We use the VIXY which is an ETF of VIX and focuses on short term performance.We find that the ask side of high frequency trading activities has far more significant impactfor both the level of VIXY and return of VIXY, while the bid side seems to be trivial. At last, we analyze the role of traded volume and trend in technical analysis. Theusefulness of technical analysis has been confirmed in the beginning part of this study byrejecting EMH, then a continuing topic for we to discuss is possible variables which couldcontribute to technical analysis. As known to finance literature, volume is frequently takento validify the trend of stock or discover the reversal of the trend. But now there is oppositeopinion maintaining that the role of volume has become trivial. Our findings complementprevious researches and confirm both the usefulness and the fading of such usefulness oftraded volume.
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Title: ACTIVE LEARNING WITH RICH FEEDBACK
Creator: Sharma, Manali
Date: 2017, 2017-07
Description: One of the goals of artificial intelligence is to build predictive models that can learn from examples and make predictions. Predictive models...
Show moreOne of the goals of artificial intelligence is to build predictive models that can learn from examples and make predictions. Predictive models are useful in many domains and applications such as predicting fraud in credit card transactions, predicting whether a patient has heart-disease, predicting whether an email is a spam, predicting crime, recognizing images, recognizing speech, and many more. Building predictive models often requires supervision from a human expert. Since there is a human in the loop, the supervision needs to be as resource-efficient as possible to save the human’s time, cost, and effort in providing supervision. One solution to make the supervision resource-efficient is active learning, in which the active learner interacts with the human to acquire supervision, usually in the form of labels, for a few selected examples to effectively learn a function that can be used to make predictions. In this thesis, I explore more intuitive and effective use of human supervision through richer interactions between the human expert and the learner, so that the human can understand the learner’s reasoning for querying examples, and provide information beyond just the labels for examples. Traditional active learning approaches select informative examples for labeling, but the human does not get to know why those examples are useful to the learner. While interacting with the learner to annotate examples, humans can provide rich feedback, such as provide their prior knowledge and understanding of the domain, explain certain characteristics of the data, suggest important attributes of the data, give rationales for why an example belongs to a certain category, and provide explanations by pointing out features that are indicative of certain labels. The challenge, however, is that traditional supervised learning algorithms can learn from labeled examples, but they are not equipped to readily absorb the rich feedback. In this thesis, we enable the learner to explain its reasons for selecting instances and devise novel methods to incorporate rich feedback from humans into the training of predictive models. Specifically, I build and evaluate four novel active learning frameworks to enrich the interactions between the human and learner. First, I introduce an active learning framework to reveal the learner’s perception of informative instances. Specifically, we enable the learner to provide its reasons for uncertainty on examples and utilize the learner’s perception of uncertainty to select better examples for training the predictive models. Second, I introduce a framework to enrich the interaction between the human and learner for document classification task. Specifically, we ask the human to annotate documents and provide rationales for their annotation by highlighting phrases that convinced them to choose a particular label for a document. Third, I introduce a framework to enrich the interaction between the human and learner for the aviation domain, where we ask subject matter experts to examine flights and provide rationales for why certain flights have safety concerns. Fourth, I introduce a framework to enrich the interaction between the human and learner for document classification task, where we ask humans to provide explanations for classification by highlighting phrases that reinforce their belief in the document’s label and striking-out phrases that weaken their belief in the document’s label. We show that enabling richer interactions between the human and learner and incorporating rich feedback into learning lead to more effective training of predictive models and better utilization of human supervision.
Ph.D. in Computer Science, July 2017
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Title: REPLENISHiE THE VOID
Creator: Monteleagre, Ryan
Date: 2012-12-02, 2012-12
Description: When a site’s program becomes detrimental to the area, should it be deconstructed and completely rebuilt? Or could the celebration,...
Show moreWhen a site’s program becomes detrimental to the area, should it be deconstructed and completely rebuilt? Or could the celebration, preservation and integration of the site’s history yield a much more sustainable, successful and ultimately satisfying result? If the area has fallen into urban blight, can this serve as a catalyst for urban renewal? Can focusing on such projects eventually lead to more sustainable and safer cities while cutting down on urban sprawl? The Union Stock Yards in Chicago was once the center of the meat packing industry in America, at one point the most productive in the world. Famous for its massive size, terrible working conditions and struggle for labor rights, the Stockyards had enormous effects on its surrounding neighborhoods which are still being felt today. The site has since been gutted and is currently an industrial park. This has created a gaping hole in the urban fabric, and much of it is still abandoned, resulting in an urban wasteland. This project uses the troubled history of the site positively and adaptively reuses the Chiappetti Lamb and Veal building. Taking cues from the site’s history as a food producer, an urban farm will offer both fresh produce and jobs for residents of the area. An on site market will be added which will sell locally grown produce, create more jobs and act as a potential draw for residents within and outside the district. Given the infamous history of the stockyards, a Museum of Labor Rights is an appropriate addition. This will attract people from outside the site’s immediate surroundings and preserve the gradually disappearing history of the district. This catalyst for urban renewal in the New City neighborhood of Chicago will be the first phase of the masterplan envisioned for the Union Stock Yard site.
M.S. in Architecture, December 2012
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Title: ANYTIME ACTIVE LEARNING DISSERTATION
Creator: Ramirez Loaiza, Maria E.
Date: 2016, 2016-05
Description: Machine learning is a subfield of artificial intelligence which deals with algorithms that can learn from data. These methods provide...
Show moreMachine learning is a subfield of artificial intelligence which deals with algorithms that can learn from data. These methods provide computers with the ability to learn from past data and make predictions for new data. A few examples of machine learning applications include automated document categorization, spam detection, speech recognition, face detection and recognition, language translation, and self-driving cars. A common scenario for machine learning is supervised learning where the algorithm analyzes known examples to train a model that can identify a concept. For instance, given example documents that are pre-annotated as personal, work, family, etc., a machine learning algorithm can be trained to automate organizing your documents folder. In order to train a model that makes as few mistakes as possible, the algorithm needs many training examples (e.g., documents and their categories). Obtaining these examples often involves consulting the human user/expert whose time is limited and valuable. Hence, the algorithm needs to utilize the human’s time as efficiently as possible by focusing on the most cost-effective and informative examples that would make learning more efficient. Active learning is a technique where the algorithm selects which examples would be most cost-effective and beneficial for consultation with the human. In a typical active learning setting, the algorithm simply chooses the examples that should be asked to the expert. In this thesis, we take this one step further: we observe that we can make even better use of the expert’s time by showing not the full example but only the relevant pieces of it, so that the expert can focus on what is relevant and can provide the answer faster. For example, in document classification, the expert does not need to see the full document to categorize it; if the algorithm can show only the relevant snippet to the expert, the expert should be able to categorize the document much faster. However, automatically finding the relevant snippet is not a trivial task; showing an incorrect snippet can either hinder the expert’s ability to provide an answer at all (if the snippet is irrelevant) or even cause the expert to provide incorrect information (if the snippet is misleading). For this to work, the algorithm needs to find a snippet to show the expert, estimate how much time the expert will spend on that snippet, and predict if the expert will return an answer at all. Further, the algorithm would estimate the likelihood of the expert returning the correct answer. Similar to anytime algorithms that can find better solutions as they are given more time, we call the proposed set of methods anytime active learning where the experts are expected to give better answers as they are shown longer snippets. In this thesis, we focus on three aspects of anytime active learning: i) anytime active learning with document truncation where the algorithm assumes that the first words, sentences, and paragraphs of the document are most informative and it has to decide on the snippet length, i.e., where to truncate the document, ii) given a document, the algorithm optimizes for both snippet location and length, and lastly, iii) the algorithm chooses not only the snippet location and size but also chooses which documents to choose snippets from so that the snippet length, the correctness of the expert’s response, and the informativeness of the document are all optimized in a unified framework.
Ph.D. in Computer Science, May 2016
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