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Title: FACILITATORS AND BARRIERS TO PRE-EXPOSURE PROPHYLAXIS (PREP) UPTAKE WILLINGNESS FOR FULL-SERVICE SEX WORKERS
Creator: Ramos, Stephen D
Date: 2022
Description: Full-service sex workers (FSSW) are individuals who exchange direct sexual services for goods, money, or other services (Centers for Disease...
Show moreFull-service sex workers (FSSW) are individuals who exchange direct sexual services for goods, money, or other services (Centers for Disease Control and Prevention, 2022a). FSSW report relatively poorer physical and mental health compared to others (Ramos et al., 2022; Rekart, 2005). Related, the CDC indicates that due to the nature of sex work, sex workers may be disproportionately at-risk for contracting Human Immunodeficiency Virus (HIV; Centers for Disease Control and Prevention, 2022a). However, a variety of factors may relate to HIV-risk in this population. Specifically, different multi-level factors may relate to sex workers’ willingness to use pre-exposure prophylaxis (PrEP), a once-daily HIV preventative medication (Centers for Disease Control and Prevention, 2022a). While highly effective against HIV, PrEP uptake in several key HIV populations is slow (Holloway et al., 2017). Here, I adapted the Social-Ecological Model (Kaufman et al., 2014), with the assistance of lived-experience members and community organizations in developing and disseminating the study, to assess barriers and facilitators towards PrEP uptake willingness for FSSW and investigated a distal-proximal stigma-based mediation analysis to PrEP willingness. I found that two barriers and two facilitators initially emerged as significant predictor of PrEP uptake willingness. However, in adopting a more conservative approach, only (a) anticipating stigmatizing disapproval from others, and (b) providing others with PrEP knowledge, independently remained as a significant barrier and facilitator to PrEP uptake willingness, respectfully. Mediation analysis did not yield a distal-proximal stigma-based mediation of PrEP uptake willingness. Implications for future research, clinical work, and policy are discussed.
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Title: POLYTRAUMA CLINICAL TRIAD ASSOCIATED ATTENTION AND MEMORY FUNCTIONING
Creator: Ramirez, Amanda M.
Date: 2021
Description: The purpose of the current study was to explore cognitive functioning associated with the polytrauma clinical triad in a sample of post-9/11...
Show moreThe purpose of the current study was to explore cognitive functioning associated with the polytrauma clinical triad in a sample of post-9/11 veterans. More specifically, it sought to determine if a component (i.e., PTSD, mTBI, or pain), in the context of the triad, accounted for variability in attention and memory functioning as measured by neuropsychological assessments. The study also sought to evaluate the relation between PTSD and cognition more comprehensively by examining if the four PTSD symptom clusters were associated with differential patterns of neuropsychological performances. Participants included 111 veterans who served in Operation Enduring Freedom, Operation Iraqi Freedom, and Operation New Dawn, otherwise known as post-9/11 veterans. Participants completed a brief structured interview and neuropsychological battery. Several hierarchical regressions examined the association between the polytrauma clinical triad and performances on select measures of attention and memory. Results indicated that the triad did not significantly predict sustained attention, visual memory, or verbal memory. These findings suggested that despite the rates of the polytrauma clinical triad among a significant portion of post-9/11 veterans, the current evidence does not support the presence of related cognitive impairment.
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Title: Contract Rollover and Volatility
Creator: Chen, Yue
Date: 2022
Description: In futures markets, approaching the expiration days, most market participants close out existing positions of front month contract and open...
Show moreIn futures markets, approaching the expiration days, most market participants close out existing positions of front month contract and open new positions of next month contract. The object of this dissertation is to evaluate the impact of contract rollover activities on unconditional volatility and conditional volatility modeling. First, two contract rollover measures, volume ratio and open interest ratio of front contract over next contract are created. Second, this study investigates the impact of contract rollover measures on both unconditional volatility estimation models and conditional volatility estimation models. Third, it examines the roles of contract rollover activities in unconditional volatility prediction models. Last, to further explore the relationship between contract rollover measures and unconditional volatilities, the vector autoregressive model is conducted to test granger causality. The findings show that the volume ratio and open interest ratio have significant impact on unconditional volatilities and conditional volatility in soybean, wheat, gold, copper, crude oil, and natural gas futures markets, except on conditional volatility in silver futures market. Alternative models that incorporate contract rollover measures outperform benchmark models that do not incorporate contract rollover measures in both estimation models and prediction models. Moreover, the findings provide the strong evidence that there is significant bidirectional granger causality among volume ratio, open interest ratio and unconditional volatilities in all investigated futures markets. The empirical results confirm the important role of contract rollover on volatility behavior and are beneficial to futures exchanges to set and monitor margins precisely for their customer’s trading accounts in commodity futures markets.
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Title: Implicit Theory of Willpower and Life Satisfaction Among Persons with Spinal Cord Injury
Creator: Cerny, Brian M.
Date: 2022
Description: Spinal cord injury (SCI) is a leading cause of physical disability and physical, functional, and psychosocial outcomes vary among persons with...
Show moreSpinal cord injury (SCI) is a leading cause of physical disability and physical, functional, and psychosocial outcomes vary among persons with SCI. Persons with SCI are at risk for poor psychosocial adjustment, evidenced by higher rates of mood disorders and lower reported life satisfaction (LS) when compared to the general population. LS among persons with SCI is influenced by sociodemographic, injury-related, and psychosocial factors. Implicit theory of willpower (TOW) refers to individuals’ beliefs about their capacity for self-regulation; specifically, whether or not self-regulatory capacity (i.e., willpower) is depleted with use. TOW has previously been associated with LS and other aspects of subjective well-being. This is the first study to assess TOW among persons with SCI, and aims to investigate the association between TOW and LS among persons with SCI. The study sample consisted of 156 adults with SCI who completed an anonymous online questionnaire. Associations between demographic- and disability-related factors, global perceived stress, TOW, engagement coping, disengagement coping, and LS were assessed via bivariate Pearson correlations and a 3-block hierarchical multiple linear regression with LS as the primary outcome. LS was significantly correlated with age, perceived physical health, self-reported participation, and perceived stress. After controlling for the influence of other variables, age, perceived physical health, and perceived stress were significantly associated with LS, consistent with prior work. Neither TOW nor the interaction between perceived stress and TOW were significantly associated with LS. Post hoc analyses suggest a chronic disability population may have different interpretations of the TOW construct or measure items than populations previously investigated. Clinical implications and future directions for research are discussed.
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Title: INFORMATION EFFICIENCY AND THE EFFECT OF HIGH FREQUENCY TRADING IN THE U.S. FUTURES MARKETS
Creator: CHA, SEUNG YOUN
Date: 2021
Description: The paper gives an empirical analysis with the U.S. futures market data on how High Frequency Trading, HFT can improve the information...
Show moreThe paper gives an empirical analysis with the U.S. futures market data on how High Frequency Trading, HFT can improve the information efficiency of asset prices. Various analyses were conducted to determine the degree of efficiency of information in futures high-frequency trading. The paper tries to explain the effect of high-frequency trading on the efficiency of the market in various ways and tries to propose stepping stones for developing a new market analysis measure.The research builds a coherent framework for analyzing both linear and non-linear market efficiency and applies it to a variety of futures contracts using high- frequency data. The major finding of this paper is that market efficiency levels vary widely over time depending on market characteristics. The paper also finds that HFT activities are higher when the market is inefficient. The paper analyzes the relationship between high frequency trading activities and market efficiency and discovers the mechanism. The story that HFT activity responds to market efficiency needs is especially strong in the E-mini, S&P500 futures contract.
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Title: Distribution-aware Visual Semantic Understanding
Creator: Chen, Ying
Date: 2021
Description: Understanding visual semantics, including change detection and semantic segmentation, is an essential task in many computer vision and image...
Show moreUnderstanding visual semantics, including change detection and semantic segmentation, is an essential task in many computer vision and image processing applications. Examples of visual semantics understanding in images include land cover monitoring, urban expansion evaluation, autonomous driving, and scene understanding. The goal is to locate and recognize appropriate pixel-wise semantic labels in images. Classical computer vision algorithms involve sophisticated semi-heuristic pre-processing steps and potentially manual interaction. In this thesis, I propose and evaluate end-to-end deep neural approaches for processing images which achieve better performance compared with existing approaches. Supervised semantic segmentation has been widely studied and achieved great success with deep learning. However, existing deep learning methods typically suffer from generalization issues where a well-trained model may not work well on unseen samples from a different dataset. This is due to a distribution change or domain shift between the training and test sets that can degrade performance. Providing more labeled samples covering many possible variations can further improve the generalization of models, but acquiring labeled data is typically time-consuming, labor-intensive and requires domain knowledge. To tackle this label scarcity bottleneck for supervised learning, we propose to apply unsupervised domain adaptation, semi-supervised learning, and semi-supervised domain adaptation for neural semantic segmentation. The motivation behind unsupervised domain adaptation for semantic segmentation is to transfer learned knowledge from one or more source domains with sufficient labeled samples to a different but relevant target domain where labeled data is sparse or non-existent. The adaptation algorithm tends to learn a common representation space where the distributions over both source and target domains are matched. In this way, we expect a classifier working well in the source domain to generalize well to the target domain. More specifically, we try to learn class-aware source-target domain distribution differences, and transfer the knowledge learned from labeled synthetic data on the source domain to the unlabeled real data on the target domain. Different from domain adaptation, semi-supervised semantic segmentation aims at utilizing a large amount of unlabeled data to improve semantic classification trained on a small amount of labeled data from the same distribution. Specifically, supervised semantic segmentation is trained together with an unsupervised model by applying perturbations on encoded states of the network instead of the input, or using mask-based data augmentation techniques to encourage consistent predictions over mixed samples. In this way, learned representation which capture many kinds of unseen variations in unlabeled data, benefit the supervised semantic classifier. We propose a mask-based data augmentation semi-supervised learning network to utilize structure information from a variety of unlabeled examples to improve the learning on a limited number of labeled examples.Both unsupervised domain adaptation (UDA) with full source supervision but without target supervision and semi-supervised learning (SSL) with partial supervision have shown to be able to address the generalization problem to some extent. While such methods are effective at aligning different feature distributions, their inability to efficiently exploit unlabeled data leads to intra-domain discrepancy in the target domain, where the target domain is separated into two unaligned sub-distributions due to source-aligned and target-aligned data. That is, enforcing partial alignment between full labeled source data and a few labeled target data does not guarantee that the remaining unlabeled target samples will be aligned with source feature clusters, thus leaving them unaligned. Hence, I propose methods for incorporating the advantages of both UDA and SSL, termed semi-supervised domain adaptation (SSDA), with a goal to align cross-domain features as well as addressing the intra-domain discrepancy within the target domain. I propose a simple yet effective semi-supervised domain adaptation approach by utilizing a two-step domain adaptation addressing both cross-domain and intra-domain shifts.
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Title: Factors Influencing the Level of Detection of Testing Listeria monocytogenes in Ice Cream
Creator: Chen, Bairu
Date: 2022
Description: The increasing evidence has shown that having a sensitive detection method for Listeria monocytogenes in food products is critical for public...
Show moreThe increasing evidence has shown that having a sensitive detection method for Listeria monocytogenes in food products is critical for public health as well as industrial economics. L. monocytogenes was associated with foodborne illness outbreaks linked to ice cream in the United States from 2010 to 2015, with another recent outbreak under investigation. The FDA Bacteriological Analytical Manual (BAM) method was commonly used for L. monocytogenes detection. However, the performance characteristics of the chromogenic methods (MOX, RLM, and R&F agars) remain to be elucidated. The factorial effect on Level of Detection (LOD) as an essential element of the International Organization for Standardization (ISO) approach for qualitative method validation was investigated in this study.For examining the LOD of L. monocytogenes in ice cream, fractional contaminated samples were prepared with the ice cream obtained from the 2015 outbreak and enumerated using the FDA BAM Most Probable Number (MPN) method for Listeria. The effect of test portion size was determined by comparing 10g and 25g using the BAM method with chromogenic agars (MOX, RLM, and R&F). The ISO single-lab validation requirement was followed for the factorial effect study, including four different factors: sample size (10g and 25g), ice cream types (commercially available regular vanilla ice cream and vanilla ice cream with low fat and no added sugar), re-freezing process (with re-freezing and without re-freezing process), and thawing process (slow thaw and fast thaw). LOD and relative LOD (RLOD) were computed using MiBiVal software to compare the sensitivity of the three chromogenic agars and the different factors. For all of the detection experiments, presumptive colonies were identified using the API listeria kit. The 2015 naturally contaminated ice cream was enumerated and resulted in an average contamination level of 2.15 MPN/g. At fractional levels of 0.25 MPN/10g and 0.75 MPN/10g, the positive rates of L. monocytogenes detected from 10g and 25g of sample portions were consistent with the statistically theoretical positive rates. The RLOD values for the reference method (MOX) and the alternative methods (RLM, R&F) were above 1 in both portion sizes, which suggested that MOX was slightly more sensitive than RLM and R&F. The factorial effect study indicated that the four factors have no significant influence on the LOD of L. monocytogenes detection at the fractional contamination levels. However, the test portion size of 25g provided more consistent results among the chromogenic media than the 10g portion size. Fat content was shown to have an effect on L. monocytogenes detection in a large test portion. The information from this study will be useful for the improvement of the reproducibility of a qualitative detection method and can also be used for data analysis standards such as ISO 16140 in method validation studies.
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Title: Habitat 2022+
Creator: Cho, San Lae Lae
Date: 2021
Description: Globalization and urbanization have led a vast group of population to shift from rural and suburban areas to urban centers. According to the...
Show moreGlobalization and urbanization have led a vast group of population to shift from rural and suburban areas to urban centers. According to the United Nation’s (UN) projection, around 2.5 billion more people will live in the cities by 2050. For cities to accommodate such a population, cities can grow horizontally or vertically. If cities were to grow horizontally, this population would need to commute majorly by car which has a very high carbon footprint. By building vertically, the population’s commute becomes more efficient if the vertical structure is built as such is a city within a city. Although having existed for 500 years, tall buildings and skyscrapers have been topics of controversy for their excessive use of resources, ignoring environmental and contextual problems. In order to mitigate this sustainability issue, mass timber as a construction material has been recently introduced into the American market. Mass timber is the building material for a carbon neutral future according to Council on Tall Buildings and Urban Habitats (CTBUH). Another means is to grow plants around the buildings meaning they are regrowing greenery back into nature with the hopes to take a step towards mitigating climate change. In addition, with the argument of tall buildings lacking neighborhood-like qualities that for instance, a Chicago block of housing may give, more and more tall buildings around the world have tried to implement the lush spaces onto the building perimeters. These spaces with plants and trees mimicking nature can exist as both indoor and outdoor spaces. While neighborhoods have parks, these spaces or sky gardens will substitute as modified parks for skyscrapers.With psychological benefits tied to spaces linked to nature in high-rises, such spaces should be integrated in all high rise, residential buildings. Today, in addition to Covid pandemic, many people practice working from home. Meanwhile, it is human nature since birth to want to be surrounded by plants and nature, either spending time under a tree or lying on the grass. To mitigate spending excessive amounts of hours indoors, spaces linked to nature have become crucial in providing a sense of peace and reconnecting back to nature. However, currently architecture that has nature integrated, results in spaces only for the wealthy. Furthermore, architecture in the US is so commercially driven with developers aiming for the most cost effective, most efficient and largest floor areas. For nature to be more available to the living spaces of middle class and ideally working class, the city planners and city development authorities need to get involved. For instance, the city could aid by allocating some of the tax collected to build more green architecture. With the tax fund assistance from the city, high quality living of green architecture will become available and much more affordable.Another option to making living more affordable is by implementing co-living. Despite the Covid-19 pandemic, co-living remains relatively high in demand in Chicago according to Chicago Tribune. For the younger generations such as the Millennials and younger, it is predicted that due to the nature of student loan debts, these generations are less likely to be financially well off after graduation. Hence, they are less likely to establish a family as soon as the previous generations, as most people tend to start a family when they feel financially stable.Hence, in this design thesis, a certain prototype will be tested out regarding some of these issues. This is just one option to the issues above and there are many options and other nuances still available to explore.
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Title: RE-DEFINING LABOR HOUSING COMMUNITIES IN DUBAI
Creator: Chavadi, Sanjana Mrutyunjaya
Date: 2022
Description: From the moment they are duped into believing in a utopian lifestyle, to their day to-day working and living conditions once they arrive in...
Show moreFrom the moment they are duped into believing in a utopian lifestyle, to their day to-day working and living conditions once they arrive in the country, to the denial of theirrights for years after they are stuck in the country, the migration of workers from other countries into the Emirates is a neglected humanitarian issue. The United Arab Emirates and several other GCC countries have a heinous habit of recruiting illiterate foreigners into their country and abusing them as if they were modern-day slaves. Many nations and individuals have legal control over immigrants by applying the Kafala sponsorship system and ignoring international norms and rights established by organizations like the International Labor Organization. While the Kafala system is only one of many aspects of the whole problem, this thesis will seek to identify the influence of architecture on the lives of the South Asian labor force who work behind the beautiful façade of the United Arab Emirates by reconstructing the labor community. These colossal structures are built and shown while the individuals who construct them are ignored and hidden. Immigrant low income labor employees bear the brunt of their hard life-work imbalance with the intention of providing assistance to their family in their home nations. The labor force of Dubai becomes the major target of the terrorists, isolated from Dubai's pleasure amenities, socially detached from the rest of the population, and separated from their creations. By creating a landmark and developing a sense of social coherence, integration, and empowerment among the labor community, this initiative intends to bridge the divide and commemorate the labor community. This project will propose a new sort of community and housing typology that embraces the ideals of modularity, adaptability, and community as a solution to the immigrant worker dilemma.
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Title: Hedge Fund Replication With Deep Neural Networks And Generative Adversarial Networks
Creator: Chatterji, Devin Mathew
Date: 2022
Description: Hedge fund replication is a means for allowing investors to achieve hedge fund-like returns, which are usually only available to institutions....
Show moreHedge fund replication is a means for allowing investors to achieve hedge fund-like returns, which are usually only available to institutions. Hedge funds in total have over $3 trillion in assets under management (AUM). More traditional money managers would like to offer hedge fund-like returns to retail investors by replicating their performance. There are two primary challenges with existing hedge fund replication methods, difficulty capturing the nonlinear and dynamic exposures of hedge funds with respect to the factors, and difficulty in identifying the right factors that reflect those exposures. It has been shown in previous research that deep neural networks (DNN) outperform other linear and machine learning models when working with financial applications. This is due to the ability of DNNs to model complex relationships, such as non-linearities and interaction effects, between input features without over-fitting. My research investigates DNNs and generative adversarial networks (GAN) in order to address the challenges of factor-based hedge fund replication. Neither of these methods have been applied to the hedge fund replication problem. My research contributes to the literature by showing that the use of these DNNs and GANs addresses the existing challenges in hedge fund replication and improves on results in the literature.
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Title: Improving Utility and Efficiency for Privacy Preserving Data Analysis
Creator: Liu, Bingyu
Date: 2022
Description: In recent decades, the smart cities are incorporating with Internet-of-Things (IoT) infrastructures for improving the citizens’ quality of...
Show moreIn recent decades, the smart cities are incorporating with Internet-of-Things (IoT) infrastructures for improving the citizens’ quality of life by leveraging information/data. The huge amount of data is extracted and generated from the devices (e.g., mobile applications, GPS navigation systems, urban traffic cameras, etc.), or city sectors such as Intelligent Transportation Systems (ITS), Resource Allocation, Utilities, Crime Detection, Hospitals, and other community services.This dissertation aims to systematically research the Data Analysis in IoT System, which mainly consists of two aspects: Utility and Efficiency. First, ITS as a representative system in IoT in the smart city, I present the work on privacy preserving for the trajectories data, which is achieved by the differential privacy technique with a novel sanitation framework. Moreover, I have studied the resource allocation problem in two different approaches: Cryptographic computation and Hardware en- claves with the utility and efficiency accordingly. For the Cryptographic computation approach, I utilize Secure Multi-party Computation (SMC) technique for achieving the privacy-aware divisible double auction without a mediator. Besides, I also pro- pose a hardware-based solution Trusted Execution Environment (TEE) for performance improvement. At the same time, integrity and confidentiality are also able to be guaranteed. The proposed hybridized Trusted Execution Environment (TEE)- Blockchain System is designed for securely executing smart contract. Finally, I have studied the Cryptographic Video DNN Inference for the smart city surveillance, which privately inferring videos (e.g., action recognition, and video, and classification) on 3D spatial-temporal features with the C3D and I3D pre-trained DNN models with high performance. This dissertation proposes the privacy preserving frameworks and mechanisms are able to be applied efficiently for IoT in the real-world.
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Title: A BIM-BASED LIFE CYCLE ASSESSMENT TOOL OF EMBODIED ENERGY AND ENVIRONMENTAL IMPACTS OF REINFORCED CONCRETE TALL BUILDINGS
Creator: Ma, Lijian
Date: 2022
Description: Today 55 percent of population in the world lives in urban areas which is expected to increase to 68 percent by the year 2050. In the cities,...
Show moreToday 55 percent of population in the world lives in urban areas which is expected to increase to 68 percent by the year 2050. In the cities, high-rise buildings as symbols of the modern cityscape are dominating the skylines, but the data to demonstrate their embodied energy and environmental impacts are scarce, compared to low- or mid-rise buildings. Reducing the embodied energy and environmental impacts of buildings is critical as about 42 percent of primary energy use and 39 percent of the global greenhouse gas (GHG) emissions come from the building sector. However, it is an overlooked area in embodied energy and environmental impacts of tall buildings. This doctoral research aims to investigate the effects of tall buildings on embodied energy and environmental impacts by using process-based life cycle assessment (LCA) methodology within Building Information Modelling (BIM) environment, which provides construction industry platform to incorporate sustainability information in architectural design. This doctoral research is carried out through a literature review on embodied energy of high-rise buildings. Current LCA methods of buildings are also discussed in the literature review. It then develops a framework for BIM-based assessment of the embodied energy and environmental impacts of tall buildings. To achieve that, a case study of tall reinforced concrete building is applied, by using ISO 14040 and 14044 guidelines with available database, Revit and Tally application in Revit. The author concentrates on embodied energy and environmental impacts of reinforced concrete tall buildings. Finally, the association between design and construction variables with embodied energy and environmental impacts is explored. This research will lead to significant contributions. A comprehensive study on embodied energy and environmental impacts of high-rise building will address a major gap in LCA literature. Researchers and environmental consultants can use the results of this research to create design tools to evaluate environmental impacts of high-rise buildings. Also, architects can use the results of this research to develop insight into the environmental performance of tall buildings in early design stage. Architects and engineers can also use the results to optimize tall building design for low embodied energy and environmental impacts. Finally, the results of this research will enable architects, engineers, planners, and policymakers develop more sustainable built environments.
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Title: Active Load Control in a Synchronized and Democratized (SYNDEM) Smart Grid
Creator: Lv, Zijun
Date: 2021
Description: Smart grid is envisioned to take advantage of modern information and communication technologies in achieving a more intelligent grid in order...
Show moreSmart grid is envisioned to take advantage of modern information and communication technologies in achieving a more intelligent grid in order to facilitate: Integration of renewable resources; Integration of all types of energy storage; Two-way communication between the consumer and utility so that end users can actively participate. The Synchronized and Democratized (SYNDEM) smart grid is regarded as the next generation smart grid. The objective of the SYNDEM smart grid is for all active players in a grid, large or small, conventional or renewable, supplying or consuming, to be able to equally and laterally regulate the grid in a synchronous manner to enhance the stability, reliability, and resiliency of future power systems. In a SYNDEM smart grid, power electronic converters are controlled to behave as conventional synchronous machines. Such converters are called virtual synchronous machines (VSMs).Following the SYNDEM structure, this thesis mainly focuses on developing the VSM technology for the automatic grid regulation at the demand side. The major aim and objective is to achieve active or intelligent loads that can flexibly and automatically take part in grid regulation. Moreover, the active load is expected to have similar grid regulation behavior as other active players in the grid, for e.g., renewable generations. To achieve this, a droop-controlled rectifier is proposed that acts as a general interface for a load to grid. The rectifier is controlled as a VSM so that a load equipped with such a rectifier can take part in grid regulation continuously like a traditional synchronous machine. Such a rectifier has a built-in storage port, in addition to the normal AC and DC ports. The flexibility required by the AC port to support the grid is provided by the storage port. The DC-bus voltage of the storage port is able to fluctuate with in a wide range to exchange energy with the grid.In order to further take use of the energy in the storage port (DC-bus capacitor) of a rectifier more reasonably and increase the support time to grid, an adaptive droop mechanism is proposed. Under such a droop mechanism, the rectifier can automatically change the power consumed according to the grid voltage variations as well as its potential to provide grid support. To achieve this, a flexibility coefficient is introduced to indicate the power flexibility level of the DC-bus capacitor. Then this flexibility coefficient is embedded into the universal droop controller (UDC) to make it adaptive. Hence, the adaptive droop controller has a changing droop coefficient corresponding to the power flexibility of a rectifier, so it can take advantage of the energy stored in its DC-bus capacitor wisely to support the grid. This droop controller can also be applied into connection between two SYNDEM smart grids. To achieve this, a grid bridge (GB) that enables autonomous and equal regulation between two SYNDEM grids is proposed. The real power transferred through a GB has linear relationship with the voltage deviation between the two micro-grids connected. The micro-grid with a higher voltage will automatically provide power to the lower one. Moreover, the power direction of a GB is bidirectional and determined by the grid voltage difference, this makes the two micro-grids equal to each other. The GB is physically a back-to-back converter. In order to achieve autonomous and equal regulation, both sides of the back-to-back converter are controlled under droop controller with the same droop coefficients. The VSM control technology is also developed to control Modular multilevel converters (MMCs) for high voltage applications. Like active loads introduced above, the MMCs can take part in the grid regulation according to the droop mechanism designed. In order to eliminate the circulating current that exists in MMCs, proportional-resonant (PR) controllers are adopted to inject second-order harmonics to the MMCs to suppress the second order circulating current. The dynamics, implementation and operation of the VSM-like MMC are introduced and analyzed. Particularly, how the VSM control algorithm works with the circulating current control in MMCs is presented. An IIT SYNDEM Smart Grid Testbed is built in an aim of achieving a minimize realization of the SYNDEM system. Extensive experiments are done on the system to show the operational scenarios when the proposed active loads are integrated in the system. There are in total eight nodes in the IIT SYNDEM testbed, which contains two utility grids, one AC load, one DC load, two solar farms and two wind farms. All the nodes are connected to a local grid through VSMs, so that they can take part in the local grid regulations in similar ways. The IIT SYNDEM Smart Grid Testbed is described in details and experimental results are provided to show the dynamic and steady performance of the IIT SYNDEM smart grid.
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Title: Modeling, Analysis and Computation of Tumor Growth
Creator: Lu, Min-Jhe
Date: 2022
Description: In this thesis we investigate the modeling, analysis and computation of tumor growth.The sharp interface model we considered is to understand...
Show moreIn this thesis we investigate the modeling, analysis and computation of tumor growth.The sharp interface model we considered is to understand how the two key factors of (1) the mechanical interaction between the tumor cells and their surroundings, and (2) the biochemical reactions in the microenvironment of tumor cells can influence the dynamics of tumor growth. From this general model we give its energy formulation and solve it numerically using the boundary integral methods and the small-scale decomposition under three different scenarios.The first application is the two-phase Stokes model, in which tumor cells and the extracellular matrix are both assumed to behave like viscous fluids. We compared the effect of membrane elasticity on the tumor interface and the curvature-weakening one and found the latter would promote the development of branching patterns.The second application is the two-phase nutrient model under complex far-field geometries, which represents the heterogeneous vascular distribution. Our nonlinear simulations reveal that vascular heterogeneity plays an important role in the development of morphological instabilities that range from fingering and chain-like morphologies to compact,plate-like shapes in two-dimensions.The third application is for the effect of angiogenesis, chemotaxis and the control of necrosis. Our nonlinear simulations reveal the stabilizing effects of angiogenesis and the destabilizing ones of chemotaxisand necrosis in the development of tumor morphological instabilities if the necrotic core is fixed. We also perform the bifurcation analysis for this model.In the end, as a future work, we propose new models through Energetic Variational Approach (EnVarA) to shed light on the modeling issues.
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Title: GLOBAL ESTIMATION AND ANALYSIS OF IONOSPHERIC DRIVERS WITH A DATA ASSIMILATION ALGORITHM
Creator: López Rubio, Aurora
Date: 2022
Description: This dissertation studies a data assimilation algorithm that estimates the drivers of the ionosphere-thermosphere (IT) region of the Earth....
Show moreThis dissertation studies a data assimilation algorithm that estimates the drivers of the ionosphere-thermosphere (IT) region of the Earth. The algorithm, EMPIRE (Estimating Model Parameters from Ionospheric Reverse Engineering) can estimate 2 main drivers of the ionospheric behavior: neutral winds and electric potential by ingesting mainly ionospheric densities obtained through Global Satellite System (GNSS) measurements. Additionally, the algorithm can ingest FPI (Fabry-Perot interferometer) neutral wind measurements. The contributions include 1) Vector spherical harmonic basis function for neutral wind estimation, 2) Quantification of the representation error of the estimations of the algorithm EMPIRE, 3) Analysis of Nighttime Ionospheric Localized density Enhancement (NILE) events and 4) Ingestion of global ICON (Ionospheric Connection Explorer) neutral winds measurements. The IT region in the atmosphere is characterized by having a large concentration of free ions and electrons, electromagnetic radiation and Earth's magnetic field. The behavior of the region is dominated by the solar activity, that ionizes the free electrons of the region, forming ionospheric plasma and determining its density. Unusual solar activity or any atmospheric disturbance affects the distribution of the ionospheric plasma and the behavior of the IT region. The redistribution of the ionospheric density impacts technology widely used such as telecommunication or satellite navigation, so it is increasingly important to study the IT system response. The IT behavior can be characterized by what drives its changes. Two drivers that play a key role, the ones we focus on this dissertation, are electric potential, that directly affects the charged ions in the system, and neutral winds, that refers to the velocity of the neutral particles that form the thermosphere. To quantify these drivers, measurements and climate models are available. Measurements are limited as the IT region is vast and covers the entire globe. Climate models can provide information in all the region, but they are usually not as reliable during the unusual solar activity conditions or disturbances. In this dissertation we use a data assimilation algorithm, EMPIRE, that combines both sources of data, measurements and models, to estimate the IT drivers, neutral winds and electric potential. EMPIRE ingests measurements of the plasma density rate and models the physics of the region with the ion continuity equation. The drivers are represented with basis functions and their coefficients are estimated by fitting the expansions with a Kalman filter. In previous work and use of the algorithm, the neutral winds were expanded using power series basis function for each of the components of the vector. The first contribution of the dissertation is to use a vector spherical harmonic expansion to describe the winds, allowing a continuous expansion around the globe and self-consistent components of the vector. Before, EMPIRE estimated the correction of the drivers with respect climate model values. In this work, EMPIRE is also modified to directly estimate the drivers. Then, a study of the representation error, which is the discrepancy between the true physics and the discrete model that represents the physics of EMPIRE and its quantification is done. Next, EMPIRE is used to analyze two NILE events, using the global estimation of both winds, from the first contribution, and the electric potential, derived in previous work. Finally, global estimation of winds allows us to implement the ingestion of ICON global winds in EMPIRE, in addition to the plasma density rate measurements.
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Title: Machine learning applications to video surveillance camera placement and medical imaging quality assessment
Creator: Lorente Gomez, Iris
Date: 2022
Description: In this work, we used machine learning techniques and data analysis to approach two applications. The first one, in collaboration with the...
Show moreIn this work, we used machine learning techniques and data analysis to approach two applications. The first one, in collaboration with the Chicago Police Department (CPD), involves analyzing and quantifying the effect that the installation of cameras had on crime, and developing a predictive model with the goal of optimizing video surveillance camera location in the streets. While video surveillance has become increasingly prevalent in policing, its intended effect on crime prevention has not been comprehensively studied in major cities in the US. In this study, we retrospectively analyzed the crime activities in the vicinity of 2,021 surveillance cameras installed between 2005 and 2016 in the city of Chicago. Using Difference-in-Differences (DiD) analysis, we examined the daily crime counts that occurred within the fields-of-view of these cameras over a 12-month period, both before and after the cameras were installed. We also investigated their potential effect on crime displacement and diffusion by examining the crime activities in a buffer zone (up to 900 ft) extended from the cameras. The results show that, collectively, there was an 18.6% reduction in crime counts within the direct viewsheds of all of the study cameras (excluding District 01 where the Loop -Chicago's business center- is located). In addition, we adapted the methodology to quantify the effect of individual cameras. The quantified effect on crime is the prediction target of our 2-stage machine learning algorithm that aims to estimate the effect that installing a videocamera in a given location will have on crime. In the first stage, we trained a classifier to predict if installing a videocamera in a given location will result in a statistically significant decrease in crime. If so, the data goes through a regression model trained to estimate the quantified effect on crime that the camera installation will have. Finally, we propose two strategies, using our 2-stage predictive model, to find the optimal locations for camera installations given a budget. Our proposed strategies result in a larger decrease in crime than a baseline strategy based on choosing the locations with higher crime density.The second application that forms this thesis belongs to the field of model observers for medical imaging quality assessment. With the advance of medical imaging devices and technology, there is a need to evaluate and validate new image reconstruction algorithms. Image quality is traditionally evaluated by using numerical figures of merit that indicate similarity between the reconstruction and the original. In medical imaging, a good reconstruction strategy should be one that helps the radiologist perform a correct diagnosis. For this reason, medical imaging reconstruction strategies should be evaluated on a task-based approach by measuring human diagnosis accuracy. Model observers (MO) are algorithms capable of acting as human surrogates to evaluate reconstruction strategies, reducing significantly the time and cost of organizing sessions with expert radiologists. In this work, we develop a methodology to estimate a deep learning based model observer for a defect localization task using a synthetic dataset that simulates images with statistical properties similar to trans-axial sections of X-ray computed tomography (CT). In addition, we explore how the models access diagnostic information from the images using psychophysical methods that have been previously employed to analyze how the humans extract the information. Our models are independently trained for five different humans and are able to generalize to images with noise statistic backgrounds that were not seen during the model training stage. In addition, our results indicate that the diagnostic information extracted by the models matches the one extracted by the humans.
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Title: EXAMINING PERFORMANCE DEGRADATION OF LI-ION BATTERIES WITH SILICON-BASED ANODE AND POSSIBLE SOLUTIONS TO IMPROVE THE SILICON ANODE BEHAVIOR
Creator: Luo, Mei
Date: 2022
Description: Si has been investigated as a promising alternative to conventional graphite because of its high specific capacity and wide operating voltage;...
Show moreSi has been investigated as a promising alternative to conventional graphite because of its high specific capacity and wide operating voltage; however, technical challenges related to volume change in the silicon anode have hampered their practical application. In this work, the effects of silicon volume change on electrochemical performance has been studied in NMC532/Si full cells. First, different area specific capacity ratios of the negative to positive electrode (N:P ratio) were investigated using three-electrode cells. With individual electrode potentials monitored by a reference electrode, different depths of lithiation/delithiation at the anode and cathode were found to play an important role on cell performance; the cell with higher N:P ratio displays superior electrochemical performance due to its smaller silicon volume change. Further, calendar-life aging and cycle-life aging of NMC532/Si cells were compared with their electrode potentials monitored using a reference electrode. The observation of larger capacity decay and impedance growth of cycle-life aging cells illustrates the important effect of silicon volume change; significant capacity decay of calendar-life aged cell was observed as well, revealing an essential role of chemical effect of ongoing side reactions at Si anode. Specially-designed silicon with different protocols and electrolyte additives were investigated to address the intrinsic challenges of Si anodes for lithium-ion batteries.
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Title: Mothers’ Vaccination Decision: The Relation Between Science Skepticism, Social Networks, Vaccination Beliefs, and Fear of ASD
Creator: Lockwood, Maria Izabel Kugelmas Guarita
Date: 2021
Description: Vaccines are instrumental in stopping the spread of disease, yet some parents choose to not vaccinate their children. Despite scientific...
Show moreVaccines are instrumental in stopping the spread of disease, yet some parents choose to not vaccinate their children. Despite scientific evidence that childhood vaccines are safe, there is an increasing number of children in the United States and the United Kingdom who are not getting vaccinated. The current study investigates different factors that may be associated with mothers’ decision to vaccinate their children. This study examines the relations between skepticism in science, vaccination beliefs, fear of having a child with Autism Spectrum Disorder (ASD), social network recommendations, and maternal decision to vaccinate. Participants included 293 expectant mothers in the United States and the United Kingdom. Results indicated that mothers who are pro-vaccine and mothers who are vaccine-hesitant have different score profiles across scales that measure skepticism in science, vaccination beliefs, and fear of having a child with ASD. Specifically, we found that relative to mothers who are vaccine-hesitant, mothers who are pro-vaccine: (1) indicated less skepticism in science; (2) had fewer anti-vaccination beliefs; (3) did not statistically differ on their fear of having a child with ASD; and (4) had a smaller percentage of their social network advocating against vaccination. Thus, the study adds to the research literature, as it illustrates that mothers who are vaccine-hesitant differ from mothers who are pro-vaccine on key factors.
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Title: Data-Driven Methods for Soft Robot Control and Turbulent Flow Models
Creator: Lopez, Esteban Fernando
Date: 2022
Description: The world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies,...
Show moreThe world today has seen an exponential increase in its usage of computers for communication and measurement. Thanks to recent technologies, we are now able to collect more data than ever before. This has dawned a new age of data-driven methods which can describe systems and behaviors with increasing accuracy. Whereas before we relied on the expertise of a few professionals with domain-specific knowledge developed over years of rigorous study, we are now able to rely on collected data to reveal patterns, develop novel ideas, and offer solutions to the world’s engineering problems. No domain is safe. Within the engineering realm, data-driven methods have seen vast usage in the areas of control and system identification. In this thesis we explore two areas of data-driven methods, namely reinforcement learning and data-driven causality. Reinforcement learning is a method by which an agent learns to increase its selection of ideal actions and behaviors which result in an increasing reward. This method was applied to a soft-robotic concept called the JAMoEBA to solve various tasks of interest in the robotics community, specifically tunnel navigation, obstacle field navigation, and object manipulation. A validation study was conducted to show the complications that arise when applying reinforcement learning to such a complex system. Nevertheless, it was shown that reinforcement learning is capable of solving three key tasks (static tunnel navigation, obstacle field navigation, and object manipulation) using specific simulation and learning hyperparameters. Data-driven causality encompasses a range of metrics and methods which attempt to uncover causal relationships between variables in a system. Several information theoretic causal metrics were developed and applied to nine mode turbulent flow data set which represents the Moehlis model. It was shown that careful consideration into the method used was required to identify significant causal relationships. Causal relationships were shown to converge over several hundred realizations of the turbulent model. Furthermore, these results match the expected causal relationships given known information of self-sustaining processes in turbulence, validating the method’s ability to identify causal relationships in turbulence.
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Title: ARCHITECTURE FOR COLLABORATIVE CREATIVITY - SPACE WE-Q: SPACE INTELLIGENCE EMPOWERING CREATIVE WE CULTURE IN LEARNING-DRIVEN ENVIRONMENTS
Creator: Mor-Avi, Anat
Date: 2020
Description: Changes in societal culture, along with research on how we learn, challenge current architectural solutions. Education’s shifting paradigms...
Show moreChanges in societal culture, along with research on how we learn, challenge current architectural solutions. Education’s shifting paradigms align with these changes and move teaching strategies from teacher-centered to learner-centered, and from formal and passive, to informal and active modes. Another shift emphasizes collaboration and participatory creativity, which evolve the idea of the “collective,” or “We” versus “I” scenarios. In addition, studies show that creativity flourishes in specific contradictory performances. Supporting these reported changes, new knowledge, and paradigm shifts, this research studied how an active, adaptive architectural design approach might emerge into the learning and creative processes. Evidence indicates that “design and space do matter,” particularly in learning- and working-driven domains. Empirical research has been weak in addressing this understanding relative to architectural solutions, affordances, behaviors, and emotions, promoting collaborative creativity. This research aimed to investigate patterns of architectural affordances believing to impact and empower collaborative cultures and behaviors in learning environments (“WE CULTURE”), specifically motions and emotions. A Mixed-method research design was conducted, using two techniques: (a) a content analysis of awarded learning and working environments, and (b) a post-occupancy evaluation using ethnographic techniques to study the Kaplan Innovation Institute at the Illinois Institute of Technology in Chicago, Illinois, USA. In an effort to provide an applied design study, a visual pattern language related to cultures of learning, environment behavior, and emotions was developed. The pattern language is the platform for designing intelligent spaces, SPACE WE-Q, promoting collaborative behaviors, and creativity through adaptive and behavior-based systems of active affordances. SPACE WE-Q offers a planned adaptive system for unplanned creative processes that emerges into learning and suggesting a new relationship between architecture and education, between architects and users, and between users and space.
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