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Title: SIMULTANEOUS RELEASE OF BIOACTIVE AFLIBERCEPT AND DEXAMETHASONE FROM A MICROSPHERE- AND NANOPARTICLE-HYDROGEL OCULAR DRUG DELIVERY SYSTEM FOR THE ENHANCED TREATMENT OF NON-RESPONSIVE PATIENTS WITH CHOROIDAL NEOVASCULARIZATION
Creator: Rudeen, Kayla M
Date: 2022
Description: There is a growing subset of wet age-related macular degeneration (AMD) patients who do not fully respond to standard of care treatment, which...
Show moreThere is a growing subset of wet age-related macular degeneration (AMD) patients who do not fully respond to standard of care treatment, which consists of bimonthly/monthly bolus intravitreal injections of anti-vascular endothelial growth factors (anti-VEGFs). Some of these patients may respond to a combination therapy of anti-VEGF and corticosteroids. One treatment option uses a dexamethasone implant that releases for six months. This regimen, however, requires both the bimonthly/monthly intravitreal injections of anti-VEGF and semiannual intravitreal injections of the dexamethasone implant. Combining the two treatments into a single drug delivery system (DDS) would reduce the total number of injections, reducing the risk of potential complications (endophthalmitis, retinal detachment, intravitreal hemorrhage, increased intraocular pressure, and cataract) as well as the socioeconomic burden of treatment.The overarching goal of this study was to develop a single DDS that simultaneously releases anti-VEGF (aflibercept) and corticosteroid (dexamethasone) for the treatment of non-responsive wet AMD patients. Our laboratory previously developed a thermoresponsive, biodegradable microparticle-hydrogel DDS that releases anti-VEGF over a period of six months. The aims of the study were to (1) modify this system to include dexamethasone-loaded nanoparticles, optimize release kinetics for both drugs, and characterize the DDS; (2) evaluate the in vivo treatment efficacy in a laser-induced choroidal neovascularization (CNV) model; and (3) investigate the impacts of temperature and storage on the DDS integrity.
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Title: DEVELOPMENT AND EVALUATION OF MRI TEMPLATES OF THE MIITRA ATLAS
Creator: RIDWAN, ABDUR RAQUIB
Date: 2021
Description: Digital human brain atlases play a pivotal role in conducting wide range of neuroimaging studies and are commonly used as references for...
Show moreDigital human brain atlases play a pivotal role in conducting wide range of neuroimaging studies and are commonly used as references for spatial normalization in voxel-wise analysis, region-of interest analyses, automated tissue-segmentation, functional connectivity analyses, etc. A brain atlas typically consists of MRI-based multi-modal templates and semantic labels delineating brain regions according to the characteristics of the underlying tissue. In recent times there has been a plethora of magnetic resonance imaging (MRI) studies on older adults without dementia to explore the role of brain characteristics associated with cognitive functions in old age with the ultimate goal to develop strategies for prevention of cognitive decline. Increasing the accuracy in terms of sensitivity and specificity of such neuroimaging studies require an atlas with a comprehensive set of high-quality templates representative of the brain characteristics typical of older adults and detailed labels accurately mapping brain regions of interest. However, such an atlas has not been constructed for older adults without dementia. Hence this thesis aims to build high quality MRI templates which are the cornerstone resources needed for the development of a comprehensive, high quality, multi-channel, longitudinal, probabilistic digital human brain atlas for older adults termed as Multi-channel Illinois Institute of Technology and Rush University Aging (MIITRA) atlas. This dissertation focuses on a) to develop and evaluate a high performing 1mm isotropic structural T1-weighted brain template, b) to investigate the development and evaluation of a spatio-temporally consistent longitudinal structural T1-weighted template of the older adult brain, c) to develop and evaluate an unbiased 0.5 mm isotropic super-resolved high resolution and detail-preserving structural T1 weighted template of the older adult brain, d) to develop an unbiased 0.5 mm super-resolved high resolution and detail-preserving structural PD weighted and T2-weighted template of the older adult brain, e) to investigate and provide future directions in the development of a 0.5 mm super-resolved high resolution DTI template of the older adult brain, and f) to construct a novel approach in the development of MRI templates using both space and frequency information of spatially normalized older adult data. The thesis based on the aforementioned foundational points was constructed as follows: Firstly, this thesis presents the development of a 1mm isotropic T1-weighted structural template of the older adult brain utilizing state of the art registration algorithm ANTs with parameters carefully optimized for older adults, in an iterative groupwise spatial normalization framework. The preprocessing steps were also thoroughly investigated to ensure high quality data. It was demonstrated through systematic comparison of this new template to several other standardized and study-specific T1-weighted templates that a) it exhibited high image sharpness, b) allowed for high spatial normalization accuracy and detection of smaller inter-group morphometric differences compared to other standardized templates, c) had similar performance to that of study-specific templates and d) was highly representative of the older adult brain. Secondly, with the acquired technical know-how from the aforementioned research findings a new method was introduced for the construction of a spatio-temporally consistent longitudinal template based on high quality cross-sectional older adult data from a large cohort. The new template was compared to templates generated with previously published methods in terms of spatio-temporal consistency and image quality and was shown to have superior performance. In addition, a novel approach was introduced for image quality enhancement of the longitudinal templates utilizing both space and frequency information. Thirdly, the thesis presents a method that involves a) thoroughly refining registration parameters, b) patch-based tissue-guided sparse-representation approach in a super-resolved unbiased minimum deformation space to construct and evaluate an unbiased 0.5 mm isotropic super-resolved high resolution and detail-preserving structural T1 weighted template of the older adult brain. This method accounts for misregistration specially in the cortical regions, ensuring sharp delineation of structures representative of the older adult brain. The new template developed using this approach maintained high anatomical consistency with sharp and detailed cortical features in the brain and exhibited higher image sharpness compared to other high-resolution standardized templates and allowed for high spatial normalization accuracy when used as a reference for normalization of older adult data. Additionally, this approach of template building was investigated on DTI tensors of older adult participants, and the constructed DTI template was shown to perform better than templates developed using the best approach currently present in the literature. Finally, the thesis presents the development of an unbiased 0.5 mm super-resolved high resolution and detail-preserving structural PD weighted and T2-weighted template of the older adult brain, from nonlocal super-resolution based upsampled PD and T2w older adult participant data, using this new template building approach.
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Title: Influence Of Internal Factors In Construction Organizations On The Implementation Of Integrated Project Delivery Viewed From The Organizational Change Theory
Creator: Rashed, Ahmed
Date: 2022
Description: Integrated Project Delivery (IPD) is an emerging construction project delivery system that is collaborative oriented. It involves the critical...
Show moreIntegrated Project Delivery (IPD) is an emerging construction project delivery system that is collaborative oriented. It involves the critical participants in an early stage of the project timeline. Recently, IPD is becoming increasingly common. Many organizations are interested in contributing to the Architecture, Engineering, and Construction (AEC) industry. No research studies have previously observed and studied the effect of IPD implementation through an organizational change theory lens. The presented research work was designed to explore the role of organizational factors in the implementing first domain, reflecting the organizational level factors, including cultural and economic considerations. In contrast, the second domain focuses on member-level factors, i.e., employee involvement and readiness to change. Together, these domains influence the organization’s intention and adoption to change toward the IPD as a project delivery system. This impact is viewed through the lens of the OCT based on the contributions and theories discussed by various researchers. These researchers are from a variety of disciplines. A data collection survey was developed to gather quantitative data from the industry. Data was collected from N=128 employees from the construction industry. Data analysis was performed through Structure Equation Modeling using Smart PLS 3. Results showed that communication, integration significantly associated IPD implementation. Moreover, involvement and readiness change also positively predicted the implementation of IPD. The empirical result of current study validates all the constructs of the hypothetical model except reward system.
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Title: Apalutamide Modulates the Expression of Regulatory Genes for Prostate Cancer Cell Invasion and Migration In Vivo and In Vitro
Creator: Qualter, Gina E.
Date: 2022
Description: The next generation antiandrogen, Apalutamide (Apa), improves both overall survival and metastasis-free survival in men with castration...
Show moreThe next generation antiandrogen, Apalutamide (Apa), improves both overall survival and metastasis-free survival in men with castration-resistant prostate cancer (CRPC). In vitro and in vivo studies were performed to characterize the mechanistic effects of Apalutamide on prostate cancer cell proliferation, invasion, and migration, and the expression of genes that regulate these processes. Apalutamide inhibited the proliferation of LNCaP human prostate cancer cells in both the presence and absence of dihydrotestosterone (DHT), and also inhibited LNCaP cell migration/invasion. At the mRNA level (RT-PCR), Apalutamide down-regulated the expression of androgen receptor (AR), c-Myc, MMP-2, MMP-9, DANCR, and lncRNA, and up-regulated TIMP-2 expression. Similar data were obtained for protein expression (western blot). In the in vivo study, male Hi-Myc mice received daily oral administration of Apalutamide beginning at age 8 weeks for 2 months, 3.5 months, or 5 months. Daily oral administration of Apalutamide reduced accessory sex gland weights by over 50% at all three time points, inhibited the progression of prostatic intraepithelial neoplasms (PIN) to cancer, and significantly affected the expression of genes that regulate invasion and migration. However, in vitro findings indicated that resistance to Apalutamide through the emergence of the AR splice variant 7 (AR-V7) following extended treatment is possible and may be reversed following knockdown of AR-V7 gene expression.In summary, these results suggest that while Apalutamide is an effective inhibitor of prostate cancer invasion/migration, further investigation into the mechanism of AR-V7 mediated Apalutamide-resistance and strategies to overcome resistance may be indicated to improve prostate cancer patient outcomes following extended periods of treatment.
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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: 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: 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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