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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: 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: The Detection of Emerging Pathogenic Arcobacter Species In Poultry and Poultry By-Products
Creator: Nguyen, Paul
Date: 2022
Description: Arcobacter species are emerging foodborne pathogens that are associated with human gastrointestinal illness. Typical symptoms of Arcobacter...
Show moreArcobacter species are emerging foodborne pathogens that are associated with human gastrointestinal illness. Typical symptoms of Arcobacter infection that have been reported include diarrhea, abdominal cramps, nausea, vomiting, and in severe cases, bacteremia. Consumption of contaminated food and water is the most common transmission source that leads to human infection. When consumed, pathogenic Arcobacter spp. pass through the stomach and establishes themselves in the host intestinal tract, where they cause gastroenteritis. Currently, there is no standard isolation method to detect pathogenic Arcobacter spp. from food and environment sample matrices. The research detailed in this thesis describes the development of the Nguyen-Restaino-Juárez Arcobacter detection system (NRJ) comprised of a selective enrichment broth and a chromogenic agar plate used to isolate three pathogenic species: Arcobacter butzleri, Arcobacter cryaerophilus, and Arcobacter skirrowii. Results revealed that NRJ yielded 97.8% inclusivity and 100.0% exclusivity when evaluating against select bacterial strains found in foods. Our research group internally validated the novel chromogenic detection system by comparing its efficacy against the modified Houf reference method (HB). Method-performance evaluations determined the NRJ method was significantly more sensitive and specific than modified HB when isolating the three Arcobacter species from ground chicken samples. Furthermore, 16S amplicon sequencing data identified that greater than 97% of bacterial isolates recovered using the NRJ detection system were Arcobacter species. This thesis presents the development and validation of a new gold standard method for isolating these emerging pathogens in food, clinical and environmental sampling.
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Title: Asztalos_iit_0091N_11584

Title: Ausloos_iit_0091N_11542

Title: Evolution and adaptations to host plants in the beetle genus Diabrotica
Creator: Lata, Dimpal
Date: 2022
Description: Corn rootworms (Diabrotica spp.) are among the most destructive pests impacting agriculture in the U.S and are an emerging model for insect...
Show moreCorn rootworms (Diabrotica spp.) are among the most destructive pests impacting agriculture in the U.S and are an emerging model for insect-plant interactions. We have a limited understanding of the genome-scale level difference between specialist and generalist corn rootworm species and their interaction with their host plants. Genome sizesof several species in the genus Diabrotica and an outgroup were estimated using flow cytometry. Results indicated that there has been a recent expansion in genome size in the common ancestor of the virgifera group leading to Diabrotica barberi, Diabrotica virgifera virgifera, and Diabrotica virgifera zeae. Comparative genomic studies between the fucata and virgifera groups of Diabrotica revealed that repeat elements, mostly miniature inverted-transposable elements (MITEs) and gypsy-like long terminal repeat (LTR) retroelements, contributed to genome size expansion. The initial transcriptional profile in western corn rootworm neonates when fed on different potential host plants demonstrated a strong association between western corn rootworm and maize, which was very distinct from other possible hosts and non-host plants. The results also showed presence of several larval development related transcripts unique to host plants and the presence of several muscle development and stress response related transcripts unique to non-host plants. The effect of the maize defensive metabolite DIMBOA on corn rootworms was studied using a novel plant-free system. The survival of both southern and western corn rootworms was not affected at a low concentration of DIMBOA. However, the concentration above the physiological dose found in plants affected the survival of corn rootworms. DIMBOA had no plant independent effect on these corn rootworms weight gain.
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Title: The Impact of Supplementary Cementitious Materials on Strength Development in Concrete
Creator: Lallas, Zoe Nicole
Date: 2022
Description: This thesis outlines the specific properties of fly ash, silica fume, slag, and a variety of natural pozzolans that affect strength...
Show moreThis thesis outlines the specific properties of fly ash, silica fume, slag, and a variety of natural pozzolans that affect strength development in concrete mix designs. It presents a comprehensive summary of select research studies which examined the fresh and hardened properties of concrete made with supplementary cementitious materials (SCMs) to better understand how these materials affect compressive strength development in concrete. The considerations necessary for precast fabrication and other applications in which early-age strength is a crucial concern are of particular importance, as SCMs often slow the rate of strength development in concrete. While SCM usage is common in concrete, replacement quantities are limited and heavily regulated, with the potential for further incorporation into concrete in higher replacement amounts, given continued research on how to best integrate SCMs to maximize strength properties, through the use of chemical admixtures, accelerators, and heat controlled curing regimes as needed.
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Title: PER – AND POLYFLUOROALKYL SUBSTANCES FATE AND TRANSPORT IN SEDIMENTS, SAND, AND ADSORBENT MEDIA
Creator: Manwatkar, Prashik
Date: 2022
Description: Perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) are two important organic chemicals of the per- and polyfuoroalkyl...
Show morePerfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) are two important organic chemicals of the per- and polyfuoroalkyl substances (PFAS) group that have contaminated land, water, and the air since 1950. The continuous release of PFAS from the surface of land into water is not easy to forecast and an appropriate treatment method needs to be economically viable since there are currently around 42,000 suspect industrial and municipal sites in the United States. For a true reproduction of real-world pollution patterns, we constructed polypropylene tanks, performed laboratory-based experiments, and analyzed the samples using EPA method 533. In this study, we examined the fate and transport of long- and short-chain PFAS, including PFOA, PFOS, and perfluorobutanesulfonic acid (PFBS), from sediments, adsorbent media, and sands under overlaying water tanks. Granular activated carbon (GAC), biochar (BC), and Fluorosorb® (FS) were also added between the contaminated sediments and the sand layer in order to observe capping effectiveness. As one of the best ways to treat contaminated sediments on a large scale, adsorbent beds may reduce contaminants migration and support the degradation of contaminants. We found that all three chemicals were able to pass through the adsorbent layers of 3-4 inches from 4-5 inches of contaminated sediments and reach the top surface of the beds (25-30 inches). In the top 5-7 inches, PFBS concentration varied from 0.28 ppb to 0.78 ppb for all adsorbent tanks for 7 days. Whereas the bottom contaminated sediments concentrations of PFBS were 8518 ppb to 9481 ppb. We also observed the concentrations at top ports increased by 0.59 ppb to 2.31 ppb in 21 days, and ultimately, 0.58 ppb to 7.07 ppb in 69 days. While PFOA and PFOS found different metabolites in all layers, they provided noticeably lesser concentrations in contaminated sediments compared to PFBS. Further, the results of this study can be useful for validating the contaminant transport model predictions by identifying linear or nonlinear sorption equilibrium processes and diffusion-dispersion processes in sediment, sand, and various adsorbent media.
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Title: Comparing the effects of an adjunct brief action planning intervention to standard treatment in a heterogeneous sample of chronic pain patients
Creator: Mikrut, Cassandra Leona
Date: 2022
Description: Objectives: Behavioral treatments for chronic pain have been associated with positive outcomes, but they are often time consuming in nature....
Show moreObjectives: Behavioral treatments for chronic pain have been associated with positive outcomes, but they are often time consuming in nature. The aim of the present study was to investigate the effectiveness of a brief behavioral treatment for chronic pain and compare Brief Action Planning used in conjunction with treatment as usual (BAP + TAU) to TAU, on changes in pain severity, pain interference, pain self-efficacy, quality of life, and anxiety and depression in a heterogeneous sample of chronic pain patients. Methods: A total of 172 participants were recruited from an urban pain clinic. Eighty-five participants were quasi-randomly assigned to the BAP + TAU group and 87 participants were quasi-randomly assigned to the TAU control group. After completing T1 measures, two iterations of the BAP protocol were delivered to the intervention group by a trained clinician over the phone, with two weeks in between iterations. The TAU group received check-in calls, collecting brief mood and pain scores, to control for clinician contact. All participants completed T2 measures following the last phone call. Validated measures were used at T1 and T2 to examine participant outcomes. Results: Two-way repeated measures analysis of variance (ANOVA) tests were used to test the primary hypotheses that there would be a Group x Time interaction, on pain severity, pain interference, pain self-efficacy, quality of life (QOL), and anxiety and depression, such that participants assigned to the BAP + TAU group would endorse improved scores from T1 to T2, while TAU participants would not. Results showed a significant Group x Time interaction on pain severity and anxiety and depression. However, there was not a significant Group x Time interaction on pain interference, pain self-efficacy, or QOL. Discussion: These findings provide preliminary support for the effectiveness of BAP, as an adjunctive treatment to TAU, when provided by a trained clinician, as a treatment for reducing pain severity and anxiety and depression, in a heterogeneous chronic pain population. These results advance the current BAP literature, providing preliminary support for using BAP with individuals with a wide variety of chronic pain diagnoses.
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Title: EMBEDDING RELATIONSHIPS: THE INDIRECT EFFECTS OF WORK RELATIONSHIPS ON TURNOVER INTENT
Creator: McDonald, Jordan C.
Date: 2022
Description: With the onset of the “Great Resignation” following the onset of the COVID-19 pandemic, employees are quitting jobs at unprecedented levels....
Show moreWith the onset of the “Great Resignation” following the onset of the COVID-19 pandemic, employees are quitting jobs at unprecedented levels. Although the traditional model of turnover (Mobley, 1977; Mobley, Griffeth, Hand, & Meglino, 1979) links job attitudes and turnover intentions as key determinants in understanding the turnover process, there is a growing recognition of the importance of studying contextual variables, namely social relations, in expanding our understanding of employee turnover and retention. Job embeddedness (Mitchell et al., 2001) and social capital theories (Granovetter, 1973; Burt, 1992; Lin, 1982) implicate employees’ social networks as additional factors worth investigating in understanding employee turnover. The aim of the current study was to study an expanded model of turnover by examining whether different types of social relationships at work differentially related to work experiences and attitudes that, in turn, related to turnover intentions. The current research leveraged an ego-centric method to collect information on employees’ social networks at work along with work experience and attitudinal constructs. The results of the study found that expressive relationship networks (i.e., friendship networks) had a positive, significant effect on employees’ job embeddedness, with an indication of a marginal indirect effect with organizational commitment. Surprisingly, employees’ instrumental networks were not significantly related to any work experience or attitudinal factors. There was no support for the hypothesized indirect effects linking social networks, work experiences and attitudes, and turnover intentions. Practical implications and directions for future research are discussed.
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Title: AN IMPROVED VALIDATED METHOD FOR THE DETERMINATION OF SHORT-CHAIN FATTY ACIDS IN HUMAN FECAL SAMPLES BY GC-FID
Creator: Freeman, Morganne M
Date: 2022
Description: Short-chain fatty acids (SCFAs) are metabolites produced by the gut microbiota through the fermentation of non-digestible carbohydrates....
Show moreShort-chain fatty acids (SCFAs) are metabolites produced by the gut microbiota through the fermentation of non-digestible carbohydrates. Recent studies suggest that gut microbiota composition, diet and metabolic status play an important role in the production of SCFAs. Current methods for the analysis of SCFAs are complex and inconsistent between research studies. The primary objective of this study was to develop a simplified method for standardized SCFA analysis in human fecal samples by gas chromatography with flame ionization detection (GC-FID). A secondary objective was to apply the method to fecal samples from a previous randomized, crossover clinical trial comparing participants with pre-diabetes mellitus and insulin resistance (IR-group, n=20) to a metabolically healthy reference group (R-group, n=9) after daily consumption of a red raspberry smoothie (RRB, 1 cup fresh-weight equivalent) with or without fructo-oligosaccharide (RRB + FOS, 1 cup RRB + 8g FOS) over a 4-week intervention period. Extraction parameters, including solvent selection and water content of the sample, were investigated before finalizing the method. Freeze-dried fecal samples (0.5 g) were suspended in 5 mL of milli-Q water, vortexed and centrifuged at 3,214 x g for 10 minutes. The supernatant was transferred to a clean tube, acidified with 5.0 M HCl and centrifuged again at 12,857 x g for 5 minutes. The resulting supernatant was transferred to a GC vial for analysis by GC-FID. Linear regression data for standards at concentrations 5-2000 ppm ranged from 0.99994-0.99998. Limit of detection (LOD) ranged from 0.02-0.23 µg/mL. Limit of quantification (LOQ) ranged from 0.08-0.78 µg/mL. The validated method was then applied to fecal samples collected from a previously conducted study. Nine SCFAs were identified and quantified (acetic, propionic, iso-butyric, butyric, iso-valeric, valeric, 4-methyl valeric, hexanoic and heptanoic acids). Statistical analysis (Student’s t-test, ANCOVA) was performed on PC-SAS 9.4 (SAS Institute). Acetic acid was significantly lower in the IR-group compared to the R-group before starting intervention (baseline, Week 0, IR v R-group, p=0.014). Intervention analysis comparing RRB to RRB + FOS at 4 weeks (WK4) showed a significant difference in 4-methyl valeric acid (p = 0.040) in the R-group. Trends of decreased SCFA content after 4-weeks of RRB and RRB + FOS compared to baseline were observed in both groups, though changes were not significantly different between dietary interventions at 4 weeks (p>0.05). Metabolic status and dietary intervention are discussed in relation to their impact on SCFA content in fecal samples and mechanisms of biological use as a metabolite. Limitations of the study include sample size and using only feces and not other biological samples for SCFAs analysis, which may be considered for future research.
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Title: Two Essays on Cryptocurrency Markets
Creator: Fan, Lei
Date: 2022
Description: Understanding the dependence relationships among cryptocurrencies and equity markets is of interest to both academics and researchers. This...
Show moreUnderstanding the dependence relationships among cryptocurrencies and equity markets is of interest to both academics and researchers. This dissertation is comprised of two essays to add to this understanding. In the first essay, I investigate the interdependencies among the level of informational efficiency of four cryptocurrencies. I examine the correlations between the market efficiencies of cryptocurrencies using the rolling window method. I find that the correlations between those levels of market efficiencies are time-varying and influenced by the market condition and external events. I extend the study by employing Granger causality tests to analyze the causal relationships among these levels of market efficiency. I find that the Granger causalities among the levels of the cryptocurrency market efficiencies are time-varying and impacted by the level of the market efficiencies. In the second essay, I investigate the pairwise dependencies and causalities between the returns of the cryptocurrencies and six equity market indices. I examine the pairwise dependencies between the returns of cryptocurrencies and those of the equity indices by using the DCC-GARCH framework. I find the dynamic conditional correlations between the cryptocurrencies and equity indices are time-varying and generally weak. Furthermore, I study the causal relationship between cryptocurrencies and equity indices by employing the rolling Granger causality test. I find that the Granger causalities between cryptocurrencies and equity indices are time-varying, and more unidirectional Granger causalities are found from cryptocurrencies to equity indices. In addition, I examine the impact of cryptocurrency returns on the correlations between the equity market indices, and likewise, the impact of equity market returns on the correlations between the cryptocurrencies. I find that the cryptocurrency price fluctuations have minimal impact on the correlations between equity indices. Moreover, the dynamic conditional correlation between cryptocurrencies is unaffected by equity price innovations except for some extreme events. These findings could have implications for understanding the relationships among cryptocurrencies and equity markets and for investors wishing to incorporate these relationships in their portfolio choices.
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Title: PLAYER MOTIVATION AND TRAINING EFFECTIVENESS: INSIGHTS FROM A STRUCTURAL MODEL OF GAME-BASED LEARNING
Creator: Gandara, Daniel A.
Date: 2022
Description: Digital game-based learning (DGBL) delivers training through video games. Practitioners are using DGBL in attempts to increase motivation,...
Show moreDigital game-based learning (DGBL) delivers training through video games. Practitioners are using DGBL in attempts to increase motivation, promote learning, and increase transfer in training. Theory and models of DGBL aim to explain how motivation is created to yield these benefits, and studies have compared DGBL to traditional methods, yet the tenets of these theories remain largely unexamined. The present study tested the process-outcome link of Garris et al.’s (2002) input-process-outcome model, examined the effect of positive and negative user judgments on behavior and learning, and expanded the model to include trainee reactions and adaptive transfer. Participants (N = 254) learned about identifying misinformation online by playing Fake It to Make It, a social-impact game that teaches core critical thinking skills. Autoregressive cross-lagged (ARCL) panel analysis was used to analyze and compare models to test the hypothesized relationships among judgments and behavior scores across six game levels in predicting six learning outcomes, including adaptive transfer tasks evaluating online sources. Findings indicated that each judgment was predicted by its own lagged judgment and lagged behavior. Additionally, positive user judgments predicted reactions, post-training self-efficacy, and motivation to transfer, while frustration inhibited declarative knowledge. Results also demonstrated that behavior and declarative knowledge predicted performance on the adaptive transfer tasks. Research recommendations and practice implications are discussed relative to using games to deliver training with emphasis on motivational properties and targeted outcomes.
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Title: TOPICDP – ENSURING DIFFERENTIAL PRIVACY FOR TOPIC MINING
Creator: Sharma, Jayashree
Date: 2021
Description: Topic mining enables applications to recognize patterns and draw insights from text data, which can be used for applications such as sentiment...
Show moreTopic mining enables applications to recognize patterns and draw insights from text data, which can be used for applications such as sentiment analysis, building of recommender systems and classifiers. The text data can be a set of documents or emails or product feedback and reviews. Each document is analysed using probabilistic models and statistical analysis to discover patterns that reflects underlying topics.TopicDP is a differentially private topic mining technique, which injects well-calibrated Gaussian noise into the matrix output of the topic mining model generated from LDA algorithm. This method ensures differential privacy and good utility of the topic mining model. We derive smooth sensitivity for the Gaussian mechanism via sensitivity sampling, which resses the major challenges of high sensitivity in case of topic mining for differential privacy. Furthermore, we theoretically prove the differential privacy guarantee and utility error bounds of TopicDP. Finally, we conduct extensive experiments on two real-word text datasets (Enron email and Amazon Product Reviews), and the experimental results demonstrate that TopicDP can generate better privacy preserving performance for topic mining as compared against other state-of-the-art differential privacy mechanisms.
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Title: THE IMPACT OF SHARED RECRUITMENT INFORMATION ON APPLICANT OUTCOMES AND THE INFLUENCE OF MODERATING VARIABLES
Creator: Savage, Catherine M.
Date: 2022
Description: Organizations are currently experiencing one of the most challenging environments when it comes to recruiting talent. What started in the...
Show moreOrganizations are currently experiencing one of the most challenging environments when it comes to recruiting talent. What started in the 1990s as the “War for Talent,” in which organizations faced fierce competition when hiring and retaining employees, has persisted, and grown more competitive, post-pandemic. As a result, organizations must re-evaluate their recruitment strategies and find ways to connect with job candidates that will increase the probability that they will pursue open job positions. Thus, we examined how sharing different information regarding pay, diversity statements, and mentoring benefits with 250 potential job applicants, based in the US, may influence their attraction to an organization, perceived person-organization fit, and their intention to pursue the job that was posted. We also examined how ethnicity, gender, and age can influence the job candidates’ perception of the information provided. Results from this research partially supported our hypothesized outcomes. Presenting more information to participants (rather than less) generally had a positive impact on organization attraction and intentions to pursue the position posted in the job advertisement. However, the amount of information shared to participants did not influence perceptions of person-organization fit. Additionally, while ethnicity did not moderate the relationship between amount of information shared and the outcome variables, gender and age were found to influence participants’ reaction to the information provided and their subsequent level of organizational attraction and intention to pursue. Implications and avenues for future research are discussed.
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Title: WHAT IMPACT DO NUMBER TALKS HAVE ON ELEMENTARY CLASSROOM MATHEMATICAL DISCOURSE AND STUDENT AND TEACHER ATTITUDES TOWARD MATHEMATICS?
Creator: Sleezer, Meghan V
Date: 2021
Description: Number Talks, created in the early 1990s by Ruth Parker and Kathy Richardson, have gained popularity in the mathematics education community...
Show moreNumber Talks, created in the early 1990s by Ruth Parker and Kathy Richardson, have gained popularity in the mathematics education community over the past decade with the publication of the book series Number Talks (Parrish, 2010, 2014), and especially since the publication of Making Number Talks Matter (Humphreys & Parker, 2015). All in all, the authors contend Number Talks can bring joy into the classroom (Humphreys and Parker, 2015, p. 6), improving student attitudes about mathematics and ultimately allowing for a more productive disposition. The characteristic that separates Number Talks from other pedagogical tools is the disconnectedness from the rest of the lesson: Number Talks need not build up to or build upon the day’s objective. Thus, what the authors argue is that the activity of Number Talks itself – albeit disconnected from the day’s objective – improves all of the aforementioned skills, regardless of what occurs during the remainder of each class session.Eight teachers from five different Chicago-area private grade schools implemented Number Talks in their 3rd-5th grade classrooms for four to six weeks in the early part of the year 2020. Student attitudes toward mathematics and toward mathematical discourse were assessed by way of survey and classroom observation before and after implementation. Classroom interactions and levels of mathematical discourse during the normal class time (outside of the Number Talk session) were assessed before and during implementation. No significant changes (positive or negative) relating to any measure were found. Teachers noticed that students who enjoyed math before the implementation also enjoyed Number Talks, while students who struggled with math were mostly disenchanted with Number Talks. Future research includes exploring whether tailoring Number Talks to relate to the upcoming lesson improves the positive effects advertised by the authors. Teacher professional development related to ambitious teaching practices (NCTM, 2017) and growth mindset (Boaler, 2016b) may complement the use of Number Talks to result in improved attitudes and discourse.
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Title: Do Numeric Performance Ratings Have Any Merit?
Creator: Sanders, Emily Kathleen
Date: 2022
Description: Numeric performance ratings have been a component of performance evaluation for decades (Prowse & Prowse, 2009; Pulakos, Mueller-Hanson & Arad...
Show moreNumeric performance ratings have been a component of performance evaluation for decades (Prowse & Prowse, 2009; Pulakos, Mueller-Hanson & Arad, 2019). Yet, in recent years their necessity has been questioned (Adler, Campion, Colquitt, Grubb, Murphy, Ollander-Krane, & Pulakos, 2016), with some organizations going so far as to remove numeric ratings entirely (Capelli & Tavis, 2016; Rock, Davis & Jones, 2014; Burkus, 2016). Unfortunately, this practice has been largely unexamined in an empirical manner. The present study tested whether the claim – that numeric ratings do not matter – holds up in all cases. This is done by exploring whether the presence or absence of numeric ratings, impacts employee perceptions of fairness associated with the appraisal. As numeric ratings are argued to be a mechanism for communicating a fair, standard, and consistent practice, the study aimed to understand if the mere presence of numeric ratings may offset some of the negative reaction employees have toward performance appraisal when they have poor-quality relationships with their supervisors. Findings indicated that while employee-manager relationship quality (assessed via Leader-Member Exchange) has a direct relationship with perceptions of fairness associated with the appraisal, the presence of numeric ratings did not moderate this relationship. Practical implications and future research recommendations are discussed.
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