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Title: VERSATILE AND DYNAMIC INCENTIVE-BASED WELLNESS PROGRAM
Creator: Janik, Raymond George
Date: 2020
Description: Rising healthcare spending is prompting companies to implement health promotion programs for their employees to reduce health cost. Several...
Show moreRising healthcare spending is prompting companies to implement health promotion programs for their employees to reduce health cost. Several studies have indicated that workplace health promotion programs do not always improve employee wellbeing or reduce company healthcare cost. Focus on short-term financial results rather than long-term employee health behavior and ineffective use of incentives have been blamed for this failure.The main goal of this research is to introduce a wellness program and incentive plan with focus on changing long-term employee health behavior so it would lead to sustainable improvement in productivity and reduction in healthcare cost. The proposed program includes multiple yearly wellness follow up events, along with wellness and fitness data collection questionnaires for timely feedback and diversified outcome-based incentives. Regression models are developed to provide estimates of biometric data that are critical to performance feedback and for estimating healthcare cost savings.The proposed wellness program is currently being tested at a 700-employee lighting company in southeast united states. The healthcare cost models estimate a return on investment of $1.8 for every dollar spent on the program.
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Title: Distributed Resource Management for Wireless Networks Over Unlicensed Spectrum
Creator: Han, Mengqi
Date: 2020
Description: In the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local...
Show moreIn the past decades, a variety of wireless networks have been deployed, e.g., long term evolution (LTE) cellular networks, wireless local network networks (WLANs), cloud radio access network (C-RANs), wireless metropolitan area networks (WMANs), wireless body area networks (WBANs) and etc.To meet the exponential growth of traffic demands and improve the network throughput, different enhancement in the MAC protocols have been proposed for the emerging networks. For example, U-LTE (Unlicensed LTE) is proposed for LTE users to aggregate the spacious unlicensed spectrum with the licensed spectrum to boost the network throughput. Meanwhile, Wi-Fi users are allowed to opportunistically bond available channels for high data rate transmissions to improve the spectrum efficiency and network throughput. But the performance of the emerging networks with the new techniques has not been well investigated. Thus, in this thesis, we comprehensively investigate the network performance in different network scenarios. In each scenario, we first develop mathematical models to identify the performance bottlenecks in the existing MAC protocols. We then propose an algorithm to intelligently tune the protocol parameters to maximize network performance. Finally, the proposed algorithm is compared with some existing algorithms. Specifically, in the first scenario, we evaluate the coexistence performance between the Wi-Fi users with channel bonding capability and the legacy users without channel bonding capability. Specifically, the channel bonding probability and the channel access delay of wireless users are first analyzed, considering the contentions among legacy and multi-channel users in the same channel and across multiple channels. Based on the analysis, the network capacity, i.e., the maximum number of traffic flows that can be supported with the bounded delay performance in a multi-channel Wi-Fi with and without channel bonding, is then derived. Based on the analytical results, we propose a heuristic bonding policythat can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. In addition, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints for multi-channel users with channel aggregation capability. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput.In the second scenario, we consider that U-LTE users are coexisting with the legacy users without channel bonding capability in the same unlicensed spectrum. The throughput of both Wi-Fi and U-LTE users are both derived when U-LTE users adopting two Load Based Equipment(LBE) random access protocols and Category 4 (Cat 4) algorithm agreed in 3GPP release 13.Based on the analysis, we find that the current protocols of U-LTE users are far from perfect to achieve harmony coexistence. Subject to the system fairness constraint, the aggregate throughput of U-LTE and Wi-Fi networks is maximized based on a semi branch and bound algorithm. To make the complex optimization tractable, reinforcement learning techniques are introduced to intelligently tune the contention window size for both U-LTE and Wi-Fi users. Specifically, a cooperative learning algorithm is developed assuming that the information between different systems is exchangeable. A non-cooperative version is subsequently developed to remove the previous assumption for better practicability. Extensive simulations are conducted to demonstrate the performance of the proposed learning algorithms in contrast to the analytical upper bound under various conditions. It is shown that both proposed learning algorithms can significantly improve the total throughput performance while satisfying the fairness constraints.Finally, by considering the energy constraints, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. In an IoT network with energy harvesting, an IoT device can contend for channel access only when it is ready, i.e., it has data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by adapting the transmission probability p of IoT devices. Given that the wireless environment is unknown at different IoT devices, e.g., the total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.
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Title: 3D reconstruction of lake surface using camera and lidar sensor fusion
Creator: Khan, Shahrukh
Date: 2020
Description: Global Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing...
Show moreGlobal Navigation Satellite System Reflectometry (GNSS-R) relies upon detecting the GNSS signals reflected off a surface and then analyzing the reflected signal to obtain surface characteristics. GNSS-R has become one of the many additional applications of the readily available GNSS signals, alongside more traditional remote sensing of ionospheric monitoring, beyond the intended GNSS purposes of providing position, navigation, and timing estimation. In previous work, GPS signals reflected off Lake Michigan in Chicago have been collected using a specially designed portable sensor suite. The data collected is then analyzed to differentiate between surface ice and water conditions, as well as obtain other characteristic information such as surface reflectivity. The goal is to provide a way for remote sensing of seasonal ice formation beyond just satellite imagery which can be affected by cloud cover. To confirm the validity of the GNSS-R results there needs to be a separate reference against which to compare. This work demonstrates the sensor fusion between camera and lidar to reconstruct the lake surface, to provide that truth reference for comparison against the results of the GPS reflectometry signal processing. For this setup, the camera provides visual information about the lake surface, while the lidar provides distance information with respect to the sensor suite. Combining the data from the two sensors allows backward projection of the camera image to reconstruct the lake surface and its features. The backward projection relies upon knowledge of the camera's intrinsic properties alongside distance information of the features captured by the camera. Each pixel of the camera image is then transformed to its 3D position relative to the sensor system. This produces a 3D map of the lake surface, as captured by the sensors. The estimated point at which the GPS signal reflects off the surface, the specular point, is calculated by the satellite position at the time of interest and the receiver location. This point is then mapped onto the reconstructed surface to identify the exact location where the signal reflected and compare the surface visually to the results from the signal analysis.Time-varying camera-lidar-specular-point maps of the data campaigns conducted for this project are created for comparison with the GPS signal analysis. Multiple data campaigns were performed during which the Lake Michigan surface had surface ice, water or a mixture of the two. The lake surface is reconstructed for different timestamps, using the appropriate image frame and lidar frame. Combining chronologically, the changes in the lake surface can then be observed along with the movement of the specular point, due to the movement of the GPS satellites. Any satellites passing over a boundary between water and ice on the lake surface are identified and time stamped, to then be compared to the GPS signal analysis results.
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Title: The Relation Between Community Violence Exposure and Young Children's Psychopathology Symptoms
Creator: Gibson, Lynda L
Date: 2020
Description: Chronic community violence exposure (CVE) has become an everyday issue for many children living in inner-city neighborhoods. However, few...
Show moreChronic community violence exposure (CVE) has become an everyday issue for many children living in inner-city neighborhoods. However, few studies have examined the effects of CVE on symptoms of psychopathology in young children. The primary aim of this study was to examine the relation between CVE and internalizing and externalizing symptoms in preschool children. Another goal was to determine if the relations between CVE and both types of symptoms were affected by the type of exposure, the location of the event, and the relationship between the child and the individual involved in the event. A signal-contingent ecological momentary assessment (EMA) design was used in which 32 caregivers reported on their age 3-5 year-old children’s exposure to community violence, and their internalizing and externalizing symptoms for one week. A total of 152 events of community violence were reported during this period, revealing that the children were exposed to an alarmingly high frequency of these events. Results of multilevel model (MLM) analyses showed that increased frequency of momentary CVE was associated with more severe caregiver-reported internalizing and externalizing symptoms throughout one-week. Additionally, witnessed CVE, situations occurring near home, and situations involving someone known by the child strengthened the association between CVE and symptom severity. The present findings reveal that some of the long-term negative effects associated with trauma exposure occur in a shorter time-span when children are exposed to community violence on a daily basis. They also provide guidelines that can be used to inform future assessment of CVE and strategies that may be effective for intervention.
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Title: Gaussian Process Assisted Active Learning of Physical Laws
Creator: Chen, Jiuhai
Date: 2020
Description: In many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential...
Show moreIn many areas of science and engineering, discovering the governing differential equations from the noisy experimental data is an essential challenge. It is also a critical step in understanding the physical phenomena and prediction of the future behaviors of the systems. However, in many cases, it is expensive or time-consuming to collect experimental data. This article provides an active learning approach to estimate the unknown differential equations accurately with reduced experimental data size. We propose an adaptive design criterion combining the D-optimality and the maximin space-filling criterion. The D-optimality involves the unknown solution of the differential equations and derivatives of the solution. Gaussian process models are estimated using the available experimental data and used as surrogates of these unknown solution functions. The derivatives of the estimated Gaussian process models are derived and used to substitute the derivatives of the solution. Variable-selection-based regression methods are used to learn the differential equations from the experimental data. The proposed active learning approach is entirely data-driven and requires no tuning parameters. Through three case studies, we demonstrate the proposed approach outperforms the standard randomized design in terms of model accuracy and data economy.
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Title: THE EFFECTS OF PHYSICAL AND CHEMICAL PROPERTIES OF DOLOMITE ON DOLOMITE DECOMPOSITION
Creator: Huang, Hsiang-Jung
Date: 2020
Description: Dolomite comprises approximately two percent of the Earth’s crust and has a widespread geological distribution throughout the world. It is an...
Show moreDolomite comprises approximately two percent of the Earth’s crust and has a widespread geological distribution throughout the world. It is an abundant, low cost, and promising raw base material for many applications in industry, such as sorbents for capturing CO2 from coal gas and a heterogeneous catalyst for reducing tar content in biomass gasification. Dolomite decomposition has been intensively studied over the past decades. However, to date, there is hardly any systematic literature available that addresses the effects of naturally occurring impurities on dolomite decomposition due to the difference in various experimental setups, sample size, particle size, and so on. Therefore, this research focuses on employing a systematic and comprehensive investigation to develop a better understanding of the effects of the physical and chemical properties of raw dolomites on dolomite decomposition. This study involves experimental, theoretical, and modeling work. There are several experimental techniques utilized for the exploration of the physical and chemical properties of dolomites from different sources, such as the Thermogravimetric analysis (TGA), the X-ray powder diffraction (XRD) and the Brunauer–Emmett–Teller theory (BET), respectively. In the study, it has been discovered that the excess weight loss of samples during thermal decomposition experiments was owing to the explosive disintegration of the nature of dolomite. The physical properties of dolomites are not the main factor affecting dolomite decomposition but thermodynamic properties and crystal structure. The initial equilibrium constant of dolomite which is dominated by the amount of silicate-based impurities plays a major role in the decomposition rate. A two-stage reaction model was developed that included a reversible reaction of uniform solid ordered-disordered crystal transformation of dolomite followed by a "Quasi-Shrinking Core" reaction of disordered dolomite decomposition. This model is capable of describing the reaction rate of half-calcination of dolomite with acceptable accuracy.
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Title: ASSET PRICING AND RETURN REVERSAL IN KOREAN AND JAPANESE STOCK MARKET
Creator: Kim, Pil Joon
Date: 2020
Description: The stock market in Asia achieved a rapid development in the 1980’s, mainly in Japan and Korea. In particular, stock market in Japan and Korea...
Show moreThe stock market in Asia achieved a rapid development in the 1980’s, mainly in Japan and Korea. In particular, stock market in Japan and Korea is deeply related to the US stock market. However, in 1997, a major financial crisis hit Asia, and IMF decided to provide financial support to Korea. In addition, in 2011, a nuclear accident at the Fukushima Daiichi Nuclear Power Plant was the most severe nuclear accident since the 26 April 1986 Chernobyl disaster. Nevertheless, the Japanese and Korean markets experienced stable growths. Were Japanese and Korean stock markets truly stable and efficient? This study empirically studied market efficiency through stock market return reversal in the Japanese and Korean stock market and the characteristics of these two stock markets were compared and analyzed.Significant return reversal phenomenon was observed as a result of validating return reversal phenomenon against the stock markets in Korea and Japan. Furthermore, return reversal level differed based on the abnormal (excess) return calculation method used in the test model. Return reversal phenomenon can be found more clearly in loser portfolio than in winner portfolio in general. In particular, when the abnormal (excess) return was calculated using CAPM model, different result from existing research was observed. I also found that the Fama-French 3 factor model can compensate for the CAPM problem. I concluded that this phenomenon is observed in Korea and Japan stock market supporting DeBondt & Talher that CAPM misleads theoretical stock price return reversal and Brown and Warner (1980), who found that sophisticated CAPM do not perform better than simple model like market adjusted returns model. This is interpreted that the stock markets in Korea and Japan are not efficient and continue to have unstable factors. These findings provide full of suggestions to further research. CAPM is to explain market equilibrium price as a one-factor model, the Fama-French 3 factor model is a multi-factor model, and it can be said to more accurately describe the equilibrium price by adding size and gross value factor in describing the market equilibrium price. These results show that if Fama-French 3 factor model uses, it can solve the problem when using CAPM.The January effect is found significantly in both the Korean and Japanese markets. In the Korean stock market, the short-term seasonal reversal effect is more pronounced than in the long-term, and in the Japanese stock market, the long-term seasonal reversal effect is more pronounced than in the short-term.
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Title: THE MODERATING AND MEDIATING ROLE OF SELF-REPORTED FAMILY ACCOMMODATION ON THE RELATION BETWEEN OBSESSIVE-COMPULSIVE SYMPTOMS AND RELATIONSHIP SATISFACTION IN AN ADULT, CLINICAL SAMPLE OF INDIVIDUALS IN ROMANTIC RELATIONSHIPS
Creator: De Leonardis, Andrew J
Date: 2020
Description: Severity of obsessive-compulsive symptoms (OCS) is associated with treatment resistance, and in an interpersonal context, is associated with...
Show moreSeverity of obsessive-compulsive symptoms (OCS) is associated with treatment resistance, and in an interpersonal context, is associated with increased relationship distress and decreased relationship satisfaction. In addition, caregivers for those with clinical levels of OCS often engage in family accommodation (FA) behaviors that serve as an extension of the OCD patient’s compulsive behavior. However, the literature on the interchange of OCS, FA, and relationship satisfaction is limited in scope because it focuses mainly on the perspective of the caregiver or partner of the individual with OCD. The current study aims to address this limitation by examining OCS, FA, and relationship satisfaction variables from the perspective of the individual with OCD. Participants included 78 adults with self-reported OCD who were recruited in the US through clinics and clinicians specializing in OCD treatment, as well as from OCD non-profit organizations to target non-treatment-seeking participants. After controlling for demographic variables, results indicated the following: (1) a significant positive association between OCS and FA, (2) a significant negative association between OCS and relationship satisfaction, and (3) a lack of an interaction between FA and OCS when predicting relationship satisfaction. However, the third result was trending towards significance and may be statistically underpowered. Exploratory analyses found FA to be a partial mediator of the association of OCS and relationship satisfaction. The findings support current trends in the research literature as well as contradict extant research on the associations between OCS, FA, and relationship satisfaction. Additionally, findings continue to show the importance of addressing family accommodation in treatment of individuals with OCD.
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Title: Technology News, December 11, 1945
Creator: Illinois Institute of Technology
Date: 1945-12-11, 1945-12-11
Collection: Technology News Microfilm collection, 1928-1981

Title: Technology News, March 26, 1946
Creator: Illinois Institute of Technology
Date: 1946-03-26, 1946-03-26
Collection: Technology News Microfilm collection, 1928-1981

Title: Technology News, April 01, 1946
Creator: Illinois Institute of Technology
Date: 1946-04-01, 1946-04-01
Collection: Technology News Microfilm collection, 1928-1981

Title: Evaluation of Salmonella Proliferation on Alfalfa Sprouts during Storage at Different Temperatures
Creator: Lin, Chih Tso
Date: 2020
Description: Sprouts, a low-calorie vegetable rich in nutrition, have been a popular ingredient in many meals in the USA. They are grown either at...
Show moreSprouts, a low-calorie vegetable rich in nutrition, have been a popular ingredient in many meals in the USA. They are grown either at commercial sprout farms or at home and served raw or lightly cooked. However, sprouts are also known as a source of foodborne illness outbreaks. FDA Food Code identifies raw sprouts as a time/temperature control for safety food. However, little information is known about the growth profile of foodborne pathogens in sprouts stored at different temperatures. This study aimed at evaluating the proliferation of Salmonella in alfalfa sprouts during storage at 4, 10, and 25℃ under two different contamination routes: 1) sprouts that were inoculated with Salmonella after harvest and 2) sprouts that were grown from contaminated seeds. Alfalfa sprouts grown from uninoculated seeds and harvested after 5 days of sprouting were divided into 25-g portions. Each portion was inoculated with a cocktail of five Salmonella serovars at levels of 10^1, 10^3 or 10^5 CFU/g prior to storage at 4, 10, or 25℃. Alternatively, sprouts grown for five days from seeds spiked with 1% of seeds previously inoculated with the Salmonella cocktail were divided into 25-g portions and stored at 4, 10, or 25℃. At defined time points (Days 0, 2, 4, 7, 14, and 21), levels of Salmonella and background microflora in stored sprouts were determined by plate count. Alfalfa sprouts appeared fresh during the 21 days of storage at 4 or 10℃ but started to show signs of spoilage after 4 days of storage at 25℃. The total plate counts maintained at a level above 9 log CFU/g throughout 21 d of storage at 4 and 10℃ or during the first 7 d of storage at 25℃. Storing sprouts at 4 or 10℃ could inhibit the proliferation of Salmonella. After 21 d of storage, the Salmonella counts in inoculated sprouts decreased slightly, by 0.88 or 0.93 log units, respectively. For sprouts stored at 25℃, the Salmonella growth profile differed depending on the route of contamination and the level of Salmonella at the start of storage. In sprouts inoculated at levels of 1.41, 2.83, and 4.75 log CFU/g, the Salmonella counts increased to 6.62, 6.86, and 6.68 log units, respectively, during the first 4-7 days of storage. For alfalfa sprouts grown from contaminated seeds, the Salmonella counts remained at a level similar to that in the harvested sprouts (8.16 log CFU/g) during the first 7 d. Results from this study further the understanding of pathogen growth in sprouts and will aid in the development of guidelines for proper storage of sprouts.
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Title: Public Event Identification Traffic Data Using Machine Learning Approach
Creator: Yang, Hanyi
Date: 2020
Description: This study developed a shock waved diagram based deep learning model (SW-DLM) to predict the occurrence of public events in real-time...
Show moreThis study developed a shock waved diagram based deep learning model (SW-DLM) to predict the occurrence of public events in real-time according to their impacts on nearby highway traffic. Specifically, using point traffic volume data as a boundary condition, shock wave analysis is first conducted to understand the impacts and features of a public event on a nearby highway-ramp intersection. Next, this analysis develops the SWG algorithm to efficiently generate and expand shock wave diagrams in real-time according to the data collection rate. Built upon that, this study contributes a novel approach, which encodes a shock wave diagram with an optimal grid of pixels balancing resolution and computation load. Using the features extracted from encoded time-series shock wave diagrams as inputs, a deep learning approach, Long-short term memory (LSTM) model, is applied to predict the occurring of a public event. The numerical experiments based on the field data demonstrate that using encoded shock wave diagrams rather than point traffic data can significantly improve the accuracy of the deep learning for predicting the occurring of a public event. The SW-DLM presents satisfied prediction performance on the average as well as on an individual day with or without traffic accident interference, happening nearby the venue of a public event. The implementation of this approach to real-time traffic provision tools such as GPS will alert travelers en route on-going events in a transportation network and help travelers to make a smart trip plan and avoid traffic congestion. Moreover, it promotes smart city development by providing a strong capability to monitor the transportation system and conduct real-time traffic management intelligently.
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Title: Using Computational Approaches to Investigate Streptococcal Species from the Food Industry
Creator: Sun, Yukun
Date: 2020
Description: Streptococcal species are a major cause of concern in the Food industry. In the swine industry, Streptococcus suis is not only a major...
Show moreStreptococcal species are a major cause of concern in the Food industry. In the swine industry, Streptococcus suis is not only a major pathogen that can cause several systemic issues in pigs, but also an emergent zoonotic agent that can infect workers and consumers alike. However, not all S. suis strains are pathogenic and strain 90-1330, an avirulent Canadian serotype 2/sequence type 28 isolate, was previously found to produce a lantibiotic bacteriocin that kills several pathogenic Streptococcus species. This finding led to the suggestion that S. suis 90-1330 could be used as a probiotic in the swine industry for prophylactic purposes.As part of my thesis (Chapter 2), I sequenced the complete genome of S. suis 90-1330 and used comparative genomic approaches to predict if this strain is indeed avirulent and suitable for probiotic purposes. Results from our comparative analyses suggested that this strain may not be as harmless as initially thought, as its genome was found to code for several virulence factors including a hemolysin that lyses blood cells. The bacteriocin it produces and the products that confer resistance to its effect were found encoded in a functional, mobile integrative and conjugative element (ICE), suggesting that use of this strain as probiotic without further engineering would facilitate the spread of resistance to this bacteriocin. Furthermore, the bacteriocin was found widely distributed across several streptococcal species, indicating that the use of this strain as a probiotic might provide fewer health benefits than originally thought.In Chapter 3, I used the approaches and tools we developed as part of our work on S. suis strains to investigate the genetic diversity pertaining to Streptococcus parasuis and Streptococcus ruminantium. These two streptococcal species were recently removed taxonomically from S. suis based on phenotype assays and on limited genotype data, and the extent of their intra- and interspecific diversity as well as their potential for virulence were unknown. The two novel species were found to be genetically distinct from S. suis in our comparative analyses, as expected from previous studies, but the genetic differences responsible for their phenotypic differences could not be ascertained in large part due to the presence of many unique proteins of unknown functions, highlighting a need for improved methods to infer functions computationally.In Chapter 4, I describe the computer pipeline that we built to facilitate and automate genetic diversity analyses between bacterial species, and which was used extensively for Chapters 2 and 3. Notably, to palliate for data missing from sequencing archives, we implemented a simple solution that generates in silico sequencing datasets from complete and/or draft genomes. I tested and validated our pipeline extensively and describe in this chapter its pros and cons and current limitations.
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Title: A three-dimensional tissue molecular imaging system based on angular domain optical projection tomography: Applications in lymph node biopsy
Creator: Torres, Veronica Calliste
Date: 2020
Description: Sentinel lymph node biopsy is a good prognostic factor for several cancers as therapeutic decisions are often determined by the results....
Show moreSentinel lymph node biopsy is a good prognostic factor for several cancers as therapeutic decisions are often determined by the results. Despite this importance, false negatives remain common because of standard pathology procedures that aim only to detect macrometastases (> 2 mm diameter) and leave more than 99% of lymph node volumes unassessed. While it is possible to section tissue samples more thoroughly, a subsequent 10x increase in pathologist read time is undesirable. Therefore, a more sensitive and rapid approach for lymph node evaluation is warranted.Our proposed solution was the development of an angle-restricted optical projection tomography system to provide high resolution quantitative imaging of whole lymph nodes prior to conventional pathology. Two main strategies were employed: 1) early photon imaging achieved with angular restriction to minimize the number of detected multiply scattered photons that add to imaging blur; and 2) paired-agent molecular imaging, which can quantify targeted biomolecule concentrations through co-administration of targeted and control imaging agents.This thesis focused primarily on the first aspect; however, all work was performed with paired-agent imaging in mind, such that the technique can be implemented directly in future studies. The first chapter presents a proof-of-concept that verifies the utility of angle-domain imaging for evaluation of low scattering lymph nodes. Filtered backprojection and strict angle restriction for scatter rejection were sufficient to detect and localize clinically relevant metastases. In the second chapter, improvements were made to the system so that detection efficiency could be improved, and the system was more rigorously characterized in terms of reconstruction accuracy and limits of detection. Finally, the third chapter presents the investigation of alternate reconstruction techniques to push the limits of achievable resolution and image quality. The overall findings of this work demonstrate the potential for an angle-restricted tomography system to provide significant improvements of metastases detection sensitivity in excised lymph nodes compared to conventional pathology at a fraction of the time and cost.
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Title: Ultraviolet photo-chemical degradation of polyethylene terephthalate for use as an alternative recycling method
Creator: Smith, Andrew Thomas
Date: 2020
Description: Consumer plastics are a deeply integrated part of the modern world. Their inherent properties which make them cheap, durable, moldable, and...
Show moreConsumer plastics are a deeply integrated part of the modern world. Their inherent properties which make them cheap, durable, moldable, and versatile have caused plastics to be used in many consumer products available for market. However, these same properties have made them a detriment to local and global environments. plastic has begun accumulating in the world’s waterways and oceans, leading to severe ecological consequences. Polyethylene terephthalate (PET) is one of the most pervasive consumer plastic, and a large contributor to the amount of waste. Because of its prevalence in the market, PET has been the focus of research into its recycling and reuse. However, all methods face issues of profitability due to operation and equipment costs, preventing widespread recycling of plastic waste. This leaves the door open to explore other processes of plastic recycling.In this study, ultraviolet photo-chemical degradation of PET was explored as an alternate route to plastic recycling. Ultraviolet irradiation has long been known to depolymerize PET plastic products, but has not been studied in order to enhance these effects. This method has the potential to reduce operation and equipment costs associated with traditional chemical recycling methods by carrying out depolymerization in the solid state. By harnessing this process, PET could be used to degrade material down to a state usable in in other, higher value products. An irradiation chamber was built as a preliminary prototype. This chamber used light of a specific ultraviolet wavelength determined from the absorbance spectrum of PET samples. This allowed the irradiation to be safer, while still maintaining absorption.Ultraviolet degradation of PET was first examined using infrared, contact angle, and fluorescence analysis, and birefringence observation to analyze the chemical and surface effects of irradiation. The results were used to understand the complex mechanisms behind the photo-chemical degradation process. Results were then discussed alongside similar experiments performed in the literature for a deeper understanding of the underlying mechanism.The molecular weight of exposed bottle samples was evaluated using both viscosity and dynamic light scattering methods. This information is key, as it is the main metric that determines the success of the process. In addition, the ultraviolet absorbance of the sample was analyzed along with the principles of Beer’s law. This yielded quantitative analysis on the effect of thickness of the sample, the degradation rate, and the quantum yield of the process.Finally, building upon the information gathered in the study, two key process modifications are made. Thinner samples are first produced, and receive irradiation on both surfaces. The degradation of the modified process was compared to that of previous results on the basis of molecular weight reduction, reaction rate and quantum yield. Using these results, conclusions were drawn about using ultraviolet photo-chemical degradation as a recycling process.
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Title: High Frequency Trading and Its Impact on Market Quality in U.S. Futures Market
Creator: Wang, Chao
Date: 2020
Description: This research focuses on the effects of high frequency trading (HFT) on market liquidity in US futures market. This research utilizes a unique...
Show moreThis research focuses on the effects of high frequency trading (HFT) on market liquidity in US futures market. This research utilizes a unique data set consisting of all book events for multiple underlying assets and contracts during calendar year 2018, covering all trading days information of E-mini S&P 500, Gold, Eurodollar, Crude Oil, Corn and Soybean futures with their nearby and deferred contract data each day. This study extends findings from existing HFT equity research (e.g. Brocher et al., 2016; Frino et al., 2019, etc.) that HFT promotes market liquidity, into the commodity market. It also addresses HFT’s contributions to price discovery, and find it varies by types of commodities. Furthermore, the research identifies how an HFT phenomenon, the Cancel Cluster, impacts the futures market. Also, this research verifies and extends the models in Frino et al. (2019) to multiple commodities. Finally, a series of promising future analyses are suggested.
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Title: THREE ESSAYS ON CORPORATE FINANCE
Creator: Wang, Jianrong
Date: 2020
Description: This dissertation is comprised of three distinct but related essays in corporate finance. In the first essay, I examine how the CEOs paid with...
Show moreThis dissertation is comprised of three distinct but related essays in corporate finance. In the first essay, I examine how the CEOs paid with inside debt utilize corporate social responsibility activities to reduce firm risk taking. In the second and third essays, I explore the possible determinants of private placement. The first essay focuses on managerial incentive induced by debt-like compensation such as deferred compensation and defined benefit pensions. Building on cumulative prospect theory and instrumental stakeholder theory, I hypothesize that CEOs paid with debt in their own firms have risk-reduction incentives, and corporate social responsibility (CSR) activities mediate the relationship between debt-like compensation and firm risk taking. Furthermore, I argue that the mediated relationship between CEO debt-like compensation and firm risk taking is context dependent, and I propose that two contingencies, namely environmental dynamism and munificence, moderate the mediated process. My analyses, based on a large longitudinal dataset of nonfinancial US firms, lend strong support for these hypotheses. The second essay examines the impact of firm’s social capital on the cost of Rule 144A debt. I find that Rule 144A debt issuing firms headquartered in the high social capital county pay lower yield spread their peers. Furthermore, the finding suggests that the effect of social capital becomes weak when the issuers have more firm-specific public information and credit records. The relation between social capital and the cost of debt is contingent on industry environment. The results reveal that firms located in high social capital counties have low bankruptcy likelihood and low risk level after Rule 144A debt issuance. The third essay focuses on the role of prior technology alliances in the PIPE issuance. Relying on the data collected from Placement Tracker and SDC platinum, I empirically investigate the relationship between issuers’ technology alliance experiences and their PIPE offering contracts. I document that the greater alliance experiences at the time of the PIPE issuance, the smaller PIPE price discount and fewer contract terms that are favorable to investors. The results indicate that the technology alliance experience alleviates the information asymmetry between issuers and investors and improve issuers’ bargaining power. I further find that issuers with more alliance activities exhibit a more positive announcement effect and outperform in the long run. Moreover, the effect of technology alliance experience is stronger if the issuers partner with large firms, whereas the effect is weaker if the issuers are in high-tech industries.
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Title: AGENT-BASED MODELING OF IMMUNE RESPONSE IN THE DEVELOPMENT OF TYPE 1 DIABETES
Creator: Xu, Qian
Date: 2020
Description: Diabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular...
Show moreDiabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular diseases, neuropathy, retinopathy and blindness and kidney failure. The economic burden induced by diabetes is not only caused by the wage loss and medical burden, but also with the cost of treatment of diabetes and co-morbidities caused by diabetes. Clinical research for treatment and cure of diabetes is costly. Computer modeling and simulation studies provide an economical alternative to conduct preliminary evaluation of new hypotheses and alternatives in new therapies. The most promising results obtained from simulations can then be investigates experimentally, improving the efficiency of experiments and clinical studies. This work focuses on the development of an agent-based model to describe the destruction of islets and β cells and the development of Type 1 diabetes. The whole process of inflammation related to diabetes takes place in pancreatic lymph node, circulation, and pancreatic tissue with islets. The infiltration to islets and insulin-producing β cell damage happens in the pancreatic tissue with islets; the lymphocytes activation and antigen presentation majorly happened in the pancreatic lymph node. Therefore, the model described activities taking place in the islets in the pancreatic tissue section and pancreatic lymph nodes, the interactions among T cells, α/β cells, antigen presentation cells and immunosuppression cells. Cell behavior was obtained from the literature that published experiment results and used to develop the rules followed by the agents representing various types of cells and their interactions. The agent-based model provides a framework to describe relationship between lymphocytes and β cell through the trends of cell variations in the inflammation and demonstrates the effects of these cells in the disease development. Two different systems, a mouse model and a human model have been developed. The simulation results with the mouse model indicate that the different types of regulatory cells play different roles in suppressing inflammation. Among them, the regulatory T cells play the most important role in suppressing inflammation, but the B regulatory cell conversion is the key to induce the cascade of regulatory cell generation in inflammatory environment when there are no regulatory cytokines in the environment. The simulation results with the human model are mostly similar with mouse model, however, their effect of potential therapies such as addition of Tregs did not do as well as that in mouse model. The treatment method might be adjusted by combining other cytokines or immunosuppression cells in human assays.
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Title: Developing Leader Identity via Structured Reflection
Creator: Standish, Melanie P.
Date: 2020
Description: As leader identity research in the context of leader development continues to expand, it is important to understand the mechanisms through...
Show moreAs leader identity research in the context of leader development continues to expand, it is important to understand the mechanisms through which leader identity becomes more central to one’s self concept. Structured leadership reflection is proposed to positively impact leader development but has not been experimentally manipulated to understand what its impact is on leader identity change. In this study, 90 participants were assigned into one of three reflection conditions and were asked to respond to reflection prompts over the course of four days. Participants were divided into the control condition, the reflecting on others as leaders condition, or the reflecting on oneself as a leader condition. Results showed no significant differences between reflection groups and their impact on leader identity change. Though our results do not provide support for the use of structured reflection to elicit leader identity development, we suggest future research should further study structured self-reflection over a longer period of time.
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