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Title: DEEP LEARNING IMAGE-DENOISING FOR IMPROVING DIAGNOSTIC ACCURACY IN CARDIAC SPECT
Creator: Liu, Junchi
Date: 2022
Description: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a noninvasive imaging modality widely utilized...
Show moreMyocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a noninvasive imaging modality widely utilized for diagnosis of coronary artery diseases (CAD) in nuclear medicine. Because of the concern of potential radiation risks, the imaging dose administered to patients is limited in SPECT-MPI. Due to the low count statistics in acquired data, SPECT images can suffer from high levels of noise. In this study, we investigate the potential benefit of applying deep learning (DL) techniques for denoising in SPECT-MPI studies. Owing to the lack of ground truth in clinical studies, we adopt a noise-to-noise (N2N) training approach for denoising in full-dose studies. Afterwards, we investigate the benefit of applying N2N DL on reduced-dose studies to improve the detection accuracy of perfusion defects. To address the great variability in noise level among different subjects, we propose a scheme to account for the inter-subject variabilities in training a DL denoising network to improve its generalizability. In addition, we propose a dose-blind training approach for denoising at multiple reduced-dose levels. Moreover, we investigate several training schemes to address the issue that defect and non-defect image regions are highly unbalanced in a data set, where the overwhelming majority by non-defect regions tends to have a more pronounced contribution to the conventional loss function. We investigate whether these training schemes can effectively improve preservation of perfusion defects and yield better defect detection accuracy. In the experiments we demonstrated the proposed approaches with a set of 895 clinical acquisitions. The results show promising performance in denoising and improving the detectability of perfusion-defects with the proposed approaches.
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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: Understanding and Combating Filter Bubbles in News Recommender Systems
Creator: Liu, Ping
Date: 2022
Description: Algorithmic personalization of news and social media content aims to improve user experience. However, there is evidence that this filtering...
Show moreAlgorithmic personalization of news and social media content aims to improve user experience. However, there is evidence that this filtering can have the unintended side effect of creating homogeneous ``filter bubbles'' in which users are over-exposed to ideas that conform with their pre-existing perceptions and beliefs. In this thesis, I investigate this phenomenon in political news recommendation algorithms, which have important implications for civil discourse.I first collect and curate a collection of over 900K news articles from over 40 sources. The dataset was annotated in the topic and partisan leaning dimensions by conducting an initial pilot study and later via Amazon Mturk. This dataset is studied and used consistently throughout this thesis. In the first part of the thesis, I conduct simulation studies to investigate how different algorithmic strategies affect filter bubble formation. Drawing on Pew studies of political typologies, we identify heterogeneous effects based on the user's pre-existing preferences. For example, I find that i) users with more extreme preferences are shown less diverse content but have higher click-through rates than users with less extreme preferences, ii) content-based and collaborative-filtering recommenders result in markedly different filter bubbles, and iii) when users have divergent views on different topics, recommenders tend to have a homogenization effect.Secondly, I conduct a content analysis of the news to understand language usage among and across various topics and political stances. I examine words and phrases used by the liberal media and by the conservative media on each topic. I first study what differentiates the liberal media from the conservative media on each topic. I then study common phrases that are used by the liberals and the conservatives on different topics. For example, I examine which phrases are shared by the liberal articles on guns and conservative articles on abortion. Finally, I compare and visualize these words using different clustering algorithms and supervised classification methods.In the last chapter, I conduct an extensive user study to find possible solutions to combat the filter bubbles in the political news recommender systems. I designed a self-contained website that enables a content-based news recommender system and indexed 40,000 U.S.~political articles. I recruited over 800 U.S.~participants from Amazon Mechanical Turk (approved by IRB). The qualified participants are split into control and treatment groups. The users in the treatment group are provided transparency and interaction mechanisms, which grant them more control over the recommendations. Our results show that providing interaction and transparency a) increases click-through rates, b) has the potential to reduce the filter bubbles, and c) raises more awareness about filter bubbles.
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Title: SYNTHESIS AND APPLICATION OF ORGANOMETALLIC PRECURSORS FOR TUNGSTEN AND MOLYBDENUM SULFIDE
Creator: Liu, Bo
Date: 2021
Description: Transition metal chalcogenides (TMCs) have unique properties. They are promising materials for the next generation electrical devices due to...
Show moreTransition metal chalcogenides (TMCs) have unique properties. They are promising materials for the next generation electrical devices due to their suitable band gap, outstanding electron mobility, and controllable atomic thickness. In the last few decades, atomic layer deposition (ALD) has been one of the hottest research frontiers for the fabrication of TMCs films. Signification progress has been made on the varieties of material grown by ALD and the improvement of ALD equipment. However, the fast-evolving microelectronic industry set higher requirements for the ALD application. In the potential electronic fabrication process, low-temperature preparation and non-corrosive procedure are critical for the advanced device architecture. Thus the novel precursor development and the investigation of reaction mechanism are necessary. In addition, as the comprehensive research of film deposition, the prevailing crystallographic defects on the as-prepared films are another appealing thing for us to think about and try to eliminate for better film quality. Therefore, this dissertation will describe the precursor ligand design and its effect on the morphology, the development of W/Mo precursors for tungsten/molybdenum disulfide, and the defect passivation of tungsten diselenide films.In chapter 2, a series of heteroleptic tungsten precursors of tetrathiotungstates (WS42-) were prepared through the facile ligand transfer method. Ligand variation has a significant effect on the crystallinity of the resulting tetrathiotungstate products. Crystalline tetrathiotungstates with preferred orientation were prepared from the reaction of synthesized precursors with H2S at room temperature. Results indicated the morphologies and crystallinities of the tetrathiotungstates can be well controlled by their ligand behaviors which give us a better understanding of the growth mechanism. Chapters 3 and 4 focus on the development of W and Mo precursors for W/Mo disulfide and their performance in wet chemistry reactions and ALD. WS2 can be synthesized at the ambient temperature in solution by the non-redox reaction. WS2 film growth can be achieved at the exciting low temperature of 125°C by ALD. Based on the performance of the tungsten precursor, a new molybdenum dimer precursor with improved reactivity was synthesized, and MoSx can be prepared at the ambient temperature in seconds. X-ray absorption spectroscopy (XAS) was also utilized to investigate the interaction between the organometallic precursor and the SiO2 surface. Chapter 5 will focus on the defect passivation of WSe2 films for the improvement of their electrical performance. Precursors were synthesized, and the wet chemistry method was designed for oxidation removal and vacancy healing. Raman spectroscopy was used as the express characterization method to reveal the treatment results. A promising healing reagent was screened out, and the repaired films were fabricated to field-effect transistors (FETs) for electrical measurements. The final results showed the electrical performance of the WSe2 films was improved after the convenient chemical treatment.
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Title: IDENTIFICATION OF THE RIBOFLAVIN BINDING SITE IN VIBRIO CHOLERAE ION PUMPING NQR COMPLEX
Creator: Lee, Chia-Hsing
Date: 2021
Description: NQR is a six-subunit complex that transfers electrons from NADH to ubiquinone, one of the essential enzymes in the bacterial respiratory chain...
Show moreNQR is a six-subunit complex that transfers electrons from NADH to ubiquinone, one of the essential enzymes in the bacterial respiratory chain of many pathogens such as Vibrio cholerae, Pseudomonas aeruginosa, Chlamydia trachomatis. Its electron transfer path requires three different flavin cofactors to facilitate: FAD, FMN, and riboflavin. The FMN in subunit B (FMNB) brings electrons to riboflavin and then transfers it to the final electron receptor UQ in subunit B, coupled with the Na+ pumping mechanism. NQR has a unique evolutionary history, and one of the pieces of evidence is that NQR has been reported as the only one flavoenzyme that uses riboflavin as its redox cofactor. However, the binding site of riboflavin has not been well understood. To gain insight into the electron transfer at this site in V.cholerae NQR, we generated mutants at the interface of subunits B, D, and E where the possible location of riboflavin is. To characterize these mutants, we assessed NQR properties with different approaches including enzyme kinetics and flavin radical profiling. We found that the mutagenesis surrounding the hydrophobic pocket disrupted the NQR activity, and cause the loss of neutral radical, but did not interfere with the binding affinity between the substrates and NQR. This study will help to understand electron transfer better in NQR and develop the drugs targeting the riboflavin binding site in the future.
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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: Enhancing Explanation Generation in the CaJaDE system using Interactive User Feedback
Creator: Lee, Juseung
Date: 2022
Description: In today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several...
Show moreIn today’s data-driven world, it is becoming increasingly difficult to interpret and understand query results after going through several manipulation steps, especially on a large database. There is a need for automated techniques that explain query results in a meaningful way. A recent study, CaJaDE(Context-Aware Join-Augmented Deep Explanations), presents a novel approach to generating explanations of query results including crucial contextual information. However, it becomes difficult to interpret explanations since the search space increases exponentially.In this thesis, we propose a new approach that introduces a user interaction model for a purpose of enhancing the generation of explanations in the CaJaDE system. We implemented a user interaction model that consists of three modules: User Selection, Recommendation Score, and User Rating. With these modules, our approach guides a user while exploring relevant join graphs, and lets them be involved in the decision-making process while generating join graphs. We demonstrate through performance experiments and user study that our approach is an effective method for users to understand explanations.
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Title: Relations Between Inhibitory Control, Teacher Support, and Externalizing Behaviors in Elementary School Children
Creator: Kurian, Jennifer
Date: 2021
Description: The aim of this study was to examine the relation between child hot and cool inhibitory control (IC) at the beginning of the school year and...
Show moreThe aim of this study was to examine the relation between child hot and cool inhibitory control (IC) at the beginning of the school year and externalizing behaviors at the end of the year, and to determine if teacher support moderates this relation in early elementary school. Participants included a diverse sample of 138 children in grades 1 (n = 62) and 2 (n = 76), with a mean age of 7.2 years (SD = 10.1 months), about half of whom were male. Hot IC was assessed with the Puzzle Box Task and cool IC with the Happy-Sad Stroop Task. Teacher support was rated by independent observers using the Adapted Teaching Style Rating Scale. A composite teacher-report score based on ratings on subscales from two measures, the Strengths and Weaknesses of Attention Deficit Hyperactive Disorder Symptoms and Normal Behavior and the Strengths and Difficulties Questionnaire, was used to assess externalizing behavior at both time points. Results of hierarchical regression analyses revealed that, contrary to expectation, neither hot nor cool IC significantly predicted child externalizing behavior at the end of the school year. A moderation analysis also failed to show a significant moderating effect for teacher support. The only variable that significantly predicted externalizing behavior at the end of the year was externalizing behavior at the beginning of the year. There were significant concurrent associations between hot IC and externalizing behaviors at both the beginning and end of the school year. These findings suggest that externalizing behaviors in early elementary school are relatively stable. Thus, early and comprehensive intervention may be critical for implementing prevention strategies designed to increase self-regulation and thereby decrease externalizing behaviors after formal school entry.
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Title: Growth kinetics of Salmonella enterica and Listeria monocytogenes during rehydration of dehydrated corn and subsequent storage
Creator: Mate, Madhuri
Date: 2022
Description: Dehydrated vegetables, including corn, are often used in restaurants and retail grocers. They do not support the growth of pathogens as their...
Show moreDehydrated vegetables, including corn, are often used in restaurants and retail grocers. They do not support the growth of pathogens as their moisture content is very low. After rehydration, these food products attain high water activity values suitable with neutral pH for the survival and proliferation of foodborne pathogens, including Salmonella enterica and Listeria monocytogenes. The purpose of the study was to examine the extent to which dehydrated corn supports the growth of S. enterica and L. monocytogenes during rehydration at 5 or 25°C water and following storage at 5, 10, and 25°C temperatures at 1, 3, 5 and 7 d intervals. Fresh corn was dehydrated at 60°C for 24 h. Dehydrated corn was inoculated with a 4-strain cocktail of either S. enterica or rifampicin-resistant L. monocytogenes, resulting in 4 log CFU/g, and held at ambient temperature for 24 h. This corn was then rehydrated using either 5 or 25°C water for 24 h. Throughout rehydration, corn samples were removed at intervals and enumerated. To enumerate S. enterica and L. monocytogenes, the samples were homogenized with BPB and BLEB respectively and cultivated on TSAYE with overlaid XLD or BHIARif200, respectively. Rehydrated corn was then stored at 5, 10, or 25°C and enumerated at intervals 1,3,5 and 7 d. Triplicate samples were assessed at each timepoint and three independent experiments were conducted for each rehydration water temperature. Growth rates were determined by DMFit and statistically analyzed using Student t-test. A p-value ≤0.05 was considered significant. Overall the growth rate of S. enterica was higher when rehydrated in 5°C water temperature and then stored at 25°C and was determined to be 0.61 ± 0.23 log CFU/g per d. This timepoint was also the shortest time required to increase by 1 log which was: 1.64 d, i.e. 39 h. For L. monocytogenes, the 25°C water rehydration showed the fastest growth rate when stored at 25°C. It took only 1.58 d or 37.8 h for 1 log increase in the population. After 5°C water rehydration of corn the highest populations of mesophilic bacteria and yeasts and molds were observed for 25°C storage ranging from 8.43 to 9.39 log CFU/g and 4.75 to 7.87 log CFU/g, respectively. After 25°C water rehydration, the highest population of mesophilic bacteria, 8.88 log CFU/g, was observed at 5°C storage at 5 d; yeasts and molds were 8.70 log CFU/g for 25°C storage on the same day. The results of this study determined that S. enterica and L.monocytogenes could survive and grow in dehydrated plant foods during rehydration and storage, highlighting the need for product assessments for these types of foods.
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Title: RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Creator: Mo, Fei
Date: 2021
Description: Deep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine...
Show moreDeep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine fields, capable of complementing human operators in disease diagnosis and treatment but optimizing and streamlining medical image display. While incredibly powerful, images produced via Deep Learning or Artificial Intelligence should be analyzed critically in order to be cognizant of how the algorithms are producing the new image and what the new imagine is. One such opportunity arose in the form of a unique collaborative project: the technical development of an image assessment tool that would analyze outputs between DL-based and non DL-based Magnetic Resonance Imaging reconstruction methods.More specifically, we examine the operator input dependence of the existing reference method in terms of accuracy and precision performance, and subsequently propose a new metric approach that preserves the heuristics of the intended quantification, overcomes operator dependence, and provides a relative comparative scoring approach that may normalize for angular dependence of examined images. In chapter 2 of this thesis, we provide a background description pertaining to the two imaging science principles that yielded our proposed method description and study design. First, if treated naively, the examined linear measurement approach exhibits potential bias with respect to the coordinate lattice space of the examined image. Second, the examined DL-based image reconstruction methods used in this thesis warrants an elaborate and explicit description of the measured noise and signal present in the reconstructed images. This specific reconstruction approach employs an iterative scheme with an embedded DL-based substep or filter to which we are blinded. In chapters 3 and 4 of this thesis, the imaging and DL-based image reconstruction experiments are described. These experiments employ cardiac MRI datasets from multiple clinical centers. We first outline the clinical and technical background for this approach, and then examine the quality of DL-based reconstructed image sharpness by two alternative methods: 1) by employing the gold-standard method that addresses the lattice point irregularity using a ‘re-gridding’ method, and 2) by applying our novel proposed method inspired by radial MRI k-space sampling, which exploits the mathematical properties of uniform radial sampling to yield the target voxel counts in the ‘gridded’ polar coordinate system. This new measure of voxel counts is shown to overcome the limitation due to the operator-dependence for the conventional approach. Furthermore, we propose this metric as a relative and comparative index between two alternative reconstruction methods from the same MRI k-space.
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Title: DO GENERAL EDUCATION HIGH SCHOOL STUDENTS IN A BASIC PHYSICAL SCIENCE COURSE IMPROVE UPON ATTITUDES TOWARD SCIENCE LEARNING AND CONTENT MASTERY FOLLOWING VIRTUAL/REMOTE FLIPPED INSTRUCTION OR VIRTUAL/REMOTE NON – FLIPPED INQUIRY – BASED INSTRUCTION?
Creator: Martino, Robert S.
Date: 2022
Description: As we progress further into the 21st Century, high school science is being challenged on how to best deliver instruction to students. Teacher ...
Show moreAs we progress further into the 21st Century, high school science is being challenged on how to best deliver instruction to students. Teacher – centered instruction has long been de – emphasized in favor of inquiry – based instruction, although teacher – centered instruction still exists to a noticeable extent. Inquiry – based instruction, while more student – centered in its common practice, still involves the teacher as a guide during classroom direct instruction. Research has been ongoing to identify new and dynamic forms of science concept delivery that serve the needs of diversified science instruction (Keys & Bryan, 2001; Saldanha, 2007). Virtual instruction has become more commonplace, and it was fully implemented during this study. It has become incumbent upon science education researchers to explore and identify the most effective means of virtual instruction, means that are student – centered, engaging, interesting, and that both improve student science content understanding and attitudes toward science. Flipped instruction is a more recently – incorporated form of student – centered instruction that has students experiencing classroom routines at home and homework routines in class, and that is why this instruction is referred to as being “flipped.” Hunley (2016) examined teacher and student perception of flipped instruction in a science classroom, while Howell (2013) explored it in a ninth – grade physical science honors classroom. At the onset of this study, relatively few studies were available about this newer form of instruction within high school science instruction, no studies were available that involved high school general education physical science courses, and certainly no studies were available that compared virtual/flipped and non – flipped general education physical science instruction at the onset of this study. This study researched the effect of virtually – implemented flipped instruction on high school students’ understanding and attitude toward science. Instruction was completely virtual/remote (online), and at home, for all students in this study. In investigating the effect of this type of instruction, this study examined student academic performance and attitudes (and intentions and beliefs) toward science in two units of a high school Integrated Chemistry and Physics (Physical Science) course. Sixty – six students from Southlake High School, a midwestern U.S. high school, took part in the study. Sixty – four of those students took the unit assessments. Half of the students (test group) were instructed via virtual/remote flipped instruction and the other half (control group) were instructed via virtual/remote non – flipped, inquiry – based instruction during the first unit. During the second unit, the test group students who were instructed via virtual/remote flipped instruction switched with the control group and were instructed via virtual/remote non-flipped inquiry – based instruction, while the control group students who were instructed via virtual/remote non-flipped instruction were instructed via virtual/remote flipped instruction. The students in both groups were surveyed three times, using the Behaviors, Related Attitudes, and Intentions Toward Science (BRAINS) (Summers, 2016) instrument student questionnaire and survey for their attitudes (and beliefs and intentions) toward science (once prior to the first unit, once after the first unit, and once following the second unit). Student test results and survey responses were then analyzed to identify which instructional style was more effective for student learning and whether student attitudes (and intentions, and beliefs) favored one instructional style over the other. Student science attitudes (and beliefs and intentions) and academic performance were evaluated throughout the study. There was an increase in control group student science attitudes (and beliefs and intentions), from the pre – study survey to the post – unit 1 survey following their receipt of non – flipped virtual/remote instruction in the first unit. There was a lower increase in test group student science attitudes (and beliefs and intentions), from lower pre – study attitudes (compared with the control group) following the test group’s receipt of flipped virtual/remote instruction in the first unit,. Following the second unit, both the control group and test group again showed increases in attitude (and beliefs and intentions) compared with the pre – study survey results, with the control group again showing greater increases than the study group. Student academic performance favored the control group as it outperformed the test group in both the first unit and the second unit, even when the test group received the virtually – delivered flipped instruction in the first unit. The findings of the study showed that virtually implemented flipped instruction resulted in no advantage for the test group in terms of greater improvement in attitudes (or beliefs or intentions) toward science and no advantage for the test group in terms of learning science content in general education Integrated Chemistry and Physics (Physical Science). These results indicate that this form of teaching may not be effective in improving general education Physical Science student learning and student attitudes (and beliefs and intentions) toward science. Therefore, the use of virtually implemented flipped instruction in this general education science course will need to be further studied to determine its effect on student learning and student attitudes (or even beliefs and intentions) toward science.
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Title: Sensemaking for Power Asymmetries in Anti-Oppressive Design Practice
Creator: Meharry, Jessica J
Date: 2022
Description: Within professional design practice in capitalist market contexts, the goals of user-centered and human-centered design methodologies is to...
Show moreWithin professional design practice in capitalist market contexts, the goals of user-centered and human-centered design methodologies is to make algorithmically-based technologies understandable for users, satisfy customer needs and desires, and thereby increase corporate profitability. However, there is growing concern that the computational methods, data management, and business models that drive these technologies are leading to global asymmetries of knowledge, information, and power. The asymmetries of power generated by these designed interactions can be considered the kind of wicked problem that design seeks to address. Yet the dominant goals and methods of professional design practice limit their ability to design ethically within market contexts. These methodologies fail to adequately consider systemic context and power relations, potential for bias in algorithmic computation, and specific forms of systemic oppression. These gaps then lead to inadequate design solutions. This study explores these gaps in design methodologies that could be transferable to a range of professional (and non-professional) practices by looking at potential new levers within familiar design methods and their effectiveness as facilitating problem reframing towards equitable solutions. This dissertation advances knowledge in design by exploring how professional designers can better understand how to use sensemaking processes for salience of power asymmetries, algorithmic materiality, and systemic oppression. It proposes an anti-oppressive design framework that is rooted in a critically-informed design praxis. These orientations rethink and recreate design knowledge by helping professional designers shift the market-focused paradigm for which they are designing.
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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: Intelligent Job Scheduling on High Performance Computing Systems
Creator: Fan, Yuping
Date: 2021
Description: Job scheduler is a crucial component in high-performance computing (HPC) systems. It sorts and allocates jobs according to site policies and...
Show moreJob scheduler is a crucial component in high-performance computing (HPC) systems. It sorts and allocates jobs according to site policies and resource availability. It plays an important role in the efficient use of system resources and users satisfaction. Existing HPC job schedulers typically leverage simple heuristics to schedule jobs. However, the rapid growth in system infrastructure and the introduction of diverse workloads pose serious challenges to the traditional heuristic approaches. First, the current approaches concentrate on CPU footprint and ignore the performance of other resources. Second, the scheduling policies are manually designed and only consider some isolated job information, such as job size and runtime estimate. Such a manual design process prevents the schedulers from making informative decisions by extracting the abundant environment information (i.e., system and queue information). Moreover, they can hardly adapt to workload changes, leading to degraded scheduling performance. These challenges call for a new job scheduling framework that can extract useful information from diverse workloads and the increasingly complicated system environment, and finally make well-informed scheduling decisions in real time.In this work, we propose an intelligent HPC job scheduling framework to address these emerging challenges. Our research takes advantage of advanced machine learning and optimization methods to extract useful workload- and system-specific information and to further educate the framework to make efficient scheduling decisions under various system configurations and diverse workloads. The framework contains four major efforts. First, we focus on providing more accurate job runtime estimations. Estimated job runtime is one of the most important factors affecting scheduling decisions. However, user provided runtime estimates are highly inaccurate and existing solutions are prone to underestimation which causes jobs to be killed. We leverage and enhance a machine learning method called Tobit model to improve the accuracy of job runtime estimates at the same time reduce underestimation rate. More importantly, using TRIP’s improved job runtime estimates boosts scheduling performance by up to 45%. Second, we conduct research on multi-resource scheduling. HPC systems are undergoing significant changes in recent years. New hardware devices, such as GPU and burst buffer, have been integrated into production HPC systems, which significantly expands the schedulable resources. Unfortunately, the current production schedulers allocate jobs solely based on CPU footprint, which severely hurts system performance. In our work, we propose a framework taking all scalable resources into consideration by transforming this problem into multi-objective optimization (MOO) problem and rapid solving it via genetic algorithm. Next, we leverage reinforcement learning (RL) to automatically learn efficient workload- and system-specific scheduling policies. Existing HPC schedulers either use generalized and simple heuristics or optimization methods that ignore workload and system characteristics. To overcome this issue, we design a new scheduling agent DRAS to automatically learn efficient scheduling policies. DRAS leverages the advance in deep reinforcement learning and incorporates the key features of HPC scheduling in the form of a hierarchical neural network structure. We develop a three-phase training process to help DRAS effectively learn the scheduling environment (i.e., the system and its workloads) and to rapidly converge to an optimal policy. Finally, we explore the problem of scheduling mixed workloads, i.e., rigid, malleable and on-demand workloads, on a single HPC system. Traditionally, rigid jobs are the main tenants of HPC systems. In recent years, malleable applications, i.e., jobs that can change sizes before and during execution, are emerging on HPC systems. In addition, dedicated clusters were the main platforms to run on-demand jobs, i.e., jobs needed to be completed in the shortest time possible. As the sizes of on-demand jobs are growing, HPC systems become more cost-efficient platforms for on-demand jobs. However, existing studies do not consider the problem of scheduling all three types of workloads. In our work, we propose six mechanisms, which combine checkpointing, shrink, expansion techniques, to schedule the mixed workloads on one HPC system.
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Title: Developing Novel Optimization Algorithms Applied To Building Energy Performance and Indoor Air Quality
Creator: Faramarzi, Afshin
Date: 2021
Description: Residential and commercial buildings account for 23% of global energy use. In the United States, space heating, cooling, and lighting energy...
Show moreResidential and commercial buildings account for 23% of global energy use. In the United States, space heating, cooling, and lighting energy use accounts for 38%, 9%, and 7% of building energy consumption, which results in 54% of the total energy consumption of the building. Energy efficiency improvements in buildings require consideration of optimal design, operation, and control of building components (e.g., mechanical and envelope systems). We can address this task by taking advantage of computational optimization methods throughout the design, operation, and control processes.Non-gradient metaheuristic optimization methods known as metaheuristics are some of the most popular and widely used optimization methods in Building Performance Optimization (BPO) problems. Conventional metaheuristics usually have simple mathematical models with low rate of convergence. On the other hand, high-performance metaheuristic optimizers are efficient and usually have a fast rate of convergence, but their mathematical models are hard to understand and implement. As such, researchers are usually not inclined to employ them in solving their problems. To this end, we aimed at developing optimization algorithms which borrow simplicity from conventional methods and efficiency from high-performance optimizers to solve problems fast and efficiently while being welcomed by users from throughout the world. Therefore, the overarching objective of this work is defined to first develop novel optimization algorithms which are simple in mathematical models and still efficient in solving optimization benchmark problems and then apply the methods to building energy performance and indoor air quality (IAQ) problems. In the first objective of this work, which is the development phase, two continuous optimization methods and one binary optimizer are developed and are separately described in three different tasks. The first method called Equilibrium Optimizer (EO) is a simple method inspired by the mass balance equation in a control volume. The second optimization method called Marine Predators Algorithm (MPA) is a more complicated method compared to EO and is inspired by widespread foraging strategies between marine predators in the ocean ecosystem. Finally, the third method is the binary version of an already developed equilibrium optimizer called Binary Equilibrium Optimizer (BEO). The second objective of the dissertation is the application phase which focuses on the application of the developed methods and other widely used methods in research and industry for solving the almost new BPO and IAQ problems. The results showed that the developed methods were able to either reach more energy-efficient solutions compared to the other methods or to show a considerably faster rate of convergence compared to other methods in the problems in which the optimal solutions are similarly obtained by different methods.
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Title: The Feasibility of Honeycomb Structure to Enhance Daylighting and Energy Performance for High-Rise Buildings
Creator: Geng, Camelia Mina
Date: 2022
Description: The world population is increasing at a fast rate and the projection is that there will be more than 12 billion people by the year 2050. It is...
Show moreThe world population is increasing at a fast rate and the projection is that there will be more than 12 billion people by the year 2050. It is also expected that at least 70% of the population will reside and work in urban areas (mostly cities) in some sort of high-rise building. At the same time, the climate is rapidly changing to increase the effects of man-made global warming. Conceivably, energy conservation, daylighting performance, thermal comfort and environmentally friendly high-rise buildings are necessary to facilitate sustainable working and living environments. The roles of the architects and planners are paramount at this critical era of history of mankind; for one thing they are responsible for the planning and design of sustainable high-rise buildings.Recently, there has been significant research to connect a branch of Biophilia design, which is Biomorphic architecture. This has developed a wonderful design approach, termed the Biomorphic idea. This focuses on the enhancement of the physical and psychological connection with nature, to acquire more natural light and the outside connection targeting energy saving. More and more, high-rise buildings are being designed following Biomorphic approaches. As such, these buildings are defined as sustainable and primarily, because they are energy efficient and, and in many cases tend to minimize the use of fossil fuels while promoting the use of renewable and clean energy sources. As such, a honeycomb structure approach successfully applies to high-rise building design. The intend of this research document is to simulate Biomorphic honeycomb structure which is the hexagonal rotation ring structure including 32 stories in18 different hexagon high-rise building configurations, to develop true daylighting and energy. performance. This is achieved by the using Grasshopper-Climate Studio simulation tool and multiple fuzzy mathematics for decision making. This document will provide a comparison of daylighting including sDA, ASE, sDG and the illuminance results from these 3 series of the 18 models configuring different honeycomb structures of high-rise buildings. The results prove that the hexagon honeycomb structure for high-rise building is feasibility and targets green buildings standards such as LEED V4.1 The success of the method depends on developing multiple criteria of Poisson ratio and Gaussian curvature within the hexagon structure to create different honeycomb facades and rotation of the ring for office high-rise building which is also a qualitative nature of the Biomorphic design parameters.
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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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