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Title: Investigating anti-biofilm and anti-persister activities of natural compounds and antimicrobial proteins
Creator: Jin, Xing
Date: 2020
Description: Bacterial biofilm formation is frequently involved in the development of chronic infectious diseases. Inhibiting biofilms is challenging due...
Show moreBacterial biofilm formation is frequently involved in the development of chronic infectious diseases. Inhibiting biofilms is challenging due to their tolerance against conventional antibiotics which are not effective to penetrating biofilm matrix to kill the cells residing in biofilms. Metabolically dormant cells known as persisters are also not eradicated by antibiotic treatment. Therefore, novel antimicrobial drugs that can kill non-growing persisters or inhibit biofilms are needed urgently. Here, we investigate the anti-biofilm and anti-persister activities of new drug candidates including plant extracts, fatty acids and colicins. We firstly screened 50 different plant extracts on enterohemorrhagic E. coli and Listeria monocytogenes, and identified Cancavalia ensiformis-derived lectin Concanavalin A (ConA) inhibits biofilm formation of enterohemorrhagic E. coli and Listeria monocytogenes by binding to carbohydrates on bacterial cell surface. Biofilm results support that ConA lectin can be applied for developing anti-adherent and anti-biofilm agents to control biofilms. Also, fatty acids may be promising candidates as anti-persister or anti-biofilm agents, because some fatty acids exhibit antimicrobial effects. We screened a fatty acid library consisting of 65 different fatty acid molecules for altered persister formation. We found that undecanoic acid, lauric acid, and N-tridecanoic acid inhibited E. coli persister cell formation including enterohemorrhagic E. coli EDL933. These fatty acids were all medium chain saturated forms. Furthermore, the fatty acids repressed EHEC biofilm formation (for example, by 8-fold for lauric acid) without having antimicrobial activity. This study demonstrates that medium chain saturated fatty acids can serve as anti-persister and anti-biofilm agents that may be applied to treat bacterial infections. Colicins, a type of antimicrobial bacteriocins, are considered as a viable alternative of conventional antibiotics due to their unique cell killing mechanisms that can damage cells by pore-forming on the cell membrane, nuclease activity, and cell wall synthesis inhibition. In this study, we utilized cell-free protein synthesis to produce colicins with different modes of action. We optimized the production yield and activity of colicins in cell-free system. Also, we tested effect of cell-free produced colicins on persister cell formation and biofilm formation. We illustrated that colicins kill persister cells and biofilm cells. Moreover, colicins produced from the engineered probiotic E. coli cells, which can be used as a living medicine, specifically and significantly eradicate target biofilms without affecting other bacterial population. Colicins have great potential to be an antibiotic alternative, and engineered probiotic E. coli is a potential candidate for engineered bacterial therapeutics.
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Title: DIAGNOSING AND TREATING ADHD: CLINICIAN CHARACTERISTICS, METHODS OF DIAGNOSIS, DIAGNOSTIC RATES, AND TREATMENT RECOMMENDATIONS
Creator: Haak, Christopher Luke
Date: 2019
Description: Attention-deficit/hyperactivity disorder (ADHD) is one of the top five most common referrals among all neuropsychologists (Sweet et al. 2015)...
Show moreAttention-deficit/hyperactivity disorder (ADHD) is one of the top five most common referrals among all neuropsychologists (Sweet et al. 2015) and continues to elicit public and professional concern about over-diagnosis in children (Sciutto & Eisenberg, 2007) and under-diagnosis in adults (Asheron et al., 2012; Kooji et al., 2010). In recent years, the prevalence of ADHD has increased (Polanczyk et al., 2007 & 2014, Thomas et al., 2015). It is unclear what is driving these changes though changes in criteria may be playing a role (van de Voort et al., 2014). Further, there has been little research on whether professional training, beliefs, and practice factors can influence the likelihood to diagnose ADHD. The purpose of this study was to examine the extent to which neuropsychologists’ professional characteristics, training, and beliefs about ADHD diagnosis and treatment influence their likelihood to diagnose ADHD. The study also evaluated whether there are differences in assessing and treating ADHD based upon the client population focus (child, lifespan, or adult) of neuropsychologists. Participants in this study were 106 neuropsychologists from across the United States and Canada who were recruited through neuropsychology listservs to participate in an online survey. Results indicated that population focus was associated with significant differences in approach to diagnosing and treating ADHD, with child- and lifespan-focused neuropsychologists reporting higher rates of ADHD diagnosis. Additionally, having a higher percent of clinical cases in which ADHD is a referral question and greater self-reported adherence to following full diagnostic criteria for making a diagnosis were associated with higher ADHD diagnostic rates, controlling for age, gender, ethnicity, and other professional characteristics. This study is among the first to examine specific clinician factors impacting diagnostic rates and its findings have several implications for practice and research.
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Title: Characterization of turbulent mixing near roadways based on measurements of short-term turbulence kinetic energy and traffic conditions
Creator: Hu, Zhice
Date: 2020
Description: Turbulence determines how vehicle emissions mix with the surrounding air and determine the distribution of pollutants on the roadway and...
Show moreTurbulence determines how vehicle emissions mix with the surrounding air and determine the distribution of pollutants on the roadway and downwind. 600 5-min near roadway simultaneous measurements (2016 to 2018) of turbulent kinetic energy (TKE), meteorological conditions, and traffic information (vehicle flow rate, density, and traffic mix (LDVs & HDVs) were used to characterize TKE. Short-term measurements (5 min.) were required to characterize the large variation in traffic flow rate that occurred in short time periods. Two roadways (Lakeshore Drive (LSD), Dan Ryan Expressway (DRE)) with distinctly different traffic composition (HDV%) and road configurations were selected for monitoring. Results indicate that variations in near-road wind speed (0.5 to 3.5 m s-1) had only a slight influence on TKE measurements. Background contributed 40% of the total measured TKE. The average dissipation rate traffic-induced TKE from on-road to near-road measurement was 90%. The average near roadway TKE (background subtracted) was 0.6 (m2 s-2) (0.2 st. dev) for LDVs only, and 0.8 (m2 s-2) (0.3 st. dev) for mixed fleet traffic flow (HDV averaged 8.4%). The increase in TKE was related to the increase in the HDV flow rate for free-flow traffic conditions but not for congestion conditions. TKE generated by individual HDVs was significantly higher than TKE generated from individual LDVs for free-flow traffic conditions. HDVs represent only a small fraction of the vehicle fleet mix (typical 1 to 10%) so that the overall effect of HDVs in changing vehicle fleet is difficult to quantify. However, the single HDV can induce near 11 times TKE than a single LDV in free-flow condition, which can validate the significant variation in the ensemble mean traffic-induced TKE under the same traffic fleet flow that is due to HDVs.
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Title: Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
Creator: Arnold, David James
Date: 2024
Description: Digitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To...
Show moreDigitization and modernization efforts have yielded greater efficiency, safety, and cost-savings for Industrial Control Systems (ICS). To achieve these gains, the Internet of Things (IoT) has become an integral component of network infrastructures. However, integrating embedded devices expands the network footprint and softens cyberattack resilience. Additionally, legacy devices and improper security configurations are weak points for ICS networks. As a result, ICSs are a valuable target for hackers searching for monetary gains or planning to cause destruction and chaos. Furthermore, recent attacks demonstrate a heightened understanding of ICS network configurations within hacking communities. A Defense-in-Depth strategy is the solution to these threats, applying multiple security layers to detect, interrupt, and prevent cyber threats before they cause damage. Our solution detects threats by deploying an Enhanced Data Historian for Detecting Cyberattacks. By introducing Machine Learning (ML), we enhance cyberattack detection by fusing network traffic and sensor data. Two computing models are examined: 1) a distributed computing model and 2) a localized computing model. The distributed computing model is powered by Apache Spark, introducing redundancy for detecting cyberattacks. In contrast, the localized computing model relies on a network traffic visualization methodology for efficiently detecting cyberattacks with a Convolutional Neural Network. These applications are effective in detecting cyberattacks with nearly 100% accuracy. Next, we prevent eavesdropping by applying Homomorphic Encryption for Secure Computing. HE cryptosystems are a unique family of public key algorithms that permit operations on encrypted data without revealing the underlying information. Through the Microsoft SEAL implementation of the CKKS algorithm, we explored the challenges of introducing Homomorphic Encryption to real-world applications. Despite these challenges, we implemented two ML models: 1) a Neural Network and 2) Principal Component Analysis. Finally, we hinder attackers by integrating a Cyberattack Lockdown Network with Secure Ultrasonic Communication. When a cyberattack is detected, communication for safety-critical elements is redirected through an ultrasonic communication channel, establishing physical network segmentation with compromised devices. We present proof-of-concept work in transmitting video via ultrasonic communication over an Aluminum Rectangular Bar. Within industrial environments, existing piping infrastructure presents an optimal solution for cost-effectively preventing eavesdropping. The effectiveness of these solutions is discussed within the scope of the nuclear industry.
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Title: Extremal and Enumerative Problems on DP-Coloring of Graphs
Creator: Sharma, Gunjan
Date: 2024
Description: Graph coloring is the mathematical model for studying problems related to conflict-free allocation of resources. DP-coloring (also known as...
Show moreGraph coloring is the mathematical model for studying problems related to conflict-free allocation of resources. DP-coloring (also known as correspondence coloring) of graphs is a vast generalization of classic graph coloring, and many more concepts of colorings studied in the past 150+ years. We study problems in DP-coloring of graphs that combine questions and ideas from extremal, structural, probabilistic, and enumerative aspects of graph coloring. In particular, we study (i) DP-coloring Cartesian products of graphs using the DP-color function, the DP coloring counterpart of the Chromatic polynomial, and robust criticality, a new notion of graph criticality; (ii) Shameful conjecture on the mean number of colors used in a graph coloring, in the context of list coloring and DP-coloring; and (iii) asymptotic bounds on the difference between the chromatic polynomial and the DP color function, as well as the difference between the dual DP color function and the chromatic polynomial, in terms of the cycle structure of a graph. These results respectively give an upper bound and a lower bound on the chromatic polynomial in terms of DP colorings of a graph.
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Title: Three Essays on the Internet Economy
Creator: Sun, Yidan
Date: 2024
Description: In an era of digital platforms, the integrity and visibility of consumer reviews, the dynamics of digital advertising markets, and the role of...
Show moreIn an era of digital platforms, the integrity and visibility of consumer reviews, the dynamics of digital advertising markets, and the role of software development kits (SDKs) emerge as pivotal elements shaping user experiences and platform economics. My research spans three distinct but interconnected domains: the impact of safety reviews on Airbnb, the effects of privacy protections on digital advertising markets, and the significance of SDK releases in the evolution of Apple's iOS app market. We find that critical reviews concerning the safety of an Airbnb listing's vicinity influence guest bookings negatively and, therefore, could boost platform revenues if such reviews were obscured, highlighting a misalignment between consumer interests and platform revenue objectives. This effect is more pronounced in low-income and minority neighborhoods, suggesting a nuanced impact on different community segments. In the digital advertising sector, we identify that data frictions disproportionately harm small publishers, especially when associated with smaller ad intermediaries, underscoring the vulnerability of niche players to market and regulatory changes. Lastly, our analysis of the iOS app market reveals the instrumental role of SDK releases in fostering the app ecosystem's growth, independent of the expanding iPhone user base. Together, these findings underscore the complex interplay between consumer feedback, technological advancements, and market dynamics in digital environments, urging a balanced approach that safeguards consumer interests while fostering innovation and equitable market practices.
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Title: Integrating Deep Learning And Innovative Feature Selection For Improved Short-Term Price Prediction In Futures Markets
Creator: Tian, Tian
Date: 2024
Description: This study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely...
Show moreThis study presents a novel approach for predicting short-term price movements in futures markets using advanced deep-learning models, namely LSTM, CNN_LSTM, and GRU_LSTM. By incorporating cophenetic correlation in feature preparation, the study addresses the challenges posed by sudden fluctuations and price spikes while maintaining diversification and utilizing a limited number of variables derived from daily public data. However, the effectiveness of adding features relies on appropriate feature selection, even when employing powerful deep-learning models. To overcome this limitation, an innovative feature selection method is proposed, which combines cophenetic correlation-based hierarchical linkage clustering with the XGBoost importance listing function. This method efficiently identifies and integrates the most relevant features, significantly improving price prediction accuracy. The empirical findings contribute valuable insights into price prediction accuracy and the potential integration of algorithmic and intuitive approaches in futures markets. Moreover, the developed feature preparation method enhances the performance of all deep learning models, including LSTM, CNN_LSTM, and GRU_LSTM. This study contributes to the advancement of price prediction techniques by demonstrating the potential of integrating deep learning models with innovative feature selection methods. Traders and investors can leverage this approach to enhance their decision-making processes and optimize trading strategies in dynamic and complex futures markets.
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Title: Design and Synthesis of New Sulfur Cathodes Containing Polysulfide Adsorbing Materials
Creator: Suzanowicz, Artur M
Date: 2023
Description: Lithium-sulfur battery (LSB) technology has tremendous prospects to substitute lithium-ion battery (LIB) technology due to its high...
Show moreLithium-sulfur battery (LSB) technology has tremendous prospects to substitute lithium-ion battery (LIB) technology due to its high theoretical specific capacity and energy density. However, escaping polysulfide intermediates (produced during the redox reaction process) from the cathode structure is the primary reason for rapid capacity fading. Suppressing the polysulfide shuttle (PSS) is a viable solution for this technology to move closer to commercialization and supersede the established LIB technology. In this dissertation, I have analyzed the challenges faced by LSBs and selected methods and materials to address these problems. I have concluded that in order to further pioneer LSBs, it is necessary to address these essential features of the sulfur cathode: superior electrical conductivity to ensure faster redox reaction kinetics and high discharge capacity, high pore volume of the cathode host to maximize sulfur loading/utilization, and polar polysulfide-resistive materials to anchor and suppress the migration of lithium polysulfides.Furthermore, a versatile, low-cost, and practical scalable synthesis method is essential for translating bench-level development to large-scale production. This dissertation covers designing and synthesizing new scalable cathode structures for lithium-sulfur batteries that are inexpensive and highly functional. The rationally chosen cathode components accommodate sulfur, suppress the migration of polysulfide intermediates via chemical interactions, enhance redox kinetics, and provide electrical conductivity to sulfur, rendering excellent electrochemical performance in terms of high initial specific capacity and good long-term cycling performance. TiO2, Ni12P5, and g-C3N4 as polysulfide adsorbing materials (PAMs) have been fully studied in this thesis along with three distinct types of host structures for lithium-sulfur batteries: Polymer, Carbon Cloth, and Reduced Graphene Oxide. I have created adaptable bulk synthesis techniques that are inexpensive, easily scalable, and suitable for bench-level research as well as large-scale manufacturing. The exceptional performance and scalability of these materials make my cathodes attractive options for the commercialization of lithium-sulfur batteries.
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Title: In situ EXAFS studies of novel Palladium-based anode catalysts for direct ethanol and formic acid fuel cells
Creator: Su, Ning
Date: 2024
Description: In this work we made nanoscale uniform deposition of Pd based anode catalyst on the transition metal Au (with atomic ratio Pd:Au=1:10) support...
Show moreIn this work we made nanoscale uniform deposition of Pd based anode catalyst on the transition metal Au (with atomic ratio Pd:Au=1:10) support of direct liquid ethanol fuel cells (DLEFCs) and direct liquid formic acid fuel cells (DLFAFCs). Synthesizing with uniform dispersion and catalyst nanoparticle dimensions understand the role of Pd reaction on its support in the direct EOR (ethanol oxidation reaction) and FOR (formic acid reaction) pathways, we performed in situ Pd K-edge X-ray absorption spectroscopy measurements as a function of potential using a custom-designed flow cell with the catalyst deposited on the glassy carbon window. We did in-situ EXAFS to better understand the reaction mechanism of Pd1@Au10 anode catalyst with EOR and AOR in nanoscale. Compared EOR with FOR electrochemical performance showed Pd@Au&C played better in ethanol than HCOOH and more stable which the the current density can reach up to 1216.25 mA·mg-1 Pd of EOR with Pd1@Au10&C in 1M KOH+1M EtOH (CH3CH2OH) on the ethanol fuel cells (DLEFCs), and 3.56 times higher of the EOR current compared with commercial Pd@C
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Title: Financialization in the Structured Products Market
Creator: Zhu, Lizi
Date: 2023
Description: This dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major...
Show moreThis dissertation aims to study financialization in the structured products market. The structured products market has been undergoing a major transformation in recent years. The market used to mainly serve institutional investors. However, as a few trading platforms powered by fintech companies emerged on the horizon, more and more banks are starting to compete in this market. The average trade size has also been declining significantly, thereby making the market increasingly accessible to retail investors. What are the factors that facilitate the development of this market? What are the economic incentives of issuers and investors? How do issuers compete? What does the future hold for this market? The main finding of this dissertation is that structured products provide utility to retail investors; As the level of risk aversion increases, an investor increasingly prefers structured products to other traditional asset classes; issuers develop three sources of competitive advantage to be a satisficer; the rise of fintech and improvement of financial education are the key to opening this market to retail investors.
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Title: UTILIZING BACTERIAL INTERACTIONS TO CONTROL PATHOGENIC BIOFILM FORMATION
Creator: Fang, Kuili
Date: 2020
Description: Many chronic infections involve bacterial biofilms, which are difficult to eliminate using conventional antibiotic treatments. Biofilm...
Show moreMany chronic infections involve bacterial biofilms, which are difficult to eliminate using conventional antibiotic treatments. Biofilm formation is a result of dynamic intra- or inter-species interactions. However, the nature of molecular interactions between bacteria in multi-species biofilms are not well understood compared to those in mono-species biofilms. The first project (Chapter 3) investigated the ability of probiotic Escherichia coli Nissle 1917 (EcN) to outcompete the biofilm formation of pathogens including enterohemorrhagic E. coli (EHEC), Pseudomonas aeruginosa, Staphylococcus aureus, and S. epidermidis. When dual-species biofilms were formed, EcN inhibited the EHEC biofilm population by 14-fold compared to EHEC mono-species biofilms. This figure was 1,100-fold for S. aureus and 8,300-fold for S. epidermidis; however, EcN did not inhibit P. aeruginosa biofilms. In contrast, commensal E. coli did not exhibit any inhibitory effect toward other bacterial biofilms. We identified that EcN secretes DegP, a bifunctional (protease and chaperone) periplasmic protein, outside the cells and controls other biofilms. Although three E. coli strains tested in this study expressed degP, only the EcN strain secreted DegP outside the cells. The deletion of degP disabled the activity of EcN in inhibiting EHEC biofilms, and purified DegP directly repressed EHEC biofilm formation. Hence, probiotic E. coli outcompetes pathogenic biofilms via extracellular DegP activity during dual-species biofilm formation. Enterohemorrhagic Escherichia coli O157:H7 (EHEC) is a pathogen causing the outbreaks of hemorrhagic colitis. Conventional antibiotics treatment is not recommended for EHEC infection as antibiotics trigger Shiga toxin production of EHEC and aggravate hemolytic-uremic syndrome. EHEC biofilm formation is closely associated with its virulence expression. Previously, we identified that probiotic E. coli Nissle 1917 (EcN) secretes DegP resulting in the inhibition of EHEC biofilm formation in a dual culture. DegP is a serine protease exhibiting both proteolytic and chaperone functions and binds to outer membrane proteins (OMPs) of target cells. However, the extracellular function of DegP is not clear. We hypothesized that binding of DegP to OMPs of EHEC might inhibit EHEC biofilm formation by affecting the adhesion ability or changing biofilm-related gene regulations of EHEC. We constructed EHEC mutants lacking ompA, ompC, or ompF individually and in combination and assessed their biofilm formation in the presence of DegP-secreting EcN in the co-culture or by adding purified DegP. It was found that both ompA and ompC double deletion decreased EHEC single species biofilm, and also caused that DegP inhibited more EHEC biofilm (about 25 fold inhibition) than DegP inhibited EHEC wt biofilm (about 10 fold), indicating that OmpA and OmpC are more related to EHEC biofilm than OmpF, and OmpA and OmpC might deplete DegP inhibitory functions. On the other hand, DegP S210A, a DegP mutant lacking protease function, inhibited EHEC wt biofilm, indicating that DegP’s biofilm inhibition function is not from its protease activity. Additionally, EHEC transcription profiles in the presence of DegP showed that DegP up-regulated expressions of cellulose production related genes (csgD and bcsA) and motility related genes (flhD, qseB), which were all involved in EHEC biofilm inhibition, and down-regulated Shiga toxin 2 virulence gene (stx2). Besdies, DegP promoted EHEC cellulose production and motility, which is consistent with transcription profile, and Shiga toxin 2 production will be further tested. This study reveals a new function of DegP secreted by EcN in controlling biofilms and leads us to develop an alternative strategy to control biofilm-related infections. Foodborne pathogen Listeria monocytogenes biofilm formation renders these cells highly resistant to current sanitation methods, and probiotics may be a promising approach to the efficient inhibition of Listeria biofilms. In the Chapter 5 study, three Leuconostoc mesenteroides strains of lactic acid bacteria isolated from kimchi were shown to be effective probiotics for inhibiting Listeria biofilm formation. Biofilms of two L. monocytogenes serotypes, 1/2a (ATCC15313) and 4b (ATCC19115), in dual-species culture with each probiotic strain were decreased by more than 40-fold as compared with single-species Listeria biofilms; for instance, a reduction from 5.4 times 10^6 CFU/cm2 L. monocytogenes ATCC19115 in single-species biofilms to 1.1 times 10^5 CFU/cm2 in dual-species biofilms. Most likely, one of the Leuconostoc strains, L. mesenteroides W51, led to the highest Listeria biofilm inhibition without affecting the growth of L. monocytogenes. The cell-free supernatant from the L. mesenteroides W51 culture containing large protein molecules (> 30 kDa) also inhibited Listeria biofilms. These data indicate that Leuconostoc probiotics can be used to repress L. monocytogenes biofilm contamination on surfaces at food processing facilities.
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Title: A MICROFLUIDIC INTESTINAL-MICROBIOTA PLATFORM TO STUDY DRUG METABOLISM
Creator: Wang, Chengyao
Date: 2020
Description: The intestine is the main site that orally administered drugs are primarily metabolized, absorbed, and distributed. The trillions of bacteria...
Show moreThe intestine is the main site that orally administered drugs are primarily metabolized, absorbed, and distributed. The trillions of bacteria that inhabit the intestine influence health and regulate important biochemical factors, such as the activity of enzymes pertinent to drug metabolism. However, this has not been systematically studied partly due to the challenges of recapitulating the unique and complex intestinal microenvironment that includes (1) the presence of mammalian and microbial cells and (2) a unique partitioned oxygenation proﬁle across the lumen to the subepithelial mucosa from anaerobic to the richly vascularized oxygenated. This thesis reports the development of a microfluidic device in which is integrated a membrane synthesized from a key element of mucosal basal lamina, collagen, and precisely controlled partitioned oxygen environment. The device enabled excellent cell viability and long-term function. More importantly, it enabled the coculture of intestinal epithelial cells and aerobic and anaerobic bacteria in the partitioned oxygen environment. These experiments on one hand allowed the measurement of cellular oxygen consumption rate under perfusion, which could be used to study microbial regulation of oxidative metabolism in epithelial cells. On the other hand, the device allowed a systematic examination of the role of different gut bacteria strains on the regulation of factors that are important in drug metabolism, namely, transporters and phase I enzymes. Our studies highlighted the importance of direct communication between the intestinal cells and the gut bacteria with major findings being that species-specific differences exist in the regulation of drug metabolism. This work will be useful for (1) the discovery of novel regulators of drug metabolizing enzymes, (2) developing new pharmacokinetic models, and (3) advancing precision medicine models for patients.
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Title: Investigation in the Uncertainty of Chassis Dynamometer Testing for the Energy Characterization of Conventional, Electric and Automated Vehicles
Creator: Di Russo, Miriam
Date: 2023
Description: For conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their...
Show moreFor conventional and electric vehicles tested in a standard chassis dynamometer environment precise regulations on the evaluation of their energy performance exist. However, the regulations do not include requirements on the confidence value to associate with the results. As vehicles become more and more efficient to meet the stricter regulations mandates on emissions, fuel and energy consumption, traditional testing methods may become insufficient to validate these improvements, and may need revision. Without information about the accuracy associated with the results of those procedures however, adjustments and improvements are not possible, since no frame of reference exists. For connected and automated vehicles, there are no standard testing procedures, and researchers are still in the process of determining if current evaluation methods can be extended to test intelligent technologies and which metrics best represent their performance. For these vehicles is even more important to determine the uncertainty associated with these experimental methods and how they propagate to the final results. The work presented in this dissertation focuses on the development of a systematic framework for the evaluation of the uncertainty associated with the energy performance of conventional, electric and automated vehicles. The framework is based on a known statistical method, to determine the uncertainty associated with the different stages and processes involved in the experimental testing, and to evaluate how the accuracy of each parameter involved impacts the final results. The results demonstrate that the framework can be successfully applied to existing testing methods and provides a trustworthy value of accuracy to associate with the energy performance results, and can be easily extended to connected-automated vehicle testing to evaluate how novel experimental methods impact the accuracy and the confidence of the outputs. The framework can be easily be implemented into an existing laboratory environment to incorporate the uncertainty evaluation among the current results analyzed at the end of each test, and provide a reference for researchers to evaluate the actual benefits of new algorithms and optimization methods and understand margins for improvements, and by regulators to assess which parameters to enforce to ensure compliance and ensure projected benefits.
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Title: Using Niobium surface encapsulation and Rhenium to enhance the coherence of superconducting devices
Creator: Crisa, Francesco
Date: 2024
Description: In recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling...
Show moreIn recent decades, the scientific community has grappled with escalating complexity, necessitating a more advanced tool capable of tackling increasingly intricate simulations beyond the capabilities of classical computers. This tool, known as a quantum computer, features processors composed of individual units termed qubits. While various methods exist for constructing qubits, superconducting circuits have emerged as a leading approach, owing to their parallels with semiconductor technology.In recent years, significant strides have been made in optimizing the geometry and design of qubits. However, the current bottleneck in the performance of superconducting qubits lies in the presence of defects and impurities within the materials used. Niobium, owing to its desirable properties, such as high critical temperature and low kinetic inductance, stands out as the most prevalent superconducting material. Nonetheless, it is encumbered by a relatively thick oxide layer (approximately 5 nm) exhibiting three distinct oxidation states: NbO, NbO$_2$, and Nb$_2$O$_5$. The primary challenge with niobium lies in the multitude of defects localized within the highly disordered Nb$_2$O$_5$ layer and at the interfaces between the different oxides. In this study, I present an encapsulation strategy aimed at restraining surface oxide growth by depositing a thin layer (5 to 10 nm) of another material in vacuum atop the Nb thin film. This approach exploits the superconducting proximity effect, and it was successfully employed in the development of Josephson junction devices on Nb during the 1980s.In the past two years, tantalum and titanium nitride have emerged as promising alternative materials, with breakthrough qubit publications showcasing coherence times five to ten times superior to those achieved in Nb. The focus will be on the fabrication and RF testing of Nb-based qubits with Ta and Au capping layers. With Ta capping, we have achieved the best T1 (not average) decay time of nearly 600 us, which is more than a factor of 10 improvements over the bare Nb. This establishes the unique capping layer approach as a significant new direction for the development of superconducting qubits.Concurrently with the exploration of materials for encapsulation strategies, identifying materials conducive to enhancing the performance of superconducting qubits is imperative. Ideal candidates should exhibit a thin, low-loss surface oxide and establish a clean interface with the substrate, thereby minimizing defects and potential sources of losses. Rhenium, characterized by an extremely thin surface oxide (less than 1 nm) and nearly perfect crystal structure alignment with commonly used substrates such as sapphire, emerges as a promising material platform poised to elevate the performance of superconducting qubits.
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Title: The Double-edged Sword of Executive Pay: How the CEO-TMT Pay Gap Influences Firm Performance
Creator: Haddadian Nekah, Pouya
Date: 2024
Description: This study examines the relationship between the chief executive officer (CEO) and top management team (TMT) pay gap and consequent firm...
Show moreThis study examines the relationship between the chief executive officer (CEO) and top management team (TMT) pay gap and consequent firm performance. Drawing on tournament theory and equity theory, I argue that the effect of the CEO-TMT pay gap on consequent firm performance is non-monotonic. Using data from 1995 to 2022 from S&P 1500 US firms, I explicate an inverted U-shaped relationship, such that an increase in the pay gap leads to an increase in firm performance up to a certain point, after which it declines. Additionally, multilevel analyses reveal that this curvilinear relationship is moderated by attributes of the TMT, and the industry in which the firm competes. My findings show that firms with higher TMT gender diversity suffer lower performance loss due to wider pay gaps. Furthermore, when firm executives are paid more compared to the industry norms, or when the firm has a long-tenured CEO, firm performance becomes less sensitive to larger CEO-TMT pay gaps. Lastly, when the firm competes in a masculine industry, firm performance is more negatively affected by larger CEO-TMT pay gaps. Contrary to my expectations, firm gender-diversity friendly policies failed to influence the CEO-TMT pay gap-firm performance relationship.
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Title: Improving Localization Safety for Landmark-Based LiDAR Localization System
Creator: Chen, Yihe
Date: 2024
Description: Autonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem...
Show moreAutonomous ground robots have gained traction in various commercial applications, with established safety protocols covering subsystem reliability, control algorithm stability, path planning, and localization. This thesis specifically delves into the localizer, a critical component responsible for determining the vehicle’s state (e.g., position and orientation), assessing compliance with localization safety requirements, and proposing methods for enhancing localization safety.Within the robotics domain, diverse localizers are utilized, such as scan-matching techniques like normal distribution transformations (NDT), the iterative closest point (ICP) algorithm,probabilistic maps method, and semantic map-based localization.Notably, NDT stands out as a widely adopted standalone laser localization method, prevalent in autonomous driving software such as Autoware and Apollo platforms.In addition to the mentioned localizers, common state estimators include variants of Kalman Filter, particle filter-based, and factor graph-based estimators. The evaluation of localization performance typically involves quantifying the estimated state variance for these state estimators.While various localizer options exist, this study focuses on those utilizing extended Kalman filters and factor graph methods. Unlike methods like NDT and ICP algorithms, extended Kalman filters and factor graph based approaches guarantee bounding of estimated state uncertainty and have been extensively researched for integrity monitoring.Common variance analysis, employed for sensor readings and state estimators, has limitations, primarily focusing on non-faulted scenarios under nominal conditions. This approach proves impractical for real-world scenarios and falls short for safety-critical applications like autonomous vehicles (AVs).To overcome these limitations, this thesis utilizes a dedicated safety metric: integrity risk. Integrity risk assesses the reliability of a robot’s sensory readings and localization algorithm performance under both faulted and non-faulted conditions. With a proven track record in aviation, integrity risk has recently been applied to robotics applications, particularly for evaluating the safety of lidar localization.Despite the significance of improving localization integrity risk through laser landmark manipulation, this remains an under explored territory. Existing research on robot integrity risk primarily focuses on the vehicles themselves. To comprehensively understand the integrity risk of a lidar-based localization system, as addressed in this thesis, an exploration of lidar measurement faults’ modes is essential, a topic covered in this thesis.The primary contributions of this thesis include: A realistic error estimation method for state estimators in autonomous vehicles navigating using pole-shape lidar landmark maps, along with a compensatory method; A method for quantifying the risk associated with unmapped associations in urban environments, enhancing the realism of values provided by the integrity risk estimator; a novel approach to improve the localization integrity of autonomous vehicles equipped with lidar feature extractors in urban environments through minimal environmental modifications, mitigating the impact of unmapped association faults. Simulation results and experimental results are presented and discussed to illustrate the impact of each method, providing further insights into their contributions to localization safety.
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Title: DEVELOPMENT AND APPLICATION OF A NATIONALLY REPRESENTATIVE MODEL SET TO PREDICT THE IMPACTS OF CLIMATE CHANGE ON ENERGY CONSUMPTION AND INDOOR AIR QUALITY (IAQ) IN U.S. RESIDENCES
Creator: Fazli, Torkan
Date: 2020
Description: Americans spend most of their time inside residences where they are exposed to a number of pollutants of both indoor and outdoor origin....
Show moreAmericans spend most of their time inside residences where they are exposed to a number of pollutants of both indoor and outdoor origin. Residential buildings also account for over 20% of total primary energy consumption in the U.S. and a similar proportion of greenhouse gas emissions. Moreover, climate change is expected to affect building energy use and indoor air quality (IAQ) through both building design (i.e., via our societal responses to climate change) and building operation (i.e., via changing meteorological and ambient air quality conditions). The overarching objectives of this work are to develop a set of combined building energy and indoor air mass balance models that are generally representative of both the current (i.e., ~2010s) and future (i.e., ~2050s) U.S. residential building stock and to apply them using both current and future climate scenarios to estimate the impacts of climate change and climate change policies on building energy use, IAQ, and the prevalence of chronic health hazards in U.S. homes. The developed model set includes over 4000 individual building models with detailed characteristics of both building operation and indoor pollutant physics/chemistry, and is linked to a disability-adjusted life years (DALYs) approach for estimating chronic health outcomes associated with indoor pollutant exposure. The future building stock model incorporates a combination of predicted changes in future meteorological conditions, ambient air quality, the U.S. housing stock, and population demographics. Using the model set, we estimate the total site and source energy consumption for space conditioning in U.S. residences is predicted to decrease by ~37% and ~20% by mid-century (~2050s) compared to 2012, respectively, driven by decreases in heating energy use across the building stock that are larger than coincident increases in cooling energy use in warmer climates. Indoor concentrations of most pollutants of ambient origin are expected to decrease, driven by predicted reductions in ambient concentrations due to tighter emissions controls, with one notable exception of ozone, which is expected to increase in future climate scenarios. This work provides the first known estimates of the potential magnitude of impacts of expected climate changes on building energy use, IAQ, and the prevalence of chronic health hazards in U.S. homes.
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Title: Data-Driven Modeling for Advancing Near-Optimal Control of Water-Cooled Chillers
Creator: Salimian Rizi, Behzad
Date: 2023
Description: Hydronic heating and cooling systems are among the most common types of heating and cooling systems installed in older existing buildings,...
Show moreHydronic heating and cooling systems are among the most common types of heating and cooling systems installed in older existing buildings, especially commercial buildings. The results of this study based on the Commercial Building Energy Consumption Survey (CBECS) indicates chillers account for providing cooling in more than half of the commercial office building floorspaces in the U.S. Therefore, to address the need of improving energy efficiency of chillers systems operation, research studies developed different models to investigate different chiller sequencing approaches. Engineering-based models and empirical models are among the popular approaches for developing prediction models. Engineering-based models utilize the physical principles to calculate the thermal dynamics and energy behaviors of the systems and require detailed system information, while the empirical models deploy machine learning algorithms to develop relationships between input and output data. The empirical models compared to the engineering-based approach are more practical in a system’s energy prediction because of accessibility to required data, superiority in model implementation and prediction accuracy. Moreover, selecting near accurate chiller prediction models for the chiller sequencing needs to consider the importance of each input variable and its contribution to the overall performance of a chiller system, as well as the ease of application and computational time. Among the empirical modeling methods, ensemble learning techniques overcome the instability of the learning algorithm as well as improve prediction accuracy and identify input variable importance. Ensemble models combine multiple individual models, often called base or weak models, to produce a more accurate and robust predictive model. Random Forest (RF) and Extra Gradient Boosting (XGBoost) models are considered as ensemble models which offer built-in mechanisms for assessing feature importance. These techniques work by measuring how much each feature contributes to the overall predictive performance of the ensemble.In the first objective of this work the frequency of hydronic cooling systems in the U.S. building stock for applying potential energy efficiency measures (EEMs) on chiller plants are explored. Results show that the central chillers inside the buildings are responsible for providing cooling for more than 50% of the commercial buildings with areas greater than 9,000 m2(~100,000 ft2). In addition, hydronic cooling systems contribute to the highest Energy Use Intensity (EUI) among other systems, with EUI of 410.0 kWh/m2 (130.0 kBtu/ft2). Therefore, the results of this objective support developing accurate prediction models to assess the chiller performance parameters as an implication for chiller sequencing control strategies in older existing buildings. The second objective of the dissertation is to evaluate the performance of chiller sequencing strategy for the existing water-cooled chiller plant in a high-rise commercial building and develop highly accurate RF chiller models to investigate and determine the input variables of greatest importance to chiller power consumption predictions. The results show that the average value of mean absolute percentage error (MAPE) and root mean squared error (RMSE) for all three RF chiller models are 5.3% and 30 kW, respectively, for the validation dataset, which confirms a good agreement between measured and predicted values. On the other hand, understanding prediction uncertainty is an important task to confidently reporting smaller savings estimates for different chiller sequencing control strategies. This study aims to quantify prediction uncertainty as a percentile for selecting an appropriate confidence level for chillers models which could lead to better prediction of the peak electricity load and participate in demand response programs more efficiently. The results show that by increasing the confidence level from 80% to 90%, the upper and lower bounds of the demand charge differ from the actual value by a factor of 3.3 and 1.7 times greater, respectively. Therefore, it proves the significance of selecting appropriate confidence levels for implementation of chiller sequencing strategy and demand response programs in commercial buildings. As the third objective of this study, the accuracy of these prediction models with respect to the preprocessing, selection of data, noise analysis, effect of chiller control system performance on the recorded data were investigated. Therefore, this study attempts to investigate the impacts of different data resolution, level of noise and data smoothing methods on the chiller power consumption and chiller COP prediction based on time-series Extra Gradient Boosting (XGBoost) models. The results of applying the smoothing methods indicate that the performance of chiller COP and the chiller power consumption models have improved by 2.8% and 4.8%, respectively. Overall, this study would guide the development of data-driven chiller power consumption and chiller COP prediction models in practice.
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Title: Computational Genomics of Human-Infecting Microsporidia Species from the Genus Encephalitozoon
Creator: Mascarenhas dos Santos, Anne Caroline
Date: 2023
Description: Microsporidia are obligate intracellular pathogens classified as category B priority pathogens by the National Institute of Allergy and...
Show moreMicrosporidia are obligate intracellular pathogens classified as category B priority pathogens by the National Institute of Allergy and Infectious Diseases (NIAID), a division of the National Institutes of Health (NIH). Microsporidian species from the genus Encephalitozoon infect humans and can cause encephalitis, keratoconjunctivitis or enteric diseases in both immunocompromised and immunocompetent individuals. The main treatment for disseminated microsporidiosis available in the United States is albendazole, an anthelmintic benzimidazole that is also used to treat fungal infections, but species from the Encephalitozoonidae have already shown signs of resistance against this drug. The Encephalitozoonidae harbors highly specialized pathogens with the smallest known eukaryote genomes, with Encephalitozoon cuniculi featuring a genome of only 2.9 Mbp and coding for a proteome of roughly 2,000 proteins. Pathogens are in an everlasting race to quicken their adaptation pace against host defenses. This adaptation is often driven by gene duplication, recombination and/or mutation, and due to the potentially disruptive nature of duplication and recombination processes, many of these evolutions in pathogens are taking place outside conserved genomic loci. As such, genes involved in virulence and drug resistance in pathogens are often localized in the (sub)telomeres rather than in chromosome cores. The small and streamlined nature of microsporidian genomes makes them excellent candidates to investigate the adaptation of pathogens to host defenses, the evolution of their virulence, and the development of their resistance to drugs from a genomic perspective. However, microsporidian genomes are highly divergent at the DNA sequence level and the ones that have been sequenced so far are incomplete and are lacking the telomeres. This high level of sequence divergence hinders standard sequence homology-based functional annotations, blurring our understanding of what these organisms are capable of from a metabolic perspective. The gap in our knowledge of what is encoded in the microsporidia telomeres could lead to an underestimation of their pathogenic capabilities. Therefore, deciphering the functions of unknown proteins in microsporidia genomes and unraveling the content of their telomeres is important to fully assess their potential for adaptability to host defenses and predisposition to drug resistance. Likewise, a better understanding of the genetic diversity in microsporidia will help assess the extent by which host-pathogen interactions are shaping the adaptation of these parasites to humans. As observed in the COVID-19 pandemic, genetic diversity can influence the speed at which pathogens adapt to host defenses and thus can pose a big challenge to disease control. The development of strategies for controlling microsporidiosis outbreaks will likely benefit from the work performed in this thesis. As part of my PhD work, I investigated the virulence and host-adaptation capabilities of human-infecting microsporidia species from the genus Encephalitozoon with computational genomic approaches. This work included: 1) using structural homology to infer the functions of unknown proteins from the microsporidia proteome; 2) sequencing the complete genomes from telomere-to-telomere of three distinct Encephalitozoon spp. (E. cuniculi, E. hellem and E. intestinalis) to determine the genetic makeup of their telomeres and better understand the extent of their diversity; and 3) assessing the intraspecies genetic diversity that exists between Encephalitozoon species.
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Title: Eating disorder support group utilization: Associations with psychological health and eating disorder psychopathology among support group attendees
Creator: Murray, Matthew F.
Date: 2023
Description: Individuals with eating disorders (EDs) report psychosocial impairments that may persist beyond ED symptom remission, suggesting a need to...
Show moreIndividuals with eating disorders (EDs) report psychosocial impairments that may persist beyond ED symptom remission, suggesting a need to examine ED treatment-adjunctive services that foster psychosocial health. One promising resource is support groups, as evidence across medical and psychiatric illnesses shows associations between group utilization and wellbeing. However, virtually no literature has examined ED-specific support groups and psychosocial health, and it is also unknown how use of supportive services relates to ED symptoms. The present study examined associations between past-month ED support group attendance and participation frequency, psychosocial health indices, and ED symptoms. A total of 215 participants who attended weekly virtual clinician-moderated ED support groups completed measures of psychosocial health, internalized stigma of mental illness, psychosocial impairment from an ED, specific types of social support elicited in group, and ED psychopathology. Adjusting for past-month ED treatment, Benjamini-Hochberg-corrected partial correlation analyses indicated that more frequent attendance was negatively related to body dissatisfaction, purging, excessive exercise, and negative attitudes toward obesity, and positively related to social support. More frequent verbal and chat participation were positively related to emotional and informational support and social companionship. Chat participation was additionally negatively related to excessive exercise and negative attitudes toward obesity. Results suggest that utilizing and participating in clinician-moderated ED support groups could provide an outlet for ED symptom management and solicitation of social support. Findings highlight areas for further consideration in the delivery of and future research on ED support groups.
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