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- Title
- BIG DATA AS A SERVICE WITH PRIVACY AND SECURITY
- Creator
- Hou, Jiahui
- Date
- 2020
- Description
-
With the increase of data production sources like IoT devices (e.g., smartwatches, smartphones) and data from smart home (health sensor,...
Show moreWith the increase of data production sources like IoT devices (e.g., smartwatches, smartphones) and data from smart home (health sensor, energy sensors), truly mind-boggling amounts of data are generated daily. Building a big data as a service system, that combines big data technologies and cloud computing, will enhance the huge value of big data and tremendously boost the economic growth in various areas. Big data as a service has evolved into a booming market, but with the emergence of larger privacy and security challenges. Privacy and security concerns limit the development of big data as a service and increasingly become one of the main reasons why most data are not shared and well utilized. This dissertation aims to build a new incrementally deployable middleware for the current and future big data as a service eco-system in order to guarantee privacy and security. This middleware will retain privacy and security in the data querying and ensure privacy preservation in data analysis. In addition, emerging cloud computing contributes to providing valuable services associated with machine learning (ML) techniques. We consider privacy issues in both traditional queries and ML queries (i.e., ML classification) in this dissertation. The final goal is to design and develop a demonstrable system that can be deployed in the big data as a service system in order to guarantee the privacy of data/ service owners as well as users, enabling secure data analysis and services.Firstly, we consider a private dataset composed of a set of individuals, and the data is outsourced to a remote cloud server. We revisit the classic query auditing problem in the outsourcing scenario. Secondly, we study privacy preserving neural network classification where source data is randomly partitioned. Thirdly, we concern the privacy of confidential training dataset and models which are typically trained in a centralized cloud server but publicly accessible, \ie online ML-as-a-Service (MLaaS). Lastly, we consider the offline MLaaS systems. We design, implement, and evaluate a secure ML framework to enable MLaaS on clients' edge devices, where a ``encrypted'' ML models are stored locally.
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- Title
- Investigating anti-biofilm and anti-persister activities of natural compounds and antimicrobial proteins
- Creator
- Jin, Xing
- Date
- 2020
- Description
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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
- Systematic Serendipity: A Study in Discovering Anomalous Astrophysics
- Creator
- Giles, Daniel K
- Date
- 2020
- Description
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In the present era of large scale surveys, big data presents new challenges to the discovery process for anomalous data. Advances in astronomy...
Show moreIn the present era of large scale surveys, big data presents new challenges to the discovery process for anomalous data. Advances in astronomy are often driven by serendipitous discoveries. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena which exhibit as-of-yet unobserved behaviors. As survey astronomy continues to grow, the size and complexity of astronomical databases will increase, and the ability of astronomers to manually scour data and make such discoveries decreases. In this work, we introduce a machine learning-based method to identify anomalies in large datasets to facilitate such discoveries, and apply this method to long cadence light curves from NASA's Kepler Mission. Our method clusters data based on density, identifying anomalies as data that lie outside of dense regions in a derived feature space. First we present a proof-of-concept case study and we test our method on four quarters of the Kepler long cadence light curves. We use Kepler's most notorious anomaly, Boyajian's Star (KIC 8462852), as a rare `ground truth' for testing outlier identification to verify that objects of genuine scientific interest are included among the identified anomalies. Additionally, we report the full list of identified anomalies for these quarters, and present a sample subset of identified outliers that includes unusual phenomena, objects that are rare in the Kepler field, and data artifacts. By identifying <4% of each quarter as outlying data, under 6k individual targets for the dataset used, we demonstrate that this anomaly detection method can create a more targeted approach in searching for rare and novel phenomena.We further present an outlier scoring methodology to provide a framework of prioritization of the most potentially interesting anomalies. We have developed a data mining method based on k-Nearest Neighbor distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k, and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (<1000) object classes higher than common classes, meaning that rarer objects are successfully prioritized over common objects. The most common class, categorized as miscellaneous stars without any major variability, and rotational variables compose well over two-thirds of the KIC, yet are considerably underrepresented in the top outliers. We have applied scoring to all long cadence light curves of quarters 1 to 17 of Kepler's prime mission and present outlier scores for all 2.8 million light curves for the roughly 200k objects.
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- Title
- High-latitude plasma drift structuring from a first principles ionospheric model
- Creator
- Kim, Heejin
- Date
- 2020
- Description
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In the high-latitude ionosphere dense plasma formations called polar cap patches are sometimes observed. These patches are often associated...
Show moreIn the high-latitude ionosphere dense plasma formations called polar cap patches are sometimes observed. These patches are often associated with ionospheric scintillation, a rapid fluctuation in the amplitude and phase of a radio signal that degrades communications and navigation systems. Predicting polar cap patch movement across the polar cap is an important subject for enabling forecasting of the scintillation.Lagrangian coherent structures (LCSs) are ridges indicating regions of maximum fluid separation in a time-varying flow. In previous studies, the Ionosphere-Thermosphere Algorithm for Lagrangian Coherent Structures (ITALCS) predicted the location of LCSs. These LCSs were shown to constrain polar cap patch source and transport regions for flow assumed to due to $\vec{E} \times \vec{B}$ plasma drift. The LCSs were predicted based on an empirical model of the high-latitude electric field for $\vec{E}$. In this thesis, the LCSs are generated using the first principles ionospheric model SAMI3 (SAMI3 is Another Model of the Ionosphere) as the model for electric field. The work relies on an understanding of various magnetic coordinate systems in space science, and includes three different approaches for attempting to generate the $\vec{E} \times \vec{B}$ drift as the flow fields that are to input to ITALCS. Finally, a representative LCS result is obtained with SAMI3 and shown to be at the high latitudes on the dayside, similar to prior work, but spanning a shorter longitudinal range.
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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
- APPLICATIONS OF INTEGRATED DESIGN METHODOLOGIES: HYBRID AUTOMATION OF DESIGN SEQUENCING AND ITS INFLUENCE ON COMPLEX DESIGN PROJECTS
- Creator
- Elshanshoury, Waleed Farouk Omar
- Date
- 2020
- Description
-
After the early development of Sketchpad in 1963 by Ivan Sutherland at MIT, the first system permitted drawing geometries parametrically;...
Show moreAfter the early development of Sketchpad in 1963 by Ivan Sutherland at MIT, the first system permitted drawing geometries parametrically; computation and algorithm aided design have significantly influenced the design practice. Computation and AAD are design approaches in which the medium of expression is logic instead of geometry. These approaches raised the curtain to various utilities, including but not limited to form-finding, automation, optimization, and robotic fabrication. Computational design and algorithm aided design are becoming fundamental approaches in most design practices because of their capability to solve complex problems.This thesis begins with a timeline presenting the evolution in design derivers and manifests how designers considered ideal design throughout history. This timeline starts with architecture approaches in ancient times when beauty, durability, and functions were the first principles to identify good architecture. It ends with the creation of computational technologies, which affected the design process and its logic. It will also investigate relations between software engineering and building design, where both fields intertwine with each other in general methodologies.This research examines how computation can generate integrated design systems to approach city planning and architectural design. IDS employs data, forces, and algorithms to construct a design system instead of solid geometries. This system combines the different design processes and chronological phases in interconnected blocks. This approach manages big data and assists in decision-making using automation, optimization, and machine learning technologies.This paper examines existing precedents, applications, and design projects that utilize IDS, including form-finding, materials, and energy. It will establish how evaluation criteria, simulations, solution optimizations, and processes automation play a vital role in integrated design systems. IDS is a dynamic workflow centered on principles and consists of components and aiding tools. This research explores technological aiding tools for these systems that help increase design performance and efficiencies using voice commands and automated functions.
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- Title
- Defense-in-Depth for Cyber-Secure Network Architectures of Industrial Control Systems
- Creator
- Arnold, David James
- Date
- 2024
- Description
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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
- Estimation of Platinum Oxide Degradation in Proton Exchange Membrane Fuel Cells
- Creator
- Ahmed, Niyaz Afnan
- Date
- 2024
- Description
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The performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the...
Show moreThe performance and durability of Proton Exchange Membrane Fuel Cells (PEMFCs) can be significantly hampered due to the degradation of the platinum catalyst. The production of platinum oxide is a major cause of the degradation of the fuel cell system, negatively affecting its performance and durability. In order to predict and prevent this degradation, this research examines a novel method to estimate degradation due to platinum oxide formation and predict the level of platinum oxide coverage over time. Mechanisms of platinum oxide formation are outlined and two methods are compared for platinum oxide estimation. Linear regression and two Artificial Neural Network (ANN) models, including a Recurrent Neural Network (RNN) and Feed-forward Back Propagation Neural Network (FFBPNN), are compared for estimation. The estimation model takes into account the influence of cell temperature and relative humidity.Evaluation of relative errors (RE) and root mean square error (RMSE) illustrates the superior performance of RNN in contrast to GT-Suite and FFBPNN. However, both RNN and GT-Suite showcase an average error rate below 5% while the FFBPNN had a higher error rate of approximately 7%. The RMSE of RNN shows mostly less compared to FFBPNN and GT-Suite, however, at 50% training data, GT-Suite shows lowest RMSE. These findings indicate that GT-Suite can be a valuable tool for estimating platinum oxide in fuel cells with a relatively low RE, but the RNN model may be more suitable for real-time estimation of platinum oxide degradation in PEM fuel cells, due to its accurate predictions and shorter computational time. This comprehensive approach provides crucial insights for optimizing fuel cell efficiency and implementing effective maintenance strategies.
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- Title
- Extremal and Enumerative Problems on DP-Coloring of Graphs
- Creator
- Sharma, Gunjan
- Date
- 2024
- Description
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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
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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
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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
- The Voderettes: Gender, Labor, and Techno-Utopia at the 1939 New York World's Fair
- Creator
- Simon, Sara M. B.
- Date
- 2024
- Description
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This thesis explores the labor demands of the Voder, the electrical speech synthesis machine developed by Bell Labs to be a major component of...
Show moreThis thesis explores the labor demands of the Voder, the electrical speech synthesis machine developed by Bell Labs to be a major component of AT&T's 1939 New York World's Fair exhibit. With the United States emerging from the Great Depression, and with political tensions escalating across the globe, the paper situates the Voder's labor demands within the historical context of the fair. Specifically, I explore the decision to have young women operate the Voder, the intricacies of the machine cloaked by the warm presence of its highly-skilled female operator. Using archival records from Bell Labs engineers, the paper exposes the previously unacknowledged engineering contributions of Voder operators in the years before the fair. These young women not only influenced major decisions about the Voder's mechanics but also gave early credence to the notion that developing a performance with the machine could make for a thrilling fair exhibit. Moreover, the paper argues that at the fair itself, AT&T and Bell Labs executives used the Voder operators to normalize a new vision of a technological utopia that relied heavily and conspicuously on the infrastructural labor of women. Given the Voder's legacy, as a tool that laid critical groundwork for voice encryption technology, the paper adds important context to the historical record, highlighting the young women at the heart of the machine.
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- Title
- Design and Synthesis of New Sulfur Cathodes Containing Polysulfide Adsorbing Materials
- Creator
- Suzanowicz, Artur M
- Date
- 2023
- Description
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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
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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
- Agency and Pathway Thinking as Mediators of The Relationship Between Caregiver Burden And Life Satisfaction Among Family Caregivers Of People With Parkinson’s Disease: An Application Of Snyder’s Hope Theory
- Creator
- Springer, Jessica Gabrielle
- Date
- 2024
- Description
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In the United States, there are 47.9 million caregivers providing care to family members with disabilities. Those providing care to someone...
Show moreIn the United States, there are 47.9 million caregivers providing care to family members with disabilities. Those providing care to someone who has Parkinson’s Disease (PD), a complex degenerative movement disorder, may have a unique caregiving experience, given that disease-related factors (e.g. motor and non-motor symptoms) can contribute to worsening caregiver burden and life satisfactions (LS). PD has an increasing incidence of 90,000 new cases per year, likely resulting in an increased need for caregivers. Caregiving research frequently focuses on the mediators between caregiver burden and LS including social support, coping skills, and appraisals. Research that has specifically focused on caregivers of people with PD (Pw/PD) is significantly limited. Hope is a “positive motivational characteristic comprised of agency and pathways thinking that can help facilitate drive towards one’s goal while also serving as a buffer against negative events” (Snyder et al.,1991). The goal of this study is to understand Snyder’s hope theory as it relates to caregiver burden and LS for caregivers of Pw/PD. Specifically, we hypothesized that (a) caregiver burden will be negatively correlated with agency thinking, pathways thinking, and LS among caregivers of Pw/PD. In addition, pathways thinking, and agency thinking will be positively associated with LS, and (b) agency thinking, and pathways thinking will mediate the relationship between caregiver burden and LS among caregivers of Pw/PD. The study sample consisted of 249 caregivers of Pw/PD who completed an online anonymous questionnaire. Correlations between agency and pathways thinking, LS, caregiver burden, and sociodemographic factors were evaluated. A parallel mediation analysis was run to evaluate the mediating roles of pathways and agency thinking in the relationship between caregiver burden and LS. Results indicated that LS was significantly and negatively correlated with caregiver burden. LS was significantly and positively correlated with both pathways and agency thinking. Pathways thinking had no indirect effect on the relationship of caregiver burden on LS. Agency thinking had a negative, indirect effect on the relationship suggesting that agency thinking partially mediated the relationship between caregiver burden and LS. Clinical implications and future directions are discussed.
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- Title
- Three-Dimensional Co-Culture Systems for Vascularization of Cardiac Tissue
- Creator
- Rodriguez Arias, Jessica A.
- Date
- 2023
- Description
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Myocardial Infarction (MI) is the partial or complete blockage of blood flow to the myocardial tissue resulting in damage and therefore loss...
Show moreMyocardial Infarction (MI) is the partial or complete blockage of blood flow to the myocardial tissue resulting in damage and therefore loss of heart function. In the U.S. every 40 seconds, someone will suffer from MI and the only available treatment is medication to treat the symptoms of heart function loss, but do not treat the underlying cause. Some attempts to treat the underlying cause have arisen in the last decades including cell-based therapies or tissue engineering therapies such as spheroid-based cardiac patches that have shown to be promising. Improvement in the mechanical properties to create suturable engineered tissues remain to be improved for ease of implantation purposes. Cell-laden hydrogel scaffolds can provide improved mechanical properties compared to biomaterial free cell-based therapies but need to allow for vascularization of the engineered tissue. Thus, the goal of this thesis is to provide preliminary studies for the use of a cell adhesive, proteolytically degradable PEG hydrogel scaffold that eventually would be used as an invitro model to evaluate engineered tissue vascularization for cardiac tissue engineering. To construct this model, important cell spheroid parameters on vascular invasion in 3D culture were investigated including the total number of cells/spheroid, the supporting cell for endothelial cells. In order to scale-up scaffolds to size of clinically relevant dimensions, a multilayered hydrogel construct visible light free-radical polymerization approach encapsulating vascular spheroids in multiple layers was also investigated. Results indicate that a total cell number of 5000 cells/spheroid aggregate were feasible due to cell sourcing. In addition, co-cultures of endothelial and mesenchymal stem cells led to maximized vascular invasion of the spheroids compared to fibroblast/endothelial co-culture and endothelial monoculture of spheroids in the hydrogel. Finally, the extent of vascularization of spheroids in each layer of the multilayered hydrogel constructs varied due to the observed differences in mechanical properties and swelling ratio of each layer due to incomplete polymerization of layers. This study demonstrated the importance of support cells and hydrogel mechanical properties in promoting vascularization of spheroid which serves as basis for building cell-laden hydrogel scaffolds for vascularization for cardiac tissues.
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- Title
- Financialization in the Structured Products Market
- Creator
- Zhu, Lizi
- Date
- 2023
- Description
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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
- Pilgrim Baptist Church, Chicago, Illinois, ca. 1964
- Creator
- Weil, F. Peter
- Date
- 1952-1964
- Description
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Pilgrim Baptist Church (3301 S. Indiana Ave, Chicago, IL) photographed from the northwest by Institute of Design student F. Peter Weil. Date...
Show morePilgrim Baptist Church (3301 S. Indiana Ave, Chicago, IL) photographed from the northwest by Institute of Design student F. Peter Weil. Date is estimated as 1964 from other evidence in the collection.
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- F. Peter Weil photographs, 1952-1964
- Title
- Multimodal Learning and Generation Toward a Multisensory and Creative AI System
- Creator
- Zhu, Ye
- Date
- 2023
- Description
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We are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed...
Show moreWe are perceiving and communicating with the world in a multisensory manner, where different information sources are sophisticatedly processed and interpreted by separate parts of the human brain to constitute a complex, yet harmonious and unified intelligent system. To endow the machines with true intelligence, multimodal machine learning that incorporates data from various modalities including vision, audio, and text, has become an increasingly popular research area with emerging technical advances in recent years. Under the context of multimodal learning, the creativity to generate and synthesize novel and meaningful data is a critical criterion to assess machine intelligence.As a step towards a multisensory and creative AI system, we study the problem of multimodal generation in this thesis by exploring the field from multiple perspectives. Firstly, we analyze different data modalities in a comprehensive manner by comparing the data natures, the semantics, and their corresponding mainstream technical designs. We then propose to investigate three multimodal generation application scenarios, namely text generation from visual data, audio generation from visual data, and visual generation from textual data, with diverse approaches to give an overview of the field. For the direction of text generation from visual data, we study a novel multimodal task in which the model is expected to summarize a given video with textual descriptions, under a challenging condition where the video can only be partially seen. We propose to supplement the missing visual information via a dialogue interaction and introduce QA-Cooperative network with a dynamic dialogue history update learning mechanism to tackle the challenge. For the direction of audio generation from visual data, we present a new multimodal task that aims to generate music for a given silent dance video clip. Unlike most existing conditional music generation works that generate specific types of mono-instrumental sounds using symbolic audio representations (e.g., MIDI), and that heavily rely on pre-defined musical synthesizers, we generate dance music in complex styles (e.g., pop, breaking, etc.) by employing a Vector-Quantized (VQ) audio representation via our proposed Dance2Music-GAN (D2M-GAN) framework. For the direction of visual generation from textual data, we tackle a key desideratum in conditional synthesis, which is to achieve high correspondence between the conditioning input and generated output using the state-of-the-art generative model -- Diffusion Probabilistic Model. While most existing methods learn such relationships implicitly, by incorporating the prior into the variational lower bound in model training. In this work, we take a different route by explicitly enhancing input-output connections by maximizing their mutual information, which is achieved by our proposed Conditional Discrete Contrastive Diffusion (CDCD) framework. For each direction, we conduct extensive experiments on multiple multimodal datasets and demonstrate that all of our proposed frameworks are able to effectively and substantially improve task performance in their corresponding contexts.
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