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Title: Investigating The Impact of Tall Building Ordinances (TBOs) on the Evolution of Ultra-Tall Buildings Typology: Case Studies in Chicago and Dubai
Creator: Alkoud, Amjad
Date: 2023
Description: Zoning ordinances are instruments that tangibly and intangibly shape cities; control urban morphology, demography, and visual identity; and...
Show moreZoning ordinances are instruments that tangibly and intangibly shape cities; control urban morphology, demography, and visual identity; and determine the inhabitants' life quality, well-being, and comfort. Tall building ordinances (TBOs), in turn, control the vertical growth of cities and the development of tall buildings as distinctive actors in the built environment today. With the recent proliferation of developing Ultra-tall buildings in cities around the world, ordinances should offer flexibility, adaptability, and responsiveness to the dynamic nature of emerging needs and technological potentials.This dissertation investigates the emergence of Ultra-tall buildings as a new typology in major metropolises and the interaction between the building ordinances and the construction of Ultra-tall. The work presented in this dissertation implements two primary research methods: cross-sectional surveys and longitudinal studies, documenting supertall buildings completed in two major cities, Chicago and Dubai. The discussions and findings are supported by structured interviews with architects and engineers actively involved in designing and constructing Ultra-tall buildings. The cross-sectional survey comprises all supertall buildings (i.e., buildings above 1000 feet in height) completed as of 2022 in Chicago, the cradle of the "modern" high-rise with 318 towers of 100-plus meters and eight supertall towers of 300-plus meters; and Dubai, the new experimental land of supertall construction with 298 towers of 100-plus meters and 28 towers of 300-plus meters height. The longitudinal case studies provide additional information and knowledge about selected examples in Chicago and Dubai, derived from personal structured interviews conducted in both cities. Several additional survey cases from China, NYC, and London were investigated for their importance and uniqueness in supporting the research discussions and findings. This research aims to bridge the gap between the building ordinance literature and Ultra-tall building design practices on the one hand. On the other hand, it sheds light on the necessity to realize Ultra-tall buildings as a distinct typology entitled to its particular set of ordinances.The research findings are intended to help architects, engineers, policymakers, and planning authorities ensure a sustainable socioeconomic future and mitigate the negative impact of Ultra-tall constructions in major cities. This goal is assumed to be achieved by developing a set of recommendations, strategies, and universal criteria to implement a more flexible and responsive approach toward emerging human needs and technologies.
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Title: Quantifying Localization Safety for State-of-the-Art Mobile Robot Estimation Algorithms
Creator: Abdul Hafez, Osama Mutie Fahad
Date: 2023
Description: In mobile robotics, localization safety is quantified using covariance matrix or particle spread.However, such methods are insufficient for...
Show moreIn mobile robotics, localization safety is quantified using covariance matrix or particle spread.However, such methods are insufficient for mission or life-critical applications, like Autonomous Vehicles (AVs), because they only reflect nominal sensor noise without considering sensor measurement faults. Sensor faults are unknown deterministic errors that cannot be modeled using a zero mean Gaussian distribution. Ignoring sensor faults, in such applications, might result in large localization errors, which in turn deceives other reliant systems, like the controller, leading to catastrophic consequences, such as traffic accidents for AVs. Thus, other techniques need to be used to conservatively quantify pose safety.This thesis builds upon previous research in aviation safety, or what is referred to as \textit{integrity monitoring}, to quantify localization safety for mobile robots that use state-of-the-art state estimators (as localizers).Specifically, this thesis utilizes the localization \textit{integrity risk} metric, as a measure of localization safety, which is defined as the probability of the robot's pose estimate error to lie outside pre-determined acceptable limits while an alarm is not triggered. Unlike open-sky aviation applications, where Global Navigation Satellite Systems (GNSS) signals are available, mobile robots operate in GNSS-denied, or in the best case GNSS-degraded, environments, which demands utilizing more complex set of sensors to guarantee an acceptable level of localization safety. This thesis provides a conservative measure of localization safety by rigorously upper-bounding the integrity risk while accounting for both nominal lidar noise and unmodeled lidar measurement faults.The contributions of this thesis include the design and analysis of practical integrity monitoring and failure detection procedures for mobile robots utilizing map-based particle filtering, a recursive integrity monitoring method for mobile robots utilizing map-based fixed lag smoothing for both solution-separation and chi-squared as failure detectors, the synthesis of an integrity monitoring procedure for mobile robots utilizing Extended Kalman Filter-based Simultaneous Localization And Mapping (EKF-based SLAM), and a Model Predictive Control (MPC) framework that is capable of planning mobile robot's trajectory to follow a predefined robot path while maintaining a predefined minimum level of mobile robot localization safety. The proposed methodologies are validated using both simulation and experimental results conducted in real-world urban university campus environments.
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Title: Designs and Optimizations of Oblivious Data Access for Mitigating Access Pattern Leakage
Creator: Che, Yuezhi
Date: 2023
Description: In today’s data-driven world, data outsourcing has grown, increasing the importance of data security and privacy. Data encryption, while...
Show moreIn today’s data-driven world, data outsourcing has grown, increasing the importance of data security and privacy. Data encryption, while providing some protection, is insufficient against side-channel attacks such as access pattern leakage. This thesis focuses on designing and optimizing efficient oblivious access methods to enhance data security and privacy. Traditional solutions, like Oblivious RAM (ORAM), often impose significant overheads, limiting their market adoption. Our research proposes novel oblivious data access schemes tailored to specific applications, systems, and contexts. This approach enables us to identify critical vulnerabilities and performance bottlenecks, and balance performance, security, and other relevant parameters. In this thesis, I present four published works in Chapters 3 to 6, demonstrating the effectiveness of my proposed methods: (1) optimizing Ring ORAM for multi-channel memory systems, (2) introducing a multi-range supported ORAM for locality-aware applications, (3) proposing an oblivious data access solution for NVM hybrid memory systems, and (4) developing an oblivious access method for deep neural networks (DNNs), ensuring privacy without sacrificing performance. These contributions address unique challenges across application domains, enhancing data security and privacy in contemporary computing systems. This thesis provides a comprehensive investigation of targeted oblivious access methods, highlighting the benefits of the proposed designs, and contributing to more effective solutions for access pattern leakage mitigation, ultimately improving data security and privacy in contemporary computing systems.
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Title: Developing Adaptive and Predictive Modules for the Second Generation of Multivariable Insulin Delivery System for People with Type-1 Diabetes
Creator: Askari, Mohammad Reza
Date: 2023
Description: In this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1...
Show moreIn this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1 diabetes (T1D). AID system is improved by integrating missing data from sensors into the system, reconciling outliers in the data, and eliminating the effects of artifacts in signals from wearable devices. Behavioral patterns of individuals with T1D are captured by data-driven models. The model predictive control algorithm of the mvAID uses these patterns for making decisions and predicting glucose concentrations in the future more accurately. A pipeline algorithm is developed for removing noise and motion artifacts from wristband signals. Then, energy expenditure, physical activity, and acute psychological stress (APS) are estimated from wearable device signals to detect and quantify disturbances affecting the concentration of blood glucose concentration. Additionally, different modules were designed for predicting risky glycemic episodes and are used to build the second generation of the mvAID system. The techniques developed are tested with historical data sets from various clinical experiments and free-living data, and with simulations made by using our multivariable glucose, insulin and physiological variables simulator (mGIPsim).
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Title: Applications of Optimal Contract Theory in Brokerage
Creator: Alonso Alvarez, Guillermo
Date: 2023
Description: In this thesis we study optimal brokerage problems in different scenarios. The thesisis structured in two parts:...
Show moreIn this thesis we study optimal brokerage problems in different scenarios. The thesisis structured in two parts: In the first part of this thesis, corresponding to Chapter 2 and 3, we construct optimal brokerage contracts for multiple (heterogeneous) clients trading a single asset whose price follows the Almgren-Chriss model. The distinctive features of this work are as follows: (i) the reservation values of the clients are determined endogenously, and (ii) the broker is allowed to not offer a contract to some of the potential clients, thus choosing her portfolio of clients strategically. We find a computationally tractable characterization of the optimal portfolios of clients (up to a digital optimization problem, which can be solved efficiently if the number of potential clients is small) and conduct numerical experiments which illustrate how these portfolios, as well as the equilibrium profits of all market participants, depend on the price impact coefficients. In the second part of this thesis, corresponding to Chapter 4, we establish existence of a solution to the optimal contract problem in models where the state process is given by a multidimensional diffusion with linearly controlled drift. Then, under certain concavity assumptions, we show that the optimal contracts in the relaxed formulation also solve the associated strong optimal contract problem. The main advantages of this approach, relative to the existing methods, are due to the fact that it allows (i) to obtain the existence of an optimal contract (as a limit point of epsilon-optimal ones), and (ii) to include various additional constraints on the associated control problems (e.g., state constraints, difference in filtrations of the agent and of the principal, etc.). Finally, we apply our results to the problem of brokerage fees when the agent has access to a larger filtration.
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Title: Qualitative Investigation of Stigma Experiences of Individuals Living with Hoarding Disorder
Creator: Bates, Sage
Date: 2023
Description: Hoarding disorder (HD) is characterized by significant difficulty discarding items, resulting in an accumulation of clutter. HD is a public...
Show moreHoarding disorder (HD) is characterized by significant difficulty discarding items, resulting in an accumulation of clutter. HD is a public health concern and is associated with treatment ambivalence (e.g., refusal to initiate treatment, dropout, and limited treatment compliance). While low insight and motivation may account for some of the treatment ambivalence, it also could be due to a number of other factors related to how HD is being perceived by others, such as stigma. Yet, there is very little research on the relationship between stigma and hoarding, and what these studies have shown is that HD is judged negatively by the general population (i.e., public stigma) in a variety of ways. However, despite these initial findings, there are no in-depth studies examining stigma of HD from the perspective of those with lived experience. Further, previous research of stigma and HD utilized stigma measures that were significantly modified from their original intent to measure severe mental illness, and it is possible that general measures of stigma may not capture the specific features of HD or public perceptions of HD. The present study is a qualitative analysis to investigate stigma pertinent to HD.
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Title: Towards Utility-Driven Data Analytics with Differential Privacy
Creator: Wang, Han
Date: 2023
Description: The widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information...
Show moreThe widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information or data. Some of them are high-dimensional and unstructured data, such as video and location data. Analyzing these data can provide significant benefits in real-world scenarios, such as videos for monitoring and location data for traffic analysis. However, while providing benefits, these complicated data always raise serious privacy concerns since all of them involve personal information. To address privacy issues, existing privacy protection methods often fail to provide adequate utility in practical applications due to the complexity of high-dimensional and unstructured data. For example, most video sanitization techniques merely obscure the video by detecting and blurring sensitive regions, such as faces, vehicle plates, locations, and timestamps. Unfortunately, privacy breaches in blurred videos cannot be effectively contained, especially against unknown background knowledge. In this thesis, we propose three different differentially private frameworks to preserve the utility of video and location data (both are high-dimensional and unstructured data) while meeting the privacy requirements, under different well-known privacy settings. Specifically, to our best knowledge, wepropose the first differentially private video analytics platform (VideoDP) which flexibly supports different video queries or query-based analyze with a rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Secondly, we define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans, vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. Third, we propose the first strict local differential privacy (LDP) framework for location-based service (LBS) (“L-SRR”) to privately collect and analyze user locations or trajectories with ε-LDP guarantees. Specifically, we design a novel LDP mechanism “staircase randomized response” (SRR) and extend the empirical estimation to further boost the utility for a diverse set of LBS Apps (e.g., traffic density estimation, k nearest neighbors search, origin-destination analysis, and traffic-aware GPS navigation). Finally, we conduct experiments on real videos and location dataset, and the experimental results demonstrate all frameworks can have good performance.
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Title: Latent Price Model for Market Microstructure: Estimation and Simulation
Creator: Yin, Yuan
Date: 2023
Description: This thesis focuses on exploring and solving several problems based on partiallyobserved diffusion models. The thesis has two parts....
Show moreThis thesis focuses on exploring and solving several problems based on partiallyobserved diffusion models. The thesis has two parts. In the first part we present a tractable sufficient condition for the consistency of maximum likelihood estimators (MLEs) in partially observed diffusion models, stated in terms of stationary distributions of the associated test processes, under the assumption that the set of unknown parameter values is finite. We illustrate the tractability of this sufficient condition by verifying it in the context of a latent price model of market microstructure. Finally, we describe an algorithm for computing MLEs in partially observed diffusion models and test it on historical data to estimate the parameters of the latent price model. In the second part we provide a thorough analysis of the particle filtering algorithm for estimating the conditional distribution in partially observed diffusion models. Specifically, we focus on estimating the distribution of unobserved processes using observed data. The algorithm involves several steps and assumptions, which are described in detail. We also examine the convergence of the algorithm and identify the sufficient conditions under which it converges. Finally, we derive an explicit upper bound of the convergence rate of the algorithm, which depends on the set of parameters and the choice of time frequency. This bound provides a measure of the algorithm’s performance and can be used to optimize its parameters to achieve faster convergence.
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Title: Electric Machine Windings with Reduced Space Harmonic Content
Creator: Tang, Nanjun
Date: 2023
Description: The reduction of magnetomotive force (MMF) space harmonic content in electric machine windings can significantly improve the machine's...
Show moreThe reduction of magnetomotive force (MMF) space harmonic content in electric machine windings can significantly improve the machine's electromagnetic performance. Potential benefits include a reduction of torque ripple, a more sinusoidal back EMF, and reduced power losses. With the proposal of a uniform mathematical representation that applies to both distributed windings and fractional-slot concentrated windings (FSCWs), closed-form expressions can be derived for harmonic magnitudes, winding factors, etc. These expressions can then be used to formulate the MMF space harmonic suppression problem for windings, which looks for improved windings with certain harmonic orders reduced or even eliminated, by varying the slot distribution and coil turns. Different solution techniques are explored to gain additional insights about the solution space. The underlying mathematical relations between different harmonic orders are mathematically proved to establish the family phenomenon, which presents clear pictures of the higher order part of the harmonic spectrum and is the foundation for exact calculation of the total harmonic distortion (THD) of windings. The exact THD calculation further indicates how the minimal THD can be achieved for a winding. Windings can also be analyzed and designed from the view of subsets to incorporate distribution and excitation phase shift effects. With reduced or the minimal space harmonic content, new winding designs can help significantly improve the Pareto front when combined with motor geometry optimization. Design examples including a 12-slot 2-pole mixed-layer distributed winding, a 18-slot 2-pole mixed-layer distributed winding, and a four-layer 24-slot 22-pole FSCW with excitation phase shift are presented with finite element analysis (FEA) results to verify the performance improvements.
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Title: Polarization Induced by A Terahertz Electric Field in A Semiconductor Nanodimer in the Overlapping Regime
Creator: Wang, Zi
Date: 2023
Description: Boltzmann transport equation is a theoretical framework for the description of thermodynamics or charge reactions in a system not in...
Show moreBoltzmann transport equation is a theoretical framework for the description of thermodynamics or charge reactions in a system not in equilibrium, which can be applied to the analysis of the interactions of mobile charges with an electromagnetic wave. When the dimensions of the object are small compared to the wavelength, the induced dipole moment provides a means to characterize the collective response while providing insight to the nature of the charge-field interactions. Semiconductor nanoparticles exhibit surface plasmon resonance in the terahertz frequency range and are of current interest for the development of components and circuits in that part of the electromagnetic spectrum. By changing the plasmon frequencies of doped semiconductors through the change of carrier concentration, new opportunities arise for plasmonic manipulation in terahertz region leading to various promising applications. Despite the Drude model's long-term success and convenience in describing the electrical conductivity of metals in terms of dielectric functions, some aspects of polarization are not accounted for by bulk properties. By incorporating the transport equations of the charge carriers with Maxwell's equations, screening effects of charge carriers can be accounted for, enabling the internal field, space charge and induced dipole moment of a semiconductor nanoparticle to be studied.The computations performed for elementary dimer structures in overlapping cases revealed the internal field screening, while the complex dipole moments show dispersion and absorption effects. The numerical algorithms are implemented using the finite element method to investigate the surface plasmon resonance (SPR) induced on the semiconductor particles. Unique SPR modes evolution is observed as the thickness of the overlap region is varied. The characteristics can be interpreted by the migration of local space charge as the level of overlap is varied. This degree of freedom provided by a semiconductor nanodimer could be employed to control the local field near a simple cluster of nanoparticles, with potential for application in sensing and circuit components in the terahertz frequency range.
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Title: High Frequency Trading and the Impact of Volume-Duration on Market Quality in the U.S. Futures Markets
Creator: Xu, Xiaoruo
Date: 2023
Description: This paper examines the impact of High Frequency Trading (HFT) on market quality in the U.S. futures market through the lens of Adjusted...
Show moreThis paper examines the impact of High Frequency Trading (HFT) on market quality in the U.S. futures market through the lens of Adjusted Volume-Durations (AVD). By using the unique nanosecond level TAQ CME datasets of commodities futures in 2018, which include Crude Oil, E-mini S&P 500, Eurodollar, Gold, Corn and Soybean, I create the AVDs of each dataset, then conduct the regression analysis on market quality variables with the independent variables including AVDs and other key variables, and the results show that as AVD decreases, the market quality deteriorates, thus HFT positively affects market quality in the U.S. futures market. In order to explore the main driver of AVD on market quality in the futures market, I use the Autoregressive Conditional Duration Model to decompose AVDs into expected AVDs (AEVD), which is the component of AVD that is influenced by past AVDs and unexpected AVDs (AUVD), which is the component of AVD that is not captured by past AVDs but by unanticipated events, and then conduct the regression analysis on market quality variables with the independent variables including AEVD, AUVD and other key variables. The result shows that AEVD has a higher impact on liquidity than AUVD, but the impact of AEVD and AUVD on volatility is mixed in the U.S. futures market. However, except for the conclusions get from the based multivariate regression results, I also explain why there are some outliers for the Eurodollar, soybean and gold, and why HFT has more explicit impact on agricultural futures market.
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Title: Phase field modeling and computation of vesicle growth or shrinkage
Creator: Tang, Xiaoxia
Date: 2023
Description: Lipid bilayers are the basic structural component of all biological cell membranes. It is a semipermeable barrier to most solutes, including...
Show moreLipid bilayers are the basic structural component of all biological cell membranes. It is a semipermeable barrier to most solutes, including ions, glucoses, proteins and other molecules. Vesicles formed by a bilayer lipid membrane are often used as a model system for studying fundamental physics underlying complicated biological systems such as cells and microcapsules. Mathematical modeling of membrane deformation has become an important topic in biological and industrial system for a long time. In this thesis, we develop a phase field model for vesicle growth or shrinkage based on osmotic pressure that arises due to a chemical potential gradient. This thesis consists of three main parts.In the first part, we establish a phase field model for vesicle growth or shrinkage without flow. It consists of an Allen-Cahn equation, which describes the evolution of the phase field parameter (the shape of the vesicle), and a Cahn-Hilliard-type equation, which simulates the evolution of the ionic fluid. The model is mass conserved and surface area constrained during the membrane deformation. Conditions for vesicle growth or shrinkage are analyzed via the common tangent construction. We develop the numerical computing in two-dimensional space using a nonlinear multigrid method which is a combination of nonlinear Gauss-Seidel relaxation operator and V-cycles multigrid solver, and perform convergence tests that suggest an $\mathcal{O}(t+h^2)$ accuracy. Numerical results demonstrate the growth and shrinkage effects graphically and numerically, which agree with the conditions analyzed via the common tangent construction.In the second part, we present a model for vesicle growth or shrinkage with flow. The dynamical equations considered are an Allen-Cahn equation, which describes the phase field evolution, a Cahn-Hilliard-type equation, which simulates the fluid concentration, and a Stokes-type equation, which models the flow. The numerical scheme in two-dimensional space includes a nonlinear multigrid method comprised of a standard FAS method for the Allen-Cahn and Cahn-Hilliard part, and the Vanka smoothing strategy for the Stokes part. Convergence tests imply an $\mathcal{O}(t+h^2)$ accuracy. Numerical results are demonstrated under zero velocity boundary condition and with boundary-driven shear flows, respectively.In the last part, we give an unconditionally energy stable and uniquely solvable finite difference scheme for the model established in the first part. The finite difference scheme is based on a convex splitting of the discrete energy and is semi-implicit. One key difficulty associated with the energy stability is due to the fact that some nonlinear energy functional terms in the expansion is neither convex nor concave. To overcome this subtle difficulty, we add auxiliary terms to make the combined term convex, which in turn yields a convex–concave decomposition of the physical energy. As a result, both the unique solvability and energy stability of the proposed numerical scheme are assured. In addition, we show the scheme is stable in the defined discrete norm.
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Title: Leader Identity Claiming and Granting Process: The Role of Gender on Perceptions of Leadership
Creator: Standish, Melanie P.
Date: 2023
Description: Ely, Ibarra, and Kolb (2011) theorize that the leader identity work among women is an area of work wherein subtle gender bias is pervasive and...
Show moreEly, Ibarra, and Kolb (2011) theorize that the leader identity work among women is an area of work wherein subtle gender bias is pervasive and impacting women’s advancement in the workplace. Interferences with the leader identity development process not only impact how a woman views herself as a leader, but how others collectively come to endorse her as a leader. Simply observing an individual claiming leadership and having that leadership be granted by someone else is known to influence how an observer classifies an individual as a leader or a non-leader. This study examines how the gender of an individual claiming leadership impacts external perceptions of how leader-like they are to others, when they are granted vs. not granted leadership. To examine this gap, this study uses an experimental vignette methodology to explore the impact of gender on leadership perceptions, during a claiming and granting process. Specifically, this work examines the mediating roles of competence and likability, as potential drivers through which differences may occur. Though women today are evaluated as equally competent as their male counterparts, engaging in dominant, agentic, behaviors, may make them less likable, and rated less leader-like as a result. The results of this study did not find an interaction between gender and granting, on perceived likability. The results did replicate existing findings that claiming leadership is not enough to be relationally recognized as a leader, and that granting from others plays an important role in how competent, and subsequently leader-like, an individual is perceived to be.
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Title: Approximation Algorithms for Selected Network and Graph Problems
Creator: Wang, Xiaolang
Date: 2023
Description: This dissertation proposes new polynomial-time approximation algorithms for selected optimization problems, including network and classic...
Show moreThis dissertation proposes new polynomial-time approximation algorithms for selected optimization problems, including network and classic graph problems. We employed distinct strategies and techniques to solve these problems. In Chapter 1, we consider a problem we term FCSA, which aims to find an optimum way how clients are assigned to servers such that the largest latency on an interactivity path between two clients (client 1 to server 1, server 1 to server 2, then server 2 to client 2) is minimized. We present a (3/2)-approximation algorithm for FCSA and a (3/2)-approximation algorithm when server capacity constraints are considered. In Chapter 2, we focus on two variants of the Steiner Tree Problem and present better approximation ratios using known algorithms. For the Steiner Tree with minimum number of Steiner points and bounded edge length problem, we provide a polynomial time algorithm with ratio 2.277. For the Steiner Tree in quasi-bipartite graphs, we improve the best-known approximation ratio to 298/245 . In Chapter 3, we address the problem of searching for a maximum weighted series-parallel subgraph in a given graph, and present a (1/2 + 1/60)-approximation for this problem. Although there is currently no known real-life application of this problem, it remains an important and challenging open question in the field.
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Title: Research through Provocation: A Structured Process to Design New Information Technologies
Creator: Rivera Gomez, Jaime Alejandro
Date: 2023
Description: This doctoral research presents a structured way to generate provocative prototypes, called provotypes, to design new information technologies...
Show moreThis doctoral research presents a structured way to generate provocative prototypes, called provotypes, to design new information technologies. Emphasizing the exploration of alternative interaction models beyond the current archetypes, this study considers emerging complexities about our relationship with technology in the long term to incorporate knowledge from science models in the early stages of the project when cross-disciplinary consensus is required. Thus, avoiding personal biases that are not aligned with how people use technology.The methodology analyzes six case studies using provotypes in multiple contexts, including academic research explorations, corporate innovation projects, and students applying the approach in educational settings. The research also involved a controlled experiment studying how different interactive configurations influence one's motivation to engage in positive behavior change. The results can be summarized in three main contributions: A provocation model to influence the shared meaning inside cross-functional teams, a tool to create provocations exploring alternative interaction models, and finally, the heuristics of provotyping to guide researchers and designers to generate early low fidelity prototyping.
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Title: Two Essays on Corporate Finance and Fixed-income Securities
Creator: Shen, Hao
Date: 2023
Description: In this study, we empirically investigate the relation between corporate finance and fixed income securities. Specifically, we employ...
Show moreIn this study, we empirically investigate the relation between corporate finance and fixed income securities. Specifically, we employ staggered changes in state corporate income tax rates as exogenous shocks and estimate how these state tax changes affect bond at-issue yield spreads. We find a significant increase in bond yield spreads after state tax increases but not after state tax decrease. Tax increases result in a 36 basis points increase in the yield spreads, which translates into a $12 million increase in interest expenses for firms experiencing tax increases. Besides, we employ the staggered adoption of universal demand (UD) laws by different states in the United States as a quasi-experimental setting and investigate the effect of UD laws on bond yield spreads at issuance. The adoption of UD laws raises the hurdle for shareholders to bring derivate lawsuits against firms and weakens shareholder litigation rights. Using a sample of bond issuances from 1985 to 2009, we find that the adoption of UD laws is positively associated with yield spreads of bonds issued by U.S. firms.
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Title: Melt Growth of Indium-Iodide on Earth and in Microgravity
Creator: Riabov, Vladimir
Date: 2023
Description: Indium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray...
Show moreIndium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray detectors. Its physical properties are similar to those of other materials used as room temperature radiation detectors. Over the years the technology of purification and crystal growth of InI was developed. Significant advances were made to improve purity, crystal structure and resulting electronic properties of the material. Nevertheless, the desired detector performance has not been achieved yet. Stress-induced crystal lattice defects resulting from solidification in contact with crucible are suspected to be responsible for the limited performance. Microgravity environment was previously used to study its effects on the process of crystal growth from the melt applied to semiconductors. It was observed that unlike on Earth materials can solidify without contact with the wall, when the sample is confined by the crucible. It was also shown that such detached solidification can drastically reduce stress-induced defects of the crystal lattice and improve electronic properties of the material. In this study crystal growth of InI was studied in microgravity, attempting to achieve detached solidification, and observe it in a transparent zone of a furnace. Partially detached solidification (a large free surface) has occurred in one of the samples. The resulting crystals were characterized by measuring their electronic properties and estimating the radiation detector performance of the devices manufactured using the crystals.
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Title: Establishing Bisphenol A Degradation and Enhancing Microbial Fuel Cell Performance by Biofilm Optimization of Shewanella Oneidensis MR1
Creator: Zhou, Jiacheng
Date: 2023
Description: Bisphenol A (BPA) has been widely used as a plasticizer in the production of synthetic polymers, such as those used in food storage containers...
Show moreBisphenol A (BPA) has been widely used as a plasticizer in the production of synthetic polymers, such as those used in food storage containers and bottles. However, BPA interferes with endocrine systems, causing carcinogenicity, immunotoxicity, and embryotoxicity. Biological water treatment processes scarcely remove BPA, owing to the poor BPA degradability and efficiency of the applied microorganisms. Shewanella oneidensis has been studied and used for the biodegradation process in wastewater treatment because of its excellent extracellular electron transfer properties. In this work, we engineered S. oneidensis MR1 to enable BPA degradation by producing ferredoxin (Fdbisd) and cytochrome P450 (P450bisd) originating from Sphingomonas bisphenolicum AO1. The engineered S. oneidensis exhibited a higher BPA degradation efficiency than that of Escherichia coli producing the same enzymes. The endogenous ferredoxin and ferredoxin reductase of S. oneidensis participated in BPA degradation, and overexpression of mtrC, omcA, and So0521, which encode S. oneidensis cytochromes, decreased BPA. We developed BPA-degrading S. oneidensis biofilms. We measured these optimized BPA-degrading S. oneidensis biofilm in a single chamber microbial fuel cell formed on different carbon electrodes by morphology. Cyclic voltammetry and electrochemical impedance spectroscopy were measured to analyze the biofilm-electrode performance. The biofilm colonization was also measured by confocal laser scanning microscope and scanning electron microscope. And the developed microbial fuel cell was used to degrade BPA and the biofilm developed on different type of carbon anodes was identified. This study provides insights into biocatalyst utilization for the biological degradation of toxic organic compounds.
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Title: Machine Learning for NDE Imaging Applications
Creator: Zhang, Xin
Date: 2023
Description: Infrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges...
Show moreInfrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges to be analyzed and characterized due to the nature of complex signal patterns and poor signal-to-noise ratios (SNR). Industries such as nuclear energy, are constructed with components produced using high-strength superalloys. These metallic components face challenges for wide deployment because material defects and mechanical conditions need to be non-destructively evaluated to identify potential danger before they enter service. Low NDE performance and lack of automation, particularly considering the complex environment in the in-situation NDE and nuclear power plant, present a major challenge to implement conventional NDE. This study solves the problems of using the advantages of machine learning as signal processing methods for Infrared Thermography and Ultrasonic NDE imaging applications. In Pulsed Infrared Thermography (PIT), for quality control of metal additive manufacturing, we proposed an intelligent PIT NDE system and developed innovative unsupervised learning models and thermal tomography 3D imaging algorithms to detect calibrated internal defects (pores) of various sizes and depths for different nuclear-grade metallic structures. Unsupervised learning aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Difficulties to detect defects by using PIT are thermal imaging noise patterns; uneven heating of the specimen; defects of micron-level size with overly weak temperature signals and so on. The unsupervised learning methods overcome these barriers and achieve the high defect detection accuracies (F-score) of 0.96 to detect large defects and 0.89 to detect microscopic defects, and can successfully detect defects with diameter of only 0.101-mm. In addition, we researched and developed innovative unsupervised learning models to compress high-resolution PIT imaging data and achieve the average high compression ratio >30 and a highest compression of 46 with reconstruction accuracy peak signal-to-noise ratio (PSNR) >73dB while preserving weak thermal features corresponding to microscopic defects. In ultrasonic NDE imaging, for structural health monitoring of materials, we built a high-performance ultrasonic computational system to inspect the integrity of high-strength metallic materials which are used in high-temperature corrosive environments of nuclear reactors. For system automation, we have been developing neural networks with various architectures for grain size estimation by characterizing the ultrasonic backscattered signals with high accuracy and data-efficiency. In addition, we introduce a response-based teacher-student knowledge distillation training framework to train neural networks and achieve 99.27% characterization accuracy with a high image processed throughput of 192 images/second on testing. Furthermore, we introduce a reinforcement learning based neural architecture search framework to automatically model the optimal neural networks design for ultrasonic flaws detection. At last, we comprehensively researched the performance of using unsupervised learning methods to compress 3D ultrasonic data and achieve high compression performance using only 4.25% of the acquired experimental data.
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Title: Image Synthesis with Generative Adversarial Networks
Creator: Ouyang, Xu
Date: 2023
Description: Image synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely...
Show moreImage synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely resemble the target images, learned from the source data distribution. This technique has a wide range of applications, including transforming captions into images, deblurring blurred images, and enhancing low-resolution images. In recent years, deep learning techniques, particularly Generative Adversarial Network (GAN), has achieved significant success in this field. GAN consists of a generator (G) and a discriminator (D) and employ adversarial learning to synthesize images. Researchers have developed various strategies to improve GAN performance, such as controlling learning rates for different models and modifying the loss functions. This thesis focuses on image synthesis from captions using GANs and aims to improve the quality of generated images. The study is divided into four main parts:In the first part, we investigate the LSTM conditional GAN which is to generate images from captions. We use the word2vec as the caption features and combine these features’ information by LSTM and generate images via conditional GAN. In the second part, to improve the quality of generated images, we address the issue of convergence speed and enhance GAN performance using an adaptive WGAN update strategy. We demonstrate that this update strategy is applicable to Wasserstein GAN(WGAN) and other GANs that utilize WGAN-related loss functions. The proposed update strategy is based on a loss change ratio comparison between G and D. In the third part, to further enhance the quality of synthesized images, we investigate a transformer-based Uformer GAN for image restoration and propose a two-step refinement strategy. Initially, we train a Uformer model until convergence, followed by training a Uformer GAN using the restoration results obtained from the first step.In the fourth part, to generate fine-grained image from captions, we delve into the Recurrent Affine Transformation (RAT) GAN for fine-grained text-to-image synthesis. By incorporating an auxiliary classifier in the discriminator and employing a contrastive learning method, we improve the accuracy and fine-grained details of the synthesized images.Throughout this thesis, we strive to enhance the capabilities of GANs in various image synthesis applications and contribute valuable insights to the field of deep learning and image processing.
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