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- Title
- Towards a Self-Programmable Storage Solution in Extreme-Scale Environments
- Creator
- Devarajan, Hariharan
- Date
- 2021
- Description
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Traditional compute-centric scientific discovery has led to a growing gap between computation power and storage capabilities. However, in the...
Show moreTraditional compute-centric scientific discovery has led to a growing gap between computation power and storage capabilities. However, in the data explosion era, where data analysis is essential for scientific discovery, slow storage systems led to the research conundrum known as the I/O bottleneck. Scientists have proposed several optimizations to address the I/O bottleneck. However, selecting and applying the appropriate optimization is a complex task, often left to the users. Additionally, the explosion of data has led to the proliferation of applications as well as storage technologies. This has created a complex matching problem between diverse application requirements and heterogeneous storage resources for the users. We need to move towards a Self-Programmable storage system that can automatically understand the I/O requirements of applications, transparently leverage the heterogeneity of storage, and reconfigures itself dynamically by utilizing application and storage information. In this work, we present the Jal System for building Self-Programmable storage. The Jal System consists of three layers: the application layer, the transfer layer, and the storage layer. The application layer uses automatic extraction of I/O requirements from applications using a source-code-based profiler. The storage layer defines a data abstraction, using a shared log store, to efficiently unify heterogeneous storage resources under a single platform. Finally, the transfer layer defines data management algorithms that consider multi-application and multi-storage information to optimize data operations. Additionally, we illustrate the benefits of utilizing the technologies within the Jal System on modern scientific AI applications. Our evaluations have demonstrated that each technology within the Jal System can accelerate I/O for modern scientific workflows. We have implemented software, tools, and system libraries for modern HPC systems. In the future, we envision building a fully integrated system that efficiently utilizes all the Jal System technologies. Additionally, we plan to extend the strategies and techniques in Jal System to other scientific domains such as AI and IoT.
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- Title
- IDEOLOGICALLY MOTIVATED INTENTIONAL ADULTERATION: THEORY INTO INDUSTRIAL APPLICATION
- Creator
- DeVuyst, Adrian Jeffrey
- Date
- 2021
- Description
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Ideologically motivated intentional adulteration is an attempt to cause harm to consumers of food. Within the context of the United States of...
Show moreIdeologically motivated intentional adulteration is an attempt to cause harm to consumers of food. Within the context of the United States of America (US), the current methods of addressing this risk are evolving in the modern post-Food Safety Modernization Act (FSMA) era. Currently, the US has the Food and Drug Administration (FDA), which requires companies to have a food defense plan with a risk assessment, mitigation strategies, and recordkeeping. Additional options from Global Food Safety Initiatives (GFSI) benchmarked standards offer additional options for a company. However, even with these standards companies are still being impacted by intentional adulteration. Historical examples from the poisoning of bread in Hong Kong during British occupation and spreading of bacteria on salad bars by the followers of Rajneesh, to more modern examples of putting needles in strawberries and urinating on production equipment show a food defense system that is not always able to address intentional adulteration. The question of why companies are still having intentional adulteration comes up. The lack of food defense events and primary research on the topic creates a system where individual companies must gather data. Evaluations and surveys at a manufacturing site, N=11, indicates that there is high confidence among front line workers about their level of knowledge, but workers are unable to articulate the basic principles of food defense. Each individual company is required to create a personalized food defense system in the status quo, but the results of the survey given suggests that the data they could gather may be insufficient to create an effective food defense system.
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- Title
- Modeling the Aerodynamic Response to Impulsive Active Flow Control
- Creator
- Asztalos, Katherine
- Date
- 2021
- Description
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In unsteady aerodynamics the response to external disturbances can depend significantly on the initial condition, and the extent to which this...
Show moreIn unsteady aerodynamics the response to external disturbances can depend significantly on the initial condition, and the extent to which this impacts the ability to model the flowfield can vary. In this work, we look to develop a model that can capture and predict the long-time response to actuation, which we suspect to be sensitive to the instantaneous state. We investigate whether a physical understanding of the short-time response to impulsive actuation can be obtained, with the goal of understanding the observed physical phenomenon present in the immediate response to this type of actuation. We find that the response to impulsive actuation is sensitive to the instantaneous wake, and that the short-time response is directly proportional to the time rate of change of the actuation input. Computational simulations of a stalled NACA 0009 airfoil subject to leading-edge synthetic jet actuation were performed. Full state information, as well as force response measurements, were collected using an immersed boundary method (IBM) numerical code. The numerical simulations performed sought to characterize the response to actuation by varying the actuation parameters, such as the strength, direction, and phase at which the onset of actuation occurs. It was found that the long-time response to actuation can be sensitive to the instantaneous wake state at the onset of actuation. The ability to extract models that describe the complex behavior of the system provides additional insight into the dominant features governing the response of such systems, as well as achieves predictive capabilities of the systems' response. The data-driven models, which are identified using variants of dynamic mode decomposition, can capture both the short- and long-time response of the system to actuation. Predictive models are identified using multiple trajectories of data corresponding to varying the phase of vortex shedding at which the onset of actuation occurs. These models achieve accurate predictions for off-design cases as well. It is also shown that multiple control objectives with the same actuator can be achieved. Classical theory aids in understanding the physics governing unsteady aerodynamic motion and the response to disturbances. Theoretical models are developed using the assumptions from classical unsteady aerodynamic theory, which provide insight into the forms that the data-driven models take. The effect of short-duration momentum injection actuation is modeled through a combination of source/sink, doublet, and vortex elements. Regardless of the precise elements used in the theoretical model, the lift response is composed of a contribution directly proportional to the rate of change of actuation strength, and a contribution that persists after the actuation burst ends that arises due to the enforcement of the Kutta condition. Methodologies that retain the physics inherent to the system by projecting the governing equations of motion onto a well-suited basis are extremely valuable for gaining physical insight and understanding into the dynamics of the flowfield. A new methodology is proposed for extracting spectral content from systems with limited data available using projection-based modeling approaches. There are challenges associated with using modal decomposition-based modeling techniques for systems exhibiting large transient dynamics due to external inputs, which is applicable in this particular instance and for related systems. The methodology presented here shows how the dynamics of this system can be understood through analysis of optimal finite-time horizon transient energy growth, applied to reduced-order models identified using actuation response data with either data-driven or physics-based models. A novel methodology is proposed to guide future experimental actuation design to achieve maximal response by considering an optimal forcing mode, identified from considering the optimal perturbation of the full unactuated system, which maximizes a given output.
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- Title
- Computationally Efficient Predictive Control Strategies for Autonomous Vehicles
- Creator
- Bhattacharyya, Viranjan
- Date
- 2021
- Description
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This thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the...
Show moreThis thesis aims at developing computationally efficient (hence real-time applicable) control strategies for autonomous vehicles in the presence of uncertainty, while incorporating high fidelity vehicle dynamics. The motivation for the control strategies is to ensure safety and improve energy efficiency of the vehicles. In this research, an effort has been made to develop control strategies to strike a balance between these competing factors. The specific contributions are: development of a new hierarchical control framework that can guarantee avoidance of red-light idling in the presence of uncertainty in preceding vehicle information/prediction in connected environment (hence improves system mobility); exploitation of a data-driven modeling approach for identifying a linear predictor for the nonlinear vehicle dynamics, which facilitates formulation of a convex equivalent problem of the original non-convex problem (hence facilitates computational tractability); introduction of a novel vehicle dynamics-aware fast game-theoretic planner for behavior and motion planning of vehicles in uncertain and unconnected environments. This thesis explores both the possible directions of future autonomous vehicles: connected and unconnected autonomous vehicles. In particular, the first problem relates to longitudinal fuel efficient driving (eco-driving) in a connected urban environment, where the connected and automated vehicles (CAVs) aim at the improvement of fuel efficiency and reduction of red-light idling (stop and go motion). The CAVs also focus on ensuring collision avoidance with the preceding vehicles despite the prediction uncertainty in future trajectory of preceding vehicles. This problem assumes vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, and is a longitudinal control problem. The next problem considers the uncertainty in prediction of future states of neighbouring vehicles in an unconnected environment and involves both lateral and longitudinal control. Following previous research, the interactive nature of driving is modeled using game-theory and a computationally efficient game-theoretic planner is introduced. Simulation results show the efficacy of the proposed methods in terms of computational tractability and fuel-efficiency.
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- Title
- CORPORATE SOCIAL RESPONSIBILITY AND SUSTAINABLE ECONOMIC DEVELOPMENT IN CHINA
- Creator
- Cheng, Weiquan
- Date
- 2021
- Description
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This paper examines corporate social responsibility (CSR) strategies and their anticipated impacts on both company’s performance and climate...
Show moreThis paper examines corporate social responsibility (CSR) strategies and their anticipated impacts on both company’s performance and climate change mitigation in mainland China. It performs analysis on the effectiveness of the policies/efforts undertaken by Chinese publicly traded companies to carry on CSR projects through CSR disclosure system, and specifically focuses on determining if CSR projects could help to enhance companies’ profitability while promoting sustainable development in China. It utilizes companies’ financial statements and CSR reports from China Stock Market & Accounting Research Database (CSMAR), and regional macroeconomic data from National Bureau of Statistics of China from 2006 to 2016. The modeling results indicate that industry types, and socioeconomic conditions within which they operate control the anticipated outcome of implementing CSR projects specifically if those projects are designed to reduce companies' carbon emissions. This research provides valuable insights for CSR development in the future according to company types and socioeconomic imbalance in China.
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- Title
- Evaluation of Bax∆2 Positive-Staining in Skin Samples Using Two Immunohistochemical Methods
- Creator
- Basheer, Sana
- Date
- 2021
- Description
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BaxΔ2 is a pro-death and tumor suppressor protein that sensitizes cells to certain chemotherapies. Previous diaminobenzidine (DAB)-based...
Show moreBaxΔ2 is a pro-death and tumor suppressor protein that sensitizes cells to certain chemotherapies. Previous diaminobenzidine (DAB)-based staining revealed that Bax∆2 is found in all organs, including breast, colon, and skin tissues. In the skin, the Bax∆2 positive cells were mainly found in the basal cell layer of the epidermis with a few Bax∆2 positive cells in the connective tissue of the dermis, although their cellular identity was unknown. Previous literature has shown that melanin, which is found throughout the cells of the epidermis, is a brown color that provides no visual contrast to the DAB staining. While the DAB-based immunostaining showed cells that appeared to be Bax∆2 positive, this result needed to be confirmed. For this, a set of human skin samples from normal and cancerous tissue of various patients was examined. The co-staining of these samples for Bax∆2 and basal cells using immunofluorescence revealed that the apparent Bax∆2-positve DAB staining in epidermal basal cells and squamous cell carcinoma as false-positive, but the Bax∆2 positive cells found in the dermal connective tissue were not false positive—which is consistent with both previous DAB-based and fluorescence-based immunostaining. Using co-immunostaining for Bax∆2 with different cellular markers, the Bax2-positive cells in the connective tissue were identified potentially as macrophages and fibroblasts. Further studies are required to confirm the identity of the Bax∆2 positive cells in the connective tissue.
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- Title
- WIENER-HOPF FACTORIZATION FOR TIME-INHOMOGENEOUS MARKOV CHAINS AND BAYESIAN ESTIMATIONS FOR DIAGONALIZABLE BILINEAR STOCHASTIC PARTIAL DIFFERENTIAL EQUATIONS
- Creator
- Cheng, Ziteng
- Date
- 2021
- Description
-
This thesis consists of two major parts, and contributes to two areas of research in stochastic analysis: (i) Wiener-Hopf factorization (WHf)...
Show moreThis thesis consists of two major parts, and contributes to two areas of research in stochastic analysis: (i) Wiener-Hopf factorization (WHf) for Markov Chains, (ii) statistical inference for Stochastic Partial Differential Equations (SPDEs).WHf for Markov chains is a methodology concerned with computation of expectation of some types of functionals of the underlying Markov chain. Most results in WHf for Markov chains are done in the framework of time-homogeneous Markov chains. The major contribution of this thesis in the area of WHf for Markov chains are: • We extend the classical theory to the framework of time-inhomogeneous Markov chains. • In particular, we establish the existence and uniqueness of solutions for a new class of operator Riccati equations. • We connect the solution of the Riccati equation to some expectations of interest related to a time-inhomogeneous Markov chain. Statistical inference for SPDEs regards estimating parameters of a SPDE based on available and relevant observations of the underlying phenomenon that is modeled by the given SPDE. We summarize the contribution of this thesis in the area statistical inference for SPDEs as follows: • We conduct the statistical inference for a diagonalizable SPDE driven by a multiplicative noise of special structure, using spectral approach. We show that the corresponding statistical model fits the classical uniform asymptotic normality (UAN) paradigm. • We prove a Bernstein-Von Mises type result that strengthens the existing results in the literature. • We prove the asymptotic consistency, asymptotic normality and asymptotic efficiency of two Bayesian type estimators.
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- Title
- Modeling and Control Methods for Boundary Constrained Soft Robots
- Creator
- Zhou, Qiyuan
- Date
- 2021
- Description
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Soft and deformable robots have been an active field of research in the past few years. However, they are limited in that they cannot apply...
Show moreSoft and deformable robots have been an active field of research in the past few years. However, they are limited in that they cannot apply much force to an environment due to the limitations of the flexible materials from which they are made of. To help overcome this limitation, a new architecture named the Jamming and Morphing Enabled Bot Array (JAMoEBA) system was conceived. This system consists of a flexible outer membrane which encloses an interior composed of a granular medium. Active sub-units along the flexible outer membrane allow for actuation and locomotion of the system. The granular material coupled with the flexible outer membrane allows the robot to maintain the characteristics typically associated with soft robots (continuum, compliant, configurable). At the same time, the granular material is also able to undergo a solid phase transition with the application of pressure to the flexible outer membrane and allow the system to behave more like a rigid robot if needed. This allows for the robot system to exploit the desirable characteristics of both soft and rigid robots in its tasks.The purpose of this thesis is to offer a discussion and demonstration of various simulation methods for the physically accurate modeling of the JAMoEBA constrained boundary robotic system and to show some of the control methods which have been investigated within the selected modeling framework. Simulation methods based on Lennard-Jones (L-J) potentials, non-smooth contact dynamics (NSCD), as well as the discrete element methods based on complementarity (DEM-C) and penalty (DEM-P) conditions as implemented in the open source physics library Project Chrono are considered. Comparisons are made in the areas of physical accuracy, computational efficiency, and feature availability in the consideration of the best simulation method for the JAMoEBA system. Investigations of control strategies such as leader-follower and heuristics based approaches are carried out using the selected simulation method. Finally, a framework for self contained localization which relies on measurements from onboard sensors and linear Kalman filtering is tested within the simulation framework, and the effectiveness of approximating the shape of the JAMoEBA system using elliptical Fourier descriptors is shown.The main contributions made in this thesis are in the areas of suitable modeling methods, controls strategies, and localization techniques for the novel boundary constrained JAMoEBA soft robot architecture. The work done serves as a solid foundation for the future study of this novel soft robotic architecture due to the demonstration of successful methods for modeling, control, and localization of the system. The work presented is not meant to be a comprehensive or deep dive into any one specific area, but rather a jumping off point for future areas of research.
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- Title
- EFFECTIVENESS OF CLEANING STRATEGIES FOR REMOVING MILK CHOCOLATE FROM PILOT-SCALE PIPE/VALVE ASSEMBLY AND CHOCOLATE PROCESSING EQUIPMENT
- Creator
- Zhang, Liyun
- Date
- 2019
- Description
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Dark chocolate manufactured on shared processing lines with milk chocolate is a high-risk food for consumers with milk allergy. Inadequate...
Show moreDark chocolate manufactured on shared processing lines with milk chocolate is a high-risk food for consumers with milk allergy. Inadequate cleaning of shared chocolate manufacturing equipment can result in milk contamination of subsequent products, and product recalls. Limited information is available on the effectiveness of different cleaning procedures for preventing the transfer of milk to dark chocolate processed on shared equipment. Pilot-scale experiments investigated the effectiveness of three dry cleaning methods: 1) no cleaning, 2) pig purging, and 3) a cocoa butter flush (40°C, 1 hour) for removing milk chocolate residue from a heated (40ºC) standard (1.5” OD) sanitary stainless steel pipe (30.5 cm length) and attached butterfly or ball valve. After cleaning, milk-free dark chocolate (~27 kg, 40°C) was pumped through the pipe/valve combination. Dark chocolate push-through samples were collected and analyzed for milk concentrations with a Neogen Veratox total milk ELISA kit. Experiments with no cleaning resulted in initial milk concentrations up to 6,070 (9.6% CV) ppm milk and up to 14,900 (0.3% CV) ppm milk for the pipe/butterfly valve and the pipe/ball valve, respectively. Cocoa butter recirculation through the pipe/butterfly valve decreased initial milk concentrations to 680 (10.3% CV) – 2720 (2.6% CV) ppm milk. Use of a pig purging dramatically reduced milk levels to 45 (4.3% CV) – 180 (15.7% CV) for the pipe/butterfly valve and below limit of quantification of ELISA (LOQ, 2.5 ppm milk) for the pipe/ball valve. After most cleaning treatments, > 14 kg of dark chocolate push-through was required to obtain milk levels < LOQ.A second set of pilot-scale experiments determined the efficacy of cleaning procedures for removing milk chocolate from selected chocolate processing equipment. Three cleaning methods explored removal of milk chocolate from a ball mill and conche: 1) no cleaning, 2) a cocoa butter flush (40°C, 5 min), and 3) wet cleaning (detergent-rinse-air dry). After cleaning, three batches of milk-free dark chocolate (40°C) were processed in the ball mill (~0.35 kg) and conche (2.5 kg), and each batch was collected and analyzed for milk. Milk chocolate (1.5 kg) was processed on a 3-roll refiner, followed by push-through with dark chocolate (~8 kg), with 0.3 kg samples collected at 5-min intervals. Milk was not detected (Show less
- Title
- Synthesis and Processing of NaSICON Membranes with High Ionic Conductivity and Good Mechanical Strength
- Creator
- Chiang, Shan-Ju
- Date
- 2019
- Description
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Natrium super ion conductors (NaSICONs), Na1+xZr2SixP3-xO12 (0 ≤ x ≤ 3) are compounds that commonly used as solid electrolytes and membranes...
Show moreNatrium super ion conductors (NaSICONs), Na1+xZr2SixP3-xO12 (0 ≤ x ≤ 3) are compounds that commonly used as solid electrolytes and membranes of sodium based batteries, or in gas sensors and fuel cells due to their high sodium ion conductivity, low thermal expansion, and ability to accommodate ions in the lattice. However, NaSICON with high relative density (> 97%) and minimum impurity phases is found to be very difficult to obtain. Furthermore, the cost of the general synthesis methods is a serious drawback. Multi-high-temperature heating procedures is often employed to increase the density and to attain the single phase NaSICON because the particle size and free ZrO2 are better reduced. This research explores the possibility of densification and synthesis of NaSICON in one high-temperature reaction through a novel process termed Integrated Mechanical and Thermal Activation (IMTA) and the co-sintering behavior as well as the NaSICON composite membranes from tape casting. The sintering temperature of NaSICON was decreased by mechanical activation at room temperature using high-energy ball milling. Sintered NaSICON-based materials showed highest total ionic conductivity of 1.45 × 10-3 S cm-1 at room temperature and high density of 3.155 g cm-3 (96.5%). An alternative to obtaining full densification (99%) of NaSICON ceramics was developed utilizing traditional solid-state reaction. This sintered NaSICON without any sintering aid exhibited the total conductivity, 6.59 × 10-4 S cm-1 at 25 °C, and the highest density of 3.238 g cm-3, a better than 2.6% enhancement from the original samples.The second part of the work has comprised of successful fabrication of NaSICON/polymer composite membranes and bi-layered NaSICON/stainless steel membranes to enhance the mechanical flexibility of pure NaSICON films. The effect of different particle sizes of stainless steel on the sintering behavior and shrinkage rate were studied systematically. The effect of solid content in the slurry was also studied to control the density of both support layer and NaSICON body. The affect structural ratios have on co-sintered tapes along with ionic conductivity was investigated using Electrochemical Impedance Spectroscopy (EIS). The co-sintered membrane exhibited a total conductivity as high as 4.580 × 10-4 S/cm at room temperature. EIS results showed the high Na-ions conductivity strongly depends on the feature of grain boundary and the high densification of NaSICON layer.
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- Title
- An aircraft hangar and the study of long-span metal structures
- Creator
- Sharpe, David C. (David Carold)
- Date
- 1962-06
- Description
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The study of long-span structures developed from a design problem for an aircraft hangar. The problem of the aircraft hangar was concerned...
Show moreThe study of long-span structures developed from a design problem for an aircraft hangar. The problem of the aircraft hangar was concerned with the development of a reasonable structurtal type into an architectural solution. Several types were considered; the truss, the arch, a rigid frame system in prestressed concrete. These were discarded in favor of a rigid frame system of steel that seemed to give the best visual solution.
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- Title
- Advances in Machine Learning: Theory and Applications in Time Series Prediction
- Creator
- London, Justin J.
- Date
- 2021
- Description
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A new time series modeling framework for forecasting, prediction and regime switching for recurrent neural networks (RNNs) using machine...
Show moreA new time series modeling framework for forecasting, prediction and regime switching for recurrent neural networks (RNNs) using machine learning is introduced. In this framework, we replace the perceptron with an econometric modeling unit. This cell/unit is a functionally dedicated to processing the prediction component from the econometric model. These supervised learning methods overcome the parameter estimation and convergence problems of traditional econometric autoregression (AR) models that use MLE and expectation-maximization (EM) methods which are computationally expensive, assume linearity, Gaussian distributed errors, and suffer from the curse of dimensionality. Consequently, due to these estimation problems and lower number of lags that can be estimated, AR models are limited in their ability to capture long memory or dependencies. On the other hand, plain RNNs suffer from the vanishing and gradient problem that also limits their ability to have long-memory. We introduce a new class of RNN models, the $\alpha$-RNN and dynamic $\alpha_{t}$-RNNs that does not suffer from these problems by utilizing an exponential smoothing parameter. We also introduce MS-RNNs, MS-LSTMs, and MS-GRUs., novel models that overcome the limitations of MS-ARs but enable regime (Markov) switching and detection of structural breaks in the data. These models have long memory, can handle non-linear dynamics, do not require data stationarity or assume error distributions. Thus, they make no assumptions about the data generating process and have the ability to better capture temporal dependencies leading to better forecasting and prediction accuracy over traditional econometric models and plain RNNs. Yet, the partial autocorrelation function and econometric tools, such as the the ADF, Ljung-Box, and AIC test statistics, can be used to determine optimal sequence lag lengths to input into these RNN models and to diagnose serial correlation. The new framework has capacity to characterize the non-linear partial autocorrelation of time series and directly capture dynamic effects such as trends and seasonality. The optimal sequence lag order can greatly influence prediction performance on test data. This structure provides more interpretability to ML models since traditional econometric models are embedded into RNNs. The ability to embed econometric models into RNNs will allow firms to improve prediction accuracy compared to traditional econometric or traditional ML models by creating a hybrid utilizing a well understood traditional econometric model and a ML. In theory the traditional econometric model should focus on the portion of the estimation error that is best managed by a traditional model and the ML should focus the non-linear portion of the model. This combined structure is a step towards explainable AI and lays the framework for econometric AI.
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- Title
- DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
- Creator
- Khairy, Sami
- Date
- 2021
- Description
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The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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- Title
- Towards Assisting Human-Human Conversations
- Creator
- Nanaware, Tejas Suryakant
- Date
- 2021
- Description
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The idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in...
Show moreThe idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in initiating conversations and overcome social anxiety so as to be able to talk and have successful conversations. By providing humans with assistive conversational support, we can augment the conversation that can be carried out. The AdvisorBot can also help to reduce the time taken to type and convey the message if the AdvisorBot is context aware and capable of providing good responses.There has been a significant research for creating conversational chatbots in open-domain conversations that have claimed to have passed the Turing Test and can converse with humans while not seeming like a bot. However, if these chatbots can converse like humans, can they provide actual assistance in human conversations? This research study observes and improves the advanced open-domain conversational chatbots that are put in practice for providing conversational assistance.While performing this thesis research, the chatbots were deployed to provide conversational assistance and a human study was performed to identify and improve the ways to tackle social anxiety by connecting strangers to perform conversations that would be aided by AdvisorBot. Through the questionnaires that the research subjects filled during their participation, and by performing linguistic analysis, the quality of the AdvisorBot can be improved so that humans can achieve better conversational skills and are able to clearly convey their message while conversing. The results were further enhanced by using transfer learning techniques and quickly improve the quality of the AdvisorBot.
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- Title
- Efficient Power System Transient Simulation for Stability Studies Based on Frequency Response Optimized Approximation
- Creator
- Lei, Sheng
- Date
- 2021
- Description
-
Power systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on...
Show morePower systems world-wide are going through a paradigm change with dramatically increasing power electronics integration and more emphasis on the intrinsically unbalanced distribution side. The new features of power systems violate the fundamental assumptions and challenge the feasibility of transient stability simulation, a traditional tool for stability studies. Electromagnetic transient simulation is applicable to power systems with the new features, but its computational efficiency is too low with the typical microsecond-level step sizes.This dissertation aims at enabling millisecond-level step sizes, typically used in traditional transient stability simulation, in efficient electromagnetic transient simulation for system-level stability studies on unbalanced power systems, while assuring satisfactory accuracy. The approach taken is to introduce novel highly accurate numerical methods into electromagnetic transient simulation.Several implicit one-step frequency response optimized integrators considering second order derivative are proposed. Some existing numerical integrators in the literature of this category are reviewed. Their numerical properties are studied. Some of these numerical integrators are especially suitable to be used as numerical differentiators.A novel power system transient simulation scheme is put forward using the implicit one-step frequency response optimized integrators as the main numerical integrators and differentiators. Large step sizes are applicable with the proposed simulation scheme to achieve efficient electromagnetic transient simulation without sacrificing accuracy. Execution of the proposed simulation scheme is detailed.Several explicit and implicit multistep frequency response optimized integrators considering first or second order derivative are proposed. Some existing numerical integrators of these types are reviewed from the error frequency response viewpoint. Based on these numerical integrators, a prediction method is put forward to further accelerate the proposed simulation scheme without impacting its accuracy.Initialization process of the proposed simulation scheme is put forward. The initialization process calculates the periodic steady state solution of unbalanced power systems considering power flow conditions. The requirements of power system stability studies on the initial conditions for transient simulation runs are thus satisfied. Effectiveness and efficiency of the initialization process are demonstrated.Computational models of power system network elements in the proposed simulation scheme are detailed. The extended nodal analysis is put forward for the proposed simulation scheme to organize the computational models of most network elements in an efficient and elegant manner.Some power system devices are implemented with the proposed simulation scheme, including single-phase grid-feeding converter system, three-phase grid-feeding converter system, three-phase synchronous machine and three-phase induction machine. The proposed simulation scheme is shown to simultaneously achieve efficiency and accuracy as applied to these devices.The proposed simulation scheme is applied to different types of power systems, including transmission system, distribution system and combined transmission and distribution system. Its versatility is revealed. Its efficiency and accuracy are demonstrated with numerical case studies as applied to these systems.
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- Title
- DATA-DRIVEN FIRST-PRINCIPLES STUDY OF ORDERING PHENOMENA IN COMPLEX ALLOYS
- Creator
- Kim, George
- Date
- 2021
- Description
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Determining the chemical (dis)ordering behavior in materials such as high entropy alloys (HEAs), and ternary Laves phases is fundamental to...
Show moreDetermining the chemical (dis)ordering behavior in materials such as high entropy alloys (HEAs), and ternary Laves phases is fundamental to developing structure-property relations that can be used as guiding principles for alloy design. A common obstacle in materials engineering is that an improvement of a material property comes at the expense of some other desirable properties. For example, trade-offs may be made between strength and ductility, or strength and density, etc. The large compositional and configuration space of possible HEAs, and Laves phases contain potential candidate materials with a balance of optimized properties and tunable structural and functional properties. However, fully exploring the large compositional and configurational space with experimental or even high-throughput Density Functional Theory (DFT) approaches is infeasible, and as of yet, predictive rules for phase stability and chemical (dis)order in HEAs, and Laves phases are still open questions.In this thesis, a HEA with chemical disorder, Al0.3CoCrFeNi, was studied using complementary experimental, DFT, and ML methods. The chemical disorder within the HEA resulted in a severely distorted lattice leading to a reduction in stiffness. Temperature dependence of chemical ordering behavior is studied in NbTaTiV and NbTaTiVZr HEAs using Monte Carlo (MC) simulations, which predicts short-range ordering (SRO) as well as short-range clustering (SRC) behavior in both HEAs. The compositional dependent behavior of substitutional ordering in two ternary Laves phases is evaluated and compared using cluster expansion (CE) models and Monte Carlo (MC) simulations.
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- Title
- ENERGY INNOVATIONS IN BUILDINGS AND URBAN FABRICS
- Creator
- Hirematt, Chandrasekharaiah Ashish
- Date
- 2021
- Description
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In his keynote speech on the "Infrastructures of Integration" at the 5th International LafargeHolcim Forum for Sustainable Construction, Ricky...
Show moreIn his keynote speech on the "Infrastructures of Integration" at the 5th International LafargeHolcim Forum for Sustainable Construction, Ricky Burdett, Professor of Urban Studies at the London School of Economics & Political Science (LSE), said “…you can actually invest in better infrastructure to do things better.” However, the population grows at the rate of almost one billion per decade. With about four fifths of it happening in urban areas, the challenge for sustainability is huge and the key for the future.Urban fabrics are expanding both vertically as well as horizontally to accommodate the population growth. With the scale of expansion happening, challenges such deforestation, resource depletion, habitat destruction, energy production and consumption are some of the major challenges that need to be focused on ecologically. It is also important to note that ecological solutions are very highly dependent on social and economic progress of the society. Energy efficient design is one which does zero or minimal damage to the environment while meeting the energy needs of the society. This thesis will discuss the concept of developing energy efficient designs as well as net zero designs in urban settings. With the help of three projects, this thesis aims to discover the challenges along with the obvious advantages of such designs. The first experiment is to look at the reduction of energy consumption in the city of Chicago with multiple neighborhoods set up in an iron grid. It was observed that taller buildings are much more energy efficient due to the reduction of surface area exposed to the external environment. This observation was used to develop a climate specific energy efficient urban fabric design in the city of Shenzhen. The design of the off-shore tower involves tackling larger issues such as the pandemic while having energy production as a bi-product of the same. Thus, the thesis argues that investment in infrastructure to build a better infrastructure should be done to solve social and economic challenges which will, in turn make it easier to produce energy efficient designs.
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- Title
- Two essays on corporate finance and risk management
- Creator
- LI, YANFENG
- Date
- 2021
- Description
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This dissertation consists of two essays. The first essay examines the interaction effect of human capital investment in firms with dual-class...
Show moreThis dissertation consists of two essays. The first essay examines the interaction effect of human capital investment in firms with dual-class shares (DCS) structure. In this study, I find that although more input in human capital, measured by employee welfare index (EWI), can enhance the valuation of single-class (SCS) firms, human capital investment in DCS firms is not valued by the market, but even hurts firm value. This result is consistent with the prediction of agency theory. The management entrenchment effect in DCS firms causes valuation discount when managers can transfer private benefit through investing in humans. To get a robust result, I use propensity score matched data of SCS and DCS firms and get the same conclusion. Overall, my paper provides the evidence that human capital investment plays a different role in firm value under different circumstances, especially under different ownership structures.The second essay examines the relationship between director network centrality and firm credit risk. By using a comprehensive data including both rated firms and unrated firms, I discover that director network is positively associated with firm’s probability of default. This positive effect is more robust in firms without agency credit ratings. I further examine that when firm’s cash flow increases, firm’s default risk increases with director network. But when investment increases and firm’s debt finance increases, the default risk decreases with director network. These combined results imply that director network leads to more agency problems when firms have plenty of cash flow but benefit firms when the cash flow goes into investment or when directors utilize their network to get more debt finance for firms. Also, I find that director network helps loss firms other than profitable firms and decreases firm default risk during the financial crisis.
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- Title
- TIMING STRATEGY OF COMMODITY MANAGERS
- Creator
- Lara Prado, Camila Cristina
- Date
- 2021
- Description
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The purpose of this research is to study whether commodity managers have the ability to time factor exposures. I utilize the methodology...
Show moreThe purpose of this research is to study whether commodity managers have the ability to time factor exposures. I utilize the methodology developed by Treynor and Mazuy (1966), and Henriksson and Merton (1981), and apply the four-factor commodity model of Blocher et al (2018). Specifically, I measure market timing, momentum timing, the high term (realized term premia for the commodities with above‐median basis), and low term (realized term premia for the commodities with below‐median basis) skills. These factors are chosen because each one, separately, captures a risk premium embedded in commodity futures.My results indicate that commodity managers’ returns have some statistically significant market timing abilities. This means that many managers increase exposure to the nearest contract when the spot premium return is high and decrease exposure when the spot premium return is low. Momentum timing, high term timing, and low term timing are not observed. When looking at different strategies, technical managers demonstrate stronger market timing ability than fundamental managers.
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- Title
- EXAMINING THE ROLES OF PUBLIC STIGMA AND ACCULTURATION ON CARE-SEEKING IN PAKISTANIS
- Creator
- Laique, Aamir
- Date
- 2021
- Description
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Pakistani Americans face bi-directional cultural influences related to their heritage culture and the mainstream culture of the host. The...
Show morePakistani Americans face bi-directional cultural influences related to their heritage culture and the mainstream culture of the host. The present study examined the impact of culture on the relationship between public stigma and care-seeking attitudes. A sample of 158 Pakistani Americans was collected using MTurk. Hierarchical regression was conducted to examine the moderating effect of heritage acculturation and mainstream acculturation on the relationship between public stigma and care-seeking. Multiple regression analysis predicting care-seeking from public stigma, heritage acculturation, and mainstream acculturation did not yield a statistically significant model. Hierarchical regression analyses examining the moderating effect of heritage acculturation and mainstream acculturation were non-significant. Acculturation had no notable impact on stigma and care-seeking. This study was unable to demonstrate significant results. Future considerations should include inter-generational differences, other forms of stigma that may play a crucial role, and inclusion of different measures to determine if there are other scales better suited for the target population.
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