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Title: Efficacy of Power Ultrasound Technology on the Reduction of Listeria monocytogenes and Salmonella enterica on Produce Matrices
Creator: Biswas, Priya
Date: 2023
Description: Fresh produces are considered as ready-to-eat (RTE) and are minimally processed before the distribution to retailers and consumers. Fresh...
Show moreFresh produces are considered as ready-to-eat (RTE) and are minimally processed before the distribution to retailers and consumers. Fresh produce recalls are frequently linked with pathogenic bacteria like Salmonella enterica and Listeria monocytogenes because of minimal processing. This study evaluated the use of power ultrasound coupled with organic acids like citric, acetic, and lactic acid which are generally recognized as safe and often helps to maintain the quality and prolong the shelf life of fresh RTE fruits and vegetables.All the produce matrices which include cucumbers, romaine lettuce, tomatoes, and strawberry were inoculated with four-strain cocktails of rifampicin-resistant S. enterica or L. monocytogenes at approximately 8 log CFU/ matrix. The produce matrices were dried for 1 h and treated for 2 minutes using 2 % or 5 % citric, lactic, or malic acid. This treatment was conducted with or without power ultrasound treatment at 40 kHz. Samples were taken in sets of three and placed into a stomacher bags. The bag contained 225 ml of water or acid. Following a 2 min treatment period, the samples were placed in separate stomacher bags, each containing 225ml of BPB or BLEB, for S. enterica or L. monocytogenes respectively. Followed serial dilutions, samples were then plated on BHIARif plates. For each condition, triplicate samples were taken, and three separate trials were conducted. The use of Student's t-test allowed for the evaluation of population differences, with a significance level of p<0.05 being deemed significant. Cucumber, romaine lettuce, tomatoes, and strawberries treated with 5 % concentration of citric, lactic, and malic acids, with addition of ultrasound showed a greater result in reductions of S. enterica to populations of 5.54 ± 0.47, 4.54 ± 0.83, and 4.69 ± x 0.36, log CFU/cucumber, 6.66 ± 0.51, 4.12 ± 0.32, and 5.51 ± 0.68, log CFU/ lettuce, 4.38 ± 0. 47, 3.12, and 5.04 ± 0.37 log CFU/ tomato, 4.66 ± 0.49, 4.69 ± 0.06, and 6.22 ± 0.39, log CFU/ strawberries, respectively. For L. monocytogenes, 5 % concentration of acids with the addition of ultrasound resulted in populations of 7.69 ± 0.35, 6.04 ± 0.24, and 6.96 ± 0.41, log CFU/ cucumbers, 7.57 ± 0.12, 5.49 ± 0.55, and 5.78 ± 0.73 log CFU/ lettuce, 6.44 ± 0.13, 5.08 ± 0.12, and 6.04 ± 0.22 log CFU/ tomato, 6.16 ± 0.37, 5.18 ± 0.22, and 5.64 ± 0.50, log CFU/ strawberries, respectively. The most effective acid was lactic when compared with citric and malic acids. The objective of this study is to investigate the effectiveness of power ultrasound as a novel non-thermal processing technology, in order to contribute to the existing knowledge base on this topic.
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Title: Critical Understanding of Multi-Mode Luminescence Properties of Eu3+ Doped LaAlO3
Creator: Alolayan, Abdulelah Abdulaziz H
Date: 2023
Description: Fluorescent anti-counterfeit materials with multi-luminescent modes under different external excitation sources are always advantageous over...
Show moreFluorescent anti-counterfeit materials with multi-luminescent modes under different external excitation sources are always advantageous over the conventional anti-counterfeit techniques. In the present thesis, our aim is to develop efficient Eu3+ doped LaAlO3 phosphor materials with different modes of luminescence properties such as down conversion-luminescence (DCL), persistent-luminescence (PersL), and optically stimulated luminescence (OSL), Thermo-luminescence (TL), radioluminescence (RL) Although, there are many reports on persistent-luminescence and optically stimulated luminescence based on Eu3+ doped matrices but the red persistent luminescence of Eu3+ ion on those matrices is not very long and the OSL intensities are also low. Herein, we report a long red persistent luminescence which lasted for 17 hrs. and the OSL intensity is very high. Furthermore, we have observed that the OSL property can be achieved even after 35 days of UV excitation and indicating its potential application for optical storage phosphor. From carrying out TSL studies we have found that three different types of traps namely Trap 1, Trap 2 and Trap 3 with trap depth 0.63 eV, 0.82 eV, 1.02 eV respectively are responsible for the persistent and OSL properties. It has been concluded that Trap 1 is mostly responsible for the persistent luminescence in short term while Trap 2 and Trap 3 are responsible for intermediate and long persistent luminescence. Further, Trap 2 and Trap 3 were also found to be present even after 35 days and responsible for the OSL properties. Anti-counterfeiting PersL composite has been developed in which AC real-life application is demonstrated.
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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: Neuropsychological Pattern of Verbal and Nonverbal Processing Speed Discrepancy in Veterans with Co-Occurring mTBI and PTSD
Creator: VanLandingham, Hannah B.
Date: 2023
Description: Rates of traumatic brain injury (TBI) exposure have increased over time (CDC, 2022). This pattern of increased TBI risk is additionally...
Show moreRates of traumatic brain injury (TBI) exposure have increased over time (CDC, 2022). This pattern of increased TBI risk is additionally associated with risk for development of posttraumatic stress disorder (PTSD; APA, 2013). Ongoing PTSD symptomology can lead to neuropsychological profiles in which deficits are more pronounced for verbally constrained performances when compared to nonverbal performances. However, less is known about this performance discrepancy in patients with a history of head injury with comorbid PTSD. Moreover, the little existing research focuses on the domains of executive functioning, learning, and memory, with little to no research on processing speed discrepancies. These findings could have significant implications for healthcare and cognitive intervention pre- and post-mTBI and/or trauma exposure because this discrepancy may impact clinical assessment and subsequent diagnosis. The analysis will include 1) determination of statistically and clinically significant differences for those with co-occurring PTSD and mTBI, and 2) examine within-subjects differences with and without the inclusion of covariates. The present research found that there are no differences between those with co-occurring PTSD and head injury compared to individuals without a co-occurring diagnosis, in addition to no significant discrepancies notes within the PTSD and mTBI group alone
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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: Modeling and Optimization of Power Plant Cooling Tower Systems Using Physics-Based and Neural-Network-Based Models
Creator: Salomon, Basile Clément Paul
Date: 2023
Description: Condensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed...
Show moreCondensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed-water into the boiler in a closed-loop system. These condensers typically use cooling water drawn from a water body (lake, river etc) to condense the steam. Cooling towers are used to lower the temperature of the warm water exiting the condenser. Since the steam condensation temperature plays an important role in the power plant efficiency, cool- ing tower performance which is limited by the wet-bulb temperature of the ambient air has been extensively studied. This work investigates the modeling of an enhanced cooling tower technology using a new pre-cooling and dehumidifying system (PDHS). This new system, based on a reversed Brayton cycle, is made out of a compressor, an air-cooled heat exchanger (HX), a heat and mass exchanger (HMX) and an expander. The goal of this PDHS concept is to pre-cool the air entering the cooling tower in order to improve its performance. In this work, a systems model has been developed. Thermodynamic models have been used for the compressor, the air-cooled heat exchanger and the expander. For the remaining components, i.e. the heat and mass exchanger, the cooling tower and the condenser, physics-based models have been developed and tested. Once tested and validated, each model can be integrated into the integrated PDHS-cooling tower-condenser system. Two different configurations of the PDHS have been considered in this thesis. In the open water loop configuration, the water in the HMX is obtained from the municipal water supply (or an alternate water source) and is released back to the source after exiting the HMX. In the closed water loop configuration, the water used to cool down the air in the HMX is being recirculated and cooled in the power plant cooling tower. The physics-based model of the PDHS developed in this work has been validated using results from an empirical model of the PDHS by GTI Energy. This first case study also shows how the PDHS can be used to save water in the cooling tower (CT). Indeed, when using the PDHS, a 37% reduction in the cooling tower evaporation rate can be observed when comparing to the baseline. This decrease in the CT evaporation rate is the main source of make-up water savings. Moreover, the water harvested by condensation in the PDHS can be redirected towards the CT, bringing another source of water savings. These two combined lead to an overall 46% decrease of the make-up water usage in the cooling tower. Another case study has been conducted on a 500 MW condenser unit. It shows that, under summer ambient conditions i.e. Ta,db = 35°C and φ = 47%, the PDHS can help the condenser restore its designed cooling load of 453 MW. Finally, using the physics-based model to create a dataset, an artificial neural network model of the PDHS has been developed to constitute a black box for the PDHS that would be able to predict with sufficient accuracy the condenser and HMX loads, the air conditions at the inlet of the CT and water temperature at both ends of the condenser and CT given the ambient air condition, the compressor pressure ratio and the water split between the condenser and the heat and mass exchanger.
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Title: Functionalized 2D Materials as Enablers of High Energy and High Power Energy Storage Devices
Creator: Radhakrishnan, Sivaviswa
Date: 2023
Description: The present Thesis concerns with the synthesis of novel functionalized 2D materials for applications as cathodes in lithium-ion batteries. It...
Show moreThe present Thesis concerns with the synthesis of novel functionalized 2D materials for applications as cathodes in lithium-ion batteries. It further concerns with the role of porosity in these novel cathode materials to achieve simultaneously high energy and power density. Examples of the novel cathode materials synthesized here include several functionalized hexagonal boron nitride (hBN) and graphene (G) species. hBN was functionalized with Li₂C₂O₄, LiBF₄, -OBF₂ groups, NOBF₄, etc. The color of the functionalized hBN species ranges from white through brown to black indicating drastic changes in the band structure of hBN due to functionalization. Functionalized G species include Li₂C₂O₄ and -OBF₃ functionalized ones. Preliminary electrochemical tests were carried out for an initial assessment of the properties of these materials. Additionally, the role of the DOL solvent was also investigated in high power CFx batteries
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Title: Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Creator: Riley, Christopher W.
Date: 2023
Description: The scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam...
Show moreThe scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam radiator control system or retrofitting legacy heating systems in existing buildings” (Syed Ali et al., 2020) by describing and testing the mechanical components of the developed controller in full detail. Other aspects of radiator efficiency are also explored. Primarily, this work aims to elaborate on the importance of material selection and mechanical properties of the design process. It also proposes initiative-taking solutions for the building’s energy recovery by monitoring the initial set up and focusing on certain details such as cardinal direction, thermal breaks, etc. These legacy systems are generally problematic when attempting to calculate energy efficiency, as a majority of radiator controls tend to be manual. Though there are comparable products within the European market, they cater to hot water systems and not steam, and in some instances require an internet bridge for operation (Tahersima et al., 2010). Since this is an extension of our earlier project, I will refer to it as Battery Operated Radiator Control (BORC) and the previous version as BERG’s Automated Radiator Control (ARC).
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Title: The Effect of Time Step on HSPF Model Performance
Creator: Rubinstein, Benjamin J.
Date: 2023
Description: Hydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time...
Show moreHydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time intervals there is very little research on the effect of using high resolution data as inputs for the models. A Python tool for downloading high resolution five minute interval data from the Oklahoma Mesonet was created and the PyHSPF Python package was used to generate, calibrate, and validate HSPF models using five minute, one hour, and daily time steps. Flow errors, R², and Nash-Sutcliffe efficiency for simulated outflows, and resource usage were compared for each model. The hourly and five minute models performed similarly well, and the daily model performed significantly worse. The results of this work could prove useful for policy makers and researchers looking to update or create new climate data collection protocols, and the tools used can be applied to many different kinds of future research.
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Title: Understanding Location Bias in Fake News Datasets of Twitter
Creator: Patil, Kayenat Kailas
Date: 2023
Description: Fake news tends to spread faster and wider than real news. It has a greater impact and can lead to negative and dangerous outcomes. With the...
Show moreFake news tends to spread faster and wider than real news. It has a greater impact and can lead to negative and dangerous outcomes. With the world spending an increasing amount of time on their mobile devices, people tend to get more of their news from their desired social media platform. It has become part of our daily lives, whether it is to keep in touch with friends and family, to getting gossip on celebrities or even shopping. In 2022, the average time a person spends per day on the internet on a social media platform has been accounted to be about 147 minutes,[1] indicating an increase in time spent scrolling through information online.It has become a widespread phenomenon in recent years, thanks in part to the rapid spread of information through social media and other online channels. It is increasingly important to explore and understand fake news and its impact on society, as well as to develop effective tools and methods for detecting and combating it. There are several factors that can tamper with the successful detection of fake news. Machine learning models often fall to such biases that result in inaccurate predictions. There are several biases that have been identified like age, gender, sex and many more. In this thesis, we are exploring location as a form of a bias and if it hinders prediction. We have looked at location from two perspectives. One, taking location as co-ordinates in the form of latitude and longitude and analyzing the likelihood of a tweet coming from a location to be fake or not. The second method we have used is that we have considered location as an entity and used natural language processing model to see if its able to predict if the given tweet is fake or not, along with masking the location mentioned in the tweet and analyzing how the performance of the model changes. Machine learning models can play an important role in fake news detection models, by analyzing large amounts of data and identifying patterns and indicators that suggest a piece of information may be false or misleading, but they are often susceptible to some form of biases. By studying biases on machine learning models on fake news datasets, we can develop more effective tools for identifying fake news and taking steps towards mitigating it, ultimately helping to protect the integrity of information and promote informed decision-making in society.
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Title: Effect of Pre-Processing Data on Fairness and Fairness Debugging using GOPHER
Creator: Sarkar, Mousam
Date: 2023
Description: At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed...
Show moreAt present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model.
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