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Title: THE EFFECT OF NANOPARTICLE SELF-STRUCTURING ON WETTING AND SPREADING OF NANOFLUIDS ON SOLID SURFACES
Creator: Kondiparty, Kirtiprakash
Date: 2011-11, 2011-12
Description: Nanofluids are suspensions of nanometer-sized particles in liquids. The nanoparticles self-structure at the three-phase contact region...
Show moreNanofluids are suspensions of nanometer-sized particles in liquids. The nanoparticles self-structure at the three-phase contact region resulting in the structural disjoining pressure gradient which causes enhanced the spreading of nanofluids compared to simple fluids without nanoparticles. In this thesis, we attempt to understand the effect of the structural disjoining pressure on the spreading dynamics of nanofluids on solid surfaces. We observed nanoparticle self-structuring phenomena during film thinning on a smooth hydrophilic glass surface using a silica-nanoparticle aqueous suspension and reflected light interferometry. Our experiments revealed that film formed from small drop is thicker and contains more particle layers than a film formed from large drop. The data for the film-meniscus contact angle verses film thickness were obtained and used to calculate the structural energy isotherm of an asymmetric film. We studied the effect of structural disjoining pressure on the wedge meniscus profile formed by an oil drop on solid surface surrounded by nanofluid using Laplace Equation augmented with the structural disjoining pressure. Our analyses indicate that a suitable combination of the nanoparticle concentration, nanoparticle size, contact angle, and capillary pressure can result not only in the displacement of the three-phase contact line, but also in the spontaneous spreading of the nanofluid as a film on solid surface. We validated our theoretical predictions using experiments where we observed spreading of nanofluid on glass surface displacing a sessile drop of canola oil. The dynamic spreading of the nanofluid on a solid surface between a sessile oil drop on solid surface was experimentally measured using reflected light microscopy. We xiv obtained the rate of nanofluid spreading by plotting the position of the inner contact line with time. The nanofluid film was found to spread at a constant velocity. We modeled the spreading dynamics of the nanofluid film using the lubrication approximation of the Navier-Stokes Equation, taking into consideration the structural disjoining pressure in the over-all pressure balance. The model was evaluated by estimating the rate of nanofluid spreading for the 10v% nanofluid. The rate of spreading thus predicted by the dynamics model for 10v% nanofluid was in good agreement with the experimental observations.
Ph.D. in Chemical Engineering, December 2011
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Title: AN OPTIMIZATION OF MANUFACTURING PROCESS SELECTION THROUGH COSTING AND SYSTEMATIC ANALYSIS
Creator: Klima, Kevin
Date: 2017, 2017-05
Description: In order for any company to remain competitive, there is a constant push to cut costs while keeping customers satisfied by providing quality,...
Show moreIn order for any company to remain competitive, there is a constant push to cut costs while keeping customers satisfied by providing quality, robust products. While current methods for predicting the most cost effective manufacturing process have proven to offer a significant amount of utility for design engineers, the issue remains that for a cost estimation to be truly accurate, the component has to be completely or nearly completely designed, which could require a significant amount of upfront development time. The goal was to develop a design tool to predict the most cost effective process to manufacture a new component based solely on already available historical data and basic knowledge of the design requirements of the new part. This study focused on steel components that could be manufactured either as a fabrication or as a casting. Two real-world applications were studied from two separate industries, with each application being designed with each process. Common cost estimation techniques were used to develop models for predicting the cost for each component to offer insight for how the cost would be expected to vary with quantity. As a means of ensuring robustness and that each competing model was structurally equivalent, each model had to pass critical exceptional and fatigue load cases in FEA while also meeting predefined success criteria. Using the results from the structural analysis and cost estimation, a design tool was developed as a means of objectively predicting how a component with similar application requirements would most cost effectively be manufactured based on the desired quantity of parts that needed to be produced. By using historical information of similar components that have the possibility of being manufactured in more than one way, more effective and systematic decision making for how a new component should be manufactured was shown to be possible. A third, independent case study was also selected as a real world example from industry as a means of validating and assessing the sensitivity of the weighting used in the development of the design tool. This was used to further refine the tool for the use in analyzing future components.
M.S. in Manufacturing Engineering, May 2017
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Title: OPTIMIZATION AND EXPERIMENTAL VALIDATION OF ELECTROSTATIC ADHESIVES
Creator: Shah, Jainam
Date: 2013-04-25, 2013-05
Description: Electrostatic adhesion provides an attachment mechanism for robotic grippers that is both controllable and e ective over a wide range of...
Show moreElectrostatic adhesion provides an attachment mechanism for robotic grippers that is both controllable and e ective over a wide range of surfaces including conduc- tive, semi-conductive and insulating materials. The adhesives function by utilizing a set of high voltage electrodes that generate an electric eld. This electric eld polarizes the substrate material, thus generating an adhesion force. Optimizing the geometry of these conductive electrodes provides enhanced adhesion forces that in- creases attachment robustness. Previous researchers have attempted to increase the adhesion level of an electrostatic adhesive but no e ort has been made to optimize the geometry and con guration of the electrodes. This thesis presents a method to increase the adhesion level of electrostatic adhesives by optimizing the electrode geo- metric parameters: width of the electrodes, thickness of the electrodes, gap between the electrodes and number of electrodes. With the optimized electrode geometry, an improvement of up to 500 percent in shear stress is achieved compared to previously published values.
M.S. in Mechanical Engineering, May 2013
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Title: ESSAYS IN ENVIRONMENTAL FINANCE
Creator: Li, Jing
Date: 2013, 2013-07
Description: The Clean Development Mechanism (CDM) is a mechanism de ned in the Ky- oto protocol that incentivizes parties to the protocol to fund...
Show moreThe Clean Development Mechanism (CDM) is a mechanism de ned in the Ky- oto protocol that incentivizes parties to the protocol to fund sustainable development projects in countries that are not party to the protocol. In the rst chapter of this paper, I introduce the CDM and how the nancing mechanism works. In the second chapter, I analyze a target contract nancing structure for di erent CDM projects in order to see under what conditions the nancing structure is e cient and to explore the contract's allocation of pro t among the rms. In the two broad categories of CDM projects I consider, I nd the optimal investment decision for the investor and for the overall system. I also analyze how the residual value of technology would a ect the nancing, target contract's e ciency, and allocation of pro t. In the third chapter, I conduct empirical analysis on the actual CDM outputs, Certi ed Emission Reduction units (CERs), for a sample of wind CDM projects in China. I nd that CDM projects greatly under perform relative to the promises they make. Based on this under-performing records, in the fourth chapter, I analyze the economic bene ts investors could gain if they were able to directly fund a portfolio of CDM projects and obtain returns from the anticipated CER issuances and underlying energy generated from the portfolio of CDM projects. I consider a variety of funding constraints that the CDM fund/portfolio manager (CDM-PM) may face and determine their economic performance against actual CDM project data for wind CDM projects in China.
PH.D in Management Science, July 2013
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Title: Rapid Rail Transit Oriented City for One Million in Calfornia
Creator: Moore, Jeremy Edward
Date: 2011-11-22, 2011-12
Description: The city of Aubretia will be a city for approximately one million people situated at the northern divergence point of the proposed California...
Show moreThe city of Aubretia will be a city for approximately one million people situated at the northern divergence point of the proposed California High-Speed Rail Authority system and the current San Joaquin Amtrak passenger rail service, located near Madera city. Having the largest population out of all 50 states and three cities —Los Angeles, San Diego and San Jose—in the top ten list of most populous US cities, California certainly has the demand to support a high-speed rail system. The 2010 United States Census has also demonstrated a continuing trend of residents relocating from the Midwest and East Coast to the Sun belt states. All inhabitants of Aubretia will have access to a heavy two-rail system providing transportation to any other point in the city in 45 minutes or less. Development will be restricted to an area approximately one half mile in radius or a ten minute walking distance centered on each transit stop. Along with standard rapid transit stops in each development area there is also an interface with a central transportation hub connecting the Aubretia Metro to Amtrak and California High-Speed Rail as well as ground transportation options like regional bus and for-hire vehicles. The planned population level for Aubretia is based on a city size that can be served with a two-track automated metro system. The system will be capable of accommodating a majority of rush hour commuters at three persons per square meter density with a minimum headway of 90 seconds.
M.S. in Architecture, December 2011
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Title: GOTTA EAT TO LIVE, GOTTA STEAL TO EAT: THE INVESTIGATION OF SERIOUS DISRUPTIVE BEHAVIOR, TEMPERAMENT, AND EXECUTIVE DYSFUNCTION AMONG HOMELESS YOUTH
Creator: Kaszynski, Katie
Date: 2014, 2014-07
Description: Background: Homeless youth are at risk for many adverse outcomes, including poor physical health, traumatic experiences, victimization, poor...
Show moreBackground: Homeless youth are at risk for many adverse outcomes, including poor physical health, traumatic experiences, victimization, poor academic achievement, cognitive deficits, psychopathology, and substance use. Research demonstrates that these individuals engage in substantial disruptive behavior (e.g., stealing, dealing drugs, breaking and entering, engaging in prostitution), which further increases their risk of negative outcomes. Individual factors, including innate temperament and executive functioning skills have been shown to relate to one another and be independently related to behavior problems, as evidenced by research investigating housed youth. Homeless youth are shown to exhibit poor effortful control, high distress, executive dysfunction, and substance abuse; factors of which have not been fully examined in relationship to persistent behavior problems as reflected in antisocial personality disorder (ASPD). Study Aim: The current study evaluated the association between temperament, executive functioning, and substance use disorders in their relation to the likelihood of meeting criteria for ASPD among homeless youth (ages 18-22). It was hypothesized that these variables would significantly relate to meeting criteria for ASPD in this population. Procedure: 87 homeless individuals (mean age = 19.27) who were residing at a homeless shelter at the time of the study (in Chicago or Los Angeles) participated over the course of two testing sessions. Each individual completed measures of ASPD and substance use disorders (MINI), temperament (ATQ), and executive functioning (D-KEFS), among other measures that are part of a larger studying conducted at University of Chicago Medical Center (UCMC). Results: Results suggested that temperament (specifically effortful control) executive dysfunction (specifically cognitive shifting), and substance use disorder (specifically substance abuse) were significantly related to the likelihood of a homeless individual meeting criteria for ASPD. Youth who showed poorer effortful control, better ability to shift attention between sets of information, and substance abuse were at a greater likelihood of meeting criteria for ASPD. Conclusions: These findings indicate that aspects of temperament, specific executive skills, and substance abuse are important variables in determining the likelihood of ASPD among a population of homeless individuals. Clinical implications, limitations, and suggestions for interventions are discussed.
Ph.D. in Psychology, July 2014
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Title: NON-INTRUSIVE LOAD MONITORING AND DEMAND RESPONSE FOR RESIDENTIAL ENERGY MANAGEMENT
Creator: Iwayemi, Abiodun
Date: 2016, 2016-05
Description: Compared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque....
Show moreCompared to cellphone bills which itemize billing into local, international, text messaging, and data, todays electricity bills are opaque. Residential electricity customers receive a monthly bill detailing their aggregate energy usage, without any insight into which appliances are responsible for what proportions of their bill. We therefore created a Non-intrusive load monitoring framework that uses only data available from smart meters and the price signals from the Electric utility, and combine it with Optimal Stopping Rule-based schedulers to create a framework to equip residents with the information they need to be more energy efficient while balancing their costs and comfort. Non-intrusive load monitoring provides homeowners with detailed feedback on their electricity usage, but an open area is automated appliance labeling and the creation of generalizable appliance models that can be trained in one home, and deployed in another. Manually labeling such events to use them for disaggregating residential appliances is a costly and tedious task, and we developed two approaches for semisupervised learning of appliance signatures. The first approach uses 1-Nearest neighbor semi-supervised learning, and we developed a stopping criterion which reduces the likelihood of mislabeling appliance instances. This approach was extended to a cluster-then-label semi-supervised learning approach which can use only 3 labeled samples of each appliance to label and classify similar appliances within the home. Our approach enables the comparison of unequal length time series, and incorporates additional features extracted from the appliance time series. Finally, we develop a hybrid framework that combines detailed appliance models learned via Non-intrusive load monitoring with optimal stopping rule schedulers. We evaluated the performance of these models in terms of cost and delay, and explored the effect that errors in the real-time price and appliance models have on appliance running costs to demonstrate how our approach outperforms scheduling using only day head prices.
Ph.D. in Electrical Engineering, May 2016
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Title: AIRFOIL LONGITUDINAL GUST RESPONSE IN ATTACHED, SEPARATING, AND DETACHED SURGING FLOW
Creator: Weirich, Jeremy Michael
Date: 2013, 2013-12
Description: Longitudinally gusting ow over a nominally two dimensional airfoil is exam- ined over a range of incidence angles and reduced frequencies. The...
Show moreLongitudinally gusting ow over a nominally two dimensional airfoil is exam- ined over a range of incidence angles and reduced frequencies. The response of the airfoil to these gusting ows is compared with classical theory, and is found to follow theory moderately well at low incidence angles across all reduced frequencies. At high incidence angles, the predictive power of the classical model is found to decrease signi cantly. The aerodynamic forces are also decomposed into circulatory and non- circulatory e ects and their relative strength is examined. The circulatory e ects are found to be minimal and constant at low incidence angles, while high incidence angles show strong variation, indication that the wake structure experiences signi - cant changes. The noncirculatory e ects are found to depend linearly on the reduced frequency and a ect the ow more strongly at lower incidence angles. An argument is made for the utility of dividing analysis of the ow into attached ow, separating ow, and fully detached ow regimes. The division is clear when examining how the normalized force coe cients change with reduced frequency, and provides a useful tool for predicting the transition of the ow between regimes.
M.S. in Aerospace Engineering, December 2013
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Title: Stigma, depression, and help-seeking: Experiences of parents/caregivers of children with mental health challenges
Creator: Serchuk, Marisa D.
Date: 2023
Description: The impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The...
Show moreThe impacts of stigma on people with lived-experience are widely recognized, however, stigma has been noted to extend to family members. The current investigation examines how specific types of stigma experienced by parents/caregivers (N=250) of children with mental health challenges are related to symptoms of depression and attitudes towards help-seeking. Results found that higher levels of public stigma, self-stigma, and vicarious stigma were associated with higher levels of depression and were differentially associated with attitudes towards help-seeking. Findings from this investigation add to the small body of literature examining stigma experienced by parents/caregivers of children with mental health challenges.
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Title: Certified Rehabilitation Counselors' Knowledge, Stigma, and Self-Efficacy in Working with Non-Suicidal Self-Injury
Creator: Tseng, Yen Chun
Date: 2023
Description: Certified Rehabilitation Counselors (CRCs) are professionals who are responsible for supporting the rights and independence of people with...
Show moreCertified Rehabilitation Counselors (CRCs) are professionals who are responsible for supporting the rights and independence of people with disabilities. They provide services such as mental health counseling, vocational counseling, advocacy, and psychoeducation to people with disabilities. Suicide prevention and safety education are within the scope of services provided by CRCs as well. Non-suicidal self-injury (NSSI), one of the strongest risk factors for suicide attempts (Franklin et al., 2017; Kiekens et al., 2018), has received more attention as people with disabilities have elevated risk to engage in such behaviors (Coduti et al., 2016). NSSI refers to the socially unacceptable behavior causing intentional and direct injury to one’s own body tissue without conscious suicidal intent (Nock & Favazza, 2009). As the prevalence of NSSI increases, it is likely that in their professional tenure, CRCs will interact with clients who have engaged in NSSI. It is within CRCs scope of practice to advocate at individual, group, institutional, and societal levels to promote opportunity and access, improve quality of life for individuals with disabilities (Commission on Rehabilitation Counselor Certification, [CRCC], 2023). However, few studies have explored CRCs’ training, stigma, and self- efficacy when working with NSSI. The purpose of this study was to explore the nature and extent of NSSI training received by CRCs, CRCs’ stigma towards individuals engaging in NSSI, and factors associated with CRCs’ self-efficacy for working with clients with NSSI. CRCs practicing in the United States participated in the study (N = 91). Less than half of the participants reported that they received NSSI training in the past. In addition, they demonstrated some knowledge of NSSI while holding some misconceptions of NSSI at the same time. In addition, CRCs reported generally positive attitudes toward NSSI. Predictors examined in the study included training, knowledge, familiarity, and attitudes toward individuals engaged in NSSI while controlling for participants’ age and gender. Hierarchical regression analysis was used to analyze whether these factors were associated with self-efficacy to work with clients with NSSI. Results indicated that training and stigma (helping attitude) were significant predictors of CRCs’ self-efficacy for working with clients experiencing NSSI. Additionally, the variance in self-efficacy was accounted for by NSSI training and stigma. CRCs who received NSSI training in the past reported more positive attitudes and perceived themselves as more capable to work with clients who engaged in NSSI. This study is among the few to examine specific factors impacting CRCs’ self-efficacy in working NSSI. Implications for practice and research are discussed.
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Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
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Title: REDUCED-ORDER MODELING OF UNSTEADY FLOW OVER TWO COLLINEAR PLATES AT LOW REYNOLDS NUMBERS
Creator: Almashjary, Abdulrahman N
Date: 2021
Description: Wakes of bluff bodies that exhibit unsteady behavior are a topic of great interest in the study of fluid dynamics. Vortex formation in these...
Show moreWakes of bluff bodies that exhibit unsteady behavior are a topic of great interest in the study of fluid dynamics. Vortex formation in these wakes depends significantly on the Reynolds number and the arrangement of the bluff bodies in the computation domain. To attain a comprehensive understanding of the unsteady wakes of adjacent bodies, we examine the emerged flow patterns in the wake of two bodies when subjected to different flow regimes and geometric configurations. This work aims to develop a reduced-order model that can capture the dynamics and predict the time evolution of specific parameters in the flowfield. Investigations including direct numerical simulations of two collinear plates normal to the flow were performed. Flowfield data and forces exerted on the plates were collected using a numerical code of an immersed boundary projection method (IBPM). The conducted numerical simulations pursued classifying the flow patterns by systematically varying the Reynolds number and the gap between the two plates. It was found that at small gap spacings, a typical von Karman vortex street is observed. Whereas at larger gap spacings, both a biased and a flip-flopping gap flow are detected. Prevalent coherent structures present in various flow regimes can be extracted via data-driven modeling techniques. The proper orthogonal decomposition (POD) method is used in this framework, from which projection-based reduced-order models are developed utilizing the governing equations of fluid flows. Single and broadband spectra are observed in the unsteady wake of the two-plate configuration. The amplitude and frequency of the time-evolution of the true POD modes and the predicted models are assessed using the spectral proper orthogonal decomposition (SPOD), an empirical method to extract coherent structures one frequency at a time from fluid flows. It was found that these reduced-order models are able to recover the frequency content from non-time resolved data.
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Title: Dynamic Risk and Dynamic Performance Measures Generated by Distortion Functions and Diversification Benefits Optimization
Creator: Liu, Hao
Date: 2023
Description: This thesis consists of two major parts, and it contributes to the fields of risk management and optimization.One contribution to risk...
Show moreThis thesis consists of two major parts, and it contributes to the fields of risk management and optimization.One contribution to risk management is made via developing dynamic risk measures and dynamic acceptability indices that can be characterized by distortion functions. In particular, we proved a representation theorem illustrating that the class of dynamic coherent risk measures generated by distortion functions coincides with a specific type of dynamic risk measures, the dynamic WV@R. We also investigate thoroughly various types of time consistencies for dynamic risk measures and dynamic acceptability indices in terms of distortion functions. Another contribution to risk management is proving strong consistency and asymptotic normality of two estimators of dynamic WV@R. In contrast to the exist- ing literature, our results do not rely on the assumptions of distribution of random variables. Instead, we investigate the asymptotic normality of estimators in terms of the generating distortion functions. Last but not least, we give counterexample to show that a sufficient condition of asymptotic normality is not necessary. The contribution to optimization is twofold. On the one hand, we formulate the (scalar) diversification optimization problem as a vector optimization problem (VOP), and show that a set-valued Bellman principle is satisfied by this VOP. On the other hand, we derive explicit policy gradient formula and implement the deep neural network to solve diversification optimization problem numerically. This deep learning technique allows to overcome computation difficulty caused by the non-convexity of VOP.
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Title: Development of data assimilation for analysis of ion drifts during geomagnetic storms
Creator: Hu, Jiahui
Date: 2024
Description: The primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity....
Show moreThe primary objective of this dissertation is to gain insight into geomagnetic storm effects at mid-latitudes induced by solar activity. Geomagnetic storms affect our everyday lives because they give rise to transient signal loss, data transmission errors, negatively impacting users of satellite navigation systems. The Nighttime Localized Ionospheric Enhancement (NILE) is a localized plasma enhancement that because it is not well understood, drives the design of satellite-based augmentationsystems. To better secure operation of technological infrastructure, it is essential to build a comprehensive understanding of the atmospheric drivers, especially during solar active periods. Instrument measurements and climate models serve as valuable tools in obtaining information regarding the occurrence of space weather events; nonetheless, both sources exhibit quantitative and qualitative limitations. Data assimilation, an evolving technique, integrates measurements and model information to optimize the state estimations. This dissertation presents developments in a data assimilation algorithm known as Estimating Model Parameters from Ionospheric Reverse Engineering (EMPIRE), and its applications in investigating the atmospheric behaviors under varying solar conditions. EMPIRE is a data assimilation algorithm specifically designed for upper atmospheric driver estimation of neutral wind and ion drifts at user-defined spatial and temporal scales. The EMPIRE application in this work aims to contribute to a more comprehensive understanding of the effects of the NILE. EMPIRE utilizes the Kalman filter to optimize state calculations primarily based on electron density rates, provided by other data assimilation algorithms. Earlier runs of the algorithm used pre-defined values for the background state covariance cross time. To address model limitations under changing geomagnetic conditions, the algorithm is enhanced by concurrently updating the background state covariance during assimilation processes. Additionally, representation error is incor- porated as a component of the observation error, and error analysis is performed through a synthetic-data study. Previously, EMPIRE fused Fabry-Perot Interferometer (FPI) neutral wind measurements, demonstrating increased agreement with validation neutral wind data. In this work, this approach is extended to augment Coherent Scatter Radar (CSR) ion drift measurements from Super Dual Auroral Radar Network (SuperDARN), providing additional insights into EMPIRE’s estimated field-perpendicular ion motion. For an in-depth exploration of storm-related NILE, both EMPIRE and another data assimilation method, the Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension coupled with Data Assimilation Research Testbed (WACCM-X + DART), is implemented for a storm event to test the proposed NILE driving mechanism. Furthermore, this dissertation introduces a Kalman smoother technique into the EMPIRE to enhance its ability to assess past storm events, and to explore the potential for algorithm improvements.
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Title: Self-Stigma, Disclosure, and Care-Seeking in People with Self-Reported Mental Illness
Creator: Shah, Binoy Biren
Date: 2023
Description: Objective: The longstanding mental illness treatment gap has only been exacerbated by the COVID-19 pandemic. One reason for this is the self...
Show moreObjective: The longstanding mental illness treatment gap has only been exacerbated by the COVID-19 pandemic. One reason for this is the self-stigma of mental illness, which has been shown to decrease care-seeking. This study aims to better understand the relationships between self-stigma and care-seeking by identifying novel mediators of this relationship. Method: A sample of 125 individuals with mental health difficulties, obtained from MTurk, completed measures of self-stigma, disclosure, care-seeking. Self-stigma was conceptualized as a distal antecedent to disclosure, and novel proximal antecedents of disclosure (i.e., approach goals, avoidance goals, and the “Why Try?” effect) were unpacked. Hypotheses were tested in steps via path analysis. Results: We found partial evidence to support our model of self-stigma. Disclosure did not mediate the relationship between self-stigma and care-seeking. Findings regarding proximal antecedents of disclosure were mixed. Conclusion: Results should be interpreted with caution due to data quality concerns. Additional research is needed to better understand how self-stigma impacts disclosure. This line of inquiry has noteworthy implications for research, policy, and clinical practice.
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Title: Learning Stochastic Governing Laws from Noisy Data Using Normalizing Flows
Creator: McClure, William Jacob
Date: 2021
Description: With the increasing availability of massive collections of data, researchers in all sciences need tools to synthesize useful and pertinent...
Show moreWith the increasing availability of massive collections of data, researchers in all sciences need tools to synthesize useful and pertinent descriptors of the systems they study. Perhaps the most fundamental knowledge of a dynamical system is its governing laws, which describe its evolution through time and can be lever-aged for a number of analyses about its behavior. We present a novel technique for learning the infinitesimal generator of a Markovian stochastic process from large, noisy datasets generated by a stochastic system. Knowledge of the generator in turn allows us to find the governing laws for the process. This technique relies on normalizing flows, neural networks that estimate probability densities, to learn the density of time-dependent stochastic processes. We establish the efficacy of this technique on multiple systems with Brownian noise, and use our learned governing laws to perform analysis on one system by solving for its mean exit time. Our approach also allows us to learn other dynamical behaviors such as escape probability and most probable pathways in a system. The potential impact of this technique is far-reaching, since most stochastic processes in various fields are assumed to be Markovian, and the only restriction for applying our method is available data from a time near the beginning of an experiment or recording.
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Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
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Title: UNDERSTANDING MARIJUANA USE AS A TREATMENT OPTION FOR PEOPLE WITH EPILEPSY: USE, ATTITUDES, AND QUALITY OF LIFE
Creator: Johnson, Kristina
Date: 2021
Description: Epilepsy is the most common neurological disorder worldwide with a heterogeneous range of negative symptoms. Current treatments for epilepsy...
Show moreEpilepsy is the most common neurological disorder worldwide with a heterogeneous range of negative symptoms. Current treatments for epilepsy have side effects that can negatively impact the quality of a person’s life. Alternative treatments are being explored, including marijuana. This study aimed to understand marijuana use in adults with epilepsy across U.S. states. Rates of use, preferred method of use, and reasons to use and not use marijuana were examined. Additionally, levels of comfort discussing marijuana compared to other treatment options and with different types of providers were explored. Lastly, this was one of the first studies to examine the relationship between quality of life (QOL) and marijuana use for people with epilepsy. Participants included 128 individuals with epilepsy from 26 states, with non-legal states having significantly fewer people who reported using marijuana. Smoking was reported as the primary method of use, knowing someone else that uses as their primary reason for using, and health concerns as the primary reason not to use. There was no difference in level of comfort discussing marijuana compared to other treatments, and participants reported feeling most comfortable discussing marijuana with neurologists compared to other providers. Finally, total QOLIE-31 and the social functioning subscale were significantly lower among marijuana users; however, this difference did not remain when anxiety was entered as a covariate. In fact, the relationship between anxiety and QOL was significant, with anxiety accounting for η2 = .12 to η2 = .57 of the variance in QOLIE-31 subscale scores, controlling for marijuana use. Findings from this study further the understanding of marijuana use by people with epilepsy in the United States.
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Title: Large Language Model Based Machine Learning Techniques for Fake News Detection
Creator: Chen, Pin-Chien
Date: 2024
Description: With advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into...
Show moreWith advanced technology, it’s widely recognized that everyone owns one or more personal devices. Consequently, people are evolving into content creators on social media or the streaming platforms sharing their personal ideas regardless of their education or expertise level. Distinguishing fake news is becoming increasingly crucial. However, the recent research only presents comparisons of detecting fake news between one or more models across different datasets. In this work, we applied Natural Language Processing (NLP) techniques with Naïve Bayes and DistilBERT machine learning method combing and augmenting four datasets. The results show that the balanced accuracy is higher than the average in the recent studies. This suggests that our approach holds for improving fake news detection in the era of widespread content creation.
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Title: Two essays on corporate finance and risk management
Creator: LI, YANFENG
Date: 2021
Description: 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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