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To address the challenges of granular emotion detection in social media text (EMDISM), I have investigated ensemble approaches that combine a variety of individual classifiers to address tradeoffs in performance. This involved first investigating EMDISM performance for individual traditional machine learning (ML), deep learning (DL), and transformer learning (TL) classifiers. Based on this analysis, the second stage investigated the creation of ensembles of the most accurate classifiers across these general classes which offer comparatively improved performance. I provide results and analysis for each classifier I considered as well as the most accurate ensembles I created from the most accurate singleton classifiers. Results show that the proposed ensemble approaches improve upon the state of the art for average accuracy, weighted precision, weighted recall, and weighted f-measure as compared to the most accurate single classifier for EMDISM.
In recent years there has been a significant increase in the prevalence of mental illness among millennials (White-Cummings, 2017). However, there is still a significantly lower rate of Black millennials, specifically Black men, utilizing mental health services compared to other marginalized groups (Cadaret & Speight, 2018). Black men have reportedly have a higher prevalence of mental illness with little to no treatment engagement, which has been linked to the increasingly high rates of suicide. Black men and their lack of mental health treatment seeking has become an increasingly popular topic in scholarly literature, yet the research is still scarce thus far. The purpose of this study was to explore the influence of social constructs on millennial Black men’s decisions about seeking mental health treatment through the lens of Critical Race Theory (CRT), Black Critical Theory (BlackCrit) and Black Masculinity. Based on past reported themes, Black Masculinity, CRT, and BlackCrit were utilized as a multidimensional framework for this critical phenomenology qualitative study. The researcher used semi-structured interviews to investigate the experiences of 16 participants who identified as millennial Black men that had considered seeking mental health treatment regardless of their decision to seek help or not. Following a modified version of Moustakas (1994) phenomenological analysis, results indicated three themes Racialized Gendered Socialization, Cultural Distrust, and Invisibility. All themes were related to racial and masculine factors. Implications and recommendations are provided for future research and improving advocacy efforts to engage more Black men in mental health treatment.
ABSTRACT
SHOHREH SHADALOU. Dynamic Illumination Systems using Freeform Optics. (Under the direction of DR. THOMAS J. SULESKI)
Illumination systems that can create light patterns of varying sizes or shapes with high efficiency and uniformity are advantageous for a range of applications, including lighting, augmented/virtual reality, laser-based manufacturing, medicine/dermatology, and lithography. Previous approaches for continuous variable illumination have utilized longitudinal movement of the source or other optical components along the optical axis, which increases both system size and light pattern non uniformity. Liquid lenses with adjustable membranes have also been used for tunable illumination, but leakage and manufacturing complexity can be significant issues. Thus, new approaches that enable dynamically tunable illumination patterns in compact, robust packages are of interest.
Recent advances in design, production and metrology have enabled the use of freeform surfaces in a wide range of optical imaging applications. As one example, the Alvarez lens consists of a pair of cubic freeform surfaces that enable variable focal length with small lateral displacements between the two elements. Complex freeform surfaces are also regularly used in static illumination systems such as automotive headlights and luminaires.
The primary objectives of this dissertation are to explore and characterize dynamic freeform optical systems enabling continuously variable illumination. Results are addressed through three articles. The first article introduces the use of arrays of freeform Alvarez lenses with LED sources to enable tunable illumination. The second article builds from this work to present the design, manufacturing, and characterization of a compact tunable illumination system. The third article introduces a general design method using freeform optics to enable variable optical illumination between two arbitrary boundary conditions. These three articles demonstrate the methods and utility of freeform optics for dynamic illumination systems.
Employee overqualification is becoming increasingly relevant in a post-pandemic world. While there have been theoretical advancements in the overqualification literature, several methodological issues remain unresolved. Specifically, the conceptualization and operationalization of perceived overqualification (POQ) are often not aligned. To date, the perception of overqualification is not yet fully understood. Thus, the main goal of this dissertation is to address these methodological limitations. In Study 1, I refined the scope of POQ by offering an explicit construct conceptualization grounded in person-job fit theory and developed a new scale to measure the multidimensional construct. In Study 2, I validated the psychometric properties of the Perceived Overqualification at Work Scale (POQWS) and explored the relationship of POQ with various work-related outcomes. Taking a person-centric approach, I used latent profile analyses (LPA) to identify different profiles of overqualified employees in Study 3 based on the POQWS dimensions. This study is the first to examine the process by which patterns of variables are identified in POQ profiles and how these combinations differentially relate to outcomes. Results from a series of exploratory and confirmatory factor analyses clearly supported a four-factor model. In the subsequent study, four distinct profiles emerged from the latent profile analyses. One-way analyses of variance (ANOVA) provided further criterion-related validity evidence for these four profiles. Taken together, the findings from this dissertation lay the grounds for future person-centered research.
Driver errors are the leading cause and contribute to about 94% of traffic crashes. To mitigate this issue, improve mobility, and enhance safety, automobile manufacturers are striving to develop various types of advanced driver assistance systems (ADAS). These ADAS are designed to assist or in some cases take over certain driving maneuvers. On the other hand, the acceptance levels of ADAS among drivers are questionable. Many surveys determined that drivers are unaware of the applications and limitations of ADAS. While ADAS are designed to enhance safer driving, their indirect effects on driver behavior have been seldom ventured and widely debated.
The focus of this research is on developing different driving scenarios that replicate real-world driving conditions using a driving simulator. Selected participants were prompted to interact with traffic within the simulation environment through a setup equipped with warning (lane departure warning, blind-spot warning, and over speed warning) or automated (lane keep assist and adaptive cruise control) features. The responses of participants when driving a vehicle with warning features, advanced features, and without ADAS in the simulation were captured, analyzed, and compared to understand their effects. The findings are valuable insights to automobile manufacturers as well as policymakers to better design ADAS such that their applicability is streamlined from both safety and user perspective.
Hydrogen bonds play a vital role in protein-DNA interactions. In particular, side chain-base hydrogen bonds are crucial to the binding specificity between protein and DNA. Mutations effecting interface hydrogen bonds in protein-DNA complexes have been linked to changes in binding specificity and are implicated in various diseases. However, knowledge about the distribution of hydrogen bond energy (HBE) in protein-DNA complexes as compared to other important biomolecular complexes is unknown. Here, we performed a systematic comparative analysis of hydrogen bond energy (HBE) in three protein-ligand complexes; protein-DNA, protein-protein and protein-peptide. Our results show that while the hydrogen bonds in protein-protein and protein-peptide complexes are predominantly strong, a unique, almost equal distribution of strong and weak hydrogen bonds is observed in protein-DNA complexes. More importantly, more strong hydrogen bonds are observed in the minor grooves of highly specific protein-DNA complexes than multispecific complexes indicating the role of minor groove hydrogen bonds in protein-DNA binding specificity. The knowledge gained from these analyses was applied to develop a novel hydrogen bond energy-based method to assess the similarity between protein-DNA complex models and reference structures, an important step towards computational prediction of complex structures. We show that HBE based method provides more accurate assessment of similarity for models generated by both homology modeling and computational docking methods.
The construct of “customer satisfaction” has been used for several decades in marketing to achieve outcomes such as customer loyalty, word-of-mouth communication, resistance to competition, and customer equity. Recent research, however, has indicated little to no correlation between customer satisfaction and many of these outcomes. A more recent marketing construct is “customer delight,” where affective bonds and positive associations are the foundations for customer relationships. While customer delight has numerous advantages, an important limitation is that it can only be used with certain types of products and consumption situations.
This study introduces the academic construct of “customer success,” an objective tool that could redefine customer relationships, and define it as an objective and mathematically based strategic process to maximize customer-desired outcomes. A long-term customer success strategy is customer-driven and designed to be mutually beneficial to both an organization and its customers. While the construct of customer success has been sporadically used by practitioners in the past, the use of the term has often been arbitrary, and the construct has never been precisely defined.
First, drawing on the reverse logic framework (RLF) of relationship marketing, the customer valuation model, and return on relationships (ROR), this study will use Hunt’s indigenous theory, inductive realist approach to help build the initial theoretical framework for the construct of customer success. Then, this study uses this construct in a government-to-customer (G2C) market scenario to test a series of hypotheses to evaluate government-achieved customer success for COVID-19 pandemic response outcomes. This study will conclude with theoretical and managerial research contributions and provide directions for future research.
Human-automation teaming (HAT) is gaining importance in military and commercial applications with autonomous vehicles because it promises to improve performance, reduce the cost of operating and designing platforms, and increase adaptability to new situations. Given that both humans and automation systems are subject to misses, faults, or errors, to ensure the HAT performance in unpredictable conditions, it is critical to address the hand-off problem -- how to transition control between a human driver and automation system. Current solutions for control transfer in semi-automated ground vehicles face issues such as protracted transfer time, misinterpretations, or misappropriations of responsibility, and incomplete or inaccurate understandings of the vehicle and environment state. Transitions involving such issues are often "bumpy'' and implicated in safety compromises.
In this dissertation, we designed and tested an adaptive haptic shared control wherein a driver and an automation system are physically connected through a motorized steering wheel. We model the structure of the automation system like the structure of the human-driver, including a higher-level intent generator and lower-level impedance controller. In the first phase of the project, we developed a
nonlinear stochastic model predictive approach to determine how automation's impedance should be modulated in different interaction modes to enable the smooth and dynamic transition of control authority. Then, we tested our controller through a set of human-subject studies using a fixed-base driving simulator. Our findings showed that by adaptively modulating the impedance of the automation system, the control transfer time is reduced, and the performance of HAT is significantly improved.
In the second phase of this dissertation, we studied the principles of convention formation in a haptic shared control framework to narrow down the many possible strategies for resolving a conflict to those that a driver might be more gravitated. To this end, we proposed a modular platform to separate partner-specific conventions from task-dependent representations and use this platform to learn various forms of conventions between a human-driver and automation system. Using this platform, we will create a map from human-automation interaction outcomes to the space of conventions. This map will then be used to design an adaptable automation system. To design an adaptable automation system, we developed a reinforcement-learning model predictive controller wherein the characteristic of the model-predictive controller, including the weights of its cost function, is updated in different interaction modes using the learned convention map. Finally, we tested the proposed platform on the problem of intent negotiation between the driver and the automation system. The results demonstrated that the conflict between humans and automation could be further reduced using the convention-based approach.
This correlational study utilized secondary, longitudinal data to examine the extent to which student-influenced and institution-influenced factors predict the academic success and degree completion of engineering transfer students at public four-year institutions in North Carolina. The sample included students who transferred from community colleges to pursue baccalaureate degrees at UNC System institutions that offered engineering or engineering technology programs from 2009 to 2016. Based on the data structure, regression analyses were utilized to examine the factors that predict first-semester academic performance and persistence to degree attainment at the receiving institutions. The hierarchical organization of student-influenced, institution-influenced, and both student and institution-influenced factors were based on a modified version of Smith and Van Aken’s (2020) literature-based conceptual framework on engineering transfer student persistence.
Results indicated that first-term academic performance is impacted by student background, college/department of engineering characteristics, and attempted and earned hours in the first semester. Further, persistence was affected by age, the amount of transfer credit, college/department of engineering characteristics, and cumulative GPA and total earned hours at the receiving institution by the student. This study provides practical and actionable findings that will aid four-year engineering institutions in increasing the academic success and persistence of vertical transfer students pursuing baccalaureate engineering degrees.
Please email me at csgreen2@gmail.com for the Zoom link if you would like to attend.
