Dissertation Defense Announcements

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.

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.

Although women and racial minority entrepreneurs make considerable contributions to society by creating their ventures, they often face additional barriers and limitations that explain the differential rate of new venture creation between men and women, White and racial minorities. Therefore, it is crucial to uncover mechanisms to help support women and racial minorities in the venture creation process. One such mechanism is supportive entrepreneurial figures such as entrepreneurial role models, mentors, and founders, all of which can play an essential role in the decision to become an entrepreneur. Despite understanding the positive influence that these supportive entrepreneurial figures can have on entrepreneurial behavior and outcomes, research has yet to examine how these relationships are shaped by the gender and race of the supportive entrepreneurial figure in the process of new venture creation. I test hypotheses with a sample of 417 entrepreneurs across two-time points. Results are intricate and complex, illustrating how in some cases, the positive influence of the entrepreneurial role model, mentor, or founder is dependent on the gender or race of that individual. My findings contribute to how supportive entrepreneurial figures shape new venture creation for women and racial minority entrepreneurs.