Dissertation Defense Announcements

The writhe is a quantity calculated from crossing signs of a link diagram. It is known that the writhe calculated from any reduced alternating link diagram of the same alternating link has the same value. That is, it is a link invariant if we restrict ourselves to reduced alternating link diagrams. This is due to the fact that reduced alternating link diagrams of the same link are obtainable from each other via flypes and flypes do not change writhe.
This dissertation introduces new invariants for the class of reduced alternating links. It also analyzes the strength of these invariants, called writhe-like invariants, in comparison to a few general link invariants. It examines how these quantities can be used in solving other knot theory problems. A part of the dissertation is dedicated to describing the computer program that computes a few writhe-like invariants and to reporting on the computed data of several alternating knots and links.

Connected and autonomous vehicles (CAVs) are a type of emerging technology that has promising potentials in improving many aspects of the existing transportation infrastructure, including operations, safety, and the environment. With the capability of traveling on the roads with shorter headways and more stable speeds, CAVs can yield a larger road capacity compared to human-driven vehicles (HDVs). Additionally, since the CAVs run on the roads with the guidance of computers or algorithms, accidents caused by errors from human drivers may be prevented, which can greatly reduce significant economic and societal losses. Less speed fluctuations are also beneficial to decrease emissions and contribute to the environment.
Thanks to the rapid development of computer science and communication technology, CAVs have evolved from theoretical experiments in academic labs to reliable products by commercial companies. Since both academic and industrial professionals have high expectations for CAVs, many studies have been conducted to explore and identify the impacts of CAV technologies on the transportation performances in many scenarios. These scenarios included conventional intersections, highway segments, on/off ramps, and roundabouts. Through extensive investigations on CAVs in different scenarios, it can be concluded that CAVs can perform better overall than HDVs. Nevertheless, it has also been found that the performances of CAVs are affected by many factors such as communication range, acceleration capabilities, and market penetration rates. Improvement in operational performance has been confirmed by existing studies when the market penetration of CAVs reaches a certain rate.
Superstreet is one of the innovative intersection designs and was proposed to alleviate the road congestion especially where unbalanced traffic volumes from main street and minor street exist. Superstreets have been successfully implemented in numerous states. Nevertheless, how CAVs would affect the performances of superstreets has not been explored, even to a minimum extent. This research is designed to investigate how CAVs with different technologies perform in the environment of superstreets. To be specific, the following questions will be answered: (1) at what market penetration rate CAVs would bring benefits towards operational performances; (2) at what extent CAVs would bring benefits towards operational performances of superstreets; (3) how the impact of CAVs on the operational performance would vary across different traffic scales and market penetration rates.
To achieve the research goals, models for CAV platooning, trajectory planning, and signal optimization have been developed, respectively. The effects of these models are tested respectively in a simulation environment where relevant traffic measures are extracted to evaluate the performances. The finding of this research may also be applied to other innovative intersection designs which have similar geometric characteristics and traffic patterns.

Using employee referral programs is generally considered a best practice for organizations seeking top quality talent. However, research on whether or not these programs result in positive outcomes equally for all applicants is mixed. To data, most research examining employee referral programs focuses on how status characteristics (such as race and gender) of applicants can result in unequal outcomes (such as being hired or promoted) for applicants with different identities. Little is known, however, about the influence of referring employee’s status characteristics during hiring processes and whether or not decision makers’ biases toward certain referring employees may lead to different hiring process outcomes for the applicants they refer. Using Status Characteristics Theory and the theory of Status Beliefs Transfer, hypotheses were tested regarding how status characteristics of referring employees, namely race and gender, might lead to a transfer of evaluators’ status beliefs from the referring employee to the applicant and affect subsequent applicant evaluations. Four hundred and thirty-seven U.S. individuals with hiring experience served as participants for an online resume evaluation experiment where the only difference between resumes was the name of the referring employee noted at the top of the document. Referring employee names were selected via pre-test to signal the referrer was either a white man, black man, white woman, or black women. Results of quantitative analyses revealed a positive statistically significant difference in average ratings of competence, recommendations for interviews, and starting salary between referred and non-referred applicants, with participants rating referred applicants more favorably. In addition, a positive statistically significant effect of race, but not gender, was found in average ratings of competence, commitment, interview recommendations, and salary recommendations for black referring compared to white referring employees. Additional qualitative thematic analysis of open response data describing rationale for participant ratings revealed additional intersectional evaluative differences among applicants referred by employees with different race/gender statuses. Taken together, and viewed through the lens of intersectional theories, findings suggest evaluations of applicants may have been influenced by a status beliefs transfer process whereby the intersectional status characteristics of referring employees were transferred onto and used to evaluate the applicants they referred. Implications for theory, practice, and future research are discussed.