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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.
Pressures associated with accountability testing have resulted in a narrowing of both the curriculum and pedagogy that does not meet the needs of high ability learners. This study proposed that either a different measurement (an above-level computer adaptive assessment) or a different model (Tobit model) should be used to more accurately demonstrate high ability student achievement and growth in order to lessen the pressures on teachers and therefore create an environment better suited for high ability student learning. To answer the research questions under study, a two-part design was used. The first part of the study used an above-level assessment and imposed an artificial ceiling at grade-level with the goal of using Tobit modeling to reproduce uncensored growth estimates using censored data. The second part of the study used naturally censored data with the goal of increasing growth estimates through Tobit modeling. Ultimately, the Tobit models using artificially censored data were able to come close to replicating the uncensored growth estimates under certain conditions. The results indicated that Tobit regression was necessary when examining homogeneous groups of high ability students. Finally, the Tobit regression models were able to increase the growth estimates for high ability students using naturally censored data. The degree to which the models increased, and under which conditions the increases existed are described in detail.
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.
Connected and autonomous vehicle (CAV) technologies could significantly change the car-following behaviors and affect the performance of the intersection systems. As it is expected to have a long transition time during which human driven vehicles (HDVs) and CAVs will coexist, it is important to investigate the impacts of CAVs on the intersection systems under different market penetration rates (MPRs). Also, the currently used Highway Capacity Manual does not consider the impacts of CAVs when calculating the intersection capacity. Though highly needed, a new guideline for estimating the intersection capacity under different MPRs of CAVs is becoming a critical issue for transportation planners and engineers. Furthermore, combining the intersection traffic signal control (TSC) systems with deep reinforcement learning (DRL) provides a new potential solution to improve the efficiency, safety, and sustainability of the intersection system. However, the training procedure of the DRL TSC system requires large samples and takes a long time to converge. Furthermore, it is common to have several intersections along corridors or in networks. A single DRL agent is unable to control several intersections as this may result in exponential explosion in the action space. Hence, a modification of the DRL TSC framework to improve the training efficiency and a multi-agent control framework to control several intersections are needed.
To better prepare and guide both intersection planning and operations under different MPRs of CAVs and traffic demands, this dissertation provides an intensive evaluation of the impacts of CAVs in several signal intersection systems, as well as an in-depth analysis on intersection capacity adjustments that consider varying MPRs of CAVs. Also, a transfer-based DRL TSC framework is proposed and tested at different MPRs of CAVs and traffic demand levels. A multi-agent DRL TSC with shared traffic states between downstream and upstream intersections is investigated in a corridor. It is concluded that 100% MPR of CAVs can increase the saturation flow rate of the through-only lane by 126.8%. Meanwhile, transfer-based models could significantly improve training efficiency and model performance. The multi-agent DRL TSC also enables coordination between intersections. The insights of this research should be helpful and valuable to transportation researchers and traffic engineers in calculating intersection capacity, designing intelligent intersections, improving intersection efficiency, and implementing DRL-controlled traffic signals under the mixed flow with CAVs.
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.
Police departments located in states allowing payday lending report 14.34% more property crimes than the police departments located in states not allowing payday lending. I also find that the police departments located in counties bordering with states allowing payday lending report more property crimes. Those results are driven by the financial pressure induced by payday loans. Furthermore, the impact of payday lending concentrates in areas with a higher proportion of the minority population.
Using a large sample over the period 1986 to 2017, we show that companies with higher exposure to climate change risk induced by sea-level rise (SLR) tend to acquire firms that are unlikely to be directly affected by SLR. We find that acquirers with higher SLR exposure experience significantly higher announcement-period abnormal stock returns. Post-merger, analyst forecasts become more accurate and environmental-related as well as overall ESG scores improve.
In this paper, we examine the impact of shareholder-creditor conflict on firm hedging behavior. We use mergers between corporate shareholders and creditors as exogenous shocks and find a positive causal relationship between reduced shareholder-creditor conflicts and corporate hedging behavior. Specifically, we find that treated firms that experience shareholder and creditor consolidation are not only more likely to hedge using financial instruments, but also hedge more in terms of the notional value of the hedge contract. In a cross-sectional test, we find that the results are stronger for firms in financial distress.
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.
