Dissertation Defense Announcements

Fertility preservation would benefit young males who must undergo treatments that can result in sterilization, such as radiation treatments for cancer. This can be achieved by removing some testicular tissue before treatment and putting it into frozen storage for later use, a process known as cryopreservation. Cryopreservation requires the use of cryoprotective agents (CPAs), such as dimethyl sulfoxide (DMSO), to reduce injury from ice crystal formation. Because DMSO can be toxic at high exposure levels, it is important to determine the exposure time that is necessary to achieve adequate concentrations for freeze protection, without over-exposing the tissue. Mass diffusion models can be used to predict this loading time, but these models depend on property parameters that are often unknown, such as the mass diffusion coefficient for a given CPA in a specific tissue.
To facilitate the development of cryopreservation protocols for testicular tissue, we determined the mass diffusion coefficient for DMSO in thin (~1 mm) tissue sections that were precision cut from feline testes that were discarded from veterinary sterilization procedures. Samples were placed in a custom tube that was mounted on the surface of an Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) and then exposed to DMSO on the opposite side. Spectra were recorded for 60 minutes, and the area of peak centered at 950 cm-1 was determined as a function of time. This time course absorbance data was then fit to an equation developed by Barbari and Fieldson (1993) that considers both mass diffusion and tissue absorption properties. By minimizing the sum of squares, estimates for the DMSO diffusion coefficient were obtained from each time sequence. Samples were analyzed at 22°C and 4°C.
Because of the inherent variability in biological tissues, alginate-gelatin and agarose were also evaluated for their potential as reference standard materials, to facilitate methodology development and training. Alginate compacted to thicknesses of 1.7 ± 0.2 mm resulting in an effective DMSO diffusion coefficient of 4.3 ± 0.3 x 10-6 cm2/s (n=4). Agarose compacted to thicknesses of 1.1 ± 0.1 mm. The effective diffusion coefficient of DMSO in agarose was 9.2 ± 0.2 x 10-6 cm2/s at 22°C (n=9) and 5.6 ± 0.2 x 10-6 cm2/s (n=9) at 4°C. Although alginate and agarose had similar variability in their thicknesses, agarose had much lower within batch and between batch variability than alginate-gelatin for the effective diffusion coefficients and thus is the preferred reference material for ATR-FTIR diffusion studies. Testicular tissue samples compacted to 2.1 ± 0.7 mm. The effective diffusion coefficient was 10.3 ± 4 x 10-6 cm2/s at 22°C and 7.1 ± 5 x 10-6 cm2/s at 4°C. The high variability is likely due to native variability in the testicular tissue samples. However, these nominal values can be used to inform preservation procedure planning.

Diversity initiatives are often ineffective because they characterize differences at the group-level, and therefore, do not adequately address individuals’ specific identity-related challenges. The purpose of this study is to use a network-based approach to studying identity to provide a comprehensive examination of the wide range of identities that are salient and important for individuals who are members of diverse race and gender groups, namely White and Black men and women at work. Additionally, I apply intersectionality theory to understand how multiple identities are constructed into overall self-concepts at work and more specifically, how individuals perceive intersections between their multiple identities. According to intersectionality theory, I expect that multiple identities will co-exist and subordinate (i.e., historically marginalized) social identities will be more central for women and racial minorities employees as opposed to dominant (i.e., historically non-marginalized) identities. I also integrate job-demands resources theory to develop and test hypotheses concerning the structural relationships between identities (i.e., conflict, compatibility, centrality) and authenticity at work. Specifically, I propose that identity conflict, compatibility, and centrality are identity structures that serve as resources that can enable or constrain authentic self-expression at work. I test these hypotheses across two studies. In summary, this work sheds theoretical and empirical light on the complex nature of multiple identities at work and how diversity initiatives can more effectively address identity dimensions that intersect and affect personal work experiences.

Autonomous vehicles (AVs) are imminent and they are not in people’s dreams now. Now the burning questions the research community is interested in include how quickly AVs would be implemented for public use, whether people would accept them, and how AVs would change the ecosystem of transportation and the built environment. Stimulated by these questions, this dissertation aims to investigate the factors that influence people’s behavioral intention (BI) to adopt AVs and shared AVs (SAVs). In addition, this study is intended to investigate the potential impacts of AVs on land use patterns and people’s travel behaviors. This dissertation consists of six papers as discussed hereunder.

The first article presents a state-of-the-art literature review to understand people’s perceptions and opinions of AVs and the factors that influence AV adoption. Results show that the socioeconomic profile of individuals and their household, their psychological factors (e.g., usefulness, ease of use, risk), and knowledge and familiarity with AV technologies would affect AV adoption. Additionally, urban form (e.g., density, land use diversity), transportation factors (e.g., travel mode, distance, and time), affinity to new technology, and institutional regulations would influence the AV adoption rate.

The second review study critically analyzes the extant literature and summarizes the short, medium, and long-term effects of AVs based on a SWOT (Strength, Weakness, Opportunity, and Threat) analysis. Results show that AV would influence transportation and human mobility by reducing vehicle ownership, vehicle miles traveled (VMT), congestion, travel costs, energy use, and increasing accessibility, mobility, safety and security, and revenue generation for commercial operators. AVs would encourage dispersed urban development, reduce parking demand, and enhance network capacity. Additionally, AVs would increase the convenience and productivity of passengers by providing amenities for multitasking opportunities.

The third paper investigates the key factors that influence people’s tendency to purchase and use personal AVs after collecting data from the 2019 California Vehicle Survey. Results from the Structural Equation Model (SEM) indicate that working-age adults, children, household income, per capita income, and educational attainment are positively associated with AV purchase intention. Similarly, psychological factors (e.g., perceived enjoyment, usefulness, and safety), prior knowledge of AVs, and experience with emerging technologies significantly influence people’s BI to purchase AVs. This study finds that family structure and psychological factors are the most influential factors in AV purchase intention of households than the built environment, other socioeconomic, and transportation factors.

The fourth paper investigates the key elements of a household’s intentions to use pooled SAVs using the SEM framework. Collecting data from the 2019 California Vehicle survey, this study finds that higher educational attainment, income, labor force participation, Asian population, and urban living are negatively associated with SAVs. In contrast, young and working-age adults are positively associated with SAVs. Study results also show that people who prefer public transportation, car-sharing, ride-hailing, and ride-sharing services are likely to use SAVs. The perceived usefulness, enjoyment, safety associated with AVs, and prior knowledge of AVs significantly influence people to use SAVs. The study concludes that people’s travel behaviors, positive attitudes to shared mobility, and psychological features are the key determinants of SAVs.

The fifth paper studies the potential impacts of AVs on the spatial distribution of household and employment locations using the existing Swindon model of the TRANUS urban simulation platform. Results show that the adoption of AVs encourages people to live outside of the city center by increasing convenience and reducing travel costs. On the other hand, AVs would increase employment opportunities in the city center by inducing more economic activities. This study finds that AVs would allow densification of the existing city center by releasing extra space from parking land areas along with peripheral new development over time.

With the same TRANUS simulation platform, the sixth paper aims to assess the potential impacts of AVs on people’s travel behaviors such as trip generation, travel distance, travel time, and travel costs. Results indicate that AVs would intensify people’s overall travel demand by increasing accessibility. On the other hand, AVs are likely to reduce vehicle ownership, travel distance, travel time, travel costs, and vehicle hours traveled by reducing solo driving and by inducing shared mobility. AVs also have the potential to reduce public and active transportation.

This study makes significant contributions by unraveling critical issues of AVs and their short-, medium-, and long-term impacts. The findings will be helpful for policymakers and professionals to implement appropriate policies to manage travel demand and urban growth, and to urban and transportation scholars in the understanding of the complex mutual relationships between transportation, mobility, and the conditions of urban environments.

With the widespread use of computers and networks, cybersecurity has emerged as a crucial concern for many businesses as they fight off growing cyber threats by vulnerability exploitation. To identify and mitigate zero-day or unpatched vulnerabilities, intensive defensive measures are required, which calls for a thorough understanding of vulnerability characteristics and threat behavior from several angles. This compels enterprises to spend a considerable amount of money to safeguard their infrastructure from cyberattacks, relying on the costly, ineffective, error-prone, and slow process of experts' input. Therefore, security automation has been a solution for many business owners in the battle against the growing number of cyber threats by vulnerability exploitation.

The modern text analytics architectures have been built in novel ways for a variety of applications, assisting cybersecurity professionals in developing resilient mechanisms against threats. Utilizing such technologies can therefore be a viable approach for processing, understanding, and predicting vulnerabilities that are typically reported through unstructured text.

This dissertation utilizes deep learning, natural language processing, and Information Retrieval to build a series of models that are able to effectively and efficiently parse, assess, analyze, and mitigate the vulnerabilities based on their textual descriptions reported in Common Vulnerabilities and Exposures (CVE) format.
This research offers a cybersecurity language model, as the core component, which is then utilized for characterizing the vulnerabilities as well as retrieving the corresponding course of defense actions. As a result of this work, enterprises and cybersecurity researchers will be able to automatically process domain-specific texts, classify vulnerabilities to cybersecurity standards to obtain high-level knowledge, and retrieve the course of defense actions for the underlying threats.