There is little consensus in the literature as to which approach for classification of Whole Genome Shotgun (WGS) sequences is most accurate. In this defense, two of the most popular classification algorithms, Kraken2 and Metaphlan2, were examined using four publicly available datasets. Surprisingly, Kraken2 reported not only more taxa but many more taxa that were significantly associated with metadata. By comparing the Spearman correlation coefficients of each taxa in the dataset against more abundant taxa, it was found that Kraken2, but not Metaphlan2, showed a consistent pattern of classifying low abundance taxa that were highly correlated with the more abundant taxa. Neither Metaphlan2, nor 16S sequences that were available for two of four datasets, showed this pattern. These results suggest that Kraken2 consistently misclassified high abundance taxa into the same erroneous low abundance taxa. These “phantom” taxa have a similar pattern of inference as the high abundance source. Because of the ever-increasing sequencing depths of modern WGS cohorts, these “phantom” taxa will appear statistically significant in statistical models even with a low classification error rate from Kraken2. These findings suggest a novel metric for evaluating classifier accuracy.
Customer churn leads to higher customer acquisition cost, lower volume of service consumption and reduced product purchase. Reducing the outflow of the customers by 5% can double the profit of a typical company. Therefore, it is of significant value for companies to reduce customer outflow. In this dissertation research, we mainly focus on identifying the customers with high chance of attrition and providing valid and trustworthy recommendations to reduce customer churn.
We designed and developed a customer attrition management system that can predict customer churn and yield actionable and measurable recommendations for the decision makers to reduce customer churn. Moreover, reviews from leaving customers reflect their unfulfilled needs, while reviews of active customers show their satisfactory experience. In order to extract the action knowledge from the unstructured customer review data, we thoroughly applied aspect-based sentiment analysis to transform the unstructured review text data into a structured table. Then, we utilized rough set theory, action rule mining and meta-action triggering mechanism on the structured table to generate effective recommendations for reducing customer churn. Lastly, in practical applications, an action rule is regarded as interesting only if its support and confidence exceed the predefined threshold values. If an action rule has a large support and high confidence, it indicates that this action can be applied to a large portion of customers with a high chance. However, there is little research focused on improving the confidence and coverage of action rules. Therefore, we proposed a guided semantic-aided agglomerative clustering algorithm to improve the discovered action rules.
Each year in the United States (U.S), one in five adults experience mental illness and one in six youth ages 6-17 experience a mental disorder (NAMI, 2020). While mental illness can affect individuals at similar rates, minority populations suffer from existent disparities in mental healthcare that have been exacerbated by the impact of COVID-19. Help-seeking behaviors of racial and ethnic minorities in the US have historically been influenced by the lack of trust in the medical system. When experiences of prejudice and discrimination are present in the counseling experience, they lead to damaging outcomes for minorities including misdiagnosis, receipt of less preferred forms of treatment, increased rate of premature termination, and overall dissatisfaction with service delivery in minority clients (Ridley et al., 2010; Rutgers University, 2019). Counselors who do not address biases, assumptions, and their own epistemological views risk operating within the oppressive framework of the dominant culture (Katz, 2014; Owen, 2017; Owen et al., 2018; Sue et al., 1992). Despite the growing support of cultural humility as complementary or even an alternative to cultural competence in counselor multicultural pedagogy, little has been examined about the ways in which this perspective can be enhanced in counselor education programs. Therefore, a standard multiple regression was utilized to examine the impact of intrinsic spirituality, common humanity, and affective empathy on cultural humility in counseling students. Results indicated that common humanity contributed significantly to the prediction of cultural humility accounting for 16% of the variance. Implications, limitations, and recommendations for future research are discussed.
Studies assessing health disparities in the United States primarily compare White and Black individuals without accounting for the heterogeneity within racial groups. The present study utilizes the racial context of origin framework to identify potential mechanisms that can explain differences in health between foreign-born Black (FBB) and US-born Black (USB) individuals. Using self-report questionnaires, this study examined the interactive effects of internalized racism, perceived discrimination, and racial context of origin on physical health and perceived discrimination reactivity. Further, motivation to succeed, belief in meritocracy, shared racial fate, and connection and belonging to the Black race were assessed to discern factors contributing to differential interactions by racial context of origin. Results indicate that internalized racism is negatively associated with physical health for FBB, but not USB. The 3-way interactions of internalized racism, perceived discrimination, and racial context of origin on physical health and perceived discrimination reactivity were not significant. Motivation to succeed, belief in meritocracy, shared racial fate, and connection and belonging to the Black race did not provide insight to differences in the role of racial context of origin in the association between internalized racism and physical health. Exploratory analyses revealed that racial centrality is a promising factor in understanding health differences by racial context of origin. Notable preliminary analyses and group differences are also discussed. These findings contribute to the understanding of racial context of origin and provide insight to race-related variables that may aid in understanding of differences in health by racial context of origin.
The identification of the artist of a painting is also known as art authentication, and the answer to this question is manifest through art gallery exhibition and is reinforced through financial transactions. Art authentication has visual influence via the uniqueness of the artist’s style in contrast to the style of another artist. The significance of this contrast is proportional to the number of artists involved and the degree of uniqueness of an artist’s collection. This visual uniqueness of style can be captured in a mathematical model produced by a machine learning algorithm on painting images. Art authentication is not always possible since art can be anonymous, forged, gifted, or stolen. Here we show an image only art authentication attribute marker for WikiArt, Rijksmuseum, and ArtFinder galleries. Contributions to the field of art authentication include the identification of a state-of-the-art machine learning algorithm, an extension to this algorithm, standard data sources for art galleries, standard performance measurements, standard combined measurement for accuracy and multi-class cardinality, limits to multi-class cardinality, and application recommendations for the produced models.
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.
This dissertation addresses a novel approach to assessing users' interaction tendencies on social media as a basis for personalized interventions that can make the truth louder and mitigate the spread of misinformation. This research leverages users' high and low interaction tendencies to amplify truth by increasing users' interactions with verified posts and decreasing their interactions with unverified posts. For designing personalized interaction-focused interventions, this dissertation presents an Active-Passive (AP) framework and three principles of social media interactions to make the truth louder on social media. This dissertation presents a study including tasks and questionnaires to investigate users' differences in the Active-Passive (AP) framework for utilizing platforms' basic interaction functionalities, such as like, comment, or share buttons. The results show that users use the interaction functionalities differently due to their interaction tendencies; users with high interaction tendencies use more interaction functionalities, and users with low interaction tendencies use less.
This dissertation presents an analysis of participants' responses to the design principles and investigates users' additional sharing functionality usage and preference for platform-based incentives. The results show that users with lower interaction tendencies share verified information more when they receive additional interaction support. Furthermore, due to the interaction tendencies, users exhibit opposite preferences for platform-based incentives that can encourage their participation in making the truth louder. Users with high interaction tendencies prefer incentives that highlight their presence on the platform, and users with low interaction tendencies favor incentives that can educate them about the impact of their participation on their friends and community. This dissertation concludes with a discussion on personalized interaction-focused interventions and provides directions for future research.
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.
Indoor environmental conditions play a significant role in protecting occupants’ well-being. The thermal characteristics are one of the primary factors of Indoor Environmental Qualities (IEQ) that can influence occupants’ health. In this regard, schedule-based and predefined environmental control is one of the main reasons for the current discomfort and dissatisfaction with the thermal environment. Recent research is attempting to leverage occupants’ demand in the control loop of the buildings to consider the well-being of each individual based on their own physiological properties. These thermal comfort models are called "personalized comfort models". In this regard, studies are trying to utilize skin temperature recorded by infrared thermal cameras for developing personal comfort models through machine learning prediction algorithms. However, some critical gaps in the current methods have limited the application of this platform in real buildings. The contribution of this dissertation is in the three main aspects of literature review, data collection, and model development. This study presents a comprehensive and systematic review of the current machine learning-based personalized thermal comfort studies. In addition, we introduce "Charlotte-ThermalFace", our recently developed dataset, and how it addresses some of the existing gaps in the subject. Charlotte-ThermalFace contains more than 10,000 infrared thermal images in varying thermal conditions, several distances from the camera, and different head positions. Using this dataset, we have developed a personalized comfort model for subjects farther away in a completely non-intrusive method.
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.