-
Prospective Students
-
Faculty and Staff
-
Current Students
-
Alumni and Friends
The environment where a person lives impacts their health more than clinical care provided. (RWJF, 2013) This research posits that the determinants of health (DOH) are best understood as a combination of social, structural, spatial and temporal aspects, not just “social”. Literature to date acknowledges these dimensions, although researchers have yet to fully explore. Utilizing a mixed-method approach, this research explores various DOH and their interactions spatially, structurally and temporally at the neighborhood level and how changes to those determinants are impacted by restructuring forces adversely affecting a Hispanic immigrant population. Specifically, this research aims to answer the following questions (1) How are the DOH impacted by the social, spatial, structural and temporal elements individually and in concert; (2) How has urban restructuring been a factor in the DOH for the Hispanic immigrant population in Southwest (SW) Charlotte; and (3) How does the acknowledgement of the structural, spatial and temporal aspects of DOH inform action to address the social and health needs of Hispanic immigrants living in Charlotte, NC. The South Boulevard corridor in the SW area of the city is the ideal case study location as it is simultaneously experiencing several forms of urban restructuring and an on-going influx of Hispanic immigrants. Ultimately, urban restructuring is an overlooked DOH in its own right - especially as it impacts vulnerable communities such as Hispanic immigrants as well as the importance of viewing the DOH in a nuanced manner acknowledging the influence and interactions of the various aspects.
This dissertation presents a new class of power converter topologies that realize galvanic
isolation by utilizing active transistor devices instead of conventional transformers.
The power converters employ standard switch-mode topologies but isolate the
ground connections with the addition of active switches on the ground side of the
power path. Compared to transformer isolation, the Active Isolated (AI) converters
have reduced size and cost with increased efficiency. A generalized approach is given
that is used to create thrity-six new active isolated topologies based on the following
basic converters: buck, boost, buck-boost, Cuk, SEPIC, and Zeta. Of these, the
buck-boost and boost-buck are determined optimum topologies since they achieve
pulsating and non-pulsating galvanic isolated conversion with the fewest component
count, respectively. The two optium converters are modeled mathematically and various
protoypes are developed that confirms proper galvanic isolation. The concept of
unipolar and bipolar isolation is explored and it is found that in many applications,
including the application choosen for this work, that unipolar isolation is adequate
to provide proper operation and safety for the user. Commom-mode transient and
steady-state models of the converters are developed and correlated to experimental
results. The two optimum convertes are used in two appliations: PV microinverter
and offline AC-DC power supply with fault protection.
Perfectionism was once thought to be a detrimental personality trait that impacts health and psychological outcomes in negative ways. However, modern conceptualizations demonstrate that this trait is multidimensional and that impacts on outcomes are complex. Additionally, person-environment interaction (PEX) theories stipulate that personality traits are only triggered and expressed in environments that are relevant for that trait, that individuals are drawn to environments that “fit” with their underlying personality traits, and that personality traits can interact with environmental conditions in unique ways. Thus, the present study was designed to apply this perspective and examine the impact of perfectionism on psychological outcomes in the context of one particularly perfection-focused environment: the social networking site of Instagram. Secondary analysis of an existing data set was undertaken to address three research questions: (1) Are perfectionists drawn to the social media environment of Instagram? (2) Does perfectionism impact specific aspects of Instagram use? and (3) Is Instagram a more detrimental environment for perfectionists than non-perfectionists? An overall pattern of findings across 70 regression analyses provided preliminary answers to these questions. Results demonstrate that individuals high in one dimension of perfectionism, evaluative concerns perfectionism (ECP), are more likely to use Instagram and that these individuals tend to engage in active and problematic Instagram behaviors. Additionally, results demonstrate that these specific Instagram behaviors exacerbate the detrimental impact of ECP on psychological outcomes. Results of this study shed new light on both perfectionism and Instagram use, as well as highlight the importance of contextualizing both person-level and environment-level determinants of health-related psychological outcomes in general. Empirical and applied implications are discussed.
Given multiple budget and revenue constraints that the transportation sector encounters, predictive analytics enables maintenance agencies to make effective decisions, prioritize maintenance tasks, and provide efficient life-cycle planning. To this end, risk-based predictive models have provided promising results in representing the susceptibility of assets to future defects. Hence, the main objective of this study is to provide an integrated framework for predicting the occurrence probability of multiple defects on different highway asset types. Several gaps in previous models were identified, including limitations in predictive frameworks given the inadequate scope of available inspection data, expert-based selection of contributing factors, and ignoring the interrelationships between neighboring assets. Therefore, this study proposes a risk-based method that combines a risk score generator and a Machine Learning (ML) algorithm to predict the hotspots of multiple defects in a given roadway. To find the best fit, the model is chosen from a pool of ML algorithms selected from different categories. To measure the efficiency of the proposed model, its performance is investigated on a selected case study. The proposed framework produced significant accurate results within the extent of available data in the case study for calculating risk scores of erosion, obstruction, and cracking on paved ditches given historical weather, traffic, maintenance, and inspection data of five selected neighboring assets (flexible pavements, unpaved ditches, slopes, small pipes and box culverts, and under drain pipes and edge drains). Additionally, the contribution of the considered factors was investigated to further study the importance of individual contributors. The framework offers decision-makers a holistic view of degradation risks of multiple assets, which could enable them to prepare an integrated asset management program. Additionally, a similar framework can be applied to other linear infrastructure systems such as sanitary sewers, water networks, and railroads.
We consider the approximation of unknown or intractable integrals using quadrature when the evaluation of the integrand is considered costly. This is a central problem in machine learning, including model averaging, (hyper-)parameter marginalization, and computing posterior predictive distributions.
Recently Batch Bayesian Quadrature (BBQ) has combined the probabilistic integration techniques of Bayesian Quadrature with the parallelization techniques of Batch Bayesian Optimization, resulting in improved performance compared to Monte Carlo techniques, especially when parallelization is increased. While the selection of batches in BBQ mitigates costs of individual point selection, every point within every batch is nevertheless chosen serially, impeding the full potential of batch selection. We resolve this shortcoming.
We developed a novel BBQ method which updates points within a batch without the costs of non-serial point selection. To implement this, we devise a dynamic domain decomposition. Combining these efficiently reduces uncertainty, lowers error estimates of the integrand, and results in more numerically robust integral estimates. Furthermore, we close an open question about the cessation criteria, which we establish and support using numerical methods.
We present our findings within the context of the history of quadrature, show how our novel methods significantly improve the literature, and provide possibilities for future research.
The essential oil (EO) industry continues to grow as consumers search for more alternative and complementary therapies. When possible, EO users are quick to turn to EOs for basic medical ailments instead of traditional medications/pharmaceuticals. With the continually high growth of EO consumers, the scientific research to support their many applications is inadequate. Due to the large gap in EO research, users do not have enough scientifically proven sources to aid in their understanding of these oils. There is a crucial need for more EO related research. A large portion of my dissertation work will provide a solid platform for users to educate themselves on EOs from a scientifically driven stand point. It will also provide new data and insights on the application and molecular mechanisms of Boswellia carterii (frankincense) EO for targeting inflammation.
The enactment of the federal G.I. Bill in 1944 and subsequent amendments over the past 76 years have provided greater access to higher education for veteran service members (Servicemen’s Readjustment Act, 1944; Steele et al., 2018; U.S. Department of Veterans Affairs, 2018a). Military-affiliated students represent the largest number of non-traditional learners entering higher education (Osborne, 2014; U.S. Department of Education, 2016; U.S. Department of Veterans Affairs, 2013; VA Campus Toolkit, 2019) with continued growth estimated in future years (VA Campus Toolkit, 2019). This current and anticipated influx of student veterans necessitates post-secondary institutions to prepare for the unique strengths, challenges, and stressors presented by student veterans in their transition from the military to college.
This phenomenological case study explored the experiences of 12 faculty and staff members in a campus-based Green Zone professional development training program intended to support the transition of student veterans into higher education. Empirical research focused on faculty and staff experiences in Green Zone training is nonexistent. Aiming to fill a void in scholarly knowledge, this study investigated how faculty and staff experienced the phenomenon of Green Zone training. The exploration was guided by four research questions: 1) What are the initial motivations of participants to engage in Green Zone training?; 2) How do faculty and staff characterize their overall experiences in the Green Zone training program?; 3) What kind of perspective changes did participants experience during the training?; and 4) What are the post-training outcomes of participants’ attendance in Green Zone training? An iterative cycle of inductive analysis yielded 12 major themes and 31 subthemes from participant narratives and triangulated by additional contextual data. Due to the interpretive nature of the study, no single theoretical framework guided the research. Instead, highlighted thematic findings were situated against theories of organizational culture and transformative learning to provide robust context to the experiences of faculty and staff in Green Zone training. Additional scholarly literature added insight to discussion of research discoveries. Findings of the study showed that organizational culture was a contributory element in participants’ overall experience in the Green Zone program, while engagement in learning that exposed them to real-life experiences of a veteran served as a pivotal point of new understanding and connection to the material. An unexpected discovery of the research was the cognitive tension that participants experienced in navigating competing ideological forces to redefine the concept of a supportive campus community for all students. Implications of this study inform application of professional development practices for higher education leaders and training practitioners in support of student veterans and other invisible and marginalized student populations.
In recent years, emotion detection in text has become more popular due to its vast potential applications in marketing, political science, psychology, human-computer interaction, artificial intelligence, etc. Access to a huge amount of textual data, especially opinionated and self-expression text, also played a special role in bringing attention to this field. In this work, we review the work that has been done in identifying emotion expressions in text and argue that although many techniques, methodologies, and models have been created to detect emotion in text, these methods, due to their handcrafted features and lexicon-based nature, are not capable of capturing the nuance of emotional language. By losing the information in the sequential nature of the text, and inability to capture the context, these methods cannot grasp the intricacy of emotional expressions, therefore, are insufficient to create a reliable and generalizable methodology for emotion detection. By understanding these limitations, we present our deep neural network methodology based on bidirectional GRU and attention mechanism and the fine-tuned transformer model (BERT) to show that we can significantly improve the performance of emotion detection models by capturing more informative text representation. Our results show a huge improvement over conventional machine learning methods on the same dataset with an average of 26.8 point increase in F-measure on the test data and a 38.6 point increase on a new dataset unseen by our model. We Show that a bidirectional-GRU with attention could perform slightly better than BERT. We also present a new methodology to create emotionally fitted embeddings and show that these embeddings perform up to 13% better in emotion similarity metrics.
Afterschool programs play a significant role in the lives of minoritized students, offering a safe space for them to develop academically, socially, and emotionally. Program administrators are responsible for the oversight of the organization and must ensure that all staff members receive the necessary professional development to impact the lives of the students and families they serve. The purpose of this qualitative study was to understand the professional development needs of afterschool and out-of-school time administrators regarding culturally relevant pedagogy. The study was framed in culturally relevant pedagogy as theorized by Gloria Ladson-Billings. A case study methodology using interview data from 5 afterschool program administrators and a document analysis addressed the three research questions. Using a thematic data analysis, three themes were derived from the data: (1) making meaning of culture; (2) seeking knowledge; and (3) enacting culturally relevant pedagogy. The findings of the study revealed that afterschool programs engage in culturally-related activities but do not institute the tenets of culturally relevant pedagogy with intent. In order to build the understanding of these paraprofessionals, culturally relevant trainings should demonstrate disparate treatment through interactive activities, offer opportunities for collaboration and include ways to link current practices to the theory of culturally relevant pedagogy. Moreover, administrators must understand the content so that they can, when necessary, deliver the training to their staff with fidelity.
Despite its long history in the United States and abroad, the unconventional drilling industry, and specifically hydraulic fracturing technology, remain controversial. While the competing demands of energy from oil and gas are contrasted with environmental safety and protection, it is likely that unconventional drilling will remain a source of social friction and a wicked problem. From the viewpoint of social resilience in hydraulic fracturing communities, social conflict represents a potential threat to the bonds that are formed within a community. This research seeks to understand the impact of planning in communities that have implemented unconventional drilling technology by using a metric of litigation as a proxy for conflict. By seeking to illuminate how conflict is affected by both municipal and industry planning efforts this research seeks to answer the question of whether planning can reduce conflict and build resilience in communities where unconventional drilling is occurring. If conflict through litigation can be reduced through planning in these communities, then resilience may be preserved, enabling these extractive communities to reduce their exposure to disruption. This research begins with a quantitative analysis of the counties in Pennsylvania to determine which counties have detailed comprehensive plans that address unconventional drilling. The comprehensive plan data was then compared to the civil lawsuit data for each county to determine which counties have both detailed comprehensive plans and low rates of fracking related civil lawsuits. Using this quantitative data, three counties were chosen as case studies for the second phase of this research. Two counties demonstrating a high level of planning and also a corresponding level of social resilience were selected (Sullivan and Clinton counties). For contrast, one county with a high level of planning, but a low level of social resilience as measured by a high incidence of civil lawsuits per well was also studied (Lawrence). A series of semi structured interviews were conducted with community members and government staff to investigate the impact of planning in those counties relative to the unconventional drilling industry. While most unconventional drilling companies declined to be interviewed for this research, one company and an industry group were also interviewed. In Sullivan County, the social resilience appears to stem from the interconnectivity of residents, government, and industry that is encouraged by the comprehensive plan and further nurtured through industry involvement in the community. In contrast, Clinton’s plan provides a guiding vision for the industry, encouraging development upon prescribed paths that promotes conscientious and environmentally and socially responsible activity. In contrast, Lawrence county’s plan addressing unconventional drilling but is stymied by a lack of reciprocal interconnectivity from industry, though the county as adapted by transitioning to related industry by leveraging their manufacturing know-how. Social resilience is notoriously difficult to measure, but this research does provide support for the theory that counties that engage in high levels of planning and also have fracking companies that are active in community engagement may have improved social resilience through the building of social bonds.
