Ultra-precision manufacturing is a deterministic method of producing optical-grade components. Continuous and interruptive machine operations are the focus of this research with the goal of improving the manufacturing community’s knowledge. The original contributions of this research are: (a) a comprehensive analysis of the cutting mechanics of single-crystal germanium, specifically studying the effects of major crystal orientation in germanium and cutting speed; (b) methodology for producing flat, damage-free test samples in single-crystal germanium; and (c) machine learning model for estimating surface finish parameters Sa, Sq, and Sz for SPDT of single-crystal germanium and oxygen-free high-conductivity copper. As a final product of this research, a pair of collimating lenses were produced. AFRL funded the research for development of these lenses.
Ultra-precision manufacturing is a deterministic method of producing optical-grade components. Continuous and interruptive machine operations are the main focus of this research with the goal of improving the manufacturing communities knowledge. The original contributions of this research are: (a) a comprehensive analysis of the cutting mechanics of single-crystal germanium, specifically studying the effects of major crystal orientation in germanium and cutting speed; (b) methodology for producing
flat, damage-free test samples in single-crystal germanium; and (c) machine learning model for estimating surface finish parameters Sa, Sq, and Sz for SPDT of single-crystal germanium and oxygen-free high-conductivity copper. As a final product of this research, a pair of collimating lenses were produced. AFRL funded the research for development of these lenses.
Demand for clean and safe drinking water is a global challenge because of water scarcity, growth of human population, urbanization, and anthropogenic pollution. Purification of water involves removal of small molecules and ions from ground water addressed as “emerging contaminants” which are extremely mobile and toxic in nature, do not degrade or hydrolyze easily, and highly soluble in water resulting in bioaccumulation. Most of the current water treatment systems have complex deficiencies that affect their overall performance. We have synthesized carbon nanostructures assisted ion exchange resins in aqueous medium that help remove these emerging contaminants in a fast, easy, and high capacity manner while supporting less contact time and low transmembrane resistance primarily achieved using thin film assemblies. We have developed a novel sonochemistry assisted atom transfer radical polymerization (SONO-ATRP) process for synthesis of polyelectrolyte anion exchange resins in water without use of any external initiator or reducing agents while using only a few ppm of catalyst. We successfully performed high-density functionalization of polyelectrolyte anion exchange resin strands onto single walled carbon nanotubes sidewalls using the SONO-ATRP process while at low reaction temperatures thereby providing a less energy intensive alternative for green chemistry. We have developed green processes to defluorinate fluorographite in water and simultaneously perform covalent grafting of anionic short brushes of poly(vinyl benzyl trimethylammonium chloride) to its surface under mild reaction conditions without need of any external reactive reagents. Field Emission Scanning Electron microscopy of thin film of functionalized carbon nanotubes demonstrated pin-hole free mesoporous architecture illustrating scaffold robustness while thin films of functionalized fluorographite exhibited stacked arrangement of plate-like structures. Exfoliation and functionalization of fluorographite was revealed through Transmission Electron Microscopy. Both the resins demonstrated high water flux (>1500 L m^(-2) h^(-1) bar^(-1)) due to their intrinsic architecture and high percent removal (>90%) of contaminants due to the tortuous path length during molecular transport through the membrane. These properties enable adsorption of impurities at environmentally relevant concentrations. These materials exhibited facile regeneration and reusage of the thin films, thus supporting sustainability. In conclusion, these processes abide by the principles of green chemistry and their processability opens new avenues for smart point-of-use water purification systems.
Decades of discriminatory housing policies have resulted in geographic segregation, forcing low-income minorities into areas of concentrated poverty (Massey & Kanaiaupuni, 1993; Stoloff, 2004). Areas of concentrated poverty are typically marked by poor housing quality, under performing schools, high crime rates, and limited access to resources such as healthcare and grocery stores, lack of social cohesion, and poor health outcomes (Crump, 2002; Dutko, Ver Ploeg, & Farrigan, 2012; Kawachi & Berkman, 2000; Massey, 1990). To combat the challenges associated with concentrated poverty and build healthy communities, place-based interventions have become increasingly popular (Arias, Escobedo, Kennedy, Fu, & Cisewski, 2018; Diez-Roux, 2017; Jutte, Miller, & Erickson, 2015). Several place-based models (e.g., Harlem Children’s Zone, Purpose Built Communities) have shown positive outcomes (Bridgespan 2004; 2011), however evaluation to guide replication and best practices have lagged.
This study examined data from a nonprofit replicating the Purpose Built Communities model in the southeastern U.S. Renaissance West Community Initiative (RWCI) is a place-based nonprofit that coordinates activities and services for residents living in a newly redeveloped mixed-income community and an adjacent low-income community. Activities coordinated by RWCI include college and career readiness programs, health education programs, health resources, community engagement activities, and children’s programs. Data from program participation and community surveys were assessed to understand the characteristics of adult residents, such as their education level, employment status, income, health, social networks, perceptions of their neighbors, participation in the nonprofit’s activities, and the degree to which each of these variables are related. Additionally, longitudinal analyses examined changes in these variables over a twelve to eighteen-month period.
Findings show that residents’ socioeconomic status (SES) and social network size were the primary predictors of the types of RWCI activities in which they participated and the frequency of participation. Participation in RWCI’s activities was not related to changes in SES, health, or neighborhood perceptions, but participation in activities was related to increased social network size. Social networks also played a role in neighborhood perceptions, such that residents with stronger neighborhood social networks had more positive perceptions of their neighbors overall. Residents with a disability had the lowest perceptions of their neighbors and reported worse health status.
The present study provides an example of how even limited quantitative data can be used by place-based nonprofits to understand the characteristics and experiences of adults living in their service area, to monitor implementation and outcomes, and provide guidance for improvements in use of resources to improve the community. The findings have implications for RWCI and their ongoing efforts to revitalize this low-income neighborhood into a healthy mixed-income community. Recommendations for ongoing data collection and analyses, targeting of services, and community building strategies are provided.
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.