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The need for ultra-precision optical components with intricate geometric profiles has grown rapidly in the last few decades. Applications of precision optical lenses range from consumer electronic products to optical sensing instruments, microscopy, astronomy, etc. Typically, polymer-based lenses have been used and have dominated the industry so far, but due to the advantages of using glass components, the demand for ultra-precision glass aspherical components has been steadily raising. In fact, it is estimated that the demand for aspherical glass lenses will grow at a rate of 6.5% in the next five years. However, the conventional manufacturing processes when used for producing aspherical glass components become time-consuming and expensive.
Precision glass molding (PGM) technology provides an alternative manufacturing technique to fabricate aspherical glass lenses and irregular optical products. It has the advantages of high forming accuracy, short manufacturing cycles, low cost, and high volume production compared to the traditional manufacturing process. However, the process has a few drawbacks such as lens profile deviations, stress birefringence, etc. Typically, the mold surfaces are machined to be exact negatives of the required lens profile, assuming the lens would take the shape of the molds. But in reality, the complex mechanical behavior of the glass and its high-temperature dependence affects the final lens profile at room temperature. In addition to geometric deviations, the rapid temperature changes, often as much as several hundreds of degrees, in a short time affect the performance of the molded lens. These drawbacks need to be addressed before the glass molding process can be used as a viable option for mass-producing optical components.
As such, in this dissertation, a coupled thermo-mechanical finite element model is established to simulate the precision glass molding process on two different glass types, D-ZK3 (CDGM) and P-SK57 (Schott). The glass is modeled as a thermo-viscoelastic material by defining the stress and structural relaxation parameters. A new testing technique based on the cylinder compression test is developed in this study to extract the viscoelastic parameters at different temperatures. The obtained material parameters when used in the numerical simulations showed a good agreement with the experimental data throughout the testing temperature range. Further, the viscosity of the glass (a highly sought-after property of glass in precision molding) is obtained as a by-product of the proposed material calibration test. Finally, the structural relaxation parameters are obtained from the impulse excitation test based on ASTM standard E1876. All the experiments required for fully calibrating the viscoelastic response of the glass are performed on a precision glass molding machine, Moore Nanotech GPM170 machine. The obtained material parameters are used in the finite element model to predict the lens deviations and the stresses in the molded lens. A mold compensation technique is used to correct the mold profiles for any deviations. The lens molded using the corrected molds is shown to fall within the designer's specifications.
The process parameters used during the molding process play a vital role in determining the profile accuracy and the optical quality of the molded lens. Hence, it is important to determine an optimal parameter set before applying any mold compensation techniques. But due to the obscure and complex nature of the process, determining the parameter sets empirically is a tedious process. As such, in this study, the developed numerical model is used to individually analyze the different process steps and the corresponding process parameters on the profile deviations and the residual stresses in the molded lens. The results obtained in this study can be used as a reference to fast-track the manufacturing process.
Human-AI co-creativity involves a human and an AI collaborating as partners on creative tasks such as generating music or art. This research domain is particularly timely as AI becomes increasingly prevalent in collaborative spaces. With the availability of ChatGPT, DALL.E 2 and other generative AI tools, co-creative AI is gaining increased popularity. Unlike general human-computer interaction, human-AI co-creation establishes a complex relationship where AI actively contributes, assumes human-like roles, and generates novel content blended with the user's contribution. Therefore, designing engaging and ethical co-creative systems poses challenges due to the open-ended nature of human-AI interaction. This dissertation contributes empirically and theoretically to the design of engaging and ethical human-centered co-creative AI. It focuses on four main areas: designing interaction, the impact of AI-to-human communication, ethical guidelines and understanding users' mental models of co-creative AI in human-AI co-creation. Firstly, this dissertation introduces the Co-Creative Framework for Interaction Design (COFI), which describes the broad range of possibilities for designing interactions in co-creative AI. Additionally, an analysis of 92 existing co-creative AI identifies common interaction design trends and research gaps. The analysis reveals a notable gap in commonly employed interaction designs: the absence of two-way communication between humans and AI, where AI cannot communicate with humans, limiting their potential as partners. Inspired by the research gap identified, this dissertation delves into examining the impact of AI-to-human communication on user experience and perception of co-creative AI. Two prototypes of a co-creative system, with and without AI-to-human communication, were developed to facilitate a comparative study. The results show improved collaborative experience and user engagement with the AI that can communicate. Moreover, the results shed light on emerging ethical concerns alongside increased user engagement. Inspired by the findings, this dissertation further explores the ethical challenges in human-AI co-creation by taking a human-centered approach. A design fiction study is presented to explore several ethical dilemmas and challenges in human-AI co-creation from the perspective of potential users. Findings provide potential users' perspectives, stances, and expectations, serving as a foundation for designing human-centered ethical AI partners in human-AI co-creation. Finally, this dissertation investigates users' mental models of co-creative AI, a crucial aspect of designing human-centered co-creative AI. A survey study is used to delve into users' mental models of co-creative AI and their association with user demographics to identify ways to design value-sensitive co-creative AI. The results also lay the groundwork for future research on personalized and adaptive co-creative AI in human-AI co-creativity.
Exclusive breastfeeding in the first six months of life and continued complementary breastfeeding up to 24 months is encouraged to ensure optimal infant and young child nutrition and health. The WHO and UNICEF emphasize these optimal Infant and Young Child Feeding (IYCF) practices, especially for regions of the world where extensive child nutrition and healthcare support is lacking or inaccessible.
This dissertation explores the epidemiology of IYCF practices and child health outcomes in sub-Saharan Africa. It also examines the status of IYCF policies and programs in this relatively less studied region of the world. I use publicly available data from the Nigerian Demographic and Health Survey (NDHS) and the WHO/UNICEF Breastfeeding Collective scorecard to answer important questions explored across three studies.
Findings from the first study suggest that longer durations of breastfeeding are associated with fewer reported acute illnesses post-infancy at 24 to 59 months; demonstrating the long-term protective effect of breast milk from illnesses that contribute to the high under-five mortality rates recorded for decades in sub-Saharan Africa. Another important finding from the second study is that the relationship between exclusive breastfeeding, household living environmental conditions, and acute health outcomes in infancy is complex. The results suggest that the efficacy of exclusive breastfeeding in reducing the incidence of diarrhea and acute respiratory illness is strongest for infants living in households with poor sanitation facilities and inadequate building materials respectively. Lastly, findings from the third study indicate that sub-Saharan Africa as a region is yet to meet global and World Health Assembly targets for the implementation of recommended IYCF policies and programs. These findings have implications for child nutrition and health outcomes in especially for a region already disproportionately impacted by high under-five mortality rates.
Exclusive breastfeeding in the first six months of life and continued complementary breastfeeding until a child is 24 months is encouraged to ensure optimal infant and young child nutrition and health. The WHO and UNICEF emphasize these optimal Infant and Young Child Feeding (IYCF) practices, especially for regions of the world where extensive child nutrition and healthcare support is lacking or inaccessible.
This dissertation explores the epidemiology of IYCF practices and child health outcomes in sub-Saharan Africa. It also examines the status of IYCF policies and programs in this relatively less studied region of the world. I use publicly available data from the Nigerian Demographic and Health Survey (NDHS) and the WHO/UNICEF Breastfeeding Collective scorecard to answer important questions explored across three studies.
Results from the first study suggest that longer durations of breastfeeding are associated with fewer reported acute illnesses post-infancy at 24 to 59 months; demonstrating the long-term protective effect of breast milk from illnesses that contribute to the high under-five mortality rates recorded for decades in sub-Saharan Africa.
Another important finding from the second study is that the relationship between exclusive breastfeeding, household living environmental conditions, and acute health outcomes in infancy is complex. The results suggest that the efficacy of exclusive breastfeeding in reducing the incidence of diarrhea and acute respiratory illness is strongest for infants living in households with poor sanitation facilities and inadequate building materials respectively. Lastly, results from the third study indicate that sub-Saharan Africa as a region is yet to meet global and WHA targets on the implementation of many IYCF policies and programs. These findings have implications for child nutrition and health outcomes in especially for a region already disproportionately impacted by high under-five mortality rates.
Nucleic acids are highly integrated into molecular biology and exhibit very interesting character with immense engineering potential to improve human health and influence molecular biology. Consequently, these biopolymers are the quintessential material for facilitating natural and therapeutic functions in basic research, biomedicine, and biological sciences. The field of nucleic acid nanotechnology is a massive research endeavor to understand and take advantage of DNA and RNA. Progression of the field is evident with an increasing amount of therapeutic nucleic acids (TNAs) approved for clinical use and several TNAs (mRNA vaccines) proved to be highly efficient to address the SARS-CoV-2 pandemic. Nucleic acid nanoparticles (NANPs) are an innovative class of structures. Herein, a review of the field of nucleic acid nanotechnology is given to summarize the potential of the field. Then, a focus on NANPs is taken though experimental work that offers novel methods of restructuring and functional options. A novel assembly method via selective nuclease degradation of RNA/DNA hybrids is introduced and DNA templated silver nanoclusters, as a new class of therapeutics, are characterized to optimize their antibacterial function. These studies advance the development of functional nucleic acids for the treatment of diseases and the improvement of the quality of life.
Enhanced power density factors can be achieved in the new generation of power electronics by utilizing wide-bandgap semiconductor switching devices with higher switching speeds and lower losses. These characteristics make high-frequency switching (wide-bandgap-based) power converters superior to silicon-based converters in several respects, including better size, weight, efficiency, and power density than silicon-based converters. The design and manufacturing of these power converters have significantly different requirements compared to traditional converters, making it challenging to integrate components and sensors with tighter tolerances. Wideband current sensors are also necessary for diagnosing, monitoring, and controlling wide-bandgap power converters. Speed is not the only concern when developing power converter layouts; size and invasiveness are also significant considerations. Several properties, such as size, speed, noise immunity, accuracy, linearity, capacity, isolation, and non-invasiveness, are required for the next generation of power converters that cannot be achieved with currently available commercial current sensors. Due to size and cost constraints, these converters cannot be equipped with current probes either. Therefore, non-invasive, ultrafast, high-capacity, switch noise-immune sensors are required by wide-bandgap-based power electronics converters.
In this thesis, comprehensive studies of single-scheme and hybrid current sensors are presented as well as issues regarding their integration into power electronics. The present study illustrates that there is no specific method of current sensing that can combine all the required sensing factors at once. The results of a feasibility study have been used to develop guidelines for the design of current sensors that provide high-quality output signals and are readily applicable to the next generation of power converters. Frequency response verification using vector network analyzers and also different types of current waveform comparisons will prove the functionality of proposed light-size and low-cost sensing solutions.
The real, complex network environment consists of an ever-increasingly diverse and large amount of data encapsulated in packets. Surveillance and monitoring of this traffic is a necessary task for law enforcement, cybersecurity, and intelligence agencies. Intercepted network traffic must be classified into multiple categories, such as the protocol encapsulation layers contained, application it originates from, user generating the traffic, and the traffic's malicious or benign nature. There is a lack of solutions which are able to classify packets individually without flow-based features. In order to address the gaps in current traffic classification and DPI techniques, we propose the initial release of the Forager toolkit, a software consisting of tools to extract hidden representations from individual packets and use these features in deep learning models to perform traffic classification. It uses data mining techniques to perform automatic generation of regular expression signatures, locality-sensitive hash fingerprints, and matrix and point cloud representations of packets. These are used as input features for corresponding deep learning models which can perform traffic classification on single packets in a real system. The models are multi-modal to capture multiple angles and dimensions of features for increased complexity of classification problems. They can be run in parallel for optimal throughput and scalability. Our experiments use these models in multiple configurations and scenarios to demonstrate superior performance and classification capability to advance the state of the art in complex network traffic surveillance and hidden representation learning.
DNA and RNA are structurally and functionally diverse biopolymers that have shown promise in recent years as a powerful biomedical tool, in the form of nucleic acid nanotechnologies. The applications of these technologies include biosensing, diagnostics, cancer therapeutics, vaccines, and many more. A relatively unexplored area to which nucleic acid nanotechnology is being applied is the field of antibacterial research. By combining short DNA oligos with silver cations, folding the DNA into its proper secondary and tertiary structures, then reducing the silver, DNA may template the formation of few-atom silver nanoclusters (AgNCs). Silver has been well understood for centuries to be an effective antibacterial agent. Many silver nanostructures have been investigated for their potential efficacy as antibiotics. DNA-AgNCs have been shown to be effective at preventing bacterial growth in a variety of conditions. A unique advantage of DNA-AgNCs is that, unlike many larger silver nanostructures which typically absorb light through surface plasmon resonance, AgNCs fluoresce in a manner dependent on the sequence and structure of the templating oligonucleotide(s). Due to the unique structure-function relationship of AgNCs, further investigation of their structure is warranted. Presented herein is a thorough review of silver nanomaterials, along with work demonstrating the effectiveness of a DNA-AgNC hairpin system against a model E. coli system, and the characterization of an RNA ring which may serve as the scaffold for a multitude of functionalities, including DNA-AgNCs, in preparation for future work.
Community violence occurs primarily in public settings, frequently involves high-risk behaviors such as firearm use, and is often geographically concentrated as a result of racial and economic segregation enforced through policy and practice. Community violence has risen in Mecklenburg County, North Carolina over the past five years, with a plurality of incidents concentrated in neighborhoods which also have high rates of social, economic, and health-related risk factors. This dissertation builds on my work with the City of Charlotte and Mecklenburg County as part of a multi-sector collaboration intended to leverage resources and align programs and policies to disrupt, reduce, and prevent community violence. In this dissertation, guided by the Ecological Systems Theory and Social Determinants of Health Framework for Action, I used qualitative, quantitative, photographic, and geospatial data to (1) explore residents’ perceptions of safety and experiences of community violence; (2) describe an integrated, place-based methodology that can be used in community violence research; and (3) explore how positionality informs cross-sector, collaborative data sharing efforts to address community violence.
In study one, participants identified neighborhood features across ecological levels that contributed to them feeling safe or unsafe. Notably, participants perceived historical and on-going disinvestment, enacted through structural racism, as contributing to unsafe conditions. In study two, which grows out of study one, we found that walking interviews generated more findings specific to place and situated within the micro-, meso-, and exosystem levels, while more traditional, semi-structured sedentary interviews yielded results that were largely centered within the individual and microsystem levels. In addition, using an integrated methodology highlighted gaps in the publicly available quantitative data and demonstrated the utility of employing multiple methods to capture data related to place, most notably by generating data that informed actionable insights across ecological levels. In study three, we found that individuals’ and organizations’ social identities (e.g., individuals’ level of data knowledge and data sharing experiences, and organizations’ use of formal data sharing processes) as well as power (specifically, individuals’ sense of empowerment, and organizations’ use of resources and data sharing capacity) interacted to influence barriers and facilitators to data sharing.
Findings point to areas for future research and suggest local implications including (a) the need for increased attention in research and practice related to how structural racism contributes to unsafe neighborhood conditions; (b) the potential benefits of considering how the described integrated, place-based methodology can be scaled to capture residents’ perceptions of safety and experience of violence across neighborhoods; and (c) the salience of attending explicitly to how the positionality of the individual and organization contributes to barriers and facilitators to cross-sector data sharing. Results from my dissertation can be used locally to inform cross-sector, collaborative solutions to community violence that incorporate residents’ perspectives and address risk factors across ecological levels. While conducted in Mecklenburg County, results also have implications for community violence prevention and intervention efforts in communities across the country.
Transgressions perpetrated by an institution against an individual that trusts or relies upon that institution is a construct known as institutional betrayal and is a burgeoning area of research in the healthcare setting. These transgressions can include either an action that is committed or an omission on behalf of the system. Healthcare institutional betrayal has been associated with lower trust in healthcare providers, greater negative expectations for healthcare, and healthcare avoidance, suggesting that past experiences with the healthcare system affect one’s ongoing and future relationship with these systems. The current study employed a between-group experimental design with participants randomly assigned to read vignettes with varying levels of healthcare institutional betrayal. Participants (N = 473) completed baseline measures of trust in healthcare providers, medical mistrust, institutional betrayal. Participants were then randomly assigned to read one of three vignettes that depicted differing levels of healthcare institutional betrayal (control, low institutional betrayal, high institutional betrayal). Following the experimental manipulation, participants completed measures of healthcare avoidance and negative expectations for future healthcare. They also completed a task which allowed for collection of implicit cognitive measures (captured by mouse-tracking software) designed to assess conflict related to seeking healthcare post institutional betrayal. As hypothesized, participants assigned to the low and high institutional betrayal conditions endorsed greater negative expectations for healthcare and lower trust in healthcare providers post-manipulation. Contrary to our hypotheses, participants randomly assigned to the low and high institutional betrayal conditions did not indicate greater healthcare avoidance as measured via self-reported healthcare avoidance or via implicit measures of healthcare avoidance. However, there was an interaction of response type (“probably yes” versus “probably no”) and condition, indicating that following institutional betrayal, there may be greater hesitation, even when choosing to seek healthcare. Overall, results indicated that institutional betrayal can cause lower levels of trust and higher levels of negative expectations of healthcare. Additionally, the results shed light on how participants make decisions to seek healthcare following the experience of institutional betrayal.
