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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.
Trauma-informed care has been the subject of numerous scholarly studies in various contexts for nearly four decades. Thus, TIC practices have been shown to reduce educators' stress (Jaycox et al., 2019), increase teacher awareness of student trauma experiences (Kuhn et al., 2019; McIntyre et al., 2019), and improve teachers' self-confidence in engaging with and helping students with trauma (Gruman et al., 2013). However, while most education research has focused on how school personnel can implement trauma-informed practices, no research supports the comprehension of the lived experiences of K-12 trauma-trained teachers who implement TIC intervention strategies in their classrooms. This study aimed to explore the lived experiences of trauma-trained K-12 teachers that have incorporated TIC intervention strategies within their classrooms to help understand their perceptions of the impact of trauma training and the use of TIC intervention strategies. The researcher used a purposive sampling and semi-structured interview format to investigate the experiences of six trauma-trained K-12 teachers using TIC intervention approaches in their classrooms. The results generated four major themes: The Impact of Training, Implemented TIC Strategies and Practices, Factors and Practices Contributing to Trauma-Informed Education, and Challenges. All the themes were fundamental to answering the research question related to lived experiences and perceptions of trauma-trained teachers. In an attempt to tackle ACE among K–12 students, this study provides implications and recommendations for future research that may improve the efficacy of TIC intervention strategies among teachers and school counselors.
With the modernization of power grids, high penetration of distributed energy-based resources (DERs), and modern loads, optimal power flow (OPF) analysis is one of the essential tools for reliable power system planning and operation. This research proposes novel OPF models for power distribution and transmission networks using second-order cone programming (SOCP). The advantages of SOCP-based convex OPF models are the efficient computational ability for large network systems and the global optimality. To confirm solution accuracy, the necessary conditions for the tightness of the angle and conic relaxations of power flow models are addressed in this research work for the proposed OPF models. In this dissertation, an OPF architecture is proposed to retrieve the bus voltage angle difference for radial distribution networks and thus control the reactive power flow, leading to better voltage regulation in the network and promising a globally optimal solution. This research also presents a SOCP-based AC-OPF model for unbalanced three-phase radial power distribution networks. Mutual coupling effects are generally ignored in the existing multi-phase SOCP AC-OPF models. The proposed SOCP-OPF model introduces a coupling coefficient for the mutual coupling effects on the three-phase unbalanced lines to overcome this critical issue. The derivation of the coupling coefficients has been illustrated with the required proof that the relaxation is tight and the solution from the proposed OPF model is optimal for an unbalanced multi-phase distribution network. Besides the distribution networks, this work also presents a novel SOCP-based OPF formulation for transmission system power networks. Power transmission networks generally have meshed orientation. For meshed power networks, though the conic relaxation is exact due to the cyclic angle constraints, the angle relaxation may not be exact. An OPF model is proposed for the SOCP-OPF model for power transmission networks satisfying the cyclic angle constraints. For that, the model defines a convex envelope to represent the relative bus voltage angles that satisfy the cyclic constraint criteria for a mesh in the network. This dissertation also presents an OPF formulation for AC-DC hybrid power distribution networks. The model determines the optimal modulation index for the converters for minimum network loss. In addition, this dissertation also proposes a distributed OPF (D-OPF) model for distribution networks and a time-dependent (T-OPF) model for real-time OPF analysis. All the proposed models in this research are tested in multiple and extensive networks, and the results show that the models are exact to produce globally optimal solutions for the reliable operation of the power grid.
Power electronics converters are an integral part of modern power and energy system. Power semiconductor switching devices are the most significant component in a power electronics converter. Power semiconductor devices such as MOSFETs/IGBTs age through different degradation mechanisms in long-term applications. Failure of MOSFETs/IGBTs is one of the primary causes of power electronics failure. And MOSFETs on-state resistance (RDSON) is a key health-indicating parameter. This dissertation presents sensing circuit designs that enable real-time monitoring of on-state voltage of the MOSFETs/IGBTs. In addition a complete online in-situ health monitoring system of the MOSFETs in a three-phase inverter is presented. A new on-state drain-source voltage (VDSON) sensing circuit has been used to monitor the RDSON of the high-side MOSFETs. This sensor references the drain of the high-side transistors for their VDSON measurement and allows VDSON measurement of multiple high-side transistors with respect to the same ground reference. The high-side VDSON measurement circuits combined with low-side VDSON measurement circuits have been used for a complete RDSON monitoring of all the MOSFETs in the inverter. The drain current (IDS) is captured from measurements using an off-the-shelf current sensor located at the output filter inductor. Accounting for propagation delays in the measurement circuitry, both the VDSON and IDS are sampled and converted into digital data multiple times in a switching cycle, filtered, and stored in a digital signal processor (DSP). The DSP, originally used for the inverter control, then processes the sensor data captured over one grid cycle and calculates the average RDSON of the MOSFETs of the inverter. Validations of all the sensing circuits, using theoretical analysis and hardware experiments along with the software implementation for data processing and handling are presented in the dissertation for this real-time, in-situ RDSON measurement. Furthermore, a method of mapping the MOSFET's RDSON to the junction temperature is presented which can be used for real-time accurate junction temperature estimation of the MOSFETs.
Maternal eating patterns during pregnancy and the first year postpartum contribute to short and long term maternal and child health outcomes. Food choices are thought to result from an interaction between individual-level appetite and the diversity and quantity of foods available to the individual. Appetite, the motivational drive to eat, is regulated by both internal and environmental factors and occurs both within and outside of physiological energy deprivation. Through a series of three manuscripts, this work examined psychophysiological influences on maternal appetite and their interrelations to understand how these factors present in pregnancy and postpartum, how they change over time, and their role in predicting the development of specific food desires. The Power of Food Scale (PFS), a measure of hedonic hunger, assesses perceived responsiveness to food stimuli in the environment. PFS retains stable psychometric properties and remains at similar levels across its subscales through pregnancy and the first year postpartum. In contrast, leptin, a hormone with roles in satiety, reward, and reproduction, shows positive mean change over the same time. Neither of these appetitive influences nor dietary restraint were associated with variability in cravings concurrently or prospectively, during pregnancy or postpartum. Overall, results of these studies suggest that these appetite influences vary relatively independently during pregnancy and postpartum, in contrast to relationships observed outside this time. Future research could build upon these findings by incorporating additional appetitive influences and/or increasing the frequency of assessments to capture fluctuations within trimesters or within the first year postpartum.
Sexual violence on college campus is a salient threat to the health and well-being of students in higher education. Title IX legislation was developed to address and help reduce sex-based discrimination, including incidences of sexual violence, on college campuses. However, existing data suggests that a relatively small number of campus survivors make a formal report and subsequently have an interaction with the Title IX Office (Cantor et al., 2015). Additionally, little is known about the implementation of Title IX processes, the nature of Title IX sexual violence reports, or the outcomes of survivors involved in Title IX reports. The current study adds to our understanding of these survivors’ experiences. Specifically, the study utilized archival Title IX report data obtained from one large public university during the 2018-2019 and 2019-2020 academic years (n = 151) to explore the nature and scope of Title IX sexual violence reports and the academic health outcomes of survivors post-report. The study utilized data extracted from Title IX sexual violence reports to describe the characteristics of Complainants (i.e., survivors), Respondents (i.e., perpetrators), incident characteristics, reporting processes, and characteristics of cases involved in formal university hearings. The study also utilized aggregated data from the Title IX sexual violence reports in conjunction with data obtained from UNC Charlotte Maxient system, which contains student GPA and enrollment status, to examine the academic health of survivors over time. Complainants predominantly identified as Caucasian (65%) and female (93%). Respondents predominantly identified as Caucasian (42%) and male (99%). Complainants most often identified Respondents as friends (16%), ex romantic partners (16%), or acquaintances (14%). Only 11% of Respondents were identified as strangers. Complainants were most often referred to the Title IX Office by mandated reporters (87%). Over half of the Complainants (62%) engaged with Title IX staff following initial outreach. Many cases had incomplete academic data (no pre-report or post-report semester GPA). However, in the sample with three GPA time points (n = 57 survivors), academic outcomes over time were not significantly associated with Respondent’s affiliation to the university, source of referral to the Title IX Office, engagement in the reporting process, or involvement in a formal university hearing. Seventeen percent (n = 25) of Complaints dropped out of the university. However, Complainant engagement with the Title IX Office was not significantly associated with dropout. These findings increase our understanding of the Title IX process and the experiences of campus sexual assault survivors who are involved with the Title IX Office.
