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GaN Device Characterization, Converter Optimization and Development for Enhanced Aviation Systems
No full textGaN device is one of the most promising candidates in high-efficiency and high-density power conversion applications. Due to the low ON-Resistance GaN devices are widely adopted in various soft-switching converters. Based on superior Baliga's Figure of Merit, GaN devices also have advantages over SiC and Si devices in hard switching conditions. However, the existence of dynamic weakens the advantage on the low conduction loss. The small footprint limits the heat dissipation ability hence the maximum switching frequency.Further improvement of power density may have constraints from passive components, which requires systematic optimization of GaN-based high-density converters.\\ By designing a multi-purpose testing platform, the dynamic of one GaN device is characterized under both DPT and soft-switching continuous tests, with different junction temperatures . Normalized is quantified and compared. The data can then be used to estimate extra loss from the dynamic under realistic power converter operating conditions.\\ The advantage of lower is especially prominent on low-Voltage rated GaN devices, which makes multi-level topology more preferable for GaN-based converters. A three-level Totem pole PFC converter for aircraft in-seat power supply is designed and optimized. The three-level topology enables the selection of the 200 V GaN device, leading to smaller conduction loss and a 98.4\% peak efficiency. With the help of the PR compensator and input voltage feedforward, the phase-leading problem caused by the digital delay is greatly reduced and proven in 800Hz line frequency. The THD can meet the standard by doubling the sampling frequency and improving sensing. The EMI performance also meets the requirement with a one-stage filter at ac side and a common-mode inductor on the DC bus.\\ For inverter design of unmanned aerial vehicle applications where there's a lower voltage DC-link, two-level gains advantage over three level after systematic optimization and evaluation on the weight and loss. Paralleling up to four GaN devices effectively balanced the switching and conduction loss. By designing the power loop inductance to 0.11 nH, 100V GaN devices were safely operated under a 70 V DC bus with only a maximum 9.8 V overshoot. To prevent the inverter from short-circuit faults, the short-circuit protection based on the measurement of the voltage on the power loop inductor with a low-pass filter is successfully applied with only 115 ns protection time. The designed prototype shows expected thermal performance under rated 3.3 kVA power and survives a two-second 5.1 kVA transient power.Doctor of PhilosophyGallium nitride (GaN) power devices are emerging as a leading technology for making power converters smaller, lighter, and more energy efficient. Compared with traditional silicon and silicon carbide devices, GaN can switch faster and conduct electricity with lower losses, which enables higher power density and improved overall system performance. However, practical limitations—such as changes in device resistance during operation, limited heat dissipation due to small device size, and constraints from passive components—can reduce these benefits and limit further improvements in switching frequency and power density.\\ In this work, a flexible experimental platform is developed to accurately measure how GaN device resistance changes under realistic operating conditions and different temperatures. These measurements are used to quantify additional losses that occur in real power converters, providing designers with more accurate data for predicting performance and optimizing system efficiency.\\ The study also demonstrates how converter topology and system-level design choices can maximize the benefits of GaN devices. For an aircraft in-seat power supply, a three-level power factor correction (PFC) converter is designed to enable the use of lower-voltage GaN devices with very low resistance. This approach achieves a peak efficiency of 98.4\% while meeting stringent requirements for power quality and electromagnetic interference at high line frequencies.\\ For unmanned aerial vehicle (UAV) inverter applications with lower operating voltages, a systematic comparison shows that a two-level topology is more advantageous in terms of weight and efficiency. By paralleling multiple GaN devices and carefully minimizing circuit inductance, the inverter achieves safe, reliable operation with fast protection against short-circuit faults. The prototype demonstrates strong thermal performance at rated power and successfully handles short-term overload conditions.\\ Overall, this work shows that combining accurate device characterization with careful system-level optimization is essential to fully realize the potential of GaN technology for next-generation high-density, high-efficiency power conversion systems
Youth Transformational Leadership Development: Identifying Bottlenecks and Barriers
No full textPublished versionYes, abstract only (Peer reviewed?
Journal of Demographic Economics
No full textA growing body of evidence finds that rural electrification reduces fertility, typically by expanding women’s opportunities outside the home and raising the opportunity cost of childbearing. We examine electrification in post-revolutionary rural Iran, where electricity expanded rapidly but female labor force participation remained low. Using a large panel of villages observed in the 1986, 1996, and 2006 censuses, we show that while Ordinary Least Squares estimates align with the broader literature in suggesting a negative association between electrification and fertility, instrumental variable estimates exploiting elevation-based variation reveal the opposite: villages with longer exposure to electricity experienced higher fertility. This positive effect is strongest in provinces with lower female labor force participation, indicating that the substitution channel emphasized in prior research was weak in the Iranian context. These findings highlight the importance of context in shaping demographic responses to infrastructure and suggest that electrification’s effects on fertility are not universally negative.Published versio
Science Advances
No full textBiogenic amines are fundamental for physiological homeostasis and behavioral control in both vertebrates and invertebrates. Monoamine neurotransmitters released in target brain regions conjointly regulate adaptive learning and plasticity. However, our understanding of these multianalyte mechanisms remains nascent, in part due to limitations in measurement technology. Here, during associative conditioning in honey bees, we concurrently tracked subsecond fluctuations in octopamine, tyramine, dopamine, and serotonin in the antennal lobe, where plasticity influences odorant representations. By repeatedly pairing an odorant with subsequent sucrose delivery, we observed individual differences in the conditioned response to odor, which occurred after a variable number of pairings (learners) or not at all (non-learners). The distinction between learners and non-learners was reflected in neurotransmitter responses across experimental conditions. The speed of learning, the number of pairings prior to a proboscis extension reflex, could be predicted from monoamine opponent signaling (octopamine-tyramine), from both the first presentation of the odorant alone, prior to any pairing with sucrose, and the first conditioned response to the odorant, coming after a number of sucrose pairings. These results suggest that monoamine signaling phenotypes may relate directly to the now widely reported socially relevant genetic differences in honey bee learning.Published versio
Dentistry Journal
No full textObjective: This study aimed to compare the accuracy of two large language models (LLMs)—ChatGPT (version 3.5) and Google Gemini (formerly Bard)—in answering dental caries-related multiple-choice questions (MCQs) using a simulated student examination framework across seven examination lengths. Materials and Methods: A total of 125 validated dental caries MCQs were extracted from Dental Decks and Oxford University Press question banks. Seven examination groups were constructed with varying question counts (25, 35, 45, 55, 65, 75, and 85 questions). For each group, 100 simulations were generated per LLM (ChatGPT and Gemini), resulting in 1400 simulated examinations. Each simulated student received a unique randomized subset of questions. MCQs were answered by each LLM using a standardized prompt to minimize ambiguity. Outcomes included mean score, passing rate (≥60%), and performance differences between LLMs. Statistical analyses included independent t-tests, one-way ANOVA within each LLM, and two-way ANOVA examining interactions between LLM type and question count. Results: Across all seven examination formats, Gemini significantly outperformed ChatGPT (p < 0.001). Gemini achieved higher passing rates and higher mean scores in every examination length. One-way ANOVA revealed significant score variation with increasing exam length for both LLMs (p < 0.05). Two-way ANOVA demonstrated significant main effects of LLM type and question count, with no significant interaction. Randomization had no measurable effect on Gemini performance but influenced ChatGPT scores. Conclusions: Gemini demonstrated superior accuracy and higher passing rates compared to ChatGPT in all simulated examination formats. While both LLMs struggled with complex caries-related content, Gemini provided more reliable performance across question quantities. Educators should exercise caution in relying on LLMs for automated assessment or self-study, and future research should evaluate human–AI hybrid models and LLM performance across broader dental domains.Published versio
Biology of Sex Differences
No full textBackground: Selective serotonin reuptake inhibitors are widely prescribed during pregnancy. Their main route of administration is through the gut. However, their impact on the maternal and offspring gut microbiome and microbial metabolic pathways remains poorly understood. This study used metagenomic shotgun sequencing to examine the effects of perinatal citalopram exposure in rat dams and their offspring on gut composition and downstream metabolic pathways.
Methods: We treated pregnant and nursing rat dams with either citalopram or vehicle (water). Their feces were collected, DNA from these samples was extracted and then sequenced using shotgun metagenomic sequencing. The BioBakery suite of microbiome analysis tools was utilized in tandem with RStudio to analyze the gut composition and microbial metabolic pathways of the rat dams and their offspring.
Results: Pregnant and nursing dams treated with citalopram exhibited marked shifts in microbial community structure, including phylum-level alterations in Proteobacteria and Defferibacteria. Citalopram treated dams displayed significantly altered beta diversity. Species level alterations due to treatment were composed of five significantly altered microbes, two of which belong to the Proteobacteria phylum. These changes were highly diverse and were not congruent with microbe-level alterations observed in offspring. Alpha diversity of microbial metabolic pathways was compared using the Gini-Simpson index, which was significantly increased in dams suggesting greater metabolic functional diversity with age. Female offspring perinatally exposed to citalopram showed significant changes in gut beta diversity, with seven significant alterations at the microbe level. These microbial shifts were accompanied by twenty-one significantly altered microbial metabolic pathways. In contrast, male offspring showed no significant differences in microbial composition or beta diversity and only minor metabolic changes.
Conclusions: These findings demonstrate that maternal citalopram exposure during pregnancy and lactation has lasting, sex-specific impacts on the offspring’s gut microbiome and microbial metabolic pathways. The pronounced alterations in female, but not male offspring, suggest that host sex may be a critical determinant in the developmental response to citalopram exposure. This work underscores the value of metagenomic approaches in uncovering complex host-microbiome interactions and highlights the need to consider offspring sex in evaluating the safety and long-term effects of antidepressant use during pregnancy.Published versio
Enhancing Chinese Heritage Language Education Through Technology: A Two-Part Design and Evaluation Study
No full textThis dissertation addresses critical gaps in Chinese Heritage Language (CHL) education by exploring the role of technology in bridging literacy-oracy disparities and supporting instructional practices. The project comprises two distinct but interconnected manuscripts that collectively aim to enhance the learning experiences of heritage students and the professional capabilities of CHL educators.
The first manuscript, a design case study, evaluates the implementation of the I Chinese Reader mobile application at a Chinese language school in Virginia through a mixed-methods approach involving 142 students, the study assesses the app's impact on reading proficiency, vocabulary acquisition, and student engagement. Findings indicate significant improvements in reading scores, particularly among intermediate learners, and highlight the importance of culturally relevant content and dialect support features. The study identifies key design principles for heritage learner applications, including the necessity of addressing disconnects between sound and writing systems and providing offline functionality for rural access.
The second manuscript adopts a design and development research methodology to create a research-based CHL Teacher Toolkit. Recognizing the challenges of "material overload" and the lack of structured guidance for technology integration, this study details the systematic design, development, and formative evaluation of a digital resource for K–12 CHL teachers.
The toolkit operationalizes theoretical frameworks such as TPACK and SAMR into practical lesson plans and decision-making matrices. Formative evaluation with practitioners confirmed the toolkit's usability and effectiveness in helping teachers select appropriate digital tools to scaffold literacy and affirm student identity.
Together, these manuscripts contribute to the fields of instructional design and heritage language education by providing empirical evidence on the efficacy of mobile-assisted language learning and practical resources for educators navigating the digital landscape.Doctor of PhilosophyMany children growing up in Chinese-speaking households in the United States face a unique challenge: while they can often speak and understand their heritage language, they frequently struggle to read and write it. Furthermore, teachers in community Chinese schools often feel overwhelmed by the vast amount of technology available and lack clear guidance on how to use it effectively. This dissertation addresses these two problems through two related studies designed to improve Chinese Heritage Language education.
The first study tested the effectiveness of a mobile app called I Chinese Reader with 142 students at the Chinese LanguageSchool. The results showed that students who used the app found that students were most successful when the app provided culturally relevant stories, support for different dialects, and the ability to work offline in areas with poor internet access.
The second study tackled the problem of "material overload" for teachers. Instead of just studying the problem, a practical "Teacher Toolkit" was designed and developed to help K–12 Chinese teachers navigate the digital landscape. This toolkit transforms complex educational theories into easy-to-use lesson plans and decision-making guides. Feedback from teachers confirmed that the toolkit was easy to use and helped them select the right digital tools to teach reading and support their students' cultural identity.
Together, these studies show that when technology is designed with the specific needs of heritage students and teachers in mind, it can bridge the gap between speaking and reading and making language education more effective
Reinforcement Learning–Based Discrete Prompt Optimization for Neuro-Symbolic Structured Simplification of Complex Game Descriptions with Large Language Models
No full textThis thesis investigates how large language models can be trained to perform structured simplification of complex, free-form game descriptions for the GameChangineer platform. The work formalizes simplification as a discrete prompt optimization problem and introduces a neuro-symbolic pipeline that maps raw natural language into controlled GameChangineer sentences via scenario normalization, retrieval-augmented code generation, and AST-based FACTS extraction. A reinforcement learning framework based on Proximal Policy Optimization optimizes discrete prompt edits using task-specific rewards that combine grammar compliance, semantic agreement with the FACTS contract, and compiler validity of the resulting games. Experiments on diverse arcade-style game descriptions show that the proposed GC-Repair and sentence correction agents significantly improve grammar-constrained generation, robustness to noisy user input, and end-to-end code correctness compared to direct LLM rewriting baselines.Master of ScienceThis thesis explores how to help people describe video games in everyday English while still producing reliable computer code behind the scenes. Many students and beginners write game ideas as long, tangled sentences that are hard for both humans and software tools to turn into clear rules. I design a system that first asks a large language model (an AI system trained to work with text) to break these complex descriptions into smaller, simpler sentences and then convert them into executable game code. The system checks and repairs its own code, extracts the key facts about the game, and finally rewrites those facts back into clean, structured English that a teaching platform called GameChangineer can understand. This approach aims to make it easier for learners to practice computational thinking, receive precise feedback on their ideas, and quickly turn rough game concepts into working educational games
Materials Science and Engineering: R: Reports
No full textWe report a solid-state additive manufacturing route for producing shape-memory ceramic (Zr0.88Ce0.12O2) reinforced metal matrix composites. Using additive friction stir deposition, we implement two feedstock engineering strategies: (i) pre-mixing of powders using a Cu matrix and (ii) hole-pattern drilling using an Al-Mg-Si matrix, where the specific matrix materials are chosen for their distinct shear flow behaviors. The process yields fully dense composites with uniform particle dispersion (20 vol%) and dynamically recrystallized metal matrices. The severe thermomechanical processing conditions also reduce the ceramic particle size, resulting in unique composite microstructures unattainable by alternative processing routes. The as-printed composites can withstand high compressive loads without cracking and retain functionality enabled by thermally and mechanically triggered martensitic transformations. Notably, for the first time, stress-induced martensitic transformation (tetragonal to monoclinic) is observed in bulk-scale composites—but it is only present in the Cu matrix composite, not the Al-Mg-Si counterpart. Micromechanics modeling attributes this contrast to differences in the load transfer and strain hardening capabilities. Complementary to global transformation characterization, Raman mapping reveals that transformation typically initiates at the particle-matrix interface. Together, these results establish a potential pathway for scalable manufacturing of multi-functional metal–shape memory ceramic composites with tunable microstructures and transformation responses.Accepted versio
Navigating the Unseen: A Haptic Glove for Peripersonal Navigation in Low-Visibility Scenarios
No full textEffective interaction with objects within arm's reach, known as peripersonal navigation, is essential to performing everyday activities as well as specialized tasks in fields such as healthcare, construction, and assistive technology. However, this ability can be substantially impaired in cluttered or unfamiliar environments, or when visual input is limited by fog, smoke, or visual impairments. While augmented reality (AR) and audio-based systems have been explored to enhance spatial awareness, AR devices are often costly and uncomfortable for extended use, and auditory cues can be difficult to interpret in noisy or attention-demanding settings. Vibrotactile feedback, which delivers directional and proximity information through touch, offers a promising alternative. It is discreet, intuitive, and resilient to environmental distractions, making it well suited for guiding movement under low-visibility conditions. Yet, critical questions remain regarding how to design effective vibrotactile cues, identify intuitive guidance strategies, and adapt feedback to users' dynamic behaviors.
To address these gaps, my dissertation pursues three integrated goals centered on developing and evaluating a haptic glove that delivers vibrotactile feedback to support peripersonal navigation in low-visibility scenarios.
Study 1 examines how tactor placement, hand motion, and temporal patterns influence vibrotactile perception. Twenty-two right-handed participants identified vibrating tactors placed on the dorsal hand or wrist while performing controlled hand movements. Results showed that recognition accuracy decreased during hand motion and when vibrations occured with shorter onset intervals, and that stimuli on the hand were more distinguishable than those on the wrist.
Study 2 evaluates vibrotactile guidance strategies using a custom haptic glove. Blindfolded participants navigated toward virtual targets under multiple feedback metaphors and proximity cue designs. Among the tested strategies, the two-tactor vector approach and a "pull" metaphor yielded the fastest target acquisition times and smoothest hand trajectories, demonstrating their potential for intuitive spatial guidance.
Study 3 examines how different temporal feedback patterns—Continuous, Fixed-Interval, Distance-Based, and Behavior-Based (which adjusts timing based on hand speed)—affect navigation performance and user experience in accuracy- and speed-priority scenarios. Participants performed a simulated object-search task under reduced visibility while performance metrics and subjective usability ratings were collected. The Distance-Based temporal pattern showed the stronger overall performance and highest user preference.
Together, these studies advance the understanding of vibrotactile design for spatial guidance and lay the foundation for haptic interfaces that adapt to user needs and environmental conditions. The findings have broad implications for assistive technologies supporting individuals with visual impairments and for applications in search and rescue, firefighting, industrial safety, and immersive AR/VR systems. By refining how tactile cues are structured and delivered, this work contributes to the development of more effective, inclusive, and context-aware haptic navigation systems.Doctor of PhilosophyBeing able to locate and interact with objects within arm's reach is essential for everyday lift, from cooking or getting dressed to performing complex tasks. This ability, known as peripersonal navigation, can become challenging in cluttered or low-visibility environments affected by smoke, fog, or poor lighting, and for individuals with visual impairments. Existing tools, such as augmented reality headsets or audio-based guidance systems, often fall short because they can be expensive, uncomfortable, or hard to use in noisy or distracting settings.
This dissertation explores vibrotactile feedback, information delivered through gentle vibrations on the skin, as an alternative way to support navigation when vision is limited. Vibrotactile cues allow users to feel direction and distance without relying on sight or sound. The goal of this work is to design and evaluate a haptic glove that uses vibration-based signals to guide hand movements and enhance spatial awareness.
The research consists of three studies. Study 1 examines how the location and timing of vibrations, along with hand motion, influence how accurately people can identify where vibrations occur. Study 2 evaluates different vibration patterns and guiding strategies to determine which ones help people navigate most quickly and clearly. Study 3 compares several timing approaches—Continuous, Fixed-Interval, Distance-Based, and Behavior-Based (which adjusts vibration timing based on hand speed)—to understand how they influence performance and user experience during a simulated search task under low-visibility conditions, with either accuracy or speed emphasized.
Together, these studies help advance touch-based technologies that can improve safety, accessibility, and performance in a range of settings. The findings have important implications for assistive devices for individuals with visual impairments and for high-risk environments such as search and rescue, firefighting, and industrial operations. Overall, this research deepens our understanding of how tactile information can be effectively designed to support human interaction with the environment