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Novel Approach to Traveling-Wave-Based Fault Location in Nonhomogeneous Transmission Lines
Full text linkAccurate fault location is a critical aspect of power system protection, ensuring grid reliability and minimizing downtime. Traditional traveling-wave-based fault location methods face limitations when applied to nonhomogeneous transmission lines due to the reliance on precise segment velocities and propagation time data. This thesis addresses these challenges by proposing a novel algorithm that leverages historical fault data to estimate segment velocities and refine these estimates as more faults occur. The algorithm was rigorously tested using the digital model of an 11-segment, 65.694 km real overhead transmission line and validated using both simulations and hardware tests using commercially available time-domain protective relays. Testing included 57 simulated faults and 57 physical relay tests, with results demonstrating consistent error reductions, with average improvements of up to 140 meters in fault location accuracy. This work highlights the algorithm’s ability to mitigate linear error trends and significantly improve fault location precision across diverse fault scenarios, even with limited training data. The proposed methodology offers a practical, low-cost solution to a longstanding challenge in fault location for nonhomogeneous transmission lines, showcasing strong potential for real-world applications and widespread implementation
The Effect of Internal Consistency on NCAA Women’s Gymnastics Scores
Full text linkThe aim of my research is to examine internal consistency in relation to the validity of scores in NCAA Women’s Gymnastics. This study focuses on scores from the now infamous 2024 Tennessee Collegiate Classic. Patterns from several different deviations and correlation coefficients are analyzed, and a “gold standard” score to test against is created. This allows me to identify several patterns in results that could signify invalid scores
Faculty Diversity and Minority Enrollment in Advanced STEM Courses: A Case Study at Neville High School
Full text linkThis study investigates the correlation between the diversity of STEM faculty and the enrollment rates of minority students in honors, Advanced Placement (AP), and dual enrollment STEM courses at Neville High School. Recognizing the long-standing underrepresented minority students in advanced STEM education, this research explores whether a more diverse faculty positively influences student participation in these courses. Using a quantitative correlational design, data on faculty demographics and student enrollment patterns were analyzed through descriptive statistics and chi-square tests of independence. The results are expected to provide information on the role of faculty diversity in fostering equitable academic opportunities. The findings may inform educational policies and encourage the recruitment and retention of diverse educators, ultimately contributing to a more inclusive learning environment in STEM education
The Design of Coplanar Waveguide Traveling-Wave Kinetic-Impedance Parametric Amplifiers
Full text linkAstronomical observations and many physics experiments rely on cryogenic amplifiers for readout. Current sensitivity is limited by the noise figure of high-electronmobility transistor (HEMT) amplifiers, which have proven di!cult to decrease further in recent years. Traveling-wave kinetic-impedance parametric amplifiers (TKIPAs) are an emerging class of amplifiers which have the potential to substantially improve the sensitivity of microwave low-noise amplifiers (LNAs) while also accepting relatively high input powers and amplifying over a wide bandwidth. In this thesis, I present the design, modeling, and testing procedures for coplanar waveguide (CPW) TKIPAs developed by our group at the National Radio Astronomy Observatory. Using these procedures I designed an amplifier meant to operate over 5-9 GHz with a maximum gain of 15 dB. Unfortunately, the fabricated prototype did unfortunately did not work due to incomplete etching. I also developed a design proposal for a W-band (75-110 GHz) amplifier using the same techniques as used for the lower frequency amplifier as well as a waveguide transition for this amplifier. The highly constrained design space suggests that interdigitated CPW TKIPAs using conventional dispersion engineering is not practical
Quarterback Statistics vs. Season Success
Full text linkThe purpose of this research is to determine which quarterback statistic most significantly impacts team success in the National Football League. By analyzing data from quarterbacks with at least 100 pass attempts per season from 2006 to 2023, we examine the relationship between quarterback rating, passer rating, completion percentage, and TD-INT ratio with end-of-season power rankings. We ran the data through multiple linear regression models to identify which statistic has the strongest correlation with team performance. Our model considers variations across different seasons and accounts for statistical trends over time. With over 17 seasons of data analyzed, further exploration could refine the findings by incorporating additional variables or alternative modeling approaches
Privacy-Preserving Structure Learning for Geospatial Data Using Information-Theoretic Dependency Measures
Full text linkThis dissertation proposes a privacy-preserving framework for structure learning in Bayesian networks (BNs) that addresses the challenges of distributed geospatial data face. Geospatial datasets often exhibit region-specific patterns such as sparsity and nonlinear dependencies. These patterns undermine the effectiveness of traditional machine learning models. Additionally, learned BN structures may reveal sensitive relationships in the generated graph by BNs. These relationships pose a significant privacy risk if reverse-engineered. To address these issues, three novel algorithms are introduced. First, the Selective Naïve Bayes with HSIC (SNB-HSIC) algorithm applies a kernel-based dependency measure to filter redundant and irrelevant features in sparse datasets, improving structure clarity without compromising classification accuracy. Second, the Controlled K-Dependence Bayesian Network (CKDBN) extends traditional K-dependence models by giving the option to select the optimal number of parents each node can have based on data-driven thresholds. THE CKDBN enables a flexible structure learning algorithm that can handle complex or high-dimensional settings. Third, the BNVeil algorithm introduces a privacy-preserving method that can obfuscate highly connected nodes using Laplace noise to protect the model’s logic from adversarial inference. All the frameworks are validated on both the full and partitioned geospatial datasets via a series of experiments that evaluate the structure quality, the predictive performance, and the robustness of privacy-preserving concerns. The results of the experiments indicate that the proposed methods in this dissertation achieve better accuracy than traditional BN models and significantly enhance interpretability and structural privacy. The three algorithms offer a practical and secure solution for region-based geospatial data
Advanced Plant Growth Using Halloysite Nanotubes (HNTs) and Waste Extraction for Medical Applications
No full textThis dissertation presents an integrated research framework that bridges nanotechnology, green chemistry, and sustainable agriculture, aiming to address two critical global challenges: enhancing plant growth under resource-limited conditions and valorizing agricultural waste for bioactive compound extraction. The study is divided into three major projects that together highlight the innovative application of magnesium oxide-coated halloysite nanotubes (MgO-HNTs) and the development of environmentally conscious extraction methods for high-value phytochemicals. The first component of this work investigates the design, fabrication, and functional evaluation of MgO-HNTs as advanced nanocarriers for promoting seed germination and early root development in tomato plants. MgO-HNTs were synthesized via an electrodeposition technique and structurally characterized using scanning electron microscopy (SEM), confirming successful surface modification. Growth experiments were conducted under hydroponic and soil-based conditions using Heirloom Cherry Tomato and Golden Tomato seeds, and further extended to simulated extraterrestrial environments. A Response Surface Methodology (RSM) approach, based on a Box–Behnken Design, was applied to assess the interaction of three independent variables—temperature, MgO-HNT concentration, and light duration—on multiple physiological responses. Among eight growth indicators evaluated, seedling length and root length stress tolerance index (RLSI) emerged as key response variables for optimization. Optimal conditions (25 °C, 12-hour light exposure, 100 mg/L MgO-HNTs) led to maximum root and shoot elongation in Earth soil simulants. These optimized conditions were validated using lunar and Martian regolith simulants to explore applications in space agriculture. The MgO-HNTs treatment substantially improved root penetration, seedling vigor, and germination rates even under nutrient-deficient, high-stress soil analogs. Notably, lunar regolith supported peak root development at 100 mg/L, while Martian regolith showed optimal results at lower concentrations (10 mg/L), reflecting the distinct mineralogical and oxidative properties of the soils. These results underscore the potential of MgO-HNTs to support in-situ resource utilization (ISRU) strategies for sustainable extraterrestrial crop production. The second component of the dissertation focuses on developing a sequential microwave–ultrasound-assisted extraction (MUAE) method to recover lycopene—a potent antioxidant and natural pigment—from tomato pomace waste. A one-factor-at-a-time (OFAT) experimental design was employed to individually investigate the effects of particle size, solvent composition, solvent-to-solid ratio, microwave time, and ultrasound conditions on extraction efficiency. Ethanol–water mixtures were chosen as a green solvent system, with 60% ethanol providing the best compromise between extraction power and environmental safety. Optimal extraction conditions included microwave irradiation at 180 W for 60 seconds followed by ultrasound treatment at 70 °C for 30 minutes, using a solvent-to-solid ratio of 40:1 (mL/g) and a particle size of 180 μm. Under these conditions, the lycopene yield reached 8.56 ± 0.30%, the highest among all tested methods. Visual and spectrophotometric analyses confirmed that the MUAE approach not only improved pigment release but also minimized thermal degradation due to its synergistic thermal-mechanical mechanism. The third component builds on the findings of the OFAT study and applies RSM to statistically optimize the simultaneous extraction of lycopene and total carotenoids. A Box–Behnken Design was used to model the effects of five variables: microwave time, ultrasound time, ultrasound temperature, ethanol concentration, and solvent-to-solid ratio. A total of 46 experimental runs were performed to develop predictive quadratic models. Statistical analysis (ANOVA) revealed that several linear and interaction terms significantly influenced extraction outcomes. The optimal extraction conditions predicted by the models were validated experimentally, yielding 8.25% lycopene and 10.50% total carotenoids. The reliability of the models was supported by high R² values, low residual errors, and good agreement between predicted and observed responses. Comparative evaluation of extraction strategies—MAE, UAE, UMAE, MUAE (single-factor), and MUAE (RSM-optimized)—confirmed that the optimized MUAE method offered the highest extraction efficiency and reproducibility. Furthermore, UV–Vis spectrophotometric calibration curves and High-Performance Liquid Chromatography (HPLC) were employed to quantify and verify the composition of the extracts. HPLC analysis identified eleven carotenoids, with lycopene as the major compound (5.43 μg/g DW), followed by β-carotene, lutein, rubixanthin, phytoene, and phytofluene, reflecting a diverse antioxidant profile suitable for nutraceutical and pharmaceutical applications. Together, these three research threads establish a cohesive narrative that merges space-inspired agricultural innovation with sustainable waste valorization. The novel use of MgO-HNTs for plant growth enhancement presents a feasible strategy for improving agricultural productivity in extreme or extraterrestrial environments, while the green extraction techniques developed for lycopene and carotenoid recovery offer scalable approaches for transforming food waste into high-value functional compounds. This work contributes to the advancement of molecular science and nanotechnology by offering practical solutions for two of the 21st century’s pressing needs: food security and sustainable resource utilization—on Earth and beyond
Enhancing the Efficiency of EAOP Degradation Using Ultrasound: A Study with Methylene Blue and PFOA as Model Pollutants
Full text linkMultiscale Materials Characterization and In-Situ Process Monitoring in Additive Manufacturing via Non-Contact Optical Thermometry Techniques
Full text linkWhile additive manufacturing (AM) is experiencing rapid growth, its development is uneven across different branches. Some areas are still emerging, while even the more established branches are still facing ongoing challenges that require further development. Regardless of their development stage, both emerging and mature AM require process monitoring and part characterization. Process monitoring helps to achieve more control over the process and build a self-adaptive system, while characterization of printed parts speeds up process optimization and ensures required quality. Together, process monitoring and build characterization will transform AM into a more dependable and commercially viable technique. Build surface temperature is a critical parameter for the majority of AM processes. For solid-state AM processes, build surface temperature helps with the evaluation of process-structure-property relations. For fused deposition AM processes, build melt pool temperature can facilitate the real-time detection of various printing defects. This study developed an in situ, multi-sensor approach for monitoring build temperature in which noncontact infrared temperature sensors with customized field of view move along with the moving print head and sense build temperature regardless of the print head’s X-Y translational movements. Different in situ infrared sensing implementations are facilitated in this work for two different categories of AM processes: a solid-state friction stir deposition AM, and a fused deposition modeling type AM. For metallic feedstock materials, a high-temperature calibration method was developed, while for polymer materials, a different and comparatively low-temperature calibration method has been developed. A statistical method for defect detection is also developed and utilized to identify temperature deviations caused by intentionally implemented defects. Effective detection of FDM print defects is demonstrated using both a simple L-shaped test geometry and a more complex industry standard test article. The effect of spindle speed and spindle torque on AFSD build temperature has been illustrated. Strengths and limitations of this approach are presented, and the potential for expansion via more advanced data analysis techniques such as machine learning are discussed. Thermal conductivity is a physical property that changes with material, microstructure, physical state, and scaling of material from bulk to micro/nanoscale. As microstructure evaluation is a common phenomenon in solid-state AM processes, it is essential to understand how microstructure affects the thermal transport properties in printed parts. This research develops a nanosecond thermoreflectance (NSTR) technique for thermal transport property characterization of bulk and thin films, which is then utilized to analyze transport property change in additive friction stir deposited layers. This work designs and constructs the NSTR setup and develops standard experimental procedures for thermoreflectance signal acquisition. A heat transfer model is also developed using a finite difference model to simulate the experimental conditions. The model finds the best fit of experimental data in order to extract thermal transport properties. Prediction of thermal penetration depth is also facilitated using the model. Finally, a combined analysis of thermal characterization and microstructure is presented to better understand the observed thermal conductivity change from substrate to print layers
Handle Traversals of Toroidal Graphs
Full text linkIn this paper we define a characteristic of toroidal graphs called the handle number. The handle number is the minimum number of edges which must traverse the handle in a toroidal embedding of a graph. After defining the characteristic, flat polygon projections are used to explore efficient toroidal embeddings. Using this exploration we then show an upper bound for the handle number of toroidal graphs. Finally, we prove an inequality between the handle number and graph skewness and conjecture an equality