Master's Theses

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https://hdl.handle.net/10057/351

This collection consists of digital copies of master's theses submitted for degree at the colleges and departments of Wichita State University. The collection includes theses beginning of fall 2005 -- summer 2022 as well as selected historical theses.

The complete set of all WSU theses may be found in the WSU Library Catalog. University Libraries has two paper copies of each theses submitted before 2006 and archival microfilm copies in the Libraries Special Collections for theses after 2006.

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The latest addition to this collection is theses defended in fall 2023

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Now showing 1 - 5 of 2139

Dung beetle (Coleoptera: Scarabaeinae) community diversity: Influence of grazing regimes and dung type in a grassland ecosystem
(Wichita State University, 2024-07) Woodard, Bryanna Grace; Jameson, Mary Liz
Dung beetles (Coleoptera: Scarabaeinae) are critical ecosystem engineers that improve grasslands through waste removal, bioturbation, and carbon sequestration. Therefore, grassland management that supports diverse dung beetle communities is essential for maintaining the sustainability of these ecosystems. Historically, grasslands in the Great Plains region were home to keystone herbivores such as bison (Bison bison bison Linnaeus), but as agriculture spread due to European expansion, bison were extirpated and replaced with cattle (Bos taurus Linnaeus). Dung beetles are closely associated with these large herbivores because they break down and distribute their dung, contributing to key ecosystem services such as nutrient cycling and reduced pasture fouling. In rangelands, pastures are managed with grazing herbivores, haying and mowing, or they are fallow (ungrazed), which allows for woody plant encroachment. To date, few studies have examined the effects of dung type on dung beetle communities and no studies have examined these effects under different management regimes. We examined dung beetle community structure and composition on bison-grazed, cattle-grazed, and ungrazed pastures at two study sites in the northern Flint Hills tallgrass ecoregion in Kansas, USA. Dung beetle community data were collected using pitfall traps baited separately with either bison, cattle, or rabbit dung. Redundancy analyses showed that dung beetle communities associated with each dung type and grazing regime differed. These results suggest that different management regimes support diverse, abundant dung beetle communities, and multiple species of herbivores are needed to support these communities, as no dung type was able to attract all species. Our findings highlight the importance of grassland management plans that implement a mosaic of grazed and ungrazed patches to maximize dung beetle diversity, thus benefiting the ecosystem services of this critically imperiled ecosystem.
Role of multifunctional 3D structured conductive nanofibers for flexible and wearable health monitoring systems
(Wichita State University, 2024-07) Yeasmin, Farzana; Asmatulu, Ramazan
This study aimed to synthesize and manufacture electrically conductive electrospun fibers for wearable health monitoring devices. Wearable health monitoring devices are essential for early and quick detection of diseases. High electrical conductivity is crucial for their performance and fast response. Electrically conductive nanofibers have the potential to enhance the performance of wearable biosensors. In this study, 18 different nanofibers were synthesized by electrospinning. PAN, SPEEK, PVDF, and polystyrene fibers were prepared, and different weight ratios of PEG and PVP inclusions were added with PVDF and PS to make fibers hydrophilic. It was observed that both PVDF and PS fibers showed hydrophilic characteristics after adding hydrophilic polymers into the solution at a certain amount. The measured water contact angle (WCA) for many fibers was 0°. The synthesized fibers were then dip-coated in an electrically conductive PEDOT: PSS solution under different conditions. Ultrasonication and desiccation effects were applied to enhance the impact of the dip coating procedure. It was found that ultrasonication and desiccation effect during the dip coating process has an impact on adsorbing the conductive solution and enhancing the electrical conductivity of the fibers. In this study, PAN fiber showed the highest electrical conductivity of 23.08 S/cm among all fibers. Polystyrene and PVDF-based fibers also showed good electrical conductivity of 17.52 S/cm and 6.72 S/cm, respectively. In this work, four-point probe test, WCA, FTIR, SEM, and EDS tests were performed to study fiber morphology. The conductive fibers prepared in this study have promising applications in wearable health monitoring devices and biosensors applications. This study may open novel opportunities for preparing electrically conductive fibers and their applications.
Artificial intelligence -based distance relay behavior for future power systems with 100% clean electricity
(Wichita State University, 2024-07) Oke, Kolade Oladimeji; Pang, Chengzong
The production of electricity in any society is an essential tool and symbol of development for such a community. In the past, the generation of electrical power has primarily relied on fossil fuels, such as coal, which emit CO2 into the atmosphere, thereby depleting the ozone layer and leaving our planet at risk of destruction. In today's world, efforts are being made to ensure the generation of electrical power through carbon-free generators. Regional Transmission Operators (RTOs) and Independent System Operators (ISOs) have set targets to ensure carbon-free generation in the near future. Owing to this development, there is an increasing addition of renewable generators to the grid, and a future of a 100% renewable generation mix is envisaged. The integration of renewables into the existing grid is also subject to changes in power flow within the power system, which may lead to surges in voltage or current, potentially causing electrical faults in the transmission line. In this research, the behavior and operation of distance relays are simulated, and a protective dataset is created using artificial intelligence methods to predict faults in the line in a 100% clean electricity. This study explores the application of machine learning models in predicting fault conditions in transmission lines based on resistance, inductance, impedance, power, voltage, and current values of the electrical power supply. Three models—logistic regress (Korstanje, sept 2022)ion, support vector machine (SVM), and K-nearest neighbors (KNN)[6]—were trained and evaluated using a dataset emphasizing the power flow results of the transmission line model derived from the Matpower. Hyperparameter tuning was performed to improve predictive accuracy, with SVM [5] (W.Urooj, 2021)and logistic regression showing superior performance in using indices such as current, voltage, and impedance to determine any presumed line faults.
PVDF and pan-based piezoelectric nanocomposite fibers integrated on fiber composites for improved vibration energy harvesting rates
(Wichita State University, 2024-07) Todmal, Purva; Asmatulu, Ramazan
This research explores the development and characterization of electrospun nanofibers integrated with fiber reinforced composites for effective vibration energy harvesting applications. Polyvinylidene fluoride (PVDF) and polyacrylonitrile (PAN) nanocomposite fibers were fabricated using electrospinning with piezoelectric inclusions such as lead zirconate titanate (PZT), barium titanate (BaTiO3), and zinc oxide (ZnO) to enhance their piezoelectric properties. The nanofibers were then integrated into 'open' and 'sandwich' composite configurations using glass, Kevlar, and carbon fiber prepregs. The energy harvesting tests showed that the PVDF+2 wt% BaTiO3 composite with glass prepregs in the open configuration exhibited the highest performance, achieving 22.4 V and 0.00853 W/m². PAN composites peaked at 13.6 V for PAN+2 wt% PZT with carbon prepregs, with a power density of 0.00315 W/m². Among the 'sandwich' composites, PVDF+4 wt% BaTiO3 with glass prepregs achieved 15.8 V and 0.00567 W/m², while PAN+4 wt% PZT with carbon prepregs reached 10.2 V and 0.00324 W/m². The 'sandwich' configuration generally showed lower performance due to reduced exposure of the piezoelectric fibers. Fourier Transform Infrared (FTIR) Spectroscopy confirmed the presence of the β-phase in PVDF, essential for piezoelectric properties, with significant peaks around 840 cm⁻¹, 1070 cm⁻¹, and 1170 cm⁻¹. FTIR also highlighted the structural integrity of PAN fibers, showing prominent nitrile stretching peaks at 2240-2260 cm⁻¹. PVDF nanofibers exhibited diameters ranging from 0.276 μm to 0.721 μm, while PVDF+4 wt% BaTiO3 fibers ranged from 0.377 μm to 2.456 μm, indicating substantial variability due to the inclusion aggregation. The PAN+4 wt% PZT fibers showed more uniform diameters ranging from 0.106 μm to 0.468 μm. This study provides valuable insights into optimizing nanofiber integrated composites for energy harvesting.
Magnetic field simulation studies in the muon spectrometer
(Wichita State University, 2024-07) Shivakoti, Sushil; Muether, Mathew
The Deep Underground Neutrino Experiment (DUNE) is focused on addressing important questions in neutrino physics such as matter-antimatter asymmetry and neutrino mass. The experiment utilizes advanced technologies to study muon neutrino disappearance ( ) and electron neutrino ( e) appearance events. An important challenge is distinguishing wrong-sign events, such as antineutrinos, in a neutrino beam. The magnetized TMS is crucial for differentiating muons and antimuons, which allows for accurate oscillation rate predictions.Our study examines the impact of magnetic fields on charge identification in the TMS. Our goal is to determine the optimal field strength for accurate charge determination. We have developed a signed distance metric for charge identification: S.D>0 (for muons) and S.D<0 (for antimuons). We discovered that higher magnetic fields increased the signed distance, which improved the particle’s charge identification. Additionally, as opposed to lower momentum and lower magnetic fields, particle recognition was better at the low momentum range and higher magnetic fields, and even better results were achieved at higher magnetic fields and higher momentum ranges by reducing overlap between the distributions. Plots of Fraction vs. True muon kinetic energy and Fraction vs. momentumTMSStart demonstrate improved charge particle identification with increased magnetic field values.

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