Engineering Commons^™

Thermal Properties Of Novel Materials, Wilarachchige D.C. Bhagya Gunatilleke

USF Tampa Graduate Theses and Dissertations

Thermal properties play an important role in various aspects of applications of technology startingfrom the materials property determinations to device performance improvements and their safety and stability. Measurements of the thermal properties of such materials of interest pose challenges due to the demands of these measurements for determination of intrinsic materials properties. The research included in this dissertation involved both synthesis of novel materials and their measurement of thermal properties. Solid-state materials synthesis involves various synthesis and processing techniques, while specific technological applications demand synthesis of defect-free single crystals. Such synthesis techniques were explored for the research included in this …

Enhancing Nitrogen Removal In High Permeability Bioretention Media: The Combined Role Of Biochar, Wood Chips And Hydraulic Design, Joshelyn Guimaray

USF Tampa Graduate Theses and Dissertations

Urban stormwater runoff can transport high nutrient loads to receiving waters, leading toeutrophication, harmful algal blooms, and the formation of hypoxic zones. Bioretention systems are a form of green infrastructure designed to mitigate these impacts, but their application in dense, ultra-urban environments is often limited by large land area requirements. This has led to the adoption of high permeability media (HPM) to accommodate higher hydraulic loading rates (HLRs) and reduce the system footprint. However, conventional HPM systems often show inconsistent removal of dissolved nitrogen species due to short hydraulic retention times (HRTs), a lack of organic carbon, and insufficient anoxic …

Advancing Power System Reliability And Security With Efficient And Resilient Graph Neural Network Frameworks, Seyed Hamed Haghshenas

USF Tampa Graduate Theses and Dissertations

Enhancing the reliability and security of smart grids is critical for ensuring their seamless operation and resilience against disruptions. The increasing integration of distributed energy resources, advanced measurement devices, and cyber-physical elements introduces both opportunities and challenges for grid management. While these advancements provide enhanced visibility and operational control, they also expose the grid to vulnerabilities from cyber-physical stresses, such as cyber-attacks, equipment failures, and fluctuating power demands. Traditional methods for reliability assessment and threat detection often rely on model-based approaches that struggle to adapt to the complexity and dynamic nature of modern smart grids. These limitations necessitate novel data-driven …

Mosquito Classification And Explainability From Image Data Via Deep Learning Techniques, Farhat Binte Azam

USF Tampa Graduate Theses and Dissertations

According to the World Health Organization (WHO), mosquitoes are the deadliest animals on Earth, responsible for more human deaths annually than any other species. Mosquito-borne illnesses continue to pose severe risks to global health. In 2015 alone, there were an estimated 214 million malaria cases worldwide. Similarly, a 2016 report from the Centers for Disease Control and Prevention (CDC) revealed that Puerto Rico’s Department of Health received over 62,500 suspected cases of Zika, with 29,345 confirmed positive cases. In 2019, Southeast Asia experienced its worst dengue outbreak in recorded history. Of the approximately 4,500 mosquito species distributed across 34 genera, …

Cross-Linked Cellulose Ester/Linseed Oil Composites For Controlled Release Fertilizers, Ian Stark

USF Tampa Graduate Theses and Dissertations

Food scarcity is an ever-present concern for a growing population but can be mitigated through innovations in agriculture. Fertilizers are required to produce a sufficient amount of food, and while they contain key nutrients for crop growth, these nutrients can leech into the surrounding environment. Controlled release fertilizers slow the release of nutrients, encasing the prills in a semi permeable coating. Current coatings are non-biodegradable, causing microplastic soil contamination, which is very difficult to remove. Cellulose esters offer a compelling alternative, with an ability to cross-link into thin films that can be applied as coatings. The cellulose esterification was completed …

Metabolic Insights Into Cyst Fluid Composition And The Role Of Taurine In Polycystic Kidney Disease, Vineetha Garimella

USF Tampa Graduate Theses and Dissertations

Polycystic kidney disease (PKD) is the most common genetic disorder affecting kidneys and the fourth leading cause of kidney failure. It occurs in two major forms, autosomal dominant PKD (ADPKD), and autosomal recessive PKD (ARPKD). ADPKD is typically adult-onset and caused by mutations in the PKD1 and PKD2 genes, and ARPKD generally manifests in utero and is caused by mutations in the PKHD1 gene. Despite the severity of the disease, only one FDA-approved drug, tolvaptan, is currently available for ADPKD and remains under clinical investigation for ARPKD. However, its use is limited by adverse side effects including polyuria, polydipsia, and …

Ai-Assisted Spectral Fingerprinting Of Mg-Doped Zno Tracer Phosphors: Tuning Photoluminescence Emission And Investigating Lifetime Via Sol-Gel Synthesis Parameters, Thomas M. Brandte

USF Tampa Graduate Theses and Dissertations

Plastic sorting remains a major driver of recycling costs, and existing infrared (IR) optical identification systems cannot determine material prominence or quality, nor reliably identify dark polymers [1]. This work explores a PVA-stabilized sol–gel and thermal synthesis route for magnesium-doped zinc oxide (Mg:ZnO) as a tunable photoluminescent tracer phosphor.

Lattice-defect control forms the foundation of phosphorescent emission tuning, enabling unique spectral fingerprints to be generated based on synthesis parameters—Mg molar ratio (Mg%), PVA mass (g), and calcination temperature (°C). Photoluminescence (PL) spectra (400–850 nm) were analyzed using Gaussian peak fitting and principal component analysis (PCA), reducing hundreds of spectral features …

Design And Implementation Of A Cross-Platform, Modular Electronic-Nose System, Kaushika Rudraraju

USF Tampa Graduate Theses and Dissertations

Electronic noses (e-noses) are a subject of great research. They are useful in multiple platforms and serve a great purpose in both studies and safety. This thesis presents a portable, modular e-nose platform that spans the full pipeline from sensing to visualization, with emphasis on scalability, repeatability, and straightforward integration. The hardware is organized as swappable sensor modules each hosting multiple commercial sensors and local signal-conditioning networked to a master microcontroller via structured communication protocols. High-resolution acquisition, time alignment, and efficient streaming to a host enable reliable logging and analysis. A controlled exposure chamber and a repeatable sampling protocol (baseline-exposure-purge …

Graphene Screen-Printed Electrode For Wound-Related Biosensing, Chanyapat Sriviroch

USF Tampa Graduate Theses and Dissertations

Monitoring wound healing is critical for improving patient outcomes, especially for chronic wounds, which often have complications like delayed healing and infections. Current methods for monitoring wound healing are invasive and costly. Also, these methods have a limited ability to provide real-time, continuous data. This study seeks to explore a graphene screen-printed electrode (SPE) electrochemical biosensor that is non-invasive and efficient to constantly monitor wound healing in real time. This research study focuses specifically on the electrochemical detection of the Human Neutrophil Elastase (HNE) and Secretory Leukocyte Protease Inhibitor (SLPI), which is one of the most abundant neutral proteinases in …

Formulation And Characterization Of Albumin Nanoparticles For Nsclc Photochemotherapy, Faisal Mohammad Shamim Khan

USF Tampa Graduate Theses and Dissertations

Non-small Cell Lung Cancer (NSCLC) is the most common type of lung cancer and is a leading cause of worldwide cancer-related death, which is a strong motivation to develop more efficacious approaches to its treatment. This thesis outlines the construction of folate-targeted bovine serum albumin (FA-BSA) nanoparticles co-loading Chlorin e6 (Ce6), a photodynamic therapy photosensitizer, and Evofosfamide (EVO), a hypoxia-activated prodrug, to enable synergistic photochemotherapy. The 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC) is a water-soluble carbodiimide coupling reagent and used for amide bonds between carboxylic acid and amines. FA-BSA nanoparticles were prepared through a desolvation approach and stabilized through crosslinking by the carbodiimide reagent …

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

Mode Engineering And Functional Enhancement In Piezoelectric-On-Silicon Mems Resonators With Magnetic Field Sensing Applications, Ugur Guneroglu

USF Tampa Graduate Theses and Dissertations

This dissertation significantly advances the field of micro-electro-mechanical systems (MEMS) resonators by exploring novel post-fabrication tuning techniques, electrode configuration effects, and innovative sensor applications for thin-film piezoelectric-on-silicon (TPoS) resonator devices. Driven by the increasing demand for tunable, high-precision and robust MEMS resonators in radio frequency (RF) applications such as sensing, timing, and filtering, this research aim to provide foundational knowledge and practical solutions for overcoming existing technological limitations. The dissertation is organized into three main research thrusts, each addressing critical challenges and opportunities in the design, fabrication, and application of TPoS MEMS resonators.

The first research thrust investigates an innovative …

Approximating Data-Driven Combinatorial Search In The Feature Space: Pwfs, Mehmet Bugrahan Ayanoglu

USF Tampa Graduate Theses and Dissertations

This dissertation presents the development and application of a novel feature selection method, Probability Weighted Feature Selection (PWFS), designed to address key challenges in machine learning involving high-dimensional, noisy, or biased datasets. It is a comprehensive exploration of feature selection strategies to enhance the performance, interpretability, and practicality of machine learning models in both engineering systems and educational data mining. PWFS introduces a structured, probabilistic approach to feature selection that adaptively weights features based on their empirical contribution to model performance. This method not only accelerates convergence to near-optimal feature subsets but also enhances flexibility for ensemble learning by encouraging …

Edge-Streamer: A Lightweight, Self-Supervised Event Segmentation Model, Lucas Miller

USF Tampa Graduate Theses and Dissertations

Event segmentation is the practice of autonomously detecting the boundaries of semantically connected sequences of actions within a video. It is connected to many areas of computer vision, including action recognition and event understanding. Nearly all current work requires the entire video to be stored in memory to be processed in multiple passes. This takes up valuable resources, increases processing time, prevents the use of low-power, low-memory devices, and precludes long-form content and live videos from undergoing event segmentation.

This thesis introduces EDGE-STREAMER, a real-time, lightweight, self-supervised, transformer architecture capable of performing event segmentation in a single pass. EDGE-STREAMER uses …

Corrosion Mitigation And Monitoring Of Bridge Tendons And Failure Forecasting Of Wastewater Force Mains, Stanley Chike Agbakansi

USF Tampa Graduate Theses and Dissertations

Corrosion remains a significant threat to civil infrastructure, resulting in costly repairsand potential structural failures. This dissertation addresses three critical aspects of corrosion: mitigation and monitoring of post-tensioned bridge tendons, and failure forecasting of wastewater force mains due to internal corrosion.

First, the research investigates the effectiveness of corrosion inhibitors (CIs) applied via pressure injection (impregnation) in post-tensioned tendons. The protective mechanisms and long-term performance of various inhibitors were evaluated to identify the most effective formulations. Among the tested inhibitors, MCI-2020 — a water-based, migratory, mixed-type corrosion inhibitor — demonstrated the most promising results, showing excellent corrosion protection while preserving …

Efficient Methods And Algorithms For Analyzing Stochastic Systems, Mohammad Ahmadi

USF Tampa Graduate Theses and Dissertations

This dissertation addresses the challenges of stochastic analysis of safety-critical systems with biological components, where unexpected behavior can lead to catastrophic events. Two fundamental challenges hinder the analysis of such systems: their typically large or infinite state spaces, and the extreme rarity of error states of interest. While Monte Carlo simulation can analyze biochemical systems without storing the state space, accurately estimating rare event probabilities becomes computationally prohibitive. Conversely, probabilistic model checking excels at analyzing extremely low probability events but becomes impractical for systems with large or infinite state spaces due to memory constraints.This work proposes two main contributions to …

The Role Of Langmuir Circulation In Coastal Flows Driven By Wind, Waves And Tides, Thathsarani Dilini Herath Herath Mudiyanselage

USF Tampa Graduate Theses and Dissertations

Langmuir Circulation (LC) is a wind and wave-driven process and is a key driver of vertical mixing and transport in the ocean. In coastal shelf regions, LC can span the entire water column, linking surface and bottom boundary layers and amplifying turbulent mixing. However, the influence of LC on vertical mixing and cross-shelf transport in shallow, tidally forced coastal waters remains poorly understood. This dissertation addresses this knowledge gap by developing a novel modeling framework based on the Reynolds-Averaged Navier–Stokes (RANS) equations, offering a computationally efficient alternative to more resource-intensive Large Eddy Simulations (LES).

The proposed RANS-based approach is first …

Hierarchical Reinforcement Learning (Hrl) In Multi-Goal Spatial Navigation With Autonomous Mobile Robots, Brendon Johnson

USF Tampa Graduate Theses and Dissertations

Hierarchical reinforcement learning (HRL) is hypothesized to be able to take advantage of the inherent hierarchy in robot learning tasks with sparse reward schemes, in contrast to more traditional reinforcement learning algorithms. In this research, hierarchical reinforcement learning is evaluated and contrasted with standard reinforcement learning in complex navigation tasks. We evaluate unique characteristics of HRL, including their ability to create sub-goals and the termination function. We constructed experiments to test the differences between PPO and HRL, different ways of creating sub-goals, manual vs automatic sub-goal creation, and the effects of the frequency of termination on performance. These experiments highlight …

Effect Of Cage Eccentricity On Moment Capacity Of Drilled Shafts, Jason Schaefer Steinbach

USF Tampa Graduate Theses and Dissertations

During the construction of drilled shafts, reinforcement cages can be inadvertently placed eccentrically. Current codes, such as ACI 318 and AASHTO LRFD Bridge Specifications, assume there is little impact from such movements within the drilled shaft or that cage centering provisions are sufficiently robust. This thesis will explore the impact that reinforcement cage eccentricity has on the bending capacity of drilled shafts and propose new strength reduction and resistance factors for tension-controlled failure in drilled shafts.

A total of 208 drilled shafts across eleven counties within the state of Florida were tested using thermal integrity profiling to identify the worst …

Bridging Virtual Robots And Physical Tasks Via Augmented Reality, Xiangfei Kong

USF Tampa Graduate Theses and Dissertations

Entry to human-robot interaction research, e.g., conducting empirical experiments, faces a significant economic barrier due to the high cost of physical robots, ranging from thousands to tens of thousands. This cost issue also severely limits the field’s ability to replicate user studies and reproduce the results to verify their reliability, thus offering more confidence to incorporate these findings. Although virtual reality (VR) user studies present a potential solution, it is unclear whether we can confidently transfer the findings to physical robots and physical environments because VR isolates both the physical robot and the physical world where robots operate. To address …

Embedding-Based Deep Learning Frameworks For Multimodal Oncology Data Integration, Aakash Gireesh Tripathi

USF Tampa Graduate Theses and Dissertations

This dissertation presents a cohesive set of novel frameworks developed to address critical challenges in oncology data integration, representation learning, and clinical information extraction. The work encompasses four interconnected projects: MINDS (Multimodal Integration of Oncology Data System), HoneyBee (Harmonized ONcologY Biomedical Embedding Encoder), LLM Extraction (Large Language Model-based Extraction from Pathology Reports), and EAGLE (Embedding Analysis for Generalized Learning in Oncology). Together, these systems enable the unification of diverse cancer data modalities—from genomics and clinical records to histopathology images and radiological scans—creating a robust foundation for advanced machine learning applications in precision oncology. By addressing key barriers in data accessibility, …

Quantifying Wellbore Effects During Co2 Sequestration In A Saline Aquifer, Uzoma Johnpaul Ajugwe

USF Tampa Graduate Theses and Dissertations

Carbon capture and storage (CCS) is a strategy for mitigating climate change by reducing greenhouse gas emissions from large stationary sources like fossil-fuel-fired power stations. One method of CCS involves carbon dioxide (CO2) sequestration in deep saline aquifers, where supercritical CO2 is injected into an aquifer. The success of such projects depends on accurate predictions of CO2 behavior within the subsurface, which are obtained through numerical simulations.

TOUGHREACT, a renowned simulation tool, models subsurface fluid flow, geochemical interactions, and solute transport, providing insights into the feasibility of CO2 sequestration projects. A critical aspect of these simulations is the apportioning of …

New Attack Surfaces Against Emerging Cloud And Web Based Infrastructures And Defenses, Junjie Xiong

USF Tampa Graduate Theses and Dissertations

Emerging network security threats, ranging from cloud-based infrastructure attacks to web-based content subversion, pose significant challenges to modern computing environments. In this dissertation, we explore two novel attack vectors that disrupt both cloud-based infrastructures and web-based content systems.

In this dissertation, we first introduce the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a …

Surface Acoustic Wave Energy Driven Degradation Of Aqueous-Based Methylene Blue, Xieqi Gu

USF Tampa Graduate Theses and Dissertations

This study investigates the degradation behavior of methylene blue (MB) under surface acoustic wave (SAW) excitation, focusing on the effects of power, frequency, hydrogen peroxide (H2O2) concentration, and the use of perovskite oxide catalysts (LFO and LSF). Initial experiments showed that MB concentrations remained stable under SAW at ~10 MHz and ~30 MHz with power levels of 1–3 W in the absence of H2O2, though solution temperatures increased. Upon introducing 1 M H2O2, MB degradation followed a second-order rate constant, with degradation rates increasing with power and significantly higher at ~30 MHz, suggesting that both frequency and power enhance free …

Wireless In-Situ Slurry Testing Device, Andrew Ward

USF Tampa Graduate Theses and Dissertations

Deep foundations are a critical piece of infrastructure used in bridge and high-rise constructions. A type of deep foundation called a drilled shaft is commonly used for these types of large structures due to their ability to resist large axial and lateral loads. Excavations in many soils require hole stabilization methods often involving drilling fluids like polymer or bentonite slurries. When used properly, these slurries prevent groundwater intrusion and side wall sloughing into the excavation. Slurry specifications are set by local or state jurisdictions, research findings, or product manufacturers. Over the years, the Florida standard specifications on slurry integrity testing …

Pathways To Efficient And Equitable Solutions For Large-Scale Routing Problems, Abhay Sobhanan

USF Tampa Graduate Theses and Dissertations

This dissertation addresses large-scale optimization problems in transportation emerging from hierarchical decision-making, equitable workload allocation, and innovative routing logistics. It presents three sets of contributions, each detailed in a separate chapter, and offers computational tools and insights to advance both the theory and practice of transportation systems.

The first work introduces a deep learning-enhanced genetic algorithm framework for solving the Hierarchical Vehicle Routing Problems (HVRPs). Traditional optimization approaches to such problems require extensive evaluation of multiple lower-level routing solutions and are computationally intensive. Our innovative method integrates a genetic algorithm with a pretrained graph neural network, which is trained on …

Simulation Of Multi-Receiver Solar Power Tower By Enhancement Of Solarpilot Software And A Review Of Absorber Coating Materials For The Receivers, Yasser Mirza Baig

USF Tampa Graduate Theses and Dissertations

Central receiver concentrating solar power (CSP) towers offer significant potential for cost-effective renewable electricity but face increasing optical losses with larger heliostat fields. This thesis introduces enhancements to NREL’s SolarPILOT software through a Python-based API named CoPylot, which enables comprehensive optimization of heliostat field layouts for multi-receiver systems. The enhanced methodology incorporates per-heliostat ray tracing, receiver orientation optimization (tilt angle β), and an incidence-angle-dependent absorptance (α(θ)) model to evaluate optical performance, thermal losses, and techno-economic metrics within an integrated simulation framework.

A case study for Daggett, California demonstrates that transitioning from a baseline single-receiver system (180 m tower, 5×5 m …

Volatile Organic Compound (Voc) Sensing Trend Among Novel 2d/3d Materials, Mohammad Shakhawat Hossain

USF Tampa Graduate Theses and Dissertations

Volatile Organic Compounds (VOCs) present significant health risks to both humans and animals. Long term exposure to certain VOCs belonging to certain chemical classes can cause both short- and long-term effects including nausea, damage to the central nervous system, and cancer. VOCs can be found in various everyday products like varnishes, paints, cooking items, cleaning products, nail polishes etc. Because of their ubiquitous nature, health concerns and risks regarding VOC exposure have become even more pressing. In this study, the VOC sensing capabilities and trends of novel 2D, and 3D materials (perovskite, and phthalocyanines) have been explored through electrochemical and …

Automated Data Analysis For Concussion Patient Records: A Flutter-Based Desktop Application, Fhaheem Tadamarry

USF Tampa Graduate Theses and Dissertations

Concussions are a prevalent and complex medical condition requiring careful clinical assessment and data-driven insights for effective management. This thesis presents the development of an automated data analysis system for concussion patient records, integrating Flutter-based desktop application development with SQL-driven data processing. The system provides a streamlined, interactive interface for clincians and researchers to upload, visualize, and analyze patient data efficiently.

The proposed solution automates data cleaning, preprocessing, and statistical analysis, ensuring robust and reliable insights into demographic, clinical, and recovery-related factors. Key analyses include sex-based differences injury mechanisms, prior head injury impact, mood disorder correlations, and time-to-treatment variations. The …

Multimodal Ai-Driven Biomarker For Early Detection Of Cancer Cachexia, Sabeen Ahmed

USF Tampa Graduate Theses and Dissertations

Cancer cachexia is a metabolic syndrome characterized by substantial skeletal muscle loss, impacting cancer patients' survival and quality of life. Despite its clinical significance, early detection remains a challenge due to the lack of standardized diagnostic criteria and the reliance on indirect markers. This work presents an AI-driven approach to enhance cachexia detection and monitoring by integrating multiple deep learning methodologies. We explore transformer architectures for time-series analysis to model sequential medical data, enabling disease prediction and progression modeling. To ensure robust and reliable decision-making in clinical settings, we explore Bayesian deep neural networks for uncertainty estimation. Additionally, we introduce …