Digital Commons Network^™

Guarding The Grid: Exploring Iot And Iiot Security Vulnerabilities In Smart Power Systems, Nicole G. Parker

Honors Undergraduate Theses

The Internet of Things (IoT) encompasses the collective network of electrical devices and the technology that enables them to send and receive data. The use of IoT technologies in industrial settings, such as transportation, manufacturing, and energy is referred to as the Industrial Internet of Things (IIoT). With the expansion of IoT in homes and IIoT in the energy sector has come an increase in the number of devices connected with each other. Engineers have utilized this network to develop sophisticated smart systems that combine sensing, processing, actuation, and control to produce smart environments. Along with the benefits of IoT …

Serious Game-Based Training For Improved Utilization Of A Novel Temporalis Emg Interface For Controlling Powered Wheelchairs, Calvin Macdonald

Honors Undergraduate Theses

Amyotrophic lateral sclerosis (ALS) is a terminal neurodegenerative disease that leads to a lack of independent mobility. One solution uses a unilateral surface EMG (sEMG) interface on the temporalis muscle to provide autonomous control of a powered wheelchair. Limbitless Journey, an EMG-controlled serious game, intends to provide users with a virtual environment to train in before use in a real-world scenario. A recent study analyzed the effect of video game training on the use of sEMG systems on the forearm, showing significant improvement in the usage of the interface but no difference between Free Play and structured play. The study …

A Human-Centered Framework For Oncological Healthcare System Modeling Applied To Mobile Radiation Delivery, Alex Price

Doctoral Dissertations

"Healthcare systems engineering is challenging due to the complexity, geographic expansiveness, inertial properties, and the heterogeneous sub-systems, with varying vested interests. Additionally, humans comprise many of the forms and functions, and are independent, with resistance and uncertainty in their decision-making that adds complexity. Human-centered systems modeling approaches are therefore needed to capture this unique aspect of a healthcare system and to enhance understanding of emergent healthcare properties. This dissertation focuses on integrating systems modeling within healthcare and emphasizes the importance of incorporating human decision-making. Moreover, the incorporation of a human-centered approach yields particularly valuable insights into the decision-making processes that …

Optimization Of Fire Emergency Preparedness In Underground Mining Environments: An Agent-Based Evacuation Approach, Oluwafemi Babatunde Salami

Doctoral Dissertations

"Underground mining environments present a complex interplay of thermal and non-thermal hazards, with fires constituting a significant risk due to the release of heat smoke from incomplete combustion. The propagation of these hazards through the ventilation networks underscores the critical need for understanding fire scenarios and their interaction with the underground environment. Existing knowledge regarding past mine fire disasters, and quantification of associated hazards and risks are insufficient for optimizing emergency evacuation strategies in the event of an underground fire emergency.

This research endeavors to bridge this gap by developing advanced quantitative risk assessment and evacuation models tailored specifically for …

Investigating The Potential Of Blended Feedstocks For Biofuel Production Via Pyrolysis Process: Experimental And Aspen Simulation Approach, Hasan Jawad Hammood Al-Abedi

Doctoral Dissertations

"The development of renewable energy sources has become increasingly urgent considering the climate crisis and the need to reduce greenhouse gas emissions. Pyrolysis, which is decomposition of an organic feedstock without oxygen, is a thermo-chemical process that has garnered significant attention as a promising technology for sustainable energy production, capable of converting biomass and waste materials into biofuels.

The primary objective of this study is to investigate the pyrolysis and co-pyrolysis behaviors of corn stover (CS), refuse-derived fuel (RDF), and oil shale (OS) in equal mass ratios. Utilizing a thermogravimetric analyzer and differential scanning calorimetry (DSC), the co-pyrolysis process is …

Optical Fiber Sensors Based On Advanced Vernier Effect - A Review, Wassana Naku, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

The Optical Vernier Effect Has Emerged as a Powerful Tool for Enhancing the Sensitivity of Optical Fiber Interferometer-Based Sensors, Ushering in a New Era of Highly Sensitive Fiber Sensing Systems. While Previous Research Has Primarily Focused on the Physical Implementation of Vernier Effect-Based Sensors using Different Combinations of Interferometers, Conventional Vernier Sensors Face Several Challenges. These Include the Stringent Requirements on the Sensor Fabrication Accuracy to Achieve a Large Amplification Factor, the Necessity of using a Source with a Very Large Bandwidth and a Bulky Optical Spectrum Analyzer, and the Associated Complex Signal Demodulation Processes. This Article Delves into Recent …

Empowering Urban Traffic Management: Elevated 3d Lidar For Data Collection And Advanced Object Detection Analysis, Nawfal Guefrachi, Hakim Ghazzai, Ahmad Alsharoa

Electrical and Computer Engineering Faculty Research & Creative Works

The 3D object detection capabilities in urban environments have been enormously improved by recent developments in Light Detection and Range (LiDAR) technology. This paper presents a novel framework that transforms the detection and analysis of 3D objects in traffic scenarios by utilizing the power of elevated LiDAR sensors. We are presenting our methodology's remarkable capacity to collect complex 3D point cloud data, which allows us to accurately and in detail capture the dynamics of urban traffic. Due to the limitation in obtaining real-world traffic datasets, we utilize the simulator to generate 3D point cloud for specific scenarios. To support our …

Additively Manufactured Aperture-Based Fss, Alexander Hook, Doyle T. Motes, Cody Morrow, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Frequency selective surfaces (FSSs) are arrays of patch- or aperture-based elements with specific high frequency reflective and/or transmissive properties. The specific FSS response of a given design is dictated by the element dimensions and spacing relative to adjacent elements (collectively referred to as the unit cell), along with the substrate (and superstrate if applicable) properties. As it relates to sensing, the FSS response may be affected by environmental (e.g., temperature) or structural (e.g., strain) changes. To this end, FSS-based sensors have been considered in recent years for a myriad of sensing applications including structural health monitoring (SHM). Concurrent to this, …

Explainable Automated Inconsistency Detection In Biomedical And Health Literature, Prajwol Lamichhane

UNF Graduate Theses and Dissertations

Given the exponential growth of scientific information online, researchers often face the daunting task of detecting contradictory statements on crucial health topics. This work develops a comprehensive pipeline for automated contradiction detection that integrates an Information Retrieval (IR) system, machine learning classifiers, and Explainable AI (XAI). The Information Retrieval system is tailored for biomedical data and comprises a datastore, syntactic, and semantic components. Users can input queries, initiating a pipeline that identifies top documents through syntactic analysis and refines results via semantic examination for relevant research claims. Employing a diverse range of Large Language Models such as pre-trained Distil-BERT, BioBERT, …

Implementation Of Algorithms For Sorting By Strip Swaps Using Alternating Cycle Graphs, Harsh S. Bhakharia

UNF Graduate Theses and Dissertations

Challenges in computational biology, such as genome rearrangement, and applications in optical character recognition, like zoning techniques, often require solving optimization problems. These involve reordering scrambled sequences of numbers into a sequential arrangement known as the identity permutation.

Rearranging any given permutation into the identity permutation with the minimum number of specific operations related to strips gives rise to various combinatorial optimization scenarios. Operations such as strip moves, transpositions, reversals, and block interchanges have been extensively researched.

This thesis builds on the foundational work of Roy et al., targeting the refinement of their cycle graph algorithm for sorting by strip …

Spatial Equity In Accessing Critical Facilities During Hurricanes, Pooya Komeilian

UNF Graduate Theses and Dissertations

Florida encounters multiple natural disasters every year, making evacuation a critical mitigation plan when severe impacts are predicted. Evacuation is a complex procedure that involves everything from household decision-making to large-scale traffic network analysis. It also addresses logistical issues, such as transporting impacted people to shelters or medical centers. Access to shelters, gas stations, and lodgings is of utmost importance, as numerous studies and surveys have shown that some people prefer not to evacuate, even if they perceive the disaster risk to be very high.

Based on this, the present study follows two phases to address gaps in evacuation studies. …

Integrating Therapy Into Play: Stand-On Ride-On For A Child With Cerebral Palsy, Kira Flanagan

UNF Graduate Theses and Dissertations

This study focuses on the development of an adaptive ride-on toy specifically designed for a 2.5-year-old child with spastic diplegic cerebral palsy. As a feasibility study, the primary objective of this innovative device is to enhance motor function, foster autonomy, and improve the overall quality of life for the child. The Power Mobility Device (PMD) integrates actuation and steering modifications, and a harness mechanism tailored to meet the child's unique needs. Comprehensive assessments of the child's spatiotemporal gait characteristics were conducted before and after a three-month usage period. The results reveal significant improvements in the child's ability to control dynamic …

Factors Influencing Household Travel Behavior And Trip Distances In Florida: A Rural Vs. Urban Perspective, Kadeem Lafargue

UNF Graduate Theses and Dissertations

This thesis investigates disparities in travel behaviors between rural and urban populations in the United States, with a specific focus on Florida. Leveraging data from the 2017 National Household Travel Survey (NHTS), the study examines demographic factors such as age, income, and household size, and their influence on travel patterns. By employing statistical methods including Poisson and negative binomial regression models, the research explores two key variables: the frequency of trips made per day and the average trip distance in miles. These models are particularly effective in handling overdispersion in count data, ensuring a robust analysis of travel behaviors across …

Development And Characterization Of Ceramic Devices For Capillary Action In Microgravity For The Purpose Of Growing Vegetables In Space, Lucas S. Mougeot

UNF Graduate Theses and Dissertations

The future of humanity is often rendered in spectacular visions of a multiplanetary civilization with a booming space economy. Reaching this reality requires addressing one of the core needs of survival: food. Porous ceramics used for Passive Porous Tube Nutrient Delivery Systems (PPTNDS) offer a promising solution. In microgravity environments such as the International Space Station (ISS), fluid dynamics and liquid coagulation do not support conventional agricultural practices. Porous ceramic materials present a solution that enables nutrient solution diffusion via capillary action.

In this research, it was determined that extruding a wadding ceramic tube and sintering it at 1900 °F …

Designing A Use-Error Robust Machine Learning Model For Quantitative Analysis Of Diffuse Reflectance Spectra, Allison Scarbrough, Keke Chen, Bing Yu

Biomedical Engineering Faculty Research and Publications

Significance

Machine learning (ML)-enabled diffuse reflectance spectroscopy (DRS) is increasingly used as an alternative to the computation-intensive inverse Monte Carlo (MCI) simulation to predict tissue’s optical properties, including the absorption coefficient,μaand reduced scattering coefficient, μs′.

Aim

We aim to develop a use-error-robust ML algorithm for optical property prediction from DRS spectra.

Approach

We developed a wavelength-independent regressor (WIR) to predict optical properties from DRS data. For validation, we generated 1520 simulated DRS spectra with the forward Monte Carlo model, where μa=0.44 to 2.45cm−1, and μs′=6.53 to 9.58cm−1. We introduced common use-errors, such as wavelength miscalibrations and intensity fluctuations. Finally, we …

Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa

Dissertations, Master's Theses and Master's Reports

Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …

Aluminum Critical Mineral Production Feasibility Via Landfill Mining: Preliminary Study Of Potential Project Locations And Co-Benefits, Anabel M. Needham

Dissertations, Master's Theses and Master's Reports

In 2022, aluminum was named a critical mineral by the United States Geological Survey (USGS) and the global demand for aluminum is projected to increase by 40% from 2020 to 2030 (Aleksić 2023). There are currently no large-scale bauxite mines in the United States to contribute to aluminum production, and this study aims to investigate the feasibility of aluminum landfill mining in the United States to produce secondary aluminum. The feasibility of landfill mining for the purpose of recovering materials and energy is a relatively new technology, and often co-benefits are required to make these projects economically viable. Publicly available …

Forecasting Of The Next Three (3) Decades Global Lithium Demand And Pricing Using Multivariate Stochastic Model., Enoch Nii- Okai

Dissertations, Master's Theses and Master's Reports

The transition to electric vehicles (EVs) is pivotal in achieving global climate goals, with nations aiming to integrate a significant portion of new passenger vehicles as electric by 2030. This shift towards electric mobility includes a notable rise in battery-electric, plug-in hybrid, and fuel-cell electric vehicles to curb carbon emissions and foster sustainable transportation. Central to this transition is the critical role of lithium, often referred to as the "white gold of the 21st century," in electric vehicle batteries, especially rechargeable lithium-ion batteries renowned for their high energy efficiency, density, and durability. While lithium has historically found extensive use across …

Study Of Nanoparticle Dispersed Phase Change Materials And The Impact Of Temperature Gradient On The Potential For Particle Migration, Udit Sharma

Dissertations, Master's Theses and Master's Reports

Supercooling in phase change materials (PCMs) and the associated challenges in enhancing thermal conductivity through nanoparticle dispersion prompted this investigation. Existing literature exhibits inconsistencies in thermal conductivity improvements, suggesting a potential correlation with nanoparticle migration induced by thermophoresis. To address this, a novel temperature-dependent scaling parameter, \(\xi\), was introduced to predict particle migration propensity. A strong association was observed between higher \(\xi\) values and diminished thermal conductivity enhancements, indicating a significant influence of nanoparticle movement on heat transfer.

To further elucidate this relationship, a Nanoparticle Interaction Parameter \(N_\text{{pl}}\) was developed, incorporating critical fluid properties and interfacial effects. The derived critical …

Continuous, Platform Purification Of Viral Products Using Aqueous Two-Phase Extraction, Natalie M. Nold

Dissertations, Master's Theses and Master's Reports

Cell and gene therapies, a ground-breaking class of drugs that can heal genetic diseases and cure cancer, often have treatment costs over $1 million. Most cell and gene therapies are delivered using virus-based delivery vectors, which are incredibly expensive to manufacture. Liquid chromatography is the favored method of viral vector purification since it is easily scalable and can be functionalized to purify by biospecificity, charge, hydrophobicity, or size. However, chromatography-based purification processes are highly expensive and usually only recover 20-50% of the viral vectors. There is currently no platform purification strategy for viral vectors due to differences in their surface …

Sustainable Materials For Environment − Multifunctional Material Made From Dead Leaves And Nanostructured Materials For Antibiotic Degradation, Siyuan Fang

Dissertations, Master's Theses and Master's Reports

As environmental pollution from industrial processes and human activities continues to rise, finding efficient approaches to recycle waste materials and degrade persistent contaminants becomes increasingly critical. Dead leaves, an abundant but underutilized biomass, present an opportunity for creating value-added materials if their biocomponents can be preserved and transformed. Simultaneously, antibiotics such as tetracycline are widely found in water bodies but pose serious ecological and health risks, necessitating effective degradation methods. This dissertation addresses these challenges by leveraging natural and nanostructured materials to develop multifunctional products and advanced photocatalytic processes.

Chapter 3 of this dissertation addresses the challenge of converting waste …

Multi-Functional Gelatin-Dithiolane Hydrogels For Tissue Engineering, Saad Asim

Dissertations, Master's Theses and Master's Reports

Biomaterials that integrate multiple functionalities, mimic the extracellular matrix (ECM) microenvironment to support cellular growth, and adhere robustly to damaged tissues are highly needed to advance tissue engineering. Protein-based biomaterials are promising due to their inherent biocompatibility, biomimicry, biodegradation, and cell-supportive properties. Herein, by leveraging the unique ability of dithiolanes to generate on-demand in-situ thiols, we developed a new class of dithiolane-modified, protein-based biomaterial that combines unique, seemingly opposing functions for tissue engineering. Dithiolane-modified gelatin, a model protein used herein, enabled photoinitiator-free photo-crosslinking to form multi-functional gelatin-dithiolane (GelDT) hydrogels, which displayed exceptional long-term stability in cell culture media (>28 …

Optimizing Diabetes Diagnostics: Electrophoretic Separation And Detection Of Hemoglobin A1c With Boronate, Rixlie Fozilova

Dissertations, Master's Theses and Master's Reports

Diabetes affects 537 million people worldwide, while 136 million Americans have diabetes or prediabetes; roughly 22.6% of individuals are undiagnosed. Diabetes is the inability to regulate blood sugar levels. Hemoglobin A1c (HbA1c) is glycosylated hemoglobin and indicates average blood sugar levels over the past three months.

We adapted the selective boronic acid binding to the HbA1c sugar moieties from boronate affinity chromatography and translated it into a portable microchip paper-based electrophoresis platform. We explored a range of boronate concentrations at a steady voltage and sample volume to determine the optimal HbA1c binding and subsequent electrophoretic separation. A pH above 11 …

Analytical And Machine Learning Modeling Of Direct Carbonation Of Natural Silicate Minerals, William Hanson

Dissertations, Master's Theses and Master's Reports

The climate-altering effects of atmospheric CO₂ make it necessary to develop innovative technologies that can remove it from the atmosphere. Mineral carbonation is a scalable technology to sequestrate and store CO2 in a stable carbonate mineral form. The efficiency of the mineral carbonation process depends on both feed mineralogy and process parameters. Comprehensive modeling and prediction of mineral carbonation efficiency for different CO₂ – reactive rocks has not been achieved in the past. In this thesis work, analytical and machine learning models are developed to predict the carbonation efficiency of dunite reactions. It is found that random forest …

Interplay Of Discharge Rates, Slope, And Sediment Size In River Meandering Dynamics: Insights From Experimental Studies, Yifan Zhang

Dissertations, Master's Theses and Master's Reports

Sinuosity is an important concept in various fields including hydrology, geomorphology, and ecology. It not only presents the degree of curviness of a river channel but also reflects the ecological sustaining ability. By studying the sinuosity of a stream or river channel, important implications could also be found including the transportation and deposition of the sediments, the habitats of the riverbanks, and the types of species that are able to live on the watershed and river. To study the factors that could heavily affect the sinuosity of a river channel, a full set of laboratory experiments was designed to perform …

Enhancing Privacy While Revealing Vulnerabilities: Strategies For Adaptation, Optimization, And Model Extraction, Madhureeta Das

Dissertations, Master's Theses and Master's Reports

In the evolving landscape of machine learning and artificial intelligence, this dissertation presents a series of innovative contributions spanning several critical areas: embracing semi-supervised domain adaptation for secure knowledge transfer, enhancing the model performance of tiny models, and executing model stealing attacks via diversified prompts. The overarching goal is to enhance the performance, scalability, and security of AI models across various applications.

The first research focus is on semi-supervised domain adaptation within federated learning frameworks. By leveraging semi-supervised learning techniques, this work addresses the challenge of adapting models trained on a source domain to perform effectively on a target domain …

Computational Analysis Of Vascular Aneurysms, Seyedmostafa Rezaeitaleshmahalleh

Dissertations, Master's Theses and Master's Reports

Vascular aneurysms, such as intracranial aneurysms (IAs) and abdominal aortic aneurysms (AAAs), pose significant health risks, particularly upon rupture. Disturbed hemodynamics, characterized by swirling flow patterns, can lead to cellular and structural changes that disrupt normal physiological processes and contribute to destructive vascular remodeling. Integrating hemodynamic analysis into clinical assessments of aneurysms has become crucial, especially through patient-specific computational fluid dynamics (CFD) simulations based on medical imaging. However, the complexity of CFD tools presents challenges in model creation and post-simulation analysis, limiting their accessibility to clinicians.
This project aimed to advance aneurysm management by exploring the role of intraluminal thrombus …

Applications Of Computational Intelligence And Data Fusion Techniques For Biomedical Images, Anand Krishnadas Nambisan

Doctoral Dissertations

"The realm of melanoma diagnosis has been significantly advanced by deep learning (DL) techniques, yet the current approaches are not without limitations, including missed diagnoses and the challenge of interpreting these "black box" models. The research is comprised of three studies, each contributing uniquely towards advancing melanoma detection accuracy and interpretability. The first study focuses on improving the detection of specific dermoscopic structures through a deep learning-based segmentation approach, while the second study builds upon this by employing a fusion technique that combines traditional image features with advanced deep learning models. This method significantly improves melanoma detection, particularly in recall …

Monitoring And Control Of Coal Dust Exposure Using Low-Cost Pm Sensors And Computational Fluid Dynamics, Nana Kobina Amoako Amoah

Doctoral Dissertations

"Overexposure to respirable coal mine dust has been linked to causing serious health problems including coal workers pneumoconiosis (CWP) and chronic massive fibrosis. The prevalence of these health problems has been on the increase since the 2000s due to increased exposure levels with roof bolter operators having the highest exposure levels. The currently used PDM3700 is too expensive, heavy and bulky which limits their use only for regulatory monitoring failing to measure miners’ personal exposure levels. Also, since roof bolter operators are more prone to elevated coal dust levels, the canopy air curtain (CAC) was developed to protect then high …

Study On Skin Cancer Classification Architectures Based-On Cnn With Channel And Spatial Attention Mechanism, Zahid Maqbool

Chulalongkorn University Theses and Dissertations (Chula ETD)

The early and accurate detection of skin cancer is crucial for effective treatment and improving patient survival rates. This research explores innovative CNN-based architectures enhanced with Channel Attention and Spatial Attention (CASA) mechanisms for classifying skin cancer types from dermoscopic images. Building on the strengths of transfer learning, the study applies pre-trained models VGG16, InceptionResNetV2, and EfficientNet variants (EfficientNet B0 to B7) and customizes them for skin cancer classification using CASA modules. The CASA integration allows the models to selectively focus on critical features within images, enhancing both spatial and channel-based feature extraction. Experiments utilize the HAM10000 dataset. Which contains …