Engineering Commons^™

Increased Visual Response Delay Impact On Sensorimotor Control In Persons With Multiple Sclerosis, Julie C. Wagner, Frankie M. Ingram, Vincenzo Daniele Boccia, Matilde Inglese, Maura Casadio, Camilla Pierella, Andrea Canessa, Robert A. Scheidt, Scott A. Beardsley

Biomedical Engineering Faculty Research and Publications

Multiple Sclerosis negatively affects hand function in 60% of cases. Upper extremity dysfunction in persons with Multiple Sclerosis (PwMS) has previously been linked to slower, more variable movement, increased visual response delays (T_v), and neuroimaging evidence of altered brain activity; yet no one knows how these aspects relate to each other. This work combines clinical, kinematic, sensorimotor control, and neural imaging techniques to gain a more complete understanding of how upper extremity dysfunction arises in PwMS. Twenty PwMS and 20 Controls completed a reach and hold task with simultaneous electroencephalography recorded to determine if increased T_v in …

Power Utilization In Open Ran: Key Findings From A Usa Testbed, Saish Urumkar, Byrav Ramamurthy, Seshu Tirupathi, Sachin Sharma

Articles

Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single …

Long-Term Assessment Of The Morphological Behavior Of The Beaches North Of Sebastian Inlet, Florida, Aidan O'Gorman

UNF Graduate Theses and Dissertations

The morphological behavior of the beaches north of Sebastian Inlet, Florida is investigated by applying Empirical Orthogonal Function (EOF) analyses to a series of beach surveys conducted semi-annually (winter and summer) over a period of fifteen years (2005-2019), with the primary objectives being to 1) determine the extent to which the inlet’s influence to the north typically fluctuates on a short-term basis (e.g. annually), and 2) identify any long-term trends of accretion or erosion that might exist. Each survey is comprised of thirty-one cross-shore profiles that are referenced to permanently installed range monuments (“R-monuments”) that are spaced nominally 305 …

Assessment Of Wave Climate Conditions At An Important Historical And Ecological Site Along The Fort George River In Northeast Florida, Ally O. Kasuwi

UNF Graduate Theses and Dissertations

This study investigated wave climate conditions at the Timucuan Preserve in northeast Florida, addressing gaps in previous shoreline erosion research that largely attributed erosion to boat wakes without detailed wave data. Field data were collected using several do-it-yourself water wave gauges. Each data collection event was for approximately one week, with an emphasis on collecting data before, during, and after periods of expected peak wave activity (i.e., holiday weekends and large-scale storms). Data were analyzed in the frequency domain to establish the likely cause of waves. Results suggested that, counterintuitively, watercraft traffic may have produced higher dominant wave periods than …

Assessment Of Surface Area Application Ratios For Beach Sand Treated Via Surface Percolated Micp-Bioslurry For Erosion Mitigation, Connor J. Marlin

UNF Graduate Theses and Dissertations

Rapid shoreline erosion associated with sea-level rise is an ongoing threat to coastal infrastructure. Traditional stabilization techniques, such as revetments, seawalls, and periodic nourishment, often prove costly, ecologically disruptive, or provide only temporary protection. Microbially Induced Calcite Precipitation (MICP) and its operational variant, bioslurry, present a biologically inspired alternative capable of forming a cemented surface crust that reduces soil erodibility. Previous laboratory studies typically prescribed a solution dosage as a percentage of pore volume (%PV). While geotechnically sound, this metric is poorly suited to field deployment across a large area. This thesis evaluates surface-area-based dosing (L/m²) as the primary dosage …

Dielectric Performance Of Fused Filament Fabricated Ultem 9085 For Naval Radome Application, Carson Rogers

UNF Graduate Theses and Dissertations

ULTEM 9085 is a high-performance thermoplastic used in the fused filament fabrication (FFF) additive manufacturing process. It has found use in applications where mechanical strength, chemical integrity, and thermal stability are of paramount importance. In this thesis, the effects of process parameters, annealing, and weathering on the mechanical and dielectric properties of FFF ULTEM 9085 have been investigated.

The first part reviews existing research on how printing parameters such as nozzle temperature, chamber temperature, print speed, infill, and layer height affect the mechanical performance of FFF ULTEM 9085. Data from the manufacturer of commonly used ULTEM 9085 filament, Stratasys, has …

Computational Pangenomics And Machine Learning For Genotype-Phenotype Analysis, Tejaswi Vemuri

UNF Graduate Theses and Dissertations

Phenotypes are the observable characteristics of an individual organism. Predicting quantitative phenotypes from genomic variation remains challenging when causal signals span both local motifs and distal regulatory contexts. Building on Frequented Regions (FRs)—subsequences conserved across genomes and extracted from a pangenome graph generated from a large collection of closely related species—we compare several modeling strategies across 35 Saccharomyces cerevisiae growth phenotypes: Random Forest (RF) on FR counts (called RFCounts), RF on FR sequences, 1D convolutional neural networks (CNN) on FR sequences, Long Short-Term Memory (LSTM) networks on FR sequences, a Genomewide Association Study (GWAS) baseline, and a sequence-based transformer model, …

Connecting Bicyclists And Transit: A Multimodal Routing Tool With Bicycle Facilities Scoring, Raphael Y. Mrema

UNF Graduate Theses and Dissertations

This research develops a comprehensive, data-driven framework for assessing multimodal bicycle accessibility using open-source technologies and real-time routing data. Traditional active transportation studies often depend on proprietary GIS tools and static network datasets, which limit scalability, reproducibility, and integration with live mobility systems. In contrast, this study introduces a Python-based approach that leverages GeoPandas, the Google Maps Directions API, and General Transit Feed Specification (GTFS) data to dynamically evaluate infrastructure quality, operational stress, and multimodal connectivity. The framework was applied to Duval County, Florida, to examine how roadway design, facility type, and transit availability jointly influence bicycle network performance and …

A Comprehensive Guide To Construction Documentation: From Hand Drafting To Bim, Cesar Salazar

Open Educational Resources

The construction industry relies heavily on precise and clear documentation to ensure the successful completion of projects. This book is a comprehensive guide to creating construction drawings, focusing on the principles and best practices that lead to high-quality outputs. Unlike manuals that teach specific software tools, this book emphasizes the effective use of various drawing tools to produce detailed graphic narratives that support the entire construction process, from inception to completion.

Factors Contributing To Fatalities In Helicopter Emergency Medical Service Accidents, Jenna Korentsides, Joseph R. Keebler, Mihhail Berezovski, Alex Chaparro

Publications

INTRODUCTION: T his study aimed to update and reinforce previous research on helicopter emergency medical service accidents in the United States. By investigating predictors of fatalities after helicopter emergency medical service crashes through the application of machine learning techniques, we updated existing data sets and sought to uncover patterns that traditional analysis might not reveal.

METHODS: Using the National Transportation Safety Board database, the authors analyzed a dataset of 267 helicopter emergency medical service accidents between 1991–2022. We first calculated fatalities odds ratios for each condition. We then plotted geospatial locations of all reported accidents. Finally, we used XGBoost regression …

Investigation Of A Busemann Intake At Negative Angle Of Attack, Mark E. Noftz, Andrew N. Bustard, Nicholas J. Bisek, Thomas J. Juliano, Joseph S. Jewell

Publications

A high-speed, shape-transitioned, inward-turning intake was tested in Purdue’s Boeing/AFOSR Mach 6 Quiet Tunnel. The inlet model, called the Indiana Inlet (INlet), had a total contraction ratio of 4.68:1 and a design point of Mach 6 at 0° angle of attack. The model was outfitted with a suite of high-frequency pressure transducers, and the external flowfield was imaged with high-speed schlieren photography. The INlet was tested under low freestream disturbance levels for a variety of freestream unit Reynolds numbers and at-4° angle of attack. An unsteady shockwave near the leading edge of the inlet forebody, indicative of boundary layer separation, …

Power To Liquid And Hydrogen Use For Saf Production; A Policy And Techno-Economic Overview For Aviation​, Eva Maleviti

Publications

What is Sustainable Development: The development that meets the needs of the present without compromising the ability of future generations to meet their own needs.

1. Environmental Sustainability

2. Social Sustainability

3. Economic Sustainability

An Operational Field Study: A Comparison Of Piloting Uncrewed Underwater Vehicles And Uncrewed Aircraft Systems, David Thirtyacre, Joseph Cerreta, Pete Miller, Kimberly Luthi, Jolee Thirtyacre

Publications

and operations, the ability to cross-train personnel in both Uncrewed Underwater Vehicles and Small Uncrewed Aircraft System operations has become a focal point for efficiency and workforce optimization. This study presents a comparative analysis of the operational and human factor considerations involved in piloting mini UUV and sUASs, highlighting the key similarities and differences in control methods, environmental influences, navigation, emergency procedures, and situational awareness. A qualitative experimental field study was conducted between July 2024 and October 2024, involving real-world deployments of both systems in maritime and aerial environments. Findings indicated that while UUV and sUAS operators relied on remote …

The Digital Loophole: Evaluating The Effectiveness Of Child Age Verification Methods On Social Media, Fatmaelzahraa Eltaher, Rahul Gajula, Luis Miralles-Pechuán, Christina Thorpe, Susan Mckeever

Conference papers

Social media platforms are an integral part of daily life for nearly five billion people worldwide. However, the growing presence of underage users on these platforms raises significant concerns regarding children's exposure to harmful content and its impact on their mental health. This paper examines the effectiveness of age verification measures implemented on leading platforms Facebook, YouTube, Instagram, TikTok, Snapchat, and X. We evaluate the age verification processes required for account creation by simulating the registration steps for minors on these platforms. We also compare these methods to best practices in online age assurance in finance, betting and public transportation …

Delivery Of Medical Supplies To Remote Locations Via Unmanned Aerial Vehicles: Approaches, Challenges, And Solutions, Meshari Aljohani, Ravi Mukkamala, Stephanie Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming more important in improving healthcare logistics, in particular due to their cost effectiveness, minimized risk, and versatile operational capabilities. This study explores the deployment of autonomous UAVs to deliver medical supplies to remote areas. Advances in ledger technology, smart contracts, and machine learning have transformed tasks previously managed by human teams or manually controlled UAVs into fully autonomous missions. We present a comprehensive analysis of the challenges and initial solutions vital for the effective use of autonomous UAVs in the delivery of medical supplies. In addition, we propose a machine-learning model to optimize UAV …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Computer Science Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Uncertainty-Aware Deep Learning Framework For Forecasting Coastal Water Level In Virginia Beach, Md Mahmudul Hasan, Malachi Schram, Sridhar Katragadda, Diana Mcspadden, Alisa N. Udomvisawakul, Heather Richter, Frank Liu

Computer Science Faculty Publications

Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and …

Benchmarking Batch-Effect Correction Methods Towards The Construction Of A Triple-Negative Breast Cancer Cell Atlas, Peter Scheible, Amy H. Tang, Jing He, Jiangwen Sun

Computer Science Faculty Publications

Triple-negative breast cancer (TNBC) requires detailed cellular mapping given its aggressive nature, immense tumor heterogeneity and genetic diversity. We integrated 156,794 cells from six scRNA-seq datasets—including tumors, metastases, and cell lines—to build a TNBC scRNA cell atlas, focusing on batch effect mitigation while maintaining biological and molecular details. Preprocessing f ilters noise, normalizes data, and leverages PCA for integration readiness. We utilized scANVI, a semi-supervised tool, to align datasets, preserving TNBC’s complex tumor heterogeneity via marker annotations [1]. UMAPs demonstrate biological clustering in integrated data, contrasted with datasetdriven unintegrated patterns. Assessments verifying effective batch correction. This method aligns with NASA’s …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Normalizing Images In Various Weather And Lighting Conditions Using Colorpix2pix Generative Adversarial Network, Sanjida Tasnim, Ashif Mahmud Mostafa, Azmain Morshed, Namreen Shaiyaz, Shakib Mahmud Dipto, Saad Aloteibi, Mohammad Ali Moni, Md. Golam Rabiul Alam, Md. Ashraful Alam

Computer Science Faculty Publications

Autonomous vehicles (AVs) are widely regarded as the future of transportation due to their tremendous benefits and user comfort. However, the AVs have been struggling with very crucial challenges, such as achieving reliable accuracy in object detection as well as faster computation required for quick decision-making. In recent years, perception systems in driverless cars have been significantly enhanced, mainly due to advances in deep-learning-based object detection systems. However, these perception systems are still heavily affected by environmental variables, such as changes in illumination, refractive interference, and adverse weather conditions, which may compromise their reliability and safety. This research proposes an …

Insights In Adaptation: Examining Self-Reflection Strategies Of Job Seekers With Visual Impairments In India, Akshay Kolgar Nayak, Yash Prakash, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection …

S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala

Computer Science Faculty Publications

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model’s ability to adapt to new knowledge. In this paper, we propose Structurally Stable Incremental Learning (S²IL), a FD method for CIL that mitigates forgetting by focusing on preserving the overall spatial patterns of features which promote flexible (plasticity) yet stable representations that preserve old knowledge (stability). We also demonstrate that our proposed method S²IL achieves strong incremental accuracy and outperforms other FD …

An Optimized Generalized Multi-Color Point Implicit Solver For Intel Gpus Using Oneapi Esimd, Joseph Wassell, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Timothée Ewart

Computer Science Faculty Publications

This paper presents an efficient implementation of a linear-solver kernel relevant to FUN3D, a suite of computational fluid dynamics software developed at NASA’s Langley Research Center. The linear solver is optimized for a range of block sizes commonly used in FUN3D. The implementation targets Aurora, the Argonne Leadership Computing Facility’s (ALCF) exascale machine featuring Intel Data Center Max 1550 GPUs. The linear solver’s performance is memory bandwidth-bound due to its low arithmetic intensity. The primary performance challenges stem from variable matrix row lengths and indirect memory access patterns inherent in unstructured-grid applications. Variable block sizes introduce additional complexity through differing …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Improving Torque Distribution Of The Baja Car's Drive Shaft, Natalie E. Falerios

Senior Honors Theses and Projects

This honors thesis analyzes torque variation in the drive shaft system of the Baja SAE vehicle, with a focus on the effects of universal joint (U-joint) geometry and phasing in an inherited and new drive shaft design. The original drivetrain was inherited from a previous team and affected the seat placement that did not comply with the Baja SAE rules. This resulted in a redesign of the drive shaft and evaluation of both the inherited and redesigned configurations’ performance. An initial experiment was created and attempted to analyze the torque variation in the drive shaft, however, there were time constraints …

New Insights Into Fertilisation With Animal Manure For Annual Double-Cropping Systems In Nitrate-Vulnerable Zones Of Northeastern Spain, Dolores Quilez, Monica Guillen, Marta Valles, Arturo Dauden, Beatriz Moreno-Garcia

Biological and Agricultural Engineering Faculty Publications and Presentations

Maize double-cropping production systems in Mediterranean areas have a great nitrogen extraction capacity and high nitrogen (N) requirements. This study aims to assess whether in these farming systems, animal manure can be applied, using adequate management practices, at levels exceeding the maximum annual amount of livestock manure established in the European Nitrate Directive for vulnerable zones (170 kg N ha-1) without increasing the risk of water nitrate contamination. We compare the risk of nitrate leaching under two fertilisation strategies, one with synthetic fertilisers and the second with a maximised application of pig slurry, exceeding the limits of the EU Nitrate …

Online Parameter Adaptation Of Lqr Controllers Via Rls For Prosthetic Joint Control: Experimental Validation On A Quanser Qube-Servo 2 Platform, Cynthia Lopez-Jordan

Theses and Dissertations

This thesis develops and experimentally validates an online adaptive Linear Quadratic Regulator (LQR) control method for prosthetic joint systems using Recursive Least Squares (RLS)-based real-time parameter estimation on the Quanser QUBE-Servo 2 platform. Traditional LQR controllers assume a fxed system model, which limits adaptability and results in reduced tracking accuracy, poor robustness, and loss of optimal performance when applied to dynamically changing prosthetic joints, infuenced by load variations, user gait changes, and mechanical wear. Limited experimental validation exists for combining RLS with online LQR adaptation in prosthetic-like systems.

To address these limitations, an RLS-driven Adaptive LQR framework was implemented to …

Integrating Satellite-Based Precipitation Analysis: A Case Study In Norfolk, Virginia, Imiya M. Chathuranika, Dalya Ismael

Engineering Technology Faculty Publications

In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data over the long term, thereby enabling more accurate environmental analysis and decision-making. Specifically, the effectiveness of SbPPs in Norfolk, Virginia, is assessed by comparing them with observed precipitation data from Norfolk International Airport (NIA) using common bias adjustment methods. The study applies three different methods to correct biases caused by sensor limitations and calibration discrepancies and then …

A Review Of Infrared Thermography Applications For Civil Infrastructure, Prabal Shrestha, Onur Avci, Sahabeddin Rifai, Feras Abla, Michael Seek, Karl Barth, Udaya Halabe

Engineering Technology Faculty Publications

Civil infrastructure is continuously subject to aging and deterioration due to multiple factors, which lead to a decline in performance and impact structural health. Accumulated damage on structures increases operational costs and poses significant risks to public safety. Effective maintenance, repair, and rehabilitation strategies are needed to ensure civil infrastructure’s overall safety and reliability. Non-Destructive Evaluation (NDE) methods are utilized to assess latent damage and provide decision-makers with real-time information for mitigating hazards. Within the last decade, there has been a significant increase in the research and development of innovative NDE techniques to improve data processing and promote efficient and …