Digital Commons Network^™

Analyzing The Global Changes In The Seasonal Cycle Of Sea Level, Storm Surge, And Ocean Waves, Amanda Barroso

Graduate Thesis and Dissertation post-2024

Changes in the seasonal cycle of extreme sea level components can modulate the flooding risk along coastlines. This dissertation evaluates the changes in and potential co-occurrence of the seasonal cycles of mean sea level, storm surge, and ocean waves. Seasonal cycles characterize the generally expected climate conditions throughout the year with distinct high and low patterns. These regular and predictable cycles have shown non-stationarity in some locations across the globe. This means the timing of the expected peak may shift to occur earlier or later. Shifts in the timing of the maximum sea level impact coastlines with increased risk of …

Numerical Evaluation Of A Novel Transpiration Cooling Scheme For Use In Dynamic Control Of Atmospheric Entry System, Caroline J. Anderson

Graduate Thesis and Dissertation post-2024

Future space exploration missions require sustainable, efficient thermal protection systems for atmospheric entry, instead of disposable ablative shields. Transpiration cooling has interest as a long studied method but never used for missions to date, prompting further study to address concerns of flow transition downstream. In consideration of additional need for aerodynamic maneuvering for greater landing accuracy, a novel asymmetric scheme of transpiration cooling is presented as a method of using onboard coolant to meet entry trajectory maneuvering requirements. This numerical study creates a coupled model of vehicle-scale flow domain and material-scale thermal response to converge on accurate vehicle surface values.

Design And Development Of A Laser Diagnostic System For Ammonia And Nox Measurements In A Toroidal Jet Stirred Reactor At Atmospheric Pressure, Marzuqa Ahmed

Graduate Thesis and Dissertation post-2024

The need for more sustainable fuel alternatives in the modern aircraft industry is growing. SAF is the current sustainable fuel in use, but still produces harmful carbon emissions. A promising alternative to SAF is ammonia, which produces zero carbon emissions. By decomposing ammonia (NH₃) into its constituents of N₂ and H₂ using a chemical catalyst, a hydrogen-ammonia fuel mix is created. H₂/NH₃ mixtures still have the desired characteristic of burning without carbon emissions, but are less reactive than pure hydrogen. Additionally, ammonia is a widely used compound, so infrastructure already exists for production, …

Scalable Embedded Parallel System With Implementation Of Multi-Node Encryption, Binh Pham

Graduate Thesis and Dissertation post-2024

The advancements in artificial intelligence (AI) and other innovative technologies promote a demand for high-performance processing systems, underscoring the importance of parallel computing. As a result, the application of parallel systems has spread across various domains. Most of the existing parallel systems are implemented on Field-Programmable Gate Arrays (FPGAs), which require complex designs and adjustments from the user and are often costly despite some performance benefits. This work explores an alternative approach by implementing a parallel system with a distributed memory protocol on affordable and commercially available microcontroller platforms to provide a scalable and cost-effective system. The proposed parallel system …

Marine Ecosystem Services Disruption And Social Violence, Rafael Hurtado-Valdivieso

Graduate Thesis and Dissertation post-2024

Over the past century, human activities have profoundly altered ocean and coastal ecosystems, significantly impacting communities whose livelihoods depend on these environments. This dissertation explores how disruptions in marine ecosystem services contribute to the emergence of social violence in coastal communities, with a specific focus on the Colombian Caribbean. While prior research suggests a potential link between environmental degradation and conflict, empirical evidence remains limited and inconclusive. This study addresses that gap through a multi-method, interdisciplinary approach that integrates environmental science, political science, and computational modeling to examine the mechanisms linking ecosystem disruption to social violence.

The research follows a …

Bridging Data Gaps And Forecasting Challenges In Water Quality Monitoring Using Deep Learning, Rohan Gudla

Graduate Thesis and Dissertation post-2024

Reliable water quality monitoring is critical for protecting public health and enabling sustainable environmental management. However, high rates of missing data often exceed 40% due to sensor failures and operational disruptions severely undermine the utility of environmental monitoring systems. To address this challenge, this thesis presents a three-stage deep learning framework designed to enhance the accuracy and resilience of water quality forecasting. First, a Bidirectional Long Short-Term Memory model with Attention and Autoencoder Modules (BiLSTM-AAM) is introduced to impute long, irregular gaps in multivariate time series. Applied to real-world data from the St. Lucie River Basin, the model achieved a …

Enhancing Intelligent Transportation Systems: Scalability, Emulation, And Applications Of V2x Communication For Connected And Autonomous Vehicles, Ghayoor Shah

Graduate Thesis and Dissertation post-2024

The integration of Vehicle-to-Everything (V2X) communication is a key advancement for Connected and Autonomous Vehicles (CAVs), enhancing the safety, efficiency, and intelligence of modern Intelligent Transportation Systems (ITS). While Autonomous Vehicles (AVs) rely heavily on onboard sensor data for situational awareness, they remain limited in non-line-of-sight scenarios, which can lead to critical driver errors and accidents. V2X communication extends this awareness by enabling real-time exchange of information beyond sensor range, improving decision-making and reducing collisions.

Two primary V2X technologies are Dedicated Short-Range Communication (DSRC) and Cellular-V2X (C-V2X). While DSRC, based on IEEE 802.11p, was initially prominent in research, C-V2X, standardized …

Accurate And Efficient Orbit Probability Approximation Framework For Space Situational Awareness, Pugazhenthi Sivasankar

Graduate Thesis and Dissertation post-2024

Uncertainty Propagation in astrodynamics has gained importance in space situational awareness (SSA) problems such as space debris tracking, collision avoidance, and Cislunar operations. In this dissertation, the technique of Orbit Probability Approximation (OPA) is developed. OPA propagates orbital uncertainty using Liouville’s theorem with different functional approximations. First, OPA is formulated with Chebyshev polynomials to propagate the uncertainty on a geocentric planar orbit problem and then validated using two sources of satellite data: GRACE navigation data from the Jet Propulsion Laboratory (JPL) database, and FireOPAL ground-based observer provided by Lockheed Martin. In this validation process, OPA propagates uncertainty without using any …

Computational Approaches For Rna Structural Motif Analysis: Similarities, Variations, And Spatial Relationships, Md Mahfuzur Rahaman

Graduate Thesis and Dissertation post-2024

The three-dimensional structures of RNA play a critical role in understanding their functions. One way to study the RNA 3D structure is by analyzing recurrent segments within the loop regions of RNAs, known as RNA structural motifs. These motifs can be grouped into families based on their structural features. However, the inherent structural diversity presents challenges in characterizing the similarities, variations, and spatial relationships within RNA motif families. This dissertation presents three computational methods that together provide a comprehensive framework for RNA structural motif analysis. First, RNAMotifComp establishes and visualizes relational networks among structural motif families. By analyzing instances of …

From Failure To Fidelity: Enabling Scalable Sim2real Lidar Perception Through Realistic Digital Twins, Muhammad Shahbaz

Graduate Thesis and Dissertation post-2024

LiDAR technology plays a key role in enabling intelligent perception across urban systems. Its ability to capture accurate 3D data regardless of lighting conditions makes it well-suited for applications in autonomous vehicles, traffic monitoring, security, and privacy-aware smart city infrastructure, offering advantages over camera-based systems in depth, precision, and reliability. However, scaling deployment faces substantial challenges due to the time, effort, and cost of gathering and labeling LiDAR data used to train and test perception algorithms. This dissertation addresses these challenges by tracing a comprehensive research trajectory from early failures to a successful, fidelity-driven solution. Initially, (1) it explores transfer …

From Microns To Mons: Investigating Airless Body Surface Properties And Processes, Autumn N. Shackelford

Graduate Thesis and Dissertation post-2024

Understanding how airless bodies have evolved in the space environment is essential for interpreting remote sensing data and constraining the weathering processes that alter airless body surfaces over time. This dissertation utilizes coordinated experimental, observational, and analytical techniques to investigate airless body surface properties and processes. Our first study seeks to characterize mechanical breakdown of the lunar regolith caused by ice-regolith interactions within the lunar polar regolith environment. The second and third studies investigate the effects of simulated micrometeorite bombardment via a novel dual pulsed laser experimental setup on iron-poor, carbon-rich Mercury and carbonaceous asteroid analogs. We analyze the spectral, …

Preparing For The Habitable Worlds Observatory: Spectropolarimetric Radiative Transfer Models Of Terrestrial (Exo)Planets, Kenneth E. Goodis Gordon

Graduate Thesis and Dissertation post-2024

The next major step for the exoplanet community lies in the characterization of terrestrial exoplanets, especially when it comes to identifying biosignatures and determining the habitability of these worlds. In response to this, NASA has proposed the Habitable Worlds Observatory (HWO), with the primary goal of searching for and characterizing Earth-sized planets in the habitable zones (HZ) of their stars. However, current characterization strategies that only rely on the unpolarized flux from the planets lose some of the informational content of the observed light and therefore suffer from degeneracies in the calculated planetary parameters. The sensitivity of polarization to the …

Phase Retrieval And Fidelity Preserving Quantum Channels, Kai Liu

Graduate Thesis and Dissertation post-2024

This thesis investigates the preservation of fundamental quantum properties under noisy quantum processes. Motivated by the challenges of maintaining coherence and information integrity in realistic quantum systems, we study three distinct structural properties: phase retrievability, orbit injectivity, and fidelity preservation. Each property reflects a unique facet of how quantum information can be robustly encoded, distinguished, or compared in the presence of environmental interactions.

In the first part of the thesis, we analyze phase retrievable quantum channels which allow recovery of a quantum state up to a global phase. We provide structural characterizations using the Kraus representation and the joint spectrum …

The Role Individual Differences And Environmental Factors In Automated Driving Performance, John M. Duany

Graduate Thesis and Dissertation post-2024

A series of experiments were conducted to investigate the effects of ADHD diagnosis, inclement weather, non-driving-related tasks (NDRT), automation failures, and traffic density on driving performance and takeover performance. This study also examined the relationship between dispositional factors (i.e., trust in automation, cognitive failures, and self-efficacy) and dynamic factors (i.e., mental workload, situation awareness, and situational trust) in relation to automated driving performance. A total of 156 participants were recruited for this study, including drivers with and without attention-deficit hyperactivity disorder (ADHD). Each participant completed four experimental drives and two baseline drives. Mental workload, heart rate variability (HRV), situational trust, …

Examining Educational Inequalities And Driving Science’S Next Generation Forward: Geobus Program Theory Evaluation, Elise M. Lorenzo

Graduate Thesis and Dissertation post-2024

This study presents a theory-driven evaluation of GeoBus, a mobile science lab designed to deliver hands-on, “fun learning experiences” via a solar powered, technology-rich learning environment brought directly to K-12 students in Central Florida Title 1 schools. While STEM education is widely recognized as essential for promoting equity, many schools, particularly in under-resourced communities, continue to face barriers to accessing meaningful and inclusive science learning opportunities. GeoBus was created in response to this need, offering students an informal learning environment with exposure to geospatial technologies and interactive science content.

Drawing on mixed-methods data, including content from the GeoBus website and …

Ucf Pegasus Plan - 5th Grade - Early American Inventions, Del M. Herman, Jaclyne Casella, Kathryn Garner

Pegasus Plans: Social Studies Unit Plans for Florida Teachers

During this unit, students will trace early American inventions' effects on the early United States and the business productivity of that era. Students will complete primary source analyses on the different inventions, take guided notes in a foldable on each invention, and participate in a collaborative project where students will create a PowerPoint on a particular early American invention in order to demonstrate proficiency in this unit.

Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst The Covid-19 Pandemic, Mahyar Alinejad

Data Science and Data Mining

Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance …

Multiclass Cyberbullying Detection Using Advanced Neural Network Architectures: A Comparative Study Amidst The Covid-19 Pandemic, Mahyar Alinejad

Data Science and Data Mining

Amidst the COVID-19 pandemic, the digital communication landscape has seen an unprecedented rise in cyberbullying incidents. Addressing this critical issue, our study develops and evaluates a novel multiclass cyberbullying detection framework employing several advanced neural network architectures—namely Neural Networks (NN), Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Gated Recurrent Units (GRU). Utilizing a balanced dataset created through Dynamic Query Expansion, this research benchmarks the performance of these models in accurately classifying cyberbullying according to specific victim attributes such as age, ethnicity, gender, and religion. Our results demonstrate that LSTM and GRU models, in particular, exhibit superior performance …

Design Of A Simplified Biomimetic Autonomous Entertainment Robot, Jessica E. Morris, Shanny May Ruiz, Morgan Snyder, Hannah Guild, Gabriel Cardona-Tous

Graduate Scholarship and Creative Works

The advancement of technology has paved the way for robots with the purpose of entertainment to become more popular. However, the exploration of biomimetic actions as a factor of entertainment is majorly limited to the mimicry of young domesticated animals such as puppies and kittens, with some notable exceptions. However, there is a wide range of possibilities for robotics that mimic animals marketed as pets, outside of the common examples. This work seeks to explore the opportunity for autonomous systems with entertainment driven and expressive actions in the family of Testudines (turtles and tortoises), an undomesticated species, but still often …

Artificial Intelligence (Ai) Literacy: A Necessity In Modern Language Education, Samarnh Pang, Engheang Nol, Kimkong Heng

Journal of English Learner Education

This article aims to discuss the importance of artificial intelligence (AI) literacy in the context of an AI-driven world and argues that it is a necessity in modern language education. The article answers a few questions, focusing on why AI literacy matters for language students and educators, what AI competencies should be promoted, and how AI literacy can be effectively integrated into the curriculum and instruction. The article also brings to the fore key considerations for implementing AI literacy in language education. These considerations include providing support to students and teachers to navigate the complexity of AI use, offering AI …

Comparative Analysis Of Matrix Factorization And Neural Collaborative Filtering For Movie Recommendation Systems, Mahyar Alinejad

Data Science and Data Mining

This paper presents a comparative study of two recommendation system approaches for predicting movie ratings: Matrix Factorization with Stochastic Gradient Descent (SGD) optimization and Neural Collaborative Filtering (NCF) using Tensor Flow. The study aims to evaluate the effectiveness of these methods in recommending movies to users based on the MovieLens 100K dataset. The Matrix Factorization approach utilizes latent features to model user preferences and item characteristics, optimizing parameters through SGD. On the other hand, NCF integrates traditional collaborative filtering with neural networks to capture complex user-item interactions. Experimental results demonstrate the performance of both models in terms of Root Mean …

Detecting Physical Activity Using Wearable Sensor Data, Dipok Deb

Data Science and Data Mining

This study focuses on detecting physical activity using wearable sensor data, specifically distinguishing between walking and running. A dataset comprising accelerometer and gyroscope readings is used to train and evaluate various machine learning models, including logistic regression, random forest, k-nearest neighbors, naïve Bayes, and XGBoost. Extensive preprocessing, such as creating lag features and rolling statistics, is performed to enhance temporal data representation. The models are evaluated using metrics like accuracy, precision, recall, and F1 score. Incorporating lag and rolling features significantly improves model performance, with logistic regression achieving perfect scores across all metrics. These findings demonstrate the effectiveness of enhanced …

Data Science Job Salary Prediction Using Linear Regression, Dipok Deb

Data Science and Data Mining

In the evolving landscape of data science, accurate salary prediction plays a crucial role in shaping career expectations, informing educational strategies, and guiding organizational hiring decisions. This study investigates the key factors influencing entry-level data science salaries in the United States by applying a multiple linear regression model to a recent dataset spanning from 2020 to 2024. Through data preprocessing, transformation, and diagnostic evaluation, we identify how job roles, experience levels, employment types, work arrangements, residency status, and company size impact compensation. Despite challenges such as outliers, heteroscedasticity, and non-normal residuals, model refinements like the Box-Cox transformation and variable selection …

Integrating Background Music Into Instructional Videos To Support Learner Engagement And Motivation, Efren De La Mora Velasco

Teaching Online Pedagogical Repository

This strategy explores how integrating background music (BM) into instructional videos enhances learner engagement and motivation by aligning musical elements with multimedia learning principles. The implementation involved designing seven videos with expert narration, purposeful music composition, and sound effects to guide attention and support pacing without causing cognitive overload. Results showed increased learner attentiveness and motivation, with positive outcomes across all dimensions of Keller’s ARCS model. Practical recommendations for integrating music into online courses are included.

Using Generative Ai To Enrich Instructional Videos With Embedded Quizzes, Efren De La Mora Velasco, Shaurya Agarwal

Teaching Online Pedagogical Repository

This entry describes a strategy to enhance student engagement in asynchronous online courses by embedding AI-generated quizzes into instructional videos. Using ChatGPT and video transcripts, the instructor created targeted multiple-choice questions that aligned with key video topics and improved interactivity. Results from the CGN5341 course showed increased video view duration, higher engagement, and positive student feedback on the usefulness of short, segmented videos with embedded quizzes.

Creating Smaller, Stable Discussion Communities Within Larger Online Courses, Mary Adler

Teaching Online Pedagogical Repository

Given the adverse effects of large online discussions for student engagement and learning, the entry offers a strategy to establish smaller communities of learners by utilizing stable group sets for a series of learning logs. Steps include soliciting student input on their interests; grouping sets to join together students with common interests; using community-building prompts that include icebreakers, connection to lived experience, and soliciting thoughtful responses; and providing faculty feedback and modeling. Students were enthusiastic about the approach in survey data, with 100% recommending continued use, either as is (76%) or with minor changes (24%).

Introduction To Prompt Engineering, Mary Rubin, Rachel Vacek

Faculty Scholarship and Creative Works

This presentation explores the fundamentals of effective prompt engineering, which involves well-crafted instructions for AI models to generate meaningful responses. Effective prompts can be written using frameworks like CLEAR and TRACI. The CLEAR Framework focuses on conciseness, logical structure, explicitness, adaptability, and reflection, while TRACI emphasizes task, role, audience, creation, and intent.

Additionally, the presentation addresses ethical considerations and best practices in prompt engineering, including academic integrity, attribution and transparency, and the importance of evaluation and assessment. By mastering these techniques, users can optimize their interactions with AI models, ensuring accuracy, relevancy, and minimizing bias.

Methods And System For Efficientindexing For Genetic Genealogicaldiscovery In Large Genotypedatabases, Degui Zhi, Shaojie Zhang, Ardalan Naseri, Ahsan Sanaullah

UCF Patents

A system for indexing, updating, and search haplotypes for genetic genealogical discovery in genotype databases. The system includes a pool of genetic indexes, a haplotype ingestion engine, and a haplotype query engine. The haplotypes of a number of individuals in a genotype database are indexed by a pool of multiple panels, and each panel pool can be dynamically updated by the insertion or deletion of individual haplotypes. A genetic genealogical search of a query haplotype against the database is achieved by first projecting the query onto a subset of panels in the pool, then conducting long match queries over each …

Comparative Evaluation Of Linear Regression, Cross Validation And Regularization Approaches In Multivariate Data Analysis, Ransford Owusu, Felix Yeboah, Francis Effah Boateng

Data Science and Data Mining

This study evaluates linear regression and its enhanced variants incorporating cross-validation and regularization techniques for high-dimensional, multivariate datasets. We address challenges such as multicollinearity and overfitting. Methods including Ridge, LASSO, and Elastic Net are compared against ordinary least squares regression. Empirical analysis using an automobile dataset for fuel efficiency prediction shows that while OLS regression captures basic relationships, its limitations are mitigated through regularization and cross-validation, resulting in improved model interpretability. The findings provide a comprehensive framework for predictive modeling in complex data environments and offer insights into statistical methodology and practical applications in the automobile industry.

Exploring Generative Artificial Intelligence (Ai) Applications In Fashion: Ethical Concerns In Human-Like Technology, Melissa Kathryn Gonzalez

Faculty Scholarship and Creative Works

No abstract provided.