Digital Commons Network^™

Kaleidoscopic Portraits Of The Joycean Reader, Zoe Patterson

James Joyce Literary Supplement

No abstract provided.

Without Borders, Lauri A. Niskanen

James Joyce Literary Supplement

No abstract provided.

Studying Electromagnetic Wave Scattering From Small Dielectric Particles Using Neural Networks, Bryan Taylan, Patrick Corzo, Chris Velissaris, Theodoros Panagiotakopoulos

Undergraduate Scholarship and Creative Works

Physics-informed neural networks solve the Helmholtz equation without labeled data, but a low residual alone does not confirm that a solution preserves the physical distinction a downstream task depends on. We trained a four-layer SIREN with physics-based residuals, a Sommerfeld condition, Adam, and L-BFGS, modeling a Gaussian source and a plane wave scattering from a small dielectric inclusion. Fine-tuning from four base models produced 600 complex fields spanning omega = 4 to 20. A compact CNN, three ResNet-18 variants, and a frozen OpenCLIP encoder classified these fields under five random seeds. The CNN achieved (95.65 +/- 0.77)% accuracy, outperforming OpenCLIP …

University Of Central Florida Undergraduate Catalog, 2025-2026, University Of Central Florida

UCF Catalogs

No abstract provided.

Data For Meta-Analysis Of Personalized Adaptive Learning In Undergraduate Mathematics, Debbie Hahs-Vaughn, Patsy Moskal, Katiuscia Teixeira, Tammy Muhs, Oluwaseun Farotimi, Christina Carassas, Corinne Bishop

Research Data and Datasets

No abstract provided.

"The First Web Novel At 30: The Collection And The Creative Process", Robert Arellano, Scott Rettberg

ELO (un)supervised 2026

Summer 2026 marks the 30th anniversary of Sunshine '69, recognized as the first novelistic hypertext fiction published on the web. While the full work remains accessible online—an "(un)supervised" preservation achievement in itself—the archive remains split between boxes and memory. This conversation between the work's creator and a major scholar in electronic literature documents both specific preservation challenges and systemic patterns in what the field chooses to preserve.

Topics include: figuring out web-born composition before established methodologies existed; the three decades of technical decisions that kept a 1996 work alive through format obsolescence and server migrations; and what gets lost …

Compressed Cinema As A Study In Llm Latent Spaces, Mallen Clifton

ELO (un)supervised 2026

In his article “Spec Acts” (2021), Matthew Kirschenbaum analyzes the AI-generated novel 1 the Road to develop his titular concept of the spec act, “the future in its multitudes collapsing into an actionable present.” With the proliferation of texts produced by generative AI and subsequent critical analyses of them, one element in particular calls for further theorization: “the future in its multitudes,” or more directly, the latent space. This echoes arguments by critics such as Antonio Somaini, who offered his own “Theory of Latent Spaces” last year. However, where Somaini’s attention is towards visual culture, I turn mine to the …

Out Of This World: An Exoplanetary Workshop, Monica Storss, Bart Kuipers

ELO (un)supervised 2026

The workshop investigates how scientific constraints can productively defamiliarize human experience and create new frameworks for creativity and expression. Participants will produce original work while considering broader questions about environment, perception, and cultural formation in speculative contexts, and how the arts, sciences, and technology inform each other in relational multiplicities. This generative workshop explores how astronomical data from confirmed exoplanets can generate new poetic forms and linguistic constraints. Participants will examine how exoplanetary conditions can shape language, metaphor, and narrative structure. We'll develop from writing exercises that respond to non-terrestrial physical parameters: alternative light spectra, gravitational variations, and atmospheric compositions. …

Establishing New Territorializations In A Community-Engaged Tesol Program: Insights From College Student Experiences, Lourdes Cardozo-Gaibisso, Savanah Stewart, Daniela Coral Patino, Haylee Morman

Journal of English Learner Education

This article presents a community engaged learning experience and its instructional implications for TESOL. The instructional cultures established in policies and practices that dictate college students’ engagement with the “real world” are dependent upon the ways they respond to and translate existing territories in social, cultural, political, and linguistic interactions with multilingual learners and communities. From a Deleuzoguattarian perspective on territorialization and deterritorialization, this article problematizes how college students perceive, and question established norms and taken-for-granted ways of doing emphasizing the importance of reflective practices, ongoing supports for all participants involved, and partnerships with local communities, advocating for a nuanced …

Sustained Reading: Multilingual Learners Exploring The Language Of A Text, Priscila J.B.M. Costa, Luciana C. De Oliveira, Meghan Love

Journal of English Learner Education

In US schools, multilingual learners (MLs) are expected to meet the same rigorous disciplinary standards as their English-speaking peers while also developing proficiency in English. This article demonstrates how sustained reading, as part of the Teaching and Learning Cycle, can support MLs' access to disciplinary literacy through the explicit attention to language features. Grounded on the Language-based Approach to Content Instruction (LACI), the article presents a classroom vignette from a 5th-grade social studies lesson in which MLs dissected a mentor text. The lesson illustrates how code-breaking within sustained reading makes disciplinary language visible, helping students notice the linguistic choices common …

Physics-Informed Neural Network Solution Of The 2d Helmholtz Equation With A Gaussian Source, Theodoros Panagiotakopoulos, Chris Velissaris, Aristotelis Nikolaos Rapsomanikis

Faculty Scholarship and Creative Works

We present a physics-informed neural network (PINN) framework for solving the complex-valued two-dimensional Helmholtz equation with a localized Gaussian source and spatially varying permittivity. Starting from Maxwell’s equations, the frequency-domain scalar Helmholtz formulation under transverse electric (TE) polarization is derived and enforced directly within the neural network loss function. The model employs a sinusoidal representation network (SIREN) architecture to capture the oscillatory nature of wave solutions and incorporates the Sommerfeld radiation condition to impose open boundary conditions. Training is performed using a hybrid collocation strategy combined with a two-stage optimization procedure consisting of Adam followed by L-BFGS. Numerical experiments in …

Ai As Scaffold To Re-Center Human Reasoning In Student Learning, Rachid Ait Maalem Lahcen

Teaching Online Pedagogical Repository

The increasing availability of generative artificial intelligence (AI) tools has renewed concerns about the role of human reasoning in student learning. Although AI systems can efficiently generate explanations, uncritical reliance on automated outputs risks diminishing students’ engagement with interpretation and justification. This paper presents an instructional strategy that attempts to re-centers human reasoning by positioning AI as a scaffold rather than a substitute for thinking. The strategy emphasizes structured evaluation of AI-generated content through activities such as step-checking with reflection, error analysis, and dual-method verification. These activities are grounded in research‑based principles that have been shown to effectively support student …

Are Testing Accommodations Helping Or Not? A Review Of English Learner (El) Accommodations On Standardized Tests, Cole Forbes

Journal of English Learner Education

This paper examines and critiques English Learner (EL) testing accommodation policies. They have been ineffective at increasing test scores and accessibility. The policies have mandated that EL students participate in high-stakes assessments with accommodations, and sometimes without them. The purpose of the accommodation is to create an even playing field for all test-takers, so that no subgroup is put at a disadvantage, which would compromise their scores and lead to misclassification of students. Laws and Acts such as the Bilingual Education Act of 1968, No Child Left Behind, and the Every Student Succeeds Act have updated and mandated a more …

Differentiated Instruction For Multilingual Learners: A Thematic Analysis Of Research And Practice, Mohsine Bensaid, Jiayuan Jiang

Journal of English Learner Education

Differentiated instruction (DI) is widely promoted as a means of supporting diverse learners, yet its enactment for multilingual learners (MLs) remains uneven and under-theorized in mainstream classrooms. This study examines how DI is conceptualized and implemented to support MLs by attending to learners’ linguistic, cultural, and academic profiles. Using reflexive thematic analysis, this qualitative synthesis examines 20 peer-reviewed empirical studies published between 2010 and 2024 in English-medium K–12 contexts in the United States, with particular attention to elementary settings when specified. Analytic interpretation was supported by practitioner-based reflection functioning as an interpretive lens rather than a separate data source. Four …

Evaluating Regularized Logistic Regression And K-Nn On Mnist Under Increasing Random Missingness, Daniel Markwei

Data Science and Data Mining

This paper investigates the effect of random missingness on the performance of regularized multinomial logistic regression and the k-nearest neighbors (k-NN) classifier for handwritten digit recognition on the MNIST dataset. In particular, we study L1-regularized (LASSO) logistic regression and L2-regularized (Ridge) logistic regression alongside k-NN. Varying percentages of random missingness were introduced into the original dataset, and each model was evaluated in terms of its classification performance. The results show that random missingness degrades the performance of all three classifiers. Overall, k-NN consistently achieves higher accuracy than both L1- and L2-regularized logistic regression across all missingness levels; however, its performance …

Ai-Powered Pathways To Open Education: Enhancing Practice And Expanding Discovery, Rebecca Mcnulty, Lily Dubach

Faculty Scholarship and Creative Works

Generative AI is changing how we teach and learn, and this session will show practical ways it can support open education. We will look at two everyday challenges: finding aligned open materials and spotting places in a course where open practices could enhance opportunities for collaborative learning. An instructional designer will demonstrate how custom GPTs can review a syllabus and suggest places where open pedagogy could involve students in co-creating and sharing knowledge. A librarian will show how large language models can help locate courses and syllabi that already use open practices, making it easier for faculty to discover and …

Creating A Flipped-Classroom With Universal Instructional Design In A Hybrid Stem Course To Facilitate Active Learning, Lori L. Dunlop-Pyle

Teaching Online Pedagogical Repository

The strategy describes using Universal Instructional Design (UID) principles to create a hybrid STEM course to facilitate active learning exercises in class for a diverse group of learners.

Interpretable Linear Models For Heart Disease Prediction: A Comparative Study, Dipok Deb, Emran Hossain

Data Science and Data Mining

Heart disease remains a leading cause of mortality worldwide, underscoring the importance of accurate and transparent methods for early diagnosis. While many machine learning and artificial intelligence models have demonstrated strong predictive performance, their limited interpretability poses challenges for clinical adoption. In this study, we evaluate three interpretable linear classification models—Generalized Linear Model (GLM) logistic regression, L1-regularized (Lasso) logistic regression, and Linear Discriminant Analysis (LDA)—for heart disease prediction using the Cleveland Heart Disease dataset. Following comprehensive data preprocessing, the models are assessed on a held-out test set using standard evaluation metrics, including accuracy, precision, recall, F1-score, and the area under …

Predicting Male Flowering Time In Maize Using Machine Learning Technique, Dipok Deb

Data Science and Data Mining

This study compares three machine learning approaches—Elastic Net, Principal Component Regression (PCR), and Partial Least Squares (PLS)—for variable selection and prediction within a high-dimensional Maize-GWAS framework. The goal was to accurately predict the complex polygenic trait of time to male flowering while managing the challenges of numerous, highly correlated genetic markers. The ENET model, which combines l1 and l2 penalties, delivered the highest predictive accuracy and successfully identified a select subset of the most influential genetic variants. In contrast, PCR and PLS, both utilizing dimension reduction, offered a significant advantage in computational speed and model stability. The findings confirm that …

Data-Driven Prediction Of Superconducting Critical Temperature: A Linear And Regularized Linear Modeling Approach, Dipok Deb

Data Science and Data Mining

This study adopts a data-driven approach to estimate the critical temperature of superconducting materials using linear machine learning models. A comprehensive dataset derived from material physico-chemical properties was analyzed after systematic preprocessing and standardization. Three linear modeling strategies—Linear Regression, Ridge Regression, and Linear Regression with Subset Selection—were developed and evaluated using standard regression performance metrics. The findings demonstrate that both basic and regularized linear models can effectively capture the relationship between material features and superconducting behavior, offering robust and interpretable predictions. While feature selection enhances model transparency, it comes with a modest reduction in predictive capability. Overall, this work emphasizes …

Digital Redlining In The Smart City: Artificial Intelligence, Housing Law, And Structural Urban Inequality, Spurthi Nrusimhadevara

Undergraduate Scholarship and Creative Works

Artificial intelligence is increasingly used in urban housing systems, where it shapes decisions about tenant screening, rent pricing, lending, zoning, and neighborhood investment. Although these tools are often promoted as efficient and impartial, they frequently rely on historical data that reflect racial, economic, and spatial inequality. As a result, AI systems can reproduce discriminatory outcomes even when protected characteristics are not directly used. This paper examines digital redlining in the smart city and argues that algorithmic housing tools mirror long standing structural inequities that raise significant concerns under fair housing and civil rights law. It evaluates how automated screening, predictive …

University Of Central Florida Graduate Catalog, 2025-2026, University Of Central Florida

UCF Catalogs

No abstract provided.

What Truly Drives Graduate Earnings?, Aiden Akbarov

High Impact Practices Student Showcase Spring 2026

The goal of my project, Predicting the Paycheck: What Factors Truly Influence Graduate Earnings?, was to see if we could actually predict a college graduate's starting salary using data instead of just guessing. I used a dataset of 172 majors from the American Community Survey to look at how a student's field of study, the gender balance of their major, and the current job market all impact their first paycheck. I wanted to create a tool that helps students understand the financial reality of their degree before they even graduate.

To get my results, I used a Multiple Linear Regression …

Laser Beam Shaping Using A Neural Network, Azeem A. Hakim

Honors Undergraduate Theses

Multiphoton lithography (MPL) is a method of laser-based 3D-printing for fabricating micron-scale structures point-by-point in a photopolymerizable medium. MPL’s high-resolution capabilities have made it a powerful method for the fabrication of many devices, such as microelectromechanical systems (MEMS) and tissue scaffolds.  The throughput of MPL processes can be increased by using spatially shaped laser beams that expose large areas or volumes simultaneously. A laser beam can be reshaped by passing it through a spatial light modulator (SLM) displaying a pre-designed phase mask. One type of laser beam profile used for this purpose is the Bessel beam, a type of structured …

The Impact Of Gastric Sleeve And Roux-En-Y Gastric Bypass On Chief Cell Function And Protein Digestion In Obese Patients, Garikoitz Mikel Gainza

Honors Undergraduate Theses

Obesity has become one of the largest global health concerns of the 21^st century, correlating with numerous associated diseases. To help address the growing prevalence and burden of obesity, sleeve gastrectomy and Roux-en-Y gastric bypass have become two commonly performed surgical procedures done to promote weight loss by altering the anatomical structure of the stomach and surrounding organs of the gastrointestinal system. Although the benefits of improved metabolism linked to these surgical procedures are well studied, their impact on the physiology of gastric cells, and more specifically chief cells as they relate to protein digestion post-surgery, is yet to …

Beyond Full Fine-Tuning: The New Playbook For Adapting Deep Neural Networks, Cristian S. Mcgee

Honors Undergraduate Theses

Fine-tuning is the process of teaching and specializing a pre-trained neural network on a downstream task. Fine-tuning is a rapidly growing topic in artificial intelligence domains; however, many fine-tuning endeavors are highly specialized without a coherent framework connecting them. This work presents a unified perspective on fine-tuning methods and performance metrics. Our perspective organizes the methods in terms of how they are applied to fine-tuning. This framework showcases methods that (i) update effective subspaces of the pre-trained model, (ii) change the adaptation optimization procedure, and (iii) alter the representations of the embedded input. Additionally, we present unconventional metrics such as …

Transforming Florida Chemistry Education: Exploring Information Literacy Strategies From Singapore, Han N. Le

Honors Undergraduate Theses

This study explores the applicability of Singapore’s information literacy (IL) strategies within Central Floridian higher education environments. Singapore’s national IL framework, supported by its Ministry of Education, integrates information and communication technology (ICT), professional development, and lifelong learning initiatives through a standardized, systemwide approach. In contrast, IL instruction in the United States is largely decentralized, often limited to one-time sessions or elective courses. To investigate the feasibility of adapting Singapore’s practices, surveys were distributed to faculty and librarians across Florida’s twelve public universities, yielding responses that highlighted varying levels of IL integration, limited institutional support, and minimal professional training related …

A Systematic Literature Review Of The Possible Negative Impacts Of Overconsumption Of Mass Media And Technology On Elementary-Aged Members Of Generation Z (1997–2012) And Generation Alpha (2013–2024), Ashya Y. Warren

Honors Undergraduate Theses

This thesis explores the possible negative impacts of overconsumption of mass media and technology on the elementary-aged members of Generation Z (1997-2012) and Generation Alpha (2013-2024). This research is aimed at establishing the relationship between excessive and uncontrolled exposure to digital media and cognitive, emotional, social, behavioral, and academic difficulties at a critical period of child growth. The purpose of this systematic literature review can be explained by the author's experience with technology overuse and professional interests as a future teacher, who should be prepared to help promote healthy childhood and development. The systematic literature review was based on peer-reviewed …

Investigating The Interaction Of Gram-Positive Lactobacillus Species With Mucin In The Context Of Barrett's Esophagus, Ritisha Suresh

Honors Undergraduate Theses

Gastroesophageal Reflux Disease (GERD) can progress to the precancerous Barrett’s Esophagus (BE) condition, in which the normal esophageal squamous epithelium is replaced with a columnar epithelium containing goblet cells. Mucins, sticky substances, are secreted in response to bile acid exposure during reflux episodes to protect the esophageal epithelium. Goblet cells secrete these mucins, which can also scaffold microbial colonization. Probiotic Lactobacillus bacteria can use glycosidase enzymes to process mucin and metabolize them into their component sugars (glycans). This project aims to identify the role of the Lactobacillus-mucin interaction in BE, as glycan availability could confer an advantage to Lactobacillus …

Unmasking Impression Management: Large Language Models For Detecting Socially Desirable Responding In Open-Ended Situational Judgment Tests, Barret Vermilion

Graduate Studies Theses and Dissertations 2026

Socially desirable responding (SDR) — the tendency to present oneself in an overly favorable light — remains a persistent validity concern in industrial-organizational psychology. Contemporary theory frames SDR as multidimensional, distinguishing agentic impression management (AM; inflating competence, dominance, and achievement) from communal impression management (CM; inflating warmth, cooperation, and prosocial qualities). Existing measures of AM and CM rely on self-report, which captures these constructs only at the level of generalized trait-like tendencies rather than as they naturally unfold in applicants' own language. This dissertation proposes a text-based alternative: detecting AM and CM in open-ended situational judgment tests (SJTs) of Big …