Digital Commons Network^™

Supporting Lgbtq Youth Through A Cross-Cultural Lens Of Wellbeing, Correna Bartow

UN SDG Open Pedagogy Faculty Fellowship: Renewable Assignments

No abstract provided.

The Multimodal Video Evaluator (Mve): Codifying Decades Of Empirical Research Into A Systematic Multimedia Design & Evaluation Framework, Koichi Sato

Teaching & Learning with AI

Online higher education relies heavily on instructional videos, yet many faculty-produced media assets inadvertently induce high extraneous cognitive load. Instructional designers frequently face an “expert bottleneck,” lacking the operational bandwidth required to conduct manual, frame-by-frame pedagogical audits across expanding institutional media catalogs. To bridge this research-practice gap, this paper presents an operational blueprint for the Multimodal Video Evaluator (MVE), an open-access, AI-driven workflow designed to automate multimedia quality assurance. Built on a native multimodal artificial intelligence architecture, the MVE concurrently processes visual, auditory, and textual streams to evaluate media assets against 127 research-derived design guidelines rooted in the Cognitive Theory …

Accessible By Design: Leveraging Ai For Inclusive Education, Lauren S. Kehoe, Rebecca Weber

Teaching & Learning with AI

No abstract provided.

Neongooey; V1, Carlos Ortiz Quintana, Jonathan Keathley, Ramses Ramirez

Laboratory of Planetary Habitability Atmospheric Models

This is the 1D magma ocean code used to create the NEONGOOEY data files for the Coupled atmospHere Interior modeL Intercomparison (CHILI) papers. This is a modified version of the GOOEY code from Schaefer et al. 2016, which includes carbon dioxide, a primordial hydrogen envelope, a new atmospheric escape framework and a new analytical corrected gray atmosphere approach for computing the outgoing longwave radiation (OLR). It has been used to model Earth, Venus, and Earth-sized scenarios at the orbits of TRAPPIST-1b and e.

Ucf Pegasus Plan - 1st Grade - National Holidays, Ania Calbough, Paola Cedeno, Danielle Linare

Pegasus Plans: Social Studies Unit Plans for Florida Teachers

No abstract provided.

Ucf Pegasus Plan - 2nd Grade - Early Native American Cultures, Kendall Childs, Megan Klein, Tiffany Salkin

Pegasus Plans: Social Studies Unit Plans for Florida Teachers

No abstract provided.

Propp, Ong, And Jung: The Order Of The Folktale Functions In Star Wars Rebels, Nathaniel Sorrentino

The Pegasus Review: UCF Undergraduate Research Journal

While the original Star Wars films have long been seen as a modern fairy tale (Gordon, 1978, p. 314), I argue that the animated television show Star Wars Rebels could also be considered a modern fairy tale due to its narrative structure. To evaluate Star Wars Rebels' structural elements, I examine its plot through Vladimir Propp’s functions of a folktale, as fairy tales and folktales share many structural elements. Vladimir Propp defined folktales around a series of 31 functions, with an emphasis on the importance of their order. Although there are certain areas where Rebels appears to diverge, careful examination …

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 …

Arriving With The Five Senses: Resilient Whole Being In The Classroom, Mitra Emad

Teaching Online Pedagogical Repository

Using a simple sensory arrival exercise to welcome and orient students to the learning environment encourages a whole being wellbeing approach to learning. Moving through each of the five senses as a way to fully arrive “in class” promotes self-regulation and engagement. In online environments, this exercise gives students an opportunity to settle into a familiar space, as well as to engage with that familiarity in ways that “render the familiar strange,” enhancing curiosity and opening up engagement with the course materials and learning experiences that follow.

Co-Working With Machines: How Service Robots Shape Frontline Employee Experiences And Well-Being, Cynthia Mejia, Hannah A. Crandell, Emily Broker, Mindy Shoss

Rosen Research Review

The research by Mejia, Crandell, Broker, and Shoss reveals the complex realities faced by restaurant workers navigating the rise of service robots. Through in-depth interviews with 42 frontline employees, the study uncovers how robot adoption affects collaboration, productivity, emotional well-being, and perceptions of job security. While robots reduce physical strain and offer entertainment value, they also spark frustration, resentment, and fears of displacement. This research offers hospitality leaders a human-centered roadmap for integrating service robots in ways that support—not replace—the workforce.

When Customers Collide Online: The Hidden Power Of Approval In Digital Service Failures, Wei Wei, Lu Zhang, Bobbie Rathjens, Sean Mcginley

Rosen Research Review

The research by Wei, Zhang, Rathjens, and McGinley reveals a surprising twist in how restaurant customers respond to online interactions after a service failure. When fellow customers chime in—either with support or mockery—their comments can dramatically shape how the original complainant feels, engages, and judges fairness. But the real game-changer? Whether the customer craves approval. Those with low need for approval react strongly to the tone of others’ comments, while high-approval seekers remain emotionally steady. This study uncovers the psychological undercurrents of electronic customerto- customer interaction (eCCI), offering fresh insights into empathy, justice, and digital engagement in hospitality.

Putting Humanity First In Service Robotics: For Me, Robotics Is About Giving Hospitality Teams The Breathing Room To Care More Deeply For People., Manuel Rivera, Cynthia Mejia

Rosen Research Review

In an engaging conversation with Juan Higueros, Co-Founder and COO of Bear Robotics, we explore his vision for human-centered robotics and how technology can empower hospitality teams to deliver deeper, more meaningful guest experiences.

Boarding By Biometrics: How Smart Technology Is Reshaping Passenger Experience, Xiaoting Chi, Frank Badu-Baiden, Seongseop (Sam) Kim, Junwei Cao, Elena-Nicoleta Untaru, Heekyoung Jung, Heesup Han

Rosen Research Review

The research by Chi, Badu-Baiden, Kim, Cao, Untaru, Jung, and Han explores how biometric boarding transforms airline travel. Using technology acceptance, agenda setting, and behavioral reasoning theories, the study reveals how ease of use, usefulness, media exposure, and attitudes shape adoption. Findings stress convenience, engagement, and targeted communication, with age and gender influencing responses. For airport managers and designers, the research offers practical strategies for secure, efficient, and passenger-centered boarding systems.

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 …

Rethinking The Mindfulness And Mindlessness In Media Evocation Of Human-Machine Communication: A Case Study On The In-Car Robot “Nomi”, Bing Wang, Longxiang Luo, Weizi Liu

Human-Machine Communication

The commonly used Media Equation framework in human-machine communication research exhibits limitations when explaining human-machine relationships in the AI era, giving rise to the Media Evocation paradigm. Based on the Media Evocation framework, this study employs in-depth interviews to investigate whether interactions between humans and the in-car robot Nomi are mindful or mindless, while also examining whether these interactions prompt reflections on the machine’s ontology. The findings indicate that most participants perceive Nomi as a human-like actor and interact with it mindfully. Highquality social cues, such as voice and facial expressions, can trigger mindful anthropomorphic responses in human-machine interaction, eliciting …

Human-Machine Communication: Complete Volume. Volume 12

Human-Machine Communication

This is the complete volume of HMC Volume 12.

Ai Humanizers, Pragmatists, Skeptics: A Cluster Analysis Of Normative Attitudes For Ai’S Capabilities And Roles, Kate K. Mays, Ekaterina Novozhilova

Human-Machine Communication

This study explores people’s attitudes about artificial intelligence’s (AI) capabilities through a normative lens. Drawing from literature on phenomenological, ontological, and heuristic judgments about machines, we aim to better understand what capabilities people think AI should be able to have and how that relates to enthusiasm about having AI take certain roles in their lives. Survey participants (N = 601) were grouped via two-step clustering based on their attitudes, revealing three clusters: AI humanizers, who expect empathy and social interaction from AI; AI pragmatists, who expect cognitive capabilities; and AI skeptics, who prefer AI with limited human-like traits and emphasize …

The Epistemic Power Of Human-Machine Communication, Katrin Etzrodt, Autumn P. Edwards

Human-Machine Communication

This editorial introduces Volume 12 of Human–Machine Communication by positioning communicative machines as epistemic provocateurs that unsettle inherited distinctions among agency, meaning, and relation. Rather than treating these disruptions as anomalies to be resolved, the editorial argues that Human–Machine Communication (HMC) is distinctively equipped to register, analyze, and sustain such epistemological tensions. Drawing on philosophical traditions from Aristotle to Marx and Heidegger, the authors show how contemporary communicative machines simultaneously strain multiple historical settlements that once stabilized human–machine boundaries. The volume frames HMC as a “seismograph” for and as a “practice of” epistemological change, tracing how agency and meaning are …

Whole Earth Machines: Human-Machine Communication For A Green Transition, Klaus Bruhn Jensen

Human-Machine Communication

The climate crisis of the 21st century represents an existential risk to humanity and biodiversity, posing essential questions of how communication may serve to coordinate mitigation of and adaptation to climate change. One recent response has been massive investments by governments and corporations in systems providing feedback on the state of Earth— Whole Earth Machines (WEMs). For human-machine communication (HMC) studies, WEMs invite sustained engagement with communication infrastructures as a key constituent of research agendas, beyond the interface encounters at the center of many HMC studies to date. The article presents a conceptualization and operationalization of WEMs as critical infrastructures …

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 …

Covid-19 Infection And Hypertension In Correlation With Race, Elizabeth Durkin

The Pegasus Review: UCF Undergraduate Research Journal

Healthcare disparities exist in the U.S. between different races. This study investigated the frequency of COVID-19 infections and hypertension cases among five different racial groups (White, Black, Asian, Native American, and Native Hawaiian). The study also examined the correlation between COVID-19 infections and hypertension with the hypothesis that because of disease predisposition and socioeconomic barriers, Black populations would have the highest rates of COVID-19 infections and hypertension. We used data from the Kaiser Family Foundation regarding COVID-19 cases and race in conjunction with Census population data to determine whether COVID-19 case frequency means differed by race. Data on hypertension and …

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 …

The Cypress Dome 2026, Issue 37, The Cypress Dome Society

The Cypress Dome

Featuring the work of Stephanie Arrarte, Miranda Attales, Al Basbano, Sophia Camarena, Sophia Ferrara, Leo Hall, Sofia Infante Roca, Amelia Kimberling, Emma Kolmetz, Antonio Vincenzo Lanni, Erik Leon, Niko Martin, Mariana Martinez, Esha Modi, Lela Norman, Mariana Perozo, Elio Rotolo, Raiya Shaw, Daniela Torres, Gabriel Walters, and Skyelar Wiedrich.

Founded in 1989 and named after a natural feature of our campus, The Cypress Dome is the University of Central Florida’s undergraduate literary journal. It is edited, designed, and produced by a selected editorial team of students from our literary organization, The Cypress Dome Society, as part of a year-long internship. …

Music Interventions For Critically Ill Patients: Evaluating Effects Of Music On Psychoemotional Symptoms In Critically Ill Patients Receiving Respiratory Support, Allyson Crighton

Honors Undergraduate Theses

Critically ill adults receiving respiratory support in intensive care units (ICUs) frequently experience psychoemotional disturbances such as agitation, anxiety, and delirium, which contribute to prolonged ventilation, increased sedative use, and adverse patient outcomes. While pharmacologic management remains standard practice, associated risks including oversedation and delirium highlight the need for safe, complementary interventions. Emerging evidence suggests that music-based interventions may offer a non-invasive, cost-effective strategy to reduce psychological or emotional distress in this vulnerable population. This integrative literature review examined the effects of music interventions, including passive music listening and structured music therapy, on agitation and related psychoemotional outcomes in critically …

Parenting Stress And Its Association With Parental Technoference: An Integrative Literature Review, Emily S. Machuca

Honors Undergraduate Theses

Introduction: Today, digital devices are a part of daily life, but can serve as a distraction that interrupts important relationships, a process referred to as technoference. In the context of parenting, device use during child interactions may reduce parental attentiveness and engagement, with potential implications for child development and behavior. Parenting stress may influence how and why parents engage with digital devices in the presence of their children. However, the association between parenting stress and parental technoference remains unclear in the current literature. This integrative literature review aimed to synthesize existing evidence examining the relationship between parenting stress and parental …

Examining The Effectiveness Of Icu Diaries In Reducing Post–Intensive Care Syndrome (Pics) Psychological Symptoms: A Rapid Review Of United States–Based Studies, Lananh Tran

Honors Undergraduate Theses

Background: The growing recognition of Post-Intensive Care Syndrome (PICS) has prompted investigation into interventions, such as ICU diaries, to mitigate the long-term psychological effects of PICS. Intensive care unit (ICU) diaries – journals written by clinicians and loved ones during an ICU admission – are a tool that can help patients reconstruct the events of their critical care experience. Entries in the diary may include clinical updates, explanation of treatments, and encouraging words from loved ones or staff. Previous research in European countries found ICU diaries may be effective in reducing psychological effects post ICU admission.

Research question: The aim …

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

UCF Catalogs

No abstract provided.