University of Central Florida
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AI Resistant Assignments in Critical Writing for Engineer Majors
No full textCritical Reasoning 1301 is a writing class that all students at our university must take. AI has invaded our student\u27 s writing. So far it has been rather easy to identify. AI creates repetitive papers, papers without adequate citations that sound like rants, and papers that sound like they have been written by multiple authors. Students turn in these papers because they cannot distinguish good writing from poor writing
Closing Session
No full textClosing Session of the First Teaching and Learning with AI Conference at the University of Central Florida, 2023
Encapsulation of Phytochemicals through Polymer-Catechin Nanoparticles Self-Assembled via Tea-Steeping
No full textThe brewing of teas has been an integral pillar of human food culture for millennia. These beverages have always aimed to extract the phytochemicals from berries, leaves, and many other food products. Leaves from Camellia sinensis are used to prepare green, black, white, oolong, and pu’er teas that are highly regarded for their organoleptic properties. Moreover, many of the widely regarded health benefits are ascribed to the high polyphenolic content within the teas. It has been established that polyphenols can interact with hydrophilic polymers, through hydrogen bonding, to form stable small particle suspensions. In this work, polymer-catechin nanoparticles were synthesized in situ to load and deliver active ingredients without requiring any behavioral change by the end user. Particle size and morphology were assessed via dynamic light scattering (DLS) and scanning electron microscopy (SEM). Furthermore, the chemical composition of the particle was studied with Liquid Chromatography-Mass Spectrometry (LC-MS), Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES), UV-Vis, and fluorescence spectroscopy. A 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay was utilized to assess radical scavenging activity and a Folin-Ciocâlteu (FC) assay was also utilized to quantify the polyphenolic concentration. Furthermore, a minimum inhibitory concentration (MIC) assay was employed with a resazurin cell viability assay to assess the impact of the nanoparticle on model gut microbes. The co-loading of hibiscus anthocyanins, turmeric curcuminoids, and metal ions exemplify the system’s ability to load a wide range of compounds and food products for the selection of desired particle characteristics. These findings demonstrate the potential to create a simple and economical alternative to nutraceutical delivery simply by the brewing of tea
Sparse Low-rank Tensor Logistic Regression with Applications in Neuroimaging
No full textDiscriminant analysis techniques, while extensively studied for traditional datasets, have rarely been adapted to address the complexities of high-dimensional tensor data. Recent advances in tensor decomposition, as reviewed by Burch et al. (2025), highlight methods such as CP, Tucker, HOSVD, and t-SVD, which offer robust frameworks for handling multi-dimensional biomedical data, including neuroimaging. In this paper, we propose a novel approach that integrates low-rank regularization techniques with tensor structures to tackle high-dimensional matrix and tensor data. Building on the Multi-Projection Optimal Scoring Discriminant Analysis (ROSDA) method by Huang & Zhang (2020), which employs multi-directional projection pursuit for robust classification, our method leverages Tucker decomposition to enforce low-rank constraints, enhancing scalability and interpretability. We validate the proposed techniques using real neuroimaging datasets from Alzheimer’s patients, sourced from the Open Access Series of Imaging Studies (OASIS) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), demonstrating their efficacy in capturing latent patterns in high-dimensional biomedical data
Down by the Tracks, Ova ‘Cross the Trussle: Exploring Systemic and Structural Barriers to Perinatal Healthcare Access for Black Women in Rural Alabama
No full textThis dissertation uses semi-structured interviews (n=42), participant observation, archival research, and storytelling, it employs frameworks of structural violence, intersectionality, and reproductive justice to situate Black women’s maternal health within broader histories of racialized medical neglect and reproductive governance. The study explores two questions: (1) What obstacles do Black women in rural Alabama face in accessing perinatal healthcare? (2) How do they navigate these barriers, and what strategies do they employ? A mixed-methods approach—including ethnography, quantitative analysis, and GIS—guides this research, with storytelling serving as both a methodological intervention and a challenge to dominant anthropological narratives. Findings highlight three major themes: cultural and historical mistrust, health literacy and language, and resilience. Black women rely on kinship networks, community-based support systems, and informal reproductive knowledge to counter institutional failures, forming alternative care infrastructures that preserve communal knowledge and enable adaptation in structurally constrained healthcare environments. The Barriers to Access Scale assesses transportation, provider availability, finances, health literacy, discrimination, and comfort in care, revealing that higher CIS scores (≥0.85) correlate with longer travel distances, greater discrimination, and lower financial resources, with provider shortages linked to medical mistrust. CIS strongly correlates with provider comprehension (r=0.80), discrimination (r=0.78), and financial barriers (r=0.51), underscoring racism and economic instability as primary obstacles. Grounded in Black feminist thought and womanist theology, it frames Black women’s lived experiences as epistemic resistance, asserting healthcare access as a moral imperative and contributing to medical anthropology, reproductive justice, and Black feminist theology while informing policy interventions that address racial and geographic maternal health disparities
Going Green: Testing an Ambient Nature Work Environment as an Employee Energy Charger
No full textAmidst rising levels of employee burnout, research on recovery from work stress has been on the rise. Organizational scholars examining predictors of recovery have almost exclusively relied on the Effort-Recovery Model (Meijman & Mulder, 1998), positing the absence of work demands as a prerequisite for resource restoration. Departing from this perspective and envisioning recovery akin to recharging a battery, the Nature Charger Model was proposed. It suggests that employee energy rejuvenation can occur even in the presence of demands. Specifically, drawing from alternative frameworks (Kaplan & Kaplan’s 1989 Attention Restoration Theory; Brosschot et al.’s 2018 Generalized Unsafety Theory of Stress), ambient nature can provide the context allowing employees to remain “plugged in,” preserving energy while working. This study, utilizing a 2x2 experimental design with nature and work demand manipulations among a sample of working adults, provided partial support for the Effort-Recovery Model, highlighting the energizing effects of brief virtual nature breaks. Additionally, the findings partially validated my Nature Charger Model, reinforcing the concept that exposure to nature during work tasks can sustain energy levels, even without a formal break. This research effort helps advance recovery theory by integrating and testing interdisciplinary perspectives and provides organizations with practical advice on how to foster and retain an energetic workforce. Future research avenues are discussed
Exploring the Security Landscape of AR/VR Applications: A Multi-Dimensional Perspective
No full textThe rapid evolution of Augmented Reality (AR) and Virtual Reality (VR) technologies on mobile platforms has significantly impacted the digital landscape, raising concerns about security and privacy. As these technologies integrate into everyday life, understanding their security infrastructure and privacy policies is crucial to protect user data.
To address this, our first study analyzes AR/VR applications from a security and performance perspective. Recognizing the lack of benchmark datasets for security research, we compiled a dataset of 408 AR/VR applications from the Google Play Store. The dataset includes control flow graphs, strings, functions, permissions, API calls, hexdump, and metadata, providing a valuable resource for improving application security.
In the second study, we use BERT to analyze the privacy policies of AR/VR applications. A comparative analysis reveals that while AR/VR apps offer more comprehensive privacy policies than free content websites, they still lag behind premium websites. Additionally, we assess 20 U.S. state privacy regulations using the Coverage Quality Metric (CQM), identifying strengths, gaps, and enforcement measures. This study highlights the importance of critical privacy practices and key terms to enhance policy effectiveness and align industry standards with evolving regulations.
Finally, our third study introduces a scalable approach to malware detection using machine learning models, specifically Random Forest (RF) and Graph Neural Networks (GNN). Utilizing two datasets—one with Android apps, including AR/VR, and Executable and Linkable Format (ELF) files—this research incorporates features such as API call groups and Android-specific features. The GNN model outperforms RF, demonstrating its ability to capture complex feature relationships and significantly improve malware detection accuracy. This work contributes to enhancing AR/VR application security, improving privacy practices, and advancing malware detection techniques
In Depth Analytics of Vehicle-Vehicle and Vehicle-Pedestrian Conflicts across Various Conditions
No full textThis study explores key traffic safety challenges by analyzing threshold discrepancies in rear-end conflicts under different weather conditions, conflict likelihood and severity at interconnected intersections, and interaction patterns between right-turning vehicles and pedestrians at signalized intersections. First, using CitySim trajectory data, the study examines the impact of weather conditions on surrogate safety measure (SSMs) thresholds for Modified Time to Collision (MTTC), Deceleration Rate to Avoid a Crash (DRAC), and Single-step Probabilistic Driving Risk Field (S-PDRF). Results indicate that MTTC and DRAC thresholds vary significantly under clear (2.3s v.s 3.0 m/s²) and rainy (4s v.s. 2.4 m/s²) conditions, whereas S-PDRF remains unaffected, supporting a universal threshold for this measure. Second, the study evaluates conflict risks at unsignalized intersections near signalized intersections using the Joint Generalized Linear Mixed Model (JGLMM) with drone-based trajectory data. The findings reveal that angled conflicts are common in weaving areas, longer right-turn queues help reduce conflicts, while increased left-turn lane queue lengths heighten risk.
Additionally, Structural Equation Modeling (SEM) confirms that higher upstream traffic volumes can mitigate downstream risks, highlighting the importance of enhanced lane markings and pre-intersection left-turn signage. Lastly, the study investigates right-turn vehicle-pedestrian conflicts through combined crash and conflict datasets, identifying four interaction patterns and demonstrating that Relative Time-to-Collision (RTTC) outperforms Post Encroachment Time (PET) for crash prediction. The study finds RTTC thresholds of 1.5s and 3.8s for Approach and Departure crosswalks, respectively, with vehicle speeds between 5-15 mph significantly increasing conflict severity. Countermeasures such as blank-out No Right Turn signs and dynamic Yield to Pedestrians signs are proposed to enhance pedestrian safety and optimize advanced driver-assistance systems (ADAS). These findings highlight the importance of data-driven safety interventions, helping to develop adaptive safety measures for traffic management, intersection design, and automated vehicle technologies
Weakly-Supervised Scaling for Open-Vocabulary Action Detection
Get PDFIn this work, we focus on scaling open-vocabulary action detection. Existing approaches for action detection are predominantly limited to closed-set scenarios and rely on complex, parameter-heavy architectures. Extending these models to the open-vocabulary setting poses two key challenges: (1) the lack of large-scale datasets with many action classes for robust training, and (2) parameter-heavy adaptations to a pretrained vision-language contrastive model to convert it for detection, risking overfitting the additional non-pretrained parameters to base action classes.
Firstly, we introduce an encoder-only multimodal model for video action detection, reducing the reliance on parameter-heavy additions for video action detection. Secondly, we introduce a simple weakly supervised training strategy to exploit an existing closed-set action detection dataset for pretraining. Finally, we depart from the ill-posed base-to-novel benchmark used by prior works in open-vocabulary action detection and devise a new benchmark to evaluate on existing closed-set action detection datasets without ever using them for training, showing novel results to serve as baselines for future work
Optical and Tunneling Spectroscopy of Single-Layer van der Waals Materials on Metals and Probe Tip Engineering of High Resolution STM and AFM for Imaging of Molecules on Silicon Surfaces
No full textThis research investigates the electronic, structural, and ferroic properties of two-dimensional (2D) materials, focusing on transition metal dichalcogenides (TMDs) and metal thio- and selenophosphates (MTPs). Using scanning tunneling microscopy (STM), scanning tunneling spectroscopy (STS), Raman spectroscopy, and photoluminescence spectroscopy (PL), this work explores how dimensional confinement, substrate interactions, and thickness-dependent effects influence material behavior. Advancements in STM probe fabrication for high-resolution molecular imaging are also presented, with implications for nanoelectronics and quantum materials.
The first study examines monolayer MoS2 on metallic substrates (gold and graphite). Raman spectroscopy revealed substrate-induced strain and charge transfer in MoS2 on Au. STM and STS on MoS2/graphite heterostructures primarily reflected the underlying graphite, highlighting challenges in probing specific layers and the impact of substrate interactions on electronic properties.
The second study focuses on NiTe2, a semimetal TMD. Low-temperature STM/STS revealed multiple electronic features near the Fermi level. Au-assisted exfoliation enabled imaging of thin NiTe2 on Au(111), which exhibited hexagonal, rectangular, and grain boundary patterns, likely resulting from the polycrystalline Au substrate. These observations emphasize the influence of substrate morphology on structural properties.
The third study investigates ferroelectric and strain-dependent behavior in MTPs CuInP2S6 (CIPS) and CuCrP2S6 (CCPS). Piezoresponse force microscopy and Raman spectroscopy revealed strain-modulated ferroelectric properties in CIPS and layer-dependent vibrational behavior in CCPS, offering insights into thickness-induced modifications in ferroic behavior.
The final study presents a field-directed sputter sharpening (FDSS) technique adapted for in-situ fabrication of ultra-sharp STM probes. A custom adapter enabled reliable production of probes with sub-2 nm radii, validated via atomic-scale imaging of C60 molecules. This advancement enhanced tip performance while minimizing contamination and downtime.
Together, these studies advance understanding of how reduced dimensionality, substrate interactions, and spatial confinement affect low-dimensional materials, with implications for future quantum and electronic applications