Digital Commons Network^™

Beetroot Juice And Vitamin C Co-Supplementation Enhances Anaerobic Performance And Reduces Post-Exercise Glycemia In Wrestlers: A Randomized, Double-Blind, Placebo-Controlled Crossover Trial, Maedeh Nojoumi, Ali Jafari, Alireza Hosseini Kakhiki, Ali Jafarzadeh Esfehani, Hossein Rafiei, Chad Kerksick, Oluwatoyosi Owoeye, Reza Rezvani

Faculty Scholarship

Background

Wrestling, characterized by high-intensity intermittent efforts, demands exceptional anaerobic power and recovery capacity. Nitrate-rich beetroot juice (BRJ), supplemented with vitamin C, has emerged as a potential ergogenic aid through increased nitric oxide bioavailability. However, limited data exists regarding its acute effects on anaerobic performance in combat sport athletes. This study investigated the acute effects of BRJ supplemented with vitamin C on upper- and lower-body anaerobic test performance and selected biochemical markers in collegiate wrestlers.

Methods

In a randomized, double-blind, placebo-controlled crossover trial, 28 collegiate male wrestlers (18–24 years) consumed a single 250-ml BRJ drink (8.4 mmol nitrate + 90 …

Effects Of Dietary Nitrate And Caffeine On End Power And Work Above End Power During A 3 Min All-Out Test In Trained Male Cyclists, Anthony M. Hagele, Kyle Sunderland, Petey W. Mumford, Chad Kerksick

Faculty Scholarship

Background: The purpose of this study was to examine the effects of acute dietary nitrate (NO3−) and caffeine (CAF) supplementation on end power (EP) and work performed above EP (WEP) in trained male cyclists during a 3 min all-out test (3MT) on a cycle ergometer.

Methods: Fifteen healthy, trained male cyclists (28.5 ± 5.3 years, 79.2 ± 9.1 kg, VO2peak 55.2 ± 5.6 mL·kg−1·min−1) completed four exercise trials in a randomized, double-blind, placebo-controlled, crossover study design separated by 3–7 days. The four experimental conditions were placebo beverage (nitrate-depleted) + placebo capsule, nitrate-rich beetroot juice + placebo capsule (BR), …

Terra: Bodies + Territories, Devin Arne

Sustainability Research & Practice Seminar Presentations

Dr. Devin Arne, Music Theory, History, and Composition, and Silvana Cardell, Creator of TERRA: Bodies + Territories, present on the performance of "TERRA: Bodies + Territories".

Egyptian Labor Migration To Europe: Brain Drain, Brain Waste, And Brain Gain In The 2020s, Dina Abdel Fattah, Ayman Zohry

Faculty Journal Articles

This study, Egyptian Labor Migration to Europe: Brain Drain, Brain Waste, and Brain Gain in the 2020s, is shaped by a key transition of mobility among high-skilled Egyptian migrants towards Europe. Historically, mobility among high-skilled Egyptian migrants has been towards the Arab region, particularly the Gulf Cooperation Council (GCC) Countries. The additional demand in the European labor market, alongside demographic changes, has encouraged high-skilled migration to Europe. This study, conducted under the framework of the Egyptian Migration Hub (EHUB) II project by the Center for Migration and Refugee Studies (CMRS) at The American University in Cairo, provides a timely contribution …

Inflating Your Success: An Algorithmic Approach To Improving Outcomes In Pediatric Tissue Expansion, Jackson C Green, Samuel G Ruiz, Kylie R Swiekatowski, Ellen B Wang, Paul Won, Chioma G Obinero, Tien Do, Danielle Sobol, Matthew R Greives

Faculty, Staff and Student Publications

Background: Tissue expanders (TEs) are vital for creating soft tissue for reconstruction. This study examines the challenges of pediatric TEs and proposes an algorithm to minimize complications and optimize outcomes.

Methods: A retrospective review (2014-2024) included patients younger than 18 years with TEs, excluding breast reconstruction cases. Initial TE fills were performed in-clinic 2 weeks postoperatively with parent education, whereas subsequent expansions were managed at home. Parents monitored for complications, started antibiotics, and scheduled urgent visits if needed. Data collected included demographics, TE characteristics, indications, defect size, and outcomes.

Results: Forty pediatric patients (median age: 5.2 y) had 96 TEs …

White Matter Microstructure Alterations In Social Anxiety Disorder: A Mega-Analysis Across Twelve Cohorts In The Enigma-Anxiety Working Group, Eline F Roelofs, Nynke A Groenewold, Kinga Farkas, Alyssa H Zhu, Si Gao, Tiana Borgers, Udo Dannlowski, Kira Flinkenflügel, Dominik Grotegerd, Tim Hahn, Andreas Jansen, Elisabeth J Leehr, Tilo T J Kircher, Hannah Meinert, Igor Nenadić, Frederike Stein, Benjamin Straube, Tamer Demiralp, Raşit Tükel, P Michiel Westenberg, Jochen Bauer, Anna Kraus, Alexander G G Doruyter, Christine Lochner, David Hofmann, Thomas Straube, André Zugman, Monica E Calkins, Raquel E Gur, Ruben C Gur, Bart S Larsen, Theodore D Satterthwaite, Theresa M Slump, Roman A Vogler, Suzanne N Avery, Jennifer U Blackford, Jacqueline A Clauss, Su Lui, Sophia I Thomopoulos, Robert R J M Vermeiren, Neda Jahanshad, Peter V Kochunov, Paul M Thompson, Daniel S Pine, Dan J Stein, Nic J A Van Der Wee, Janna Marie Bas-Hoogendam

Faculty, Staff and Student Publications

Background: Studies investigating social anxiety disorder (SAD) have reported inconsistent alterations in white matter (WM) microstructure. The ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Anxiety Working Group investigated differences in the microstructure of 25 WM tracts between individuals with SAD and healthy control (HC) participants in a mega-analysis.

Methods: We analyzed data from 487 individuals with SAD and 1604 HC participants (ages 8-65 years) from 12 cohorts worldwide. Analyses and quality control were performed using standardized ENIGMA diffusion tensor imaging protocols. We primarily examined fractional anisotropy (FA) as the main parameter of WM microstructure. Linear mixed-effects analyses were conducted to …

56-Year-Old Man With Dyspnea And Volume Overload, Altamish Daredia, Han Yu, Ryan Walsh, Benjamin Karfunkle

Faculty, Staff and Student Publications

No abstract provided.

Place-Based Environmental Education: Forming Connections To Your Local Environment And Community, Deanna Grelecki

School of Education and Leadership Student Capstone Projects

Growing research shows that engaging children in environmental education produces a myriad of benefits, such as improved social, physical, mental, and cognitive aspects of children' s wellbeing. Even with these beneficial outcomes, researchers find that a disconnect between youth and the natural world exists and is growing. This capstone answers the question, How can educators create place-based environmental education opportunities for middle school students?, and ultimately help students build positive relationships with the environment. Grounded in place-based environmental education and created for a rural community in Northern Illinois, this capstone project centers on a field study situated in a local …

Voices From Beyond The Grave: Protecting The Audio Of Murder Victims Through (Intellectual) Property Law, Alexandra M. Hudson

Washington and Lee Law Review

For most, the audio of a deceased loved one is a treasured keepsake. For the families of violent crime victims, it can be a harrowing reminder of their loved one’s death. And it can also be a source of content for true crime podcasters.

When a person dies from a violent crime and their killer is prosecuted, the audio associated with the crime (body camera footage, 911 calls, surveillance footage, etc.) frequently becomes public record. Public record laws vary greatly across the United States but typically err toward disclosure to promote government transparency. Broad public record laws benefit the public …

Energy Management And Water Balance In Migrating Hummingbirds, Shayne R. Halter

Biology ETDs

Small body size and rapid metabolism make annual migrations challenging for North American hummingbirds. Many of these birds make annual journeys of over 10,000 km. During migrations, hummingbirds use stopover sites, where they remain for several days to accumulate body fat. At these stopovers, they often encounter uncertainties in food resources, competition, and weather. To save energy, hummingbirds sometimes enter nocturnal torpor. My dissertation uses respirometry data, lipid measurements from Quantitative Magnetic Resonance, and feather hydrogen stable isotopes to understand how energy levels, migration patterns, torpor use, and water balance interact in four species of migrating hummingbirds. I discovered lipid …

Biological Soil Crusts: Effects Of Environmental Change On Dryland Microbes, Mariah T. Patton

Biology ETDs

Climate change is increasing rainfall variability, altering nutrient availability, and causing warming in drylands, which comprise approximately ~36% of Earth's terrestrial surface. These expanding ecosystems contribute the most to interannual variability in the global carbon cycling. Biological soil crusts (biocrusts)—often called the "skin of the Earth"—are critical components of dryland surfaces, consisting of successional microbial communities dominated initially by Cyanobacteria and later by a more diverse composition with other bacteria, archaea, lichen, fungi, and algae. These communities play essential roles in soil aggregation, erosion prevention, and carbon and nitrogen cycling. However, our ability to predict biocrust responses to environmental change …

How Did We Get Here? The Rise Of Anti-Immigrant Rhetoric As A Sociopolitical Force In U.S. Immigration Discourse And Enforcement Policy Through A Latino Lens, Andrea Maldonado

Honors Theses

No abstract provided.

Racing To Safety: Tax Policy For Ai Safety-By-Design, Mirit Eyal, Yonathan Arbel

Articles

The White House recently announced its vision of artificial intelligence (AI) policy: AI development is a race and America must win it. To that end, a new America's AI Action Plan directs federal agencies and states to remove regulatory barriers to AI development and accelerate innovation. This approach leaves limited room for regulatory measures that would address the safety risks of powerful AI systems: their behavior in novel domains remains unpredictable, their decision-making opaqueness, and their alignment with human values is uncertain. While experts warn of large-scale accidents, policymakers find themselves in a bind: Regulate AI and cede ground to …

Predicting Property Sale Prices In Dubai Using Machine Learning Regression Models, Mohammad Buabdulla

Theses

It has been observed that the fastness of digital real estate’s application has resulted in availability of large-scale property data which forms an opportunity to more precise and open property valuation practices. A lot of conventional real estate appraisal methodologies with much emphasis on manual evaluation and historical comparative value find it difficult to respond to the market dynamics or the dynamics that exist within the market at an alarming rate. This research will help mitigate these shortcomings by generating and testing machine learning regression models to determine the price of residential property at sale in Dubai by using real-life …

Nlp Crowdsourcing For Predominantly Oral Languages: The Case Of Bambara, Allahsera Auguste Tapo

Theses

Predominantly oral languages (POLs) face a significant "digital divide," as they are often excluded from the benefits of modern natural language processing (NLP) technologies, due to a lack of extensive, readily available machine learning (ML) datasets. We investigate methods to overcome this data scarcity for Bambara, a Manding language, spoken primarily in Mali, with a rich oral tradition but limited digital presence.     The research leverages crowdsourcing and community engagement to build high-quality ML ready dataset resources. Key contributions include methods for automatic speech recognition (ASR) and machine translation (MT) dataset collection and curation and for educational resource creation.      Our findings …

The Best Practices For Prefabrication Of Industrial Buildings, Derek Gines

Theses

Prefabrication has demonstrated measurable and repeatable advantages in productivity, cost certainty, and environmental performance, yet its adoption within industrial building typologies remains largely inconsistent. Existing research largely evaluates prefabrication through downstream performance outcomes, while offering limited insight into the upstream design and organizational decisions that enable or undermine its reliability. This thesis reframes prefabrication as a design‑led methodology rather than a construction optimization, arguing that successful hybrid prefabrication is determined primarily by early decision timing, governance structures, and the control of spatial and logistical interfaces. This study adopts a qualitative design‑research approach that combines comparative case study analysis with expert …

Predicting Luxury Car Sales Using Machine Learning: A Comparative Study Of Linear Regression, K-Nearest Neighbors, And Support Vector Machines, Khalifa Jamal Mohammad Saleh Alblooshi

Theses

This paper explores the use of machine learning to predict the sales of luxury cars in the globe, and BMW as a case study of its sales data in the global market between the period 2010 and 2024. The study focuses on three regression algorithms, which include the Linear Regression, K-Nearest Neighbours (KNN) and Support Vector Machines (SVM) and sees their predictive accuracy, generalisation performance and business applicability. In Python with the help of the Google Colab, an end-to-end analytical pipeline was developed entailing data preprocessing, outlier management, feature engineering, and time-sensitive traintest division. RMSE, MAE, MAPE, and R2 were …

Toward Reliable Computational Social Science: Inconsistency-Aware Methods For Human Annotation And Ai Inference, Sujan Dutta

Theses

As artificial intelligence (AI) becomes increasingly common in computational social science, \textit{inconsistency} has emerged as a key challenge. AI models often contradict themselves when given equivalent inputs, disagree with other models on the same data, and diverge from human judgments in seemingly opaque ways. Human annotators exhibit their own inconsistencies, both within individuals and across groups shaped by differing values and identities. Rather than treating these inconsistencies simply as noise, this dissertation argues that they contain meaningful signals that can be leveraged to improve learning efficiency, strengthen evaluation, and increase the reliability of large-scale social measurement. To study this phenomenon, …

Toward A Unified Framework For Open World Visual Learning, Yuansheng Zhu

Theses

Artificial intelligence systems have achieved remarkable performance across a wide range of visual tasks. However, most existing models operate under the unrealistic closed-world assumption, where training and test data are drawn from the same distribution. In real-world applications such as anomaly detection, autonomous driving, and medical diagnosis, learning systems frequently encounter novel or out-of-distribution scenarios. These settings require models that can recognize unknown inputs, adapt to new information over time, and maintain reliable performance under evolving conditions. This dissertation studies the problem of Open World Visual Learning, a paradigm that enables visual learning systems to operate robustly in dynamic and …

Alternative Color Mode Design: Supporting Mobile App Creators With Evidence-Based Guidelines, Sarah Andrew

Theses

Alternative color modes, such as light, dark, dim, and high contrast modes, in mobile apps can improve accessibility for people with vision impairments and usability for people without vision impairments across situational contexts. However, current mobile apps exhibit inconsistent color implementations for UI elements (e.g., background, text, buttons, images, and non-selectable icons), leaving users with limited accessible options. My dissertation addresses a central question in human-computer interaction and accessibility: How can mobile app designers be supported to implement alternative color modes that meet the accessibility and usability needs of people with and without vision impairments? Through an eight-study mixed-methods investigation, …

Optimizing Virtual Scrolling Performance In Angular: A Comparative Study Of Cdk And Custom Implementation, Guri Sokoli

Theses

Modern web applications often display large datasets with tens of thousands of items, such as e-commerce catalogs, data tables, and social media feeds. Rendering all items in the Document Object Model (DOM) at once causes browser freezing, high memory use, and slow interfaces. Virtual scrolling solves this problem. It is widely adopted but rarely studied through direct performance comparison. Few empirical studies measure how different implementations behave under varying dataset sizes, devices, or browsers. This research conducts a comparative analysis of Angular CDK Virtual Scroll as an industry-standard baseline and develops an optimized implementation incorporating framework-specific enhancements: OnPush change detection …

Maintenance Insights For Power Transformers In Energy Networks, Saleh Hassan Al-Ali

Theses

This thesis examines machine learning approaches for predicting failures in electrical power distribution transformers, with the goal of helping utility operators intervene before outages occur. The dataset covers 16,000 distribution transformers operated by Compa ˜n´ıa Energ ´etica de Occidente (CEO), a Colombian utility serving 42 municipalities in the Cauca Department. Each transformer record includes geographic location, rated power capacity, self-protection features, ceramic insulation criticality levels, removable connector configurations, customer categories, user counts, estimated un-supplied energy, installation types, network topology, and secondary line lengths. Failure event histories were also included, which allowed the problem to be framed as a supervised binary …

Towards Reliable And Trustworthy Deep Learning Through Explainability And Interpretability, Dipkamal Bhusal

Theses

Deep neural networks achieve state-of-the-art performance across many domains, yet their deployment in high-stakes settings is constrained by two challenges: opaque decision-making and vulnerability to adversarial manipulation. This thesis investigates explainability and interpretability as principled mechanisms for improving the reliability and trustworthiness of deep learning models. First, we develop new post-hoc explanation methods that improve feature attribution and concept-based explanations. These methods provide faithful decision cues by modeling meaningful feature interactions and extracting faithful coherent concepts, enabling more reliable understanding of why a model predicts a given label. Second, we show that explanation quality is not solely a property of …

Emote - A Modular Action Figure For Childhood Emotional Growth, Terrence Li

Theses

Emotional regulation and communication are one of the most important skills that we can learn. This skill allows us not only to recognize and effectively communicate our feelings to others but also allows us to recognize them in others. Although learning and recognizing these emotions may be a pursuit in which progress varies from person to person, this skill is especially invaluable to young children. Beginning as early as the age of 3, many children begin to show early awareness of their own emotions, such as reacting to discomfort or comfort, or starting to use words for feelings. This learning …

How Policing Data Visualizations Affect Comprehension, Decision Confidence, And Perceptions Of Racial Disparities, Abeer Mustafa

Theses

Police departments use public dashboards to share use-of-force data for policymaking and public awareness, but it remains unclear how visualization formats affect how people interpret this information. This between-subjects study with 64 participants compares absolute use-of-force incident counts (Totals) and population-adjusted rates (Rates) across four United States cities. The research included a quantitative analysis of graph comprehension, policy prioritization, confidence ratings, and attitude change, as well as a qualitative examination of open-ended responses. Results showed a strong framing effect: those who viewed absolute numbers prioritized Aurora, Colorado (highest incidents) for policy intervention, often disregarding population baselines, while those viewing per-capita …

Defining Her2 Associated Proteogenomic Features In Breast Cancer And Extending To Gynecologic Cancers, Maya Anand

Theses

HER2 amplification is a well-established driver of breast cancer and serves as the primary basis for clinical classification and treatment selection. However, this framework assumes that HER2-driven tumor biology is defined solely by ERBB2 amplification or overexpression. The goal of this study was to evaluate whether HER2-associated signaling is represented as a pathway-level activation state and whether this framework could help identify tumors with clinically relevant HER2 activity beyond current routine classification methods. HER2-associated transcriptional programs were identified across three independent breast cancer cohorts, resulting in conserved gene sets (P76 and P25). Amplification-independent HER2 activation was assessed using the HER2 …

Leveraging Machine Learning For Traffic Congestion Management In Smart Cities, Omar Alhasai

Theses

Traffic congestion continues to be a major urban issue, leading to traffic delays, higher fuel costs, and air pollution problems. Traffic management systems currently function in reactive mode because their algorithms only operate following congestion development rather than preventing it. Smart cities need predictive systems based on data analytics and machine learning to actively control urban traffic movements because traffic continues to rise as a result of urbanization and population growth. The proposed research designs a machine learning–driven traffic congestion prediction system that uses genuine data obtained from Aarhus, Denmark, and METR-LA, Los Angeles. The study will analyze fundamental traffic …

A Comparative Study Of Inference-Time Scaling Strategies For Large Language Models, Oluwamayowa Owolabi

Theses

Large language models (LLMs) have demonstrated strong performance on a range of reasoning tasks, however, their reliability often depends not only on model size or training data, but also on inference-time strategies. However, existing inference-time methods are typically evaluated in isolation and under differing experimental assumptions, making it difficult to draw systematic conclusions about their relative effectiveness. This thesis proposes a controlled empirical study of inference-time scaling strategies for large language models under fixed inference-time compute budgets. The findings reveal that no single strategy dominates uniformly. PRM guided selection with the IBM Granite verifier achieves the highest absolute accuracy across …

Wegmans School Of Health And Nutrition’S Culinary Kitchen Cart Manual Development, Neena Bhala

Theses

To facilitate the initiation of culinary medicine at RIT, a manual to guide the use of a mobile kitchen cart was developed and evaluated. This manual was developed to support faculty, staff, and students’ use of a Mobile Kitchen Cart to be able to support culinary medicine and nutrition education activities. The Manual was directed to RIT faculty, staff, and students who have experience with the cart or intend to have future use with the cart. A qualitative evaluation study was conducted with ten participants including RIT faculty (n=2), students (n=4), and staff (n=4). Feedback on the manual was obtained …

Hardware Integrity Checking On An Fpga Through Power Side-Channel Analysis, Ethan Vuong

Theses

FPGAs have seen extensive usage in applications such as cloud-computing, hardware acceleration, mobile devices, and military alike. While the reconfigurability of these devices allow them to be as adaptable as they are fast, it raises concerns of adversaries modifying not mere software, but hardware itself. Moreover, designers face an IP trust issue where they cannot be sure that a third-party IP was not modified in transaction, programming, or even post-programming. Cloud computing centers are hesitant to rent fabric on multi-tenant FPGAs due to the plethora of vulnerabilities and uncertainties that come with allowing users to reconfigure hardware. This thesis aims …