Digital Commons Network^™

Evaluating The Effects Of Behavioral Skills Training On Defensive Back Skills In High School Football Players: A Focus On Man Coverage, Malachi James Jackson-Talmadge

Theses

Behavioral Skills Training (BST) is an intervention package that involves instructions, modeling, rehearsal, and feedback. BST has been used to enhance athletic performance in several sports (e.g., soccer, field hockey, bowling). In football, two prior studies have shown improved football-specific skills following BST. Although effective, both studies implemented BST individually with each player, which can be time-consuming during football practices. Providing BST to groups of players may be an efficient alternative. Currently, no study has evaluated the effects of group BST in sports. The current research used a multiple baseline design to evaluate the effects of group BST on man …

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, …

Branding Through Semiotics, Minimalism, & Branding Psychology: The Eurekafit Brand, Torri Salem

Theses

This paper examines how the combination of minimalism, semiotics, and branding psychology work seamlessly together to create an inclusive brand with an emotionally resonate visual identity system-- a brand that is cohesive and bridges the sub-brands of EurekaFit and Eureka CrossFit. What initially started as a logo redesign of Eureka CrossFit has now expanded into this dual-branding visual system, that communicates approachability, a sense of belonging and fitness, without being intimated, to its audience. This study investigates visual signifiers like color, typography and imagery, through a semiotic analysis and approach. I reduced clutter and visual noise and was able to …

Petals Of The Mind: A Digital Floral Interpretation Of Mental States, Corine D. Arrington

Theses

Petals of the Mind is an applied studio art project that explores the use of symbolic floral imagery to visually represent internal emotional states, specifically depression, anxiety, and ADHD. This project consists of six large format digital posters created in Procreate. Each emotional category is represented by a pair of flowers selected for their symbolic and structural characteristics: Blue Poppy and Black Rose for depression, Tulip and Aster for anxiety, and Honeysuckle and Wildflower Mix for ADHD. These flowers are intentionally deconstructed through layering, fragmentation, and motion cues to mirror common internal experiences such as emotional heaviness, vigilance, cognitive scattering, …

Llm-Driven Mission Control And Autonomous Planning For Search-And-Rescue Uavs: A Simulation-Based Evaluation, Naser Bader Alsaedi

Theses

Unmanned aerial vehicles (UAVs) are increasingly used in search‑and‑rescue (SAR) missions, yet many systems still rely on fragmented software where mission design, perception, and flight control are configured separately. This thesis examines whether a unified AI‑driven framework can reduce configuration effort and operator workload in UAV‑based SAR operations. The proposed system integrates natural‑language mission specification using a large language model (LLM) (LLaMA 3.1), autonomous coverage planning, YOLOv8‑based victim detection, and PX4/MAVSDK control within a single architecture. Operators describe missions through free‑form text or a graphical interface; the model converts these descriptions into structured mission parameters that are automatically planned and …

Analysis Of A Coaxial Transmission Line Filled With An Orthorhombic Dielectric-Magnetic Medium, Fathima Manikunnath Abdul Akbar

Theses

Coaxial transmission lines are fundamental means for Transverse Electromagnetic (TEM) wave propagation in RF, microwave and high-speed electronic systems. The study of transmission lines is often familiar when they are filled with isotropic materials; however modern engineered direction dependent materials reshape field distributions. In this thesis, we consider a coaxial transmission line of an inner radius and outer radius b filled with an orthorhombic dielectric-magnetic material, which is described by two anisotropy parameters αx and αy. The potential and field distributions are studied in relation to the ratio b/a as well as the anisotropy parameters αx and αy. Due to …

Efficient Fpga Implementation Of A 1 Million-Point Fft, Jwaher Abdulqader Al Tamimi

Theses

The one-million-point Fast Fourier Transform is implemented using a radix-2 single-path delay feedback pipeline architecture. To minimize the computational overhead, twiddle factors were pre-computed and stored in memory. The design uses a fixed-point representation with two integer bits and seven fractional bits, achieving a measured signal-to-noise ratio of 37.98. Given the substantial memory requirements, a memory partitioning approach was used. It mapped the delay buffers in each stage lookup table memory, block random-access memory, or ultra random-access memory, based on word width and memory depth.

The implementation operates successfully at 100 megahertz on a mid-scale field-programmable gate array. Post-implementation reported …

Study Of The Effect Of Biodiesel Derived From Jojoba Oil With Alcohols Blends On Diesel Engine Performance, Exhaust Emissions, Vibration, And Noise, Yassir Abdelrahman Elbashier

Theses

This study presents a mutual investigation on the properties of the fuels and the performance of an engine using biofuels from mixtures of jojoba oil biodiesel with alcohols. The work consists of mainly two phases, examining the properties of the jojoba biodiesel-alcohols mixtures and the performance and emission behavior in a single piston diesel engine. A base mixture of 50% jojoba biodiesel and 50% diesel produced, and this was fractionally mixed with 5, 10, and 20% methanol and with 5, 10, and 20% diethyl ether (DEE). The rheological properties namely viscosity and shear stresses of all biodiesel blend mixtures were …

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 …

An Artist’S Playground: Building Bridges Between Children’S Education And Their Knowledge Of Art Through Multi-Modal Learning, Alice J. Cauchi

Theses

Not all museums welcome children as part of their main audience with open arms. Research suggests that experiencing museums as a child supports classroom learning and development, but only when the museums engage young children properly. This paper explores how incorporating multi-modal interactives into art museums can effectively engage children, ages 4 to 8, and build a foundation that supports lifelong learning. Drawing on ideas from early-childhood education, museum visitor-engagement studies, and interactivity sciences, this project results in a framework that guides art museums on how they can overlap play, interactives, and content to incorporate this age group effectively to …

Modeling And Autonomous Control Systems For A Delivery Drone Application, Dana Helal Alnuaimi

Theses

This thesis focuses on the development of a multi-drone navigation and control system, aiming to enhance payload capacities beyond the limits of single-drone systems. By integrating multiple drones to work collaboratively as a unit, this study addresses the challenges associated with lifting and transporting heavier payloads.

The primary objective is to design and evaluate a multi-drone system capable of working in tandem to transport larger payloads efficiently. The research aims to develop robust control algorithms, supported by system identification for dynamic modeling, and navigation strategies to enable effective coordination between drones.

The study employs a combination of simulation and real-world …

A Comparative Study On Performance Of Iot-Driven Ml-Enabled Forecasting Models For Efficient Air Quality Monitoring, Bara Ksiksi

Theses

Air pollution is one of the most critical environmental challenges affecting public health globally, responsible for approximately 4.2 million premature deaths annually according to the World Health Organisation. This thesis presents a comparative study of IoT-driven machine learning forecasting models for air quality monitoring in Abu Dhabi, UAE, introducing a zonal approach combined with satellite-based spatial validation. The primary objective is to evaluate forecasting performance across three distinct activity zones using ground station data from the Environment Agency Abu Dhabi (EAD), and to incorporate a spatial validation component using satellite imagery to assess the consistency of ground-based predictions at a …

Breath-Based Detection Of Liver Cancer Biomarkers Using An Swcnt-Fet Nano-Biosensor: Quantumatk, Mohamed Mohieb Rashdan

Theses

Early detection of liver cancer remains limited by the slow pace and invasiveness of current testing methods. This study proposes a single-walled carbon nanotube field-effect transistor (SWCNT-FET) designed to detect hexanal—a volatile organic compound (VOC) elevated in liver cancer—directly from exhaled breath. The device is modeled in QuantumATK using a semi-empirical Extended Hückel Hamiltonian within the non-equilibrium Green's function (NEGF) framework, emphasizing realistic contact physics by employing metallic SWCNT electrodes instead of conventional metal films. Zigzag channels with (11,0) and (12,0) chiralities are examined to analyze how geometry and contact matching influence charge transport. Simulations include current–voltage (I–V) characteristics and …

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 …

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 …

Dissent By Design: Graphic Communication And The Continuity Of Resistance As A Civic Practice In The United States, Ryan "Rain" Milligan

Theses

Graphic communication has played a role in shaping civic life in the United States throughout much of its history, influencing how individuals recognize injustice, understand systems of power, and participate in collective action. Typically associated with moments of protest or political upheaval, printed materials such as pamphlets, broadsides, newspapers, and posters have also functioned as everyday tools that structure public engagement. This project examines these materials through the framework of agitation, education, and organization, considering how graphic communication operates across different historical contexts to provoke recognition, make complex ideas accessible, and coordinate collective effort. Drawing from a range of historical …

Beyond Anomaly Detection: Classifying Attacker Automation Level From Ssh Honeypot Behavioral Signatures, Ashley Alt

Theses

The proliferation of AI-assisted offensive tools has introduced a new category of cyber attacker that combines the speed of automation with the adaptive reasoning previously associated only with skilled human operators. Despite the richness of behavioral data captured by SSH honeypots, existing analyses treat interaction logs primarily as evidence of malicious activity rather than as a dataset capable of distinguishing between attacker types. This thesis investigates whether human-driven, traditionally automated, and AI-assisted attackers produce distinguishable behavioral signatures within SSH honeypot interactions, and whether machine learning techniques can reliably classify attacker behavior from session-level features. A controlled experimental architecture was developed …

Traditional And Machine-Learning Equalization Techniques For Bandwidth-Limited Short-Reach Optical Communication Channels, Abdullah Khawatmi

Theses

This thesis investigates equalization techniques for bandwidth-limited short-reach optical communication systems, with a focus on Visible Light Communication (VLC) and Step-Index Plastic Optical Fiber (SI-POF) links. Commercial light-emitting diodes and photodiode receivers impose severe bandwidth constraints, inter-symbol interference, and noise sensitivity, which fundamentally limit achievable data rates. The work addresses these impairments through systematic evaluation of traditional digital signal processing–based equalizers and modern machine-learning-based post-equalization methods. The primary aim of this thesis is to enhance the achievable data rate and reliability of commercial short-reach optical links while maintaining practical computational complexity. Specifically, the objectives are to (i) design and experimentally …

A Computational Assessment Of Halobacterium Salinarum Glutamate Dehydrogenase Enzymes, Maaz Abdalla

Theses

Glutamate dehydrogenase (GDH) is a hexameric enzyme. GDH is involved in several pathways and cellular processes such as oxidation-reduction homeostasis, ammonia metabolism, lipid biosynthesis, insulin and lactate production, and acid-base equilibrium. The main objective of this thesis is to understand the structural and biochemical properties of this enzyme and why some organisms, for e.g., Halobacterium salinarum, have more than one GDH with different coenzyme specificities. The catabolism of glutamate is linked to NAD+ specific GDHs, meanwhile, NADP+ specific GDHs play an anabolic role in ammonia assimilation. Molecular docking and binding free energy calculations were employed followed by long-scale (500 nanoseconds) …

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 …

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 …

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 …

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 …

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 …

Software Vulnerability Recidivism In Open-Source Projects, Brandon Keller

Theses

Software vulnerabilities present a major threat to businesses and individuals alike and it is therefore critical that a culture exists among software engineers to encourage the discovery and patching of security flaws. Vulnerability counts are a common way of evaluating a project’s security. However, this metric can run counter to building a developer culture of fault recognition if more vulnerabilities is always seen as a bad thing. While these counts can present a rough idea of a project’s history with security, they provide no insight into how the development team improves and learns as a result of a vulnerability. A …

Evaluating Expected Real-World Tactile Experience From Virtual Fabric Perception, Julianna Gross

Theses

Most people have experienced the disappointment of ordering products online and receiving a product completely different to what was expected. For example, a sweater may appear that it is made out of extremely soft blue cotton, yet when seen in person is an itchy purple polyester blend. The current research seeks to ameliorate this confusion by evaluating various visual conditions that have the potential to lead to disparities between real-world feel and online images. Lighting and material characteristics are two major indicators of fabric, specifically real life look and feel. The angle of lighting and which light source is chosen …

Optimizing Urban Commute Quality Through Traffic Congestion Analysis And Predictive Modeling, Obaid Almansoori

Theses

Urban traffic congestion imposes significant economic, environmental, and social costs on rapidly growing cities worldwide. This research investigates how predictive analytics and machine  learning can be leveraged to classify and forecast traffic congestion severity in real time,  enabling data-driven decision-making for transportation planning, signal optimization, and  congestion management. A real-world traffic monitoring dataset comprising 5,952 observations collected over two months via  computer vision sensors at an urban intersection was analysed under the CRISP-DM frame- work. The  dataset records counts of four vehicle classes including cars, bikes, buses, and trucks at  15-minute intervals, alongside temporal variables such as time of day, …

Phrases, Crystal Ching-Lam Tam

Theses

Motivational posters are widely used in educational environments to encourage perseverance, build confidence, and promote positive thinking. However, many of these visuals rely on cliché imagery, generic language, and overly decorative styles, making them feel inauthentic and easy to ignore. In visually saturated campus environments, they often fade into the background, acting as noise rather than as meaningful communication. How can motivation be communicated in a more engaging, intentional, and relevant way for college students? This thesis introduces Phrases, a visual communication system that reimagines motivational design through abstraction, clarity, and restraint. Rooted in Swiss design principles and Gestalt theory, …

Imaging Performance Analysis Of Euv Mask Stacks For Sub-24 Nm Features At High And Hyper Numerical Aperture, Abdusame Omran N. Berish

Theses

As extreme ultraviolet (EUV) lithography advances toward high numerical apertures (NAs) of 0.55 and 0.75, mask-3D effects degrade image fidelity, limiting resolution and the process window. This work investigates the performance of alternative EUV absorber materials for sub-12 nm half-pitch patterning under high-NA and hyper-NA conditions. The lithographic simulations were conducted using the PROLITH Maxwell rigorous model under dipole illumination optimized for sub-24 nm resolution dense line/space features. The EUV mask stacks were derived from five fabricated and tested absorbers: TaBN, Ru/Ta, Pt2W, Ni/CrN, and TaSi2.  The absorber thickness was recalibrated to an optimal value, ensuring a maximum normalized image …

In-Context Retrieval For Molecules And Chemical Synthesis Pathways, Abhisek Dey

Theses

Contrastive learning methods require well-defined positive pairs, limiting their applicability to domains where complete, high-fidelity pairings are available. In practice, large-scale scientific corpora --including patents, publications, and web-scale data -- contain vast quantities of contextually relevant but incompletely paired samples that are discarded under standard training paradigms. In this work, we demonstrate that hard negative mining can be leveraged to construct pseudo-positive supervision signals from unpaired or partially paired data, enabling contrastive learning to exploit the full breadth of available corpora without sacrificing representational quality. Using a large-scale chemical drug patent corpus as a testbed, we train a cross-modal contrastive …