Digital Commons Network^™

How Do You Know? Parsing The Infocalypse Using Art-Based Data Visualization, Michelle Sylvia Weintraub

Theses

Since the advent of the World Wide Web, vast interconnected digital networks have increasingly provided access to diverse data sources for Internet users. Towards the turn of the 21st century, social media sites opened opportunities to produce and consume (i.e., prosume) data, empowering users to publish their own content online (Manovich, 2009). The ability to publish user-generated content results in more voices being heard, creating more seats at the proverbial table. However, more recently, persuasive media has increasingly been weaponized to disseminate misinformation and disinformation online (Howard, 2020). Consequently, the agency exercised by Internet and social media users is diminished. …

Connecting Children With Artists Through A Children's Art History Book, Katie Meyer

Theses

This project is a children’s art history book that is set in an art museum. The goal of the book is to help children connect with Contemporary artists and their art. A museum setting is utilized to introduce children to museums and as an appropriate environment for viewing art. The theme of the exhibit that will be presented in the book is titled the “Innovators Exhibit.” This exhibit features six Contemporary artists from different art styles/mediums. The artists come from diverse backgrounds to expose children to a variety of cultures. Thought provoking questions will also be asked throughout the book …

Heroic Sacrifice And Christ-Like Bodies: Conceptions Of Immortality In Jacques-Louis David's Death Of Marat, Ingelise Johnson

Theses

This thesis posits a reinterpretation of Jacques-Louis David’s late-eighteenth-century martyr-portrait, The Death of Marat, within its French Revolutionary political function and theological associations. The revolutionary body in Marat is rendered from a corporeal language of antique heroic figures and Renaissance depositions David observed during his study in Rome. It is especially in the iconography of the hanging arm that David illustrates Marat’s association with heroic and sacrificial deaths of antique and Christian imagery. These references convey the artist’s exploration of death within religious beliefs germane to the sociocultural contexts of the revolutionary period. Within antique and Christian theologies, David explores …

The Evolution Of Agriculture Marketing: Harnessing Data Analytics To Understand Generational Shifts And Influencer Trends, Lexi Schweigert

Theses

The findings reveal that data analytics plays a crucial role in shaping marketing strategies in agriculture. Generational shifts, particularly among Gen Z and Alpha, are driving changes in consumer preferences and expectations. Influencers are also playing an increasingly important role in shaping the perception of agriculture and food among these generations.

Customizing Ai Strategies Across Multiple Generations, Matthew Harrer

Theses

This project investigates how artificial intelligence can help brands and marketers connect more effectively with Generation X, Millennials, and Generation Z. The literature review lays the groundwork that focuses on consumer behaviors and the integration of AI into digital marketing practices for each generation. The second part of the project involves a secondary data analysis of 21 recent marketing surveys and reports that explores topics related to trust, personalization, and social media. By integrating the findings into an insightful guidebook, marketers will be able to maximize these insights into clear actionable strategies.

Elisabetta Sirani’S Timoclea: Baroque Agency And The Aesthetics Of Feminine Rage, Shaylen Grace Gardner

Theses

This thesis examines Timoclea Kills the Captain of Alexander the Great (1659) by Elisabetta Sirani as a foundational example of feminine rage within the Baroque period. By framing Timoclea’s violent act not as an outlier or solely reactive gesture, but as a deliberate, composed assertion of power, Sirani constructs a model of feminine agency that defies patriarchal expectations of female submission and passivity. Through interdisciplinary analysis grounded in feminist theory, formal visual analysis, and retrospective cultural studies, this research argues that Sirani’s Timoclea exemplifies the Baroque’s strategic use of socially acceptable iconography to veil explicit challenges to gendered expectations. This …

Network And Tree-Based Approaches To Data-Driven Modeling Of Complex Climate Systems, Adam M. Giammarese

Theses

The guiding thread throughout this thesis work is data-driven modeling of Earth's climate. We investigate a variety of settings within the field of complex climate systems, such as gridded, high-resolution reanalysis data, lower-resolution climate indices, and incomplete paleoclimate records with missing values. We begin with reanalysis data of high spatial and temporal resolution, and use climate network analysis to uncover hidden trends in the data in regards to the Amazon rainforest and its changing role in the global climate system. We introduce a novel formulation of k-nearest neighbors climate networks to study changing trends at a local level, and apply …

Investigating Spectral Rendering Techniques To Improve Colour Matching In Virtual Production, Vlad Simion

Theses

As rendering engines become increasingly important in film and television, with their use in virtual production (VP) to display rendered imagery, some underlying issues become more apparent. This thesis aims to investigate how we can improve asset color matching of VP elements with real-life objects found on sets. Experiments were conducted in which objects were exposed to various types of lighting setups, and digital twins were rendered using traditional computer graphics techniques. The renderings occurred in both classic RGB spaces and the spectral domain. Additionally, data reduction techniques were used for the spectral renderings to determine if any of them …

Advancements In Fabry-Pérot Cavity Fabrication And Its Applications In Photothermal Spectroscopy For Trace Gas Sensing., Giulia Malvicini

Theses

The detection of trace gases is essential in various applications, from environmental monitoring and industrial process control to medical diagnostics. The need for high-sensitivity, compact, and costeffective gas sensing systems drives continuous advancements in sensor technology. Among optical sensors, photothermal spectroscopy (PTS) offers distinct advantages, detecting the indirect effects of thermal waves induced in the sample by exciting the molecules with a laser. More particularly, photothermal interferometry (PTI) uses interferometric methods, such as the Fabry-Pérot (FP) interferometer for the read-out.

Multi-Point And Multi-Station Orbit Propagation For Non-Functional Drifting Geo Satellites, Hritik Mitra

Theses

Currently, there are thousands of man-made space objects that are orbiting the Earth. These satellites serve a host of essential purposes (scientific research, technical applications, services) and they are all required to remain within their mission parameters. Most critical for them is to maintain the orbital parameters that are specified to achieve a particular mission’s objectives. If a satellite deviates beyond a certain limit, there is not only a risk of mission failure but there is a major hazard for possible collisions with other objects such as active satellites, space debris and even natural objects in some cases. There is …

Measurement And Improvement Of Photon Identification Efficiencies Using Machine Learning Techniques In The Atlas Detector At The Lhc, Abdulla Esam Mahboub

Theses

Photons play a crucial role in numerous analyses at the Large Hadron Collider (LHC), particularly in studies like the Higgs boson decay to two photons. Precise photon identification is essential for enhancing the sensitivity and accuracy of such measurements. This thesis focuses on the development of a machine learning (ML)-based photon identification algorithm to improve the photon identification efficiency within the ATLAS detector, using a Deep Neural Network (DNN) approach. The primary goal is to boost photon identification efficiency by using advanced neural network techniques. Traditional photon identification relies on cuts applied to shower shape variables, which can limit the …

Determining Sphincs+ Readiness For Standardization Of Slh-Dsa Signature, Jessica Ancillotti

Theses

As quantum computing advances, public-key cryptographic algorithms risk becoming obsolete, requiring the development and implementation of quantum-resistant alternatives. This thesis evaluates SPHINCS+, a stateless hash-based digital signature scheme recently standardized by NIST under the FIPS 205 standard named Stateless Hash-Based Digital Signature Algorithm (SLH-DSA), which was selected for being a conservative and robust choice due to its reliance solely on well-understood cryptographic primitives. In the context of the growing need for quantum-resistant cryptographic solutions, determining the readiness of SPHINCS+ involves assessing its practical viability across different application domains and evaluating how well it meets today’s and future needs. To achieve …

Performance Comparison Of Learning With Errors Cryptosystems, Gabriel Johnson

Theses

Due to recent advancements in quantum computing, there has been great interest in finding quantum-resistant public key encryption algorithms. Much focus has been given to lattice-based cryptosystems, as certain lattice problems appear difficult even in the quantum setting. In particular, the Learning with Errors (LWE) problem, introduced by Regev, gives a means for constructing numerous public key cryptosystems with very strong proofs of security based on the hardness of finding a short vector in a lattice. We analyze and compare the performance, in terms of memory usage and speed, of four different Learning with Errors cryptosystems: basic LWE, normal-form LWE, …

Parsing Of Math Formulas And Chemical Diagrams Using Graph-Based Representation And Attention Models, Ayush Kumar Shah

Theses

Mathematical formulas and chemical diagrams appear frequently in scientific documents but are often embedded as visual content, either rasterized or vector-based images, limiting their accessibility and automated analysis. This thesis aims to bridge this gap by presenting a graph-based visual parsing framework that recognizes and parses these notations from both vector and raster image inputs in digital documents. For mathematical formulas in born-digital PDFs, we construct Symbol Layout Trees (SLTs) using a graph defined over vector-based primitives, capturing spatial relationships, avoiding relying on OCR. For born-digital chemical diagrams, we introduce a Minimum Spanning Tree (MST)-based technique that extracts molecular structure …

Data-Driven Machine Learning Applications For Predictive Modeling Of Petrochemical And Ecofriendly Systems, Noora Saleh Al Mansoori

Theses

Traditional experimental approaches in industrial processes, such as Fourier Transform Infrared Spectroscopy (FTIR) spectroscopy, thermogravimetric analysis (TGA), and well-drilling operations, are often constrained by time, cost, and operational limitations. This research explores the application of data-driven Machine Learning (ML)-based predictive modeling to improve efficiency and reduce dependency on resource-intensive experimentation. The study develops ML models for three distinct processes: FTIR intensity prediction of bitumen thermal cracking products, thermal degradation of Medium-Density Fibreboard (MDF) using TGA data, and Rate of Penetration (ROP) prediction in petrochemical industry. Six algorithms: Linear Regression (LinReg), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Gradient …

Design Of A Deployable Rolled Antenna System For Satellite Applications, Ashwaq Abdulla Alkaabi

Theses

This project presents the development of a deployable Synthetic Aperture Radar (SAR) antenna designed for a 16U CubeSat platform. The primary challenge in SAR satellite design lies in the need for large antennas to achieve high-resolution imaging, which traditionally results in increased satellite size and cost. To address this, the proposed solution employs a scalable 4 × 21 patch antenna array operating at 1.275 GHz, fabricated on a flexible Polyimide-based Printed Circuit Board (PCB). This flexible PCB allows the antenna to safely roll during deployment, avoiding damage and facilitating compact storage. However, Polyimide poses challenges due to higher losses and …

Advancing Academic Advising With Knowledge Graphs: Integrating Machine Learning And Llms For Personalized Course Planning, Sara Alshamsi

Theses

Academic advising plays a critical role in helping students make informed decisions, improve academic performance, and successfully navigate their university journey. However, with increasing university enrollment, traditional advising methods often struggle to scale, leading to student frustration and overburdened advisors. Additionally, designing course offerings that match student demand is a complex and error-prone process involving multiple stakeholders. To address these challenges, this thesis proposes an automated, data-driven system for generating personalized academic plans for students. The primary aim of this thesis is to develop a system that reduces students’ dependency on advisors while simultaneously providing accurate estimates of course demand …

Numerical Methods For Approximating Line Integrals Over Implicitly Defined Curves, Raghd Alsaadawi

Theses

In this thesis, we develop and investigate a predictor-corrector method for the numerical tracing of implicitly defined curves. The study begins with the introduction of modified numerical integration techniques — specifically, the modified trapezoidal and modified midpoint rules — for evaluating the line integral of a vector field along an implicitly defined curve. Furthermore, we explore higher-order methods aimed at improving the accuracy of such integrals. Theoretical and numerical results, including asymptotic error expansions, are presented to support the analysis. In addition, several numerical experiments are carried out to illustrate the effectiveness and robustness of the proposed approaches.

Wavelet-Based Multi-Step Methods For Systems Of Differential Equations, Rashad Assad Hijji

Theses

Wavelets have been widely used in many areas of engineering and mathematics, including the development of multistep algorithms to solve initial value problems (IVPs) in the context of the Galerkin method using Daubechies' wavelets. The main scope of our work is to build a comprehensive framework for solving Systems of Differential equations using the compactly supported wavelets proposed by I. Daubechies. Wavelets are mathematical functions that decompose data into distinct frequency components, and each element is analyzed with a resolution that matches its scale. Compact support of Daubechies wavelets is key in allowing them to be computationally efficient for high-dimensional …

Modeling Femtosecond Laser Interaction With Glass For Optical Fabrication, Nathan Klein

Theses

The fabrication of precision optics is critical for a wide range of applications, including biosensors, virtual and augmented reality, medical imaging, and micro-electronics. However, it is challenging to meet the demands of these applications with conventional chemical and mechanical fabrication methods, as they can introduce chemical waste, mid-spatial-frequency errors, and subsurface damage that degrades image quality. Femtosecond lasers have emerged as a promising alternative, offering fast, non-contact, and chemical-free machining with single digit nanometer precision. This thesis presents a computational model designed to investigate the interaction process between a high-intensity femtosecond laser pulse and dielectric material. A pulse propagation model …

Investigating Hardware Injections In Ligo O3 Data: Simulated Signals From A Neutron Star In A Low-Mass X-Ray Binary, Jediah Gofhaone Tau

Theses

Simulated continuous gravitational wave (CW) signals, called hardware (HW) injections were added to the data in the LIGO detectors' third observing run (O3), including two periodic signals mimicking a spinning neutron star in a binary system, similar to the Low-Mass X-Ray Binary (LMXB) Scorpius X-1 (Sco X-1). Using a cross-correlation pipeline, which searched for CWs from Sco X-1 in O3, we searched for these HW injections, using an uncertainty around the true signal parameters akin to the uncertainty associated with the parameters of Sco X-1. One of the signals, residing in the 230-235 Hz frequency band, was detected. The other …

Observational Predictions For Convective Common Envelopes, Nikki Noughani

Theses

Common envelopes (CEs) are thought to be the main method for producing tight binary systems in the universe, as the orbital period shrinks by several orders of magnitude during this phase. Despite their importance for many stellar evolution channels, direct detections are rare, and thus observational constraints on common envelope physics are often inferred from post-CE populations. Recently, galactic population observations suggest that the CE phase must be highly inefficient at using orbital energy to drive envelope ejection for low-mass systems and highly efficient for high-mass systems. Such a dichotomy has been explained by an interplay between convection, radiation, and …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …

A Novel Framework For Dynamic Graph Representation Learning With Mamba, Ashish Pandey

Theses

Dynamic graph embedding is a key technique for modeling temporal dependencies in evolving networks. While transformer-based models perform well, their quadratic complexity limits scalability on long graph sequences. This thesis compares transformer approaches with the Mamba architecture-a linear-complexity state-space model—for temporal graph embedding.

Two frameworks are proposed: DG-Mamba and GDG-Mamba. DG-Mamba uses standard GCN-based spatial encoding, while GDG-Mamba incorporates domain-aware edge features using Graph Isomorphism Network with Edge Convolution (GraphGINE). Experiments on UCI, Reality Mining, Slashdot, Bitcoin-OTC, and SBM datasets show that Mamba-based models match or exceed transformer performance, especially on graphs with high temporal variability.

The thesis also applies …

Predicting Patient No-Show Rate In Healthcare To Improve Operational Excellence, Saeed Ahmed Al Wari

Theses

The challenge of integrating no-show predictive models into healthcare environments is complex and multifaceted, extending far beyond the distinct technical challenge of building accurate models. This thesis will discuss more on the subject of incorporating machine learning into healthcare. While algorithmic accuracy and model validation are critical, they are a mere sub-system of a much larger operational, technological, ethical, legal and human ecosystem. Predictive models face mass deployment in healthcare systems which are complex networks of stakeholders across strict regulatory frameworks and deeply embedded workflows (AlMuhaideb et al., 2019). However, for these models to result in long term and real …

Predicting Customer Churn In E-Commerce, Hind Tawfiq Kazim

Theses

Customer retention has become a critical focus for businesses seeking to sustain growth and profitability in an increasingly competitive market. In this thesis, advanced machine learning techniques are used to develop a data-driven churn prediction model for Majid Al Futtaim's customer base. In an e-commerce environment, the research focuses on identifying the key transactional and behavioral factors that influence customer churn, based on customer relationship management theory. The study is contextualized in the context of increased digital consumer engagement and high customer acquisition costs, which make retaining existing customers more cost- effective than acquiring new ones. Our primary research questions …

Future Of Inclusive Education In The Uae: Supporting Learning Disabilities In Higher Education, Christina Ayub

Theses

Inclusive education is a critical component of higher education, ensuring that students with learning disabilities (LDs) receive equitable access to academic opportunities and institutional support. While global advancements in inclusive education have been significant, the UAE’s higher education sector faces unique challenges in accommodating students with LDs, particularly as technological disruptions, demographic shifts, and evolving policies reshape the broader educational landscape. This research investigates how a higher education institution in the UAE, such as RIT Dubai, might adapt its support systems for students with LDs, such as ADHD, dyslexia, dyscalculia, dysgraphia, and dyspraxia, under a range of plausible future conditions …

On The Use Of Nonlinear Electrophoresis For Separating Particles And Cells In Insulator-Based Electrokinetic Systems, Alaleh Vaghef-Koodehi

Theses

The increasing demand for efficient separation processes of micron-sized particles, including microorganisms, highlights the importance of developing low-cost, non-labor-intensive, and time-efficient techniques. Insulator-based electrokinetic (iEK) systems have proven to be a practical alternative to bench-scale methodologies such as centrifugation and membrane filtration. Several studies have reported the separation of microparticles by employing direct current (DC) voltages. However, all of them have overlooked the phenomenon of nonlinear electrophoresis (EPNL) and its role in the electrokinetic migration of particles. This study combines mathematical modeling and experiments to extend the capabilities of iEK systems. This dissertation has four aims: (1) to separate microparticles …

Wooden Spatial Analogies: Playful, Transformative Design Process Kit With Inclusion In Mind, Sarah R. Baxter

Theses

This paper concerns solutions to insufficient support in educational inclusivity through the subject of the five design process steps. It leverages the insights of researchers, such as Linda Silverman, regarding ‘visual-spatial learners’, individuals who perceive the world through images rather than words and thrive in navigating complex tasks, yet often face challenges with linear, step-by-step processes. This work critiques the conventional educational methods that frequently overlook the profound potential of spatially adept students. A final prototyped solution was assembled. The outcome was shaped, sanded, and painted wooden spatial analogies of play, designed for rearrangement, and interactively representative of the design …

End-To-End Systems Limitations In Hyperspectral Target Detection Using Parametric Modeling And Subpixel Lattice Targets For Validation, Chase Canas

Theses

Hyperspectral target detection applies algorithms to high-dimensional images to identify rare targets, or objects of interest, within cluttered scenes of diverse backgrounds. Computational demands of processing hyperspectral datasets, along with their limited availability in both public and private sectors, imposes challenges in assessing system-level limitations of detection. This research presents a methodology to quantify end-to-end sensitivities and limitations through statistical modeling across thousands of target detection scenarios. The objective is to identify specific parametric thresholds where detection begins to degrade, based on “knees” in detection curves derived from model outputs of various scenarios. The model considers subpixel targets, where an …