Digital Commons Network^™

Hybridize Functions: A Tool For Automatically Refactoring Imperative Deep Learning Programs To Graph Execution, Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, Anita Raja

Publications and Research

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the "best of both worlds," using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a …

“I Was Just Doing My Job”: An Oral History Of Vietnam Veterans And The Impact Of Their Voices In Collective War Memory, Alyssa Morris

Masters Theses, 2020-current

This master's thesis explores how oral histories from Vietnam veterans provide vital counter-narratives to traditional, often impersonal, military histories. Inspired by the author's grandfather, a Vietnam veteran, the project collects and analyzes ten interviews conducted as part of the Library of Congress’ Veterans History Project. The study emphasizes how oral history not only preserves overlooked personal stories but also restores humanity and empathy to historical understanding. Not only does this project highlight the findings of the interviews but also details the process the author went through before and during the interviews, accompanying that is advice on best practices and how …

Fayetteville State University Spring Commencement May 9, 2025, Fayetteville St University

Graduation Exhibit

Program for Fayetteville State University Spring Commencement May 9, 2025

Keynote Speaker: Mayor Mitch Colvin, City of Fayetteville Mayor 2017-2025

Nuwc Uav Release From A Uuv, Callum Melrose, Isaac Hernandez, Nick Ensign, Clayton Peel

Mechanical Engineering Capstone Design Projects

This design report focuses on the development of an innovative system that enables the deployment of a small encapsulated unmanned aerial vehicle (UAV) from a submerged unmanned underwater vehicle (UUV). Addressing a significant need within the underwater marine community, this capability has applications in advanced scouting, aerial imaging, and enhancing situational awareness. The project centers on designing a UUV hull form or modifying an existing design to incorporate a specialized payload section capable of housing and deploying an encapsulated UAV in submerged conditions.

The primary design challenges include integrating the payload system without compromising the hydrodynamic efficiency, stability, or operational …

Autonomous Drone Launch Pad, Jennifer Radulski, David O'Connor, Zach Smith, Colby Landry, David Janczar

Mechanical Engineering Capstone Design Projects

Launch Tech was tasked by the Naval Undersea Warfare Center (NUWC) to create an autonomous drone launch pad to be used offshore for reconnaissance missions by the United States military. NUWC provided the team with $2,000 to design, test, and assemble a single-use launch pad made out of a flexible composite material that fits inside a 3-inch outer diameter by 1-foot-long cylindrical capsule. The capsule is to be released from an unmanned underwater vehicle, float to the water’s surface, and, upon command, open to expose the launch pad. It must be able to hold the drone in position during transit, …

Team 16h Hybrid Rocket Motor Stand, Lilian Allory, Amelia Lambert, Ethan Leary, Chris Silveira, Matthew Zannini

Mechanical Engineering Capstone Design Projects

This project focuses on the design and construction of a hybrid rocket motor test stand to be used in a future hybrid rocket class that will be offered at the University of Rhode Island. Team 16H began coordination with sponsor, Dean Marchese, in September 2024 to develop a problem definition and customer requirements. The test stand should have the ability to measure and record essential parameters such as thrust, chamber pressure, and mass flow rate. To ensure user safety, the implementation of a remote-control ignition system is required. The table is expected to consume gaseous oxygen with an inline nitrogen …

Nasa University Student Launch Initiative (Nasa Usli), James Collier, Chris Grivers, Ryan Hirsch, Alex Jedson, Jack Lague

Mechanical Engineering Capstone Design Projects

The NASA University Student Launch Initiative (NASA USLI) is an annual engineering competition that challenges university teams to design, build, and launch high-powered rockets (HPRs) capable of meeting specific objectives. For the 2025 competition, the primary mission is to accurately predict the rocket's apogee, demonstrating precision in design and execution. The University of Rhode Island’s Mechanical Engineering Capstone Team 17H designed, built, and flown the rocket, using the engineering method. The team began by reviewing literature, patents, and prior competition entries to gain a thorough understanding of high-powered rocketry and to identify proven design strategies. Customer requirements were carefully analyzed, …

Preludios Colombianos: A Recording Project Of The Piano Preludes By Adolfo Mejía And The Preludes Op.48 And Op.56 By Guillermo Uribe Holguín, Natalia Vanegas Escobar

Electronic Theses and Dissertations Archive

Preludios Colombianos is an audio recording album dedicated to the piano works of Colombian composers Adolfo Mejía Navarro and Guillermo Uribe Holguín, performed by pianist Natalia Vanegas Escobar. The album features Uribe Holguín’s Preludes op. 48 and op. 56 and Mejía’s Four Preludes, offering listeners a focused look at two important yet underrepresented voices in classical music. Despite the significance of these composers in the Colombian national repertoire, many of their pieces have never been professionally recorded, and some have no audio record whatsoever. This project responds to that need, providing accessible, high quality, and more performance interpretations of these …

Single Vesicle Surface Protein Profiling And Machine Learning - Assisted Dual Image Analysis For Breast Cancer Detection, Mitchell Lee Taylor

Electronic Theses and Dissertations Archive

Single vesicle molecular profiling of cancer-associated EVs is increasingly recognized as a powerful tool for cancer detection and monitoring. Mask and target dual imaging offers a straightforward method to quantify the fraction of molecularly targeted EVs in biofluids at the single vesicle level. However, accurate and efficient dual imaging analysis remains challenging due to interference from false signals in mask images and the need to process large volumes of clinical sample data. A fully automated dual imaging analysis method based on machine learning, integrated with dual imaging single-vesicle technology (DISVT), for breast cancer detection at various stages is presented here. …

Optimal Placement Of Electric Vehicle Charging Stations Using Modified Artificial Hummingbird Algorithm In An Electric Grid, Sravan Kumar Dumpeti

Electronic Theses and Dissertations Archive

The rapid adoption of Electric Vehicles (EVs) necessitates to optimize the grid through strategic placement of Electric Vehicle Charging Station (EVCS). Conventional algorithms such as Greywolf Optimization Algorithm and Particle Swarm Optimization have been used for the placement of EVCS, but they have difficulties in dealing with dynamic and complex environments. With this background, this thesis proposes a Modified Artificial Hummingbird Algorithm (MAHA) for optimal placement of EVCS in the IEEE 9-bus, 57-bus, and 118-bus systems. The first approach uses a single objective function that minimizes transmission losses due to EV charging, while the second approach considers a multi-objective function …

Invisible Scholars, Emmaline Payne

Bachelor of Architecture Theses - 5th Year

In the United States, every year, there are approximately 1.3 million students who experience homelessness, an individual who lacks a fixed, regular, and adequate nighttime residence. Homelessness among students has a significant impact on their academic performance, mental health, and behavior. Factors like higher absenteeism, constant movement, and lack of support can all contribute to decreased academic achievement. Ultimately, this leads to students having to repeat grades or not graduating high school, compromising their abilities to succeed in the future. In addition to the decrease in academic achievement, behavioral and mental challenges exacerbated by the stress of housing instability also …

Aquatic Boundaries: Reimagining BeléN, A City Carried By Water And Resilience., Kassidy R. Mcclain

Bachelor of Architecture Theses - 5th Year

Rising water levels are not a distant threat. It is a reality that many cities already face. According to the Intergovernmental Panel on Climate Change, the global mean sea level has risen approximately 8–9 inches since 1880. The rising sea levels affect not only coastal cities but also cities along rivers and lakes attached to the sea by causing baseline water levels along rivers to increase. The rise in baseline water levels reduces the river's ability to drain effectively, thus causing riverside areas to become more susceptible to flooding. Traditional building methods meant to keep water out of buildings often …

Noninvasive Assessment Of The Tumor Using Cfdna, Irfan Alahi

McKelvey School of Engineering Graduate Student Theses & Dissertations

Next-generation high-throughput sequencing, which is increasingly generating vast amounts of genomic data, offers opportunities for a deeper understanding of the multifaceted nature of cancer and, hence, better patient care. However, the inherently complex and heterogeneous nature of cancer and the significant challenges in the generated data demand advanced data-driven frameworks to decode the molecular underpinnings of cancer. Moreover, the undeniable need for non-invasive approaches presents additional technical challenges in this domain. This dissertation proposes novel frameworks addressing three key challenges in computational oncology. The first study of this dissertation develops a data-driven algorithm to identify stemness signatures in metastatic castration-resistant …

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Butte Treatment Lagoons (Btl) Groundwater Treatment System Quarterly Operation And Maintenance Report – Quarter 4 2024 (Dated March 28, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Implementation Of Classroom Technology For Elementary Students With Adhd, Melanie Flemming

Walden Dissertations and Doctoral Studies

The problem addressed in this study is the teacher-reported increase in behavioral problems among students with attention-deficit hyperactivity disorder (ADHD) since a mandated introduction of educational technology in the classroom aimed at improving student performance. Using the PICRAT (passive, interactive, creative, replacement, amplification, transformation) describes the impact of technology on a teacher's previous practice model as the conceptual scaffold, this study was conducted to explore teachers’ experiences integrating educational technology and any corresponding student behavioral changes in classrooms that include students with ADHD. Following a basic qualitative design, data were collected through semistructured interviews, using a self-designed interview protocol, with …

The Effectiveness Of Postincarceration Reentry Programming In Fostering Community Reintegration For Women, La Shawn B. Kelley

Walden Dissertations and Doctoral Studies

Identifying gaps in reentry programs for formerly incarcerated women is challenging due to the lack of evaluations that include the perspectives of the transitioning women on which components most effectively support their reintegration. The purpose of this generic qualitative study was to explore the experiences and perceptions of transitioning women who received reentry services, in order to assess program effectiveness in community reintegration and inform future interventions. The research question for this study was, how do women served by Organization A’s reentry program describe the program’s effectiveness in preparing them for community reintegration after incarceration? To answer the practice-focused research …

Acquisition Algorithms For Protein Mass Spectrometry And Their Application To Clinical Proteomics, Nathan Wamsley

Arts & Sciences Graduate Student Theses and Dissertations

Bottom-up protein mass spectrometry aims for the identification and quantification of protein species by proxy of their proteolytic peptides. This dissertation presents methodological advances in three areas of protein mass spectrometry: acquisition methods, clinical appli- cations, and data analysis algorithms. The second chapter presents an optimized internal standard-triggered parallel reaction monitoring (OIS-PRM) method and demonstrates its application to study prognostic biomarkers in head and neck squamous cell carcinoma (HN- SCC). OIS-PRM uses intelligent scan scheduling and real-time monitoring of peptide elution profiles to reduce cycle-times and improve quantitative precision and accuracy. The assay measured NRF2 activation in both cell lines …

Mechanisms Of Protective Humoral Immunity And Pathogenesis In Alphavirus Infection, Natasha Monir Kafai

Arts & Sciences Graduate Student Theses and Dissertations

Alphaviruses are enveloped, mosquito-transmitted, positive-sense RNA viruses of the Togaviridae family classified into Old and New World groups based on endemic regions and human disease characteristics. Old World alphaviruses, including chikungunya virus preferentially cause arthritogenic disease, whereas New World viruses like Venezuelan equine encephalitis virus (VEEV), Eastern equine encephalitis virus (EEEV), and Western equine encephalitis virus (WEEV) can cause neurological disease. Since the 1920s, VEEV has been responsible for major periodic outbreaks affecting hundreds of thousands of humans and equines in South and Central America. Expansion of mosquito vectors raises concern for possible VEEV reemergence. In addition, VEEV is a …

Architecture Through Modularity, Jafet Estrada

Bachelor of Architecture Theses - 5th Year

Globally, an estimated 1.8 billion people lack access to adequate housing with an included 150 million people who live in permanent homelessness. Additionally, Housing costs have significantly outpaced income growth globally. Between 2014 and 2022, house price-to-income ratios have risen, with the global median ratio now at 6.1 (up from around 5 in 2014). Architecture through modularity could transform these lives with low-cost solutions. Modularity more broadly refers to a system’s ability to be broken down into smaller, self-contained, and interchangeable parts or modules. These modules function independently but are designed to work together as part of a cohesive whole. …

Interactive Bodies: Creating A Haptic Dialogue, Evan R. Haller

Bachelor of Architecture Theses - 5th Year

BUILDINGS ARE BORING. The majority of modern buildings often fail to have meaningful engagements with their occupants, resulting in static spaces that prioritize function over experience. If we continue to design for pure functionality, are we any different from machines? My thesis, “Interactive Bodies: Creating a Haptic Dialogue,” explores a shift in architectural design where spaces can adapt and change depending on the users’ ability to interact with them.

Through various case studies on kinetic architecture and interactive design experiments, as well as inspiration from Archigram, my research investigates how kinetic components can foster a more meaningful relationship between people …

The Rising Returns To R&D: Ideas Are Not Getting Harder To Find, Yoshiki Ando, James Bessen, Xiupeng Wang

Faculty Scholarship

R&D investment has grown robustly, yet aggregate productivity growth has stagnated. Is this because “ideas are getting harder to find”? This paper uses micro-data from the US Census Bureau to explore the relationship between R&D and productivity in the manufacturing sector from 1976 to 2018. We find that both the elasticity of output (TFP) with respect to R&D and the marginal returns to R&D have risen sharply. Exploring factors affecting returns, we conclude that R&D obsolescence rates must have risen. Using a novel estimation approach, we find consistent evidence of sharply rising technological rivalry. These findings suggest that R&D has …

Data-Driven Dynamic Decision-Making Using Discrete Optimization And Supervised Machine Learning, Navid Rashedi

Dartmouth College Ph.D Dissertations

In recent years, the operations research community has developed data-driven optimization techniques to solve complex combinatorial problems with the aid of machine learning. This thesis contributes to these efforts by combining machine learning with optimization to expedite online decision-making, with applications in transportation and healthcare.

In the domain of airline operations recovery, the focus is on the aircraft recovery process—repairing disrupted schedules by minimizing overall disruption costs. Traditional exact methods are too time-consuming, while heuristic approaches often yield poor solution quality and lack generalizability across varying formulations. To address these challenges, this research employs supervised machine learning to identify near-optimal …

First-Principles Studies Of Electrical Polarization Effects In Ferroelectrics, Antiferroelectrics And Defects, Louis Alaerts

Dartmouth College Ph.D Dissertations

Over the last few decades, density functional theory (DFT) has emerged as a formidable tool in the field of computational material science. Not only has it become a complementary method approach to experiments by rationalizing the many complex properties of materials but its formidable predictive power can also be used to identify the most promising candidates for specific applications. Point defects in semiconductors have become an important platform for the development of quantum networks due to their ability to act as spin-photon interfaces. Spectral diffusion, the broadening of the optical emission line, can significantly impact the performance of these defect-based …

Catecholaminergic Polymorphic Ventricular Tachycardia-Linked Ryanodine Receptor Variants Exhibit Domain-Specific Calcium Leak And Calmodulin Affinity Properties, Yi Yang, Eduardo Hertel Ribeiro, Bengt Svensson, Xander H T Wehrens, Takeshi Yamamoto, Razvan L Cornea, Robyn T Rebbeck, Masafumi Yano, Donald M Bers

Faculty, Staff and Students Publications

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an inherited stress-induced arrhythmogenic disease often caused by point variants in the cardiac ryanodine receptor (RyR2) that enhance diastolic sarcoplasmic reticulum (SR) Ca2+ leak. These RyR2 variants cluster in three hot-spots (N-terminal, central and C-terminal domains). We previously demonstrated a pathologic diastolic RyR2 conformation in heart failure, oxidative stress and CaMKII phosphorylation exhibiting a trilogy of effects: (1) reduced calmodulin (CaM)-RyR2 affinity, (2) enhanced unzipping peptide (DPc10) binding and (3) elevated diastolic SR Ca2+ leak that are dantrolene-sensitive. Here we test whether this pathological trilogy occurs in CPVT knock-in (KI) mice bearing N-terminal (R176Q/+), …

Aggn: Adaptive Granularity Graph Networks For Heterophilic Environments

Defensive Publications Series

Graph neural networks (GNNs) have shown significant success in learning graph representations. However, recent studies reveal that GNNs often fail to outperform simple MLPs on heterophilous graph tasks, where connected nodes may differ in features or labels, challenging the homophily assumption. Existing methods addressing this issue often overlook the importance of information granularity and rarely consider implicit relationships between distant nodes. To overcome these limitations, we propose the Adaptive Granularity Graph Network (AGGN), a novel GNN model specifically designed for heterophilous graphs. AGGN enhances node embeddings by aggregating multi-view information at various granularity levels and incorporating implicit data from distant, …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

Learning Resilient Graph Structures In Heterophilic Settings

Defensive Publications Series

Graphs provide a fundamental way to model relationships between entities and are central to numerous machine learning tasks. Standard graph-based methods often assume the provided graph structure is both accurate and complete. However, real-world graphs frequently suffer from noise and sparsity, negatively impacting downstream tasks like node classification and clustering. While graph representation learning has advanced significantly, many methods implicitly assume graph homophily (connections predominantly between nodes of the same class), struggling when faced with heterophily (connections predominantly between different classes). This paper introduces a novel method, Resilient Graph Learning for Heterophily (RGLH), designed to learn high-quality graph structures directly …

Quantum-Coherent Graphene Via Entangled Plasma Deposition (Coherium™) Room Temp Superconductor, Peter Branton

Defensive Publications Series

Twisted bilayer graphene (TBG) at a magic angle (~1.1°) has demonstrated superconductivity below ~1.7 K due to emergent flat-band behaviour. However, its mechanical assembly limitations, thermal fragility, and scalability issues hinder real-world applications. The Coherium™ process addresses these limitations by synthesizing entangled carbon plasma into a structurally coherent graphene lattice with integrated AI stabilization, targeting room-temperature Superconducting properties.

Efficient Partitioning For Devices With Multiple User Modes, Harshad Dhabu, Abhijit Adsule

Defensive Publications Series

This document describes techniques that enable a computing device (e.g., a smart watch, ring, glasses, etc.) to more efficiently transition between multiple user modes, such as an adult mode and a child mode. Using a single system image including applications installed on the computing device for every user mode may lead to system image bloat, diminished performance and power inefficiency. Alternatively, creating separate stock keeping units (SKUs) for each user mode may introduce manufacturing and inventory complexities. Instead, the device may divide the device's storage and the applications corresponding to each user mode into three partitions. The first partition may …

Machine Learning System And Method For Predicting Risk Of Drilling Component Failure, Baker Hughes Company

Defensive Publications Series

The invention addresses the problem of drilling tool failure risk assessment in the oil and gas industry. Tool failure in drilling operations can lead to significant financial losses, downtime, and safety risks. The invention aims to provide a solution for predicting and preventing these failures, which is crucial for maintaining safe and efficient drilling operations.

The invention proposes a solution by combining data-driven techniques with expert knowledge. It begins with data collection, including historical data on tool performance, and involves data cleaning and pre-processing. Relevant features are identified, and a predictive model is developed using machine learning techniques. The model …