Digital Commons Network^™

Testing The Efficacy Of A Non-Invasive, Epidermal, Marker-Based Method For Conducting X-Ray Reconstruction Of Moving Morphology (Xromm) On A Cadaver Chicken, Brian Skillman

Theses and Dissertations

X-Ray Reconstruction of Moving Morphology (XROMM) is widely used to understand the kinematics of living organisms. The first of two standard methods for utlizing XROMM is to surgically implant X-ray dense markers within the bones of interest and euthanize the subject at the conclusion of testing, referred altogether as marker-based. The other standard, known Scientific Rotoscoping, or markerless, involves using thorough videography of the subject and manual bone orientation within the data collection program. These standards both pose unique issues, such as post-surgery complications for marker-based and time constraints for markerless. We investigate an intermediate method for utilizing XROMM involving …

Investing In Tomorrow's Citizens: Exploring Generation Alpha Middle School Students' Perspectives On Civics And Education, Talia Luccia Vazquez

Theses and Dissertations

Democracy requires maintenance from an active, informed citizenry, yet civic literacy in the United States has been rapidly declining while its democratic structures face systematic subversion. To reverse this, educational researchers have recommended increasing time for K–12 civics education. While devoting more time to middle school civics education could help rebuild a civically literate citizenry, public middle schools face the unique challenge of teaching a student population that is experiencing rapid physical and psychological development, alongside a well-documented dip in academic motivation and engagement. Tailoring civics education to be academically motivating to the current student demographic—Generation Alpha—requires an understanding of …

The Design And Economics Of An Electrolysis-Based System For Tribal Nation Green Hydrogen Infrastructure, Frank Yeboah

Theses and Dissertations

This thesis develops technical and economic analyses for a community-scale system that utilizes excess renewable energy to generate green hydrogen and oxygen which can be used by Tribal Nations to improve their energy infrastructure. Many Tribal Nations, such as the Mandan, Hidatsa and Arikara Nation, the Turtle Mountain Band of Chippewa Indians and the Prairie Band Potawatomi Nation, are still energy insecure due to aging infrastructure, geographic remoteness and reliance on outside utilities. The use of renewable energy is growing, but the intermittency of solar and wind generation without long-duration storage is an issue. One possible solution is to use …

When Does Emigration Reduce Domestic Economic Activities? Evidence On The Loss Of Management Capacity From A Visa Lottery, Marup Hossain, Ahmed Mushfiq Mobarak

Cowles Foundation Discussion Papers

Economic development is often conceived as structural transformation and migration out of rural agriculture (Lewis, 1954; Fei & Ranis, 1964). Understanding development therefore requires us to identify how rural economies transform in response to emigration. We study how international migration reshapes domestic economic activity in origin households using a randomized visa lottery that gave Bangladeshi men job opportunities in Malaysia. Winning the lottery raises total household income through remittances, but income earned in Bangladesh declines due to contractions in nonfarm business activity. In contrast, crop income remains stable despite large reductions in agricultural input expenditures, because farming gets delegated through …

Modeling Intercellular And Intracellular Organization And Dynamics Of The Bacteria Escherichia Coli : From Population Dynamics In The Gut Microbiome, To Condensate Formation In Starved Cells, Alberto Alonso

Theses

Escherichia coli is a predominantly non-pathogenic species of bacteria present in the gut of most healthy adults. It plays a critical role in the health of the gut microbiome and is also widely used as a model organism in microbiology and synthetic biology studies. This thesis discusses results from two ongoing projects on E. Coli organization and emergent biophysical properties at two very distinct length scales. The first project focuses on using genetically modified engineered E. coli bacteria for early detection and intervention in colorectal cancer (CRC), a significant public health issue in the US. The success of these engineered …

Using Ai Strategically For Internship & Job Search: Using Ai To Think Better, Not Just Faster, Hoang Bao Han Tran

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation examines how students can use artificial intelligence strategically throughout the internship and job search process while maintaining critical thinking and human judgment. It explores the shift from traditional job-search platforms toward AI-assisted career research, including using personal experience, career goals, leadership activities, and individual context to evaluate potential opportunities. The presentation demonstrates how AI can support students in analyzing job descriptions, identifying relevant skills and qualifications, researching employers, and preparing for interviews. It also distinguishes the role of AI from that of career advisors, emphasizing that AI can help collect and process information while career advisors help students …

Ethical Use Of Ai In Education: Ai-Driven Learning: Good Or Bad?, Moumita Saha

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation examines the ethical use of artificial intelligence in education and the opportunities and challenges associated with AI-driven learning. It explores the growing adoption of artificial intelligence, concerns surrounding excessive reliance on generative AI, and issues including privacy, algorithmic bias, misuse of AI, job displacement, transparency, plagiarism, intellectual property, equitable access, and academic integrity. The presentation also considers how AI may affect critical thinking, learning processes, assessment, and student career readiness while highlighting research on the potential of AI to support personalized learning and education. Through case examples, research findings, discussion, and a hands-on group activity comparing work completed …

Ai For Leaders: Co-Design Your Career Workshops, Rami Huu Nguyen

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation explores the role of career identity and leadership development in helping students navigate an increasingly AI-driven professional landscape. It introduces a framework for understanding career identity through interests, skills, motivation, experiences, and professional roles, while examining the importance of maintaining individual identity when using artificial intelligence tools. The presentation emphasizes role-based career identity as a strategy for helping students establish direction, develop leadership skills, and remain competitive in the job market. Through an interactive co-design activity, career advisors work in teams to develop career workshops or mini-projects using search engines and AI tools, considering student roles, instructional processes, …

Machine Learning Pipeline For Tcga-Luad Clinical Outcome Prediction, Cassandra Tellez, Maryam Khazaei Pool

Faculty Research, Scholarly, and Creative Activity

Background and Context: The Cancer Genome Atlas (TCGA) is a large public collection of medical data from thousands of cancer patients, managed by the NCI and NHGRI [1]. This project focuses on the TCGA-LUAD (Lung Adenocarcinoma) dataset, which contains clinical and molecular data from patients with lung adenocarcinoma [2]. We use Machine Learning (ML) to see if a patient's basic medical info can predict how well their lung cancer treatment will work.

Cae-If: An Anomaly Detection Approach Based On Temporal Representation Of The Reconstruction Error, Haijun Geng, Zhi Zhang, Yuhua Qian, Bo Yang, Qi Ma, Haotian Chi, Jing Yang, Xia Yin

Faculty Research, Scholarly, and Creative Activity

In the field of network traffic anomaly detection, unsupervised learning plays a critical role yet encounters significant challenges, including accurately determining anomaly thresholds and modeling the intricate temporal dynamics of network traffic. To address these challenges, we present a novel approach, termed Convolutional Autoencoder-Isolation Forest (CAE-IF). By leveraging packet-level reconstruction errors with contextual information, our approach obviates the need for manual threshold setting and effectively captures temporal dynamics. The process commences with the application of the damped incremental statistics algorithm to extract statistical features from network traffic with temporal information. Subsequently, the Convolutional Autoencoder (CAE) is employed to compute the …

A Review Of The Safety Alert Systems In Commercial Vehicles: Implementation Challenges And Trends For Electronic Logic Devices And In-Cab Alerts, Md Sadman Islam, Deep Patel, Ahmed Sajid Hasan, Mohammad Jalayer, Abdul S. Ngereza, Emmanuel Kidando

Civil and Environmental Engineering Faculty Publications

The trucking industry is crucial to the US economy, accounting for 73.1% of the value, 71.1% of the cargo, and 42.0% of the ton-miles of all commodities transported nationwide. Despite its pivotal role, the industry is affected by high crash rates, with truck crashes resulting in 5,472 fatalities in 2023 alone. These incidents not only exhibit a higher fatality rate per vehicle mile compared with passenger cars but also cause substantial economic disruptions. Predominantly, these crashes are attributed to the failure to stop or slow down because of insufficient sight distances. In recent years, trucking companies have implemented various technologies, …

Spring Ai 2026 Workshop And Speaker Series Report, Gregory Blike, Hannah (Nyingi) Brown, Dora (Dawn) Nguyen, Rami Huu Nguyen, Ajanee Igharo, Frayni Calderon, Moumita Saha, Chengjie Zheng

Paul English Applied Artificial Intelligence (AI) Institute Publications

The AI Workshop and Speaker Series was organized by the Student Advisory Council of the Paul English Applied Artificial Intelligence Institute at the University of Massachusetts Boston with the guidance of Distinguished Professor of Computer Science Wei Ding to create a practical and student-centered AI learning space. The Spring 2026 series included workshops on LinkedIn Optimization, Data Mining, GitHub, Prompt Engineering, Machine Learning of Structured Data, and Applied LLMs with Responsible AI Use in Research, while the broader speaker series introduced students to AI career development, generative AI and LLMs for cybersecurity, transportation security, political sciences, and AI in biomedicine. …

Atomic-Scale Investigation Of Functional Molecular Systems And Graphene Nanoribbons Using Scanning Probe Microscopy, A.M. Shashika D. Wijerathna

Physics Theses & Dissertations

Emerging molecular electronics, molecular devices, and carbon-based nanotechnologies increasingly rely on the precise control of molecular structures and interfaces at the atomic scale. Advancing these systems requires experimental techniques capable of resolving atomic structures, quantifying molecular behavior, and revealing the mechanisms governing nanoscale assembly. Scanning probe microscopy uniquely provides these capabilities by combining atomic-resolution imaging with local spectroscopy and manipulation. This dissertation employs ultra-high-vacuum low-temperature scanning tunneling microscopy (UHV-LT-STM) and qPlus atomic force microscopy (qPlus AFM) to address three complementary challenges: (i) atomic-scale characterization of complex supramolecular architectures, (ii) quantitative investigation of mechanically induced conformational switching of individual molecules, and …

Drones In Wildfire Airspace Management: Developing A Scalable Framework For Mid-Sized Cities, Victoria Denise Tanner Williamson

Engineering Management & Systems Engineering Theses & Dissertations

Wildfires are becoming increasingly frequent and severe, creating growing risks for mid-sized communities with limited emergency response resources. The January 2025 Los Angeles wildfires—which burned more than 57,000 acres and destroyed over 18,000 structures—underscore the urgent need to strengthen wildfire response strategies through the integration of unmanned aerial systems (UAS) (Hardy, 2025).

This study develops a scalable framework for drone-assisted wildfire response in resource-constrained environments. Using the city of Waynesboro, Virginia, as a case study, the research examines how mid-sized jurisdictions can integrate unmanned aerial vehicles (UAVs) to improve preparedness, response, and recovery. A three-round, mixed-methods Delphi study engages emergency …

Nanostructured Negative Electron Affinity Gallium Arsenide Photocathodes With Enhanced Quantum Efficiency And Extended Charge Lifetime For Advanced Photoinjectors, Md Aziz Ar Rahman

Physics Theses & Dissertations

This dissertation presents a comprehensive experimental and computational investigation of nanostructured negative electron affinity (NEA) gallium arsenide (GaAs) photocathodes. This work was undertaken to achieve higher quantum efficiency (QE) and longer charge lifetime than conventional flat NEA GaAs electron sources commonly used in photoinjectors. Finite-Difference Time-Domain and charge-transport simulations of three types of nanostructure geometries reveal substantial QE enhancement over flat GaAs via Mie resonance mode excitation. Consequently, the truncated nanocone array (TNCA) photocathodes with heights of 400, 700, and 1000 nm were fabricated and NEA activated via Cs-NF₃ deposition. In a low-voltage ultra-high vacuum chamber with a base pressure …

Technical And Vocational High School Training And Graduates' Socioeconomic Insertion In Burkina Faso, Jacqueline Nikièma

Educational Leadership & Workforce Development Theses & Dissertations

Technical and vocational education and training (TVET) is widely promoted as a pathway to improving employment opportunities, supporting economic growth, and facilitating successful labor market transition. However, empirical evidence on its effectiveness remains limited in Burkina Faso. This study examined the relationship between TVET high schools and graduates’ socioeconomic insertion, with particular emphasis on employment in occupations related to the field of study (horizontal match) and examined the relationship between gender and this employment outcome. The study used descriptive statistics to summarize the characteristics of the sample, and logistic regressions to analyze the relationship between TVET training and employment in …

Analyzing The Institutional Factors Associated With Sense Of Belonging Among Stem Adjuncts At Community Colleges, Karuna Taneja

Educational Leadership & Workforce Development Theses & Dissertations

In this study I examined the relationships among organizational support, workplace conditions, and sense of belonging among adjunct faculty teaching STEM courses in community colleges. Grounded in the need-to-belong theory (Baumeister & Leary, 1995) and Herzberg’s two-factor theory (1968), I distinguished between relational and structural dimensions of faculty experiences. Perceived organizational support was conceptualized as a relational construct reflecting faculty members’ perceptions of being valued and included within their institutions, whereas workplace conditions were examined as structural factors influencing job satisfaction.

Using a quantitative, cross-sectional correlational survey design, data were collected from adjunct STEM faculty (N = 64) teaching at …

Modeling And Generating Crash Avoidance Behaviors In Safety-Critical Vehicle–Pedestrian Interactions Using Deep Reinforcement Learning, Qingwen Pu

Civil & Environmental Engineering Theses & Dissertations

Traffic crashes between vehicles and pedestrians arise from complex, split-second interactions in which both parties make rapid evasive decisions. Four fundamental gaps persist in existing research. Surrogate safety measures assume linear trajectories, failing to capture the curved movements of turning vehicles and crossing pedestrians at intersections. Single-agent modeling treats one party as a fixed obstacle, ignoring the joint decision-making that governs near-miss outcomes. The effect of vehicle type on pedestrian avoidance behavior—whether pedestrians respond differently to automated vehicles (AVs) than to human-driven vehicles (HDVs)—remains poorly understood. Finally, automated driving system development is hampered by a severe scarcity of large-scale, behaviorally …

Structure Meets Context: A Simulation Study Of Centralized And Decentralized Decision-Making In Complex Environments, Murat Balci

Engineering Management & Systems Engineering Theses & Dissertations

The question of when to centralize or decentralize decision-making authority is a foundational problem in organizational theory and management science. Yet existing research often favors decentralized structures, frequently treating them as universally superior, and provides limited guidance on when centralization may be more effective. Leaning toward decentralization without thoroughly exploring options leaves organizational designers without clear direction under varying environmental conditions. This study addresses that gap by developing and applying a simulation-based framework to evaluate the context-dependent effectiveness of centralized and decentralized decision-making structures.

Drawing on contingency theory and decision science, the study conceptualizes environmental complexity along two dimensions: dynamism …

Empirical Rhetorical Modeling: New Analytical Tools To Evaluate The Intensity Of Rhetoric, August Immel

English Theses & Dissertations

Since 2020, the United States has experienced a surge of incendiary rhetoric in contemporary political discourse, which exacerbates societal polarization and incites violence as evidenced by heightened politically motivated attacks. Numerous actors characterize the impetus of such violence as “extreme rhetoric.” No standard definition exists for such a term, and this gap necessitates the central research question of this dissertation, which interrogates how rhetoric can be systematically evaluated for “extremeness.” I propose that the issue of extreme rhetoric can be fruitfully examined from within the context of rhetorical theory, but instead of attempting to create a new definition of “extreme …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

A Causal Inference Protocol For Root-Cause Diagnosis In Non-Stationary Industrial Time Series, Cansu Yalim

Engineering Management & Systems Engineering Theses & Dissertations

Industrial fault diagnosis increasingly relies on data-driven methods, yet most approaches assume process stationarity and treat association-based rankings as sufficient evidence for root-cause identification. Neither assumption holds when operating phases and fault progression create distinct behavioral regimes with shifting relationships among variables, and neither answers which feasible corrective action would alter a fault trajectory. The absence of a procedure addressing non-stationarity, causal identifiability, and decision-relevant effect estimation constitutes the gap this dissertation targets. In this context, a regime is a time period in which the process operates under a relatively consistent setting, so the relationships among variables are approximately stable …

Spatial Structure And Energy Content Of Wakes Behind Single And Arrayed Undulated Cylinders, Taiga Naomi Drucker-Boisvert, OndˇRej FerˇCák, Jérémie Auzoux, Sarah E. Smith, Zein Sadek, Rowan Moore, Christain T. Murphy, Trevor Dunt, Jennifer A. Franck, Raul Bayoan Cal

Mechanical and Materials Engineering Faculty Publications and Presentations

Biologically inspired undulated cylinder geometries significantly influence wake structure and vortex-induced vibrations in turbulent flows, yet the underlying mechanisms are not fully understood. It is further unknown how these influences extend to multi-cylinder arrays, where interacting wakes accumulate and mix. This study examines the wake characteristics of an undulated cylinder and a smooth elliptic cylinder in single- and multi-cylinder array configurations, using particle image velocimetry and proper orthogonal decomposition (POD). Relative to the smooth elliptic cylinder, the undulated geometry induces pronounced spanwise-axial variability, characterized by enhanced near-wake mean momentum and weak far-wake signatures, consistent with reduced turbulent kinetic energy. While …

Personalization And Generalization In Machine Learning For Affective Ai, Rajesh Titung

Theses

Human emotional communication involves subtle, low-intensity, and context-dependent expressions influenced by sociocultural and interpersonal factors. Their inherent ambiguity and variability create heterogeneous data distributions, making affective phenomena difficult to model in naturalistic settings where expressions differ from exaggerated affect behaviors in acted environments. Affective AI faces the fundamental challenge of balancing personalization, which captures individual nuances, with generalization, which ensures robustness of affective AI systems across individuals. Traditional centralized models emphasizing generalization often fail to capture fine-grained expressive heterogeneity, while fully personalized models improve system performance for individuals but lack transferability and robustness. The central task is thus to develop …

Quantitative Surface-Enhanced Raman Spectroscopy For Antiretroviral Drug Monitoring: Novel Quality Index And Distributional Approaches In Aqueous And Plasma Matrices, Marguerite Rose Butler

Chemistry & Biochemistry Theses & Dissertations

Surface-enhanced Raman spectroscopy (SERS) offers high sensitivity for molecular detection but is limited for quantitative applications by spatial heterogeneity in signal enhancement on dried colloidal substrates. This Dissertation addresses these limitations through the development and evaluation of novel quantitative methods applied to the antiretroviral drugs tenofovir and emtricitabine.

A quality index (Qi) metric was developed to rank individual SERS spectra within each well based on analyte peak prominence relative to local baseline. SERS spectra were acquired from evaporated silver colloid deposits on aluminum well plates using a custom raster-scanning Raman system. Calibration curves were constructed using the top-ranked spectra (Top-Qi …

Computational And Ai Tools For Understanding Telomere-Associated Cancer Mechanisms, Eleni Adam

Computer Science Theses & Dissertations

Telomeres are the protective caps of the human chromosomes and are critical for genome stability. Dysfunctional telomeres caused by their erosion with age and cell proliferation as well as by defects in their maintenance is a major early event leading to genome changes and cancer. Subtelomeres possess the critical role of regulating adjacent telomeres. Due to their complex repeat structure and high variance from one person to another, these areas have not been analyzed in detail. We present a set of computational and machine learning tools to aid in the understanding of subtelomere structure and its rearrangements in cancer.

Initially, …

Public Participation In Community Redevelopment Agencies: A Mixed Methods Approach To Understanding The Impacts Of Collaboration In Public-Private Partnerships, Shannon Mcbride-Lewis

School of Public Service Theses & Dissertations

Local governments create community redevelopment agencies (CRAs) to enhance economic and redevelopment opportunities within their communities through public-private partnerships funded with tax increment financing (TIF). The use of increment revenue, the complexity of public-private partnerships, and the controversies surrounding CRAs can lead to community concerns and opposition. Collaboratively engaging the public in the development, implementation, and performance of CRA public-private partnerships is one way to improve program and project outcomes by enhancing engagement, equity, and accountability. This study asks does the level of resources (dedicated professional staff, consequential incentives, and TIF contribution) and formal public-private partnership programs of CRAs influence …

Residual Stress Informed Reconstruction And Failure Analysis Of Manufacturing Induced Composite Meso-Structure, Mohammad Nazmus Saquib

Mechanical & Aerospace Engineering Theses & Dissertations

A physics informed, AI-augmented framework is developed to enable digital twins of molded composite materials by linking manufacturing induced residual stresses to a local meso-structure. By reconstructing and explicitly modeling these architectures, the framework reveals how processing driven meso-structural variability governs stiffness, damage evolution, and failure. The work focuses on two technologically important systems: prepreg platelet molded composites (PPMC), characterized by inherently stochastic platelet architectures, and 2/2 twill glass fiber/PA6 organosheets, whose periodic but locally variable weave geometry influences mechanical response. For PPMC, the Residual Stress Informed Microstructure Reconstruction (ResIMiR) framework is introduced, combining thermoelastic strain fields with deep learning …

Time-Frequency Analysis Of Arterial Pulse Signals Of Cardiovascular Disease Patients Measured By A Microfluidic-Based Tactile Sensor, Md Mahfuzur Rahman

Mechanical & Aerospace Engineering Theses & Dissertations

This dissertation presents a single-degree-of-freedom (SDOF) based time–frequency analysis framework that can simultaneously extract a comprehensive set of cardiovascular autonomic indices from arterial pulse wave recordings obtained at rest and post-exercise, supporting non-invasive monitoring of autonomic recovery during cardiac rehabilitation. The framework rests on three technical contributions.

First, an SDOF analytical model of motion artefacts quantifies two physically distinct distortion mechanisms: additive baseline drift and Time-Varying System Parameter (TVSP) generated distortion of the Tissue-Contact-Sensor (TCS) stack. Baseline drift is slowly varying and removable by filtering; TVSP distortion rides on all harmonics of the true pulse signal and cannot be removed …

Deep Multimodal Sensor Fusion For Autonomous Driving: A Real-World Deployment On Little Blue, Raphael T. Pamie-George

Electrical & Computer Engineering Theses & Dissertations

Accurate perception and control systems are essential for autonomous vehicles to safely interpret and navigate complex environments. Individual sensing modalities such as cameras, LiDAR, and inertial measurement units (IMUs) provide valuable environmental and vehicle state information; however, each sensor exhibits inherent limitations when used independently. Camera performance can degrade under varying lighting conditions, LiDAR measurements may be sparse or affected by environmental noise, and IMU measurements are susceptible to drift over time. Little Blue, a research autonomous vehicle platform from ODU (Old Dominion University), previously relied primarily on camera-based data for neural network training, limiting the system’s ability to exploit …