Engineering Commons^™

Safety And Resilience In Disaster Debris Removal And Clean-Up Operations: A Field And Training Guide For Resilience Workers In Disaster Debris Removal And Cleanup, Mohsen Shahandashti, Ph.D, P.E., Abhijit Roy, Ph.D, Zhe Yin, Ph.D, P.E., Pmp, Leed Ap, Antonio Balderrama, Ph.D, P.E., Sandesh Adhikari, Santosh Acharya

Mavs Open Press Open Educational Resources

A practical safety guide for the people who clear debris after disasters, and for the trainers who prepare them. Built for crews of every experience level--from seasoned operators to first-time volunteers--it teaches how to read a damaged site, recognize hidden and shifting hazards, work safely around heavy equipment and hazardous materials, and make sound decisions under pressure. Every chapter pairs clear guidance with a one-page field job aid, facilitator notes for trainers, and a real-world scenario, all grounded in OSHA, FEMA, EPA, and NIOSH guidance.

Safety and Resilience in Disaster Debris Removal and Clean-Up Operations Copyright © 2026 by Mohsen …

Interpretable Machine Learning Of Plasma Proteomics Reveals Stage-Specific Signatures Across The Alzheimer's Disease Continuum, Hemshankar Laugi

2026 Spring Honors Capstones Projects

Alzheimer’s pathology begins years before clinical symptoms, starting early with amyloid-β accumulation, followed by tau deposition and neurodegeneration. Although existing tools, such as cerebrospinal fluid (CSF) screening and PET/MRI imaging, can accurately track disease progression, they are invasive, expensive, and not scalable for population-level screening. So, there is a growing interest in using blood-based plasma biomarkers as a scalable alternative. While recent studies demonstrate strong predictive performance with plasma biomarkers, most rely on a small set of canonical blood-based protein biomarkers such as amyloid-β, p-tau, and neurofilament light. These biomarkers primarily reflect downstream brain pathology and may fail to capture …

Testing And Qualification Of Low-Voltage Power Supplies For The Atlas Tile Hadronic Calorimeter Phase-Ii Upgrade, Justice A. Jones

2026 Spring Honors Capstones Projects

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) places increased thermal and operational demands on detector electronics, requiring highly reliable power systems. The ATLAS Tile Hadronic Calorimeter (TileCal) uses low-voltage power supply (LVPS) bricks to power front-end electronics, but these units operate in inaccessible regions, making failures difficult to repair. Therefore, rigorous qualification procedures are essential. This work focuses on improving LVPS reliability through a structured burn-in process. Each unit undergoes pre-burn-in electrical verification using a Single Test Stand (STS), followed by sustained operation under load and elevated temperature, and post-burn-in requalification. Standard cooling conditions limit temperatures to …

The Waldo Dataset, Mary E. Koone, Rosie Kallie, Vassilis Athisos, Laurel S. Stvan

Computer Science and Engineering Datasets - Archive

Distinct from the task of predicting the author of a document (authorship attribution), we focus on addressing the issue of how to estimate the similarity between the written language styles of authors. To do so, we present a dataset of metadata derived by asking human annotators, who were presented with three documents, to identify which two were written by the same author and which was written by a different author. The dataset has over 400 such annotations, creating a companion to the Amazon Web Services (AWS) customer review dataset, laying the groundwork for crowdsourcing applications to other natural language processing …

Latex: Overleaf, Anette Moreno-Lozano Phd, John Connolly Phd

Day Family Research Lab Workshop Series

This workshop introduces participants to LaTeX for scientific writing. Attendees will learn to create and compile documents in Overleaf, insert and format equations, figures, tables, and references, use collaboration and version control tools, and find discipline-specific resources and templates.

Identify Top Journals, Ibis Anette Moreno-Lozano Phd.

Day Family Research Lab Workshop Series

No abstract provided.

Finding Research Datasets And Evaluating Data Quality, Ibis Anette Moreno-Lozano Phd.

Day Family Research Lab Workshop Series

No abstract provided.

Data Management Plans For Grant Proposals, Rubab Shahzad

Day Family Research Lab Workshop Series

Fundamentals of research data management and how to create effective Data Management Plans (DMPs) and Data Management Sharing Plans (DMSP)

Universal Sound Separation: Distance-Aware Mixture Simulation, Co-Occurrence Conditioning, And Chain-Of-Inference, Wonjun Park

Computer Science and Engineering Theses - Archive

Universal Sound Separation (USS) -- the task of disentangling arbitrary sound sources from a single-channel acoustic mixture -- remains an open challenge due to the ill-posed nature of the problem and the distributional gap between synthetic training data and real-world recordings. This thesis addresses three distinct bottlenecks in the USS pipeline: training data realism, inference strategy, and conditioning richness. We first present two knowledge-guided approaches to sound source separation. The first is a distance-aware mixing strategy that leverages Large Language Models (LLMs) to assign plausible loudness relationships between audio sources during training data synthesis. By querying an LLM about the …

Mechanistic Auditing Of Privacy Retention In Llm Summarization, Samreen Begum

Computer Science and Engineering Theses - Archive

Anonymized summarization aims to produce useful summaries while replacing person names with placeholders such as [PERSON]. In practice, privacy is often evaluated by checking that no real names appear in the generated text. However, suppressing a name in the output does not guarantee that the model has removed identity information from its internal computation. This gap is critical in collaborative inference frameworks, where intermediate internal states (ISs) are exposed for safety auditing. We study this question in a controlled setting where a language model reads a document containing a real name and is required to generate a summary that replaces …

Data-Driven Prediction Of Concrete Bridge Deck Corrosion Using Ground Penetrating Radar (Gpr), Nafisa Shafiullah

Civil Engineering Theses

Corrosion detection remains a crucial factor in the proper maintenance of Reinforced Concrete (RC) structures. While several studies have used Ground Penetrating Radar (GPR) for corrosion detection, quantitative models for prediction are still limited. This study presents an approach for the quantitative prediction of rebar corrosion in concrete using GPR data obtained from a prior experimental program involving accelerated corrosion. A total of 36 RC samples were cast, varying in cover depth, rebar diameter, and concrete porosity. The samples were subjected to an impressed current of 0.65 A in a 5% NaCl solution over three phases: 10, 20, and 30 …

Numerical And Machine Learning Based Recreation Of Damage Morphologies Of Barely Visible Impact Damage, Oscar A. Valdez

Mechanical and Aerospace Engineering Dissertations

Composite laminates are highly sought after in the aerospace industry as they provide strength without dramatically increasing the weight of manufactured structures. However, composite laminates are susceptible to barely visible impact damage caused by routine activities. This type of damage can easily go unnoticed while significantly reducing the load-carrying capability of the laminate. Current non-destructive evaluation techniques, such as ultrasonic scanning, can reveal the damage footprint but provide no insight into delamination through-the-thickness due to the shadowing effect. Micro-computed tomography offers ply-by-ply damage resolution but is unsuitable for field inspections and is constrained by specimen size. This study aims to …

Set-Theoretic Reachability-Informed Model Predictive Control For Mechanical And Aerospace Systems, Jinaykumar Nitinkumar Patel

Mechanical and Aerospace Engineering Dissertations

Modern mechanical and aerospace systems increasingly operate autonomously in environments characterized by nonlinear dynamics, uncertainty, and safety constraints. In these settings, estimation and control methods based on nominal models and single-point trajectory predictions are usually insufficient to ensure safe and reliable operation. This dissertation uses a set-theoretic perspective, in which the system state, uncertainty, and admissible behavior are described by sets instead of point estimates. The key question is not only what the state is, but what set of states remains consistent with the dynamics, disturbances, control limits, and available measurements. This provides bounded descriptions of uncertainty and supports control …

Ai Data Center Dynamic Load Effects On Current Transformer Saturation, Sergio A. Hernandez

Electrical Engineering Theses

AI data centers can produce rapid changes in electrical demand that may influence current transformer performance during faults. This study evaluates the effect of an AI data center transient on CT saturation during single line-to-ground faults using a 400 V, 60 Hz grid connected inverter model in MATLAB/Simulink. The normal condition transient produced a maximum RMS current rate of approximately 211 A/ms, which was used along with the maximum power condition to define fault inception cases. A MATLAB time-domain CT model then swept the fault current DC offset coefficient to determine the minimum offset required for CT saturation. The calculated …

Experimental Thermal Characterization And Comparison Of Direct Liquid Cooling And Air Cooling For A High-Density Gpu Server Thermal Test Vehicle, Oluwagbolahan Esan

Mechanical and Aerospace Engineering Theses

As the power density of artificial intelligence (AI) and high-performance computing (HPC) servers escalates, air cooling is approaching fundamental scaling limits set by air's low convective heat transfer coefficient and rising parasitic fan power. This thesis presents an experimental comparison of direct-to-chip liquid cooling and air cooling for a high-density GPU server, using a Thermal Test Vehicle (TTV) that emulates the device layout and power-density regime of an 8-GPU SXM5-class baseboard (eight emulated GPU blocks and four NVLink Switch modules). The liquid configuration was tested at 8.8 kW (8.0 kW GPU, 800 W switch) across three Coolant Distribution Unit (CDU) …

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang

Computer Science and Engineering Dissertations

Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning …

Ai-Driven Network Orchestration: Adaptive Routing For Federated Learning Over Software-Defined Hybrid Wans, Osama Abu Hamdan

Computer Science and Engineering Dissertations - Archive

Modern wide-area networks increasingly adopt hybrid architectures that combine high-capacity wired backbones with flexible wireless links to extend connectivity to remote and underserved locations. However, the bandwidth variability inherent in wireless segments creates routing challenges that traditional protocols, designed for static link capacities, cannot adequately address. Simultaneously, Federated Learning (FL) has emerged as a privacy-preserving distributed machine learning paradigm in which geographically dispersed clients collaboratively train shared models without exchanging raw data. When deployed over wide-area networks, FL training is severely bottlenecked by communication overhead, particularly in cross-silo settings where model payloads reach hundreds of megabytes and synchronous aggregation protocols …

Characterization Of Tomographic Piv System For Single-Phase Immersion Cooling Applications, Joel Joshua Oommen

Mechanical and Aerospace Engineering Theses

As the rapid expansion of Artificial Intelligence (AI) infrastructure and High-Performance Computing (HPC) pushes power requirements to new heights, traditional air-cooling methods have become inadequate for modern server demands. Consequently, single-phase immersion cooling (SPIC) has emerged as one of the effective and sustainable alternatives, utilizing dielectric fluids to remove heat through direct contact with electronic components. Despite numerous thermal and reliability studies, the complex three-dimensional movement of buoyant thermal plumes around intricate component shapes are not yet fully understood in confined spaces.

This thesis provides a detailed characterization of Tomo-PIV and a component level experimental study of flow patterns in …

Scaling Llm Inference: From Novel Attention Mechanisms To Efficient Kv Cache Management, Weishu Deng

Computer Science and Engineering Dissertations

The rapid scaling of artificial intelligence workloads has shifted the dominant performance bottleneck of modern computing systems from compute to memory. Graph neural networks (GNNs) issue increasingly irregular memory accesses, while large language models (LLMs) issue increasingly large ones; in both cases, the relative scaling of memory bandwidth and capacity continues to lag behind the scaling of compute. Consequently, naively executing these workloads on commodity GPUs results in stalled streaming multiprocessors, exhausted high-bandwidth memory (HBM), and serving stacks that incur PCIe transfers on the critical path. This dissertation argues that the efficient scaling of attention-based AI workloads requires the joint …

Proposed Design Approach Of Bolted Cruciform Stiffened End-Plate Connections And Bolted Circular Flange Plate Connections Under Combined Loading For Enhanced Economy And Performance, Cesar E. Aguirre

Civil Engineering Dissertations

This research will deliver a substantive contribution to the advancement of design practice for bolted circular flange connections and cruciform stiffened end-plate connections — critically evaluating existing approaches, identifying their limitations, and proposing evidence-based design frameworks that enhance structural performance, predictability, and safety across relevant engineering applications.In addition provide economical benefits.

Virtualized And Distributed Neighborhood Data Centers, Benjamin T. Niccum

Computer Science and Engineering Theses

This thesis evaluates whether PCIe-fabric-based resource pooling can support a decentralized neighborhood micro-data-center model under real implementation constraints. The work combines architecture design, prototype deployment, performance benchmarking, and security assessment. Results show strong prototype-scale feasibility with low-latency and high-throughput behavior, while also identifying deployment-blocking security gaps and operational maturity requirements. The thesis contributes an evidence-traceable path from concept validation to deployment-grade roadmap planning.

The Rise In Height And Curb Weight Of The Top-Selling Vehicles In The United States From 1991 - 2025: Implications For Engineering Design And Vulnerable Road Users, Samuel L. Steinman-Friedman

Civil Engineering Theses

Road fatalities are higher in the United States than in other high-income regions. In 2024, 39,254 people died on roads in the United States compared to 19,940 in the European Union. These two regions have comparable exposure metrics, such as population size, roadway network length, and fatalities per mile traveled. Light trucks dominate the U.S. market, whereas hatchbacks are prevalent in Europe. The trendline of the average height and curb weight of the top-selling vehicles in the United States has increased by 0.24 inches and 23.52 pounds per year since 1991. The average 2025 top-selling U.S. passenger vehicle is 7.5 …

Integrated Thermal, Energy, And Structural Performance Assessment Of A Novel In-Service Shallow Geothermal Bridge Deicing System, Alireza Fakhrabadi

Civil Engineering Dissertations

Understanding the performance of bridge deck deicing systems under winter weather conditions is important for improving transportation safety and reducing long-term deterioration of bridge infrastructure. Bridge decks are more vulnerable to icing than ground-supported pavements because they are exposed to ambient air, wind, and radiation from multiple directions, while they do not receive thermal support from the underlying soil. Conventional deicing methods, especially chloride-based salts, can reduce ice formation in the short term, but repeated application may accelerate reinforcement corrosion, concrete damage, and environmental concerns. Therefore, shallow geothermal energy has been considered as a sustainable alternative for bridge deck deicing …

Deterministic Black-Box Context-Free Grammar Inference: Scaling From Small To Big Languages, Mohammad Rifat Arefin

Computer Science and Engineering Dissertations

Context-free grammars are essential for a number of software engineering tasks like program understanding, debugging, and grammar-based testing. However, formal language descriptions (i.e. grammars) are often unavailable, outdated, or inaccessible because parsers are closed-source or cannot be instrumented. Black-box grammar inference addresses this gap using only valid sample programs and parser acceptance decisions. The problem is challenging because finite samples rarely exercise all language features or their combinations, while the inference process cannot inspect the parser’s internal representation of syntax. This dissertation develops deterministic techniques for black-box context-free grammar inference. The first tool, TreeVada introduces the structural assumption that balanced …

Cross-Layer Supervisory Control For Low-Altitude Uav Swarm Networks, Nitin Singh Rathore

Computer Science and Engineering Theses

Low-altitude unmanned aerial vehicle (UAV) swarms are increasingly used in applications such as aerial sensing, disaster response, and communication support, where reliable operation under dynamic and uncertain conditions is essential. In these environments, performance degradation arises from multiple sources, including external disturbances, sensing uncertainty, and communication impairments. Although these effects originate from different layers of the system, such as dynamics, observation, and networking, they often manifest as similar tracking or coordination errors. Conventional control approaches, which rely primarily on error-driven feedback, do not explicitly account for the underlying cause of these deviations, limiting their effectiveness in multi-agent settings. This thesis …

Effects Of Metals On Persulfate Activation For The Decomposition Of Pfas, Malu Deav Devadasan

Civil Engineering Dissertations

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants widely used in industrial and consumer products due to their unique physicochemical properties. Their resistance to degradation, potential for bioaccumulation, and associated adverse health effects have prompted increasing regulatory attention, including the establishment of drinking water standards for six PFAS compounds by the U.S. Environmental Protection Agency in 2024.

Current PFAS treatment technologies are often energy-intensive and costly or generate secondary waste streams requiring further management. Meanwhile, this research investigated transition metal-catalyzed advanced oxidation processes for PFAS decomposition in water under ambient conditions. Previous studies demonstrated effective degradation of perfluorocarboxylic acids …

Gaussian Process Regression–Based Uncertainty Quantification For Unmanned Aircraft System Traffic Management And Advanced Air Mobility Applications, Aakarshan Khanal

Mechanical and Aerospace Engineering Dissertations

Uncertainty quantification has gained significant attention in recent years as a research area in dynamical systems. Mathematical representations of the physical system, combined with an understanding of model uncertainties, enable the propagation of uncertainty in temporal space, which allows us to make informed decisions. However, what if the true dynamics of the system is unknown or too complex to define explicitly? In such cases, the system’s behavior can instead be inferred or learned from observed input–output data rather than from an analytical or physics-based model. To this end, this dissertation focuses on developing a data-driven framework for nonparametric dynamics modeling, …

Beyond Permissions: A Privacy And Security Analysis Of Sensor And Network Activity On Xr Devices Using Xrmonitor, Gayatri Sravya Siripurapu

Computer Science and Engineering Theses - Archive

Standalone virtual reality headsets integrate multiple sensors and cameras whose runtime behavior is not transparently documented, creating potential privacy and security risks that are difficult to audit using existing tools. The relationship between software feature activation and the underlying hardware engaged at runtime remains opaque, limiting the ability of users and researchers to assess what data is being collected and when.

This thesis presents XRMonitor, a unified framework for monitoring and analyzing sensor activation behavior and network traffic on Meta Quest devices. The framework is motivated by the lack of transparency in how standalone XR devices engage their hardware sensors …

Pre-Training And Interpretability In Deep Learning Models, Weizhi An

Computer Science and Engineering Dissertations

In the evolving field of artificial intelligence, the efficacy of deep learning models is often gated by the quality of their training and the clarity of their decision-making processes. This dissertation addresses these crucial challenges by focusing on two key areas: enhancing pre-training strategies and improving model interpretability. Our approach is twofold, integrating novel pre-training methodologies that embed domain-specific knowledge early in the model training process, and developing advanced techniques for disentangling and clarifying the decision-making mechanisms within these models. The first direction of our research employs MoDNA, a motif-oriented pre-training framework specifically designed for DNA language models. By leveraging …

Uncertainty Quantification, Propagation & Conjunction Assessment In Orbital Mechanics Using Generalized Polynomial Chaos Expansion & 2-Dimensional Conjunction Plane Analysis Techniques, Monalisa Karim

Mechanical and Aerospace Engineering Theses

Uncertainties, that are inherent to dynamic models, can be associated with state initial conditions, force modelling errors, navigation and actuation errors. In system modelling stochastic differential equations are used to represent dynamic phenomena with uncertainties, for which the solutions are probability density functions of quantities of interest characterizing the realization of the stochastic processes. In Polynomial Chaos Expansion (PCE) propagation, these solutions are represented as weighted sums of multivariate spectral polynomials that are functions of the input random variables. Generalized polynomial chaos expansion (gPC) is an extension to the original homogenous PCE which projects the random solution onto a basis …