Physical Sciences and Mathematics Commons^™

Exploring Causes Of Beam Loss At Cebaf, C. Matthews, J. Benesch, R. M. Bodenstein, B. Freeman, D. Moser, D. Turner, K. Price, J. Samari, B. Terzić

Physics Faculty Publications

At Jefferson Lab, the Continuous Electron Beam Accelerator (CEBAF) features a unique design with two linear accelerators and two arc sections allowing for multiple turns of the electron beam, as well as four experimental end stations. This topology leads to increased beam losses, especially in the spreader and recombiner regions connecting the arcs to the LINACs and in the extraction regions connecting the experimental end stations to the accelerator. These losses result in equipment activation and operational interruptions. Recent upgrades to the facility’s diagnostic systems, including the addition of xenon ion chambers, have provided higher-resolution data regarding these loss events. …

Real-Space Imaging Of The Electron-Pair Density Hole In Molecular Auger-Meitner Decay, Mats Simmermacher, Nathan Goff, Andres Moreno Carrascosa, Elke Fasshauer, Thomas Northey, Lingyu Ma, Haiwang Yong, Brian Stankus, Asami Odate, Xuan Xu, Wenpeng Du, Kyle Acheson, Joseph C. Cooper, Daniel Ratner, Mengning Liang, Ruaridh Forbes, Michael P. Minitti, Adam Kirrander, Peter M. Weber

Physics Faculty Publications

Electrons in matter can rearrange extremely quickly under external perturbations, underpinning subsequent structural and chemical transformations. Coulomb interactions between neighbouring electrons often shape this response, giving rise to correlated motion and strongly affecting the distribution of electrons in the system. Here we show that non-resonant hard X-ray scattering can directly access changes in the radial electron-pair density during the rapid rearrangement of core and valence electrons. We do this by studying sulfur hexafluoride molecules undergoing Auger–Meitner decay. We exploit a second-order interaction between the X-ray photons and the molecules to trigger and probe the decay dynamics with a single pulse, …

Reaching The Intrinsic Performance Limits Of Superconducting Nanowire Single-Photon Detectors Up To 0.1 Mm Wide, Kristen M. Parzuchowski, Eli Mueller, Bakhrom G. Oripov, Benedikt Hampel, Ravin A. Chowdhury, Sahil R. Patel, Daniel Kuznesof, Emma K. Batson, Ryan Morgenstern, Robert H. Hadfield, Varun B. Verma, Matthew D. Shaw, Jason P. Allmaras, Martina J. Stevens, Alex Gurevich, Adam N. Mccaughan

Physics Faculty Publications

Superconducting nanowire single-photon detectors combine high detection efficiency, low noise, and excellent timing resolution, making them a leading platform for photon-counting applications. However, despite decades of materials and fabrication research, detector performance has never been shown to match theoretical performance expectations. Here, we demonstrate in situ tuning of a detector from its typical, suboptimal operation, to a regime limited only by material quality, allowing the device to reach its intrinsic performance limit. Our approach is based on current-biased superconducting “rails” placed on either side of the detector that redistribute current across its width to achieve peak performance. This technique reduces …

Rf Design Progress Of The 197 Mhz Crab Cavity For Eic, B. Xiao, J. C. Brutus, A. Blednykh, Z. Conway, J. Cox, J. R. Delayen, S. U. De Silva, R. Fernandes, J. Guo, D. Holmes, N. Huque, G. Li, Z. Li, J. B. Mandel, S. Verdú-Andrés, G. Wang, Q. Wu, W. Xu

Physics Faculty Publications

The interaction region (IR) crab cavity system is a special RF system designed to compensate for the luminosity loss caused by the 25 mrad crossing angle at the interaction point (IP) of the Electron-Ion collider (EIC). The configuration includes six crab cavities in the Hadron (proton or ion) Storage Ring (HSR) - four operating at 197 MHz and two at 394 MHz - installed on each side of the IP, along with one 394 MHz crab cavity on each side of the IP in the Electron Storage Ring (ESR). This paper presents the recent progress in the RF design of …

First Measurement Of Polarized Spin-Density Matrix Elements And Differential Cross Sections Dσ/Dt In Ω Photoproduction Off The Proton For 2.7 < E𝛾 < 5.2 Gev Using Clas At Jefferson Lab, T. Hu, Z. Akbar, V. Crede, J. M. Laget, V. Mathieu, M. J. Amaryan, W. R. Armstrong, H. Atac, N. A. Baltzell, L. Barion, M. Battaglieri, I. Bedlinskiy, B. Benkel, F. Benmokhtar, A. Bianconi, L. Biondo, A. Biselli, M. Bondi, F. Bossu, S. Boiarinov, W. J. Briscoe, S. Bueltmann, D. Bulumulla, V. D. Burkert, R. Capobianco, D. S. Carman, J. C. Carvajal, A. Celentano, T. Chetry, G. Ciullo, G. Clash, P. L. Cole, M. Contalbrigo, G. Costantini, A. D'Angelo, N. Dashyan, R. De Vita, M. Defurne, A. Deur, S. Diehl, C. Djalali, R. Dupre, H. Egiyan, A. El Alaoui, L. El Fassi, L. Elouadrhiri, P. Eugenio, S. Fegan, A. Filippi, G. Gavalian, Y. Ghandilyan, G. P. Gilfoyle, F. X. Girod, A. A. Golubenko, G. Gosta, R. W. Gothe, K. A. Griffioen, M. Guidal, K. Hafidi, H. Hakobyan, M. Hattawy, T. B. Hayward, D. Heddle, A. Hobart, M. Holtrop, Y. Ilieva, D. G. Ireland, E. L. Isupov, D. Jenkins, H. S. Jo, K. Joo, S. Joosten, D. Keller, A. Khanal, M. Khandaker, A. Kim, W. Kim, F. J. Klein, V. Klimenko, A. Kripko, V. Kubarovsky, V. Lagerquist, L. Lanza, M. Leali, P. Lenisa, K. Livingston, I.J.D. Macgregor, D. Marchand, L. Marsicano, V. Mascagna, M. E. Mccracken, B. Mckinnon, Z. E. Meziani, S. Migliorati, R. G. Milner, T. Mineeva, M. Mirazita, V. Mokeev, C. Munoz Camacho, P. Nadel-Turonski, K. Neupane, J. Newton, S. Niccoai, M. Nicol, G. Niculescu, M. Osipenko, A. I. Ostrovidov, P. Pandey, M. Paolone, L. L. Pappalardo, R. Paremuzyan, E. Pasyuk, S. J. Paul, W. Phelps, N. Pilleux, O. Pogorelko, J. W. Price, Y. Prok, B. A. Raue, T. Reed, M. Ripani, J. Ritman, A. Rizzo, G. Rosner, F. Sabatié, C. Salgado, S. Schadmand, A. Schmidt, R. A. Schumacher, E. V. Shirokov, U. Shrestha, P. Simmerling, D. Sokhan, N. Sparveris, S. Stepanyan, I. I. Strakovsky, S. Strauch, R. Tyson, M. Ungaro, L. Venturelli, H. Voskanyan, A. Vossen, E. Voutier, D. P. Watts, X. Wei, M. H. Wood, B. Yale, M. Yurov, N. Zachariou, J. Zhang, The Clas Collaboration At Jefferson Laboratory

Physics Faculty Publications

We report on the differential cross sections dσ/dt, the unpolarized spin-density matrix elements ρ⁰₀₀, ρ⁰₁₋₁, Re ρ⁰₁₀, and the first extraction of the polarized elements Im ρ³₁₀, Im ρ³₁₋₁ for the reaction γp → pω using the CLAS spectrometer at Jefferson Laboratory. The ω mesons were detected in their dominant charged decay mode, ω→π+π−π⁰, and all t-dependent results are presented in a fine binning for incident photon energies between 2.73 and 5.16 GeV (corresponding to the center-of-mass energy range W ∈ [ 2.45, 3.25 ] GeV). All matrix elements are first measurements for −t>0.6 GeV². …

Efficient Dynamic And Momentum Aperture Optimization For Lattice Design Using Multipoint Bayesian Algorithm Execution, Z. Zhang, I. Agapov, S. Gasiorowski, T. Hellert, W. Neiswanger, X. Huang, D. Ratner

Physics Faculty Publications

We demonstrate that multipoint Bayesian algorithm execution (multipointBAX) can overcome fundamental computational challenges in storage ring design optimization. Dynamic aperture and momentum aperture optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current production black-box optimization methods require extensive particle-tracking simulations for each trial configuration; the high computational cost restricts the extent of the search to ~ 10³ configurations and therefore limits the quality of the final design. We remove this bottleneck using multipointBAX, which selects, simulates, and models each trial configuration at the single-particle level. We demonstrate …

Interpolation Between Sudakov And Bfkl Rapidity Evolutions For Tmd Factorization At Small X, Ian Balitsky

Physics Faculty Publications

Within the framework of rapidity-only small-x TMD factorization, the evolution with respect to the rapidity cutoff is initially governed by Sudakov double logarithms and subsequently by BFKL/BK single logarithms. The evolution equation proposed in this paper correctly reproduces both the Sudakov and BFKL limits, while providing a consistent interpolation between these two regimes.

Comparative Evaluation Of Data Augmentation Strategies For Rare-Class Intrusion Detection: Assessing Conditional Generative Adversarial Networks Across Benchmark Datasets And Classifiers, Brogan Oconnor

CCAC Theses and Dissertations

Intrusion Detection Systems (IDS) are widely used to identify malicious network activity, unauthorized access, and anomalous system behavior. Although machine learning and deep learning classifiers often achieve strong aggregate performance on IDS benchmark datasets, rare attack detection remains difficult because of severe class imbalance. Minority attack categories may contain very few training observations, which can bias classifiers toward normal traffic and more frequent attack classes. This problem is especially important because rare attacks may represent high-impact security events even when they occur infrequently. This quantitative experimental study evaluated whether conditional generative adversarial network (cGAN) augmentation improved rare attack detection in …

Adaptive Electric Vehicle Routing And Charging With Deep Reinforcement Learning, Mandana Farhang Ghahfarokhi, Hyoshin Park, Venktesh Pandey, Gyugeun Yoon

Engineering Management & Systems Engineering Faculty Publications

As electric vehicles (EVs) gain popularity, efficient routing and charging solutions remain challenging due to time-dependent travel variability, sparse charging infrastructure, and heterogeneous user preferences. To address these challenges, this paper introduces a decision-support system that integrates three complementary methods: Temporal Multimodal Multivariate Learning (TMML) for real-time characterization of travel time uncertainty, Time-Dependent Shortest Path (TDSP) for reliability-aware route choice, and Deep Q-Network (DQN) reinforcement learning for adaptive charging decisions in sparse infrastructure environments. TMML updates link-level travel time distributions in real-time through Bayesian inference with cluster-based propagation, reducing uncertainties across the network. TDSP leverages these updated distributions to estimate …

A Hybrid Response Surface Methodology And Machine Learning Framework For Quantifying Effects Of Physicochemical Parameters On Pfas Distribution, Harsh V. Patel, Jazmin Green, Hyoshin Park, Stephanie Luster-Teasley Pass, Renzun Zhao

Engineering Management & Systems Engineering Faculty Publications

Predicting PFAS adsorption across diverse adsorbents and environmental matrices remains challenging because adsorbent physicochemical properties, PFAS molecular descriptors, and operational conditions simultaneously influence adsorption. This study develops and evaluates a unified hybrid modeling framework that integrates Response Surface Model (RSM) with machine-learning algorithms to quantify how six key variables, surface area, Log K_ow, pH_pzc, pK_a, log dose, and log-initial concentration, affect PFAS distribution coefficients (Log K_d). A data set of more than 1000 adsorption observations spanning 15 PFAS compounds, multiple adsorbent types, and a broad operational range was compiled and preprocessed using …

A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?, Joshit Mohanty

Engineering Management & Systems Engineering Faculty Publications

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within …

Technological Interventions For Reducing Climate Change Impacts On Health: An Overview Of Future Possibilities, Sujatha Alla, Vijay Kumar Chattu, Bawa Singh

Engineering Management & Systems Engineering Faculty Publications

Globally, over five million deaths annually are attributed to extreme weather events exacerbated by climate change, such as heatwaves, hurricanes, wildfires, droughts, and floods, which create health crises and economic losses. The 2015 Paris Declaration emphasized climate technology mechanisms, including research and development, to enhance resilience and reduce greenhouse gas emissions. Biotechnological advancements have mitigated about 20% of economic losses since the 1960s, highlighting technology’s potential to address climate-induced health risks.

Information-Theoretic Path Planning For Safe And Adaptive Nanobot Drug Delivery, Nagesh Bheesetty, John H. Park, Sujatha Alla, Winifred M. Lo, Aditya Nimmagadda, Mohamadmahdi Samandari, Jae H. Lee

Engineering Management & Systems Engineering Faculty Publications

We present a safe and efficient path planning framework for nanobot navigation in complex vascular networks, enabling precise drug delivery to hard-to-reach tumor regions such as those found in the lungs. The proposed method introduces a safe and efficient path planning strategy that integrates a utility-based function to balance two key goals: exploring uncertain areas to gather valuable information and avoiding hazardous regions to ensure safe and reliable delivery. We evaluate a simplified swarm-based nanobot navigation scenario using synthetic vascular maps that mimic the structural complexity of lung tumor blood vessels. Environmental uncertainty is modeled from vessel topology, blockage density, …

Small Language Model Jailbreak Defender Framework, Eduardo Alberto Castro Puello

CCAC Theses and Dissertations

Small Language Models (SLMs) are increasingly deployed in enterprise, edge, and domain‑specialized applications due to their lower computational cost and latency compared to Large Language Models (LLMs). However, SLMs typically exhibit weaker safety alignment and are disproportionately vulnerable to jailbreak, prompt‑injection, and fine‑tuning attacks that can erode refusal behavior and enable unsafe outputs. Existing defensive techniques such as rule‑based filtering or heavy safety fine‑tuning often degrade task performance or introduce excessive over‑refusal, limiting the safe adoption of SLMs in regulated or security‑sensitive environments. This dissertation proposes the design and empirical evaluation of a modular security alignment framework for fine‑tuned SLMs …

Modeling And Mitigating Atmospheric Degradation In Computer Vision With Application In Renewable Energy Prediction, Sumit Laha

Graduate Studies Theses and Dissertations 2026

Weather-induced variability poses significant challenges to the reliability and performance of modern computational systems, particularly those relying on visual perception and environmental prediction. This dissertation focuses on enhancing computer vision and machine learning based predictive models that operate under varying atmospheric conditions. Two representative weather-impacted applications are investigated: image dehazing and solar photovoltaic (PV) power output forecasting. Image dehazing focuses on the restoration of clear, unobstructed visuals from hazy or foggy images, a task that is vital for various applications. On the other hand, photovoltaic (PV) power forecasting aims to predict future solar energy generation based on historical sky images …

Concept Drift Detection For Streaming Data Using One-Class Classification, Poorna Sandamini Senaratne

Graduate Studies Theses and Dissertations 2026

Modern machine learning systems are increasingly deployed in streaming environments where data arrive sequentially and the underlying data-generating process may evolve over time. This phenomenon, known as concept drift, can significantly degrade model performance if not detected and addressed in a timely manner. This dissertation proposes a principled framework for concept drift detection based on one-class classification, integrating neural network embeddings with Support Vector methodologies.

The proposed approach leverages neural networks to learn compact and informative embeddings of input data, capturing complex nonlinear structures in a lower-dimensional latent space. These embeddings are then used to construct a statistical description of …

Ecological And Evolutionary Divergence Of A Shared Sympatric Color Polymorphism In Paracirrhites Hawkfish, Samuel Greaves

Graduate Studies Theses and Dissertations 2026

Color polymorphism provides a tractable window into how selection, gene flow, and ecology interact to maintain phenotypic diversity and initiate divergence. I investigate the ecological and genomic basis of sympatric light and melanistic morphs in two reef hawkfishes, Paracirrhites arcatus and P. hemistictus, across Guam and Hawai‘i. This study aims to determine whether the color morphs of these hawkfishes are a stable polymorphism or have begun the process of ecological speciation. For P. arcatus revealed substantially lower density on Guam (0.086 ind/m²) than reported for Hawai‘i (0.292 ind/m²) and a strong skew away from the melanistic morph on Guam …

Computational Methods For Rna 3d Structure Classification And Structural Motif Discovery, Nabila Shahnaz Khan

Graduate Studies Theses and Dissertations 2026

Non-coding RNAs perform diverse and essential cellular functions, and the study of RNA structures is expanding rapidly. The growing volume of RNA 3D structures highlights the need for efficient computational methods to identify recurring structural motifs, which are often conserved and critical to RNA function. However, the complexity and redundancy of RNA structures in existing databases pose significant challenges for motif discovery and structural analysis. To address these challenges, this dissertation presents three complementary computational approaches. First, a non-redundant RNA structural dataset, RNA-NRD, was developed to systematically reduce redundancy in RNA structural data and provide a reliable foundation for downstream …

Advancing Multi-Modal Learning, Swetha Sirnam

Graduate Studies Theses and Dissertations 2026

Multimodal learning aims to develop systems that understand and reason across multiple sources of information such as images, videos, audio, and text. These capabilities are central to applications including visual search, digital assistants, robotics, and visual question answering. Despite recent progress in multimodal large language models, current approaches struggle to capture the structured complexity of real-world visual data. Fine-grained visual details are often lost during multimodal alignment, models frequently fail to capture nuanced human intent, and existing methods remain limited in understanding long-form videos with complex temporal dynamics. Furthermore, many evaluation benchmarks rely heavily on explicit visual cues, which can …

Charge Transport Modeling Of Multifunctional Molecular Transistors, Romena Akter

Graduate Studies Theses and Dissertations 2026

Distinguishing between the current–voltage characteristic curves associated with different transport mechanisms in a molecular electronic device and an artifact arising from device-level disorder is a central challenge in the study of molecular electronics. Characterizing these mechanisms and connecting them qualitatively to molecular-level orbital alignment, electrode coupling, voltage division, energetic disorder, and reorganization energy requires not only experimental data but also a rigorous theoretical framework for extracting physically meaningful parameters from measured transfer characteristics. This dissertation addresses this challenge in Chapters 2, 3, and 4 by developing and applying four complementary transport models —Simmons tunneling, single-level Landauer, Marcus hopping, and McConnell …

First-Principles Insights Into Electronic And Magnetic Structure At Hybrid Interfaces And Low-Dimensional Materials And Reaction On Surfaces, Bushra Ashraf

Graduate Studies Theses and Dissertations 2026

Low-dimensional materials and transition metal surfaces/interfaces provide complementary platforms for understanding how dimensionality, symmetry, spin–orbit coupling (SOC), and interfacial charge transfer/hybridization jointly control electronic structure, emergent magnetism, and surface reactivity. Using first-principles modeling with experimental comparisons, this work aims to establish interfacial structure–property relationships across low-dimensional systems.

Strained Pd adlayers on 2H-MoS₂(0001) provide a controlled platform to separate strain-driven exchange from interface-induced quenching in an itinerant d-electron metal. Although expansive strain promotes magnetism in free-standing Pd, strong Pd–MoS₂ hybridization and charge transfer suppress the monolayer moment and leave a narrow thickness window where magnetism re-emerges in …

Restoring Digital Trust: Decentralized Frameworks For Identity, Consent, And Media Provenance, Raghu N. Avula

Graduate Studies Theses and Dissertations 2026

Digital platforms are deeply embedded in modern society through centralized network architectures. As online interactions transition to the physical world handling sensitive data, user privacy and cryptographic security require enhancement beyond traditional limits. This dissertation presents a decentralized “Tri-Layer Trust Architecture” that leverages Verifiable Trust Registries, Cryptographic Binding, and Edge Verification to prioritize data sovereignty and local authentication. To demonstrate the architecture’s applicability across distinct operational environments, we implement three solutions: (1) Verifiable Presence for peer-to-peer ride-sharing via D-RideX, which encrypts biometric data on physical hardware (TEEs) and Soulbound Tokens (SBTs) to prevent impersonation without cloud database exposure; (2) Verifiable …

Understanding The Potential Biogeochemical Effects Of Alternative Restoration Materials: A Study Of Basalt And Other Materials, Cadie L. Barnes

Graduate Studies Theses and Dissertations 2026

Concern over the effects of microplastics on estuarine ecosystems and public health has prompted many restoration practitioners to switch from plastic materials to more “natural” substrates. These natural (BESE-biopolymer, cement-jute structures) and non-plastic (basalt fiber) materials should not contain harmful substances; however, their chemical composition is unknown. Natural materials may degrade faster in subtropical environments and release nutrients into surrounding waters, which is concerning for eutrophic restoration areas. Basalt fiber bags, made from melted basalt rock spun into fibers, have not been evaluated for biogeochemical impacts within estuaries. To address this knowledge gap, two field experiments and two laboratory experiments …

An Integrated Framework For Last-Mile Delivery Optimization Using Reinforcement Learning And Social Media-Based Traffic Prediction In Underdeveloped Megacities, Vasanth Bhat

Graduate Studies Theses and Dissertations 2026

Supply chain management has become increasingly critical as transportation costs rise and urban delivery systems place additional pressure on already congested networks. Last-mile delivery—the movement of goods from distribution centers to end users—represents the most expensive and operationally complex segment of the logistics process, particularly in underdeveloped megacities characterized by unpredictable traffic, limited infrastructure, and sparse real-time data. This dissertation proposes an integrated framework for optimizing last-mile delivery in such environments by combining social media–based traffic intelligence, machine learning, and deep reinforcement learning. The framework leverages unstructured, real-time data from social media platforms to enhance traffic prediction and incorporates these …

Investigation Of Exotic Phenomena In Topological Quantum Materials, Iftakhar Bin Elius

Graduate Studies Theses and Dissertations 2026

he First Quantum Revolution established quantum mechanics as the foundation of modern physics, enabling transformative technologies such as semiconductors, lasers, and transistors through prin-ciples of quantization, wave-particle duality, and uncertainty. The ongoing Second Quantum Rev-olution harnesses entanglement, superposition, and coherence to advance quantum computation, communication, simulation, and sensing. Central to this endeavor is the study of quantum mate-rials hosting novel electronic structures and emergent phases, including topological insulators and Dirac, Weyl, and nodal-line semimetals. In this thesis, using angle-resolved photoemission spec-troscopy (ARPES), transport measurements, and density functional theory (DFT) calculations, we investigate three classes of materials: (i) magnetic topological semimetals, …

Learning-Based Optimization For Collaborative Truck-Drone Routing And Scheduling, Ahmad A. Bany Abdelnabi

Graduate Studies Theses and Dissertations 2026

Modern delivery networks increasingly rely on innovative technologies such as drones working alongside trucks to meet rising demands for fast and efficient last–mile transportation. Hybrid truck–drone delivery offers strong potential, but its effectiveness depends on careful synchronization between trucks and drones. Although drones provide fast, direct travel, their limited endurance and the need to coordinate launch and recovery with trucks create tightly coupled routing and scheduling interactions. Poor dispatch decisions can cause truck waiting, reduce parallelism, and increase mission completion time. These challenges motivate decision frameworks that explicitly model synchronization and develop scalable methods for coordinated truck–drone operations. This dissertation …

Detonability Limits Of Liquid Fuel Cloud, Khaoula Chougag

Graduate Studies Theses and Dissertations 2026

Detonation-based propulsion systems are of significant interest owing to their high thermodynamic efficiency, rapid energy release, and favorable entropy production relative to deflagration-based systems. Liquid fuels offer practical advantages in storage and volumetric energy density, making them attractive candidates for next-generation detonation engines. This dissertation investigates the detonability characteristics of liquid hydrocarbon fuel clouds through three interconnected studies conducted in a turbulent shock-tube environment using high-speed imaging and pressure diagnostics. The first study maps the detonability limits of monodisperse n-dodecane clouds by systematically varying droplet diameter (5-50 μm) and O₂/N₂ oxidizer composition at equivalence ratios spanning ϕ = 0.8-2.0. Results …

Detection And Discrimination Of Targets In Infrared Imagery, Adam T. Cuellar

Graduate Studies Theses and Dissertations 2026

Automated infrared (IR) imagery analysis is essential for persistent surveillance, defense, and security, yet it remains difficult when sensors move and when unknown objects appear. This dissertation addresses two fundamental problems: (1) detecting small moving targets amid platform-motion induced parallax and (2) distinguishing between known stationary targets and out-of-distribution (OOD) objects.   For detecting moving targets while the platform itself is in motion, auxiliary Global Positioning System and Inertial Navigation System data are combined with image analysis. Direction Cosine Matrices from calibrated inertial measurements enable sub-pixel frame alignment unattainable with purely image-based registration. The stabilized sequence is processed by a Reed–Xiaoli …

Repeatability And Reproducibility Of Likelihood Ratio Estimation For Forensics Using Machine Learning: An Application To In-Silico Fire Debris Data, Cameron Ezazi

Graduate Studies Theses and Dissertations 2026

The importance of using statistical evidence to help inform the interpretation of forensic evidence has grown greater over time. This is especially evident in the field of forensic fire debris analysis, where the reliability and reproducibility of statistical results are crucial. This thesis investigates how the application of 5 different common machine learning methods on an in-silico fire debris dataset can be reduced to a univariate forensic score to create score-based likelihood ratios (SLRs), and how these ratios should be interpreted. These SLRs are crucial in supporting or rejecting a Prosecutor’s or Defense’s hypothesis, thus making the previously mentioned reliability …

Calculus In Context: The Five College Calculus Project, James Callahan, Kenneth Hoffman, David Cox, Donal O'Shea, Harriet Pollatsek, Lester Senechal

Open Educational Resources: Textbooks

Calculus in Context: The Five College Calculus Project is a thorough reframing of what it means to learn calculus. It starts with a “clean slate;” there is no presumptive commitment to any aspect of the traditional course. Instead, the curriculum is guided by the following principles:

• Calculus is fundamentally a way of dealing with functional relationships that occur in scientific and mathematical contexts. The techniques of calculus must be subordinate to an overall view of the underlying questions.
• Technology radically enlarges the range of questions we can explore and the ways we can answer them. Computers can be engaged as …