Physical Sciences and Mathematics Commons^™

An Approach To Stylometry Using Causal Language Models, Harrison F. Stropkay

Computer Science Senior Theses

We present a novel stylometric approach using large language models. By training separate models on individual authors' works, we find that each model achieves lower cross-entropy loss when predicting text from its training author compared to other authors' texts. Moreover, for any given text, the model trained on its true author’s corpus yields the lowest loss. We suggest that, in this way, a model trained on one author's works embodies the unique writing style of that author. We demonstrate our approach on works by eight known authors. This approach also confirms that R. P. Thompson wrote the well-studied 15th book …

Re: Approval Letter With Comment For The Butte Priority Soils Operable Unit (Bpsou) 2025 Revised Draft Final Unreclaimed Sites: Ur-03 Site Remedial Action Work Plan (Rawp) (Dated June 2, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Final 2025 Residential Metals Abatement Program (Rmap) Rock Creek Cattle Company Borrow Submittal #1 (Dated May 23, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) 2022 Draft Final Insufficiently Reclaimed Sites Sampling: Bres No. 37 – Josephine Shaft Site Evaluation Summary Report (December 6, 2024), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Response To Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) Butte Reclamation Evaluation System (Bres) Draft 2024 Field Evaluation Summary And Technical Recommendations Report (Dated April 10, 2025), Abby Peltomaa

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Investigation Into Thermodynamic And Kinetic Phenomena In Arylation Reactions With Diaryliodonium Salts, Soocheta Jha

Dissertations and Theses

Benzenoid rings are fundamental structural motifs in organic chemistry, widely recognized for their prevalence in natural products, pharmaceuticals, agrochemicals, and functional materials. Transition metal-catalyzed arylation, using catalysts such as palladium, nickel, or copper, has revolutionized C-C, C-O, and C-N bond formation, offering broad substrate scope and high efficiency. Among the reagents developed for efficient aryl group transfer, diaryliodonium salts have emerged as particularly powerful tools. Diaryliodonium salts facilitate metal-free arylations across a broad spectrum of nucleophiles, offering high reactivity, operational simplicity and environmental compatibility. The versatility of diaryliodonium salts has led to their widespread adoption as efficient arylating reagent in …

Enhancing Iot Decentralization With Iota 2.0: A Dag-Based Fast Probabilistic Consensus Framework, Ayat N. Kadhum, Ahmed M. Al-Salih

Journal of Intelligent Informatics, Networking, and Cybersecurity

The integration of IoT and blockchain enhances security, trust, and data integrity but is hindered by security attacks, scalability, and high latency. In this work, a more efficient method of consensus using Directed Acyclic Graph (DAG)-based Fast Probabilistic Consensus (FPC) and Edwards-Curve Digital Signature Algorithm (EdDSA) is proposed to yield better security, efficiency, and decentralization. By eliminating mining, resource use is optimized, and consensus is hastened. Experimental results show a high throughput of 7228.05 Transactions Per Second (TPS), rapid consensus formation in just 7.62 rounds on average, and high adversary resilience, with the system successfully mitigating 89% of adversarial attacks. …

Detecting Physical Activity Using Wearable Sensor Data, Dipok Deb

Data Science and Data Mining

This study focuses on detecting physical activity using wearable sensor data, specifically distinguishing between walking and running. A dataset comprising accelerometer and gyroscope readings is used to train and evaluate various machine learning models, including logistic regression, random forest, k-nearest neighbors, naïve Bayes, and XGBoost. Extensive preprocessing, such as creating lag features and rolling statistics, is performed to enhance temporal data representation. The models are evaluated using metrics like accuracy, precision, recall, and F1 score. Incorporating lag and rolling features significantly improves model performance, with logistic regression achieving perfect scores across all metrics. These findings demonstrate the effectiveness of enhanced …

Convergence Research For Microplastic Pollution At The Watershed Scale, Heejun Chang, Elise Granek, Amanda Gannon, Jordyn M. Wolfand, Janice Brahney

Watershed Sciences Faculty Publications

Microplastics are found in Earth's atmosphere, lithosphere, hydrosphere, pedosphere, and ecosphere. While there is a growing interest and need to solve this grand challenge in both the academic and policy realms, few have engaged with academics, policymakers, and community partners to co-identify the problem, co-design research, and co-produce knowledge in tackling this issue. Using a convergence research framework, we investigated the perception of microplastic pollution among different end users, delivered educational materials to K-12 teachers and practitioners, and identified key sampling points for assessing environmental microplastic concentrations in the Columbia River Basin, United States. Three community partner workshops identified regional …

Cross-Modality Learning For Predicting Ihc Biomarkers From H&E-Stained Whole-Slide Images, Amit Das

Computer Science Senior Theses

Hematoxylin and Eosin (H&E) staining is a cornerstone of pathological analysis, offering reliable visualization of cellular morphology and tissue architecture for cancer diagnosis, subtyping, and grading. Immunohistochemistry (IHC) staining, an important ancillary study, provides molecular insights by detecting specific proteins within tissues, enhancing diagnostic accuracy, and improving treatment planning. However, IHC staining is costly, time-consuming, and resource-intensive, requiring specialized expertise. To address these limitations, this study proposes HistoStainAlign, a novel deep learning framework that predicts IHC staining patterns directly from H&E whole-slide images (WSIs) by learning joint representations of morphological and molecular features. The framework integrates paired H&E and IHC …

The Other Side Of The Equation: De-Simplification, A Prerequisite For Calculus, Stephen L. Brown

ACMS Conference Proceedings 2005

No abstract provided.

Multi-Modal Emotion Appraisals Using Large Language Models, Carlos Guerrero Alvarez

Computer Science Senior Theses

This thesis investigates the capabilities of large language models (LLMs), specifically GPT4 and GPT4o, in appraising human emotional responses within strategic social scenarios. Building on the work of Houlihan et. al[7], we evaluate LLMs using a dataset of human emotion ratings from game-theoretic situations, later extending the experimental paradigm to include both text and multimodal (image and profession) inputs. Our methodology introduces novel prompting techniques for the experiment at hand, and we compare the performance of these techniques with expert perspectives to assess how different prompts influence model predictions. Results show that LLMs can approximate human emotional appraisals, with the …

Artificial Intelligence And Astronomy: Lab Manual For Generative Ai-Based Learning Activities, Vasiliy Znamenskiy

Open Educational Resources

This lab manual introduces community college students to the intersection of artificial intelligence (AI) and astronomy through hands-on, inquiry-based activities. It explores how generative AI platforms—ranging from text-based chat systems to image and video generators—can enhance scientific understanding, creativity, and critical thinking in space science education. Students engage with tools such as ChatGPT, Gemini, DALL·E, and Synthesia to formulate and evaluate astronomy-related questions, generate scientifically inspired visualizations, and produce dynamic representations of cosmic phenomena. Through structured exercises, the manual promotes digital literacy, fosters analytical skills, and illustrates how AI assists in astronomical research, including data analysis, image enhancement, spacecraft autonomy, …

Biomarker-Guided Imaging And Ai-Augmented Diagnosis Of Degenerative Joint Disease, Rahul Kumar, Kyle Sporn, Aryan Borole, Akshay Khanna, Chirag Gowda, Phani Paladugu, Alex Ngo, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

Department of Medicine Faculty Papers

Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies that integrate biochemical biomarkers, advanced imaging techniques, and machine learning models relevant to osteoarthritis. We evaluate the diagnostic utility of cartilage degradation markers (e.g., CTX-II, COMP), inflammatory cytokines (e.g., IL-1β, TNF-α), and synovial fluid microRNA profiles, and how they correlate with quantitative imaging readouts from T2-mapping MRI, ultrasound elastography, and dual-energy CT. Furthermore, we highlight recent developments …

Machine Verification Of Correctness For A Concurrent Union-Find Object, Karun Ram

Computer Science Senior Theses

We consider a family of near-optimal randomized multiprocessor implementations for the union-find problem due to Jayanti and Tarjan–known as the Jayanti-Tarjan Randomized-Linking (JT-RL) union-find objects–and provide the first formal and fully machine-verified proof of their strong linearizability (i.e., correctness). Their algorithms are efficient both in theory and in practice: numerous benchmarking works demonstrate that they perform faster, or as fast, as all other known implementations for the union-find object on both CPUs and GPUs.

The correctness of the JT-RL algorithms is subtle, which motivates the need for formal verification. To this end, we first specify the JT-RL objects in TLA+, …

A Survey Of Feature Attribution Techniques In Explainable Ai: Taxonomy, Analysis And Comparison, Mohd Amril Nazir, Edmund Evangelista, Syed Muhammad Salman Bukhari, Ravishankar Sharma

All Works

The feature attribution methods have become central to explainable artificial intelligence (XAI), providing critical insights into how machine learning (ML) models make individual and aggregate decisions. This survey presents a complete taxonomy of feature attribution techniques, organizing them into model-agnostic and model-specific categories while highlighting extensions such as rule-based and attention-based explanations. We analyze formal definitions of each method, mathematical formulations, application contexts, strengths, and limitations. A comparative analysis highlights key trade-offs among model flexibility, computational cost, explanation fidelity, and interpretability. In addition to theoretical perspectives, we provide practical comparisons of selected methods on benchmark tasks to guide real-world applicability. …

Level Of Service Criteria For Urban Arterials With Heterogeneous And Undisciplined Traffic Streams, Afzal Ahmed, Farah Khan, Syed Faraz Abbas Rizvi, Fatma Outay, Muhammad Faiq Ahmed, Muhammad Adnan

All Works

Accurate evaluation of the prevailing traffic operations plays an important part in developing sustainable transport systems. This research examines the suitability of the level of service (LOS) criteria developed by the Indian and United States (US) Highway Capacity Manuals (HCM) for heterogeneous and undisciplined traffic streams and proposes new criteria using a data-driven approach. Traffic data were collected from a selected major arterial in Karachi, and fundamental diagrams were developed using these data. These fundamental diagrams and field-collected data were analyzed using the K-mean clustering approach to examine the actual traffic states at various LOS bands used in practice. Associating …

Atomistic Characterization Of Hydration-Dependent Fuel Cell Ionomer Nanostructure: Validation By Vibrational Spectroscopy, Dan J. Donnelly, Moon Young Yang, Nicholas Dimakis, Seung Soon Jang, William A. Goddard Iii, Eugene S. Smotkin

Physics & Astronomy Faculty Publications

The development of Nafion alternatives for fuel cells and electrolyzers requires a fundamental understanding of hydration-dependent ion-exchange site acid/base chemistry. We present here reactive force field (ReaxFF) molecular dynamics (MD) simulations of Nafion at varying molar water/ion-exchange-site ratios (λ), which we correlate to our experimental and density functional theory-based vibrational spectra. ReaxFF describes the formation and breaking of covalent bonds, enabling simulations of proton exchange between sulfonic acid/sulfonate groups and water/hydronium. Our MD simulations determine the λ-dependent equilibrium proportions of protonated and deprotonated sites. We find that protonated sites persist across all λ from 0 to 20, challenging the widely …

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

Thesis/ Dissertation Defenses

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

Simulation And Molecular Design Of Antimonene-Based Materials With High Optoelectronic Performance., Eman Mohammed Ghanem

Thesis/ Dissertation Defenses

Two-dimensional materials have undergone a rapid development toward many applications since the discovery of graphene. Then, 2D binary compounds such as transition-metal chalcogenides emerged as complementary materials for graphene due to their sizable bandgap and moderate electrical properties. Silicene, hexagonal boron nitride, molybdenum disulfide, phosphorene and germanene et al have received increasing attention owing to their unique properties and promising applications. Recently, research interests have turned to monoelemental 2D materials. A new ultrathin 2D semiconductor material in group-V, namely, antimonene (Sb). Since its discovery in 2015, antimonene, as an emerging 2D material, has rapidly gained popularity due to its unique …

Siliciclastic Cave Or Diamond Mine? Multiapproach Investigation In Igatu Village, Chapada Diamantina, Northeastern Brazil, Raphael Parra, Ricardo G. F. De A. Pereira, Leonardo F. Vieira, Rubson P. Maia

International Journal of Speleology

The territory around Igatu, as that of many villages in southeastern Chapada Diamantina (Bahia, Brazil), was deeply modified by diamond mine activity. Caves were special targets for miners, since they can act as diamond-bearing sediment traps, and the Canal da Fumaça Cave was one of the most important and rich of them. The question that arises is whether the formation is a natural cave or an artificial diamond mine. In order to answer this question, we developed speleological and geological studies with detailed cave surveys, remote sensing and field measurements for structural analysis, optical and electronic microscope for petrographic characterization, …

Investigation Of A Polymer-Based Holographic Grating For Visible Light Dosimetry Using A Bleachable Dye, Saoirse Maher, Denise Denning, Jackie Mccavana Dr., Seán Cournane Dr., Suzanne Martin Dr., Dervil Cody

Conference Papers

Holographic sensors are of interest for a range of sensing tasks because of their high sensitivity, rapid response time, broad dynamic range, lightweight characteristics, and design flexibility. The practical application of holography makes it possible to design optical sensors that are effective in the visible and near-infrared ranges. The objective of this research is to construct a holographic grating that is customised to provide quantitative data in visible light dosimetry applications. Upon exposure to visible light, the proposed holographic grating will produce a measurable change in diffraction efficiency based on the bleaching of the grating material. In the evolution of …

Using Oblique, Bistatic Receptions Of Superdarn Signals To Measure Hf Propagation In The Auroral And Polar Cap Region, Riley Troyer, Jeffrey Holmes, Evan Thomas, Simon Shepherd, Eugene Dao, John Carilli

Space Dynamics Laboratory Publications

Experiment Geometry

Three receiver locations:

• Pituffik (formerly Thule)
• Utqiagvik (formerly Barrow)
• USCGC Healy – Beaufort Sea

Two SuperDARN locations:

• Christmas Valley (CVE, CVW)
• Iceland (ICE, ICW)

Gnss Technology, Utah State University Space Dynamics Laboratory

Space Dynamics Laboratory Publications

SDL presentation on GNSS Technology

Arts-Based Sustainability: From New York To Malawi, Martha B. Lerski

Publications and Research

Recognizing that libraries serve multiple constituencies and subject areas, this chapter documents and advocates for development of transdisciplinary arts-based research (ABR) and culture-related projects linked to environmental challenges. Libraries contribute collections and spaces, as well as the research of library and information scientists. Libraries are currently among invisible contributors to sustainability planning and services. The chapter will link this invisibility to the value of what visual arts refer to as negative space elements in subjects ranging from traditional ecological knowledge to environmental science. Library collections, projects, and research contribute to education for sustainable development (ESD) as required to achieve the …

Having A Cow: Exploring The Economic And Mental Health Challenges Of Dairying In The Upper Valley, Ava J. Ori

Environmental Studies Senior Theses

Until late in the 20th century, the dairyman was an influential actor in the fabric of the Upper Valley consisting of Grafton, Orange, Sullivan and Windsor counties (Rozwenc, 1981 & Weld, 1905). Dairy farmers were critical to both the economic growth of the region and the construction of a local identity centered around distinctive high quality dairy products. However, the dairy farmers responsible for the bucolic feel and scrappy independence unique to the Upper Valley are being traded in for massive operations lacking both quality of milk and the cultural intricacy specific to local dairies. In 1905 there were 4,173 …

The Ecophysiology Of The Enigmatic Namib Succulent, Lithops Ruschiorum, Garrett Macleod Althausen

Environmental Studies Senior Theses

Water availability is the primary constraint for plant survival and reproduction in hyper-arid ecosystems. In such environments, plant fitness depends on an alignment between physiological traits and limited water resources. The Namib Desert of Namibia exemplifies this extreme selection pressure. Within the Namib Desert, Lithops ruschiorum, a succulent endemic to the gravel plains, exhibits habitat specificity and numerous physiological adaptations to cope with the hyper-aridity.

This research explores the intersection of two fitness strategies for L. ruschiorum: habitat selection and exploitation of alternative moisture sources. The first chapter explores the population dynamics and micro-site habitat conditions preferred by …

Efficient Signal Synthesis For Data Augmentation Using Generative Ai, Yagna Veera Narayan Kaasaragadda

Theses and Dissertations

Modern signal processing AI applications face increasing demands for diverse training data while operating under computational constraints. State-of-the-art generative models, though effective, often require prohibitive resources, limiting their deployment in real-time or embedded systems. This thesis proposes a computationally efficient framework for synthetic signal generation using a two-stage architecture that combines a Vector Quantized Variational Autoencoder (VQ-VAE) with either a decoder-only transformer or a discrete diffusion model. The VQ-VAE encodes high-dimensional signals into discrete latent tokens, significantly reducing model complexity while enabling symbolic sequence modeling. These discrete representations are then modeled using transformer-based autoregressive models or Score Entropy Discrete Diffusion …

Advances In And Applications Of Capillary Scale Liquid Chromatography, Samuel William Foster

Theses and Dissertations

Analytical-scale high performance liquid chromatography (HPLC) is one of the most widely used analytical techniques in the world. Efforts have been made to reduce the mobile phase consumption of HPLC by reducing the inner diameter (I.D.) of the column. By using capillary-scale columns (< 0.5 mm I.D.), separations can be performed with orders of magnitude less solvent consumption. This work describes advances made to capillary scale instrumentation as well as applications enabled by these advances. A compact capillary-scale LC was used for the analysis of numerous pharmaceutical and illicit drug compounds for point of need analysis. A miniaturized mass spectrometer was coupled to this instrument for on-line reaction monitoring within a standard chemical fume hood. Kinetic profiles of commercially available capillary columns were developed in order to provide guidance to others during column selection. The implementation of a column oven improved the analysis of large biopharmaceuticals. Several critical quality attributes of monoclonal antibodies including fragmentation, charge variants, and glycosylation profiles were able to be determined on the capillary scale. These results will help to make capillary-scale liquid chromatography a more viable solution for chromatographic analysis.

Age, Provenance, And Geochemical Relationships Amongst The Great Valley Group, Coast Range Ophiolite, And Franciscan Subduction Complex At Del Puerto Canyon, Central California, Devon A. Orme, Natalee Weis, Kathleen D. Surpless, Andrew K. Laskowski, James L. Crowley, Nicole Stine, Ian Colliver, Carly Ross, Emma Sweet

Earth and Environmental Geosciences Faculty Research

The Great Valley Forearc (GVF) basin of California, USA, preserves an extensive rock record of the Jurassic–Paleogene tectonic development of the California segment of the North American Cordillera. We present new U-Pb geochronology, zircon and whole-rock geochemistry, and petrographic analyses from the Great Valley Group (GVG), Franciscan subduction complex, and Coast Range Ophiolite (CRO) in the northern San Joaquin Valley to better understand the timing and location of initial forearc sedimentation, and how sediment routing systems may have evolved during Cretaceous time. Basal GVG strata of the Knoxville Formation were deposited ca. 145–140 Ma and are separated by an ~40 …