Physical Sciences and Mathematics Commons^™

Land Instability Compounds The Risk Of Sea Level Rise In Alexandria, Egypt, Rejoice Thomas, Sara Zouriq, Shahryar Fazli, Amr Fawzy, Nikolay Grisel Todorov, Surendra Maharjan, Wenzhao Li, Erik Linstead, Daniele Struppa, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

The coastal region of Alexandria Governorate in Egypt holds significant strategic importance for trade while being susceptible to extreme weather events. It confronts a dual challenge of the rising sea levels and, as found in this study, land instability. While much attention has been rightly directed towards sea level rise (SLR), the stability of the land warrants equal consideration. Here, a comprehensive analysis of land stability is conducted in Alexandria by measuring Line of Sight (LOS) displacements and assessing their topographical, hydrological, and coastal impacts. Persistent Scatterer Interferometry technique is used to measure the LOS displacements in association with land …

Ensemble-Based Binding Free Energy Profiling And Network Analysis Of The Kras Interactions With Darpin Proteins Targeting Distinct Binding Sites: Revealing Molecular Determinants And Universal Architecture Of Regulatory Hotspots And Allosteric Binding, Mohammed Alshahrani, Vedant Parikh, Brandon Foley, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

KRAS is a pivotal oncoprotein that regulates cell proliferation and survival through interactions with downstream effectors such as RAF1. Despite significant advances in understanding KRAS biology, the structural and dynamic mechanisms of KRAS allostery remain poorly understood. In this study, we employ microsecond molecular dynamics simulations, mutational scanning, and binding free energy calculations together with dynamic network modeling to dissect how engineered DARPin proteins K27, K55, K13, and K19 engage KRAS through diverse molecular mechanisms ranging from effector mimicry to conformational restriction and allosteric modulation. Mutational scanning across all four DARPin systems identifies a core set of evolutionarily constrained residues …

Tilings In The 3 Dimensional Lattice With L-Tetrominoes, Ian N. Bridges

Rose-Hulman Undergraduate Mathematics Journal

We consider three dimensional L-tetrominoes. We show that there exists at least one way to tile every three dimensional rectangle whose side lengths are at least $3$ and area is congruent to $1 \pmod 4$ such that one square goes untiled. In addition, we show that every three dimensional rectangle is tileable provided one side has length at least $2$ and the other is a multiple of $4$.

K-Mshc: Unmasking Minimally Sufficient Head Circuits In Large Language Models With Experiments On Syntactic Classification Tasks, Pratim Chowdhary, Peter Chin, Deepernab Chakrabarty

Computer Science Senior Theses

Understanding which neural components drive specific capabilities in mid-sized language models ($\leq$10B parameters) remains a key challenge. We introduce the $(\bm{K}, \epsilon)$-Minimum Sufficient Head Circuit ($K$-MSHC), a methodology to identify minimal sets of attention heads crucial for classification tasks as well as Search-K-MSHC, an efficient algorithm for discovering these circuits. Applying our Search-K-MSHC algorithm to Gemma-9B, we analyze three syntactic task families: grammar acceptability, arithmetic verification, and arithmetic word problems. Our findings reveal distinct task-specific head circuits, with grammar tasks predominantly utilizing early layers, word problems showing pronounced activity in both shallow and deep regions, and arithmetic verification demonstrating a …

Evaluating Vision Language Model Capabilities For Time Series Interpretation: An Empirical Study With Conversation Duration And Psychological Flourishing Data From The Studentlife Dataset, Jusung Park

Computer Science Senior Theses

This research investigates the capability of Vision Language Models (VLMs), specifically ChatGPT‑4o, to interpret and predict psychological outcomes based on visual representations of time series data. Leveraging conversation duration metrics and psychological flourishing scores from the StudentLife dataset, this study rigorously evaluates the predictive accuracy of VLMs using various methods, including zero‑shot on raw data, zero‑shot on graph data, few‑shot learning, qualitative labeling, and chain‑of‑thought reasoning. Despite multiple methodological enhancements, predictive performance remains modest, revealing significant challenges in quantitative interpretation of visualized temporal data by current multimodal models. We demonstrate that standardizing input tokens by using graph images rather than …

A Steiner Tree Vc Set System In Minor-Free (Di)Graphs, Eli Friedman

Computer Science Senior Theses

We propose a set system of maximum-covering minimum-density partial Steiner trees for planar and minor-free graphs. We show that this system has VC dimension at most h-1 for edge-weighted K_h-minor-free graphs, both directed and undirected. We also consider its geometric interpretation as a range space, proving it to be piercing.

In addition, we demonstrate how one can form a junction tree set system of bounded VC dimension from such Steiner trees. This is motivated by refining the junction tree set cover approach used in Chekuri and Jain's polylogarithmic approximation algorithm for Directed Steiner Forest in planar graphs [CJ25].

Character Relationship Prediction In Movies: Toward Emotionally-Aware Automatic Audio Descriptions, Seung Hyun Hahm

Computer Science Senior Theses

Automatic audio description (AD) systems support visually impaired audiences by narrating visual content, but they often fail to capture the interpersonal dynamics that underpin narrative understanding. In this work, we introduce a novel framework for character relationship prediction as a means of enriching audio descriptions with socially grounded context. Our contributions are threefold: (1) we propose the Character Relationship Module (CRM), which extends identity-aware video captioning with directed sentiment inference between character pairs; (2) we develop a scalable weak supervision pipeline that uses large language models to generate 669,520 relationship annotations across 202 films; and (3) we construct a complementary …

Bayesian Segmentation–Driven Informative Path Planning For Uav-Based Water Orthomosaic Generation, Phuc Dai Tran

Computer Science Senior Theses

This paper presents a comprehensive implementation

study of an informative path planning (IPP) algorithm

for autonomous water body detection and mapping using

unmanned aerial vehicles (UAVs). We propose a hybrid IPP

framework that seamlessly integrates Bayesian probabilistic

classification and real-time uncertainty quantification to achieve

superior flight efficiency and mapping accuracy compared

to conventional systematic coverage methods. Our approach

employs the state-of-the-art SegFormer deep learning segmentation

model in conjunction with log-odds-based orthomosaic

generation to produce high-fidelity water body maps under

diverse environmental conditions. Through random sampling of

the FloodNet dataset, we demonstrate that our IPP algorithm

maintains flight distance while achieving …

A First Look Into Parental Strategies, And Challenges Around Children’S Device Usage In Urban Nepal, Rizu Paudel, Prakriti Dumaru, Ankit Shrestha, Mahdi Nasrullah Al-Ameen

Computer Science Student Research

There have been substantial changes in the landscape of technology use by children in Global South during COVID-19, when the shift to online learning platforms necessitated parents to avail personal devices (e.g., smartphones, computers) for their children to fulfill their educational needs. However, the use of devices by children are not limited to serving educational purpose only. Our study positions itself in a critical post-pandemic period in Nepal, characterized by the increase in device use by children, while a little study to date, investigated parental mediation in this developing country. To this end, we conducted semi-structured interviews with 20 parents, …

Temperature, Albedo, And Emissivity Of Triton And Proteus From Voyager 2 Iris Data, Garrett Nobis, Vincent Chevrier

Space and Planetary Science Faculty Publications and Presentations

The Voyager 2 infrared interferometer and spectrometer (IRIS) spectra for Triton are revisited, and the Proteus IRIS spectra are looked at for the first time. Fifty more spectra are added to the average of 16 previously used for Triton. The unit emissivity Planck function temperature result is 37.9  ±  0.5 K. Iterating IRIS temperatures over several emissivities allows for constraints on emissivity and Bond albedo based on past data. Upper and lower bounds on Triton’s temperature and albedo of 37.9  ±  0.5 to 46.1  ±  0.7 K for temperature and 0.67−0.86 for albedo are determined from the IRIS data and …

Motionteller: Multi-Modal Integration Of Wearable Time-Series With Llms For Health And Behavioral Understanding, Aiwei Zhang, Arvind Pillai, Andrew Campbell, Nicholas C. Jacobson

Computer Science Senior Theses

As wearable sensing becomes increasingly pervasive, a key challenge remains: how can we generate natural language summaries from raw physiological signals such as actigraphy - minute-level movement data collected via accelerometers? In this work, we introduce MotionTeller, a generative framework that natively integrates minute-level wearable activity data with large language models (LLMs). MotionTeller combines a pretrained actigraphy encoder with a lightweight projection module that maps behavioral embeddings into the token space of a frozen decoder-only LLM, enabling free-text, autoregressive generation of daily behavioral summaries.

We construct a novel dataset of 54,383 ⟨actigraphy, text⟩ pairs derived from real-world NHANES recordings, and …

An Efficient Algorithm For Finding High Harmonic Centrality Vertices In Graphs, John Balson

Computer Science Senior Theses

In this thesis we consider the problem of harmonic centrality in graphs. This measure is used widely in the study of real-world complex networks. In particular, we consider the problem of finding a high harmonic centrality vertex in time much faster than $O(mn)$, the time required to calculate the exact harmonic centrality of all vertices in a graph. The problem of calculating centrality measures faster has received much attention in recent years, since calculating the exact harmonic centrality of all vertices can be infeasible for large graphs; hence, faster algorithms are needed. This thesis proposes a new algorithm for finding …

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 …