Digital Commons Network^™

Ai, Translation, And Telling The Truth, David I. Smith

University Faculty Publications and Creative Works

I am working on a large translation project this year. I have been surprised to find several conversation partners voicing the assumption that I am getting AI to do the translating for me. I’ve been wondering how to respond.

A short, but in the end inadequate answer is that, impressive as the current variations on machine translation are, they still get things wrong. Neural machine translation services such as Google Translate and DeepL still produce oddities fairly regularly. I have been working lately with seventeenth-century Czech texts, an area in which I would expect machine translation to struggle a little …

Winddensity-Mbir: Model-Based Iterative Reconstruction For Wind Tunnel 3d Density Estimation, Karl J. Weisenburger, Gregery T. Buzzard, Charles A. Bouman, Matthew R. Kemnetz

Faculty Publications

Experimentalists often use wind tunnels to study aerodynamic turbulence, but most wind tunnel imaging techniques are limited in their ability to take non-invasive three-dimensional (3D) density measurements of turbulence. Wavefront tomography is a technique that uses multiple wavefront measurements from various viewing angles to non-invasively measure the 3D density field of a turbulent medium. Existing methods make strong assumptions, such as a spline basis representation, to address the ill-conditioned nature of this problem. We formulate this problem as a Bayesian, sparse-view tomographic reconstruction problem and develop a model-based iterative reconstruction algorithm for measuring the volumetric 3D density field inside a …

A Comprehensive Survey Of Artificial Intelligence Applications In Predicting Mining-Induced Subsidence, Deformation, And Landslides: Strengths, Limitations, And Future Trends, Long Quoc Nguyen, Dung Ba Nguyen, Minh Tuyet Dang

Journal of Sustainable Mining

Mining activities often cause mining-induced ground deformation, including subsidence and landslides, and related geo-environmental impacts, posing significant risks to infrastructure and safety. This study conducts a systematic assessment to identify, categorize, and evaluate AI-based methods (machine learning, deep learning, and hybrid models) for predicting and monitoring mining-induced ground deformation. The literature search was performed across major scientific databases, using predefined keywords and selection criteria, resulting in a final dataset of relevant peer-reviewed studies. The reviewed works were classified into three methodological groups: traditional machine learning, deep learning-based approaches, and hybrid methods. The results show that ML still dominates in terms …

The Interplay Of Seasonality, Evolution, And Density-Dependence In Discrete-Time Predator-Prey Dynamics, Narendra Pant

Doctoral Dissertations

We extend the discrete-time mathematical models developed in (Ackleh et al., 2019) and (Ackleh et al., 2024) to account for seasonal prey reproduction and build a class of discrete-time predator-prey seasonal models. Each model distinguishes between breeding and non-breeding seasons, representing prey reproduction as a periodic function of period 2. Altogether, three different models are analyzed. In the first part, we extend the predator-prey model from (Ackleh et al., 2019) to incorporate seasonality. We study the resulting dynamics and show that when the inherent reproduction number of the prey and the invasion reproduction number of the predator are larger than …

Building And Restoring Trust In Deep Learning: From Multimodal Sensing To Generative Synthesis And Model Integrity, Liqun Shan

Doctoral Dissertations

Deep learning has become a foundational technology for modern intelligent systems used in sensing, authentication, media generation, and automated decision-making. As these systems are increasingly deployed in security- and privacy-sensitive settings, ensuring their trustworthiness has become a critical challenge. Yet deep learning models remain vulnerable to spoofed sensory inputs, synthetic media, and malicious behaviors hidden within trained networks. These vulnerabilities undermine reliability and raise serious concerns about whether such systems can be trusted under adversarial and deceptive scenarios. This dissertation investigates how to build and restore trust in deep learning across three tightly connected dimensions: multimodal sensing, generative authenticity, and …

Learning And Predicting The Performance Of Gradual Type System, Mohammad Wahiduzzaman Khan

Doctoral Dissertations

Gradual typing reconciles the complementary strengths of static and dynamic typing by allowing programmers to incrementally introduce type annotations while preserving the flexibility of dynamically typed code. This approach improves reliability, documentation, and tooling support without sacrificing rapid prototyping. To ensure soundness, gradual type systems enforce annotations through runtime checks, typically implemented via cast insertion. Although these checks guarantee correctness, they can introduce substantial and highly variable runtime overhead, making performance prediction and optimization a central challenge for practical adoption. Prior work has largely focused on coarse-grained (macro-level) configurations, where entire modules are either fully typed or untyped. While such …

Investigating The Distribution And Origin Of Pitted Mounds On Mars Using Machine Learning-Based Mapping And Integrated Geological Analysis: Application To Isidis Planitia, Precious Batubo

Doctoral Dissertations

Pitted mounds are widespread landforms across the northern plains of Mars, yet their origin remains uncertain. These features have been interpreted as possible expressions of subsurface fluid activity, including sedimentary volcanism, magmatic processes, or other fluid-assisted mechanisms. Determining their distribution, morphology, and geology is therefore important for understanding the evolution of subsurface hydrological processes and the planet’s potential for habitability. However, the large spatial extent of mound-bearing terrains makes comprehensive manual mapping impractical. This dissertation presents an automated approach to mound detection using Faster Region-Based Convolutional Neural Network (Faster R-CNN) from high-resolution Context Camera (CTX) images including morphometric and mineralogical …

Micro-Hyperspectral Imaging Of Phytoplankton Enables Deconvolution Of Mixed Communities For Spaceborne Hyperspectral Remote Sensing, Chisom Okwuchi Emeghiebo

Masters Theses

Characterizing phytoplankton diversity is critical for large-scale biodiversity monitoring and harmful algal bloom (HAB) detection. Here, we present a micro hyperspectral imaging method to characterize reflectance signatures, for the first time across major phytoplankton groups. Spectral variability among taxa in laboratory cultures is interpreted using algal pigment and absorption analyses, demonstrating that pigment composition and absorption govern reflectance features. Chlorophytes and cryptophytes exhibit distinct spectral signatures and are readily distinguishable, whereas diatoms and dinoflagellates show greater spectral similarity, with differentiation relying on subtle reflectance features associated with chlorophyll c₁c₂ near ~463 nm and within 620–650 nm. We further applied this …

Linking Greenhouse Gas Fluxes And Plant Responses To Saltwater Intrusion Events Using Remote Sensing, Madeline Jean Moore

Masters Theses

As sea level rise and coastal subsidence increase coastal Louisiana's vulnerability to saltwater intrusion (SWI) events, accurate and rapid monitoring of vegetation responses to these events is becoming increasingly important. In this study, we utilized unmanned aerial vehicle (UAV) multispectral imagery to assess short-term vegetation responses to simulated SWI events in the Visser’s Experimental Wetland Complex in Saint Martinville, Louisiana. Vegetation patches of two common freshwater wetland species, Panicum hemitomon (Maidencane) and Typha domingensis (Southern Cattail), were exposed to 5 ppt salinity for 6-, 10-, and 17-day durations. Multispectral imagery of both species was collected before and after each SWI …

Classifying Storm Surges Along Louisiana Coast Using Machine Learning, Faith Ewere Okunbor

Masters Theses

Storm surges are among the most significant threats to coastal wetlands, causing flooding and saltwater intrusion that alter hydrology, vegetation structure, and long-term ecosystem resilience. Along the Louisiana coast, storm surge effects vary depending on storm behavior, shoreline geometry, and marsh hydrologic connectivity, yet are commonly characterized using broad storm metrics rather than site-specific environmental responses. This study develops a data-driven framework to classify storm surges across coastal Louisiana wetlands using observations from the Coastwide Reference Monitoring System (CRMS). Hourly water-level and salinity time series from twelve hurricanes (2007-2024) were analyzed using a 40-day storm-centered window. Hydrologic and salinity metrics …

Regioselective Tellurination Reactions And Their Utilities, Itunu Comfort Olanrewaju

Masters Theses

This thesis provides a comprehensive study of regioselective tellurination and its applications by integrating experimental and computational techniques, with an aim of synthesizing libraries of organotellurium heterocycles. This study addresses existing challenges in regioselective tellurination, such as unpredictable regioselectivity and avoidance of toxic reagents by harnessing intramolecular Te…O coordination and electrophilic aromatic tellurination. This strategy enables the development of previously poorly accessible compounds including benzotellurazinones, 2-acylaminobenzotellurazoles, and previously unknown 2,3-dihydrobenzo [b] oxatelluranes, which were prepared by antiMarkovnikov anti-addition of TeCl4 to C-C triple bonds. Experimental studies were complemented by computational DFT analyses to provide mechanistic and energetic insight into the …

Scientific Crosstalk: Natural Language Processing, Praveshika Bhandari

Theses and Dissertations

While sentiment analysis has made significant strides in domains such as social media and personal correspondence, its application to formal scientific writings remains under-explored. The crosstalk between emotional expressions in personal and professional communications has also received limited attention despite its potential to reveal insights into the emotional drivers of scientific creativity. Our research introduces a computational framework designed to detect and quantify emotional expressions across various documents over time. Leveraging state-of-the-art transformer models fine-tuned on domain-specific corpora, the framework models emotional tone distribution. Integrating emotion analysis with knowledge graph modeling enables the exploration of emotional trends alongside key scientific …

30 Db On-Chip Ultra-High Inverse Weak Value Amplification, Yuhan Mei, Meiting Song, Andrew N. Jordan, Jaime Cardenas

Mathematics, Physics, and Computer Science Faculty Articles and Research

Weak value amplification (WVA) has emerged as a powerful technique that enhances measurement precision. However, traditional table-top WVA setups and on-chip demonstrations have not achieved an amplification over 20 dB. The practical limit of the amplification is the fidelity of photon post-selection. To address this limit, we design a weak value device with an over 30 dB interferometer extinction ratio, where the stray light in the dark port is minimized with thermally tunable phase shifters. As a result, the device successfully shows 30 dB WVA, termed ultra-high WVA. This WVA optimization strategy by improving interferometer extinction is extensible to a …

Olaf: Towards Robust Llm-Based Annotation Framework In Empirical Software Engineering, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the Operationalization for LLM-based Annotation Framework (OLAF), a conceptual framework that organizes key constructs: reliability, calibration, drift, consensus, aggregation, and transparency. The paper …

C*-Extreme Quantum Instruments; Completion And Disintegration Of Completely Positive Maps, Arghya Chongdar

Doctoral Theses

This thesis develops a comprehensive operator-algebraic framework for the study of completely positive (CP) instruments, with contributions spanning convexity theory, integration theory, completion problems, and disintegration theory. We begin by laying the foundational groundwork in the theory of C*-algebras and von Neumann algebras, introducing key structures such as CP maps, positive operator-valued measures (POVMs), and CP instruments, along with their dilation-theoretic properties. Pure and decomposable instruments are characterized via minimal bi-dilations, and CP instruments are realized as bivariate maps, providing a rigorous quantum analogue of classical joint measures. The thesis then investigates convexity-theoretic aspects of instruments. A structural characterization of …

Public Acceptance Of Meat-Reduction Policies Across Cultures: Comparing Singapore And Switzerland, Bianca Wassmann, Shu Tian Ng, Mark Chong, Angela K. Y. Leung, Michael Siegrist

Research Collection Lee Kong Chian School Of Business

Purpose – As meat-reduction policies are discussed across the globe, many are met with public resistance. Cultural values may help explain this pushback, yet their role in shaping support for food policy remains poorly understood. This study is the first to apply cultural cognition theory to food policy by examining how cultural worldviews shape the acceptance of meat-reduction interventions in Singapore and Switzerland—two economically developed countries with contrasting cultural profiles. Design/methodology/approach – In an online survey, participants (Singapore: n = 357; Switzerland: n = 495) rated their acceptance of 11 meat-reduction interventions (e.g. taxes, subsidies, labelling). We then analysed to …

Cfalr: Collaborative Filtering-Augmented Large Language Model For Personalized Fashion Outfit Recommendation, Yujuan Ding, Junrong Liao, Yunshan Ma, Yi Bin, Wenqi Fan, Tat-Seng Chua, Qing Li

Research Collection School Of Computing and Information Systems

Personalized outfit recommendation poses a significant challenge in e-commerce and social media platforms, requiring systems that balance user preferences with aesthetic compatibility. Collaborative filtering (CF) provides a traditional solution for this, but it struggles with data-sparse scenarios and complex user-item-outfit relationships. Meanwhile, existing template-based approaches are constrained by rigid pre-designed structures. To bridge these research gaps, we introduce CFALR (Collaborative Filtering-Augmented Large Language Model for Recommendation), a novel framework that synergizes collaborative filtering with large language models for personalized outfit recommendation. Specifically, CFALR describes user-outfit interactions in natural language and leverages LLMs to capture fashion semantics while employing CF-enhanced embeddings …

Breadquest: Enhancing Roguelike Accessibility Through Procedural Generation And Thematic Design, Hahns Pena

Computer Science and Software Engineering

BreadQuest is a top-down roguelike dungeon crawler with a whimsical dessert theme that aims to make the genre more accessible while preserving strategic depth and replayability. Players explore procedurally generated dungeons, fight pastry-themed enemies, and collect bakery-inspired items that support a flavor-elemental combat system, with each run offering unique layouts, encounters, and rewards. Built in Unity with a modular, data-driven architecture, the game uses procedural generation techniques like Binary Space Partitioning, Voronoi diagrams, and Perlin noise to create varied and replayable levels. The project emphasizes approachable gameplay, cultural dessert inspiration, and replayability, with success evaluated through playtesting and player feedback.

Active Galaxies In A New Light: The Broad Line Region In The Near-Infrared, Sky O'Donnell

Physics

At the center of most massive galaxies, there is a supermassive black hole with a mass millions to billions of times that of the Sun. In some of these galaxies, so-called Active Galactic Nuclei (AGNs), the supermassive black hole is converting the gas that falls toward it into radiation energy, creating large amounts of luminosity. AGNs are composed of the central supermassive black hole, its accretion disk, and a region just outside, composed of fast-moving ionized gas clouds called the Broad Line Region (BLR). Using spectra obtained with NASA’s Infrared Telescope Facility, we use a technique known as reverberation mapping …

Oer Organic Chemistry I Lab Manual, Ji Kim

Open Educational Resources

This Organic Chemistry I Lab Manual presents ten inquiry-based modules that integrate green chemistry principles into every stage of the microscale laboratory experience. Students use Kontes microscale glassware to perform essential techniques, including distillation, recrystallization, extraction, chromatography, and nucleophilic substitution reactions. Each module features standardized learning objectives, pre-lab questions, microscale procedures, data tables, and post-lab analysis. Green Chemistry Connections explicitly links each experiment to the Twelve Principles, emphasizing waste prevention, safer solvents, renewable feedstocks, and accident reduction. From isolating caffeine from tea and lycopene from tomato paste to synthesizing cyclohexene via E1 elimination, students learn classical organic chemistry through an …

Real-Time Simulation Of Bio-Luminescent Light Propagation Using Compute Shaders Within Unreal Engine, Jaden D. Halevi

Computer Science Senior Theses

Presented in this paper is a GPU-native approach to interactive fluid simulation within Unreal Engine 5. The system, BioFluidSim, implements an incompressible Navier-Stokes solver using Unreal’s Niagara Grid2D compute shader pipeline, with a modular biological emission output stage parameterized from experimentally measured Lingulodinium polyedrum bioluminescence behavior. The system is evaluated against FluidNinja Live, a commercially available fragment shader fluid implementation, as a performance baseline. Beyond performance, BioFluidSim offers greater physical fidelity than the fragment shader baseline. Helmholtz–Hodge pressure projection enforces a divergence-free velocity field at runtime, a physical constraint approximated but not enforced by fragment shader approaches. The biological emission …

A Systematic Review Of Intrusion Detection Systems For Internet Of Medical Things: Performance, Efficiency, Explainability, And Generalization, Oswald Adohinzin, Youssef Harrath

Research & Publications

The Internet of Medical Things (IoMT) has transformed health care delivery through medical devices, remote patient monitoring, and real-time clinical decision support. However, the proliferation of IoMT devices introduces security vulnerabilities that put patient safety and data privacy at risk. Intrusion Detection Systems (IDS) have emerged as essential components for protecting IoMT networks from cyberattacks. This article presents a systematic review of IoMT-IDS research, analyzing 53 high-quality papers published between 2020 and 2025, identified through database searches spanning 2016–2025 across IEEE Xplore, Springer, ScienceDirect, and ACM Digital Library. We organize the literature through a comprehensive taxonomy spanning classical machine learning …

Exploring Regenerative Vegetable Systems On The Central Coast Of California, Una S. O'Connell

Master's Theses

As climate change and current agricultural practices place pressure on the long term sustainability and resilience of food production systems, there is growing interest in whether existing agricultural systems can adapt to maintain productivity while improving environmental outcomes.

This study evaluated the effects of regenerative versus standard organic vegetable production systems on yield, insect communities, and weed suppression across three field trials conducted on California's Central Coast. In addition, I collected baseline soil health data for all trials. Trials 1 and 2 were carried out at the Cal Poly San Luis Obispo Organic Farm using cabbage and broccoli as cash …

Developing A Humpback Whale Vocalization Detector Using Machine Learning Models, Lucas Kantorowski

Master's Theses

Humpback whale songs are notoriously complex. Identification of humpback whale song units requires bioacousticians to tediously listen, analyze, and annotate collected sound data. Even sparse data requires listening to the entirety of the collected acoustic data. In this study, three hours of audio containing over one-thousand humpback whale song units was collected in Monterey Bay, California.

Prior studies have seen success using convolutional neural networks by performing image classification on hundreds of hours worth of spectrograms. Our study uses traditional machine learning models, as they are less computationally demanding, and require less data.

We use time splitting and Mel-frequency cepstrum …

Fairlinked: Data Fairification Tools For Materials Data Science, Van D. Tran, Brandon Lee, Ritika Lamba, Henry Dirks, Quynh D. Tran, Balashanmuga Priyan Rajamohan, Ozan Dernek, Laura S. Bruckman, Yinghui Wu, Erika I. Barcelos, Roger H. French

Student Scholarship

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of …

Experimental Methods For Cryogenic Rare Event Detection: Cuore And Cupid, Alessandro Libenson

Physics

This senior project was written with the primary purpose of serving as a introductory guide for undergraduate students learning about the experimental methods used in the CUORE and CUPID Collaborations. In particular, this project is geared towards getting students in Dr. Gutierrez's research group at Cal Poly up to speed on what CUORE and CUPID actually do. I wrote this as an undergraduate aiming to help future undergraduates. For Cal Poly students, I would suggest the following prerequisites before reading this paper: Electronics and Instrumentation, Quantum Laboratory 1, and the equivalent of the coding/analysis class. It is also important to …

Western Kentucky University Stormwater Utility Survey 2026, Warren Campbell

SEAS Faculty Publications

In the U.S., more than 2200 stormwater utilities have been enacted. This year, we have expanded the survey to include stormwater utilities in other countries. Many of these stormwater assessments are called taxes, but if the assessment is recurring, is not an ad valorem tax, and is dedicated to stormwater purposes, we call it a stormwater utility. With the help of ChatGPT we have been able to identify 25 stormwater fees in Australia. We were also able to identify 3 in New Zealand, and some in Europe as well.

Identifying Textual Predictors Of Early Termination In Clinical Trials In Medicine: An Explainable Machine-Learning Study, Rohan Ramnarain

Dissertations, Theses, and Capstone Projects

About one in five clinical trials in medicine ends early, wasting valuable resources and reducing the evidence available for developing life-saving medical treatments. This project uses a method called Trial2Vec, which is a self-supervised machine-learning method that converts clinical trial documents into dense numerical representations that capture their key design and clinical characteristics, to turn each proposed clinical trial’s written protocol into a compact numerical profile (a process referred to as embedding). These profiles are then paired with a predictive machine learning models to identify the words and phrases in the trial documents that can signal a higher risk of …

Muon Lifetime: Theory, Experiment, And Simulation, Soren Agustin Munoz

Physics

This senior project investigates the muon lifetime through three complementary approaches: theoretical calculation, laboratory measurement, and computational simulation. The theoretical component develops the necessary background from relativistic field theory to the effective weak interaction, culminating in the leading-order Fermi-theory prediction of τ ≈ 2.2 μs, which explains why the muon lifetime lies on the microsecond scale.

The experimental component measures the lifetime of stopped cosmic-ray muons using a plastic scintillator, photomultiplier tube, and analog timing electronics. A binned Poisson likelihood fit to the primary 15-day acquisition run τ = 2.17^+0.03_-0.09 μs, consistent with the accepted value within the …

Ppg-Sport: A Dataset For Reliable Heart Rate Monitoring From Wrist Ppg Under Dynamic Sports Conditions, Changshuo Hu, Hung Manh Pham, Yiming Zhang, Guanru Yan, Xiao Ma, Yuezhong Wu, Thivya Kandappu, Archan Misra, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) has become a cornerstone of physiological sensing in wearable devices, enabling non-invasive monitoring of heart rate and related biomarkers. However, its reliability deteriorates sharply under dynamic, high-intensity, or non-periodic motions such as those in sports, where existing datasets fail to capture realistic wrist dynamics. To address this gap, we introduce PPG-Sport, the first large-scale dataset designed for heart rate monitoring from wrist-worn PPG under real sports conditions. The PPG-Sport dataset includes synchronized PPG, inertial measurement unit (IMU), and electrocardiography (ECG) recordings from both wrists of 30 participants across six representative activities: stationary, walking, running, badminton, table tennis, and …