Digital Commons Network^™

Representing Post-Fire Reality: An Empirically Informed Temperature Index Approach To Post-Fire Hydrologic Modeling In Oregon's Western Cascades, Logan Hastings

Dissertations and Theses

The invaluable water resources stored in snowpack face mounting threats from rising temperatures, drought, shifts in precipitation regimes, and increased fire frequency, which pose challenges to operational hydrologic modeling in Oregon's sensitive maritime snowpacks. The gridded temperature index (GTI) approach in operational models like the Hydrological Engineering Center's Hydrologic Modeling System (HEC-HMS) from the U.S. Army Corps of Engineers remains in common use for its minimal data requirements, but how GTI parameterizations should adapt to post-fire conditions is largely unknown.

This research assessed the impact of the 2020 Lionshead Fire on HEC-HMS performance in the North Santiam River basin across …

Broadening The Community Of Nudibranch Enthusiasts Through Multilingual Educational Programming To Increase Climate Advocacy Engagement, Richelle Li Tanner, Cintya Felix Mendivil, Ashley Lam, Lorena Muñoz, Micah Kim, Elena G. Morales Poot, Gabrielle Keeler-May

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Environmental literacy and advocacy for environmental protections are essential components of responsible stewardship. For coastal communities, stewardship requires knowledge of ocean processes and the biological communities living within these ecosystems. While many studies highlight difficulties in engaging American audiences in environmental concern and stewardship due to hyper-individualistic societal values, tidepooling is one recreational pathway to coastal community engagement that strengthens sense of place, and therefore, responsibility to protect natural resources. We sought to broaden the tidepooling community to include more diverse voices through a participatory science program with inland city-dwelling, multilingual, and non-English speaking adults. Using nudibranchs as an environmental …

A Transformer-Based Approach With Data Augmentation For Multilabel Emotional Context Detection, Mohsin Hasan Hussein, Marem H. Abdulabas, Azha Talal Mohammed Ali, Homam Aziz Ghazi

Al-Bahir

Emotion identification in texts is becoming increasingly difficult because of the wide variety of ways emotions are represented. This study uses a fine-tuned Robustly Optimized Bidirectional Encoder Representations from Transformers Approach

(RoBERTa) to offer a Transformer-based model for identifying multilabel emotional context in textual data. To balance emotion categories and enhance the model's capacity for generalization, data augmentation is applied on two different datasets: Semantic Evaluation and Cross-lingual Emotion Dataset (SemEval and XED) English corpus. This stage is considered one of the most important steps in preprocessing as it greatly helps to improve the results. The RoBERTa model was then …

A Hybrid Rule-Based And Large Language Model Framework For Extracting Acronym–Definition Pairs From Scientific Literature, Petro Skrypnyk

Theses and Dissertations

Authors of scientific papers rely heavily on acronyms and often use them without defining them, making the literature harder to read and index. This thesis develops and evaluates a hybrid rule-based and large language model (LLM) framework that extracts acronym–definition pairs from scientific PDF documents. It extends an earlier Rowan University system that combined a regular-expression parser with a single LLM on 200 papers. That system showed that neither the parser nor the LLM alone is sufficient for accurate extraction of the pairs. The framework is a fully automated pipeline from PDF input to scored results. It compares four LLM …

Using Graphic Novels In The Teaching And Learning Of Mathematics And Physics, Jason Ho, David Klanderman, Sarah Klanderman, James Turner

University Faculty Publications and Creative Works

Are you looking for innovative teaching strategies for geometry or other mathematics and physics courses? In this article, we o!er a discussion of several graphic novels and their potential for successful teaching and learning at the high school and university levels. We describe how engaging stories, combined with mathematical and scientific meaning found in both text and image, can help to excite students, enrich learning, and explain mathematical concepts. We report on recent data collected from multiple mathematics and physics classes that extend prior research on the use of graphic novels to teach English Language Arts (Boerman-Cornell and Kim, 2020) …

Fastest-Warming States And Cities In The Mountain West, 2025, Maisoon Faris, Kahlen Coss, Kevin Yang, Caitlin J. Saladino, William E. Brown Jr.

Environment

This fact sheet presents data published by Climate Central on the change in average annual temperatures for the five Mountain West states of Arizona, Colorado, Nevada, New Mexico, and Utah and 15 Mountain West cities from 1970 to 2025. City-level data are derived from Applied Climate Information System (ACIS) operated by National Oceanic and Atmospheric Administration (NOAA), state-level data are derived from NOAA’s National Centers for Environmental Information (NCEI).

Studying Electromagnetic Wave Scattering From Small Dielectric Particles Using Neural Networks, Bryan Taylan, Patrick Corzo, Chris Velissaris, Theodoros Panagiotakopoulos

Undergraduate Scholarship and Creative Works

Physics-informed neural networks solve the Helmholtz equation without labeled data, but a low residual alone does not confirm that a solution preserves the physical distinction a downstream task depends on. We trained a four-layer SIREN with physics-based residuals, a Sommerfeld condition, Adam, and L-BFGS, modeling a Gaussian source and a plane wave scattering from a small dielectric inclusion. Fine-tuning from four base models produced 600 complex fields spanning omega = 4 to 20. A compact CNN, three ResNet-18 variants, and a frozen OpenCLIP encoder classified these fields under five random seeds. The CNN achieved (95.65 +/- 0.77)% accuracy, outperforming OpenCLIP …

Fast Discovery Of Motivic Patterns In Symbolic Music Via Lossy Compression, Adam James Wilson

Publications and Research

Generative systems that react to live musicians require rapid analysis of musical data, which rules out deep learning models: they cannot be trained within the time constraints of live performance. But because analysis results are often transformed before use, we are free to reduce the parameters that undergo transformation to a small set of primitive states. We address this coincidence of constraint and opportunity with an algorithm for online discovery of maximal musical motives that achieves speed through lossy compression: the pitch and inter-onset-interval deltas for all pairs of events in a potential motive are reduced to two-bit values, conceptualized …

Learning-Based Entanglement Generation For Quantum Routing, Tasdiqul Islam, Rasman Mubtasim Swargo, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Entanglement generation in long-distance quantum networks is challenging because resources are limited and entanglement swapping is probabilistic. To maximize the rate of successful requests, existing quantum routing algorithms often rely on computationally expensive methods such as Integer Linear Programming (ILP) to determine which links to entangle and use for end-To-end entanglement generation. However, these approaches fail to meet the latency requirements of real-world quantum networks. In this study, we propose a Reinforcement Learning (RL)-based model that determines which links to entangle in each time slot, replacing the slow ILP-based link-selection phase used in prior algorithms. The proposed Deep Q-learning model …

Artificial Intelligence Models For Automated And Semiautomated Analysis And Interpretation Of Clinical Electroencephalography, Sándor Beniczky, Birgit Frauscher, Fábio Nascimento, Shobi Sivathamboo, Catalina Rojas, Michael Sperling, Samden Lhatoo, Philippe Ryvlin, Levin Kuhlmann

Department of Neurology Faculty Papers

Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of data that cannot be managed through traditional visual interpretation by experts. Artificial intelligence (AI) has the potential to augment human expertise and reduce workloads. The application of artificial neural networks in analysing clinical EEG recordings has led to major breakthroughs, bringing AI-based EEG interpretation closer to clinical implementation. In this Review, we summarise the most important research and development results in this field from …

Chemistry In The City Module, Ji Kim

Open Educational Resources

This project integrates a historical environmental health report from the CUNY Digital History Archive into an online Introductory Chemistry course to connect core chemical concepts with real urban pollution issues. Students analyze perchloroethylene contamination in apartments above dry cleaners, apply concepts such as volatility, vapor pressure, and concentration units, and reflect on chemical principles underlying indoor air pollution.

Chemistry In The City Module, Ji Kim

Open Educational Resources

This project integrates a historical environmental health report from the CUNY Digital History Archive into an online Introductory Chemistry course to connect core chemical concepts with real urban pollution issues. Students analyze perchloroethylene contamination in apartments above dry cleaners, apply concepts such as volatility, vapor pressure, and concentration units, and reflect on chemical principles underlying indoor air pollution.

Readme Template For Geospatial Data, Alyssa Renteria

Library Faculty Research

A readme file provides descriptive information about a dataset, file(s), or software. The goal is to ensure that the files and data can be correctly interpreted by your future self or others when sharing or publishing data, code, or software.

Waste Streams From Next Generation Molten Salt Reactors: The Challenge Of Creating An Insoluble Ceramic For Chloride Salt Waste, Alevtina A. Maksimova, Jake W. Amoroso, Matthew Page, Gregory Morrison, Hans Conrad Zur Loye

Faculty Publications

Molten salt reactors (MSRs) design development is of interest to many research groups and companies. Waste management for MSRs is one of the most important issues that needs to be resolved due to its impact on environmental safety, primarily via the dissolution of radionuclides in water. Furthermore, the waste treatment strategy will have a significant influence on the operating cost of MSRs, which is why a reliable process for the immobilization, transportation, storage and disposal of MSR waste must be addressed. This work presents a new approach for the immobilization of chloride salts from molten salt reactors in stable oxyhalide …

Enhancing Programming Productivity For Individuals With Adhd Through Generative Artificial Intelligence: An Inductive Analysis, Lionel Mew

School of Professional and Continuing Studies Faculty Publications

Attention-deficit/hyperactivity disorder (ADHD) significantly impacts computer programmers through challenges in sustained attention, executive functioning, and organizational skills. While traditional intervention strategies have shown varying degrees of success, the emergence of generative artificial intelligence (AI) presents novel opportunities to address ADHD-related programming challenges. This paper presents an inductive analysis synthesizing current research on ADHD's effects on programming, traditional productivity enhancement techniques, and the potential of generative AI tools. Through examination of recent literature and field studies, we propose that generative AI can serve as a transformative intervention by providing personalized cognitive support, reducing executive function demands, and enhancing code generation efficiency. …

Bridging Data Gaps In Retinal Imaging: From Structural Domain Adaptation To Topology-Aware Synthesis, Gözde Merve Demirci

Dissertations, Theses, and Capstone Projects

Comprehensive visualization of the retina is essential for diagnosing and monitoring blinding diseases such as Diabetic Retinopathy and Retinopathy of Prematurity (ROP), where pathological changes often extend beyond a single field of view. Despite significant advances in automated retinal image analysis, clinical deployment remains limited by two fundamental data gaps: a structural learning gap, arising from scarce expert annotations and poor generalization across imaging domains, and a spatial coverage gap, caused by the difficulty of acquiring multi-view retinal images in fragile populations. Although these challenges are often addressed independently, this dissertation argues that they are tightly coupled: accurate, …

Potential Energy Landscape Formalism For Quantum Liquids, Yang Zhou

Dissertations, Theses, and Capstone Projects

Atomic delocalization due to nuclear quantum effects (NQE) remains poorly understood in low-temperature liquids near the glass state and during vitrification. Many liquids can be described accurately by treating their nuclei as classical particles, but this approximation fails for light elements such as He and H₂, small hydrogen-containing molecules such as water, and systems in which zero-point motion or isotope-substitution effects are important. Developing a general thermodynamic and statistical-mechanical description of such liquids has been challenging. This dissertation extends the potential energy landscape (PEL) formalism, originally developed for classical liquids and glasses, to liquids that obey quantum mechanics and exhibit …

Mapping The Water Quality Of Jamaica Bay, New York (1996-2024): Principal Component Analysis And K-Means Clustering, Sneha Srivastava

Dissertations, Theses, and Capstone Projects

Jamaica Bay, located along the southeastern coast of New York City, acts as a biodiverse estuary of wetlands, meadows, and salt marsh islands. The purpose of this study is to analyze the water quality conditions of the region over time, comparing locations around the bay to identify hyperlocal features that influence larger trends in the hydrological system. Ten variables were used as water quality indicators, including total Kjeldahl nitrogen, salinity, pH, Secchi disk depth, and total phosphorus, among others, across five stations in the bay, between 1994 and 2024. After data cleaning and standardization methods were applied, principal component analysis …

On Families Of Finsler Metrics, İsmai̇l Sağlam, Ken'ichi Ohshika, Athanase Papadopoulos

Turkish Journal of Mathematics

In this paper, we answer some natural questions concerning symmetrisation and more general combinations of Finsler metrics, with a view to applications to Funk and Hilbert geometries and metrics on Teichmüller spaces. The metrics on Teichmüller space that we consider are the Thurston metric and the earthquake metric, both introduced by Thurston. The first metric has been thoroughly studied over the last couple of decades, and the second over the last few years. The Funk metric and its symmetrisation, the Hilbert metric, are classical metrics that have been investigated in the contexts of hyperbolic geometry and geometric function theory and, …

From Withdrawal To Impact: Smu Libraries’ Book Rehoming Initiatives, Kai Leong Heng, Eng Ling Lynn Yeo

Research Collection Library

Academic libraries are no strangers to large-scale deselection exercises. Driven by space constraints, evolving curricula, and the shift towards digital resources, the question is no longer whether to withdraw print materials, but what comes next. In the past year, SMU Libraries explored a different answer: instead of recycling withdrawn books, could we reimagine their next chapter, and rehome them to create meaningful impact for the community?

Catalytic Synthesis Of Protoberberine Alkaloids And Their Evaluation As Selective Dopamine Receptor Ligands, Ashok Reddy Gudipally

Dissertations, Theses, and Capstone Projects

Dopamine regulates motor control, cognition, reward, motivation, and neuroendocrine signaling. Disruption of dopaminergic neurotransmission is closely linked to central nervous system disorders, including Parkinson’s disease, schizophrenia, addiction, and related neuropsychiatric conditions. Because these functions are mediated by multiple dopamine receptor subtypes with distinct roles in physiology and disease, the discovery of subtype-selective ligands remains an important goal in neuropharmacology. Within this context, protoberberine alkaloids, particularly tetrahydroprotoberberines (THPBs), are attractive molecular frameworks because they combine structural complexity with broad biological relevance, including measurable activity in CNS-related diseases. Prior work from our laboratory and others established (–)-stepholidine (1.60) as an important lead …

Model-Theoretic Arguments In Philosophy, Peter Susanszky

Dissertations, Theses, and Capstone Projects

This dissertation is on model-theoretic arguments in philosophy, especially those of Quine, Davidson, and Putnam. In the first part, to ground the debate, I give a rigorous introduction to the salient parts of first-order model theory. I start the second part by giving an introduction to Quine's philosophy, and how the model-theoretic arguments fit into it. After considering how Donald Davidson adopted the Quinean lesson, I move on to Putnam's model-theoretic arguments. Putnam's spin on these model-theoretic considerations significantly departs from Quine and Davidson, while retaining many of the core ideas. Most importantly, I argue that the target of Putnam's …

Rethinking Road Racing Standards: Comparing New York Road Runners Race Results To Running Industry Standards, Jennie Coughlin

Dissertations, Theses, and Capstone Projects

Running has undergone a third boom in participation since 2020, driven largely by people seeking fitness options that were outside and socially distanced during the COVID-19 pandemic. Social media has also allowed runners from groups that did not traditionally participate to find community. As the road racing population has expanded to include more people, some road racing industry standards have not kept up. This project assesses what the road racing community looks like and measures industry standards against the community of participants to see what changes in those standards would be needed to make them inclusive for all participants.

To …

Mesoscopic Scaling Of Microwave Transmittance And Disorder-Induced Alignment, Israel Kurtz

Dissertations, Theses, and Capstone Projects

This thesis presents microwave measurements and numerical simulations of wave propagation and transmission through random mesoscopic waveguides. We analyze the scaling of mesoscopic microwave conductance, including departures from Ohm’s law near channel openings, which are frequencies at which new propagating modes enter the system, and the evolution of modal phase alignment within the medium, which leads to the observed diversity of transmission eigenvalues. We extend the transmission matrix formalism to all sample depths using the flux matrix, which relates the flux amplitude within the sample to the incident flux, and decompose the flux at each sample depth into forward- and …

Self-Organized Criticality In Biased Activated Random Walk, Joshua Meisel

Dissertations, Theses, and Capstone Projects

We demonstrate many of the conjectured universality and self-organized criticality (SOC) properties of the interacting particle system Activated Random Walk (ARW) in the setting of one-dimensional biased walks, as formulated for instance in Levine and Silvestri’s 2024 survey. Namely, there exists the same limiting critical state for two finite ARW models, as well as for the infinite model as the density approaches its critical value from below. Many of the desired properties hold, including heavy-tailed avalanches, but spatial correlations decay exponentially fast, a deviation from the SOC paradigm. Nonetheless, the obtained decay rate vanishes with the bias. Since the critical …

Individualized Bayesian Inference Identifies Novel Genetic Variants For Parkinson's Disease, Jin Ren, Yasaman J. Soofi, Md Asad Rahman, Qing Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Parkinson's disease (PD) is a complex neurodegenerative disorder with a significant genetic component. While genome-wide association studies (GWAS) have been instrumental in identifying genetic variants associated with PD, the reliance on large sample sizes and population-level analyses may overlook variants with lower minor allele frequencies or individual-specific relevance. Individualized Bayesian Inference (IBI) offers a promising method to complement GWAS by identifying and prioritizing candidate genetic markers at both the individual and patients-like-me subgroup levels. This study evaluates the application of IBI to PD genetics, using GWAS as a baseline for comparison. We analyzed genetic data from the Fox Insight online …

Building Wellbeing Through Nature And Adventure For Justice-Involved Youth, Lewis Kogan, Meagan Ricks, Janna Coulter, Miranda Margetts, Lori Butterfield, Ben Ukoh-Eke

Outcomes and Impact Quarterly

Justice-involved youth often experience elevated levels of stress, mental health challenges, and reduced access to positive developmental opportunities. A nature- and adventure-based youth development program designed to strengthen resilience, self-efficacy, nature-connectedness, and well-being among justice-involved youth was piloted in 2025 to address these challenges. Pilot findings indicated improvements in resilience, self-efficacy, hopefulness, and connectedness to nature.

Bayesian And Multi-Objective Decision Support For Incident Mitigation In Cyber-Physical Systems, Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Research Collection School of Computing and Information Systems

Cyber-physical systems increasingly rely on interconnected physical and digital systems whose security incidents can escalate rapidly into safety and operational failures. Existing decision-support approaches struggle to support incident response because they rely on static assumptions, incomplete vulnerability data, and single-objective risk models that do not adequately capture trade-offs between attack success likelihood, impact severity, and system availability. This paper proposes an adaptive decision-support framework for incident mitigation in cyber-physical systems that integrates hierarchical Bayesian Network modelling, confidence-calibrated exposure estimation, and multi-objective optimisation into a unified, adaptive pipeline. The framework constructs probabilistic models from system architecture and vulnerability data, incorporating complementary …

Prune: A Patching Based Repair Framework For Certifiable And Privacy-Robust Unlearning Of Neural Networks, Xuran Li, Jingyi Wang, Xiaohan Yuan, Peixin Zhang

Research Collection School Of Computing and Information Systems

Machine unlearning has emerged as a key mechanism for enabling the “right to be forgotten” in neural network models, allowing the selective removal of specific training data upon request. Existing approaches typically rely on retraining models with the remaining data, which is computationally expensive and difficult to verify, especially when deployed models are distributed or resource-constrained. To address this challenge, our prior conference work introduced PRUNE, a patching-based framework that formulates unlearning as a neural network repair problem. PRUNE achieves targeted forgetting by learning lightweight patch networks that redirect model predictions on the data to be unlearned while preserving performance …

Neural Symphony Of Flow Experience: Evidence For High-Dimensional Metastable Dynamics, Abdelrahman B. M. Eldaly, Kris Zhangguang Kang, Fiona Fui-Hoon Nah, Leanne Lai-Hang Chan, Keng Siau, Xiao Fan Liu, Richard Huskey, Langtao Chen, Tejaswini Yelamanchili, Rene Weber

Research Collection School Of Computing and Information Systems

Flow, an optimal experience characterized by deep immersion and engagement in an activity, has been extensively studied in behavioral research. However, its neural dynamic mechanism remains poorly understood. In a within-subject video gaming experiment, we captured neural activity underlying flow, boredom, and anxiety using a 64-channel electroencephalogram (EEG) system. Compared to boredom and anxiety, flow exhibits the highest global functional connectivity, metastability, and dimensionality of dynamic functional connectivity patterns, suggesting that flow is a highly adaptable process that is supported by high-dimensional neural dynamics. Unlike previous studies that focused on identifying static or localized brain activity, we examine the neural …