Physical Sciences and Mathematics Commons^™

Demonstrating Co2 Storage In The Pcor Partnership Region And Beyond, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet about the Plains CO₂ Reduction (PCOR) Partnership region and the importance of CO₂ storage.

Red Forest, Lucas Shilts

Senior Project

Sustaining large numbers of astronauts on Mars will require greenhouses capable of supplementing astronaut dietary needs. Lower solar irradiation and high levels of ionizing radiation on the Martian surface complicate the growth of crops on the red planet. Crops on Mars must be protected from radiation sources while still receiving sufficient illumination to grow. This research seeks to develop a Martian greenhouse illumination and heating design capable of efficiently providing plants the illumination and warmth needed while protecting them from the Martian radiation environment. This research explores a design for a direct-light subsurface Martian greenhouse which employs sun-tracking mirrors, vertical …

Learn, Sleep, Remember, Repeat, Charlie Martinez, Christian Fink

Physics Student Scholarship

My research focused on using computational methods to study the effect of sleep on memory. There are many simplifications that we used to break down this hefty question, but two are essential to understand: (1) we modeled a simple network (connection diagram included in poster) that comprised of only about 800 neurons (for reference the human brain contains about 80 billion neurons) and represented the connection between two brain regions the thalamus and the cortex, and (2) we modeled memory as a simple learning rule that strengthened connections between neurons that fired in sequence and weakened connections that pointed in …

Understanding Stellar Element Formation Through Nuclear Reactions, Irena Langer, Mally Panek, Kiyah Young-Wilson, Adam Fritsch

Physics Student Scholarship

Studying nuclear structure has many applications, such as developing a stronger understanding of stellar nucleosynthesis, or how elements are formed inside of stars. Our research focuses on analysis of the proton evaporation nuclear reaction of Carbon-12 on Aluminum-27 in order to extract information about the structure of the nuclei involved. We are studying the cross section, or likelihood, of this reaction, using both experimental and modeled data. Since the cross section of a nuclear reaction depends on the physical characteristics of the nuclei involved, we can use the cross section to analyze our work.

Measuring How Much Judges Matter For Case Outcomes, Ryan W. Copus, Ryan Hübert

Faculty Works

A large empirical literature examines how judges’ traits affect how cases get resolved. This literature has led many to conclude that judges matter for case outcomes. But how much do they matter? Existing empirical findings understate the true extent of judicial influence over case outcomes since standard estimation techniques hide some disagreement among judges. We devise a machine learning method to reveal additional sources of disagreement. Applying this method to the Ninth Circuit, we estimate that at least 38% of cases could be decided differently based solely on the panel they were assigned to.

In Situ Thrust Measurement Of Fish During Locomotion; Test Case: Sharks, Braedon Payne, Bryan A. Keller, Daniel Weihs, Roi Gurka

Physics and Engineering Science

We present a novel method of measuring thrust of aquatic animals using in situ video data of swimming motions. To demonstrate its utility, the method was applied to several large elasmobranch species, which are typically highly challenging to measure. Using motion tracking software, we analyzed video footage of wild and captive sharks to track their instantaneous position and speed. In order to estimate the force output, we used the tail/body motion based on the swimming modes of the fish to calculate the water displaced by this motion during locomotion. Using Newton 3rd law, we have calculated the instantaneous force exerted …

Statistical Power To Detect Simultaneous Violation Of Proportionality In Hazards And Additive Assumption In Cox Regression Model, Lawrence Mensah Agbota

Theses and Dissertations

The Cox proportional hazards regression model is a widely employed semi-parametric tool in epidemiological and medical research for analyzing time-to-event data and assessing the relationship between patient survival times and one or more predictors. This method involves regression analyses necessitating a meticulous approach to careful examination of the covariates and the relationship among covariates included in the model through a series of critical decisions and steps. Violation of the additivity of the effects and the proportionality in hazards (PH) assumption can lead to biased results and misleading scientific findings. We conducted a Monte Carlo simulation study to assess the performance …

Serving Others Using Generative Ai, Kenneth C. Arnold

University Faculty Publications and Creative Works

Ken Arnold, computer science professor at Calvin University, explores the idea of use Generative AI as a tool to help us serve others.

Expanding And Mainstreaming Sociohydrology Toward Transdisciplinary Praxis, Shinichiro Nakamura, Heidi Kreibich, Melissa Haeffner, Jenia Mukherjee, Giuliano Di Baldassarre, Maiko Sakamoto, Mikiko Sugiura, Günter Blöschl, Taikan Oki, Murugesu Sivapalan

Environmental Science and Management Faculty Publications and Presentations

Since its development in the early 2010s, sociohydrology has deepened our understanding of the long-term coevolution of humans and water by integrating insights from both the natural and social sciences, while also fostering an interdisciplinary community. Its modus operandi to date has been to focus on emergent phenomena, manifesting as unintended consequences, in a variety of contexts. The compound disaster that struck Japan’s Noto Peninsula in 2024, and similar experiences in other parts of the world, underscore the urgent need for systemic approaches that are co-developed by academia and practitioners and focus on context-specific solutions. This perspective piece thus calls …

Response Of Soybean To Variable Irrigation Levels In Eastern Nebraska Using The Aquacrop Model, Anmol Singh

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

AquaCrop was calibrated and validated for soybean [Glycine max (L.) Merr.] using 19 irrigation treatments from five years using data from field experiments. The model accurately simulated canopy cover (CC), soil water content (SWC), and grain yield, with overall validation nRMSE values of 12%, 7%, and 7%, respectively. The overall validation nRMSE for SWC in individual 30-cm soil layers was comparatively higher, at 19%. The model was subsequently applied for long-term simulations (2010–2024) to estimate soybean yield and water requirements for two dominant soil types, Yutan silty clay loam and Tomek silt loam under three irrigation levels - rainfed, …

Empowering Weight Loss: A Pragmatic Randomized Controlled Trial Of A Theory-Driven Self-Regulation Mobile App For Young Adults With Excess Body Weight, H. S. J. Chew, J. W. Ngooi, R. C. Du, P. Z. Chan, M. Jansson, B. Zhu, Y. Cao, Chong-Wah Ngo, R. Foo, A. Shabbir, D. Ho, N. Sevdalis, K. Y. Ngiam

Research Collection School Of Computing and Information Systems

Background/Introduction: Obesity is projected to affect more than half of the global population by 2035, posing significant health and economic challenges. While lifestyle modification is considered a cornerstone of weight management, its effectiveness often relies on substantial support systems. Purpose: This study aimed to evaluate the effectiveness of a 12-week, standalone Temporal Self-Regulation Theory (TST)-based weight loss mobile application, which integrates self-regulation techniques, food logging, and dietary nudging, in promoting weight loss among young adults with excess body weight. Methods: A two-arm, parallel-group, 1:1 randomized controlled trial was conducted, adhering to the CONSORT-Outcomes 2022 Extension guidelines. Participants completed a face-to-face …

Hdifftg: A Lightweight Hybrid Diffusion-Transformer-Gcn Architecture For 3d Human Pose Estimation, Yajie Fu, Chaorui Huang, Junwei Li, Hui Kong, Yibin Tian, Huakang Li, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

We propose HDiffTG, a novel 3D Human Pose Estimation (3DHPE) method that integrates Transformer, Graph Convolutional Network (GCN), and diffusion model into a unified framework. HDiffTG leverages the strengths of these techniques to significantly improve pose estimation accuracy and robustness while maintaining a lightweight design. The Transformer captures global spatiotemporal dependencies, the GCN models local skeletal structures, and the diffusion model provides step-by-step optimization for fine-tuning, achieving a complementary balance between global and local features. This integration enhances the model’s ability to handle pose estimation under occlusions and in complex scenarios. Furthermore, we introduce lightweight optimizations to the integrated model …

Mitigating Regression Faults Induced By Feature Evolution In Deep Learning Systems, Hanmo Yu, Zan Wang, Xuyang Chen, Junjie Chen, Jun Sun, Shuang Liu, Zishuo Dong

Research Collection School Of Computing and Information Systems

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To …

Dual-Target Disjointed Cross-Domain Recommendation Mediated Via Latent User Preferences, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Users often navigate multiple platforms online, each characterized by its own set of scarce data. Recommender systems face a significant challenge in such fragmented environments. This paper proposes a novel approach to enhance recommendation systems by leveraging connections across distinct yet conceptually similar datasets from multiple platforms. We introduce a unique scenario of dual-target overlapping-free cross-platform recommendation, presenting a bridging mechanism to mutually improve across platforms and learn latent user preferences. Our approach addresses the data sparsity prevalent in each platform and enhances recommendation quality by harnessing redundant, rich, and similar domain data. Experiments validate the effectiveness of our method, …

Advancing Food Nutrition Estimation Via Visual-Ingredient Feature Fusion, Huiyan Qi, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is limited due to the lack of datasets with nutritional annotations. To address this issue, we introduce FastFood, a dataset with 84,446 images across 908 fast food categories, featuring ingredient and nutritional annotations. In addition, we propose a new model-agnostic Visual-Ingredient Feature Fusion (VIF2 ) method to enhance nutrition estimation by integrating visual and ingredient features. Ingredient robustness is improved through synonym replacement and resampling strategies during training. The ingredient-aware …

Do Supplier Ceo's National Cultural Origins Affect Supplier-Customer Relationships?, Peng Liang, Hasan Cavusoglu, Nan Hu

Research Collection School Of Computing and Information Systems

This study investigates how the national cultural origins of supplier chief executive officers (CEOs), as characterized by Hofstede’s cross-cultural dimensions, influence the duration of supplier–customer relationships. By analyzing the cultural origins of supplier CEOs from 20 countries over a 25-year period, we find that supplier CEOs with high long-term orientation (LTO) and high uncertainty avoidance (UNA) are associated with longer lasting supplier–customer relationships, while those with high individualism (IND) are associated with shorter relationship durations. These findings are robust to several alternative explanations of customer and supplier CEO variables. To address potential endogeneity—specifically, the concern that CEOs with certain cultural …

Wa-Fdnet: A Unified Weight Adaptation Network For Multimodal Image Fusion And Object Detection, Yanyin Guo, Ying Luo, Junwei Li, Zhiyuan Zhang

Research Collection School Of Computing and Information Systems

Multimodal image fusion and object detection are critical tasks in computer vision, particularly in scenarios requiring robust perception under low illumination conditions. Existing approaches that attempt to combine these tasks often rely on cascaded or loosely coupled designs, which can result in suboptimal performance due to gradient conflicts and task imbalance. In this paper, we propose WA-FDNet, a novel Weight Adaptation Fusion Detection Network that unifies multimodal image fusion and object detection into a single end-to-end framework. WA-FDNet adopts a shared encoder–private decoder architecture, enabling efficient feature sharing while preserving task-specific characteristics. The image fusion branch employs a spatial attention-based …

A Comparative Study Of Machine Learning And Deep Learning Models In Binary And Multiclass Classification For Intrusion Detection Systems, Ayesha Alharthi, Meera Alaryani, Sanaa Kaddoura

All Works

Network infrastructure evolution has significantly expanded the attack surface, leading to increasingly complex and sophisticated cybersecurity threats. Traditional rule-based intrusion detection systems (IDS) often fail to detect emerging attack vectors, prompting the need for intelligent, data-driven approaches. This study evaluates and compares the performance of machine learning (ML) and deep learning (DL) models for network intrusion detection. Two publicly available datasets were utilized: a binary-labeled software-defined networking (SDN) dataset and a multiclass industrial control system dataset based on the IEC 60870-5-104 protocol. Preprocessing steps included normalization, label encoding, and a 70:10:20 train-validation-test split. Seven models, Random Forest, Decision Tree, K-Nearest …

P-Adic Cellular Neural Networks With Delay, Baboucarr Dibba

Theses and Dissertations

This dissertation presents a novel framework for p-adic reaction-diffusion cellular neural networks (CNNs) with delay, offering new insights into the stability and dynamic behavior of these networks. Through numerical simulations, we explore their response to various conditions, highlighting their capability to model complex systems. Additionally, this work reviews cutting-edge developments in p-adic CNNs, particularly their application to advanced image processing tasks such as edge detection and noise filtering, demonstrating their effectiveness in preserving critical image features while filtering out noise. This dissertation is written in collaboration with my Ph.D supervisor and Dr. Zambrano-Luna, Brian.

A Study Of Quasigroups, A Computational Approach On Isotopism And Isomorphism Hierarchical Structure, Runaldo Montrose

Theses and Dissertations

Quasigroups morphism classification can present a real computational challenge. As of today, the number of quasigroups that can be generated from a finite set S of cardinality n is known for very small value of n. Though we know the number of quasigroups for small n, generating them in real time is a whole other issue that leads to a bigger challenge of building their isomorphy classes, because the algorithm used required large computing memory resources. In this paper we will expose the computational complexity of constructing their hierarchical morphism structure from isotopism to isomorphism. As an improvement, …

Electrocatalytic Degradation Of Methylene Blue Using Graphene Oxide, Antimony Oxide, And Graphene Oxide-Supported Antimony Oxide Ink-Based Electrodes, Maria Irene Myers Armas

Theses and Dissertations

This study investigates the electrocatalytic degradation of methylene blue (MB) using copper mesh electrodes coated with graphene oxide (GO), antimony oxide (Sb₂O₃), and their composite (GO/Sb₂O₃). These materials were evaluated across a pH range of 2 to 8 using sodium sulfate as the supporting electrolyte. UV-Vis spectroscopy at 665 nm confirmed dye degradation, with removal efficiencies reaching up to 95% at pH 2. However, degradation decreased at higher pH, with 40 – 60% removal at pH 8, depending on the electrode. Kinetic analyses revealed optimum performance under acidic conditions. GO/Sb₂O₃ electrodes demonstrated the most consistent and effective performance across all …

Predicting Enzyme-Substrate Association Using Heterogeneous Knowledge Graph, Jannatul Ferdaus

Theses and Dissertations

Phosphorylation and dephosphorylation are dynamic processes that control many aspects of cellular activity, such as metabolic pathways, cell cycle progression, and signal transduction. Protein activity and interactions are modulated by the reversible addition or removal of phosphate groups, which allows cells to react abruptly to evolving conditions. Although kinase-specific phosphorylation site prediction has advanced, phosphatase-specific dephosphorylation site computational prediction is still a major obstacle that prevents us from fully comprehending the extent of cellular regulation. In this study, we constructed a knowledge graph for the prediction of enzymes (kinases and phosphatases) and their associated substrates with specific phosphosites. As part …

Using Radium Isotopes To Compare Nutrient Inputs Via Submarine Groundwater Discharge To Offshore Export On The West Florida Shelf, Andrew L. Lindgren

OES Theses and Dissertations

Radium isotopes have been used to trace submarine groundwater discharge (SGD) and associated nutrient and metal inputs into the coastal ocean. They have also been used to estimate rates of diffusive mixing and export fluxes of dissolved constituents offshore. However, use of both approaches in tandem to determine the fate of groundwater inputs after they enter the coastal ocean is still relatively unexplored. Radium isotopes and nutrients were measured in two cross-shelf cruises on the West Florida Shelf in different seasons and in quarterly sampling of offshore wells. Through 226Ra mass balance and 223Ra-derived diffusive mixing rates, inputs of nutrients …

From Palimpsest To Prompt: Rewriting Shakespeare, Creative Authorship, And The Generative Logics Of Large Language Models In Contemporary Theatre, Michael Harding, James Hutson

Faculty Scholarship

This article examines the convergence of creative authorship, adaptation, and generative artificial intelligence within contemporary theatre, taking Michael Harding‘s Awake, Young King as a central case study. Through the rewriting of Shakespearean drama, Harding‘s creative process demonstrates how theatrical meaning emerges through ongoing negotiation among playwright, performer, and audience, with scripts historically subject to revision, improvisation, and reinterpretation. Concerns regarding copyright, intellectual property, and the role of AI in the performing arts are reframed as extensions of enduring debates over originality and authorship, rather than novel threats. Tracing the evolution from The Rise of James VI to Awake, Young King, …

Unraveling Nonperturbative Qcd With Transverse Momentum Hadronic Structures, Tommaso Rainaldi

Physics Theses & Dissertations

The main purpose of this thesis is to investigate the theoretical foundations of the factorization theorems that involve transverse quark and gluon momentum distributions, and to better understand the interface between foundational questions and phenomenological applications. Many of these applications involve using transverse parton momentum dependent (TMD) correlation functions as tools for probing the complex nonperturbative structures of the hadrons.

We first introduce the concepts of collinear and TMD factorization in a toy-model theory and, by leveraging the comparative simplicity of such a model, we test the range of validity and limitations of the factorization approach. At the same time, …

Future-Proofing Careers: Unlocking The Potential Of Ai-Driven Digital Badges, Harika Rao

Faculty and Staff Publications & Presentations

This poster explores the transformative role of AI-driven digital badges and microcredentials in addressing the urgent need for adaptable, workforce-ready graduates in an era of rapid technological change. By offering personalized, stackable, and industry-aligned credentials, higher education institutions can empower learners with just-in-time skills that traditional degree programs often fail to provide. This poster highlights how AI can support granular competency assessment, flexible online delivery, and lifelong learning pathways, enabling students to showcase evidence of skill mastery in ways that are both affordable and scalable.

Modeling Multiple Tasks In Recommendation Systems, Dinh Hieu Do

Dissertations and Theses Collection (Open Access)

Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.

However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

From Disruption To Integration: Cryptocurrency Prices, Financial Fluctuations, And Macroeconomy, Zhengyang Chen

Faculty Publications

This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency's deep financial system …

An Efficient Detection And Deep Clustering Based Pipeline For Reliable Rodent Ultrasonic Vocalization Analysis, Sabah S. Anis

Theses and Dissertations

Ultrasonic vocalizations (USVs) are critical for understanding rodents' emotional states and social behaviors. However, manual analysis of USVs is time-consuming, subjective, and prone to errors. This thesis presents an automated pipeline that addresses these challenges by performing efficient USV detection and clustering. The proposed approach significantly reduces the time and effort needed to analyze USV data while improving accuracy and reproducibility.

To address this gap, we introduce ContourUSV, a five-step pipeline for USV detection. First, it begins with generating spectrograms from audio recordings, which are then pre-processed to enhance the contrast between USVs and background noise. Key steps include median …