Physical Sciences and Mathematics Commons^™

Computing Optimal Multi-Level Stress Testing Plans Using A Combined Variable Neighborhood Search Algorithm Under Progressive Type-Ii Censoring Scheme, Michael Obuobi

Open Access Theses & Dissertations

In multi-level stress life tests under Type-II progressive censoring, determining optimal allocation poses significant computational challenges due to the vast solution space. Efficient methods are essential for exploring the admissible censoring schemes effectively. This thesis introduces a novel meta-heuristic algorithm, the Combined Variable Neighborhood Search (CVNS), which computes optimal schemes at different stress levels simultaneously. Unlike methods focusing on marginal stress levels or one-step progressive censoring, this approach leverages a unified framework to ensure enhanced computational efficiency and solution quality. By integrating the components of the design parameters into a cohesive optimization process, the algorithm effectively reduces computational time while …

Investigation Of High-Frequency Electron Spin Dynamics In The Kagome-Lattice Ymn6sn6 Crystal, Lovia Ofori

Open Access Theses & Dissertations

Kagome magnetic crystals received a great deal of research attention in the recent past for their intriguing magnetic properties. Recently, YMn6Sn6 (Y166) has shown to exhibit several nontrivial magnetic phases (such as the Distorted Spiral (DS) phase, Transverse Conical Spiral (TCS) phase, Fan-like (FL) phase and the Forced-Ferromagnetic (FF) phase) and complex magnetic interactions. In this work, we employed very high-frequency electron paramagnetic resonance (VHF-EPR) spectroscopy to investigate the local microscopic magnetic interactions of Mn ions in the Kagome Crystal Y166 to better understand the new observed magnetic phases. Particularly, we studied the temperature-dependent EPR behavior at variable very-high microwave …

From Land To Lagoon: The Spatiotemporal Biogeochemical Dynamics Of Arctic Coastal Erosion In Elson Lagoon, Alaska, Sasha Victoria Peterson

Open Access Theses & Dissertations

TThe Arctic is warming rapidly, driving widespread environmental change across coastal landscapes, including permafrost thaw, shoreline erosion, and shifts in sediment and carbon dynamics, pose growing challenges for predicting carbon cycling and climate feedbacks. This dissertation investigates the spatiotemporal variability of erosion, decomposition, and nearshore turbidity in Elson Lagoon, Alaska (1955–2023), to better understand how carbon, nitrogen, and sediment are mobilized, transformed, and potentially emitted as greenhouse gases in Arctic coastal systems. Chapter 2 presents a high-resolution analysis of shoreline change and erosion-driven material fluxes spanning nearly seven decades. Drawing on 795 soil data points, airborne LiDAR, and five shoreline …

Enhancing Security And Resiliency In Operational Technology Environments Through Network Slicing And Federated Learning, Brian Giovanni Rodiles Delgado

Open Access Theses & Dissertations

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation.

The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, …

A Numerical Study Of Self-Assembling Amphiphilic Systems, Joshua Albert Sackey

Open Access Theses & Dissertations

Mixing processes in ternary mixtures involve immiscible fluids such as oil and water, and a surface-active molecule called surfactant. These physical processes find applications in various fields, including enhanced oil recovery, drug delivery design systems, and the formulation of cleaning products. Even though this process has several applications, the mathematical models describing it and the numerical methods solving it are not well understood. The underlying mathematical model is a nonlinear initial-boundary value problem involving sixth-order derivatives and belongs to the class of sixth-order Cahn-Hilliard equations. Authors Sharma and Tierra recently proposed a numerical method to approximate its solutions in two …

Machine Learning And Time Series Forecasting For Hydropower Predictions, Jose Reynaldo Vega

Open Access Theses & Dissertations

Recent advancements in machine learning have led to the design of many neural network architectures aimed at solving real-world problems. Each network works to make predictions by finding patterns in the provided data. One common application is time series forecasting, where a model predicts future events based on historical time series data. Time series forecasting is used in a variety of fields, one example being in predicting water releases of Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. As global population growth drives an increase in energy demand, the need for resilient and sustainable energy generation has become urgent. HFPVH systems have emerged …

A Study Of End-Cut Preference In Tree-Based Modeling, Xiangya Wang

Open Access Theses & Dissertations

Decision trees, particularly those built using the Classification and Regression Trees (CART) algorithm, are widely used for their interpretability and flexibility. However, the greedy nature of the CART splitting procedure gives rise to the end-cut preference (ECP) phenomenon, wherein split points near the extremes of predictor ranges are favored. This study offers a comprehensive investigation of ECP, exploring its theoretical underpinnings, practical manifestations, and implications for both single decision trees and ensemble methods such as Random Forests. Through theoretical analysis and simulation studies, we examine how ECP affects tree structure, variable selection, and predictive accuracy across tree-structured, linear, and nonlinear …

Car Price Prediction Using Machine Learning: Analyzing The Dvm-Car Dataset, Yaman Abu Ghareebaih

Electronic Theses and Dissertations

The objective of this study is to predict car prices using machine learning models and the DVM-CAR dataset, which includes over 1.4 million images and car specifi- cations from 899 car models. Key factors such as mileage, engine power, and year of registration were analyzed for their correlation with car prices. Extensive data cleaning was performed, including filling missing values, identifying outliers, and normalizing numerical variables. Discrete variables like car make and body type were encoded using one-hot encoding. Linear relationships were analyzed with Multiple Logistic Regression, and Random Forest models were used for nonlinear patterns. Model performance was evaluated …

Alphamissense Predictions And Clinvar Annotations: A Deep Learning Approach To Uveal Melanoma, David J. Taylor Gonzalez, Mak B. Djulbegovic, Meghan Sharma, Michael Antonietti, Colin K. Kim, Vladimir N. Uversky, Carol L. Karp, Carol L. Shields, Matthew W. Wilson

Wills Eye Hospital Papers

OBJECTIVE: Uveal melanoma (UM) poses significant diagnostic and prognostic challenges due to its variable genetic landscape. We explore the use of a novel deep learning tool to assess the functional impact of genetic mutations in UM.

DESIGN: A cross-sectional bioinformatics exploratory data analysis of genetic mutations from UM cases.

SUBJECTS: Genetic data from patients diagnosed with UM were analyzed, explicitly focusing on missense mutations sourced from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.

METHODS: We identified missense mutations frequently observed in UM using the COSMIC database, assessed their potential pathogenicity using AlphaMissense, and visualized mutations using AlphaFold. Clinical …

The Improved New Intersection Theorem Revisited, Lars Winther, Luigi Ferraro

School of Mathematical & Statistical Sciences Faculty Publications

We prove a generalized version of Evans and Griffith’s improved new intersection theorem: Let I be an ideal in a local ring R. If a finite free R-complex, concentrated in nonnegative degrees, has I-torsion homology in positive degrees, and the homology in degree 0 has an I-torsion minimal generator, then the length of the complex is at least dimR−dimR/I. This improves the bound htI obtained by Avramov, Iyengar, and Neeman in 2018.

Multiobjective Linear Ensembles For Robust And Sparse Training Of Few-Bit Neural Networks, Ambrogio Maria Bernardelli, Stefano Gualandi, Simone Milanesi, Hoong Chuin Lau, Neil Yorke-Smith

Research Collection School Of Computing and Information Systems

Training neural networks (NNs) using combinatorial optimization solvers has gained attention in recent years. In low-data settings, the use of state-of-the-art mixed integer linear programming solvers, for instance, has the potential to exactly train an NN while avoiding computing-intensive training and hyperparameter tuning and simultaneously training and sparsifying the network. We study the case of few-bit discrete-valued neural networks, both binarized neural networks (BNNs) whose values are restricted to ±1 and integer-valued neural networks (INNs) whose values lie in the range {−P,…,P}. Few-bit NNs receive increasing recognition because of their lightweight architecture and ability to run on low-power devices: for …

Augsso: Secure Threshold Single-Sign-On Authentication With Popular Password Collection, Changsong Jiang, Chunxiang Xu, Guomin Yang

Research Collection School Of Computing and Information Systems

Single-sign-on authentication is widely deployed in mobile systems, which allows an identity server to authenticate a mobile user and issue her/him with a token, such that the user can access diverse mobile services. To address the single-point-offailure problem, threshold single-sign-on authentication (PbTA) is a feasible solution, where multiple identity servers perform user authentication and token issuance in a threshold way. However, existing PbTA schemes confront critical drawbacks. Specifically, these schemes are vulnerable to perpetual secret leakage attacks (PSLA): an adversary perpetually compromises secrets of identity servers (e.g., secret key shares or credentials) to break security. Besides, they fail to achieve …

Reverse Modeling In Large Language Models, Sicheng Yu, Yuanchen Xu, Cunxiao Du, Yanying Zhou, Minghui Qiu, Qianru Sun, Hao Zhang, Jiawei Wu

Research Collection School Of Computing and Information Systems

Humans are accustomed to reading and writing in a forward manner, and this natural bias extends to text understanding in auto-regressive large language models (LLMs). This paper investigates whether LLMs, like humans, struggle with reverse modeling, specifically with reversed text inputs. We found that publicly available pre-trained LLMs cannot understand such inputs. However, LLMs trained from scratch with both forward and reverse texts can understand them equally well during inference. Our case study shows that different-content texts result in different losses if input (to LLMs) in different directions---some get lower losses for forward while some for reverse. This leads us …

Intention Is All You Need: Refining Your Code From Your Intention, Qi Guo, Xiaofei Xie, Shangqing Liu, Ming Hu, Xiaohong Li, Lei Bu

Research Collection School Of Computing and Information Systems

Code refinement aims to enhance existing code by addressing issues, refactoring, and optimizing to improve quality and meet specific requirements. As software projects scale in size and complexity, the traditional iterative exchange between reviewers and developers becomes increasingly burdensome. While recent deep learning techniques have been explored to accelerate this process, their performance remains limited, primarily due to challenges in accurately understanding reviewers’ intents. This paper proposes an intention-based code refinement technique that enhances the conventional comment-to-code process by explicitly extracting reviewer intentions from the comments. Our approach consists of two key phases: Intention Extraction and Intention Guided Revision Generation. …

Tensorjsfuzz: Effective Testing Of Web-Based Deep Learning Frameworks Via Input-Constraint Extraction, Lili Quan, Xiaofei Xie, Qianyu Guo, Lingxiao Jiang, Sen Chen, Junjie Wang, Xiaohong Li

Research Collection School Of Computing and Information Systems

The 2025 ACM Web Conference (WWW '25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its logo, featuring the Sydney Harbour Bridge, symbolizes the core "connecting" function of the Web. Formerly known as the International World Wide Web Conference (WWW), this event originated at CERN in 1994 and has long served as the premier venue for presenting and discussing research, development, standards, and applications related to the Web.The 2025 ACM Web Conference (WWW'25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its …

Dissecting Global Search: A Simple Yet Effective Method To Boost Individual Discrimination Testing And Repair, Lili Quan, Tianlin Li, Xiaofei Xie, Zhenpeng Chen, Sen Chen, Lingxiao Jiang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Deep Learning (DL) has achieved significant success in socially critical decision-making applications but often exhibits unfair behaviors, raising social concerns. Among these unfair behaviors, individual discrimination-examining inequalities between instance pairs with identical profiles differing only in sensitive attributes such as gender, race, and age-is extremely socially impactful. Existing methods have made significant and commendable efforts in testing individual discrimination before deployment. However, their efficiency and effectiveness remain limited, particularly when evaluating relatively fairer models. It remains unclear which phase of the existing testing framework (global or local) is the primary bottleneck limiting performance. Facing the above issues, we first identify …

Enhancing Sub-Optimal Trajectory Stitching: Spatial Composition Rvs For Offline Rl, Sheng Zang, Zhiguang Cao, Bo An, Senthilnath Jayavelu, Xiaoli Li

Research Collection School Of Computing and Information Systems

Reinforcement learning via supervised learning (RvS) has been known as a burgeoning paradigm for offline reinforcement learning (RL). While return-conditioned RvS (RvS-R) predominates across a wide range of datasets pertaining to the offline RL tasks, recent findings suggest that goal-conditioned RvS (RvS-G) outperforms in specific sub-optimal datasets where trajectory stitching is crucial for achieving optimal performance. However, the underlying reasons for this superiority remain insufficiently explored. In this paper, employing didactic experiments and theoretical analysis, we reveal that the proficiency of RvS-G in stitching trajectories arises from its adeptness in generalizing to unknown goals during evaluation. Building on this insight, …

Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-Awareness In Computing, Tadashi Okoshi, Zexiong Gao, Yi Zhen Tan, Takumi Karasawa, Takeshi Miki, Wataru Sasaki, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In Affective computing, recognizing users’ emotions accurately is the basis of affective human–computer interaction. Understanding users’ interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users’ interoception challenging. This study aims to determine other forms of data that can explain users’ interoceptive or similar states in their real-world lives and propose a novel hypothetical concept “cyberoception,” a new sense (1) which …

Mosmac: A Multi-Agent Reinforcement Learning Benchmark On Sequential Multi-Objective Tasks, Minghong Geng, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Recent advancements in multi-agent reinforcement learning (MARL) have demonstrated success on various cooperative multi-agent tasks. However, current benchmarks often fall short of representing realistic scenarios that demand agents to execute sequential tasks over long temporal horizons while balancing multiple objectives. To address this limitation, we introduce multi-objective SMAC (MOSMAC), a comprehensive MARL benchmark designed to evaluate MARL methods on tasks involving multiple objectives, sequential subtask assignments, and varying temporal horizons. MOSMAC requires agents to tackle a series of interconnected subtasks in StarCraft II while simultaneously optimizing for multiple objectives, including combat, safety, and navigation. Through rigorous evaluation of nine state-of-the-art …

Course Insight Portfolio: Math 1060 Calculus Of One Variable I, Julianne Barnhart, Rayna Elizabeth Maleki, Antsa Rakotondrafara

Publications

We presented this Course Insight Portfolio project as a final assignment for the class Teaching Undergraduate Math MATH 9700 in Summer 2025. It covers the Clemson class MATH 1060 Calculus of One Variable I and is a fruit of the collaborative work of Julianne Barnhart, Rayna Maleki, and Antsa Rakotondrafara.

Quotients, Equivalence Relations, And Normality In Non-Associative Algebra With Regards To Loops And Quasigroups, Matthew L. Mulholland

All NMU Master's Theses

This thesis will contain a detailed overview of relations, quotients, normality, loops, quasigroups, and related theorems and varieties. Nonassociative algebra is a relatively new area of mathematics, it came about in the past hundred years, and has started making progress in the past 60 years. In nonassociative algebra, varieties do not necessarily satisfy associativity. Several interesting problems with relations, quotients, and normality arise from the setting of nonassociative algebra. In the language of equivalence relations, quotients, and subsets what are the conditions of normality, or existence of a subalgebra in quasigroups and loops? A quasigroup, Q, is defined to be …

Exploring The Biasing Effects Of Gender On Personality Disorder Diagnoses Formulated By Artificial Intelligence, Zoe Colclough

Student Theses

Gender bias is prevalent in personality disorder assessments, and while artificial intelligence has been posited as a solution to improve diagnostic objectivity and accuracy, the potential for such technologies to propagate human gender bias in mental health contexts remains underexplored. This study investigated the influences of gender bias on the diagnostic performance of ChatGPT-4o for personality disorders using three factorial research designs, which involved experimentally manipulating patient gender in a combined sample of 360 vignettes and case studies. Vignettes were synthesized through a novel artificial intelligence-assisted methodology established for this research, and case studies were identified from the literature. Significant …

Mechanistic Investigation Of Ring-Opening Polymerization Of Polycaprolactone Using Tin(Ii) Catalysts: Ligand Effects And Biomedical Applications, Eva-Larue M. Barber

Honors Scholar Theses

This research explores the mechanistic aspects of ring-opening polymerization (ROP) of ε-caprolactone (CL) to produce polycaprolactone (PCL), a biodegradable polymer widely used in biomedical applications. The study investigates how light exposure and catalyst concentration influence polymerization efficiency, using tin(II) 2-ethylhexanoate [Sn(Oct)₂] as the catalyst in a non-polar toluene solvent at 90 °C. Reactions were conducted under either ambient light or black light bulb (BLB) illumination, with monomer-to-catalyst ratios of 1:1 and 200:1.

Proton nuclear magnetic resonance (¹H NMR) spectroscopy was used to analyze conversion efficiency by tracking the disappearance of monomer signals and appearance of characteristic PCL peaks. Results revealed …

Latitudinal Gradients In Arctic Willows And Their Effects On Stream-Riparian Systems, Donal Heaney

Honors Scholar Theses

Extreme warming in the Arctic due to climate change presents the opportunity to study rapid changes to ecosystems and their potential resilience. Willow (Salix) shrubs form the base of many Arctic riparian ecosystems, and they have increased in height and range as temperatures have warmed. The effects of warming on willows could alter riparian ecosystems and neighboring streams, so understanding how climate change will affect willows is integral to understanding how the stream-riparian meta-ecosystem might change. I used data from the EvoME Institute to compare willow canopy cover above Arctic streams along a latitudinal transect and investigate how …

St. Cloud Dam: Is The Renewable Energy Worth The Ecological Consequences, Adam Novak

Celebrating Scholarship and Creativity Day (2018-)

The St. Cloud hydroelectric dam is a smaller dam located in central Minnesota on the Mississippi river. The dam causes many environmental issues related to stopping freshwater fish migration, lack of nutrient loading at the bottom of the dam, and a general loss of biodiversity on the Mississippi. Dam removal is an option to solve many of the problems that the dam produces, but it will remove a large amount of renewable energy that powers St Cloud. Do the renewable energy benefits of the St Cloud dam outweigh the ecological benefits of its removal? This poster examines whether the renewable …

Advancing Precision And Autonomy In Agriculture And Medical Imaging Through Ai And Computer Visions, Tan-Hanh Pham

Theses and Dissertations

Deep learning has revolutionized numerous fields by enhancing precision, automation, and decision-making capabilities. This dissertation explores its applications in agriculture and medical image processing, introducing novel methodologies to improve accuracy and efficiency in these domains. These fields hold critical societal importance -- agriculture underpins global food security and sustainability, while medical imaging drives advancements in diagnostics and personalized healthcare, both benefiting significantly from data-driven innovations. In agriculture, deep learning is applied to precision spray systems through droplet analysis. Specifically, a generative model is designed to create synthetic droplet images, addressing the challenge of limited training samples, which are expensive …

More Concerns About Atmospheric Methane Removal Efforts, Joshua Luczak

Research Collection College of Integrative Studies

The National Oceanic and Atmospheric Administration reported record-high global atmospheric carbon dioxide levels (419.3 ppm) in 2023, alongside atmospheric methane levels (1922.6 ppb) now over 160% above pre-industrial levels. The World Meteorological Organization predicts 2024 could surpass 2023 as the warmest year on record, with more frequent and severe extreme weather events. Meeting the Paris Climate goal of limiting global warming to well below 2°C—or ideally 1.5°C—above pre-industrial levels is becoming increasingly difficult. To address these challenges, efforts are expanding beyond reducing greenhouse gas emissions. While carbon dioxide removal (CDR) technologies have been the focus, attention is now turning to …

Stability Analysis Of Turbulent Fluid Flow, Adam D. Schroeder

Mathematics, Statistics, and Computer Science Honors Projects

Hydrodynamic stability refers to the study of when and how laminar flows transition to turbulence. This includes investigations of the mechanisms of transition, as well as the classification of known flow configurations as either stable or unstable and the identification of critical values of flow parameters at which this bifurcation occurs. In this thesis, we introduce the mathematical theory behind continuum mechanics and fluid dynamics as well as some tools from the study of dynamical systems. We apply these concepts to the linear stability analysis of zero pressure gradient flat plate flow via numerical simulations in OpenFOAM, discussing both the …

Cover Crop-Induced Improvements To Soil Hydraulic Properties And The Role Of Termination Time In Mitigating Negative Soil Moisture Impacts, Fabrizio Javier Pilco

Theses and Dissertations

In the Lower Rio Grande Valley of South Texas, water scarcity and drought pose ongoing challenges for farmers. Although cover crops are promoted for improving soil health and moisture, their effects on soil hydraulic properties in this region remain understudied. This thesis addresses both long-term (Chapter 2) and short-term (Chapter 3) impacts of cover crops on soil hydraulic properties and soil moisture. Long-term effects were evaluated through a three-year participatory field trial across four farms using a BACI design, while short terms were evaluated using a complete randomized block design. Soil hydraulic properties (residual water content θr, saturated water content …

The Effect Of Hiv-1 Infection Associated Bacterial Lipopolysaccharide (Lps) On Human Oral Keratinocytes: Implications For Promoting Chronic Inflammation, Md Shafayat Jamil

Theses and Dissertations

Human immunodeficiency virus type 1 (HIV-1) remains a major global health concern, affecting approximately 39 million people worldwide, with 500,000 new infections reported in 2022. While combined antiretroviral therapy (cART) has significantly reduced viral replication, HIV-1 infection is linked to various comorbidities, including oral dysbiosis and accelerated aging. Immune dysregulation in HIV-1–infected individuals promotes the proliferation of pathogenic gram-negative bacteria like Porphyromonas gingivalis, a key contributor to periodontal disease. P. gingivalis secretes lipopolysaccharide (LPS), which adversely affects human oral keratinocytes (HOK), the first line of defense against microbial threats in the oral cavity. HOK recognize LPS as a threat, …