Digital Commons Network^™

Gradient-Based Model Shortcut Detection For Time Series Classification, Salomon Ibarra, Frida Cantu, Kaixiong Zhou, Li Zhang

Computer Science Faculty Publications

Deep learning models have attracted lots of research attention in time series classification (TSC) task in the past two decades. Recently, deep neural networks (DNN) have surpassed classical distance-based methods and achieved state-of-the-art performance. Despite their promising performance, deep neural networks (DNNs) have been shown to rely on spurious correlations present in the training data, which can hinder generalization. For instance, a model might incorrectly associate the presence of grass with the label "cat" if the training set have majority of cats lying in grassy backgrounds. However, the shortcut behavior of DNNs in time series remain under-explored. Most existing shortcut …

Early Conceptual Sketches Of Blended Reality And The Precursor To The Bbs Quad (2022), David Smith

Publications and Research

This document contains two original hand-drawn conceptual sketches created in early 2022, representing the earliest visual formulations of what would later evolve into the Balanced Blended Space (BBS) framework. The drawings predate my first conversations with ChatGPT and were produced as part of my independent sabbatical research into blended environments, mediated performance, and human–machine interaction.

The first drawing examines human–computational mediation, perception, and internal mapping. The second sketch—later referred to informally as the “BBS Quad”—extends this idea by reconciling cognition–computation symmetry with physical–virtual spatial relationships. Published together, these images document the conceptual foundations of the BBS framework prior to its …

Self-Assessment In Calculus I, Bertha Naa Dei Neequaye

All Graduate Theses and Dissertations, Fall 2023 to Present

The purpose of this research investigate how undergraduate students in Calculus I at Utah State University learn and reflect on their level of understanding of Calculus I. In the study, self-assessment practices refer to students reflecting on what they have learned, rating their level of understanding and providing examples that illustrate their level of understanding. The structured and unstructured self-assessment rubrics were the two types of self-assessment rubrics used in the study. The structured self-assessment rubric provides a clear list of objectives of Calculus I topics for students to rate themselves on, while the unstructured self-assessment rubric is an open …

Machine Learning Applications: Cell Tracking And Nonparametric Estimation Of Non-Smooth Divergences, Mina Mahbub Hossain

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis brings together two important research directions: how to compare different sets of data more accurately, and how to better understand how brain cancer cells move and change shape.

In the first part, we look at a problem in statistics: measuring how different two data sources are from each other. Traditional methods often make strong assumptions, which may not always hold in real situations. Our approach avoids those assumptions by using an ensemble method, a way of combining many weak estimators into one stronger result. This makes the method more flexible and reliable, especially when dealing with complex or …

A Software Framework For Translating Onnx Models Onto The Lace-C3a Hardware, Shawn Jones

All Graduate Theses and Dissertations, Fall 2023 to Present

Modern computers are powerful, but they are not always efficient enough for small, low power systems like those used on satellites and scientific instruments. To solve this problem, engineers often turn to FPGAs—reconfigurable computer chips that can be customized to run specific tasks much faster and with far less energy than ordinary processors. However, finding the best possible design for an FPGA program is extremely difficult because there are millions of ways a design could be built, and only a small fraction of them actually perform well.

This thesis presents SNOW, a new framework that helps automate the search for …

An Investigation Of Paleoclimate Through A Sedimentological Lens In The Gulf Of Mexico And Western North Pacific, Sarah Monica

Dissertations

This dissertation explores the use of sediment cores as proxy data for the reconstruction of paleoclimatic and environmental conditions. Human-caused climate change is leading to dramatic shifts in the global climate. As the instrumental record of climate is relatively short compared to the amount of time Earth has experienced weather, this work aims to extend our knowledge of climate beyond the instrumental record, thereby improving our holistic understanding of the climate system. In Chapter one, sediment cores from the central Texas inner shelf are used to produce a record of intense tropical cyclone (TC) activity over a ~4500-year period. X-Ray …

Leveraging Disparate Wildland Fire Datasets To Address Longstanding Fire Management Questions, Kendra Fallon

Boise State University Theses and Dissertations

While wildfire season is perceived as occurring in the warm months, and expanding, wildfire management is a year-long process. Juxtaposed to wildfire seasons are the reactive seasons of mitigating for post-fire effects, namely first-order effects of potential hydrological impacts in burn footprints, and the proactive implementation of fuels treatments to modify fire behavior to lessen the reactive workload. In this dissertation, I focus on answering lingering questions that are critical to the management of the shoulder seasons of wildfire season. To assist in resource allocation and identification of post-hydrological risks, I present a model identifying the major drivers of soil …

Deep Reinforcement Learning For Adaptive Mesh Refinement: A Training Parameter Study, Antone Chacartegui

Boise State University Theses and Dissertations

Adaptive mesh refinement (AMR) enables efficient solution of partial differential equations by dynamically allocating computational resources to regions requiring higher resolution. It has been demonstrated that deep reinforcement learning agents could learn effective mesh adaptation policies. However, practical deployment requires a systematic understanding of how training parameters affect performance across diverse computational scenarios.

This thesis presents a parameter optimization study analyzing the relationship between deep reinforcement learning for adaptive mesh refinement (DRL-AMR) training parameters and model performance. We implement an independent DRL-AMR system with a discontinuous Galerkin solver and actor-critic (A2C) algorithm for one-dimensional linear advection. The study explores four …

Combinatorics Of Trees Via Derivatives Of Polynomial Functors In Homotopy Type Theory, Warren David Hatton

Boise State University Theses and Dissertations

Polynomial functors are, essentially, polynomials whose variables take values in sets, groupoids, or some other suitable category, rather than in numbers. In this thesis, we will explore three related aspects of polynomial functors over $\infty$-groupoids in the framework of homotopy type theory: first, polynomial functors provide a foundation for inductively defined data types as wellfounded trees; second, polynomial functors have a notion of derivative which, viewed in light of the connection to data types, yields a computational interpretation of differentiation; and third, suitably finite polynomial functors over (higher) groupoids yield categorified generating functions, and thus subsume the theory of combinatorial …

A Comparative Study Of Novel Methods For Variances, Taoreed Muritala

Boise State University Theses and Dissertations

Understanding variability is fundamental to knowledge advancement across various disciplines, including manufacturing, clinical research, biology and genetics. Population variances play a crucial role in processes such as quality control, treatment evaluation and the interpretation of biological mechanisms. However, statistical procedures for comparing variances across multiple populations remain less developed than those for comparing means. Classical omnibus tests, such as Bartlett's and Levene's, can detect overall variance heterogeneity but fail to identify which populations differ, thereby limiting their usefulness in multi-population analyses and increasing the risk of inflated family-wise error rates through repeated testing.

This thesis develops and evaluates three multiple …

Abelian Sandpile Models On Edge Periodic Graphs, Payton Lyons

Boise State University Theses and Dissertations

The abelian sandpile model is a Markov chain that is able to decompose any recurrent configuration into a basic alive divisor, some amount of sand placed on the sink vertex, and a series of vertex firings. In this paper we begin to explore some of the dynamics of this system when the edges of the graph are allowed to alternate between two different edge sets. This system will be framed in a similar fashion as the classical model, and then some basic numerical simulations were used to observe the threshold behavior of the system. We lay some of the initial …

An Intelligent Healthcare System For Rare Disease Diagnosis Utilizing Electronic Health Records Based On A Knowledge-Guided Multimodal Transformer Framework, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Ankur Pandey

All Works

Rare diseases are a common problem with millions of patients globally, but their diagnosis is difficult because of varied clinical presentations, small sample size, and disparate biomedical data sources. Current diagnostic tools are not able to combine multimodal information effectively, which results in a timely or wrong diagnosis. To fill this gap, this paper suggests a smart multimodal healthcare framework integrating electronic health records (EHRs), genomic sequences, and medical imaging to improve the detection of rare diseases. The framework uses Swin Transformer to extract hierarchical visual features in radiographic scans, Med-BERT and Transformer-XL to learn semantic and long-term temporal relations …

Big Data Transfer Service Architecture For Cloud Data Centers: Problems, Methods, Applications, And Future Trends, Muhammad Umar Majigi, Ismaila Idris, Shafi’I Muhammad Abdulhamid, Richard A. Ikuesan

All Works

Data volume, velocity, and structure have significantly evolved over the years. The complex networking architectures of current infrastructures, and the development, and accessibility of cloud services to a diverse user base have introduced numerous challenges which have raised concerns regarding the quality-of-service performance in data processing for both service providers and customers. Key issues identified in the context of big data transfer services for cloud data centers include storage, big data transfer, service transfer architecture, data processing, bandwidth, and security, all of which demand extensive research. After thoroughly screening selected peer-reviewed articles, the primary open issues are: incorporating a data …

A Hybrid Fog-Edge Computing Architecture For Real-Time Health Monitoring In Iomt Systems With Optimized Latency And Threat Resilience, Umar Islam, Mohammed Naif Alatawi, Ali Alqazzaz, Sulaiman Alamro, Babar Shah, Fernando Moreira

All Works

The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitive patient data. Traditional cloud-based architectures often struggle with latency and data protection, making them inefficient for real-time healthcare scenarios. To address these challenges, we propose a Hybrid Fog-Edge Computing Architecture tailored for effective real-time health monitoring in IoMT systems. Fog computing enables processing of time-critical data closer to the data source, reducing response time and relieving cloud system overload. Simultaneously, edge computing nodes handle data preprocessing …

A Deep Learning Framework For Automated Breast Cancer Diagnosis Using Intelligent Segmentation And Classification, Ahed Abugabah

All Works

Breast cancer is the most commonly diagnosed cancer among women worldwide, accounting for a significant proportion of new cases. Deep learning (DL) has emerged as a powerful tool for the detection and diagnosis of breast cancer, particularly through the analysis of histological images, a critical component of automated diagnostic systems that directly impact patient management. The BreakHis dataset and the Wisconsin Breast Cancer Database (WBCD) are widely used publicly available resources for deep learning–based analyses of breast cancer histological images in cross-disciplinary healthcare research. A computer-assisted approach employs colour normalisation to reduce the effects of the differences in the distribution …

Connecting The Dots: Iot, Sustainability, And Sdgs, Saadat M. Alhashmi, Islam Al-Qudah, Ibrahim Abaker Hashem, Belal Alsinglawi, Raiza Borreo, Hassan S․ Migdadi, Weisi Chen

All Works

Internet of Things (IoT) technologies can transform various sectors by converging with global sustainability goals. This paper systematically reviews how IoT supports fulfilling the United Nations Sustainable Development Goals (SDGs). This study initially identified publications that are most relevant to IoT and sustainability. Each publication was carefully examined and mapped to its corresponding SDG, methodology, context, and country. This work presents an opportunity to learn about country contributions, collaborations, and IoT and SDG research trends over the past decade. India, China, and the US were among the top contributors to the IoT and SDG literature, with India accounting for 68 …

Wildfire Smoke And Public Health: Comparing 2023 Canadian Wildfire Events With Hospital Admissions In Douglas County, Nebraska, Jeremy Poell

Capstone Experience: Master of Public Health

Wildfires are becoming increasingly common in Canada and the United States. Smoke produced from these fires creates a multitude of air pollution constituents that can cause breathing and other health issues for humans, particularly those with asthma and other respiratory conditions. Of these pollutants, PM2.5 (particulate matter that is 2.5 microns or smaller) is particularly problematic as these particles are inhaled deep into lung tissue, where they create inflammation and oxidative stress. Poor air quality can also trigger asthma and respiratory issues, leading to an increase in emergency department admissions for breathing treatments. The goal of this study is to …

Evaluation Of Graphite And Activated Carbon As Suitable Water Purification Media For In-Situ Resource Utilisation (Isru) On Mars, Ciaran Archenoul

The Plymouth Student Scientist

Eventual human missions to the red planet will require large-scale in-situ resource utilisation to meet their objectives. Perhaps the most critical element for developing a sustainable architecture is propellant production from the products of water electrolysis and atmospheric carbon dioxide. The purpose of this study was to assess how graphitic materials may be used as porous filters to remove solvated ions from Martian water prior to electrolysis, reducing the overall complexity of ISRU systems. The two materials under study were PG25 graphite and activated carbon powder.

A comprehensive three-technique characterisation of both materials was performed, using helium pycnometry, N_{2 …}

Vacuum-Assisted Resin Transfer Moulding (Vartm): Can Ultrasonic Vibration Decrease Fill Time, Increase Composite Properties, And Reduce Void Formation, Iulia D. Precup

The Plymouth Student Scientist

This study investigated the potential of ultrasonic vibration (28 kHz) to optimize the Vacuum-Assisted Resin Transfer Moulding (VARTM) process. The aim was to assess whether introducing ultrasonic vibration during the resin infusion phase could reduce fill time, improve mechanical properties, and minimize void formation in glass fibre-reinforced polyester composites. Two flexible tooling systems were compared: a reusable silicone membrane and a traditional vacuum bag. Composite plates were manufactured with and without ultrasonic vibration for both systems. Fill time was observed, and post-cured samples were subjected to mechanical testing for flexural modulus, flexural strength, and interlaminar shear strength (ILSS). Void content …

Analysis Of Organic Compounds Extracted From Recycled Polypropylene Via Gc-Fid/Ms And Pyr-Gc-Ms For Future Pharmaceutical Applications, India E. Hateley

The Plymouth Student Scientist

A mandatory target for 30% of plastic packaging to be recycled has been proposed by 2030, in order to mitigate the problem of plastic waste being incinerated or deposited in landfills. However, during the recycling process additives are added and with polypropylene being used for plastic packaging and surgical masks, these additives need to be analysed for the presence of contaminants. Examples include VOCs, PAHs, phthalates, and brominated diphenyl’s, all of which have health issues. Two samples of recycled polypropylene pellets were obtained from a national recycling company, along with a reference pellet purchased through Sigma-Aldrich. All pellets underwent ultrasound-assisted …

A Generational Analysis Of Psychological Motivators Influencing Seafarer Retention, Perisah Tahanci

The Plymouth Student Scientist

Seafarer retention is a growing concern amid increasing global demand for skilled maritime officers. This study investigates the generational differences in psychological motivators influencing seafarer retention, where ‘psychological motivators’ encompass the cognitive factors, including mental and physical wellbeing, work-life balance, compensation, working conditions, and external factors, that shape retention intentions. A cross-sectional survey (n=214) combined quantitative and qualitative analyses to systematically assess how the perceived importance and experience of these factors vary across Generation Z, Millennials, Generation X, and Baby Boomers.

The findings reveal that significant generational differences exist across most factors, particularly in work-life balance and mental wellbeing, in …

What Environmental Circumstances Generate Hazardous Wave Overtopping Conditions For Infrastructure Disruption: Dawlish (United Kingdom) Case Study, Zbigniew Tomasz Zielonka

The Plymouth Student Scientist

Coastal infrastructure faces increasing threats from wave overtopping due to climate change, rising sea levels, and intensifying storms. This research investigates the environmental conditions that drive hazardous wave overtopping using Dawlish, a vulnerable section of the UK’s railway, as a case study. Understanding these conditions is essential for improving knowledge, developing effective mitigation strategies, and enhancing the resilience of shoreline defences against future challenges. Using one-year observations from WireWall instruments, offshore wave buoy, and meteorological data, the research applies a Random Forest model to assess the influence of metocean conditions on overtopping. The results indicate that wave height, tidal level, …

Method Optimisation And Environmental Analysis Of Methanesulfonic Acid (Msa) In Rainwater By Ion Chromatography, Thomas Levett

The Plymouth Student Scientist

This research optimised a method of analysis for methanesulfonic acid (MSA) alongside common inorganic anions using ion chromatographic (IC) apparatus developed at the University of Plymouth. The aim was to assess whether MSA could be more easily and routinely monitored alongside common rainwater anions. The method’s analytical quality was evaluated through each development stage of the research and its limitations found. Historical rainwater samples collected at Penlee Point, Plymouth, UK (2015–2018) were also used for testing. The method successfully provided detectable ranges of MSA between 0.001 – 5 mg/L for F^-, Cl^-, NO_2-, Br …

Quantification Of Trace Metal(Loids) In The River Seaton, Cornwall, Uk And The Influence Of The South Caradon Mine On Ecological Risk, Bjorn Piñol

The Plymouth Student Scientist

Freshwater quality is critical to the health of aquatic ecosystems and human water security, yet anthropogenic activities, such as historic mining, continue to degrade aquatic systems. While extensive research has examined metal contamination in major rivers like the River Tamar, smaller tributaries affected by legacy mining remain understudied. This study investigates trace metal contamination and its controlling factors in the River Seaton, Cornwall, a small river influenced by historic mining from the South Caradon Mine. Water samples collected from four study sites along the river gradient and a reference site at Siblyback Lake were subjected to ICP-MS analysis and physico-chemical …

Scaling Wind Farm Layout Optimisation To Meet Uk Energy Demands, Grace Richardson

The Plymouth Student Scientist

This research aims to explore the Wind Farm Layout Optimisation Problem (WFLOP) through both single- and multi-objective optimisation, to show that it can be used to create farm layouts that provide enough power for all homes in the UK. We highlight the intricacies of the problem, including the application of different algorithms and models, and the challenges of scaling the models to achieve optimal results. We employ both a Hill Climbing Algorithm (HCA) and an evolutionary algorithm (NSGA-II) to obtain wind farm layouts, and compare these to the well-known results proposed by Mosetti et al. Moreover, we present a novel …

Collective Agency And Community Resilience Among Farmworkers In The Lower Rio Grande Valley, Angel Velazquez

Theses and Dissertations

This research assesses how farmworkers in the Lower Rio Grande Valley of Texas use everyday practices to build resilience and adapt to changes in their lives. I apply two interconnected theories, White’s (2017, 2019) framework of collective agency and community resilience and Castro and Sen’s (2022) framework of everyday adaptation actions, to study these practices. I conducted 19 semi-structured interviews with Latino farmworkers and professionals who work with local farmworker organizations in the Rio Grande Valley (RGV) of Texas. Interview transcripts were analyzed to determine if and how Latino farmworkers in the RGV can work towards community resilience and implement …

Determination Of An Effective Natural Boxelder Bug (Boisea Trivittata) Repellent, Reyna Chavez

Theses and Dissertations

This study investigates the use of ecofriendly repellant alternatives to pest management to control boxelder bugs (Boisea trivittata). This bug gathers by the thousands on warm surfaces of exterior suburban homes during early fall and late spring. I determined bug repellency to turmeric oil (Curcuma longa), catnip (Nepeta cataria), citronella (Cymbopogon nardus), and lavender (Lavandula angustifolia). The treatments consisted of essential oils containing 20% methanol. All treatments demonstrated repellency of Boxelder bugs. Citronella was the most effective and had the highest level of repellency range of 90-99%. Catnip was the least effective with a repellency range of 63-82%.

Clustering 24-Hour Ambulatory Blood Pressure Time Series With Dynamic Time Warping And Time Warp Edit Distance, John Knight

Theses and Dissertations

Ambulatory blood pressure monitoring (ABPM) captures dynamic circadian changes in blood pressure (BP) that are not reflected in static clinic readings. This study applied time-series clustering with two elastic distance measures—Dynamic Time Warping (DTW) and Time Warp Edit Distance (TWED)—to identify distinct phenotypes in 2,155 24-hour ABPM time series from participants in the Maracaibo Aging Study. DTW and TWED both yielded three clusters corresponding to non-dipping, moderate-dipping, and strong-dipping patterns. The non-dipping group showed elevated nighttime BP, associated with greater cardiovascular and cognitive risk, while the strong-dipping group was associated with higher education and younger age in baseline clinic measurements. …

A Picture Tells A Thousand Words—, But Ecg Signals Have More To Say, Ashley N. Gomez

Theses and Dissertations

With the increasing adoption of deep learning classification models in the medical domain, a critical challenge remains: achieving high predictive accuracy while maintaining clinical Inter-pretability. This study examines how model architecture, dataset origin, and the use of full versus subset data affect both classification performance and Interpretability in Electrocardiogram (ECG) signal analysis. ResNet18 is evaluated using an open-source ECG Image Dataset, thus a custom dataset derived from digitized ECG images. Post-hoc explainability methods, such as Integrated Gradients, are applied to determine which time steps have the most significant influence on model decisions. The findings demonstrate that model architecture and dataset …

Efficient Self-Supervised Representation Learning For Large-Scale Time Series Classification, Kevin Garcia

Theses and Dissertations

Recently, there has been a significant advancement in designing Self-Supervised Learning (SSL) frameworks for time series data to reduce the dependency on data labels. Among these works, hierarchical contrastive learning-based SSL frameworks, which learn representations by contrasting data embeddings at multiple resolutions, have gained considerable attention. Due to their ability to gather more information, they exhibit better generalization in various downstream tasks. However, when the time series data length is significant long, the computational cost is often significantly higher than that of other SSL frameworks. In this paper, to address this challenge, we propose an efficient way to train hierarchical …