Digital Commons Network^™

Perceptions Of Blind Adults On Non-Visual Mobile Text Entry, Dylan Gaines, Keith Vertanen

Michigan Tech Publications

Text input on mobile devices without physical keys can be challenging for people who are blind or low-vision. We interview 12 blind adults about their experiences with current mobile text input to provide insights into what sorts of interface improvements may be the most beneficial. We identify three primary themes that were experiences or opinions shared by participants: the poor accuracy of dictation, difficulty entering text in noisy environments, and difficulty correcting errors in entered text. We also discuss an experimental non-visual text input method with each participant to solicit opinions on the method and probe their willingness to learn …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Using Confidence Scores To Improve Eyes-Free Detection Of Speech Recognition Errors, Sadia Nowrin, Keith Vertanen

Michigan Tech Publications

Conversational systems rely heavily on speech recognition to interpret and respond to user commands and queries. Despite progress on speech recognition accuracy, errors may still sometimes occur and can significantly affect the end-user utility of such systems. While visual feedback can help detect errors, it may not always be practical, especially for people who are blind or low-vision. In this study, we investigate ways to improve error detection by manipulating the audio output of the transcribed text based on the recognizer's confidence level in its result. Our findings show that selectively slowing down the audio when the recognizer exhibited uncertainty …

The Response Of Mitochondrial Position To Glucose Stimulation In The Min6 Model System, Luis Perez

Arts & Sciences Graduate Student Theses and Dissertations

The compartmentalization of eukaryotic cells into membrane-bound organelles with specific subcellular positioning enables precise spatial and temporal control of cellular functions. While functionally significant mitochondrial localization has been demonstrated in cells such as neurons, it remains unclear how general these cell principles are. Here, we examine the spatial organization of mitochondria within MIN6 pancreatic beta cells under variable glucose conditions. We observe glucose-dependent redistributions of mitochondria, favoring peripheral localization at elevated glucose levels when insulin secretion is also elevated. Our results suggest that active mitochondrial transport along microtubules and calcium activity, but not ATP synthesis, are critical regulators of this …

Machine Learning Crime Prediction Models And The Gap Between Research And Implementation: A Systematic Review, Ricardo Huamantingo, Miguel Cano-Lengua, Ciro Rodriguez

Karbala International Journal of Modern Science

A crime is an illegal or violent act committed by one individual against another. The increasing crime rate has become a major concern as it negatively affects people's quality of life and generates significant social and economic costs. This study aims to identify the most widely used machine learning (ML) models for crime prediction, determine evaluation metrics for assessing model performance, and analyze key data characteristics to enhance real-world implementation. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A search string was formulated using the population, intervention, comparison, and outcomes (PICO) framework and applied …

Research And Education In Robotics: A Comprehensive Review, Trends, Challenges, And Future Directions, Mutaz Ryalat, Natheer Almtireen, Ghaith Al-Refai, Hisham Elmoaqet, Nathir Rawashdeh

Michigan Tech Publications

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is …

The People's Rivers: Early Environmental Regulation And The Oregon State Sanitary Authority, Avery Morgan Fischer

Dissertations and Theses

The environmental movement in Oregon can trace its origins to the turn of the twentieth century, where citizen activism and initiative led to the creation of an early environmental regulatory agency, the Oregon State Sanitary Authority. This creation established that the State of Oregon had a duty to preserve, not just the navigability or usability of rivers and waterways, but the quality and purity of its water. The Oregon State Sanitary Authority played an active role in maintaining and protecting the environment as a mandate of the people, and throughout its thirty-year existence battling industrial polluters, reluctant municipalities, and disinterested …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

A Study Of Machine Learning Techniques In Solving Biochemical And Chemical Problems, Kenneth Micheal Plackowski

Chemistry and Chemical Biology ETDs

Data-driven approaches to solving problems in biology and chemistry require utilization of reliable techniques and machine learning algorithms are the modern reliable approach. This work presents three problems that involve use of supervised learning techniques when classification is the goal and unsupervised learning techniques when global data representation is the goal.

In the first problem, we demonstrate the use of unsupervised clustering techniques, self-organizing maps and K-means, to ascertain analyte detection capabilities of carbon nitride dots. In the second problem, we add scalability features to a functional group classification model applied to infrared data and evaluate its ability to inform …

How Multi-Scale Modeling Can Help Examine Social Determinants Of Health And Resulting Disparities, Kyoko Yoshida, Elsje Pienaar, Shalanda A. Bynum, Naomi Chesler, Mitchel J. Colebank, Jessie Heneghan, Nadra Tyus, Jasmine Miller-Kleinhenz, Bruce Y. Lee

Faculty Publications

Social determinants of health (SDOH) are the conditions in which people live, work, and play, and the wider set of factors (e.g., social and economic systems and policies) that shape a person’s daily life. SDOH can differ significantly across communities and populations, having positive impacts for some and negative impacts for others. Ultimately, this results in differences in health and disease distribution, that are known as health disparities. Despite the known impacts of SDOH and calls to characterize, address, reduce, and eliminate health disparities, they persist and, in some cases, have worsened. To address this challenge, a session at the …

Using Linear Programming And Game Theory To Optimize The Relation Between Us And China, Junhao Su

Student Works

This paper develops an optimization model to analyze U.S.–China bilateral trade dynamics and competition in artificial intelligence (AI). First, grounded in WTO tariff limits, we formulate a linear programming model to maximize the combined trade volume and conduct a comprehensive sensitivity analysis on tariff parameters. Second, we integrate zero‑sum and non‑zero‑sum game‑theoretic frameworks to identify the Nash equilibria governing both trade negotiations and technological rivalry. The model is implemented in Python using PuLP and is empirically validated with real‑world tariff data to highlight the policy relevance of the optimal solutions. Our results reveal a high concordance between the zero‑sum game …

Analyzing Environmental Health: Air Pollution And Human Health Using Geospatial Data Science, Yanhong Huang

Geography ETDs

Air pollution from industrial emissions and wildfire smoke poses growing threats to public health. This dissertation employs geospatial data science to examine these challenges through three interrelated studies. The first study investigates the relationship between maternal residential exposure to industrial pollutants and low birth weight, identifying five chemicals as significant risk factors. The second study assesses the impact of industrial air pollution on lung and bronchus cancer survival, finding that exposure to 1,1,1-trichloroethane and cobalt is associated with reduced survival. The third study explores disparities in wildfire smoke PM_2.5 exposure and its association with asthma exacerbations. Results show disparities …

Theoretical And Experimental Investigation Of Liquid-Liquid Phase Separation: Characterizing Elastin-Like-Polypeptides, Adam D. Quintana

Chemical and Biological Engineering ETDs

This dissertation develops and validates a semi-empirical Flory–Huggins-based interaction model, combined with Cahn–Hilliard simulations, for predicting multi-component liquid–liquid phase separation (LLPS) in elastin-like polypeptide (ELP) systems. Equilibrium droplet compositions, measured using a PDMS-based microfluidic device, enabled direct parameterization of interaction coefficients. The model was applied to generate phase diagrams and assess composition dependence in ternary mixtures. Cahn–Hilliard simulations were conducted to explore potential phase morphologies under different interfacial conditions. Multi-component Lattice Boltzmann simulations were implemented to model droplet morphology evolution under varying interfacial and diffusive parameters, reproducing experimentally relevant morphologies. A three-phase wetting study revealed conditions for selective wetting and …

A Machine Learning Approach To Quantitative X-Ray Diffraction Analysis, Spencer Snow Chandler

Theses and Dissertations

X-ray Powder Diffraction (XRPD) is a powerful method in material sciences that gives insights into the atomical and crystallographic structure of a material, revealing information into the material's properties and suitability for industrial and scientific application. In geology, XRPD analysis is frequently leveraged to identify and quantify the present mineral phases in an unknown mixture. Despite it's widespread use, interpreting XRPD patterns requires highly-specialized knowledge, making the analysis largely dependent upon the background experience of the analyst. To assist experts, computational methods have been developed over the years. Some of these techniques involve fitting diffraction patterns using pseudo-Voigt functions, which …

Approximating Transport Maps Using Minibatch Optimal Transport Plans, Dawson Collins

Theses and Dissertations

Optimal Transport theory (OT) has gained much attention in the past decades due to its rich mathematical content and striking connections to other areas of math. It has also naturally found a place in data science as a way to compare and transform probability measures. Unfortunately, there remain significant computational burdens that restrict its effectiveness in large-scale data regimes. In this thesis, we consider Minibatch Optimal Transport (MiniOT), which breaks up the task of computing the optimal transport plan between datasets into several smaller tasks of computing the optimal transport plan between minibatches of the datasets. After obtaining the minibatch …

Seeking Structure In Complex Systems: From Feature Analysis To Space-Time Causal Discovery With Earth Science Applications, Jeffrey J. Nichol

Computer Science ETDs

Complex systems are difficult to study because of their many interacting parts, emergent phenomena, and feedback loops. These systems underpin all life on Earth. We need improved tools for seeking an understanding of them. This body of research presents my investigations into data-driven methods for understanding complex systems, including my invention of a novel causal discovery meta-algorithm for space-time gridded data. I demonstrated machine learning feature importance and causal discovery capabilities for comparing simulated and observed climate data. I developed a new benchmark for modeling space-time dynamics of locally driven phenomena and examined a prominent causal discovery algorithm. Finding that …

The Role Of The Cytosolic Chaperonin Cct In The Folding And Assembly Of G Protein Complexes, Mikaila I. Sass

Theses and Dissertations

All organisms depend on cellular signaling pathways to dictate cell growth, responses to stimuli, and all other cellular functions necessary to maintain health and survival. Many of these signals rely on G protein coupled receptors (GPCRs) and their associated heterotrimeric G proteins for signal detection and transduction. To perform this function, the three subunits of the G protein must be assembled after synthesis on the ribosome. This process requires additional proteins called molecular chaperones to assist in folding of the subunits and assembly into the G protein heterotrimer. Among these chaperones, the cytosolic chaperonin CCT plays an essential role in …

A New Approach To Stability Of Delay Differential Equations With Time-Varying Delays Via Isospectral Reduction, Quinlan Leishman

Theses and Dissertations

Understanding how time delays impact the stability of a delay differential equation is important for modeling many natural and technological systems that experience time delays. Here we introduce a new stability criterion for delay-independent stability of these equations, called intrinsic stability, showing global exponential stability for a large class of nonautonomous nonlinear systems. Our approach is able to incorporate bounded time-varying delays, including those with certain types of discontinuities. The approach we take to prove this result is novel, associating the delay differential equation with a sequence of finite-dimensional matrices of increasing size and using the graph-theoretic technique of isospectral …

Math 112 At Byu: Student Averages On Various Exam Questions, Makayla Mcleod

Theses and Dissertations

In Math 112 at BYU, exams play a crucial role in evaluating students' understanding of the course content. Traditionally, these exams are a combination of multiple-choice and free-response questions, with the latter serving as a means to assess a student's thought process while solving problems. Will students perform as well on a multiple-choice exam? Or will students' averages be higher on free-response exams? In this thesis, we test 7 hypotheses related to these questions to provide more insight into the answer. We examine other factors that may affect students' averages, including various Calculus 1 topics, modes of questioning, and difficulty …

"Chatgpt Told Me To Say It": Ai Chatbots And Class Participation Apprehension In University Students, Daisuke Akiba

Publications and Research

The growing prevalence of AI chatbots in everyday life has prompted educators to explore their potential applications in promoting student success, including support for classroom engagement and communication. This exploratory study emerged from semester-long observations of class participation apprehensions in an introductory educational psychology course, examining how chatbots might scaffold students toward active and independent classroom contribution. Four students experiencing situational participation anxiety voluntarily participated in a pilot intervention using AI chatbots as virtual peer partners. Following comprehensive training in AI use and prompt design given to the entire class, participants employed systematic consultation frameworks for managing classroom discourse trepidations. …

Qder: Query-Specific Document And Entity Representations For Multi-Vector Document Re-Ranking, Shubham Chatterjee, Jeff Dalton

Computer Science Faculty Research & Creative Works

Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDER, a neural re-ranking model that unifies these approaches by integrating knowledge graph semantics into a multi-vector model. QDER's key innovation lies in its modeling of query-document relationships: rather than computing similarity scores on aggregated embeddings, we maintain individual token and entity representations throughout the ranking process, performing aggregation only at the final scoring stage-an approach we call "late aggregation." We first transform these fine-grained representations through learned attention patterns, then apply carefully chosen mathematical operations for precise matches. …

The Kentucky Microbiome Dataset, Rachel L. Washburn, Douglas C. Curl

Research Data--KGS

The Kentucky Microbiome Dataset is a continuously updated geolocated dataset which describes the distribution and diversity of microorganisms across the Commonwealth of Kentucky. Each geolocated point represents a location reported in the peer-reviewed literature where microorganisms were sampled from soil, water, geologic substrates, or clinical settings. Environmental studies include only non-host-associated, non-clinical microorganisms sampled from natural environments. Clinical studies are mapped separately to highlight locations where investigations into host-associated or human health-related microbes have occurred.

For each location, associated metadata include study information, taxonomic classifications, sample type, sampling method, latitude and longitude, sampling date, publication reference, and a direct link …

Ideal Mathematical Pendulum: Examples Of Physics Problem Conditions Expressed Primarily Through Diagrams, Vasiliy Znamenskiy

Open Educational Resources

This article explores an innovative approach to physics education centered on the formulation of problems using visual diagrams with minimal textual description. Departing from traditional word-heavy methods, the proposed technique leverages schematic drawings as the primary mode of problem presentation. This visual-first strategy facilitates intuitive understanding, promotes spatial reasoning, and supports the development of abstract thinking in students. The method is especially effective in diverse classrooms where language barriers may hinder comprehension. By engaging learners through image-based tasks, the approach reduces cognitive load, fosters independent discovery of physical principles, and enhances overall conceptual clarity. Examples from mechanics and oscillations illustrate …

Detecting Misuse Of Security Apis: A Systematic Review, Zahra Mousavi, Chadni Islam, Muhammad Ali Babar, Alsharif Abuadbba, Kristen Moore

Research outputs 2022 to 2026

Security Application Programming Interfaces (APIs) are crucial for ensuring software security. However, their misuse introduces vulnerabilities, potentially leading to severe data breaches and substantial financial loss. Complex API design, inadequate documentation, and insufficient security training often lead to unintentional misuse by developers. The software security community has devised and evaluated several approaches to detecting security API misuse to help developers and organizations. This study rigorously reviews the literature on detecting misuse of security APIs to gain a comprehensive understanding of this critical domain. Our goal is to identify and analyze security API misuses, the detection approaches developed, and the evaluation …

Occurrence Of Pesticides In Oregon Coastal Waters And Effects On The Intertidal Sea Anemone, Anthopleura Elegantissima, Gabriella Mae Bleil

Dissertations and Theses

Pesticides are used globally for a wide range of applications including agricultural, forestry, roadsides, freshwater systems, and personal use. While pesticides have ensured efficient crop production, they are frequently transported away from application sites and are ubiquitous in terrestrial and aquatic environments. Pesticides are often detected in watersheds and rivers globally, which can have unintentional toxic effects on non-target organisms by interfering with cellular processes, behavior, feeding, reproduction, and disrupting endocrine processes. The aggregating anemone, Anthopleura elegantissima, is an important species along the North American Pacific coast due to its symbiotic relationships that contribute to high productivity, and its …

Recent Insights Into Electrochemical Sensing Of Per- And Polyfluoroalkyl Substances, Christopher T. Lebarron, Pavithra Hosseini, Seyyedamirhossein Hosseini

Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) are widely studied due to their persistence, prevalence, and harmful effects on human and environmental health. Key challenges include improving detection methodologies, such as lowering detection limits, and developing more sensitive and selective approaches. Given the ubiquity of PFAS, effective sensing must be paired with remediation strategies that transform these pollutants into value-added or less toxic products. This perspective explores recent advances in electrochemical PFAS detection, highlighting challenges and future opportunities.

Recent Insights Into Electrochemical Sensing Of Per- And Polyfluoroalkyl Substances, Christopher T. Lebarron, Pavithra Hosseini, Seyyedamirhossein Hosseini

Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) are widely studied due to their persistence, prevalence, and harmful effects on human and environmental health. Key challenges include improving detection methodologies, such as lowering detection limits, and developing more sensitive and selective approaches. Given the ubiquity of PFAS, effective sensing must be paired with remediation strategies that transform these pollutants into value-added or less toxic products. This perspective explores recent advances in electrochemical PFAS detection, highlighting challenges and future opportunities.

Framework For Mapping Sublimation Features On Mars’ South Polar Cap Using Object-Based Image Analysis, Racine D. Cleveland, Vincent F. Chevrier, Jason A. Tullis

Geosciences Faculty Publications and Presentations

Mars’ south polar cap hosts dynamic landforms known as Swiss cheese features (SCFs), which form through the sublimation of carbon dioxide (CO2) ice driven by the planet’s extreme seasonal and diurnal solar insolation cycles. These shallow, rounded depressions—first identified by Mars Global Surveyor in 1999 and later monitored by the Mars Reconnaissance Orbiter (MRO)—have been observed to increase in size over time. However, large-scale analysis of SCF formation and growth has been limited by the slow and labor-intensive nature of manual mapping techniques. This study applies object-based image analysis (OBIA) to automate the detection and measurement of SCFs using High-Resolution …

Large Bandgap Observed On The Surfaces Of Euzn2As2 Single Crystals, Dejia Kong, Siavash Karbasizadeh, Ganesh Narasimha, Paras Regmi, Chenggang Tao, Chenggang Tao, Sai Mu, Rama Vasudevan, Ian Harrison, Rongying Jin, Zheng Gai

Faculty Publications

EuZn₂As₂ (M = Zn, Cd, In, Sn etc.) is an excellent material system for studying magnetism-tuned topological properties. However, discrepancies exist between experimental data and theoretical calculations regarding the bulk and surface bandgaps. In this work, cleaved EuZn₂As₂ crystals are studied using scanning tunneling microscopy/spectroscopy and density functional theory calculations. Triangular-shaped defect-induced modifications in the local density of states help distinguish between Eu-terminated and AsZn-terminated surfaces. While large bandgaps ( ~1.5 eV at 77 K) are observed on both pristine surfaces, the bandgap size is found to be highly sensitive to local heterogeneity, tending …

An Upper-Mesopelagic-Zone Carbon Budget For The Subarctic North Pacific, Brandon M. Stephens, Montserrat Roca-Martí, Amy E. Maas, Vinícius J. Amaral, Samantha Clevenger, Shawnee Traylor, Claudia R. Benitez-Nelson, Philip W. Boyd, Ken O. Buesseler, Craig A. Carlson, Nicolas Cassar, Margaret Estapa, Andrea J. Fassbender, Yibin Huang, Phoebe J. Lam, Oliver Marchal, Susanne Menden-Deuer, Nicola L. Paul, Alyson E. Santoro, David A. Siegel, David P. Nicolson

Faculty Publications

Mesopelagic zone (MZ) carbon budgets comparing supply with demand can be difficult to constrain due to the temporal and spatial offsets between key sources and sinks, their associated uncertainties, and potential sampling biases. To address these challenges, the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) campaign increased the number and variety of simultaneous measurements to monitor temporal variability in the MZ carbon budget using both a Lagrangian frame of reference and long-term autonomous observations. In this study, we collate a comprehensive combination of new and previously published organic carbon supply and demand measurements collected from the surface (5 …