Physical Sciences and Mathematics Commons^™

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 …

Hierarchical Frameworks For Scaling-Up Multi-Agent Coordination, Minghong Geng

Research Collection School Of Computing and Information Systems

Multi-agent reinforcement learning has emerged as a powerful framework for developing collaborative behaviors in autonomous systems. However, existing MARL methods often struggle with scalability in terms of both the number of agents and decision-making horizons. My research focuses on developing hierarchicalapproaches to scale up MARL systems through two complementary directions: structural scaling by increasing the number of coordinated agents and temporal scaling by extending planning horizons. My initial work introduced HiSOMA, a hierarchical framework integrating self-organizing neural networks with MARL forlong-horizon planning, and MOSMAC, a benchmark for evaluating MARL methods on multi-objective MARL scenarios. Building on these foundations, my recent …

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 …

Climate Prediction As An Adaptive Force-Multiplier: Reasons For Hope In A Rapidly Changing World, Matthew D. Laplante

All Graduate Theses and Dissertations, Fall 2023 to Present

It's not unreasonable to feel pessimistic about our future under climate change. But "believing the science" doesn't require you to accept that we are headed toward apocalypse. It is true that human-caused climate change is real, it is underway, and our societies face many increased risks because of it. But that's just part of what climate science tells us about our future world. This same field is responsible for the recent and rapid development of tools and techniques that permit the identification of patterns in our oceans and atmosphere that will aid us in making increasingly accurate predictions about climate—the …

Determining The Structure And Function Of Type Iv-A Anti-Crisprs, Olivine Redman

All Graduate Theses and Dissertations, Fall 2023 to Present

Bacteria face a constant existential threat in the form of infection by viruses along with other forms of mobile genetic elements, such as bacteriophage and transposable elements. To survive, bacteria and other prokaryotes have evolved various immune systems to evade these would-be invaders. One such immune system is the CRISPR-Cas system, an adaptive immune system able to record the genetic signature of invading viruses in order to recognize and destroy them should they be encountered again in the future. In this thesis I present data that sheds light on the mechanism of one particular subtype of CRISPR-Cas systems: the type …

Reinforcement Learning, Modeling Markets, And Professional Basketball Free Agency, Jacob Cohn

Computational and Data Sciences (PhD) Dissertations

This dissertation presents a reinforcement learning-based approach to modeling and optimizing decision-making in professional basketball free agency and related economic environments. A Markov Decision Process (MDP) framework is introduced to capture the strategic interactions of NBA teams bidding for free agents under budgetary and roster constraints. To address computational scalability challenges, a reinforcement learning (RL) environment is developed, leveraging Proximal Policy Optimization (PPO) to approximate optimal policies for team decision-making.

Empirical results demonstrate that the RL agent successfully learns strategic bidding behavior that aligns with dynamic programming benchmarks in simplified settings while scaling effectively to larger, intractable environments. The study …

Designing Accessible And Dependable Tools For Vocational Rehabilitation Data Analysis, Ruth E. Taylor

All Graduate Theses and Dissertations, Fall 2023 to Present

Since 1973, the U.S. Rehabilitation Services Administration (RSA) has partnered with state vocational rehabilitation (VR) agencies to help individuals with disabilities achieve meaningful employment and independence. RSA-911 datasets play a crucial role in this effort by capturing detailed participant data, but their complexity can hinder effective analysis.

To simplify this process, we present an R software package to streamline the cleaning and analysis of RSA-911 and Transition Readiness Toolkit (TRT) data, a new measure of program effectiveness. We also deliver a user-friendly dashboard, empowering both VR researchers and counselors with the opportunity to conduct analyses. Using our developed tools, we …

Potamogeton Crispus Invasion: Impacts On Native Aquatic Plants And Associated Faunal Habitat, Meghan-Grace Slocombe

All Graduate Theses and Dissertations, Fall 2023 to Present

Aquatic invasive plants are non-native plants that invade a water body and displace native aquatic plants. Invasion depletes the native plant community's ability to provide ecosystem services, including improved water quality, decreased problematic algae, and increased animal habitat. The loss of these ecosystem services reduces the value of fishing, boating, and swimming opportunities in aquatic ecosystems. The potential changes in plant communities and services are particularly important at sites targeted for restoration because project goals typically include increasing ecosystem services, including wildlife habitat. Curly-leaf pondweed (Potamogeton crispus L.) is a problematic invasive species in North America. To better manage …

Contrastive Representation Learning For Highly Imbalanced Multivariate Time Series With Extreme Instance Strategy, Onur Vural

All Graduate Theses and Dissertations, Fall 2023 to Present

Time series data refers to a sequence of data points collected or recorded at regular time intervals. In many fields including space weather, healthcare, and finance, predicting events from such data is crucial because these predictions can help protect infrastructures, improve healthcare outcomes, and forecast financial trends. However, one challenge in working with time series data is the presence of rare events, which are often underrepresented in the data. This imbalance makes it difficult for traditional prediction methods to provide accurate results, as they tend to focus more on the more frequent events and overlook the rare ones. To tackle …

Describing Functionality In Natural Language May Improve Decomposition Behaviors, Matthew R. Burns

All Graduate Theses and Dissertations, Fall 2023 to Present

Problem decomposition—the ability to break complex problems into simpler parts—is a critical skill for computer programming that many beginning students struggle to develop. This research examines how using natural language to describe program functionality can help students develop better problem-solving approaches.

We created a tool called ”Natural Language Functions” (NLFs) that allows students to write descriptions of what they want their code to do in plain English, which then generates working Python functions. We studied how students used this tool compared to students who solved programming problems in traditional ways.

Our findings show that students who used the NLFs tool …

Managing For Disturbance: Trade-Offs Associated With Population Resilience And Ecosystem Size, Ellie Wallace

All Graduate Theses and Dissertations, Fall 2023 to Present

Populations can recover from and withstand disturbance through characteristics such as reproduction and maintaining large abundances. While these traits are beneficial to native species conservation, they can simultaneously challenge effective invasive species management. Effective invasive species control requires sufficient efforts to overcome a population's ability to recover from disturbance, such as harvest. My dissertation aimed to address how the size of an invasive population and its ability to recover from disturbance impact a suite of invasive species control methods, including those that (1) target mainly larger and older individuals (i.e., mechanical removal), (2) target all sizes of individuals (i.e., targeted …

Advancing Multi-Agent Robotics Simulations Through Heterogeneous Reinforcement Learning In Isaaclab, Jacob R. Haight

All Graduate Theses and Dissertations, Fall 2023 to Present

Robots increasingly operate in collaborative teams across domains such as search-and- rescue, warehouse automation, and autonomous driving—scenarios that demand advanced coordination strategies enabled by multi-agent reinforcement learning (MARL). However, existing simulation frameworks often struggle to balance realism, speed, and scalability, especially when supporting diverse, heterogeneous robot teams. This research extends Isaac Lab, a high-performance robotics simulator, by integrating heterogeneous-agent reinforcement learning (HARL) capabilities. The result is a flexible and GPU-accelerated platform for training both homogeneous and heterogeneous robot teams in complex, physics-based environments. These enhancements significantly narrow the gap between simulation and real-world deployment for multi-robot systems.

A Survey Of Master's Qualifying Exam Practices And Content In Real Analysis, Zachary M. Coverstone

All Graduate Theses and Dissertations, Fall 2023 to Present

Graduate programs in mathematics are intended to develop experts in mathematics. As part of a graduate program, many students are expected to engage in a qualifying examination that can serve myriad purposes. This dissertation investigates real analysis qualifying examinations at the master's level through a nation-wide survey of institutions in the Association of Public and Land-Grant Universities (APLU). This study reveals the practices in administration of real analysis qualifying exams, institutions reported offering traditional, pencil-and-paper exams and generally asked students to prepare individually using previously administered exams. The survey results show that approximately two in three APLU institutions offering master's …

Conservation Laws For Asymptotically Perfect Fluid Spacetimes, Lyle Arnett Jr.

All Graduate Theses and Dissertations, Fall 2023 to Present

In physics, conservation laws, like those for energy or momentum, are powerful tools that help us understand how systems evolve and interact. In general relativity, where spacetime itself is curved by matter and energy, defining such conservation laws becomes especially subtle and complex. Traditional methods rely on idealized conditions like empty space or special symmetries, which limit their usefulness in more realistic, dynamic settings such as an expanding universe.

This dissertation explores a modern mathematical framework, developed by Iyer and Wald, that allows conservation laws to be derived directly from the equations governing spacetime. Using this approach, I examine not …

Sabrina Vs Steph: The Battle Between The Wnba And Nba, Naysha Mcgriff

Symposium of Student Scholars

The average salary of a Women’s National Basketball Association (WNBA) player is 110 times less than a National Basketball Association (NBA) player’s. Despite growing WNBA viewership, gender inequality in sports remains high, with critics claiming female athletes are less skilled. Gender bias in sports is severely understudied, making direct comparisons to men’s leagues unfair due to long-term lack of investment in women’s sports. This study investigates whether the perceived disparity in skill levels between WNBA and NBA players' is genuine or influenced more by external factors by developing an unbiased measure of player efficiency to compare athletic performance. This dataset …

Structure Disorder And Magnetic Behavior Of An Olivine-Type Cathode Material, Hamida Hassan, Madalynn Marshall

Symposium of Student Scholars

Our research investigates the structure disorder and magnetic behavior of Li(Mn,Fe)PO₄, an olivine-type material relevant for lithium-ion battery applications. Using single-crystal neutron diffraction at Oak Ridge National Laboratory, we precisely determined Mn and Fe occupancy, revealing a 56% Fe and 44% Mn distribution at the atomic 4c site within the Pnma space group. Additionally, we observed significant lithium site vacancies (78% occupied), influencing the material's electrochemical and magnetic properties. The varying Mn/Fe ratio affected spin reorientation transitions, shifting from antiferromagnetic alignment along the a-axis to the b-axis. Our findings provide crucial insights for optimizing olivine-based cathodes, enhancing energy …

Computational Study Of The Proton Transfer In The H7o3+ Cluster, Anna James, Martina Kaledin

Symposium of Student Scholars

Proton transfer (PT) from one molecule to another is among the most studied phenomena in chemistry. PT requires the bond cleavage and the formation of a new one, AH⁺ + B -> A+ BH⁺. In protonated water clusters, such a process consists of the interconversion of hydrogen bonds. Experimentally, such a process can be observed as a significant increase of a dipole moment. However, other vibrational transitions often occur with small changes in the dipole moment while large changes in polarizability. In this work, we study the PT process in a protonated water cluster, H₇O …

Overcoming Motor Imagery Bci Illiteracy: Adaptive Decoding And Knowledge Transfer In Eeg-Based Brain-Computer Interfaces, Zaid Shuqfa Shuqfa

Thesis/ Dissertation Defenses

Brain Computer Interface (BCI), Also known as brain-machine interface (BMI) is a mean of controlling machines without the need to activate peripheral nerves or muscles. It has received the attention of research for decades. Motor imagery-based BCI is a paradigm that is characterized by its user friendliness where users can generate control commands at their freewill, without waiting for a que from the BCI module. Motor imagery brain–computer interface (MI–BCI) has considerable potential in increasing the quality of the lives for people with mobility impairment and the healthy ones as well. Though, its diffusion in application still has many pitfalls …

Large Language Model Enabled Mental Health App Recommendations Using Structured Datasets, Kris Prasad, Md Abdullah Al Hafiz Khan

Symposium of Student Scholars

The increasing use of large language models (LLMs) in mental health support necessitates detailed evaluation of their recommendation capabilities. This study compares four modern LLMs—GPT-4o, Claude 3.5 Sonnet, and dataset-enhanced Gemma 2 and GPT-3.5-Turbo—in recommending mental health applications. We constructed a structured dataset of 55 mental health apps using RoBERTa-based sentiment analysis and keyword similarity scoring, focusing on depression, anxiety, ADHD, and insomnia. Standard LLMs demonstrated inconsistent accuracy and often relied on outdated or generic information. In contrast, our retrieval-augmented generation (RAG) pipeline enabled lower-cost models to achieve up to 55% higher accuracy than baseline models while recommending apps with …

Properties Of Eigenvalues Of The Fractal Laplacian, Eric Stachura, Andrew Chincea

Symposium of Student Scholars

We investigate the properties of the eigenvalues of the fractal Laplacian. We begin by defining the fractal Laplacian operator in one dimension and formulate the corresponding Dirichlet eigenvalue problem. Analytical solutions are obtained for specific fractal parameters, and computational results illustrate the structure of eigenvalues and their associated eigenfunctions. We extend our analysis to two dimensions using separation of variables. Our findings contribute to a deeper understanding of how fractal geometry affects the spectral characteristics of differential operators.

Older Groundwater Reservoirs In Nebraska, Marvin P. Carlson, Steven S. Sibray

Conservation and Survey Division: Faculty and Staff Publications

Sedimentary rocks below Nebraska's regional water table are saturated down to the crystalline igneous and metamorphic rocks of Precambrian age. The total thickness of these sedimentary rocks ranges from about 600 feet to over 10,000 feet. Nearly all current wells in Nebraska produce water from the near-surface, unconsolidated or partly consolidated rocks. Most studies to date -- interpretive reports, test drilling, inventory of wells, monitoring water levels, and water quality determinations -- have concentrated on these near-surface sources of groundwater.

Opioid Epidemic In Maine: An Analysis Of Increasing Overdose-Related Deaths Following The Coronavirus, Aysel S. Hamlin

Thinking Matters Student Research Symposium

The rate of drug overdose resulting in death doubled in Maine following the COVID-19 pandemic from the onset of the COVID-19 pandemic in late 2019 through 2022. The correlation between increased isolation during the pandemic and overdose death rates sheds a concerning light on the insufficient resources for people struggling with Opioid Use Disorder (OUD) throughout Maine. The increasing trade and access to fentanyl following the pandemic accounted for the majority of drug-related deaths in Maine in 2021 and 2022. This study examines the need for long-term access to drug treatment in rural and urban Maine, both environments with varying …

Predicting Healthcare Service Quality Based On A Kalman-Optimized Bi-Lstm-Inspired Deep Learning Model, Mohammed K. Al-Khafaji, Eman S. Al-Shamery

Karbala International Journal of Modern Science

Health is one of the most important aspects of human well-being, and access to high-quality healthcare is essential for a good quality of life. Providing top-level health services at all times is crucial. However, the research in healthcare poses significant challenges due to the diversity and variations of medical practices across different hospitals. This paper aims to tackle the challenge of data missing and scattering during data collection. Then, the quality of services (QoS) offered by healthcare facilities will be analyzed and predicted from the patient's perspective. The model begins preprocessing data by data cleaning, handling missing values, and scattering …

The Role Of Individual Values In Bryant University's Sustainability Efforts, John Boccuzzi Iii

Honors Projects in Data Science

This research examines student, faculty, and staff perspectives on sustainability at Bryant University, with the goal of understanding how individual values align with the university's environmental initiatives. The objective is to assess perceptions of Bryant's current sustainability practices, explore how effectively these efforts are communicated across campus, and identify potential gaps between institutional action and community awareness. To achieve this, the study gathers qualitative data through an open-ended survey and applies sentiment analysis to interpret student attitudes toward sustainability. By analyzing these responses alongside Bryant's sustainability marketing efforts, this research will identify gaps between student engagement and institutional messaging The …

Promoting Sustainable Drinking Water Consumption At Bryant University, Matthew Gerdenich

Honors Projects in Biological and Biomedical Sciences

Many higher education institutions have either banned or restricted the sale of disposable plastic water bottles to address environmental pollution and climate change. This project seeks to gather knowledge and data related to disposable water bottle consumption at Bryant University that can be used to provide recommendations for promoting reusable water bottle use on campus. Additionally, the project seeks to find out why community members choose single-use water bottles over reusables ones and how to make reusable water bottle use more attractive and accessible. Primary research included a survey distributed to undergraduate students on campus as well as water samples …

Exploring The Evolution Of Global Warming Discourse: A Twitter-Based Analysis Across The United States, United Kingdom, And India, Trevor Christensen

Honors Projects in Data Science

Global warming has gained increasing attention over the past decade, with public discourse intensifying on social media platforms, particularly on Twitter. This increased discussion stems from political controversies surrounding climate change and the rise in extreme weather events. This study explores the evolution of global warming discourse on Twitter, with a focus on the United States, United Kingdom, and India. This research used a dataset of historical tweets ranging from 2010 to 2023 containing the keyword "global warming." Using natural language processing (NLP) techniques such as emotion analysis, word cloud visualization, and topic modeling (LDA), approximately twenty-eight million tweets were …

Synthesis Of Surface Bound Ligands Through Click Chemistry For A Tethered Catalyst System Towards The Reduction Of Carbon Dioxide, Emma J. Goehner

Departmental Honors & Graduate Capstone Projects

The electrochemical reduction of carbon dioxide (CO₂) offers a promising strategy for reducing excess atmospheric CO₂ by converting it into valuable chemical products. This process can be catalyzed by transition metal complexes, particularly those with extended aromatic ligands such as terpyridines which coordinate with metals in a tridentate fashion. When tethered to an electrode, the terpyridine-metal catalyst complex allows for stable binding and reusability without the need for chemical separation to recover the catalyst. Self-assembled monolayers (SAMs) of (3-bromopropyl) phosphonic acid were formed on copper surfaces, enabling attachment of terpyridine ligands through a click reaction between a …

Developing Predictive Mathematical Model For Optimizing Coating Weight Variation In Galvalume Production: A Case Study Of A Metal Industry, Victoria Mahabi

Tanzania Journal of Engineering and Technology (TJET)

Variations in coating weight for galvanized steel sheets can result in notable differences between batches. Such variations may cause various issues, such as diminished corrosion resistance, lower mechanical strength, and visual defects, which can ultimately drive-up costs, lead to customer dissatisfaction, and pose safety risks. Even with attempts to manage elements like air knife pressure and line speed, coating weight inconsistencies remain challenging. The research focuses on developing a predictive mathematical model designed to optimize variations in coating weight during Galvalume production. The critical parameters influencing coating weight variation were identified and analysed using a systematic literature review, primary data …

Artificial Intelligence In Orthopedic Medical Education: A Comprehensive Review Of Emerging Technologies And Their Applications, Kyle Sporn, Rahul Kumar, Phani Paladugu, Tejas Sekhar, Swapna Vaja, Tamer Hage, Ethan Waisberg, Chirag Gowda, Ram Jagadeesan, Nasif Zaman, Alireza Tavakkoli

SKMC Student Presentations and Publications

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, …

Thermodynamic Study Of Ligand Coordination On Lead Halide Perovskite Quantum Dots, Bishal Adhikari

Theses and Dissertations

Inorganic lead halide perovskite (ILHP) has been extensively studied in thin films and bulk crystals as promising materials for their photovoltaic and optoelectronic properties. These are stable enough to be prepared as colloidal quantum dots that can be dispersed, deposited and used to achieve quantum confinement; however, selection of the suitable surface passivating ligand remains challenge for long term stability of the colloidal quantum dots. Several ligand coordination chemistries used to achieve greater stability or specific functions: these can be installed at time of synthesis in some cases they can also be installed to preexisting quantum dots by ligand exchange …