Physical Sciences and Mathematics Commons^™

Reinventing Mathematics Learning: An Exploration Of Undergraduate Mathematics Learning Post-Pandemic, Morgan R. Balesano

Honors Scholar Theses

In response to the COVID-19 pandemic, beginning in March 2020 nearly all secondary and undergraduate mathematics students were forced to adapt to new methods of learning and testing. As a result, the current experience for these mathematics students has changed vastly, as have the opinions and preferences of these students in terms of their learning. This study aims to identify instruction and testing resources and methods that students prefer and those that students find less beneficial in their current, post-pandemic educational experience. The past few years have seen a focus on the direct impacts of online learning during the pandemic, …

Collaborative Network Traffic Management Strategies Using The Distributed Reinforcement Learning And Large Language Models, Saeed Rashed Alkuwaiti

Thesis/ Dissertation Defenses

The focus of this research is to explore collaborative network traffic management strategies using the Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs) approaches. It emphasizes exploring a new tool for addressing network traffic by utilizing Distributed Reinforcement Learning (DRL) and Large Language Models (LLMs). This is achieved by utilizing self-organizing and self-directing techniques to optimize the network performance. Using the NF-TON-IOT dataset, various classifiers such as Random Forest, AdaBoost, C4. 5, Multi-Layer Perceptron (MLP), and SVM with an RBF kernel were tested for traffic classification and intrusion detection. Research recommends that DRL optimizes the complexity of the network …

Detection And Parameter Estimation Of Supermassive Black Hole Ringdown Signals Using A Pulsar Timing Array, Xuan Tao, Yan Wang, Soumya D. Mohanty

Physics & Astronomy Faculty Publications

Gravitational wave (GW) searches using pulsar timing arrays (PTAs) are commonly assumed to be limited to a GW frequency of ≲4 ×10−7  Hz given by the Nyquist rate associated with the average observational cadence of 2 weeks for a single pulsar. However, by taking advantage of asynchronous observations of multiple pulsars, a PTA can detect GW signals at higher frequencies. This allows a sufficiently large PTA to detect and characterize the ringdown signals emitted following the merger of supermassive binary black holes (SMBBHs), leading to stringent tests of the no-hair theorem in the mass range of such systems. Such large-scale …

Perceptions Of Employability With Ai Skills, Brandy Whitford, Patrick J. Cooper

Student Publications and Presentations

Artificial intelligence (AI) is making AI proficiency a key factor in hiring and career advancement. By late 2023, 75% of knowledge workers integrated AI into their workflows, with 92% reporting increased productivity and creativity (Kimbrough, 2024). Employers are adapting—66% prefer candidates with AI expertise, and 77% consider AI skills essential for career growth (Microsoft & LinkedIn, 2024). However, hiring biases related to AI-skilled applicants remain underexplored, particularly concerning gender disparities in employability perceptions. This study examines how AI-related skills influence perceived employability and whether these perceptions vary based on applicant gender. Specifically, it explores whether AI-skilled female applicants receive higher …

Using Mathematical Modeling To Study The Dynamics Of Legionnaires’ Disease And Consider Management Options, Mark Z. Wang, Christina J. Edholm, Lihong Zhao

Faculty Articles

Legionnaires' disease (LD) is a largely understudied and underreported pneumonic environmentally transmitted disease caused by the bacteria \textit{Legionella}. It primarily occurs in places with poorly maintained artificial sources of water. There is currently a lack of mathematical models on the dynamics of LD. In this paper, we formulate a novel ordinary differential equation-based susceptible-exposed-infected-recovered (SEIR) model for LD. One issue with LD is the difficulty in its detection, as the majority of countries around the world lack the proper surveillance and diagnosis methods. Thus, there is not much publicly available data or literature on LD. We use parameter estimation for …

From Food To Fashion: Bio-Based Textiles Advancing Sustainability And Socioeconomic Growth In Emerging Markets, Andrew Burnstine

Faculty and Staff Publications & Presentations

The global fashion industry is undergoing a profound shift towards sustainability, driven by innovations in bio-based textiles derived from agricultural byproducts such as cactus, pineapple, banana, coconut, fungi, and bacterial cultures. This article comprehensively explores the technological advancements, industry adoption trends, socioeconomic impact on emerging markets, and barriers to widespread adoption of bio-based textiles. The alignment of these innovations with global sustainability initiatives, key industry collaborations, and policy interventions are also examined.

Re: Comment Letter For The Butte Priority Soils Operable Unit (Bpsou) Butte Reclamation Evaluation System (Bres) Draft 2024 Corrective Action Plans (Dated January 27, 2025), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Exploring Teacher Knowledge In Meeting The Expectations Of Integrated Content Language Development In Mathematics: A Descriptive Case Study, Carrissa Rozelaar-Miller

Doctoral Dissertations and Projects

The purpose of this descriptive case study was to discover how knowledge is applied to meet the expectations of integrated content and language development in mathematics for Title I elementary teachers at an urban school district in the central United States. Halliday’s theory on systemic functional linguistics guided the research as it relates to the function and purpose of language through meaning and social interaction in different genres. The central research question was: How do Title I elementary teachers apply specific integrated content and language development knowledge in mathematics? The participants consisted of 10 teachers with at least three years …

Existence And Nonexistence Of Positive Solutions For Fractional Boundary Value Problems With Lidstone-Inspired Fractional Conditions, Jeffrey Lyons, Jeffrey T. Neugebauer, Aaron G. Wingo

EKU Faculty and Staff Scholarship

This paper investigates the existence and nonexistence of positive solutions for a class of nonlinear Riemann–Liouville fractional boundary value problems of order 𝛼 +2⁢𝑛, where 𝛼 ∈(𝑚 −1,𝑚] with 𝑚 ≥3 and 𝑚,𝑛 ∈ℕ. The conjugate fractional boundary conditions are inspired by Lidstone conditions. The nonlinearity depends on a positive parameter on which we identify constraints that determine the existence or nonexistence of positive solutions. Our method involves constructing Green’s function by convolving the Green functions of a lower-order fractional boundary value problem and a conjugate boundary value problem and using properties of this Green function to apply the Guo–Krasnosel’skii …

Gender Bias Within Ai Imaging, Drew Quattrocchi

Student Publications and Presentations

This study investigates AI-created gender bias in AI-created images through content analysis, contrasting the way gender is depicted in professions in leading AI image-creation tools such as Chat smith, Adobe Firefly, Midjourney, and Stable Diffusion. Employing a quantitative research method, this study contrasts AI-created images of gender-stereotypical careers for both male and female. Non-gendered careers will be used as well to identify patterns of stereotyping and bias. The area of emphasis lies in individual subjects within the images and scrutinizing visual elements such as clothing, accessories, background, face expressions, and gendered roles assigned to each. Particular emphasis is focused to …

Technology Anxiety In Virtual Reality Adoption: Examining The Impact Of Age, Past Experience, And Cybersickness, Eman Al Khalifah, Ramy Hammady, Mahmoud Abdelrahman, Ons Al-Shamaileh, Mostafa Marghany, Hatana El-Jarn, Alyaa Darwish, Yusuf Kurt

All Works

This study examines the role of Technology Anxiety (TA), age, past use, and cybersickness in the adoption of Virtual Reality (VR) technology. Using an extended Technology Acceptance Model (TAM), the research integrates age and past use as antecedents of TA and evaluates their influence on perceived ease of use (PEoU), perceived enjoyment (PENJ), and user attitudes. Data from 206 participants were analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM) following a VR pilgrimage experience. The findings challenge conventional assumptions, revealing that past VR use increased TA, contradicting prior studies that associate familiarity with reduced anxiety. Additionally, older users exhibited …

A Complete Guide To Multi-Fidelity No-U-Turn Sampling With Application To An Inverse Wave Problem, Paul Smith

Theses and Dissertations

We employ a Bayesian framework to solve an inverse problem that aims to identify the initial condition distribution of wave propagation, given a limited set of observable data. To optimize sampling efficiency, we introduce a multi-fidelity No-U-Turn Sampler (MF-NUTS) with an adaptive mass matrix. Maximizing sampling efficiency is crucial, as our ultimate goal is to extend this methodology to a large-scale inverse problem to identify the source parameters of historical tsunami-generating earthquakes. The forward model for tsunami propagation (GeoClaw) requires over 20 minutes of supercomputer time on 24 cores to evaluate each sample for a triple-fault rupture, necessitating a strategy …

Optimizing Aircraft Taxi Processes: A Novel Cost-Minimizing Queue Discipline For The Pushback To Runway Process, Kevin James

Theses and Dissertations

Optimization of the aircraft taxi process can significantly decrease operating costs for air carriers, especially at major airports where congestion on the ramp is a primary contributor to taxi times. To improve the efficiency of this process, we propose a novel queue discipline which mitigates pushback delays through a dynamic reordering of clearance deliveries. As an alternative to FIFO, the proposed discipline accounts for aircraft delays at the gate before the aircraft enters the queueing system. Our proposed system implements pushback control to allow for controller-imposed variability in runway queueing. To validate our proposed queue discipline, we provide a computational …

The Universe Through The Lens Of Gravitational Waves, Bartosz Fornal

Mathematics Colloquium Series

Gravitational waves were predicted by Einstein’s theory of general relativity back in 1916, but it took 100 years of scientific and technological progress to measure them directly at the Laser Interferometer Gravitational-Wave Observatory (LIGO). Although the signals detected by LIGO originated from cataclysmic astrophysical events such as black hole and neutron star mergers, one expects the presence of a primordial gravitational wave background permeating space today emitted within one trillionth of a second after the Big Bang, some 13.8 billion years ago. Such a signal could have been produced by various particle physics phenomena in the early Universe, including phase …

Application For Prediction Of Heart Failure; The Next Step In Machine Learning For Healthcare, Amy Adyanthaya, Dawn Bowerman, Rachel Liercke, Robert Slater

SMU Data Science Review

Heart failure (HF) is a serious medical condition affecting approximately 6.7 million U.S. adults and is expected to impact 8.5 million Americans by 2030 [1]. Heart failure is a complicated clinical ailment and characterizes the final course of numerous heart diseases [2]. This paper introduces a machine-learning-based application that utilizes Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and XGBoost models, implemented through the Python Flask framework, to predict HF risk using clinical data. The results indicate high model performance, with precision and recall metrics underscoring the application’s reliability in identifying at-risk patients. By providing real-time, accessible insights, this tool aims …

Multi-Agent Translation Team (Matt): Enhancing Low-Resource Language Translation Through Multi-Agent Workflow, Anishka Peter, Mai Dang, Michael Liu, Joaquin Dominguez, Nibhrat Lohia

SMU Data Science Review

Like humans, large language models (LLMs) benefit from revision and refinement, especially for complex tasks requiring critical thinking. Inspired by human collaborative problem-solving, this study introduces a novel multi-agent workflow designed to enhance LLM translations from English to low-resource languages. Multi-Agent Translation Team (MATT) involves the collaboration of agents that are assigned specific roles, such as translator, evaluation coordinator, and various levels of editing, to refine the initial translation into the most desired version possible. The agents work collaboratively in an iterative loop until the translation loss meets a satisfactory threshold. It stands out from other multi-agent workflows by combining …

Enhancing Animal Shelter Operations With Time Series And Machine Learning, Sakava L. Kiv, Donald L. Anderson, Shivam Negi, Jacquelyn Cheun

SMU Data Science Review

Enhancing animal shelter operations through machine learning involves employing a variety of advanced techniques aimed at increasing efficiency, promoting animal welfare, and optimizing resource allocation. This paper explores predictive analytics for adoption rates using regression models to estimate the likelihood of adoption based on historical data, encompassing variables such as breed, health status, and previous adoption trends. Additionally, classification algorithms are utilized to categorize animals by adoption probability, facilitating better resources and marketing prioritization. Clustering algorithms are employed to group animals according to behavior patterns and/or physical health, enabling tailored medical care and enrichment activities that improve their mental and …

Enhancing Network Security Through Dual-Layer Log Analysis: Integrating Machine Learning Classifiers With Large Language Models For Intelligent Anomaly Detection, Anthony Burton-Cordova, O'Neil Gray, Mohammad Al Rousan

SMU Data Science Review

This paper presents an innovative approach to enhancing network security by integrating machine learning algorithms with fine-tuned large language models (LLMs) to provide an expert assistant querying. The proposed method utilizes machine learning for efficient preprocessing and feature extraction from log data, followed by the application of a fine-tuned LLM to analyze and interpret anomalies with greater accuracy. This dual-layer detection system is designed to improve the identification of subtle and sophisticated security threats. The research team’s extensive evaluation using real-world log datasets indicates that the combined approach increases detection rates and communicates results in an understandable manner, demonstrating its …

Survey On Intelligent Planning Methods From Large Language Models Perspective, Yanzhong Zhou, Junren Luo, Xueqiang Gu, Wanpeng Zhang

Journal of System Simulation

Abstract: Starting from the perspective of large language models, this paper gives an overview of the definition and development of intelligent planning, and briefly introduces the traditional methods of intelligent planning; based on the close relationship between large language model intelligent agents and intelligent planning, introduces the architecture of large language models and typical large model intelligent agents; focusing on the intelligent planning for large language models, combs through the learning of planning languages, chain of thought, feedback optimization, and process automation; combining with the current challenges and difficulties, introduces the outlook of cutting-edge research on intelligent planning with large …

Simulation Environment Construction Of Track Segment Association And Algorithm Performance Evaluation, Dian Ding, Guangfen Wei, Zheng Cao, Shaohui Wen

Journal of System Simulation

Abstract: In order to study the applicability of Track Segment Association (TSA) algorithms in actual radar working environment , a TSA simulation environment which can simulate the real movement of the target is constructed. By constructing a rich set of target motion sets, the state switching process of target motion is described based on Markov state transition matrix, and the density is flexibly controlled through track translation. The simulation results show that this environment can evaluate the performance of the current classical TSA algorithms. The evaluation results provide a good reference for the practical engineering application of interrupted track association.

A Method For Road Extraction Using Masked Image Modeling And Contrastive Learning, Jiangjiang Wu, Zhenghong Li, Zhichao Sha, Hao Chen, Shuang Peng, Chun Du, Jun Li

Journal of System Simulation

Abstract: Aiming at the occlusion problem of road extraction from remote sensing images, a road extraction method combining MIM and CL is proposed, the model training process includes a masked pretraining stage and a contrast training stage. The masked pre-training stage mainly carries out mask image reconstruction, and trains the model to recover the whole image from some areas that are randomly occluded. The comparison training stage is mainly for the prediction error and low confidence regions to learn the comparison, to narrow the distance between the features of the same category and increase the distance between the features of …

Fids: Accelerating Network Intrusion Detection Through Strategic Feature Selection, James Elmore

Cybersecurity Undergraduate Research Showcase

Network intrusion detection systems (IDS) typically analyze complete network flows to identify malicious traffic, requiring flows to conclude before classification. This approach creates detection delays for attacks like Slowloris that intentionally keep connections open for extended periods of time. This paper introduces a novel approach that classifies network traffic using only features available from the first few packets of a flow, enabling faster detection while maintaining high accuracy. We evaluate three random forest models on the CICIDS2017 dataset using expanding sets of features: the first-packet model trained on on features available from the first backward packet, the few-packet model which …

The Evolution Of Russian And Chinese Disinformation Tactics And The Threat They Pose To The U.S. Cybersecurity, Jehovani Sese

Cybersecurity Undergraduate Research Showcase

This paper aims to discuss how disinformation has become the most powerful tool used against the United State by foreign operatives. Of these foreign operatives, Russia and China have shown the ability to use advanced tactics to truly affect the United States security. Often these tools came in the form of state-sponsored media, influence campaigns and fake online identities. This literature review explores the evolution of Russian and Chinese disinformation tactics, examining how these approaches have changed over time and become more sophisticated. This paper will highlight major campaigns which use tools such as bot networks, and social media manipulation. …

Shaped Adversarial Patches And The Ability They Hold, Nathan Hallberg

Cybersecurity Undergraduate Research Showcase

In more recent years the development of computer vision has advanced to be more comprehensive than in the past, with newer applications ranging from autonomous vehicles to security systems. The main application I will be talking about throughout this paper is an object detection algorithm called YOLO (You only look once), this algorithm is particularly significant due to their real-time performance of being able to identify and localize objects within an image in quick timing. However, the strength of these computer vision models is increasingly challenged by adversarial attacks, which manipulate the computer's vision to block a certain part of …

Cohens_D, Manish Rami

Software

This Python script calculates the effect size Cohen's d in a two group situation with known means and Standard Deviations.

Use this effect size if the sample size in your experiment is large and the two SDs are similar.

2025 April 17 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

A Transfer Learning-Based Hybrid Model For Pm2.5 Concentration Prediction, Xinbiao Lu, Chunlin Ye, Yisen Chen, Wen Wu, Yudan Chen

Journal of System Simulation

Abstract: In order to solve the problems of increased computational cost due to irrelevant features and decreased prediction accuracy due to the difference in probability distribution caused by the change of data distribution over time in PM2.5 concentration prediction, this paper constructs a hybrid deep learning model TraTCN-LSTM-BiGRU based on migration learning. The meteorological factors related to PM2.5 concentration are selected as the model input using the mean-value heat map algorithm features; the source domain data and target domain data are divided by KL scatter and an adaptive layer is introduced into the model to achieve inter-domain distribution adaptation; the …

An Event Ontology And Dataset Construction Method For Strategic Operations Analysis, Quanlin Chen, Jun Jia

Journal of System Simulation

Abstract: Aiming at the lack of professional datasets for information extraction technology research in the field of strategic operations research analysis, this paper proposes an event ontology and dataset construction method for strategic operations research analysis. The method proposes an event ontology model for strategic operations research analysis according to the needs of situation judgment in strategic operations research analysis, and uses the method of "a small amount of manual annotation + fine-tuned large language model annotation" to construct the event dataset EfSOA for strategic operations research analysis. The dataset construction method proposed in this paper and the constructed dataset …

Statistical Study Of Solar Wind Conditions Prior To Substorm Onsets, Luke H. Francis

Doctoral Dissertations and Master's Theses

Due to complex, multi-region, coupled plasma systems, auroral substorm onsets have been historically difficult to predict. The northward turning of the interplanetary magnetic field was considered the primary candidate as an external triggering mechanism for substorm onsets. However, that was later shown to be coincidental in nature. This study is motivated by recent multi-spacecraft observations that show how several magnetosheath jets at the bow shock were heavily correlated to substorm onsets, indicated by a strongly radial IMF interval. In the past, studies have looked at small samples of substorms in order to make large-scale predictions. However in this study, a …

Trajectory Planning Of Quadruped Robot Over Obstacle With Single Leg Based On Deep Reinforcement Learning, Min Li, Sen Zhang, Xiangguang Zeng, Gang Wang, Tongwei Zhang, Dijie Xie, Wenzhe Ren, Tao Zhang

Journal of System Simulation

Abstract: Aiming at the problems of joint vibration and high energy consumption of quadruped robot in the process of walking over obstacles, a foot trajectory planning method of quadruped robot based on deep reinforcement learning SAC algorithm is proposed. Based on robot kinematics and Monte Carlo method, the motion space of the single-legged foot of quadruped robot is analyzed. A compound seventhdegree polynomial trajectory of the quadruped robot is planned. The SAC algorithm is used to train and obtain the low energy consumption obstacle crossing strategy of four-legged robot under different obstacle environment. The simulation results show that the compound …