Physical Sciences and Mathematics Commons^™

Spatial Patterns And Morphometric Evolution Of Sinkholes In Response To Watershed Hydrogeomorphology: Examples From A Karst Landscape In The Missouri Ozarks, Usa, Marcell Tóth

Graduate Theses/Dissertations

Sinkholes are characteristic enclosed depressions with internal drainage connecting the surface and subsurface karst hydrologic system. Their morphological evolution involves gradual subsidence of the ground surface and hazardous collapses. Therefore, understanding sinkhole morphometry and linked spatial distribution is critical for managing geohazards in karst watersheds. Sinkhole occurrence shows spatial variability based on watershed geomorphological attributes, surficial hydrological patterns, hydrogeological and geological settings. The aim of this research is to investigate morphometric properties and distribution of sinkholes in the carbonate karst-dominated lands of Mark Twain National Forest in Missouri, USA. The specific objectives were to (1) identify sinkhole clusters based on …

31p Solution Nmr Investigation Of Abasic Dna, Clarissa R. Krimmel

Graduate Theses/Dissertations

Base excision repair (BER) mechanisms fix single base lesions in DNA, such as T:G mismatches. During the base excision repair mechanism, an abasic site (AP site) is formed as an intermediate. AP sites are unstable and highly mutagenic; they can stop DNA replication. This research investigates how the conformational properties of abasic sites in DNA affect the binding recognition of enzymes involved in DNA repair mechanisms, including BER. Three different abasic sequences are being analyzed for this research project. A second project looks at the effects of a naturally occurring purine derivative, hypoxanthine, on the DNA backbone. These hypoxanthine lesions …

Differential Impact Of Admission Type And Clinical Complexity On Diabetes Hospitalization Costs Among African American And Hispanic Patients In Southeastern Virginia, Ismail El Moudden, Asra Amidi, Reem Sharaf-Alddin, Michael C. Bittner, Qi Zhang

Department of Obstetrics & Gynecology Faculty Publications

Background

Diabetes mellitus (DM) imposes substantial healthcare costs with documented disparities among African Americans and Hispanic patients. To inform care delivery and resource allocation, this study identified hospitalization cost predictors among African American and Hispanic patients with diabetes in Southeastern Virginia.

Methods

We analyzed 6,011 hospital discharges from the Virginia Health Information database (2016-2020) for adults aged 18-85 with diabetes. Discharges were classified by Medicare Severity Diagnosis-Related Groups: DM with complications/comorbidities (DCC, n = 3,328), DM with major complications/comorbidities (DMCC, n = 1,518), and DM without major complications/comorbidities (DWO, n = 1,165). Because cost distributions were right-skewed (skewness 3.5-8.24), we …

Srgan-Based Deep Learning Framework For Wind Turbine Damage Detection From Sentinel-2 Imagery, Kübra Çakir, Onur Elma, Murat Kuzlu

Engineering Technology Faculty Publications

The operational reliability of wind turbines is critical for sustainable energy production in smart grids. This study proposes a remote monitoring approach using perceptually enhanced satellite imagery. Sentinel-2 multispectral data (10 m resolution) has been processed with a Super-Resolution Generative Adversarial Network (SRGAN) to improve visual quality to a perceptual resolution of 30 cm. Although true spatial refinement is not achieved, the sharper structural details enhance classification accuracy. The data set comprises 15,000 images—10,000 SRGAN-enhanced and 5000 augmented through rotation, zoom in, increasing brightness, noise addition, and blurring. A custom Convolutional Neural Network (CNN) has been trained to classify turbines …

Interpretable Machine Learning For Bridge-Pier Scour Prediction And Flood Resilience, Adil Khan, Dalya Ismael

Engineering Technology Faculty Publications

Bridge-pier scour is a leading cause of flood-induced bridge failure, yet practice still lacks transparent, physics-informed tools that link data-driven prediction with design guidance. This study develops an interpretable, physics-aware machine-learning framework to predict equilibrium scour depth and translate those predictions into actionable strategies for flood-resilient infrastructure. Using the 2014 U.S. Geological Survey Pier-Scour Database (569 laboratory cases), five models: Gradient Boosting, AdaBoost (Tree), XGBoost, Gaussian Process (RBF kernel), and Kernel Ridge (polynomial), were trained and evaluated with K-fold cross-validation. Model performance was evaluated using R², RMSE, and MAE. Gradient Boosting performed best, achieving training and testing R² of 0.99 …

Generative Ai And Llm Applications In Renewable Energy And Smart Grids: A Systematic Review For The Sustainable Energy Transition, Umit Cali, Ugur Halden, Merlinda Andoni, Ferhat Ozgur Catak, Si Chen, Benoit Couraud, Emre Kantar, Samuel Knapper, Ibrahim Kucukdemiral, Huseyin Kusetogullari, Murat Kuzlu, Yashar Mousavi, Sonam Norbu, Taha Selim Ustun, David Flynn

Engineering Technology Faculty Publications

The global energy transition toward decarbonization and digitalization requires advanced methods to manage decentralized, data-intensive cyber-physical energy systems. This systematic review analyzes 106 research studies on Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) in renewable energy and smart grids, organized into seven application clusters covering forecasting, system design, operation, reliability, data and cybersecurity, and energy markets. The review situates these applications within a Cyber-Physical-Social Systems (CPSS) framework. Results show that GANs dominate current applications (47.2%), followed by LLMs (10.4%) and VAEs (9.4%), with growing adoption of diffusion and score-based models (7.5% each). Selected studies report improved probabilistic forecasting …

A Large Language Model-Based Academic Advising Assistant For Engineering Technology Students, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Otilia Popescu, Adel El-Shahat, Ryan Cotton, Kayla Marie Seegers, William Austin Henderson

Engineering Technology Faculty Publications

In recent years, Artificial Intelligence (AI)-based solutions, particularly Large Language Models (LLMs), have been applied to a variety of domains, such as energy, finance, transportation, healthcare, and education. Among these domains, education has become increasingly popular due to strong interest among educators and students. This study proposes an academic advising assistant system that uses LLMs to help Engineering Technology (ET) students plan their course load based on their educational history, departmental course offerings, and personal constraints, such as their preferred semester course load. The proposed LLM-based academic advising assistant system maintains a database of students' course histories and upcoming course …

Artificial Intelligence (Ai) In Educating Next Generation Of Engineering Technology Students, Adel El-Shahat, Murat Kuzlu, Vukica M. Jovanovic, Katherine Smith, Abdullah Al Mamun, Otilia Popescu

Engineering Technology Faculty Publications

Artificial Intelligence (AI) is transforming education, particularly for electrical engineering technology (EET) students, by presenting adaptive learning, immediate responses, and unconventional tools. Therefore, this paper proposes investigating modern learning to employ AI in educating future electrical engineering technology students. Firstly, the paper explores how to shape AI knowledge for EET students, supplying them with hands-on skills in AI tasks, clarifying coding, data analysis, and AI ethical usage. Then, as educators, what are the efficient AI tools to utilize in teaching, such as tailored tutoring, automated code assessment, AI-driven design/simulation, lecture dictation, and smart content creation? Key tools, for instance, Google …

Deep Learning-Based High-Resolution Spectrogram Reconstruction For Robust Bearing Fault Diagnosis, Sabyasachi Biswas, Saifur Rahman Tushar, Md Ebtidaul Karim, Abdullah Alam Mamun, Mahathir Mohammad Bappy

Engineering Technology Faculty Publications

Accurate and reliable bearing fault diagnosis is critical for the safe and efficient operation of rotating machinery. Conventional time–frequency (TF) analysis techniques, such as the short-time Fourier transform (STFT), are limited by their fixed window sizes and the inherent trade-off between time and frequency resolution, often leading to suboptimal fault representation under varying operating conditions. To address these challenges, this paper proposes a deep learning-based framework that reconstructs high-resolution, noise-robust TF representations directly from raw vibration signals. The proposed model integrates a denoising encoder with a learnable TF transformation network to adaptively capture fault related frequency patterns without manual parameter …

Limits Of Machine Learning For Predicting Stillbirth At Term: A Population-Based Cohort Of 302,192 Pregnancies In Northern Italy, M. Benuzzi, T. Kawakita, F. Monari, B. Melis, G. Guariglia, A. La Marca, I. Neri, G. Saade, F. Facchinetti, I. El Moudden

Department of Obstetrics & Gynecology Faculty Publications

Background

Objective

Study Design

Modeling Healthcare Data With Logistic Quantile And Uniform-Based Mixture Polynomial Distributions, Mohan D. Pant, Aditya Chakraborty, Jovanna A. Tracz

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Continuous healthcare data often deviate from normality, which can substantially increase the risk of making invalid inferences, given that many inferential statistical procedures rely on normality assumption. To obviate this issue, we propose a new family of non-normal distributions based on a linear combination of the quantile functions of standard logistic and uniform (0, 1) distributions. This new family of non-normal distributions is studied within three different methods: L-moments, conventional moments, and percentiles. Its performance is compared among the three methods in the context of parameter estimation and data modeling. The results of Monte Carlo simulation and bootstrapping techniques indicate …

Understanding Cancer Information Burden And Quality Concerns: The Roles Of Medical Statistics Literacy, Online Health Information Confidence, And Social Media, Aditya Chakraborty, Sikta Das, Amy Myat Phyo

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Understanding factors shaping perceptions of cancer-related information is important for public health communication. This study examined associations of three information-related predictors, understanding medical statistics, confidence in online health information, and difficulty interpreting social media information, with two outcomes: cancer information was too hard to understand (CTU) and concern about cancer information quality (CCQ). Data came from the 2022 Health Information National Trends Survey. The cross-sectional study included 1972 participants. Survey-weighted univariable and multivariable logistic regression models assessed associations between the three predictors and CTU and CCQ, adjusting for sociodemographic characteristics, health insurance, and cancer history. LASSO logistic regression, random forest, …

Zero-Shot Segmentation Of Estuary Mudflats Using The Segment Anything Model, Jaren Unzen

Honors Theses and Capstones

Estuary mudflats are ecologically sensitive environments that require consistent monitoring. Traditional satellite-based classification workflows are often constrained by the high cost and labor-intensive nature of manual data annotation. This study evaluates the utility of Segment Anything Model 3 (SAM 3), a foundational computer vision model, to automate mudflat segmentation without domain-specific fine-tuning. By leveraging the model’s text-prompting capabilities alongside specialized pre- and post-processing techniques, we generated segmentation masks in a zero-shot framework. Our approach achieved an F1- score of 0.51, demonstrating the inherent challenges of spectrally complex coastal features. Despite this, the results highlight a promising pathway for adapting large-scale …

Blens: Biomedical Literature Extraction And Scoring System, Tyler J. Simone

Honors Theses and Capstones

Systematic reviews and meta-analyses represent the gold standard for evidence synthesis in healthcare, yet their manual execution remains labor-intensive, time-consuming, and vulnerable to human bias. With the exponential growth of biomedical literature, traditional literature screening and analysis has become increasingly unstable and noncomprehensive. This thesis presents the development and validation of an automate literature gathering and review system that integrates multiple scientific databases through a unified desktop application. The platform combines APIs from PubMed (NCBI Entrez), ClinicalTrials.gov, bioRxiv and medRxiv to enable simultaneous, standardized searching across peerreviewed and preprint sources. Built in Python with a PySide6 graphical interface, this standalone …

Investigating A Nanoparticle-Assisted Approach For Localized Strain Engineering In Two-Dimensional Materials, Skye Martichuski

Honors Theses and Capstones

This work investigated a fabrication strategy for inducing localized nanoscale deformation and strain in transferred MoS2 flakes using gold nanoparticle-coated substrates as a potential platform for strain engineering in two-dimensional materials. Substrate type, nanoparticle concentration, drying conditions, cleaning procedures, and flake transfer methods were investigated using optical microscopy and atomic force microscopy (AFM). Au-coated SiO2 substrates produced improved nanoparticle coverage compared to bare SiO2, while passive air drying yielded the most favorable nanoparticle distributions. Salt crystals introduced during passive drying were successfully removed with a gentle deionized water rinse, and a 50% diluted gold nanoparticle suspension provided improved particle spacing …

Exploring Caring Pedagogy Through Self-Study: A Mathematics Educator’S Journey, Kien H. Lim

Departmental Papers (Math)

This self-study examines my efforts to cultivate a positive learning environment in a capstone mathematics course for preservice middle school teachers. Motivated by earlier negative course evaluations and guided by heart-centered pedagogy, I explored how my beliefs, emotions, and instructional practices evolved as I sought to integrate kindness, foster mathematical thinking, and support students’ well-being. Drawing on dialogue transcripts, reflective journals, and reconstructed tutoring notes, I analyzed tensions between content coverage and relational pedagogy, as well as between deficit- and strength-based perspectives of students. Through critical dialogue and reflective inquiry, I gained deeper insight into my identity as a caring …

Creating A Positive Learning Environment In A Math Course For Preservice Teachers, Kien H. Lim, Hilda Y. Sotelo

Departmental Papers (Math)

This study examines how a mathematics instructor cultivated a positive learning environment in a synchronous online course for 23 preservice teachers. Guided by self-determination theory, growth mindset, and mathematical caring, the course integrated high-cognitive-demand tasks, collaborative problem solving, flexible policies, and motivational supports. Data sources included reflection logs (Likert-scale items and open-ended responses) and a focus group; the instructor’s reflexive self-study informed result interpretation. Wilcoxon signed-rank tests indicated significant gains in students’ sense of inclusion and trust, though some instructor- related ratings declined late in the semester. Qualitative findings emphasized students’ appreciation for flexibility, encouragement, and growth-oriented feedback. Despite time …

Cognitive Prosthetic: An Ai-Enabled Multimodal System For Episodic Recall In Knowledge Work, Lawrence Obiuwevwi, Krzystof J. Rechowicz, Vikas Ashok, Sachin Shetty, Sampath Jayarathna

Computer Science Faculty Publications

Modern knowledge workplaces increasingly strain human episodic memory as individuals navigate fragmented attention, overlapping meetings, and multimodal information streams. Existing workplace tools provide partial support through note-taking or analytics but rarely integrate cognitive, physiological, and attentional context into retrievable memory representations. This paper presents the Cognitive Prosthetic Multimodal System (CPMS)—an AI-enabled proof-of-concept designed to support episodic recall in knowledge work through structured episodic capture and natural language retrieval. CPMS synchronizes speech transcripts, physiological signals, and gaze behavior into temporally aligned, JSON-based episodic records processed locally for privacy. Beyond data logging, the system includes a web-based retrieval interface that allows users …

Adaptive Boundary-Aware Fact-Checker Placement For Misinformation Suppression In Social Networks, Mostafa Taghizade Firouzjaee, Ghazal Naderi, Ross Gore, Neda Moghim

School of Cybersecurity Faculty Publications

The spread of fake news on online social networks is driven by imitation-based user behavior and network topology, often leading to persistent misinformation clusters and echo chambers. In this study, we develop a spatial evolutionary game-theoretic framework in which agents update their latent opinions through payoff-biased imitation, while external fact-checkers act as non-imitative intervention nodes. Building on this formulation, we propose an adaptive, boundary-aware intervention mechanism that dynamically regulates both the density and spatial allocation of fact-checkers according to real-time system conditions. Competing information clusters are identified through local neighborhood composition, enabling boundary nodes, i.e., interfaces between fake-news and non-fake-news …

Framework For Task Offloading In O-Ran Architecture For Heterogeneous Computing Applications, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Computational offloading transfers tasks from resource-constrained devices to more capable servers or cloud platforms, improving processing speed and user experience. Open radio access networks (O-RAN's) disaggregated architecture and open interfaces make it suitable for offloading delay-sensitive tasks, enhancing real-time application performance. This study focuses on task offloading in O-RAN, a reference network architecture. Although research on O-RAN is limited, existing work lacks a comprehensive approach to offloading, including offloading layer determination, node selection, and resource allocation based on task types and their latency needs. We propose a delay-aware task offloading framework within O-RAN to support diverse delay requirements, improving offloading …

A. Vs. I In Ai: Is There A Threshold To "Engineered" Intelligence?, Joshit Mohanty

Engineering Management & Systems Engineering Faculty Publications

Despite artificial intelligence reshaping the world, its development generates uncertainties regarding future capabilities. AI simultaneously exists as an artifact of engineering design and as autonomous intelligence, creating an observer-participant feedback loop. This paper proposes that embodied AI faces a bandwidth-limited intelligence threshold T_h that it arises from B = min(C_sens,C_Act). However, Shannon capacity measures bits while intelligence operates on concepts, necessitating a dual-channel model separating physical bandwidth B_io from representational capacity B_rep. Intelligence emerges as multi-dimensional rather than scalar, with components exhibiting different bandwidth dependencies. Surpassing T_h requires either new sensing methods expanding B, enhanced representational frameworks, or reconceptualization within …

Technological Interventions For Reducing Climate Change Impacts On Health: An Overview Of Future Possibilities, Sujatha Alla, Vijay Kumar Chattu, Bawa Singh

Engineering Management & Systems Engineering Faculty Publications

Globally, over five million deaths annually are attributed to extreme weather events exacerbated by climate change, such as heatwaves, hurricanes, wildfires, droughts, and floods, which create health crises and economic losses. The 2015 Paris Declaration emphasized climate technology mechanisms, including research and development, to enhance resilience and reduce greenhouse gas emissions. Biotechnological advancements have mitigated about 20% of economic losses since the 1960s, highlighting technology’s potential to address climate-induced health risks.

Potassium For High Rainfall Pastures In Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Potassium (K) deficiency can lead to a decline in clover content and severely limit pasture productivity in high rainfall pastures. Large amounts of potassium can be lost each year through leaching and removal of potassium in hay or silage.

This information is specific to high rainfall pastures receiving more than 600mm average annual rainfall in the south-west of Western Australia.

Tissue Sampling And Analysis For High Rainfall Pastures In Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Tissue testing helps identify nutrient deficiencies that impact plant health. In high rainfall areas ( > 600 mm), pastures are highly productive and have greater demand for both macro- and micronutrients. Micronutrient status is best assessed through plant tissue, as soil levels are difficult to measure accurately.

The Department of Primary Industries and Regional Development recommends tissue testing to assess the effectiveness of fertiliser and soil testing programs, and diagnose emerging macronutrient and micronutrient deficiencies that could cause production losses.

Sulfur For High Rainfall Pastures In Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Sulfur deficiency can substantially reduce pasture yield on sandy soils, particularly in wet years where the sulfate form of sulfur leaches below the root zone of pasture plants, making it unavailable.

This information applies specifically to high rainfall pastures (over 600mm average annual rainfall) in the south-west of Western Australia.

The Department of Primary Industries and Regional Development recommends basing fertiliser decisions on good evidence (soil and tissue test results)

Soil Sampling High Rainfall Pastures In Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

Soil sampling and testing is an essential part of making good fertiliser decisions. Fertilisers are a large cost to farming pastures in high rainfall areas. This page is specific to sampling high rainfall pastures (more than 600mm average annual rainfall) in the south-west of Western Australia.

The Department of Primary Industries and Regional Development recommends soil sampling and testing as part of a plant nutrient management program.

Environmental Impact Of Nitrogen And Phosphorus Fertilisers In High Rainfall Pasture Areas Of Western Australia, Department Of Primary Industries And Regional Development, Western Australia

Natural resources factsheets

The most commonly used nitrogen and phosphorus fertilisers in the high rainfall (>600mm annual average) south-west of Western Australia can be leached into the groundwater and washed into waterways, resulting in serious environmental and economic damage.

The Department of Primary Industries and Regional Development recommends regular soil testing and consultation with FertCare-accredited advisers to reduce leaching losses and increase profit from added nutrients.

Protomix: Unified Prototype Learning For Semantic Change Detection In Remote Sensing, Hengzhi Chen, Kun Hu, Patrick Filippi, Wei Xiang, Thomas Francis Bishop, Zhiyong Wang

Research outputs 2022 to 2026

As an emerging topic, semantic change detection (SCD) in remote sensing aims to both identify the locations of changes before and after events and determine the land cover (LC) classes of changed regions. While deep learning has demonstrated its great potential for SCD, the following two fundamental challenges remain: large variations among the same LC class as well as close similarity between different LC classes, and the severe underutilization of massive unchanged regions, which dominate remote sensing scenes but lack detailed semantic annotations. To address these issues, we propose a novel prototype learning framework for SCD, namely ProtoMix, by leveraging …

Rrto: A High-Performance Transparent Offloading System For Model Inference In Mobile Edge Computing, Zekai Sun, Xiuxian Guan, Zheng Lin, Yuhao Qing, Haoze Song, Zihan Fang, Zhe Chen, Fangming Liu, Heming Cui, Wei Ni, Jun Luo

Research outputs 2022 to 2026

Deploying Machine Learning (ML) applications on resource-constrained mobile devices remains challenging due to limited computational resources and poor platform compatibility. While Mobile Edge Computing (MEC) offers an offloading-based inference paradigm using GPU servers, existing approaches can be divided into non-transparent and transparent methods, with the former necessitating source-code modifications. Non-transparent offloading achieves high performance but requires intrusive code changes, limiting compatibility with diverse applications. Transparent offloading, in contrast, offers wide compatibility but introduces two challenges: performance degradation under MEC's low and fluctuating bandwidth, where per-operator remote procedure calls (RPCs) inflate end-to-end latency and energy consumption; and black-box logic reconstruction, which …

Redox-Mediator Enhanced Electrochemiluminescence Under Non-Aqueous Conditions, Steven J. Blom, Fazeleh Mesgari, El M.S. Martin, Egan H. Doeven, David J. Hayne, Timothy U. Connell, Peter J. Barnard, Narges Saeedizadeh, Seyed Mohammad Jafar Jalali, Paul S. Francis

Research outputs 2022 to 2026

The introduction of small-molecule redox mediators into aqueous co-reactant electrochemiluminescence (ECL) systems has emerged as an effective strategy to increase signal intensity. Herein, we investigate the influence of a series of neutral iridium(iii) complexes (Ir(pmi)3, Ir(ppy)3, Ir(ppz)3 and Ir(ppy)2(acac)) as redox mediators for co-reactant ECL in acetonitrile. Using [Ru(bpy)3]²⁺ as a benchmark luminophore and tri-n-propylamine (TPrA) as a co-reactant, the redox mediators elicit similar effects in this solvent to those of their sulfonated [Ir(sppy)3]³⁻ and [Ir(sppz)3]³⁻ analogues under aqueous conditions. The Ir(ppz)3 complex was most effective; at a concentration of 100 µM it produced an 11-fold increase …