Physical Sciences and Mathematics Commons^™

Toward Biocompatible Assessment Of N-15 Hyperpolarized Metronidazole For Hypoxia Sensing, Faisal Asif

Wayne State University Theses

Recent advances in hyperpolarization, particularly Signal Amplification by Reversible Exchange (SABRE), have enhanced molecular magnetic resonance imaging (MRI) for in vivo applications. This study investigates [15N3]metronidazole, a tri-nitrogen-labeled derivative of an FDA-approved antibiotic, as a hypoxia-sensitive imaging agent. Under hypoxic conditions, the nitroimidazole group undergoes stepwise reduction to an amino group, inducing significant 15N chemical shift changes (30–600 ppm). We aim to develop an iridium catalyst-free, biocompatible hyperpolarized (HP) [15N3]metronidazole formulation exhibiting robust polarization (up to 15%) and extended T1 lifetimes (~10 min). Three specific objectives guide this work: (1) formulation of a HP injectable with enhanced 15N detection, (2) …

Lead Bioaccessibility And Commonly Measured Soil Characteristics (Detroit, Mi, Usa) Phase Ii, Conor T. Gowan, Sabrina R. Good, Allison R. Harris, Patrick Crouch, Shawn P. Mcelmurry

Open Data at Wayne State

Urban soil contaminated with anthropogenic sources of Pb is a major contributor to child Pb exposure. To assess the Pb risk in urban areas, composite samples were collected from 142 voluntary privately owned 142 residential parcels in Detroit, Hamtramck, and Highland Park, Michigan. Samples were collected at two time periods, before and after treatment applied for those denoted as treatment sites. Samples were collected from areas covered with turf grass on the parcels from the front, middle and rear sections. Soils were collected with a bulb planter at approximately 10cm depth and were cleaned with a tap water-Liquinox detergent solution …

The Local Deep Galerkin Method Applied To The (2+1)-D Cahn-Hilliard Equation, Caden Fischer

Schultz-Werth Award Papers

Physics-informed Neural Networks (PINNs) are an alternative approach to solving Partial Differential Equations. This study examines the Local Deep Galerkin Method (LDGM), and its application to the (2+1)-Dimensional Cahn-Hilliard Equation (2D-CH). The 2D-CH models phase separation. One application of this is to modeling biofilm. The LDGM is trained on a loss function that minimizes the sum of the squares of the residuals of the system of equations. The LDGM is compared to a numerical simulation based on the Finite Element Method. The results of this study show that LDGM does not produce accurate results for the 2D-CH equation for large …

Association Between Lifetime Interpersonal Violence And Post– Covid-19 Condition Among Women In Kentucky, 2020-2022, Ayşe Güler, Heather M. Bush, Katie Schill, Nurlan Kussainov, Ann L. Coker

Biostatistics Faculty Publications

Objective: The COVID-19 pandemic increased the risk of interpersonal violence. We investigated the association between lifetime interpersonal violence experience and risk of post–COVID-19 condition (the persistence of symptoms of COVID-19 and severity of health problems associated with COVID-19 that last a few weeks, months, or years) among women with lifetime interpersonal violence experience.   Methods: Women participants aged ≥18 years in Kentucky’s Wellness, Health & You—COVID-19 study completed online quantitative surveys about the impacts of the pandemic, developing COVID-19, and symptoms of post–COVID-19 condition. We conducted cross-sectional analyses estimating rate ratios of developing COVID-19 and symptoms of post–COVID-19 condition during the …

Eugenol Loaded Calcium Citrate Particle As A Ph Triggered Drug Release System And A Reducing Agent For Au (Iii), Sadia Afrin

Dissertations and Theses

The study demonstrated that eugenol affects the morphology of calcium citrate particles during their formation, as confirmed by SEM micrography and PXRD analysis. Coating of eugenol loaded calcium citrate particles (CaCit MPs) with casein improves their known pH-dependent drug release profile, as confirmed by UV absorption spectroscopy. The casein-coated eugenol-loaded CaCit MPs (CCaCit(eu) MPs) can effectively control and sustain drug release under different pH conditions, particularly at acidic pH values (4.8 and 5.5), which mimics the environment of tumor tissues. To further explore the biomedical applications of calcium citrate, a bulk nanostructured precursor to the known anti-cancer calcium citrate phosphate-based …

Elements Of Statistics, Paul Flesher, Jeffrey Sadler, Jonathan Rehmert, Lanee Young

All Open Educational Resources

This undergraduate text, intended for an introductory statistics course, motivates and develops basic inferential statistics. The first chapter establishes the necessity of inferential statistics, lays the basis of a scientific worldview, and finishes by introducing sampling methods and variables. Subsequent chapters develop visualizations, descriptive statistics, probability, and random variables. Sampling distributions are treated in the fifth chapter which is immediately followed by the development of confidence intervals and hypothesis testing. The text concludes with a treatment of linear regression. The use of Excel is emphasized throughout. The text is best viewed online through LibreTexts.

Classifying Microlensing Events From Rome/Rea, Alaina Schweitzer, Rachel Street, Katarzyna Kruszyńska, Daniel Godines

Open Pedagogy: Selected Projects

The Roman Galactic Bulge Time Domain Survey will complete our census of free-floating and bound exoplanets by detecting microlensing events from timeseries photometry (M. T. Penny et al. 2019). But the literature on classifying microlensing events is limited compared with other types of transients. Building on recent developments in machine learning techniques, this research describes the preparation and training of an eXtreme Gradient Boosting classifier to detect microlensing events in the recent data release from the ROME/REA Survey. We evaluate the classifier’s ability to distinguish microlensing from various other categories of stars, both variable and constant. In this note we …

Ripples: An Automated Embedding Generation Algorithm For The Forward-Forward Algorithm, Spencer Connolly

All Graduate Theses, Dissertations, and Other Capstone Projects

The Forward-Forward algorithm (FF) is yet another novel invention by Geoffrey Hinton, the creator of the famous backpropagation algorithm (BP). Since its proposal, many papers have been published exploring its potential, and good progress has been made in increasing its viability. Though FF continually falls short of BP, its purpose is not to replace BP and preliminary research shows that there is plenty of room for growth. In this paper, we present a literature review for FF algorithms applied to Convolution Neural Networks (CNN) for image classification tasks and set the stage for applying FF to more complex datasets. The …

Sites 45ch01104 And 45ch00402 Determinations Of Eligibility, Chelan County, Washington, Benjamin Aa Hodges

All Graduate Projects

This Master of Applied Science (MAS) project report assesses the National Register of Historic Places (NRHP) eligibility of archaeological sites 45CH01104 and 45CH00402 near Wells Dam in Chelan County, Washington. Field investigations conducted in 2024 included pedestrian surveys, subsurface testing, and ground-penetrating radar (GPR) analysis to gather data on site conditions and potential cultural features. Site 45CH01104 was characterized by pre-contact lithic and fire-cracked rock (FCR) scatters and is recommended as eligible in this report for the NRHP under Criteria A and D due to its association with Indigenous subsistence activities and its potential to yield important archaeological information. It …

Collaborative Wildfire Governance Within The Southwest Idaho Wildfire Crisis Strategy Landscape: Survey Results 2025, Mahmood Muttaqee, Cody R. Evers, Lindsay K. Campbell, Max Nielsen-Pincus, Olivia Awbrey, Kathryn Hixson, Michelle Johnson, Erika Svendsen

Environmental Science and Management Faculty Publications and Presentations

Collaboration plays a critical role in wildfire risk mitigation efforts. This study examines collaborative wildfire governance networks within Southwest Idaho, a priority landscape designated in 2022 under the USDA Forest Service's Wildfire Crisis Strategy (WCS). Through survey data from 64 respondents, the Fueling Adaptation study aimed to identify the structure, capacity, and gaps in governance networks addressing wildfire risk in Southwest Idaho and understand how WCS investments have influenced wildfire management in the area. The findings reveal a growing governance network centered on the USDA Forest Service and the Idaho Department of Lands and supported by numerous non-governmental organizations and …

Nebraska Summary #1293: Fendt 618 Vario, Nebraska Tractor Test Lab

Nebraska Tractor Tests

ABOUT THE TEST REPORT AND USE OF THE DATA The test data contained in this report are a tabulation of the results of a series of tests. Due to the restricted format of these pages, only a limited amount of data and not all of the tractor specifications are included. The full OECD report contains usually about 30 pages of data and specifications. The test data were obtained for each tractor under similar conditions and therefore, provide a means of comparison of performance based on a limited set of reported data. EXPLANATION OF THE TEST PROCEDURES Purpose The purpose of …

Aviation Sustainability & Infrastructure, Eva Maleviti

Publications

MS Aviation & Aerospace Sustainability

What Makes The Embry-Riddle Program Unique?

► Industry-driven program
► Technology pathways
► 100% online asynchronous teaching through Canvas learning platform
► 10 courses/30 U.S. credits hours (60 ECTs) all on Aviation & Aerospace Sustainability topics
► Covers the latest trends in the industry
► Utilizes real case studies

Hydrogen Standards And Aviation Sustainability, Eva Maleviti, Evan Stamoulis, Elen Paraschi

Publications

Standards from ISO, SAE, and ASTM are essential for certification, safety, and sustainability validation.

Hydrogen Readiness In Aviation & Challenges- Technology Meets Regulation And Market Demand, Eva Maleviti

Publications

Where technology meets regulation and market demand.

Examining The Disclosure Of Sensitive Information Through Mobile Applications: A Privacy Calculus And Warning Experiment On Location-Based Services, Dwayne A. Ford

CCAC Theses and Dissertations

Smartphones and mobile applications have become all but ubiquitous in society. These applications provide a plethora of functions both in standalone and network configurations. Many of these popular applications utilize Location-Based Services (LBS) to deliver value to the user. Whether for navigation, transportation or social interactions, sharing information is essential when using these applications. While LBS applications provide various benefits, the sharing of location data also creates significant privacy risks. In many cases, users are unaware of the real risks and continue to share their location data in exchange for the benefits the application provides.

The problem identified in this …

A Hybrid Deep Learning Model For Iot Network Anomaly Detection, Yonas Getachew Mulissa

CCAC Theses and Dissertations

The rapid expansion of Internet of Things (IoT) networks has heightened the need for intelligent, automated Anomaly Detection (AD) systems to identify sophisticated and evolving cyber threats. This study designed, implemented, and evaluated a broad range of deep learning models—including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs) (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BiGRU), Bidirectional Long Short-Term Memory (BiLSTM)), Transformer-based architectures, Autoencoders, and hybrid combinations—to address the challenge of multiclass anomaly classification in IoT traffic. Using two benchmark datasets, IoT-DS-2 and CIC-IoT-2023, we conducted extensive experiments to assess classification performance, training efficiency, and …

Development Of A Phishing Risk Exposure Taxonomy On Mobile Devices In The Healthcare Industry, Christopher P. Collins

CCAC Theses and Dissertations

Phishing emails accessed on mobile devices present substantial risks to healthcare organizations when employees often operate under high cognitive load and with limited cybersecurity training. Despite widespread security awareness initiatives, healthcare workers continue to engage with phishing content on mobile platforms, posing threats to organizational data. Given the high value of healthcare data and the increasing sophistication of phishing schemes targeting healthcare professionals, there is a pressing need to enhance their ability to recognize phishing indicators on mobile devices.

This study developed and validated a Healthcare Workers Phishing Risk Exposure (HWPRE) taxonomy, designed to classify healthcare workers based on their …

An Evaluation Of Data Protection And Privacy Issues Introduced By Byod In Financial Institutions, Andy Miguel Santana

CCAC Theses and Dissertations

The idea of “bring your own device” (BYOD) allows organizational employees to conduct their tasks or processes on their own personal devices, has increased organizational efficiency significantly while allowing employees more flexibility. However, this approach also introduces major concerns about the security of organizational data as employees take their devices everywhere with them, opening more opportunities for unauthorized access to important data. Another major concern is the privacy of employee personal data. As many organizations implement BYOD, employees worry that with organizational monitoring and device management, their personal data is at risk as well. The problem this study tackles is …

Architectural Technical Debt Migrating Object Oriented Systems To Modular Architectures, Lionel Standridge

CCAC Theses and Dissertations

Architectural Technical Debt (ATD), a subset of Technical Debt (TD), arises when outdated architectural decisions present significant challenges to the maintenance and evolution of legacy object-oriented monolithic systems. These systems tend to have tightly coupled components and rigid dependencies, making it difficult to scale, adapt, and modernize. This dissertation investigated strategies for managing ATD during the transition from monolithic architectures to modular systems. By identifying the root causes of ATD in a legacy objectoriented system and evaluating various decomposition strategies, this research proposed a framework to guide practitioners in reducing ATD and improving a system’s modularity. Using quantitative metrics, the …

Enhanced Network Anomaly Detection Using Machine Learning Models, Ousmane Barry

CCAC Theses and Dissertations

This dissertation investigates enhanced network anomaly detection using Machine Learning (ML) models. The study addresses two distinct classification problems: binary classification and multiclass classification. In the binary classification task, network traffic data is categorized as either "normal" or "abnormal," where abnormal includes all non-normal traffic. Leveraging the balanced nature of the dataset, this study develops optimized models that achieve consistently high classification performance. Key metrics, including precision, recall, and F1 scores, are used to ensure robust evaluation and reliable detection across all classes.

For multiclass classification, only classes present in both training and test datasets are included to ensure meaningful …

A Vision Transformer Based Assistive System For Dermatological Diagnosis In Systemic Lupus Erythematosus, Syeda Lamima Farhat

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) is a complex and often underdiagnosed autoimmune disease that affects multiple organs and presents with a wide range of symptoms-ranging from fatigue and joint pain to life-threatening organ damage. One of its most visible and diagnostically significant indicators is the Butterfly Malar Rash (BMR), a distinctive facial rash that often resembles other common dermatological conditions like rosacea, acne, eczema, and fifth disease. This overlap can lead to misdiagnosis or delayed detection, especially in busy clinical environments. To assist dermatologists in distinguishing BMR from similar facial rashes, this study explores the development of an AI-powered image classification …

Evaluating Aspect-Based Sentiment Analysis In Healthcare Drug Reviews Across Machine Learning, Deep Neural Networks, And Transformer Models, Eun Soo Park

All Graduate Theses, Dissertations, and Other Capstone Projects

Sentiment analysis has become a critical area of research in Natural Language Processing (NLP), enabling insights from unstructured text. Within this field, Aspect-Based Sentiment Analysis (ABSA) plays a practical role in domains such as healthcare, where patients drug reviews often contain diverse opinions across multiple aspects, including overall comments, perceived benefits, and side effects. However, aspect-level classification remains challenging due to class imbalance, subtle sentiment expression, and the limitations of traditional models. This research investigates the performance of three modeling paradigms: traditional machine learning (SVM, SVC, and XGBoost), deep learning (CNN-BiLSTM), and transformer-based approaches (DistilBERT sentence-pair classification). Using the UCI …

Identifying All Matches Of A Rigid Object In An Input Image Using Visible Triangles, Abdullah N. Arslan

Faculty Publications

It has been suggested that for objects identifiable by their corners, every triangle formed by these corner points can serve as a reference for detecting other corner points. This approach enables effective rigid object detection, including partial matches. However, when there are many corner points, the implementation becomes impractical due to excessive memory requirements. To overcome this, we propose a new algorithm that leverages Delaunay triangulation, considering only the triangles generated by the Delaunay triangulation to reduce the complexity of the original approach. Our algorithm is significantly faster and requires significantly less memory, offering a viable solution for large problem …

Program And Proceedings: Nebraska Academy Of Sciences 1880–2025, 145th Anniversary Year, One Hundred-Thirty-Fifth Annual Meeting

Nebraska Academy of Sciences: Programs and Proceedings

Program

Aeronautics and Space Science

Biological and Medical Sciences

Biology

Chemistry

Earth Sciences

Science Education

Anthropology

Applied Science and Technology

Physics and Engineering

Forensic Sciences

Ecology, Sustainability, and Environmental Science

Maiben Lecture: Mary Ann Vinton, "State of the Academy"

Friends of Science Awards: David Crouse and Daniel Sitzman

Predicting Lung Cancer Severity Using Machine Learning Algorithms: Enhanced By Statistical Analysis, Esin Bilgin

Theses, Dissertations and Culminating Projects

Cancer is a serious and severe cause seen in every region of the world and severely affects the quality of life and life span. Among the various types of cancer, lung cancer is one of the most critical, having a fatal impact on life. While medical imaging techniques, laboratory results, and biomarkers play a significant role in diagnosis and prognosis, clinical studies are also crucial in monitoring the progression of cancer and identifying diagnostic and prognostic factors. The findings demonstrate satisfactory accuracy, and the analysis incorporates statistical data with machine learning techniques. These findings play a pivotal role in supporting …

Quantifying The Vulnerability Of Smooth Dogfish And Winter Skate To Electromagnetic Fields From Offshore Wind Transmission Cables In The Mid-Atlantic Shelf, Rachel A. Sechrist

Theses, Dissertations and Culminating Projects

The development of offshore wind structures in the northeastern U.S. will contribute to renewable energy goals, but will overlap with many marine species as well as economically important fisheries. High voltage transmission cables from offshore wind farms emit electromagnetic fields (EMFs) that elasmobranchs may detect using sensory organs known as ampullae of Lorenzini. However, sensitivity to EMFs varies by species due to differences in the number and arrangement of pores, the length of subdermal canals within the ampullae of Lorenzini sensory network, and the habitats they reside and regions they forage. For species who reside or forage in benthic regions, …

Brain Tumor Classification Using Deep Learning: Custom Cnn Vs. Resnet50, Rayen Bentemessek

ICT

The project presents a deep learning solution to classify brain tumors through MRI images. Following the CRISP-DM framework, two Convolutional Neural Network (CNN) models were developed and evaluated, a custom CNN designed from scratch and a pretrained ResNet50 that was transfer learned and fine-tuned. Both models were assessed using standard performance metrics such as accuracy, precision, recall and F1-score. Despite the higher test accuracy achieved by the custom CNN, further interpretability indicated inconsistent attention to the actual tumor regions also known as shortcut learning. On the other hand, ResNet50 showed more reliable and clinically relevant focus which supported its selection …

Player Transfer Market In European Football Using Machine Learning To Analyse The Evolution Of The European Football., Pablo Lopes De Souza Oliveira

ICT

The study uses machine learning to analyse the European football transfer market from 2015 to 2025, revealing patterns in transfer fees and market values influenced by player attributes, highlighting the potential of data-driven insights.

Predicting Early Customer Inactivity In The Banking Sector Using Machine Learning: A Churn Prevention, Ivana Mc Fadden

ICT

Customer churn, when customers stop using a company’s services, is a challenge for the banking sector (Singh et al., 2023). High churn rates often signal poor customer experiences, resulting in revenue losses and increased costs to obtain new clients. Goyal and Srivastava (2015) stress that fostering loyalty through exceptional service and understanding customer needs is important for long-term retention.

This project aims to predict early customer inactivity, an indicator of churn, by using machine learning. Early identification of at-risk customers will allow banks to apply targeted interventions, reduce acquisition costs, and improve customer satisfaction (Singh et al., 2023). By analysing …

Using Machine Learning To Predict Credit Card Fraud, Pedro Henrique Das Chagas Morais

ICT

Credit card fraud poses a significant challenge to financial institutions, leading to substantial financial losses and declining customer trust. This project develops and evaluates machine learning models to detect fraudulent credit card transactions using a large, realistic synthetic dataset. Following data preprocessing, exploratory analysis, and class-balancing using SMOTE, four supervised models—Logistic Regression, Decision Tree, Random Forest, and XGBoost—were trained and compared. Performance was assessed using metrics suited to imbalanced classification, including AUC, Recall, Precision, F1-score, and Average Precision. Results show that XGBoost, particularly after hyperparameter optimisation, delivered the strongest performance (AUC 0.99, Recall 0.83, AP 0.70), outperforming other models and …