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The Impact Of Instructional School Design: The 6–12 School Model As A Strategic Response To Increase Black Boys Self-Efficacy, Xavier E. Edwards

Education Doctoral

This dissertation investigated the impact of the 6–12 instructional school design on the self-efficacy of Black boys, utilizing the theoretical frameworks of Bandura's self-efficacy model and Yosso's community cultural wealth model. The research explored how this specialized school structure supports Black boys' academic achievement, aspirations, and resilience—addressing longstanding disparities in educational and social outcomes. Through qualitative interviews with Black male alumni of 6–12 school models, the study identified factors within the design that positively influence self-efficacy both within and beyond the classroom. Findings indicated that sustained relationships with supportive teachers, a keen sense of brotherhood among peers, and consistent engagement …

Measuring Teaching Success In College Foreign Literature Programs: An Evaluation Perspective Using Double Framed Superhypersoft Set, Lingling Chen

Neutrosophic Sets and Systems

No abstract provided.

An Approach To Neutrosophic Hyper Soft Rough Matrix And Its Application, J. Boobalan, E. Mathivadhana

Neutrosophic Sets and Systems

No abstract provided.

, General, General Weak, Anti, Balanced, And Semi-Neutrosophic Graph, Takaaki Fujita, Florentin Smarandache

Neutrosophic Sets and Systems

No abstract provided.

Machine Learning For Parkinson’S Disease: A Comprehensive Review Of Datasets, Algorithms, And Challenges, Sahar Shokrpour, Amir Mehdi Moghadamfarid, Sepideh Bazzaz Abkenar, Mostafa Haghi Kashani, Mohammad Akbari, Mostafa Sarvizadeh

Michigan Tech Publications

Parkinson’s disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive …

Atmospheric Teleconnection Patterns And Hydrological Whiplashes In The Western U.S., Wenzhao Li, Surendra Maharjan, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Western U.S. is undergoing notable transformations in its hydrological patterns, distinguished by rising variability and recurrent “whiplash” shifts between extreme wet and dry phases. Our comprehensive analysis of 469 streamflow stations from 1981 to 2023 reveals a substantial increase in hydrological whiplash events, with a peak of 206 stations experiencing dry-to-wet whiplash in the early 1990s. We establish strong links between these streamflow extremes and sub-seasonal to seasonal teleconnection factors, particularly the Western Pacific Oscillation (WP) and Eastern Pacific/North Pacific Oscillation (EPO). Additionally, we demonstrate the combined impacts of the El Niño-Southern Oscillation (ENSO) and the Madden-Julian Oscillation (MJO), …

P-Adic Cellular Neural Networks With Delay, Baboucarr Dibba

Theses and Dissertations

This dissertation presents a novel framework for p-adic reaction-diffusion cellular neural networks (CNNs) with delay, offering new insights into the stability and dynamic behavior of these networks. Through numerical simulations, we explore their response to various conditions, highlighting their capability to model complex systems. Additionally, this work reviews cutting-edge developments in p-adic CNNs, particularly their application to advanced image processing tasks such as edge detection and noise filtering, demonstrating their effectiveness in preserving critical image features while filtering out noise. This dissertation is written in collaboration with my Ph.D supervisor and Dr. Zambrano-Luna, Brian.

A Study Of Quasigroups, A Computational Approach On Isotopism And Isomorphism Hierarchical Structure, Runaldo Montrose

Theses and Dissertations

Quasigroups morphism classification can present a real computational challenge. As of today, the number of quasigroups that can be generated from a finite set S of cardinality n is known for very small value of n. Though we know the number of quasigroups for small n, generating them in real time is a whole other issue that leads to a bigger challenge of building their isomorphy classes, because the algorithm used required large computing memory resources. In this paper we will expose the computational complexity of constructing their hierarchical morphism structure from isotopism to isomorphism. As an improvement, …

Electrocatalytic Degradation Of Methylene Blue Using Graphene Oxide, Antimony Oxide, And Graphene Oxide-Supported Antimony Oxide Ink-Based Electrodes, Maria Irene Myers Armas

Theses and Dissertations

This study investigates the electrocatalytic degradation of methylene blue (MB) using copper mesh electrodes coated with graphene oxide (GO), antimony oxide (Sb₂O₃), and their composite (GO/Sb₂O₃). These materials were evaluated across a pH range of 2 to 8 using sodium sulfate as the supporting electrolyte. UV-Vis spectroscopy at 665 nm confirmed dye degradation, with removal efficiencies reaching up to 95% at pH 2. However, degradation decreased at higher pH, with 40 – 60% removal at pH 8, depending on the electrode. Kinetic analyses revealed optimum performance under acidic conditions. GO/Sb₂O₃ electrodes demonstrated the most consistent and effective performance across all …

Predicting Enzyme-Substrate Association Using Heterogeneous Knowledge Graph, Jannatul Ferdaus

Theses and Dissertations

Phosphorylation and dephosphorylation are dynamic processes that control many aspects of cellular activity, such as metabolic pathways, cell cycle progression, and signal transduction. Protein activity and interactions are modulated by the reversible addition or removal of phosphate groups, which allows cells to react abruptly to evolving conditions. Although kinase-specific phosphorylation site prediction has advanced, phosphatase-specific dephosphorylation site computational prediction is still a major obstacle that prevents us from fully comprehending the extent of cellular regulation. In this study, we constructed a knowledge graph for the prediction of enzymes (kinases and phosphatases) and their associated substrates with specific phosphosites. As part …

Sequential Data Modeling Of Influenza A Via Traditional, Dwt-Gpr Hybrid, And Deep Learning Architectures, Edmund Fosu Agyemang

Theses and Dissertations

Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all …

The Evidential Problem Of Evil And The Aesthetics Of Surprise, James S. Spiegel

Faith and Philosophy: Journal of the Society of Christian Philosophers

No abstract provided.

Secure Query On Encrypted Data By Using Fully Homomorphic Encryption, Mrinmoy Bera

Master’s Dissertations

Cloud service providers typically store user data in an encrypted form (data at rest). However, when a user performs a query, the server first decrypts the data, processes the query on plaintext, and then sends the result back to the user (data in transit). This process exposes a critical vulnerability—if the cloud server is ever compromised, the decrypted data becomes accessible to the attacker. To address this security gap, we design a secure query protocol that eliminates the need to decrypt data on the server side. Fully Homomorphic Encryption (FHE) offers a groundbreaking solution by enabling arbitrary computations directly on …

Federated Learning Using Fully Homomorphic Encryption, Sk Golam Kuddus

Master’s Dissertations

Traditional machine learning approaches require centralizing data for training, which raises significant privacy concerns when dealing with sensitive information. Federated learning (FL) addresses this by keeping data local and enabling multiple users to collaboratively train a shared machine learning model. In spite of this, FL remains vulnerable to inference attacks, as sensitive information can still be extracted from the model’s learned parameters. While traditional privacy-enhancing techniques such as di!erential privacy introduce noise to model updates to obscure individual data points, they often present a fundamental trade-o! between privacy and utility. Furthermore, these approaches still carry risks of data leakage if …

The Impact Of Students' Attendance On Their Academic Success: A Ypar Study, Quennella Smith

Theses and Dissertations

Youth participatory action research (YPAR) is a collaborative research approach that empowers students to examine issues affecting their lives and communities. This action research aimed to investigate the relationship between students’ attendance and academic success on middle school students, focusing specifically on eighth-grade students based on the theoretical framework of the critical race theory (CRT) in education and the self-determination theory (SDT). Three research questions were explored related to the impact of students’ attendance on their academic success: 1) What do students consider factors that lead to their poor attendance? 2) How do students feel they can be supported in …

First Detrital Zircon Provenance Data From Oligocene-Pleistocene Sediment In The Beaufort-Mackenzie Basin, Arctic Ocean, Joseph Martina

Theses and Dissertations

Sediment, runoff, and nutrient input to the Arctic Ocean plays a critical role in both physical and biological processes that impact regional and global systems. The Beaufort-Mackenzie Basin, situated in the Arctic Ocean offshore northwestern Canada, contains more than 14 km of clastic strata and provides a record of erosion and sediment transport from the Cretaceous to present. Pliocene sedimentation rates within the basin jump nearly 2000% relative to underlying units, but the cause of this shift remains unknown. We used cuttings from the N. Issungnak L-86 well, focusing on the Oligocene-lower Miocene Kugmallit and Mackenzie Bay sequences and the …

Understanding Community College Students’ Thoughts And Motivations Regarding Tutoring In An Undergraduate Mathematics Course, Whitney Paige Cherekos

Theses and Dissertations

This mixed-methods study examines community college students’ thoughts and motivations regarding tutoring, with the aim of identifying disparities in tutoring utilization and engagement at a community college in North Carolina. The purpose of the dissertation is to evaluate tutoring effectiveness and explore students’ perceptions of tutoring. The findings of this study led to proposed solutions to reduce or eliminate the barriers that today’s community college students encounter when needing academic support.

To evaluate tutoring effectiveness, I collected quantitative data from institution registration reports, transcripts, tutoring logs, and an anonymous student survey for students enrolled in multiple sections of a single …

Examining The Impact Of Teacher Participation In Pedagogical Content Knowledge Development Intervention On The Academic Achievement Of Culturally And Socioeconomically Diverse Students, Emelyne Viteve Baucicaut

Theses and Dissertations

This action research study examines the impact of a Pedagogical Content Knowledge (PCK) intervention on elementary teachers' instructional practices and student outcomes in Math and English Language Arts (ELA). The goal was to address student underperformance by enhancing teachers' PCK and exploring how improved content knowledge and pedagogical strategies influence classroom teaching and student achievement. Using a concurrent mixed-methods design, baseline data were collected through surveys, classroom observations, and student assessments. Over five weeks, teachers participated in professional development focused on lesson planning, student exemplars, and success criteria. Due to scheduling constraints, computer-based assessments were replaced by analyzing student work …

Who Do I Want To Become?: Validating Identity As A Key Component Of A Teacher Leadership Framework, Denae Kizys

Theses and Dissertations

Historically, teaching has been a solitary profession within a hierarchy, where educators work in isolation and their roles are primarily focused on instruction. However, as education has evolved, especially post COVID-19, there is a shift towards a need for a more collaborative, ecosystem-based approach, where teachers are not only in their classrooms but acting as teacher leaders within this ecosystem. This research aims to understand the development of teacher-leader identities through the use of the proposed, Ecosystem of Teacher Leadership Framework, which uses elements of self-leadership, recognition, and the ecosystem. Through both qualitative and quantitative methods, a survey-like interview was …

Spatio-Temporal Modeling & Goodness Of Fit Testing For Ecological Fire Data, Jedidiah Olof Lindborg

Theses and Dissertations

An analysis of fire data sets resulting from controlled burns was performed. Spatio-temporal models were applied to the data sets to determine which covariates are significant in predicting fire temperature. The data sets were censored and only contain temperatures above 300C, due to a limitation in the measuring device. To handle the censoring, an extrapolation was used to reconstruct the temperatures below 300C. Models were created and run for a data set with the extrapolated temperatures and a data set with all censored temperatures removed. Several aspects of the model were evaluated, such as the chosen hyperparameters, the spatial covariance …

Promoting The Mathematical Success Of High School Geometry Students Through Project-Based Learning, Amanda Shankle-Erdman

Theses and Dissertations

This action research study explores the impact of project-based learning (PBL) on the mathematical success of high school geometry students, particularly those from low socioeconomic backgrounds. Students from low SES households often face challenges in acquiring foundational math skills, which in turn leads to low math-related self-efficacy and poor academic performance. The study aimed to determine whether implementing PBL enhances students' understanding of geometry, improves students’ math-related self-efficacy and anxiety, and changes students’ attitude toward math by creating real-world connections to their lived experiences. Using a convergent mixed-methods approach, the study collected both qualitative and quantitative data from high school …

Bridging The Gap: Implementation And Perception Of High-Leverage Practices Among General And Special Educators, Jacqueline Ann Viotto

Theses and Dissertations

This dissertation explores the use and perception of High Leverage Practices (HLPs) among general and special education teachers within educational settings. Utilizing a concurrent mixed-methods research design, the study integrates quantitative surveys and qualitative interviews to examine how HLP knowledge and use by educators. This dissertation investigates the knowledge and use of HLPs in inclusive settings through the following research questions:

1. What is the self-reported knowledge and use of HLPs by teachers?

2. What are the differences in self-reported knowledge and usage of HLPs in inclusive environments when comparing general education and special education teachers?

3. How do general …

Filling In The Gaps: Evaluating A Self-Regulated Learning Strategy For Middle School Students Using Action Research, Leslie Eller Southerland

Theses and Dissertations

The purpose of this action research study was to evaluate the effects of a self-regulated learning innovation on middle school students’ self-regulated learning skills. One way to address problems such as student motivation, student engagement, and distracting behaviors in the classroom is to explicitly teach self-regulated learning skills such as goal setting, strategy use, self-monitoring, and systematic practice. Two research questions guided this study: (1) How and to what extent does a self-regulated learning innovation impact 7th and 8th grade students’ self-regulated learning skills (planning, self-monitoring, evaluation, reflection)? (2) In what ways do 7^th and 8^th grade students …

Functional Data Analysis On Life Expectancy And Healthcare Expenditure, Hagen Sanchez

Theses and Dissertations

This study analyzes the life expectancy for 237 countries from 1950-2023 and the healthcare expenditure for 50 countries from 1970-2022, and how life expectancy and healthcare expenditure relate to each other. Functional Principal Components Analysis was used to analyze the life expectancy and healthcare expenditure for each of the countries. Additionally, the regions of the countries were analyzed to identify any regional trends for the life expectancy data. Due to missing data and the structure of the healthcare data, multiple imputation methods and Principal Component Analysis techniques were explored for the healthcare data. Furthermore, a simulation study was conducted to …

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

Modeling Multiple Tasks In Recommendation Systems, Dinh Hieu Do

Dissertations and Theses Collection (Open Access)

Traditional research in recommendation systems has largely centered on the static offline supervised learning setting. In this paradigm, all available user-item interaction data is collected and partitioned into fixed training, validation, and test sets. Models are developed and evaluated in this controlled environment, where the underlying data distribution is assumed to remain unchanged. This approach offers clear advantages: it simplifies experimentation, enables reproducible benchmarking, and allows for straightforward comparisons between algorithms.

However, this static offline setting does not reflect the realities faced by modern recommendation systems. In real-world applications, data is dynamic and ever-evolving, where new users and items are …

From Sparse Feedback To Sequential Decision-Making: Learning Safety Constraints With Weak Supervision, Siow Meng Low

Dissertations and Theses Collection (Open Access)

Real-world decision-making often involves safety constraints that are implicit, non-Markovian, or difficult to specify directly. Standard reinforcement learning (RL) approaches typically assume access to fully specified cost functions and constraint budgets—assumptions that limit their applicability in domains where such structure must instead be inferred from data. This dissertation develops a sequence of methods for learning safety-relevant structure from weak supervision, such as sparse binary feedback on trajectory segments, and using these signals to guide planning and policy optimization.

The first part of the dissertation introduces a sample-efficient method for planning in continuous Markov Decision Processes (MDPs) using deep reactive policies. …

From Disruption To Integration: Cryptocurrency Prices, Financial Fluctuations, And Macroeconomy, Zhengyang Chen

Faculty Publications

This paper examines cryptocurrency shock transmission to financial markets and the macroeconomy using a Bayesian structural VAR with Pandemic Priors from 2015 to 2024. By affecting overall risk appetite, cryptocurrency price shocks generate positive financial market spillovers, accounting for 18% of equity and 27% of commodity price fluctuations. Real economic effects are significant in driving investment but remain limited, contributing only 4% to unemployment and 6% to industrial production variance. However, cryptocurrency shocks explain 18% of price-level forecast error variance at long horizons. Narrative analysis reveals sentiment and technology as primary shock drivers. These findings demonstrate cryptocurrency's deep financial system …

Network And Tree-Based Approaches To Data-Driven Modeling Of Complex Climate Systems, Adam M. Giammarese

Theses

The guiding thread throughout this thesis work is data-driven modeling of Earth's climate. We investigate a variety of settings within the field of complex climate systems, such as gridded, high-resolution reanalysis data, lower-resolution climate indices, and incomplete paleoclimate records with missing values. We begin with reanalysis data of high spatial and temporal resolution, and use climate network analysis to uncover hidden trends in the data in regards to the Amazon rainforest and its changing role in the global climate system. We introduce a novel formulation of k-nearest neighbors climate networks to study changing trends at a local level, and apply …

Functional Time Transformation Model With Applications To Digital Health, Rahul Ghosal Ph.D., Marcos Matabuena, Sujit K. Ghosh

Faculty Publications

The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and …