Digital Commons Network^™

Unh/Noaa Joint Hydrographic Center 2025 Performance And Progress Report Executive Summary, Larry A. Mayer

Center for Coastal and Ocean Mapping

No abstract provided.

Forming Of Economies Of Wellbeing Through Responsive Practice In Everyday Life, Julie Richardson

School of Art, Design and Architecture Theses

In recent years, most Western economies have increasingly focused on the narrow pursuit of economic growth with its unintended and undesirable impacts on the wellbeing of people and planet being well-documented. This has led to the growth in ‘alternative economics’ that addresses these critiques and proposes new economic models. However, alongside these critiques, there is another that questions the modern scientific approach underpinning economics, arguing that it overlooks the importance of lived experience, creating feelings of detachment in the economic practitioner as well as undermining the relevance and effectiveness of economics and policy making. The research develops a more responsive …

Visual Thinking Informed By Repetition: A Practice-Based Methodology, Julia Heurling

School of Art, Design and Architecture Theses

This artistic research proceeds from the idea that visual thinking is thinking about images through images. It explores how ideas are instigated, developed, and articulated exclusively through visual elements from a practice-based perspective. The use of repetition as a method of thinking is rooted in my previous practice as a pattern designer. I consider this a cognitive process based on the assumption that repetition is a tool for abstraction, as it changes one’s perspective from the particular to the general. Abstraction, in this context, becomes a method for processing information (i.e. to think visually).My artistic practice is engaged in combining …

Artist Designed Systems In Community Radio, Lucinda Guy

School of Art, Design and Architecture Theses

Skylark is a full-time licensed radio station, founded by the author of this thesis. Since 2020 it has broadcast a continuous and ever-changing sound installation, rejecting the conventional radio schedules of presenters, programmes, music tracks, adverts etc. Its content is drawn from contributors within Dartmoor National Park, and includes oral histories, music, poems, field recordings, archive material and fragments of bespoke music. Skylark is also a public service and research project that intends to offer an innovative way forward for community radio in the UK. Such stations are intended to respond to their locations, include multiple voices, and be not-for-profit. …

Educational Trouble In Paradise: Student Perception Of Teaching Behaviors And It's Relation To Intrinsic Motivation And Academic Achievement, David Mchale Davis

Dissertations and Theses

Grounded in self-determination theory, this study explored students’ perceptions of provision of choice, teacher feedback, and emotional support, and how these teaching behaviors relate to intrinsic motivation and academic achievement. This study is a cross-sectional survey design and has data from 197 public high school students in the Bahamas. Path analysis was used to evaluate a model in which the three teaching behaviors predicted intrinsic motivation, which in turn was hypothesized to mediate their effects on academic achievement, measured by GPA, math grade, and students’ perceived likelihood of graduating. Results supported the model, indicating that higher levels of perceived teacher …

Calcium Carbonate Microparticles As Acid Responsive Targeted Drug Delivery Carriers For Oral Diseases And Its Anti-Bacterial Activity, Hem Raj Joshi

Dissertations and Theses

Dental diseases are mostly caused by bacterial infections in the teeth, forming biofilms that release acids and decay the enamel of the teeth. More than half of the world's population were affected by oral diseases, including periodontitis (gum disease), dental caries (cavities), and oral cancer. Chlorhexidine (CHX) is the mostly used antimicrobial drug for dental diseases, but its overexposure to the healthy teeth causes many side effects, such as tooth staining, taste alteration, and many more. Taking advantage of the acidic environment of infected teeth, we designed a pH responsive targeted drug delivery system using calcium carbonate microparticles (CCMPs) as …

Convergence In Cancer: Integrative Multi-Modal Modeling For Pan-Cancer Drug Response Prediction, Jessica Zylla

Dissertations and Theses

Heterogeneity and rarity can make cancer difficult to study and treat. Cancer's heterogeneity, evident in tumor locations, tumor cell types, subtypes, and microbiota within the tumor microenvironment, complicates diagnosis, prognosis, and treatment. Research into the tumor microenvironment and its microbiota is an evolving area of oncology that may advance cancer drug-patient response. Cancer-omics signatures are crucial to drug response, yet there is no tool that physicians use as a gold standard. Research indicates that microbial presence can alter drug metabolism and immune response, affecting cancer-drug efficacy on a patient-specific basis. This work highlights the need for training readiness in transdisciplinary …

A Computational Approach To The Game Of Cycles, Jocelyn Garcia, Mike Janssen, Eliza Kautz

Faculty Work Comprehensive List

In Mathematics for Human Flourishing, Francis E. Su introduced The Game of Cycles, a game played on a finite simple planar graph. In game play, opponents alternate adding direction to the edges of the graph with the goal of creating a cycle or making the last legal move. Recent work has sought to determine winning strategies on certain classes of graphs. We introduce a tabular representation of a game state and provide a computer program that determines which player has a winning strategy on any legal game board. The program builds a directed graph of all possible game states, utilizing …

Graduate Catalog 2025-2026, Southwestern Oklahoma State University

Graduate Catalogs

No abstract provided.

Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …

Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.

A Comprehensive Exploration Of 6g Wireless Communication Technologies, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md. Shahriar Uzzal, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into …

Optimizing Ai Language Models: A Study Of Chatgpt-4 Vs. Chatgpt-4o, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md Fayaz Bin Hossen, Muhammad Rezaur Rahman, Md. Shahadat Jaman

Electrical & Computer Engineering Faculty Publications

This paper presents a comparative analysis of OpenAI's GPT-4 and its optimized variant, GPT-4o, focusing on their architectural differences, performance, and real-world applications. GPT-4, built upon the Transformer architecture, has set new standards in natural language processing (NLP) with its capacity to generate coherent and contextually relevant text across a wide range of tasks. However, its computational demands, requiring substantial hardware resources, make it less accessible for smaller organizations and real-time applications. In contrast, GPT-4o addresses these challenges by incorporating optimizations such as model compression, parameter pruning, and memory-efficient computation, allowing it to deliver similar performance with significantly lower computational …

Differentiating Opioid Use Disorder From Healthy Controls Via Ml Analysis Of Rs-Fmri Networks, Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique, F. Gerard Moeller, C. Kenneth, Dianne Wright, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Objectives/Goals: This work aims to identify functional brain networks that differentiate opioid use disorder (OUD) subjects from healthy controls (HC) using machine learning (ML) analysis of resting-state fMRI (rs-fMRI). We investigate the default mode network (DMN), salience network (SN), and executive control network (ECN), as well as demographic features. Methods/Study Population: This work uses high-resolution rs-fMRI data from a National Institute on Drug Abuse study (IRB #HM20023630) with 31 OUD and 45 HC subjects. We extract rs-fMRI blood oxygenation level-dependent (BOLD) features from the DMN, SN, and ECN. The Boruta ML algorithm identifies statistically significant features and brain activity mapping …

A Potentially Fruitful Path Toward A Cleaner And Safer Environment: Mxenes Uses In Environmental Remediation, Ali Mohammad Amani, Milad Abbasi, Atena Najdian, Farzaneh Mohamadpour, Seyed Reza Kasaee, Hesam Kamyab, Shreeshivadasan Chelliapan, Hanieh Ardeshiri, Lobat Tayebi, Ehsan Vafa, Sarah Mosleh-Shirazi, Alireza Jahanbin, Saravanan Rajendran, Daniel Simancas-Racines

Electrical & Computer Engineering Faculty Publications

The rapid industrialization of the world has resulted in severe environmental pollution, necessitating the development of new materials such as pollution remediation. Two-dimensional (2D) MXenes have emerged as a promising family of materials due to their unique physicochemical properties, making them ideal for environmental remediation. The article sheds light on the new opportunities of MXenes in the removal of organic and inorganic contaminants, including organic dyes, pharmaceuticals, heavy metals, radionuclides, and gas pollutants. MXenes also show excellent performance in photocatalytic degradation, adsorption, and microbial inactivation with environmental safety. Moreover, their application in recovering valuable elements from waste streams is also …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Détermination De La Concentration Des Métaux Dans L'Air Par Icp-Ms Avec Prélèvement Effectué Sur Filtre Encapsulé Digérable : Solu-Sert, Sébastien Gagné, Luminita Damian, Saphos Vlavianos

Environnementales

No abstract provided.

Détermination De La Concentration Des Métaux De La Faction Inhalable Dans L'Air Par Icp-Ms Avec Prélèvement Effectué Sur Dis, Sébastien Gagné, Luminita Damian, Isabelle Madore

Environnementales

No abstract provided.

Ml Model To Better Identify Instances Of Bullying Faced By Members Of The Lgbtq+ Community, Arslan Bisharat

Master's Theses

Cyberbullying poses a significant threat to online communities, with the LGBTQ+ community facing disproportionately higher rates of harassment. While existing cyberbullying detection systems have made progress in identifying general instances of online harassment, they often fail to capture the nuanced and context-dependent nature of LGBTQ+-targeted bullying. This thesis presents a novel approach to this challenge by developing SpectrumNet, an LGBTQ+-centric transformer-based model for cyberbullying detection. Our research was conducted in two phases. In Phase 1, we evaluated the effectiveness of pre-trained transformer models (RoBERTa, BERT, and GPT-2) in identifying LGBTQ+-related cyberbullying. Building on these findings, Phase 2 introduced SpectrumNet which …

A Qualitative Analysis Of Open Banking In Poland: The Perspective Of Market Experts, Sylwia Ciechanowska

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: The purpose of this article is to present an in-depth analysis of the development of open banking in Poland based on interviews conducted with market experts representing financial institutions and regulatory authorities. The article identifies both the benefits and implementation barriers of open banking, with particular emphasis on the quality of APIs (application programming interfaces), system interoperability, and the impact of regulatory frameworks on service innovation and security.

Methodology: This qualitative study was conducted using structured interviews with market experts representing banks, fintech companies, regulatory authorities, and independent industry consultants. The selection of experts was purposive, with …

Exploring The Factors That Influence K-12 Teachers’ Use Of Open Educational Resources, Yu-Chun Kuo, Yu-Tung Kuo, Hungwei Tseng

Research, Publications & Creative Work

The features of open educational resources (OERs) have the potential of enhancing teaching effectiveness and student learning experiences in K-12 education. Encouraging K-12 teachers to use OERs requires an understanding of the factors that have an influence on teachers’ decisions to adopt or use OERs in their teaching. In this regard, this study explored the factors related to how teachers perceived their use of OERs, including the perceived ease of use of OERs, perceived usefulness of OERs, intention to adopt OERs, attitudes, OER self-efficacy, and so on. The relationships of several proposed factors were explored. The participants were teachers from …

The Impact Of International Baccalaureate (Ib) Courses: Experience And Outcome Differences For Full Ib Diploma Earners Vs. Non-Diploma Earners In An Urban School In The Us, Christopher Jennens, Nilufer Guler

Research, Publications & Creative Work

This study examines differences in educational experiences and outcomes for high school(HS) students who participated in the International Baccalaureate (IB) Diploma Program and earned full diplomas compared to those who participated in the IB Diploma Program but did not earn the diploma. ACT scores, after-high school placement, and HS graduation rates of 256 participants were collected. 122 participants completed an online survey, and 12 participated in interviews. The results revealed that the educational outcomes and experiences for diploma and non-diploma earners were vastly similar. Diploma earners had significantly higher ACT scores, but their college entrance and other academic outcomes were …

Nutritional Management Of The Child With Chronic Kidney Disease And On Dialysis., Vanessa Shaw, Caroline Anderson, An Desloovere, Larry A. Greenbaum, Lyndsay Harshman, Christina L. Nelms, Pearl Pugh, Nonnie Polderman, José Renken-Terhaerdt, Evelien Snauwaert, Stella Stabouli, Jetta Tuokkola, Johan Vande Walle, Bradley A. Warady, Fabio Paglialonga, Rukshana Shroff

Manuscripts, Articles, Book Chapters and Other Papers

While it is widely accepted that the nutritional management of the infant with chronic kidney disease (CKD) is paramount to achieve normal growth and development, nutritional management is also of importance beyond 1 year of age, particularly in toddlers, to support the delayed infantile stage of growth that may extend to 2-3 years of age. Puberty is also a vulnerable period when nutritional needs are higher to support the expected growth spurt. Inadequate nutritional intake throughout childhood can result in failure to achieve full adult height potential, and there is an increased risk for abnormal neurodevelopment. Conversely, the rising prevalence …

Digital Twins In Healthcare: A Comprehensive Review And Future Directions, Hamid Khoshfekr Rudsari, Becky Tseng, Hongxu Zhu, Lulu Song, Chunhui Gu, Abhishikta Roy, Ehsan Irajizad, Joseph Butner, James Long, Kim-Anh Do

Faculty, Staff and Student Publications

Digital Twin (DT) technology has emerged as a transformative force in healthcare, offering unprecedented opportunities for personalized medicine, treatment optimization, and disease prevention. This comprehensive review examines the current state of DTs in healthcare, analyzing their implementation across different physiological levels-from cellular to whole-body systems. We systematically review the latest developments, methodologies, and applications while identifying challenges and opportunities. Our analysis encompasses technical frameworks for cardiovascular, neurological, respiratory, metabolic, hepatic, oncological, and cellular DTs, highlighting significant achievements such as population-scale cardiac modeling (3,461 patient cohort), reduced atrial fibrillation recurrence rates through patient-specific cardiac models, improved brain tumor radiotherapy planning, advanced …

Combining Biology-Based And Mri Data-Driven Modeling To Predict Response To Neoadjuvant Chemotherapy In Patients With Triple-Negative Breast Cancer, Casey E Stowers, Chengyue Wu, Zhan Xu, Sidharth Kumar, Clinton Yam, Jong Bum Son, Jingfei Ma, Jonathan I Tamir, Gaiane M Rauch, Thomas E Yankeelov

Faculty, Staff and Student Publications

Purpose

To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC).

Materials and Methods

In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no. NCT02276443) between April 2018 and May 2021. To relate the calibrated parameters in the biology-based model and pretreatment MRI data, a convolutional neural network (CNN) was employed. The CNN …

The Comparison Of Human And Machine Performance In Object Recognition, Gokcek Kul

School of Psychology Theses

Deep learning algorithms have advanced significantly due to better hardware and large training sets, with some claiming they now match or exceed human performance. Recent findings indicate improvements in robustness against Out-Of-Distribution benchmark sets and alignment with human categorisation behaviour. This thesis evaluates various machine learning models to ensure a fair comparison with human performance using principles from comparative psychology. Chapter 2 introduces a psychologically grounded framework for comparing human and machine object categorisation performance, focusing on different presentation times and task complexities. The results indicate that while recent machine learning models have reached or exceeded human performance under optimised …

“Fish In Different Water, To Survive Or To Thrive?” Understanding Direct-Entry Pre-Sessional Chinese Students’ Learning Experiences During Covid-19 And Its Aftermath, Lingyuan Osgathorpe

School of Law, Humanities and Social Sciences Theses

This thesis explores the learning experiences of direct-entry pre-sessional Chinese students in the UK during the COVID-19 pandemic and its aftermath, using Pierre Bourdieu’s theoretical framework. Through the metaphor of students as "fish in different water", the research examines how these learners navigate and adapt to new educational environments that often starkly contrast with their prior experiences.The study employs a two-stage semi-structured interview process with student participants, first in China during their online pre-sessional courses and later in the UK during their degree programmes. All interviews were conducted in Mandarin Chinese to ensure linguistic and cultural accessibility. Additionally, expert interviews …

Unraveling Complex Temporal Patterns In Ehrs Via Robust Irregular Tensor Factorization, Ren Yifei, Linghui Zeng, Jian Lou, Li Xiong, Joyce C Ho, Xiaoqian Jiang, Sivasubramanium V Bhavani

Faculty, Staff and Student Publications

Electronic health records (EHRs) contain diverse patient data with varying visit frequencies. While irregular tensor factorization techniques such as PARAFAC2 have been used for extracting meaningful medical concepts from EHRs, existing methods fail to capture non-linear and complex temporal patterns and struggle with missing entries. In this paper, we propose REPAR, an RNN REgularized Robust PARAFAC2 method to model complex temporal dependencies and enhance robustness in the presence of missing data. Our approach employs Recurrent Neural Networks (RNNs) for temporal regularization and a low-rank constraint for robustness, enabling precise patient subgroup identification and improved clinical decision-making in noisy EHR data. …

Explainable Diagnosis Prediction Through Neuro-Symbolic Integration, Qiuhao Lu, Rui Li, Elham Sagheb, Andrew Wen, Jinlian Wang, Liwei Wang, Jungwei W Fan, Hongfang Liu

Faculty, Staff and Student Publications

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models …

Rank Aggregation Methods And Tools In Genomic Data Analysis, Wenping Zou, Savannah Mwesigwa, Sayed-Rzgar Hosseini, Zhongming Zhao

Faculty, Staff and Student Publications

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. …