Digital Commons Network^™

Comprehensive Study Of Integrating Clustering And Adversarial Learning For Enhanced Recommender Systems: A Systematic Review Of Hybrid Methodologies And Applications, M. E. Alqaysi, Murtadha M. Hamad, Ahmed Subhi Abdalkafor

Mesopotamian Journal of CyberSecurity

Recommender systems (RSs) have become critical elements in modern instances of information and decision-support systems, resulting in a transformation of user experiences through highly personalized suggestions for an undeniably vast range of items. Although RSs have become commonplace, they continue to evolve, and their challenges, including sparsity, cold-start, scalability, and vulnerability to adversarial challenges remain. The use of clustering methods has proven highly effective in resolving these issues through the discovery and exploitation of latent user behaviour patterns, segmenting user groups that contribute more towards personalized and adaptable RSs. Additionally, adversarial learning has become a growing focus of study as …

Blockchain-Based Accountability: A Systematic Literature Review, Nabeel Z. Tawfeeq, Dujan B. Taha

Mesopotamian Journal of CyberSecurity

The Blockchain algorithm has advanced the accountability and transparency of modern digital infrastructures. Enforcing responsible behavior and data integrity across distributed environments involves several key components, such as smart contracts, access control models, cryptographic techniques, and a decentralized identity framework. Because the blockchain ledger is immutable and transparent, once a transaction is recorded, it cannot be altered without detection, making fraudulent actions easily traceable and thereby ensuring accountability. However, the need for hybrid approaches that combine on-chain and off-chain solutions for an efficient reliability system introduces challenges, including privacy preservation, scalability, and regulatory compliance. This paper analyzes the effective features …

A New Framework Enhancing, Evaluation, And Benchmarking For Cybersecurity Ddos Attack Detection Models Through The Integration Of Bwm And Vikor Methods, Alaa Mohammed Mahmood, İsa Avci, Sahar Yousif Mohammed

Mesopotamian Journal of CyberSecurity

The proposed work aims to develop a DDoS attack detection model that targets web servers by selecting the appropriate classifier based on several criteria and enhancing the accuracy. The detection system has been constructed using machine-learning algorithms to train and test the CIC-DDoS2019 dataset, and it is integrated with a stacked classifier to enhance accuracy. The classifiers depend on multiple criteria. For that, we employed the BWM to calculate the criteria weights and VIKOR to rank the classifiers, which are part of the MCDM methods. We consulted three experts to establish weights for the categories. Also, we utilized two methods …

Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based On Distributed Federated Learning And Deep Neural Networks, Mazin Abed Mohammed, Mohd Khanapi Abd Ghani, Israa Badr Al-Mashhadani, Sajida Memon, Sajida Memon, Haydar Abdulameer Marhoon, Marwan Ali Albahar

Mesopotamian Journal of CyberSecurity

The exponential growth of healthcare Internet of Things (IoT) data necessitates secure, low-latency analytics that extend beyond centralized architectures. This paper presents BDAFL DNN, a blockchain-integrated data analytics framework that combines Federated Learning (FL) and Deep Neural Networks (DNNs) for real-time, privacy-preserving healthcare analytics across edge and cloud resources. Local devices such as smartwatches and phones collect noninvasive time series sensor streams (heart rate, temperature, and abdomen sensors), perform on device DNN training, and send only model updates to healthcare edge nodes, where a blockchain ledger validates updates for integrity and traceability; validated updates are then aggregated in the cloud …

Improvement Of The Face Recognition Systems Security Against Morph Attacks Using The Developed Siamese Neural Network, Sura Abed Sarab Hussien, Thair Abed Sarab Hussien, Nada Hussein M. Ali

Mesopotamian Journal of CyberSecurity

Face Recognition Systems (FRS) are increasingly targeted by morphing attacks, where facial features of multiple individuals are blended into a synthetic image to deceive biometric verification. This paper proposes an enhanced Siamese Neural Network (SNN)-based system for robust morph detection. The methodology involves four stages. First, a dataset of real and morphed images is generated using StyleGAN, producing high-quality facial images. Second, facial regions are extracted using Faster Region-based Convolutional Neural Networks (R-CNN) to isolate relevant features and eliminate background noise. Third, a Local Binary Pattern-Convolutional Neural Network (LBP-CNN) is used to build a baseline FRS and assess its susceptibility …

Global Research Trends And Collaboration In Mitre Att&Ck Framework: A Bibliometric And Network Analysis In Cybersecurity, Asad Maqsood, Syifak Izhar Hisham, Ahmad Firdaus

Mesopotamian Journal of CyberSecurity

The MITRE ATT&CK framework has become a foundational tool for organizing adversarial behaviors and techniques in cybersecurity. While its adoption in practice and academia has grown significantly, no prior bibliometric review has comprehensively mapped the global research landscape surrounding this framework. This study addresses that gap by conducting a systematic bibliometric and network analysis of publications related to ATT&CK indexed in Scopus from 2017 to 2025. Using VOSviewer and quantitative bibliometric methods, we analyzed 391 publications to identify trends in research output, influential works, key contributors, thematic areas, and patterns of collaboration. Results show exponential growth in studies related to …

A Proposed Algorithm For Avoiding Jammer In Structure-Free Wireless Sensor Networks, Zainab Shaker Matar Al-Husseini, Hussain K. Chaiel, Amel Meddeb, Ahmed Fakhfakh, Ahmed Fakhfakh

Mesopotamian Journal of CyberSecurity

Wireless Sensor Networks (WSNs) have emerged as a transformative technology with applications in critical and often inaccessible environments, including military and security domains. These networks comprise cooperative sensor nodes that gather and relay data to a base station. However, their inherent resource constraints—particularly the non-rechargeable nature of energy—pose a major challenge to network longevity and reliability. Conventional fixed-path transmission protocols exacerbate this issue, as energy-depleted or jammed nodes can disrupt communication, leading to partial or complete data loss. The primary objective of this study is to design and evaluate a structure-free transmission protocol that dynamically adapts data routing in order …

Exploring Design-Based Research: Enhancing Methodologies For Educational Innovation, Awang Najib Awang Mohamad, Zaimuariffudin Nordin, Mohd Hafizan Hashim

Journal of Research Initiatives

Design-based research (DBR) has proven to be a robust methodology for transforming educational practice through a systematic cycle of analysis, design, development, and real-world implementation. Its distinctive strength lies in fostering close collaboration between researchers and educators within authentic learning contexts, producing design principles and theories that are deeply responsive to local needs. This paper presents a critical and comprehensive examination of the three core phases of the McKenney and Reeves (2012) generic DBR model, illustrating how it integrates rigorous inquiry with practical problem-solving. It also introduces Sandoval's (2014) concept of conjecture mapping as a strategic tool for aligning theoretical …

Preparing The Next Generation Of Health Education Teachers For The Ethical Use Of Ai, Melissa J. Haithcox-Dennis, Reeshemah Johnson

Journal of Research Initiatives

This article examines how health education teacher preparation programs can prepare future educators to use artificial intelligence (AI) in responsible and effective ways. It begins with a brief overview of the use of AI in education, considering the growing impact of generative AI technologies on teaching and learning. The article outlines international and national standards that govern the application of AI in education, addressing key ethical concerns, including the dissemination of inaccurate health information, violations of student privacy, bias, and inequitable access to AI tools. The article emphasizes the importance of ethical decision-making and professional responsibility when integrating AI into …

Enhancing Critical Thinking Through Interactive Learning Approaches For First-Year University Students, Nana Yaw B. Agyeman, Venessa Vela Aphane Ms

Journal of Research Initiatives

The use of interactive learning methods is considered crucial in equipping university students with critical skills. This could successfully deal with, and address issues encountered in real-world contexts. It has been noted that most first-year university students often face challenges adjusting to university life due to their background. The research methodology utilized in this study was qualitative, as it was deemed appropriate for the research objectives. A case study design was employed to investigate the issue comprehensively. A purposive sample of eight participants was selected. Thematic analysis was employed as the method of data analysis. The study's findings suggest that …

Uinta Basin Snow Shadow: Impact Of Snow-Depth Variation On Winter Ozone Formation, Michael J. Davies, John R. Lawson, Trevor O’Neil, Seth N. Lyman, Karlee Zager, Tristan D. Coxson

Bingham Research Center

After heavy snowfall in the Uinta Basin, Utah, elevated surface ozone occurs if a cold-air pool persists and traps emissions from oil and gas industry operations. Sunlight and actinic flux from a high-albedo snowpack drive ozone buildup via photolysis. Snow coverage is paramount in initiating the cold pool and driving ozone generation. Its depth is critical for predicting ozone concentrations. The Basin’s location leeward of the Wasatch Mountains provides conditions for a precipitation shadow, where sinking air suppresses snowfall. We analyzed multiple years of ground-based snow depth measurements, surface ozone data, and meteorological observations; we found that ozone levels track …

The Global Phase Space Of The Three-Vortex Interaction System And Its Application To Vortex-Dipole Scattering, Atul Anurag

Dissertations

This dissertation presents a global reduction of the classical three-vortex problem that is free from coordinate singularities, enabling a comprehensive analysis of the system's dynamics across all circulation regimes.

To achieve this, a two-step symplectic reduction procedure is developed. The first step introduces Jacobi coordinates adapted to the symplectic structure of the vortex system, and the second applies a Lie-Poisson reduction to the resulting system. This formulation eliminates the non-physical singularities associated with collinear vortex configurations and facilitates a global phase space analysis, including a detailed and novel investigation of bifurcations.

Within this reduced framework, all relative fixed points are …

Robust Ai Solutions For Financial Markets Through Generative Modeling, Dynamic Graph Learning, And Reinforcement-Based Portfolio Optimization, Jingyi Gu

Dissertations

Financial markets are inherently uncertain and dynamic, driven by complex factors such as macroeconomic signals, investor sentiment, and evolving inter-asset relationships. While machine learning has advanced financial modeling, existing approaches often fall short in addressing the real-world intricacies of finance. This dissertation confronts two critical challenges, human-driven stochasticity and risk-intensive decision-making under real-world trading constraints, while seizing a pivotal opportunity, the structural dynamics of evolving financial systems. These elements are foundational to advancing robust and practical financial intelligence.

To this end, this dissertation develops a unified framework for robust financial modeling and decision-making. The framework is architected as a progressive, …

Large-Scale Graph Algorithms And Applications With An Emphasis On Fintech Data, Fuhuan Li

Dissertations

Graph algorithms are essential analytical tools with applications spanning cybersecurity, biology, social media, and increasingly, financial technology (FinTech). The complex and interconnected nature of financial data, particularly in cryptocurrency networks, presents unique opportunities for graph-based analysis in fraud detection and anomaly identification.

This dissertation presents the design and implementation of scalable graph algorithms tailored for large-scale networks, with particular emphasis on FinTech applications. The primary contributions include: (1) novel cover-edge based triangle counting algorithms that significantly reduce computational overhead through breadth-first search preprocessing, achieving substantial speedups over traditional methods and dramatic communication reduction in distributed settings, (2) optimized parallel implementations …

Bodipy-Based Photocages For The Controlled Release Of Hydrogen Sulfide And Persulfides, Yanmei Li

Dissertations

Reactive sulfur species (RSS) such as hydrogen sulfide (H2S) and hydropersulfides (RSSH) are crucial mediators in a variety of physiological and pathological processes. Their regulatory roles span from maintaining cellular redox balance to influencing signaling pathways critical for homeostasis and stress responses. This dissertation investigates H2S and RSSH as biologically important molecules and explores the development of boron-dipyrromethene (BODIPY)-based photocages for its controlled, light-triggered release. Given H2S and RSSH's critical role in redox regulation and cellular protection, achieving spatiotemporal control over its delivery is essential for both therapeutic and research applications. BODIPY scaffolds offer favorable photophysical properties, including visible-light activation …

Reduced Order Models Of Hydrodynamically Interacting Flapping Wings, Jose Pabon

Dissertations

Fish schools exhibit a collective behavior and self-organization that is mediated by hydrodynamic interactions between individual fish. However, the long-time evolution of hydrodynamically interacting collectives is challenging to investigate due to the persistent influence of long-lived vortical structures, and the high-resolution requirements of direct numerical simulation at large Reynolds numbers. Reduced-order models have therefore played an important role in theoretical investigations of collectives of swimming bodies. The main results detailed herein are several new reduced-order models of swimmers that self-propel by flapping, i.e., by executing a prescribed periodic rigid body motion. The models are extensions of a discrete-time dynamical system …

Harnessing Graphs For Knowledge Representation In Natural Language Processing, Uras Varolgunes

Dissertations

This work proposes innovative methods for integrating domain-specific knowledge into natural language processing tasks through the use of graphs, aiming to enhance the performance of models across various domains, including finance and healthcare. Several novel approaches are proposed that fuse graph structures with modern deep learning techniques, addressing the challenges of missing word embeddings, label prediction, and graph representation learning for large language models.

First, a powerful embedding method built on top of the recent advances in latent graph learning is introduced to address the critical problem of word embedding imputation. Second, a graph-enhanced label attention model designed for medical …

Optimizing Hip And Knee Assistance For Walking And Sit-To-Stand Transitions: An Intrinsic Muscle Mechanics Based Predictive Approach, Neethan Ratnakumar

Dissertations

As the global population ages, the demand for wearable assistive technologies continues to rise, driven by their potential to enhance mobility and independence in older adults. Effectively designed controllers for lower-limb exoskeletons to assist sit-to-stand (STS) and walking are crucial for delivering efficient, safe, and comfortable assistance during daily activities. Traditionally, controller optimization involves biomechanical modeling and user-specific customization. Musculoskeletal simulations play a central role in this process by providing insights into human-exoskeleton interaction dynamics, thereby informing and refining control strategies.

This work presents a simulation-driven approach for developing exoskeleton controllers for walking and STS using two distinct methods: optimal …

Development And Implementation Of Novel Mass Spectrometric Methods For Metabolism Studies In Drug Discoveries, Huifang Yao

Dissertations

The field of drug discovery has evolved significantly over the past few decades, with a growing emphasis on understanding the metabolic pathways and biotransformation processes that influence the pharmacokinetics and pharmacodynamics of drug candidates. Novel mass spectrometric methods have emerged as powerful tools in this domain, facilitating the comprehensive analysis of metabolic profiles and enabling the identification of metabolites with implications for safety, efficacy, and overall drug development. However, challenges remain in quantifying metabolic flux—an essential aspect of understanding drug action—particularly due to the limitations of traditional isotope kinetic assays, which often involve lengthy sample preparation and the use of …

Multi-Messenger Diagnostics Of The Origin And Transport Of Solar Energetic Particles, Meiqi Wang

Dissertations

Impulsive solar energetic electron events (SEEs) are a common phenomenon originating from the Sun. They are characterized by rapid onset and brief duration, and are commonly associated with solar flares. Solar radio bursts, which offer a rich variety of diagnostic tools for solar activities, exhibits a close relationship with impulsive solar energetic electron events. Such diagnostics are particularly powerful when combined with multi-wavelength remote sensing and in situ observations. The dissertation focuses on combining state-of-the-art microwave imaging spectroscopy data obtained by the Expanded Owens Valley Solar Array (EOVSA) with other multi-messenger observations to investigate the origin of impulsive solar energetic …

Access To Information On Toddler Family Development Program And Family Participation In Child Growth And Development, Dita Dhammayanti, Demsa Simbolon, Lissa Ervina, Yusran Fauzi

Kesmas

The comprehension of the Toddler Family Development (TFD) Program among families in Indonesia remains limited, likely due to insufficient access to information and low participation rates. Limited participation can negatively affect a family’s ability to support optimal child growth and development. This study examined the relationship between access to information on the TFD Program and family participation in child growth and development. Using secondary data from the 2019 Program Performance and Accountability Survey in Indonesia, the cross-sectional analysis included 21,497 respondents. The results revealed an association between access to information on the TFD Program and family participation in child growth …

Bridging The Gap: Harnessing Emerging Technology To Narrow The Academic Achievement Gaps Of Minority Students, Dionne Williams, Terence Vinson, Kristi Young, Danielle Williams, James R. Mixon

The Journal of the Research Association of Minority Professors

The academic achievement gap of Black, Hispanic, and Native American students has historically lagged as it relates to Caucasian students. This work defines academic achievement as standardized test scores, graduation rates, and college completion rates. The persistent educational disparities faced by minority students remain a significant challenge for school systems. The integration of technology offers a promising solution to help narrow these academic achievement gaps and provide equitable learning opportunities. Technology offers educators the ability to utilize self-paced instructional programming, ensuring that the educational needs and deficiencies of minority students are addressed. This work explores how innovative technological tools, ranging …

The Pure Yang-Mills Field, James Glimm

Department of Applied Mathematics & Statistics Faculty Publications

We construct a pure Yang-Mills quantum gauge field theory. The construction is based on the axial gauge, ghost states, the BRST framework and the entropy production maximizing Gribov extension of the Hamiltonian, with a loop expansion to all finite orders for the dynamics. The construction is established by renormalized perturbation theory to finite order. Reflection positivity and the reconstruction theorem are established. Hilbert space convergence of renormalized perturbation theory is a consequence of the reconstruction theorem. All Osterwalder-Schrader axioms are verified. A two-sided mass gap exists in the energy spectrum for the weak coupling limit of low temperature. The conditions …

Perceptions Of College Students With Learning Disabilities On High School Transition Services Related To Self-Determination Skills, Julie Ann O'Malley

Graduate Theses & Dissertations

This phenomenological qualitative study explored college students' recalled experiences with high school special education secondary transition programming and its impact on their college success and perceived readiness. Guided by Self-Determination Theory (SDT), focusing on the competence need, the research examined students' perceptions of instruction quality and quantity, and their preparedness in disability awareness and knowledge of strengths and limits.

Analysis of survey data and semi-structured interviews revealed key findings consistently showed students perceived high school transition services as inadequate in both quantity and quality. They reported a lack of formal instruction, including the absence of designated classes, a lack of …

Newsletter, Summer 2025, Cedar Oaks Guild

Cedar Oaks Guild Newsletter

No abstract provided.

Pipeline Vs. Choice: The Global Gender Gap In Stem Applications, Isaac Ahimbisibwe, Adam Altjmed, Georgy Artemov, Andres Barrios-Fernandez, Aspasia Bizopoulou, Martti Kaila, Jin-Tan Liu, Rigissa Megalokonomou, José Montalban, Christopher Neilson, Jintao Sun, Sebastian Otero, Xiaoyang Ye

Cowles Foundation Discussion Papers

Women account for only 35% of global STEM graduates, a share unchanged for a decade. We use administrative microdata from centralized university admissions in ten systems to deliver the first crossnational decomposition of the STEM gender gap into a pipeline gap (academic preparedness) and a choice gap (first-choice field conditional on eligibility). In deferred-acceptance platforms where eligibility is score-based, we isolate preferences from access. The pipeline gap varies widely, from -19 to +31 percentage points across education systems. By contrast, the choice gap is remarkably stable: high-scoring women are 25 percentage points less likely than men to rank STEM first.

Design And Development Of A Standalone Digital Holographic Microscope Employing Phase-Driven Reconstruction And Classification For Biomedical Imaging And Optical Diagnostics, Charlotte Kyeremah

Graduate Doctoral Dissertations

Access to advanced biomedical imaging technologies remains a significant challenge in resource-limited settings, especially for early disease detection and monitoring of diseases such as malaria, HIV, and other blood-borne diseases. Although point-of-care (POC) devices have gained popularity in global health, many rely on antibody-based tests, lateral flow strips, or optical readouts that often lack quantitative capabilities, sensitivity to early infections, or versatility in different diagnostic targets. In addition, these systems are typically dependent on disposable reagents or manual interpretation, which limits their effectiveness in remote areas. Digital Holographic Microscopy (DHM) presents a promising alternative as a label-free imaging method capable …

Low-Cost Cutaneous Protoporphyrin Ix (Ppix) Detection (Cpd) Device For Follow-Up Monitoring Of Patients After Photodynamic Therapy, Md Asaduzzaman Rasel

Graduate Masters Theses

Background: Photodynamic Therapy (PDT) utilizes specific wavelengths of light to activate photosensitizing chemical compounds, known as photosensitizers, which induce the generation of cytotoxic reactive oxygen species (ROS) for the targeted destruction of cancer cells. Among various photosensitizers for PDT, Protoporphyrin IX (PpIX) is widely employed in oncology and dermatology due to its natural in situ generation via the metabolic conversion of 5- aminolevulinic acid (ALA), a non-phototoxic prodrug. Systemic administration of ALA after 3-6 hr drug delay leads to peak PpIX accumulation in tissues, facilitating therapeutic and diagnostic applications. However, PpIX can persist in the skin for 24–48 hours post-treatment, …

Sugar: A Sequence Unfolding Based Transformer Model For Group Activity Recognition, Yash U. Gondkar

Graduate Masters Theses

Large Language Models have improved significantly in the past couple of years due to the adoption of transformers. However, transformers still find it challenging to process videos due to limited context size caused by their quadratic computing cost. Therefore, we studied a booming field in machine learning which powers applications like social scene analysis and video surveillance systems called Group Activity Recognition (GAR). We found that recent models were able to achieve more than 90% accuracy on popular datasets like the Volleyball dataset, however, it turned out that even they relied on transformers.

Therefore, in this work, we developed a …

On Being The Only: Using Black Feminist Thought And Hermeneutic Phenomenology To Deeply Understand Onliness As Experienced By Black Women In Stem, Karen M. Jarvis-Thorne, Karen M. Jarvis-Thorne

Graduate Doctoral Dissertations

When a Black woman persists in a STEM undergraduate academic space as the only Black woman in that space, how is she shaped as a human being? How does she fare? How does the experience influence her decision making as she contemplates graduate school applications? Does the experience guide her decision to stay or leave STEM? In this study, the author used Black Feminist Thought to provide cultural and critical oversight of her engagement with Hermeneutic Phenomenology. Hermeneutic Phenomenology requires this oversight because the methodology’s theoretical foundations are grounded in Heidegger’s Nazism and should be critiqued if and when appropriate …