Digital Commons Network^™

Urinary Metabolome Study For Monitoring Prostate Cancer Recurrence Following Radical Prostatectomy., Sabur Badmos, Elizabeth Noriega Landa, Kiana L. Holbrook, George E. Quaye, Xiaogang Su, Wen-Yee Lee

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND/OBJECTIVES: Prostate cancer (PCa) is the most common cancer among males. Approximately 20-40% of patients with clinically localized PCa will present with a biochemical recurrence after a radical prostatectomy (RP), while some will present with recurrent metastasis. Monitoring the disease post-treatment is crucial for detecting a potential cancer recurrence early. Urinary volatile organic compounds (VOCs) have shown potential to detect PCa. However, their application in disease monitoring remains unexplored.

METHODS: A total of 165 urine samples were collected from male adults with biopsy-designated PCa-positive results before (n = 55) and after a RP (n = 55), and with …

Novel Nano-Composite Materials (Nncms) With The Designations J1, J2, And J3 For Removal Several Harmful Ions, Mohammed K. Al-Doseri, Safaa. R. Fouda, Mohamed Mossad, Mahmoud H. Mahmoud

Mansoura Engineering Journal

The most toxic material to the environment is heavy metals. remove heavy metals from wastewater is done by adsorption. Many papers have been done using different materials to take off toxic dangerous ions from wastewater. Noval Nano-composites material (NNCM)as designated as J1, J2, and J3 the most used adsorbents in this study. To conduct the experiments, synthetic wastewater was generated in the lab. Batch experiments were conducted to create the best conditions for removing these ions from the wastewater. The optimum conditions obtained were 90 min optimum contact time, and pH equal seven, optimal NNCM dose was 0.5 g/L achieving …

Can Correction Messages Reduce The Spread Of Fake News On Social Media? The Impact Of Information Updates On The Effectiveness Of Corrections, Kelvin K. King, Diego Escobari

School of Economics and Finance Faculty Publications

Although government agencies and fact-checking organizations issue correction messages to mitigate misinformation dissemination on social media, their efforts have been largely ineffective. Drawing on Information Manipulation Theory (IMT) and information extracted from over 84 million tweets, we examine the impact of correction messages and explore how different information updates affect their effectiveness. Our empirical approach differentiates between subpopulations of correction messages. The results support the existence of two distinct types: correction messages that significantly mitigate the spread of fake news—a 1 percent increase in “effective” messages decreases the spread of fake news by 0.89 percent; and those that amplify the …

A Comparison Of Diving Behavior Of Goose-Beaked And Dense-Beaked Whales From Tagging Studies In Multiple Ocean Basins, Jay Barlow, Robin W. Baird, Janelle Badger, Gregory S. Schorr, Erin A. Falcone, Shannon N. Coates, Peter L. Tyack, Andrew J. Read, Leigh S. Hickmott, John W. Durban, Diane Claridge, Natacha Aguilar De Soto, Daniel Miranda Gonzalez, Fleur Visser, Machiel G. Oudejans, Stacy L. De Ruiter, David A. Sweeney, Brenda K. Rone, Stephanie L. Watwood

University Faculty Publications and Creative Works

Studies of cetacean diving behavior in multiple locations in different ocean basins allow for an assessment of variability within and among populations. We examine foraging dive behaviors of goose-beaked whales (Ziphius cavirostris) and dense-beaked whales (Mesoplodon densirostris) using data from 132 tagged whales in seven locations in the Pacific and Atlantic Oceans and the Mediterranean Sea. Acoustic recording tags are used to identify foraging dives by the presence of echolocation. For other tag types, foraging dives are identified based on maximum dive depth. Five parameters are used to characterize foraging dives: maximum dive depth, foraging dive duration, dive cycle duration, …

Performance Analysis Of Computational Methods For Predicting Protein Function In Rare Diseases, Aichetou Mohamed Sidiya, Hanin Alzaher, Razan Almahdi, Tayeb Brahimi

Effat Undergraduate Research Journal

Protein function prediction is crucial for understanding the underlying mechanisms of rare diseases. With the increasing availability of computational methods including machine learning-based approaches, network-based methods, and sequence-based methods, predicting protein functions has become more accessible. However, it is not clear which of these methods performs better or how they compare to each other in terms of accuracy, efficiency, and scalability. In this study, we evaluate several computational methods for predicting protein functions in rare diseases using key performance indicators (KPIs). We analyze the strengths and weaknesses of each method and provide recommendations for researchers and clinicians interested in using …

Electrical Energy Consumption Forecasting Based On Conventional And Artificial Intelligence Methods: A Comparison, Haya H. Binsalim, Jana Kamal, Salma Badaam, Danah Milyani, Ghadah S. Alyami, Aziza I. Hussein

Effat Undergraduate Research Journal

Artificial Intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review and compare the classical techniques and the emerging new AI-based techniques used for forecasting electrical energy consumption in buildings. The findings revealed that the Artificial Neural Network (ANN) model achieved the lowest Mean Absolute Percentage Error (MAPE) of 0.928%. AI-based techniques have many advantages over classical techniques, such as their ability to handle a large amount of data and provide accurate and fast results.

Mapping The Research Landscape Of Covid-19 And Artificial Intelligence Using The Lens Database, Aichetou Mohamed Sidiya, Jailan Fouad Aljizawi

Effat Undergraduate Research Journal

Due to the rapid spread of the COVID-19 pandemic, all countries faced a significant challenge in their efforts to monitor and halt the spread of the virus. Moreover, researchers around the world raced into publishing to get to understand the effects of this pandemic on every aspect of our life whether that was economic, medical, or social. Thus, this research seeks to assess the quality of research on the application of Artificial Intelligence in the Covid-19 pandemic with an emphasis on Saudi Arabia. The research methodology is based on Bibliometric Analysis techniques and VosViewer for various bibliometric visualizations based on …

The Relationship Between Self-Talk And Self Esteem Amongst University Students, Weam Batarfi, Saddiga Al-Ghalib

Effat Undergraduate Research Journal

The theory of self-talk is important to human nature and has the capacity to enhance person's life. In addition, self-esteem plays a major role in mankind life, either in a positive or a negative way. This work was done to analyze the relationship between self-talk and self-esteem among university students. This work was carried out based on quantitative method. For this work, the sample population was university students and the sample size used was 100 students. Data collection was done using survey method. Furthermore, the scales that were used are the Self-Talk Scale development, Factor Analysis, and Validation and self-esteem …

Wideband Spectrum Sensing For Cognitive Radio Using Under-Sampled Successive Approximation Adc, Aziza I. Hussein

Effat Undergraduate Research Journal

The radio spectrum, an inherently limited resource, has been increasingly utilized owing to the recent exponential growth of wireless services. This has led to a new approach, termed cognitive radio, predicated upon exploitation of spectrum holes for omnipresent spectrum utilization. This is made possible via cognitive radio networks’ employment of spectrum sensing. Wideband spectrum sensing has been the focal challenge point in cognitive radio technology, since existing techniques are reliant on analog to digital converters (ADC) with sampling at the Nyquist rate. Unfortunately, in order to perform digitization of wideband RF signals at the Nyquist rate, a very high sampling …

Intelligent Energy Management Mechanisms For Electric Vehicles: A Review, Aziza I. Hussein

Effat Undergraduate Research Journal

This paper investigates intelligent methods used for energy management (EM) in Electric Vehicles (EV). The key role of EM in EVs to increase the performance of the vehicle and reduce Fuel Consumption (FC) thus producing less greenhouse effect. However, the used tactics had limitations. An introduced model of Intelligent Energy Management System (IEMS) for Plug-in hybrid electric vehicles (PHEVs) was efficient for FC reduction. Whereas for IEMS Based on Kalman Filtering showed a low percentage of error. While EM using Tags Threshold Admission And Greedy Scheduling improves EV’s performance, but it can only manage energy per one EV. Another model …

Electrical Energy Consumption Forecasting Analysis Based On Conventional And Artificial Intelligence Methods: A Comparison, Aziza I. Hussein

Effat Undergraduate Research Journal

Artificial intelligence (AI)-based models have been widely applied for energy consumption forecasting over the past decades. The purpose of this paper is to review the classical techniques and the emerging new techniques based on AI of the building electrical energy consumption forecasting. The advantages of AI-based techniques over the classical are that AI methods can handle a large amount of data yet gives accurate results, the results can be found very quickly, in addition to AI having the ability to solve complex nonlinear patterns of raw data. This paper will discuss several studies using different models of forecasting based on …

Potential Pitfalls In Visual Models Of Tipping Points - And How To Fix Them, Jonathan Dechert, Svetlana Gurevich, Stefan Heusler

Northeast Journal of Complex Systems (NEJCS)

Visual models play a crucial role in both science and science communication. However, the distinction between mere analogies and mathematically sound graphical representations is not easy and can be misunderstood not only by laypeople but also within academic literature itself. Moreover, even when the graphical representation exactly corresponds to the mathematical model, its interpretation is often far from obvious. In this paper we discuss the potential landscape visualization commonly used for tipping points in the context of nonlinear dynamics and reveal potential pitfalls, in particular when distinguishing bifurcation induced tipping (B-tipping) from noise-induced tipping (N-tipping).

We propose new visualization techniques …

Optimizing K-Prototype Or Scalable Mixed-Data Clustering: A Vectorized Approach To Accelerate Convergence And Accuracy In Big Data, Faten Fagr Alkourdy

Al-Farahidi Expert Systems Journal

The exponential growth of data stored in databases and data warehouses has intensified the demand for advanced analytical tools capable of efficiently extracting actionable insights. Data mining has emerged as a critical methodology for transforming vast datasets into knowledge. However, traditional techniques often face scalability challenges, particularly due to prolonged computational times, which hinder their effectiveness in large-scale applications. This research aims to improve the K-prototypes clustering algorithm to optimize computational efficiency and enhance clustering precision, enabling more effective knowledge discovery. Key modifications involve implementing vectorization techniques to accelerate processing, redesigning the initialization strategy for cluster centers, and refining the …

Intelligent Control Of Wind Turbines: A Neural-Fuzzy Controller For Pitch And Yaw Angle Regulation Under Variable Wind Conditions, Salah Ali Daoud, Samir Ali Amin, Salah Sabeeh Abed Al Kareem

Al-Farahidi Expert Systems Journal

By modifying the pitch and yaw angles of the blades, the study presents a hybrid neural-fuzzy controller for wind turbines that enhances performance in variable wind conditions. The suggested system integrates fuzzy logic (for decision-making) and artificial neural networks (for learning), unlike conventional PID and fuzzy PID controllers, which have trouble with nonlinear dynamics. It employs two stages of control: pitch control at high wind speeds and yaw control at low wind speeds. The hybrid controller performs noticeably better than PID-based techniques, according to MATLAB/Simulink simulations, lowering pitch control error by 40%, stabilizing the system in 0.1 seconds instead of …

Finite Element Simulation Of Composite Rectangular Cantilever Plates, Muhsin J. Jweeg, Dhia Al-Azzawi, Mohammed Abbas, Mohsin Abdullah Al-Shammari

Al-Farahidi Expert Systems Journal

This work concentrates on the application of the finite element simulation and an experimental program for the composite cantilever plates. The mechanical properties of the models were used to manufacture plates with different aspect ratios and thicknesses. The used models include different aspect ratios of carbon fiber materials and Kevlar composites. In the numerical simulation, the ANSYS package was used to simulate the cantilever plates to obtain the natural frequencies and mode shapes using the mechanical properties obtained experimentally for both types of materials. The findings indicate a strong correlation between the plate geometry and the mode shapes. This gives …

A Study Of Rural Health Care For Chronic Care Management Using Machine Learning To Determine Costs, Joanna Maria Keeling

Doctoral Dissertations

Management of diabetes or heart disease may be uniquely challenging for older individuals with multiple chronic conditions[1]. Chronic Care Management would target patients living with comorbidities in Louisiana, who are not receiving sufficient healthcare services and help them receive the care they deserve[2]. This study is aimed at comparing the results of rural Louisiana Medicaid recipients with comorbidities to the National Averages. We will also use Machine Learning to predict Churn and Medical Costs. To help us identify these patients with comorbidities, we decided to use NCQA Quality Measures. We identified several measures from NCQA that we wanted to use, …

The Design Of Coplanar Waveguide Traveling-Wave Kinetic-Impedance Parametric Amplifiers, Jordan Scott Savoie

Master's Theses

Astronomical observations and many physics experiments rely on cryogenic amplifiers for readout. Current sensitivity is limited by the noise figure of high-electronmobility transistor (HEMT) amplifiers, which have proven di!cult to decrease further in recent years. Traveling-wave kinetic-impedance parametric amplifiers (TKIPAs) are an emerging class of amplifiers which have the potential to substantially improve the sensitivity of microwave low-noise amplifiers (LNAs) while also accepting relatively high input powers and amplifying over a wide bandwidth. In this thesis, I present the design, modeling, and testing procedures for coplanar waveguide (CPW) TKIPAs developed by our group at the National Radio Astronomy Observatory. Using …

Structural And Computational Results On Equitably Dissectible Graphs, Ann Wells Clifton

Doctoral Dissertations

This dissertation introduces the class of equitably dissectible graphs — graphs whose vertex set can be recursively partitioned into two parts whose sizes differ by at most one where the two induced subgraphs are connected. In Chapter 2, we show that a graph is equitably dissectible if and only if it has a spanning tree that is equitably dissectible, and we use this result to establish connections between the class of equitably dissectible graphs and Hamiltonian graphs. We also fully characterize the equitably dissectible spiders of the form S3(a) and S3(a, b), identifying a family of unruly spiders that are …

A Backtracking Algorithm For Determining The Existence Of Regular Graphs Of Specified Girth And Excess, Stetson Ray Bosecker

Doctoral Dissertations

The study of cages focuses on finding (k, g)-graphs of minimal order. This dissertation generalizes the problem of finding cages to the determination of graphs with specified excess, thereby broadening the significance of the results. The (k, g, ε)-graph problem seeks to determine the existence or nonexistence of k-regular graphs with girth g and excess ε = n(G)−M(k, g) (where M(k, g) represents the Moore bound for cage graphs). Motivated by heuristic methods used to determine properties within the study of cages, we present a backtracking algorithm capable of constructing (k, g, ε)-graphs or determining their nonexistence. Chapter 2 provides …

Privacy-Preserving Structure Learning For Geospatial Data Using Information-Theoretic Dependency Measures, Ahmed Mudhish

Doctoral Dissertations

This dissertation proposes a privacy-preserving framework for structure learning in Bayesian networks (BNs) that addresses the challenges of distributed geospatial data face. Geospatial datasets often exhibit region-specific patterns such as sparsity and nonlinear dependencies. These patterns undermine the effectiveness of traditional machine learning models. Additionally, learned BN structures may reveal sensitive relationships in the generated graph by BNs. These relationships pose a significant privacy risk if reverse-engineered. To address these issues, three novel algorithms are introduced. First, the Selective Naïve Bayes with HSIC (SNB-HSIC) algorithm applies a kernel-based dependency measure to filter redundant and irrelevant features in sparse datasets, improving …

Advancing Rfid Systems: From Head Orientation To Robotic Localization Using Passive Tags, Guilherme Ricardo Mendes Da Silva Barreto De Figueiredo

Doctoral Dissertations

No abstract provided.

An Envisioned And Efficient Design Of Next Generation Decentralized Iot Bot Detection Model, Ahmed Abdullah Almalki

Doctoral Dissertations

The Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are revolutionizing critical infrastructures, but their expansion has also introduced severe cybersecurity vulnerabilities. Traditional IoT Bot Detection Systems (IBDS) struggle to scale in environments characterized by high-dimensional, large-scale, and redundant network traffic. These challenges hinder the development of reliable cloud-based intrusion detection systems. The limitations of static and rulebased methods in detecting evolving IoT botnet attacks—such as those launched by Mirai and Gafgyt—underscore the need for intelligent, adaptive approaches. To address this, the present study proposes a machine learning and deep learning-driven IoT Botnet Detection Model, validated through …

Federated Boolean Matrix Factorization Using Integer Programming, Quynh Anh Nguyen, Ngoc Nguyen, Nhat Phan

Research from the Berry Summer Thesis Institute, 2025

Identifying the underlying structural patterns in data and extracting meaningful insights is a key challenge in data analysis. One effective approach to this problem is matrix factorization (MF), which approximates large matrices with lower-dimensional representations, making it effective for uncovering hidden patterns. MF techniques are widely applicable across various domains, such as recommender systems, cancer genomics, system identification, clustering, and image processing. Despite their effectiveness, existing MF methods often struggle with computational constraints and convergence challenges when tackling large-scale, nonsmooth, and nonconvex optimization problems, which are common in real-world applications.

This project aims to explore both the theoretical understanding and …

Comparing Ordinary Least Squares And Quantile Regression: A Causal-Comparative Approach To Modeling Conditional Relationships, Samuel Nnorom

Electronic Theses and Dissertations

Ordinary Least Squares (OLS) regression has traditionally been the preferred quantitative method for estimating linear relationships. However, it assumes that the effect of a predictor variable remains constant across the entire outcome distribution, which can miss important insights when data are heterogeneous. Quantile Regression (QR), on the other hand, offers a more detailed analysis by focusing on the full response variable distribution, thereby revealing different relationship patterns at various quantiles within the outcome. This study compares how OLS and QR perform in modeling conditional relationships within a causal-comparative framework based on ex post facto research. Using the mortality data from …

Code Before Clause: Building Canada’S Digital Defences Before Negotiating Trade, Barry Appleton

Articles & Chapters

Canada is about to give away the keys to its digital future — before it even decides what future it wants.

This paper argues that any exploratory discussions between Canada and the European Union on a digital trade agreement must be paused until Canada legislates a domestic digital sovereignty framework and builds the governance capacity to implement it.

Characterization Of L-Band Resonators And Relaxation Times Of Soₓ- Radicals At X-Band, Georgina Naa Odey Amassah

Electronic Theses and Dissertations

Electron Paramagnetic Resonance (EPR) analysis requires resonators optimized for specific experimental challenges. Volume resonators enclose samples, while surface coil resonators allow imaging of larger subjects. For in vivo studies, L-band frequencies are commonly used because they offer a good balance between signal-to-noise ratio (S/N) and the ability to penetrate tissue. Rapid Scan (RS) EPR at L-band frequencies was chosen for my studies of resonators because of this balance but presents a challenge for sensitivity.

The spatial position dependence of signal intensity was evaluated for selected paramagnetic samples using 10 mm and 30 mm surface coils. The 10 mm coil, with …

Self Spin-Orbit Torques, Ryan Greening

Electronic Theses and Dissertations

Spin–Orbit torques (SOTs) provide an energy-efficient pathway to control magnetization in nanoscale heterostructures. Recently, spin-orbit coupling within ferromagnets has been shown to generate self spin-orbit torques (SSOTs) on the magnetization itself. Previous work on self spin-orbit torques relies on thick ferromagnets with complicated film structures with chemical disorder or compositional gradients to generate inversion symmetry breaking. This dissertation advances experimental methodology and presents data on the quantification of self spin-orbit torques in magnetic thin films due to the spin-orbit coupling of standard 3d ferromagnets.

First, I develop an oblique-incidence magneto-optical Kerr effect (MOKE) technique that exploits a mirror-assisted double reflection …

Developing Deep Learning Methods For Cognitive Impairment Detection Based On Non-Invasive Data, Muath Alsuhaibani

Electronic Theses and Dissertations

Cognitive impairment detection is on the rise to help reduce the burden of healthcare costs on institutions and individuals. Mild Cognitive Impairment (MCI) is an early stage of cognitive decline progressing to Alzheimer’s disease (AD) or AD-related Dementia (ADRD). Detecting the early stages of AD/ADRD is crucial for early interventions among older adults to mitigate cognitive decline over time. However, the current diagnostic methods are often costly and/or invasive, such as MRI and PET scans. Thus, the search for non-invasive and cost-effective screening tools for the early detection of cognitive impairment using speech, language, visual, and motor data is growing. …

Centrosomal And Non-Centrosomal Microtubule Networks Coordinate To Drive Nuclear Dynamics In Developing Epithelium, Rashmi Budhathoki

Electronic Theses and Dissertations

The morphogenesis of developing tissues relies on extensive cellular rearrangements in shape, position, and identity. A key process in reshaping tissues is cell intercalation-driven elongation, where epithelial cells align and intercalate along a common axis. Typically, analyses focus on how peripheral cortical forces influence cell shape changes. Less attention is given to how inhomogeneities in internal structures, particularly the nucleus, impact cell shaping. Here, we examine how pulsed contractile and extension dynamics interact with the nucleus in elongating Drosophila embryos. Our data show that tightly packed nuclei in apical layers hinder tissue remodeling/oscillatory behaviors. We identify two mechanisms for resolving …

Belonging: Stories From Multilingual Learners In The Linguistic Minority, Riley Stouffer

Electronic Theses and Dissertations

This qualitative case study examines how multilingual learners in the linguistic minority (MLLMs) experience connection and belonging in a Rocky Mountain middle school. Although multilingual learners (MLs) have received increased attention, MLLMs, whose home languages are not widely spoken in the school context, remain overlooked. Grounded in Krashen’s (1982) Affective Filter Hypothesis and Wills and Shinar’s (2000) Supportive Functions of Belonging, this study explores how MLLMs navigate school, what fosters or hinders belonging, and how these experiences shape engagement.

Data from semistructured interviews, classroom observations, and field notes with eight MLLM students in grades 6–8 revealed that belonging was fostered …