Digital Commons Network^™

Heartbeat Sound Classification Using Mel-Spectrogram And Cnn Optimized By Frilled Lizard Algorithm For Cardiovascular Disease Detection, Ahmed T. Alhasani, Zainab H. Albakaa, Shahad A. Alabidi, Osamah Qasim Abd Zaid Gburi, Ammar Kadi, Irina Potoroko

Mesopotamian Journal of Artificial Intelligence in Healthcare

Cardiovascular disease (CVD) continues to be the predominant cause of mortality globally, underscoring the critical necessity for prompt and precise diagnostic techniques.  This paper introduces an innovative machine learning framework for categorizing heartbeat sounds into four classifications—normal, murmur, additional heart sound, and artifact—utilizing audio recordings from the PhysioNet/CinC Challenge 2016 dataset.  The methodology employs Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, converting raw heart sound data into comprehensive time-frequency representations.  A Convolutional Neural Network (CNN) is utilized for classification, with its hyperparameters refined by the recently developed Frilled Lizard Optimization (FLO) method, a bio-inspired metaheuristic that emulates the …

Α Novel Ai-Based Modeling With Bias Classification Hybrid Risk Evaluation System For Confidence Enhanced Network Meta-Analysis Of Occupational Hazards And Burnout Risk Among Public Health Inspectors, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Public Health Inspectors (PHIs) serve a critical role in enforcing health and safety regulations, particularly under the growing pressures of climate change. With rising exposure to occupational hazards such as heat waves, air pollution, and vector-borne diseases, PHIs now face escalating stress and burnout. Geographical variability, limited resources, and institutional gaps in training and support further shape their complex risk profile. Despite growing concern, systematic, confidence-based evaluations of these occupational risks—especially tailored to PHIs—remain rare. This study addresses that gap using a Confidence in Network Meta-Analysis (CINeMA)-enhanced framework to assess domain-specific occupational risk profiles of PHIs working in climate-stressed environments. …

Chemoproteomic Profiling Of A Carbon-Stabilized Gold(Iii) Macrocycle Reveals Cellular Engagement With Hmox2, Sailajah Gukathasan, Chibuzor Ngozi Olelewe, Libby Ratliff, Jong H. Kim, Alyson M. Ackerman, J. Robert Mccorkle, Sean Parkin, Gunnar F. Kwakye, Jill M. Kolesar, Samuel G. Awuah

Chemistry Faculty Publications

In this work, we discovered a novel organometallic gold(III) macrocycle, Au-Mac1, that demonstrates anticancer potency in a panel of triple-negative breast cancer cells (TNBC), and based on this complex, a biotinylated-Au-Mac1 probe was designed for target identification via chemoproteomics, which uncovered the engagement of HMOX2 of the heme-energy metabolism pathway. Using orthogonal chemical biology and molecular biology approaches, including immunoblotting, flow cytometry, and cellular thermal shift assays, it was confirmed that Au-Mac1 engages HMOX2 in cells. Downstream effects of Au-Mac1 on the depletion of mitochondrial membrane proteins and bioenergetics point to the potential role of HMOX2 in cancer. Importantly, Au-Mac1 …

Electrochemical Observation And Ph Dependence Of All Three Expected Redox Couples In An Extremophilic Bifurcating Electron Transfer Flavoprotein With Fused Subunits, Debarati Das, Wassim El Housseini, Monica Brachi, Shelley D. Minteer, Anne-Frances Miller

Chemistry Faculty Publications

Bifurcating enzymes employ energy from a favorable electron transfer to drive unfavorable transfer of a second electron, thereby generating a more reactive product. They are therefore highly desirable in catalytic systems, for example, to drive challenging reactions such as nitrogen fixation. While most bifurcating enzymes contain air-sensitive metal centers, bifurcating electron transfer flavoproteins (bETFs) employ flavins. However, they have not been successfully deployed on electrodes. Herein, we demonstrate immobilization and expected thermodynamic reactivity of a bETF from a hyperthermophilic archaeon, Sulfolobus acidocaldarius (SaETF). SaETF differs from previously biochemically characterized bETFs in being a single protein, representing a …

Functional Utility Of Gold Complexes With Phosphorus Donor Ligands In Biological Systems, Adedamola S. Arojojoye, Samuel G. Awuah

Chemistry Faculty Publications

Metallo-phosphines are ubiquitous in organometallic chemistry with widespread applications as catalysts in various chemical transformations, precursors for organic electronics, and chemotherapeutic agents or chemical probes. Here, we provide a comprehensive review of the exploration of the current biological applications of Au complexes bearing phosphine donor ligands. The goal is to deepen our understanding of the synthetic utility and reactivity of Au-phosphine complexes to provide insights that could lead to the design of new molecules and enhance the cross-application or repurposing of these complexes.

Pathways To Nursing And Midwifery Education In Tanzania With Reflection To The Global Perspectives: A Narrative Review, Tumbwene Mwansisya, Mary Lyimo, Eunice Pallangyo

School of Nursing & Midwifery, East Africa

Background/Objectives:

This review aimed to explore the pathways of nursing and midwifery education in Tanzania and compare them with global perspectives. The goal was to identify similarities, differences, and areas for potential improvement to align with international standards.

Methods:

A narrative literature review was carried out through databases with published studies in nursing and gray literature. The database search included Medline, PubMed, Google Scholar, EBSCO, PsycINFO, clinical nursing, and gray literature from January 2014 up to December 2024. The search process was carried out by the authors with the following key words: admission, pathway to nursing profession, delivery mode, …

Caspase-11 And Nlrp3 Exacerbate Systemic Klebsiella Infection Through Reducing Mitochondrial Ros Production, Yuqi Zhou, Zhuodong Chai, Ankit Pandeya, Ling Yang, Yan Zhang, Guoying Zhang, Congqing Wu, Zhenyu Li, Yinan Wei

Chemistry Faculty Publications

Introduction: Klebsiella pneumoniae is a Gram-negative bacterium and the third most commonly isolated microorganism in blood cultures from septic patients. Despite extensive research, the mechanisms underlying K. pneumoniae-induced sepsis and its pathogenesis remain unclear. Acute respiratory failure is a leading cause of mortality in systemic K. pneumoniae infections, highlighting the need to better understand the host immune response and bacterial clearance mechanisms. Method: To investigate the impact of K. pneumoniae infection on organ function and immune response, we utilized a systemic infection model through intraperitoneal injection in mice. Bacterial loads in key organs were quantified, and lung injury was assessed. …

Iodinated Disinfection Byproduct Formation From Iohexol In Sunlit And Chlorinated Urban Wastewaters, Reagan Patton Witt, Marcelo I. Guzman

Chemistry Faculty Publications

Iodinated disinfection by-products (I-DBPs) are of growing concern due to their elevated toxicity compared to their chlorinated counterparts, with links to adverse health effects such as bladder cancer and miscarriages. Medical imaging agents like iohexol, commonly used in healthcare facilities, introduce iodine into wastewater systems. This study investigates the photodegradation of iohexol and the subsequent formation of products, including I-DBPs, during simulated final wastewater treatment under chlorination and sunlight exposure. Experiments were conducted with solutions containing 30 μM iohexol, 3 mg L⁻¹ humic acids, and 5.5 mg L⁻¹ hypochlorite. Samples were irradiated at λ ≥ 295 nm and …

Improving Proteostasis Of Trafficking-Deficient GabaAReceptor Variants By Activating Ire1, Xu Fu, Ya Juan Wang, Kyung A. Lee, Lucie Y. Ahn, Xi Chen, Brock T. Harvey, Meng Wang, Hailey Seibert, Pei Pei Zhang, Adrian Guerrero, Ashleigh E. Schaffer, Christopher I. Richards, R. Luke Wiseman, Jeffery W. Kelly, Ting Wei Mu

Chemistry Faculty Publications

Gamma-aminobutyric acid type A receptors (GABA_ARs) are essential for maintaining the excitation–inhibition balance in the central nervous system. Genetic variations of GABA_ARs result in a variety of neurological disorders, such as epilepsy. A key pathogenic mechanism involves protein misfolding and defective assembly of GABA_ARs in the endoplasmic reticulum (ER), resulting in impaired surface expression and loss of function. Here, we investigated three trafficking-deficient variants of the GABA_AR α1 subunit (GABRA1), including D219N (ClinVar Variation ID: 127232), G251D (Variation ID: 419523), and P260L. We demonstrated that selective pharmacological activation of the IRE1/XBP1s signaling …

Girls First Fund: External Evaluation—Endline Evaluation Report, Brian Medina Carranza, Neelanjana Pandey, Angel Del Valle, Fatima Zahra

Adolescents and Young People

The Population Council is leading an evaluation of programs funded by the Girls First Fund (GFF) in the Dominican Republic (DR), India, and Niger. These three countries were chosen to maximize geographic, social, cultural, and economic diversity across contexts, but common to all three is the continued persistence of child marriage and the presence of collective efforts to eliminate it. The broader goal of this evaluation is to: 1) demonstrate proof of concept for the impact of  GFF-supported programs; 2) examine key outcomes of interest to the GFF and the community-based organizations that it funds, including education, child marriage, and …

Tackling Paradoxes And Double Binds For A Healthier Workplace: Insights From The Early Covid-19 Responses In Quebec And Ontario, Daniel Côté, Amelia León, Ai-Thuy Huynh, Jessica Dubé, Ellen Maceachen, Pamela Hopwood, Marie Laberge, Samantha Meyer, Meghan K. Crouch, Joyceline Amoako

Études primaires

The urgency of managing the COVID-19 health crisis in workplaces led to tensions, work overload, and confusion about preventive measures. This study presents a secondary analysis of qualitative data on paradoxes and double binds (PDBs) experienced by precarious essential workers in Canada who interacted with the public and their supervisors. Based on 13 interviews from a larger qualitative dataset, we examine how workers navigated public health recommendations and organisational demands during the pandemic. Findings reveal multiple organisational and managerial PDBs—both COVID-19-related and pre-existing—that contributed to psychological distress and compromised well-being. We argue that PDBs represent a significant occupational health hazard …

Parkinson's Disease Detection Using Deep Learning Approach Based On Wearable Sensor-Based Daily Monitoring, Bahaulddin N. Adday, Khalid Shaker, Ihsan Salman, Hothefa Shaker

Mesopotamian Journal of Artificial Intelligence in Healthcare

Parkinson's disease (PD) is a movement disorder characterized by motor dysfunction commonly bradyphemia, tremor, rigidity, akinesia, or slowness of movement. Noting that motor states can fluctuate in PD the primary aim of this current paper was to differentiate multiple states using wearable sensors in the patients and detection of this PD based on deep learning (CNN). Methodology: In this paper, the researchers recorded the signals of the accelerometer and gyroscope fixed on the wrist of PD in their regular daily functioning after using this dataset collection. The deep learning architecture developed was to optimize a CNN for analyzing the sensor …

Optimizing Hospital Operational Efficiency Using Ai: A Multi-Objective Nsga-Ii Model For Real-World Medical Data In Syria, Khder Alakkari, Bushra Ali, Teba Majed Hameed

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study presents an AI-driven multi-objective optimization approach using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to enhance hospital operational efficiency in Syria. Using real-world data from the Tishreen University Hospital over a 60-day period, the research addresses three conflicting objectives: minimizing average patient waiting time, reducing daily operational costs, and maximizing the number of patients treated. Six key operational variables were selected to build the optimization model, including bed availability, physician count, and daily admissions. The NSGA-II algorithm successfully generated a set of Pareto-optimal solutions, each reflecting different trade-offs among the objectives. Statistical analysis and visualizations confirmed the complexity …

Artificial Intelligence-Powered Robotic Technology For Transforming Palliative Care, Adebo Thomas, Asiku Denis, Wamusi Robert, Simon Peter Kabiito, Zaward Morish, Aziku Samuel, Malik Sallam, Ioannis Adamopoulos

Mesopotamian Journal of Artificial Intelligence in Healthcare

Palliative care seeks to improve the quality of life of patients with life-threatening illnesses by addressing their physical, emotional, and psychological needs. However, global challenges such as workforce shortages, limited access to specialized care, and inconsistent care quality demand innovative solutions. Advances in artificial intelligence (AI)-powered robotics offer transformative potential to overcome these barriers and strengthen palliative care delivery. This study explores how AI-driven robotic technologies support palliative care through applications in symptom monitoring, clinical decision-making, emotional companionship, and personalized care planning. It reviews cutting-edge robotic systems, including assistive, companion, diagnostic, nursing, procedural, service, and rehabilitation robots. Enabled by machine …

The Role Of Artificial Intelligence In Early Tumor Detection: An Xgboost Risk Assessment Model For Egyptian Patients, Toufik Mzili, Mourad Mzili, Saif Islam Bouderba, Ahmed Abatal, Widi Aribowo, Zahra Oughannou

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study developed an XGBoost-based risk assessment model to enhance early tumor detection among Egyptian patients, addressing the challenges of late diagnosis and limited healthcare resources. Utilizing a retrospective dataset of 178 patients, the model incorporated demographic, clinical, and biochemical variables, including AFP levels, viral hepatitis status (HBV/HCV), and liver function markers. The model demonstrated strong predictive performance, achieving an accuracy of 0.833, precision of 0.846, and an AUC of 0.86, though recall remained moderate (0.688), indicating room for improvement in identifying high-risk cases. Feature importance analysis highlighted AFP levels and hepatitis status as the most influential predictors, aligning with …

Clinical Data Analysis Using Machine Learning Algorithms To Predict The Progression Of Type 2 Diabetes, Niki Syrou, George Mpourazanis, Panagiotis Tsirkas, Jovanna Adamopoulou, Jovanna Adamopoulou

Mesopotamian Journal of Artificial Intelligence in Healthcare

Type 2 diabetes mellitus (T2DM) is a growing global health concern requiring early detection strategies. This study applies a Random Forest machine learning model to predict diabetes progression using a structured clinical dataset of 100 patients. The dataset includes demographic, physiological, and biochemical variables such as age, BMI, blood pressure, glucose levels, and lipid profiles. After preprocessing and training, the model achieved strong performance metrics: accuracy of 0.80, precision of 0.84, recall of 0.94, and an AUC of 0.88. Feature importance analysis revealed that systolic blood pressure, fasting glucose, and BMI are the most critical predictors. These findings are consistent …

Optimizing Decision Tree Classifiers For Healthcare Predictions: A Comparative Analysis Of Model Depth, Pruning, And Performance, Hussein Alkattan, Ali Subhi Alhumaima, Mohammed Shakir Mohmood, Ghassan Dhahir Mohammed Al-Thabhawee

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study presents of Decision Tree classifiers for predictive modeling in medicine, focusing on model depth optimization, pruning techniques, and performance evaluation. On the basis of a synthetic healthcare dataset containing over 55,000 records, each with features such as age, gender, blood type, bill amount, and medical condition, we investigate the impact of varying tree depth from 1 to 5 on predictive accuracy, interpretability, and generalizability. Shallow models have strong transparency but poor classification strength, and deep models obtain stronger interactions but suffer from overfitting. With pruning, we find a balance between model simplicity and precision and yield strong classifiers …

Data Mining Driven Segmentation Of Health Insurance Policyholders Using K-Means Clustering, Farah Ali Khairi, Laith Farhan, Oluwaseun A. Adelaja

Mesopotamian Journal of Artificial Intelligence in Healthcare

This study illustrates a data‐driven approach to the segmentation of health insurance policyholders based on K-Means clustering of an open insurance dataset. Key demographic and financial features like age, body mass index (BMI), dependents, annual medical spending, and premium payment were normalized first to ensure comparability. The optimal number of clusters (k = 3) was determined using silhouette analysis, and three clusters were formed: (1) young, low‐cost individuals, (2) middle‐aged medium‐cost individuals, and (3) old, high‐cost individuals. Cluster centroids provide actionable profiles that can be utilized by insurers for target marketing, risk profiling, and development of customized plans. A set …

Machine Learning Approaches For Predicting Breast Cancer Recurrence: A Comparative Analysis, Noor Razzaq Abbas, Hussein Alkattan, Isam Bahaa Aldallal

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper reports a comparative analysis of four supervised machine learning algorithms: RF, SVM (using radial and linear kernels), Logistic Regression, and Multi-Layer Perceptron, for breast cancer recurrence prediction on a carefully curated clinical dataset. The data set, first collected by Royston and Altman and subsequently released on Kaggle, has patient age, menopausal status, tumor size, histological grade, lymph node status, estrogen and progesterone receptor levels, hormone therapy for treatment, recurrence-free survival time, and a binary recurrence outcome. The data set was then divided after the elimination of identifiers and z-score normalization in an 80:20 ratio using stratified sampling. Models …

Order Of The Coif Program, August 18, 2025, University Of South Carolina School Of Law Order Of The Coif

Order of the Coif Programs and Documents

No abstract provided.

Deep Learning Approaches For Predicting Strain Energy In Heterogeneous Materials, Junesh Gautam

Electronic Theses and Dissertations

Finite Element Analysis (FEA) faces computational challenges when analyzing nonlinear and heterogeneous materials. Utilizing the Mechanical MNIST dataset, comprising 60,000 simulated samples of 28x28 pixel domains under large deformation, the study evaluates classical regression methods (Linear Regression, Random Forest, Gradient Boosting) and advanced deep learning architectures (Convolutional Neural Networks (CNN) and Residual Networks (ResNet)). CNN models achieved superior performance, with a Mean Squared Error (MSE) of 4.21 and an R2 value of approximately 0.982, outperforming classical regression models and slightly surpassing ResNet architectures. These deep learning methods automatically learn spatial relationships from pixel-based representations, eliminating the need for manual feature …

Satellite And Uas Synergy For Large-Scale Crop Canopy Cover Mapping With Deep Learning, Muhammad Ali Irshad

Electronic Theses and Dissertations

No abstract provided.

Effects Of Essential Oils Alone Or In Combination With Monensin In An In Vitro Model Of Ruminal Acidosis, Jorge L. Bonilla Urbina

Electronic Theses and Dissertations

The objective of this study was to evaluate the effect of essential oils and/or monensin on the concentration of volatile fatty acids (VFA), ammonia, and pH in a ruminal acidosis in vitro model. Red Angus steers (n = 4; BW = 435 ± 9 kg) with ruminal and duodenal cannulas were allocated in a 4 × 4 Latin square design. Treatments were 1) Fed no essential oils (RB) or monensin sodium (MON:CON); 2) Fed RB at a rate of 14 g daily with no Mon (RB+); 3) Fed no RB and fed Mon at 400 mg daily (MON+); 4) Fed …

Minds, Made Up Or Malleable? Covert Commands At The Eclipse Of Vaccine Communications, David H. Lee

Publications and Research

Abstract: Although millions have died from SARS-CoV-2 illness, COVID vaccines are credited with stemming the tide. Vaccinations and boosters significantly decrease the incidence of severe illness and death (even if they don’t usually prevent COVID-19 infection). Despite the success of global vaccination efforts, frequent reports of vaccine hesitance and refusal suggest that public health communications need improvement.

The chapter begins by acknowledging adverse events associated with COVID vaccination, including rare cases of anaphylaxis, GBS, and myocarditis. An inoculation-theoretic rationale is offered for this admission of vaccine risks: just as immune defenses mount when organisms are exposed to pathogens, so too …

The Role Of Mentorship In The Advancement Of Black Women In Higher Education Administrative Roles, Kimberley Colclough

Publications and Research

This research study focused on the lived experiences of Black women administrators in higher education institutions, the obstacles they face in pursuit of support and career advancement, and how they benefited from a relationship with a mentor. This descriptive phenomenological qualitative study was implemented by conducting in-depth interviews with a small sample of (6) six African American women administrators from various higher education institutions located in the Northeast, West Coast, and Midwest regions of the United States. This phenomenological qualitative study was conducted to understand and describe the lived experiences mentorship for a select group of Black women leaders in …

Don’T Do More With Less: Sustainable Work As A Management Value, Amanda Koziura

Library Faculty Research

Discusses a series of decisions a middle manager made to keep the work of her department sustainable and how she incorporated her values into her process.

The Legacy Of Dobbs: How The Supreme Court’S Decision To Review Gender- Affirming Care Bans Signals Its Intent To Eliminate The Protections Of Bostock And Obergefell Against Laws Designed To Discriminate Against Lgtbq+ Individuals, Jennifer S. Bard

Minnesota Journal of Law & Inequality

No abstract provided.

Design And Fabrication Of Tpms-Based Bone Scaffolds: A Cost-Effective Approach Using Plga-Nha And 3d-Printed Pva Molds, Yasser Ahmed

Theses and Dissertations

This thesis introduces a new cost-effective method for fabricating bone scaffolds from triply periodic minimal surface (TPMS) geometries—namely Gyroid and Diamond structure—using a poly(lactic-co-glycolic acid)-nanohydroxyapatite (PLGA-nHA) composite and 3D-printed polyvinyl alcohol (PVA) molds. Current TPMS scaffold fabrication techniques depend on costly laser-based techniques like selective laser sintering and thus remain inaccessible. Herein, we reveal a cost-effective and repeatable indirect approach via fused deposition modeling (FDM) for printing water-soluble PVA molds, which are then iteratively cast filled with a PLGA-nHA solution. The protocol was optimized through concentration modulation and controlled evaporation of the solvent for optimum infiltration and uniformity in the …

Concept Application Of Active Magnetic Bearing Technology For Offshore Horizontal Axis Wind Turbines, Isaac Ansah

Theses and Dissertations

Wind energy continues to lead the global transition to renewable power, driven by its minimal environmental impact and high scalability. Offshore wind farms benefit from stronger and consistent wind resources. However, these advantages are tempered by challenges related to installation, maintenance, and drivetrain reliability. Conventional bearings, which support the drivetrain system, are prone to wear related failures due to lubrication breakdown and mechanical fatigue from continuous loading. This study explores the integration of Active Magnetic Bearings as an alternative to conventional bearings in offshore Horizontal Axis Wind Turbines. Using advanced engineering simulation tools, the main driveshaft of a 130 MW …

Hillslope Degradation: Coarse Sands May Move Faster But Fines Dominate Flux, Robert Chance

Theses and Dissertations

Hillslopes are one of the most pervasive landforms on Earth yet the details of the processes that drive their evolution are not completely understood. Excluding boulders, the majority of mass and volume in soil-mantled hillslopes is composed of sand-sized and finer grains. Understanding the transport rates and governing transport mechanisms of these grains is therefore key to advancing our knowledge of hillslope evolution.This research investigates sand-sized sediment transport through field experiments at 20 sites tracking surface sediment motion and 11 sites measuring depth-dependent transport. These empirical results are compared against outputs from a novel grain-based hillslope diffusion simulation (GBHDS), which …