Digital Commons Network^™

Light-Assisted 3d Printing Of Continuous Carbon Fiber Reinforced Thermoset Composites, Md Zahirul Islam

Mechanical Engineering and Applied Mechanics

Polymer 3D printing has become an emerging manufacturing technique, due to its design flexibility, however its application to produce structural components is still limited due to the poor mechanical strength and thermal stability of most 3D printed parts. Because of the superior mechanical strength of carbon fiber, 3D printing of continuous carbon fiber reinforced thermoset composites have recently been studied overcome this barrier of mechanical strength and thermal stability. Light- curing based 3D printing of continuous carbon fiber shows a promising potential, however this process also has limitations in making custom object due to fiber loop creation as the nozzle …

An Examination Of Selected Chinese Ethnic Choral Music From Inner Mongolia, Yunnan Province, And Central Plains Area, Kewen Cheng

Music

The landscape of choral music in modern China is characterized by a rich and varied repertoire, despite its relatively brief history within the nation. Diverging from the trajectories of traditionally Christian countries, the evolution of Chinese choral music has been shaped by unique historical, social, religious, and folk music traditions. This paper embarks on an examination of selected Chinese ethnic choral music, commencing with an overview of the development path of Chinese choral music in Chapter One. It then delves into an analysis of choral traditions across three distinct ethnic regions in the following three chapters. Each of them is …

Improving Nitrogen Management, Including The Use Of Cover Crops, In North Dakota Cropping Systems, Brady Goettl

Natural Resources Management

Crop production in North Dakota covers nearly 64 million ha, playing a crucial role in the state’s economy. However, wide-spread agriculture also poses environmental risks resulting from soil erosion and loss of N to ground and surface water. To address these concerns, agriculturists must adopt practices to decrease soil erosion and responsibly manage N. Between 2020 and 2024, several studies in North Dakota focused on improving N management in two-row malting barley (Hordeum vulgare L.), explored opportunities for cover crop integration, and assessed the rotational impact cover crops on crop productivity and soil factors. Research indicated N recommendations ranging from …

Identification, Investigation, And Spatial Analysis Of Various Contributing Factors To Crash And Injury Severity In Different Crash Types, Ihsan Khan

Transportation Logistics and Finance

This dissertation had three main objectives related to improving road safety by investigating factors that contribute to injury severity in different types of single-vehicle crashes. The first objective was to develop a generalized ordered logit model to examine factors affecting injury severity of occupants in single-vehicle rollover crashes using 5 years of U.S. crash data from 2012-2016. Results showed likelihood of serious/fatal injuries increased in rollovers with occupant ejection, speeding, higher speed limits, roadside/median rollovers, undulating terrain, blacktop surfaces, rural roads, evenings, weekdays, older drivers, lack of occupant protection, previous driver crashes, distracted/aggressive driving, and passenger cars. Airbag deployment reduced …

Graph Two-Sample Test Via Empirical Likelihood, Ting Zou

Statistics

In the past two decades, there has been a notable surge in network data. This proliferation has spurred significant advancements in methods for analyzing networks across various disciplines, including computer science, information sciences, biology, bioinformatics, physics, economics, sociology, and health science. Graph two-sample hypothesis testing, aimed at discerning differences between two populations of networks, arises naturally in diverse scenarios. In this paper, we delve into the essential yet intricate task of testing for equivalence between two networks. There are many testing procedures available. For instance, the t-test based on subgraph counts is one of the methods. In this paper, we …

Monte-Carlo Method For Identifying Aberrantly Expressed Genes In Cancer, Matthew M. Beltran

Theses and Dissertations

Gene expression provides insight into the functional variations on the cellular level that shape biological phenomena. Several recent sequencing technologies have produced an abundance of expression profiles spurring entirely new disciplines of biological study. With this myriad of data, the new task is deciding how to assess and extract meaningful insights. Identifying genes with expression changes in disease conditions is often the first step in finding potential biomarkers for diagnosis, and targets for pharmaceutical treatments. Parametric statistical tests at the individual gene level have been the conventional approach for finding differentially expressed genes. These tests exhibit high statistical power but …

Mass Political Murder: What And Where Is The Hate?, Clark Mccauley

Psychology Faculty Research and Scholarship

This article explores the meaning and importance of hate in intergroup conflict, especially in conflict that moves to genocide or politicide. Review of controversies in defining hate leads to definition of hate as an extreme form of negative identification that includes perception of bad essence. Negative identification is inverse caring for others, as seen in studies of schadenfreude and gluckschmerz. Studies of dehumanization suggest that two forms of bad essence can be distinguished: evil human (entitativity essentializing) and infrahuman animal (natural kind essentializing). Studies also show that those who essentialize more are more ready to punish indiscriminately all …

Prevalence And Determinants Of Diarrhoea And Acute Respiratory Infections Among Children Aged Under Five Years In West Africa: Evidence From Demographic And Health Surveys, Derrick Nyantakyi Owusu, Henry Ofori Duah, Duah Dwomoh, Yakubu Alhassan

College of Nursing Faculty Research and Publications

Background Diarrhoea and pneumonia are the leading causes of morbidity and mortality in children aged < 5 y (under five) globally. This study sought to investigate the prevalence and determinants of diarrhoea and acute respiratory infections (ARIs) among children under five in West Africa. Methods The most recent demographic and health survey (DHS) standard for 13 West African countries was used in the study. We calculated the prevalence of diarrhoea and ARIs (2 wk prior to the survey) and performed multivariable complex logistic regression analysis to identify possible predictors of diarrhoea and ARIs. Results The weighted prevalence of diarrhoea and ARI was 13.7% and 15.9%, respectively. The prevalence of comorbid diarrhoea and ARI was 4.4%. Children aged < 2 y (p< 0.001), mothers aged < 30 y (p< 0.003), mothers without formal education (p< 0.001), poor households (p< 0.001) and poor nutritional status, wasting (p=0.005) and underweight (p< 0.001), were the independent predictors of diarrhoea. The independent predictors of ARIs were children with no childhood vaccinations (p=0.002), use of solid fuel in the household (p=0.007), being underweight (p=0.05) and diarrhoea (p< 0.001). Conclusions The findings imply the need for holistic public health interventions such as increased vaccination coverage, population-based nutritional programmes and campaigns on the use of cleaner cooking fuel targeted at high-risk subgroups in the population to reduce the burden and adverse effects of diarrhoea and ARIs in the West African region.

Process Of Maintaining Self In Individuals Living With Systemic Sclerosis: A Grounded Theory Study Of American Women, Donald D. Miller, Jennifer J. Doering

College of Nursing Faculty Research and Publications

Background: People with chronic illnesses may struggle to adapt psychologically to the illness experience and have feelings of identity loss, self-diminishment, and biographical disruption. This may limit people’s ability to engage in optimal selfmanagement. Systemic sclerosis is a debilitating, stigmatizing, and life-limiting progressive chronic illness with significant disfiguring effects. Little is known about the identity management process in people with disfiguring and debilitating conditions such as systemic sclerosis.

Purpose: The purpose of this study was to generate a grounded theory explicating the process of maintaining a sense of self in people living with systemic sclerosis.

Methods: Fifteen women with systemic …

Enhancement Pattern Mapping For Early Detection Of Hepatocellular Carcinoma In Patients With Cirrhosis, Newsha Nikzad, David Thomas Fuentes, Millicent Roach, Tasadduk Chowdhury, Matthew Cagley, Mohamed Badawy, Ahmed Elkhesen, Manal Hassan, Khaled M Elsayes, Laura Beretta, Eugene Jon Koay, Prasun Kumar Jalal

Faculty, Staff and Student Publications

BACKGROUND AND AIMS: Limited methods exist to accurately characterize the risk of malignant progression of liver lesions. Enhancement pattern mapping (EPM) measures voxel-based root mean square deviation (RMSD) of parenchyma and the contrast-to-noise (CNR) ratio enhances in malignant lesions. This study investigates the utilization of EPM to differentiate between HCC versus cirrhotic parenchyma with and without benign lesions.

METHODS: Patients with cirrhosis undergoing MRI surveillance were studied prospectively. Cases (n=48) were defined as patients with LI-RADS 3 and 4 lesions who developed HCC during surveillance. Controls (n=99) were patients with and without LI-RADS 3 and 4 lesions who did not …

Exploration Of Dopaminergic And Serotonergic Pathways Utilizing Pleiotropic Agents, Charles B. Jones Iii

Theses and Dissertations

2-(Benzoyl)piperidines (BPs) are at a crossroads of therapy and abuse. These compounds are a hybrid between the ADHD medication methylphenidate and abused stimulant cathinones. Here we use computational molecular modeling, synthesis, mutagenesis, and epifluorescence microscopy to further improve the understanding of and potential treatments for several neuropsychiatric disorders. Utilizing the BP scaffold and controlling chirality and aryl substitution, these agents were examined at DAT and SERT. From these investigations it has now been established that R isomer BPs are favored for DAT while S isomer BPs are favored for SERT. Aryl-substitution can greatly affect the selectivity of these agents with …

Hack24f: Careplus: Ai Chatbot, Arianna Fernandez, Sophia Cherkaoui

Paul English Applied Artificial Intelligence (AI) Institute Publications

90% of patients Google symptoms before they talk to their doctor. What are we developing? An app that asks for user symptoms and personal information, users AI to determine medical conditions and provides a plan of action, including recommended medications and suggested doctors to visit.

Hack24f: Llms And World Knowledge Problems, Chengtaon Lin, Joseph Lobo, Huuthanhvy Nguyen, Brian Santos, Ting Xu

Paul English Applied Artificial Intelligence (AI) Institute Publications

While AI “hallucinations” have been widely publicized by the media as shortcomings of Large Language Models (LLMs), there is much less awareness about shortcomings that derive from the potential lack of understanding of the world, from a potential lack of commonsense knowledge. We want to raise awareness among our student body about the potential fallibility of LLMs when they the problems that they need to solve require a basic understanding of the world. We hope that this awareness will prompt everyone to question more carefully the outputs generated by LLMs.

Hack24f: The Empathy Architects, Michael Rejtig, Ajanee Igharo, Emma Kelminson, Jackson Comeau

Paul English Applied Artificial Intelligence (AI) Institute Publications

Problem Statement: We want to help patients from underserved communities feel more safe, respected and comfortable in interacting with healthcare providers during the initial in-person encounters while also helping providers build empathy and implement socio-emotionally competent communication behaviors. Solution: Empathy Evolution is an AI-powered role-playing game that helps patients simulate their ideal communication with healthcare providers.

Accelerated Hypofractionated Magnetic Resonance Guided Adaptive Radiation Therapy For Ultracentral Lung Tumors, Alonso La Rosa De Los Rios, Kathryn Mittauer, Nema Bassiri-Gharb, Amy Rzepczynski, Michael Chuong, Sreenija Yarlagadda, Tugce Kutuk, Nicole Mcallister, Matthew Hall, Alonso Gutierrez, Ranjini Tolakanahalli, Minesh Mehta, Rupesh Kotecha

All Publications

No abstract provided.

Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …

Different Visions From Biosview: A Brief Report, Lucas N. Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

In this collaborative research endeavor at the intersection of biological safety and cybersecurity for BiosView labs, the authors highlight their engagement with a diverse student cohort. The chapter delves into the motivation behind collaborations extending beyond traditional academic research environments, emphasizing inclusivity. The meticulous examination of student demographics, including gender, self-reported ethnicity, and national origin, is detailed in the methodology. A student-centric approach is central to the exploration, focusing on aligning teaching and management styles with unique student needs. The chapter elaborates on effective teaching methodologies and management practices tailored for BiosView labs. A dedicated section emphasizes the purpose of …

Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …

Deapsecure Computational Training For Cybersecurity: Progress Toward Widespread Community Adoption, Wirawan Purwanto, Bahador Dodge, Karina Arcaute, Masha Sosonkina, Hongyi Wu

Electrical & Computer Engineering Faculty Publications

The Data-Enabled Advanced Computational Training Program for Cybersecurity Research and Education (DeapSECURE) is a non-degree training consisting of six modules covering a broad range of cyberinfrastructure techniques, including high performance computing, big data, machine learning and advanced cryptography, aimed at reducing the gap between current cybersecurity curricula and requirements needed for advanced research and industrial projects. Since 2020, these lesson modules have been updated and retooled to suit fully-online delivery. Hands-on activities were reformatted to accommodate self-paced learning. In this paper, we summarize the four years of the project comparing in-person and on-line only instruction methods as well as outlining …

Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn

Electrical & Computer Engineering Faculty Publications

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. …

Toward Inclusivity: Rethinking Islamophobic Content Classification In The Digital Age, Esraa Aldreabi, Mukul Dev Chhangani, Khawlah M. Harahsheh, Justin M. Lee, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In this paper, we implement a comprehensive three-class system to categorize social media discussions about Islam and Muslims, enhancing the typical binary approach. These classes are: I) General Discourse About Islam and Muslims, II) Criticism of Islamic Teachings and Figures, and III) Comments Against Muslims. These categories are designed to balance the nuances of free speech while protecting diverse groups like Muslims, ex-Muslims, LGBTQ+ communities, and atheists. By utilizing machine learning and employing transformer-based models, we analyze the distribution and characteristics of these classes in social media content. Our findings reveal distinct patterns of user engagement with topics related to …

Runtime Performance Of Gamess Quantum Chemistry Application Offloaded To Gpus, Masha Sosonkina, Gabriel Mateescu, Peng Xu, Tosaporn Sattasathuchana, Buu Pham, Mark S. Gordon, Sarom S. Leang

Electrical & Computer Engineering Faculty Publications

Computational chemistry is at the forefront of solving urgent societal problems, such as polymer upcycling and carbon capture. The complexity of modeling these processes at appropriate length and time scales is mainly manifested in the number and types of chemical species involved in the reactions and may require models of several thousand atoms and large basis sets to accurately capture the chemical complexity and heterogeneity in the physical and chemical processes. The quantum chemistry package General Atomic and Molecular Electronic Structure System (GAMESS) has a wide array of methods that can efficiently and accurately treat complex chemical systems. In this …

Real-Time Spectroscopic Ellipsometry For Flux Calibrations In Multi-Source Co-Evaporation Of Thin Films: Application To Rate Variations In Cuinse₂ Deposition, Dhurba R. Sapkota, Balaji Ramanujam, Puja Pradhan, Mohammed A. Razooqi Alaani, Ambalanath Shan, Michael J. Heben, Sylvain Marsillac, Nikolas J. Podraza, Robert W. Collins

Electrical & Computer Engineering Faculty Publications

Flux calibrations in multi-source thermal co-evaporation of thin films have been developed based on real-time spectroscopic ellipsometry (RTSE) measurements. This methodology has been applied to fabricate CuInSe₂ (CIS) thin film photovoltaic (PV) absorbers, as an illustrative example, and their properties as functions of deposition rate have been studied. In this example, multiple Cu layers are deposited step-wise onto the same Si wafer substrate at different Cu evaporation source temperatures (TCu). Multiple In₂Se₃ layers are deposited similarly at different In source temperatures (TIn). Using RTSE, the Cu and In₂Se₃ deposition rates are determined as …

Visual Representation Based Creative Problem Solving (Cps) For Microelectronic Course, Gon Namkoong, Tian Luo

Electrical & Computer Engineering Faculty Publications

Microelectronic technologies become the basis for many modern revolutions and require technological innovation and creative transformation of new knowledge into products. This demands our engineering students to be more innovative and creative than ever. It also requires solid resources and strategies to cultivate higher-order thinking skills for engineering students. Presently, microelectronic course covers a wide range of topic areas, including basic semiconductor, pn junction diode, metal oxide semiconductor field effect transistor (MOSFET) and bipolar junction transistor (BJT) circuits. A traditional teaching strategy in a typical educational setting encompasses instructors delivering lectures to teach the basics and principles of microelectronic devices …

Predictions Of Lattice Parameters In Niti High-Entropy Shape-Memory Alloys Using Different Machine Learning Models, Tu-Ngoc Lam, Jiajun Jiang, Min-Cheng Hsu, Shr-Ruei Tsai, Mao-Yuan Luo, Shuo-Ting Hsu, Wen-Jay Lee, Chung-Hao Chen, E-Wen Huang

Electrical & Computer Engineering Faculty Publications

This work applied three machine learning (ML) models—linear regression (LR), random forest (RF), and support vector regression (SVR)—to predict the lattice parameters of the monoclinic B19′ phase in two distinct training datasets: previously published ZrO₂-based shape-memory ceramics (SMCs) and NiTi-based high-entropy shape-memory alloys (HESMAs). Our findings showed that LR provided the most accurate predictions for a_c, a_m, b_m, and c_m in NiTi-based HESMAs, while RF excelled in computing βm for both datasets. SVR disclosed the largest deviation between the predicted and actual values of lattice parameters for both training datasets. A combination approach …

Parametrization Of Fluid Models For Electrical Breakdown Of Nitrogen At Atmospheric Pressure, Shirshak K. Dhali

Electrical & Computer Engineering Faculty Publications

In the transient phase of an atmospheric pressure discharge, the avalanche turns into a streamer discharge with time. Hydrodynamic fluid models are frequently used to describe the formation and propagation of streamers, where charge particle transport is dominated by the creation of space charge. The required electron transport data and rate coefficients for the fluid model are parameterized using the local mean energy approximation (LMEA) and the local field approximation (LFA). In atmospheric pressure applications, the excited species produced in the electrical discharge determine the subsequent conversion chemistry. We performed the fluid model simulation of streamers in nitrogen gas at …

Scene Classification Of Remote Sensing Image Based On Multi-Path Reconfigurable Neural Network, Wenyi Hu, Chunjie Lan, Tian Chen, Shan Liu, Lirong Yin, Lei Wang

Electrical & Computer Engineering Faculty Publications

Land image recognition and classification and land environment detection are important research fields in remote sensing applications. Because of the diversity and complexity of different tasks of land environment recognition and classification, it is difficult for researchers to use a single model to achieve the best performance in scene classification of multiple remote sensing land images. Therefore, to determine which model is the best for the current recognition classification tasks, it is often necessary to select and experiment with many different models. However, finding the optimal model is accompanied by an increase in trial-and-error costs and is a waste of …

Paper-Based Dna Biosensor For Rapid And Selective Detection Of Mir-21, Alexander Hunt, Sri Ramulu Torati, Gymama Slaughter

Electrical & Computer Engineering Faculty Publications

Cancer is the second leading cause of death globally, with 9.7 million fatalities in 2022. While routine screenings are vital for early detection, healthcare disparities persist, highlighting the need for equitable solutions. Recent advancements in cancer biomarker identification, particularly microRNAs (miRs), have improved early detection. MiR-21 is notably overexpressed in various cancers and can be a valuable diagnostic tool. Traditional detection methods, though accurate, are costly and complex, limiting their use in resource-limited settings. Paper-based electrochemical biosensors offer a promising alternative, providing cost-effective, sensitive, and rapid diagnostics suitable for point-of-care use. This study introduces an innovative electrochemical paper-based biosensor that …

An Overview Of The Relationships Between The Food Industry And Nanotechnology, Mehdi Koushki, Nasrin Amiri-Dashatan, Hossein Pourghadamyari, Hadi Khodabandehloo, Fatemeh Bagheri, Masoumeh Farahani, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

Background and Objective: Due to the growth of the global population, food demands are increasing. Hence, the need to develop more efficient methods for producing better quality, safer, and more sustainable food seems essential. In the past decades, the use of nanoscale materials has increased greatly due to the unique chemical, physical, and biological characteristics of nanomaterials compared to bulk materials. This research presents nanotechnology role in improving sensorial properties (taste, appearance, and texture) and safety aspects as well as processing and packaging of foods. The use of nano-omics-based technologies and artificial intelligence-nanotechnology-based technologies in the food industry is also …

Integrated Machine Learning And Deep Learning Models For Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran

Electrical & Computer Engineering Faculty Publications

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the …