Digital Commons Network^™

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 …

Thermal Diffusivity And Acoustic Properties Of Nb Thin Films Studied By Time-Domain Thermoreflectance, Md. Obidul Islam, Hani Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

The thermal diffusion and acoustic properties of Nb impacts the thermal management of devices incorporating Nb thin films such as superconducting radiofrequency (SRF) cavities and superconducting high-speed electronic devices. The diffusion and acoustic properties of 200-800 nm thick Nb films deposited on Cu substrates were investigated using time-domain thermoreflectance (TDTR). The films were examined by X-ray diffraction, scanning electron microscopy, and atomic force microscopy. The grain size and thermal diffusivity increase with film thickness. The thermal diffusivity increased from 0.100± 0.002 cm²s^-1 to 0.237± 0.002 cm²s^-1 with the increase in film thickness from 200 …

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 …

Immersive Virtual Labs For Enhancing In-Person And Online Education, Yiyang Li, Yuzhong Shen, Charles I. Sukenik

Electrical & Computer Engineering Faculty Publications

Labs play a critical role in science and engineering education, offering practical insights and hands-on experience to students that can't be achieved through theoretical learning alone. With the continuous advancement in technology, education is being reshaped and many universities are now offering online programs. This shift in educational paradigm offers students access to a wider range of academic resources, without being limited by geographical boundaries, time constraints, among others. However, the rise of online education also brings unique challenges, such as lack of face-to-face interaction and limited hands-on learning experiences. Virtual labs, which allow students to conduct experiments in a …

Accelerating Cavity Fault Prediction Using Deep Learning At Jefferson Laboratory, Md M. Rahman, A. Carpenter, K. Iftekharuddin, C. Tennant

Electrical & Computer Engineering Faculty Publications

Accelerating cavities are an integral part of the continuous electron beam accelerator facility (CEBAF) at Jefferson Laboratory. When any of the over 400 cavities in CEBAF experiences a fault, it disrupts beam delivery to experimental user halls. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults. By utilizing pre-fault signals, we train a long short-term memory-convolutional neural network binary classifier to distinguish between radio-frequency (RF) signals during normal operation and RF signals indicative of impending faults. We optimize the model by adjusting the fault confidence threshold and implementing a multiple consecutive …

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 …

Generalized Functions In The Study Of Signals And Systems, Erik I. Verriest, Gunther Dirr, W. Steven Gray

Electrical & Computer Engineering Faculty Publications

We collect three instances where the theory of generalized functions may still make contributions to the study of signals and systems. In the first, a purely algebraic approach is presented for LTI-ODE's, in terms of two operators, D and T, respectively the differentiation operator and the multiplication-by-the-independent-variable operator. This formalism adds simplicity, a duality theory, and nicely generalizes to other classes of operator equations and their solutions. In the second part we extend the classical bilateral Laplace transform to include Bohl functions with support in ℝ by invoking Sato's hyperfunctions. Finally, in the third case we use the Colombeau algebra …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …

An Enhanced Real-Time Intrusion Detection Framework Using Federated Transfer Learning In Large-Scale Iot Networks, Khawlah Harahsheh, Malek Alzaqebah, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The exponential growth of Internet of Things (IoT) devices has introduced critical security challenges, particularly in scalability, privacy, and resource constraints. Traditional centralized intrusion detection systems (IDS) struggle to address these issues effectively. To overcome these limitations, this study proposes a novel Federated Transfer Learning (FTL)-based intrusion detection framework tailored for large-scale IoT networks. By integrating Federated Learning (FL) with Transfer Learning (TL), the framework enhances detection capabilities while ensuring data privacy and reducing communication overhead. The hybrid model incorporates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), attention mechanisms, and ensemble learning. To address the class imbalance, Synthetic …

A Review Of Emerging Sensor Technologies For Tank Inspection: A Focus On Lidar And Hyperspectral Imaging And Their Automation And Deployment, Sergio Pallas Enguita, Chung-Hao Chen, Samuel Kovacic

Electrical & Computer Engineering Faculty Publications

This paper reviews various sensor technologies for tank inspection, focusing on Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) as advanced solutions for corrosion detection. These technologies are evaluated alongside traditional methods such as ultrasonic, electromagnetic, and thermographic inspections. This review highlights their potential to enhance inspection accuracy, reduce the limitations of manual inspection, and support integrated data analysis for comprehensive asset management. Additionally, this paper proposes a pathway for automating these techniques to streamline inspection processes and improve implementation in practical applications.

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

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 …

Predictive Modeling Of Healthcare Traffic Using Machine Learning: A Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md. Rafid Hassan, Nondon Lal Dey, Md. Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Effective healthcare traffic management is critical for ensuring prompt medical services, particularly in emergencies where delays can have life-threatening consequences. This study conducts a comparative analysis of three popular machine learning models—Linear Regression, Decision Trees, and Random Forests—for predicting healthcare-related traffic volumes. Utilizing a comprehensive dataset from a metropolitan interstate traffic system, the models were evaluated based on key performance metrics, including Mean Squared Error (MSE), R² Score, and execution time. The findings demonstrate that the Random Forest model outperforms the others, offering superior predictive accuracy and efficiency. These insights are valuable for optimizing traffic management in healthcare, ultimately contributing …

Direct Measurement Of Microwave Loss In Nb Films For Superconducting Qubits, B. Abdisatarov, D. Bafia, A. Murthy, G. Eremeev, H. E. Elsayed-Ali, J. Lee, A. Netepenko, C. P. A. Carlos, S. Leith, G. J. Rosaz, A. Romanenko, A. Grassellino

Electrical & Computer Engineering Faculty Publications

Niobium films are a key component in modern two-dimensional superconducting qubits, yet their contribution to the total qubit decay rate is not fully understood. The presence of different layers of materials and interfaces makes it difficult to identify the dominant loss channels in present two-dimensional qubit designs. In this paper, we present the study that directly correlates measurements of RF losses in such films to material parameters by investigating a high-power impulse magnetron sputtered (HiPIMS) film atop a three-dimensional niobium superconducting radio frequency (SRF) resonator. By using a 3D SRF structure, we are able to isolate the niobium film loss …

Distance Estimation Based On Step Frequency Using Accelerometer Data, Rami M. Al-Naimat, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

In recent years, smartphone sensors have become one of the most important and easily available sensors to facilitate people's lives, especially in health care and positioning (indoor environments). However, the data coming from smartphone sensors can be distorted during the user’s movement such as irrelevant movements, walk mode, and speed of walking. This distortion (noise) impairs the estimated distance accuracy (accumulative error) which increases with increasing walking distance. In addition, the accuracy of the distance traveled is affected by the user's speed, as the speed affects the step length. This work proposes a novel approach for calculating step length in …

A Day In The Life Of A Beanhead, Patrick Potter

Mighty Pen Project Anthology & Archive

It's a beautiful spring morning at West Point Academy, and Cadet Potter is barefoot, late, and an easy target.

For freshman, or Beanheads, just trying to get by at West Point, life is tough. This short humorous memoir recounts a harrowing school day of one such cadet.

Articles, stories, and other compositions in this archive were written by participants in the Mighty Pen Project. The program, developed by author David L. Robbins, and in partnership with Virginia Commonwealth University and the Virginia War Memorial in Richmond, Virginia, offers veterans and their family members a customized twelve-week writing class, free of …

Confessions Of A Bomb Tech, Harry Mayer

Mighty Pen Project Anthology & Archive

An EOD technician, or bomb tech, recounts a render safe procedure conducted on a rainy December day. A study on luck, defusing bombs, and the many EOD protocols written in blood.

Articles, stories, and other compositions in this archive were written by participants in the Mighty Pen Project. The program, developed by author David L. Robbins, and in partnership with Virginia Commonwealth University and the Virginia War Memorial in Richmond, Virginia, offers veterans and their family members a customized twelve-week writing class, free of charge. The program encourages, supports, and assists participants in sharing their stories and experiences of military …

The Emerald Knights, Harry Mayer

Mighty Pen Project Anthology & Archive

A Marine is transferred to the Navy, and is forced to adapt to a drastically different set of rules and attitudes.

Articles, stories, and other compositions in this archive were written by participants in the Mighty Pen Project. The program, developed by author David L. Robbins, and in partnership with Virginia Commonwealth University and the Virginia War Memorial in Richmond, Virginia, offers veterans and their family members a customized twelve-week writing class, free of charge. The program encourages, supports, and assists participants in sharing their stories and experiences of military experience so both writer and audience may benefit.

Entrepreneurship, Resource Rents And Institutions, André Varella Mollick, Lu Sui

School of Economics and Finance Faculty Publications

The resource curse hypothesis supposes that rents generated by resource booms create opportunities for rent-seeking activities, which weaken innovation and economic development due to the potentially adverse effects in competitive markets. The quality of institutions is an important channel to transform society from a rent-seeking economy to an entrepreneurial economy. We address in this paper these channels by examining necessity, opportunity, productive and innovative measures of entrepreneurship. Using annual data from 2002 to 2017, we estimate the relationship between entrepreneurship and natural resource rents for 60 countries. Allowing for measures of institutions (corruption, government policies, and cultural and social norms) …

Table Top Roleplaying & Communication: Rituals, Rhetoric & Reclamation, Logan Cyrus Sayles

Undergraduate Scholarship

Broadly the topic is about the effect reclaiming a passionate will to play, act, and tell stories has on our lives and how doing so changes communication practices and the way people communicate. Narrowed further the focus is on the observable effects of Table Top Role Playing Games (linguistically narrowed down to the most popular Table Top Role Playing Game, Dungeons & Dragons) as a kind of social tool, with a further sub-focus on the stimulation and promotion of the following values and activities; acting, creative thinking, critical thinking, public speaking, self-confidence, self-empowerment, risk-taking, experimentation, active listening, teamwork/group work, storytelling, …

A Symbolic Interactionism’S Perspective Of The Attack On The U.S. Capitol., Odeli Hernandez

Undergraduate Scholarship

This paper aims to draw the line between Symbolic Interactionism and how it ties into the political world. Symbolic Interactionism states that humans interpret and assign meanings to symbols through social interaction, thus influencing their behaviors and shaping their realities. It is said that symbolic interactionism could only be applied to micro levels of social interactions, and not to something as big and intricate as politics. This paper will focus on a stand-alone event, the insurrection of the U.S. Capitol on January 6, 2021. Numerous symbols were displayed before, during, and after the attack on the U.S. Capitol, each of …

Rhetoric On Climate Change And The Effects On Public Perceptions, Hailey Mina Rose Dossey

Undergraduate Scholarship

One of the extraordinary challenges humans are facing today is climate change and over the past two decades, the majority of the world has moved from questioning its legitimacy to accepting it as fact. With most people having accepted climate change as a reality, the goal of climate communication has shifted from spreading awareness to motivating audiences to take action. Through this analytical discussion, we will look into effective and ineffective methods of rhetoric as it can help guide us to a better understanding of why certain rhetoric that has previously been successful in other fields may not be the …

The Online Education Landscape, Digital Tools, And Covid-19, Rosetta S. Burk

Undergraduate Scholarship

The 2020 COVID-19 pandemic has been the ultimate stress test for communication technologies, online learning methods, digital pedagogies, students and educators as the world faced school closures and emergency online learning situations during the pandemic lockdowns. There has been an influx in communication tools, digital paradigms, and teaching methodologies in addition to relying on existing digital tools and methods to continue students' education while unable to meet in person. The level of remote learning and digital movement is unprecedented and the results have been varied and illuminating. Research has been conducted on student attitudes in the immediate aftermath of the …

Radiation Induced Skin Fibrosis (Risf): Opportunity For Angiotensin Ii-Dependent Intervention, Patricia F. Boothe, Vidya P. Kumar, Yali Kong, Kan Wang, Howard Levinson, David Mu, Milton L. Brown

Department of Biomedical and Translational Sciences Faculty Publications

Medical procedures, such as radiation therapy, are a vital element in treating many cancers, significantly contributing to improved survival rates. However, a common long-term complication of such exposure is radiation-induced skin fibrosis (RISF), a complex condition that poses substantial physical and psychological challenges. Notably, about 50% of patients undergoing radiation therapy may achieve long-term remission, resulting in a significant number of survivors managing the aftereffects of their treatment. This article delves into the intricate relationship between RISF, reactive oxygen species (ROS), and angiotensin II (Ang II) signaling. It proposes the underlying mechanisms and examines potential treatments for mitigating skin fibrosis. …

Gene Expression Of Tight Junctions In Foreskin Is Not Affected By Hiv Pre-Exposure Prophylaxis, Emily L. Webb, Stefan Petkov, Heejin Yun, Laura Else, Limakatso Lebina, Jennifer Serwanga, Azure-Dee A.P. Pillay, Thabiso B. Seiphetlo, Susan Mugaba, Patricia Namubiru, Geoffrey Odoch, Daniel Opoka, Andrew S. Ssemata, Pontiano Kaleebu, Saye Khoo, Neil Martinson, Julie Fox, Clive M. Gray, Carolina Herrera, Francesca Chiodi

Department of Obstetrics & Gynecology Faculty Publications

Introduction: Tight junctions (TJs) serve as permeability filters between the internal and external cellular environment. A large number of proteins have been identified to be localized at the TJs. Due to limitations in tissue collection, TJs in the male genital tract have been understudied.

Methods: We analysed the transcriptomics of 132 TJ genes in foreskin tissue of men requesting voluntary medical male circumcision (VMMC) and enrolled in the Combined HIV Adolescent Prevention Study (CHAPS) trial conducted in South Africa and Uganda (NCT03986970). The trial evaluated the dose requirements for event-driven HIV pre-exposure prophylaxis (PrEP) with emtricitabine-tenofovir (FTC-TDF) or emtricitabine-tenofovir alafenamide …

Sign Here: How Parental Waivers Exceed The Bounds Of Parents' Fundamental Rights, Carissa Hansen

Mitchell Hamline Law Review

No abstract provided.

Minnesota's Misclassification Solutions Lead The Way But Leave Undocumented Workers Behind: How Minnesota Can Increase Worker Protections, Mckayla Murphy Zelaya

Mitchell Hamline Law Review

No abstract provided.