Digital Commons Network^™

Sports Science: An Entrepreneurial Venture, Nicole J. Jones

Senior Honors Projects

In sports science, ensuring maximum athlete safety and optimizing data utilization are pivotal yet leave room for further work. My project, Unbeaten SafeWare, addresses these critical issues by focusing on two primary concerns: preventing heat-related and cardiac illnesses, which are significant causes of athlete fatalities, and enhancing the transparency and utility of sports data. This initiative involves developing a shirt integrated with sensors to monitor vital signs and an athlete management system to handle data input, storage, analysis, and accessibility for athletes.

The project has advanced through the efforts of a multidisciplinary team, which includes biomedical engineering undergraduates, two faculty …

Spectrophotometric Device For Early Malaria Detection, Adrian Wasylewski, Christopher Faber, Victor Lin

Honors Thesis

Malaria remains a significant global health challenge, with millions of cases reported annually, particularly in resource-limited regions. Timely and accurate diagnosis is crucial for effective treatment and disease management. Unfortunately, there are still significant challenges in existing testing procedures, which are often expensive, inaccurate, or impractical for mass testing. While spectrophotometry has been proposed as a detection tool for other tropical diseases such as dengue fever, no prototype has been made for any disease, including malaria. This report addresses this gap by presenting a spectrophotometric-based malaria detection tool that achieves high sensitivity at a low cost. The inclusion of a …

Mapping Star Formation Histories In Nearby Galaxies To Source Lyman Continuum Photon Escape, Katie Kudla '24

Engineering & Physics Student Scholarship

Katie Kudla ’24, Physics major
Faculty Mentor: Dr. Joseph Ribaudo, Engineering and Physics

Teacher Candidate Perceptions Of Stem Following An Informal Learning Experience, Megan Rismiller

Honors Projects

In this study, six teacher candidates are interviewed to discover their perceptions of teaching integrated STEM following an informal learning experience. As team leaders at a summer STEM camp, teacher candidates make observations and connections to STEM content areas and practices. Teacher candidates reflect on the behaviors of themselves and their students throughout the process, and note their discoveries about STEM in the classroom setting. They report these findings through a series of questions during a semi-structured interview. Findings show teacher candidates perceive STEM education as a method for engaging learning experiences, as a route to engage students in social-emotional …

High Powered Rocket Modification, Joshua Gage

SACAD: Scholarly Activities

Rocketry has always been a fun challenge for me. Since not only was I able to learn something new every time I did it, but I was able to do something with my hands as well. One area that has been very challenging for me is how to put a tracker onto a rocket that has no electronics bay. And studying for the L2 Certification tests. And this poster shows my thoughts and process I did to pass my L2 Certification Flight.

Igniting Passion:​ A Detailed Journey Through Rocketry Course Activities, Krish M. Patel, Hannah Caycedo, Joshua Gage, Josi Maness, Kevin Park, Mufeng Shen

SACAD: Scholarly Activities

This course is a semester-long adventure in rocketry, led by Dr. Paul Adams. It covers everything about building and flying rockets, starting from the basics to more advanced rocketry. Students learn how to build rockets and use equipment used I payload systems like and altimeter and a GPS. We also learned about the importance of safety involved with building and launching rockets.

Physics Alumni Newsletter Spring 2024, Terry Goforth

Physics Alumni Newsletter

Physics Alumni Newsletter

The Physics Alumni Newsletter is produced by the SWOSU Physics Department.

Our Engineering Physics students are recruited in fields such as electronics, aerospace, mechanical engineering, petroleum engineering and software engineering. Graduates also have careers in meteorology, architecture, education and more.

Design And Development Of Deep Eutectic Solvent Assisted Pressurized Liquid Extraction Of Nutraceuticals, Harish Babu B

Theses and Dissertations

Over recent decades, the usage of nutraceuticals has been exponentially increasing as alternates to pharmaceutical drugs. However, extraction of these therapeutically significant compounds from their corresponding sources requires the use of volatile organic solvents like hexane, ethanol and chloroform which increases the global warming potential. This problem has been overcome in recent decades by the use of supercritical carbon dioxide (SCO2) as the extraction solvent. Since SCO2 is non-polar in nature, the extraction of polar compounds with better efficiency is questionable.

On the other hand, modernization in solvent engineering has created generations of green solvents like ionic liquids (ILs), deep …

Clpfudatabase: A Suite Of R Packages For Energy Conversion Chain Analysis, Matthew Kuperus Heun, Zeke Marshall, Emmanuel Aramendia

University Faculty Publications and Creative Works

Energy flowing through societal energy conversion chains (ECCs) enables economic activity and facilitates human flourishing. To understand economic growth and human well-being, the field of societal energy analysis (Common, 1976) evaluates ECCs from the primary stage (resources extracted from the environment, such as coal, oil, natural gas, wind, and solar), to the final stage (energy purchased by consumers, such as refined petroleum and electricity), to the useful stage (energy desired by the end user, such as heat, motion, and light), and sometimes to energy services (such as thermal comfort, transport, and illumination). Societal exergy analysis (SEA) (Ertesvåg, 2001), an extension …

Assessing The Relationship Between Contralateral Limb Synergy And Rock Climbing Efficiency, Jeromy Miramontes

Theses, Dissertations and Capstones

Rock climbing is a total body sport testing the mental and physical capabilities of athletes. Efficiency of movement can improve the ability of climbers to perform at a higher capacity and reduce the risk of injury. Data on the relationship between the continuous relative phase (CRP) of contralateral joint rotations and time to completion of a bouldering move are ways to assess rock climbing. This study aimed to assess this relationship, as well as the geometric entropy (GE) of the center of mass displacement when completing the bouldering move, and the metabolic efficiency over 4 min of bouldering. The study …

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

Browse all Theses and Dissertations

Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …

Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

Browse all Theses and Dissertations

Graph Neural Networks (GNNs) have increasingly gained popularity as tools for analyzing graph data in areas like biology, knowledge-graphs, social networks, biology, and recommendation systems. However, their vulnerability to adversarial attacks - small, targeted manipulations of graph structures or node features - raises serious concerns about their reliability in real-world applications. Existing defense strategies, such as adversarial training, edge filtering, low-rank approximations, and randomization-based methods, often suffer from high computational costs, scalability issues, or reduced clean-data performance. Unlike these methods, the proposed approach integrates multi-hop relationships, applies adaptive regularization, and maintains a balance between feature-based and structural embeddings, ensuring improved …

Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram

Browse all Theses and Dissertations

In today's technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively …

Investigating The Impact Of Stress And Irradiation Flux On Latent Track Formation In Tio2 Under Swift Heavy Ion Irradiation: A Phase Field Study, Ebrahim Ebrahimi

Browse all Theses and Dissertations

Swift Heavy Ions (SHI) irradiation, characterized by high kinetic energy ions, induces significant material/structural modification, e.g., latent track. However, the intricate interaction among various physics, i.e., mechanical stress, phase transition, and heat transfer, has been ignored in the continuum-based approaches in favor of simplicity. Here, we developed a two-dimensional coupled phase-field inelastic-thermal spike (PF-iTS) model to investigate the effect of thermal crosstalk, elastic energy, and irradiation flux on latent track formation. A particular focus is placed on investigating the influence of internal mechanical stress on latent track formation. Simulation results reveal a shift in critical stopping energy and a reduction …

Reinforcement Learning For Optimal Kicking Actions In Humanoid Robotics: Advancing Robotic Autonomy And Versatility, Suresh Dodda, Sathish Kumar Chintala, Sukender Reddy Mallreddy, Sharath Chandra Macha, Yashwanth Vasa, Sapan Bharadwaj Bonala, Navin Kamuni, Sujatha Alla

Engineering Management & Systems Engineering Faculty Publications

Acquiring the necessary skills to perform a work effectively and efficiently requires a significant investment of time and computing power. Previous applications of Reinforcement Learning (RL) for action optimization in humanoid robotics have shown how promising this technology is for moving robotics towards true autonomy and versatility. Therefore, this study offers the first use of RL to create an entirely optimal kicking action for the Alderbaran Nao robot. Kicking motions that were steady, precise, quick, and able to kick farther than any existing RoboCup squad were generated by optimizing for a multi-objective reward function. We demonstrate that the ideal kicking …

Data Driven Trade-Off Analysis For Cybersecurity, Goskel Kucukkaya, Murat Ozer, Murat Balci, Emrah Ugurlu

Engineering Management & Systems Engineering Faculty Publications

Trade-off analysis, a specialization of systems engineering, addresses design criteria like security, cost, performance, and compliance. Monte Carlo simulations are commonly employed to generate impact scenarios for trade-off analysis combined with solution alternatives that accommodate industry-specific considerations and uncertainties. In the cyber domain, this paper proposes a methodology for data-driven trade-off analysis in cybersecurity, leveraging industry reports as primary data sources using confidentiality, integrity, and availability as trade-off analysis objectives. Distribution functions are derived to manage and model uncertainties for various industries. The approach given in this study aims to facilitate informed choices and to enhance cybersecurity decision making and …

A Pade-Eno Flux Reconstruction For High-Speed Flows, Blake Martin

Browse all Theses and Dissertations

The development of high order numerical schemes has been instrumental in advancing computational fluid dynamics (CFD), particularly for applications requiring high resolution of discontinuities and complex flow phenomena prevalent in high-speed flows. This thesis introduces the Pade-ENO scheme, a high-order method that integrates Essentially Non-Oscillatory (ENO) techniques with compact Pade stencils to achieve superior accuracy, up to 7th order, while maintaining stability in harsh environments. The scheme’s performance is evaluated through benchmark tests, including the advection equation, Burgers’ equation, and the Euler equations. For high Mach number flows, such as the sod shock tube the Pade-ENO method demonstrates its ability …

Advanced Digital Wideband Receiver Design: High Dynamic Range And Enhanced Multi-Signal Detection With Fpga-Based Custom Fft And Nyquist Folding, Kiran Jayarama

Browse all Theses and Dissertations

In modern wideband receiver standards, efficient frequency spectrum utilization is essential to meet demands for high data rates, reduced latency, and enhanced connectivity. The Fast Fourier Transform (FFT) stands as a pivotal technology, particularly in radar signal processing, where it supports tasks such as target detection, range estimation, and velocity estimation by analyzing the frequency content of the received radar signals. This dissertation introduces the design of an advanced digital wideband receiver featuring a high dynamic range for multiple signals, with a focus on improved performance, compact size, and reduced power consumption, implemented on an FPGA using custom hardware. Key …

Digitalization Of Railway Transportation Through Ai-Powered Services: Digital Twin Trains, Salih Sarp, Murat Kuzlu, Vukica Jovanovic, Zekeriya Polat, Ozgur Guler

Engineering Technology Faculty Publications

Digitalization is a key concept that transformed the various industries through technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin (DT). Although innovations provided by the advancement of digitalization have paved the way for more efficient operations and products for transportation, the rail transportation sector struggles to keep up with the rest of the transportation industry, since trains are designed to last for decades, and the insufficient infrastructure investment leads to multiple railroad derailments across the globe. Therefore, the primary aim is to transform current railway systems into human-centric, adaptable, sustainable and future-proof networks, aligning with Industry …

An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

Browse all Theses and Dissertations

Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize …

An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

Browse all Theses and Dissertations

Traffic surveillance and enforcement heavily depend on the real-time detection of helmets and license plates, particularly in high-density urban environments. This study presents a dynamic and optimized lightweight model, the proposed G-YOLOv8n, designed for resource constrained edge devices like the Raspberry Pi. By integrating the GhostNet module into the YOLOv8n architecture, this research achieves a nearly 50% reduction in model size and computational load, while maintaining comparable detection accuracy to the original YOLOv8n. These enhancements enable real-time processing capabilities crucial for traffic monitoring operations. The growing demand for real-time, low-power solutions in intelligent transportation systems necessitates lightweight, efficient detection models. …

Integrative Machine Learning Approaches For Enhanced Classification Of Genomic Sequences: A Next-Generation Sequencing Perspective, Sujatha Alla, Nagesh Bheesetty, Sai Gireesh Komaragiri, Prasanthi Chidipudi, Joshit Mohanty, Sathish Kumar Chintala, Jubin Thomas, Jayapal Vummadi, Hemanth Volikatla, Navin Kamuni

Engineering Management & Systems Engineering Faculty Publications

The advent of Next-Generation Sequencing (NGS) techniques has revolutionized genomic research by enabling the rapid sequencing of DNA and RNA. This data can be used for various applications, including genome sequencing, transcriptome profiling, metagenomics, and epigenetics studies. For this study, DNA classifier dataset was extracted from UCI repository of machine learning databases. This vast amount of genomic data necessitates the development of sophisticated machine learning (ML) models for effective classification and analysis. This study presents a comprehensive comparison of various ML models, including Support Vector Machines (SVM), Random Forests (RF), and Neural Networks (NNs), approaches, in classifying genomic data. We …

Project Control System (Pcs) Implementation In Engineering And Construction Projects: An Empirical Study In Saudi's Petroleum And Chemical Industry, Sahar Jawad, Ann Ledwith, Rashid Khan

School of Cybersecurity Faculty Publications

Purpose

There is growing recognition that effective project control systems (PCS) are critical to the success of projects. The relationship between the individual elements of PCS and successfully achieving project objectives has yet to be explored. This research investigates the enablers and barriers that influence the elements of PCS success and drive project objectives.

Design/methodology/approach

This study adopts a mixed approach of descriptive analysis and regression models to explore the impact of six PCS elements on project outcomes. Petroleum and chemical projects in Saudi Arabia were selected as a case study to validate the research model.

Findings

Data from a …

Void Fraction And Quality Correlation Analysis Using The Separated Flow Model For Pulsed-Power Heat Loads, Zachary Joseph Carner

Browse all Theses and Dissertations

Aircraft platforms are continually upgraded with increasingly high quantities of high-powered electronics. As such, efficient thermal management systems are crucially important to overcome system instabilities and support pulsed power profiles. To create effective thermal control systems, it is imperative to thoroughly examine and account for a variety of potential system behaviors caused by transient changes in the flow regime. If left unconstrained, these changes will create thermal instabilities, which can severely damage electronics and hurt the overall reliability of the aircraft. These instabilities can be described by both void fraction and quality. Electrical Capacitance Tomography (ECT) allows for the collection …

Integrating Knowledge Graphs With Large Language Models For Natural Language Querying, Rakesh Kandula

Browse all Theses and Dissertations

This research explores the integration of knowledge graphs with large language models that have already been trained on a vast pool of unstructured text data. Large language models trained on this type of data have a tendency to hallucinate and produce factually inaccurate results. This behavior is primarily due to the data being trained is unstructured and huge text corpus, and large language model uses predictive text analysis methods to obtain a response. These issues can be addressed by applying Retrieval Augmented Generation and Fine-tuning to large language models, employing an underlying domainspecific knowledge graph. Integrating knowledge graph and large …

Prediction Interpretations Of Ensemble Models In Chronic Kidney Disease Using Explainable Ai, K M Tawsik Jawad

Browse all Theses and Dissertations

Chronic Kidney Disease (CKD) poses significant health and financial threat to millions of patients all around the world. The irreversible nature of this disease not just leads to comorbid diseases like Diabetes Mellitus, Hypertension, Anemia, Bone Disease, Neurological Implants etc. It can permanently damage the kidney by progressing to Acute Kidney Injury (AKI) or End Stage Renal Diseases (ESRD). The risk factors of CKD become more dangerous as patients suffering from it have little to no idea about the presence of CKD in their body until it takes the shape of AKI or ESRD. There are severe economic burdens for …

Biomechanical Simulation Of Cardiovascular Implantable Electronic Device Leads With Residual Properties, Anmar Mahdi Salih

Browse all Theses and Dissertations

Implantable leads used in pacemakers, defibrillators, and cardiac resynchronization therapy are designed for in-vivo applications, yet their longevity is inevitably shaped by the conditions within the human body. The mechanical behavior of these leads can be affected over time, necessitating the evaluation of their residual properties. Two main insulators, silicone, and polyurethane are commonly used for the outer insulation of cardiac leads. Understanding the long-term performance of these insulators is crucial for ensuring the reliability and safety of cardiac implantable devices. The research aims to assess the long-term mechanical properties and performance of implantable leads utilized in cardiovascular implantable electronic …

An Ml-Assisted Golden-Free Hardware Trojan Localization And Detection Approach For Trusted Microelectronics, Ashutosh Ghimire

Browse all Theses and Dissertations

Hardware Trojans are malicious circuits, hidden in integrated circuits (ICs) which pose a significant threat to security. Detection of hardware Trojans is important to build trust, verify, and make the semiconductor ICs process secure. The existing hardware Trojan detection methods are generally destructive, require intricate comparisons, or require a long time for reverse engineering. In the initial phase of this study, the substitution of supervised hardware Trojan detection methods in ASICs chips is explored with unsupervised approaches, thereby eliminating the dependence on golden references. The Trojan detection uses a ring oscillator (RO) based on NAND as the power monitor. Frequency …

Investigation Of Spalart-Allmaras Turbulence Model For Vortex Flows, Abigail Rayna Kerestes

Browse all Theses and Dissertations

Conventional turbulence models often predict behaviors opposite as to what is observed in flows subject to rotation. In this type of flow scenario, rotation typically induces turbulence suppression. To address this limitation, a modification to the Spalart Allmaras Model with Rotation Correction (SA-R) was proposed to enhance the original Spalart Allmaras Model’s sensitivity to rotation and curvature. To test the validity and accuracy of this modification, two cases were investigated. The first case involved an axisymmetric rotating pipe. A Reynolds Number of 37,000 was implemented and the initial and boundary conditions established by Zaets et. al. were utilized. Initially non-rotating, …

Understanding Impact Of Graph Structure On Knowledge Graph Embedding, Brandon Dave

Browse all Theses and Dissertations

The effectiveness of a deployed knowledge graph is commonly evaluated with defined use-cases from domain experts. This poses challenges during the development cycle in determining how to represent data. Developers of a knowledge graph can optionally include semantics into a knowledge graph by abstracting the data representation in such a way that mirrors information as it exists in the real world. Consequently, the abstraction is represented by additional layers, resulting in performant differences in knowledge graph embedding; such as, the embedded model's ability to infer facts between entities through link predictions. This thesis presents a comprehensive analysis of the performance …