Engineering Commons^™

Transforming Organizational Cyber Security With Artificial Intelligence And Data-Driven Optimization, Soumyadeep Hore

USF Tampa Graduate Theses and Dissertations

This dissertation presents a comprehensive framework for enhancing organizational cybersecurity through data-driven intelligence. The research integrates multiple methodologies to tackle challenges in network intrusion detection and vulnerability management within cybersecurity operations centers (CSOCs). First, the research investigates vulnerability prioritization and mitigation techniques currently employed by CSOCs. To further streamline the vulnerability prioritization and mitigation process a machine learning (ML)-based Vulnerability Priority Scoring System (VPSS) is introduced, significantly improving the prioritization and mitigation of context-sensitive vulnerabilities. The VPSS outperforms traditional methods, reducing the cumulative vulnerability exposure score by up to 30% by considering both organizational context and vulnerability severity. Next, the …

Deep Learning Framework For Graph-Based Molecular Drug Discovery, Daniel Manu

Electrical and Computer Engineering ETDs

This dissertation proposes the GraphGANFed framework, which combines Graph Convolutional Networks (GCN), Generative Adversarial Networks (GAN), and Federated Learning (FL) to generate novel molecules while preserving data privacy. FL allows learning from distributed clients without sharing local datasets, GCN extracts molecular structural properties, and GAN generates new molecules retaining the learned properties. Extensive simulations on three benchmark datasets show GraphGANFed's effectiveness, producing molecules with high novelty (≈ 100) and diversity (> 0.9). Results indicate a trade-off among evaluation metrics, and the right dropout ratio avoids mode collapse. Additionally, Conditional GraphGANFed (cGraphGANFed) incorporates a critic network from Reinforcement Learning (RL) to …

Automating An Industrial Dishwashing System Using Hardware-In-The-Loop Plc Simulation With Factory I/O, Paniz Khanmohammadi Hazaveh, Nathir Rawashdeh, Dunsten M. X. Dsouza, Joshua Olusola, Joshua Albrecht, Eric Houck

Michigan Tech Publications

While industrial automation is growing in many industries, the hospitality industry has not seen much technological innovation recently. This leaves a wealth of potential for the implementation of automated solutions, especially at large-scale operations, like university dining halls or cruise ship restaurants. This paper details a student project developed in the advanced programmable logic controllers class. It is part of the master program in mechatronics. Students work in groups in a creative setting, where they learn to integrate various automation technologies and learn to write scientific publications. The project implements the automation of a student dining hall dishwashing system using …

Plc Multi-Robot Integration Via Ethernet For Human Operated Quality Sampling, Jeevan S. Devagiri, Paniz Khanmohammadi Hazaveh, Nathir Rawashdeh, Sai Revanth Reddy Dudipala, Pratik Mohan Desmuhk, Aditya Prasad Karmarkar

Michigan Tech Publications

In automation, quality control inspection is a critical requirement to ensure product standards. The goal of this work is to insure product quality without interrupting the production line flow. The multi-robot system presented, connects a programmable logic controller (PLC), as the main controller, to a conveyor belt and two FANUC industrial robotic arms via EtherNet/IP. Human interaction is implemented to pick a work piece from the moving conveyor and return it with a quality label. This label is used by the PLC to execute the correct robot action; either to return the inspected part to the conveyor or discard it …

A Robust Data-Driven Framework For Artificial Intelligent Systems, Quoc H. Nguyen

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) systems have demonstrated remarkable performance across various domains. However, their robustness remains a critical concern, particularly in terms of data and model reliability. This dissertation aims to address the challenges associated with building robust AI systems by focusing on two key aspects: data robustness and model robustness. Data robustness poses significant challenges, including data shift, concept shifting, limited and imbalanced datasets, and interoperability issues in IoT systems for data collection. Existing methods fall short in handling dynamic business objectives and evolving data landscapes effectively. To bridge these gaps, we propose an IoT framework that ensures interoperability, seamless …

Ai-Based Concept Inventories: Using Cognitive Diagnostic Computer Adaptivetesting In Lasso For Classroom Assessment, Jason Morphew, Amirreza Mehrabi, Ben Van Dusen, Jayson Nissen

School of Engineering Education Faculty Publications

Research-based concept inventories, such as the Force Concept Inventory, the Force and Motion Conceptual Evaluation, or the Energy and Momentum Conceptual Survey have played a pivotal role in designing and evaluating instruction in introductory physics courses. While these concept inventories have helped identify inequity in course instruction and have led to improved pedagogical methods, concerns about their ability to provide actionable feedback for formative assessment remain. In addition, using the same questions for both a pre- and a post- assessment leaves the potential for students learning the correct answers without undergoing conceptual change. To address these issues, we designed a …

Creative Insights Into Motion: Enhancing Human Activity Understanding With 3d Data Visualization And Annotation, Isaac Browen, Hector M. Camarillo-Abad, Franceli L. Cibrian, Trudi Di Qi

Engineering Faculty Articles and Research

This paper presents a novel 3D system for human motion analysis - Motion Data Visualization and Annotation (MoViAn). Designed to provide a comprehensive visual representation of 3D human motion data, MoViAn incorporates detailed visualization of gaze direction, hand movements, and object interactions, alongside an interactive interface for efficient data annotation. A user study involving eight participants indicates that MoViAn enables users to thoroughly explore and annotate human motion data, with System Usability Scale (SUS) results demonstrating a satisfactory usability level. The contribution of this paper lies in the development of an interactive and usable data analytics tool aimed at deepening …

Wip: A Novel Learning Log Application For Classifying Learning Events Using Bloom’S Taxonomy, Alex M. Phan, Jenna Metera, Sonia Fereidoona, Cham Yang, Minju Kim, Carolyn L. Sandoval, Phuong Truong

Psychology Faculty Articles and Research

Learning can be a daunting and challenging process, particularly in engineering. While cognitive models for learning such as Bloom's taxonomy have been developed since the 1950s and evidenced to be useful in designing engineering courses, these models are not commonly explicitly taught in classrooms to help students manage and regulate their own learning. In highly demanding curriculum such as engineering, ineffective strategies can lead to poor academic performance that cascades throughout a student’s academic career. Feedback from traditional examinations often do not provide personalized and actionable changes to study habits (i.e., with suboptimal scores, students may know they need to …

Board 125: Work In Progress: Faculty Experiences And Learning Through Oral-Assessment Implementation In Engineering Courses, Minju Kim, Carolyn L. Sandoval, Josephine Relaford-Doyle, Torus Washington Ii, Saharnaz Baghdadchi, Nathan Delson, Marko Lubarda, Alex M. Phan, Curt Schurgers, Huihui Qi

Psychology Faculty Articles and Research

This WIP qualitative research paper explores faculty experiences with implementing oral exams in high-enrollment undergraduate engineering courses at a research university. With a primary goal to improve conceptual mastery, as well as a desire to ensure academic integrity in the sudden move to remote instruction at the height of the pandemic, a group of faculty in Mechanical and Aerospace Engineering and Electrical and Computer Engineering departments implemented oral assessments in their own engineering courses across several quarters. They formed a research group with educational developers in a teaching and learning center on campus, and investigated the impact of oral assessments …

Board 303: Implementing Oral Exams In Engineering Classes To Positively Impact Students' Learning, Huihui Qi, Carolyn L. Sandoval, Curt Schurgers, Marko Lubarda, Alex M. Phan, Saharnaz Baghdadchi, Maziar Ghazinejad, Minju Kim, Zongnan Wang, Nathan Delson

Psychology Faculty Articles and Research

In this paper, we present the outcomes of a three-year NSF IUSE project focused on the integration of oral examinations in engineering classes. Our exploration begins with an examination of the manifold advantages of oral exams, benefiting both students and instructors. We delve into oral exams as an assessment tool, elucidating their learning benefits and emotional advantages. While some of these aspects align with existing literature, we also unveil novel findings. Additionally, we address the benefits for instructors and Teaching Assistants (TAs), encompassing informative insights that can catalyze instructional improvements. Next, we share our strategies for mitigating the scalability challenges …

How Could Future Climate Conditions Reshape A Devastating Lake-Effect Snow Storm?, Miraj Kayastha, Chenfu Huang, Jiali Wang, Yun Qian, Zhao Yang, T. C. Chakraborty, William J. Pringle, Robert D. Hetland, Pengfei Xue

Michigan Tech Publications

Lake-effect snow (LES) storms, characterized by heavy convective precipitation downwind of large lakes, pose significant coastal hazards with severe socioeconomic consequences in vulnerable areas. In this study, we investigate how devastating LES storms could evolve in the future by employing a storyline approach, using the LES storm that occurred over Buffalo, New York, in November 2022 as an example. Using a Pseudo-Global Warming method with a fully three-dimensional two-way coupled lake-land-atmosphere modeling system at a cloud-resolving 4 km resolution, we show a 14% increase in storm precipitation under the end-century warming. This increase in precipitation is accompanied by a transition …

Model-Based Systems Engineering For Engineering Education Systems Simulation, Pallavi Singh

USF Tampa Graduate Theses and Dissertations

As the engineering education system continuously evolves to meet the demands of modern industry and society, there is a need for a methodology that would manage and resolve the complexities inherent in engineering educational systems. Model-based Systems Engineering (MBSE) is a structured approach to system design that utilizes models across all stages of the system's life cycle and supports requirements management, design, analysis, verification, and validation processes. While MBSE has been applied successfully in industries like defense, aerospace, and automotive, its application in engineering educational systems remains unexplored. This dissertation develops a dynamic model of the university-level engineering education system …

A First Step Towards Understanding Thermomechanical Behavior Of The Nb-Cr System Through Interatomic Potential Development And Molecular Dynamics Simulations, Lucas A. Heaton, Adib J. Samin

Faculty Publications

Utilizing a preliminary interatomic potential, this work represents an initial exploration into the thermomechanical behavior of NbCr solid solutions. Specifically, it examines the effect of different amounts of Cr solute, for which information in the literature is limited. The employed interatomic potential was developed according to the embedded atom model (EAM), and was trained on data derived from density functional theory calculations. While the potential demonstrated reasonable accuracy and predictive power when tested, various results highlight deficiencies and encourage further development and training. Mechanical strength, heat capacities, thermal expansion coefficients, and thermal conductivities were found to decrease with Cr content. …

Authentic Impediments: The Influence Of Identity Threat, Cultivated Perceptions, And Personality On Robophobia, Kate K. Mays

Human-Machine Communication

Considering possible impediments to authentic interactions with machines, this study explores contributors to robophobia from the potential dual influence of technological features and individual traits. Through a 2 x 2 x 3 online experiment, a robot’s physical human-likeness, gender, and status were manipulated and individual differences in robot beliefs and personality traits were measured. The effects of robot traits on phobia were non-significant. Overall, subjective beliefs about what robots are, cultivated by media portrayals, whether they threaten human identity, are moral, and have agency were the strongest predictors of robophobia. Those with higher internal locus of control and neuroticism, and …

What’S In A Name And/Or A Frame? Ontological Framing And Naming Of Social Actors And Social Responses, David Westerman, Michael Vosburg, Xinyue Liu, Patric R. Spence

Human-Machine Communication

Artificial intelligence (AI) is fundamentally a communication field. Thus, the study of how AI interacts with us is likely to be heavily driven by communication. The current study examined two things that may impact people’s perceptions of socialness of a social actor: one nonverbal (ontological frame) and one verbal (providing a name) with a 2 (human vs. robot) x 2 (named or not) experiment. Participants saw one of four videos of a study “host” crossing these conditions and responded to various perceptual measures about the socialness and task ability of that host. Overall, data were consistent with hypotheses that whether …

Exploring The Experiences Of African American Female Students In Engineering: A Narrative Literature Review, Anissa Guerin, Phd

Tapestry: Journal of Research in Education

Abstract

This narrative literature review delves into the experiences of African American female students in engineering, addressing their persistent underrepresentation in the field despite advancements in postsecondary education. By examining a broad range of research, the study explores the unique challenges faced by these students and investigates tools for their persistence and degree completion. The theoretical framework draws from intersectionality theory, emphasizing the intersecting identities of race and gender in shaping these experiences. Methodologically, a narrative literature review approach was employed, facilitating a comprehensive analysis of existing scholarship. Data collection involved rigorous criteria focusing specifically on empirical research or original …

Estimation Of Vehicle Traffic Parameters Using An Optical Distance Sensor For Use In Smart City Road Infrastructure, Rafał Burdzik, Ireneusz Celiński, Minvydas Ragulskis, Vinayak Ranjan, Jonas Matijošius

Henry M. Rowan College of Engineering Departmental Research

In recent decades, the dynamics of road vehicle traffic have significantly evolved, compelling traffic engineers to develop innovative traffic monitoring solutions, especially for dense road networks. Traditional methods for measuring traffic volume along road sections may no longer suffice for modern traffic control systems. This is particularly true for induction loops, a widely used method since the last century. In contrast, measuring techniques using microwaves or visible light offer better accuracy but are often hindered by the high cost of sensors. This paper presents new techniques for measuring traffic flow and other parameters that adapt to changing traffic dynamics using …

Cellular Energy Cycle Mediates An Advection-Like Forward Cell Flow To Support Collective Invasion, Jian Zhang, Jenna Mosier, Yusheng Wu, Logan Waddle, Paul V. Taufelele, Wenjun Wang, Heng Sun, Cynthia A. Reinhart-King

Biomedical Engineering Faculty Publications and Presentations

Collective cell migration is a model for nonequilibrium biological dynamics, which is important for morphogenesis, pattern formation, and cancer metastasis. The current understanding of cellular collective dynamics is based primarily on cells moving within a 2D epithelial monolayer. However, solid tumors often invade surrounding tissues in the form of a stream-like 3D structure, and how biophysical cues are integrated at the cellular level to give rise to this collective streaming remains unclear. Here, it is shown that cell cycle-mediated bioenergetics drive a forward advective flow of cells and energy to the front to support 3D collective invasion. The cell division …

Rotational Rheometry Test Of Portland Cement-Based Materials – A Systematic Literature Review, Laura Silvestro, Artur Spat Ruviaro, Geannina Lima, Luís Urbano Durlo Tambara Júnior, Dimitri Feys, Ana Paula Kirchheim

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This Study Systematically Reviews 62 Papers on the Use of Rotational Rheometry to Assess the Fresh State Behavior of Portland Cement-Based Materials. the Research Highlights the Wide Variation in Test Methods and Aims to Provide a Comprehensive overview. Findings Reveal that 50.0% of Studies Employed Vane Geometry, despite its Limitations in Providing Transformation Equations. Regarding Dynamic Shearing Tests, 67.0% Followed a Consensus using a Pre-Shearing Step and a Stepwise Routine with Stabilization Times ≥ 10 S. While the Bingham Model is Commonly Used, the Study Emphasizes the Importance of Considering Shear-Thinning Behavior in Cementitious Materials. Models Like Herschel-Bulkley and Modified …

Anonymized Identity Recognition And Classification Using Privacy Preserving Facial Encoding, Manas Sanjay Pakalapati

USF Tampa Graduate Theses and Dissertations

The need for sharing large-scale datasets to train deep neural network models, particularly in healthcare, raises significant data security and privacy concerns. To address these issues, methods such as data encryption or encoding are utilized. These techniques can encrypt the data and make it unreadable to humans, while still retaining its usefulness for training models.

In this study, we investigate various image encoding techniques designed to protect privacy by making images unrecognizable while still retaining their usefulness for model training. Our investigation utilized publicly available facial databases and focused on evaluating the trade-offs inherent in image encoding techniques, with a …

Study On The Tribological Properties Of Din 16mncr5 Steel After Duplex Gas-Nitriding And Pack Boriding, Rafael Carrera Espinoza, Melvyn Alvarez-Vera, Marc Wettlaufer, Manuel Kerl, Stefan Barth, Pablo Moreno Garibaldi, Juan Carlos Díaz Guillen, Héctor Manuel Hernández García, Rita Muñoz Arroyo, Javier A. Ortega

Mechanical Engineering Faculty Publications

DIN 16MnCr5 is commonly used in mechanical engineering contact applications such as gears, joint parts, shafts, gear wheels, camshafts, bolts, pins, and cardan joints, among others. This study examined the microstructural and mechanical properties and tribological behavior of different surface treatments applied to DIN 16MnCr5 steel. The samples were hardened at 870 °C for 15 min and then quenched in water. The surface conditions evaluated were as follows: quenched and tempered DIN 16MnCr5 steel samples without surface treatments (control group), quenched and tempered DIN 16MnCr5 steel samples with gas-nitriding at 560 °C for 6 h, quenched and tempered DIN 16MnCr5 …

Protocol For Recording Neural Activity Evoked By Electrical Stimulation In Mice Using Two-Photon Calcium Imaging, Seungbin Park, Megan Lipton, Yujiao J. Sun, Maria Dadarlat

Purdue University Libraries Open Access Publishing Fund

Electrical stimulation provides a clinically viable approach for treating neurological disorders. Here, we present a protocol for recording neural activity evoked by electrical stimulation in mice using two-photon calcium imaging. We detail steps for chronically implanting a head fixation bar, a stimulating electrode, and a glass imaging window.We additionally describe the procedures for viral injections and awake head-fixed recordings.

Comparing Human Text Classification Performance And Explainability With Large Language And Machine Learning Models Using Eye-Tracking, Jeevithashree Divya Venkatesh, Aparajita Jaiswal, Gaurav Nanda

Purdue University Libraries Open Access Publishing Fund

To understand the alignment between reasonings of humans and artificial intelligence (AI) models, this empirical study compared the human text classification performance and explainability with a traditional machine learning (ML) model and large language model (LLM). A domain-specific noisy textual dataset of 204 injury narratives had to be classified into 6 cause-of-injury codes. The narratives varied in terms of complexity and ease of categorization based on the distinctive nature of cause-of-injury code. The user study involved 51 participants whose eye-tracking data was recorded while they performed the text classification task. While the ML model was trained on 120,000 pre-labelled injury …

Black Box Modeling Of Hvcb: A Comparative Approach, Raghdaa Elbahy, Ahmed Abdelbaset, Mahmoud Hammoda, Mansour H. Abdel-Rahman, Ebrahim A. Badran

Mansoura Engineering Journal

The extinction of an electric arc is a multifaceted process influenced by electric, magnetic, thermodynamic, and chemical phenomena. Black box models provide a mathematical depiction of the electrical characteristics of the arc. Although these models do not offer a complete explanation of the physical processes within a circuit breaker, they effectively describe its electric behavior. This study explores the modeling of electric arcs and the analysis of high-voltage circuit breakers using black box models such as Cassie, Mayr, and Schavemaker. To represent the circuit breaker model in ATPdraw, a customized MODELS block was employed. The paper presents a comparative evaluation …

Continuum Mechanics Modeling Of High Strain Rate Impact Of Thermoplastic Polymer Particles, Jeeva Muthulingam

Theses and Dissertations

The study of particle impact at high strain rates is crucial in fields ranging from materials science and engineering to space exploration and environmental science. In the particular case of cold spray, a particle undergoes intense plastic deformation upon impact, causing it to adhere to the target substrate. While metallic particles impacting on metallic substrates have been extensively studied, understanding the mechanical characteristics of polymeric particles on polymer and metal substrates requires further research. This study focuses on single particle impact modeling, analyzing impact parameters such as particle size, velocity, and angle of incidence using continuum mechanics finite element modeling …

Understanding The Mechanical And Electrochemical Impacts Of Binder Systems On Silicon Anodes In Lithium-Ion Batteries, Fei Sun

Theses and Dissertations

Silicon has emerged as a promising alternative to traditional graphite as an anode material in battery technology, primarily due to its high theoretical capacity and abundance. However, its application is hindered by significant challenges, including severe volume expansion in the active material (~275%) during cycling, which can lead to a series of electrode failure issues. Polymer binder plays an essential role in addressing these challenges as it accommodates silicon's volume expansion and the rearrangement of particles. This work conducted an analysis of how different binders influence mechanical and electrochemical properties of silicon electrodes. Our findings are supported by a series …

Cognitive Manufacturing: Definition And Current Trends, Fadi El Kalach, Ibrahim Yousif, Thorsten Wuest, Amit Sheth, Ramy Harik

Publications

Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the …

Integration Of Machine Learning In Structural Health Monitoring For Damage Identification And Response Prediction In Bridges, Naga Lakshmi Chittitalli Ravuri

Theses and Dissertations

Machine learning-based structural health monitoring (ML-SHM) plays a pivotal role in enhancing structural resilience. By recognizing potential hazards, implementing resistance measures, facilitating swift recovery, and continuously monitoring structural health, ML-SHM ensures proactive maintenance and minimizes recovery delays post-events. Leveraging machine learning algorithms and sensor data, ML-SHM enables early detection of anomalies, prediction of failures, and adaptive responses, enhancing the structure's ability to withstand and recover from adverse conditions. This integrated approach not only improves the structure's performance and adaptability but also contributes to overall safety and longevity. This thesis presents a comprehensive exploration of structural health monitoring (SHM) techniques for …

Large-Scale Research Infrastructure Empowers High-Quality Development Of Private Enterprises: Current Situation, Challenges, And Policy Recommendations, Lingling Zhang, Fuqiang Wang, Mingze Zhang, Zexia Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

Private enterprises have become an important source of innovation in China’s economic development. Large-scale research infrastructure, as a crucial strategic support and innovation element for breaking through key technologies, provides a platform for the innovative development of private enterprises. At present, some large-scale research infrastructures in China, such as the China Spallation Neutron Source and the Shanghai Synchrotron Radiation Facility, have begun actively exploring mechanisms to serve private enterprises. These efforts have helped a number of private companies overcome critical technological bottlenecks and achieve original innovations. Nevertheless, in the current process of opening up large-scale research infrastructures to private enterprises …

Tool Life Characterization In Refill Friction Stir Spot Welding, Ruth Guadalupe Belnap

Theses and Dissertations

As light-weighting becomes a priority for the automotive industry, refill friction stir spot welding emerges with enormous potential to supplement or replace conventional spot joining processes. This thesis addresses the limitations of current tooling options by examining materials beyond steel for use in RFSSW. Contained herein is an analysis of weld quality as a function of tool material, a production evaluation of RFSSW using various tool materials, and an assessment of long-term performance of a tungsten carbide tool. Over the course of this research, tungsten carbide emerged as a viable candidate for long-lasting RFSSW tooling.