Engineering Commons^™

The Effects Of Low-Frequency, Low-Intensity Ultrasound On Gene Expression In Macrophage-Endothelial Cell Co-Culture, Evelyn Hergenhan

Honors Theses

Introduction

Low-frequency, low-intensity ultrasound (LFLI US) has been shown to be a promising treatment for noninvasive accelerated chronic wound healing. However, the biological mechanisms of this ultrasound-assisted healing are not yet understood, limiting clinical adoption. The objective of this study is to characterize the effects of LFLI US on gene expression in a simulated chronic wound environment to gain a better understanding of the biological mechanisms underlying this therapeutic.

Methods

A simulated chronic wound environment consisting of endothelial cells and inflammatory M1 macrophages co-cultured in 3D was subject to LFLI US at 50, 100, or 150 mW/cm² Spatial-Peak, Temporal-Peak …

Comparative Evaluation Of Deep Learning Models: Resnet18, Minivgg, And Yolov8 For Five-Class Blood Cell Classification, Keita Sakurai

Master's Theses or Doctor of Nursing Practice

Accurate classification of blood cell types is a critical task in automated hematological analysis. This study presents a comparative evaluation of three deep learning architectures, ResNet18, MiniVGG, and YOLOv8, for five-class blood cell image classification. To ensure a fair comparison, all models were trained under standardized conditions, including a consistent 90:10 training–validation split, controlled dataset size, and fixed training epochs. ResNet18 was trained to establish a baseline using residual learning. MiniVGG employed a compact VGG-inspired design with regularization to balance efficiency and accuracy, while YOLOv8 leveraged a lightweight, pretrained classification backbone with integrated data augmentation. Experimental results demonstrate a clear …

Heat Input Control And Deep Learning-Based Indirect Measure Of Process And Deposition Stability In Wire Arc Additive Manufacturing, Alessandra Caggiano, Giulio Mattera, Yuming Zhang, Roberto Teti

Electrical and Computer Engineering Faculty Publications

A process qualification-oriented data-driven framework for Wire Arc Additive Manufacturing (WAAM) integrating qualification data, process monitoring and feedback control, is presented. A proportional control strategy regulating heat input by varying the Contact Tip–to–Workpiece Distance (CTWD) is developed to enhance process stability, ensure consistent layer geometry and maintain the qualified heat-input conditions for process qualification. To assess the control strategy stability, deep learning-based CTWD soft sensing from high-frequency welding signals is combined with an uncertainty-aware process quality index. The framework is validated on Invar 36 alloy, but it supports extension to other alloys and arc welding-based additive processes.

Investigation Of Factors Influencing Electric Vehicle Adoption In Indonesia: Ev Owners’ Perspectives, Desrina Yusi Irawatia, Nur Aini Masruroh, Nur Mayke Eka Normasari

ASEAN Journal on Science and Technology for Development

Electric vehicle (EV) uptake in Indonesia remains markedly below policy benchmarks. This study applies the Unified Theory of Acceptance and Use of Technology version 3 (UTAUT3), an extension of UTAUT2 that incorporates personal innovativeness as an additional construct to examine its impact on both behavioral intention and actual EV adoption within the Indonesian context. Unlike studies that typically survey the general public, this study focuses on actual EV users and owners, providing more representative and responsive insights into real-world EV usage. A total of 208 respondents participated, with 135 from the Jabodetabek area and 73 from Surabaya. The UTAUT3 framework …

A Deep Neural Approach To Network-Based Obfuscated Malware, Meera Parmar, Sunil Gautam

ASEAN Journal on Science and Technology for Development

The rising prevalence of obfuscated malware poses a critical threat to network security, undermining traditional detection methods and jeopardizing data integrity and system reliability in an increasingly connected world. This growing danger highlights the urgent need for advanced solutions to protect against evolving cyber risks. This research intro-duces a novel framework to enhance malware detection, employing a hybrid architecture that integrates spatial and temporal analysis with attention mechanisms. The approach leverages a large dataset subsample, focusing on key feature selection and augmentation to improve robustness against evasion techniques. This innovative framework offers a significant advancement in identifying malicious network traffic, …

Part Ii: Industrial Information Integration Review 2020-2025, Jinzhi Li

Information Technology & Decision Sciences Faculty Publications

Industrial Information Integration Engineering (IIIE) has become increasingly essential for improving operational efficiency and harmonizing heterogeneous industrial systems through advanced digital integration approaches. Fueled by rapid advancements in Industry 4.0 technologies—including digital twins, artificial intelligence, immersive interfaces, and IoT infrastructures—IIIE is substantially transforming traditional enterprise architecture and integration frameworks. This systematic review synthesizes recent developments and emerging trends, with particular attention to the accelerating adoption of digital twins and the deepening convergence between operational technologies (OT) and information technologies (IT) across multiple sectors. While notable progress has been made, significant challenges persist, especially in developing resilient integration architectures and fully …

When Words Flow Like Water: How The Enbridge Line 3 Pipeline Environmental Impact Statement Failed To Prevent Hydrogeologic Harm In Minnesota, Carly Gutzmann

Journal of Earth and Life Science

For the aquifers of Minnesota, the environmental impact statement (EIS) was a promise of protection that never left the page. An environmental impact statement is meant to be an aid in the decision making process in order to ensure that projects consider potential environmental harms that may occur. However, they are often used instead as another regulatory box to check, rather than as active considerations when planning. As such, a project plan can be flawed from the start—if project developers only consider environmental impacts after they have already put considerable time, effort, and funding into their project as-is, they may …

Captive Model Test For Hydrodynamic Derivatives Of Ship Maneuvering In Regular Head Waves, Pin-Yuan Huang, Zih-Yao Lin, Tsung-Yueh Lin, Fu-En Lee, Yan-Wei Lai

Journal of Marine Science and Technology–Taiwan

The motion of ship in wave can be discussed in seakeeping and maneuvering. In model tests, the former is found by the motion response of waves while the latter requires captive model test to acquire hydrodynamic derivatives. The hydrodynamic derivatives in waves are discovered to be different from those in calm water. To understand better the behavior of maneuverability in waves, maneuvering tests in a wide range of waves are performed in this study. This study presents an experimental investigation into the maneuvering behavior of a container ship in regular waves, covering a wide range of wavelengths from half to …

Measuring The Impact Of 3d Printers On The Health Of Youth, Alexis Kostin, Olla Obeid, Oz Trost

Wetterhahn Science Symposium Posters

3D printers are used more widely than ever before; they can be found in factories, hospitals, and even schools. However, the health effects of the VOCs and UFPs that these machines emit have yet to be fully understood. In collaboration with the University of Nevada, Las Vegas, this study utilizes an Air Gradient One sensor at the Diamond Lab to monitor VOC and UFP emissions before, during, and after 3D printing sessions. By comparing these levels to EPA and CDC safety standards, we aim to quantify the respiratory risks posed to youth.

Numerical And Machine Learning Based Recreation Of Damage Morphologies Of Barely Visible Impact Damage, Oscar A. Valdez

Mechanical and Aerospace Engineering Dissertations

Composite laminates are highly sought after in the aerospace industry as they provide strength without dramatically increasing the weight of manufactured structures. However, composite laminates are susceptible to barely visible impact damage caused by routine activities. This type of damage can easily go unnoticed while significantly reducing the load-carrying capability of the laminate. Current non-destructive evaluation techniques, such as ultrasonic scanning, can reveal the damage footprint but provide no insight into delamination through-the-thickness due to the shadowing effect. Micro-computed tomography offers ply-by-ply damage resolution but is unsuitable for field inspections and is constrained by specimen size. This study aims to …

Set-Theoretic Reachability-Informed Model Predictive Control For Mechanical And Aerospace Systems, Jinaykumar Nitinkumar Patel

Mechanical and Aerospace Engineering Dissertations

Modern mechanical and aerospace systems increasingly operate autonomously in environments characterized by nonlinear dynamics, uncertainty, and safety constraints. In these settings, estimation and control methods based on nominal models and single-point trajectory predictions are usually insufficient to ensure safe and reliable operation. This dissertation uses a set-theoretic perspective, in which the system state, uncertainty, and admissible behavior are described by sets instead of point estimates. The key question is not only what the state is, but what set of states remains consistent with the dynamics, disturbances, control limits, and available measurements. This provides bounded descriptions of uncertainty and supports control …

Beyond The Honeypot: Increasing Adversarial Cognitive Load Through Interactionless Cyber Deception, Marcus Rozier, Connor Dedic, Ted Mcdaniel, Hadley Westover

Space Dynamics Laboratory Publications

Traditional cyber deception mechanisms, such as honeypots and honey-tokens, typically rely on adversary interaction to trigger alerts and provide defensive value. This paper introduces the concept of interactionless deception—a defensive paradigm focused on the strategic manipulation of the environment to disrupt the cyber kill chain without requiring direct engagement. By utilizing a greedy top-k selection method to analyze MITRE ATT&CK® data across 178 Advanced Persistent Threat (APT) groups, this research identifies seven high-frequency sub-techniques that represent common attacker behavior. To counter these, the authors developed a series of proof-of-concept tools, including deceptive web proxies, simulated command-and-control (C2) beacons, and …

A Low Noise, High Qe, Large Format Ccd Camera System For The Nasa Mighti Instrument, Jed Hancock, Joel Cardon, Mike Watson, James Cook, Mitch Whiteley, James Beukers, Chris Englert, Charlie Brown, John Harlander

Space Dynamics Laboratory Publications

Outline

• MIGHTI Instrument Overview and Configuration
• Camera System Block Diagram
• Flight Camera Heads, Electronics Box, and CCDs
• CCD Testing at e2v
• AI&T at the Space Dynamics Lab
• Electro-Optical Characterization and Performance Results
• Conclusions

Deep Learning Framework For Sow Posture Classification From Depth Images: Comparison With Data Transformation Techniques, Models, And Cross-Validation, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Suzanne M. Leonard, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Piglet preweaning mortality (PWM) in the United States averages ≈14–15%, with sow overlaying causing about one third proportion of these losses. This research aimed to develop and evaluate deep learning models to classify six sow postures using depth images to monitor behaviors linked to overlaying risk. Top-down depth images were captured with Kinect V2^® cameras at 10 frames min^-1 for five consecutive days (2 days before to 2 days after farrowing), yielding 26,506 training images from 18 sows, 17,901 testing images from 12 sows, and 4,697 additional images from three sows in diagonal stalls for external validation. Three …

Assessment Of Soybean Response To Irrigation Variability In Eastern Nebraska Using The Aquacrop Model, Anmol Singh, Saleh Taghvaeian, Yufeng Ge, Derek M. Heeren, Frank Bai

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Soybean [Glycine max (L.) Merr.] is a major irrigated crop in eastern Nebraska, where recent expansions in irrigated agriculture and projected trends in irrigation demand necessitate thorough investigations of approaches that can optimize its irrigation management. In this study, the AquaCrop model was calibrated and validated using 19 variable irrigation treatments from a five-year field experiment. The model accurately simulated canopy cover, soil water content, and grain yield, with validation normalized root mean square error (nRMSE) of 12%, 7%, and 7%, respectively. The model was subsequently applied to estimate soybean yield and water requirement during a 15-year period (2010–2024) …

Ai Data Center Dynamic Load Effects On Current Transformer Saturation, Sergio A. Hernandez

Electrical Engineering Theses

AI data centers can produce rapid changes in electrical demand that may influence current transformer performance during faults. This study evaluates the effect of an AI data center transient on CT saturation during single line-to-ground faults using a 400 V, 60 Hz grid connected inverter model in MATLAB/Simulink. The normal condition transient produced a maximum RMS current rate of approximately 211 A/ms, which was used along with the maximum power condition to define fault inception cases. A MATLAB time-domain CT model then swept the fault current DC offset coefficient to determine the minimum offset required for CT saturation. The calculated …

Experimental Thermal Characterization And Comparison Of Direct Liquid Cooling And Air Cooling For A High-Density Gpu Server Thermal Test Vehicle, Oluwagbolahan Esan

Mechanical and Aerospace Engineering Theses

As the power density of artificial intelligence (AI) and high-performance computing (HPC) servers escalates, air cooling is approaching fundamental scaling limits set by air's low convective heat transfer coefficient and rising parasitic fan power. This thesis presents an experimental comparison of direct-to-chip liquid cooling and air cooling for a high-density GPU server, using a Thermal Test Vehicle (TTV) that emulates the device layout and power-density regime of an 8-GPU SXM5-class baseboard (eight emulated GPU blocks and four NVLink Switch modules). The liquid configuration was tested at 8.8 kW (8.0 kW GPU, 800 W switch) across three Coolant Distribution Unit (CDU) …

A Taxonomy-Driven Modular Defense Against Non-Canonical Language In Vision-Language-Action Models, Viraj Samson

Electronic Theses and Dissertations

Vision-Language-Action (VLA) models have recently achieved strong performance across manipulation benchmarks, but these benchmarks rely on highly templated instructions on which the models are typically fine-tuned, leaving their behavior under realistic language-side perturbation unclear. It remains an open question whether the linguistic flexibility inherited from vision-language pretraining survives this fine-tuning, or whether the resulting policies become narrowly tuned to benchmark phrasing and brittle to the intent-preserving language variation that real users naturally produce. We address this gap with a systematic study of VLA robustness under non-canonical instructions, comprising three components: a structured taxonomy of intent-preserving variations spanning linguistic, orthographic, and …

Data-Driven Optimization Of Memory Effects And Critical Components In Cascading Failure Interaction Networks, Md Farhan Tanvir

Electronic Theses and Dissertations

Cascading failures are a major concern for modern power systems‚ where a small failure can cascade through the network to create a large blackout. Because such an event could have catastrophic economic and social costs it is important to understand cascading failures‚ how to model them‚ and the possibility of reducing them. In this thesis‚ we develop a data-driven framework based on actual utility outage data to model and reduce cascading failures. The proposed methodology is based on generation-dependent interaction models and in this study interaction matrices obtained from historical outage events are used to describe the interaction between generations. …

Computational Modeling Of Biophysical Transport: Respiratory Drug Delivery And Tissue-Level Physics, Md Tariqul Hossain

Electronic Theses and Dissertations

The nasopharynx, the upper part of the pharynx located at the back of the nose serves as a critical hotspot for initial respiratory infections via inhaled transmission, largely due to the presence of specific surface receptors that pathogens can exploit for cell invasion, combined with a relatively sparse local mucociliary substrate. To enhance the therapeutic efficacy against certain pathogens, such as the SARS and Influenza viruses, it is therefore essential to improve the targeted delivery of drugs to the nasopharynx. This study explores the use of intranasal sprays as a method for drug administration and models the transport of sprayed …

Numerical Investigation Of Injector Location And Equivalence Ratio In A Flame‐Holder Cavity Scramjet, Matthew Sorenson

Electronic Theses and Dissertations

This thesis presents a CFD-based parametric study of injector location and fuel rate in an ethylene-fueled flame-holder cavity scramjet using STAR-CCM+. Simulations are performed for three injector locations (x/D = 18, 36, and 54) and nine equivalence ratios spanning from ϕ = 0.2 to 1.0 in 0.1 increments. The study aims to determine how injector placement and fueling level alter scramjet performance across a broad operating sweep. The results show a clear regime change between ϕ = 0.2 and ϕ ≥ 0.3. At ϕ = 0.2, combustor behavior is weakly sensitive to injector location compared to the higher-fueling cases. For …

Wearable Biosensors For Continuous Monitoring Of Chronic Kidney Disease: Materials, Biofluids, And Digital Health Integration, Anupamaa Sivasubramanian, Shankara Narayanan, Gymama Slaughter

Center for Bioelectronics Publications

Chronic kidney disease (CKD) is a progressive and irreversible disorder affecting over 850 million individuals globally and is associated with significant morbidity, mortality, and healthcare burden. Conventional diagnostic approaches rely on intermittent laboratory measurements, including serum creatinine, estimated glomerular filtration rate (eGFR), and urinary albumin, which provide limited temporal resolution and fail to capture dynamic physiological changes. Recent advances in wearable biosensing technologies offer new opportunities for continuous, non-invasive monitoring of biochemical and physiological markers relevant to renal function. This review provides a comprehensive analysis of wearable biosensors for CKD monitoring, focusing on sensing mechanisms (electrochemical, optical, and field-effect transistor), …

Cross-Linking Peg Microgels With Mesoporous Organosilica Nanoparticles To Engineer Microporous Annealed Particle Scaffold Properties, Jonathan M. Zuidema, Alessandro Ajo, Marco Carofiglio, Luisa De Cola

Mechanical Engineering Faculty Publications

Microporous annealed particle (MAP) scaffolds composed of cross-linked microgels are emerging as important biomaterials due to their modular design, high porosity, and large surface area, which allow for highly tailored properties, cellular infiltration, nutrient and waste exchange, and improved drug release characteristics. To further expand on these technologies and enhance their modular design, thiolated mesoporous organosilica nanoparticles (ssMSN-SH), which degrade in response to glutathione, were first cross-linked inside of 4-arm poly(ethylene glycol) maleimide microgels. The microgel size, rheological properties, and payload release characteristics were determined. Hierarchical MAP scaffolds were then created by cross-linking 4-arm poly(ethylene glycol) maleimide microgels with ssMSN-SH. …

Data Set, R Scripts And Script Outputs For Manuscript: Manufacturing Systems: Characteristics And Dynamics, Alan J. Fitzmorris

Graduate Research Data

This file repository contains fifteen computer-generated datasets, two naturally occurring datasets, nineteen shipbuilding shop datasets, and sixty-one concrete plant datasets.  Each data set contains subdirectories associated with various aspects of the data analysis, including stationarity analysis, dynamic system analysis, and complexity analysis.  In each case, applicable R scripts are included.  The text file entitled “Directory_and_File_Names” depicts the directory structure and associated file names for a total of 225 directories encompassing 8008 files.

Effects Of Matched Freespace And Wall Damping On Stiffness Perception, Madeleine Toland, M Bourgeois, Emma Treadway

Engineering Faculty Research

Stiffness perception has previously been shown to be influenced by damping. This work addresses open questions about what damping conditions are best for enabling classification of virtual walls by their stiffness. Through a forced-choice classification experiment with 12 participants, we found that the highest mean classification performance (in terms of both information transfer and percent correct responses) occurred when freespace damping matched wall damping, rather than being higher or lower. However, statistically significant differences did not appear between the closest damping values employed, suggesting that the effects do not become pronounced until the damping is quite mismatched.

Work In Progress: The Impact Of Classroom Interventions On Student Success And Retention In Electric Circuits, Cherian Mathews

All Faculty Articles - School of Engineering and Computer Science

University of the Pacific has embarked on a university-wide endeavor to improve student success and retention. This effort involves academic coaches being placed in each unit, early grade reporting in the fifth week of the semester, follow-ups by faculty advisors and academic coaches for struggling students, and documentation of interactions with such students in the university CRM system. These follow-ups with students typically involve conversations to explore factors contributing to poor performance (e.g., health issues, excessive work hours, study skills), suggesting approaches to improve performance, and making referrals to care teams and supplemental instruction. Despite all these efforts, DFW rates …

Ai For Life Sciences: From Geometric Protein Modeling To Multimodal Drug Design, Feng Jiang

Computer Science and Engineering Dissertations

Predicting biomolecular interactions, from immune recognition to drug–target binding, is a central problem in the life sciences and computational drug discovery. Deep learning has advanced this area, yet three challenges persist: the topology of large, highly imbalanced interaction networks; structural noise in computationally predicted protein models; and the integration of multimodal information such as functional text and taxonomic annotations. This dissertation develops a coherent set of models spanning immune complex prediction and small-molecule drug design: graph learning that addresses network topology and severe class imbalance; a noise-tolerant method that fuses predicted structures with evolutionary sequence features; and multimodal representation learning …

Data For: Optimizing Finite Structures To Suppress The Photonic Density Of States, Prakash Mishra, Sukhad Dnyanesh Joshi, Quintin A. Hatzis, Aditya Bahulikar, M. Cenk Gursoy, Rodrick Kuate Defo

Electrical Engineering and Computer Science - All Scholarship

We propose a topology-optimization framework for optimizing finite structures of arbitrary shape by combining density-based methods with level-set approaches. We first optimize regular polygonal structures to suppress the photonic density of states and find that the best performing polygon is consistent with a tiling of space with hexagonal unit cells. We next show that introducing cavities into hexagonal structures further suppresses the photonic density of states, particularly when the cavity is also hexagonal. Such a result would find application in the design of fiber-optic cables. We then describe an approach for optimizing arbitrary x-simple or y-simple designs that can recover …

Localized Air Pollution Impacts From Data Centers In Northern Virginia, Damian Pitt, Ivan Suen, Ellie Plisko

Institute for Sustainable Energy and Environment Publications and Presentations

This report examins the extent of air pollution emissions resulting from the use of backup generators at data centers in Northern Virginia, including both actual current emissions and potential future emissions, and how those emissions totals compare to other sources of air pollution in the Northern Virginia region. It also examines how exposure to data center emissions correlates with demographic characteristics such as race, income, and education.

We while the air pollution emissions from individual data centers is minor, the collective impact from the 100+ such facilities in the Northern Virginia region is significant. Notably, the cumulative emissions exposure in …

Ransomware As Organization: A Comparative Analysis Of Corporate And Criminal Structures In Conti, George Urling

Theses, Dissertations and Capstones

Cybercriminal groups continue to pose major threats to global cybersecurity. One of the most common types of cybercriminal groups are, “Ransomware-as-a-Service (RaaS)" groups, who create and sell ransomware. While research is conducted into the development of ransomware, there is limited reporting on the organizational structure and habits of RaaS groups. In 2022, prominent RaaS group Conti had their chat logs leaked, with the logs ranging from 2020 to 2022. This study seeks to provide a deeper understanding of RaaS group structures by utilizing the Conti leaked logs as a case study. The study, entitled “Ransomware as Organization: A Comparative Analysis …