Engineering Commons^™

Development Of Bio-Based Coating Materials For Environmentally Friendly Controlled-Release Nitrogen Fertilizer Applications, Anne Carolyne Mendonca Cidreira

Electronic Theses and Dissertations

No abstract provided.

Navigating Big Data In Cyber Archaeology: An Updated Four-Field Approach Applied To San Miceli, Jared Wilson

Dissertations

Problem

This dissertation examines the evolving role of digital technologies in archaeological research, focusing on the management and interpretation of extensive digital datasets generated by modern excavations. Drawing on six years of comprehensive fieldwork at San Miceli, Sicily, this study critically evaluates and updates the established four-field methodology proposed by Levy et al. (2012), encompassing acquisition, analysis, dissemination, and curation. While traditional archaeological documentation methods rely heavily on analog techniques, these approaches increasingly fall short due to the rapid expansion and complexity of digital data.

Method

To address this gap, the dissertation refines and expands the current methodological framework by …

Lunar Payload Logistics: Advancing Autonomous Control For Lunar Surface Transporter Vehicles, Liam Murray

Electronic Theses and Dissertations

No abstract provided.

Global Spectral Clustering Of Temporally Stable Pixels For Epics Identification, Validation, And Hyperspectral Assignment For Satellite Calibration, Juliana Maria Fajardo Rueda

Electronic Theses and Dissertations

No abstract provided.

Refinement Of Trend-To-Trend Cross Calibration Total Uncertainties Utilizing Extended Pseudo Invariant Calibration Sites (Epics) Global Temporally Stable Target, Minura Samaranayake

Electronic Theses and Dissertations

Cross-calibration is an essential technique for calibrating Earth Observation satellite sensors, which involves taking nearly simultaneous images of a ground target to compare uncalibrated sensor to a well-calibrated reference sensor. This study introduces the hyperspectral Trend-to-Trend (T2T) cross-calibration technique utilizing EPICS Cluster 13 Global Temporally Stable (Cluster 13-GTS) as the calibration target, offering better temporal stability than previous targets used in T2T cross-calibration by an absolute difference of 0.4%, between coefficients of variation across all bands excluding CA band. A multispectral sensor-specific normalized hyperspectral profile was developed using the EO-1 Hyperion hyperspectral profile over Cluster 13-GTS to improve Spectral Band …

Exploring Research And Tools In Ai Security: A Systematic Mapping Study, Sidhant Narula, Mohammad Ghasemigol, Javier Carnerero-Cano, Amanda Minnich, Emil Lupu, Daniel Takabi

School of Cybersecurity Faculty Publications

With the pervasive integration of artificial intelligence (AI) in various facets of modern technology, the importance of AI security has been thrust into the spotlight. The field is rapidly evolving, with new challenges and solutions emerging at a swift pace. However, the breadth and depth of AI security research have not been comprehensively mapped in recent times, presenting a crucial need for an extensive review and synthesis of existing literature. Given the increasing reliance on AI in critical domains such as healthcare, finance, and national security, ensuring the resilience and trustworthiness of these systems is imperative. This survey fulfills the …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Zero Trust Architecture As A Risk Countermeasure In Small-Medium Enterprises And Advanced Technology Systems, Ahmed M. Abdelmagid, Rafael Diaz

Engineering Management & Systems Engineering Faculty Publications

The growing sophistication of cyberattacks exposes small- and medium-sized businesses (SMBs) to a widening range of security risks. As these threats evolve in complexity, the need for advanced security measures becomes increasingly pressing. This necessitates a proactive approach to defending against potential cyber intrusions. Emerging technologies, such as blockchain, artificial intelligence, and Zero Trust security framework, offer crucial tools for strengthening the digital infrastructure of SMBs. The Zero Trust architecture (ZTA) holds significant promise as a critical strategy for protecting SMBs. While existing literature explores the implementation of ZTA in various business settings, discussions specifically addressing the financial, human resource, …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Security Enhancement In Aav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Autonomous Aerial Vehicles (AAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These AAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

Leaf-Based Varietal Categorization Of Sweetpotato (Ipomoea Batatas L. Lam.), A Potentially Healthful Vegetable, Using Image Processing And K-Means Clustering, Shahidul Islam, Md Towfiqur Rahman, Md Hamidul Rahman, Abdul Momin

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Sweetpotato (Ipomoea batatas Lam) leaves contain higher concentrations of phenolic compounds, flavonoids, and carotenoids that are remarkable in health promotion. However, the nutrient content in sweetpotato leaves varies from variety to variety, and leaf shape and color are the key identifying factors for the varietal classification of sweetpotatoes. So, detecting sweetpotato leaves is essential for the in-situ identification of sweetpotato varieties and for developing intelligent agricultural systems. This study aimed to create a leaf-shape-based varietal classification technique for sweetpotato using image processing techniques coupled with a K-means clustering algorithm. 38 leaf images (RGB) of two sweetpotato cultivars were collected …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Agency, Index & Process: Investigating The Role Of The Artist’S Body In Digital Sculpture Production, Alan Magee

Doctoral

Contemporary Digital Sculpture has emerged out of recent developments in digital art, 3d modelling and virtual modes of production. These advances range from the creation of more powerful software and hardware systems to the to nascent XR sectors, and the potential for materialisation through technologies such as 3d printing, laser-cutting, or CNC1 machining. However, critical discourse remains predominantly focused on the end product, often overlooking the embodied labour processes inherent in its creation. As revealed through indexical traces of the artist’s body, these processes encapsulate the gestures, actions and subjective agency of artistic activity. Consequently, with the limitations of digital …

Modeling And Analysis Of Amorphous Steel Transformer For Potential Loss Reduction In Power Systems., Daniel A. Muchow

Theses and Dissertations--Electrical and Computer Engineering

The energy consumed in power systems can be reduced directly by implementing new technologies and materials. The effort to reduce the carbon emissions expelled from the production of electrical energy has become a major focus. According to the U.S. energy Information Administration approximately 5% of all the electric generated and transmitted in the U.S. electrical grid is lost. The energy profile calculated from the usage of electrical power from all 50 states in 2021 is estimated to be 3.8 billion megawatts per hour, with a state average of just over 11 cents per MWh, producing just over 41.8 billion in …

Analysis And Design Optimization Of Synchronous Machines With Excitation Through Stator Dc Coils Or Magnets And Reluctance Rotors, Oluwaseun A. Badewa

Theses and Dissertations--Electrical and Computer Engineering

The design and optimization of electric machines face increasing demands for higher efficiency, improved torque density, manufacturability, and effective utilization of materials, particularly in applications such as electric traction and propulsion, where performance, reliability, and cost are critical. This work explores innovative synchronous machine configurations with reluctance rotors and stator-combined excitation, including permanent magnet and DC-excited topologies, to address these challenges through advanced design methodologies, computational modeling, and experimental validation. This research is relevant to address the growing demand for high-performance electric machines that combine high power density with costeffective manufacturing. The conventional limitations in power and torque density, thermal …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

Comparison Of Laboratory Scale Bourbon Whiskey Mashing And Fermentation To Pilot Scale, Ryan C. Sarhan, Brad J. Berron, Czarena L. Crofcheck, Glenna Joyce Welsko, Tyler J. Barzee

Biosystems and Agricultural Engineering Faculty Publications

Why the work was done: Laboratory scale methods are useful for optimising grain spirit production. While small scale methods exist, comparisons between conventional, simultaneous saccharification and fermentation, and distillery scale production are lacking. Further, there is limited understanding of how these methods affect the rheological properties of the mash and flavour compounds from fermentation. This study compared conventional and simultaneous saccharification and fermentation mashing methods to a pilot scale distillery to establish a standard laboratory method for simulating industrial bourbon production.

How the work was done: Two mashing methods for bourbon whiskey were compared: simultaneous saccharification and fermentation versus conventional …

Contaminant Mixtures And Their Impact On Nitrate Removal In Wetlands: A Mesocosm Study, Emily N. Byers, Tiffany L. Messer, Daniel N. Miller, Christopher Barton, Jason M. Unrine, Carmen Agouridis

Biosystems and Agricultural Engineering Faculty Publications

Constructed wetlands are used extensively to mitigate surface runoff. While wetland treatment for nitrogen (N) has been comprehensively studied, a knowledge gap remains regarding the implications of other contaminants (e.g., pesticides, pharmaceuticals) on nitrate-N (NO₃-N) removal. This study sought to fill that gap by determining the impact imidacloprid, caffeine, perfluorooctane sulfonic acid (PFOS), atrazine, glyphosate, and sulfate (SO₄²⁻) have on NO₃-N removal rates. The contaminants were determined based on their occurrence across Kentucky surface waters in urban (imidacloprid, caffeine, and PFOS) and rural (atrazine, glyphosate, and SO₄²⁻) environments. Two constructed …

Representation Learning Of Point Cloud Data For Process Mining And Anomaly Detection In Complex Systems, Yujing Yang

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Complex systems, e.g., advanced manufacturing systems, are largely associated with dynamic and transient behaviors, resulting in condition changes and anomalies. Sensor-based condition monitoring is critical in detecting anomalies and supporting process monitoring and performance improvement for complex manufacturing systems. Traditional sensor-based monitoring approaches primarily focus on one-dimensional (1D) signals and two-dimensional (2D) images, which are limited in their ability to capture high-resolution spatial patterns pertaining to anomalies induced by systems’ condition changes, especially subtle ones. Recent advancements in three-dimensional (3D) sensing present a unique opportunity to address this limitation by enabling the capture of 3D point cloud data with micro-level …

A Pyrrole Modified 3,4-Propylenedioxythiophene Conjugated Polymer As Hole Transport Layer For Efficient And Stable Perovskite Solar Cells, Yuanhao Tang, Ke Ma, Wenhao Shao, Yoon Ho Lee, Ashkan Abtahi, Jiaonan Sun, Hanjun Yang, Aidan H. Coffey, Harindi R. Atapattu, Mustafa Ahmed, Qixuan Hu, Wenzhan Xu, Raunak Dani, Limei Wang, Chenhui Zhu, Kenneth R. Graham, Jianguo Mei, Letian Dou

Chemical and Materials Engineering Faculty Publications

Despite the outstanding electric properties and cost-effectiveness of poly- (3,4-ethylenedioxythiophene) (PEDOT) and its derivatives, their performance as hole transport layer (HTL) materials in conventional perovskite solar cells (PSCs) has lagged behind that of widely used spirobifluorene-based molecules or poly(triaryl amine). This gap is mainly from their poor solubility and energy alignment mismatch. In this work, the design and synthesis of a pyrrole-modified HTL (PPr) based on 3,4-propylenedioxythiophene (ProDOT) are presented for efficient and stable PSCs. As a result of the superior defects passivation ability, excellent contact with perovskite, enhanced hole extraction, and high hydrophobicity, the unencapsulated PPr-based PSCs showed the …

Analysis Of Tar And Oil Derived From Pyrolysis And Copyrolysis Of Waste Plastics And Biomass, Anitha Shankaralinge Gowda, Dimitrios Karadimas, Jeffrey Seay

Chemical and Materials Engineering Faculty Publications

Pyrolysis has been proposed as a potential technology for managing the growing volume of plastic waste generated worldwide. Co-pyrolysis of plastic waste with biomass is a promising technology for generating fuel and chemical products. However, this process generates tar as a waste product. The chemical properties of this tar have yet to be thoroughly analyzed. This study presents the results of gas chromatography–mass spectrometry (GC–MS), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) of oil and tar obtained from the pyrolysis of pure plastics including high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene (PE), polystyrene (PS), and plastic-biomass mixtures. GC–MS analysis …

Membrane Filtration Of Nanoscale Biomaterials: Model System And Membrane Performance Evaluation For Aav2 Viral Vector Clarification And Recovery, Mara Leach, Kearstin Edmonds, Emily Ingram, Rebecca E. Dutch, Ranil Wickramasinghe, Malgorzata Chwatko, Dibakar Bhattacharyya

Chemical and Materials Engineering Faculty Publications

The growing demand for viral vectors as nanoscale therapeutic agents in gene therapy necessitates efficient and scalable purification methods. This study examined the role of nanoscale biomaterials in optimizing viral vector clarification through a model system mimicking real AAV2 crude harvest material. Using lysed HEK293 cells and silica nanoparticles (20 nm) as surrogates for AAV2 crude harvest, we evaluated primary (depth filters) and secondary (membrane-based) filtration processes under different process parameters and solution conditions. These filtration systems were then assessed for their ability to recover nanoscale viral vectors while reducing DNA (without the need for endonuclease treatment), protein, and turbidity. …

Fundamental And Application Of Co-Assembly Of Peptides And Proteins: Experiment And Computation, Newton A. Ihoeghian, Qing Shao

Chemical and Materials Engineering Faculty Publications

Peptides and proteins can co-assemble into various nanostructures based on complementary non-covalent interactions such as electrostatic forces, hydrogen bonding, and hydrophobic associations. These co-assemblies create a design space of functional materials for a wide spectrum of energy, environmental, and biomedical applications. This review focuses on the fundamentals and applications of three co-assembling systems: ultra-short peptides, peptides, and proteins. We will present and discuss experimental studies demonstrating applications in drug delivery, tissue engineering, and biomaterials development. We will also discuss the contribution of computational research, including molecular dynamics simulations and machine learning, to enhance our understanding of assembly mechanisms. Integrating experimental …

Accelerated Aging System For Pinniped Tags, Sean Shermer, Nathan Vasquez, Deekshitha Garige

Undergraduate Scholarship

This project focused on the design of an accelerated aging system intended to evaluate the durability of pinniped identification tags used in marine mammal research. The system was developed in collaboration with the Cal Poly Humboldt Marine Lab and NOAA, under the guidance of Dr. Dawn Goley, to address the limitations of field based durability testing, which is slow, inconsistent, and difficult to reproduce. The primary objective was to create a repeatable laboratory testing framework capable of simulating multiple environmental stressors experienced by pinniped tags. This includes the accelerated tests of UV radiation, mechanical abrasion, temperature cycling and biological fouling. …

Advances In Magnetic Nanoparticles For Molecular Medicine, Xiaoyue Yang, Sarah E. Kubican, Zhongchao Yi, Sheng Tong

Biomedical Engineering Faculty Publications

Magnetic nanoparticles (MNPs) are highly versatile nanomaterials in nanomedicine, owing to their diverse magnetic properties, which can be tailored through variations in size, shape, composition, and exposure to inductive magnetic fields. Over four decades of research have led to the clinical approval or ongoing trials of several MNP formulations, fueling continued innovation. Beyond traditional applications in drug delivery, imaging, and cancer hyperthermia, MNPs have increasingly advanced into molecular medicine. Under external magnetic fields, MNPs can generate mechano- or thermal stimuli to modulate individual molecules or cells deep within tissue, offering precise, remote control of biological processes at cellular and molecular …

Clinical Correlation Of Sars-Cov-2 Wastewater Passive Sampling In Long-Term Care Facilities And Wastewater Treatment Plants, William Strike, Alexus Lori Rockward, Blazan Mijatovic, Ann Noble, Cullen Olsson, Soroosh Torabi, Mohammad Dehghan Banadaki, Reuben Adatorwovor, James W. Keck, Scott M. Berry

Biomedical Engineering Faculty Publications

Wastewater-based epidemiology (WBE) is a promising tool for improving health outcomes through early detection and cost-effective pathogen surveillance. Long-term care facilities (LTCFs) serve and employ vulnerable populations that may particularly benefit from the use of WBE, but financial and technical costs associated with standard sampling methods limit the feasibility of WBE in the LTCF setting. In this work, we used passive sampling to simplify the wastewater analysis process and compared its performance to the standard composite sampling method. Moore swabs and automatic composite samplers were used concurrently to sample wastewater from two LTCFs, and samples were analyzed for SARS-CoV-2 concentration. …

Affordable Miniaturized Speckle Contrast Diffuse Correlation Tomography Device For Depth-Sensitive Mapping Of Cerebral Blood Flow In Rodents, Fatemeh Hamedi, Faezeh Akbari, Mehrana Mohtasebi, Chong Huang, Li Chen, Lei Chen, Guoqiang Yu

Biomedical Engineering Faculty Publications

Significance: Continuous and longitudinal monitoring of cerebral blood flow (CBF) is critical for understanding brain pathophysiology and guiding interventions. Although rodents are the primary models in neuroscience, existing imaging modalities often fail to provide the optimal combination of low cost, high spatiotemporal resolution, wide head coverage, and sufficient penetration depth for small-animal brain imaging.

Aim: Leveraging a clinical speckle contrast diffuse correlation tomography (scDCT) system, we aimed to develop an affordable, user-friendly, fast, and miniaturized scDCT (mini-scDCT) device tailored for depth-sensitive CBF imaging in small rodents.

Approach: The mini-scDCT replaces bulky and costly optoelectronic components with compact, low-cost alternatives while …

Implementing Wastewater-Based Epidemiology For Long-Read Metagenomic Sequencing Of Antimicrobial Resistance In Kampala, Uganda, William Strike, Temitope O. C. Faleye, Brian Lubega, Alexus Lori Rockward, Soroosh Torabi

Biomedical Engineering Faculty Publications

Antimicrobial resistance (AMR) is an emerging global threat that is expanding in many areas of the world. Wastewater-based epidemiology (WBE) is uniquely suited for use in areas of the world where clinical surveillance is limited or logistically slow to identify emerging threats, such as in Sub-Saharan Africa (SSA). Wastewater was analyzed from three urban areas of Kampala, including a local HIV research clinic and two informal settlements. Wastewater extraction was performed using a low-cost, magnetic bead-based protocol that minimizes consumable plastic consumption followed by sequencing on the Oxford Nanopore Technology MinION platform. The majority of the analysis was performed using …

Influence Of Actuated Air Brakes On The Apogee Of Sub-Orbital Rockets, Michael L. Farha

Graduate Theses, Dissertations, and Problem Reports (ETD)

Collegiate engineering competitions have played a foundational role in the educational outcomes of university students for as long as they have existed. Among these competitions is the Spaceport America Cup, which challenges students to design, build, and launch high-power rockets with the incentive to fly as close as possible to a pre-established target apogee. Many teams competing in this competition have chosen to approach the challenge by incorporating deployable air brakes into their rocket designs. This approach comes with many uncertainties, both in controls and aerodynamics. For teams just starting out, aerodynamic performance is often one of the most challenging …