Engineering Commons^™

The Association Between The Multidimensional Evaluation Of Attitudes Toward Security Recommendations And The Intention Of Following Those Recommendations, Miguel A. Toro-Jarrin, Pilar Pazos, Miguel A. Padilla

Engineering Management & Systems Engineering Faculty Publications

This study focused on the attitudes toward security recommendations as determinants of compliance with these recommendations in the workplace. It aimed to determine whether attitudes toward an overall concept (security recommendations in organizations) are associated with Information Security (IS) actions. We also examined the relationship between various dimensions of attitudes toward security recommendations and IS actions that follow these recommendations. We conducted a national survey of American workers. Six hundred and eighty-two responses were retained. We also collected demographic data to explore their moderating role in the relation between predictors and IS action-intention. The survey included four workplace scenarios and …

Sampling Balanced High-Quality Data To Train An Automatic Mesh Generator, Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng

Engineering Management & Systems Engineering Faculty Publications

In real-world scenarios, high-quality data are often scarce and imbalanced, yet it is essential for the optimal performance of data-driven algorithmic models. Data synthesis methods are commonly used to address this issue; however, they typically rely heavily on the original dataset, which limits their ability to significantly improve performance. This article presents a quality function-based method for directly generating high-quality data and applies it to a mesh generation algorithm to demonstrate its efficiency and effectiveness. The proposed approach samples input-output pairs of the algorithm based on their feature spaces, selects high-quality samples using a defined quality function that evaluates the …

Predictive Maintenance In Naval Vessel Propulsion Systems For Enhanced Marine Operations Using A Bigmm-Hmm Framework With Divergence-Based Clustering, Farshid Javadnejad, Hyoshin John Park, Samuel Kovacic, Andres Sousa-Poza

Engineering Management & Systems Engineering Faculty Publications

This study introduces a BiGMM-HMM Integration Framework designed to improve predictive maintenance strategies for naval vessel propulsion systems, addressing the need for efficient and reliable operation in marine engineering applications. The framework effectively manages multimodal sensor data by leveraging a unique combination of Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) in a bidirectional architecture. It analyses the dynamic interactions between sensors and subsystems. Two preprocessing methods are evaluated: Method 1 focuses on subsystem interactions, employing divergence-based root cause analysis to identify key sensor variables by clustering of sensors and subsystems. In contrast, Method 2 processes the entire dataset …

A Governance-Centric Framework For Strengthening Healthcare Cybersecurity: A Systems Perspective, Sujatha Alla, Sai Gireesh Komaragiri, Teresa Duvall, Satluk Karahan, Nagesh Bheesetty, Vijay Kumar Chattu

Engineering Management & Systems Engineering Faculty Publications

Healthcare systems face unprecedented security and privacy challenges due to increasing digitization and interconnectedness. This paper provides a comprehensive analysis of these challenges by examining various cyberattacks, defensive mechanisms, and governance frameworks within modern healthcare infrastructure. The research systematically categorizes prevalent security threats, such as ransomware, insider threats, and data breaches, identifying vulnerabilities specific to healthcare systems. Furthermore, the study evaluates current defensive strategies, including encryption techniques, access control systems, and intrusion detection tools, assessing their effectiveness against complex cyber threats. A key focus is placed on governance structures and their role in cybersecurity resilience. The research explores how regulatory …

Reduced Set-Shifting Processing Speed In Male Rats Following Low Dose (10 Cgy) Proton Exposure, Hui Ho Vanessa Chang, Gyutae Kim, Kyu-Sung Kim, Richard A. Britten

Center for Integrative Neuroscience and Inflammatory Diseases (CINID) Faculty Publications

Space radiation (SR) exposure poses significant biomedical risks, including effects on the central nervous system (CNS). These risks are particularly relevant to cognitive function during long-duration space missions. One critical cognitive skill is decision-making, which requires attentional set-shifting (ATSET)—the ability to quickly assess problems, evaluate options, and select the best actions. Previous studies have shown that exposure to However, the impact of low LET (< 1 keV/μm) protons, which significantly contribute to the total radiation flux astronauts encounter within spacecraft, on ATSET performance is unknown. To address this gap, we evaluated the effects of cranial irradiation with 10 cGy of 100 MeV/n protons (LET = 0.732 keV/μm) on ATSET performance in male Sprague-Dawley rats. We also investigated whether concurrent exposure to variable gravity (hypergravity step-up, step down, purported to have the same effect as exposure to microgravity (another major spaceflight stressor) exacerbated SR-induced cognitive deficits. Our findings indicate that proton exposure alone significantly impaired ATSET performance, as evidenced by decreased processing speed while performing compound discrimination reversal and extra-dimensional shifting. Notably, no additive or synergistic effects were observed when hypergravity was combined with proton exposure. The impact that low-dose proton exposure has on CNS functionality, particularly in reducing processing speed during complex tasks, warrant further investigation. If similar cognitive deficits were to occur in astronauts exposed to galactic cosmic rays, mission success and safety could be significantly compromised.

Contextual Augmentation In Artificial Intelligence, Emmanuel Joshua Balogun

College of Graduate Studies: Theses & Dissertations

Contextual understanding is a significant challenge of Large Language Models (LLMs), which are typically trained on general-purpose datasets. Due to this, LLMs fail to capture nuanced or domain-specific information and may struggle to interpret user queries accurately. Consequently, prompt engineering can become complex in automating, and LLMs are prone to “hallucinating”—generating random or irrelevant texts—when they lack sufficient context. This undermines their ability to provide focused, accurate responses. Accordingly, this thesis seeks to enhance the contextual understanding capabilities of Artificial Intelligence systems to facilitate more precise and relevant answer generation. Study A looks into a new approach to combating misinformation …

Sogar: Self-Supervised Spatiotemporal Attention-Based Social Group Activity Recognition, Naga Venkata Sai Raviteja Chappa, Pha Nguyen, Alexander H. Nelson, Han-Seok Seo, Xin Li, Page Daniels Dobbs, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Social group activity recognition is crucial for various applications including surveillance, human-robot interaction, and behavioral analysis. Current approaches often require extensive manual annotations and rely heavily on pre-trained detectors, limiting their practical applications. Additionally, existing methods struggle to effectively model long-term spatiotemporal relationships in group activities. This paper introduces a novel approach to Social Group Activity Recognition (SoGAR) using Self-supervised Transformers network that can effectively utilize unlabeled video data. To extract spatio-temporal information, we create local and global views with varying frame rates. Our self-supervised objective ensures that features extracted from contrasting views of the same video are consistent across …

Enhancement Of Intersystem Crossing In Asymmetrically Substituted Bodipy Photosensitizers, Mikhail Filatov, Tatsiana Mikulchyk, Maxine Hodee, Metodej Dvoracek, Venkata N.K. Mamillapalli, Aimee Sheehan, Craig Newman, Sergey M. Borisov, Daniel Escudero, Izabela Naydenova

Articles

We present a novel method to promote intersystem crossing (ISC) and triplet state formation in boron dipyrromethenes (BODIPYs) through the asymmetrical introduction of functional groups within the chromophore. This approach enables the development of new BODIPY photosensitizers without relying on the incorporation of heavy atoms or large electron-donating aromatic groups. Demonstrated on a series of 14 synthesized asymmetrical BODIPY (aBDP) compounds, it significantly enhances photosensitization efficiency compared to the reference symmetrical BODIPYs. In particular, the asymmetrical introduction of ethoxycarbonyl groups into pyrrolic rings of the BODIPY core lead to efficient ISC and singlet oxygen generation, with quantum yields reaching 0.76 …

Antennas For Emerging Satellite Services, Jakub Przepiorowski

Doctoral

The NewSpace era has revolutionized access to space, driving advancements in satellite communication (SATCOM) systems and creating demand for low-cost, compact, and high-performance user terminals. This thesis addresses these needs by developing innovative antenna solutions tailored for user terminals designed for emerging satellite constellations and satellite Internet-of-Things (S-IoT) applications.

Power Utilization In Open Ran: Key Findings From A Usa Testbed, Saish Urumkar, Byrav Ramamurthy, Seshu Tirupathi, Sachin Sharma

Articles

Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

Enhancing The Mechanical Properties Of Sla 3d-Printed Bio-Resin From Linseed Oil Using Cellulose And Lignin Biopolymers, Chukwunonso A. Ikedionu

College of Graduate Studies: Theses & Dissertations

Abstract

This research explores the development of bio resin from linseed oil by the process of epoxidation and acrylation to produce acrylated epoxidized linseed oil (AELO). Lignin and cellulose biopolymers were added as reinforcement at varying weight fractions of 1 wt.%, 2.5 wt.%, and 5 wt.%, and their mechanical performance was compared against pure AELO. The formulations were printed using an Elegoo SLA 3D printer to create standardized test specimens like tensile bars, compression cylinders, and hardness blocks, which were all prepared according to the ASTM testing standard.

The results indicate that reinforcement effects depend on both filler type and …

Visual Understanding Of Rock Wettability Distribution To Contact Angle Regions And Oil Displacement Patterns In The Silty Sand Reservoir Via 2d Pore-Scale Modeling, H. Al-Ajaj, W. Al-Bazzaz, Ralph E. Flori, S. Alsayegh, H. Almubarak, D. S. Ibrahim, H. Al-Saedi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study, conducted using a silty sand reservoir rock extracted from a Kuwait-producing oilfield, provides crucial insights into fluid distribution patterns and mineralogy, as well as wettability contact angle preferences. The method used for visual identification not only captures rock physics but also suggests effective oil recovery displacement strategies. Visual identification is presented using 2D image technology and a Scanning Electron Microscope (SEM). Analytical data are presented from electron bombardments and backscattering reflections collected in the BSE detector. These analyses were used to characterize the various surface boundary morphology parameters of the silty sand mineral surfaces, including area, perimeter, mineral …

Integration Of Physics-Informed Neural Networks And Transfer Learning For Rainfall Induced Landslide Forecasting, Shian Cao, Weibing Gong

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Rainfall-induced landslides are a significant geological hazard, causing severe economic losses and casualties. Accurate forecasting of these events is particularly challenging due to the complex interactions of spatial and temporal factors governing slope stability. The Iverson model, which uses the Richards equation to describe water infiltration in unsaturated soils, is a widely adopted framework for analyzing rainfall-induced landslides. However, its reliance on traditional numerical methods limits its scalability and efficiency, particularly for complex boundary conditions and transient behaviors near slope failure. To address these limitations, we propose a physics-informed neural network (PINN) enhanced with transfer learning (TL-PINN) to solve the …

Investigation Of Dynamic Adsorption And Desorption Of Polymer Nanogel In Porous Media Through Microfluidics, Junchen Liu, Fuqiao Bai, Abdulaziz A. Almakimi, Mingzhen Wei, Xiaoming He, Ibnelwaleed A. Hussein, Baojun Bai

Mathematics and Statistics Faculty Research & Creative Works

Understanding the transport and retention of elastic nanogel and microgel particles in porous media has been a significant research subject for decades, essential to the application of enhanced oil recovery (EOR). However, a lack of dynamic adsorption and desorption studies, in which the kinetics in porous media are seldom investigated, hinders the design and application of polymer nanogel in underground porous media. In this work, we visualized and quantified the transport and dynamic adsorption of polymer nanogel in 3D glass micromodels that were manufactured by packing glass beads in capillaries. Calibrating the linearity of fluorescence intensity to concentration, we calculated …

A Novel Technique For Improved Measurement Of Graphite And Inclusions In Ductile Iron Contaminated By Boron, Chase Schroeder, Colleen Lehrer, Simon Lekakh, Laura Bartlett

Materials Science and Engineering Faculty Research & Creative Works

The highly heterogeneous microstructure of ductile iron in casting includes graphite particles, non-metallic inclusions, microporosity, and other features, such as carbides distributed in the ferrite–pearlite matrix. Determination and comprehensive quantification of microstructural features are practically important however challenging. An advanced methodology based on an automated SEM/EDX analysis was developed and compared to the standard optical imaging method. In addition to backscattered electron contrast, sensitive to atomic number, a novel methodology added EDX data to identify and classify the multiple structural features at micron resolution thresholding. To quantify the shape of individual phases, the eight chord raster algorithms was used and …

Optical Sensor Instrumentation For Enhanced Continuous Caster Development, Hanok Wondimagegnehu Tekle

Doctoral Dissertations

Fiber-optic sensors are an emerging technology that can enhance process monitoring and control in steelmaking. They are especially valuable in the continuous caster’s harsh environment. This dissertation presents the industrial application and demonstration of three sensor types: single-mode silica fiber with Rayleigh-based Optical Frequency Domain Reflectometry (OFDR), sapphire fiber (single-crystal alumina) with Fiber Bragg Gratings (sFBG), and an in-line Raman spectroscopy probe. Each sensor served a distinct role in the continuous caster. Rayleigh OFDR sensors were embedded in tundishes for distributed thermal mapping at 7-mm spatial resolution across ≈4 m. Measurements were taken during preheating, casting, and ladle exchanges, and …

Radio Frequency Wave Propagation Modeling Within Obstructed Underground Mining Environments For Effective Miner Localization During Mine Emergencies, Emmanuel Atta Antwi

Doctoral Dissertations

The reliability of underground wireless communication systems remains a significant challenge, particularly during emergencies when the mine environment becomes severely obstructed by debris, dust, and humidity. These environmental and geometric conditions result in significant attenuation of electromagnetic (EM) waves and multipath effects that undermine miner localization and rescue operations. Existing Vector Parabolic Equation (VPE) models are efficient for straight or mildly curved tunnels but fail to account for the random and obstructive conditions typically encountered during underground emergencies. This research develops a novel radio propagation model for EM wave behavior in underground mine drifts under geometrically obstructed conditions. The model …

Re-Crosslinkable Particle For Conformance Control And Temporary Plugging, Yifu Long, Bowen Yu, Baojun Bai, Thomas P. Schuman

Patents

The present invention generally relates to the composition of particle gels for conformance control, well drilling and well treatments. More particularly, this invention relates to swellable polymer particle gels that can re-crosslink, i.e., reassociate and self-heal, at subterranean conditions. These particle gels can be deployed to improve the conformance of water flooding of especially matrix bypass events (MBEs), that are also known as void space conduits. Moreover, these particle gels can be deployed for controlling the water production and increasing of petroleum recovery. The inventive particles can also be deployed as diverter for well stimulation.

System And Method For Simulating A Respiratory System, Yang Wang, Weixing Hao, Yue-Wern Huang

Patents

A system and method for simulating a human respiratory system to facilitate investigating the functioning of the respiratory system, such as the generation and effects of secondhand smoke. An aerosol generator, which may include an atomizer, is configured to generate an aerosol. A series of filter media includes, in order, an inhaling extrathoracic filter media, an inhaling tracheobronchial filter media, an alveolar filter media, an exhaling tracheobronchial filter media, and an exhaling extrathoracic filter media. Each filter media includes one or more layers of a material having a fiber diameter and a weight that simulate the corresponding region of the …

Coating Compositions And Methods For Using The Same, Fateme Razaei, Thomas P. Schuman, Glenn C. Morrison

Patents

Coating compositions and methods for using the same are disclosed. The coating compositions can include an aminosilica adsorbent. The coating compositions can adsorb CO2 .

Effect Of Activation Temperature On Quantum Efficiency And Lifetime Of Nea Truncated Nanocone Array Gaas Photocathode, Md. Aziz Arroyo Rahman, Md Abdullah Mamun, Shukui Zhang, Hani E. Elsayed-Ali

Physics Faculty Publications

This study investigates the quantum efficiency (QE) and operational lifetime of a negative electron affinity GaAs truncated nanocone array (TNCA) photocathode benchmarked against a conventional flat GaAs photocathode under varying activation temperatures. The TNCA structure demonstrated a QE of up to 13.6% at 590 nm with room temperature (RT) activation—approximately 1.5 times higher than its flat counterpart. This enhancement is due to Mie resonance effects within the nanostructure, as confirmed by finite-difference time-domain simulations. Moreover, the TNCA photocathode exhibits significantly extended charge lifetime, with enhancement factors of ∼6.1 and ∼19.8 under RT and 50 °C activations, respectively. These gains are …

Frozen Commons: Food Security And Food Sovereignty In Rural Alaskan Communities And Tribes, Catey Selby, Weiwei Mo

Honors Theses and Capstones

This project explored the challenges, needs, and potential solutions related to food security and food sovereignty in rural Alaskan communities. These communities have long relied on traditional subsistence practices like fishing, hunting, and foraging, but those practices have been increasingly affected by climate change, regulatory restrictions, and commercial overfishing. Key issues identified included shorter hunting seasons, changes in animal migration, unsafe ice conditions, habitat loss, complex land ownership, and permitting barriers. Large-scale commercial fisheries also put additional pressure on subsistence resources, reducing access to traditional food sources.

The project focused on identifying key stakeholders—such as government agencies, tribal organizations, and …

Floating Offshore Structures: A Literature Review Of Current Technologies, Dynamic Behavior, And Energy Applications Post-2021, Stephen Michael Nolin

Honors Theses and Capstones

No abstract provided.

Optimization Of Small-Scale Bioreactors For Increased Pdna Production And Yield, Laura M. Hills

Honors Theses and Capstones

No abstract provided.

Synthesis Of Bio-Orthogonal Hydrogels For 3d Cell Culture Via Penicillamine Β-Thiolactone-Mediated Native Chemical Ligation, Kaelin E. Marshall, Matthew E. Currier, Nathan J. Oldenhuis

Honors Theses and Capstones

Many hydrogel systems designed to mimic the extracellular matrix (ECM) employ thiol-based chemistries with undesirable functional groups, including vinyl sulfones and methacrylates. In downstream applications, any unreacted functional groups are problematic and render hydrogels non-viable for human use. Here, we introduce modifications to the Native Chemical Ligation (NCL) mechanism, exploring the elimination of cytotoxic byproducts and increased reaction rate by using a β-thiolactone. The inclusion of the β-thiolactone results in retention of the thiol within the gel, as well as enhanced kinetics due to the thermodynamic favorability of the ring opening and amide formation. We achieved this by functionalizing 4-arm …

Dynamic Beamforming And Array Shape Estimation, Nicholas Ryan Costick

Open Access Master's Theses

The accurate estimation of towed sonar array shapes during complex maneuvers is a critical challenge affecting beamforming and target localization performance. When underwater arrays experience sharp turns or rapid movements, sensor positions become difficult to track precisely, negatively impacting the reliability of beamforming methods. This thesis addresses the issue of dynamic array shape uncertainty, motivated by operational challenges faced by the Navy.

A maximum-likelihood estimation (MLE) method is developed to simultaneously estimate the array shape and field directionality (spatial spectrum) during maneuvers. The proposed solution expands upon previous research, specifically the dynamic spatial spectrum estimation techniques described by Rogers and …

Experimental Validation Of Model Predictive Control For Roll Attenuation Of Scale Model Barge In Beam Sea Conditions, Callum Robbins

Open Access Master's Theses

Floating offshore wind turbines (FOWTs) are the next great frontier in the offshore energy world. However, for these machines to be safe, effective, and reliable, they must be stable. In this thesis, an active control strategy is developed that uses future wave information and model predictive control to attenuate wave induced motions of a floating platform, such as a FOWT. The future wave information is provided by a wave reconstruction and prediction (WRP) algorithm which allows for wave events to be calculated faster than real-time. This wave information is provided to a nonlinear model predictive controller (NMPC) that uses a …

Exploring Rust’S Performance In A Serverless Environment, Saoirse Mullen, Gary Clynch

Academic Poster Collection

Exploring Rust’s Performance in a Serverless Environment

Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White

Academic Poster Collection

AI-Based Predictive Analytics for Network Operations