Digital Commons Network^™

Calcium Based Carbon Negative Fillers And Additives: Effects On Hydration And Performance Of Low-Carbon Cement Systems, Smita Bhowmick Shithi

Masters Theses

Cement production is a major source of global CO₂ emissions creating an urgent need for carbon-negative fillers and functional additives that lower clinker use while maintaining cement performance. This thesis investigates the effects of organic mineral salts as fillers and additives on cement hydration, fresh properties, compressive strength and pore structure. In the first study, ordinary Portland cement (OPC) was partially replaced with calcium carbonate or calcium oxalate to enable a direct comparison under controlled particle size distribution (PSD). Rheology, flowability, hydration kinetics, phase evolution, mechanical performance and water absorption were evaluated. Calcium carbonate promoted early hydration through nucleation and …

Autonomous Navigation Development And On-Ground Validation For Satellite Rendezvous And Proximity Operations, Logan Banker

Masters Theses

Spacecraft rendezvous and docking are critical mission phases for various applications of spaceflight, including active debris removal (ADR) and in-orbit servicing, assembly, and manufacturing (ISAM). While previous missions utilized humans to perform rendezvous and docking, this style of mission greatly increases safety risks and cannot be implemented on a large scale. Autonomous servicing satellites provide a path towards scalable rendezvous and proximity operations (RPO) because these autonomous agents do not require human intervention. This work presents a lightweight convolutional neural network (CNN) for the navigation system of an agent which analyzes monocular images and predicts the target's position and orientation …

Wattage- And Composition-Dependent Ocular Surface Toxicity: An In Vitro Study On E-Cigarette Aerosol Exposure In Human Corneal Epithelial Cells, Tzu-Ling Lin

Masters Theses

As electronic cigarette consumption continues to escalate globally, its impact on non-respiratory mucosal surfaces, particularly the ocular surface, remains largely understudied. This study investigated the toxicological effects of e-cigarette aerosols using a human corneal epithelial cell (HCE-2) model. Our findings reveal that aerosol exposure induces a significant, dose-dependent reduction in cell viability, exacerbated by higher device wattage and specific flavor agents such as watermelon. Exposure was found to trigger intracellular oxidative stress and the activation of apoptotic pathways, leading to programmed cell death. Furthermore, the upregulation of proinflammatory cytokines suggests that vaping may predispose users to chronic inflammatory conditions of …

Developing Discharge Estimation Algorithm Using Low-Cost Velocity Sensor And Machine Learning, Barkha Gautam

Masters Theses

Accurate river discharge estimation is essential for flood forecasting, water resources management, and hydraulic decision-making; however, continuous discharge records are unavailable at many river locations. Traditional stage-discharge rating curves are widely used but their reliability may decrease when channel conditions change or flow conditions vary rapidly. This study develops and evaluates Long Short-Term Memory (LSTM) models for discharge prediction using 15-minute time-series data from river monitoring stations in Missouri. Two model configurations, a baseline stage-only model and an enhanced stage-plus-velocity model, are developed and evaluated independently at two river sites to determine whether the inclusion of surface velocity improves discharge …

Aura-Visionpose: Creating Datasets For Vision-Based Pose Estimation, Mohanad Alameer

Masters Theses

"Spacecraft rendezvous is an integral part of many orbital missions, including active space debris removal (ADR) and in-orbit servicing, assembly, and manufacturing (ISAM). Traditionally, rendezvous missions rely mainly on manual ground-based control to perform rendezvous and docking. While this can be effective on an individual basis, it becomes less practical at scale. Autonomous vision-based methods can reduce reliance on human control, and may improve scalability, response time, and reduce the possibility of human error for both ADR and ISAM. This work lays the necessary groundwork for developing a machine learning (ML) model for autonomous rendezvous by creating a labeled image …

Optical Fiber Eaf Refactory Lining Temperature Monitoring For Safety And Operating Efficiency, Manoj Kumar Pullagura

Masters Theses

"Electric arc furnace (EAF) sidewalls experience severe thermal, chemical, and mechanical loading, especially near burner and injector regions where refractory wear and water ingress from damaged cooling components pose safety risks. Conventional thermocouples and resistance temperature detectors provide reliable point measurements but lack the spatial resolution needed to capture steep gradients and localized anomalies. This research investigates embedded optical fiber sensors for high-resolution temperature monitoring of EAF refractory safety linings to support early water-leak detection and assessment of refractory thermal behavior. A combined Rayleigh backscattering-based optical frequency domain reflectometry (OFDR) and fiber Bragg grating (FBG) approach was evaluated through laboratory …

The Occurence Of Expansive Clay Soils In Illinois And Their Effects On Shallow Lightly Loaded Foundations, Greg James Gollaher

Masters Theses

"The damage caused to shallow foundations and slabs on grade by the shrink-swell activity of clay soils is well documented across the United States. There are local and national resources available that delineate areas that are problematic and areas that are not. These resources paint Illinois as an undemanding foundation environment. However, our experience differs from this. Our experience tells us that damage related to the shrink-swell activity of clay soils is widespread, and commonly severe causing not only cosmetic damage to structures and hard improvements but also affecting the serviceability and longevity of structures. My goal is to accurately …

Castrip Process Technology: Non-Grain Oriented (Ngo) Si Steels Extension And Carbide-Free Bainitic (Cfb) For Flat Products, Naram Naidu Meesala

Masters Theses

"This research explores the development of advanced steel grades manufactured through Castrip® twin-roll thin strip casting technology, with particular attention to non-grain-oriented electrical Si-Steels (NGOES) and carbide-free bainitic (CFB) steels. The Castrip® process offers a near-net-shape route for producing thin steel strips, which greatly decreases the number of processing stages, lowers energy demand, and reduces carbon dioxide emissions when compared with traditional steel production methods.

In the NGOES part of the study, several industrial Fe–Si steel compositions were cold rolled to final thicknesses ranging from 0.35 mm to 0.15 mm. These materials were then exposed to short-duration continuous annealing at …

Evaluation Of A Novel Re-Crosslinkable Preformed Particle Gel For Conformance Control In Ultra-High Temperature Reservoirs, Yanbo Liu, Tao Song, Caleb Kwasi Darko, Thomas P. Schuman, Mingzhen Wei, Baojun Bai

Chemistry Faculty Research & Creative Works

Preferential fluid flow remains a major challenge in subsurface energy production and gas storage operations, resulting in excessive water production in mature oil fields, reduced heat extraction in geothermal reservoirs, and low sweep and storage efficiency in CO₂-EOR projects. Polymer gels are widely used to mitigate high-permeability channels; however, conventional systems exhibit limited plugging efficiency and short lifetimes in ultra-high-temperature reservoirs due to poor thermal stability. This study presents a novel ultra-high-temperature-resistant preformed particle gel (UHT-PPG) developed for conformance control in reservoirs with temperatures of 150–275 °C and severe super-K or channeling problems. The material was evaluated in …

Progression Toward Terminal Oxidation Of Starch Via Hybrid Organic–Enzymatic Bioanode For Renewable Carbohydrate Conversion, Adam Milam, Jose Intano, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

The performance of starch-based biofuel cells is fundamentally limited by inefficient utilization of the fuel's inherent energy density. Sequential oxidation of carbohydrate fuels, such as glucose, the monomeric unit of starch, using minimal catalyst systems has therefore emerged as a promising strategy to improve biofuel cell efficiency. Here, we extend a previously reported three-catalyst, organic–enzymatic glucose oxidation cascade to polymeric carbohydrate fuels by integrating enzymatic starch depolymerization with downstream electrochemical oxidation. To our knowledge, this represents the first demonstration of a defined hybrid organic–enzymatic cascade designed to drive progression of starch-derived glucose equivalents toward terminal oxidation using a minimal catalyst …

On-Device Artificial Intelligence Solutions With Applications To Smart Environments, Fabrizio De Vita, Dario Bruneo, Sajal K. Das

Computer Science Faculty Research & Creative Works

Recent advances in Artificial Intelligence (AI) and the increasing availability of computational power have accelerated the diffusion of Intelligent Cyber-Physical Systems (ICPSs), enabling smart applications with reasoning capabilities. However, the limited resources of embedded and Edge devices significantly constrain the complexity of deep learning models that can be effectively deployed. Traditional approaches rely on cloud-based training and edge-only inference, a paradigm that becomes inadequate when low latency, privacy, security, and high customization are required. In this context, On-device AI is emerging as a new paradigm in which both training and inference are performed directly on the device, avoiding data transfer …

Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients 'selfish'. This work proposes a Fair and Robust strategy for aggregation in the Federated Learning (FL) server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel …

Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which …

Cervical Ligament Systems In Sauropod Dinosaurs: What Support Is There?, Audrey Rose Williams

Masters Theses

"Sauropod dinosaurs, such as Diplodocus, have been the subject of numerous hypotheses about what ligamentous structures could have lifted and supported their long necks. Because the supportive tissues rarely fossilize, palaeontologists rely on osteological correlates on the bone to infer their nature. Options for supportive cervical ligaments in sauropods are mammal-style nuchal ligaments, avian-style interlaminar elastic ligaments, reptilian-style supraspinal ligaments, or some combination thereof. This study tested for the presence of a mammal-style nuchal ligament via histological and micro-computed tomography of posterior cervical hemispinous processes of Apatosaurus and Diplodocus, in addition to examining gross morphological features of the cervical vertebrae …

High Temperature Diffraction From Aerodynamically Levitated Materials, Chris J. Benmore, Stephen K. Wilke, David Lipke, Richard Weber

Materials Science and Engineering Faculty Research & Creative Works

Aerodynamic levitation combined with laser beam heating has become an established technique for studying the structure of materials at ultra-high temperatures and under non-equilibrium conditions. This article briefly highlights some recent technical and scientific advancements in understanding the relationships between a material's behavior and its structure, investigated using diffraction methods. It focuses on three evolving frontiers: sophisticated sample environments for accessing metastable states and reactive chemistries, high-flux photon and neutron probes to reveal atomic structure, and advanced computational modeling frameworks. Free from contamination, containerless processing (levitation) can minimize heterogeneous nucleation at the interface, enabling access to deeply supercooled melts or …

Anisotropic Second-Harmonic Ince-Gaussian Beam Generation Using Nbobr2 Holograms, Jayanta Deka, Xiaodong Yang, Jie Gao

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Ince-Gaussian (IG) modes are a complete and orthogonal set of solutions to the paraxial wave equation in elliptic coordinates representing a continuous transition between Hermite-Gaussian modes and Laguerre-Gaussian modes. Chip-scale platforms to generate IG beams have great significance for various applications, including optical trapping and micromanipulation of particles, optical communication, and quantum optics. On the other hand, materials with high optical anisotropy are crucial for building polarization-sensitive optical devices. In this context, niobium oxide dihalides are a new class of ferroelectric materials exhibiting strong, tailorable, and highly anisotropic second-harmonic generation responses. Here, we report the generation of highly anisotropic second-harmonic …

Analytical And Empirical Data-Driven Risk-Governance Design Across The Different Phases: Creating Informed Policy Adjustments For The Transportation Project Lifecycle, Mariam A. Elazhary

Doctoral Dissertations

"Transportation infrastructure is a critical foundation for economic productivity, yet agencies still face challenges in translating the expanding transportation datasets into defensible, phase-specific risk-governance tools. This dissertation addresses this gap by developing analytical and empirical, data-driven approaches to support risk governance across the transportation project lifecycle. Module 1 uses a panel model with bid-letting data to examine how out-of-state contractor participation affects pre-award competition and to derive entry-governance rules. Module 2 analyzes nonfatal injury data through iterative multiple linear regression and fatality data through association-rule change mining to identify multi-factor safety-risk configurations and develop adaptive safety-governance provisions. Module 3 develops …

Re-Crosslinkable Hyper-Branched Polymer Gels Based On A Transamidation Reaction, Tao Song, Baojun Bai, Thomas P. Schuman

Patents

The present invention generally relates to the field of conformance control in oil fields, particularly to a re-crosslinkable preformed particle gel system, and composition thereof. The composition of the re-crosslinkable preformed particle gel system contains monomers, crosslinkers, a polymer matrix, and additives. Components in the composition for re-crosslinkable preformed particle gel in the present invention have excellent compatibility with each other, and the obtained gel has excellent long term high-temperature resistance property. Different from prior products, in the recrosslinkable preformed particle gel compositions according to embodiments of the present invention, the components that are used to re-crosslink the particle gels …

Generation Of Wave Turbulence In Dipolar Gases Driven Across Their Phase Transitions, Georgios A. Bougas, Koushik Mukherjee, Simeon Mistakidis

Physics Faculty Research & Creative Works

Ultracold quantum gases with long-range anisotropic interactions host novel exotic phases of matter, such as super solids, exhibiting both rigid and superfluid characteristics. The impact of this interplay on the out-of-equilibrium dynamics of dipolar gases, and in particular its connection with universal turbulent behavior, remains highly unexplored. Here, upon considering a dipolar Bose-Einstein condensate of dysprosium atoms being dynamically driven across the super solid-superfluid phase transition and vice versa, we unveil the emergence of a robust nonequilibrium quasi-steady state. This state displays self-similar momentum distributions exhibiting algebraic decay at large momenta, with scaling exponents supporting the existence of wave turbulence. …

Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network …

Dcmm-Transformer: Degree-Corrected Mixed-Membership Attention For Medical Imaging, Huimin Cheng, Xiaowei Yu, Shushan Wu, Luyang Fang, Chao Cao, Jing Zhang, Tianming Liu, Dajiang Zhu, Wenxuan Zhong, Ping Ma

Computer Science Faculty Research & Creative Works

Medical images exhibit latent anatomical groupings, such as organs, tissues, and pathological regions, that standard Vision Transformers (ViTs) fail to exploit. While recent work like SBM-Transformer attempts to incorporate such structures through stochastic binary masking, they suffer from non-differentiability, training instability, and the inability to model complex community structure. We present DCMM-Transformer, a novel ViT architecture for medical image analysis that incorporates a Degree-Corrected Mixed-Membership (DCMM) model as an additive bias in self-attention. Unlike prior approaches that rely on multiplicative masking and binary sampling, our method introduces community structure and degree heterogeneity in a fully differentiable and interpretable manner. Comprehensive …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …

Solubility And Dissolution Mechanism Of Novel Multi-Ester Headgroup Surfactants In Supercritical Co2, Ning Xu, Yan Ling Wang, Baojun Bai, Shi Zhang Cui, Yu Zhang, Wen Jing Shi, Zhao Nian Zhang, Wen Hui Ding, Pei Xu Ma, Zan Gao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address the limited solubility and applicability of conventional hydrocarbon surfactants in supercritical CO₂, a series of multi-ester headgroup surfactants were designed and synthesized by leveraging the CO₂-philic properties of ester groups. The molecular structures were characterized using Fourier transform infrared (FT-IR) spectroscopy and ¹H NMR. A custom-designed laser-based apparatus was developed to quantify surfactant solubility and systematically investigate phase behavior in CO2. Molecular dynamics (MD) simulations were employed to elucidate structure–solubility relationships across multiple scales, including solubility parameters, interaction energies, radial distribution functions (RDFs), and free volume fractions. Results indicate that, at 323.15 K, …

Analyzing Sleep Architecture And Brain State Transitions Via Hidden Markov Models On Fmri Data, Brileigh Jay Cates

Masters Theses

Sleep is associated with systematic changes in brain activity and functional connectivity observable in functional magnetic resonance imagining (fMRI) signals. Because subjects often fall asleep during resting-state experiments, the absence of vigilance monitoring can confound the interpretation of resting-state dynamics. Although electroencephalography (EEG) is the gold standard for sleep staging, simultaneous EEG-fMRI acquisition is not always feasible.

This study investigates whether sleep stages can be inferred directly from fMRI using a probabilistic latent-state framework. Hidden Markov Models (HMMs) are applied to blood-oxygen-level-dependent (BOLD) time series to identify latent brain states and their temporal transitions. Inferred states are aligned with EEG-derived …

A Combined Stochastic And Physical Framework For Alloys And Metal Casting Processes Modeling, Simon N. Lekakh, Oleg Neroslavsky

Materials Science and Engineering Faculty Research & Creative Works

High temperature metal casting processes have dualistic nature and conceptually consist of two parts of distinct processes: the first type is deterministic, strictly obeying the physical law, while the second type is stochastic. Therefore, the metal casting processes are not precisely predictable, and deterministic considerations cannot provide exact outcomes. To solve this problem, the combined stochastic and deterministic framework was suggested. The local processes were described using deterministic models for several parameter arrangements, while the distribution of these arrangements on macro level was calculated using stochastic approaches. The approach was used for cast alloy design, investment casting process optimization, and …

Overcoming Resolution Vs. Throughput Trade-Offs In Ceramic Material Extrusion Additive Manufacturing Via Viscoelastic Filament Stretching, Abid H. Rafi, David W. Lipke, Jeremy L. Watts, Gregory E. Hilmas, Ming C. Leu

Materials Science and Engineering Faculty Research & Creative Works

Fabricating large, monolithic ceramic parts using material-extrusion additive manufacturing remains challenging due to difficulty maintaining uniform moisture content during printing, which can lead to drying-induced defects such as warping and cracking, especially as part size and print time increase. Fabricated parts have trade-offs among print resolution, high throughput, and structural fidelity. Our study has shown that increasing the ratio of nozzle traverse speed vs. material extrusion speed increases filament stretching in viscoelastic ceramic paste, helping to overcome the trade-offs between resolution and throughput. Using aqueous ZrB2–SiC (70/30 vol.%) as a representative ultra-high temperature ceramic paste, rheological characterisation revealed viscoelastic yield-stress …

Sadqn-Based Residual Energy-Aware Beamforming For Lora-Enabled Rf Energy Harvesting For Disaster-Tolerant Underground Mining Networks, Hilary Kelechi Anabi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a …

Stall Detection In Hydraulic Excavator Operations Using Heuristics And Machine Learning: A Case Study, Mateo Fernando Montenegro Defaz, Kwame Awuah-Offei, Yixiang Gao

Mining Engineering Faculty Research & Creative Works

This work aims to develop a reliable algorithm for stall detection during excavator digging by analyzing key operational variables such as velocity and angular displacements from machine monitoring data. The work develops and validates a heuristic algorithm to detect stalling events and trains a support vector machine classification algorithm to distinguish between "normal" digging cycles and cycles with stalling. This work is a novel attempt at using a classification algorithm to categorize digging cycles into normal and those with stalling events based on machine monitoring data alone. The developed classification algorithm achieved a sensitivity of 100%, indicating it correctly identified …

Transverse Magnetic Mode Split Post Dielectric Resonator, Donghyun Kim, Xiaoning Ye, Chaofeng Li

Patents

A device for measuring a property of a substrate comprises an enclosure,two resonators,and two probes.The substrate has a top surface, and the enclosure defines a cavity for placing the substrate. The resonators extend into the cavity of the enclosure. The probes extend into the cavity, and each of the probes comprises a loop that extends about an axis that is substantially parallel to the top surface of the substrate so that the probes are operable to excite a transverse magnetic mode signal in the cavity.

Experimental Investigation Of Subcritical Neutron And Gamma Noise Methods At The Seven Percent Critical Experiment (7upcx), Cole Kostelac, Jesson Hutchinson, George Mckenzie, Robert Weldon, Nicholas Whitman, Ayodeji Babatunde Alajo

Nuclear Engineering and Radiation Science Faculty Research & Creative Works

As part of a collaborative international effort organized by Lawrence Livermore National Laboratory (LLNL), with key participants from L'Institut de radioprotection et de s ˆuret´e nucl´eaire (IRSN), Los Alamos National Laboratory (LANL), and Sandia National Laboratories (SNL), a series of high-multiplication subcritical neutron and gamma noise measurements were planned and executed. The primary aim of this research was to advance detector technology, assess the validity of gamma noise for subcriticality measurements, and nuclear criticality safety, focusing on collecting list-mode or time-series data from various reactor configurations with multiplication values ranging from 20 to 310. This comprehensive dataset enabled a detailed …