Engineering Commons^™

Simulated Lunar Gravity Testing Of A Magnetic And Electrostatic System For Beneficiating Lunar Regolith, Blake A. Coffman, Gabriel Porter, Lindsay Manteufel, Mitchell Cottrell, Jeffrey D. Smith, David J. Bayless, William Shonberg, Frank D. Han, Fateme Rezaei, Kirby Runyon

Materials Science and Engineering Faculty Research & Creative Works

We present the design and testing of a lunar regolith beneficiation device that utilizes magnetic and electrostatic separation methods to concentrate desired minerals by removing unwanted material, such as the mineral anorthite, from bulk lunar regolith. The beneficiated materials would have value for downstream in-situ resource utilization (ISRU) processes such as metal extraction, oxygen extraction, and metal oxide additive manufacturing processes. The apparatus uses a dual-strength magnet system with N52 and N42 neodymium magnets to separate particles by magnetic susceptibility. The electrostatic separation system, which acts like a sieve, sorts the regolith simulant by particle size using a single-phase 50% …

The Effects Of Mold Flux Contamination On Oxide Scale Formation And Hydro-Descaling Efficiency During Steel Processing, Tochukwu Princewill Ojiako, Richard Osei, Mario Buchely, Haiming Wen, Simon Lekakh, Ronald O'Malley

Materials Science and Engineering Faculty Research & Creative Works

Oxide scale formation during thin-slab continuous casting has a complex structure, which is influenced by mold flux contamination, that modifies interfacial reactions during solidification, subsequent reheating, and descaling. While individual aspects of the oxidation behavior of carbon steel have been previously examined, the synergetic effects of mold flux contamination during continuous casting and subsequent reheating on scale modification and the efficiency of hydraulic descaling remain inadequately studied. This study quantitatively examines the effect of flux composition on oxide scale evolution, adhesion, and hydraulic removal in low-carbon steel under simulated industrial conditions. Slab samples with as-cast, cleaned, and flux-coated surfaces were …

Five Tensions Of Artificial Intelligence Adoption For Organ Allocation: Applying The Technology–Organization–Environment Framework, Amaneh Babaee, Daniel Burton Shank, Casey I. Canfield, Joely Grace Hall, Krista L. Lentine, Henry Randall, Mark Schnitzler

Psychological Science Faculty Research & Creative Works

Background: The US organ transplantation system is pursuing modernization of the allocation process through the integration of new technologies such as artificial intelligence (AI). However, the legal and ethical issues within the transplantation industry are still of concern. Objective: We explore the opportunities and challenges for Organ Procurement Organizations (OPOs) to adopt AI. The US organ transplant system is a highly regulated industry yet open to innovation. Methods: Ten structured interviews were conducted with OPO representatives using the Extended Technology, Organization, Environment (TOE) framework. Results: Overall, we identified five core tensions in AI adoption: (1) misconceptions, (2) approach to training, …

Toward Secure And Practical Machine Learning-Based Access Control: A Framework With Real-World Constraints And Adversarial Analysis, Olusesi Balogun, Mohammad Ghasemigol, Zhipeng Cai, Daniel Takabi

School of Cybersecurity Faculty Publications

Attribute-Based Access Control (ABAC) frameworks coordinate access requests based on subject, object, and environment attributes, as well as policy rules, and are widely used in corporate security systems. Recently, machine learning has been applied to ABAC to address policy-generation imbalances, misassigned privileges, and attribute leakages. However, existing MLBAC techniques do not consider the structural constraints and attribute interdependencies present in traditional ABAC systems. Moreover, these frameworks have not been extensively evaluated under black-box attack scenarios. To address these gaps, we propose extensions to MLBAC that integrate structural constraints, attribute dynamism, and attribute weighting into the MLBAC objective function. Additionally, we …

Development Of A 3d-Printed Hemodynamic Model To Characterize Doppler Signatures Of Obstructed Tapvr, Michael R. Turner, Maher R. Saadeh, Holly D. Bauser-Heaton, Vahid Serpooshan

Summer Research Program Abstracts

No abstract provided.

The Fox Versus The Hedgehog: Firm Survival During Unprecedented Crises, Sheng Fang, Shaomin Li, Liu Wang

Management Faculty Publications

This paper examines how sales channel diversification influences firm resilience during the COVID crisis. Drawing on a series of logit regressions analyzing 11,662 firms across 31 developing countries, we find that firms embodying an open mind-set and a diverse array of sales channels, akin to foxes, are more likely to survive, as compared to their hedgehog counterparts, which focus on one particular task. Our mediation analysis suggests that this is mainly due to their faster growth in sales and exports. Moreover, we find that the impact of fox-like traits on firm survival is moderated by female leadership and corporate innovation.

The Operation And Control Strategy Of Microgrids: A Brief Review, Mohammad Alam, Salsabila Zaman

Electrical Engineering Student Publications

A microgrid is an effective approach for integrating various distributed energy resources to meet local energy demand. It possesses the capability to operate independently or in conjunction with the main utility grid. Typically, a microgrid is a small-scale power system (usually several megawatts or less) comprising three primary components: the ability to function in both grid-connected and islanded modes, distributed power generation sources, and autonomous control centers. Microgrids enhance system resilience by ensuring uninterrupted operation during grid disturbances. Additionally, they are environmentally sustainable and contribute to improved power quality. As a crucial element of modern power systems, microgrids can effectively …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Viscoelastic Behavior Of Magnetically Filled Pdms Elastomers: Influence Of Filler Loading And Particle Type, Madison Procyk

College of Graduate Studies: Theses & Dissertations

Magnetorheological elastomers (MREs) offer a promising pathway for soft, magnetically actuated drug‑delivery systems, yet material‑selection guidelines remain unclear due to limited comparative data across particle chemistries, size scales, and concentrations. This study systematically evaluates how magnetic filler type (carbonyl iron and magnetite) and particle size (micro vs. nano), at two weight percentages – 40 wt% and 50 wt% – in Sylgard 184 PDMS influence the viscoelastic behavior of MREs intended for compliant biomedical actuation. Eight particle formulations were prepared through sieving, controlled mixing, vacuum degassing, and isotropic curing, producing nineteen total specimens (16 with filler particles and 3 without). Dynamic …

A Real-Time Autonomous Mobile Robotic System For Crack Detection And 3d Laser Profiling In Construction Using Two-Stage Deep Learning, Emmanuella Ogun

College of Graduate Studies: Theses & Dissertations

This thesis presents an autonomous mobile robotic system that integrates real-time visual crack detection with laser-based three-dimensional profiling for building inspection. The system coordinates SLAM-based navigation, deep learning perception, and manipulator-guided measurement within a unified ROS 2 framework, enabling scan-on-demand workflows where laser profiling occurs selectively at visually detected defect locations. The detection pipeline employs a two-stage architecture combining U-Net semantic segmentation with Pix2Pix residual refinement, achieving 73.9% mean intersection-over-union and 76.4% F1-score on the CrackSeg9k benchmark while maintaining 16 frames-per-second real-time throughput. Real-world validation demonstrated 95% detection success on 80 campus images under challenging conditions. The profiling subsystem utilizes …

Formalizing Asymmetric Control-Telemetry Separation In Distributed Industrial Control Systems, Andrew Manison

College of Graduate Studies: Theses & Dissertations

Distributed industrial control systems often place control and telemetry traffic on the same communication substrate even though the two workloads impose different requirements. Control paths need bounded request-response latency and predictable acknowledgement semantics, whereas telemetry paths benefit from scalable publish-subscribe fanout and tolerance for consumer-side delay. This thesis argues that, for the tested class of mixed workloads on shared commodity infrastructure, these communication roles should be separated architecturally rather than forced through a single protocol. To evaluate that claim, the thesis formalizes an asymmetric control- telemetry pattern and instantiates it in the Asymtra framework using gRPC for synchronous control and …

Comprehensive Performance Evaluation Of Devops Infrastructure Under Dynamic Workloads, Abdulrazaq Mamud

College of Graduate Studies: Theses & Dissertations

This research aims to investigate performance optimization and reliability issues related to cloud-based computing environments through an analysis of three key infrastructure components: virtualized CPU resource management, distributed API rate limiting, and web server deployment architectures. This research combines machine learning and system experimentation as a way of exploring the impact of infrastructure-level behaviors on overall system performance and scalability. The first component of the research focuses on analyzing CPU Fragmentation in Virtualized Environments, where unbalanced workload allocation on Virtual CPU Cores causes increased tail latency, resulting in Service Level Agreement violations. Metrics are analyzed using the Random Forest classifier …

Effect Of Elemental Additions On The Microstructure, Macrostructure, And Mechanical Properties Of Gray Iron, August E. Rautmann

College of Graduate Studies: Theses & Dissertations

Gray iron is composed primarily of an iron matrix and flake graphite, with the higher percentage of volume taken up by the iron matrix. The iron matrix is the transformation product of austenite, and this prior austenite structure is understood to affect the final grain structure. Grain structure affects the properties of a metal, with a finer structure recognized as beneficial for most properties. Thus, the refinement of austenite structure has potential in improving the material properties of gray iron. This study aims to advance the limited knowledge about the control of austenite grain size via elemental additions. Building off …

The Effect Of Chamber Geometry On The Performance Of Oscillating Water Column, Timilehin O. Osunade

College of Graduate Studies: Theses & Dissertations

This thesis investigates the effect of internal chamber geometry on the performance of a piezoelectric oscillating water column (OWC) wave energy harvester. Four acrylic chamber configurations with internal top-surface angles of 0°, 30°, 45°, and 60° were tested under 32 controlled conditions combining synchronous (SYN) and asynchronous (ASC) forcing at input voltages of 4, 6, 8, and 10 V. A 35 mm piezoelectric disc mounted at the chamber roof served as the power take-off element and, subject to the RC-limit caveat disclosed in Section 3.2.1, as a first-order dynamic analogue of the chamber pressure for phase-based compressibility analysis. All test …

Feasibility Of Quantifying The Dosage Of Zycotherm In Its Modified Asphalt Binders, Susmita Acharjee

College of Graduate Studies: Theses & Dissertations

The quantification of low-percentage additives in asphalt binders is challenging due to their minimal concentration and the complex composition of the binder matrix. Zycotherm, a nano-organosilane anti-stripping additive, is typically used in very small dosages (0.05%–0.20%), making its detection and quantification particularly difficult. This study investigated the feasibility of estimating Zycotherm dosage in asphalt binders by examining chemical and rheological and moisture responses. Two asphalt binder grades, PG 64-22 and PG 76-22, were modified with Zycotherm at dosages of 0.05%, 0.10%, 0.15%, and 0.20% by weight of binder, along with corresponding base binders. Laboratory experiments were conducted using Fourier Transform …

Stabilized Multi-Objective Optimization For Camouflage Attack In Remote Sensing, Omid Rajabi Rostami, Kazi Aminul Islam, Liuwan Zhu, Rui Ning, Chungsheng Xin, Hongyi Wu, Jiang Li

Electrical & Computer Engineering Faculty Publications

We investigate the vulnerability of multi-task maritime surveillance systems in which object detection and semantic segmentation operate concurrently. We propose Stabilized Multi-Objective Optimization (SMO), a framework that jointly targets Mask R-CNN and U-Net with a single universal adversarial texture in a digital white-box setting, with additional cross-model transfer evaluation. SMO combines a logarithmic-inversion loss transformation, feature inconsistency, and a systematic comparison of six optimization strategies for seven heterogeneous objectives. The loss transformation controls loss-dependent gradient scaling, while a U-Net generator supplies a spatially coherent texture and a hull-aligned application function accommodates target geometry without explicit Expectation over Transformation (EoT) sampling. …

Effect Of Graphene Oxide On The Upconversion Photoluminescence Behavior Of Er/Yb Co-Doped Pvdf-Go Composite Nanofibers, Saptasree Bose, Jack Ryan Summers, Bhupendra B. Srivastava, Karen Lozano, Victoria Padilla-Gainza

Mechanical Engineering Faculty Publications

Upconversion photoluminescence (UCPL) materials, particularly rare-earth (RE) doped nanoparticles, have garnered significant attention due to their ability to convert near-infrared (NIR) excitation into visible emission, offering benefits such as high photostability, long lifetimes, low autofluorescence, and deep tissue penetration. Among various platforms, polymer-based one-dimensional (1D) nanofibers with in-situ lanthanide doping remain relatively unexplored, despite their superior mechanical flexibility, processability, and potential for improved luminescence performance. In this study, we report the fabrication and UCPL quenching behavior of Er3+/Yb3+ co-doped polyvinylidene difluoride (PVDF) nanofibers incorporated with graphene oxide (GO), synthesized for the first time using the scalable Forcespinning® technique. PVDF, a …

Autonomous Weapons And Strategic Stability, Chick Edmond

Political Science & Geography Faculty Publications

The increasing number of artificial intelligence (AI) elements within military systems has introduced new forms of security dilemmas related to speed, level of secrecy, and transfer of responsibility from humans to machines. This article addresses the question of whether or how AI enabled autonomous weapons can lead to greater levels of strategic instability. Three causal mechanisms were determined by this study to potentially create destabilizing effects due to the introduction of autonomy; the first mechanism is a reduction in time available for decision making. The second mechanism involves the creation of multiple pathways of escalation. The third mechanism is the …

Temporal Patterns Of Storm Surges: Event-Identification, Clustering, And Count Modeling, Ariadna Martin Oliva

Graduate Studies Theses and Dissertations 2026

Coastal regions face compounding risks when extreme events strike in rapid succession, yet most statistical frameworks treat such events as independent, a simplification that can critically underestimate hazard. This thesis addresses that gap through three interconnected contributions focused on the temporal clustering of storm surges at the global scale. A new automated method is developed to extract independent storm surge events from continuous tide gauge records. By leveraging event correlations and a soft-margin approach that accounts for local variability, the method identifies characteristic event durations across 1,485 global tide gauge stations, revealing the influence of both local site conditions and …

Real-Time Neck Posture Classification Using A Lightweight Wearable Imu Pendant, Muntaser Syed, Alfred Sjöqvist, Noah Sedlik, Tsing Liu

Electrical Engineering and Computer Science Student Publications

Poor neck posture during prolonged device use contributes to musculoskeletal disorders affecting millions worldwide. Existing posture monitoring solutions rely on camera-based systems or complex multi-sensor arrays, limiting their practicality for continuous daily use. We present a lightweight, chest-worn pendant using a single 6-axis IMU (accelerometer and gyroscope) for real-time classification of seven neck posture states: neutral, mild flexion, moderate flexion, severe flexion, extension, lateral tilt, and lying. Our approach employs an ensemble architecture combining bidirectional LSTM, Transformer encoder, and 1D-CNN models with learnable fusion weights. To address limited training data, we apply aggressive data augmentation (30x multiplication) including noise injection, …

Dystop: Dynamic Staleness Control And Topology Construction For Asynchronous Decentralized Federated Learning, Yizhou Shi, Qianpiao Ma, Yan Xu, Junlong Zhou, Ming Hu, Yunming Liao

Research Collection School Of Computing and Information Systems

Federated Learning (FL) has emerged as a potential distributed learning paradigm that enables model training on edge devices (i.e., workers) while preserving data privacy. However, its reliance on a centralized server leads to limited scalability. Decentralized federated learning (DFL) eliminates the dependency on a centralized server by enabling peer-to-peer model exchange. Existing DFL mechanisms mainly employ synchronous communication, which may result in training inefficiencies under heterogeneous and dynamic edge environments. Although a few recent asynchronous DFL (ADFL) mechanisms have been proposed to address these issues, they typically yield stale model aggregation and frequent model transmission, leading to degraded training performance …

Enhanced Hydrophilicity And Self-Cleaning Properties Of Tio₂ Thin Films On Glass For Photovoltaic Applications: Influence Of Precursor Concentration And Light Irradiation, Fatma Refaat, Hany Hashem, Mohamed Mahmoud Gouda, Ibrahim Ahmed, Khaled Abdelwahed, Ahmed Bakr El Basaty

Trends in advanced sciences and technology

This study investigates the modification of glass substrates with TiO₂ thin films to enhance surface hydrophilicity and self-cleaning behavior. The films were deposited on glass by spin coating using different precursor concentrations, followed by thermal treatment. Structural and optical properties were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and UV–Vis spectroscopy, while surface wettability was evaluated through water contact angle measurements. XRD confirmed the formation of pure anatase TiO₂, while FTIR spectra verified the presence of Ti–O and Ti–O–Ti bonds. Wettability analysis showed that films prepared with the lowest precursor concentration exhibited the …

The Impact Of Surveying, Nicholas A. Cosper

Williams Honors College, Honors Research Projects

My project consists of three parts. The first part will be helping the public. To do this I will be helping the Boy Scouts earn their surveying merit badge. This badge is known to be one of the hardest for scouts to obtain. In the next part of my project, I will be running a 3-wire level on the Davey Tree Seed Campus. This project will be extremely useful to them (the rest of this information is confidential). In the final part of my project, I will be conducting a topographical survey for the Cuyahoga Valley National Park for a …

Effects Of Coupling Agents On An Epoxy Coating And Consequent Corrosion Resistance Of Carbon Steel, Breck A. Mooney

Williams Honors College, Honors Research Projects

This project will include the determination of how coupling agents affect corrosion resistance on select carbon steel when combined with a polymer coating. This research will explore 2 different silane coupling agents: 3-aminopropyltriethoxysilane (APTES) and 3-glycidoxypropyl-trimethoxysilane (GPTMS). 1 polymer coating, Bispenol A diglicidyl ether (BADGE), will be explored as well. These coatings will be applied to select carbon steels in various ratios to determine their effects on corrosion.

Teleoperated Robotic Arm With Vision-Based Imitation Learning, Favour E. Emmanuel, Hunter J. Donahue

Williams Honors College, Honors Research Projects

This report presents the design, development, and deployment of a teleoperated robotic arm system capable of vision-based imitation learning. The system uses a leader-follower architecture with myArm C650 and myArm M750 robots manufactured by Elephant Robotics. The leader arm movement is controlled by the operator, whereas the follower performs actions acquired from demonstrations. Data collection included synchronized joint-angle recording and multi- camera video streams capturing human demonstrations.

These demonstrations were processed with a deep learning imitation algorithm called Action Chunking with Transformers (ACT). The main capability of this algorithm is its ability to make future action predictions based solely on …

Analyzing Network Traffic Utilizing Aws Developer Tools And Artificial Intelligence, Michael K. Spatafore

Williams Honors College, Honors Research Projects

Alert fatigue occurs during long hour shifts and high stress situations. Constant notifications and on-call shifts can create overstimulation and desensitization, which leads to delay responses and increased vulnerability. Malicious actors have weaponized alert fatigue by storming a network with a vast number of low-priority events to mask their malicious activity, also known as alert storming. This project focuses on the reduction of alert fatigue by automating log analysis with Large Language Models. The project uses Amazon Web Service and its many features, such as Amazon GuardDuty, Lambda, and Bedrock. Amazon GuardDuty detects anomalies, then lambda sends those anomalies to …

Determining Critical Micelle Concentrations Of Standard Surfactants And Rhamnolipids, Kathryn E. Kunz

Williams Honors College, Honors Research Projects

My proposed project will study the critical micelle concentration (CMC) of rhamnolipids using the pendant-drop and drop-weight methods. First, the critical micelle concentrations of ionic and nonionic common surfactants will be tested. Sodium Dodecyl Sulfate (SDS) will be used as an ionic surfactant and Tween 20 will be used as a nonionic surfactant. The critical micelle concentrations will be determined using the pendant-drop and drop-weight methods. The experimental results from the standard surfactants compared to their accepted values will be tested to assist with determining a more suitable approach. Completing the tests will provide a better understanding of critical micelle …

Areosense, Josephine Turney

Williams Honors College, Honors Research Projects

The AeroSense growing system is designed to make indoor aeroponic gardening easy and accessible for everyone. By combining sensors, automation, and AI, the system can monitor and adjust humidity, lighting, and nutrient levels to help plants thrive without requiring expert knowledge. The goal is to create a self-regulating garden that takes the guesswork out of growing fresh herbs and vegetables at home. The project involves building a working aeroponic prototype equipped with misters, pumps, and environmental sensors, all managed by a Raspberry Pi. Using computer vision and machine learning, AeroSense will be able to assess plant health and respond automatically …