Other Engineering Commons^™

Cavitation-Aware Design Of Hydrofoil Sections For Marine Propeller Blade Development, Samuel G. Bacon

Mechanical & Aerospace Engineering Theses & Dissertations

Cavitation is a major design constraint for marine propeller blade sections because it can reduce hydrodynamic performance, increase vibration and noise, and contribute to surface damage. This thesis develops and applies an exploratory computational framework for cavitation-aware hydrofoil-section design relevant to marine propeller blade development. The framework combines section-level CFD validation, CST-based geometry parameterization, pressure-based pre-screening, matched-load comparison, and focused predicted inception envelope or cavitation-bucket analysis.

The CFD methodology was validated against published YS-920 hydrofoil water-tunnel data using selected fully wetted, near-inception, and cavitating reference cases. The validation results showed that the adopted framework reproduced the key experimental trends needed …

Deep Learning Based Control System For Multi-Zone Hvac Residential Buildings, Mohammad H. Z. Alghalayeeni

Mechanical Engineering Theses

Heating, ventilation, and air conditioning (HVAC) systems are major contributors to residential energy consumption. However, most homes continue to rely on single zone thermostat control, which regulates temperature based on a single sensor and cannot account for thermal variations across multiple rooms. This often leads to uneven thermal conditions and inefficient energy use in multi-zone residential buildings. This thesis presents a deep reinforcement learning based approach for improving HVAC zoning control through dynamic airflow distribution. A physics based multi zone thermal model of a residential house was developed to simulate heat transfer processes including conduction, convection, solar and internal heat …

Multi-Modal Tensor Fusion For Alzheimer’S Disease Recognition, Mason Li, Tiffany Le, Jiajing Huang, Yuxin Wen

Engineering Faculty Articles and Research

Accurate and early diagnosis of Alzheimer’s disease (AD) is critical for effective intervention, disease monitoring, and patient care. Traditional diagnostic approaches rely on a single modality, such as clinical assessments, neuroimaging, or genetic markers, which may fail to capture the complex, multifaceted nature of AD. Multimodal learning has therefore been explored to integrate complementary information across data sources. However, conventional fusion strategies, including early feature concatenation and late decision-level fusion, often model modalities independently and fail to capture high-order cross-modal interactions. To address these limitations, we propose a multimodal tensor fusion network (MTFN) that integrates heterogeneous data sources, including visual …

Sustainability Tool Inventory, Robert Moore, James O'Donnell

Tools

The purpose of this inventory is to inform Irish organisations, including the private sector, public agencies and semi-states, about Sustainability Tool

Developing Low-Cost Gnss Remote Sensing Hardware To Measure Precipitable Water Vapor For Flash Flood Nowcasting In Southern Appalachia, Austin Gleydura

Student Research Symposium (SRS)

Flash flood prediction in Southern Appalachia is particularly challenging due to steep terrain, narrow valleys, highly localized rainfall patterns, and limited measurement coverage. Traditional remote sensing methods, such as Doppler radar and microwave radiometry, suffer from reduced resolution at extended range and signal blockage by mountains, while satellite instruments like MODIS lack sufficient spatio-temporal resolution for sub-kilometer measurements critical to flash flood nowcasting. GNSS-Meteorology offers an established alternative for measuring precipitable water vapor (PWV) and is currently integrated into several numerical weather models. Recent research demonstrates that GNSS-derived PWV products can be used to accurately predict rainfall intensity and timing …

Computational Modeling Of Enhanced Nonlinear Optical Response In Plasmonic Waveguide Devices With Epsilon-Near-Zero Films, Kevin T. Le, Mark C. Harrison

Engineering Faculty Articles and Research

Silicon photonics faces fundamental limitations in nonlinear applications due to weak Kerr nonlinearity, substantial two-photon absorption, and coupling challenges with subwavelength structures. This work investigates epsilon-near-zero (ENZ) materials integrated into plasmonic waveguide architectures for enhanced nonlinear photonic devices. ENZ materials exhibit nonlinear refractive indices several orders of magnitude higher than conventional materials near their zero-permittivity wavelength, enabling giant optical effects over deeply sub-wavelength interaction lengths. We present finite element method simulations of plasmonic ENZ waveguides, investigating layer thickness optimization for nonlinear enhancement while minimizing optical losses. Initial modal analysis confirms superior field confinement in hybrid plasmonic-ENZ structures compared to conventional …

Dual-Function Plasmonic Structure For Logic Operations And Circular Polarization Detection, Marjan Bazian, Mark C. Harrison

Engineering Faculty Articles and Research

Many integrated photonic devices have a high potential for dual applications, making them more versatile components in photonic integrated circuits. In this study, we show that a plasmonic XOR gate structure, originally designed to perform logic operations, can also be used as a circular polarization detector. This multi-purpose capability stems from the phase-encoded input mechanism that controls the output response of the structure. We adapted this mechanism to select the output based on the polarization state of light. The design of this structure, utilizing the subwavelength field confinement of plasmonic waveguides, enables the integration of logic and detection functions into …

Cancerseg-Xa: Medical Histopathology Segmentation System Based On Xception Backbone And Attention Mechanisms, Alaa Youssef, Wessam H. El-Behaidy, Aliaa Youssif

Computer Science

Accurate segmentation of histopathological images is essential to support early diagnosis and effective treatment planning in cancer care. This study presents CancerSeg-XA, a deep learning-based histopathology segmentation system designed to deliver robust performance across diverse tissue types and imaging sources. Built upon the DeepLabV3+ framework, CancerSeg-XA incorporates architectural enhancements to strengthen feature representation and improve model stability. The system was evaluated on three widely recognized datasets—BCSS, PanNuke, and PUMA—each presenting distinct structural and clinical challenges. Across all datasets, CancerSeg-XA consistently outperformed the baseline DeepLabV3+ in terms of segmentation accuracy, recall, and F1-score. Specifically, it achieved accuracy improvements of 4.78%, 4.31%, …

Dynamic Soliton Solutions And Stability Analysis Of The (2+1)-Dimensional Wazwaz Kaur Boussinesq Equation Using An Efficient Method, Nivan Mohamed Elsonbaty, Hamdy Mohamed Ahmed, Niveen Mohamed Badra, Wafaa B. Rabie

Basic Science Engineering

This paper presents the first application of the Modified Extended Direct Algebraic (MEDA) method to the (2+1)-dimensional Wazwaz-Kaur-Boussinesq equation, a model governing wave dynamics in shallow waters. The approach successfully uncovers previously unreported classes of exact solutions, including combo dark–singular solitons and Jacobi elliptic function solutions. The spectrum of obtained solutions–which also encompasses bright, dark, and singular solitons, as well as hyperbolic, periodic, exponential, and rational functions–reveals rich and complex soliton dynamics. A comprehensive stability analysis confirms the robustness of these solutions under perturbation. These results significantly advance the understanding of wave propagation in nonlinear systems, providing valuable insights for …

Mycelium-Based Composites Using Minimally Processed Industrial Hemp Biomass: Impact Of Species And Feedstock Ratio On Mechanical Performance Compared To Polystyrene Packaging, Radika Bhaskar, Tanisha Rutledge, Kevin Trangone, Oneal Latimore

School of Design and Engineering Papers

Mycelium-based composites (MBCs\) are formed from lignocellulosic substrates and biopolymer matrices derived from fungal mycelium. Due to their low fossil energy demand and biodegradability, MBCs represent a versatile and sustainable material suitable for a range of applications, with increasing interest focused on packaging. Hemp fibers are an example of natural fibers with great promise as a substrate to improve the mechanical properties of MBCs. However, the separation of bast and hurd fiber requires processing and commercial-scale facilities that are logistically challenging and may be cost-prohibitive. Here, the potential for minimally processed hemp, with no separation of fibers, is evaluated for …

Comprehensive Evaluation Of Co₂ Eor Numerical Modeling In The Clinton Sandstone Of The Appalachian Tri-State Region Using Compositional Reservoir Simulation (Cmg Gem), Bushra Aref Alqattan

Graduate Theses, Dissertations, and Problem Reports (ETD)

ABSTRACT

The Clinton Sandstone of the Appalachian Basin represents a mature hydrocarbon-producing formation with potential for carbon dioxide (CO₂) enhanced oil recovery (EOR). This study develops a compositional reservoir simulation model using the CMG software suite (WinProp, Builder, and GEM) to evaluate the performance of CO₂ injection in improving oil recovery within the Clinton formation.

A three-dimensional reservoir model was constructed using representative geological and petrophysical properties consistent with those of the Clinton Sandstone. Fluid behavior was modeled using a compositional equation-of-state (EOS) approach to accurately capture phase behavior, miscibility development, and CO₂–oil interactions under reservoir conditions. Injection scenarios were …

Machine Learning For Predicting Prosthetic Limb Movements, Jessica Alexandra Cegarra Arraiz

Theses and Dissertations

This thesis develops and evaluates a deep learning-based prediction model capable of identifying intended limb movement from surfaced electromyography (sEMG) signals using sequence learning techniques. sEMG signals change over time due to multiple factors such as muscle fatigue or user variability. Traditional prosthetics control methods rely on static feature extraction, ignoring how signals change over time, thereby limiting their ability to capture the temporal changes of muscle activity. As a result, these approaches often lead to poor accuracy, robustness, and generalization. Limited experimental validation has been conducted on sequence-based machine learning approaches using temporal sEMG data from publicly available datasets …

Ai-Driven Prediction And Reconstruction Of Missing Cased-Hole Logs For Improved Well System Understanding, Samuel Avilez Martinez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Well logging is a fundamental technique in formation evaluation providing continuous, real-time measurements of geological and petrophysical properties within a well. Through the systematic analysis of well logs, engineers and geoscientists can accurately determine critical formation characteristics, including porosity, permeability, lithology, and fluid composition. Well logging is fundamental for making informed decisions and reducing uncertainties in the exploration and development of oil and gas reservoirs.

Despite its significance, the acquisition of reliable well log data in oil and gas wells is often compromised by various operational, mechanical, and formation-related challenges, as well as pressure, fluid, and equipment constraints. In high-risk …

Ai-Based Porosity Detection In Babbitt Bore Turning, Kaylee Leggett, Hannah Adams Gemmell, Kaisa Shingleton

Williams Honors College, Honors Research Projects

For this project, an external company reached out to the University of Akron requesting assistance with defect detection during their vertical turning operations. As babbitt is removed in a vertical turning process, it occasionally reveals defects, mainly porosity, which can lead to costly downstream failures of the part. Current inspection techniques involve use of dye penetrant, which is time consuming, labor intensive, unergonomic, and a source of human error. The goal of the project is to create an alternative inspection method using an AI-based machine-learning model. After the turning operation, a camera is deployed to perform an in-place inspection, taking …

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 …

Assessing The Influence Of Land Use Land Cover Alterations On Climate Variability In Thimphu & Chhukha, Sonam Zangpo, Karma Choki, Tashi Tobgay, Phurpa Rinchen, Tandin Tshewang

ASEAN Journal on Science and Technology for Development

This study examines the spatiotemporal dynamics of land use and land cover (LULC) changes and their influence on local climate variability in Bhutan using an integrated geospatial and machine learning framework. LULC changes during the period 2004 to 2022 were analyzed to understand their relationship with localized climate responses. Despite increasing urbanization pressures, comprehensive assessments linking LULC dynamics with climate variability in Bhutan remain limited. Geographic Information System (GIS) techniques and Random Forest classification were used to map LULC changes, while non-parametric trend analyses (Mann–Kendall test and Sen’s slope estimator) were applied to evaluate trends in key meteorological variables, including …

Advancing Edge Intelligence: Strategies For Improving Machine Learning On Embedded Devices, Prakash Dhungana

Theses and Dissertations--Electrical and Computer Engineering

Artificial intelligence (AI) has demonstrated tremendous success in handling complex tasks across multiple domains, a leap largely attributed to complex deep neural network (DNN) architectures. However, the increasing demand for real-time processing, low latency, limited connectivity, and enhanced privacy necessitates edge intelligence, where computation is performed directly on resource-constrained embedded devices rather than relying on cloud infrastructures. While optimized TinyML techniques such as pruning, quantization, and neural architecture search (NAS) have made edge deployment feasible by significantly reducing computation and memory demands, achievable performance remains severely limited by hardware constraints. Furthermore, models deployed in highly dynamic real-world environments suffer from …

Automation And Habitat Development For A Space Based Marine Life Environment, Logan Trimmer, Alexander Hang

Harrisburg University Other Works

This project was a cross-collaboration between the Environmental Sciences and Advanced Manufacturing and Robotics programs for the company Monolith Space.

The goal of this project was to design an autonomous system to be able to track qualities of water in an aquaculture system designed to be sent to space.

Workshop Outcomes Report: 2nd International Workshop On Seismic Resilience Of Arctic Infrastructure And Social Systems, Majid Ghayoomi, Daniela Morganti

Faculty Publications

The report provides an overview of the second international workshop on Seismic Resilience of Arctic Infrastructure and Social Systems. The report discusses agenda, workshop activities, interdisciplinary working groups, and results. It ends with several strategic questions and investigation plans that were developed as part of the workshop activities.

Feasibility Assessment Of Energy As A Service With Third Party Ownership For Development Of Utility-Scale Solar Pv In The Consumer Goods Industry, Winda Puspadimiati, Widodo W Purwanto

CSID Journal of Infrastructure Development

Global awareness of climate change impacts has compelled the power sector to adopt renewable energy as a primary source of electricity, driving innovation in renewable energy business models. This study analyzes the feasibility of Energy as a Service (EaaS) with a Third-Party Ownership (TPO) scheme for utility-scale solar photovoltaic (PV) development in the fast-moving consumer goods (FMCG) industry. Four scenarios of utility-scale solar PV configurations under the EaaS model are evaluated: on-grid ground-mounted (OG-GM), hybrid ground-mounted (H-GM), on-grid floating (OG-F), and hybrid floating (H-F). All scenarios are developed under the assumption of utilizing the PLN grid through a power wheeling …

Assessment Of The Melt Quality Of A 30% Scrap Adc12 Aluminium Alloy Using The Inclusion And Fluidity Measurement Instrument (Ifmi) With Mullite Ceramic Filters, Gusti Ruri Ruri Lestari, Sandya Ananda Riswan, Muhammad Anis, Ahmad Ashari, Paramita Vidya Ayuningtyas, Bambang Suharno Prof, Donanta Dhaneswara

Journal of Materials Exploration and Findings

The increasing demand for sustainable practices in the metal casting industry has driven the use of recycled aluminum alloys such as ADC12. However, the addition of aluminum scrap tends to increase oxide inclusions, which reduce melt fluidity and compromise casting quality. This study utilizes the Inclusion and Fluidity Measurement Instrument (IFMI), equipped with mullite ceramic filters, to assess the melt quality of ADC12 aluminum alloy containing 30% scrap. Fluidity and inclusion characteristics were evaluated at five pouring temperatures (660°C, 680°C, 700°C, 720°C, and 740°C). The results show that fluidity increased with temperature, reaching a peak of 84.6 g·s⁻¹ at 740°C, …

Equipment Criticality Analysis To Determine Asset Integrity Management System Scheme In Supporting Production Optimization Scenarios In The Aeging Field, Donny Andryanto, Donanta Dhaneswara

Journal of Materials Exploration and Findings

This research investigates the production decline and cost increase in the X&Y oil and gas fields from 2022 to 2023. Production fell by 34%, while production costs per barrel rose by 79%. To address these issues, a series of optimization processes are proposed. These aim to restructure and enhance the production facilities to reduce current production costs. The optimizations include reducing pressure at the PPP Platform in 2024. Additional steps include shutting down CPP-ORF 14” pipelines and most processes at CPP 2 Platform by 2027 to convert it into an accommodation platform. Further, the plan involves optimizing the release of …

An Overview Of Biomass-Derived Graphene Oxide And Its Characteristics For Future Sustainable Applications, Afif Wardana, Ahmad Nabil Shahab, Donanta Dhaneswara, Nofrijon Sofyan

Journal of Materials Exploration and Findings

The production of graphene oxide (GO) from biomass presents considerable promise as a sustainable alternative substitute for traditional semiconductors. Biomass waste, abundant and often underutilized worldwide, is distinguished by its high carbon content and regenerative characteristics, rendering it an optimal resource for sustainable material production. By heating its biopolymers, lignocellulosic biomass can be used as a new material to make graphene, which forms three-dimensional turbostratic crystallites. These crystallites, composed of partially defective aromatic carbon sheets with graphite-like characteristics, make it easier to create GO with specialized functions for cutting-edge applications. Its capability underscores the revolutionary potential of biomass waste in …

Morphological Test Of Areca Nut Fiber Ceramic Membrane Using Scanning Electron Microscopy Energy Dispersive X-Ray Mapping Spectroscopy, Amelia Marzain, Siti Umi Kalsum, Marhadi Marhadi, Ahmad Nabil Shahab

Journal of Materials Exploration and Findings

This study investigates the potential of ceramic membranes derived from areca nut fiber as a cost-effective and environmentally sustainable material for the removal of iron (Fe) and manganese (Mn) from groundwater. Two types of membranes were fabricated: one without activation and one chemically activated using 10% sodium hydroxide (NaOH). The morphological and elemental characteristics of both membranes were analyzed using Scanning Electron Microscopy (SEM) and Energy Dispersive X-ray (EDX) mapping. The concentrations of Fe and Mn before and after treatment were measured using Atomic Absorption Spectroscopy (AAS). The NaOH-activated membrane exhibited a more porous surface structure and higher oxygen content, …

Analysis Of Leakage In Nearshore Pipelines Using Fitness For Service Method To Ensure Mechanical Integrity Based On Api-579 Level 3, Elriandri Elriandri, Dedi Priadi

Journal of Materials Exploration and Findings

The Fitness for Service (FFS) analysis is performed as a quantitative assessment to evaluate the integrity condition of a pipeline. Essentially, FFS assessment helps determine whether equipment components can operate safely despite existing deficiencies. This evaluation is carried out using the Finite Element Method (FEM). In the case of an underwater pipeline that experiences leak due to anchor pull at the flange connection, it undergoes plastic deformation and is lifted approximately 1 meter. As a mitigation step, inspection and repairs have been carried out by the company. Subsequently, modeling is performed to reconstruct the deformation process of the pipeline. Then, …

Quantum Sensing Of Magnetic And Thermal Fields Using Nitrogen-Vacancy Centers On Spoof Surface Plasmon Waveguides, Maxwell Stonham

UNLV Theses, Dissertations, Professional Papers, and Capstones

Nitrogen-vacancy (NV) centers in nanodiamonds are solid-state quantum defects capable of detecting magnetic fields and temperature at the nanoscale with high spatial resolution and optical addressability at room temperature. Their spin-dependent fluorescence enables optically detected magnetic resonance (ODMR), a technique widely used for quantum sensing. However, enhancing optical readout efficiency remains a critical challenge for scalable integration and various methods can be used to improve this.

This thesis explores a hybrid quantum sensing platform that integrates NV-based sensing with spoof surface plasmon waveguides designed to enhance optical excitation and emission efficiency through localized spoof plasmon modes supported by subwavelength corrugated …

The Role Of Artificial Intelligence In Reducing Internet Crimes Against Children, Malyssa Shaw

Student Scholar Symposium Abstracts and Posters

When generative artificial intelligence (AI) first surfaced and broke into the public sphere, my immediate concern was in its development, implementation, and harmful applications. I was not surprised when deepfake technology rapidly advanced alongside these new developments and impacted women and children worldwide. Disproportionately, they have been made victims of intimate media forgery as early as the 1990s, with an unprecedented uptick in recent years as a direct result of these developments. In response, I wrote "Deepfake, Real Harm: Protecting Children in the Age of AI", analyzing data specifically regarding child sexual abuse material (CSAM) created with artificial intelligence while …

Analyzing Patterns In Hofstede’S Cultural Dimensions Towards Individual Ai Receptiveness For Affective Experiences, Maggie Yang

Master's Theses

The use of artificial intelligence (AI) has significantly advanced efficient decision-making, with trust in algorithmic decisions shown to vary by cultural upbringing [1]. As AI becomes increasingly embedded in everyday life, it is essential to examine how cultural values affect trust in AI in subjective contexts that extend beyond purely quantitative analysis, like the personal interpretation of art. This work utilizes Hofstede’s cultural dimensions to investigate potential patterns in receptiveness towards AI predictions during art interpretation, providing insight into individual susceptibility to bias in AI-assisted affective analysis.

The study leveraged a cultural dimension survey and a custom Java program connected …

Experiential Learning And The Revitalization Of Manufacturing Education At The University Of Dayton, Sean Cahill

Research and Reflection on Learning and Teaching in Higher Education

This perspective paper explores the role of experiential learning in preparing future-ready manufacturing engineers at the University of Dayton, set against the backdrop of Dayton’s legacy as an industrial innovator and the broader national movement to revitalize domestic manufacturing. As automation, cyber-physical systems, and Industry 4.0 technologies reshape the manufacturing landscape, there is a growing need for engineers who possess both technical fluency and systems-thinking capabilities. To meet this need, the manufacturing engineering technology department implemented hands-on, integrated lab-and-lecture modules through support from the Experiential Learning Innovation Fund for Faculty (ELIFF). Grounded in Kolb’s Experiential Learning Theory and constructivist learning …

Spatially Mapped Statewide Estimated Potential Evapotranspiration Using An Efficient Surface Interpolation Method: A Case Study Of South Carolina, Sudhanshu S. Panda, Devendra M. Amatya, Ka Kit Liu, Augustine Muwamba, Timothy J. Callahan

Journal of South Carolina Water Resources

Potential evapotranspiration (PET) exhibits substantial spatial and temporal variability across large landscapes, necessitating site-specific estimation for accurate environmental and water resource assessments. However, obtaining PET or ET data for specific locations across an entire state remains challenging due to the limited number of weather stations and associated environmental datasets. This study aimed to develop an automated geospatial modeling framework to map PET distribution across South Carolina, USA, using PET estimated by the temperature-based Hargreaves–Samani (H–S) method with daily weather data from 59 NOAA stations. Because the accuracy of spatial interpolation depends on both the target variable and the desired spatial …