Digital Commons Network^™

Multi-Method Energy Optimization: Linear Programming, Hmm Modeling And Arduino-Based Simulations, A.E. El-Alfy, Eman Elayat, M. A. E. Sheta

Mansoura Engineering Journal

This paper presents the simulation and optimization of hybrid energy systems integrating renewable energy and storage technologies. Linear Programming (LP) is utilized to minimize operational costs, while a Hidden Markov Model (HMM) improves reliability by accurately predicting energy flow. Hardware-in-the-loop simulation in Proteus demonstrates efficient energy trading, significantly reducing dependence on conventional sources. The results from a 24-hour analysis show that the system achieves an 87.14% load factor, indicating high efficiency in utilizing the system's rated capacity, 26.23 system reliability, and a 20% improvement in cost efficiency and enhanced sustainability, providing a robust and practical framework for hybrid energy solutions …

Trident-Bladder: A Resolution-Aware Multimodal System For Robust, Calibrated Bladder Cancer Diagnosis Across Endoscopy, Histopathology, And Liquid Biopsy, K. Sankar, S. Venkata Lakshmi, Gummadi Reshma, V. Gokula Krishnan, M. Ganesan, V. Vijayaraja

Mansoura Engineering Journal

Bladder cancer diagnosis demands the integration of cystoscopy, histopathology, and biomarker testing, yet the majority of AI systems continue to be modality, specific and sensitive to acquisition variations. TRIDENT, Bladder is a resolution, aware multimodal framework that we have developed to address this issue. The method combines cystoscopic lesion segmentation, whole, slide image (WSI) molecular subtype prediction, and urine miRNAclinical data through reliability, weighted fusion and a utility, aware decision policy. The endoscopic branch features a quality, gated super, resolution component, resulting in mDice 87.5, sensitivity 92.1%, precision 91.4%, boundary, F1 85.3%, and ECE 0.023 at 32 FPS. mDice on …

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, …

Exploring Aggregate Morphological Characteristics Under Laboratory Polishing For Enhanced Pavement Skid Resistance, Ahmed S. El-Ashwah, Magdy Abdelrahman

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

As the use of recycled asphalt pavement (RAP) in pavement construction grows for sustainable development, it becomes essential to investigate potential frictional deterioration over time. This study evaluated the friction properties of recovered RAP material aggregates compared with raw aggregates across various polishing cycles. The micro-Deval test was employed to simulate aggregate loss of texture, while morphological and friction properties were measured using an aggregate imaging measurement system (AIMS-II), along with a British pendulum tester (BPT) and dynamic friction tester (DFT). Additionally, Fourier transform infrared spectroscopy (FTIR) was employed to assess its potential in determining the origin and composition of …

Evaluation Of Multiple Generative Large Language Models On Neurology Board-Style Questions, Mohammad Almomani, Vijaya Valaparla, James Weatherhead, Xiang Fang, Alok Dabi, Chih Ying Li, Peter Mccaffrey, Dan Hier, Jorge Mario Rodríguez-Fernández

Electrical and Computer Engineering Faculty Research & Creative Works

Objective: To compare the performance of eight large language models (LLMs) with neurology residents on board-style multiple-choice questions across seven subspecialties and two cognitive levels. Methods: In a cross-sectional benchmarking study, we evaluated Bard, Claude, Gemini v1, Gemini 2.5, ChatGPT-3.5, ChatGPT-4, ChatGPT-4o, and ChatGPT-5 using 107 text-only items spanning movement disorders, vascular neurology, neuroanatomy, neuroimmunology, epilepsy, neuromuscular disease, and neuro-infectious disease. Items were labeled as lower- or higher-order per Bloom's taxonomy by two neurologists. Models answered each item in a fresh session and reported confidence and Bloom classification. Residents completed the same set under exam-like conditions. Outcomes included overall and …

River Tay Refreshments Package Design, Blake Studley

Student Research Design & Innovation Symposium

This package design project was created for a junior-level Package Design course. The objective of the project was to develop a custom beverage packaging system inspired by a culturally significant river from another country. The design concept was based on Scotland's River Tay, which is known for one of the largest salmon populations and harvesting industries. The project incorporated elements of Scottish heritage and environmental significance through visual motifs and design. Featured flavors were inspired by major cities located along the River Tay, reinforcing the geographic and cultural connection. Plaid patterns, nature-inspired graphics, and imagery of an otter; an animal …

Solving High-Dimensional Differential Equations Using Recurrent And Residual Neural Network Architectures, Hind Khaled Kolaib

Knowledge Engineering and Data Science

High-dimensional Partial Differential Equations (PDEs) form the foundation of complex process modeling in various scientific and engineering applications, including finance, physics, and optimal control. However, classical numerical methods are adversely affected by the curse of dimensionality, making them inapplicable for large-scale problems. Recently, however, deep learning-based approaches have provided a new toolbox for these high-dimensional PDEs, including methods such as the Deep Backward Stochastic Differential Equation (Deep BSDE) method. Our approach draws on a more sophisticated deep learning backbone, using neural networks (in our case, a Residual Neural Network and a Long Short-Term Memory network (LSTM) integrated into the Deep …

A Full Polymer Piezoelectric Flextensional Energy Harvester, Nadia Ahbab, Sidra Naz, Bingqi Zhao, Tian-Bing Xu

Mechanical & Aerospace Engineering Faculty Publications

This study presents a full polymer piezoelectric flextensional energy harvester (FPPFEH) comprising a single-layer poly(vinylidene fluoride) (PVDF) film bonded to a 3D-printed polylactic acid (PLA) flextensional frame. For an arm inclination angle of θ=10°, the free-body model gives a theoretical geometric force-amplification factor of M_F=cot θ ≈ 5.67; this value represents an ideal upper bound and was not independently validated by local force or strain measurements. During assembly, the film was tensioned only to remove visible slack and maintain a flat configuration. No intentional pretension was applied, and any residual tension was not measured. Off-resonance force-controlled tests showed …

Structural And Magnetic Characterization Of A Magnetite Nanoparticle-Based Aggregation Assay And Its Application To In Vitro Cytokine Monitoring, Gabrielle Rose Moss

Dartmouth College Ph.D Dissertations

Magnetic particle spectroscopy (MPS) based aggregation assays rely on target induced changes in functionalized magnetic nanoparticle (fMNP) hydrodynamic diameter and/or changes in fMNP aggregation level to enable target detection. fMNP target binding can be modeled via Brownian and Néel relaxation. We are engineering an MPS-based aggregation assay to sense tumor necrosis factor (TNF)-⍺ in a 3D melanoma tissue culture to aid in the development of immunotherapies for cancer treatment. TNF-⍺ is an inflammatory cytokine, whose production can be linked to cell death.

The first part of this thesis investigates the effects of ligand density, off-target proteins, and salts on fMNP …

Advancing Cns Tumor Management: Novel Modalities In Surgery, Radiation, And Chemotherapy, Armin David Tavakkoli

Dartmouth College Ph.D Dissertations

Most tumors affecting the brain and spine are managed through a combination of surgical resection, radiation, and chemotherapy; however, operative morbidity from invasive surgery, the narrow therapeutic window of conventional radiotherapy, and poor blood-brain barrier penetration of systemic agents are critical barriers that hinder long term tumor control. This thesis presents three complementary strategies to advance brain and spine tumor therapy. First, we investigate laser interstitial thermal therapy (LITT) in the treatment of spinal tumors with metastatic epidural spinal cord compression, replacing “separation surgery” with a minimally invasive, image-guided approach that maximizes tumor ablation while preserving neurologic function. Second, we …

Extending Low-Frequency Traveling-Wave Propagation In A Water-Filled Cylindrical Waveguide Using Active Impedance Control, William Slater

Open Access Dissertations

This dissertation investigates extending the frequency range over which a one-dimensional, plane traveling wave can be generated in a water-filled, steel-walled waveguide under ocean temperature and hydrostatic pressure conditions. The wave guide considered is a thick-walled, steel cylinder filled with a water/glycol mixture with a transducer at each end and a hydrophone array along the length. One transducer excites a plane wave while the other cancels reflections, resulting in a traveling wave in the waveguide. The current (open-loop) measurement system is advertised to generate traveling waves for low frequencies where only 3.4% of a wavelength can be measured (kL …

Process Modeling, Operability Analysis, And Techno-Economic Optimization Of Soecs For Sustainable H2 Production, Krishna Murthy Busam

Graduate Theses, Dissertations, and Problem Reports (ETD)

The global imperative to decarbonize energy systems has intensified research into sustainable hydrogen production pathways, as hydrogen emerges as a critical energy carrier for sectors difficult to electrify directly. Among water electrolysis technologies, solid oxide electrolyzer cells operating at elevated temperatures offer the highest theoretical efficiency by utilizing thermal energy to reduce electrical energy requirements, yet comprehensive frameworks integrating electrochemical modeling with process operability analysis and techno-economic optimization remain scarce in the literature. This dissertation develops a process systems framework for proton-conducting solid oxide electrolyzer cells that bridges the gap between fundamental electrochemical phenomena and industrial-scale process economics. At the …

Convergence Of High Throughput Experimentation And Machine Learning To Rapidly Advance Application-Specific Polymer Development, Mashrafee Aryan, Daniel Struble, Felix Campbell, Saroj Upreti, S. M.Ashik Abedin, Aahil Khambawla, Jeetain Mittal, Michael S. Dimitriyev, Emily B. Pentzer, Svetlana A. Sukhishvili, Xiaodan Gu, Boran Ma

Faculty Publications

Machine learning (ML) has heavily influenced the way scientific study is done with demonstrated successes in nearly every field. However, furthering performance and explainability of ML models in increasingly complex systems and with increasingly demanding outcomes requires a significant influx of high-quality data. To that end, this Review covers some of the techniques, instrumentation, and methodologies that have shown promise for significantly accelerating the discovery of polymer materials, optimization of their properties, and elucidation of property–application relationships through high throughput (HT) experimentation, characterization, and analysis. Attention is given to not only ML, but also to hardware advancements and their synergy …

Skin Type Diversity In Image Datasets, Neda Alipour

Doctoral

Image-based AI systems that analyse human skin are increasingly used in healthcare and computer vision applications. However, many human skin-based image datasets do not provide reliable information about skin type, making it difficult to assess whether these systems perform consistently across the full spectrum of skin colour. The objective of this thesis is to examine how skin type diversity is represented and measured in image datasets, and to evaluate the reliability of image-based skin type measurement methods under different imaging conditions. Using publicly available skin lesion image datasets as a well-defined and widely used sub-class of skin image datasets, this …

Development Of Stronger, More Extrudable 6xxx Series Alloys For Automotive Applications, Eli A. Harma

Dissertations, Master's Theses and Master's Reports

6xxx alloys are widely used in automotive extrusion structures for their high specific strength and formability. Advanced designs require greater formability and strength, creating an opportunity for high-strength, formable alloys. The 6xxx series forms β”-Mg₅Si₆ precipitates during age hardening and develops a fibrous microstructure during extrusion, both strengthening the alloy. Increasing Mg and Si to form more β” decreases formability. Thus, modifying texture can increase strength without reducing formability. Current alloys like 6005A add Mn and Cr to form dispersoids that inhibit recrystallization and promote strengthening textures; however, high Mn and Cr concentrations reduce formability. Replacing Mn …

Aqueous Iron Electrowinning: From Electrolyte Engineering To The Unexplored Potential Of Acetate Systems, Chodwell N. Verenga

Dissertations, Master's Theses and Master's Reports

Iron electrowinning and electrodeposition sit at the intersection of classical metallurgy and developing sustainable manufacturing. With the iron and steel industry responsible for 7 - 9% of global CO₂ emissions, low-temperature electrochemical pathways for iron production have garnered significant research interest. This review examines the fundamental electrochemical principles governing iron deposition, the thermodynamic constraints imposed by the iron-water system, and the kinetic challenges posed by the parasitic hydrogen evolution reaction (HER). The three key electrolyte systems are objectively evaluated: acidic sulfate and chloride electrolytes, alkaline suspension electrolytes and water-in-salt electrolytes. Their operating parameters, faradaic efficiencies, energy consumption and deposit characteristics …

Comparison Of Fixed Tilt And Single Axis Tracking Solar Photovoltaics During Winter, Isobel R. Bowker

Dissertations, Master's Theses and Master's Reports

As solar projects are built in northern climates, it is necessary to understand how snow cover impacts power generation. While there are validated models that predict snow shedding for fixed tilt projects, there is not one for single axis tracking systems. This report aims to characterize the differences between fixed tilt and single axis tracking systems under the same snow conditions. Generation data was collected over four winter months for fixed tilt and tracking systems at Michigan Technological University. Models were created for both systems using weather information from the site, in which losses were set to zero to create …

Design, Modeling, And Experimental Development Of Nanoscale Confinement Structures On Planar Silicon-Based Microelectrode Arrays For Single-Entity Electrochemical Sensing, Parinaz Eskandari

Dissertations, Master's Theses and Master's Reports

Electrochemical sensing is widely used for chemical and biological detection due to its high sensitivity, label-free operation, and compatibility with miniaturized electronic systems. However, conventional microelectrode platforms operate in an ensemble-averaged regime in which the measured current represents the collective response of many molecules interacting with the electrode surface. This ensemble averaging masks localized nanoscale electrochemical events and limits the ability to detect rare interactions, such as single molecules or nanoparticles. Achieving single-entity electrochemical detection therefore requires strategies that confine electrochemical reactions to nanoscale regions while maintaining compatibility with scalable planar microfabrication.

This dissertation investigates nanoscale electrochemical confinement on planar …

Experimental Rate Feedback Control Of A Model-Scale Hourglass-Shaped Heaving Point Absorber, James R. Halverson

Dissertations, Master's Theses and Master's Reports

Buoy geometry greatly affects a point absorber wave energy converter's dynamic response to waves. Finding the optimal buoy shape and control method remains an open research area focused on maximizing the conversion of wave kinetic energy into electricity. This work presents an experimental comparison of closed-loop energy extraction between a cylindrical and a truncated cone buoy, both with the same submerged volume, across various wave frequencies and amplitudes. To ensure a fair comparison, the optimal rate feedback gain is calculated for each buoy at each wave condition. Multiple metrics, including power output, capture width, and actuator force, are used to …

Numerical And Experimental Evaluation Of A Simple, Low-Energy Lunar Volatile Separation Technology, Eleanor L. Zimmermann

Dissertations, Master's Theses and Master's Reports

In-situ resource utilization (ISRU) is the practice of finding, extracting, and using the local resources found on extraterrestrial bodies, such as the Moon and Mars. ISRU enables in-situ production of mission consumables—such as rocket fuel and potable water—which will be critical for establishing a sustainable human presence on the Moon, a goal NASA intends to accomplish by 2028 with the Artemis missions. The separation and liquefaction of locally extracted volatiles—such as water, CO₂, and Methane—is an essential component in lunar surface sustainability, but there are very few technologies being developed for this purpose. This report introduces the Radiative …

Nondestructive Evaluation Of Additively Manufactured Parts Using Resonant Inspection And Frequency Domain-Based Correlation Criteria, Gita Deonarain

Dissertations, Master's Theses and Master's Reports

Additive manufacturing (AM) enables the production of highly customized and geometrically complex components; however, these parts remain susceptible to a wide range of defect types and severities. The combination of complex geometries and distributed defects presents a significant challenge for post-process nondestructive evaluation (NDE). Conventional inspection techniques, such as computed tomography and ultrasonic testing, are often limited by geometry, material, accessibility, and cost, while in situ monitoring is not yet sufficiently mature to replace post-process inspection.

This dissertation establishes the Frequency Domain Assurance Criterion (FDAC) as a quantitative, vibration-based framework for defect detection in AM components. FDAC captures spatial–spectral correlations …

The Excess Path Length Distribution: A Stochastic Model For Sample-Based Path Planners, Chaz B. Cornwall

Dissertations, Master's Theses and Master's Reports

Through random sampling, sample-based path planners enable autonomous agents to quickly find paths without human intervention. However, due to the paths' randomness, sample-based path planners currently require additional verification, partially nullifying agents' ability to act autonomously. I set out to characterize this uncertainty so humans know what to expect from these path planners and know how to alter the path planner to desired specifications. To ensure the results are theoretical as well as practical, I first create a stochastic model of path length uncertainty using the trade-off between sampling time and optimality. By leveraging this model, my proposed algorithm reduces …

Shm For Incipient Buckling Detection In Overloaded Structures Using Nonlinear System Identification Algorithms On Redundant Data Sets, Padmanabh Shridhar Desai

Dissertations, Master's Theses and Master's Reports

Structural health monitoring (SHM) detects and characterizes damage to predict failure, but failures such as buckling, which are not predicated on traditional damage modalities, are harder to detect. Direct load measurements are costly and difficult to implement (especially for dead loads) as they require copious numbers of sensors, which must be installed before loading is present. Vibration-based detection is a good alternative because it can infer global structural characteristics with relatively few sensors. This dissertation presents an SHM approach for detecting incipient buckling in structures under excessive loads based on non-linear models fit from measurements of small lateral vibrations due …

Mechanical Fractionation Of Wheat Middlings For Application In Pyrolysis And Animal Feed, Okwudili I. Obiakor

Dissertations, Master's Theses and Master's Reports

Wheat middlings are grain byproducts that are underused for bioenergy production despite having a high lignocellulose content. Its use as a feedstock for catalytic pyrolysis depends on effective and sustainable fractionation to enhance efficiency and product yield. This study tested mechanical pre-fractionation (sieving and air classification) to reduce ash content of certain fractions to < 1% for catalytic pyrolysis and enrich nutrient levels of other fractions for feed applications. Wheat middlings were separated into 11 fractions using screen sizes from 106 µm to 1651 µm, and the composition of each fraction was characterized (ash, protein, structural carbohydrates, lignin, starch, and simple sugars). For cyclone separation, a full factorial design was used to study the effect of air velocity (15,18, 20 m/s) and feed rate (33, 35, 38 kg/h) on the amount of material collected in the underflow and overflow, and the composition of these fractions was characterized. Sieving showed a pronounced segregation of ash content, from 4.5% to 6.5% in wheat middlings fractions, with 5.7% being the lowest for coarse fractions which has higher lignocellulose content, and 4.5% in the fine fraction (150-212 µm). The same defined enrichment was observed in all nutrients except for protein. Starch enrichment zone were defined in the fine fractions (212 µm and below), for structural carbohydrates and lignin in the coarse fractions (600 µm and above), and for ash in coarse fractions (1180-1651 µm). In contrast, protein content was not significantly different between various fractions (p value=0.787). The fiber rich fraction (> 600 µm), suitable for pyrolysis, which have a composite ash value of 6.0% can be sorted and mixed with intermediate fractions to reduce ash to 5.3% and the remaining fractions rich in digestible nutrients used for animal feed formulation. Compared to sieving, cyclone separations did not …

Experimental Characterization Of Photonic Crystal Based Invisibility Cloak Under Tm-Polarized Microwaves, Muhammad Danyal

Dissertations, Master's Theses and Master's Reports

Electromagnetic invisibility cloaks guide waves around an object so that the transmitted wavefront remains undisturbed. Most experimental microwave cloaking studies have focused on transverse electric (TE) polarization due to established measurement techniques. In this thesis, the experimental realization and characterization of a dielectric photonic crystal cloak operating under transverse magnetic (TM) polarization are presented. A measurement system operating in the X-band was developed to map the electric field distribution of the transmitted waves. Cloaking performance was first evaluated qualitatively by comparing numerical and experimental field distributions, where restoration of a flat wavefront indicated effective cloaking. Quantitative evaluation was performed by …

Computational And Ai Frameworks For Identifying Key Regulatory Genes And Their Target Genes In Plants And Humans, Md Khairul Islam

Dissertations, Master's Theses and Master's Reports

This dissertation presents computational and AI-driven frameworks for identifying key regulatory genes and their downstream targets across plant and human biological systems. Three studies address distinct challenges in genomic regulation using advanced machine learning and bioinformatics approaches.

The first study introduces DyGAF (Dynamic Gene Attention Focus), a dual-attention transformer framework that identifies and ranks disease-relevant biomarker genes by simultaneously modeling independent molecular responses and interdependent regulatory network behavior. Two attention models provide complementary perspectives on gene importance and are fused through a novel combination metric. Applied to COVID-19 nasopharyngeal swab profiles, the attention-weighted representations achieved 94.23% classification accuracy, high sensitivity, …

Machine Learning And Multi-Scale Optimization For Control And Energy Management In Connected And Automated Vehicle Propulsion Systems, Joshua D. Orlando

Dissertations, Master's Theses and Master's Reports

This dissertation presents a multi-scale optimization framework leveraging machine learning (ML) to enhance energy efficiency in connected and automated vehicle (CAV) propulsion systems. As transportation transitions toward hybridization and automation, the integration of vehicle-to-everything (V2X) connectivity and advanced control algorithms offers unprecedented opportunities for energy reduction. This research addresses three critical scales of vehicle energy management: multiple vehicle-level coordination, component-level powertrain dynamics, and real-time vehicle parameter estimation.

First, the research investigates the energy consumption characteristics of heterogeneous propulsion systems—ranging from internal combustion engines to battery electric vehicles across light- and heavy-duty sectors—on arterial roadways. Utilizing Particle Swarm Optimization (PSO) and …

Application Of Reactive Power Theory To Wave Energy Converters Under Multi-Frequency Excitation: Analysis And Experimentation, Mckenna R. Collins

Dissertations, Master's Theses and Master's Reports

There is abundant energy surging in the oceans across the globe. However, there have been technological challenges in extracting that energy for use. Wave energy converter (WEC) technology is being developed to find innovative ways to convert wave kinetic energy into electricity, but there are still efficiency obstacles that inhibit large-scale use. Focusing on heave-point absorber control theory, this thesis continues the work to improve the efficiency of WECs. This is done by leveraging a superposition of linear mass-spring-damper models to analyze a WEC under multi-frequency excitation and applying proportional-derivative gains to each linear model. However, to analyze this system, …

Open Source Tools For Ecological Research, Alex P. Riebe

Dissertations, Master's Theses and Master's Reports

This thesis presents the development of an open source wireless sensor network for hibernacula manipulation with an emphasis on accessibility and reproducibility. It addresses the design of the electrical hardware, guidance on antennas and RF implementation, and design of an application-specific communication protocol, all with the explicit goal of enabling ecologists and other conservationists to be able to manufacture, deploy, operate, and maintain the system for their research. The resulting sensor network designed in this thesis is to control the temperature inside bat hibernacula during the winter to study the relationship between temperature and bat mortality rate due to White …

Study Of Bearing Capacity Testing And Soil Preparation Techniques In Lunar Highlands Simulant At Field Scale, Christi A. Lecaptain

Dissertations, Master's Theses and Master's Reports

Humanity seeks to return to the surface of the Moon with the advent of the Artemis missions. These endeavors plan to be the beginning of a new era as NASA and other space organizations seek to establish a lunar base. Ahead of any astronauts or mining infrastructure, the physical groundwork will need to be laid to support the arrival of rockets, boots, and rover wheels. As surfaces are prepared, they will need to be tested and verified to ensure the stability of any structures placed on top. Understanding the bearing capacity is vital to soil quality testing. A standard terrestrial …