Engineering Commons^™

Upgraded Carbon Ores To Products, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet on the Energy & Environmental Research Center project to develop domestic battery materials from upgraded carbon ores. Includes information on upgraded carbon ores to products (UCOP) technology and a timeline for development.

Construction And Performance Optimization Of Bioconjugated Nanosensors For Early Detection Of Breast Cancer And Pro-Inflammatory Diseases, Pooja Gaikwad

Dissertations, Theses, and Capstone Projects

In recent years, nanosensors have emerged as a tool with strong potential in medical diagnostics. Single-walled carbon nanotube (SWCNT) based optical nanosensors have notably garnered interest due to the unique characteristics of their near-infrared fluorescence emission, including tissue transparency, photostability, and various chiralities with discrete absorption and fluorescence emission bands. Additionally, the optoelectronic properties of SWCNT are sensitive to the surrounding environment, which makes them suitable for in vitro and in vivo biosensing. Single-stranded (ss) DNA-wrapped SWCNTs have been reported as optical nanosensors for cancers and metabolic diseases. Breast cancer and cardiovascular diseases are the most common causes of death …

Quantifying Temperature-, Pressure-, And Nuclear Quantum Effects On Hydrophobic And Hydrophilic Water-Mediated Interactions, Justin T. Engstler

Dissertations, Theses, and Capstone Projects

Water-mediated interactions (WMIs) are responsible for diverse processes in aqueous solutions, including protein folding and nanoparticle aggregation. WMI may be affected by changes in temperature and pressure, and hence, they can alter chemical/physical processes that occur in aqueous environments. Traditionally, attention has been focused on hydrophobic interactions while, in comparison, the role of hydrophilic and hybrid (hydrophobic–hydrophilic) interactions have been mostly overlooked. Here, we study the role of T and P on the WMI between nanoscale (i) hydrophobic–hydrophobic, (ii) hydrophilic–hydrophilic, and (iii) hydrophilic–hydrophobic pairs of (hydroxylated/non-hydroxylated) graphene-based surfaces. We find that hydrophobic, hydrophilic, and hybrid interactions are all sensitive to …

Tunable Linear And Nonlinear Metasurfaces Based On Hybrid Gold-Graphene Plasmons, Matthew Feinstein

Dissertations, Theses, and Capstone Projects

Optical Metasurfaces are planar structures that are patterned with subwavelength structures and are very thin compared to the wavelength of light. Despite their thinness, these structured materials can strongly interact with incident light to effect the functionalities of conventional optical components, such as rotation of the polarization state, beam steering, lensing, spectral filtering, and holography, to name a few. Metasurfaces can also facilitate nonlinear optical effects, such as the mixing of beams at different frequencies to generate a beam at a new frequency.

The ability to alter the behavior of a metasurface during operation is highly desired for applications such …

Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

Qc-Sane: Robust Control In Drl Using Quantile Critic With Spiking Actor And Normalized Ensemble, Surbhi Gupta, Gaurav Singal, Deepak Garg, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

Recently Introduced Deep Reinforcement Learning (DRL) Techniques in Discrete-Time Have Resulted in Significant Advances in Online Games, Robotics, and So On. Inspired from Recent Developments, We Have Proposed an Approach Referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC-SANE) for Continuous Control Problems, Which Uses Quantile Loss to Train Critic and a Spiking Neural Network (NN) to Train an Ensemble of Actors. the NN Does an Internal Normalization using a Scaled Exponential Linear Unit (SELU) Activation Function and Ensures Robustness. the Empirical Study on Multijoint Dynamics with Contact (MuJoCo)-Based Environments Shows Improved Training and Test Results Than …

Experimental Data Supporting Co2 Enhanced Oil Recovery And Storage Potential In The Bakken Petroleum System (Bps), University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet on CO₂ enhanced oil recovery (EOR) in the Bakken petroleum system. Highlights experimental data concerning CO₂’s role in oil recovery in the region.

Uncertainties In Retrieval Of Remote Sensing Reflectance From Ocean Color Satellite Observations, Eder I. Herrera Estrella

Dissertations, Theses, and Capstone Projects

Ocean Color radiometry uses remote sensing to interpret ocean dynamics by retrieving remote sensing reflectance (������) from satellite imagery at different scales and over different time periods. ������ spectrum characterizes the ocean color that we observe, and from which we can discern concentrations of chlorophyll, organic and inorganic particles, and carbon fluxes in the ocean and atmosphere. ������ is derived from the total radiance at the top of the atmosphere (TOA). However, it only represents up to ten percent of the total signal. Hence, the retrieval of ������ from the total radiance at TOA involves the application of atmospheric correction …

Syne1 Mutation Is Associated With Increased Tumor Mutation Burden And Immune Cell Infiltration In Ovarian Cancer, Laura M. Harbin, Nan Lin, Frederick R. Ueland, Jill M. Kolesar

Markey Cancer Center Faculty Publications

SYNE1, a nuclear envelope protein critical for cellular structure and signaling, is downregulated in numerous malignancies. SYNE1 alterations are found in 10% of gynecologic malignancies and 5% of epithelial ovarian cancers. Previous studies demonstrated an association between SYNE1 mutation, increased tumor mutation burden (TMB), and immunotherapy response. This study evaluates the SYNE1 mutation frequency, association with TMB, and downstream effects of SYNE1 mutation in ovarian cancer. Genetic information, including whole-exome sequencing, RNA analysis, and somatic tumor testing, was obtained for consenting ovarian cancer patients at an academic medical center. Mutation frequencies were compared between the institutional cohort and The Cancer …

When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Peer-to-Peer (P2P) logistics platforms, unlike traditional last-mile logistics providers, do not have dedicated delivery resources (both vehicles and drivers). Thus, the efficiency of such operating model lies in the successful matching of demand and supply, i.e., how to match the delivery tasks with suitable drivers that will result in successful assignment and completion of the tasks. We consider a Same-Day Delivery Problem (SDDP) involving a P2P logistics platform where new orders arrive dynamically and the platform operator needs to generate a list of recommended orders to the crowdsourced drivers. We formulate this problem as a Dynamic Order Recommendations Problem (DORP). …

Pattern-Of-Life Modeling With Automatic Dependent Surveillance-Broadcast (Ads-B), Sarah J. Bolton

Theses and Dissertations

This dissertation and research were sponsored by the Air Force Research Laboratory Layered Sensing Exploitation Branch (AFRL/RYA) to investigate the utility of using the data found within aircraft secondary radar to make predictions about aircraft characteristics and intent. The research focuses on making predictions on aircraft characteristics using only the kinetic data within one type of secondary radar, Automatic Dependent Surveillance-Broadcast (ADS-B), as a surrogate for primary radar. The results from this research provide a means to reduce the reliance on a type of aircraft tracking that is vulnerable to cyber attack and other integrity concerns.

Optimizing Mn-Al Permanent Magnet Performance Through Control Of The Phase Transformation, Ternary Element Addition, And Advanced Processing, Thomas R. Keller

Dartmouth College Ph.D Dissertations

The growing need for electrical power in machines and vehicles brings with it a growing need for critical materials. The current high-performance permanent magnets (PMs) based on rare-earth (RE) elements Nd and Sm cannot escape the problem of raw material cost, geographic scarcity in the earth’s crust, and lack of circular global supply chains. This poses a problem of industrial ecology: can PMs be made from inexpensive, more abundant materials while still meeting sufficient performance criteria? PMs based on the magnetic τ phase of Mn-Al offer a possible alternative to REPMs. However, years of innovation have not yet achieved real-world …

Establishing Physical And Chemical Mechanisms Of Polymerization And Pyrolysis Of Phenolic Resins For Carbon-Carbon Composites, Ivan Gallegos, Josh Kemppainen, Jacob R. Gissinger, Malgorzata Kowalik, Adri Van Duin, Kristopher E. Wise, S. Gowtham, Gregory Odegard

Michigan Tech Publications

The complex structural and chemical changes that occur during polymerization and pyrolysis critically affect material properties but are difficult to characterize in situ. This work presents a novel, experimentally validated methodology for modeling the complete polymerization and pyrolysis processes for phenolic resin using reactive molecular dynamics. The polymerization simulations produced polymerized structures with mass densities of 1.24 ± 0.01 g/cm3 and Young's moduli of 3.50 ± 0.64 GPa, which are in good agreement with experimental values. The structural properties of the subsequently pyrolyzed structures were also found to be in good agreement with experimental X-ray data for the phenolic-derived carbon …

Pymaivar: An Open-Source Python Suit For Audio-Image Representation In Human Action Recognition, Muhammad B. Shaikh, Douglas Chai, Syed M. S. Islam, Naveed Akhtar

Research outputs 2022 to 2026

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams. This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition. By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field. The package is implemented in Python and can be used across different operating systems.

Alkaline Pretreatment And Air Mixing For Improvement Of Methane Production From Anaerobic Co-Digestion Of Poultry Litter With Wheat Straw, Yuanhang Zhan, Jun Zhu, Yiting Xiao, Leland C. Schrader

Biological and Agricultural Engineering Faculty Publications and Presentations

Alkaline pretreatment (AL) and air mixing (air) both have the potential to improve anaerobic co-digestion (Co-AD) of poultry litter with wheat straw for methane production. In this study, the effects of the combination of AL (pH 12 for 12 h) and air mixing (12 mL·d⁻¹) on the Co-AD process were investigated. The substrate hydrolysis was enhanced by AL, with soluble chemical oxygen demand increased by 4.59 times and volatile fatty acids increased by 5.04 times. The cumulative methane yield in the group of Co-AD by AL integrated with air (Co-(AL + air)), being 287 mL·(g VS_added) …

Guar-Based Injectable Hydrogel For Drug Delivery And In Vitro Bone Cell Growth, Humandra Poudel, Ambar R. Rangumagar, Pooja Singh, Adeolu Oluremi, Nawab Ali, Fumiya Watanabe, Joseph Batta-Mpouma, Jin-Woo Kim, Ahona Ghosh, Anindya Ghosh

Chemistry & Biochemistry Faculty Publications and Presentations

Injectable hydrogels offer numerous advantages in various areas, which include tissue engineering and drug delivery because of their unique properties such as tunability, excellent carrier properties, and biocompatibility. These hydrogels can be administered with minimal invasiveness. In this study, we synthesized an injectable hydrogel by rehydrating lyophilized mixtures of guar adamantane (Guar-ADI) and poly-β-cyclodextrin (p-βCD) in a solution of phosphate-buffered saline (PBS) maintained at pH 7.4. The hydrogel was formed via host-guest interaction between modified guar (Guar-ADI), obtained by reacting guar gum with 1-adamantyl isocyanate (ADI) and p-βCD. Comprehensive characterization of all synthesized materials, including the hydrogel, was performed using …

Advances In Quaternion-Valued Neural Networks, Jeremiah P. Bill

Theses and Dissertations

This dissertation investigates the construction, optimization, and application of quaternion neural networks (QNNs) to Department of Defense (DoD) related problem sets. QNNs are a type of neural network wherein the weights, biases, and input values are all represented as quaternion numbers. This work provides a critical evaluation of the myriad different quaternion backpropagation derivations that exist in the literature, testing the performance of each on a range of regression problem sets. The optimization dynamics of QNNs are explored, presenting visualizations of QNN loss surfaces and a novel method for assessing the “smoothness” of these loss surfaces. Finally, this dissertation presents …

Improving Deep Reinforcement Learning Methodology For Autonomous Defense And Escort Of Military High-Value Assets, Joseph Liles Iv

Theses and Dissertations

This dissertation explores the application of machine learning to the control of autonomous unmanned combat aerial vehicles (AUCAVs). In particular, this research applies deep reinforcement learning methodologies to a defensive air combat scenario wherein a fleet of AUCAVs protects a military high-value asset (HVA). A collection of air battle management scenarios along with an original simulation environment and a set of designed computational experiments support the approximation of high-quality decision policies by employing Markov decision processes, approximate dynamic programming algorithms, and deep neural networks for value function approximation.

Atmospheric Propagation Of Qubits: Laboratory Experiments To Field Demonstrations, Keith A. Wyman

Theses and Dissertations

Free-space quantum networks can enable global-scale quantum communication via satellite-based nodes and quantum ground transceivers. To enable building of robust global quantum networks, it is critical to learn how the state of the qubit is transformed while propagating through the atmosphere. With such an overarching goal, we built a laboratory based atmospheric turbulence simulator (ATS) to characterize the effects of atmospheric turbulence on an entangled pair of photons as a function of statistical quantities such as the Fried parameter or scintillation index for long-distance communication. Specifically, the changes in the statistical properties associated with a quantum source was investigated using …

An Analytical Framework For Photonic Storage And Retrieval: Implications For Quantum Optical Networks, Billie V. Deluca

Theses and Dissertations

Quantum information and communication is the next revolution in information technology. However, communication through quantum networks will require the ability to transmit, store, and interact with photonic quantum bits (qubits). The focus of this dissertation is a theoretical description of a promising technology for storage of photonic states employing electromagnetically induced transparency (EIT). A general semiclassical model for EIT photonic storage will be developed, a key capability required for understanding the effect of storage that is independent of the photonic state being stored. This dissertation demonstrates the capability of this model to predict light pulses retrieved from EIT storage as …

Quantifying Atmospheric Turbulence Effects On Image Quality Using A Deconvolution Algorithm, Ahmad M. Almalki

Theses and Dissertations

Imaging through turbulence is affected by several factors including imaging system specifications, imaging system setup and more importantly the atmospheric turbulence as it is uncontrollable. One important parameter which is used to quantify the atmospheric turbulence severity is the atmospheric coherence diameter (��₀ ), known as Fried's parameter. This thesis explores ways to characterize the atmospheric turbulence effects on image quality using simulated and laboratory generated turbulence where ��₀ is estimated using a maximum a posteriori (MAP) estimator and frequency domain analysis algorithms. Furthermore, image quality metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM) …

Analysis Of Coronal Magnetic Field Parameters During X- And M-Class Solar Flares, Seth H. Garland

Theses and Dissertations

Using Non-Linear Force Free Field (NLFFF) extrapolation, 3D magnetic fields were modeled from the 12-minute cadence Helioseismic and Magnetic Imager (HMI) photospheric vector magnetograms, spanning a time period of one hour before through one hour after the start of 18 X-class flares and 12 M-class flares. Several magnetic field parameters were calculated from the modeled fields directly – as well as from the power spectrum of surface maps generated by summing the fields along the vertical axis – for two different regions: areas with photospheric Bz ≥ 300 G (Active Region – AR) and areas above the photosphere with the …

Brdf Measurements And Physical Optics Modeling Applied To Anisotropic Satellite Solar Cells, Madilynn E. Compean

Theses and Dissertations

Light curve analysis is often used to discern information about satellites in geosynchronous orbits, and since solar panels comprise relatively large surface areas, their reflected energy can contribute greatly to observed light curves. Data was collected using a space qualified solar cell interrogated by a green HeNe laser. The data validated certain aspects from previous work, but also identified multi-slit behavior, showed that the specular term was a second diffraction pattern, and diagnosed the out-of-plane diffraction curvature as the conical diffraction phenomenon. Two physical-optics-based models were developed to incorporate these new features and better predict solar cell BRDF solely from …

Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Duc Thien Nguyen, Akshat Kumar

Research Collection School Of Computing and Information Systems

Learning control policies for a large number of agents in a decentralized setting is challenging due to partial observability, uncertainty in the environment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale constrained MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among agents are governed by the aggregate count and types of agents, and do not depend on agents’ specific identities. Second, we show that standard Lagrangian relaxation methods, which are popular for single agent RL, do not …

Understanding Multi-Homing And Switching By Platform Drivers, Xiaotong Guo, Andreas Haupt, Hai Wang, Rida Qadri, Jinhua Zhao

Research Collection School Of Computing and Information Systems

Freelance drivers in the shared mobility market frequently switch or work for multiple platforms, affecting driver labor supply. Due to the importance of driver labor supply for the shared mobility market, understanding drivers’ switching and multi-homing behavior is vital to managing service quality on – and effective regulation of – mobility platforms. However, a lack of individual-level data on driver behavior has thus far impeded a deeper understanding. This paper taxonomizes and estimates perceived switching and multi-homing frictions on mobility platforms. Based on a structural model of driver labor supply, we estimate switching and multi-homing costs in a platform duopoly …

Exploring Topological Phonons In Different Length Scales: Microtubules And Acoustic Metamaterials, Ssu-Ying Chen

Dissertations

The topological concepts of electronic states have been extended to phononic systems, leading to the prediction of topological phonons in a variety of materials. These phonons play a crucial role in determining material properties such as thermal conductivity, thermoelectricity, superconductivity, and specific heat. The objective of this dissertation is to investigate the role of topological phonons at different length scales.

Firstly, the acoustic resonator properties of tubulin proteins, which form microtubules, will be explored The microtubule has been proposed as an analog of a topological phononic insulator due to its unique properties. One key characteristic of topological materials is the …

Quantifying Balance: Computational And Learning Frameworks For The Characterization Of Balance In Bipedal Systems, Kubra Akbas

Dissertations

In clinical practice and general healthcare settings, the lack of reliable and objective balance and stability assessment metrics hinders the tracking of patient performance progression during rehabilitation; the assessment of bipedal balance plays a crucial role in understanding stability and falls in humans and other bipeds, while providing clinicians important information regarding rehabilitation outcomes. Bipedal balance has often been examined through kinematic or kinetic quantities, such as the Zero Moment Point and Center of Pressure; however, analyzing balance specifically through the body's Center of Mass (COM) state offers a holistic and easily comprehensible view of balance and stability.

Building upon …

Bacterial Motion And Spread In Porous Environments, Yasser Almoteri

Dissertations

Micro-swimmers are ubiquitous in nature from soil and water to mammalian bodies and even many technological processes. Common known examples are microbes such as bacteria, micro-algae and micro-plankton, cells such as spermatozoa and organisms such as nematodes. These swimmers live and have evolved in multiplex environments and complex flows in the presence of other swimmers and types, inert particles and fibers, interfaces and non-trivial confinements and more. Understanding the locomotion and interactions of these individual micro-swimmers in such impure viscous fluids is crucial to understanding the emergent dynamics of such complex systems, and to further enabling us to control and …

Boundary Integral Equation Methods For Superhydrophobic Flow And Integrated Photonics, Kosuke Sugita

Dissertations

This dissertation presents fast integral equation methods (FIEMs) for solving two important problems encountered in practical engineering applications.

The first problem involves the mixed boundary value problem in two-dimensional Stokes flow, which appears commonly in computational fluid mechanics. This problem is particularly relevant to the design of microfluidic devices, especially those involving superhydrophobic (SH) flows over surfaces made of composite solid materials with alternating solid portions, grooves, or air pockets, leading to enhanced slip.

The second problem addresses waveguide devices in two dimensions, governed by the Helmholtz equation with Dirichlet conditions imposed on the boundary. This problem serves as a …

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …