Engineering Commons^™

Highly Efficient Dopamine Sensing With A Carbon Nanotube-Encapsulated Metal Chalcogenide Nanostructure, Harish Singh, Jiandong Wu, Kurt A.L. Lagemann, Manashi Nath

Chemical and Biochemical Engineering Faculty Research & Creative Works

Carbon nanotube-encapsulated nickel selenide composite nanostructures were used as nonenzymatic electrochemical sensors for dopamine detection. These composite nanostructures were synthesized through a simple, one-step, and environmentally friendly chemical vapor deposition method, wherein the CNTs were formed in situ from pyrolysis of a carbon-rich metallo-organic precursor. The composition and morphology of these hybrid NiSe2-filled carbon nanostructures were confirmed by powder X-ray diffraction, Raman, X-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy images. Electrochemical tests demonstrated that the as-synthesized hybrid nanostructures exhibited outstanding electrocatalytic performance toward dopamine oxidation, with a high sensitivity of 19.62 μA μM-1 cm-2, low detection limit, broad linear …

Accuracy Of Nitrate Hysteresis And Flushing For Agricultural Watersheds In The Midwest, Noah Rudko, Sara K. W. Mcmillian, Jane Frankenberger, François Birgand

Graduate Industrial Research Symposium

Storm event-based metrics, such as hysteresis (HI) and flushing (FI), are used to differentiate nitrate pathways and sources, which is essential for watershed management. Estimations of these event-based metrics typically use high frequency (15-minute – hourly) measurements, but daily data are also used due to their greater availability. To date, there has been no study assessing how using lower frequency samples affect the accuracy of HI and FI, which could skew interpretation of potential nutrient pathways and sources. We used continuous measurements of nitrate collected at 9 watersheds throughout the Midwest spanning 448 storms. HI and FI were estimated from …

A Machine Learning Model Of Perturb-Seq Data For Use In Space Flight Gene Expression Profile Analysis, Liam F. Johnson, James Casaletto, Lauren Sanders, Sylvain Costes

Graduate Industrial Research Symposium

The genetic perturbations caused by spaceflight on biological systems tend to have a system-wide effect which is often difficult to deconvolute it into individual signals with specific points of origin. Single cell multi-omic data can provide a profile of the perturbational effects, but does not necessarily indicate the initial point of interference within the network. The objective of this project is to take advantage of large scale and genome-wide perturbational datasets by using them to train a tuned machine learning model that is capable of predicting the effects of unseen perturbations in new data. Perturb-Seq datasets are large libraries of …

Resource Optimization For Air Mobility Under Emergency Situations, Yongxin (Jack) Liu

Math Department Colloquium Series

This project aims to improve air traffic management in emergencies. We first developed a GRU neural network to forecast weather-related airport capacity constraints using historical data, underscoring the value of real-time data analysis. We then optimized emergency evacuation air travel using Particle Swarm Optimization, demonstrating the ability to quickly aggregate evacuation flight resources cost-effectively. Finally, we provided a hybrid model combining a genetic algorithm with a neural network for evacuation planning, we show that neural network can be integrated accelerate genetic algorithms for efficient and performance assured system optimization.

Laboratory Exercise For The Radiometry Student, Michael A. Marciniak

Faculty Publications

The U.S. Air and Space Forces require optical expertise among their personnel. The Air Force Institute of Technology offers a graduate optics curriculum, which includes a three-course sequence to educate students in the optical concepts of radiometry and radiometric instrumentation. We find radiometry is often a deceptively difficult concept for students to master. To address this, we have developed an experiment in our optics-laboratory coursework to help them gain this mastery. A Fourier-transform infrared spectrometer (FTS) is used to collect spectral data from an unknown sample. FTS calibration and data collection are discussed here, as are the two specific samples …

Graph Theory And Graph Neural Network Assisted High-Throughput Crystal Structure Prediction And Screening For Energy Conversion And Storage, Joshua Ojih, Mohammed Al-Fahdi, Yagang Yao, Jianjun Hu, Ming Hu

Faculty Publications

Prediction of crystal structures with desirable material properties is a grand challenge in materials research, due to the enormous search space of possible combinations of elements and their countless arrangements in 3D space. Despite the recent progress of a few crystal structure prediction algorithms, most of those methods only target a few specific material families or are restricted to simple systems with limited element diversity. Moreover, these algorithms are usually coupled with first principles calculations and thus are computationally expensive and very time consuming. Therefore, establishing a workflow that can generate a large number of hypothetical structures with diverse elements …

Relative Vectoring Using Dual Object Detection For Autonomous Aerial Refueling, Derek B. Worth, Jeffrey L. Choate, James Lynch, Scott L. Nykl, Clark N. Taylor

Faculty Publications

Once realized, autonomous aerial refueling will revolutionize unmanned aviation by removing current range and endurance limitations. Previous attempts at establishing vision-based solutions have come close but rely heavily on near perfect extrinsic camera calibrations that often change midflight. In this paper, we propose dual object detection, a technique that overcomes such requirement by transforming aerial refueling imagery directly into receiver aircraft reference frame probe-to-drogue vectors regardless of camera position and orientation. These vectors are precisely what autonomous agents need to successfully maneuver the tanker and receiver aircraft in synchronous flight during refueling operations. Our method follows a common 4-stage process …

Western Science And Eastern Zen To Seek The Origin Of Truth: Philosophical Background Of Scale Modeling, Kozo Saito

Progress in Scale Modeling, an International Journal

This article was written to introduce philosophical background of scale modeling, where Zen philosophy was applied to overcome the limitation of logical thinking and hypotheses-driven deductive science. Three specific reasons are as follows. The first is related to the law approach in scale modeling; it uses the kufu principle, originated in Zen Buddhism, together with the other three scientific methods: experimental, theoretical, and computational. The second reason is because scale modeling seeks relativistic understanding by attempting to realize similarity; the concept is closer to Eastern philosophy rather than absolute understanding cultivated by deductive science. The third is in the educational …

03.04.2024 Orsp Connect, Liz Williamson

ORED Newsletter

ORSP On-demand Training for PIs, Liz Williamson spotlight, Grant for Studying Clean Energy Production Awarded, NIH Simplified Peer Review Framework.

Adsorption Of Crystal Violet Dye From Synthetic Wastewater By Ball-Milled Royal Palm Leaf Sheath, Neloy Sen, Nawrin Rahman Shefa, Kismot Reza, Sk Md Ali Zaker Shawon, Md. Wasikur Rahman

Physics & Astronomy Faculty Publications

The current study shows that using a batch approach to remove crystal violet dye from synthetic wastewater is feasible when using royal palm leaf sheath powder as an adsorbent. In order to investigate the effects of many parameters, including starting concentration, pH effect, dye concentration, adsorbent dose, contact time, and temperature, experiments were carried out under various operating conditions. Maximum removal was obtained at pH 6 and at a concentration of 100 ppm, which are considered as ideal values. The influence of pH and dye concentration was shown to be substantial. Langmuir, Freundlich, and Temkin isotherm models were fitted to …

How Can Generative Ai (Genai) Enhance Or Hinder Qualitative Studies? A Critical Appraisal From South Asia, Nepal, Niroj Dahal

The Qualitative Report

Qualitative researchers can benefit from using generative artificial intelligence (GenAI), such as different versions of ChatGPT—GPT-3.5 or GPT-4, Google Bard—now renamed as a Gemini, and Bing Chat—now renamed as a Copilot, in their studies. The scientific community has used artificial intelligence (AI) tools in various ways. However, using GenAI has generated concerns regarding potential research unreliability, bias, and unethical outcomes in GenAI-generated research results. Considering these concerns, the purpose of this commentary is to review the current use of GenAI in qualitative research, including its strengths, limitations, and ethical dilemmas from the perspective of critical appraisal from South Asia, Nepal. …

Development Of An Integrated Workflow For Nucleosome Modeling And Simulations, Ran Sun

Doctoral Dissertations

Nucleosomes are the building blocks of eukaryotic genomes and thus fundamental to to all genetic processes. Any protein or drug that binds DNA must either cooperate or compete with nucleosomes. Given that a nucleosome contains 147 base pairs of DNA, there are approximately 4^147 or 10^88 possible sequences for a single nucleosome. Exhaustive studies are not possible. However, genome wide association studies can identify individual nucleosomes of interest to a specific mechanism, and today's supercomputers enable comparative simulation studies of 10s to 100s of nucleosomes. The goal of this thesis is to develop and present and end-to-end workflow that serves …

The Santa Clara, 2024-03-01, Santa Clara University

The Santa Clara

No abstract provided.

The Interplay Of Spin, Charge, And Heat: From Metal/Insulator Heterostructures To Perovskite Bilayers, Sam M. Bleser

Electronic Theses and Dissertations

In this dissertation begin with an investigation of non-local spin transport in an amorphous germanium (a-Ge) sample via the inverse spin Hall effect (ISHE). In that study we show that commonly used techniques such as differential conductance and delta mode of a paired Keithley 6221/2182a for non-local resistance measurements can lead to false indicators of spin transport. Next, we turn out attention to a thickness dependent study in thermally-evaporated chromium (Cr) thin films on a bulk polycrystalline yttrium-iron-garnet (YIG) substrate. This project analyzed the spin transport in the Cr films versus thickness via the longitudinal spin Seebeck effect (LSSE). This …

Thermal, Electrical, And Spin Transport: Encompassing Low-Damping Ferromagnets And Antiferromagnetic/Ferromagnetic Heterostructures, Matthew Ryan Natale

Electronic Theses and Dissertations

Continuing technological advancements bring forth escalating challenges in global energy consumption and subsequent power dissipation, posing significant economic and environmental concerns. In response to these difficulties, the fields of thermoelectrics, spintronics, and spincaloritronics emerge as contemporary solutions, each presenting unique advantages. Thermoelectric devices, based on the Seebeck effect, other a passive, carbon-free energy generating solution from waste heat. Although current thermoelectric technology encounters hurdles in achieving optimal efficiencies without intricate designs or complex materials engineering, recently research into low-damping metallic ferromagnetic thin films have provided a new method to enhance spin wave lifetimes, thus contributing to thermoelectric voltage improvements. As …

Designing An Optical Router Based On A Multimode-Interference Silicon-On-Insulator Coupler With Tunable Power Transmittance, Dana S. Akil, Muhammad A. Othman, Sherif M. Sherif, Mohamed A. Swillam

Faculty Journal Articles

The demand on fast and high-bandwidth data transmission is in continuous increase. These demands are highly dependent on optical signal manipulation, including switching, modulation, and routing. We demonstrate a two-port silicon optical router based on the multimode interferometer (MMI) configuration. The same MMI structure was used for both inward and backward waveguiding to reduce the total length of the device. A phase shifter consisting of two ring-like waveguides made of silicon p-n junctions was used to introduce the phase shift needed for optical routing upon voltage application. Two designs for the MMI optical router were studied: Firstly, a conventional MMI …

Continual Online Learning-Based Optimal Tracking Control Of Nonlinear Strict-Feedback Systems: Application To Unmanned Aerial Vehicles, Irfan Ganie, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel optimal trajectory tracking scheme is introduced for nonlinear continuous-time systems in strict feedback form with uncertain dynamics by using neural networks (NNs). The method employs an actor-critic-based NN back-stepping technique for minimizing a discounted value function along with an identifier to approximate unknown system dynamics that are expressed in augmented form. Novel online weight update laws for the actor and critic NNs are derived by using both the NN identifier and Hamilton-Jacobi-Bellman residual error. A new continual lifelong learning technique utilizing the Fisher Information Matrix via Hamilton-Jacobi-Bellman residual error is introduced to obtain the significance of weights in …

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Performance Of Humans And Agents In A Systems Modeling Language V2 Task: A User-Centered Evaluation Approach, Allen W. Dukes

Theses and Dissertations

The Department of Defense is adopting Digital Engineering practices for its workforce. Simultaneously, the larger Systems Engineering community strives to modernize and define those Digital Engineering practices. These efforts to move from traditionally document-based approaches to pure digital implementations will require enhanced capabilities to manage and digitally track the lifecycle of a program or product. However, this growth must address tool design through an iterative process focusing on usability for many user types. Many tools and technologies exist but often lack an assessment of usability when engineers design tools for other engineers. Including usability when developing solutions for Digital Engineering …

Small Unmanned Aircraft System Detection And Tracking With Audio, Computer Vision, And Deep Learning Techniques, Anthony C. Brunson

Theses and Dissertations

sUAS present significant risks to local and federal agencies when under the control of negligent, reckless, or criminal operators. In the face of an escalating presence of sUAS in shared airspace with traditional aircraft, and their deployment in protected airspace as potential weapons, safeguarding personnel, facilities, and assets becomes paramount. This research seeks to address this emerging threat by investigating the efficacy of integrating low-cost distributed sensors and Machine learning (ML) models to enhance battlespace awareness and complement existing sensing platforms for real-time sUAS detection, classification, and localization. The thesis introduces the conceptualization and development of a Drone Detection Command …

Advanced Oxidation Of Methyl Tert Butyl Ether With Ultraviolet Light Emitting Diodes, Aaron R. Neal

Theses and Dissertations

Methyl Tert-Butyl Ether (MTBE) is a volatile, soluble, organic compound introduced to gasoline within the United States as a fuel oxygenator in 1979. MTBE is a common groundwater pollutant due to its high water solubility and resistance to bio-degradation; it commonly infiltrates groundwater sources via leaking underground storage tanks or fuel spills. This research employed ultraviolet light emitting diodes (UV-LEDs) and hydrogen peroxide (H₂O₂) induced advanced oxidation process (AOP) to treat MTBE contaminated water at the bench scale. Using four H₂O₂:MTBE molar peroxide ratios (100:1, 200:1, 400:1, 500:1), MTBE degradation was induced …

Assessing The Relationship Between The Quasi-Biennial Oscillation And D-Region Electron Density, Natalie R. Wirth

Theses and Dissertations

High frequency (HF) communication is a vital aspect of military communication and is highly reliant upon ionospheric conditions. The variation in electron density within the lowest echelon of the ionosphere, the D-region, can significantly impact HF signals, making communication inconsistent and unreliable for these wavelengths. Despite the D-region’s important, research in this area mainly focuses on understanding the impact of space weather anomalies on the upper ionosphere, particularly its higher layers, with relatively little exploration of the D-region. This region, situated closest to the troposphere and stratosphere, has been definitively linked to solar weather events like solar flares and geomagnetic …

On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo

Theses and Dissertations

The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski

Theses and Dissertations

The role of autonomy has evolved recently, demanding tighter integration between human and autonomous systems, particularly in highly contested A2AD environments. Near-peer adversaries have modernized their integrated air defense systems (IADS), diminishing the current advantages of the United States Air Force. To regain air dominance, efforts like the Collaborative Combat Aircraft (CCA) program are underway, aiming to deploy unmanned autonomous alongside manned next-generation fighter aircraft. This research assesses various operational concepts, focusing on autonomous tactics post-manned fighter loss, strike timing of independent teams, and weapon configuration observability. Using the Advanced Framework for Simulation, Integration and Modeling (AFSIM), an agent-based model …

U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan

Theses and Dissertations

The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.

Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering

Theses and Dissertations

Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the …

A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike

Theses and Dissertations

This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …

A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae

Theses and Dissertations

A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …

Advanced Oxidation Of Tert-Butanol With Ultraviolet Light Emitting Diodes And Hydrogen Peroxide, Lauren R. Mainolfi

Theses and Dissertations

Tert-butanol (TBA), a versatile chemical widely used in industrial processes, poses exposure risks through inhalation, ingestion, and skin contact. Environmental contamination, often from industrial activities, emphasizes the importance of robust waste management and monitoring to protect water supplies from potential TBA migration and ensure drinking water safety. This study employed hydrogen peroxide (H2O2): TBA molar ratios of 100, 200, 400, and 500:1 in a Continuous Flow Stirred-Tank Reactor (CSTR) with UV-LED as the TBA degradation mechanism in an Advanced Oxidation Process (AOP). The UV-LEDs and H2O2 were synergistically employed to generate hydroxyl radicals in an advanced oxidation process. Gas chromatography–mass …