Engineering Commons^™

Tribocorrosion And Metal Release From Austenitic Stainless Steels 304 And 201 In Simulated Cassava Food Contact, Robert Addai, Temitope E. Olowoyo, Thalia E. Standish, Jeffrey Daniel Henderson, Ubong Eduok, Yolanda Hedberg

Chemistry Publications

Cassava is the third most significant calorie source in the tropics. Its processing has changed from traditional methods to stainless steel processing machines. This study investigated the influence of cassava on metal release from two common stainless steels, ASTM 304 and 201, with and without friction, and on tribocorrosion (multianalytically) of 304. Cassava was relatively corrosive and hindered repassivation of the surface oxide of stainless steel, but it also acted as a lubricant against mechanical friction. The combined action of friction and cassava caused a significant increase in iron, chromium, nickel, and manganese release from the stainless steels (30–35- fold …

Understanding The Impact Of Microplastic Contamination On Soil Quality And Eco-Toxicological Risks In Horticulture: A Comprehensive Review, N. P. Gayathri, Geena Prasad, Vaishna Prabhakaran, Vishnu Priya

Research outputs 2022 to 2026

The horticulture sector, essential for global food production, confronts significant challenges with prevalent pollutants, mainly microplastics. The presence of microplastics in the food chain has induced physiological stress and a multifactorial food safety concern. The complexity of the problem, arising from intricate interactions among microplastics, organisms, and ecosystems, poses a substantial challenge to food safety, necessitating an immediate strategic perspective due to the associated risks to human health and eco-toxicology. Significant knowledge gaps persist regarding their impact on terrestrial ecosystems, especially in horticulture. This study addresses the urgent need to comprehend the implications of microplastics on soil health, eco-toxicological risks, …

Advective And Diffusive Gas Phase Transport In Vadose Zones: Importance For Defining Vapour Risks And Natural Source Zone Depletion Of Petroleum Hydrocarbons, Kaveh Sookhak Lari, Greg B. Davis, John L. Rayner, Trevor P. Bastow

Research outputs 2022 to 2026

Quantifying the interlinked behaviour of the soil microbiome, fluid flow, multi-component transport and partitioning, and biodegradation is key to characterising vapour risks and natural source zone depletion (NSZD) of light non-aqueous phase liquid (LNAPL) petroleum hydrocarbons. Critical to vapour transport and NSZD is transport of gases through the vadose zone (oxygen from the atmosphere, volatile organic compounds (VOCs), methane and carbon dioxide from the zone of LNAPL biodegradation). Volatilisation of VOCs from LNAPL, aerobic biodegradation, methanogenesis and heat production all generate gas pressure changes that may lead to enhanced gas fluxes apart from diffusion. Despite the importance of the gaseous …

Revitalizing Turtle Creek Park, Michael Hardyway, Ethan Harm, Abbey Jacoby, Casey Stephenson

Final Reports in ENST 411: Environmental Community Projects

We current ENST 411 students, Abbey Jacoby, Michael Hardyway, Ethan Harm, and Casey Stephenson have chosen to work with Jim Knight, East Buffalo Township, the Merrill Linn Land and Waterways Conservancy, and many others in an attempt to revitalize Turtle Creek Park for a plethora of reasons. Three of us are majoring in biology, and two are majoring in environmental science, which makes much of the information and techniques relevant in Turtle Creek applicable to our courses of study. This project included heavy hands on work which allowed us students to leave a memorable and impactful influence on the Lewisburg …

Star-Based Reachability Analysis Of Binary Neural Networks On Continuous Input, Mykhailo Ivashchenko

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Deep Neural Networks (DNNs) have become a popular instrument for solving various real-world problems. DNNs’ sophisticated structure allows them to learn complex representations and features. However, architecture specifics and floating-point number usage result in increased computational operations complexity. For this reason, a more lightweight type of neural networks is widely used when it comes to edge devices, such as microcomputers or microcontrollers – Binary Neural Networks (BNNs). Like other DNNs, BNNs are vulnerable to adversarial attacks; even a small perturbation to the input set may lead to an errant output. Unfortunately, only a few approaches have been proposed for verifying …

An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko

Research Collection School Of Computing and Information Systems

The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

Next-Generation Crop Monitoring Technologies: Case Studies About Edge Image Processing For Crop Monitoring And Soil Water Property Modeling Via Above-Ground Sensors, Nipuna Chamara

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

Artificial Intelligence (AI) has advanced rapidly in the past two decades. Internet of Things (IoT) technology has advanced rapidly during the last decade. Merging these two technologies has immense potential in several industries, including agriculture.

We have identified several research gaps in utilizing IoT technology in agriculture. One problem was the digital divide between rural, unconnected, or limited connected areas and urban areas for utilizing images for decision-making, which has advanced with the growth of AI. Another area for improvement was the farmers' demotivation to use in-situ soil moisture sensors for irrigation decision-making due to inherited installation difficulties. As Nebraska …

Bidding Strategy For A Wind Power Producer In Us Energy And Reserve Markets, Anne Stratman

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Wind power is one of the world's fastest-growing renewable energy resources and has expanded quickly within the US electric grid. Currently, wind power producers (WPPs) may sell energy products in US markets but are not allowed to sell reserve products, due to the uncertain and intermittent nature of wind power. However, as wind’s share of the power supply grows, it may eventually be necessary for WPPs to contribute to system-wide reserves. This paper proposes a stochastic optimization model to determine the optimal offer strategy for a WPP that participates in the day-ahead and real-time energy and spinning reserve markets. The …

Vr Circuit Simulation With Advanced Visualization For Enhancing Comprehension In Electrical Engineering, Elliott Wolbach

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technology advances, the field of electrical and computer engineering continuously demands innovative tools and methodologies to facilitate effective learning and comprehension of fundamental concepts. Through a comprehensive literature review, it was discovered that there was a gap in the current research on using VR technology to effectively visualize and comprehend non-observable electrical characteristics of electronic circuits. This thesis explores the integration of Virtual Reality (VR) technology and real-time electronic circuit simulation with enhanced visualization of non-observable concepts such as voltage distribution and current flow within these circuits. The primary objective is to develop an immersive educational platform that makes …

Optimal Molecular Dynamics System Size For Increased Precision And Efficiency For Epoxy Materials, Khatereh Kashmari, Sagar Patil, Josh Kemppainen, Shankara Gowtham, Gregory Odegard

Michigan Tech Publications, Part 2

Molecular dynamics (MD) simulation is an important tool for predicting thermo-mechanical properties of polymer resins at the nanometer length scale, which is particularly important for efficient computationally driven design of advanced composite materials and structures. Because of the statistical nature of modeling amorphous materials on the nanometer length scale, multiple MD models (replicates) are typically built and simulated for statistical sampling of predicted properties. Larger replicates generally provide higher precision in the predictions but result in higher simulation times. Unfortunately, there is insufficient information in the literature to establish guidelines between MD model size and the resulting precision in predicted …

Gate-Controlled Supercurrent Effect In Dry-Etched Dayem Bridges Of Non-Centrosymmetric Niobium Rhenium, Jennifer Koch, Carla Cirillo, Sebastiano Battisti, Leon Ruf, Zahra Makhdoumi Kakhaki, Alessandro Paghi, Armen Gulian, Serafim Teknowijoyo, Giorgio De Simoni, Francesco Giazotto, Carmine Attanasio, Elke Scheer, Angelo Di Bernardo

Mathematics, Physics, and Computer Science Faculty Articles and Research

The application of a gate voltage to control the superconducting current flowing through a nanoscale superconducting constriction, named as gate-controlled supercurrent (GCS), has raised great interest for fundamental and technological reasons. To gain a deeper understanding of this effect and develop superconducting technologies based on it, the material and physical parameters crucial for the GCS effect must be identified. Top-down fabrication protocols should also be optimized to increase device scalability, although studies suggest that top-down fabricated devices are more resilient to show a GCS. Here, we investigate gated superconducting nanobridges made with a top-down fabrication process from thin films of …

Deep Selenium Donors In Zngep2 Crystals: An Electron Paramagnetic Resonance Study Of A Nonlinear Optical Material, Timothy D. Gustafson, Larry E. Halliburton, Nancy C. Giles, Peter G. Schunemann, Kevin T. Zawilski, J. Jesenovec, Kent L. Averett, Jeremy Slagle

Faculty Publications

Zinc germanium diphosphide (ZnGeP₂) is a ternary semiconductor best known for its nonlinear optical properties. A primary application is optical parametric oscillators operating in the mid-infrared region. Controlled donor doping provides a method to minimize the acceptor-related absorption bands that limit the output power of these devices. In the present study, a ZnGeP₂ crystal is doped with selenium during growth. Selenium substitutes for phosphorus and serves as a deep donor. Significant concentrations of native defects (zinc vacancies, germanium-on-zinc antisites, and phosphorous vacancies) are also present in the crystal. Electron paramagnetic resonance (EPR) is used to establish the …

Data Engineering: Building Software Efficiency In Medium To Large Organizations, Alessandro De La Torre

Whittier Scholars Program

The introduction of PoetHQ, a mobile application, offers an economical strategy for colleges, potentially ushering in significant cost savings. These savings could be redirected towards enhancing academic programs and services, enriching the educational landscape for students. PoetHQ aims to democratize access to crucial software, effectively removing financial barriers and facilitating a richer educational experience. By providing an efficient software solution that reduces organizational overhead while maximizing accessibility for students, the project highlights the essential role of equitable education and resource optimization within academic institutions.

Radiative Cooling For Energy-Efficient Power Generation, Rickia Hanna

Celebrating Scholarship and Creativity Day (2018-)

This thesis examined radiative cooling on a small scale using a hybrid photovoltaic/radiative cooling model system, to determine its efficiency for large-scale power generation. Radiative cooling is concerned with heat transfer and this thesis’s main goal is to harness that heat energy to produce electricity. The efficiency of the system was tested using various light sources at different angles and total power output. The testing was done several times and there proved to be a proportional relationship between light intensity and current output. However, due to the performance threshold of the PTEC module, the component of the system used to …

Microplastics Fouling Mitigation In Forward Osmosis Membranes By The Molecular Assembly Of Sulfobetaine Zwitterion, Javad Farahbakhsh, Mitra Golgoli, Mehdi Khiadani, Amir Razmjou, Masoumeh Zargar

Research outputs 2022 to 2026

Forward osmosis (FO) membranes have potential for the efficient water and wastewater treatment applications. However, their development has faced significant challenges due to their fouling propensity. In this study, FO membranes modified with sulfobetaine zwitterions (i.e., [2-(Methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl) ammonium hydroxide) were fabricated and used for the first time to address microplastic (MP) fouling issue. Water flux, reverse salt flux (RSF), fouling, and flux recovery were evaluated for the membranes loaded with different quantities of the zwitterions ranging from 0.25 % to 2 %. The developed membranes were tested over 49 h with feed solutions containing polyethylene MPs and bovine serum albumin …

A Reputation System For Provably-Robust Decision Making In Iot Blockchain Networks, Charles C. Rawlins, Sarangapani Jagannathan, Venkata Sriram Siddhardh Nadendla

Electrical and Computer Engineering Faculty Research & Creative Works

Blockchain systems have been successful in discerning truthful information from interagent interaction amidst possible attackers or conflicts, which is crucial for the completion of nontrivial tasks in distributed networking. However, the state-of-the-art blockchain protocols are limited to resource-rich applications where reliably connected nodes within the network are equipped with significant computing power to run lottery-based proof-of-work (pow) consensus. The purpose of this work is to address these challenges for implementation in a severely resource-constrained distributed network with internet of things (iot) devices. The contribution of this work is a novel lightweight alternative, called weight-based reputation (wbr) scheme, to classify new …

Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler

MS in Computer Science Project Reports

In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …

Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy

Computer Science Faculty Research & Creative Works

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …

Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia

Research Collection School Of Computing and Information Systems

The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …

Effect Of Fabrication Parameters On The Ferroelectricity Of Hafnium Zirconium Oxide Films: A Statistical Study, Guillermo A. Salcedo, Ahmad E. Islam, Elizabeth Reichley, Michael Dietz, Christine M. Schubert Kabban, Kevin D. Leedy, Tyson C. Back, Weison Wang, Andrew Green, Timothy S. Wolfe, James M. Sattler

Faculty Publications

Ferroelectricity in hafnium zirconium oxide (Hf_1−xZr_xO₂) and the factors that impact it have been a popular research topic since its discovery in 2011. Although the general trends are known, the interactions between fabrication parameters and their effect on the ferroelectricity of Hf_1−xZr_xO₂ require further investigation. In this paper, we present a statistical study and a model that relates Zr concentration (x), film thickness (t_f), and annealing temperature (T_a) with the remanent polarization (P_r) in tungsten (W)-capped Hf_1−xZr_xO₂. …

Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …

Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …

Will Saf Turbocharge The Corn Ethanol Market?, Richard Perrin, Lilyan Fulginiti, Felipe Miranda De Souza Almeida

Cornhusker Economics

The long-run outlook for the corn ethanol industry is questionable, due to a transition to electric and hybrid vehicles. One source of hope for the long run is the potential demand for producing Sustainable Aviation Fuel (SAF). SAF is a key component in the United States Aviation Climate Action Plan, a path to net-zero greenhouse gas (GHG) emissions in the aviation industry by 2050. Demand for ethanol for SAF offers hope to the ethanol industry, but it depends a great deal on policy decisions that are being made now. Here we sketch out this story.

Based on the information available …

6d Single-Fluorogen Orientation-Localization Microscopy For Elucidating The Architecture Of Beta-Sheet Assemblies And Biomolecular Condensates, Tingting Wu, Weiyan Zhou, Jai S. Rudra, Rohit V. Pappu, Matthew D. Lew

Electrical & Systems Engineering Publications and Presentations

We develop six-dimensional single-molecule orientation-localization microscopy (SMOLM) to measure the 3D positions and 3D orientations simultaneously of single fluorophores. We show how careful optimization of phase and polarization modulation components can encode phase, polarization, and angular spectrum information from each fluorescence photon into a microscope’s dipole-spread function. We used the transient binding and blinking of Nile red (NR) to characterize the helical structure of fibrils formed by designed amphipathic peptides, KFE8^L and KFE8^D, and the pathological amyloid-beta peptide Aβ42. We also deployed merocyanine 540 to uncover the interfacial architectures of biomolecular condensates.

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 …

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 …

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 and Astronomy Faculty Publications and Presentations

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 …

Analyzing Biomedical Datasets With Symbolic Tree Adaptive Resonance Theory, Sasha Petrenko, Daniel B. Hier, Mary A. Bone, Tayo Obafemi-Ajayi, Erik J. Timpson, William E. Marsh, Michael Speight, Donald C. Wunsch

Chemistry Faculty Research & Creative Works

Biomedical Datasets Distill Many Mechanisms Of Human Diseases, Linking Diseases To Genes And Phenotypes (Signs And Symptoms Of Disease), Genetic Mutations To Altered Protein Structures, And Altered Proteins To Changes In Molecular Functions And Biological Processes. It Is Desirable To Gain New Insights From These Data, Especially With Regard To The Uncovering Of Hierarchical Structures Relating Disease Variants. However, Analysis To This End Has Proven Difficult Due To The Complexity Of The Connections Between Multi-Categorical Symbolic Data. This Article Proposes Symbolic Tree Adaptive Resonance Theory (START), With Additional Supervised, Dual-Vigilance (DV-START), And Distributed Dual-Vigilance (DDV-START) Formulations, For The Clustering Of …

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 …