Engineering Commons^™

Amino Acid-Based Protein-Mimic Hydrogel Incorporating Pro-Regenerative Lipid Mediator And Microvascular Fragments Promotes The Healing Of Deep Burn Wounds, Yan Lu, Shanchun Su, Chih Chang Chu, Yuichi Kobayashi, Abdul Razak Masoud, Hongying Peng, Nathan Lien, Mingyu He, Christopher Vuong, Ryan Tran, Song Hong

School of Medicine Faculty Publications

Pro-regenerative lipid mediator 1 (PreM1) is a specialized pro-resolving lipid mediator that promotes wound healing and regenerative functions of mesenchymal stem cells (MSCs), endothelial cells, and macrophages. The healing of third-degree (3°) burns and regenerative functions of MSCs are enhanced by ACgel1, an arginine-and-chitosan-based protein-mimic hybrid hydrogel. Adipose-tissue derived microvascular fragments (MVFs) are native vascularization units and a rich source of MSCs, endothelial cells, and perivascular cells for tissue regeneration. Here we describe an innovative PreM1-MVFs-ACgel1 construct that incorporated PreM1 and MVFs into ACgel1 via optimal design and fabrication. This construct delivered PreM1 to 3°-burn wounds at least up to …

Flexible Operation Of Nuclear Hybrid Energy Systems For Load Following And Water Desalination, An Ho, Blake W. Billings, John Hedengren, Kody M. Powell

Faculty Publications

Nuclear hybrid energy systems (NHES) have the potential to provide dependable and emission-free electricity to the grid while also increasing the flexibility and reliability of the electrical grid. Molten salt reactor (MSR) technology can provide consistent, carbon-free electricity while also increasing efficiency, security, and sustainability and reducing nuclear waste. This study investigates the integration of Molten Salt Reactors (MSR) and conventional Pressurized Water Reactors (PWR) with desalination technologies: Direct Contact Membrane Distillation (DCMD), Multi-Stage Flash Distillation (MSFD), and Reverse Osmosis (RO). Dynamic first-principles models were developed and tested using real grid data from the New York Independent System Operator. The …

Characterizing Biomolecular Structure Features Through An Innovative Elliptical Dichroism Spectrometry For Cancer Detection, Yusuf Asad, Keerthi Priya Jangili, Amara Arshad, Maliha Elma, Komila Rasuleva, Alfred Akinlalu, Tommy Gao, Umamaheswara Rao Tida, Wenjie Xia, Dali Sun

Electrical and Computer Engineering: Faculty Scholarship

This research introduces a novel method for evaluating the structural features of biomolecules, utilizing our innovative Elliptical Dichroism (ED) spectrometer specifically designed for stereochemical analysis. By integrating ED spectrometry with autocorrelation (AC) analysis, we investigate the conformational characteristics of biological molecules such as amino acids, proteins, and extracellular vesicles (EVs) induced by elliptically polarized UV absorption. Our streamlined approach offers a cost-effective and portable solution with minimal sample consumption and supports multiple working modes to efficiently characterize biomolecular structures. The insight from this new approach demonstrates potential applications in using biomolecular characterization for cancer detection.

Incorporating Genai Into Information Literacy Instruction: Find Engineering Standards, Lauren Todd

Generative AI Teaching Activities

Students will prompt GenAI tools to find relevant engineering standards that might apply to their mechanical engineering design project, then they must evaluate the responses.

Leveraging Convolutional Neural Network (Cnn)-Based Auto Encoders For Enhanced Anomaly Detection In High-Dimensional Datasets, Aetsam Javad, Madiha Anjum, Hassan Ahmed, Arshad Ali, H. M. Shahzad, Hamayun Lhan, Abdulaziz M. Alshahrani

Business Faculty Publications

This study presents an Auto-Encoder Convolutional Neural Network (AECNNs) approach for anomaly detection in high-dimensional datasets. Unsupervised learning-based algorithms have a strong theoretical foundation and are widely used for anomaly detection in high-dimensional datasets, but some limitations significantly reduce their performance. This study proposes an algorithm to address these limitations. The proposed AECNN combines various convolutional layers, feature extraction, dimensionality reduction, and data preprocessing and was evaluated using accuracy, precision, recall, and F1-score. The performance of the proposed model was evaluated using a large real benchmark dataset. The proposed CNN-based autoencoder distinguished anomalies with an AUC score of 0.83 and …

Hydrogen-Peroxide Intercalated Expanded Graphite Facilitates Large Enhancement In Thermal Conductivity Of Polyetherimide/Graphite Nanocomposites, Fatema Tarranum, Swapneel Danayat, Avinash Nayal, Zarin Tasmin Mona, Roshan Sameer Annam, Keisha Walters, Jivtesh Garg

Chemical Engineering Faculty Publications and Presentations

In this work, we prepared expanded graphite (EG) filler using hydrogen peroxide (H₂O₂) as an oxidizing agent to study its impact on the thermal conductivity (k) of polyetherimide (PEI) composite, compared to EG prepared using sodium chlorate (NaClO₃). Sulfuric acid was used as the primary intercalating agent for both synthesis routes. The use of H₂O₂ prepared EG (EG-H₂O₂) led to a remarkable ∼4030% enhancement (9.5 Wm⁻¹K⁻¹) in k of the PEI/EG composite, while the use of NaClO₃ prepared EG (EG-NaClO …

Irrigation Pumping Plants: Energy Consumption And Pumping Plant Performance [Class Handout], Derek M. Heeren, Saleh Taghvaeian, Abia Katimbo, Xin Qiao

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Irrigation can be a large contributor to energy consumption on an irrigated farm. It is important to understand factors that impact irrigation energy use and to identify opportunities to reduce energy consumption. This class handout introduces a set of equations for calculating energy consumption for an irrigation pumping plant based on flow conditions and pump efficiency. It also presents typical values for motor performance (including both electric motors and internal combustion engines) in both SI and USCS units. Finally, the handout introduces the concept of a field evaluation of an irrigation pumping plant along with the Nebraska Pumping Plant Performance …

Quantifying The Aging Of Lithium-Ion Pouch Cells Using Pressure Sensors, Yousof Nayfeh, Jon C. Vittitoe, Xianglin Li

Mechanical Engineering Faculty Publications

Understanding the behavior of pressure increases in lithium-ion (Li-ion) cells is essential for prolonging the lifespan of Li-ion battery cells and minimizing the safety risks associated with cell aging. This work investigates the effects of C-rates and temperature on pressure behavior in commercial lithium cobalt oxide (LCO)/graphite pouch cells. The battery is volumetrically constrained, and the mechanical pressure response is measured using a force gauge as the battery is cycled. The effect of the C-rate (1C, 2C, and 3C) and ambient temperature (10 °C, 25 °C, and 40 °C) on the increase in battery pressure is investigated. By analyzing the …

High-Fidelity Machine Learning Techniques For Driver Drowsiness Detection, Yasser Ismail, Ebenezer Essel, Abeer Abdelhamid, Mahmoud Darwich, Fahmi Khalifa, Fred Lacy

Faculty Publications

It is devastating that daily, there is an ample number of car crashes that cause damage to automobiles, onboard passengers get injured, and others tend to lose their lives. Road crashes are fast rising across the globe and have drawn many road safety commissions and concerned individuals to discuss ways to reduce this menacing situation drastically. With the introduction of artificial intelligence and technological advancement, the government and state commissions have beckoned on the various universities and research institutions to develop methods to curb the rise of automobile crashes. Some causes of these crashes include drunk driving and drowsiness, the …

Glycerol-Free Biodiesel Via Catalytic Interesterification: A Pathway To A Netzero Biodiesel Industry, Nessren Farrag, Omar Youssef Youssef, Esraa Khaled, Omar Aboelazayem

Chemical Engineering

Conventional biodiesel manufacturing uses alcohol as an acyl acceptor, resulting in glycerol as a side product. The increased demand for biodiesel has led to the production of a substantial surplus of glycerol, exceeding the market need. Consequently, glycerol is now being regarded as a byproduct, and in some cases, even as waste. The present study aims to suggest an economically viable and ecologically friendly approach for maintaining the viability of the biodiesel sector. This involves generating an alternative byproduct of higher value, rather than glycerol. Triacetin is produced through the interesterification of triglycerides with methyl acetate, and is a beneficial …

A Global Model-Agnostic Rule-Based Xai Method Based On Parameterized Event Primitives For Time Series Classifiers, Ephrem T. Mekonnen, Luca Longo, Pierpaolo Dondio

Articles

Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps …

Optimising Grid-Forming Inverters To Prevent Under-Frequency Load Shedding With Minimal Energy Storage, Indoopa Manamperi, Iftekhar Ahmad, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The increasing penetration of distributed photovoltaics decreases the available loads in distribution feeders. This makes the traditional under-frequency load shedding systems ineffective. As an alternative, large-scale energy storage systems (ESS) could be used to provide contingency frequency response. This study investigates the optimum sizing of the ESS to prevent under-frequency load shedding. The optimal size is determined for both droop and virtual synchronous generator control-based inverters, to maintain frequency within operating standards. The control parameters are treated as variables in the optimisation process, allowing the identification of the optimal set of control parameters that minimise the ESS size while preventing …

Salicylhydroxamic Acid Containing Structural Adhesive, Md Saleh Akram Bhuiyan, Kan Wang, Seyedehfatemeh Razaviamri, Bruce Lee

Michigan Tech Publications

The feasibility of utilizing salicylhydroxamic acid (SHAM) as a new adhesive molecule for designing structural adhesives is investigated in this study. SHAM-containing polymers were prepared with a hydroxyethyl methacrylate (HEMA) or methoxyethyl acrylate (MEA) backbone and mixed with polyvinylidene fluoride (PVDF). PVDF was included to increase the cohesive property of the adhesive through hydrogen bond (H-bond) formation with the adhesive polymers. SHAM-containing adhesive demonstrated lap shear adhesion strength (S ) greater than 0.9 MPa to glass, metal, and polymeric surfaces. Adhesive formulations with elevated SHAM-content also demonstrated increased adhesive properties with S values reaching as high as 4.8 MPa. Due …

Golf Club Selection With Ai-Based Game Planning, Mehdi Khazaeli, Leili Javadpour

Pacific Faculty Work

In the dynamic realm of golf, where every swing can make the difference between victory and defeat, the strategic selection of golf clubs has become a crucial factor in determining the outcome of a game. Advancements in artificial intelligence have opened new avenues for enhancing the decision-making process, empowering golfers to achieve optimal performance on the course. In this paper, we introduce an AI-based game planning system that assists players in selecting the best club for a given scenario. The system considers factors such as distance, terrain, wind strength and direction, and quality of lie. A rule-based model provides the …

Version 1 Noaa-20/Omps Nadir Mapper Total Column So2 Product: Continuation Of Nasa Long-Term Global Data Record, Can Li, Nickolay A. Krotkov, Joanna Joiner, Vitali Fioletov, Chris Mclinden, Debora Griffin, Peter J.T. Leonard, Simon Carn, Colin Seftor, Alexander Vasilkov

Michigan Tech Publications

For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic …

Career: Innovation For Inclusion: Transforming Engineering Through Scalable Accessibility, Cassandra J. Mccall

Funded Research Records

No abstract provided.

Finite Element Modeling And Analysis Of Rc Shear Walls With Cutting-Out Openings, Islam M. Saad, Mohamed Emara, Ayman El-Zohairy, Sherif El-Beshlawy

Faculty Publications

In recent decades, reinforced concrete (RC) shear walls have been one of the best structural solutions to resist lateral load in high-rise buildings. Shear wall openings are essential for preparations and architectural requirements, which weaken the wall, reducing bearing capacity, energy absorption, and stiffness while also causing stress concentrations. This paper presents a comprehensive finite element (FE) investigation of the behavior and performance of RC shear walls with openings and subjected to lateral loads. The study aims to evaluate the influence of various parameters, such as opening location, size, wall aspect ratio, axial load, and concrete strength, which affect the …

A Limited-Preemption Scheduling Model Inspired By Security Considerations, Benjamin Standaert, Fatima Raadia, Marion Sudvarg, Sanjoy Baruah, Thidapat Chantem, Nathan Fisher, Christopher Gill

Computer Science and Engineering Faculty Research

Safety-critical embedded systems such as autonomous vehicles typically have only very limited computational capabilities on board that must be carefully managed to provide required enhanced functionalities. As these systems become more complex and inter-connected, some parts may need to be secured to prevent unauthorized access, or isolated to ensure correctness.

We propose the multi-phase secure (MPS) task model as a natural extension of the widely used sporadic task model for modeling both the timing and the security (and isolation) requirements for such systems. Under MPS, task phases reflect execution using different security mechanisms which each have associated execution time costs …

Structural Changes Likely Cause Chemical Differences Between Empty And Full Aav Capsids, Caryn Heldt, Molly A. Skinner, Ganesh S. Anand

Michigan Tech Publications

Due to the success of adeno associated viruses (AAVs) in treating single-gene diseases, improved manufacturing technology is now needed to meet their demand. The largest challenge is creating a process to separate empty and full capsids. Patients received larger capsid doses than necessary due to the presence of empty capsids. By enabling the better separation of empty and full capsids, patients would receive the greatest therapeutic benefit with the least amount of virus capsids, thus limiting potential side effects from empty capsids. The two most common empty/full separation methods used in downstream processing are ultracentrifugation and anion exchange chromatography. Both …

Jovian Vortex Hunter: A Citizen Science Project To Study Jupiter’S Vortices, Ramanakumar Sankar, Shawn R. Brueshaber, Lucy Fortson, Candice Hansen-Koharcheck, Chris Lintott, Kameswara Mantha, Cooper Nesmith, Glenn S. Orton

Michigan Tech Publications

The Jovian atmosphere contains a wide diversity of vortices, which have a large range of sizes, colors, and forms in different dynamical regimes. The formation processes for these vortices are poorly understood, and aside from a few known, long-lived ovals, such as the Great Red Spot and Oval BA, vortex stability and their temporal evolution are currently largely unknown. In this study, we use JunoCam data and a citizen science project on Zooniverse to derive a catalog of vortices, some with repeated observations, from 2018 May to 2021 September, and we analyze their associated properties, such as size, location, and …

Performance-Tunable Thermal Barrier Coating For Carbon Fiber-Reinforced Plastic Composites Via Flame Spraying, Heejin Kim, Kandasamy Praveen, Min Wook Lee, Juhyeong Lee

Mechanical and Aerospace Engineering Faculty Publications

Thermal barrier coatings (TBCs) are essential for improving the heat resistance of materials operating in high-temperature environments. This paper proposes a new method for manufacturing double-layered TBC with graded porosity for carbon fiber-reinforced plastic (CFRP) composites. The TBC was created by a flame spraying process, consisting of relatively dense and porous layers: (1) a dense layer was produced by spraying yttria-stabilized zirconia (YSZ) particles directly onto neat carbon fabric substrate and (2) a porous layer was prepared by co-spraying YSZ particles with sacrificial polyetheretherketone (PEEK) particles. The porosity of the porous layer was controlled by varying a PEEK injection distance …

Techno-Economic And Multi-Objective Optimization Of A Hydrogen Liquefaction Plant Based On Rbc Integrated With Orc For Waste Heat Recovery, Erfan Ghamati, Mehdi Khiadani, Barun Das

Research outputs 2022 to 2026

To comply with the trend of emission reduction and energy-saving in the industrial sector, a novel integration of the hydrogen liquefaction process based on reversed Brayton cycle (RBC) with the organic Rankine cycle (ORC), for waste heat recovery is proposed. The high temperature gas in the compressor outlet of RBC, which is typically wasted in air-coolers, is recovered in ORC and used as the heat source for power generation for the hydrogen liquefaction process. Thermodynamic analysis showed that compared to the conventional system COP increased by 17.95% from 0.11 to 0.14 and specific energy consumption (SEC) decreased by 16.33% from …

Biomanufacturing Of High-Strength Concrete: Incorporation Of Hemp Fiber To Improve High-Temperature Performance, Srishti Banerji

Funded Research Records

No abstract provided.

Eager: Leveraging Disruptive Technologies To Promote Learner Agency In Engineering Education, Cassandra J. Mccall

Funded Research Records

No abstract provided.

Effects Of Biofilm Colonization On The Dynamics Of Microplastics In Turbulent Flow, Liyuan Joanna Hou

Funded Research Records

No abstract provided.

Practical Insights And Advances In Concrete Pumping, Daniil Mikhalev, Siamak Fakhrayee Nejad, Serina Ng, Baishakhi Bose, Robin De Schryver, Ana Brunčič, Manu K. Mohan, Yanwei Wang, A. V. Rahul, Atta Ur Rehman, Dengwu Jiao, Viktor Mechtcherine, Geert De Schutter, Dimitri Feys

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This technical letter gives a concise overview of the state-of-the-art in concrete pumping. It outlines the different pump systems, briefly describes the general flow behavior of concrete in pipes and addresses the main challenges of pumping. It also elaborates upon factors influencing the pumping behavior and how to control the pumping process.

Exploring The Relationship Between Covid-19 Transmission And Population Mobility Over Time, Tanmoy Bhowmik, Naveen Eluru

Civil and Environmental Engineering Faculty Publications and Presentations

This study explores the dynamic relationship between COVID-19 transmission and transportation mobility, with an emphasis on understanding the time-varying bidirectional interplay across the different phases of the pandemic. To gain insight into this relationship, we analyzed county-level data on transmission and mobility patterns from the United States over a 74-week period using a comprehensive list of factors including: temporal factors, socio-demographics, health indicators, health care infrastructure attributes, and spatial factors. For our analysis, we proposed a simultaneous econometric model system that explicitly accounts for the bidirectional relationship between COVID-19 transmission and mobility patterns while also accounting for the influence of …

The Evaluation Of Machine Learning Techniques For Isotope Identification Contextualized By Training And Testing Spectral Similarity, Aaron P. Fjelsted, Tyler J. Morrow, Clayton D. Scott, Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Douglas E. Wolfe

Faculty Publications

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset …

Development Of Message Passing-Based Graph Convolutional Networks For Classifying Cancer Pathology Reports, Hong Jun Yoon, Hilda B. Klasky, Andrew E Blanchard, J. Blair Christian, Eric B Durbin, Xiao Cheng Wu, Antoinette Stroup, Jennifer Doherty, Linda Coyle, Lynne Penberthy, Georgia D Tourassi

School of Public Health Faculty Publications

Background: Applying graph convolutional networks (GCN) to the classification of free-form natural language texts leveraged by graph-of-words features (TextGCN) was studied and confirmed to be an effective means of describing complex natural language texts. However, the text classification models based on the TextGCN possess weaknesses in terms of memory consumption and model dissemination and distribution. In this paper, we present a fast message passing network (FastMPN), implementing a GCN with message passing architecture that provides versatility and flexibility by allowing trainable node embedding and edge weights, helping the GCN model find the better solution. We applied the FastMPN model to …

Design Considerations For Self-Management Technologies For People Living With Dementia And Informal Carers – Perspectives Of Healthcare Professionals And Charity Workers, Dympna O'Sullivan

Conference papers

Dementia is a neurodegenerative disorder that leads to decline in memory, language, reasoning, and the ability to perform daily activities. It is linked to poorer quality of life for the person with dementia and their informal (unpaid) carers. While early intervention and access to adequate care are critical in slowing dementia's progression and better managing associated symptoms, dementia is frequently only diagnosed at an advanced stage and care is often fragmented. To better understand how to meet the complex needs of persons living with dementia and their informal carers, 10 healthcare professionals and 10 charity workers from relevant community and …