Engineering Commons^™

Integrating Sustainability Into Aviation Training: Perspectives From Industry Professionals, Eva Maleviti, Sarah Talley

Publications

This paper is part of a broader research project aimed at identifying the most appropriate training materials, formats, and delivery methods for aviation sustainability content for future aviation professionals, starting from the early stages of their training. The current paper presents the data analysis and results of a short online survey targeting aviation professionals. Specifically, it provides preliminary data on sustainability perspectives from global aviation professionals. Through qualitative analysis, the creation of a baseline from a random sample of aviation professionals offers insight into their perspectives and how they could be influenced toward a more sustainable pathway. Additionally, the paper …

Fungal Recycling, Arden Yale, Alannah Ridgway

Student Research Design & Innovation Symposium

Fungal recycling is a developing technique that turns garbage into food. To do this, the presenters created a new method of growing fungi of all different types (e.g. white oyster, lion's mane, etc.) and inoculating them into bags. These bags are sterile and contain a few key ingredients: coco coir, grain pellets, and shredded cardboard boxes. These boxes are taken from anywhere, mostly from lab equipment. Using the coco coir and grain pellets as a "starter", the fungi used the cardboard as a growing medium, literally growing food from trash. The technique is currently being refined. This project could bring …

Wearable Lanyard Acc For On-Demand Communication, Valerie Rosario, Rileigh Kearney, Hailey Soares, Kristen Martinez, Hannah Pereira

Student Research Design & Innovation Symposium

Augmentative and Alternative Communication (AAC) devices help individuals experiencing challenges with verbal communication, including English language learners, individuals who are hard of hearing, on the autism spectrum, or those experiencing anxiety disorders. Current technologies include low-tech communication boards and complex mobile applications, but both of these offer little customization. Our prototypes integrate touch-sensing panels and small LCD screens into common wearables, like necklaces and lanyards, so that the ACC discretely blends in with everyday apparel and provides on-demand communication that is customizable to the individual user. The touch-sensing pads are sensory-friendly, and the LCD screens operate without sound to support …

Generalized Reference Targeting For Spacecraft, David Geller, David Woffinden, Collin York

Space Dynamics Laboratory Publications

For spaceflight programs to achieve some of the aggressive exploration initiatives such as visiting and landing on other celestial bodies, rendezvousing with other orbiting vehicles, and ultimately returning crew safely to Earth, an assortment of targeting algorithms to compute the necessary burns to strategically maneuver a spacecraft to a variety of destinations are required. Numerous examples exist, but rather than creating and implementing multiple targeting solutions, is it possible to have a general targeting model that can accommodate a variety of applications? Originally motivated for mission design and analysis purposes, this paper outlines a generalized reference targeting algorithm for spaceflight …

High-Speed Aircraft Stability And Control Metrics, Timothy T. Takahashi, Jack A. Griffin, Ramana Grandhi

Faculty Publications

This review paper identifies key stability and control screening parameters needed to design low-risk, general-purpose high-speed aircraft. These derive from MIL-STD-8785C, MIL-STD-1797, and older AGARD reports, and are suitable for assessing conceptual high-speed vehicles. We demonstrate their applicability using published ground test, computation, and flight test data from the Bell X-2, North American X-15, Martin X-24A, Northrop HL-10, Lockheed Blackbird (YF-12/SR-71), and North American XB-70 as well as the Rockwell Space Shuttle Orbiter. The relative success of the X-15 and Blackbird and the performance limitations of the others indicate the need to scrutinize lateral-directional stability at the preliminary design phase. …

Largely Enhanced Out-Of-Plane Electromechanical Coupling Effects In Two-Dimensional Molybdenum-Disulfide/Boron-Nitride Heterostructures, Qiong Liu, Vijay Kumar Choyal, James E. Morris, Timon Rabczuk, Xiaoning Jiang, Xiaoying Zhuang

Electrical and Computer Engineering Faculty Publications and Presentations

The electromechanical coupling effects in two-dimensional (2D) transition metal dichalcogenides (TMDs) have attracted great interest. However, for 2D TMDs, piezoelectricity is confined to the basal plane, and the flexoelectricity-derived out-of-plane electromechanical response is usually faint, limiting the applications of this material family using the out-of-plane electromechanical effects. Here, this work reports a facile strategy to greatly enhance the out-of-plane electromechanical response of hexagonal molybdenum disulfide (2H-MoS₂) nanoflakes by stacking monolayer hexagonal boron nitride (h-BN) on 2H-MoS₂ nanoflakes to form MoS₂/BN heterostructures. The d^eff/₃₃ coefficient of MoS₂/BN can reach a value comparable …

Comparison Of Dynamic Mode Decomposition With Other Data-Driven Models For Lung Cancer Incidence Rate Prediction, L. Raymond Guo, Jifu Tan, M. Courtney Hughes

Faculty Articles, Papers, and Other Scholarship

Introduction: Public health data analysis is critical to understanding disease trends. Existing analysis methods struggle with the complexity of public health data, which includes both location and time factors. Machine learning offers powerful tools but can be computationally expensive and require specialized knowledge. Dynamic mode decomposition (DMD) is an alternative that offers efficient analysis with fewer resources. This study explores applying DMD in public health using lung cancer data and compares it with other machine learning models.

Methods: We analyzed lung cancer incidence data (2000–2021) from 1,013 US counties. Machine learning models (random forest, gradient boosting machine, support vector machine) …

Updating Rainfall Zones And Intensities In Nebraska For Improved Design Of Non-Bridge-Sized Drainage Structures, Rezaul Mahmood, Tirthankar Roy, Chanchal Gupta, Kanak Kanti Kar, William Sorenson

Nebraska Department of Transportation: Research Reports

This study assesses the effectiveness of precipitation zones and Intensity-Duration-Frequency (IDF) curves used by the Nebraska Department of Transportation (NDOT) for designing drainage structures. Twenty stations were selected for the analysis of revised IDF curves out of forty-two stations. Datasets have been considered from the National Center for Environmental Information (NCEI). By Comparing the new curves with existing ones, the study efforts examine the frequency, duration and severe rainfall. The results show that the IDF curves for various zones and stations varied noticeably from one another. Also, noticed that Zone A and B shows lower rainfall intensities in short to …

Qualitative Interview Coding, Caroline M. Moore

ENGS 15.11: Design and Education

This lesson introduces undergraduate students to rigorous thematic analysis as a tool for gaining insights from qualitative interviews. Aimed at students with some familiarity in Design Thinking, it emphasizes the role of interviewing in understanding user experiences and informing human-centered research. This might be the first lesson in a multi-lesson sequence, allowing the next lesson to go deeper into insights of what design researchers gather from their thematic coding. Through a mix of lecture, individual coding, and small-group coding, students learn to distinguish between inductive and deductive coding and apply these methods to sample interviews. The lesson fosters active learning …

Mangroves: A Lesson On Modeling Changing Shorelines, Willa Skye

ENGS 15.11: Design and Education

This lesson is an overview of storm barriers, specifically mangroves and how they maintain shorelines. It is meant to offer a visual experience of coastline protection and provide a background for deeper understanding about intertidal zones and their conservation. Students will spend time creating a solution to a flooding problem by making a mock coast and storm barrier. To wrap-up the lesson, students will pair up, then reflect on their model and then individually write or draw their takeaways.

Using Design Thinking To Upcycle, Lily Chabica

ENGS 15.11: Design and Education

This 60-minute lesson introduces the power of design thinking as a tool for sustainability. Through short discussions, visual examples, and a hands-on “Trash Island” design challenge, students will explore how creativity can transform waste into value. The session combines theory (understanding upcycling vs. recycling) with active practice (building prototypes from found materials). By the end, students will connect environmental responsibility to personal agency. They will realize that design can drive everyday impact.

Confronting Indecision, Lane Allison Murray

ENGS 15.11: Design and Education

This course aims to increase students' understanding of indecision and how to confront it. The class provides insight into how the fear of the unknown holds people back from making decisions and about understanding one's values as a means to combat indecision. Students and teachers alike explore instances of indecision and evaluate their own impulses & thought processes by reflecting on the deeper reasons for their choices. Students will increase their comfort level of asking themselves and their peers questions about the values that guide them and, in doing so, strengthen their relationships with themselves and others.

Cnns And Transformers For Visual Understanding: From Feature Alignment To Image Captioning, Xuehao Liu

Doctoral

Deep learning has developed rapidly since the introduction of Deep Belief Networks during the past decade. As an area of machine learning, it still has many open challenges. Among these open challenges is the issue of transparency, with deep learning models known as black boxes. Both explainability of a model for understanding the decision making process, and the transparency of the relationship between the input and output are crucial for understanding a model. The understanding of models can build trust between AI systems and humans, verify models behavior, and identify potential biases or errors.

Navigating Human-Robotic Interaction Challenges In Teaching-By-Demonstration, Shakra Mehak

Doctoral

The advancement in interdisciplinary research domains, like robotics and HRI, presents a challenging task. It is an exception rather than the norm for research to extend beyond the boundaries of individual disciplines and encompass the challenges presented by other fields of study. This project is part of ”Collaborative Intelligence for Safety Critical Systems” (CISC), which is a Marie Curie Training Network funded by the European Commission to hire and train researchers with the expertise and skillset necessary to carry out the major tasks required to develop a Collaborative Intelligence system. The program encompasses four overarching themes: Artificial Intelligence (AI), Human …

3d Bioprinting Technology Innovation In Female Reproductive System, Siyao Chen, Tongxin Wang, Jiaqi Chen, Mingxing Sui, Luyao Wang, Xueyu Zhao, Jianqiao Sun, Yingli Lu

Department of Obstetrics & Gynecology Faculty Publications

Several diseases affect the female reproductive system, and both disease factors and treatments impact its integrity and function. Consequently, understanding the mechanisms of disease occurrence and exploring treatment methods are key research focuses in obstetrics and gynecology. However, constructing accurate disease models requires a microenvironment closely resembling the human body, and current animal models and 2D in vitro cell models fall short in this regard. Thus, innovative in vitro female reproductive system models are urgently needed. Additionally, female reproductive system diseases often cause tissue loss, yet effective tissue repair and regeneration have long been a bottleneck in the medical field. …

Twitter-Based Osint For Cyber Event Analytics, Dakota S. Dale, Kylie Mcclanahan, W. Sky Elder, Qinghua Li

Electrical Engineering and Computer Science Faculty Publications and Presentations

Open-Source Intelligence (OSINT) is often regarded as a critical component for cybersecurity intelligence gathering to secure cyber infrastructures. As Artificial Intelligence (AI) and social media progress and become more commonplace, we have the unique opportunity to collect and analyze information from social media in real time. In this paper, we propose an AI-based framework for automatically filtering the Twitter stream for cybersecurity information, analyzing the natural language to aggregate the tweets around specific cybersecurity events, and certifying the information. The system's applications range from discovering events security operators may not have been aware of to helping operators investigate on-going events …

Understanding Human-Agent Teaming: A Mixed-Methods Examination Of Perceptions And Interactions, Valerie Robbins-Roth, Kendall Carmody, Bella Deloach, Meredith Carroll, Amanda Thayer, Jessica Wildman

Aeronautics Faculty Publications

While quantitative research on trust in HATs is well-established, there is a significant gap in understanding individuals’ subjective experiences during trust violations and repair. This study explores how people perceive and respond to these events, guided by three key questions: (1) How do participants describe emotional and behavioral responses to trust violations and repair? (2) How do personality traits influence reactions? (3) What qualitative themes emerge in the dissolution of trust? Using a mixed-methods approach, participants engaged in a 1-hour simulated search-and-rescue mission with four autonomous agents. During the task, one agent committed two trust violations, followed by repair attempts …

Nonlinear Control Of Buck-Type Converters For Micro-Wind Generators, Noah Wilding, Shuzan Kumar Sarkar, Shruti Pandey, Michael L. Mcintyre

Electrical and Computer Engineering Faculty Research & Creative Works

Small-scale wind turbines offer a promising solution for distributed renewable energy generation. However, this approach often leads to wasted energy when battery capacity is reached, as excess energy is typically dissipated into resistors. The reliance on batteries further increases the cost and complexity of such systems. This paper presents a nonlinear control algorithm for regulating buck-type converters, providing a more efficient energy management solution. By employing a grid-connected inverter, excess energy is utilized rather than dissipated, potentially eliminating the need for batteries and reducing micro-wind turbine installation costs. The proposed control strategy manages the DC-link voltage for the inverter by …

Filter Based Motor Control For Robotic Applications, Shuzan Kumar Sarkar, Noah Wilding, Shruti Pandey, Nicholas Hawkins, Michael L. Mcintyre

Electrical and Computer Engineering Faculty Research & Creative Works

Controlling coreless DC motor in the field of humanoid robotic application involves considering various surrounding electromagnetic environment interference and sudden change of load with parameters variation of motor dynamics which makes the system complex. This paper presents a filter-based control scheme for a coreless DC motor drive system using an H-bridge inverter as the input circuit of the motor which is easy to implement and cost effective. From the electromagnetic characteristics, the dynamic model of the motor along with the control scheme is implemented in the commercial software PLECS. Then the effectiveness of this approach is validated through simulations demonstrating …

Learning From Leads: A 1d Dilated Resnet For Ecg Chagas Disease Screening, Somesh Saini, Matheus Lima Diniz Araujo

Student Scholarship

No abstract provided.

Energy, Exergy, Economic, And Environmental Analyses (4e) Of Geothermal Power Plant With Double Flash System For Power And Heat Production, Mamdouh El Haj Assad, Yashar Aryanfar, Imen Ben Salem, Shek Atiqure Rahman, Laveet Kumar, Ahmad K. Sleiti, Mohammad Alhuyi Nazri

All Works

Geothermal energy is a reliable and sustainable renewable energy source due to its continuous availability and eliminating the need for energy storage systems. Among various types of geothermal power plants, double flash (DF) geothermal plants are among the most widely utilized. This paper presents a comprehensive thermodynamic analysis of a DF geothermal power plant, integrating energy, exergy, economic, and exergoenvironmental (4E) evaluations. The study examines the influence of key parameters, including the high-pressure separator and geothermal production well temperature, on the system performance. The results indicate that the expansion valve of the high pressure separator exhibits the highest exergy destruction …

Green Project Finance For Infrastructure And Building Construction: Instruments And Benchmarks, Yunping Liang

Durham School of Architectural Engineering and Construction: Faculty Publications

It is counterintuitive that both the practice and research on green project finance for infrastructure and building construction are ascendant and still have limited precedents, considering that individually, the two parts (i.e., project finance and sustainability in the built environment) are both familiar to the communities. To narrow the gap and facilitate the financing of the built environment’s sustainable transformation, this study systematically reviews the common instruments for financing green infrastructure and building projects. Benchmarking questions distinguishing the profile of green project finance from common construction project finance are developed based on the literature review and then are used for …

Using Electro-Peroxone Process To Remediate Soil Contaminated With Phenol, Elaheh Faghih Nasiri, Farhad Qaderi, S. Mustapha Rahmaninezhad

Civil Engineering Faculty Publications

Industrial processes are among human activities that cause production of a large volume of wastewater containing organic pollutants such as phenol and its derivatives. Soil remediation is crucial for enhancing environmental quality for both humans and other living organisms. This study investigate the use of an electro-peroxone system to remove environmental pollutants from soil. In conjunction with ozonation, the study employed electrochemically generated hydrogen peroxide using a carbon electrode, addressing concerns about transportation and storage. Experiments were structured using response surface methodology (RSM) with three variables: ozone dosages ranging from 4 to 8 l/h, initial pollutant concentrations from 20 to …

Mxene-Based Materials For Enhanced Water Quality: Advances In Remediation Strategies, Ali Mohammad Amani, Milad Abbasi, Atena Najdian, Farzaneh Mohamadpour, Seyed Reza Kasaee, Hesam Kamyab, Shreeshivadasan Chelliapan, Mostafa Shafiee, Lobat Tayebi, Ahmad Vaez, Atefeh Najafian, Ehsan Vafa, Sareh Mosleh-Shirazi

Electrical & Computer Engineering Faculty Publications

Two-dimensional MXenes are promising candidates for water treatment because of their large surface area (e.g., exceeding 1000 m²/g for certain structures), high electrical conductivity (e.g., >1000 S/m), hydrophilicity, and chemical stability. Their strong sorption selectivity and effective reduction capacity, exemplified by heavy metal adsorption efficiencies exceeding 95 % in several studies, coupled with facile surface modification, make them suitable for removing diverse contaminants. Applications include the removal of heavy metals (e.g., achieving >90 % removal of Pb(II)), dye removal (e.g., demonstrating >80 % removal of methylene blue), and radioactive waste elimination. Furthermore, 3D MXene architecture exhibit enhanced performance in antibacterial …

Detecting Anomalous Srf Cavity Behavior With Unsupervised Learning, Hal Ferguson, Jiang Li, Adam Carpenter, Chris Tennant, Dillon Thomas, Dennis Turner

Electrical & Computer Engineering Faculty Publications

We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. Key to the system’s success was the development of data acquisition systems (DAQs) that capture fast-sampled, information-rich signals, essential for detecting transient effects. The approach involves creating daily cavity-specific models using principal component analysis to handle variations in rf signal behavior and mitigate performance degradation from data drift. This unsupervised method eliminates the need for expensive labeling by continuously updating models with recent data. Deployed and operational for 3 months before a …

Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …

Effect Of Bioactive Glass On Pxdda / Pxdda-Co-Pla Nanocomposite For Hard Tissue Reconstruction: Synthesis And Characterization, Ehsan Vafa, Lobat Tayebi, Fatemeh Azizli, Somayeh Parham, Katayoon Rezaeeparto, Sedigheh Azadi, Ali Mohammad Amani, Mohammad Javad Azizli, Hesam Kamyab, Shreeshivadasan Chelliapan, Saravanan Rajendran

Electrical & Computer Engineering Faculty Publications

Newer bone graft materials face various challenges in achieving optimal mechanical strength, bioactivity, and antibacterial action simultaneously, which can result in suboptimal regeneration outcomes and increased infection risks In the present study, we developed a novel nanocomposite of poly (xylitol-co-dodecanedioic acid) (PXDDA) and poly (lactic acid) (PLA) with 1-10 wt% incorporation of bioactive glass (BG), utilizing a a PXDDAco-PLA compatibilizer for maintaining homogeneity. Extensive characterization techniques including, Fourier infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauere Emmette Teller (BET), Proton Nuclear Magnetic Resonance (¹H NMR) and contact angle measurements, revealed that the addition …

Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant

Electrical & Computer Engineering Faculty Publications

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.

Biomedical Mxene-Polymer Nanocomposites: Advancing Photothermal Therapy, Antibacterial Action, And Smart Drug Delivery: A Review, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Mohammad Javad Azizli, Milad Abbasi, Ahmad Vaez, Hesam Kamyab, Daniel Simancas-Racines, Shreeshivadasan Chelliapan

Electrical & Computer Engineering Faculty Publications

MXenes are hydrophilic, conductive, tunable, and biocompatible two-dimensional ceramic materials prepared by etching the 'A' layer from their precursor MAX phases. Although MXenes show exceptional promise in photothermal therapy, biosensing, and regenerative medicine, they face challenges such as oxidative instability in physiological environments, limited drug-loading capacity, and unpredictable immune responses. To address these limitations, MXene/polymer nanocomposites incorporating both synthetic polymers (e.g., polyethylene glycol (PEG), polyvinyl alcohol (PVA), polyvinylpyrrolidone (PVP), and polylactic-co-glycolic acid (PLGA)) and natural biopolymers (e.g., cellulose nanofiber, gelatin, chitosan, hyaluronic acid, and soybean phospholipids) have been developed. These composites enhance functionality for biomedical applications such as photothermal cancer …

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …