Engineering Commons^™

Applying Positive Unlabeled Learning Techniques And Using The Kullback-Leibler Divergence To Improve Geothermal Surveying Assessments, Martín Thomas Rodriguez

Dissertations and Theses

As we face the current climate crisis, the discovery of geothermal energy resources has the potential to greatly reduce our dependence on fossil fuels worldwide. However, the development of any new energy infrastructure is expensive and depends on the willingness of energy agencies and developers to make initial investments based on calculated risk measures. One such measure, called geothermal favorability, is the likelihood that a site has conditions favorable for geothermal systems containing recoverable energy potential. Its prediction from existing geophysical datasets proves to be a nontrivial task. The prediction of geothermal favorability can be framed as a binary classification …

Graph Neural Networks Powered Scientific Paper Recommendation, Junhao Shen

Computer Science and Engineering Theses and Dissertations

Scientific paper recommendation systems aim to help researchers discover relevant papers amidst the vast and ever-growing body of literature. With the exponential yearly increase in scientific publications, the demand for effective paper recommendation solutions has become both critical and increasingly challenging. In recent years, deep learning techniques have revolutionized recommender systems, and scientific paper recommendations have naturally integrated these advancements. In this dissertation, we address these challenges through three progressive contributions.

First, we enhance traditional content-based methods using Graph Neural Networks (GNNs) by introducing a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) approach. This method improves upon conventional topic modeling techniques …

Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor

Faculty Publications

This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise docking operations. Our study focuses on autonomous aerial refueling as a use case, demonstrating significant advancements in relative navigation and object detection. We introduce a novel method for aligning digital twins using fiducial targets and motion capture data, which facilitates accurate pose estimation from real-world imagery. Additionally, we develop cost-efficient annotation automation techniques for generating high-quality You Only Look Once training data. Experimental results indicate that …

The Impact Of Student Engagement Activities On Future Climate Change Adaptation: The Case Of Student Simulation Models, Bassel Mostafa Elkalaf

Future Journal of Social Science

This paper explores the critical role of student engagement in addressing the growing challenges of climate change, with a focus on the Model United Nations (MUN) as a case study. As climate-related security threats increase globally, educational platforms that prepare youth for effective leadership in climate politics are more essential than ever. MUN, a widely practiced student activity simulating global policy-making, provides a valuable opportunity for students to deepen their understanding of the interconnectedness between climate change, peace, and security. By participating in MUN simulations, students engage in debates, develop innovative solutions, and practice diplomatic skills, all while exploring the …

Multi-Criteria Decision-Making Approach Based On Correlation Coefficient For Multi-Polar Interval-Valued Neutrosophic Soft Set, Hamza Naveed, Saalam Ali

Neutrosophic Systems with Applications

The correlation coefficient between two factors is crucial in statistical computation, indicating the extent and evolution of the appropriate link. The precision of applicability evaluations frequently relies on the thoroughness and caliber of data obtained from a certain dataset. Statistical research sometimes entails data marked by intrinsic trade-offs and uncertainty. This study seeks to present m-polar interval-valued neutrosophic soft sets (mPIVNSSs) through the integration of m-polar fuzzy sets with interval-valued neutrosophic soft sets. The suggested mPIVNSS structure is a significantly generalized version of m-polar neutrosophic soft sets and serves as a substantial extension of interval-valued neutrosophic soft sets. In this …

Neutrosophic Cox Proportional Hazards Model For Robust Variable Selection In Survival Analysis, Ibrahim Yasser, Aya A. Abd El-Khalek, A. A. Salama, Doaa A. Abdo

Neutrosophic Systems with Applications

This paper introduces a novel approach for variable selection in survival analysis by integrating neutrosophic logic into the Cox Proportional Hazards (Cox PH) model to address the limitations of recent studies related to high dimensionality. Neutrosophic logic, is a mathematical framework that allows for uncertainty, indeterminacy, and inconsistency, and particularly well suited for handling the complexity and often-ambiguous nature of biological data. By incorporating neutrosophic sets into the Cox PH model, we aim to enhance model robustness, improve variable selection, and address the curse of dimensionality. We compare the performance of the neutrosophic-enhanced Cox PH model with traditional variable selection …

On Neutrosophic Pδs-Irresolute Functions In Neutrosophic Topological Spaces, Bishnupada Debnath, Anjan Mukherjee

Neutrosophic Systems with Applications

In general topology the notion of pds-irresolute and aδs-irresolute functions were introduced by Beceren and Noiri. In the present paper, these concepts of pδs-irresolute (briefly, Npδs-irresolute) and aδs-irresolute (briefly, Naδs-irresolute) functions are explored for the first time in neutrosophic topological spaces (NTS) as generalized version. We proved that every Naδs-irresolute function is Npδs-irresolute function but not conversely. Some characterizations, counter examples, and fundamental features are also presented. By neutrosophic pre-open, neutrosophic δ-open, and neutrosophic δ-semi-open sets, some new fundamental properties of such functions are provided. Furthermore, under Npδs-irresolute functions, the behavior of neutrosophic semi-connected, neutrosophic pre-connected, neutrosophic pre-T2, neutrosophic δ-semi-T2, …

Analysis Of Bck/Bci-Algebras Based On Bipolar Complex Intuitionistic Fuzzy Soft Ideals, Zeeshan Ali

Neutrosophic Systems with Applications

In this article, we design an informative and reliable technique of bipolar complex intuitionistic fuzzy soft sets with numerous operational laws by merging the model of soft sets, complex fuzzy sets, and bipolar intuitionistic fuzzy sets to handle imprecise data. In addition, an ideal in a BCK-algebra is derived based on bipolar complex intuitionistic fuzzy soft set theory are proposed which can capture the information of hesitancy, vagueness, and non-membership information within the circumstance of BCK-algebra. Moreover, we design union, intersection, AND, and OR based on bipolar complex intuitionistic fuzzy soft ideal and simplify it with the help of numerous …

Comparative Analysis Of Multi-Criteria Techniques In Neutrosophic Environment And Their Applications To Economic Condition Assessment, Asmaa Elsayed, Mai Mohamed

Neutrosophic Systems with Applications

In economic decision-making, the challenge of evaluating multiple, often conflicting criteria necessitates advanced Multi-Criteria Decision-Making (MCDM) techniques. Traditional methods can struggle with the inherent uncertainty, ambiguity, and imprecision of real-world data. This paper addresses these challenges by investigating the effectiveness of various MCDM techniques within neutrosophic environments, with a particular focus on the Criteria-wise Alternatives Ranking and Correlation Analysis for Composite Scoring (CARCACS) method. Neutrosophic sets, which incorporate truth, falsity, and indeterminacy, provide a robust framework for addressing the vagueness and inconsistencies found in economic indicators such as GDP growth, employment levels, inflation rates, trade balances, investment activity, and government …

(R2090) Analysis Of Heat And Mass Transfer In Mhd Boundary Layer Flow Of Chemically Reacting Fluid, Dharati Sheth, Manisha Patel, Dilip Joshi

Applications and Applied Mathematics: An International Journal (AAM)

Analysis of the effects of heat and mass transfer on the chemically responding boundary layer flow of a Casson fluid across a porous stretched sheet in the existence of a crosswise magnetic field is the aim of the existing work. The partial differential equations that control the current flow problems can be converted into similarity equations by applying the right similarity technique. Our goal is to use the DTM-Pade approximation to analytically solve the derived similarity equations. The transformed similarity equations are a group of nonlinear ordinary differential equations for the present flow problem. The analytical approach of the differential …

Everyone Brings Something To The Table: A Culturally Sustaining Analysis Of Teacher Candidates’ Mixed Reality Teaching Simulations, Erin Scott

Faculty Publications

Discussions of HBCU students’ content knowledge have centered around developing high impact, asset-based approaches to learning (Williams et al, 2022). So, HBCU teacher preparation providers (TPPs) can also focus efforts on culturally sustaining approaches to developing candidates’ content knowledge and researching how high impact practices, like teaching simulations, can help prepare candidates for classrooms. This exploratory case study uses critical cases (n=2); findings suggest candidates have a developing understanding of content, which forms a solid basis for increasing their knowledge. MRTSs are addressed as a potential high impact, culturally sustaining practice for building candidates’ content knowledge. Further, HBCU TPPs can …

Geochemical Evidence Of Drying During The 4.2 Ka Event In Sediment Cores From The Yucatán Peninsula, Mexico, Derek K. Gibson, Jonathan Obrist-Farner, Alex Correa-Metrio, Alejandra Rodriguez-Abaunza, Carlos Castañeda-Posadas

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Tropical hydroclimate variability during the Middle and Late Holocene was investigated using geochemical indicators of local-scale precipitation and evaporation preserved in sediment cores from two sites in the Mexican Yucatán Peninsula. Scanning X-Ray fluorescence spectroscopy data show generally decreasing precipitation trends during the Early and Middle Holocene. During the transition between the Middle and Late Holocene, geochemical evidence of reduced watershed erosion and increased evaporation indicate that a centennial-scale drying event impacted the region between 4.3 and 4.0 ka (kilo-anum; thousand years before present). These findings suggest that the 4.2 ka drying event, which has been previously recorded in Europe, …

Impact Of Irrigation Systems On Water Use And Water Productivity: A Case Study Using Aquacrop In Khartoum, Sudan, Abdalhakam Almagzoop

Department of Agricultural and Biological Systems Engineering: Masters Project Reports

Water scarcity significantly challenges agricultural productivity in arid regions like Khartoum, Sudan. This study evaluates the impact of three irrigation systems—furrow, sprinkler, and surface drip—on the water use and productivity of sorghum (Sorghum bicolor L.) under different climatic conditions using the AquaCrop model. Simulations were conducted for three representative years: a wet year (2022), a normal year (2015), and a dry year (2018). The results demonstrate that irrigation substantially improved water use efficiency and crop yield compared to non-irrigated (rainfed) conditions. Rainfed crops resulted in lower biomass, harvest index, and yield, particularly in the dry year, highlighting the importance …

Enhancing Water Sustainability In North Africa: Literature Review And Synthesis Of Current Knowledge Gaps In Sudan, Osman M. A. Adam

Department of Agricultural and Biological Systems Engineering: Masters Project Reports

This study delves into the critical role of groundwater in addressing global water challenges, with a focus on the Nubian Sandstone Aquifer System (NSAS) in north Africa. Groundwater constitutes a source of potable water, irrigation, and industrial use, especially in arid regions where surface water is limited. We analyzed the status of water quantity, withdrawals, recharge, and geological characteristics in the NSAS, specifically in Sudan, Egypt, and Libya. Though the NSAS is largely an untapped resource, water withdrawals from the NSAS in Egypt, Sudan, and Libya are projected to increase substantially from 2000 to 2100 due to population growth, with …

Nickel Electrocatalysts On Carbon Supports For The Hydrogen Evolution Reaction., Mohan Paudel

Electronic Theses and Dissertations

Hydrogen is an important energy storage alternative to fossil fuels due to its high energy density. Currently, most hydrogen is produced through petroleum feedstocks, which produce large amounts of carbon dioxide. However, a more eco-friendly method is the electrocatalytic splitting of water into hydrogen and oxygen. Efficient catalysts for the hydrogen evolution reaction (HER) are typically platinum group metals, which are rare and expensive. There is growing interest in developing HER electrocatalysts with earth-abundant metals for water electrolysis. Nickel, which lies in the same group in the periodic table as Pt and has similar chemical properties, is explored extensively for …

Learning To Represent Temporal Dynamics And Generative Factors For Intelligent Visual Navigation, Sahand Khoshdel

All Theses

Visual navigation systems are crucial in various applications, including autonomous driving, unmanned aerial systems (UAS), and industrial automation. For these systems to operate efficiently in dynamic environments, they must not only interpret complex surroundings but also anticipate changes over time. Temporal prediction—forecasting environmental changes like moving obstacles or shifting lighting conditions—enables navigation systems to act proactively, enhancing both safety and performance. This dissertation investigates representation learning methods both as a backbone feature extractor for RL agents as well as a proxy for systems oriented for Explainable AI (XAI). Two main projects are presented as case studies to achieve the aforementioned …

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Interpreting Strain Caused By Transient Well Testing, Soheil Roudini

All Dissertations

Understanding reservoir properties and structure is essential to managing subsurface operations, such as geologic storage of CO₂ or production of water, hydrocarbons or heat. Previous work demonstrated the potential of analyzing strain data during transient well tests by estimating hydraulic diffusivity using a type-curve-like approach. The following dissertation aims to pursue this potential by developing and evaluating methods for interpreting strain tensor data to improve subsurface characterization. The research advances the classic, intuitive method of using type-curves to interpret well tests, but it also brings together modern computational methods from machine learning, proxy modeling and Bayesian inversion, and it …

Thermal Conductivity Characterization Of High Oleic Vegetable Oils Based Hybrid Nanofluids Formulated Using Gnp, Tio2, Mos2, Al2o3 Nanoparticles For Mql Machining, Anthony Chukwujekwu Okafor, Tobechukwu Kingsley Abor, Saidanvar Esanjonovich Valiev, Ignatius Echezona Ekengwu, Abiodun Saka, Monday Uchenna Okoronkwo

Chemical and Biochemical Engineering Faculty Research & Creative Works

This paper presents the results of thermal conductivity characterization of six high oleic soybean oil (HOSO) and four high oleic canola oil (HOCO)-Based hybrid nanofluids formulated with four types of nanoparticles (Graphene nanoplatelet (xGnP), TiO₂, MoS₂, and Al₂O₃) at nanoparticles wt.% concentration from 1 % to 7 % in 1 % increment using the two-step method for use in MQL machining of difficult-to-cut metals. Thermal conductivity of the formulated hybrid nanofluids were measured using Thermtest Transient Hot Wire Liquid Thermal Conductivity Meter at temperatures from 25 °C to 75 °C in increment …

A Strategic Insight Into The Market For Carbon Management Capacity, Mahelet G. Fikru, Ting Shen, Jennifer Brodmann, Hongyan Ma

Economics Faculty Research & Creative Works

This study presents the market for carbon management capacity via carbon capture, utilization, and storage technologies, identifying demand and supply forces, as well as clarifying the potential impact of market and non-market-based shocks on technology developers versus adopters. The paper addresses a prevailing gap in market analysis, introducing a microeconomic framework and unique dataset to identify key players, market forces, and policy incentives shaping the carbon capture, utilization, and storage landscape. The analysis equips industry stakeholders, policymakers, and investors with valuable insights regarding (1) leaders in the design, development, and manufacture of carbon capture, utilization, and storage technologies (supply), (2) …

Co2 Capture Utilizing Novel Deep Eutectic Solvents: Modeling, Process Simulation, And Cost Analysis, Nazneen Aspi Kolah

Masters Theses

One of the most pressing global issues that humans face is climate change caused by anthropogenic greenhouse gas emissions, especially carbon dioxide (CO2). Conventional solvents utilized for CO₂ capture, such as amines, are widely used in industrial applications due to their high CO2 absorption capacity. However, deep eutectic solvents (DES) have attracted a lot of academic attention recently due to their biodegradability and low energy for regeneration.

This research serves as a foundational proof-of-concept for using DESs in industrial gas separation processes. Five DES are evaluated for CO2 removal from flue gases. Aspen Plus is utilized to simulate the performance …

An Investigation Into The Use Of Amateur Astronomy Equipment For Optical Orbit Determination Using A Mobile Telescope Platform, Johan C. Govaars

Master's Theses

The process of optical orbit determination has long been in the domain of large organizations and stationary observatories with highly specialized scientific equipment. This thesis seeks to determine not only if satellites can be captured regularly using purely commercial-off-the-shelf (COTS) equipment, but also if initial orbit determinations can be made using that data. Moreover, the use of a mobile telescope platform allows users to circumvent the restrictions of fixed observatories such as low horizon viewing restrictions or existing light pollution.

A completely COTS setup was utilized that included an 8-inch Celestron NextStar 8SE telescope with an f/6.3 focal reducer and …

Fitting Quality Of Nmr Relaxation Data To Differentiate Asphalt Binders, Rebecca M. Herndon, Kevin Lai, Magdy Abdelrahman, Klaus Woelk

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Asphalt binder performance grades (PGs) are important metrics in designing pavements for effective transportation infrastructure. the PG system relies on the binder's stiffness and is determined through energy- and time-intensive physical testing. Physical properties, like stiffness, can also be determined by spin–lattice NMR relaxometry, a non-destructive chemical method. NMR relaxometry can quantify the molecular mobility of materials by determining relaxation times from exponential decays of excited nuclear magnetization. While relaxation times have been used to determine physical properties of materials, a quantitative relation to the PG grades of asphalt binder is yet to be established. in this study, T1 NMR …

Surgical Suturing Skill Assessment Using Estimated Hand Roll Angle From A Deep-Learning Computer Vision Algorithm, Jianxin Gao, Amir Mehdi Shayan, Simar P. Singh, Joe Bible, Ravikiran Singapogu, Richard E. Groff

Publications

This paper proposes a deep-learning computer vision algorithm to estimate hand roll angles for metric-based assessment of surgical suturing skills. The number of rolls metric, previously calculated directly from IMU data, counts the number of hand roll reversals during a single suture. To calculate this metric using computer vision, we apply a deep-learning algorithm that can reliably estimate hand roll angles after training on suturing videos collected on the SutureCoach simulator. Results show that the estimation accuracy of the deep-learning algorithm is robust to different video backgrounds. The number of rolls metrics were used to analyze suturing performance in the …

Basic Safety Message Generation Through A Video-Based Analytics For Potential Safety Application, Abyad Enan

All Theses

With the advancement of modern artificial intelligence techniques, computer vision can play a vital role in enhancing roadway safety by reducing the risk of imminent collisions. To do so, a vision-based safety application is required, where a roadside camera can monitor the roadway traffic and predict potential risks of crashes in real-time. If any risky situation or behavior is observed that may lead to a crash, then a safety application can send warnings to the vehicles at risk. For vision-based safety applications on a roadway section, it is important to accurately monitor each vehicle’s location, speed, acceleration, heading direction, etc. …

Thermal Conductivity Characterization Of High Oleic Vegetable Oils Based Hybrid Nanofluids Formulated Using Gnp, Tio2, Mos2, Al2o3 Nanoparticles For Mql Machining, Anthony Chukwujekwu Okafor, Tobechukwu Kingsley Abor, Saidanvar Esanjonovich Valiev, Ignatius Echezona Ekengwu, Abiodun Saka, Monday Uchenna Okoronkwo

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper presents the results of thermal conductivity characterization of six high oleic soybean oil (HOSO) and four high oleic canola oil (HOCO)-based hybrid nanofluids formulated with four types of nanoparticles (Graphene nanoplatelet (xGnP), TiO2, MoS2, and Al2O3) at nanoparticles wt.% concentration from 1 % to 7 % in 1 % increment using the two-step method for use in MQL machining of difficult-to-cut metals. Thermal conductivity of the formulated hybrid nanofluids were measured using Thermtest Transient Hot Wire Liquid Thermal Conductivity Meter at temperatures from 25 °C to 75 °C in increment of 10 °C. Obtained results showed that thermal …

The Use Of Two-Dimensional Detectors For Snapshot And High Accuracy Imaging Ellipsometry, Jeremy A. Vanderslice

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

Ellipsometry is a powerful and sensitive optical technique used to characterize thin film thickness and optical properties by measuring changes in the polarization state of light upon reflection or transmission. This dissertation investigates novel ellipsometry configurations that use two-dimensional detectors to enhance measurement speed, spatial resolution, or accuracy of ellipsometry measurements. The study involves the development of three different ellipsometry configurations, encompassing mechanical prototypes, mathematical models, and test measurements to evaluate each system.

Advisor: Jeffrey Shield

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo

Knowledge Engineering and Data Science

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …