Digital Commons Network^™

Ephemeral Legacy, Allison Hall

Master's Theses

Recently, my thoughts have centered on the problem of legacy. How do we forge bonds with other humans? What happens on an interpersonal level when these humans pass away, or when we pass, and they continue in life? How do queer spaces consistently offer deeper phenomena of interpersonal legacies; how does death operate as generational catalyst? I am particularly interested in putting these questions in the context of queer spaces. In queer culture, kinship is formed in ways that break from typical western notions of kinship. Despite being incoherent to western notions of family, queer bonds are extremely strong (Bradway …

Investigation Of Magnesium-Potassium Phosphates As Potential Nuclear Waste Form For The Immobilization Of Minor Actinides, Hans Conrad Zur Loye, Petr Vecernik, Monika Kiselova, Vlastislav Kašpar, Hana Korenkova, Vlastimil Miller, Petr Bezdicka, Jan Šubrt, Natalija Murafa, Volodymyr Shkuropatenko, Sergey Sayenko

Faculty Publications

Several recent studies have evaluated technologies of spent nuclear fuel processing specifically for solidifying transuranic (TRU) waste as a by-product of fission. Of the TRU group, plutonium and the minor actinides will be responsible for the bulk of the radiotoxicity and heat generation of spent nuclear fuel in the long term (300 to 20,000 years). In this study, we investigated magnesium potassium phosphate (MKP)-based compounds as host waste forms for the encapsulation of inactive trivalent Nd and Sm as analogues of the minor trivalent actinides, Am and Cm. Waste forms were fabricated under ambient atmospheric conditions by adding 5 wt.% …

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 …

Physics-Informed Deep Learning For Pilot Parameter Estimation And Pilot-Induced Oscillation Characterization, Stephen A. Brutch

Doctoral Dissertations and Master's Theses

This thesis investigates the issue of loss-of-control in flight which is a driving contributor to fatal aviation accidents. The two main contributors tackled in this research are human pilot error and categories of pilot-induced-oscillations. The primary objective is to develop models that can capture the mathematical parameters that relate to human pilot control under the widely used McRuer Mathematical pilot model. The goal is that if the mathematical parameters that relate to human pilot control can be monitored during flight then the pilot’s input to the control system and pilot-induced-oscillations (PIO) can be monitored during flight to avoid loss-of-control. This …

Assessing General Propulsion Architectures For Fast Transit Times To Mars For Crewed Missions, Vivek Bhatt

Doctoral Dissertations and Master's Theses

As NASA and commercial contractors, such as SpaceX, continue to develop their programs with Artemis and Starship, respectively, it is becoming more and more apparent that the program developments are ultimately for finding a way to put humans onto Mars. However, when we approach the current timeline of going to Mars, it usually involves a 6-9 month one way trip. Due to this, human spaceflight to Mars becomes more dangerous as exposure to affects such as solar radiation, long exposure to low gravity fields, and isolation. These are some of the many hazards that come with interplanetary space travel for …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Understanding Levels Of Perceived Racism And Discrimination When Utilizing Perinatal Care, Alexandria S. Evans

UNLV Theses, Dissertations, Professional Papers, and Capstones

Social and structural determinants of health, grounded in historical racism and discrimination, significantly influence maternal health outcomes. Because of this, communities of color experience a higher burden of maternal health disparities. In the last several decades, there has been an increased awareness of the need to integrate critical race theory into public health research to address maternal health disparities in the context of perinatal care delivery, policy, and funding decisions.

This three-manuscript dissertation utilized mixed methods to examine the ways in which systemic racism and discrimination are perceived and how it impacts the utilization of perinatal care services in historically …

Effect Of Epistemic Cognition And Self-Regulation On The Evaluation Of Online Information, Devin T. Foschi

UNLV Theses, Dissertations, Professional Papers, and Capstones

While research has shown that epistemic cognition plays an important role in self-regulation, the way in which it does so in online contexts needs further research. The objective of this dissertation was to examine the relationship between Internet-specific epistemic cognition and Internet-specific self-regulated learning, and to use a mixed-methods approach in order to expand upon the way learners undergo the evaluation of information in online spaces beyond the traditional method of only using self-report measures and other common quantitative techniques. Using an explanatory sequential mixed-methods design, Phase 1 (N = 117) used quantitative methods to first look at the relationship …

Social Network Analysis: How Social Media Groups Can Influence Policies Among Hispanic Families, Valeria Gurr

UNLV Theses, Dissertations, Professional Papers, and Capstones

The COVID-19 pandemic in March 2020 impacted the entire world in various ways. On March 15, 2020, education in Nevada was significantly impacted when the Governor announced the closure of public schools due to the pandemic. When the schools were closed, students were forced to stay home and receive their education through virtual instruction. This transition to distance education affected the Hispanic/Latino(a) communities in pronounced ways and highlighted issues such as limited access to the Internet, technology, lack of hardware devices, as well as language barriers and more. This study uses social networking analysis techniques to examine the nature of …

Uptake Of Contaminants Of Emerging Concerns (Cecs) By Plants Irrigated With Recycled Waters, Abid Hussain

UNLV Theses, Dissertations, Professional Papers, and Capstones

The increasing demand for water in arid and semi-arid regions necessitates the use of recycled water (RW) for irrigation. RW is a viable alternative to conventional irrigation water at most locations, but it can be a major route for contaminants of emerging concerns (CECs) to be taken up by the tissues of food crops, thereby leading to their subsequent entry into the food chain. CECs include pharmaceuticals, personal care products, and industrial chemicals that are generally present at low concentrations in environmental waters, hence their common designation as trace organic compounds (TOrCs), and CECs also include microbiological constituents such as …

Using Neighborhood Information To Improve Fiber Direction Estimation From Neuroimaging Data, Anjan Mandal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurately estimating neuronal fiber directions is crucial in neuroimaging analysis. The Ball-and-Stick Model (BSM), introduced by Behrens et al. (2003), and widely used in software tools like FSL, remains one of the most popular models for this purpose. BSM analyzes each voxel individually, based solely on its signal information.In this dissertation, we propose modifications to BSM, that incorporate signal information from neighboring voxels in the estimation process, potentially improving the accuracy of the estimates. Additionally, within the estimation process, we introduce two novel proposal distributions to enhance the efficiency of the Markov chain Monte Carlo sampling procedure on the simplex …

Symptom Network Comparisons Between Pre- And Post-Concussion, Christine Salva

UNLV Theses, Dissertations, Professional Papers, and Capstones

Increased awareness about the short and long-term negative outcomes associated with sport-related concussion has resulted in active monitoring of cognitive and symptom profiles of athletes using a variety of assessments. The traditional approach to interpretation of cognitive and symptom measures has been based on a latent construct framework where domain scores reflecting latent constructs representing cognitive or symptom domains are collected at baseline and then compared to those obtained after concussion during the acute injury phase, with repeated additional assessments to determine when cognitive and symptom alterations have resolved. More recently, network approaches have been used to examine changes in …

Development And Testing The Predictability Of An Instrument Based On The Multi- Theory Model (Mtm) Of Health Behavior Change For Pre-Exposure Prophylaxis (Prep) Uptake Among People Of Color, Siddharth Raich

UNLV Theses, Dissertations, Professional Papers, and Capstones

HIV/AIDS continues to be a significant public health problem in the US and globally. In the US, it disproportionately affects people of color. In 2012, Pre-exposure Prophylaxis (PrEP) was approved by the FDA for HIV prevention. PrEP reduces the chances of acquiring HIV by 99% among people at high risk. However, PrEP uptake among people of color remains low. Therefore, this study aimed to develop and test the predictability of an instrument to describe the intent to initiate and sustain PrEP use among people of color. This research utilized an exploratory sequential mixed methods design based on the fourth-generation, multi-theory …

Removal Capabilities Of Zeolite And Zero Valent Iron With High-Level Waste Tank Radionuclides And Metals: Cesium, Strontium And Chromium, Colette R. Towles

UNLV Theses, Dissertations, Professional Papers, and Capstones

High level nuclear waste (HLW) has been disposed for temporary storage in underground steel tanks at the Department of Energy Nuclear sites. For example, at the Hanford Site, WA, and Savannah River Site (SRS), SC, HLW account for eighty-eight million gallons (Mgal) and 450 million Curies (MCi) of radioactivity. In the early nuclear activity at Hanford, the waste was discharged to single-shell steel tanks, and the lack of secondary containment allowed leaks, contaminating soil and groundwater. Despite tremendous efforts to stabilize the liquid waste at nuclear sites, treatment is complex, costly, and time-consuming; therefore, HLW may have to be stored …

Density-Driven Investigations Of Ruthenium Binary Compounds Rux2 (X = O, S, N), Melanie White

UNLV Theses, Dissertations, Professional Papers, and Capstones

Exploration and fine-tuning of the energy landscape of materials can be accessed by means of changes in material density, a key principle of high pressure research. Density-driven changes impact variations in oxidation states and coordination numbers for the element constituents of a compound. Pressure-induced modification of intrinsic density can promote the formation of new materials, variations in physical properties, and transformations in crystallographic symmetry. With this as our foundation, we focus on ruthenium binary compounds - altering the anion species to change electronegativity while probing pressure and temperature effects. This led us to the present study of ruthenium oxide RuO2, …

Uranium(Vi) & Thorium(Iv) Speciation In Organic Media Containing Organophosphorus & Diglycolamide Extractants, Joel Castillo

UNLV Theses, Dissertations, Professional Papers, and Capstones

Developing sustainable green energy systems is a continually growing concern as the world is faced with urgent issues of climate change and global warming. Nuclear energy provides one of the most efficient solutions to supplying power to the electrical grid without causing more carbon pollution. There are nearly a hundred thousand metric tons of used nuclear fuel (UNF) stored on-site at decommissioned nuclear power plants for an indefinite period of time. Achieving a finalized closed fuel cycle is a crucial part in developing the nuclear energy necessary for combating the issues of pollution and greener energy. Understanding actinide speciation especially …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Nuclear Fuel Cycle Chemistry: A Density Functional Study, Eduardo Montoya

UNLV Theses, Dissertations, Professional Papers, and Capstones

The nuclear fuel cycle aptly describes the life cycle of nuclear fuel sources such as uranium. From its early forms in naturally bearing ores, to its high-temperature sintering into a fuel pellet, the chemistry uranium experiences throughout the fuel cycle is diverse, driving many experimental efforts towards understanding its chemistry to both improve nuclear technologies and advance the safety and handling of nuclear materials. Computing techniques and methodologies can be used to supplement experimental research and are readily available to researchers interested in utilizing computing-based data to support experimental findings. Density functional theory (DFT) is commonly practiced, reliably predicting the …

Addressing Low Molecular Weight Chemical Peaking Events During Direct Potable Reuse By Granular And Biological Activated Carbon, Ceonie Washington

UNLV Theses, Dissertations, Professional Papers, and Capstones

Direct Potable Reuse (DPR) is an innovative solution designed to address water scarcity by treating wastewater to directly produce safe drinking water. As droughts and water shortages become more frequent, DPR offers a sustainable and localized water supply by incorporating advanced treatment processes. DPR systems typically rely on a combination of technologies such as ozone followed by biological activated carbon (O3/BAC), reverse osmosis (RO), and advanced oxidation processes (AOPs) to remove a wide range of contaminants. However, low molecular weight organic compounds (LMWCs), particularly volatile organic compounds, can sometimes pass through RO membranes and AOP systems.

California's DPR regulations currently …

An Adaptive And Parallel Direct Solver For Elliptic Partial Differential Equations, Damyn Chipman

Boise State University Theses and Dissertations

We introduce the quadtree-adaptive Hierarchical Poincaré-Steklov (QAHPS) method, an adaptive direct method for solving elliptic partial differential equations on a hierarchy of adaptively refined finite volume meshes. The QAHPS method builds up a solution operator set with O(N^3/2) complexity that acts as the factorization of the system matrix, with linear O(N) complexity for the application of the solution operator set to any number of right-hand side vectors. As the solution operator set is built up by merging local subdomains, it can be adapted as the mesh is refined and coarsened. The method is an …

Adapting Deep Learning Models For Downstream Web Tasks: Multimodal Models, Task Development, Agent Adaptation, Graham Annett

Boise State University Theses and Dissertations

This dissertation presents a framework for the development of deep learning models tailored for dynamic web tasks, leveraging generalized pre-trained multimodal transformers. A task generation framework, applied to multiple web datasets, is introduced, facilitating instruction fine-tuning of models for executing multi-step web workflows. This approach enhances the adaptability of pre-trained models to a spectrum of novel web tasks, which is vital for the reliable operation of web agents.

Moreover, this work proposes an encoding schema extending the Decision Transformer, which advances the adaptability of these models for downstream tasks through targeted modality tokenization, thereby broadening their practical applicability. These enhancements …

Distribution Through Taxation Versus Legal Rules, And The Epistemic Limits Of Law-And-Economics, Erick J. Sam

Utah Law Review

This Article develops an epistemic critique of the view, widely held in law-and-economics, that legal rules should always be chosen for efficiency and that redistribution to produce a fair distribution should be conducted solely through the tax-and-transfer system. In place of this orthodox view, I develop an alternative distributive theory.

The canonical ‘double distortion’ argument for the orthodox position, formulated by Kaplow and Shavell (1994), claims that opting for efficient legal rules minimizes economic distortions and maximizes aggregate social wealth, some of which can then be redistributed via the tax-and-transfer system to those who would have been better off under …

Using Gamification As Scaffolding To Support Children As They Formulate Initial Keyword-Based Search Queries, Benjamin John Bettencourt

Boise State University Theses and Dissertations

Child searchers, ages six to twelve, are known to struggle when it comes to using mainstream search engines. One such struggle that has been identified is the query formulation process, including initial query formulation. Two avenues of assistance that have shown promise in assisting users in other endeavors are gamification and scaffolding. In an attempt to provide assistance tailored to child searchers in their initial query formulation processes, this research explores the use of a gamified scaffold built with the purpose of teaching more effective, keyword-based, query formulation practices. To study the efficacy of utilizing a gamified scaffold to support …

(R2104) On The Qualitative Results Of Riemann-Liouville Fractional Order Nonlinear Neutral Singular Systems With Mixed Delays, Abdullah Yiğit, Cemil Tunç

Applications and Applied Mathematics: An International Journal (AAM)

In this manuscript, we discuss new conditions for asymptotic stability of nonlinear Riemann- Liouville fractional mixed-delay neutral singular systems. By constructing a set of improved Lyapunov-Krasovski˘ı functionals (LKFs), we establish some new delay-dependent conditions in terms of matrix inequality, which guarantees that the systems are asymptotically stable. In the particular case, we give four numerical examples with their solutions and graphs via MATLAB software demonstrating the practical applicability of these proven conditions. With this study, some conditions existing in the literature are developed and generalized.