Physical Sciences and Mathematics Commons^™

Through The Lens Of A Naturalist: How Learning About Nature Promotes Nature Connectedness Via Awe, Shu Tian Ng, Angela K. Y. Leung, Sarah Hian May Chan

Research Collection School of Social Sciences

Environmental educators stress the importance of engaging with the wonders of the Earth in promoting nature connectedness. However, it remains unclear if learning about nature has an incremental effect beyond mere exposure to nature and what psychological mechanism can explain such a learning effect if it exists. To fill this gap, we propose a mediation model in which learning about nature promotes a sense of awe—a self-transcendent emotion associated with the recognition of vastness in nature. A sense of awe, in turn, promotes nature connectedness. Study 1 employed a cross-sectional survey and offered preliminary support for the proposed model, with …

Predicting New Crescent Moon Visibility Applying Machine Learning Algorithms, Murad Al-Rajab, Samia Loucif, Yazan Al Risheh

All Works

The world's population is projected to grow 32% in the coming years, and the number of Muslims is expected to grow by 70%—from 1.8 billion in 2015 to about 3 billion in 2060. Hijri is the Islamic calendar, also known as the lunar Hijri calendar, which consists of 12 lunar months, and it is tied to the Moon phases where a new crescent Moon marks the beginning of each month. Muslims use the Hijri calendar to determine important dates and religious events such as Ramadan, Haj, Muharram, etc. Till today, there is no consensus on deciding on the beginning of …

The Underrepresentation Of Black Females In Cybersecurity, Makendra Latrice Crosby

Cybersecurity Undergraduate Research Showcase

The significance of cybersecurity methods, strategies, and programs in protecting computers and electronic devices is crucial throughout the technological infrastructure. Despite the considerable growth in the cybersecurity field and its expansive workforce, there exists a notable underrepresentation, specifically among Black/African American females. This study examines the barriers hindering the inclusion of Black women in the cybersecurity workforce such as socioeconomic factors, limited educational access, biases, and workplace culture. The urgency of addressing these challenges calls for solutions such as education programs, mentorship initiatives, creating inclusive workplace environments, and promoting advocacy and increased awareness within the cybersecurity field. Additionally, this paper …

Privacy Concerns And Proposed Solutions With Iot In Wearable Technology, Hyacinth Abad

Cybersecurity Undergraduate Research Showcase

This paper examines the dynamic relationship between IoT cybersecurity and privacy concerns associated with wearable devices. IoT, with its exponential growth, presents both opportunities and challenges in terms of accessibility, integrity, availability, scalability, confidentiality, and interoperability. Cybersecurity concerns arise as diverse attack surfaces exploit vulnerabilities in IoT systems, necessitating robust defenses. In the field of wearable technology, these devices offer benefits like health data tracking and real-time communication. However, the adoption of these devices raises privacy concerns. The paper explores proposed solutions, including mechanisms for user-controlled data collection, the implementation of Virtual Trip Line (VTL) and virtual wall approaches, and …

The Analysis And Impact Of Artificial Intelligence On Job Loss, Ava Baratz

Cybersecurity Undergraduate Research Showcase

This paper illustrates the analysis and impact of Artificial Intelligence (AI) on job loss across various industries. This paper will discuss an overview of AI technology, a brief history of AI in industry, the positive impacts of AI, the negative impacts of AI on employment, AI considerations that contribute to job loss, the future outlook of AI, and employment loss mitigation strategies Various professional source articles and reputable blog posts will be used to finalize research on this topic.

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Potential Security Vulnerabilities In Raspberry Pi Devices With Mitigation Strategies, Briana Tolleson

Cybersecurity Undergraduate Research Showcase

For this research project I used a Raspberry Pi device and conducted online research to investigate potential security vulnerabilities along with mitigation strategies. I configured the Raspberry Pi by using the proper peripherals such as an HDMI cord, a microUSB adapter that provided 5V and at least 700mA of current, a TV monitor, PiSwitch, SD Card, keyboard, and mouse. I installed the Rasbian operating system (OS). The process to install the Rasbian took about 10 minutes to boot starting at 21:08 on 10/27/2023 and ending at 21:18. 1,513 megabytes (MB) was written to the SD card running at (2.5 MB/sec). …

Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow

Cybersecurity Undergraduate Research Showcase

This paper delves into the intricate landscape of deepfakes, exploring their genesis, capabilities, and far-reaching implications. The rise of deepfake technology presents an unprecedented threat to American national security, propagating disinformation and manipulation across various media formats. Notably, deepfakes have evolved from a historical backdrop of disinformation campaigns, merging with the advancements of artificial intelligence (AI) and machine learning to craft convincing but false multimedia content.

Examining the capabilities of deepfakes reveals their potential for misuse, evidenced by instances targeting individuals, companies, and even influencing political events like the 2020 U.S. elections. The paper highlights the direct threats posed by …

Use Of Digital Twins To Mitigate Communication Failures In Microgrids, Andrew Eggebeen

Theses and Dissertations

This work investigates digital twin (DT) applications for electric power system (EPS) resilience. A novel DT architecture is proposed consisting of a physical twin, a virtual twin, an intelligent agent, and data communications. Requirements for the virtual twin are identified. Guidelines are provided for generating, capturing, and storing data to train the intelligent agent. The relationship between the DT development process and an existing controller hardware-in-the-loop (CHIL) process is discussed. To demonstrate the proposed DT architecture and development process, a DT for a battery energy storage system (BESS) is created based on the simulation of an industrial nanogrid. The creation …

Assessing The Effects Of The Spring Hunting Season Start Date On Wild Turkey Seasonal Productivity And Hunter Behavior, Joseph Quehl

Masters Theses

Many states throughout the Southeast have documented declines in wild turkey (Meleagris gallopavo) harvest and estimates of recruitment in poult-per-hen ratios. Wild turkey populations are driven by seasonal productivity, so the decline in these parameters may indicate a decline in the overall population. One hypothesis as to why we are seeing a reduction in productivity and a potential population decline is that the spring hunting season is disrupting the reproductive cycle by harvesting too many males before they have had the opportunity to breed, or by harvesting dominant males and disrupting the social hierarchy of the flock. Our …

Water Main Break Rates In The Usa And Canada: A Comprehensive Study, Steven L. Barfuss

Reports

Deteriorating Infrastructure

Municipalities and the people they serve depend on pipe networks that provide safe drinking water. This piping is underground, out of sight, and often neglected.

Overall assessment of water infrastructure condition is not good. Using the US as an example:

• In 2009, the American Society of Civil Engineers (ASCE) issued a US report card and gave a D- to drinking water infrastructure.
• In 2017, the grade improved to a D.
• In 2021, the grade was raised to a C-, better but still not good.
• Utilities are currently losing 11% of their water to leakage.
• Pipe life estimates of …

Multi-Layer Evolution Of Acoustic-Gravity Waves And Ionospheric Disturbances Over The United States After The 2022 Hunga Tonga Volcano Eruption, P. A. Inchin, A. Bhatt, S. A. Cummer, S. D. Eckermann, B. J. Harding, J. Ma, J. J. Makela, J B. Snively

Publications

e Hunga-Tonga Hunga-Ha'apai volcano underwent a series of large-magnitude eruptions that generated in the atmosphere. We investigate the spatial and temporal evolutions of fluctuations driven by atmospheric acoustic-gravity waves (AGWs) and, in particular, the Lamb wave modes in high spatial resolution data sets measured over the Continental United States (CONUS), complemented with data over the Americas and the Pacific. Along with >800 barometer sites, tropospheric observations, and Total Electron Content data from >3,000 receivers, we report detections of volcano-induced AGWs in mesopause and ionosphere-thermosphere airglow imagery and Fabry-Perot interferometry. We also report unique AGW signatures in the ionospheric D-region, measured …

Estimating Ocean Observation Impacts On Coupled Atmosphere-Ocean Models Using Ensemble Forecast Sensitivity To Observation (Efso), Chu-Chun Chang, Tse-Chun Chen, Eugenia Kalnay, Cheng Da, Safa Mote

Mathematics and Statistics Faculty Publications and Presentations

Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble-based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density-based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF and investigated the impact of ocean observations under a weakly coupled DA …

How Beavers (Castor Canadensis) Affect Habitat Availability For Two Native Oregon Turtles: Actinemys Marmorata And Chrysemys Picta Bellii, Rodé Krige

University Honors Theses

Oregon is home to two native freshwater turtle species: the northwestern pond turtle and the western painted turtle. Both turtles are Oregon conservation strategy species with a status rating of sensitive, indicating declining populations. Oregon’s decline in these turtle’s populations is thought to be predominantly due to loss and degradation of habitat that results from development and urbanization. Beavers’ ability to dam streams and create ponds may be creating habitat usable by turtles, but the relationship is under-studied. This study assessed water temperature, basking habitat, and overall turtle habitat suitability at beaver-dammed and control ponds in Portland, Oregon. Average basking …

Analysis Of Student Behavior And Score Prediction In Assistments Online Learning, Aswani Yaramala

All Graduate Theses and Dissertations, Fall 2023 to Present

Understanding and analyzing student behavior is paramount in enhancing online learning, and this thesis delves into the subject by presenting an in-depth analysis of student behavior and score prediction in the ASSISTments online learning platform. We used data from the EDM Cup 2023 Kaggle Competition to answer four key questions. First, we explored how students seeking hints and explanations affect their performance in assignments, shedding light on the role of guidance in learning. Second, we looked at the connection between students mastering specific skills and their performance in related assignments, giving insights into the effectiveness of curriculum alignment. Third, we …

Ecology And Management Of Dyer's Woad (Isatis Tinctoria) In Northern Utah, Erin Marie Hettinger

All Graduate Theses and Dissertations, Fall 2023 to Present

Dyer’s woad (Isatis tinctoria L.) is a non-native forb that continues to threaten natural areas throughout Northern Utah and much of the Intermountain West. Once introduced, dyer’s woad can become extremely invasive, decreasing forage quality, and displacing native species. While dyer’s woad is found throughout much of Northern Utah, its range in other states remains limited. If promptly managed, control success in these areas will be much higher and populations may be kept at bay before ecological damage becomes severe.

This project tested the ability of dyer’s woad seedlings to compete with common rangeland grasslands at varied densities as …

Tropical Atlantic Temperature And Hydrologic Shifts During The Paleocene-Eocene Thermal Maximum, Chels Howard

All Graduate Theses and Dissertations, Fall 2023 to Present

The Paleocene-Eocene Thermal Maximum (PETM) is a geologic climate event that is studied heavily due to the similarities to anthropogenic climate change. This event occurred approximately 56 million years ago (Ma) and is characterized by a rapid release of carbon into the atmosphere, which resulted in an increase in temperature and a change in global climate events such as changes in patterns of evaporation and precipitation. We see this effect occurring in modern anthropogenic climate change due to the burning of fossil fuels releasing carbon dioxide (CO₂) into the atmosphere. In this study I use elemental data of …

Collaborative Task Completion For Simulated Hexapod Robots Using Reinforcement Learning, Tayler Don Baker

All Graduate Theses and Dissertations, Fall 2023 to Present

There is growing interest in developing autonomous systems capable of exhibiting collaborative behaviors. Using methods such as reinforcement learning is another way to train multiple robots for collaborative task completion. This study was able to successfully in simulation train multiple hexapod robots to push a target to a designated goal collaboratively. This required each robot to learn how find the target and push that target to a goal. This work suggests that using reinforcement learning for collaborative task completion for hexapod robots may simplify the complexity of the software and improve the decisions that they make.

Numerical Quantification Of Surface Runoff And Soil Erosion Using Pcraster, Victorino A. Bato

The Philippine Agricultural Scientist

Surface runoff and soil erosion of the Upper Lagawe River Sub-watershed, Ifugao Province, Philippines were quantified using Geographic Information Systems (GIS)-based numerical simulation. PCRaster software was used as a framework for the model. Manning’s and Rose, and Freebairn’s Equations were utilized to compute surface runoff velocity and sediment mass. Analysis and comparison of the measured and predicted streamflow yielded a mean Nash and Sutcliffe Efficiency Index of 0.70, a mean percent bias of -8.38%, and a mean root mean square error (RMSE) of 0.16. Based on the model, super typhoon Lando (international name: Koppu) generated an estimated surface runoff volume …

The Effect Of Amf-Cacao Association On Varying Physicochemical, Nutrient, And Biological Soil Parameters In An Agroforest System, Diana P. Paguntalan, Nelly S. Aggangan, Inocencio E. Buot Jr.

The Philippine Agricultural Scientist

The increasing interest in sustainable methods in agroforestry directs the attention of many to arbuscular mycorrhizal fungi (AMF) for their role in maintaining or improving overall plant health and nutrition of many plants including cacao (Theobroma cacao L.) trees. The effects of different treatments of AMF and other soil amendments applied singly or in combination on physicochemical properties, nutrient levels, spore count, and rhizosphere microbial counts, were investigated and compared across different sampling periods of varying climatological conditions in an agroforest system. Results showed that there was a significant effect on the N content using the different treatments. The …

Electronic Note-String Detector, Gavin Garcia-Rossi, Tommy Smail

Electrical Engineering

As the virtual space has become a dominant part of everyone’s day-to-day lives, many normal face-to-face interactions and services have not yet been facilitated by adapting technology. One of these prevailing areas is music lessons. Over Zoom meetings, or other virtual platforms, it is tremendously challenging to teach students. These challenges include recognizing student mistakes audibly and visually, and being able to give confident feedback on the incorrect notes played by learning musicians. Without having to delve into improving the complex systems that would be required to improve audio, video, and connection quality of these connections, we have another solution …

Review Classification Using Natural Language Processing And Deep Learning, Brian Nazareth

Electronic Theses, Projects, and Dissertations

Sentiment Analysis is an ongoing research in the field of Natural Language Processing (NLP). In this project, I will evaluate my testing against an Amazon Reviews Dataset, which contains more than 100 thousand reviews from customers. This project classifies the reviews using three methods – using a sentiment score by comparing the words of the reviews based on every positive and negative word that appears in the text with the Opinion Lexicon dataset, by considering the text’s variating sentiment polarity scores with a Python library called TextBlob, and with the help of neural network training. I have created a neural …

General Population Projection Model With Census Population Data, Takenori Tsuruga

Electronic Theses, Projects, and Dissertations

The US Census Bureau offers a wide range of data, and within this array, the American Community Survey 5-Year Estimate (ACS5) serves as a valuable resource for understanding the US population. This project embarks on an exploration of Machine Learning and the Software Development process with the goal of generating effective population projections from ACS5 data. The project aims to provide methods to make predictions for every city and town in the US, encompassing their total population and population divided into 5-year age groups. It's worth noting that while the generation of these projections is grounded in the generalized statistical …

Hypothyroid Disease Analysis By Using Machine Learning, Sanjana Seelam

Electronic Theses, Projects, and Dissertations

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order …

Les Taux De Rupture De Conduites D’Eau Principales Aux États-Unis Et Au Canada : Une Étude Complète, Steven L. Barfuss

Reports

Détérioration des infrastructures

Les municipalités et les personnes qu’elles servent dépendent des réseaux de conduites qui leur fournissent de l’eau potable de bonne qualité. Ces conduites sont souterraines, invisibles et souvent négligées.

Les résultats de l’évaluation globale de l’état des infrastructures hydrauliques ne sont pas bons. Prenons les États-Unis comme exemple :

· En 2009, l’American Society of Civil Engineers (ASCE) a publié un rapport sur les États-Unis et a attribué un D- aux infrastructures d’eau potable.

· En 2017, cette note est passée à D.

· En 2021, elle affiche une hausse à C-, ce qui est mieux, mais …

Multi-Scale Characteriziation Of Pore Structure And Mass Transport In Natural Rocks, Xiaoqing Yuan

Earth & Environmental Sciences Dissertations

The mass transport process in porous natural rocks is notably influenced by the pore structure with both geometrical and topological attributes. Nevertheless, previous studies have not taken into consideration the sample size effect or the impact of diagenesis processes on petrophysical investigations of rocks, fluids, and rock-fluid interactions. Six rocks (one granodiorite, one limestone, two chalks, one mudstone, and one dolostone) with different extents of heterogeneity at six different particle sizes were studied to describe the effects of pore structure (especially connectivity) on mass transport. Thirteen geologically different rocks (two marbles, four fossiliferous limestones, six mudstones, and one sandstone) were …

Measurement, Modeling, And Analysis Of The Energy Spectra Of Electrons And Annihilation Gammas Resulting From Low Energy Positron Irradiation Of Clean And Adsorbate Covered Surfaces, Sima Lotfimarangloo

Physics Dissertations

This dissertation presents measurements and theoretical modeling demonstrating the capability of Doppler Broadened annihilation gamma Spectroscopy (DBS) to provide element-specific information from the topmost atomic layer of surfaces that are either clean or covered with adsorbates or thin films. Our measurements show that the energy spectra of Doppler-shifted annihilation gamma photons emitted following the annihilation of positrons from the topmost atomic layers of clean gold (Au) and copper (Cu) differ significantly. With the aid of the positron annihilation-induced Auger electron spectroscopy (PAES) performed simultaneously with DBS, we show that measurable differences between the Doppler broadened gamma spectra from Au and …

Synthesis And Characterization Of Pyrazine-Based Prodrugs: A Novel Approach To Combat Multi-Drug Resistant Tuberculosis, Charles Chambers

Chemistry & Biochemistry Theses

Synthesis and Characterization of Pyrazine-Based Prodrugs: A Novel Approach to Combat Multi-Drug Resistant Tuberculosis Charles Cole Chambers The University of Texas – Arlington, 2023 Supervising Professor: Dr. Joseph Buonomo. The work described in this thesis investigates the development and characterization of a novel prodrug designed to combat multi-drug resistant tuberculosis (MDR-TB), a significant and growing global health challenge. Central to this study is the synthesis of a pyrazine-based prodrug, which innovatively utilizes bicyclic orthoester (BOE) moieties to improve the therapeutic efficacy and safety profile of this tuberculosis treatment. At the core of this research project is the strategic design of …

A Design Strategy To Improve Machine Learning Resiliency Of Physically Unclonable Functions Using Modulus Process, Yuqiu Jiang

Theses and Dissertations

Physically unclonable functions (PUFs) are hardware security primitives that utilize non-reproducible manufacturing variations to provide device-specific challenge-response pairs (CRPs). Such primitives are desirable for applications such as communication and intellectual property protection. PUFs have been gaining considerable interest from both the academic and industrial communities because of their simplicity and stability. However, many recent studies have exposed PUFs to machine-learning (ML) modeling attacks. To improve the resilience of a system to general ML attacks instead of a specific ML technique, a common solution is to improve the complexity of the system. Structures, such as XOR-PUFs, can significantly increase the nonlinearity …

Automated Human Activity Recognition From Controlled Environment Videos, Pranay Mandadapu

Theses and Dissertations

This thesis explores deep learning methods for Human Activity Recognition (HAR) from videos to automate the annotation of human activities in videos. The research is particularly relevant for continuous monitoring in healthcare settings such as nursing homes and hospitals. The innovative part of the approach lies in using YOLO models to first detect humans in video frames and then isolating them from the rest of the image for activity recognition which leads to an improvement in accuracy. The study employs pre-trained deep residual networks, such as ResNet50, ResNet152-V2, and Inception-ResNetV2, which were found to work better than custom CNN-based models. …