Physical Sciences and Mathematics Commons^™

Electrodeposition Parameters Dramatically Influence The Morphology, Stability, And Performance Of N-Si/Pt Light-Addressable Electrochemical Sensors, Jocelyn B. Hernandez, Zackary D. Epright, Irina M. Terrero Rodríguez, Glen D. O'Neil

Department of Chemistry and Biochemistry Faculty Scholarship and Creative Works

Light addressable electrochemical (LAE) sensors have seen great utility in the past several years because they enable multiple localized electrochemical measurements to be performed on a single macroscopic electrode, opening up applications in imaging, biosensing, surface patterning, and multiplexing. In this study, we investigated the effects of electrodeposition on the formation of LAE sensors formed between n-Si and electrodeposited Pt. We prepared sensors by electrodepositing Pt onto freshly-etched n-Si under a variety of conditions, varying the Pt precursor concentration, electrodeposition time, supporting electrolyte, and potential waveform. We characterized the sensors using a combination of atomic force microscopy, electrochemical impedance spectroscopy, …

Predictive Machine Learning And Its Future In Professional Basketball, Zachary Harmon

Honors College Theses

Artificial Intelligence (AI) is an ever-evolving field, transforming various aspects of contemporary life. From language models to immersive gaming experiences, AI technologies have become integral to our daily existence. Among the most promising arenas for AI integration is the world of sports. This research delves into the application of machine learning models to predict NBA game outcomes, shedding light on the profound impact of machine learning in the realm of professional basketball. Beyond the scope of game prediction, this study explores the broader implications, such as optimizing the selection of televised games, assisting players in showcasing their skills, and much …

Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury

Graduate Theses and Dissertations

The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …

Satellite-Based Estimation Of Seasonal Surface Water Change And Its Relationship To Climate Variability In A Tropical Open Basin Lake, Osnar Mondragon Grios

Graduate Theses and Dissertations

This study explores the spatiotemporal dynamics of climate variability and water surface area in Lake Nicaragua, one of the largest tropical lakes on Earth. The significance of this study lies in its potential to provide valuable insights into the hydroclimatic patterns and water availability, vital for Nicaragua’s water supply. Monthly and seasonal time series of precipitation, land surface temperature, and evapotranspiration (ET), acquired from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Moderate Resolution Imaging Spectroradiometer (MODIS), were analyzed in Google Earth Engine using a non-parametric Mann-Kendall trend test and Sen's slope. The second aim of this …

Assessing The Evaporation Method For Soil-Water Retention Curve Development And Comparison Of Soil-Water Characteristics Across Different Tillage Practices In A Furrow-Irrigated Corn (Zea Mays L.) System, Jeferson Prass Pimentel

Graduate Theses and Dissertations

The thesis comprises three studies. The first study in this thesis (Chapter I) focused on a six-year corn field experiment to assess the influence of conservation agricultural practices on soil properties and their long-term effects on water-use efficiency and yield. Non-tillage management did not significantly reduce soil bulk density compared to conventional tillage, as no differences were observed between non-tillage and tillage systems throughout the 6-year experiment. There was no difference in total water-use efficiency among soil management practices in 2018, 2019, 2020, and 2021. Even though in one year of the study, a significant 24 kg ha-1 mm-1 improvement …

Environmental And Agronomic Evaluation Of Struvite In Rice Production Systems, Diego Della Lunga

Graduate Theses and Dissertations

Furrow-irrigation constitutes an alternative water regime that has been increasingly adopted in Arkansas. Among the management of nutrients in furrow-irrigated systems, phosphorus (P) represents a substantial challenge. The environmental sustainability of rice (Oryza sativa) production systems needs to be evaluated across different water regimes and fertilizer-P sources. Therefore, the objectives of the following studies were to: i) evaluate season-long carbon dioxide (CO2) and methane (CH4) emissions and global warming potential (GWP) under different tillage treatments [i.e., conventional tillage (CT) and no-tillage (NT)] and at different site positions (i.e., up-, mid-, down-slope) along the predominant slope of a production-scale, furrow-irrigated rice …

If We Add Axiom Of Choice To Constructive Analysis, We Get Classical Arithmetic: An Exercise In Reverse Constructive Mathematics, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

A recent paper in Bulletin of Symbolic Logic reminded that the Axiom of Choice is, in general, false in constructive analysis. This result is an immediate consequence of a theorem -- first proved by Tseytin -- that every computable function is continuous. In this paper, we strengthen the result about the Axiom of Choice by proving that this axiom is as non-constructive as possible: namely, that if we add this axiom to constructive analysis, then we get full classical arithmetic.

When Is A Single "And"-Condition Enough?, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, there are several possible decisions. Any general recommendation means specifying, for each possible decision, conditions under which this decision is recommended. In some cases, a single "and"-condition is sufficient: e.g., a condition under which a patient is recommended to take aspirin is that "the patient has a fever and the patient does not have stomach trouble". In other cases, conditions are more complicated. A natural question is: when is a single "and"-condition enough? In this paper, we provide an answer to this question.

Development Of Back-Scatter And Pile-Up Identification For Ucna+, Amelia Greathouse

Undergraduate Honors Theses

The UCNA Experiment at the Los Alamos Neutron Science Center (LANSCE) uses

an electron spectrometer to observe angular correlations between the neutron spin and the momenta of beta particles emitted during the process of beta (β) decay. Combined with neutron lifetime measurements, these observations probe physics beyond the standard model. In recent years there has been an effort to modernize the equipment to reduce the physical limitations of the experiment. The new prototype helps to reduce error via use of silicon photo-multipliers (SiPMs) and the SiPMs also have a greater quantum efficiency than the photomultiplier tubes (PMTs). However, there is …

Exploration And Statistical Modeling Of Profit, Caleb Gibson

Undergraduate Honors Theses

For any company involved in sales, maximization of profit is the driving force that guides all decision-making. Many factors can influence how profitable a company can be, including external factors like changes in inflation or consumer demand or internal factors like pricing and product cost. Understanding specific trends in one's own internal data, a company can readily identify problem areas or potential growth opportunities to help increase profitability.

In this discussion, we use an extensive data set to examine how a company might analyze their own data to identify potential changes the company might investigate to drive better performance. Based …

Enhancing The Classification Of Autism Spectrum Disorder From Rs-Fmri Functional Connectivity Data Using Temporal Information, Mihir Yashwant Ingole

Computer Science and Engineering Theses - Archive

Autism Spectrum Disorder (ASD) affects the patient’s cognitive development which leads to difficulties in social functioning, daily tasks, and independent living. This necessitates intervention at an early age to take preventive measures and provide vital care. Manual diagnosis methods like Autism Diagnostic Observation Schedule (ADOS) assessment adopts symptom-based criteria which typically manifest at a later age. To automate this process, correlations computed from BOLD (Blood Oxygen-level dependent) signals obtained through resting state functional magnetic resonance imaging (rs-fMRI) data of patients across sparse brain regions has been used recently as a measure of functional connectivity. The goal of this study is …

Easemarks: Using Secondary Sketch Marks To Author And Communicate Motion Interpolation, Hadrien Nguyen

Computer Science and Engineering Theses - Archive

Motion interpolation is a process where an animator transforms jerky frame transitions into rich motions that communicate anticipation, urgency, hysteresis, and even calmness. Animators leverage mathematical functions known as easing curves to modify the rate at which in-betweens are added to keyframes. While effective, easing curves are tedious to tune since they fundamentally lack the ability to encode spatial information. Inspired by timing charts and other standards from traditional cel animation, we introduce a motion animation technique where secondary marks, which we term EaseMark (e.g., hatches, loops), are used to denote motion interpolation decisions. We synthesize an EaseMark Sketching Language …

Hemln-Sd: Substructure Discovery In Heterogeneous Multilayer Networks, Kiran Bolaj

Computer Science and Engineering Theses - Archive

Graph mining analyzes the real-world graphs for finding core substructures in chemical compounds (e.g., Benzene), identify the structure that occurs frequently in a given graph or forest. These identified structures are important as they reveal an inherent feature or property in the given graph or forest. Substructures represent interesting and repeating patterns found within an application, offering insights into hidden regularities. Therefore, the process of finding these interesting and frequent patterns in an unsupervised manner is known as substructure discovery. SUBDUE was the first main-memory algorithm developed for substructure discovery. Since then, for scalability, the algorithm has been extended to …

Homln-Sd: Substructure Discovery In Homogeneous Multilayer Networks, Arshdeep Singh

Computer Science and Engineering Theses - Archive

Substructure discovery is a process in data analysis and data mining that involves identifying and extracting meaningful patterns, structures, or components within a larger dataset. These substructures can be of various types, such as frequent patterns, motifs, or any other relevant features within the data. The growth of the internet and the proliferation of mobile devices have led to the generation of enormous amounts of data. Companies like Facebook and Twitter can generate large datasets from user interactions on their websites, such as connections between users and user generated content. Moreover, advances in processing power and storage capacity have made …

Feasibility Study Of Off-The-Shelf Components On A Split-Cycle Motor And Esc Testbed, Hayden C. Lotspeich

Computer Science and Engineering Theses - Archive

This project aims to create a testbed for split-cycle flapping wing systems that allows for testing of different motors and ESC protocols to find a suitable set for a flapping wing system. In order for a flapping-wing drone to be able to maneuver, it has to be able to flap its wings at different speeds when flapping forward and flapping backwards. The arching back-and-forth motion is what the output wing would be connected to, so this system is used to calculate the maximum split-cycle time ratio that can be achieved when set up with different motors and ESC protocols.

Design Of Single Precision Floating Point Unit (32-Bit Numbers) According To Ieee 754 Standard Using Verilog, And Creation Of An Education Model For Advanced Digital Logic And Design Courses, Kartikey Sharan

Computer Science and Engineering Theses - Archive

In today’s day and age of arithmetic, Floating Point Arithmetic is by far the most industry sanctioned way of approximating real number arithmetic for making numerical calculations on all computers used by industries on an everyday basis. In the year 1985, IEEE 754 standard was established that defined a single universal standard for all different arithmetic formats [1]. Before this, for a long period each computer had a different arithmetic format and size for bases, significand, and exponents. This format allowed industries all around the world to compute floating point arithmetic in a universal way and facilitated open communication between …

Enhancing Biomedical Imaging With Ai: Compression, Prediction, And Multi-Modal Integration For Clinical Advancement, Mohammad Sadegh Nasr

Computer Science and Engineering Dissertations - Archive

This dissertation delves into the enhancement of biomedical image analysis through the deployment of artificial intelligence methodologies, focusing on the transition from theoretical innovation to practical clinical utility. Spanning four cornerstone projects, the work encapsulates the development of predictive models for spatial transcriptomics, efficient image compression for cancer pathology slides, and critical evaluations of histopathology slide search engines. The first project employs Random Forest Regression and spatial point processes to forecast cell distribution patterns, thereby offering a novel perspective on gene expression in embryogenesis at a single-molecule resolution. The second venture introduces a Variational Autoencoder (VAE) that sets a new …

Parameterized Complexity Of Feature Selection For Categorical Data Clustering, Sayan Bandyapadhyay, Fedor V. Fomin, Petr A. Golovach, Kirill Simonov

Computer Science Faculty Publications and Presentations

We develop new algorithmic methods with provable guarantees for feature selection in regard to categorical data clustering. While feature selection is one of the most common approaches to reduce dimensionality in practice, most of the known feature selection methods are heuristics. We study the following mathematical model. We assume that there are some inadvertent (or undesirable) features of the input data that unnecessarily increase the cost of clustering. Consequently, we want to select a subset of the original features from the data such that there is a small-cost clustering on the selected features. More precisely, for given integers ℓ (the …

Gated Recurrent Units For Blockage Mitigation In Mmwave Wireless, Ahmed H. Almutairi, Alireza Keshavarz-Haddad, Ehsan Aryafar

Computer Science Faculty Publications and Presentations

Millimeter-Wave (mmWave) communication is susceptible to blockages, which can significantly reduce the signal strength at the receiver. Mitigating the negative impacts of blockages is a key requirement to ensure reliable and high throughput mmWave communication links. Previous research on blockage mitigation has introduced several model and protocol based blockage mitigation solutions that focus on one technique at a time, such as handoff to a different base station or beam adaptation to the same base station. In this paper, we address the overarching problem: what blockage mitigation method should be employed? and what is the optimal sub-selection within that method? To …

Data-Driven Optimization Approaches For Dynamic Urban Logistics Operational Problems, Jingfeng Yang

Dissertations and Theses Collection (Open Access)

Given the rapid pace of urbanization, there is a pressing need to optimize urban logistics delivery operations for enhanced capacity and efficiency. Over recent decades, a multitude of optimization approaches have been put forth to address urban logistics challenges, encompassing routing and scheduling within both static and dynamic contexts. In light of the rising computational capabilities and the widespread adoption of machine learning in recent times, there is a growing body of research aimed at elucidating the seamless integration of data and machine learning within conventional urban logistics optimization models. Additionally, the ubiquitous utilization of smartphones and internet innovations presents …

Discrete Complementary Exponential And Sine Integral Functions, Samer Assaf, Tom Cuchta

Mathematics Faculty Research

Discrete analogues of the sine integral and complementary exponential integral functions are investigated. Hypergeometric representation, power series, and Laplace transforms are derived for each. The difficulties in extending these definitions to other common trigonometric integral functions are discussed.

The Ups And Downs Of Early Dark Energy Solutions To The Hubble Tension: A Review Of Models, Hints And Constraints Circa 2023, V. Poulin, Tristan L. Smith, T. Karwal

Physics & Astronomy Faculty Works

We review the current status of Early Dark Energy (EDE) models proposed to resolve the “Hubble tension”, the discrepancy between “direct” measurements of the current expansion rate of the Universe and “indirect measurements” for which the values inferred rely on the Λ CDM cosmological model calibrated on early-universe data. EDE refers to a new form of dark energy active at early times (typically a scalar-field), that quickly dilutes away at a redshift close to matter-radiation equality. The role of EDE is to decrease the sound horizon by briefly contributing to the Hubble rate in the pre-recombination era. We summarize the …

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 …

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 …

New Paths Of Attacks: Revealing The Adaptive Integration Of Artificial Intelligence In Evolving Cyber Threats Targeting Social Media Users And Their Data, Larry Teasley

Cybersecurity Undergraduate Research Showcase

The intersection between artificial intelligence tools and social media has opened doors to numerous opportunities and risks. This research delves into the escalating threat landscape in a society heavily dependent on social media. Despite the efforts by social media companies and cybersecurity professionals to mitigate cyber-attacks, the constant advancements of new technologies render social media platforms increasingly vulnerable. Malicious actors exploit generative AI to collect user data, enhancing cyber threats on social media. Notably, generative AI amplifies phishing attacks, disseminates false information, and propagates propaganda, posing substantial challenges to platform security. Ease access to large language models (LLMs) further complicates …

Lip(S) Service: A Socioethical Overview Of Social Media Platforms’ Censorship Policies Regarding Consensual Sexual Content, Sage Futrell

Cybersecurity Undergraduate Research Showcase

The regulation of sexual exploitation on social media is a pressing issue that has been addressed by government legislation. However, laws such as FOSTA-SESTA has inadvertently restricted consensual expressions of sexuality as well. In four social media case studies, this paper investigates the ways in which marginalized groups have been impacted by changing censorship guidelines on social media, and how content moderation methods can be inclusive of these groups. I emphasize the qualitative perspectives of sex workers and queer creators in these case studies, in addition to my own experiences as a content moderation and social media management intern for …

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 …

A Review Of Threat Vectors To Dna Sequencing Pipelines, Tyler Rector

Cybersecurity Undergraduate Research Showcase

Bioinformatics is a steadily growing field that focuses on the intersection of biology with computer science. Tools and techniques developed within this field are quickly becoming fixtures in genomics, forensics, epidemiology, and bioengineering. The development and analysis of DNA sequencing and synthesis have enabled this significant rise in demand for bioinformatic tools. Notwithstanding, these bioinformatic tools have developed in a research context free of significant cybersecurity threats. With the significant growth of the field and the commercialization of genetic information, this is no longer the case. This paper examines the bioinformatic landscape through reviewing the biological and cybersecurity threats within …

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. …

Cooperative Observing At A Modest-Sized Observatory, Michael D. Joner, Denzil E. Watts Iv, Seneca H. Bahr, Oliver Hancock, Michael W. Holland, Hafsa Jamil, Eden Saxton, Malaya Williams-Jones

BYU West Mountain Observatory Project

The West Mountain Observatory is an astronomical research facility operated by Brigham Young University, located on an isolated mountain approximately 22 kilometers southwest of the main Provo UT campus. The observatory was upgraded 15 years ago and has since been utilized primarily as an undergraduate research facility. The observatory is well suited to do work on projects utilizing time series photometric observations and has been particularly successful at conducting multiple projects for undergraduate student researchers that require observations of different durations and cadences using various filter combinations. This requires additional planning and cooperation between the different programs that are often …