Digital Commons Network^™

Examining The Impacts Of Demographics, Individual Experiences, And Perceptions On Range Anxiety And Electric Vehicle Road Trips, Joshua Ward

All Graduate Theses and Dissertations, Fall 2023 to Present

Range anxiety is perhaps the most widely cited barrier to electric vehicle (EV) adoption and use. Range anxiety is generally defined as a general fear that an EV has insufficient battery range or that there is insufficient charging infrastructure to travel from one place to another. Although this barrier is widely cited, there is little research that investigates how EV drivers experience range anxiety and how it might vary based on personal characteristics, perceptions, and experiences. Data on long-distance EV trips is also scarce in the United States and mainly limited to personal stories of an EV road trip.

This …

Understanding And Influencing Public Concerns About Prescribed Fire Use In Utah, Brooke Richards

All Graduate Theses and Dissertations, Fall 2023 to Present

Prescribed fire is beneficial for improving forest health and reducing wildfire risk, which is especially important for residents who live near forested areas. To expand the use of prescribed fire, Utah land managers must first ensure there is public support. Therefore, better understanding public concerns and what shapes their beliefs about prescribed fire can help managers improve outreach and education methods. Through interviews with south-central Utah land managers, private landowners, and Health departments employees, a focus group discussion with Park City natural resource managers, and observation of Summit County public commentary, two outreach messages were suggested to address the five …

Assessing Canopy Features, Its Shading Factor, And Their Influence In The Estimation Of Evapotranspiration In Almond Orchards Using Uas Information And The Two-Source Energy Balance Model, Moises R. Duran-Gomez

All Graduate Theses and Dissertations, Fall 2023 to Present

This study explored how canopy biomass and shadows affect evapotranspiration (ET) estimates, a crucial factor for irrigation management in almond orchards in California’s Central Valley. Data from 24 drone flights over three almond plots in 2021 and 2022 were analyzed using the Two-Source Energy Balance (TSEB) model, combined with meteorological data from EC-towers. The study found that filtering out shadows improved ET estimation, particularly in young orchards, and identified the importance of using new Leaf Area Index (LAI) models for better accuracy. This research is part of the T-REX initiative, which integrates drone and ground-based data to enhance water use …

Insights From Diverse Environments: Investigating The Fate And Transport Of Semivolatile Organic Contaminants In Alpine, Arctic, And Arid Regions, Jeffrey Perala-Dewey

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation investigates the fate and transport of pollutants through four field-based studies in a diverse array of environments. These pollutants travel through the atmosphere and end up in places far from their original sources. In this work I explore how chemical properties of the pollutants and the environmental processes that are present change the way the pollutants travel through the environment. In the first study, pollutants from car engines were quantified in the vegetation along transects in three alpine valleys. Using these measurements, I investigated how the chemical properties of the pollutants impacted their interplay with wind systems in …

The Temperature Trend In The Upper Mesosphere Between ∼84 Km And 98 Km Based On Diurnal Cycle Observations By A Na Lidar At Middle Latitudes Between 2002 And 2017, Melania Carolina Pena

All Graduate Theses and Dissertations, Fall 2023 to Present

Carbon dioxide is one of the main greenhouse gasses that contributes to maintaining Earth’s warm global temperature in its atmosphere. Recent studies have indicated that this cooling trend has been determined to vary between less than 1 K/decade or 2 K/decade. However, these results have been determined using data that have been obtained using nighttime lidar observations and potentially do not give the complete picture on how the cooling trend is behaving. The main goal of this research is to understand the behavior of the linear temperature trend between the daytime and nighttime profiles using a 16-year dataset (2002-2017), when …

Using Luminescence Dating To Investigate Geomorphic And Archaeologic Features At The Wiggins Fork Bison Jump Complex, Northwestern Wyoming, Emma T. Krolczyk

All Graduate Theses and Dissertations, Fall 2023 to Present

The Wiggins Fork Bison Jump Complex, located in northwestern Wyoming, is comprised of thousands of human-placed rock piles, known as cairns, that were placed within lines to form an extensive network of drivelines and funnels leading to cliffs or steep slopes. These archaeological features were used by Native Americans as a bison hunting method, where the animals were driven off cliffs for harvest. This research uses luminescence dating, which determines when sediment was last exposed to sunlight, to date the timing of driveline construction and the deposition ages of the underlying river deposits.

Research focused on Jump #4, which had …

Cellulose–Starch Composite Aerogels As Thermal Superinsulating Materials, Safoura Ahmadzadeh, Angelina Sagardui, David Huitink, Jingyi Chen, Ali Ubeyitogullari

Chemistry & Biochemistry Faculty Publications and Presentations

The demand for sustainable packaging materials is rapidly increasing due to growing environmental concerns over the impact of plastic waste. In this study, biodegradable, porous, lightweight, and high-surface-area microcrystalline cellulose–starch (MCC-S) hybrid aerogels were synthesized via supercritical carbon dioxide (SC–CO₂) drying. The samples were generated using five different MCC-S weight ratios and characterized for their morphology, crystallinity, and structural and thermal properties. When MCC and S were used together, aerogels with superior properties were obtained compared to those made from each component individually. Specifically, the 1:2 MCC-S aerogel exhibited the highest porosity (97%), the lowest density (0.058 g/cm …

Ai Regulation Has Its Own Alignment Problem: The Technical And Institutional Feasibility Of Disclosure, Registration, Licensing, And Auditing, Neel Guha, Christie M. Lawrence, Lindsey A. Gailmard, Kit T. Rodolfa, Faiz Surani, Rishi Bommasani, Inioluwa Deborah Raji, Mariano-Florentino Cuéllar, Colleen Honigsberg, Percy Liang, Daniel E. Ho

Faculty Scholarship

Calls for regulating artificial intelligence (“AI”) are widespread, but there remains little consensus on both the specific harms that regulation can and should address and the appropriate regulatory actions to take. Computer scientists propose technical solutions that may be infeasible or illegal; lawyers propose regulation that may be technically impossible; and commentators propose policies that may backfire. AI regulation, in that sense, has its own alignment problem, in which proposed interventions are often misaligned with societal values.This Article assesses the alignment and technical and institutional feasibility of four dominant proposals for AI regulation in the United States: disclosure, registration, licensing, …

Bounds On 𝑎hvp,Lo Using Hölder’S Inequalities And Finite-Energy Qcd Sum Rules, Siyuan Li, T. G. Steele, Jason N.E. Ho, R. Raza, K. Williams, R. T. Kleiv

Faculty Work Comprehensive List

This study establishes bounds on the leading-order (LO) hadronic vacuum polarization (HVP) contribution to the anomalous magnetic moment of the muon (𝑎HVP,LO 𝜇 ,𝑎𝜇 =(𝑔 −2)𝜇∕2) by using Hölder’s inequality and related inequalities in Finite-Energy QCD sum rules. Considering contributions from light quarks (𝑢, 𝑑, 𝑠) up to five-loop order in perturbation theory within the chiral limit, leading-order light-quark mass corrections, next-to-leading order for dimension-four QCD condensates, and leading-order for dimension-six QCD condensates, the study finds QCD lower and upper bounds as (657.0±34.8)×10−10 ≤𝑎HVP,LO 𝜇 ≤(788.4±41.8)×10−10.

Π-Hub: The Proteomic Navigator Of The Human Body, Fuchu He, Ruedi Aebersold, Mark S Baker, Xiuwu Bian, Xiaochen Bo, Daniel W Chan, Cheng Chang, Luonan Chen, Xiangmei Chen, Yu-Ju Chen, Heping Cheng, Ben C Collins, Fernando Corrales, Jürgen Cox, Weinan E, Jennifer E Van Eyk, Jia Fan, Pouya Faridi, Daniel Figeys, George Fu Gao, Wen Gao, Zu-Hua Gao, Keisuke Goda, Wilson Wen Bin Goh, Dongfeng Gu, Changjiang Guo, Tiannan Guo, Yuezhong He, Albert J R Heck, Henning Hermjakob, Tony Hunter, Narayanan Gopalakrishna Iyer, Ying Jiang, Connie R Jimenez, Lokesh Joshi, Neil L Kelleher, Ming Li, Yang Li, Qingsong Lin, Cui Hua Liu, Fan Liu, Guang-Hui Liu, Yansheng Liu, Zhihua Liu, Teck Yew Low, Ben Lu, Matthias Mann, Anming Meng, Robert L Moritz, Edouard Nice, Guang Ning, Gilbert S Omenn, Christopher M Overall, Giuseppe Palmisano, Yaojin Peng, Charles Pineau, Terence Chuen Wai Poon, Anthony W Purcell, Jie Qiao, Roger R Reddel, Phillip J Robinson, Paola Roncada, Chris Sander, Jiahao Sha, Erwei Song, Sanjeeva Srivastava, Aihua Sun, Siu Kwan Sze, Chao Tang, Liujun Tang, Ruijun Tian, Juan Antonio Vizcaíno, Chanjuan Wang, Chen Wang, Xiaowen Wang, Xinxing Wang, Yan Wang, Tobias Weiss, Mathias Wilhelm, Robert Winkler, Bernd Wollscheid, Limsoon Wong, Linhai Xie, Wei Xie, Tao Xu, Tianhao Xu, Liying Yan, Jing Yang, Xiao Yang, John Yates, Tao Yun, Qiwei Zhai, Bing Zhang, Hui Zhang, Lihua Zhang, Lingqiang Zhang, Pingwen Zhang, Yukui Zhang, Yu Zi Zheng, Qing Zhong, Yunping Zhu, Π-Hub Consortium

Faculty, Staff and Students Publications

The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body …

Universal Return And Factor Timing, Poh Ling Neo, Chyng Wen Tee, Jeroen Kerkhof

Research Collection Lee Kong Chian School Of Business

The authors highlight the shortcomings of commonly used performance graphs in assessing and comparing investment returns. This paper's main conceptual contribution is the introduction of Universal Return (UR) - a visual method to evaluate and rank investment strategies that challenges the traditional focus on raw performance statistics. The authors argue that the ranking of investment performance when viewed from a pool of investors over different entry time is more important than its overall absolute returns, as it better aligns investment analysis with the needs of investors, who are primarily concerned with identifying currently successful strategies or managers rather than dwelling …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

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 …

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, …

Temporal Metadata Analysis: A Learning Classifier System Approach, Michael C. Todd, Gilbert L. Peterson

Faculty Publications

Digital forensics is a complex field that requires expert knowledge (EK) and specialized tools to collect, analyze, and report on digital evidence. Temporal metadata analysis is particularly challenging, requiring expert knowledge to understand and interpret underlying traces and associate them with their source. This paper introduces Digital Trace Inspector (DTI), a Learning Classifier System (LCS)-based decision support tool for temporal metadata analysis. DTI leverages a binary Michigan-style LCS to locate and group corroborating temporal digital traces of targeted user activity. Rules are built from expert-created atomics encoded as feature vectors using patterns defined in a structured EK rule framework. The …

Nature-Based Solutions For Flood Risk Mitigation: Mapping And Hydrological Modeling In An Under-Resourced Urban Area On The Mississippi Gulf Coast, Jennifer Makenzie Holifield

Master's Theses

Flooding is the most frequent and costly disaster in the United States, affecting people disproportionately based on environmental, societal, and economic limitations. Through stakeholder engagement, open-access research, and nature-based solutions (NBS), ecology and hydrology can be bridged with local-scale politics to raise awareness of local issues and form strong interdisciplinary bonds. I approached nature-based equity from a ground front, in which I engaged with local stakeholders and city officials to gain insight and calibration on flooding experiences and flooding presence within Moss Point, MS. This engagement helped calibrate GIS models, from which, I found that the flooding hazards of Moss …

Modeling Hourly Storm Surges Using Deep Learning Techniques, Md Abu Zafor

Master's Theses

Storm surges cause coastal flooding, one of the most devastating coastal hazards. Accurate modeling of storm surges is essential for predicting and mitigating these impacts. Traditional approaches model surges at individual tide gauges and often focus on daily time scales, leading to data redundancy and limiting their ability to capture sub-daily variability. This study addresses these limitations using deep learning (DL) algorithms to model hourly surges simultaneously at multiple tide gauges along the U.S. East Coast and Gulf of Mexico. Three algorithms, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid ConvLSTM are employed to model surges using …

Medium-Entropy Engineering Of Magnetism In Layered Antiferromagnet CuXNi2(1-X)CrXP2S6, Dinesh Upreti, Rabindra Basnet, M. M. Sharma, Santosh Karki Chhetri, Gokul Acharya, Md Rafique Un Nabi, Joshua Sakon, Mourad Benamara, Mansour Mortazavi, Jin Hu

Physics Faculty Publications and Presentations

Antiferromagnetic van der Waals-type M₂P₂X₆ compounds provide a versatile material platform for studying 2D magnetism and relevant phenomena. Establishing ferromagnetism in 2D materials is technologically valuable. Though magnetism is generally tunable via a chemical way, it is challenging to induce ferromagnetism with isovalent chalcogen and bimetallic substitutions in M₂P₂X₆. Here, we report co-substitution of Cu¹⁺ and Cr³⁺ for Ni²⁺ in Ni₂P₂S₆, creating Cu_xNi_2(1-x)Cr_xP₂S₆ medium-entropy alloys spanning a full substitution range ( …

Correlates Of Creativity And Elementary School Students’ Perceptions Of Individual And Sociocultural Factors, Seokmin Kang, Hye-Sook Park

Teaching and Learning Faculty Publications

The study investigated the relationships between individual and sociocultural factors and students’ creativity based on a sociocultural perspective. The participants are 7,324 fifth graders from 242 schools obtained from the Korean Educational Longitudinal Study 2013. Through multilevel modeling, student gender, self-regulation behaviors, academic achievement, extrinsic motivation, and perceived parenting style at level 1, and school characteristics such as teacher’s teaching methods, students’ relationship with their teacher, and their teacher’s academic pressure at level 2 were incorporated. The final model incorporating both level 1 and level 2 variables showed that the most significant variable related to students’ creativity was self-regulation, followed …

Investigating Nekton Response To Changing Salinities In The Mississippi Sound: An Experimental And Statistical Approach, Adam Murray

Master's Theses

The Mississippi Sound provides nursery habitats for many coastal species and is recognized for its commercial fisheries. Previous freshening events linked to the Bonnet Carré Spillway, a Mississippi River flood diversion structure, proved catastrophic for oyster populations in the Mississippi Sound, but effects on mobile fish and shellfish species are not well-defined. The planned Mid-Breton Sediment Diversion (MBSD), an initiative to combat wetland loss, is also forecasted to lower salinities in the region, increasing the need to better understand responses of commercially and ecologically important species to freshening events. The objective of this study was to characterize effects of salinity …

Enhancing Student Learning Of The Physics Of The Human Eye In The Context Of Computer Vision Syndrome During Covid-19, Liang Zeng, Guang Zeng

Physics & Astronomy Faculty Publications

No abstract provided.

The Temperature Versus Orbital Period Relation Of Am Cvns: Insights From Their Donors, Colin W. Macrie, Liliana Rivera Sandoval, Yuri Cavecchi, Tin Long Sunny Wong, Manuel Pichardo Marcano

Physics & Astronomy Faculty Publications

We studied the spectral energy distribution of 22 known AM CVns with orbital periods (Porb) larger than 35 minutes using multiwavelength public photometric data to estimate the effective temperature of the accreting white dwarf. We find an infrared (IR) excess in all systems when compared to a single blackbody, both when the disk should be extended and when it should be truncated by the accretor's magnetic field. This suggests a dominant contribution from the donor to the IR flux. When fitting two blackbodies, the temperature of the hot component decreases with Porb, as expected by evolutionary models. …

Forward And Backward Private Searchable Encryption For Cloud-Assisted Industrial Iot, Tianqi Peng, Bei Gong, Shanshan Tu, Abdallah Namoun, Sami Alshmrany, Muhammad Waqas, Hisham Alasmary, Sheng Chen

Research outputs 2022 to 2026

In the cloud-assisted industrial Internet of Things (IIoT), since the cloud server is not always trusted, the leakage of data privacy becomes a critical problem. Dynamic symmetric searchable encryption (DSSE) allows for the secure retrieval of outsourced data stored on cloud servers while ensuring data privacy. Forward privacy and backward privacy are necessary security requirements for DSSE. However, most existing schemes either trade the server’s large storage overhead for forward privacy or trade efficiency/overhead for weak backward privacy. These schemes cannot fully meet the security requirements of cloud-assisted IIoT systems. We propose a fast and firmly secure SSE scheme called …

Locally Varying Geostatistical Machine Learning For Spatial Prediction, Francky Fouedjio, Emet Arya

Research outputs 2022 to 2026

Machine learning methods dealing with the spatial auto-correlation of the response variable have garnered significant attention in the context of spatial prediction. Nonetheless, under these methods, the relationship between the response variable and explanatory variables is assumed to be homogeneous throughout the entire study area. This assumption, known as spatial stationarity, is very questionable in real-world situations due to the influence of contextual factors. Therefore, allowing the relationship between the target variable and predictor variables to vary spatially within the study region is more reasonable. However, existing machine learning techniques accounting for the spatially varying relationship between the dependent variable …

Learning-Based Feature Identification For Rendezvous And Capture Of Non-Cooperative Space Objects, Trupti Mahendrakar

Theses and Dissertations

In recent years, On-Orbit Servicing (OOS) and Active Debris Removal (ADR) have attracted increasing interest due to growing concerns about space debris. This debris poses a significant risk to spacecraft, as collisions can catastrophically end missions and potentially trigger a cascading chain reaction of more debris. Space debris ranges from tiny paint chips to large non-functional spacecraft and even launch vehicle components. One way to mitigate the formation of additional space debris is to reduce the total number of large non-cooperative resident space objects present in operational orbits. This can be achieved through two approaches: de-orbiting these objects as part …

A Comparative Analysis Of Early Algebraic Thinking Activities From U.S. And Singapore Primary Textbooks, Christian Joel Hernandez

Theses and Dissertations

Many studies highlight the challenges students face when transitioning to algebra at the secondary level. Introducing algebraic concepts and fostering algebraic thinking at the primary level can help mitigate these difficulties. Prior to formal algebra instruction, early algebra can be cultivated as a mode of thinking known as algebraic thinking. Several international curricula, such as Singapore Math, incorporate early algebraic thinking into the early stages of schooling. Singapore Math, renowned for its high performance in international assessments, has been widely adopted by schools seeking to replicate its success.

This study compares two primary-level mathematics curricula—CCSSM-aligned textbooks and Singapore Math—specifically focusing …

Synthesis Of Photocleavable Molecules For Neurological Applications, Nishal Madujith Egodawaththa Arachchilage Don

Theses and Dissertations

In recent biomedical research, the precise control of molecular dynamics through light activation has emerged as a powerful tool, particularly in the field of neurobiology. This approach involves the use of photocleavable cages or photoprotective groups (PPGs) that are chemically tethered to biologically active molecules such as neurotransmitters or calcium chelators. These cages remain inert until exposed to specific wavelengths of light, typically in the visible range, triggering the release of the active compound with high spatiotemporal precision.

For neurotransmitter systems, such as glutamate (Glu), which plays a critical role in synaptic transmission and memory formation, researchers have developed novel …

Novel, Ultra-Fast Electrochemical Sensors For The Enhanced Detection Of Neurotransmitters And Toxic Heavy Metals In Aqueous Solutions, Noel Manring

Theses and Dissertations

Neurodegenerative diseases (NDDs) are a growing global health concern, affecting millions of people worldwide. NDDs arise from the progressive loss of neuronal cell function, ultimately leading to cell death. Abnormal levels of neurotransmitters, the chemical messengers in the brain, have also been linked to NDDs. Despite various treatments and medications aimed at alleviating physical and mental symptoms, there is currently no cure or means to halt disease progression. While the exact pathogenesis of NDDs remain poorly understood, cognitive decline is largely attributed to brain volume reduction, particularly in the hippocampus. Clinical studies suggest that environmental factors, such as exposure to …

Machine Learning Methods For Quantification Of Glacier Variations Through Satellite Imagery, Robert D. Breininger

Theses and Dissertations

Glaciers around the world have experienced a trend of recession within the past century. Quantification of glacier variations using satellite imagery is of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. The potential methods to quantify glacier variations with increasing complexity include detecting the terminus location, quantifying the glacier surface area, and measuring glacier volume. Although there are methods in literature designed purposefully for glacier area segmentation that have achieved acceptable results, they are often localized to the region where their training data were acquired and further rely on training sets …

Theoretical Advancements In Hawkes Processes And Their Practical Applications, Xi Zhang

Theses and Dissertations

Hawkes self-exciting point processes have been widely used in fields such as seismology, finance and social media analysis. Despite decades of research focusing on these processes, several key aspects remain under-explored. These include broadly applicable methods for model evaluation and selection, intuitive nonparametric inference approaches akin to kernel density estimation for probability density functions, and strategies to enhance the performance of generative point process models in predictive tasks. In this dissertation, first, we extend the time-rescaling theorem, which is traditionally limited to non-terminating processes with complete observations, to accommodate terminating processes and incomplete observations as well. This extension allows for …