Digital Commons Network^™

Dispersal And Population Structure Of Euchlanis Chihuahuaensis Via Anemochory In The Chihuahuan Desert, Tristan Chavez-Poeschel

Open Access Theses & Dissertations

Desert ecosystems present challenges for aquatic organisms as habitats are fragmented, both in space and time; however, diapausing stages of rotifers can travel hundreds of kilometers during wind events. I used the rotifer Euchlanis chihuahuaensis as a model species to investigate the influence of wind dispersal on gene flow and population genetics in Chihuahuan Desert populations. I hypothesized that anemochory facilitates gene flow from source populations in the Northern Chihuahuan Desert in Mexico and the western United States to habitats in the Trans-Pecos region via delineated wind corridors. To test this hypothesis, the genetic diversity of populations from both inside …

Water Sufficiency For Organismal Function In Dryland Critical Zones, Lindsey Dacey

Open Access Theses & Dissertations

Water availability is crucial for organismal survival and growth in dryland environments, affecting both ecological interactions and carbon dynamics. The goal of this thesis is to develop soil water release curves (SWRCs) that link soil water potentials (Ψ) to soil water content (θ). Using the SWRCs, temporal soil water sufficiency curves are developed, which quantify the amount of time that dryland critical zones have enough water to sustain the physiology of organisms. These curves allow for effectively indicating water availability across different species, coverage types, and soil conditions, enhancing our understanding of water dynamics in drylands and contributing important parameters …

Comparing The Regulatory Effects Of Overexpressed Micrornas And Xenobiotic Drugs On Cell Cycle And Apoptotic Regulators In Pc-3 Cells, Tommie Johnson

Dissertations (2016-Present)

MicroRNA was first discovered in C. elegans as small temporal RNA (stRNA) that does not code for protein. Since being discovered they have played a significant role in regulating gene expression at the post-transcriptional level. miRNAs are found in various organisms, and they bind to the 3' untranslated regions to inhibit translation and cause mRNA degradation. Some drugs are involved in cell cycle regulation such as Palbociclib, Ribociclib (LEE011), and Abemaciclib (LY2835219), that causes G1 arrest impeding cell proliferation. Cyclin D1 (CCND1) is supported by the cell cycle making it into a functional product. When undergoing a chemical reaction, the …

Development And Pilot Testing Of A Surface Discrimination Test For People With Lower Limb Amputation, Colin Kruger, Kyle Mcknight, Sharlene Lim, Samuel Straus

UNLV Theses, Dissertations, Professional Papers, and Capstones

Introduction: There is a lack of understanding as to how sensory loss and sensory deficits impact those with LLA. The purpose of this research is to determine the extent to which people with LLA can discriminate between surfaces underfoot, in order to better understand the relationship between people with LLA and their perception of the ground they are walking on. We developed a test to determine which qualities of surfaces may be easier to distinguish.

Methods: 10 unimpaired adults and 2 adults with LLA participated. Participants compared surfaces underfoot that consisted of ceramic, rough tile, gravel, sand, and sandpaper to …

Eco-Anxiety Effects College Students, Alexa Edmundson

Department of Environmental Studies: Undergraduate Student Theses

A survey was conducted on the UNL student population to evaluate whether college students, 18-25 year olds who are currently attending an accredited post-secondary school, are experiencing eco-anxiety and if eco-anxiety affects their pro-environmental behaviors. Climate change is taking a toll not only physically on us but also mentally. The mental health of people, especially college students, is not often studied in relation to climate change, but if we are to understand how to create change in this issue then we need to be looking at the whole picture. The hypothesis is that if college students are experiencing eco-anxiety, then …

Temporal Trends And Dental Metric Variation In The Macaca Sylvanus Lineage, Cristina Stan

Graduate Theses and Dissertations

The extensive collection of fossil macaques from Europe, spanning from the Late Miocene to the Late Pleistocene period, is believed to be of the species Macaca sylvanus. However, there has been a prolonged discussion regarding the classification of these specimens into a single taxonomic group as the existing dental sample exhibits a large amount of morphological and metric variation. Specifically, researchers have attempted to identify multiple subspecies based on chronological data of the paleontological locality and, secondly, based on dental metric variation. In this study, I assess temporal trends in the fossil record of Macaca sylvanus and compare overall variation …

Crystalline Colloidal Arrays: Exploring Light-Matter Interactions, Haley W. Jones

All Dissertations

Understanding and manipulating quantum light-matter interactions, especially in the context of nanostructured environments, is highly critical for technological progress and inventive solutions in the fields of telecommunications, energy-efficient lighting, and cutting-edge quantum computing technologies. The fundamental interactions of photons with a surrounding environment directly impact the performance and efficiency of devices such as lasers, light-emitting diodes (LEDs), and quantum computing elements, which leverage the unique properties of confined structured environments. Since the late 1980s, the connection between a structured environment and the decay kinetics of an embedded fluorophore has been a highly debated and unresolved topic in the literature. More …

Identifying Temporomandibular Disorder Morphological Risk Factors Via Explainable Deep Learning And Multiscale Biomechanical Modeling, Shuchun Sun

All Dissertations

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis and development of targeted prevention and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analysis of disease datasets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and …

Models Of Functional Redundancy In Ecological Communities, Sandra Annie Tsiorintsoa

All Dissertations

Functional redundancy is the number of taxa that perform a given function within a given community. In most systems, high levels of functional redundancy are important, because they contribute to ecosystem stability. However, we currently have very little understanding of why functional redundancy varies among communities. One possible factor that could affect functional redundancy is environmental complexity. Many studies show that simplified ecosystems harbor communities with lower taxon diversity. What is less clear is if this simplicity and lower taxon diversity also affects functional redundancy. To answer this question, we use metacommunity models to explore the connection between environmental complexity …

Efficient Fully Bayesian Approaches To Brain Activity Mapping With Complex-Valued Fmri Data: Analysis Of Real And Imaginary Components In A Cartesian Model And Extension To Magnitude And Phase In A Polar Model, Zhengxin Wang

All Dissertations

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data, and therefore, lead to underutilization of available data and flawed statistical assumptions. This dissertation proposes two efficient, fully Bayesian approaches for the analysis of complex-valued functional magnetic resonance imaging (cv-fMRI) time series.

Chapter 2 introduces the model, referred to as CV-sSGLMM, using the real and imaginary components of cv-fMRI data and sparse spatial generalized linear mixed model prior. This model extends the …

Oscillations Of Capillary Surfaces With Volume And Edge Effects, Dingqian Ding

All Dissertations

Capillary surfaces are defined by an interface endowed with surface tension that is partially supported by a solid substrate and are susceptible to oscillations reflecting a balance between fluid inertia and the restorative force of surface tension. The wave dynamics strongly depend upon volume change within the domain and edge effects through the boundary conditions applied at the contact-line formed at the liquid-gas-solid interface, while the spatial wave structure conforms to the geometry of the capillary surface. This dissertation develops mathematical models to address these effects for several canonical capillary surfaces, which are organized into two parts that are focused …

Domain Decomposition Methods For Fluid-Structure Interaction Problems Involving Elastic, Porous, Or Poroelastic Structures, Hemanta Kunwar

All Dissertations

We introduce two global-in-time domain decomposition methods, namely the Steklov-Poincare method and Schwarz waveform relaxation (SWR) method using Robin transmission conditions (or the Robin method), for solving fluid-structure interaction systems involving elastic, porous, or poroelastic structure. These methods allow us to formulate the coupled system as a space-time interface problem and apply iterative algorithms directly to the evolutionary problem. Each time-dependent fluid and the structure subdomain problem is solved independently, which enables the use of different time discretization schemes and time step sizes in the subsystems. This leads to an efficient way of simulating time-dependent multiphysics phenomena. For the fluid-porous …

Year-Round Co2 Emissions From The Drawdown Area Of A Tropical Reservoir: Strong Seasonal And Spatial Variation, Ícaro Barbosa, José R. Paranaíba, Sebastian Sobek, Sarian Kosten, Rafael M. Almeida, Vitor Duque, Natália Mendonça, Nathan Barros, Raquel Mendonça

School of Earth, Environmental, & Marine Sciences Faculty Publications

Highlights

• Drawdown areas emitted 80% of reservoir CO2 with just 1/5 area.

• Emissions from reservoir shorelines near forests were greater than from areas near grassland.

• CO2 emissions increase with the distance from the water.

• Estimates of reservoir drawdown CO2 flux vary by ∼ 300 % across the seasons examined here.

• There was no discernible difference in CO2 emissions between day and night-time.

• A significant increase in CO2 fluxes was observed 30 min after a rewetting event.

Abstract

A growing body of literature points to drawdown areas as important sources of atmospheric CO2 within reservoirs. Yet seasonal and temporal patterns of …

Quantum Machine Learning For Credit Scoring, Nikolaos Schetakis, Davit Aghamalyan, Micheael Boguslavsky, Agnieszka Rees, Marc Rakotomalala, Paul Robert Griffin

Research Collection School Of Computing and Information Systems

This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs …

Investigate The Importance Of Local Magnetic Moment And Magnetic Interaction In Superconducting Fese Monolayer Using Density Functional Calculations, Sudip Pokharel

Graduate Theses and Dissertations

In this dissertation, we present two projects to study superconducting FeSe monolayer using first-principles density functional calculations. Monolayer FeSe/SrTiO₃ system has very different properties as compared to its bulk counterpart in terms of critical superconducting temperature, Fermi surface topology and antiferromagnetic (AFM) stability. For FeSe monolayer, local magnetic moment (LMM) and AFM fluctuation are closely linked to superconductivity. However, LMM is not studied enough for FeSe monolayer. For FeSe/SrTiO₃ system, the substrate SrTiO₃ constrains the in-plane lattice constant for FeSe and also plays a crucial role in the onset and enhancement of superconductivity by providing charge transfer. …

Unraveling The ‘Anomaly’ In Time Series Anomaly Detection: A Self-Supervised Tri-Domain Solution, Yuting Sun, Guansong Pang, Guanhua Ye, Tong Chen, Xia Hu, Hongzhi Yin

Research Collection School Of Computing and Information Systems

The ongoing challenges in time series anomaly detection (TSAD), including the scarcity of anomaly labels and the variability in anomaly lengths and shapes, have led to the need for a more robust and efficient solution. As limited anomaly labels hinder traditional supervised models in anomaly detection, various state-of-the-art (SOTA) deep learning (DL) techniques (e.g., self-supervised learning) are introduced to tackle this issue. However, they encounter difficulties handling variations in anomaly lengths and shapes, limiting their adaptability to diverse anomalies. Additionally, many benchmark datasets suffer from the problem of having explicit anomalies that even random functions can detect. This problem is …

Anomalyclip: Object-Agnostic Prompt Learning For Zero-Shot Anomaly Detection, Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen

Research Collection School Of Computing and Information Systems

Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, e.g., data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/ organs, can vary significantly. Recently large pre-trained vision-language models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition ability in various vision tasks, including anomaly detection. However, their …

Blade Design And Validation Of Hydrokinetic Turbine To Harvest Water Current Energy, Setare Sadeqi

LSU New Orleans Theses and Dissertations

The innovative aspect of this research lies in the careful integration of cutting-edge technologies throughout the entire process of designing, fabricating, and testing the carbon fiber propeller for the 3-bladed horizontal axis ocean current turbine (OCT). SolidWorks software played a pivotal role in the initial design phase, enabling a meticulous and precise modeling of the propeller's geometry. The utilization of SolidWorks allowed for a detailed exploration of various design parameters, ensuring that the propeller's structure and form were optimized for performance in ocean current conditions. Moving beyond the realm of virtual design, the choice of carbon fiber as the fabrication …

Disaster Plant Pathology: Smart Solutions For Threats To Global Plant Health From Natural And Human-Driven Disasters, Berea A. Etherton, Robin A. Choudhury, Ricardo I. Alcalá Briseño, Romaric A. Mouafo-Tchinda, Aaron I. Plex Sulá, Manoj Choudhury, Ashish Adhikari, Si Lin Lei, Nattapol Kraisitudomsook, Jacobo Robledo Buritica

School of Earth, Environmental, & Marine Sciences Faculty Publications

Disaster plant pathology addresses how natural and human-driven disasters impact plant diseases, and the requirements for smart management solutions. Local to global drivers of plant disease change in response to disasters, often creating environments more conducive to plant disease. Most disasters have indirect effects on plant health through factors such as disrupted supply chains and damaged infrastructure. There is also the potential for direct effects from disasters, such as pathogen or vector dispersal due to floods, hurricanes, and human migration driven by war. Pulse stressors such as hurricanes and war require rapid responses, while press stressors such as climate change …

Skimming Genomes For Systematics And Dna Barcodes Of Corals, Andrea M. Quattrini, Luke J. Mccartin, Erin E. Easton, Jeremy Horowitz, Herman H. Wirshing, Hailey Bowers, Kenneth Mitchell, María Del P. González-García, Makiri Sei, Catherine S. Mcfadden

School of Earth, Environmental, & Marine Sciences Faculty Publications

Numerous genomic methods developed over the past two decades have enabled the discovery and extraction of orthologous loci to help resolve phylogenetic relationships across various taxa and scales. Genome skimming (or low-coverage genome sequencing) is a promising method to not only extract high-copy loci but also 100s to 1000s of phylogenetically informative nuclear loci (e.g., ultraconserved elements [UCEs] and exons) from contemporary and museum samples. The subphylum Anthozoa, including important ecosystem engineers (e.g., stony corals, black corals, anemones, and octocorals) in the marine environment, is in critical need of phylogenetic resolution and thus might benefit from a genome-skimming approach. We …

Conversion Of Microcrystalline Cellulose To Hexane Using Hydrogenated Metal Oxides At Low Temperature And Pressure, Mubarak Osman

Electronic Theses and Dissertations

Increasing global energy demand and environmental concerns have fueled the exploration of sustainable and efficient methods for renewable fuel production. The conversion of cellulosic waste to hydrocarbon fuels using hydrogenated metal oxides presents a novel and eco-friendly approach to sustainably address energy demand. In this study, hydrogenated metal oxides were used as catalysts in a relatively low-temperature and atmospheric-pressure environment to facilitate the conversion of cellulosic waste into hydrocarbon fuels. Hydrogenated metal oxides have been introduced as potential hydrogen transfer catalysts. The expected result is the selective conversion of cellulose to hexane at relatively low temperatures, which significantly reduces energy …

Establishing “The Fossil Record”: A Database Of Vertebrate Paleontological Sites Across The State Of Tennessee, Sarah Mclaurine

Electronic Theses and Dissertations

Fossil localities across the state of Tennessee and the data related to those sites were compiled from Tennessee Division of Geology Bulletin 84, titled “Tennessee’s Prehistoric Vertebrates,” and stored in a Microsoft Access geodatabase housed by the Department of Collections at the East Tennessee State University Museum of Natural History located at the Gray Fossil Site. Included in the database are forms to enter new site localities, view information about those already entered, view and add data to a master faunal list for the state, view sites repository information and store and add documents that are key-word searchable from the …

Vascular Brain Digital Twins For Medical Training And Education In Metaverse, Shamma Khaled Alghafri

Theses

This study addresses the need for innovative educational tools in the field of anatomy, specifically focusing on brain anatomy. The objective is to develop a virtual reality application and a 3D visualization that offer immersive and interactive learning experiences for students. The VR application, developed using Unity and designed for the Oculus Quest 2 headset, creates an immersive virtual laboratory environment. This environment includes interactive elements such as a table with buttons for displaying brain models and a whiteboard for user interaction. Users can manipulate and explore different brain structures, enhancing their understanding of complex anatomical features. Additionally, we integrated …

Bridging The Gap Between Molecular Components And Functional Electronic Chemical Sensing Devices With 2d Layered, Conductive Metal–Organic Frameworks, Emma Katherine Ambrogi

Dartmouth College Ph.D Dissertations

Layered, conductive framework materials are a class of materials with properties that make them ideal sensing materials in electronic chemical sensors. My thesis is focused on the development of these materials for electrochemical sensing of liquid phase analytes, and on understanding the material–analyte and material–electrode interfaces, and their self-assembly and deposition onto surfaces.

Chapter 1 provides a summary of the use of layered conductive metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) in electronic chemical sensors, detailing the materials and sensor architectures employed, and the analytes detected. Relevant performance metrics are compared, and investigations into material–analyte interactions are highlighted.

Chapter …

An Intelligent Academic Advising System For Course Recommendation Using Large Language Models, Malika Maya Iratni

Theses

Academic advising is an important resource for students, especially in higher education, in order to guide them to make the best possible decisions to improve their overall academic performance and overall academic journey. With the increasing number of students joining these institutions each year, traditional advising becomes a time-consuming and inefficient process that can leave students discouraged, and advisors overwhelmed. Therefore, there is a need to develop intelligent advising systems that make use of the recent advancements in technology, to support the advising process, and increase overall student satisfaction. In this work, an academic advising model that uses Recommender Systems …

Ublade: Efficient Batch Processing For Uncertainty Graph Queries, Siyuan Yao, Yuchen Li, Shixuan Sun, Jiaxin Jiang, Bingsheng He

Research Collection School Of Computing and Information Systems

The study of uncertain graphs is crucial in diverse fields, including but not limited to protein interaction analysis, viral marketing, and network reliability. Processing queries on uncertain graphs presents formidable challenges due to the vast probabilistic space they encapsulate. While existing systems employ batch processing to address these challenges, their performance is often compromised by the suboptimal selection of parallel graph traversal methods, the excessive costs in random number generation, and additional sampling-loads intrinsic to batch processing. In this paper, we introduce uBlade, an efficient batch-processing framework for uncertain graph queries on multi-core CPUs. uBlade utilizes the work-efficient graph traversal, …

Automatic Grading Of Short Answers Using Large Language Models In Software Engineering Courses, Nguyen Binh Duong Ta, Yi Meng Chai

Research Collection School Of Computing and Information Systems

Short-answer based questions have been used widely due to their effectiveness in assessing whether the desired learning outcomes have been attained by students. However, due to their open-ended nature, many different answers could be considered entirely or partially correct for the same question. In the context of computer science and software engineering courses where the enrolment has been increasing recently, manual grading of short-answer questions is a time-consuming and tedious process for instructors. In software engineering courses, assessments concern not just coding but many other aspects of software development such as system analysis, architecture design, software processes and operation methodologies …

Silver Nanoparticle Biosynthesis Utilizing Ocimum Kilimandscharicum Leaf Extract And Assessment Of Its Antibacterial Activity Against Certain Chosen Bacteria, Horyomba Siaka Ouandaogo, Souleymane Diallo, Eddy Odari, Johnson Kinyu

All Peer-Reviewed Publications

The use of plants in the biological production of silver nanoparticles for antibacterial applications is a growing field of research. In the current work, we formulated Ocimum kilimandscharicum extracts using silver nanoparticles, and evaluated its potential antibacterial activity. Aqueous and methanol plant extracts were used to reduce silver nitrate at different time intervals (30 to 150 minutes) and pH (2 to 11). The UV-visible absorption spectrum recorded for methanol and aqueous extracts revealed a successful synthesis of AgNPs for methanol and aqueous extracts. The antimicrobial activity of the AgNPs was evaluated against Escherichia coli ATCC 25922, Salmonella choleraesuius ATCC 10708, …

Use Of Modelling Tools To Assess Climate Change Impacts On Smallholder Oil Seed Yields In South Africa, Priscilla Ntuchu Kephe, Siyabusa Mkuhlani, Farirai Rusere, Abel Chemura

All Peer-Reviewed Publications

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm …

A Best Way Forward To The Organisation Of Entomological Training Courses In Sub-Saharan Africa, Kurt Jordaens, Marc De Meyer, Muriel Van Nuffel, Ashley H. Kirk-Spriggs, Christopher Sabuni, Maulid Mwatawala, Ramadhani Mujabwa, Sija Kabota, Terence Bellingan, Georg Goergen, Mervyn Mansell, Aruna Manrakhan, Antonio Sinzogan, Mark K. Schutze, Arianna Thomas-Cabianca, Robert Copeland, Burgert Muller, Massimiliano Virgilio, Ella Bert, Eva November, John Midgley

All Peer-Reviewed Publications

Despite their significant impact on human life in the Afrotropical Region, Diptera remain understudied. For a large part this is due to a lack of Diptera taxonomic experts and collection curators from the Afrotropical Region (except for South Africa perhaps), the difficulties some developing countries face in maintaining collections, and the low number of digitised specimens. This impedes the use of these Diptera collections for identification services, taxonomic and phylogenetic research, and other biological research areas. The dire need for knowledge transfer and training of young and emerging entomologists in the Afrotropical Region has prompted the Royal Museum for Central …