Physical Sciences and Mathematics Commons^™

Variation In Zero Plane Displacement And Roughness Length For Momentum Revisited, Ashvath Singh Kunadi, Richard P. Silberstein, Sally E. Thompson

Research outputs 2022 to 2026

Zero plane displacement height (d0) and momentum roughness length (z0m), describe the aerodynamic characteristics of a vegetated surface. Usually, d0 and z0m are assumed to be constant functions of the physical characteristics of the surface. Prior evidence collected from the literature and our examination of flux tower data show that d0 and z0m vary in time at sites with tree and shrub canopies, but not grasslands. The conventional explanations of these variations are based on linear functions of wind velocity and friction velocity, with little theoretical basis. This study explains the variation in aerodynamic parameters by matching four analytical canopy …

Assessing The Reactivity Of Amino Acids To Chlorination Using Capillary Electrophoresis, Pedro De Allende

Electronic Theses, Projects, and Dissertations

Amino acids (AA) are ubiquitous in wastewater systems. One of the most popular wastewater treatments is chlorination which is effective at eradicating bacteria and overall treating the wastewater, but it can also produce disinfection by-products (DBPs). Certain DBPs have been classified as Class B Carcinogens to humans and are therefore highly regulated and monitored. Past studies have shown that natural organic matter (NOM), specifically amino acids, can be precursors to DBPs if chlorinated to the correct dose. This thesis project explored AA chlorination's mechanism(s) to understand further which products arise. In particular, the investigation detailed which steps of the mechanism …

Exploring The Integration Of Blockchain In Iot Use Cases: Challenges And Opportunities, Ivannah George

Electronic Theses, Projects, and Dissertations

Blockchain and The Internet of Things (IoT) is a significant paradigm which has gained traction in today’s digital age as two complimentary technologies. The combination of IoT's connectivity with blockchain's security creates new opportunities and solves problems associated with centralized systems. This culminating project aims to delve deeper into the integration of blockchain technology in IoT applications based on select use cases to uncover potential benefits and significant challenges of blockchain integration across different sectors. The research objectives to be addressed are: (RO1) How emerging vulnerabilities manifest in the implementation of blockchain within current IoT ecosystems. (RO2) How current opportunities …

Enhancing Cybersecurity For Unmanned Systems: A Comprehensive Literature Review, Jonathan Gabriel Mardoyan

Electronic Theses, Projects, and Dissertations

This culminating experience project addresses the pressing cybersecurity challenges encountered by unmanned autonomous vehicles. The research provides a comprehensive literature review on how hybrid encryption techniques can improve the security of its communication systems. The chosen research questions guiding this study are: (Q1) How can we enhance cybersecurity measures to safeguard the communication and transmission of sensitive data from unmanned systems, thereby preventing unauthorized access by malicious actors? (Q2) How can we ensure the confidentiality and integrity of messages exchanged with unmanned systems to a command-and-control center operating on the tactical edge? (Q3) How can hybrid encryption tackle the consumption …

Story Of Your Lazy Function's Life: A Bidirectional Demand Semantics For Mechanized Cost Analysis Of Lazy Programs, Li-Yao Xia, Laura Israel, Maite Kramarz, Nicholas Coltharp, Koen Claessen, Stephanie Weirich, Yao Li

Computer Science Faculty Publications and Presentations

Lazy evaluation is a powerful tool that enables better compositionality and potentially better performance in functional programming, but it is challenging to analyze its computation cost. Existing works either require manually annotating sharing, or rely on separation logic to reason about heaps of mutable cells. In this paper, we propose a bidirectional demand semantics that allows for extrinsic reasoning about the computation cost of lazy programs without relying on special program logics. To show the effectiveness of our approach, we apply the demand semantics to a variety of case studies including insertion sort, selection sort, Okasaki's banker's queue, and the …

A Generalization Of The Graham-Pollak Tree Theorem To Even-Order Steiner Distance, Joshua N. Cooper, Gabrielle Tauscheck

Faculty Publications

Graham and Pollak showed in 1971 that the determinant of a tree’s distance matrix depends only on its number of vertices, and, in particular, it is always nonzero. The Steiner distance of a collection of 𝑘 vertices in a graph is the fewest number of edges in any connected subgraph containing those vertices; for 𝑘 =2 , this reduces to the ordinary definition of graphical distance. Here we show that the hyperdeterminant of the 𝑘 th order Steiner distance hypermatrix is always nonzero if 𝑘 is even, extending their result beyond 𝑘 =2 . Previously, the authors showed that the …

Phthalocyanine-Enabled Technologies For Water Treatment And Disinfection Strategies, Hooralain Bushnaq, Catherine Munro, Sisi Pu, Amir Razmjou, Masoumeh Zargar, Giovanni Palmisano, Srinivas Mettu, Ludovic F. Dumée

Research outputs 2022 to 2026

Water treatment and disinfection are critical factors in ensuring a safe and sustainable water supply. Phthalocyanines (Pcs), a class of versatile and robust organic compounds, have garnered significant attention for their application in various water treatment processes. This review paper offers a groundbreaking investigation of Pc-enabled technologies within the domain of water treatment applications. The core aim of this review is to meticulously analyze the chemical and optical properties of Pcs, elucidate their photosensitization mechanism and establish the crucial connection between their chemical structure and photodynamic efficacy. The various modes of Pcs application in water treatment processes - whether in …

Antimicrobial And Chemical Characterization Of Cold Atmospheric-Pressure Plasmas, Dalton Albert Miller

Boise State University Theses and Dissertations

Microbial contamination of food processing facilities is a major cause of human disease and economic loss. There are approximately 48,000,000 cases of foodborne illness in the United States annually, leading to 128,000 hospitalizations and 1,300 deaths. The total economic damages are estimated at $36 billion each year, including productive hours, medical expenses, and the disposal of contaminated foodstuffs. Current methods to reduce contamination on food processing surfaces utilize steam or concentrated chemicals (hydrogen peroxide, hypochlorite, strong acids, strong bases) that are hazardous to workers and required large volumes of water to remove prior to resuming contact with food. Ongoing outbreaks …

Radiogenic Isotope And Ionic Concentration Ratios Reveal Hydrologic Mixing, Subsurface Residence Time, And Cross-Basin Transfer In A Snow-Fed Mountain Karst Watershed, Nathaniel Ashmead

Boise State University Theses and Dissertations

Snow-fed karst aquifers support various complex ecosystems and provide a vital water source to communities worldwide. Due to their nature and distribution, these watersheds may be particularly vulnerable to the effects of climate change. Water resource managers can mitigate uncertainty from climate impacts with accurate predictions of future water availability provided by hydrologic models. While many such models exist, their effectiveness at predicting streamflow in snow-fed karst watersheds is limited by a lack of insight into the significance of cross-basin groundwater transfers. This study used radiogenic isotope and ionic concentration ratios as natural tracers to detect cross-basin transfer between two …

Using Raman Spectroscopy To Validate Metamorphic P-T Paths From Western New Hampshire, Alexis Thomas

Boise State University Theses and Dissertations

Constructing and interpreting pressure-temperature (P-T) paths in metamorphic rocks is crucial to understanding tectonic processes occurring at mid- to deep-crustal levels. Conventional methods for obtaining P-T paths rely on mineral chemistry, such as chemical zoning in garnets, which assumes growth at chemical equilibrium. However, studies show that garnet growth can occur with disequilibrium nucleation, which can result in a biased or potentially incorrect P-T path. An alternative, physics-based approach using Raman spectroscopy can also be used to determine P-T paths. This method involves using elastic thermobarometry of quartz inclusions within garnet. In this study, I tested the hypothesis that P-T …

Lca Operational Carbon Reduction Based On Energy Strategies Analysis In A Mass Timber Building, Moein Hemmati, Tahar Messadi, Hongmei Gu, Mahboobeh Hemmati

Architecture Faculty Publications and Presentations

Buildings play a significant role in the rise of energy consumption and carbon emissions. Building operations are responsible for 28% of the world’s carbon emissions. It is crucial, therefore, to evaluate the environmental impact of various buildings’ operational phase in order to implement sustainable strategies for the mitigation of their energy usage and associated carbon footprint. While numerous studies have been conducted to determine the carbon footprint of conventional building operation phases, there are still a lack of actual data on the operational carbon (OC) emissions of mass timber buildings. There is also a lack of research pertaining to the …

Numerical Simulation Of The Atmospheric Lamb Waves Generated By The Hunga Tonga-Hunga Ha'apai Eruption Using A Shallow Water Approximation, Augustus Tropea

Boise State University Theses and Dissertations

On January 15th, 2022 around 04:05 UTC the undersea volcano Hunga Tonga-Hunga Ha’apai located near the South Pacific island of Tonga violently erupted, and a large amount of energy was released into the atmosphere. The atmospheric disturbances generated by this event were detected by equipment all around the world. Analysis of this data revealed that some of these atmospheric disturbances were Lamb waves generated by the volcanic eruption. Previous works have successfully modeled these types of waves by using a shallow water approximation. Here we present results for the homogeneous shallow water equations at earth scale using high resolution finite …

Optimization Of Mass Spectrometry-Based Methods For Low-Input And Spatial Proteomics, Andikan Jones Nwosu

Theses and Dissertations

Eukaryotic cells are highly heterogeneous. These cells are arranged into different compartments, carrying out separate functions and facilitating biological processes. Proteins are the effector biomolecules targeted to subcellular locations that help fulfill specific tasks in living organisms. Spatial proteomics can help unravel molecularly how protein abundance and localization are altered in cells, which is not feasible in traditional bulk-scale proteomics. To achieve this, our lab has developed a miniaturized sample processing platform called nanoPOTS, reduced separation columns' inner diameter to increase ionization efficiency and concentrate analytes for mass spectrometers and optimized data acquisition modes for increasing proteome coverage in spatial …

A Framework For The Foundation Of The Philosophy Of Artificial Intelligence, Emily Barnes, James Hutson

Faculty Scholarship

In recent years, the rapid advancement of artificial intelligence (AI) technology has sparked profound questions about the nature of machine intelligence and the possibility of AI consciousness. As AI systems become increasingly sophisticated, examining their philosophical foundations has become imperative. This article investigates the intricate relationship between AI and existential thought, aiming to establish a comprehensive framework for understanding AI's philosophical underpinnings. The historical development of AI, from symbolic AI to contemporary machine learning paradigms, highlights the increasing complexity and sophistication of AI systems, prompting significant philosophical debates about machine consciousness. Theoretical models such as the Independent Core Observer Model …

Transformer-Based Deep Learning Prediction Of 10-Degree Humphrey Visual Field Tests From 24-Degree Data, Min Shi, Anagha Lokhande, Yu Tian, Yan Luo, Mohammad Eslami, Saber Kazeminasab, Tobias Elze, Lucy Shen, Louis Pasquale, Sarah Wellik, Carlos Gustavo De Moraes, Jonathan Myers, Nazlee Zebardast, David Friedman, Michael Boland, Mengyu Wang

Wills Eye Hospital Papers

PURPOSE: To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning.

METHODS: We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship …

First-Order Algorithms For Convex Smooth Optimization Problems With Homogeneous Linear Constraints, Yidan Guo

All Dissertations

The purpose of this dissertation is to explore the first-order methods that can be used to solve an approximate solution for convex smooth problems with homogeneous linear constraints. It consists of three interconnected research projects.

In the first project, we study the problem of computing the projection of a given vector onto the kernel of a symmetric positive semi-definite matrix. The complexity of an algorithm for computing a numerical solution is evaluated by the total number of matrix-vector multiplications required for computing an approximate solution. Such problems arise commonly in consensus optimization, in which the total number of matrix-vector multiplications …

Scalable Solution Of Time-Dependent Pdes Through Component-Wise Exponential Integrator, Chelsea Drum

Dissertations

Exponential integrators, such as exponential Runge-Kutta or Rosenbrock methods, are designed specifically for the time integration of stiff systems of ordinary differential equations (ODEs) and allow the use of larger time steps than other general-purpose ODE solvers. However, these methods rely on computing matrix function-vector products that are traditionally computed using a Krylov projection, such as Lanczos or Arnoldi iteration, that involves substantial computational expense at high spatial resolution. Krylov Subspace Spectral (KSS) methods' frequency-dependent approach, designed to circumvent stiffness in linear problems, computes these products with greater scalability. We propose the combination of such KSS methods with exponential integrators …

Single Charge Carrier Dynamics In A Single Polymer Chain, Ming Lei

All Dissertations

Understanding how charge carrier move through conjugated polymers is crucial for optimizing the functionality and efficiency of various applications, from organic solar cells and light-emitting diodes (LEDs) to field-effect transistors. Previous studies have demonstrated that tracking the fluorescence centroid displacement provides a method to determine the position of single charge carriers in a conjugated polymer nanoparticle. This method allows the observation of charge carrier trajectories over time and allow researchers to obtain insights into the dynamic processes. In addition, a detailed map of the energy landscape can be constructed by analyzing the fluorescence spectrum. These techniques were previously applied to …

Optimization Strategies To Enhance Performance In Matrix/Tensor Factorization And Multi-Source Data Integration, Mengyuan Zhang

All Dissertations

Optimization in the realm of machine learning constitutes a fundamental process aimed at refining the parameters of models to enhance their performance. It serves as the backbone of various machine learning techniques, encompassing diverse algorithms and methodologies tailored to address specific tasks and objectives.

In machine learning, datasets are commonly structured as matrices or tensors, making techniques like matrix factorization and tensor factorization indispensable for extracting meaningful representations from intricate data. Furthermore, datasets commonly comprise multiple sets of features, which has inspired our exploration of effective strategies for leveraging information from diverse sources during optimization. Additionally, the interconnected nature of …

Materials Data Science Ontology (Mds-Onto): Unifying Domain Knowledge In Materials And Applied Data Science, Van D. Tran, Jonathan E. Gordon, Alexander Harding Bradley, Balashanmuga Priyan Rajamohan, Quynh D. Tran, Gabriel Ponón, Yinghui Wu, Laura S. Bruckman, Erika I. Barcelos, Roger H. French

Student Scholarship

Ontologies have gained popularity in the scientific community as a means of standardizing concepts and terminology used in metadata across different institutions to facilitate data comprehension, sharing, and reuse. Despite the existence of frameworks and guidelines for building ontologies, the processes and standards used to develop ontologies still differ significantly, particularly in Materials Science. Our goal with the MDS-Onto Framework is to provide a unified and automated system for ontology development in the Materials and Data Sciences. This framework offers recommendations on where to publish ontologies online, how to best integrate them within the semantic web, and which formats to …

Identifying New Potential Breeding Pond Sites For The Dusky Gopher Frog In Southern Mississippi, Shelbie Kellum

Master's Theses

The main objective of this thesis was to identify potential new pond sites across southern Mississippi for the critically endangered dusky gopher frog (DGF). Ephemeral ponds are the preferred breeding habitat for the species, but they are quite rare in the region due to the diminishment of the longleaf pine ecosystem. This research addresses gaps in the current literature by providing a broad overview of dusky gopher frog breeding habitat identification. LiDAR derived datasets as well as tools within ArcGIS Pro were utilized in order to perform spatial analysis on sites of interest in south Mississippi. By using this methodology …

Understanding The Variability Of Circulation And Water Quality At Experimental Oyster Leases In The Mississippi Sound Using A High-Resolution Nested Modeling Approach, Shihab Hossain Saran

Master's Theses

Four 50-acre experimental oyster leases; two near Saint Louis Bay (StLB) and two near Pascagoula Bay (PB) were recommended to provide science-based guidance for oyster reef restoration efforts in response to continued limited productivity of oysters in the Mississippi Sound (MSS). Two-way nested simulations using ROMS within COAWST modeling framework have been run with improved forcings to analyze circulation, salinity and temperature variability within and around the lease areas under the extreme freshwater inflow from Bonnet Carré Spillway (BCS) openings and local rivers in 2019. High-resolution atmospheric forcing data is crucial because diurnal sea-land breeze affects salinity variability by increasing …

Exploring Overbank Sediment Deposition Variation In Heavily Modified Floodplains Of The Lower Mississippi River: A Sedimentological And Geophysical Analysis, Seth Fradella

Master's Theses

Since the 1930s, the Lower Mississippi River (LMR) has experienced large-scale modifications to the channel profile and surrounding floodplains through dams, dikes, revetments, dredging, and channel cutoffs. Although these changes have improved navigation and reduced flood risk, unanticipated changes to the major flood return period, individual flood severity and duration, and sediment regime have become increasingly apparent and sometimes problematic, such as the 2011 and 2018-2020 floods. Flood control levees along the LMR have reduced the natural floodplain area by 70-90%, resulting in heavily restricted overbank storage capacity of water and sediment. For the same flood events in recent history, …

Projected Range Shift Of The Atlantic Surfclam, Spisula Solidissima, And The Ocean Quahog, Arctica Islandica, Under Climate-Induced Benthic Warming: Forecasting Fishery Influence In The Mid-Atlantic Bight Throughout The 21st Century, Molly Mae Spencer

Master's Theses

The Atlantic surfclam, Spisula solidissima, and ocean quahog, Arctica islandica, are biomass dominant bivalve species on the eastern North American continental shelf, both supporting lucrative commercial fisheries in the Mid-Atlantic with a combined value of $53.6 million in ex-vessel revenue per year. Rapid climate change is expected to modify the geographic range of these bivalve populations across the Mid-Atlantic Bight (MAB), suggesting transformative consequences for the surfclam fishery. This study forecasted future distributions of the two species from years 2016 – 2095, based on projected bottom water temperatures and a temperature-dependent population dynamics model, to project subsequent fishery-based indicators …

El Centro De Almacenamiento De Carbono Roughrider, Universidad De Dakota Del Norte. Centro De Investigación De Energía Y Ambiental

EERC Brochures and Fact Sheets

Hojas informativas acerca del Centro de Almacenamiento de Carbono Roughrider, un Proyecto de CarbonSAFE. El Proyecto del Centro de Almacenamiento de Carbono Roughrider está investigando la viabilidad para el almacenamiento Seguro y permanente de CO₂ en el condado McKenzie, Dakota del Norte.

Drilling Faqs, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet about geological research drilling. Cylindrical rock samples, called cores, and geologic data will be collected from an exploratory hole drilled in McKenzie County, North Dakota.

The Roughrider Carbon Storage Hub, University Of North Dakota. Energy And Environmental Research Center

EERC Brochures and Fact Sheets

Fact sheet regarding the Roughrider Carbon Storage Hub, a CarbonSAFE project. The Roughrider Carbon Storage Hub project is investigating the feasibility of safe, permanent storage of CO₂ in McKenzie County, North Dakota.

Preguntas Más Frecuentes De Perforación, Universidad De Dakota Del Norte. Centro De Investigación De Energía Y Ambiental

EERC Brochures and Fact Sheets

Hoja informativa acerca de la investigación geológica de la perforación. Muestras de roca cilíndricas, llamadas núcleos de roca, y datos geológicos serán adquiridos en un pozo exploratorio perforado en el Condado McKenzie, Dakota del Norte

The Gavel And The Camera: Environmental Law And Photojournalism's Relationship To Appalachian Identity, Emily S. Patton

Honors Theses

Both the federal government and photojournalism have played a substantial role in the “othering” of Appalachia while simultaneously attempting to save it. Examining both institutions demonstrates the exploitative processes that alter the perception of rural communities and their surrounding environment. Stacey Kranitz’s archival exhibit, As It Was Give(n) To Me, internalizes this precarious role of the camera in a region heavily journalized. By inhabiting the area for years, establishing close relationships, and researching the documented history of Appalachia, Kranitz’s work becomes her livelihood in the dissolution of profession vs. personal. She is critical of past projects whose objective portrayal of …

Dynamic Prediction Of Disease Progression With Longitudinal Data, Wenhao Li

Dissertations and Theses (Open Access)

Dynamic prediction plays a pivotal role in clinical research, especially when forecasting time-to-event outcomes based on evolving longitudinal data. This process often leverages the integration of longitudinal and time-to-event data through joint modeling, a prevalent technique. Alongside joint modeling, landmark modeling stands as another key approach in the realm of longitudinal studies. These methodologies are instrumental in dynamically predicting clinical events by utilizing predictor variables measured over time, up until the moment predictions are made. Within this framework, Chapter 2 addresses the challenge of comparing joint modeling and landmark modeling for dynamic prediction in longitudinal studies, introducing a novel algorithm …