Physical Sciences and Mathematics Commons^™

The Effects Of Water With Anti-Inflammatory Capabilities (Wac) On Circulating Inflammatory Biomarkers At Rest And Following Resistance Exercise, Brittany Martin

Health, Human Performance and Recreation Undergraduate Honors Theses

Purpose: To determine the effects of waters with anti-inflammatory capabilities (WAC) influence circulating inflammatory biomarkers at rest and following resistance exercise. Methods: Participants completed a heavy lifting protocol and drank water with anti-inflammatory capabilities for a total of six weeks. Blood samples were taken at different at 9 different time intervals to assess concentrations of anti-inflammatory markers and to quantify inflammatory biomarkers. Those time intervals included pre-AHREP 1 and post-AHREP 1, 24-hr post-AHREP 1, 48-hr post-AHREP 1, 3-week check-up, pre-AHREP 2, post-AHREP 2, 24-hr post-AHREP 2, and 48-hr post-AHREP 2. Background: The biomarkers that were focused on were: IL-6 …

Synthesis And Function Of Ion-Selective Single-Molecule Fluorescent Imaging Molecules And Materials, Alena A. Denisenko

Chemistry & Biochemistry Dissertations - Archive

Neutrinos are fermion subatomic particles that interact only by weak subatomic forces—making them almost impossible to detect. However, they can be produced in specific radioactive decay reactions, such as beta decay. We hypothesize the detection of double beta decay through the tagging of the Ba2+ daughter ion from 136Xe (136Xe —136Ba2+ + 2ν + 2β). The proposal is to develop single-molecule fluorescence imaging (SFMI) technology to investigate gaseous chemical constituents in high-pressure gases (ca. 30-150 psi) at gas-solid interfaces. To achieve proper SMFI, heterogenous self-assembled monolayers (SAMs) containing fluorophores that operate under dry conditions and are turned on when a …

Robust Noise-Based Attacks Against Audio Event Detection Systems, Rodrigo Augusto Silva Dos Santos

Computer Science and Engineering Dissertations - Archive

The massive advances on the field of deep neural networks in the 2000 and 2010 decades led to an overwhelming adoption of these algorithms on all sorts of domains and applications. Under this widespread adoption scenario, it is natural that these neural networks have also been employed on safety-related use cases, bringing substantial improvements to the performance of existing as well as novel systems. Examples of these safety-inclined applications include scene recognition, object detection and tracking, speech recognition, audio event detection and classification, just to cite a few ones. Unfortunately, these neural network algorithms have been shown to be vulnerable …

Designing Large-Scale Key-Value Systems On High-Speed Storage Devices, Xingsheng Zhao

Computer Science and Engineering Dissertations - Archive

With the evolution of new technologies, such as edge computing, full self-driving, virtual reality, and multi-media streaming, the volume of data is growing at an accelerated speed. The global data volume could achieve 175~zettabytes by 2025. With this huge amount of data, the focus of data management has been shifted from traditional SQL databases to NoSQL databases, which provide higher performance and better scalability. Key-value (KV) stores are a common type of NoSQL database and are becoming a major storage infrastructure in various application domains. With the development of high-speed storage devices, such as NVMe SSD, Open-channel SSD, and non-volatile …

Efficient Algorithms And Human-In-The-Loop Approaches For Attribute Design And Selection, Md Abdus Salam

Computer Science and Engineering Dissertations - Archive

Feature engineering and feature selection are two important aspects of data science pipeline. Due to the advancement of data collection techniques in recent years, huge amount of data is becoming available in different industries. Consequently, the importance of data science is increasing for business analytic purpose. Different tools and techniques are being developed to assist data scientists to complete their tasks efficiently. One of the main human involvements in the data science task is for feature engineering and selection. These pre-processing steps will prepare the data in the format desired to be fed into various machine learning algorithms to accomplish …

A Necessary And Sufficient Condition For The Asymptotic Normality Of The Quantile Estimator In The Deconvolution Problem, Jeremy R. Valdez

Mathematics Dissertations - Archive

In this study, we examine the estimation of a quantile function when we have n observations coming from the convolution model contaminated by additive measurement errors. Under certain assumptions, a kernel type deconvolution quantile estimator of the unknown quantile function is proposed. Moreover, we discuss the necessary and sufficient condition on the bandwidth in order to investigate the limiting distribution of the deconvolution kernel quantile estimator when the error terms follow either an ordinary smooth or super smooth distribution. A bootstrap approach is used to select the optimal bandwidth to construct approximate distribution free confidence bands for the quantile function …

Twisting Systems And Some Quantum P³S With Point Scheme A Rank-2 Quadric, Hung Viet Tran

Mathematics Dissertations - Archive

In 1996, J. J. Zhang introduced the concept of twisting a graded algebra by a twisting system, which generalizes the concept of twisting a graded algebra by an automorphism (the latter concept having been introduced in an article by M. Artin, J. Tate and M. Van den Bergh in 1991). Twisting using a twisting system is an equivalence relation and certain important algebraic properties of the original algebra are carried over to the twisted algebra. We call a twisting system nontrivial if it is not given by an automorphism. However, there are very few known examples of nontrivial twisting systems …

Exponential Tensor Modules, Khoa Hoang Nguyen

Mathematics Dissertations - Archive

Representation theory of Lie algebra of a finite dimensional reductive Lie algebra g is a long-standing problem. The ultimate goal is to classify all representations of g. However. the only case only case when a complete classification is obtained is the case of g = sl(2). Hence, it is natural to study certain categories of representations of g for which some finiteness conditions on the action of certain elements of g is enforced. In this thesis, we introduce a class of representations T (g, V, S) of sl(n + 1) of mixed tensor type. By varying the polynomial g, the …

Modeling And Debiasing Feedback Loops In Collaborative Filtering Recommender Systems., Sami Khenissi

Electronic Theses and Dissertations

Artificial Intelligence (AI)-driven recommender systems have been gaining increasing ubiquity and influence in our daily lives, especially during time spent online on the World Wide Web or smart devices. The influence of recommender systems on who and what we can find and discover, our choices, and our behavior, has thus never been more concrete. AI can now predict and anticipate, with varying degrees of accuracy, the news article we will read, the music we will listen to, the movies we will watch, the transactions we will make, the restaurants we will eat in, the online courses we will be interested …

Fitting Physical Models To Spatiotemporal Observations: Discovering Developmental Regulatory Networks Of Drosophila, Dj Holt

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Deep learning continues to solve significant scientific and engineering problems, but the solutions found are neural networks with thousands of parameters that provide no scientific or engineering insights. A solution to this problem, explored in this work, is to learn mathematical models that represent mechanisms that can be interpreted by scientists and engineers.

A challenging learning problem is to discover the genetic regulatory mechanisms that drive pattern formation during early biological development. Using known mathematical models of these processes, consisting of coupled ordinary differential and partial differential equations, we aim to identify the model parameters that describe the biological mechanisms …

School Garden Programs, Food Pathways, And Environmental Justice: Modeling Ethical Frameworks To Reclaim The Value Of Care Work Through Education, Charlotte Borland '22

Honor Scholar Theses

No abstract provided.

Beyond Accuracy In Machine Learning., Aneseh Alvanpour

Electronic Theses and Dissertations

Machine Learning (ML) algorithms are widely used in our daily lives. The need to increase the accuracy of ML models has led to building increasingly powerful and complex algorithms known as black-box models which do not provide any explanations about the reasons behind their output. On the other hand, there are white-box ML models which are inherently interpretable while having lower accuracy compared to black-box models. To have a productive and practical algorithmic decision system, precise predictions may not be sufficient. The system may need to have transparency and be able to provide explanations, especially in applications with safety-critical contexts …

Statistical Methods For Assessing Drug Interactions And Identifying Effect Modifiers Using Observational Data., Qian Xu

Electronic Theses and Dissertations

This dissertation consists of three projects related to causal inference based on observational data. In the first project, we propose a double robust to identify the effect modifiers and estimate optimal treatment. Observational studies differ from experimental studies in that assignment of subjects to treatments is not randomized but rather occurs due to natural mechanisms, which are usually hidden from the researchers. Many statistical methods to identify the treatment effect and select the optimal personalized treatment for experimental studies may not be suitable for observational studies any more. In this project, we propose a exible outcome model to select the …

Studying Alive : An Application For The Wellness Of College Students During The Covid-19 Pandemic, Natasia Fernandez

Theses, Dissertations and Culminating Projects

Mental health awareness has become an increasingly important topic over the past couple of years due the Covid-19 pandemic. Many individuals find it difficult to discuss their mental health. An individual’s mental health is a significant factor in maintaining their overall wellness. College students, specifically, face various hurdles and challenges that can affect their mental health. They have several responsibilities weighing on their shoulders which can lead to stress, depression and/or anxiety. College students may find it difficult to express these topics and seek healthy ways to cope. During the Covid-19 pandemic, additional challenges have been added onto college students …

Association Of Phosphate-Containing Versus Phosphate-Free Solutions On Ventilator Days In Patients Requiring Continuous Kidney Replacement Therapy, Melissa L. Thompson Bastin, Arnold J. Stromberg, Sethabhisha N. Nerusu, Lucas J. Liu, Kirby P. Mayer, Kathleen D. Liu, Sean M. Bagshaw, Ron Wald, Peter E. Morris, Javier A. Neyra

Statistics Faculty Publications

Background and objectives Hypophosphatemia is commonly observed in patients receiving continuous KRT. Patients who develop hypophosphatemia may be at risk of respiratory and neuromuscular dysfunction and therefore subject to prolongation of ventilator support. We evaluated the association of phosphate-containing versus phosphate-free continuous KRT solutions with ventilator dependence in critically ill patients receiving continuous KRT.

Design, setting, participants, & measurements Our study was a single-center, retrospective, pre-post cohort study of adult patients receiving continuous KRT and mechanical ventilation during their intensive care unit stay. Zeroinflated negative binomial regression with and without propensity score matching was used to model our primary outcome: …

Iowa Waste Reduction Center Newsletter, May 2022, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- EPA adds 1-bromopropane (1-BP) to the Clean Air Act List of Hazardous Air Pollutants
--- FLPC Calls on Congress to Prioritize Food Waste Reduction in the 2023 Farm Bill
--- Sustainability & Environmental Awareness Award
--- Two Iowa Green Brewery Certified Breweries Won Prestigious 2022 World Beer Cup Award
--- Iowa Composting Council Seeks Board Nominations
--- Air Quality permits under review
--- Industry New

An Application Of Machine Learning To Analysis Of Packed Mac Malware, Kimo Bumanglag

Masters Theses & Doctoral Dissertations

The macOS operating system is increasingly targeted by malware. Software written for macOS, both benign and malicious, is in the Mach-O executable format. Malware authors may frustrate analysts through obfuscation methods such as packing. The field of malware research on Windows is well-established but is less so on the macOS platform. Thus far, no research has been identified that studies how machine learning can be used to detected packed Mach-O malware. This research applies supervised machine learning techniques to the classification of packed Mach-O malware. This research will answer three research questions. First, whether machine learning can classify packed Mach-O …

Plastic Bottles In Architecture: A How To Guide On Life-Cycle Through Reuse, Crystallyne Landram

Architecture Undergraduate Honors Theses

This booklet is a collection of 6 case studies that utilize reused plastic bottles significantly within their design. Prior to working with the case studies, much time was dedicated to research that justified the need for such a project. In-depth research on the environmental crisis, waste management trends, stewardship, sustainability, and ethics was conducted and recorded in the introduction to contextualize the need for an accessible how-to guide such as this one. Time was also given to establishing background information on bottles, reprocessing, and architecture to elaborate further on each piece of the content matter. It is curated as a …

Quantized Enveloping Superalgebra Of Type P, Saber Murad Ahmed

Mathematics Dissertations - Archive

We introduce a new quantized enveloping superalgebra $\mathfrak{U}_q\mathfrak{p}_n$ attached to the Lie superalgebra $\mathfrak{p}_n$ of type P. The superalgebra $\mathfrak{U}_q\mathfrak{p}_n$ is a quantization of a Lie bisuperalgebra structure on $\mathfrak{p}_n$ and we study some of its basic properties. We determine representations of the superalgebra $\mathfrak{U}_q\mathfrak{p}_n$ and derive its Drinfeld-Jimbo relations. We prove the triangular decomposition of $\mathfrak{U}_q\mathfrak{p}_n$ and introduce some preliminary results concerning the highest weight representation of $\mathfrak{U}_q\mathfrak{p}_n$. We also introduce the periplectic q-Brauer algebra and prove that it is the centralizer of the $\mathfrak{U}_q\mathfrak{p}_n$-module structure on $\mathbb{C}(n|n)^{\otimes \ell}$. Finally, we propose a definition for a new periplectic q-Schur …

Liquid Chromatography With Tandem Mass Spectrometry Method Development For The Determination Of Β–Defensins In Bovine Milk, Symone T. Whalin

Masters Theses & Specialist Projects

Bovine mastitis, caused by a wide array of pathogens, results in a substantial economic loss for the dairy cattle livestock industry. β-defensins are a part of the bovine’s innate immune system and act as the first line of defense against mastitis. Only foundational research has been done on β-defensins’ ability to treat and prevent mastitis. There have been no analytical methods reported in the literature for analyzing β-defensins in bovine milk. This research aims to create an analytical approach to determine β-defensins in bovine milk. It is challenging to determine an analyte in a complex sample matrix, and milk is …

Machine Learning Methods For Statistical Analysis And Representation Learning On Neuroimaging Data, Fan Yang

Computer Science and Engineering Dissertations - Archive

With the recent advance and widespread adoption of imaging technological innovations, clinical practitioner and scientists can easily acquire and store a large amount of various neuroimaging modalities, such as Diffusion Tensor Imaging (DTI), Magnetic Resonance Imaging (MRI), resting-state functional MRI (rs-fMRI) and Positron Emission Tomography (PET), etc. These novel imaging data sources cover a rich amount of factors that influence patients' cognitive health, offer an objective view of patients at unprecedented multi-resolution for the understanding of brain structure and function, and have the significant potential to improve healthcare by aiding better decision-making in diagnosing, monitoring and treating diseases. Machine Learning …

Using Antipatterns To Improve Database Code Fragments, And Utilizing Knowledge Graphs And Nlp Patterns To Extract Standardized Data Element Names, Bader Alshemaimri

Computer Science and Engineering Dissertations - Archive

Database code fragments exist in software systems by using SQL as the stan- dard language for relational databases. Traditionally, developers bind databases as backends to software systems for supporting user applications. However, these bind- ings are low-level code and implemented to persist user data, so Object Relational Mapping (ORM) frameworks take place to abstract database access details. These approaches are prone to problematic database code fragments that negatively im- pact the quality of software systems. In the first part of the dissertation, we survey problematic database code fragments in the literature and examine antipatterns that occur in low-level database access …

Investigation Of The Cellular Uptake And Cytotoxicity Of Ruthenium(Ii) Polypyridyl Complexes As A Function Of Lipophilicity, Matthew Guerrero

Chemistry & Biochemistry Theses - Archive

Ru(II) polypyridyl complexes have been one of the focal points of investigation for inorganic anti-cancer drugs, as they are robust enough to remain chemically intact in vivo and many show potent cytotoxicty. In particular, the RPC [Ru(dip)3]2+, where dip is 4,7-diphenyl-1,10-phenanthroline, is noted for its high cytotoxicity (IC50 ~2-5 uM) across a broad spectrum of cancer cell lines and more recently has been implicated as targeting microtubules, which is an unusual target for metal-based drugs. While the cellular uptake for [Ru(dip)3]Cl2 is among the highest seen for RPCs, its solubility in water or buffer is limited and problematic. In order …

Investigation Of F420-Dependent Glucose-6-Phosphate Dehydrogenase Variants Using Ph Profiles And Stopped-Flow Spectrometric Ligand-Binding Methods, Ana Laura Alvarez

Chemistry & Biochemistry Theses - Archive

F420-dependent glucose-6-phosphate dehydrogenase (FGD) from Mycobacteria tuberculosis, catalyzes a reaction the conversion of glucose-6-phosphate (G6P) to 6-phosphogluconolactone using the oxidized F420 cofactor, which is reduced to F420H2. The reaction is important within M. tuberculosis, the cause of tuberculosis disease (TB) due to its health relevance for treatment of multiple drug resistant and extreme drug resistant strains of M. tuberculosis. TB affects millions of people worldwide, and although treatable, multiple drug resistant and extreme drug resistant forms of TB adds strain on the current generation of drugs. Since this enzyme is not found in humans, is an ideal drug target. A …

Petrophysical Studies On Woodford Shale In Oklahoma And Wolfcamp Shale In Texas: A Multiple-Approach Methodology, Chen Zhao

Earth & Environmental Sciences Dissertations - Archive

The successful development of oil and gas from unconventional reservoirs in the United States proves the high petroleum potential that shale rock reserves. Petrophysical studies on shale rocks are an important part of reservoir characterization. Petrophysical studies investigate the basic properties and pore structures of the shale rock, including porosity, density, pore size distribution, specific surface area, wettability, pore connectivity, and permeability, to understand the storage and movement of oil and gas in shale rocks. Multiple experimental approaches were applied onto both outcrop and well-core samples from several U.S. shale plays. A range of complementary methodologies of X-ray diffraction, polarized …

Unsupervised Domain Adaptation With Deep Neural Networks, Jinyu Yang

Computer Science and Engineering Dissertations - Archive

Deep neural networks (DNNs) demonstrate unprecedented achievements on various machine learning problems and applications. However, such impressive performance heavily relies on massive amounts of labeled data which requires considerable time and labor efforts to collect and annotate. To remedy this limitation, unsupervised domain adaptation (UDA) has attracted more and more attention in the past decade, owing to its capability in transferring the knowledge learned from a labeled source domain to an unlabeled target domain. UDA has proved its wide applicability in various vision tasks, for example, image classification and semantic segmentation. Despite its impressive success, the limitations of existing UDA …

Learning Causal Bounds Using Marginal Independence Information With Applications To Gene Expression Analysis, Borzou Alipourfard

Computer Science and Engineering Dissertations - Archive

ABSTRACT: Discovering causal relations is a fundamental goal of science. Randomized controlled experiments were often considered to be the only reliable method for tackling this task. However, in recent years, various causal discovery methods have been proposed that are capable of identifying causal relations from purely observational data. While these causal discovery methods provide a theoretical framework for bridging the gap from statistical relations to causal conclusions, causal discovery remains a challenging task in practice; this challenge arises because many of the assumptions made in obtaining these theoretical results are often not met in practice. This is especially true when …

Optimal Utility-Based Traffic Control For Datacenter Networks, Akshit Singhal

Computer Science and Engineering Dissertations - Archive

As datacenter applications with diverse service requirements proliferate, it becomes imperative to enable datacenter network flow rate allocation that satisfies minimum user-utility requirements, while allowing for user-utility-based fair resource allocation. Multiple Path Transmission Control Protocols (MPTCPs) allow flows to explore path diversity of datacenter networks and multihoming to improve throughput, reliability, and network resource utilization. The work in this dissertation aims to develop optimal utility-based datacenter traffic control protocols to meet diverse service requirements for datacenter applications. Specifically, this dissertation makes contributions on four highly related research topics. First, we put forward a HOListic traffic control framework for datacenter NETworks …

An Examination Of The Statistics And Risk Management Concepts Behind The Patient Protection And Affordable Care Act (Ppaca) Of 2010, Scott Sinclair

Undergraduate Honors Thesis Collection

The Patient Protection and Affordable Care Act (PPACA) is the overarching federal law that has impacted the intricacies of the health insurance market for more than a decade. Using the supervised learning method of multiple linear regression, the relationship between the medical loss ratio rebates and predictor variables such as the state, health insurance market, and the number of insurance companies owing rebates will be analyzed, along with the actuarial value of metal tiers and geographic rating area factors in terms of their relationship to the insurance premium for a standard family of four, defined as a forty-year-old couple with …

Investigation Of The Analytical Utility Of The Inductively Coupled Plasma Mass Spectrometer (Icp-Ms) For The Quantification Of Proteins Via Lanthanide Labeling, Caitlin Elizabeth Dunlap

Undergraduate Honors Thesis Collection

The discovery and ability to provide an absolute concentration of biomarkers in the body can facilitate early diagnoses of diseases. This information can also be used for more accurate and unique treatments for those patients. While major advances in molecular mass spectrometry allows for a sensitive identification of these biomarkers, they are limited to only four orders of magnitude in their dynamic ranges. Due to this, detection and quantitation of low abundant proteins, which tend to make up these vital biomarkers, is very difficult. On the other hand, ICP-MS offers a wider dynamic range (up to 12 orders of magnitude) …