Physical Sciences and Mathematics Commons^™

Exploring The Diagnostic Potential Of Radiomics-Based Pet Image Analysis For T-Stage Tumor Diagnosis, Victor Aderanti

Electronic Theses and Dissertations

Cancer is a leading cause of death globally, and early detection is crucial for better

outcomes. This research aims to improve Region Of Interest (ROI) segmentation

and feature extraction in medical image analysis using Radiomics techniques

with 3D Slicer, Pyradiomics, and Python. Dimension reduction methods, including

PCA, K-means, t-SNE, ISOMAP, and Hierarchical Clustering, were applied to highdimensional features to enhance interpretability and efficiency. The study assessed the ability of the reduced feature set to predict T-staging, an essential component of the TNM system for cancer diagnosis. Multinomial logistic regression models were developed and evaluated using MSE, AIC, BIC, and Deviance …

Assessing Gtfs Accuracy, Gregory L. Newmark

Mineta Transportation Institute

The promised benefits of the General Transit Feed Specification (GTFS) Schedule and Realtime standards are dependent on the underlying quality of the data. Despite this fundamental reliance, there has been relatively little research on techniques and strategies to assess GTFS accuracy. The need for such assessment is growing as federal and state governments increasingly require transit agencies to make these data available to the public. This research fills this gap by presenting a suite of methods and metrics to assess the temporal accuracy of GTFS Realtime and the spatial accuracy of GTFS Schedule feeds. The temporal assessment demonstrates an approach …

Mesenchymal Stem Cells In Autoimmune Disease: A Systematic Review And Meta-Analysis Of Pre-Clinical Studies, Hailey N. Swain, Parker D. Boyce, Bradley A. Bromet, Kaiden Barozinksy, Lacy Hance, Dakota Shields, Gayla R. Olbricht, Julie A. Semon

Mathematics and Statistics Faculty Research & Creative Works

Mesenchymal Stem Cells (MSCs) Are of Interest in the Clinic Because of their Immunomodulation Capabilities, Capacity to Act Upstream of Inflammation, and Ability to Sense Metabolic Environments. in Standard Physiologic Conditions, They Play a Role in Maintaining the Homeostasis of Tissues and Organs; However, there is Evidence that They Can Contribute to Some Autoimmune Diseases. Gaining a Deeper Understanding of the Factors that Transition MSCs from their Physiological Function to a Pathological Role in their Native Environment, and Elucidating Mechanisms that Reduce their Therapeutic Relevance in Regenerative Medicine, is Essential. We Conducted a Systematic Review and Meta-Analysis of Human MSCs …

A Uniformly Most Powerful Test For The Mean Of A Beta Distribution, Richard Ntiamoah Kyei

Electronic Theses and Dissertations

The beta distribution is used in numerous real-world applications, including areas such as manufacturing (quality control) and analyzing patient outcomes in health care. It also plays a key role in statistical theory, including multivariate analysis of variance (MANOVA) and Bayesian statistics. It is a flexible distribution that can account for many different characteristics of real data. To our surprise, there has been very little work or discussion on performing statistical hypothesis testing for the mean when it is reasonable to assume that the population is beta distributed. Many analysts conduct traditional analyses using a t-test or nonparametric approach, try transformations, …

Two New Baseball Performance Statistics, Charles H. Smith

Faculty/Staff Personal Papers

Ever since I was a small child I have been interested in both statistics and baseball, so I guess it was inevitable I would eventually find a way to put the two together. In this short note I'd like to suggest a pair of measures that I feel might be useful in interpreting quality of play: one focusing more on hitting, the other on pitching. Let's start with the one concerning hitting.

Lactoferrin And Lysozyme To Promote Nutritional, Clinical And Enteric Recovery: A Protocol For A Factorial, Blinded, Placebo-Controlled Randomised Trial Among Children With Diarrhoea And Malnutrition (The Boresha Afya Trial), Ruchi Tiwari, Kirkby Tickell, Emily Yoshioka, Joyce Otieno, Adeel Shah, Barbra Richardson, Lucia Keter, Maureen Okello, Churchil Nyabinda, Indi Trehan

Paediatrics and Child Health, East Africa

Introduction: Children with moderate or severe wasting are at particularly high risk of recurrent or persistent diarrhoea, nutritional deterioration and death following a diarrhoeal episode. Lactoferrin and lysozyme are nutritional supplements that may reduce the risk of recurrent diarrhoeal episodes and accelerate nutritional recovery by treating or preventing underlying enteric infections and/or improving enteric function.

Methods and analysis: In this factorial, blinded, placebo-controlled randomised trial, we aim to determine the efficacy of lactoferrin and lysozyme supplementation in decreasing diarrhoea incidence and improving nutritional recovery in Kenyan children convalescing from comorbid diarrhoea and wasting. Six hundred children aged 6–24 months with …

Robust Multivariate Estimation And Inference With The Minimum Density Power Divergence Estimator, Ebenezer Nkum

Open Access Theses & Dissertations

The estimation of the location vector and scatter matrix plays a crucial role in many multivariate statistical methods. However, the classical likelihood-based estimation is greatly influenced by outliers, potentially leading to unreliable decisions. Hence, a fundamental challenge in multivariate statistics is to develop robust alternatives that can maintain performancein the presence of outliers and deviations from the assumed data distribution. Unfortunately, methods with good global robustness often substantially sacrifice efficiency. To address this, we propose the adoption of Minimum Density Power Divergence (MDPD) estimation, a well-established robust technique known for its efficiency and statistical robustness to outliers and model violations. …

Random Forest For High-Dimensional Data, George Ekow Quaye

Open Access Theses & Dissertations

The exponential growth of data has led to a rapid increase in high-dimensional datasets across various domains, presenting significant challenges in data analysis, particularly in predictive modeling tasks. Traditional Random Forest (RF), while robust, often struggles with datasets filled with numerous noisy or non-informative features, compromising both performance and accuracy. This study introduces an advanced algorithm, High-Dimensional Random Forests (HDRF), designed to address these challenges by integrating robust multivariate feature selection techniques directly into the decision tree construction process. Unlike standard RF, HDRF incorporates ridge regression-based variable screening at each decision split, enhancing its ability to identify and utilize the …

Evaluation Of Factors Impacting Predictor Importance Results In Multilevel Models, Soonhwa (Suna) Paek

Theses and Dissertations

Background: Dominance Analysis (DA) was originally proposed to determine the relative importance of predictor variables in OLS regression models by comparing the change in model fit (i.e., R2) resulting from adding each predictor to each possible subset model (Azen & Budescu, 2003; Azen, 2013; Budescu, 1993). Although various educational studies show that DA can provide useful information in research, the DA procedure has not been studied extensively with Multilevel Linear Models (MLMs), which are commonly used to analyze nested data structures.

Purpose: This study aimed to identify appropriate multilevel measures of fit for the DA procedure in various MLMs, and …

Cte Induced Premium Principles And Properties, Linjiao Wu

Theses and Dissertations

The traditional pricing approach in the insurance industry assumes independence among insureds, yet overlooks the complexities of interdependent risk profiles. This dissertation addresses this limitation by proposing a premium pricing model tailored for managing dependent risks, drawing inspiration from conditional tail expectation (CTE) theory. In our model, each individual insured's premium is contingent upon the collective loss surpassing a predefined threshold.

To validate the efficacy of our model, we introduce several key properties to ensure fairness and stability in premium determination among insured individuals, including diversification and monotonicity. Diversification ensures that adding one policyholder to the insured group does not …

Robust-Efficient Fitting Of Loss Models Via Quantile Least Squares, Mohammed Adjei Adjieteh

Theses and Dissertations

Actuaries and statisticians use statistical models to predict future losses for pricing and other purposes. However, a key challenge in modeling is estimating the unknown parameters that index these distributions. Ensuring both efficiency and robustness of the chosen method is crucial, especially given the prevalence of outliers or extreme losses in insurance claims data. The primary objective of this dissertation is to introduce a robust, efficient, and computationally easy parameter estimation method that can be applied to various loss modeling scenarios. The proposed method exploits the joint asymptotic normality of sample quantiles (of i.i.d. random variables) to construct both ordinary …

Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression And Inductive Thematic Analysis In A Mixed Methods Study, Samuel Nelson Koscelny

All Theses

High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the …

Sparse Neural Network To Enhance Performance Under Limited Parameter Constraints., Nailah Rawnaq

Graduate Theses and Dissertations

Over the past decade, the widespread adoption of deep neural networks has been a breakthrough driven by significant computational advancements. Additionally, the number of parameters of those models is exponentially increasing for performing complex tasks and achieving better performance. However, in most practical cases, often there are constraints in the number of parameters due to limited resources in storage size and computational cost. Network pruning can lead to an optimal solution to this problem. In this thesis, I present supporting evidence to the hypothesis that higher sparsity leads to better performance for a convolution-based neural network. I perform performance studies …

Gan With Skip Patch Discriminator For Biological Electron Microscopy Image Generation, Nishith Ranjon Roy

Graduate Theses and Dissertations

GAN models have been successfully used for image generation in various sections such as real-life objects like human faces, cars, animal faces, landscapes, etc. This work focuses on biological electron microscopy (EM) image generation. Unlike other real-life objects, biological EM images are obtained through electron microscopy techniques to study biological specimens. Electron microscopy offers high resolution and magnification capabilities, making it a powerful tool for visualizing biological structures at the nanoscale. However, using GAN models for biological EM image generation poses challenges due to the complex and unique arrangements of biological structures and the sparse and asymmetrical patterns in EM …

Application Of Machine Learning Algorithms In Healthcare, Dwaipayan Mukhopadhyay

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) is a subset of artificial intelligence that has made substantial strides in predicting and identifying health emergencies, disease populations, and disease state and immune response, amongst a few fields of healthcare. Here we provide a brief overview of machine learning-based approaches and learning algorithms. Second, we discuss a general procedure of ML and review some studies presented in ML application for several healthcare fields. We also briefly discuss the risks and challenges of ML application to healthcare.This dissertation also consists of four different cases in healthcare where we have applied ML techniques on real life data sets. …

Forecasting Commercial Vehicle Miles Traveled (Vmt) In Urban California Areas, Steve Chung, Jaymin Kwon, Yushin Ahn

Mineta Transportation Institute

This study investigates commercial truck vehicle miles traveled (VMT) across six diverse California counties from 2000 to 2020. The counties—Imperial, Los Angeles, Riverside, San Bernardino, San Diego, and San Francisco—represent a broad spectrum of California’s demographics, economies, and landscapes. Using a rich dataset spanning demographics, economics, and pollution variables, we aim to understand the factors influencing commercial VMT. We first visually represent the geographic distribution of the counties, highlighting their unique characteristics. Linear regression models, particularly the least absolute shrinkage and selection operator (LASSO) and elastic net regressions are employed to identify key predictors of total commercial VMT. LASSO regression …

Oh Statistics!, Heather L. Cook

Journal of Humanistic Mathematics

This poem was written about statistics and the usefulness thereof.

Pekerja Anak Di Kawasan Timur Indonesia Tahun 2022: Kondisi Dan Faktor Yang Memengaruhi, Muhammad Rafii Al Muflih, Yuliagnis Transver Wijaya

Jurnal Ekonomi Kependudukan dan Keluarga

In 2019-2022, the percentage of child labor in the eastern region of Indonesia is always higher than in the western region of Indonesia. However, there is still very little study discussing child labor in the eastern region of Indonesia. Therefore, this study aims to analyze the conditions and factors that influence child labor in eastern region of Indonesia. The data used in this study is the August 2022 Indonesia National Labor Force Survey (Sakernas) with descriptive analysis and binary logistic regression as the analysis method. The results showed that 5-6 children out of 100 children in eastern region of Indonesia …

Monetary Policy Impact On Stock Returns For Selected South Asian Countries, Neluka Devpura, Paresh Kumar Narayan, Navin Perera

Bulletin of Monetary Economics and Banking

In this paper, we examine the monetary policy impact on the stock market returns and volatility for four major South Asian countries (Bangladesh, India, Pakistan, and Sri Lanka). We test our hypothesis that monetary policy influences both the first and second order of stock returns by using monthly data. The short-term interest rate and the Treasury bill rate are employed as proxies for monetary policy. Controlling for industrial production, inflation, exchange rates (vis-à-vis the US dollar), US interest rate, and money supply, our findings indicate that there exists a statistically significant impact of short-term interest rates on stock returns only …

Gender-Specific Mental Health Outcomes In Central America: A Natural Experiment, Thea Nagasuru

Computer Science Summer Fellows

While COVID lockdown measures have had varying effects on the mental health of different demographics, several bodies of research have noted their disparate effect on women. Why is women's mental health more negatively impacted by lockdown measures, and how much more are they impacted than men? How can we predict and mitigate these negative effects on women? This paper aims to contribute to answering those questions by comparing COVID stringency measures and their effect on the gap in depression rates between men and women in two neighboring countries: Nicaragua and Honduras.

Leveraging Intersubject Representational Similarity Analysis To Explore Individual Differences In Early Life Adversity And Cortico-Amygdala Connectivity In A Preadolescent Sample, Amira Hmidan

Electronic Thesis and Dissertation Repository

Preadolescence is a critical developmental phase characterized by changes in the functional connectivity (FC) between the cortex and amygdala, which are essential for emotional processing and regulation. Early life adversity (ELA), such as exposure to childhood maltreatment, familial dysfunction, and poverty, is associated with negative physical and mental health outcomes (Felitti et al., 1998). Emerging research indicates that disturbances in cortico-amygdala FC could act as a mechanism linking ELA to various mental health issues; however, most focus on adult populations and overlook individual differences. Here, intersubject representational similarity analysis (IS-RSA) was leveraged to explore how individual variations in ELA relate …

Mixstm: Adapting The Structural Topic Model For A Quantitative Analysis Of Focus Group Data, Pascale A. Nevins

Electronic Thesis and Dissertation Repository

The Structural Topic Model (STM) incorporates external information about expected document-topic proportions to enhance the model. Motivated by focus groups, whose transcripts represent text data inherently grouped by session, we propose three extensions to the STM: 1) mean document-topic proportion estimation using a regression with random effects; 2) partitioned estimation of group-specific topic covariance matrices; and 3) a post hoc mixed effects regression on topic prevalence which incorporates latent variable uncertainty into the coefficient estimates. We explore the utility of these modifications through simulated examples and apply them to focus group transcripts from a pan-Canadian study on homelessness. The new …

On Large Language Models In National Security Applications, William N. Caballero, Philip R. Jenkins

Faculty Publications

The overwhelming success of GPT-4 in early 2023 highlighted the transformative potential of large language models (LLMs) across various sectors, including national security. This article explores the implications of LLM integration within national security contexts, analyzing their potential to revolutionize information processing, decision-making, and operational efficiency. Whereas LLMs offer substantial benefits, such as automating tasks and enhancing data analysis, they also pose significant risks, including hallucinations, data privacy concerns, and vulnerability to adversarial attacks. Through their coupling with decision-theoretic principles and Bayesian reasoning, LLMs can significantly improve decision-making processes within national security organizations. Namely, LLMs can facilitate the transition from …

Multi-Case Study Of Left-Flank Boundaries Within Supercells, Peyton B. Stevenson

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

This study investigates the prevalence and significance of forward-flank convergence boundaries (FFCBs) and left-flank convergence boundaries (LFCBs) in shaping the structure and intensity of supercells, using observational data from various field projects. Unlike previous research focusing on individual cases, this study examines a diverse range of cases to provide comprehensive insights into the relationship between these boundaries and supercell characteristics such as intensity, longevity, and tornadogenesis. By analyzing high-resolution surface data, the research addresses the frequency, location, and intensity of these boundaries, and their impact on pseudo vertical vorticity, pseudo convergence, and density gradients. A total of 228 boundary identifications …

Phylogeny And Disparity Of Ammonoid Family Acanthoceratidae Over Ocean Anoxic Event 2, Lindsey Howard

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

The widespread use of genera as proxies for species in paleobiological studies might affect the results of these studies. Although most attention has been given to taxonomic diversity studies, this could also be true of disparity and phylogenetic studies. In particular, the assumption that particular character states truly diagnose all members of a genus might distort results. This study examines the disparity of Acanthoceratid ammonoids at both the generic and species level. 149 species from 42 genera were examined with 52 characters measured. Following the measurements, an inverse modeling simulation was run 100 times to generate a simulated phylogeny with …

Design And Evaluation Of An Esa-Based Method Of Ensemble Subsetting For A Wofs (Warn On Forecast-Like System), Daniel J. Butler

Department of Earth and Atmospheric Sciences: Dissertations, Theses, and Student Research

Forecasting severe thunderstorm environments in the southeastern United States can be challenging due to mesoscale heterogeneities such as shortwave troughs, pre-existing airmass boundaries, cold fronts aloft, low-level jets, dry air intrusions, and mesoscale lows. To combat these challenges, ensemble sensitivity analysis (ESA) may be applied to a Warn-on-Forecast (WOF)-like ensemble to improve forecasts of severe convection through ensemble weighting and subsetting. Ensemble-based weighting and subsetting uses ensemble members that most accurately represent the thunderstorm environment in areas of mesoscale heterogeneity. This study creates and evaluates the ensemble-based weighting and subsetting in four cases of severe thunderstorm occurrence. The open parameter …

Quasi – Monte Carlo Estimation For Functional Generalized Linear Mixed Models, Ruvini Jayamaha

Waldo Library Student Exhibits

Functional Data Analysis (FDA) is a topic of growing interest in the Statistics community. The data in FDA are smooth curves or surfaces in time or space which can be conceptualized as functions.

We propose a Functional Generalized Linear Mixed Model (FGLMM) to fit EEG data and estimate the parameters using Quasi-Monte Carlo Method.

This proposed model deals with non-Gaussian scalar response, functional predictor, and random effects. We relax the assumption of link and variance functions.

Ocular Gene Transfer In The Spotlight: Implications Of Newspaper Content For Clinical Communications, Shelly Benjaminy, Tania M. Bubela

Office of the Provost

Background: Ocular gene transfer clinical trials are raising hopes for blindness treatments and attracting media attention. News media provide an accessible health information source for patients and the public, but are often criticized for overemphasizing benefits and underplaying risks of novel biomedical interventions. Overly optimistic portrayals of unproven interventions may influence public and patient expectations; the latter may cause patients to downplay risks and over-emphasize benefits, with implications for informed consent for clinical trials. We analyze the news media communications landscape about ocular gene transfer and make recommendations for improving communications between clinicians and potential trial participants in light of …

Evaluating Past Progress And Assessing Prediction Breeding Strategies For Sustained Genetic Gains In The Louisiana Sugarcane Variety Development Program, Brayden A. Blanchard

LSU Doctoral Dissertations

The aim of this dissertation is to outline important considerations for the Louisiana Sugarcane Variety Development Program (LSVDP) as it pertains to historical progress, impact, goal setting, and new strategies for continued genetic gains. Industry progress was evaluated with robust regression models to quantify rates of productivity gains. Over the last 50 years, statistically significant productivity gains were identified in sucrose content (45%), cane yield (32.2%), and sugar yield (93%) while pairwise comparisons of decades showed that progress was incremental rather than rapid and sustained once achieved. The decade from 1990-1999 was identified as the only decade with a significant …

Comparison Of Value At Risk Using Historical And Monte Carlo Methods On Pt Xyz Stock Portofolio, Eka Fitriani, Yulial Hikmah, Ira Rosianal Hikmah

Jurnal Administrasi Bisnis Terapan

One way to achieve profits in a company is through investment activities. However, everything has risks. Investing can also be risky. Therefore, the relationship between risk and investment is important because it will influence the determination of investment selection. The problem faced by investors is choosing an efficient portfolio, or a portfolio that provides the smallest risk. This risk can be done by measuring risk, one of which is using the Value at Risk (VaR) measure. Measurement using Value at Risk has several methods that are quite popular, namely the Historical Method, Variance-Covariance, and Monte Carlo. In this research, the …