Statistics and Probability Commons^™

Quaternary Subsurface Characterization Of The Mississippi River Valley Alluvial Aquifer: Insights From Interval Kriging And Airborne Em-Borehole Data Integration, Yuqi Song

LSU Doctoral Dissertations

The Pleistocene period significantly contributed to the formation of alluvial aquifer worldwide. These productive aquifers are crucial for domestic, industrial, and agricultural water supplies. The Mississippi River Valley alluvial aquifer (MRVA), a principal aquifer in the U.S., is crucial for national food security and global agricultural supply. This study aims to characterize the subsurface architecture of the MRVA, thereby enhancing fundamental understanding of sedimentological processes involved in the genesis of glacio-fluvial aquifers worldwide. The research objectives are threefold: (1) to provide a detailed characterization of the MRVA; (2) to develop 3D geostatistical methods for geological modeling; and (3) to develop …

Differences In Covid-19 Deaths Amongst Cancer Patients And Possible Mediators For This Relationship, Leah Vaidya, Nubaira Rizvi, Xiao Cheng Wu, Lauren S. Maniscalco, Yong Yi, Augusto Ochoa, Qingzhao Yu

School of Public Health Faculty Publications

Previous research demonstrated Non-Hispanic Black populations experience higher COVID-19 mortality rates than Non-Hispanic White individuals. Additionally, cancer status is a known risk factor for COVID-19 death. While prior studies investigated comorbidities as exploratory variables in differences in COVID-19 hospitalization, none have explored their role in COVID-19-related deaths. This study aimed to evaluate whether Charlson Comorbidity Index (CCI) and subsequently, individual diseases are potential explanatory variables for this relationship. The analysis focused on Non-Hispanic Black and Non-Hispanic White cancer patients aged 20 or older, diagnosed between 2011 and 2019, who tested positive for COVID-19 from the start of pandemic through June …

Unlocking Precision Using K-Means++- Improved Genetic Algorithm-Radial Basis Function Neural Network: Data-Driven Evolution Of Smart Gloves For Gesture Recognition, Liang Xiao Ding, Kuan Way Chee, Hong Lü, Anand Paul, Jeonghong Kim, Jang Myung Lee

School of Public Health Faculty Publications

Human-computer interaction technologies have been used since the 1970s but have only gained growing popularity in recent years with new design paradigms. Ongoing research and development in gesture recognition systems with broad application prospects have focused on improving accuracy and real-time performance as well as the robustness of specific machine learning algorithms against environmental conditions. This paper addresses the accuracy enhancement of a novel Fifth Dimension Technologies data-glove-based gesture recognition system using a genetic-algorithm (GA)-trained k-means++-improved radial basis function (RBF) or GK-RBF neural network. First, we analyzed and modeled the sensor distribution in the data glove and proposed joint constraints …

Value-Based Healthcare Reimagined: A Mixed-Methods Study On Behavioral Health Clinicians' Perspectives, Amanda L. Strickland

Electronic Theses and Dissertations

This study explores how behavioral health clinicians perceive Value-Based Healthcare (VBHC), a model designed by Porter and Teisberg (2006) to improve outcomes relative to costs. While widely promoted in healthcare reform, VBHC poses unique challenges when applied to behavioral health settings. Using an explanatory mixed-methods design, this study first assessed clinicians’ awareness of VBHC through a survey of 23 licensed clinicians at a Community Mental Health Center (CMHC) in Colorado. Quantitative findings revealed that one-third of participants were aware of VBHC with awareness differing by role prompting further exploration in a qualitative phase. Semi-structured interviews with eight clinicians provided deeper …

On Large Language Models In National Security Applications, William N. Caballero, Phillip 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 …

Glass Delta, Manish Rami

Software

A Python script to calculate the effect size Glass' delta in a two group experiment with different standard deviation.

Racial And Ethnic Inequalities In Actual Vs Nearest Delivery Hospitals, Nansi S. Boghossian, Lucy T. Greenberg, Jeffrey S. Buzas, Joshua Radack, Molly Passarella, Jeannette Rogowski, George R. Saade, Ciaran S. Phibbs, Scott A. Lorch

Faculty Publications

IMPORTANCE Minoritized racial and ethnic groups, such as American Indian and Black individuals, often receive lower quality health care compared with White individuals. There is limited understanding of how these disparities extend to obstetric care, particularly when comparing the quality of care at the actual delivery hospital vs the nearest obstetric hospital based on the birthing individual’s residence. OBJECTIVE To examine inequality in care based on the actual delivery hospital and the closest delivery hospital to the birthing individual’s residential zip code centroid. DESIGN, SETTING, AND PARTICIPANTS This population-based retrospective cohort study used data from 5 states (2008 to 2020 …

Linking Water Quality And Climate Change To Long-Term Trends In Species Abundance In Norwalk Harbor, Viktoria Savatorova, Aidan Kieft, Nicole C. Spiller, Kasey Burns

Spora: A Journal of Biomathematics

This study examines the effects of environmental changes on fish populations in Norwalk Harbor, focusing on winter flounder (Pseudopleuronectes americanus), cunner (Tautogolabrus adspersus), northern pipefish (Syngnathus fuscus), and naked goby (Gobiosoma bosci) as examples of species responding to climate-related shifts. We analyze how water temperature, salinity, and dissolved oxygen correlate with fish abundance. To assess statistically significant differences in catch per unit effort (CPUE) across harbor regions, we applied the Kruskal-Wallis test followed by Dunn's post-hoc test. Seasonal variations in CPUE were examined by comparing monthly catch data for each species. K-means …

A Deep Sparse Capsule Network For Non-Invasive Blood Glucose Level Estimation Using A Ppg Sensor, Narmatha Chellamani, Saleh Ali Albelwi, Manimurugan Shanmuganathan, Palanisamy Amirthalingam, Emad Muteb Alharbi, Hibah Qasem Salman Alatawi, Kousalya Prabahar, Jawhara Bader Aljabri, Anand Paul

School of Public Health Faculty Publications

Diabetes, a chronic medical condition, affects millions of people worldwide and requires consistent monitoring of blood glucose levels (BGLs). Traditional invasive methods for BGL monitoring can be challenging and painful for patients. This study introduces a non-invasive, deep learning (DL)-based approach to estimate BGL using photoplethysmography (PPG) signals. Specifically, a Deep Sparse Capsule Network (DSCNet) model is proposed to provide accurate and robust BGL monitoring. The proposed model’s workflow includes data collection, preprocessing, feature extraction, and predictions. A hardware module was designed using a PPG sensor and Raspberry Pi to collect patient data. In preprocessing, a Savitzky–Golay filter and moving …

Bayesian Spatio-Temporal Modeling Of African Co2 Emissions (1990–2020): A Hierarchical Approach To Identify Determinants, Regional Trends, And Local Dynamics, Ebenezer Afrifa-Yamoah, Prince Mensah Osei

Research outputs 2022 to 2026

Africa's unique position in global CO2 emissions demands rigorous analysis for effective climate policy development. Despite contributing only 4% to global emissions, the continent faces disproportionate climate impacts while undergoing rapid development and complex economic transitions. Current research lacks extensive continental analysis of the spatial dependencies, temporal evolution of emission patterns, and their key drivers, which is fundamental for evidence-based climate policy and sustainable development strategies. We applied Bayesian hierarchical spatio-temporal modeling to analyze CO2 emissions across African countries (1990–2020), integrating rotated empirical orthogonal function (REOF) analysis with spatial autocorrelation techniques (Local Moran's I and LISA) to capture complex emission …

Bayesian Nonparametric Hypothesis Testing Methods On Multiple Comparisons, Qiuchen Hai, Zhuanzhuan Ma

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we introduce Bayesian testing procedures based on the Bayes factor to compare the means across multiple populations in classical nonparametric contexts. The proposed Bayesian methods are designed to maximize the probability of rejecting the null hypothesis when the Bayes factor exceeds a specified evidence threshold. It is shown that these procedures have straightforward closed-form expressions based on classical nonparametric test statistics and their corresponding critical values, allowing for easy computation. We also demonstrate that they effectively control Type I error and enable researchers to make consistent decisions aligned with both frequentist and Bayesian approaches, provided that the …

Transcriptional Profiles Reveal Physiological Mechanisms For Compensation During A Simulated Marine Heatwave In Yellowtail Kingfish (Seriola Lalandi), Sharon E. Hook, Ryan J. Farr, Jenny Su, Alistair J. Hobday, Catherine Wingate, Lindsey Woolley, Luke Pilmer

Fisheries Research Articles

Background

Changing ocean temperatures are already causing declines in populations of marine organisms. Predicting the capacity of organisms to adjust to the pressures posed by climate change is a topic of much current research effort, particularly for species we farm or harvest. To explore one measure of phenotypic plasticity, the physiological compensations in response to heat stress as might be experienced in a marine heatwave, we exposed Yellowtail Kingfish (Seriola lalandi) to sublethal heat stress, and used the transcriptome in gill and muscle, benchmarked against heat shock proteins and oxidative stress indicators, to characterise the acute heat stress …

Discounting Effect Size When Borrowing External Data In Clinical Studies, Zhuanzhuan Ma, Chul Ahn, Bin Wang, Xuefeng Li

Research Symposium

Background: When borrowing information from external data to augment a current trial, many available methods discount the sample size but retain the effect size from previous studies. Discounting the sample size is just one way to discount the prior information. It may not be appropriate if the underlying assumption of unbiased treatment effect does not hold, for example, when the treatment effect in the historical study is likely higher than the one expected in the current trial.

Methods: To tackle this potential issue, we study some methods to shrink the effect size from previous studies assuming that the prior effect …

Sparse Bayesian Variable Selection Using Global-Local Shrinkage Priors For The Analysis Of Cancer Datasets, Zhuanzhuan Ma

Research Symposium

Background: With a rapid development of data collection technology, high dimensional data, whose model dimension k may be growing or much larger than the sample size n, is becoming increasingly prevalent in different fields of study, such as ecology, genetics, among others. This data deluge is introducing new challenges to traditional statistical procedures and theories and is thus generating a renewed interest in the problems of variable selection and classification in high dimensional regression models. In large k, small n settings, variable selection is usually the first step for dimension reduction to uncover significant covariates, which contribute to …

Pre-Exposure Prophylaxis Uptake Among Black/African American Men Who Have Sex With Other Men In Midwestern, United States: A Systematic Review, Oluwafemi Adeagbo, Oluwaseun Abdulganiyu Badru, Prince Addo, Amber Hawkins, Monique J. Brown Ph.D., Mph, Xiaoming Li, Rima Afifi

Faculty Publications

Introduction: Black/African American men who have sex with other men (BMSM) are disproportionately affected by HIV, experience significant disparities in HIV incidence, and face significant barriers to accessing HIV treatment and care services, including pre-exposure prophylaxis (PrEP). Despite evidence of individual and structural barriers to PrEP use in the Midwest, no review has synthesized this finding to have a holistic view of PrEP uptake and barriers. This review examines patterns of, barriers to, and facilitators of PrEP uptake among BMSM in the Midwest, United States (US).

Methods: Five databases (CINAHL Plus, PUBMED, PsycINFO, SCOPUS, and Web of Science) were searched …

Emerging Technologies For Forensic Genetic Identification, Lilly Llanos

Senior Honors Theses

There are many new innovations in forensic science that are being developed for the identification of biological evidence. These techniques include next-generation DNA sequencing, DNA phenotyping, and forensic genetic genealogy. This thesis will explore each, as well as newer applications of proteomics. The methodologies, reliability, practicality of cost and training, moral implications, and past research of each will be discussed. Finally, some ideas for future research and steps to drive growth and greater understanding will be suggested. This will encourage further innovations and the increased acceptance of forensic evidence in court. Each method was found to have both advantages and …

Urban Heat Dynamics In Pune: The Influence Of Land Cover And Local Climate, Arpit Tiwari, Preethi Nanjundan, Ravi Ranjan Kumar, Ananya Karmakar, Satyaban Bishoyi Ratna

Northeast Journal of Complex Systems (NEJCS)

Urban areas with high population density and extensive infrastructure development have been experiencing an increasing strain on the local heat budget, leading to a surge in heat-related illnesses and discomfort. This study examined the impact of climate and land use as heat islands in Pune, India, from 2012 to 2023 at six different locations representing varying degree of urbanization. Satellite land cover observations revealed that 55.17% of the total area was urbanized in the city itself, which was limited to 44.8% in 2012. This urbanization has significantly impacted the increasing tendency of maximum temperature (T_max; 0.13℃ to 1.63℃ …

Analysis Of Systematic Trade-Offs Between Military And Healthcare Expenditure Alongside Gdp Growth Of Select Asian And Western Exporting Economies In The 21st Century, Rahul Balamurugan, Carlos Gershenson, Preethi Nanjundan, Hiroki Sayama

Northeast Journal of Complex Systems (NEJCS)

This study explores the complexity in the trade-offs between military expenditure, healthcare expenditure, and GDP growth across select Asian nations and major weapon-exporting countries, examining how nations allocate finite resources between national security and human well-being over the past two decades. Using a systems science approach, the research integrates Granger causality testing to analyze temporal and directional relationships among GDP growth, military expenditure, and healthcare expenditure, uncovering their dynamic interdependencies. The methodology includes trend and slope analysis, Granger causality testing, outlier detection, and clustering to identify heterogeneity in resource allocation strategies. Developed, weapon-exporting nations exhibit complementary trends, with strong causality …

Smoothed Particle Hydrodynamics For Free-Surface Flows And Time Series Forecasting Approach For Computational Fluid Dynamics, Huali Ye

Doctoral Dissertations

With the increase in computing power, numerical simulation has become an essential approach to solving problems in engineering and science. Numerical simulations provide a platform for theoretical validation and facilitate novel discovery. Even though extensive mesh-based numerical methods are utilized, significant limitations exist, particularly in Computational Fluid Dynamics (CFD). Because of the grid distortion, issues related to large deformations, moving interfaces, and free surfaces may lead to considerable computational errors, constraining their efficacy in numerous applications. As a mesh-free method, Smoothed Particle Hydrodynamics (SPH) was introduced in 1977 and has been widely applied in many fields such as astrophysics and …

Maine Career Exploration: Final Evaluation Report, Julia Bergeron-Smith Mppm, Msw, Sarah Goan, Madison Burke, Becky Wurwarg, Amy Geren, Shannon Saxby

Publications

The Maine Career Exploration (MCE) Program was a two-year, $25 million pilot initiative launched in 2022 by Governor Janet Mills as part of the Maine Jobs and Recovery Plan. The program aimed to connect young people aged 16 to 24 with Maine’s economy through paid, age-appropriate career experiences that matched their interests, while also expanding career exploration opportunities in schools and communities and building long-term infrastructure to sustain these efforts. Managed by the Maine Department of Economic and Community Development, MCE distributed funding across three main streams: the Maine Children’s Cabinet Career Exploration Pilot Project, the Maine Department of Education’s …

Analysis Of Nuclear Security And Safety Integration Using Survey Responses And Pairwise Comparison Methods, Sheila V. Gbormittah, Theodore A. Thomas, Jason Timothy Harris

International Journal of Nuclear Security

Integrating nuclear security and safety is important for implementing and sustaining nuclear technology because it promises improved and effective management. This integration is an ongoing effort to ensure that both work together with minimal conflicts. This research aimed to determine the preferred level at which nuclear security and nuclear safety integrate by using the pairwise comparison methods of decision-making. This methodology used survey responses from women nuclear professionals to identify the most-desired criteria at three levels: strategic, operational, and cultural. The strategic level includes actions that government officials and regulators can take. The operational level encompasses actions that deliver the …

A New Measure Of Non-Parametric Correlation For Variables In The Likert Scale, Shubhabrata Das

Working Papers

We propose a new measure of nonparametric correlation that is especially suited for measuring association between variables measured in the Likert scale where data is ordinal and tied observations are extremely common. The proposed general structure of the measure is based on graded level of concordance and discordance between the pairs of metrics. The general form of the measure has all the desirable properties except the measure is not necessarily zero for independent variables. This limitation is acceptable given only ordinal nature of the metrics. Three versions of the measure are studied. The first is based on simple equi-distant weights. …

Modifiable And Non-Modifiable Risk Factors For Dementia Among Non-Hispanic White And Black Populations Aged 50-64 In The United States, 2006-2016, Jingkai Wei, Matthew C. Lohman Ph.D., Monique J. Brown, James W. Hardin, Chih-Hsiang Yang, Anwar T. Nerchant, Daniela B. Friedman

Faculty Publications

Background and Objectives

Research Design and Methods

Adverse Childhood Experiences, Resilience, And Syringe Services Program Attendance Among Persons Who Inject Drugs In Northeast Georgia, Usa: A Mediation Analysis, Mohammad Rifat Haider, Samantha Clinton, Monique J. Brown, Nathan B. Hansen

Faculty Publications

Background: Syringe services programs (SSP) are evidence-based venues offering harm reduction services to persons who inject drugs (PWID), such as sterile syringes, STI/HIV testing, and linkage to care to decrease drug use-related morbidities and mortalities. Adverse childhood experiences (ACEs) have been linked with reduced resilience, while increased resilience can help PWID attend SSPs. This study examined the potential mediating role of resilience between ACEs and SSP attendance among PWID.

Methods: Data were collected from adult HIV-negative PWID in northeast Georgia, between February-December 2023 (N = 173). Data were collected on SSP attendance (Yes vs. No), resilience, and ACEs. Covariates included …

Geographic Disparities In Unpaid Caregiving, Emma Kathryn Boswell, Monique J. Brown Ph.D., Mph, Lorie Donelle Phd, Rn, Fcan, Nicholas Yell, Taryn Farrell, Peiyin Hung Ph.D., Elizabeth Crouch Ph.D.

Faculty Publications

Purpose: An updated, nationally representative examination of rural–urban differences in the experiences, health, and well-being of caregivers is needed; previous research on this topic uses older data or has limited generalizability. This study examines rural–urban differences in the characteristics, experiences, and health of caregivers. Methods: The 2021–2022 Behavioral Risk Factor Surveillance System (n = 44,274 unpaid caregivers) was used, with rurality defined according to the 2013 National Center for Health Statistics (NCHS) Urban-Rural Classification Scheme. Chi-square tests compared rural–urban differences in these caregivers’ characteristics, including demographic factors, caregiving intensity (e.g., weekly hours spent caregiving, reason for caregiving, past-month ADL/IADL assistance), …

Explainable Neural Networks With Guarantee: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

Effect Of Pre-Adsorbed Species On High-Pressure Adsorption Of Methane In Zeolite 5a Using Grand Canonical Monte Carlo (Gcmc) Simulations, Kanhamardi Lao, Brooks D. Rabideau

Shelby Hall Graduate Research Forum Posters

Natural gas upgrading, which removes impurities from methane (CH4), is essential for industrial applications, including liquefied natural gas (LNG) production and power generation, as well as for residential use. Removing non-hydrocarbon impurities such as carbon dioxide (CO2), nitrogen (N2), and water vapor (H2O), among others, along with separating heavier hydrocarbon gases from raw natural gas, is required to achieve high- purity methane and prevent pipeline corrosion. Zeolite 5A is a microporous aluminosilicate material with a pore size of approximately 5 Å, containing sodium and calcium cations that balance the framework’s negative charge. Its structure offers high thermal stability and a …

Exploring The Potential Of Strongly Coupled Lagrangian Data Assimilation In An Ocean–Atmosphere System, Luyu Sun, Amit Apte, Laura Slivinski, Elaine T. Spiller

Mathematical and Statistical Science Faculty Research and Publications

Precise measurements of ocean surface flow velocities are essential for refining forecasts in a coupled ocean–atmosphere system. While oceanic data are generally sparse, surface drifters present an opportunity by providing detailed and frequently observed sea surface currents, which are a critical component in the dynamics at air–sea interface. Such observations could potentially address the usual data gaps in a coupled ocean–atmosphere assimilation system. In this study, we investigate the implications of assimilating drifter data within a coupled system with intermediate complexity based on a quasigeostrophic model—Modular Arbitrary-Order Ocean–Atmosphere Model (MAOOAM)—using observing system simulation experiments (OSSEs). Two main strategies for assimilating …

Optimal Time Series Forecasting Through The Garma Model, Adel Hassan A. Gadhi, Shelton Peiris, David E. Allen, Richard Hunt

Research outputs 2022 to 2026

This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting long-memory time series. This characteristic is confirmed using popular datasets such as sunspot data and Australian beer production data. This approach provides a robust framework for accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other implemented models, such as GARMA, TBATS, ARIMA, and ANN, highlighting its ability to address both long-memory and …

Generation Of Patient Specific Cardiac Chamber Models Using Generative Neural Networks Under A Bayesian Framework For Electroanatomical Mapping, Sunil Mathew, Jasbir Sra, Daniel B. Rowe

Mathematical and Statistical Science Faculty Research and Publications

Electroanatomical mapping is a technique used in cardiology to create a detailed 3D map of the electrical activity in the heart. It is useful for diagnosis, treatment planning and real time guidance in cardiac ablation procedures to treat arrhythmias like atrial fibrillation. A probabilistic machine learning model trained on a library of CT/MRI scans of the heart can be used during electroanatomical mapping to generate a patient-specific 3D model of the chamber being mapped. The use of probabilistic machine learning models under a Bayesian framework provides a way to quantify uncertainty in results and provide a natural framework of interpretability …