Digital Commons Network^™

Network Construction Using Sparse Gaussian Graphical Model Based On Gwas Summary Statistics, Megh Subedi, Xuewei Cao, Byung-Jun Kim, Qiuying Sha

Michigan Tech Publications

In genome-wide association studies (GWAS), thousands of genetic variants are tested to identify their associations with a phenotype. GWAS have identified many strongly associated genetic variants with phenotypes and have greatly enhanced our understanding of the genetic architecture of complex phenotypes and diseases. Joint analysis of multiple phenotypes can increase the overall statistical power to detect genetic associations and allows for the identification of pleiotropic loci. A phenotype-phenotype network (PPN) represents phenotypes as nodes and the relationships between them as edges, which allows for the visualization of complex relationships between phenotypes, making it easier to identify clusters and helps us …

Perspectives On Firearm Storage Conversations: Summary Of Health Care Provider Interviews And Parent Focus Group, Rachel M. Gallo Mph

Publications

This report summarizes perspectives from Maine parents who own firearms and health care providers on safe firearm storage and related conversations in health care settings. Through interviews and a parent focus group, the project explored how firearms are stored, what influences storage decisions, and how families and providers feel about discussing firearm safety. Parents emphasized the importance of responsible ownership, education, and individualized storage solutions, while identifying cost as a key barrier to safer storage options. Many health care providers expressed interest in addressing firearm safety but cited limited time, lack of training, and concerns about patient trust as challenges. …

‘Earth System Engineers’ And The Cumulative Impact Of Organisms In Deep Time, S. A. F. Darroch, M. M. Casey, A. T. Cribb, A. E. Bates, M. E. Clapham, D. L. Contreras, M. Craffey, I. A. P. Duijnstee, W. Gearty, N. J. Gotelli, M. J. Hamilton, R. F. Hayes, P. M. Hull, D. E. Ibarra, V. A. Korasidis, J. Lee, C. V. Looy, T. R. Lyson, B. Muddiman, P. D. Roopnarine, A. B. Shupinski, F. A. Smith, A. L. Stigall, C. P. Tomé, K. A. Turk, A. Villaseñor, P. J. Wagner, Steve C. Wang

Mathematics & Statistics Faculty Works

Understanding the role of humans as ‘ecosystem engineers’ requires a deep-time perspective rooted in evolutionary history and the fossil record. However, no conceptual framework exists for studying the rise of ecosystem engineering in deep time, requiring us to consider effects that fall outside the scope of traditional definitions. Here, we present a new framework applicable to both modern and ancient engineering-type effects. We propose a new term – ‘Earth system engineering’ – to describe biological processes that alter the structure and function of planetary spheres, and which combines core tenets of ecosystem engineering, niche construction, and legacy effects. We illustrate …

How Fair Workweek Laws Affect Labor Markets: A New York City Case Study, Joseph Pickens, Aaron Sojourner

Employment Research Newsletter

No abstract provided.

Adverse Childhood Experiences, Positive Childhood Experiences, And Digital Media Use Among Children And Adolescents In The United States, Elizabeth L. Crouch, Emma Boswell, Monique J. Brown Ph.D., Kevin J. Bennett

Publications

No abstract provided.

How Fair Workweek Laws Affect Labor Markets: A New York City Case Study, Joseph Pickens, Aaron Sojourner

Upjohn Institute Policy and Research Briefs

No abstract provided.

Teaching Statistical Literacy Through An Excel Class Project, Patricia Berchiolli, Omar Babun Codorniu

Faculty and Staff Publications & Presentations

Lynn University’s core curriculum, The Dialogues, enables students to develop critical thinking, communication, and innovation skills. As part of this curriculum, Introductory Statistics introduces students to key statistical concepts while showing how they can be applied in real-world situations using Excel. The highlight of the course is the Statistics Excel Project, where students create their own dataset with a mix of quantitative and qualitative variables. To keep the focus on learning statistical techniques rather than data collection, students use Excel’s random number generator for quantitative data, which also avoids the need for IRB approval. From there, they calculate statistical measures, …

The Effect Of Renewable Energy Consumption, Economic Growth, And Energy Use On Co₂ Emissions In Indonesia: An Ardl Analysis, Raihan Ahmad Mustofa, Mariam Kamila, Rininta Nurrachmi, Syifa Shafnastiara

Jurnal Kebijakan Ekonomi

Abstract This study examines the dynamic relationship between renewable energy consumption, total energy use, economic growth, and CO₂ emissions in Indonesia over the period 1990–2021 using an ARDL approach. The results show a stable long-run relationship with quick adjustment after shocks. In the long run, energy consumption is associated with higher CO₂ emissions, reflecting the continued dominance of fossil fuels. Renewable energy helps reduce emissions in the short term, but its long-term impact remains statistically insignificant. GDP does not show a significant effect, suggesting that meaningful decarbonization will require a more intensive shift in the energy mix.

Keywords: ARDL, renewable …

A Review Of Research And Practices On Teaching Data Visualizations For Blind And Visually Impaired Students, Shiya Cao

Statistical and Data Sciences: Faculty Publications

Around 36 million people in the world are blind and an additional 217 million have moderate to severe vision impairment. In higher education, four percent of 54,204 undergraduates who participated in the 2022 American College Health Association survey reported to be blind or have low vision. Those students frequently do not have access to data visualizations we generally teach and use in postsecondary statistics and data science classes. The design of those visualizations is premised on implicit assumptions about the user’s visual ability. Making data visualizations accessible to blind and visually impaired (BVI) people would help improve equity in higher …

Engr 597: Special Projects In Engineering Science - Data Analytics, Ali Behnood Ph.D., P.E., M., Asce

GMAS Course Syllabi

No abstract provided.

Smarter Disease Detection From Electronic Health Record Data: An End-To-End Ai-Augmented Pipeline For Computable Phenotyping, Dylan Owens

Statistical Science Theses and Dissertations

Electronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.

We …

Spatiotemporal Modeling Of Maternal Mortality In South Carolina 2018-2023, Leah Wood, Ray Bai, Emily Mann

Senior Theses

Maternal death serves as a public health indicator due to fact that it is considered preventable with the availability of modern biomedicine, however, it persists broadly throughout the United States. Current literature outlines national trends in maternal mortality with complicating, preexisting conditions, and structural upstream factors often cited as being the largest contributors to increased risk. This study utilizes publicly available, county-level data for maternal death in addition to demographic and descriptive data in order to estimate maternal mortality rates in each of South Carolina’s 46 counties from 2018 to 2023. In order to address sparsity in the outcome variable …

(Si15-140) Designing Bayesian Double Sampling Plans Based On Zero Inflated Poisson Distribution, Priyadharshini R., Shalini K., Hemalatha R., Sangeetha S.

Applications and Applied Mathematics: An International Journal (AAM)

The implementation of attribute-based sampling inspection serves as a quality control technique used across numerous industries to evaluate items or workflow processes. When the data exhibits a substantial number of zero counts, the zero-inflated Poisson (ZIP) distribution serves as an effective model for accommodating this zero-inflation. Double sampling plan (DSP) is a quality check method where the decision to approve or decline a batch comes after examining two samples, providing more conclusive information compared to a single sample plan (SSP). In practice, effective decision-making regarding submitted lots considers both within-lot and between-lot variations, which can be addressed through the use …

(Si15-142) Selection Of Single Sampling Plans Based On Zero Inflated Binomial Distribution Using Cost Optimization, Sangeetha S., Shalini K., Hemalatha R., Priyadharshini R.

Applications and Applied Mathematics: An International Journal (AAM)

Economic design of sampling plans involves creating sampling plans that minimize the total cost associated with the inspection process while ensuring quality. It aims to address the quality risk concerns of both producer and consumer, ensuring product quality while minimizing inspection costs. This article’s objective is to design single sampling plans by attributes based on Zero-inflated Binomial (ZIB) distribution, using cost optimization principles by developing an economic model aimed at achieving optimal total cost by considering the Average Total Inspection (ATI). Numerical illustration is provided to illustrate the selection of single sampling plans under ZIB distribution that minimizes producer’s total …

The Gender Pay Gap: An Occupational Analysis (2012-2023), Curtis Long

Harriet L Wilkes Honors College Theses 2020 - Present

This thesis examines the gender wage gap across thirty major U.S. occupations from 2014 to 2023 using data from the Current Population Survey (CPS), more specifically, the U.S. Bureau of Labor Statistics (BLS). The analysis measures the difference in median weekly earnings between women and men in each occupation and applies two statistical tests: a yearly z-test to evaluate whether the average wage gap is significant, and a ten-year t-test to identify persistent occupational gaps. Results show significant wage differences in every year, with the largest disparities in finance, management, and law, and smaller gaps in education and healthcare. Consistent …

Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics, Marcos Matabuena, Rahul Ghosal Ph.D., Javier Enrique Aguilar, Ayya Keshet, Robert Wagner, Carmen Fernández Merino, Juan Sánchez Castro, Vadim Zipunnikov, Jukka-Pekka Onnela, Francisco Gude

Faculty Publications

Continuous glucose monitoring (CGM) data have revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as hypoglycemia. Recently, there has been growing interest in utilizing CGM devices in clinical studies involving healthy and diabetic populations. However, efficiently exploiting the high temporal resolution of CGM profiles remains a significant challenge. Numerous indices—such as time–in–range metrics and glucose variability measures–have been proposed, but evidence suggests these metrics overlook critical aspects of dynamic glucose homeostasis. As an alternative method, this paper explores the clinical value of glucodensity metrics in capturing glucose dynamics—specifically the speed …

Association Of Estimated Plasma Volume Status With Invasive Hemodynamics And Adverse Clinical Outcomes In Patients With Pulmonary Hypertension And Chronic Kidney Disease, Andrew Geller, Jose Manuel Martinez Manzano, Esteban Kosak Lopez, Phuuwadith Wattanachayakul, John Malin, Raul Leguizamon, Tara John, Rasha Khan, Ian Mclaren, Alexander Prendergast, Simone Jarrett, Kevin Bryan Lo, Christian Witzke

Einstein Health Papers

Identifying noninvasive measures to assess intravascular volume status and risk stratify patients with pulmonary hypertension (PH) and chronic kidney disease (CKD) is needed. We assessed the predictive value of estimated plasma volume status (ePVS) using the Strauss-derived Duarte formula in PH-CKD patients. This single-center retrospective cohort analysis included patients with PH and CKD Stage 3b (CKD3b), Stage 4 (CKD4), or Stage 5 (CKD5) who underwent right heart catheterization from 2018 to 2023. Patients were categorized into low ePVS (< 6.2) and high ePVS (≥ 6.2) using Youden's J statistics. We used the Cox-proportional hazards model, adjusting for age, sex, and body mass index, to investigate the association between …

Reduced Order Approach For Peening Stress Field Variability, Langdon Feltner, Paul Mort

15th International Conference on Shot Peening

A common goal in shot peening research is to connect operational parameters to resultant residual stress fields, providing a means to control and optimize the effectiveness of surface treatment. In practice, experimental measurements of residual stresses are often averaged values over regions that are large in comparison to an impact dimple. In fact, the stochastic nature of impact locations leads to residual stress fields that are distributed. Finite element peening simulations confirm this observation. The goal of this report is to connect operational parameters to localized fluctuations in residual stress through probabilistic reasoning (Figure 1). In particular, the development of …

Heuristic Weight Initialization For Transfer Learning In Classification Problems, Musulmon Lolaev, Anand Paul, Jeonghong Kim

School of Public Health Faculty Publications

Transfer learning is the predominant method for adapting pre-trained models on another task to new domains while preserving their internal architectures and augmenting them with requisite layers in Deep Neural Network models. Training intricate pre-trained models on a sizable dataset requires significant resources to fine-tune hyperparameters carefully. Most existing initialization methods mainly focus on gradient flow-related problems, such as gradient vanishing or exploding, or other existing approaches that require extra models that do not consider our setting, which is more practical. To address these problems, we suggest employing gradient-free heuristic methods to initialize the weights of the final new-added fully …

From P-Values To Practical Relevance: An Introduction To Effect Sizes Through A Legal Psychological Example, Paul Riesthuis, Henry Otgaar, Tery Setiawan, Nathanael Sumampouw, Charlotte Bücken

Psychological Research on Urban Society

Null-hypothesis significance testing and p-values are frequently criticized for their focus on detecting non-zero differences and their inability to provide evidence for the null hypothesis. In this article, we highlight how effect sizes, when meaningfully interpreted, can address these issues. Specifically, we argue that researchers should consider the smallest effect size of interest (SESOI) - the smallest effect size that yields practical or theoretical relevance. We propose several methods for estimating the SESOI and present a consensus study among Indonesian professionals which can be used to estimate the SESOI for child eyewitness testimony research. Results suggest that most Indonesian professionals …

Effect Of Vitamin D Supplementation During Pregnancy And Lactation On The Development Of Infants Born To Tanzanian Women Living With Hiv: A Secondary Analysis Of A Randomised Controlled Trial, Temiwunmi Shobanke, Alfa Muhihi, Nandita Perumal Phd, Nzovu Ulenga, Fadhlun M. Alwy Al-Beity, Christopher P. Duggan, Wafaie W. Fawzi, Karim P. Manji, Christopher R. Sudfeld

Faculty Publications

Background Infants born to pregnant women living with HIV (WLHIV) are at greater risk for morbidity and mortality and may also have poorer developmental outcomes as compared with infants who are not exposed to HIV. Nutrition interventions in pregnancy may affect developmental outcomes.

Objectives This study evaluated the effect of maternal vitamin D supplementation on infant development outcomes.

Design We conducted a secondary analysis of a randomised, triple-blind, placebo-controlled trial of maternal vitamin D supplementation from June 2015 to October 2019.

Setting Antenatal care clinics in Dar es Salaam, Tanzania.

Participants Pregnant WLHIV and their offspring.

Interventions Daily 3000 IU …

Long-Term Behavior Of Subordinated Branching Processes With Prevailing Emigration, George Yanev

School of Mathematical & Statistical Sciences Faculty Publications

This paper deals into the long-term behavior of subordinated critical branching processes with migration. We focus on scenarios where emi­gration is the dominant factor and introduce additional randomness in timing through a subordination mechanism, involving renewal processes. The key findings highlight how the initial population size and the interar­rival mean time influence both asymptotic behavior of the non-extinction probability and corresponding Yaglom type limit theorems. We also study an alternating regenerative process, when the population cycles between zero and positive states. This research complements previous studies for processes when immigration prevails over emigration.

Comparative Evaluation Of Estimation Techniques For Purchasing Power Parity In African Countries Using The Country-Product-Dummy Regression Framework, Rokibat Adeola Tijani, Taiwo Abideen Lasisi, Dahud Kehinde Shangodoyin, Olasunkanmi James Oladapo

Al-Bahir

Purchasing Power Parity (PPP) is a popular macroeconomic analysis metric used to compare economic productivity and standards of living between countries. This study examines the estimation of PPP within the International Comparison Program (ICP) at Basic Heading (BH) level stage and leverages on the data from the 2011 ICP round. Focusing on five BHs out of 12 BHs across 50 Africa countries, to empirically evaluate the validity of the classical Ordinary Least Square (OLS) assumptions in the estimation of Country Product Dummy (CPD) regressions. Given the widespread use of OLS for BH level PPP computation, a rigorous examination of these …

Modeling Musculoskeletal Combat Casualty Care: Nato Trauma System Performance In Large Scale Combat Operations, Mark P. Cote, Kaitlyn E. Holly, Roman J. Schoenfeld, Matthew R. Bryan, Malina O. Hatton, Mitchel B. Harris, Tracey P. Koehlmoos, Andrew J. Schoenfeld

Department of Orthopaedic Surgery Faculty Papers

BACKGROUND: It is unclear whether the current North Atlantic Treaty Organization (NATO) trauma system will be effective in the setting of Large-Scale Combat Operations (LSCO). We sought to model the efficacy of the NATO trauma system in the setting of LSCO. We also intended to model novel scenarios that could better adapt the current system to LSCO.

METHODS: We developed a discrete-event simulation model for patients with combat musculoskeletal injuries treated within the standard NATO system. The primary outcome of the model was survival. The model's health states were characterized as stable, hypovolemia, sepsis, shock, or death. The model simulated …

Bayesian Survival Analysis For High-Dimensional Compositional Data, Zhenying Ding

All ETDs from UAB

Survival analysis integrating microbiome and clinical data offers powerful insights into human health. The microbiome influences immunity, inflammation, and cancer outcomes. Combining microbial profiles with clinical factors like tumor stage, treatment, and demographics enhances understanding of how host-microbe interactions affect survival. To analyze these complex datasets, advanced statistical methods, including Bayesian models, penalized Cox proportional hazards models, and machine learning techniques are employed. These approaches can uncover novel biomarkers and therapeutic targets, leading to personalized treatment strategies that optimize patient survival. However, challenges arise due to the compositionality, high-dimensionality, and phylogenetic relation between taxa in microbiome data. In this dissertation, …

Bayesian Mediation Analyses For Zero-Inflated Data, Jinhong Cui

All ETDs from UAB

Mediation analysis is a fundamental tool in causal inference, enabling researchers to disentangle the pathways through which an exposure affects an outcome via intermediate variables (mediators). It has been widely applied in social sciences, psychology, and healthcare-related studies. Traditionally, mediation analysis has been conducted within the linear structural equation modeling (LSEM) framework. However, recent advances have shifted attention toward the counterfactual framework due to its flexibility in defining causal effects and its capacity to accommodate non-linear relationships, parametric and non-parametric models. Building on this foundation, methodological developments in mediation analysis have expanded to address more complex data structures, such as …

Emerging Clinical Role Of Tavapadon, A Novel Dopamine Partial Agonist, In The Treatment Of Parkinson’S Disease, Alan D. Kaye, Bennett M. Ford, Brennan M. Abbott, Kalob M. Broocks, Sofia Novacic, Sahar Shekoohi

School of Medicine Faculty Publications

Tavapadon, a novel oral dopamine-D1R/D5R partial agonist, has been studied in recent years for the treatment of late-stage development Parkinson’s disease (PD). Levodopa, a dopamine precursor that currently remains the gold-standard first-line therapy for PD motor symptoms, serves as a benchmark against emerging dopaminergic agents. By selectively activating D1-family receptors on direct-pathway medium neurons, Tavapadon differs in that it delivers levodopa-level motor benefit while avoiding its many D2R/D3R-mediated adverse effects. In placebo-controlled trials, Tavapadon produced clear, clinically meaningful gains in motor function and day-to-day activities, as captured by the Unified Parkinson’s Disease Rating Scale (UPDRS). Recent late-stage results have revealed …

Generalized Transversality Conditions For Fuzzy Quantum-Symmetric Variational Problems Via Granular Approach, Martin Bohner, Ewa Girejko, Agnieszka B. Malinowska, Linh Nguyen, Baruch Schneider, Tri Truong

Mathematics and Statistics Faculty Research & Creative Works

This paper investigates fuzzy q-symmetric variational problems with natural boundary conditions. Based on the relative distance measure fuzzy arithmetic and horizontal membership functions (HMFs), we propose novel concepts of differentiability and integrability for fuzzy functions on quantum geometric subsets of real numbers. Then, fundamental foundations of q-symmetric calculus of variations based on HMFs are provided. With the help of HMFs and granular q-symmetric differentiability, we derive necessary optimality conditions for fuzzy q-symmetric variational problems that depend on free endpoints. Moreover, sufficient conditions for minimizers of q-symmetric variational problems are obtained. Some numerical examples illustrating the proposed approach are presented.

Novel Statistical And Topological Data Analyses Of 2d Electronic Images, Robert L. Paige, Vic Patrangenaru

Mathematics and Statistics Faculty Research & Creative Works

In this paper, novel statistical and topological data analyses of 2D images are developed. One considers methodologies based on the Region Covariance Descriptor (RCD) and Topological Data Analysis (TDA) rooted in the simplicial as well as cubical persistent homologies. These methods provide statistical methods for data from populations of complex data objects that are elements of non-Euclidean spaces. The 2D image data considered consist of pictures of two leaves—A and B—from the same tree, twenty of each leaf, from different perspectives. The novel statistical procedures developed are used for correctly determining that leaf A images and leaf B images are …

Deep Learning Framework For Option Pricing, Kyle Rytand Bistrain

Master's Theses

Accurately pricing American options with market data presents a significant challenge, as foundational models like the Black-Scholes-Merton (BSM) model rely on assumptions that deviate from real-world financial data -- such as log-normal returns, constant volatility, and no dividends -- and fail to account for the key early exercise feature of American options. While parametric models can adjust for these features, the complexity of the resulting models renders them prohibitively difficult to apply in practice for nonspecialists. In response, modern machine learning (ML) techniques provide a set of flexible and powerful alternatives, and recent research has explored the application of ML …