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Pediatric Cancer Incidence, Temporal Trends, And Mortality In The United States By Health Disparities Indicators, Seer (1973-2014), Prachi P. Chavan, Laurens Holmes Jr.

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Pediatric cancer incidence has been increasing in the United States, despite improvement in pediatric cancer survival. This steady increase in incidence trends is not completely understood but maybe associated with social and environmental factors. In this study we aimed to assess the cumulative incidence, temporal trends, and mortality rates in pediatric cancer. Additionally, we examined sub-group variability in both incidence and mortality rates. Methods: Data from Surveillance, Epidemiology, and End Results (SEER) −18 from 1973–2014 were used for the purpose of analysis in this study. Age-adjusted incidence rates were used to assess temporal trends in cancer among children aged < 1–19 years. Univariable and multivariable binomial regression models were used to examine the association between race and cancer mortality while adjusting for potential confounders. Results: There were 92,594 cancer diagnoses during this period. White children comprised 74,758, (80.7%), black children 10,030, (10.8%), and other races 6648, (7.2%). Overall the age-adjusted cumulative incidence was slightly higher among white children (16.4%) than black children (12.4%) and other (13.0%). Children aged 15–19 years and those in metropolitan regions were more likely to be diagnosed with pediatric cancer. Relative to females, males were 16% more likely to die from the disease [adjusted Risk Ratio (aRR): 1.16, 95% Confidence Interval (CI): 1.09–1.22]. Additionally, compared to white children, black children had higher mortality rates [(aRR): 1.37, 99% CI: 1.23–1.52]. Conclusions: There is an increasing trend in pediatric cancer incidence; while white children have the highest incidence, black children and males indicated a survival disadvantage, indicative of racial and sex variability in overall pediatric cancer in the United States.

Machine Learning Methods For Intrusion Detection And Response In Network Security, Ayomide Oyemaja

College of Graduate Studies: Theses & Dissertations

Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.

We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results. …

Majority Decision Using Top-Performing Neural Networks Models For Improved Credit Risk Prediction, Vincent Dey

College of Graduate Studies: Theses & Dissertations

Credit risk prediction remains both a challenging and high-interest problem due to the inherently unbalanced nature of financial datasets and the continuous drive for higher pre- dictive precision. In this work, I build upon previous advancements in credit risk modeling and introduce an ensemble-based Artificial Neural Network (ANN) architecture designed to enhance classification performance. By leveraging a selective ensemble of decision net- works, this approach not only improves prediction accuracy but also mitigates the chal- lenges posed by imbalanced data distributions. While the primary focus is on credit risk prediction, my analysis demonstrates that the proposed model can be effectively …

Generalizations Of Finiteness Conditions And Extension Monads In Algebras With Infinitely Many Or Infinitary Operations, Danielle Christienne Bowerman

Doctoral Dissertations

In this work, we extend the results of finiteness conditions and extension monads found in Insall from finitely many finitary operations to infinitely many finitary operations, as well as touching on infinitary operations. We also examine varieties of algebras, including the notion of strong varieties introduced in Insall, and common constructions of extension monads in varieties of algebras. We see that for finite collections of algebras of the same signature, the extension monad operation on a variety of algebras commutes with the direct product operation, and all retractions from an enlargement or extension monad are trivial. We also see that …

Risk Factors For Autism Spectrum Disorder: A Statistical And Machine Learning Perspective, Supti Biswas

Open Access Master's Theses

his study explores factors associated with autism spectrum disorder (ASD) through analysis of a rich dataset comprising maternal and child characteristics. The data were carefully cleaned and preprocessed to address missing values and ensure analytical consistency. Statistical analyses and advanced visualization techniques were used to uncover patterns linking ASD status with variables such as maternal age, prenatal smoking, and education level. To further assess predictive capacity and feature importance, multiple supervised machine learning algorithms - logistic regression, decision trees, random forests, and support vector machines (SVM) - were applied. ASD appears to be influenced by a complex interplay of demographic …

Hymate: A Hybrid Mamba And Transformer Model For Ehr Representation Learning, Md Mozaharul Mottalib, Thao-Ly Phan, Rahmatollah Beheshti

Department of Medicine Faculty Papers

Electronic health Records (EHRs) have become a cornerstone in modern-day healthcare. They are a crucial part for analyzing the progression of patient health; however, their complexity, characterized by long, multivariate sequences, sparsity, and missing values-poses significant challenges in traditional deep learning modeling. While Transformer-based models have demonstrated success in modeling EHR data and predicting clinical outcomes, their quadratic computational complexity and limited context length hinder their efficiency and practical applications. On the other hand, State Space Models (SSMs) like Mamba present a promising alternative offering linear-time sequence modeling and improved efficiency for handling long sequences, but focus mostly on mixing …

Implementation Of Project-Based Instruction In Statistics Higher Education Courses, Jennifer A. Daddysman

Theses and Dissertations--Education Sciences

Project-based instruction is an instructional methodology based in constructivism that aligns with recommendations from the American Statistical Association for teaching statistics, including incorporating real-world data and examples, collaboration, and scaffolding (GAISE College Report ASA Revision Committee, 2016; GAISE Steering Committee, 2024; Krajcik & Blumenfeld, 2005; Tishkovskaya & Lancaster, 2012). This study examined current use of project-based instruction in United States higher education courses in statistics and the supports for and challenges to implementing this type of instruction.

This study used qualitative research methodology in the integrative pedagogy and diffusion of innovation frameworks. Voluntary response surveys were distributed via two sections …

The Matryoshka Doll Prior – Principled Multiplicity Correction In Bayesian Model Comparison, Andrew Womack, Daniel Taylor-Rodríguez, Claudio Fuentes

Mathematics and Statistics Faculty Publications and Presentations

This paper introduces a general and principled construction of model space priors with a focus on regression problems. The proposed formulation regards each model as a “local” null hypothesis whose alternatives are the set of models that nest it. Assuming constant ratio of prior probabilities for any “local” null and its alternatives provides a natural isomorphism of model spaces (like a matryoshka doll), constituting an intuitive way to correct for multiplicity in Bayesian model selection and averaging problems. This isomorphism yields the Poisson distribution as the unique limiting distribution over model dimension under mild assumptions. We compare this model space …

Safeguard Cyberspace In Ransomware Era: Risk Analysis & Cyber Insurance, Li Huang

Electronic Theses & Dissertations (2024 - present)

The increasing frequency and severity of ransomware attacks pose significant challenges for organizational cybersecurity. Fragmentation across disciplines in cyber defense has created practical gaps in the development of the necessary capabilities needed to address rapidly evolving cyber threats. This study explores the impact of ransomware attacks and the evolving role of cyber insurance as a proactive cybersecurity partner. Bridging the gap between actuarial science and cyber risk management, it proposes an interdisciplinary framework that quantifies the impact of ransomware and integrates cyber insurance into cybersecurity strategies.

The primary contribution of this study is methodology. We present a framework that remains …

Theoretical Foundations And Applied Performance Of Periodicity-Aware Imputation: Variable Bandpass Block Bootstrap Methods For Incomplete Time Series, Asmaa Ahmad

Electronic Theses & Dissertations (2024 - present)

Time series data are prevalent across a wide range of disciplines, including health surveillance, public policy, and environmental monitoring. In the presence of underlying cyclical patterns, the integrity of time series analysis depends critically on the ability to detect, model, and impute structured missing data without compromising the temporal structure. This dissertation introduces and validates a novel imputation framework that integrates the Variable Bandpass Periodic Block Bootstrap (VBPBB) into multiple imputation procedures, improving the accuracy, robustness, and interpretability of time series models under high rates of missingness and noise. The overarching goal of this dissertation was to develop and evaluate …

Mathematical Contributions To The Study Of Chemotaxis And Cell Signaling, Hajr Zam

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation presents results from two mathematical projects concerned with the biology of cells. Chapter 1 provides biological background and places the two mathematical problems in the context of cell signaling. The larger project, with Prof. H. Hattori on a chemotaxis model is presented in Chapters 3 and 4. Work with Prof. \'{A}. Hal\'{a}sz on a chemical reaction network system with linear multimers and two types of labels is presented in Chapter 2. The chemotaxis system describes the one-dimensional dynamics of a species of cells with two chemical species, a chemo-attractant and chemo-repellent. The goal is to analyze the behavior …

Developing Consensus-Based Methods For The Examination And Interpretation Of Contemporary Vehicle, Architectural, And Portable Electronic Device Glasses By Micro-X-Ray Fluorescence Spectrometry, Zachary Bailey Andrews

Graduate Theses, Dissertations, and Problem Reports (ETD)

Glass is a trace material that is commonly encountered during investigations of violent crimes. When glass is recovered at crime scenes, it can be used to establish links between suspects, victims, and the scene itself. The most discriminatory form of analysis for glass evidence is elemental analysis, and micro-X-ray fluorescence spectrometry (µXRF) is becoming an increasingly common technique used for this purpose. Recent advances in µXRF technology, such as the introduction of silicon drift detectors (SDD) and improved polycapillary optics are promising in enhancing the capabilities for the examination of glass evidence in forensic investigations. However, along with these advances …

An Adaptive Method For Covariate Balancing In Block Randomized Clinical Trials, Ren Rasnick

Theses and Dissertations

Clinical trials are randomized in part to limit allocation bias, but also to ensure comparability between treatment arms for a baseline variable of concern. Comparable with regard to a baseline variable of concern is necessary for the validity of statistical methods. However, comparability is not guaranteed for trials of any size and is even more likely in trials with < 200 total participants. We propose a new method for adapting the allocation of participants in a sequentially allocated two-armed study with a small sample size to better ensure comparability.

The proposed method calculates the expected final imbalance (lack of comparability) based on the current participant values. Unlike several other methods, our method ensures the final desired sample size for each treatment arm, utilizes the expected final imbalance, increases comparability between …

Further Results On Learning Quantum Measurement Classes: Quantum Pac Model For Povm Hypothesis Classes, Arka Prabha Das

Electronic Theses & Dissertations (2024 - present)

This thesis investigates the problem of learning from quantum systems, where each example consists of a quantum state paired with a classical outcome. The task centers on choosing an effective measurement rule from a fixed set to enable accurate prediction of the classical outcome from the quantum state. A central focus lies in understanding whether joint measurement strategies that cannot be separated into local operations offer a real benefit in terms of the number of examples needed for successful learning. We examine conditions under which a non-separable measurement within a given hypothesis class achieves strictly better sample complexity bounds compared …

Complex Missing Data Problems In Education Surveys, Thomas Wesley Robertson

Electronic Theses & Dissertations (2024 - present)

Missing data are a nearly universal problem in human subjects research, including in education. However, reporting and addressing missing data is an issue, despite guidelines from the APA style guide and the What Works Clearinghouse, as well as guidance from prominent statisticians on the best methods to use. Prior research conducted in 2004 and 2014 found that in the field of education, most studies do not report or address missing data. In addition, no study has looked specifically at how missing data are reported and addressed in complex surveys. The current study has two main objectives: first, to determine if …

Copula-Based Bayesian Model For Detecting Differential Gene Expression, Prasansha Liyanaarachchi, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian …

In Search Of The Rational Voter In The 2020 Presidential Election: Understanding The Impact Of Voter Costs And Benefits On Turnout, Norou Diawara, Tiffany Henley, Samuel L. Brown, Md Iqbal Hossain

Mathematics & Statistics Faculty Publications

The ability to vote is one of the most valuable rights and privileges afforded by the Constitution of the United States to its citizens. For many, voting is not just a civic duty; it is also a choice. Voting is crucial to our democracy, and any changes to it may affect the efficiency of the democratic process. The bigger question is whether voters behave rationally by engaging in a cost-benefit calculus in deciding whether or not to vote. Using data science, this paper will examine the probability of voting and investigate its impact via cost and benefit among other variables …

Determination Of Electrochemical Parameters For Predicting Reaction Mechanism And Algorithmic Approaches To Pain Assessment, Huize Xue

Dissertations

This dissertation introduces novel advancements in electrochemical kinetics and pain assessment, structured into two main parts. The first part focuses on the comprehensive analysis of the kinetic and mechanistic aspects of electrochemical reactions, utilizing a combination of experimental techniques and simulation methods. A new software tool, Envismetrics, was developed using Python to facilitate the analysis of complex electrochemical data, including cyclic voltammetry (CV), chronoamperometry (CA), and hydrodynamic voltammetry (HDV). The software was rigorously tested and validated with well-characterized redox systems such as the ferricyanide/ferrocyanide couple, dimethylamine borane (DMAB), and Per- and Polyfluoroalkyl Substances (PFAS). It was successfully used to determine …

Machine Learning Methods For Pattern Recognition Analysis Of Genomic And Molecular Data, Kuang Du

Dissertations

While immune therapies achieve remarkable success in treating various cancers, only a subset of patients achieves a durable clinical response, and many exhibit innate or acquired resistance. Precision medicine aims to tailor treatments to individual patients based on specific biological markers, ensuring that each patient receives the therapy most likely to be effective. Predictive biomarkers and gene signatures offer potential for more personalized treatment strategies by identifying patients likely to benefit. Recent studies suggest that gene signatures, comprising sets of genes, hold predictive value for certain clinical variables. Typically derived from biological expert knowledge, these signatures demonstrate substantial predictive potential, …

Impact Of Urban Development On Uv Exposure: A Clustering And Machine Learning Assessment, Taufik Roni Sahroni Mr., Verdi Yasin, Lulut Alfaris, Reza Ariefka, Ruben Cornelius Siagian, Mohammad Alfin Karim, Nana Rahdiana, Ade Suhara

Journal of Environmental Science and Sustainable Development

The relocation of Indonesia's capital city is anticipated to promote inclusive economic growth while embracing cultural diversity. However, this transition may affect ultraviolet (UV) radiation exposure patterns. The study investigated variations in UV exposure in the IKN region, focusing on urban development factors such as land use and population density that affect public health, sun protection, and skin cancer prevention. The research hypothesized that UV radiation is significantly correlated with these factors. UV Index data from 2010-2023, a hierarchical clustering method, identifies complex data patterns without determining the number of clusters. XGBoost, a machine learning model, was used for handling …

Surgical Delay-Associated Mortality Risk Varies By Subtype In Loco-Regional Breast Cancer Patients In Seer-Medicare, Macall Leslie Salewon, Rashmi Pathak, William Dooley, Ronald Squires, Hallgeir Rui, Inna Chervoneva, Takemi Tanaka

Department of Pharmacology, Physiology, and Cancer Biology Faculty Papers

Substantial evidence supports that delay of surgery after breast cancer diagnosis is associated with increased mortality risk, leading to the introduction of a new Commission on Cancer quality measure for receipt of surgery within 60 days of diagnosis for non-neoadjuvant patients. Breast cancer subtype is a critical prognostic factor and determines treatment options; however, it remains unknown whether surgical delay-associated breast cancer-specific mortality (BCSM) risk differs by subtype. This retrospective cohort study aimed to assess whether the impact of delayed surgery on survival varies by subtype (hormone [HR] + /HER2 -, HR -/HER2 -, and HER2 +) in patients with …

Immune Checkpoint Inhibitor-Associated Cutaneous Adverse Events: Mechanisms Of Occurrence, Abdulaziz M. Eshaq, Thomas W. Flanagan, Abdulqader A. Ba Abbad, Zain Alabden A. Makarem, Mohammed S. Bokir, Ahmed K. Alasheq, Sara A. Al Asheikh, Abdullah M. Almashhor, Faroq Binyamani, Waleed A. Al-Amoudi, Abdulaziz S. Bawzir, Youssef Haikel, Mossad Megahed, Mohamed Hassan

School of Medicine Faculty Publications

Immunotherapy, particularly that based on blocking checkpoint proteins in many tumors, including melanoma, Merkel cell carcinoma, non-small cell lung cancer (NSCLC), triple-negative breast (TNB cancer), renal cancer, and gastrointestinal and endometrial neoplasms, is a therapeutic alternative to chemotherapy. Immune checkpoint inhibitor (ICI)-based therapies have the potential to target different pathways leading to the destruction of cancer cells. Although ICIs are an effective treatment strategy for patients with highly immune-infiltrated cancers, the development of different adverse effects including cutaneous adverse effects during and after the treatment with ICIs is common. ICI-associated cutaneous adverse effects include mostly inflammatory and bullous dermatoses, as …

Optimizing Transport Predictive Modeling With Simulation-Based Statistical Inference Authors, Mamunur Rashid, Quyen Tran

Mathematics Faculty Publications

Simulation-based statistical inference (SBI) leverages computer simulations to help scientists understand and analyze complex data. This paper explores how SBI techniques can be used to analyze transportation data. We use modern computational methods, including machine learning models, to improve the accuracy of predictions and decision-making in transportation planning. Our study focuses on two SBI methods, Approximate Bayesian Computation - Markov Chain Monte Carlo and Synthetic Likelihood, to create synthetic data for training machine learning models. These models show the potential of SBI to handle uncertain transportation data. It also highlights the practical benefits of SBI in making better decisions for …

Measurement Of Breast Artery Calcification Using An Artificial Intelligence Detection Model And Its Association With Major Adverse Cardiovascular Events, Suzanne Rose, Josette Hartnett, Zachary Estep, Daniyal Ameen, Shweta Karki, Edward Schuster, Rebecca Newman, David Hsi

Department of Medicine Faculty Papers

Breast artery calcification (BAC) obtained from standard mammographic images is currently under evaluation to stratify risk of major adverse cardiovascular events in women. Measuring BAC using artificial intelligence (AI) technology, we aimed to determine the relationship between BAC and coronary artery calcification (CAC) severity with Major Adverse Cardiac Events (MACE). This retrospective study included women who underwent chest computed tomography (CT) within one year of mammography. T-test assessed the associations between MACE and variables of interest (BAC versus MACE, CAC versus MACE). Risk differences were calculated to capture the difference in observed risk and reference groups. Chi-square tests and/or Fisher's …

How Should China Respond To “Pan-Data Sovereignty” Competition Among China, U.S., And Eu—An Analysis Based On The Digital Stack Model?, Yan Liu, Congjing Ran

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data sovereignty has become deeply intertwined with various economic and social development factors such as technology, trade, economy, culture, society, and politics, leading to a “Pan-Data Sovereignty” competition pattern in the digital space. Through the digital stack model, which examines digital technologies in a layered framework, we can more clearly assess the competitive capacities in“Pan-Data Sovereignty” of China, United States, and European Union. The analysis identifies a three-tiered global “Pan-Data Sovereignty” competition structure among China, U.S., and EU, with each entity holding distinct advantages across various layers of the digital stack. Intense future competition is anticipated in fields such as …

Interactions Of The Sars-Cov-2 Viral Genome 3’-Untranslated Region With Viral And Host Rnas, Caleb Frye, Mihaela Rita Mihailescu

Electronic Theses and Dissertations

This dissertation focuses on the characterization of RNA-RNA interactions within the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome and with host microRNAs. As the causative agent of coronavirus disease 2019 (COVID-19), SARS-CoV-2 has evolved rapidly since its appearance. This has warranted prompt characterization of the virus particularly of its single stranded RNA (ssRNA) genome. By using a combination of bioinformatics, biophysics, and/or biological assays, we analyzed the SARS-CoV-2 viral genomic RNA and uncovered interactions of genomic RNA with host RNAs, highlighting an underutilized method of targeting RNA viruses. We showed here that the conserved elements in the viral genomic …

Efficient Development Of Density-Insensitive Near-Infrared Methods For In-Line Drug Content Monitoring In Continuous Powder Streams, Natasha L. Velez-Silva

Electronic Theses and Dissertations

A well-defined action plan to respond effectively to sudden changes in product demand is critical for preventing drug shortages within the pharmaceutical industry. An effective way to increase the output of a continuous manufacturing (CM) process is through flow rate adjustments. However, robust analytical methods must be in place to ensure consistent analytical performance across varying flow rates. Existing approaches for mitigating the physical effects of flow rate on Near-Infrared (NIR) measurements are often burdensome. Thus, efficient robust modeling strategies that reduce the current calibration burden and ensure model insensitivity to the physical variations in CM systems are needed. In …

Mean From Median Estimation In Longitudinal Meta-Analysis Of Quality Of Life In Radiation Oncology Patients, Harlan R. Sayles

Theses & Dissertations

Meta-analysis of longitudinal data, where some of the studies report results with means and standard errors while others use medians and ranges, is a complex analytical problem with several challenges which must be overcome. Existing methods for estimating means from medians and either ranges, interquartile ranges, or both have not previously been evaluated in a longitudinal setting. In this work, a simulation study was used to estimate mean bias, between studies variance bias, and coverage of confidence intervals in a longitudinal setting. A second simulation study estimated the variance of meta-analysis results from a given set of studies that may …

Racial And Ethnic Differences In Outcomes Of Neonates Born At Less Than 30 Weeks' Gestation, 2018-2022, Nansi S. Boghossian, Marco Geraci, Erika M. Edwards, Jeffrey D. Horbar

Faculty Publications

Importance: Previous research has examined outcomes among very preterm newborns by the birthing parent's race and ethnicity, but knowledge about these trends during the COVID-19 pandemic is limited. Objective: To examine trends in outcomes among Black, Hispanic, and Asian preterm newborns compared with White preterm newborns. Design, Setting, and Participants: This cohort study (2018-2022) took place at 774 neonatal intensive care units in the Vermont Oxford Network. Participants were newborns born at 22 to 29 weeks' gestation. Exposure: Race and ethnicity. Main Outcomes and Measures: The primary outcomes were mortality and complications, including respiratory distress syndrome, necrotizing enterocolitis (NEC), early-onset …

Optimization Of Construction Scheme Of Ultra‑Large Special‑Shaped Foundation Pit Adjacent To Existing Station And Temporary Steel Bridge, Chen Dengwei

Journal of China & Foreign Highway

With the intensifying development density of urban underground space in China，the impact of ultra-large foundation pit construction on the normal operation of adjacent structures cannot be ignored.Relying on the Huangmugang Comprehensive Transportation Hub Project in Shenzhen，this paper studied the deformation of the envelope structure and soil caused by the excavation of the ultra-large special-shaped foundation pit，as well as the structural response of the deformation of the adjacent existing station and the temporary steel bridge.The paper optimized the construction scheme of the special-shaped foundation pit excavation.The results show that the deformation trend of the envelope structure on the north and south …