Statistics and Probability Commons^™

Hybrid Patchtst And Physics-Based Framework For Predicting Lithium-Ion Battery State Of Health, Pavan Ravuri

Honors Undergraduate Theses

Accurately forecasting the state of health of lithium-ion batteries is critical for improving performance, reliability and lifetime in energy storage applications. Battery capacity degrades into a nonlinear pattern over cycling due to electrochemical processes where neither purely data driven nor physics-based models can capture alone. This study looks at a hybrid framework combining that PatchTST patch-based transformer architecture with physics-based features derived from the solid electrolyte interphase and pseudo two-dimensional models. Physics inspired proxy features were computed from cycling data and concatenated with electrochemical measurements as added input channels before patch segmentation. There are five model configurations that were evaluated …

From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins

Honors Undergraduate Theses

With their deserts, castles, and ghost houses, the environments of Super Mario games are colorful, whimsical, and charming, but why are they so compelling, and what happens when our analysis of these environments extends beyond individual levels to expansive game worlds? Drawing on Cresswell’s theory of place (2014) and recent work on musical place-building in Mario Kart 8 (Heazlewood-Dale, 2024), I propose a spectrum between localized and globalized scale in games. As game environments become increasingly globalized, the music may be similarly altered to account for this shift in scale. Consequently, players may then encounter a broader, less musically congruent …

Type Ii Diabetes Treatment Comparison Via Compartment Modeling, Abigail M. Collins

Theses and Dissertations

Type II diabetes mellitus affects one in ten adults worldwide, yet the effects of treatment type and adherence level on developing complications and quality of life have not been well characterized at the population level, and mathematical modeling offers a structured way to examine these dynamics. This thesis adapts the Boutayeb et al. (2004) model to incorporate dynamic treatment types and levels of adherence, producing nine scenarios in which complication development rate and complication recovery rate differed, to compare peak complications and quality of life across treatment and adherence conditions. Using a system of ordinary differential equations and compartment modeling, …

Challenges And Applications Of Fine-Grained Temporal Action Understanding: Modeling Temporal Granularity And Data Efficiency, Halil I. Helvaci

Theses and Dissertations--Electrical and Computer Engineering

Fine-grained Temporal Action Segmentation (TAS) has become a cornerstone of video understanding, offering dense frame-level predictions essential for clinical assessment, surgical skill evaluation, and human-computer interaction. While TAS methods have delivered strong results on coarse-grained benchmarks, two fundamental challenges persist: (1) global attention mechanisms dilute boundary information critical for subsecond precision, a phenomenon we term the temporal granularity bottleneck, and (2) dense frame-level annotation remains prohibitively expensive, with most datasets requiring exhaustive labeling of lengthy untrimmed videos. These challenges are particularly pronounced in medical domains, where sub-second primitives define clinical outcomes while expert annotation remains scarce. In this dissertation, we …

Advancing Periodic Time Series Analysis: Application, Bias Assessment, And Optimal Window Selection In The Variable Bandpass Periodic Block Bootstrap Method, Yanan Sun

Electronic Theses & Dissertations (2024 - present)

Time series analysis is essential for understanding long-term patterns, periodic behavior, and underlying correlations in complex datasets. The periodically correlated (PC) time series is a type of time series where the correlation structure repeats over fixed intervals. The Variable Bandpass Periodic Block Bootstrap (VBPBB) has recently been proposed as a resampling method that preserves PC structures through the use of periodogram, bandpass filters, and block bootstrap resampling. Although promising, VBPBB remains underutilized, and its limitations have not been fully examined. This dissertation advances both the application and methodological development of the VBPBB.

The first project applies the VBPBB to a …

Applications Of Machine Learning For Evaluating Downward-Coupled Stratosphere-Troposphere Interactions And Subseasonal Forecasts Of Opportunity, Elena M. Fernandez

Electronic Theses & Dissertations (2024 - present)

Wintertime stratospheric dynamics provide key information for understanding atmospheric teleconnections and improving subseasonal-to-seasonal (S2S) predictions on timescales of two weeks to two months. Periods of enhanced predictability, often referred to as forecasts of opportunity, arise from large-scale teleconnected variability, within which the stratosphere serves as an important precursor for tropospheric states, such as near-surface temperatures. While traditional diagnostics of downward coupled stratosphere-troposphere interactions typically rely on zonal-mean representations of wind and geopotential height, this dissertation presents an alternative vortex-centric framework through metrics that capture the daily geometric and dynamical evolution of the stratospheric polar vortex. The proposed stratospheric …

Spectral Analysis Of Traffic Accidents In New York's Capital Region, Michael Barr

Electronic Theses & Dissertations (2024 - present)

In this paper we estimate the spectral density of traffic accident events in the Capital Distict, NY area using a band-pass filter known as the Kolmogorov-Zurbenko Fourier Transform (KZFT). The source data is provided by Moosavi, et al. (2019) and originally captured from various public entities and sensors in the road network. Spectral density estimation with KZFT suppresses noise to reveal the constituent frequencies embedded in the noisy signal. Signal reconstruction based on KZFT produces an approximate weekly accident arrivals for this noisy signal, or in other words a pattern which is proportionate to the event expectation viewed over a …

Entropic Foundation Of Finance And Physics: Securities Price Dynamics And Quantum Theory, Mohammad Abedi

Electronic Theses & Dissertations (2024 - present)

In many scientific and financial contexts, we must reason and make predictions under conditions of incomplete information. This dissertation develops Entropic Dynamics (ED) as a unified framework for deriving dynamical laws directly from principles of inference. Within this approach, probability distributions represent states of knowledge, and their evolution is determined through entropy maximization subject to relevant constraints. This leads to a novel concept of entropic time and a formulation of dynamics as an inferential process. In this talk, I will present how ED provides a common foundation across multiple domains. In physics, quantum dynamics for particles and scalar fields in …

Estimation Of Optimal Treatment Regimes In The Presence Of Missing Data: An Application To Opioid Use Disorder Treatment, Abdullah Albalawi

Electronic Theses & Dissertations (2024 - present)

Opioid use disorder (OUD) remains a major public health challenge, and identifying treatment strategies tailored to individual patients over time is an important methodological and clinical problem. Dynamic treatment regimes (DTRs) provide a framework for such individualized strategies, but conventional estimation methods are often presented under complete-data assumptions. Missingness, especially dropout in multistage studies, can introduce bias and affect estimated treatment rules. Comparatively little work has examined how missing data should be handled in sequential multiple assignment randomized trials (SMARTs), where the timing and ordering of variables are central to estimation.

This dissertation examines how different missing-data approaches affect DTR …

Dimension Reduction Involving Exogenous Variables With Applications In Manufacturing And Healthcare, Linxi Li

Theses and Dissertations

High-dimensional data analysis presents diverse challenges, including the curse of dimensionality, the complexities of working with datasets that combine large feature spaces with limited sample sizes, and difficulties in identifying meaningful relationships among variables. As datasets grow in size and complexity across different fields, it is increasingly important to develop practical approaches for extracting essential information from such data. Dimension reduction methods address these challenges by alleviating the effects of high dimensionality, enhancing the ability to reveal hidden patterns, and uncovering latent structures within the data to support further analysis. Some methods reduce dimensionality while preserving all relevant information, offering …

An Integrated Data-Driven Framework For Arctic Shipping: Analyzing Vessel Speed, Environmental And Ecological Factors Through Innovative Statistical Spatio-Temporal Methods, Inverse Optimization And Machine Learning, Mauli Pant

Theses and Dissertations

This dissertation develops an integrated data-driven framework to analyze vessel navigation and ecological risk in the United States Arctic from 2010 to 2019. As environmental change and maritime activity increase in the region, understanding how vessels respond to dynamic conditions and how those responses interact with marine ecosystems has become increasingly important. A central theme of this dissertation is the treatment of vessel speed as both an observed outcome and a decision variable reflecting trade- offs among operational, environmental, and ecological factors. The first chapter develops a predictive framework for vessel speed over ground (SOG) using Gaussian Process Boosting (GPBoost), …

Machine Learning-Based Spatio-Temporal Modeling Of Climate Dynamics And Desertification In The Sahara–Sahel Region, Stephen M. Tivenan

Theses and Dissertations

Arid climate classifications are threshold-dependent and easily interpretable mappings that are widely used in ecological, agricultural, and climate-related studies. These classifications inform scientific understanding, support policy and land management decisions, and provide an intuitive summary of environmental conditions. Despite their usefulness, traditional arid climate classifications often fail to quantify uncertainty, incorporate spatial context, or account for complex relationships among relevant environmental variables. Existing approaches to uncertainty assessment have largely relied on comparing classifications across multiple datasets or alternative formulas, but these methods generally overlook important spatial dependence and latent structure in the data.

This dissertation develops three machine learning-based statistical …

Machine Learning-Based Intrusion Detection System For Iot Networks Using The Rt-Iot 2022 Dataset, Bukunmi Ebenezer Afolabi

Theses, Dissertations and Capstones

The rapid expansion of the Internet of Things (IoT) has transformed modern computing by enabling seamless connectivity among heterogeneous devices across diverse application domains. However, this increased interconnectivity has significantly enlarged the attack surface of IoT networks, exposing them to a wide range of sophisticated cyber threats. Conventional security mechanisms often lack the capability to detect emerging attacks in real time, thereby necessitating the development of intelligent Intrusion Detection Systems (IDS) capable of accurately identifying malicious network activities. This study developed and evaluated a machine learning-based intrusion detection framework for multiclass IoT attack detection using the RT-IoT2022 dataset. The dataset …

Symbolic Logistic Regression For Interval-Valued Predictors: A Simulation Study And Application To Health Data, Soad Abdullah

Graduate Research Theses & Dissertations

This thesis investigates symbolic logistic regression for interval-valued predictors through simulation studies and a real health data application. Classical logistic regression assumes exact predictor values, whereas in many practical settings variables are available only in interval form due to coarsening or reporting uncertainty. Two simulation studies examine the impact of interval uncertainty under asymmetric intervals and measurement error. Symbolic models based on midpoint and midpoint-plus-width representations are compared with the classical approach. Results show that midpoint modeling captures the general relationship but introduces bias under asymmetry, while incorporating width reduces this distortion. Under measurement error, classical logistic regression exhibits attenuation …

Bayesian Inference For A 12-Level Galton Board Using Terminal Bin Counts, Ace Frieders

Graduate Research Theses & Dissertations

A Galton board is a physical device in which a ball passes through a triangular array of pegs and is deflected left or right before landing in one of several bins. Under an idealized model, deflections are independent with probability 0.5 of moving right, implying a binomial distribution for the terminal bin location. Physical boards may deviate from this ideal due to asymmetries and other sources of bias. This thesis develops a Bayesian model for a 12-level Galton board in which each peg has its own right-move probability. Independent Beta priors are assigned to peg probabilities, and terminal bin counts …

Statistics 103a Instructor Guide, Elizabeth R. Wentworth

Open Educational Resources

This set of slides contains reading, original videos, activities and instructions for instructors to run a complete 12 week course in any modality. These resources can be used to supplement in-person instruction or can be used for either a hybrid or asynchronous course.

Financial Literacy And Inclusion Of Philippine Jeepney And Tricycle Drivers, Bryan N. Bernabe, Jyro B. Triviño

Leadership and Strategy Faculty Publications

The study investigated how the different elements of financial literacy influence the financial inclusion of jeepney and tricycle drivers in Caloocan, Metro Manila. Pearson correlation analysis revealed a positive correlation between financial inclusion and attitude, behavior, knowledge, and skills. Additionally, analysis of variance highlighted that education and age play significant roles in enhancing financial literacy. The linear regression findings also supported the idea that income acts as a positive moderator, augmenting the impact of financial literacy on financial inclusion. The study attempted to disaggregate its financial literacy components to understand their impact on financial inclusion, but its interrelationships also require …

Machine Learning Prediction Of Federal Appellate Court Outcomes: A Multi-Circuit Analysis With Administrative Law Implications, Nicky Nuertey Apenahier

Dissertations and Theses

Federal appellate courts are the final arbiters in many cases, yet systematic machine learning analysis across all twelve circuits remains largely absent from the computational law literature. With courts of appeals deciding tens of thousands of cases annually and the Supreme Court reviewing only a fraction, understanding what predicts reversal outcomes has both theoretical importance and practical consequences for litigants, attorneys, and judicial administrators. This study addresses that gap using eleven years of federal appellate decisions from the Federal Judicial Center’s Integrated Database. A systematic comparison of twenty-five machine learning models, spanning five algorithms and five class-imbalance correction strategies, identifies …

Lessons Learned From Clinical Trials Of Thymic Stromal Lymphopoietin (Tslp) Inhibition, Jonathan Corren, Larry Borish, Christopher Brightling, Joseph K. Han, Emma Guttman-Yassky, Steven Ziegler, Jean Publicover, Jyotsna Gulati, Nestor Molfino, Christopher S. Ambrose, Jean-Pierre Llanos, Lang Chen, Jane R. Parnes, Joseph Spahn, Andrew Lindsley

Department of Otolaryngology (ENT) Faculty Publications

Type 2 (T2) immune–mediated epithelial-driven diseases are characterized by dysregulated immune responses at epithelial barrier surfaces. A key mediator of these diseases is thymic stromal lymphopoietin (TSLP), an epithelial cytokine that acts as a regulator of both T2 and non-T2 inflammation. TSLP activates dendritic cells and other immune cells, promoting the release of proinflammatory cytokines, including IL-4, IL-5, and IL-13. Given its central role in initiating and amplifying T2 inflammation, TSLP has emerged as a promising therapeutic target in multiple epithelial-driven inflammatory diseases. Tezepelumab is a human monoclonal antibody that selectively blocks TSLP from interacting with its receptor complex, thereby …

Defining Clinically Meaningful Within-Patient Changes In Heart Failure: Jacc Heart Failure Position Statement, Amin Yehya, Rebecca Hahn, Barry Borlaug, Victoria Delgado, Andrew Ambrosy, Ross Arena, Nosheen Reza, Gregory Lewis, Natalie Tapaskar, Salvatore Carbone, Cynthia Chauhan, Mitchell A. Psotka, Norman Stockbridge, James Januzzi, Javed Butler, Jennifer Cowger, Biykem Bozkurt, John Spertus

Department of Medicine Faculty Publications

Heart failure (HF) clinical trials increasingly rely on diverse endpoints beyond mortality, including hospitalizations, imaging, hemodynamic status, biomarkers, functional capacity, and patient-reported outcomes. Although statistically significant differences between treatment groups are commonly reported, interpreting whether these changes are clinically meaningful for individual patients remains challenging. This JACC: Heart Failure position statement proposes a pragmatic framework for defining clinically meaningful within-patient change across commonly used HF outcomes by integrating measurement variability, prognostic associations, and patient-anchored evidence where available. Relative and absolute risk reductions should both inform interpretation of clinical events, while imaging, hemodynamic, biomarker, and functional measures require changes exceeding measurement …