Digital Commons Network^™

Analysis Of Minor League Rule Changes Effect On Stolen Bases, Zachary Houghtaling

Williams Honors College, Honors Research Projects

This study uses various statistical analyses to evaluate the justification of rule changes for Major League Baseball that were implemented within the Minor Leagues during the 2021 minor league season. The primary focus of the study is predicting how some of these Minor League rule changes could affect the stolen base success rate and the number of attempts per game within the Major Leagues. A survey was conducted to evaluate how fans feel about stolen bases within the current game and if rules should be altered to increase the number of stolen bases that occur. Additionally, recorded Major and Minor …

Maintenance Optimization In A Digital Twin For Industry 4.0, Abhijit Gosavi, Vy Khoi Le

Engineering Management and Systems Engineering Faculty Research & Creative Works

The advent of Internet of Things and artificial intelligence in the era of Industry 4.0 has transformed decision-making within production systems. In particular, many decisions that previously required significant human activity are now made automatically with minimal human intervention via so-called digital twins (DTs). In the context of maintenance and reliability modeling, this naturally calls for new paradigms that can be seamlessly integrated within DTs for decision-making. The input data for time to failure needed in reliability computations are directly collected from the work center in a digital setting and often do not satisfy a known distribution. A neural network …

Searching For Anomalous Extensive Air Showers Using The Pierre Auger Observatory Fluorescence Detector, Andrew Puyleart

Dissertations, Master's Theses and Master's Reports

Anomalous extensive air showers have yet to be detected by cosmic ray observatories. Fluorescence detectors provide a way to view the air showers created by cosmic rays with primary energies reaching up to hundreds of EeV . The resulting air showers produced by these highly energetic collisions can contain features that deviate from average air showers. Detection of these anomalous events may provide information into unknown regions of particle physics, and place constraints on cross-sectional interaction lengths of protons. In this dissertation, I propose measurements of extensive air shower profiles that are used in a machine learning pipeline to distinguish …

Maximum Likelihood Estimator Method To Estimate Flaw Parameters For Different Glass Types, Nabhajit Goswami

Dissertations, Master's Theses and Master's Reports

Glass is commonly used in architectural applications, such as windows and in-fill panels and structural applications, such as beams and staircases. Despite the popularity of structural glass use in buildings, an engineering design standard to determine the required component or member strength for design loads does not exist. Glass is a brittle material that lacks a well-defined yield or ultimate stress, unlike ductile materials. The traditional engineering methods used to design a ductile material cannot be used to design a glass component. Glass fails in tension primarily due to the presence of microscopic flaws present on the surface that acts …

An Analysis On Trends Of Research Topics In Civic Education Using Dynamic Topic Model, Poon Thongsai

Chulalongkorn University Theses and Dissertations (Chula ETD)

The aim of this thesis is to study the trend of civic and citizenship education research from 2000 to 2020 and the influence the regional background of researches has on the research discussion. Relevant data is collected from ERIC and SCOPUS database. This includes abstracts, published year, regional background of researchers, and author h-index. The keywords used are “civic education” or “citizenship education” or “civics”. There are 4917 papers extracted in total. Upon doing further preparation, 4854 articles are prepared for analysis. We apply Structural Topic model (STM) technique to the abstracts with covariates including the published year and the …

Perception Of Health Care Access In Rural Georgia: Findings From A Community Health Needs Assessment Survey, Elisa M. Childs, Tiffany R. Washington

Journal of the Georgia Public Health Association

Background: Limited access to health care services has been cited as a barrier to care for individuals who live in rural areas, contributing to significant health disparities in this population. While perception of services has been cited as a determinant of utilization of health services, it is unknown how perception of services influences health care access in rural areas. The paucity of studies specific to areas in the United States that are medically underserved, necessitated this study and its quantification of the issues that are relevant to individuals living in rural Georgia.

Methods: This study examined the perception of health …

A Comparison Of Imbalanced Data Handling Methods For Pre-Trained Model In Multi-Label Classification Of Stack Overflow, Arisa Umparat

Chulalongkorn University Theses and Dissertations (Chula ETD)

Tag classification is essential in Stack Overflow. Instead of combining through pages or replies of irrelevant information, users can easily and quickly pinpoint relevant posts and answers using tags. Since User-submitted posts can have multiple tags, classifying tags in Stack Overflow can be challenging. This results in an imbalance problem between labels in the whole labelset. Pretrained deep learning models with small datasets can improve tag classification accuracy. Common multi-label resampling techniques with machine learning classifiers can also fix this issue. Still, few studies have explored which resampling technique can improve the performance of pre-trained deep models for predicting tags. …

An Application Of Reinforcement Learning To Credit Scoring Based On The Logistic Bandit Framework, Kantapong Visantavarakul

Chulalongkorn University Theses and Dissertations (Chula ETD)

This study applies reinforcement learning to credit scoring by using the logistic bandit framework. The credit scoring and the credit underwriting are modeled into a single sequential decision problem where the credit underwriter takes a sequence of actions over an indefinite number of time steps. The traditional credit scoring approach considers the model construction separately from the underwriting process. This approach is identified as a greedy algorithm in the reinforcement learning literature, which is commonly believed to be inferior to an efficient reinforcement learning approach such as Thompson sampling. This is true under the simple setting, i.e., granting credit to …

Multi-Output Learning For Predicting Evaluation And Reopening Of Github Pull Requests On Open-Source Projects, Peerachai Banyongrakkul

Chulalongkorn University Theses and Dissertations (Chula ETD)

GitHub's pull-based development model is widely used by software development teams to manage software complexity. Contributors create pull requests for merging changes into the main codebase, and integrators review these requests to maintain quality and stability. However, a high volume of pull requests can overburden integrators, causing feedback delays. Previous studies have used machine learning and statistical techniques with tabular data as features, but these may lose meaningful information. Additionally, acceptance and latency may not be sufficient for the pull request evaluation. Moreover, reopened pull requests can add maintenance costs and burden already-busy developers. This thesis proposes a novel multi-output …

A Non-Deterministic Deep Learning Based Surrogate For Ice Sheet Modeling, Hannah Jordan

Graduate Student Theses, Dissertations, & Professional Papers

Surrogate modeling is a new and expanding field in the world of deep learning, providing a computationally inexpensive way to approximate results from computationally demanding high-fidelity simulations. Ice sheet modeling is one of these computationally expensive models, the model used in this study currently requires between 10 and 20 minutes to complete one simulation. While this process is adequate for certain applications, the ability to use sampling approaches to perform statistical inference becomes infeasible. This issue can be overcome by using a surrogate model to approximate the ice sheet model, bringing the time to produce output down to a tenth …

Development Of Regional Landslide Susceptibility Models: A First Step Towards Model Transferability, Gina M. Belair

Graduate Student Theses, Dissertations, & Professional Papers

Landslides are a globally pervasive problem with the potential to cause significant fatalities and economic losses. Although landslides are widespread, many at-risk regions may not have the high-quality data or resources used in most landslide susceptibility analyses. This study aims to develop regional susceptibility relationships that are versatile and use publicly available data and open-sourced software. Logistic Regression and Frequency Ratio susceptibility relationships were developed in 23 regions in Washington, Utah, North Carolina, and Kentucky, with a region referring to a unique area and data combination. Regions were diverse in their geology, morphology, climate, and nature and quality of their …

Nidus Idearum. Scilogs, Ix: Neutrosophia Perennis, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this ninth book of scilogs collected from my nest of ideas, one may find new and old questions and solutions, – in email messages to research colleagues, or replies, and personal notes, some handwritten on the planes to, and from international conferences, about topics on Neutrosophy and its applications, such as: Neutrosophic Bipolar Set, Linguistic Neutrosophic Set, Neutrosophic Resonance Frequency, n-ary HyperAlgebra, n-ary NeutroHyperAlgebra, n-ary AntiHyperAlgebra, Plithogenic Crisp Graph, Plithogenic Fuzzy Graph, Plithogenic Intuitionistic Fuzzy Graph, Plithogenic Neutrosophic Graph, Plithogenic Real Number Graph, Plithogenic Complex Number Graph, Plithogenic Neutrosophic Number Graph, and many more.

Exchanging ideas with: Tareq Al-Shami, …

A Simple Algorithm For Generating A New Two Sample Type-Ii Progressive Censoring With Applications, E. M. Shokr, Rashad Mohamed El-Sagheer, Mahmoud Mansour, H. M. Faied, B. S. El-Desouky

Basic Science Engineering

In this article, we introduce a simple algorithm to generating a new type-II progressive censoring scheme for two samples. It is observed that the proposed algorithm can be applied for any continues probability distribution. Moreover, the description model and necessary assumptions are discussed. In addition, the steps of simple generation algorithm along with programming steps are also constructed on real example. The inference of two Weibull Frechet populations are discussed under the proposed algorithm. Both classical and Bayesian inferential approaches of the distribution parameters are discussed. Furthermore, approximate confidence intervals are constructed based on the asymptotic distribution of the maximum …

Continuous And Discrete Models For Optimal Harvesting In Fisheries, Nagham Abbas Al Qubbanchee

Masters Theses

"This work focuses on the logistic growth model, where the Gordon-Schaefer model is considered in continuous time. We view the Gordon-Schaefer model as a bioeconomic equation involved in the fishing business, considering biological rates, carrying capacity, and total marginal costs and revenues. In [25], the authors illustrate the analytical solution of the Schaefer model using the integration by parts method and two theorems. The theorems have many assumptions with many different strategies. Due to the nature of the problem, the optimal control system involves many equations and functions, such as the second root of the equation. We concentrate on Theorem …

Spatio-Temporal Copula-Based Graph Neural Networks For Traffic Forecasting, Pitikorn Khlaisamniang

Chulalongkorn University Theses and Dissertations (Chula ETD)

Modern cities heavily rely on complex transportation, making accurate traffic speed prediction crucial for traffic management authorities. Classical methods, including statistical techniques and traditional machine learning techniques, fail to capture complex relationships, while deep learning approaches may have weaknesses such as error accumulation, difficulty in handling long sequences, and overlooking spatial correlations. Graph neural networks (GNNs) have shown promise in extracting spatial features from non-Euclidean graph structures, but they usually initialize the adjacency matrix based on distance and may fail to detect hidden statistical correlations. The choice of correlation measure can have a significant impact on the resulting adjacency matrix …

Lake Huron Shoreline Analysis, Shubham Satish Nandanwar

Theses and Dissertations (Comprehensive)

Lake Huron is a popular tourist destination and is home to several businesses and residents. Since the shoreline is dynamic and is subject to change over the years due to several factors such as a change in water level, soil type, human encroachment, etc., these locations tend to encounter floods due to increased water levels and wind speed. This causes erosion and loss to the properties along the shoreline.

This study is based on two areas of interest named Pinery Provincial Park and Sauble Beach which are located on the shoreline of Lake Huron where Pinery Provincial Park is a …

Finding The Best Predictors For Foot Traffic In Us Seafood Restaurants, Isabel Paige Beaulieu

Honors Theses and Capstones

COVID-19 caused state and nation-wide lockdowns, which altered human foot traffic, especially in restaurants. The seafood sector in particular suffered greatly as there was an increase in illegal fishing, it is made up of perishable goods, it is seasonal in some places, and imports and exports were slowed. Foot traffic data is useful for business owners to have to know how much to order, how many employees to schedule, etc. One issue is that the data is very expensive, hard to get, and not available until months after it is recorded. Our goal is to not only find covariates that …

Reducing Print Time While Minimizing Loss In Mechanical Properties In Consumer Fdm Parts, Long Le, Mitchel A. Rabsatt, Hamid Eisazadeh, Mona Torabizadeh

Mechanical & Aerospace Engineering Faculty Publications

Fused deposition modeling (FDM), one of various additive manufacturing (AM) technologies, offers a useful and accessible tool for prototyping and manufacturing small volume functional parts. Polylactic acid (PLA) is among the commonly used materials for this process. This study explores the mechanical properties and print time of additively manufactured PLA with consideration to various process parameters. The objective of this study is to optimize the process parameters for the fastest print time possible while minimizing the loss in ultimate strength. Design of experiments (DOE) was employed using a split-plot design with five factors. Analysis of variance (ANOVA) was employed to …

A Simple And Robust Alternative To Bland-Altman Method Of Assessing Clinical Agreement, Abhaya Indrayan Prof

COBRA Preprint Series

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. These limits only describe the dispersion of disagreements in 95% cases and do not measure the degree of agreement. The interpretation regarding the presence or absence of agreement by this method is based on whether B-A limits are within the pre-specified externally determined clinical tolerance limits. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that the direct use of clinical tolerance limits for assessing agreement without the B-A limits is more effective …

Parameter Estimation And Inference Of Spatial Autoregressive Model By Stochastic Gradient Descent, Gan Luan

Dissertations

Stochastic gradient descent (SGD) is a popular iterative method for model parameter estimation in large-scale data and online learning settings since it goes through the data in only one pass. While SGD has been well studied for independent data, its application to spatially-correlated data largely remains unexplored. This dissertation develops SGD-based parameter estimation and statistical inference algorithms for the spatial autoregressive (SAR) model, a common model for spatial lattice data.

This research contains three parts. (I) The first part concerns SGD estimation and inference for the SAR mean regression model. A new SGD algorithm based on maximum likelihood estimator (MLE) …

Dependent Censoring In Survival Analysis, Zhongcheng Lin

Dissertations

This dissertation mainly consists of two parts. In the first part, some properties of bivariate Archimedean Copulas formed by two time-to-event random variables are discussed under the setting of left censoring, where these two variables are subject to one left-censored independent variable respectively. Some distributional results for their joint cdf under different censoring patterns are presented. Those results are expected to be useful in both model fitting and checking procedures for Archimedean copula models with bivariate left-censored data. As an application of the theoretical results that are obtained, a moment estimator of the dependence parameter in Archimedean copula models is …

A Poisson-Akash Inrar(1) Model For Over Dispersed Count Time Series, Emmanuel W. Okereke, Sunday N. Gideon

BAU Journal - Science and Technology

In this paper, a Poisson-Akash INAR(1) model was proposed in order to improve on the modelling of overdispersed stationary count time series. The estimators of the parameters of the model were derived using the Yule-Walker (YW) method and the conditional least squares (CLS) method. An expression for the conditional log-likelihood and the r-step ahead forecast were obtained for the model. Three overdispersed nonseasonal stationary count time series were modelled to illustrate the applicability of the proposed model as well as its capacity to outperform the competing INAR (1) models in modelling overdispersed stationary count time series and the result showed …

Genetic Contributors Of Incident Stroke In 10,700 African Americans With Hypertension: A Meta-Analysis From The Genetics Of Hypertension Associated Treatments And Reasons For Geographic And Racial Differences In Stroke Studies, Nicole D. Armstrong, Vinodh Srinivasasainagendra, Amit Patki, Rikki M. Tanner, Bertha A. Hidalgo, Hemant K. Tiwari, Nita A. Limdi, Ethan M. Lange, Leslie A. Lange, Donna K. Arnett, Marguerite R. Irvin

Epidemiology and Environmental Health Faculty Publications

Background: African Americans (AAs) suffer a higher stroke burden due to hypertension. Identifying genetic contributors to stroke among AAs with hypertension is critical to understanding the genetic basis of the disease, as well as detecting at-risk individuals.

Methods: In a population comprising over 10,700 AAs treated for hypertension from the Genetics of Hypertension Associated Treatments (GenHAT) and Reasons for Geographic and Racial Differences in Stroke (REGARDS) studies, we performed an inverse variance-weighted meta-analysis of incident stroke. Additionally, we tested the predictive accuracy of a polygenic risk score (PRS) derived from a European ancestral population in both GenHAT and REGARDS AAs …

Dysregulation Of Dna Methylation And Epigenetic Clocks In Prostate Cancer Among Puerto Rican Men, Anders Berglund, Jaime Matta, Jarline Encarnación-Medina, Carmen Ortiz-Sanchéz, Julie Dutil, Raymond Linares, Joshua Marcial, Caren Abreu-Takemura, Natasha Moreno, Ryan Putney, Ratna Chakrabarti, Hui Yi Lin, Kosj Yamoah, Carlos Diaz Osterman, Liang Wang, Jasreman Dhillon, Youngchul Kim, Seung Joon Kim, Gilberto Ruiz-Deya, Jong Y. Park

School of Public Health Faculty Publications

In 2021, approximately 248,530 new prostate cancer (PCa) cases are estimated in the United States. Hispanic/Latinos (H/L) are the second largest racial/ethnic group in the US. The objective of this study was to assess DNA methylation patterns between aggressive and indolent PCa along with ancestry proportions in 49 H/L men from Puerto Rico (PR). Prostate tumors were classified as aggressive (n = 17) and indolent (n = 32) based on the Gleason score. Genomic DNA samples were extracted by macro-dissection. DNA methylation patterns were assessed using the Illumina EPIC DNA methylation platform. We used ADMIXTURE to estimate global ancestry proportions. …

Acute Pediatric Chagas Disease In Antioquia, Colombia: A Geographic Location Of Suspected Oral Transmission, Lídia Gual-Gonzalez, Catalina Aango-Ferreira, Laura Camila Lopera-Restrepo, Omar Cantillo-Barraza, Daniela Velásquez Marín, Natalia Restrepo Bustamante, Omar Triana-Chavez, Melissa Nolan Ph.D., Mph

Faculty Publications

Chagas disease, Trypanosoma cruzi infection, is an insidious cause of heart failure in Latin America. Early diagnosis and treatment are critical to prevent irreversible myocardial damage that progressively accumulates over decades. Several structural barriers account for the less than 1% of cases in Colombia being treated, including poor physician knowledge, especially considering that some regions are considered non-endemic. The two cases reported here represent an emerging epidemiologic scenario associated with pediatric Chagas disease. Both cases are suspected oral transmitted parasitic infection in a geographic region of Colombia (Andean region of Antioquia) where no previous oral transmission of Chagas disease had …

Approximate Likelihood Based Estimations For Joint Models With Intractable Likelihoods, Karl Stessy M. Bisselou

Theses & Dissertations

This dissertation focuses on the development of approximation approaches for the joint modeling (JM) of repeated measures data and time-to-event data in the presence of analytically or numerically intractable likelihoods. Current likelihood-based inferences for JMs show several limitations including (i) intractability of integrals during marginal likelihood derivations due to the complexity in computations, and (ii) the large number of nuisance parameters (unobserved) posing a problem with convergence. The h-likelihood (HL) and synthetic likelihood (SL) are two computationally efficient estimation approaches that overcome these challenges.

In the presence of extremely high censoring rates, the HL can produce bias parameter estimates. We …

Smoking, Alcohol Consumption, And Depression In Association With Incidence Of Type 2 Diabetes Among Mexican Americans In Starr County, Texas, Gabriela Rubannelsonkumar

Honors Program Theses and Research Projects

Previous studies on conditions like obesity, hypertension, and type 2 diabetes mellitus (T2DM) have explored the correlations between them and various other human conditions, including aortic stiffness, left ventricular hypertrophy and sleep apnea, as they predict possibilities of developing certain diseases in Mexican Americans. This study aims to observe the correlation between lifestyle decisions that could relate to the onset of the depression in normal, prediabetic, and diabetic individuals. These include smoking habits and alcohol consumption. Many papers have previously conducted research on these lifestyle habits as they relate to obesity, hypertension, diabetes, however, have done so in a singular …

The Efficacy Of Plant-Based Dietary Program In Patients With Diabetes: A Pilot Study, Reuben Adatorwovor, Nisha Sharma, Dakota Mccoy, Sharon Wasserstrom, Matthew Robinson, Jacquelyn Nyenhuis, Sowmya Suryanarayanan

Biostatistics Faculty Publications

Dietary choices play a key role in insulin sensitivity among diabetes patients. An 8-week pilot study was conducted to evaluate whether a mostly plant-based dietary program will lead to improvement in biochemical markers in adults with diabetes. The dietary program included educational presentations, weekly cooking demonstrations and small group discussions. A sample of thirty-two adults with diabetes (types 1 and 2) were recruited and seventeen (53%) completed the study. Matched-pair tests and Fishers exact tests were used to compare the changes in means and proportion of the participants’ responses. There were changes in HbA1c, lipids, CRP (mg/L), cholesterol (mg/dL), HDL …

Aspect-Based Sentiment Analysis Of Movie Reviews, Samuel Onalaja, Eric Romero, Bosang Yun

SMU Data Science Review

This study investigates a comparison of classification models used to determine aspect based separated text sentiment and predict binary sentiments of movie reviews with genre and aspect specific driving factors. To gain a broader classification analysis, five machine and deep learning algorithms were compared: Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network Long-Short-Term Memory (RNN LSTM). The various movie aspects that are utilized to separate the sentences are determined through aggregating aspect words from lexicon-base, supervised and unsupervised learning. The driving factors are randomly assigned to various movie aspects and their impact tied to …

Predicting Power Using Time Series Analysis Of Power Generation And Consumption In Texas, Joshua Eysenbach, Bodie Franklin, Andrew J. Larsen, Joel Lindsey

SMU Data Science Review

Due to the recent power events in Texas, power forecasting has been brought national attention. Accurate demand forecasting is necessary to be sure that there is adequate power supply to meet consumer's needs. While Texas has a forecasting model created by the Electricity Reliability Council of Texas (ERCOT), constant efforts are required to ensure that the model stays at the state-of-the-art and is producing the most reliable forecasts possible. This research seeks to provide improved short- and medium-term forecasting models, bringing in state-of-the-art deep learning models to compare to ERCOT’s forecasts. A model that is more accurate than ERCOT’s own …