Physical Sciences and Mathematics Commons^™

Dynamic Influence Diagram-Based Deep Reinforcement Learning Framework And Application For Decision Support For Operators In Control Rooms, Joseph Mietkiewicz, Ammar N. Abbas, Chidera Winifred Amazu, Anders L. Madsen, Gabriele Baldissone

Articles

In today’s complex industrial environment, operators are often faced with challenging situations that require quick and accurate decision-making. The human-machine interface (HMI) can display too much information, leading to information overload and potentially compromising the operator’s ability to respond effectively. To address this challenge, decision support models are needed to assist operators in identifying and responding to potential safety incidents. In this paper, we present an experiment to evaluate the effectiveness of a recommendation system in addressing the challenge of information overload. The case study focuses on a formaldehyde production simulator and examines the performance of an improved Human-Machine Interface …

Approximate Confidence Distribution Computing, Suzanne Thornton, W. Li, M. Xie

Mathematics & Statistics Faculty Works

Approximate confidence distribution computing (ACDC) offers a new take on the rapidly developing field of likelihood-free inference from within a frequentist framework. The appeal of this computational method for statistical inference hinges upon the concept of a confidence distribution, a special type of estimator which is defined with respect to the repeated sampling principle. An ACDC method provides frequentist validation for computational inference in problems with unknown or intractable likelihoods. The main theoretical contribution of this work is the identification of a matching condition necessary for frequentist validity of inference from this method. In addition to providing an example of …

Differences In Clinical Presentation At First Hospitalization And The Impact On Involuntary Admissions Among First-Generation Migrant Groups With Non-Affective Psychotic Disorders., Kelly K Anderson, Rebecca Rodrigues

Epidemiology and Biostatistics Publications

BACKGROUND: Some migrant and ethnic minority groups have a higher risk of coercive pathways to care; however, it is unclear whether differences in clinical presentation contribute to this risk. We sought to assess: (i) whether there were differences in clinician-rated symptoms and behaviours across first-generation immigrant and refugee groups at the first psychiatric hospitalization after psychosis diagnosis, and (ii) whether these differences accounted for disparities in involuntary admission.

METHODS: Using population-based health administrative data from Ontario, Canada, we constructed a sample (2009-2013) of incident cases of non-affective psychotic disorder followed for two years to identify first psychiatric hospitalization. We compared …

Applying Structural Equation Modeling To Better Understand The Relationship Between Stressors, Social Support And Wellbeing In The Lives Of Spouse Dementia Caregivers, Craig Holden

Dissertations, Theses, and Capstone Projects

Applying Structural Equation Modeling to Better Understand the Relationship Between Stressors, Social Support and Wellbeing in the Lives of Spouse Dementia Caregivers considers the utility of Pearlin et al.’s (1990) stress process model in understanding the needs of spouse caregivers. Data were drawn from eight biennial waves of the University of Michigan Health and Retirement Study (HRS) and analyzed using structural equation modeling. The final study sample comprised 774 spouses, average age 73, who were categorized based on Alzheimer’s Disease and Related Dementia (ADRD) and non-ADRD caregiver status. Results showed that for the study sample as a whole, social support …

Steady State Thermal Blooming With Convection: Modeling, Simulation And Analysis, Jeremiah S. Lane

Theses and Dissertations

The modeling, simulation, and analysis of high energy laser propagation is a research topic of significant interest to the defense community. A detailed understanding of the phenomenon of thermal blooming is crucial as it is detrimental to the propagation of lasers over long distances and in the presence of aerosols. The simulation of thermal blooming has historically relied on wave optics models and scaling laws for the fluid response to the laser. Since thermal blooming occurs in the presence of natural convection, however, there is a need for simulating this coupled fluid-beam effect using a first principles approach. In this …

Improving Deep Reinforcement Learning Methodology For Autonomous Defense And Escort Of Military High-Value Assets, Joseph Liles Iv

Theses and Dissertations

This dissertation explores the application of machine learning to the control of autonomous unmanned combat aerial vehicles (AUCAVs). In particular, this research applies deep reinforcement learning methodologies to a defensive air combat scenario wherein a fleet of AUCAVs protects a military high-value asset (HVA). A collection of air battle management scenarios along with an original simulation environment and a set of designed computational experiments support the approximation of high-quality decision policies by employing Markov decision processes, approximate dynamic programming algorithms, and deep neural networks for value function approximation.

Advanced Statistical Methodology For The Modern Probability Of Detection, Christine E. Knott

Theses and Dissertations

Probability of detection (POD) is an invaluable part of the calculations used by the USAF to validate the capabilities of nondestructive inspection systems for detecting defects in critical structural components on aircraft. A POD study consists of a designed experiment, linear modeling, and a probability of detection verses defect size curve. This curve is useful for determining how often an aircraft should be re-inspected. Some POD studies are unsuccessful in creating realistic POD curves because the statistical modeling used has two common limitations: (1) a lack of convergence leading to no solution and, (2) violated assumptions leading to incorrect solutions. …

Construction And Performance Optimization Of Bioconjugated Nanosensors For Early Detection Of Breast Cancer And Pro-Inflammatory Diseases, Pooja Gaikwad

Dissertations, Theses, and Capstone Projects

In recent years, nanosensors have emerged as a tool with strong potential in medical diagnostics. Single-walled carbon nanotube (SWCNT) based optical nanosensors have notably garnered interest due to the unique characteristics of their near-infrared fluorescence emission, including tissue transparency, photostability, and various chiralities with discrete absorption and fluorescence emission bands. Additionally, the optoelectronic properties of SWCNT are sensitive to the surrounding environment, which makes them suitable for in vitro and in vivo biosensing. Single-stranded (ss) DNA-wrapped SWCNTs have been reported as optical nanosensors for cancers and metabolic diseases. Breast cancer and cardiovascular diseases are the most common causes of death …

Healthy Lifestyle Behaviors And Sociodemographic Characteristics Among Medical Students In Indonesia During The New Normal Era: A Cross-Sectional Study, Sharren Shera Vionnetta, Tommy Nugroho Tanumihardja, Kevin Kristian

Kesmas

This study aimed to identify medical students’ healthy lifestyle behaviors during the new normal era and to determine its relationship with sociodemographic factors, bearing in mind that, as future physicians and health role models, medical students play an important role in adopting and promoting healthy lifestyle behaviors to reduce the risk of future health problems as well as optimize communities’ health status. This cross-sectional study was conducted at the School of Medicine and Health Sciences of Universitas Katolik Indonesia Atma Jaya, with 111 medical students selected through stratified random sampling. Data were collected using sociodemographic characteristics (sex, residence, year of …

Prediction Of Factors For Patients With Hypertension And Dyslipidemia Using Multilayer Feedforward Neural Networks And Ordered Logistic Regression Analysis: A Robust Hybrid Methodology, Wan Muhamad Amir W Ahmad, Mohamad Nasarudin Bin Adnan, Norhayati Yusop, Hazik Bin Shahzad, Farah Muna Mohamad Ghazali, Nor Azlida Aleng, Nor Farid Mohd Noor

Makara Journal of Health Research

Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%–30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia.

Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping.

Results: Variable …

Modelling Long-Term Security Returns, Xinghan Zhu

Electronic Thesis and Dissertation Repository

This research focuses on the concerns of Canadian investors regarding portfolio diversification and preparedness for unexpected risks in retirement planning. It models market crashes and two main financial instruments as independent components to simulate clients’ portfolios. Initially exploring single distributions on mutual funds such as Laplace and t distributions, the research finds limited success. Instead, a normal-Weibull spliced distribution is introduced to model log returns. The Geometric Brownian Motion (GBM) model is employed to predict and evaluate returns on common stocks using the Maximum Likelihood Estimator (MLE), assuming that daily log returns follow a normal distribution. Additionally, the Merton Jump …

Characteristics And Source-Specific Health Risks Of Ambient Pm2.5-Bound Pahs In An Urban City Of Northern Taiwan, Yu-Chieh Ting, Chun-Hung Ku, Yu-Xuan Zou, Kai-Hsien Chi, Jhy-Charm Soo, Chin-Yu Hsu, Yu-Cheng Chen

Department of Biostatistics, Epidemiology, and Environmental Health Sciences Faculty Publications

Polycyclic aromatic hydrocarbons (PAHs) with highly toxic compounds mainly exist in small-sized particles and can induce considerable human health risks. Studies on PM2.5-bound PAHs and their source-specific human health risks still remain scarce. Daily PM2.5 samples (n = 119) were collected every three days from 2016 to 2017 in Taipei city, Taiwan. Fifteen PAHs in PM2.5 were analyzed via gas chromatography tandem mass spectrometry (GC/MS-MS). We utilized a positive matrix factorization (PMF) model, diagnostic ratios, and potential source contribution function (PSCF) to identify the origins of PM2.5-bound PAHs. The annual concentration of total PAHs (TPAH) was 0.79 ± 0.67 ng …

Making The Error Bar Overlap Myth A Reality: Comparative Confidence Intervals, Frank S. Corotto

Georgia Journal of Science

Many interpret error bars to mean that if they do not overlap the difference is statistically “significant”. This overlap rule is really an overlap myth; the rule does not hold true for any conventional type of error bar. There are rules of thumb for estimating P values, but it would be better to show error bars for which the overlap rule holds true. Here I explain how to calculate comparative confidence intervals which, when plotted as error bars, let us judge significance based on overlap or separation. Others have published on these intervals (the mathematical basis goes back to John …

Optimizing Dynamic Treatment Regimes With Q-Learning: Complications Due To Error-Prone Data And Applications To Covid-19 Data, Yasin Khadem Charvadeh

Electronic Thesis and Dissertation Repository

In this thesis, we employ statistical modeling and methods to examine COVID-19 data, and we develop new methods to address new issues that invalidate some standard methods.

In the first study, we employ semiparametric and nonparametric survival models as well as data visualization techniques to examine the epidemiological features of COVID-19. Based on our numerical results, the median incubation time is about 5 days, and the elders are more likely to have longer incubation periods.

In the second study, we use data from 175 countries and investigate possible factors associated with the case fatality rate (CFR) of COVID-19. The Q-learning …

The Influence Of Framing And Recent Experience On Farmer Choices In Experimental Games Depicting Risk-Reducing Agricultural Technologies, Ana Maria Ospina Tobar

Electronic Theses and Dissertations

Climate change is a major threat to food security, particularly in low and middle-income countries that are highly dependent on staple crops for subsistence. The vulnerability of staple crops, like maize, in the face of climate change, is increasing due to the increasing frequency of droughts. This thesis aims to evaluate two mechanisms through which farmers may be more willing to adopt new technologies that increase their resilience to climate change: First, I evaluate the effectiveness of a new virtual maize farming game as a learning tool to teach farmers about the outcomes they could obtain under different weather events …

A Review Of Recent Gene Expression-Based And Dna Methylation-Based Mathematical Cell Type Deconvolution Methods, Chenxiao Tian

Arts & Sciences Electronic Theses and Dissertations

In recent years, many cell type deconvolution methods based on DNA methylation data and gene expression data have been developed. Both of these two methods have its special advantages and disadvantages, e.g., DNA methylation-based methods’ data source is usually more stable than gene expression and DNA methylation is easier to measure in FFPE tissues or formalin-fixed paraffin-embedded, while some gene-expression data like scRNA-seq data usually has high cost and complexity. On the other hand, gene expression-based deconvolution methods currently have many more available methods than DNA methylation-based deconvolution methods, which leads to DNA methylation-based methods in many cases can learn …

Sickle Cell Disease Treatment With Arginine Therapy (Start): Study Protocol For A Phase 3 Randomized Controlled Trial., Chris A Rees, David C. Brousseau, Daniel M Cohen, Anthony Villella, Carlton Dampier, Kathleen Brown, Andrew Campbell, Corrie E Chumpitazi, Gladstone Airewele, Todd Chang, Christopher Denton, Angela Ellison, Alexis Thompson, Fahd Ahmad, Nitya Bakshi, Keli D Coleman, Sara Leibovich, Deborah Leake, Dunia Hatabah, Hagar Wilkinson, Michelle Robinson, T Charles Casper, Elliott Vichinsky, Claudia R Morris

Department of Pediatrics Faculty Papers

BACKGROUND: Despite substantial illness burden and healthcare utilization conferred by pain from vaso-occlusive episodes (VOE) in children with sickle cell disease (SCD), disease-modifying therapies to effectively treat SCD-VOE are lacking. The aim of the Sickle Cell Disease Treatment with Arginine Therapy (STArT) Trial is to provide definitive evidence regarding the efficacy of intravenous arginine as a treatment for acute SCD-VOE among children, adolescents, and young adults.

METHODS: STArT is a double-blind, placebo-controlled, randomized, phase 3, multicenter trial of intravenous arginine therapy in 360 children, adolescents, and young adults who present with SCD-VOE. The STArT Trial is being conducted at 10 …

Atrial Fibrillation Management In Hispanic Adults, Tania Borja

Dissertations

Background: Research has found atrial fibrillation (AF) to be the primary or a contributing cause of death on 183,321 death certificates, and an underlying cause of death for 26,535 Americans in 2019. Findings indicate an increased AF diagnosis in White people compared to racial and ethnic minorities, contrasting widespread findings of increased prevalence of cardiovascular disease and ischemic strokes in minorities. Significant disparities—by race and socioeconomic status in disease distribution and access to testing and lifesaving treatments—have been documented, specifically associated with social determinants of health (SDOH); i.e., the conditions in which people are born, grow, live, work, and age. …

Using Geographic Information To Explore Player-Specific Movement And Its Effects On Play Success In The Nfl, Hayley Horn, Eric Laigaie, Alexander Lopez, Shravan Reddy

SMU Data Science Review

American Football is a billion-dollar industry in the United States. The analytical aspect of the sport is an ever-growing domain, with open-source competitions like the NFL Big Data Bowl accelerating this growth. With the amount of player movement during each play, tracking data can prove valuable in many areas of football analytics. While concussion detection, catch recognition, and completion percentage prediction are all existing use cases for this data, player-specific movement attributes, such as speed and agility, may be helpful in predicting play success. This research calculates player-specific speed and agility attributes from tracking data and supplements them with descriptive …

Traditional Vs Machine Learning Approaches: A Comparison Of Time Series Modeling Methods, Miguel E. Bonilla Jr., Jason Mcdonald, Tamas Toth, Bivin Sadler

SMU Data Science Review

In recent years, various new Machine Learning and Deep Learning algorithms have been introduced, claiming to offer better performance than traditional statistical approaches when forecasting time series. Studies seeking evidence to support the usage of ML/DL over statistical approaches have been limited to comparing the forecasting performance of univariate, linear time series data. This research compares the performance of traditional statistical-based and ML/DL methods for forecasting multivariate and nonlinear time series.

A Hybrid Ensemble Of Learning Models, Bivin Sadler, Dhruba Dey, Duy Nguyen, Tavin Weeda

SMU Data Science Review

Statistical models in time series forecasting have long been challenged to be superseded by the advent of deep learning models. This research proposes a new hybrid ensemble of forecasting models that combines the strengths of several strong candidates from these two model types. The proposed ensemble aims to improve the accuracy of forecasts and reduce computational complexity by leveraging the strengths of each candidate model.

Indirect Aggression And Victimization: Investigating Instrument Psychometrics, Gender Differences, And Its Relationship To Social Information Processing, Taylor Steeves

Electronic Theses and Dissertations

The study of indirect bullying behaviors, relational aggression and social aggression, has been of theoretical importance and interest to researchers and psychologists within the last few decades. In this investigation, using a convenience sample of 451 late adolescents attending a private university in the mid-Atlantic U.S., I examined the factor structure of two measures of indirect bullying, the Young Adult Social Behavior Scale – Victim (YASB-V) and the Young Adult Social Behavior Scale – Perpetrator (YASB-P). Using confirmatory factor analysis (CFA), I found that the YASB-V comprised a four-factor model, differing from the model that had been identified in the …

Comparing Elevator Strategies For A Parking Lot, Naveed Arafat

Major Papers

In this paper, we compare elevator strategies for a parking garage. It is assumed that the parking garage has several floors and there is an elevator which can stop on each floor. We begin by considering 4 strategies detailed in page 23. For each strategy, we loop the program 100 times, and get 100 mean values for wait times. Welch's test confirms highly significant differences among the 4 strategies. Repeating the analysis multiple times we see that the best of the 4 strategies is strategy 2, which places the elevator on floor 2 (the median floor) after use.

Evaluation Of Effectiveness In Seismic Microzonation Hazard Mapping In Canada: Communication, Use, Standardization And Levels, Meredith L. Fyfe

Electronic Thesis and Dissertation Repository

Comprehensive seismic hazard maps in the form of seismic microzonation maps have been produced for many populous and seismically active Canadian cities. These maps are important tools for regional decision making for technical (engineering) and non-technical (planning, emergency management, insurance) applications. Outcomes from stakeholder engagement indicate that non-technical end users are not confident in their interpretation of the maps or the mapped site effect predictors (metrics). Users may seek seismic hazard information from lower-level mappings, such as the global Vs₃₀ mosaic, which, when compared against the higher-level Canadian mappings, was found to correctly estimate site class with 26% accuracy …

Excess Zeros Under Gam: Tweedie Or Two-Part?, Xianming Zeng

Major Papers

Positive, right-skewed data with excess zeros are encountered in many real-life situations. Two possible techniques to analyze this type of data are: Two-part models and Tweedie models. The two-part models assume existence of a separate zero generating process, while the Tweedie models are based on distributions that allow mass at zero. The paper aims to present a simulation study to investigate the performance of Generalized Additive Models (GAM) under the distribution of Tweedie and two-part models for such data with excess zero by using MSE (Mean Square Error) and relative bias to compare the performance of both methods. We found …

The "Benfordness" Of Bach Music, Chadrack Bantange, Darby Burgett, Luke Haws, Sybil Prince Nelson

Journal of Humanistic Mathematics

In this paper we analyze the distribution of musical note frequencies in Hertz to see whether they follow the logarithmic Benford distribution. Our results show that the music of Johann Sebastian Bach and Johann Christian Bach is Benford distributed while the computer-generated music is not. We also find that computer-generated music is statistically less Benford distributed than human- composed music.

Math And Democracy, Kimberly A. Roth, Erika L. Ward

Journal of Humanistic Mathematics

Math and Democracy is a math class containing topics such as voting theory, weighted voting, apportionment, and gerrymandering. It was first designed by Erika Ward for math master’s students, mostly educators, but then adapted separately by both Erika Ward and Kim Roth for a general audience of undergraduates. The course contains materials that can be explored in mathematics classes from those for non-majors through graduate students. As such, it serves students from all majors and allows for discussion of fairness, racial justice, and politics while exploring mathematics that non-major students might not otherwise encounter. This article serves as a guide …

Probabilistic Modeling Of Social Media Networks, Distinguishing Phylogenetic Networks From Trees, And Fairness In Service Queues, Md Rashidul Hasan

Mathematics & Statistics ETDs

In this dissertation, three primary issues are explored. The first subject exposes who-saw-from-whom pathways in post-specific dissemination networks in social media platforms. We describe a network-based approach for temporal, textual, and post-diffusion network inference. The conditional point process method discovers the most probable diffusion network. The tool is capable of meaningful analysis of hundreds of post shares. Inferred diffusion networks demonstrate disparities in information distribution between user groups (confirmed versus unverified, conservative versus liberal) and local communities (political, entrepreneurial, etc.). A promising approach for quantifying post-impact, we observe discrepancies in inferred networks that indicate the disproportionate amount of automated bots. …

The Importance Of Contrast Sensitivity, Color Vision, And Electrophysiological Testing In Clinical And Occupational Settings, Frances Silva

Theses & Dissertations

Visual acuity (VA) is universally accepted as the gold standard metric for ocular vision and function. Contrast sensitivity (CS), color vision, and electrophysiological testing for clinical and occupational settings are warranted despite being deemed ancillary and minimally utilized by clinicians. These assessments provide essential information to subjectively and objectively quantify and obtain optimal functional vision. They are useful for baseline data and monitoring hereditary and progressive ocular conditions and cognitive function. The studies in this dissertation highlight the value of contrast sensitivity, color vision, and cone specific electrophysiological testing, as well as the novel metrics obtained with potential practical clinical …

Robust Penalized Density Power Divergence Regression With Scad Penalty For High Dimensional Data Analysis, Maxwell Kwesi Mac-Ocloo

Open Access Theses & Dissertations

Amidst the exponential surge in big data, managing high-dimensional datasets across diverse fields and industries has emerged as a significant challenge. Conventional statistical methods struggle to handle their complexity, making analysis intricate. In response, we've formulated a robust estimator tailored to counter outliers and heavy-tailed errors. Our approach integrates the SCAD penalty into the Density Power Divergence method, effectively reducing insignificant coefficients to zero. This enhances analysis precision and result reliability.We benchmark our robust and penalized model against existing techniques like Huber, Tukey, LASSO, LAD, and LAD-LASSO. Employing both simulated and UCI machine learning repository datasets, we assess method performance …