Physical Sciences and Mathematics Commons^™

Stochastic Optimization To Reduce Aircraft Taxi-In Time At Igia, New Delhi, Rajib Das, Saileswar Ghosh, Rajendra Desai, Pijus Kanti Bhuin, Stuti Agarwal

International Journal of Aviation, Aeronautics, and Aerospace

Since there is an uncertainty in the arrival times of flights, pre-scheduled allocation of runways and stands and the subsequent first-come-first-served treatment results in a sub-optimal allocation of runways and stands, this is the prime reason for the unusual delays in taxi-in times at IGIA, New Delhi.

We simulated the arrival pattern of aircraft and utilized stochastic optimization to arrive at the best runway-stands allocation for a day. Optimization is done using a GRG Non-Linear algorithm in the Frontline Systems Analytic Solver platform. We applied this model to eight representative scenarios of two different days. Our results show that without …

Local Or Import? A Compositional Analysis Of Aztec Ritual Ceramics In The Tuxtlas Frontier, Veracruz, Mexico, Matthew T. Meyer

Murray State Theses and Dissertations

At the time of Spanish Contact in the early 16th Century the western Tuxtlas region formed part of the Aztec imperial frontier in the southern Gulf lowlands. The most apparent material manifestation of this imperial connection was Aztec-style Texcoco-Molded Censers, recovered primarily from sites that served local centralizing functions. While rare, these symbols may provide valuable information on the dynamics of frontier politics and the relations between this region and the distant core to which they were sending tax payments. Initial consideration of this adopted imperial style implies political linkages, but the mechanisms of introduction, knowledge transmission, imperial versus local …

Bayesian Structural Time Series Methods For Modeling Cattle Body Temperature In Heat-Stressed Animals, Lacey Quandt

Murray State Theses and Dissertations

Climate change has had devastating effects globally, most commonly talked about during natural disasters and rising temperatures. Notably, the climate concern is turning towards agriculture and livestock. With rising temperatures, the prolonged amount of heat stress put on animals, specifically cattle, is becoming more apparent. Heat stress has been linked to a reduction in cattle growing and fattening, feed intake, productivity, reproduction, and fertility; increased heart rates and respiration; changes in behavior; and mortality in severe cases. There are abatement strategies put in place to lower heat stress in cattle, such as improvements in shading and cooling, nutritional management, and …

Enhancing Control Room Operator Decision Making: An Application Of Dynamic Influence Diagrams In Formaldehyde Manufacturing, Joseph Mietkiewicz, Anders L. Madsen

Articles

Intoday’s rapidly evolving industrial landscape, control room operators must grapple with an ever-growing array of tasks and respon sibilities. One major challenge facing these operators is the potential for task overload, which can lead to decision fatigue and increased reliance on cognitive biases. To address this issue, we propose the use of dynamic influence diagrams (DID) as the core of our decision support system. By monitoring the process over time and identifying anomalies, DIDs can recommend the most effective course of action based on a probabilistic assessment of future outcomes. Instead of letting the operator choose or search for the …

A Duration-Over-Threshold Model For Flood Frequency And Flow Regime Characterization, Kenneth Scott Feasley Lawson

Graduate College Dissertations and Theses

Proper characterization of river flow is essential for the development of structural and non-structural measures to reduce flood damages, restore ecosystem functions, and manage environmental contaminants in riparian zones. In particular, the duration of flood events is an important feature of floods that drives riverine processes such as erosion, geomorphic adjustment, habitat suitability, nutrient and water quality dynamics, and structural damage. Despite this, most flood characterization methods focus solely on relating the magnitude of annual-maximum discharges to frequency, without addressing the duration of flood events. We investigated event-specific discharge-duration dynamics at 33 USGS stream gages within the US state of …

Automated Machine Learning: Intellient Binning Data Preparation And Regularized Regression Classfier, Jianbin Zhu

Electronic Theses and Dissertations, 2020-2023

Automated machine learning (AutoML) has become a new trend which is the process of automating the complete pipeline from the raw dataset to the development of machine learning model. It not only can relief data scientists' works but also allows non-experts to finish the jobs without solid knowledge and understanding of statistical inference and machine learning. One limitation of AutoML framework is the data quality differs significantly batch by batch. Consequently, fitted model quality for some batches of data can be very poor due to distribution shift for some numerical predictors. In this dissertation, we develop an intelligent binning to …

Beginner's Analysis Of Financial Stochastic Process Models, David Garcia

HMC Senior Theses

This thesis explores the use of geometric Brownian motion (GBM) as a financial model for predicting stock prices. The model is first introduced and its assumptions and limitations are discussed. Then, it is shown how to simulate GBM in order to predict stock price values. The performance of the GBM model is then evaluated in two different periods of time to determine whether it's accuracy has changed before and after March 23, 2020.

Graph Learning On Multi-Modality Medical Data To Generate Clinical Predictions, Justin Jiang

HMC Senior Theses

There exist petabytes of data pertaining to medical visits – everything from blood pressure recordings, X-rays, and doctor’s notes. Electronic health records (EHRs) organize this data into databases, providing an exciting opportunity for machine learning researchers to dive deeper into analyzing human health. There already exist machine learning models that aim to expedite the process of hospital visits; for example, summary models can digest a patient’s medical history and highlight certain parts of their past that merit attention. The current frontier of medical machine learning is combining the various formats of data to generate a clinical prediction – much like …

Cheiloscopy Patterns In Individuals With And Without Parafunctional Oral Habits: A Cross-Sectional Observation Pilot Study, Emily Regan, Brenda Bradshaw, Ann Bruhn, Walter Melvin, Sinjini Sikdar

Dental Hygiene Faculty Publications

Purpose

Lip prints are unique and have potential for use as a human identifier. The purpose of this study was to observe possible cheiloscopy differences of individuals with and without parafunctional oral habits such as smoking, vaping, playing a wind instrument or using an asthma inhaler.

Methods

This IRB approved blinded cross-sectional observation pilot study collected lip prints from 66 individuals, three of which were excluded. Participants cleansed their lips, then lipstick was applied to the vermillion zones of the upper and lower lips. Adhesive tape was applied to the lips and prints were transferred to white bond paper for …

A Class Of Regression Models For Pairwise Comparisons Of Forensic Handwriting Comparison Systems, Cami M. Fuglsby

Electronic Theses and Dissertations

Handwriting analysis is a complex field largely living in forensic science and the legal realm. One task of a forensic document examiner (FDE) may be to determine the writer(s) of handwritten documents. Automated identification systems (AIS) were built to aid FDEs in their examinations. Part of the uses of these AIS (such as FISH[5] [7],WANDA [6], CEDAR-FOX [17], and FLASHID®2) are tomeasure features about a handwriting sample and to provide the user with a numeric value of the evidence. These systems use their own algorithms and definitions of features to quantify the writing and can be considered a black-box. The …

Striving For Appropriate Antibiotic Use: A Biomarker Initiative, And Outcomes Associated With Azithromycin Exposure, Amanda Gusovsky

Theses and Dissertations--Pharmacy

The introduction of antibiotics into clinical practice is considered the greatest medical breakthrough of the 20^thcentury. However, the use of antibiotics can contribute to the development of resistance. In the United States (U.S.), approximately 2.8 million people are infected with antibiotic-resistant bacteria each year, and more than 35,000 people die as a result. Moreover, some antibiotics are known to cause cardiac side effects including QT prolongation, hypotension, and ventricular arrythmias. The U.S. Centers for Disease Control and Prevention (CDC) defines appropriate antibiotic use as the effort to use “the right antibiotic, at the right dose, for the right …

Statistical Intervals For Neural Network And Its Relationship With Generalized Linear Model, Sheng Yuan

Theses and Dissertations--Statistics

Neural networks have experienced widespread adoption and have become integral in cutting-edge domains like computer vision, natural language processing, and various contemporary fields. However, addressing the statistical aspects of neural networks has been a persistent challenge, with limited satisfactory results. In my research, I focused on exploring statistical intervals applied to neural networks, specifically confidence intervals and tolerance intervals. I employed variance estimation methods, such as direct estimation and resampling, to assess neural networks and their performance under outlier scenarios. Remarkably, when outliers were present, the resampling method with infinitesimal jackknife estimation yielded confidence intervals that closely aligned with nominal …

Copula-Based Models For Bivariate And Multivariate Zero-Inflated Count Time Series Data, Dimuthu Fernando, Norou Diawara

College of Sciences Posters

Count time series data have multiple applications. The applications can be found in areas of finance, climate, public health and crime data analyses. In some scenarios, count time series come as multivariate vectors that exhibit not only serial dependence within each time series but also with cross correlation among the series. When considering these observed counts, analysis presents crucial challenges when a value, say zero, occurs more often than usual. There is presence of zero-inflation in the data.

In this presentation, we mainly focus on modeling bivariate zero-inflated count time series model based on a joint distribution of the two …

The Impact Of Faculty Composition On Cost Per Student: A Mixed Model Approach, Arun Sleeba

Graduate Research Theses & Dissertations

This thesis aims to explore whether research universities in the United States, specifically those classified as Carnegie I or II institutions, utilize part time contingent faculty(rPTF) as a cost saving strategy. Additionally, it sought to determine if there was a differential impact on total costs when comparing public and private universities. Employing a linear mixed effects model with random intercept and slopes, this study analyzed the relationship between rPTF (ratio of part-time to total faculty) and total cost. This study did not provide substantial evidence to support the notion despite observing a negative correlation between rPTF and total cost. Regarding …

A State Space Modeling Approach To Eeg Artifact Removal, Patrick B. Rafael

Graduate Research Theses & Dissertations

In this work, a state space modeling approach is applied to an Electroencephalography(EEG) recording for the purpose of artifact removal, and is compared against Independent Components Analysis (ICA), the current gold standard. Issues of model identifiability are touched on, and Hamiltonian Monte Carlo (HMC) is used to estimate a linear non-Gaussian state space model. Results show that estimating such a model is a nontrivial matter, and the full utility of the state space approach remains to be demonstrated.

Enhanced Maximum Likelihood Models For Underreported Variables: Extending To Multiple Claims Dimension, Shalaka Sudhanshu Sarpotdar

Graduate Research Theses & Dissertations

This thesis builds upon the foundations laid out in Xia et al. [2023], which explored the utilizationof Maximum Likelihood approach to model misrepresentation data in Generalized Linear Models (GLM) ratemaking models. We introduce the concept of “underreported variables”, a form of insurance misrepresentation where insured individuals provide inaccurate information about risk factors that influence insurance eligibility, premiums, and insured amounts. Unlike fraudulent misrepresentation, underreported variables arise from a lack of awareness regarding the insured’s mental and physical health conditions, rather than fraudulent intent. The study rigorously tests the proposed model using health insurance data and extends its applicability to other …

Dynamic Overnight Effect On Next Day Stock Market Forecasting, Thomas J. Lee

Graduate Research Theses & Dissertations

Using a cross section of stocks that have high frequency trading data from 2007 to 2018, we document whether various intraday momentum patterns found in the financial literature over the years continue to hold over time. The first half hour return on the market is often seen as having predictive power over the last half hour of trading, or overnight returns are thought to reverse in the next day's first half hour of trading. We find that while there is some evidence for these patterns, especially in the earlier years, these patterns tend to weaken over time as investors take …

Macroeconomic Factors Influencing Foreign Direct Investment In Some Selected Countries In Africa, Richard Essel Mensah

Graduate Research Theses & Dissertations

This paper investigates the possible factors that influence foreign direct investment inflow rate to Africa after controlling for other macroeconomic factors. Using the heterogenous Toeplitz mixed method on a sample of 23 countries from 1998 – 2020, we find evidence of the statistical significance of a relationship between the amount of trade done in Africa and the FDI inflow rate in Africa. We also find a statistical relationship between the labor force participation rate and the FDI inflow rate to Africa. Although the Fixed effect and GLM method did not find the relationship between LFP rate and FDI inflow to …

Machine Learning Methods For Prediction Of Human Infectious Virus And Imputation Of Hla Alleles, Xiaoqing Gao

Dissertations, Master's Theses and Master's Reports

This dissertation contains three Chapters. The following is a concise description of each Chapters.

In Chapter 1, we introduced the Random Forest, a machine learning method, to foresee whether a virus is capable of infecting humans. The Covid pandemic informs us the importance of predicting the ability of a zoonotic virus that can infect humans from its genomic sequence. We used the -mer with and as features of a virus to predict if it can affect humans. We further employed the Boruta algorithm to select the important features, then fed those important features into the Random Forest method to train …

Longitudinal Sport Science Implementation In American Collegiate Men’S Basketball, Jason Stone

Graduate Theses, Dissertations, and Problem Reports

The expanding opportunities to implement sport science frameworks in elite-level basketball environments coincide with the sport’s increasing global prominence. Concomitant to these opportunities is the continual growth of the sport technology market (e.g., wearables, force plates) and computational power (e.g., data management tools, coding capabilities), which yields solutions and challenges for both athletes and practitioners. Due to the rapid influx of new sport technologies in high performance environments, particularly American Collegiate Men’s Basketball, more formal and ecologically valid research on how to effectively utilize data derived from them, particularly over long periods of time (i.e., multiple seasons) is needed. To …

Evaluation Of Edison's Data Science Competency Framework Through A Comparative Literature Analysis, Karl R. B. Schmitt, Linda Clark, Katherine M. Kinnaird, Ruth E. H. Wertz, Björn Sandstede

Statistical and Data Sciences: Faculty Publications

During the emergence of Data Science as a distinct discipline, discussions of what exactly constitutes Data Science have been a source of contention, with no clear resolution. These disagreements have been exacerbated by the lack of a clear single disciplinary 'parent.' Many early efforts at defining curricula and courses exist, with the EDISON Project's Data Science Framework (EDISON-DSF) from the European Union being the most complete. The EDISON-DSF includes both a Data Science Body of Knowledge (DS-BoK) and Competency Framework (CF-DS). This paper takes a critical look at how EDISON's CF-DS compares to recent work and other published curricular or …

Meta-Analysis Of Mesenchymal Stem Cell Gene Expression Data From Obese And Non-Obese Patients, Dakota William Shields

Masters Theses

"The prevalence of gene expression microarray datasets in public repositories gives opportunity to analyze biologically interesting datasets without running the laboratory aspect in house. Such experimentation is expensive in terms of finances, time, and expertise, which often results in low numbers of replicates. Meta-analysis techniques attempt to overcome issues due to few biological or technical replicates by combining separate experiments together to increase statistical power. Proper statistical considerations help to offset issues like simultaneous testing of thousands of genes, unintended hybridization, and other noises.

Microarrays contain light intensities from tens of thousands of hybridized probes giving a measure of gene …

The Chains That Bind: Gender, Disability, Race, And It Accommodations, Eleanor T. Loiacono, Shiya Cao

Statistical and Data Sciences: Faculty Books

This chapter explores intersectionality of gender, disability, and race relevant to Information Technology (IT) accommodations and employment. More specifically, we investigate individuals’ experiences and differences in receiving IT accommodations as an organizational diversity intervention that helps disabled employees integrate into the workplace. The goal of this chapter is to seek a better understanding of individual differences in the accommodation process and how to empower disabled women in the workplace. To do so, by applying the Individual Differences Theory of Gender and IT (IDTGIT), we focus on the experiences disabled men and women have with regard to IT accommodations as well …

Applications Of Transfer Learning From Malicious To Vulnerable Binaries, Sean Patrick Mcnulty

Graduate Student Theses, Dissertations, & Professional Papers

Malware detection and vulnerability detection are important cybersecurity tasks. Previous research has successfully applied a variety of machine learning methods to both. However, despite their potential synergies, previous research has yet to unite these two tasks. Given the recent success of transfer learning in many domains, such as language modeling and image recognition, this thesis investigated the use of transfer learning to improve vulnerability detection. Specifically, we pre-trained a series of models to detect malicious binaries and used the weights from those models to kickstart the detection of vulnerable binaries. In our study, we also investigated five different data representations …

The Birds And The Trees: Quantifying The Drivers Of Whitebark Pine Decline And Clark's Nutcracker Habitat Use In Glacier National Park, Vladimir Kovalenko

Graduate Student Theses, Dissertations, & Professional Papers

Whitebark pine (Pinus albicaulis), recently listed as threatened under the Endangered Species Act, is in steep decline in Glacier National Park, Montana, USA due to the non-native pathogen Cronartium ribicola, causal agent of the fatal disease white pine blister rust. A sample of the park’s population suggests that approximately 70 percent of whitebark pines have died, while 65 percent of the remaining trees are infected. Using landscape and climate variables, we show how geographic location, elevation, aspect, solar radiation, relative humidity, and snowpack interact with tree diameter to affect mortality, disease incidence, cone production, and regeneration. We also examine how …

Meta-Analysis Of Scent Detection Canines And Potential Factors Influencing Their Success Rates, Molly Marie Jaskinia

Graduate Student Theses, Dissertations, & Professional Papers

Objective: This is a meta-analysis focused on the success rates of scent detection canines and potential factors that could influence their accuracy. A series of statistical analyses were conducted to determine if certain demographic factors, such as the dog’s gender, age, and breed, have an effect on a scent dog’s accuracy during a search. Or if more circumstantial factors, like the dog’s level of experience in scent work, the type of target scent, and their handler’s awareness of the target’s location, affect the outcome of the search.

Materials and Methods: A dataset was created from 37 different articles consisting of …

The Influence Of Instrumental Sources Of Variance On Mass Spectral Comparison Algorithms, Isabel Cristina Galvez Valencia

Graduate Theses, Dissertations, and Problem Reports

Current search algorithms for the identification of substances based only on their electron ionization mass spectra provide the correct compound as their top result approximately 80% of the time. One contributing factor to the ~20% deviation in the first-hit recognition rate is that traditional algorithms work by comparing the unknown spectrum to an ‘ideal’ or consensus spectrum of each reference compound. The inclusion of replicate reference spectra in a database has been shown to improve the probability of ranking the correct identity in the number one position, but the variance in ion abundances caused by different conditions or different instruments …

Knowledge Discovery On The Integrative Analysis Of Electrical And Mechanical Dyssynchrony To Improve Cardiac Resynchronization Therapy, Zhuo He

Dissertations, Master's Theses and Master's Reports

Cardiac resynchronization therapy (CRT) is a standard method of treating heart failure by coordinating the function of the left and right ventricles. However, up to 40% of CRT recipients do not experience clinical symptoms or cardiac function improvements. The main reasons for CRT non-response include: (1) suboptimal patient selection based on electrical dyssynchrony measured by electrocardiogram (ECG) in current guidelines; (2) mechanical dyssynchrony has been shown to be effective but has not been fully explored; and (3) inappropriate placement of the CRT left ventricular (LV) lead in a significant number of patients.

In terms of mechanical dyssynchrony, we utilize an …

Statistical Modeling: Regression, Survival Analysis, And Time Series Analysis, Lawrence Leemis

Open Education Resources (OER)

Statistical Modeling provides an introduction to regression, survival analysis, and time series analysis for students who have completed calculus-based courses in probability and mathematical statistics. The book uses the R language to fit statistical models, conduct Monte Carlo simulation experiments and generate graphics. Over 300 exercises at the end of the chapters makes this an appropriate text for a class in statistical modeling.

Part 1: Regression
Chapter 1: Simple Linear Regression
Chapter 2: Inference in Simple Linear Regression
Chapter 3: Topics in Regression

Part II: Survival Analysis
Chapter 4: Probability Models in Survival Analysis
Chapter 5: Statistical Methods in Survival …

Statistical Models For Decision-Making In Professional Soccer, Sean Hellingman

Theses and Dissertations (Comprehensive)

As soccer is widely regarded as the most popular sport in the world there is high interest in methods of improving team performances. There are many ways teams and individual athletes can influence their own performances during competition. This thesis focuses on developing statistical methodologies for improving competition-based decision-making for soccer so as to allow professional soccer teams to make better informed decisions regarding player selection and in-game decision-making.

To properly capture the dynamic actions of professional soccer, Markov chains with increasing complexity are proposed. These models allow for the inclusion of potential changes in the process caused by goals …