Physical Sciences and Mathematics Commons^™

Radon Plate-Out And The Effects Of Airflow And Electric Charge For Dark Matter Experiments, Faith Fang

SMU Journal of Undergraduate Research

The Cryogenic Dark Matter Search (SuperCDMS) is an international collaboration designed to search for and detect dark matter particles, which make up ~85% of the matter in the universe. The plate-out, or deposition of naturally occurring radioactive decay byproducts onto surfaces, can create backgrounds that interfere with dark matter detection experiments. In the first series of these experiments, we analyze the amount of radon progeny, 214Pb and 214Bi, that plate-out on polycarbonate samples while controlling factors such as electric charge and airflow. These samples are exposed to radon-spiked nitrogen gas in a polycarbonate wind tunnel to simulate plate-out conditions in …

Transport And Mixing Of Water Masses Across The Southeast Caribbean Ocean Imaged By Seismic Reflection Data, Joseph Renzaglia

Earth Sciences Theses and Dissertations

The Caribbean Sea serves as a major pathway for global thermohaline circulation (THC), which is a complex and vital component of the Earth’s climate system, influencing global heat distribution and oceanic circulation. Though relatively stratified, it is the boundary layer that distributes mass and temperature between the surface waters and the deep ocean where we observe various multiscale mixing processes from mesoscale to fine-scale. In regions where bathymetry is shallower and mechanical mixing forces, such as winds and tides, are more dominant, diapycnal diffusivity is typically stronger, driving vertical mixing. This type of mixing occurs at small scales, typically as …

Interpretable Word-Level Sentiment Analysis With Attention-Based Multiple Instance Classification Models, Chenyu Yang

Statistical Science Theses and Dissertations

In this study, our main objective is to tackle the black-box nature of popular machine learning models in sentiment analysis and enhance model interpretability. We aim to gain more insight into the decision-making process of sentiment analysis models, which is often obscure in those complex models. To achieve this goal, we introduce two word-level sentiment analysis models.

The first model is called the attention-based multiple instance classification (AMIC) model. It combines the transparent model structure of multiple instance classification and the self-attention mechanism in deep learning to incorporate the contextual information from documents. As demonstrated by a wine review dataset …

Ohio Recovery Housing: Resident Risk And Outcomes Assessment, Elyjiah Potter, Bivin Sadler

SMU Data Science Review

Addiction and substance abuse disorder is a significant problem in the United States. Over the past two decades, the United States has faced a boom in substance abuse, which has resulted in an increase in death and disruption of families across the nation. The State of Ohio has been particularly hard hit by the crisis, with overdose rates nearly doubling the national average. Established in the mid 1970’s Sober Living Housing is an alcohol and substance use recovery model emphasizing personal responsibility, sober living, and community support. This model has been adopted by the Ohio Recovery Housing organization, which seeks …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Investigation Into A Practical Application Of Reinforcement Learning For The Stock Market, Philip Traxler, Sadik Aman, Will Rogers, Allyn Okun

SMU Data Science Review

A major problem of the financial industry is the ability to adapt their trading strategies at the same rate the market evolves. This paper proposes a solution using existing Reinforcement Learning libraries to help find new strategies at a practical scale. Using a wide domain of ticker symbols, an algorithm is trained in an environment that better represents reality. The supplied decision-making algorithm is tested using recorded data from the U.S stock market from 2000 through 2022. The results of this research show that existing techniques are statistically better than making decisions at random. With this result, this research shows …

Differentiation Of Human, Dog, And Cat Hair Fibers Using Dart Tofms And Machine Learning, Laura Ahumada, Erin R. Mcclure-Price, Chad Kwong, Edgard O. Espinoza, John Santerre

SMU Data Science Review

Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly …

Impact Of Covid-19 On Recruitment Of High School Athletes To Di Track And Field, Christopher Haub, Jon Paugh, Alonso Salcido, Monnie Mcgee

SMU Data Science Review

Due to COVID-19, in the spring of 2020, the NCAA gave scholarship athletes an extra year of eligibility but did not increase the number of scholarships a school could issue. This potentially led to increased competition for scholarships as coaches could choose between retaining athletes or recruiting new ones. Furthermore, the Spring 2020 track and field season for high school seniors ended early – limiting high school athletes’ chance to get their best scores, and interrupting student to college interaction. This research looks specifically at the impact of COVID-19, and the resulting NCAA policy changes, on the recruitment to DI …

A Prompt Engineering Approach To Creating Automated Commentary For Microsoft Self-Help Documentation Metric Reports Using Chatgpt, Ryan Herrin, Luke Stodgel, Brian Raffety

SMU Data Science Review

Microsoft collects an immense amount of data from the users of their product-self-help documentation. Employees use this data to identify these self-help articles' performance trends and measure their impact on business Key Performance Indicators (KPIs). Microsoft uses various tools like Power BI and Python to analyze this data. The problem is that their analysis and findings are summarized manually. Therefore, this research will improve upon their current analysis methods by applying the latest prompt engineering practices and the power of ChatGPT's large language models (LLMs). Using VBA code, Microsoft Excel, and the ChatGPT API as an Excel add-in, this research …

The Impact Of The Covid-19 Pandemic On Faculty Productivity And Gender Inequalities In Stem Disciplines, Monnie Mcgee, Raag Patel, Roslyn Smith, Satvik Ajmera

SMU Data Science Review

Women and minorities within STEM disciplines historically encounter obstacles in academic advancement, a situation compounded by the COVID-19 pandemic due to the imposition of additional responsibilities like caregiving. This study meticulously probes into the pandemic's influence on traditional academic productivity metrics – specifically publication and submission frequency, citation volume, and leadership in scholarly entities, by employing Natural Language Processing to extract and analyze data from key journals within various scientific domains. A critical revelation from the research indicates a notable downturn in publication activity during 2021, potentially attributed to pandemic-induced disruptions, with a compensatory surge observed in 2022. Although a …

Predicting Land Reclamation Of Bond Released Surface Mines, Kendall Scott, Austin Webb, Tadd Backus, Robert Slater

SMU Data Science Review

Accurately measuring the recovery of released surface mines in the UnitedStates poses crucial challenges. This study aims to develop a prediction of land classification, that considers various environmental and coal mine variables. By utilizing this prediction, the researchers and environmentalists (specifically Appalachian Voices, the group heading this research) can better understand the relevant factors for successful reclamation. Efficient management of mine recovery is essential for environmental sustainability, regulatory compliance, and resource utilization. This study focuses on the Appalachian Forest area, which risks becoming a net carbon source (a place that emits more carbon than it absorbs) due to mine recovery. …

Utilizing Computer Vision For Automated Cellular Microscopy, Ahmed Awadallah, Ryan Bass, James Burke, Robert Price, John Santerre

SMU Data Science Review

Abstract. Post-acquisition data analysis of microscopy images is a vital yet time-consuming process for researchers. Quantitative fields such as biology and microbiology often require using images as primary data sources. Finding methods to automate this process would increase the throughput, quality, and reproducibility. This research aims to provide a novel end-to-end pipeline that reduces the workload on researchers in identifying cell cytoplasm and nuclei while creating a process that can scale to the researcher's needs. The proposed methodology utilizes various image-processing techniques to rapidly identify the boundaries of cells and nuclei, including filtering, thresholding, and deep learning. The results …

Seabed Reflection Coefficient Variability In The Mono Lake Using Seismic Reflection Sparker Data, Francis Nnamdi Okeifufe

Earth Sciences Theses and Dissertations

Acoustic reflections from a lakebed provide valuable information about the dynamic boundary between water and the lakebed, where sediments and biota in the water column eventually settle. In this study, we present a comprehensive analysis of reflected sound wave amplitudes to gain insights into the physical properties of a water-sediment boundary. We decided to carry out this analysis within the unique ecosystem of the Mono Lake basin due to the fact that the Mono Lake, a meromictic and hypersaline waterbody is situated within one of the most active yet understudied volcanic regions in the United States. The fact that the …

Bayesian Statistical Modeling Of Spatially Resolved Transcriptomics Data, Xi Jiang

Statistical Science Theses and Dissertations

Spatially resolved transcriptomics (SRT) quantifies expression levels at different spatial locations, providing a new and powerful tool to investigate novel biological insights. As experimental technologies enhance both in capacity and efficiency, there arises a growing demand for the development of analytical methodologies.

One question in SRT data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process, which could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types …

Static Malware Family Clustering Via Structural And Functional Characteristics, David George, Andre Mauldin, Josh Mitchell, Sufiyan Mohammed, Robert Slater

SMU Data Science Review

Static and dynamic analyses are the two primary approaches to analyzing malicious applications. The primary distinction between the two is that the application is analyzed without execution in static analysis, whereas the dynamic approach executes the malware and records the behavior exhibited during execution. Although each approach has advantages and disadvantages, dynamic analysis has been more widely accepted and utilized by the research community whereas static analysis has not seen the same attention. This study aims to apply advancements in static analysis techniques to demonstrate the identification of fine-grained functionality, and show, through clustering, how malicious applications may be grouped …

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.

Leveraging Non-Covalent Interactions Between Small Organic Molecules And Inorganic Scaffolds In The Design Of Advanced Materials, Jonathan Lefton

Chemistry Theses and Dissertations

Powder diffraction is a powerful tool for studying crystal structures, especially as it relates to interactions of small organic molecules with inorganic compounds. The first part of this dissertation involves small organic ligands interacting with metal-organic framework, MOF-74. The first and simplest iteration involves the crystal structure solution of a neat, liquid loading of n-propylmercaptan to the open metal sites within the MOF-74 pores. Later studies investigate the leveraging of a similarly sized bitopic ligand in the solution loading of 1,2-ethanedithiol, which results in the amorphization of MOF-74. Having no crystallinity, amorphous or severely defected materials can be a …

The Investigation Of Singlet Fission From The Perspective Of Hierarchy Of Pure States (Hops), Tao (James) Chen

Chemistry Theses and Dissertations

This thesis provides a preliminary investigation of singlet fission from the perspective of Hierarchy of pure states (HOPS), which provides a numerical exact solution for the investigation of a series of open quantum systems. Since the inception of the concept of singlet fission about half a century ago, this photo-physical process has attracted the attention of a multitude of researchers and has been extensively studied theoretically and experimentally. However, these previous methods for the investigation of singlet fission focus more or less on tackling the underlying mechanisms of singlet fission from the perspective of perturbation. So far, the HOPS method …

The Investigation Of Singlet Fission From The Perspective Of Hierarchy Of Pure States (Hops), Tao (James) Chen

Chemistry Theses and Dissertations

This thesis provides a preliminary investigation of singlet fission from the perspective of Hierarchy of pure states (HOPS), which provides a numerical exact solution for the investigation of a series of open quantum systems. Since the inception of the concept of singlet fission about half a century ago, this photo-physical process has attracted the attention of a multitude of researchers and has been extensively studied theoretically and experimentally. However, these previous methods for the investigation of singlet fission focus more or less on tackling the underlying mechanisms of singlet fission from the perspective of perturbation. So far, the HOPS method …

Neural Network Learning For Pdes With Oscillatory Solutions And Causal Operators, Lizuo Liu

Mathematics Theses and Dissertations

In this thesis, we focus on developing neural networks algorithms for scientific computing. First, we proposed a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations. Several linearized learning schemes have been proposed for neural networks solving nonlinear Navier-Stokes equations. We also proposed a causality deep neural network (Causality-DeepONet) to learn the causal response of a physical system. An extension of the Causality-DeepONet to time-dependent PDE systems is also proposed. The PhaseDNN makes use of the fact that common DNNs often achieve convergence in the low frequency …

Study Of Radiation Effects In Gan-Based Devices, Han Gao

Electrical Engineering Theses and Dissertations

Radiation tolerance of wide-bandgap Gallium Nitride (GaN) high-electron-mobility transistors (HEMT) has been studied, including X-ray-induced TID effects, heavy-ion-induced single event effects, and neutron-induced single event effects. Threshold voltage shift is observed in X-ray irradiation experiments, which recovers over time, indicating no permanent damage formed inside the device. Heavy-ion radiation effects in GaN HEMTs have been studied as a function of bias voltage, ion LET, radiation flux, and total fluence. A statistically significant amount of heavy-ion-induced gate dielectric degradation was observed, which consisted of hard breakdown and soft breakdown. Specific critical injection level experiments were designed and carried out to explore …

Polygonal Faults In The Austin Chalk: Invariance Of Scale From Mud Cracks To Polygons With Implications Of Structural, Geomorphic And Isotopic Data On Polygonal Fault Geometry And Origin., Kun Shang

Earth Sciences Theses and Dissertations

The Cretaceous Austin Chalk contains large numbers of fractures and normal faults whose orientations have been attributed to either regional stresses (e.g., the Balcones fault trend) or, by analogy with the mudrocks, to polygonal faulting resulting from compaction. In this study, we present geomorphic data, field study, and stable isotope data to support that the majority of these faults in North Texas are polygonal. Field-measured fault orientations suggest randomly distributed fault strikes, indicating a polygonal fault structure. Using geomorphologic data (topographic and DEM data) on stream orientations suggests that the polygonal fault patterns are best reflected in the headwater (1st …

A Comparison Of Confidence Intervals In State Space Models, Jinyu Du

Statistical Science Theses and Dissertations

This thesis develops general procedures for constructing confidence intervals (CIs) of the error disturbance parameters (standard deviations) and transformations of the error disturbance parameters in time-invariant state space models (ssm). With only a set of observations, estimating individual error disturbance parameters accurately in the presence of other unknown parameters in ssm is a very challenging problem. We attempted to construct four different types of confidence intervals, Wald, likelihood ratio, score, and higher-order asymptotic intervals for both the simple local level model and the general time-invariant state space models (ssm). We show that for a simple local level model, both the …

Optimal Experimental Planning Of Reliability Experiments Based On Coherent Systems, Yang Yu

Statistical Science Theses and Dissertations

In industrial engineering and manufacturing, assessing the reliability of a product or system is an important topic. Life-testing and reliability experiments are commonly used reliability assessment methods to gain sound knowledge about product or system lifetime distributions. Usually, a sample of items of interest is subjected to stresses and environmental conditions that characterize the normal operating conditions. During the life-test, successive times to failure are recorded and lifetime data are collected. Life-testing is useful in many industrial environments, including the automobile, materials, telecommunications, and electronics industries.

There are different kinds of life-testing experiments that can be applied for different purposes. …

New Methods For Core-Hole Spectroscopy Based On Coupled Cluster Theory, Megan Simons

Chemistry Theses and Dissertations

X-ray absorption spectra (XAS) is a method used to investigate atomic local structure and electronic states. Coupled cluster method is a numerical method used for describing many-body systems and electron correlation in a wavefunction. When equation-of-motion coupled cluster is used in XAS calculations, the ground state is applied to the excitation operator, which excites or ionizes the electron. This causes a large orbital relaxation error, normally ~5 eV, which leads to the need for triple excitations in order to obtain accurate results.

This dissertation introduces a coupled cluster method that uses "transition potential" reference orbitals to reduce the orbital relaxation …

Optimizing Tumor Xenograft Experiments Using Bayesian Linear And Nonlinear Mixed Modelling And Reinforcement Learning, Mary Lena Bleile

Statistical Science Theses and Dissertations

Tumor xenograft experiments are a popular tool of cancer biology research. In a typical such experiment, one implants a set of animals with an aliquot of the human tumor of interest, applies various treatments of interest, and observes the subsequent response. Efficient analysis of the data from these experiments is therefore of utmost importance. This dissertation proposes three methods for optimizing cancer treatment and data analysis in the tumor xenograft context. The first of these is applicable to tumor xenograft experiments in general, and the second two seek to optimize the combination of radiotherapy with immunotherapy in the tumor xenograft …

Photonic Sensors Based On Integrated Ring Resonators, Jaime Da Silva

Mechanical Engineering Research Theses and Dissertations

This thesis investigates the application of integrated ring resonators to different sensing applications. The sensors proposed here rely on the principle of optical whispering gallery mode (WGM) resonance shifts of the resonators. Three distinct sensing applications are investigated to demonstrate the concept: a photonic seismometer, an evanescent field sensor, and a zero-drift Doppler velocimeter. These concepts can be helpful in developing lightweight, compact, and highly sensitive sensors. Successful implementation of these sensors could potentially address sensing requirements for both space and Earth-bound applications. The feasibility of this class of sensors is assessed for seismic, proximity, and vibrational measurements.

Development Of Bayesian Hierarchical Methods Involving Meta-Analysis, Jackson Barth

Statistical Science Theses and Dissertations

When conducting statistical analysis in the Bayesian paradigm, the most critical decision made by the researcher is the identification of a prior distribution for a parameter. Despite the mathematical soundness of the Bayesian approach, a wrongly specified prior can lead to biased and incorrect results. To avoid this, prior distributions should be based on real data, which are easily accessible in the "big data" era. This dissertation explores two applications of Bayesian hierarchical modelling that incorporate information obtained from a meta-analysis.

The first of these applications is in the normalization of genomics data, specifically for nanostring nCounter datasets. A meta-analysis …