Data Science Commons^™

Transition Metal Phosphides For High Performance Electrochemical Energy Storage Devices, Amina Saleh

Theses and Dissertations

Electrochemical energy storage technologies are nowadays playing a leading role in the global effort to address the energy challenges. A lot of attention has been devoted to designing hybrid devices known as supercapatteries which combine the merits of supercapacitors (high power density) and rechargeable batteries (high energy density). Transition metal phosphides (TMP) are a rising star for supercapattery anode materials thanks to their high conductivity, metalloid characteristics, and kinetic favorability for fast electron transport. Herein, new TMP-based materials were synthesized for use as supercapattery positive electrodes, via a multifaceted approach to yield devices enjoying concurrently high power and energy densities. …

Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher

Articles

In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best …

P3k14c, A Synthetic Global Database Of Archaeological Radiocarbon Dates, Erick Robinson

Anthropology Faculty Publications and Presentations

Archaeologists increasingly use large radiocarbon databases to model prehistoric human demography (also termed paleo-demography). Numerous independent projects, funded over the past decade, have assembled such databases from multiple regions of the world. These data provide unprecedented potential for comparative research on human population ecology and the evolution of social-ecological systems across the Earth. However, these databases have been developed using different sample selection criteria, which has resulted in interoperability issues for global-scale, comparative paleo-demographic research and integration with paleoclimate and paleoenvironmental data. We present a synthetic, global-scale archaeological radiocarbon database composed of 180,070 radiocarbon dates that have been cleaned according …

Machine Learning For Predicting Risk Of Early Dropout In A Recovery Program For Opioid Use Disorder, Assaf Gottlieb, Andrea Yatsco, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

BACKGROUND: An increase in opioid use has led to an opioid crisis during the last decade, leading to declarations of a public health emergency. In response to this call, the Houston Emergency Opioid Engagement System (HEROES) was established and created an emergency access pathway into long-term recovery for individuals with an opioid use disorder. A major contributor to the success of the program is retention of the enrolled individuals in the program.

METHODS: We have identified an increase in dropout from the program after 90 and 120 days. Based on more than 700 program participants, we developed a machine learning …

Development Of Advanced Machine Learning Models For Analysis Of Plutonium Surrogate Optical Emission Spectra, Ashwin P. Rao, Phillip R. Jenkins, John D. Auxier Ii, Michael B. Shattan, Anil K. Patnaik

Faculty Publications

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of …

Application Of Gravity Data For Hydrocarbon Exploration Using Machine Learning Assisted Workflow, Oluwafemi Temidayo Alaofin

LSU Master's Theses

Gravity survey has played an essential role in many geoscience fields ever since it was conducted, especially as an early screening tool for subsurface hydrocarbon exploration. With continued improvement in data processing techniques and gravity survey accuracy, in-depth gravity anomaly studies, such as characterization of Bouguer and isostatic residual anomalies, have the potential to delineate prolific regional structures and hydrocarbon basins. In this study, we focus on developing a cost-effective, quick, and computationally efficient screening tool for hydrocarbon exploration using gravity data employing machine learning techniques. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, …

On The Horizon: Nanosatellite Constellations Will Revolutionize The Internet Of Things (Iot), Diane Janosek

Seattle Journal of Technology, Environmental, & Innovation Law

The Internet of Things has experienced exponential growth and use across the globe with 25.1 billion devices currently in use. Until recently, the functionality of the IoT was dependent on secure data flow between internet terrestrial stations and the IoT devices. Now, a new alternative path of data flow is on the horizon.

IoT device manufacturers are now looking to outer space nanosatellite constellations to connect to a different type of internet. This new internet is no longer terrestrial with fiber cables six feet underground but now looking up, literally, 200 to 300 miles above the earth, to communicate, connect …

Sociodemographic And Clinical Characteristics Associated With Improvements In Quality Of Life For Participants With Opioid Use Disorder, Assaf Gottlieb, Christine Bakos-Block, James R Langabeer, Tiffany Champagne-Langabeer

Faculty, Staff and Student Publications

Background: The Houston Emergency Opioid Engagement System was established to create an access pathway into long-term recovery for individuals with opioid use disorder. The program determines effectiveness across multiple dimensions, one of which is by measuring the participant's reported quality of life (QoL) at the beginning of the program and at successive intervals.

Methods: A visual analog scale was used to measure the change in QoL among participants after joining the program. We then identified sociodemographic and clinical characteristics associated with changes in QoL.

Results: 71% of the participants (n = 494) experienced an increase in their QoL scores, …

Author’S Reflections On Making Sense Of Numbers: Quantitative Reasoning For Social Research, Jane E. Miller

Numeracy

Miller, Jane E. 2021. Making Sense of Numbers: Quantitative Reasoning for Social Research. (Los Angeles: SAGE Publications) 608 pp. ISBN 978-1544355597.

This article introduces and provides an excerpt from Making Sense of Numbers: Quantitative Reasoning for Social Research, published by Sage. The book explains and illustrates how making sense of numbers involves integrating concepts and skills from mathematics, statistics, study design, and communications, along with information about the specific topic and context under study. It teaches how to avoid making common errors of logic, calculation, and interpretation by introducing a systematic approach and a healthy dose of skepticism …

Opening A Window To Evolution: David Angelini’S Research On Genetic Adaptation Gets Push From Mcvey Data Science Initiative, Christina Nunez

Colby Magazine

Most people think of soapberry bugs as little more than a nuisance, if they think of them at all. Found across much of the southeastern United States, the oblong insect is harmless to humans and likes to hang out on plants native to the soapberry family, hence its straightforward name.

Lncrnafunc: A Knowledgebase Of Lncrna Function In Human Cancer, Mengyuan Yang, Huifen Lu, Jiajia Liu, Sijia Wu, Pora Kim, Xiaobo Zhou

Faculty, Staff and Student Publications

The long non-coding RNAs associating with other molecules can coordinate several physiological processes and their dysfunction can impact diverse human diseases. To date, systematic and intensive annotations on diverse interaction regulations of lncRNAs in human cancer were not available. Here, we built lncRNAfunc, a knowledgebase of lncRNA function in human cancer at https://ccsm.uth.edu/lncRNAfunc, aiming to provide a resource and reference for providing therapeutically targetable lncRNAs and intensive interaction regulations. To do this, we collected 15 900 lncRNAs across 33 cancer types from TCGA. For individual lncRNAs, we performed multiple interaction analyses of different biomolecules including DNA, RNA, and protein levels. …

Fusiongdb 20: Fusion Gene Annotation Updates Aided By Deep Learning, Pora Kim, Hua Tan, Jiajia Liu, Haeseung Lee, Hyesoo Jung, Himanshu Kumar, Xiaobo Zhou

Faculty, Staff and Student Publications

A knowledgebase of the systematic functional annotation of fusion genes is critical for understanding genomic breakage context and developing therapeutic strategies. FusionGDB is a unique functional annotation database of human fusion genes and has been widely used for studies with diverse aims. In this study, we report fusion gene annotation updates aided by deep learning (FusionGDB 2.0) available at https://compbio.uth.edu/FusionGDB2/. FusionGDB 2.0 has substantial updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with 44 human …

Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil

Published and Grey Literature from PhD Candidates

Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of …

Humanizing Computational Literature Analysis Through Art-Based Visualizations, Alexandria Leto

Electronic Theses and Dissertations

Inequalities in gender representation and characterization in fictional works are issues that have long been discussed by social scientists. This work addresses these inequalities with two interrelated components. First, it contributes a sentiment and word frequency analysis task focused on gender-specific nouns and pronouns in 15,000 fictional works taken from the online library, Project Gutenberg. This analysis allows for both quantifying and offering further insight on the nature of this disparity in gender representation. Then, the outcomes of the analysis are harnessed to explore novel data visualization formats using computational and studio art techniques. Our results call attention to the …

Cardiovascular Applications Of Artificial Intelligence In Research, Diagnosis, And Disease Management, Viswanathan Rajagopalan, Houwei Cao

Center for No Boundary Thinking

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different …

A Predictive Model To Predict Cyberattack Using Self-Normalizing Neural Networks, Oluwapelumi Eniodunmo

Theses, Dissertations and Capstones

Cyberattack is a never-ending war that has greatly threatened secured information systems. The development of automated and intelligent systems provides more computing power to hackers to steal information, destroy data or system resources, and has raised global security issues. Statistical and Data mining tools have received continuous research and improvements. These tools have been adopted to create sophisticated intrusion detection systems that help information systems mitigate and defend against cyberattacks. However, the advancement in technology and accessibility of information makes more identifiable elements that can be used to gain unauthorized access to systems and resources. Data mining and classification tools …

An Exploration In Health Analytics: Pediatric Burns, Care Policy Assessment And Interrupted Time Series, Chao Wang

2022

Healthcare systems globally face multiple challenges in the face of population growth and changes in disease pathology. With regard to the rising demand of the healthcare and the global threats of the pandemic, the medical datasets can be trained further to develop preventive methods. Meanwhile, policy reforms of health systems could be a critical aspect to deal with the public crisis and concerns. However, two basic problems must be addressed first: identification of key factors on a priority basis and evaluation of changes.

Thus, the paper presents a series of trials on the application of data analytics to health-related problems, …

Check Yourself Before You Wrek Yourself: Unpacking And Generalizing Randomized Extended Kaczmarz, William Gilroy

HMC Senior Theses

Linear systems are fundamental in many areas of science and engineering. With the advent of computers there now exist extremely large linear systems that we are interested in. Such linear systems lend themselves to iterative methods. One such method is the family of algorithms called Randomized Kaczmarz methods.
Among this family, there exists a Randomized Kaczmarz variant called Randomized
Extended Kaczmarz which solves for least squares solutions in inconsistent linear systems.
Among Kaczmarz variants, Randomized Extended Kaczmarz is unique in that it modifies input system in a special way to solve for the least squares solution. In this work we …

The Pandemic’S Effects On The Use Of Personal Listening Devices And Prevalence Of Hearing Damage In College Students, Morgan Fink

Senior Honors Projects

Personal listening devices (PLDs), such as earbuds and headphones, are prevalent in today’s society, and overuse of these devices can cause hearing damage. Since the pandemic caused lockdowns and online classes, college students have presumably had more time to be indoors and to use PLDs, leading to a higher risk or developing hearing damage. Previous studies have explored the PLD use and the prevalence of hearing damage in college students, but this study investigates whether the coronavirus pandemic has affected college students’ PLD listening habits and whether these changes are related to the students’ disclosure of suspected symptoms of hearing …

Understanding The Enumerated World: Making Sense Of Data As An Information Source, Kristi Thompson, Elizabeth Hill, Alexandra Cooper

Western Libraries Publications

Chapter in ACRL publication The Data Literacy Cookbook.

This recipe is a guide to preparing an instructional session aimed at postsecondary students in the social or health sciences or related disciplines on locating, evaluating, and using secondary data sources as information resources. Who collects data? Where can you access them? Why are data available on some topics and not others? Why are some statistics available at a detailed level of geography and others only nationally? What are some key limitations of official statistics, and where can information be found to fill in the gaps? This recipe uses these questions to …