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The Graphs That Have Antivoltages Using Groups Of Small Order, Vaidy Sivaraman, Dan Slilaty 2019 Wright State University - Main Campus

The Graphs That Have Antivoltages Using Groups Of Small Order, Vaidy Sivaraman, Dan Slilaty

Mathematics and Statistics Faculty Publications

Given a group Γ of order at most six, we characterize the graphs that have Γ-antivoltages and also determine the list of minor-minimal graphs that have no Γ-antivoltage. Our characterizations yield polynomial-time recognition algorithms for such graphs.


Reduced Bias For Respondent Driven Sampling: Accounting For Non-Uniform Edge Sampling Probabilities In People Who Inject Drugs In Mauritius, Miles Q. Ott, Krista J. Gile, Matthew T. Harrison, Lisa G. Johnston, Joseph W. Hogan 2019 Smith College

Reduced Bias For Respondent Driven Sampling: Accounting For Non-Uniform Edge Sampling Probabilities In People Who Inject Drugs In Mauritius, Miles Q. Ott, Krista J. Gile, Matthew T. Harrison, Lisa G. Johnston, Joseph W. Hogan

Statistical and Data Sciences: Faculty Publications

People who inject drugs are an important population to study in order to reduce transmission of blood-borne illnesses including HIV and Hepatitis. In this paper we estimate the HIV and Hepatitis C prevalence among people who inject drugs, as well as the proportion of people who inject drugs who are female in Mauritius. Respondent driven sampling (RDS), a widely adopted link-tracing sampling design used to collect samples from hard-to-reach human populations, was used to collect this sample. The random walk approximation underlying many common RDS estimators assumes that each social relation (edge) in the underlying social network has an equal …


Post-Acquisition Processing Confounds In Brain Volumetric Quantification Of White Matter Hyperintensities, Ahmed A. Bahrani, Omar M. Al-Janabi, Erin L. Abner, Shoshana H. Bardach, Richard J. Kryscio, Donna M. Wilcock, Charles D. Smith, Gregory A. Jicha 2019 University of Kentucky

Post-Acquisition Processing Confounds In Brain Volumetric Quantification Of White Matter Hyperintensities, Ahmed A. Bahrani, Omar M. Al-Janabi, Erin L. Abner, Shoshana H. Bardach, Richard J. Kryscio, Donna M. Wilcock, Charles D. Smith, Gregory A. Jicha

Neurology Faculty Publications

BACKGROUND: Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored.

NEW METHOD: 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance.

RESULTS: A series …


Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter 2019 University of South Florida

Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter

USF Tampa Graduate Theses and Dissertations

Ensemble methods are commonly used for building predictive models for classification. Models that are unstable to perturbations in the training set, such as the decision tree, often see considerable reductions in error when grouped, using bootstrapped resamples of the training data to train many models. The non-parametric bootstrap, however, has limited efficacy when used on severely imbalanced data, especially when the number of observations of one or more classes is exceptionally small. We explore the fractional random weighted bootstrap, which randomly assigns fractional weights to observations, as an alternative resampling pro cedure in training machine learning ensembles, particularly decision tree …


"Big Ideas" In Secondary Mathematics Education Programs, Eryn Stehr, Hyunyi Jung, Jill Newton 2019 Georgia Southern University

"Big Ideas" In Secondary Mathematics Education Programs, Eryn Stehr, Hyunyi Jung, Jill Newton

Mathematical and Statistical Science Faculty Research and Publications

Although research and policy documents provide recommendations to inform secondary mathematics teacher preparation, no single study has addressed the “big ideas” of courses in multiple programs and how those big ideas may be interpreted through the lens of recent research and policy documents. To answer this need, we focused on big ideas and course objectives from three courses (i.e., Linear Algebra, Secondary Mathematics Methods, and Teaching in a Diversity Society) taught in four secondary mathematics programs. Major themes emerged related to mathematical content, pedagogy, and issues of equity. We describe findings related to big ideas, course objectives, and their connections …


A Multi-Step Approach To Modeling The 24-Hour Daily Profiles Of Electricity Load Using Daily Splines, Abdelmonaem Jornaz, V. A. Samaranayake 2019 Missouri University of Science and Technology

A Multi-Step Approach To Modeling The 24-Hour Daily Profiles Of Electricity Load Using Daily Splines, Abdelmonaem Jornaz, V. A. Samaranayake

Mathematics and Statistics Faculty Research & Creative Works

Forecasting of real-time electricity load has been an important research topic over many years. Electricity load is driven by many factors, including economic conditions and weather. Furthermore, the demand for electricity varies with time, with different hours of the day and different days of the week having an effect on the load. This paper proposes a hybrid load-forecasting method that combines classical time series formulations with cubic splines to model electricity load. It is shown that this approach produces a model capable of making short-term forecasts with reasonable accuracy. In contrast to forecasting models that utilize a multitude of regressor …


Diabetes And Its Effect On Abdominal Aortic Aneurysm Growth Rate In Hispanic Patients, Monica Betancourt-Garcia, Kristina Vatcheva, Amrit Thakur, Prateek Gupta, Eugene Postevka, Ricardo Martinez, R. Armour Forse 2019 Doctors Hospital at Renaissance

Diabetes And Its Effect On Abdominal Aortic Aneurysm Growth Rate In Hispanic Patients, Monica Betancourt-Garcia, Kristina Vatcheva, Amrit Thakur, Prateek Gupta, Eugene Postevka, Ricardo Martinez, R. Armour Forse

School of Mathematical & Statistical Sciences Faculty Publications

Background

The growth rate of abdominal aortic aneurysms (AAA) can vary depending on age, baseline diameter, blood pressure, race, and history of smoking. Paradoxically, previous studies show evidence of a protective effect of diabetes on the rate of AAA expansion despite its well-established role in the morbidity and mortality of cardiovascular disease. This study aims to investigate the impact diabetes plays on AAA growth within a Hispanic population.

Methods

Data were collected from patients who were predominantly Mexican-American at a single hospital site. Baseline and follow-up measures for AAA diameter were obtained from serial imaging studies. Demographics, medical history, the …


New Measure Of Skewness Of A Probability Distribution, Ashok Singh, Laxmi Gewali, Jiwan Khatiwada 2019 University of Nevada, Las Vegas

New Measure Of Skewness Of A Probability Distribution, Ashok Singh, Laxmi Gewali, Jiwan Khatiwada

Hospitality Faculty Research

Symmetry of the underlying probability density plays an important role in statistical inference, since the sampling distribution of the sample mean for a given sample size is more likely to be approximately normal for a symmetric distribution than for an asymmetric one. In this article, two new measures of skewness are proposed and the confidence intervals for true skewness are obtained via Monte Carlo simulation experiments. One advantage of the two proposed skewness measures over the standard measures of skewness is that the proposed measures of skewness take values inside the range (-1, +1).


Establishing An Analytics Capability Within Hr, Rizwan Khan 2019 The AES Corporation - Manager, Workforce Planning & Analytics

Establishing An Analytics Capability Within Hr, Rizwan Khan

River Cities Industrial and Organizational Psychology Conference

With advancements in technology, HR finally has the tools available to collect and process people data. But is that all that is needed to successfully implement and sustain an analytics capability within HR? The purpose of this presentation/tutorial is to demonstrate a realistic journey The AES Corporation has taken thus far in developing its People Analytics capability. A framework for the implementation of People Analytics will be presented which incorporates themes in I/O Psychology that have been incorporated into the framework such as goal setting theory, job analysis, and change management. How this framework is operationalized will also be demonstrated …


Evaluating The Impact Of Proactive Care Management With Idstrat, D.J. Donahue, Lauren Staples 2019 BlueCross BlueShield of Tennessee

Evaluating The Impact Of Proactive Care Management With Idstrat, D.J. Donahue, Lauren Staples

Published and Grey Literature from PhD Candidates

This purpose of this study is to quantify potential cost savings and member care improvements as a result of engagement through BlueCross BlueShield of Tennessee’s (BCBST) Identification and Stratification (IDStrat) process. Commercial members engaged in clinical management that were identified through IDStrat were compared to commercial members identified through other means across several metrics including per-member, per-month (PMPM) cost and physician visits. Members identified by IDStrat experienced a statistically significant 7% greater reduction in costs after being engaged when compared with those identified by other methods. Members identified by IDStrat also experienced a significant reduction in emergency room visits after …


Inferring A Consensus Problem List Using Penalized Multistage Models For Ordered Data, Philip S. Boonstra, John C. Krauss 2019 The University Of Michigan

Inferring A Consensus Problem List Using Penalized Multistage Models For Ordered Data, Philip S. Boonstra, John C. Krauss

The University of Michigan Department of Biostatistics Working Paper Series

A patient's medical problem list describes his or her current health status and aids in the coordination and transfer of care between providers, among other things. Because a problem list is generated once and then subsequently modified or updated, what is not usually observable is the provider-effect. That is, to what extent does a patient's problem in the electronic medical record actually reflect a consensus communication of that patient's current health status? To that end, we report on and analyze a unique interview-based design in which multiple medical providers independently generate problem lists for each of three patient case abstracts …


Population Health Management, Data And Technology, Helena Ladd, Cody Hepp, Anna McCloud, Hannah Granger, Mary Ellen Hethcox, Samuel Calabrese 2019 Ohio Northern University

Population Health Management, Data And Technology, Helena Ladd, Cody Hepp, Anna Mccloud, Hannah Granger, Mary Ellen Hethcox, Samuel Calabrese

Pharmacy and Wellness Review

No abstract provided.


Phase Iv Clinical Trials: Postmarketing Surveillance Of Prescription Drugs, Morgan Belling, Jacquline Nunner, Jessica Stemen 2019 Ohio Northern University

Phase Iv Clinical Trials: Postmarketing Surveillance Of Prescription Drugs, Morgan Belling, Jacquline Nunner, Jessica Stemen

Pharmacy and Wellness Review

No abstract provided.


Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru 2019 University of Kentucky

Enhancing Timeliness Of Drug Overdose Mortality Surveillance: A Machine Learning Approach, Patrick J. Ward, Peter J. Rock, Svetla Slavova, April M. Young, Terry L. Bunn, Ramakanth Kavuluru

Kentucky Injury Prevention and Research Center Faculty Publications

BACKGROUND: Timely data is key to effective public health responses to epidemics. Drug overdose deaths are identified in surveillance systems through ICD-10 codes present on death certificates. ICD-10 coding takes time, but free-text information is available on death certificates prior to ICD-10 coding. The objective of this study was to develop a machine learning method to classify free-text death certificates as drug overdoses to provide faster drug overdose mortality surveillance.

METHODS: Using 2017–2018 Kentucky death certificate data, free-text fields were tokenized and features were created from these tokens using natural language processing (NLP). Word, bigram, and trigram features were created …


I Spy With My Little Eye … A Knee About To Go 'Pop'? Can Coaches And Sports Medicine Professionals Predict Who Is At Greater Risk Of Acl Rupture?, Anne Inger Mørtvedt, Tron Krosshaug, Roald Bahr, Erich Petushek 2019 Oslo Sports Trauma Research Center

I Spy With My Little Eye … A Knee About To Go 'Pop'? Can Coaches And Sports Medicine Professionals Predict Who Is At Greater Risk Of Acl Rupture?, Anne Inger Mørtvedt, Tron Krosshaug, Roald Bahr, Erich Petushek

Michigan Tech Publications, Part 1

BACKGROUND: The vertical drop jump (VDJ) test is widely used for clinical assessment of ACL injury risk, but it is not clear whether such assessments are valid.

AIM: To examine if sports medicine professionals and coaches are able to identify players at risk of sustaining an ACL injury by visually assessing player performance during a VDJ test.

METHODS: 102 video clips of elite female handball and football players performing a baseline VDJ test were randomly extracted from a 738-person prospective cohort study that tracked ACL injuries. Of the sample, 20 of 102 went on to suffer an ACL injury. These …


Teaching Data Analysis Using Students' Own Data, Dmitry Kondrashov, Stefano Allesina 2019 University of Chicago

Teaching Data Analysis Using Students' Own Data, Dmitry Kondrashov, Stefano Allesina

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.


An Agent-Based Modeling Approach For Predicting The Behavior Of Bighead Carp (Hypophthalmichthys Nobilis) Under The Influence Of Acoustic Deterrence, Joey Gaudy, Craig Garzella 2019 Valparaiso University

An Agent-Based Modeling Approach For Predicting The Behavior Of Bighead Carp (Hypophthalmichthys Nobilis) Under The Influence Of Acoustic Deterrence, Joey Gaudy, Craig Garzella

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.


Network Structure And Dynamics Of Biological Systems, Deena R. Schmidt 2019 University of Nevada, Reno

Network Structure And Dynamics Of Biological Systems, Deena R. Schmidt

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.


Quantifying Distribution In Carbon Uptake Across A Global Measurement Network Of Terrestrial Ecosystems, John Zobitz, Madeline Oswood 2019 Augsburg University

Quantifying Distribution In Carbon Uptake Across A Global Measurement Network Of Terrestrial Ecosystems, John Zobitz, Madeline Oswood

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.


Integrating Mathematics And Biology In The Classroom: A Compendium Of Case Studies And Labs, Becky Sanft, Anne Walter 2019 University of North Carolina at Asheville

Integrating Mathematics And Biology In The Classroom: A Compendium Of Case Studies And Labs, Becky Sanft, Anne Walter

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.


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