Physical Sciences and Mathematics Commons^™

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 …

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

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

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

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

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Quantifying Pollen Traits To Build A Mathematical Model Of Pollen Competition - A Biologist's Perspective, Rob Swanson, Alex Capaldi

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Study On Discrete And Discrete Fractional Pharmacokinetics-Pharmacodynamics Models For Tumor Growth And Anti-Cancer Effects, Ferhan Atici, Ngoc Nguyen

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Classification Of Coronary Artery Disease In Non-Diabetic Patients Using Artificial Neural Networks, Demond Handley

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Interpreting Patient Reported Outcomes In Orthopaedic Surgery: A Systematic Review, Shgufta Docter, Zina Fathalla, Michael Lukacs, Michaela Khan, Morgan Jennings, Shu-Hsuan Liu, Dong Zi, Dianne Bryant

Western Research Forum

Background: Reporting methods of patient reported outcome measures (PROMs) vary in orthopaedic surgery literature. While most studies report statistical significance, the interpretation of results would be improved if authors reported confidence intervals (CIs), the minimally clinically important difference (MCID), and number needed to treat (NNT).

Objective: To assess the quality and interpretability of reporting the results of PROMs. To evaluate reporting, we will assess the proportion of studies that reported (1) 95% CIs, (2) MCID, and (3) NNT. To evaluate interpretation, we will assess the proportion of studies that discussed results using the MCID or the effect sizes and how …

A Statistical Analysis Of The Roulette Martingale System: Examples, Formulas And Simulations With R, Peter Pflaumer

International Conference on Gambling & Risk Taking

Some gamblers use a martingale or doubling strategy as a way of improving their chances of winning. This paper derives important formulas for the martingale strategy, such as the distribution, the expected value, the standard deviation of the profit, the risk of a loss or the expected bet of one or multiple martingale rounds. A computer simulation study with R of the doubling strategy is presented. The results of doubling to gambling with a constant sized bet on simple chances (red or black numbers, even or odd numbers, and low (1 – 18) or high (19 – 36) numbers) and …

Kelly Fraction Estimation For Multiple Correlated Bets, William Chin

International Conference on Gambling & Risk Taking

It is well-known that expected portfolio growth is maximized by maximizing

expected logarithmic utility. This investment criterion is known as Kelly betting.

It has many optimality properties but is considered to be risky. Blackjack

teams and other advantage gamblers practice a fraction of the Kelly optimal to

decrease risk. Some hedge fund managers are thought to practice according to

Kelly principles. We use a continuous multivariate Geometric Brownian motion

model and present an interval estimate for the historical fraction for a portfolio

of correlated bets, possibly including a risk-free asset. Historical data comes

from a range of sources and the …

Characterizing The Permanence And Stationary Distribution For A Family Of Malaria Stochastic Models, Divine Wanduku

Biology and Medicine Through Mathematics Conference

No abstract provided.

Measuring Clinical Weight Loss In Young Children With Severe Obesity: Comparison Of Outcomes Using Zbmi, Modified Zbmi, And Percent Of 95th Percentile, Carolyn Bates

Research Days

No abstract provided.

Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge

MODVIS Workshop

No abstract provided.

Predictive Performance Of Existing Population Pharmacokinetic Models Of Tacrolimus In Pediatric Kidney Transplant Recipients, Alenka Chapron

Research Days

No abstract provided.

Quantifying Sleep Architecture For Pediatric Hypersomnia Conditions, Alicia K. Colclasure

Biology and Medicine Through Mathematics Conference

No abstract provided.

Prospective Evaluation Of A Population Pharmacokinetic Model Of Pantoprazole For Obese Children, Alenka Chapron

Research Days

No abstract provided.

Predictive Validity Of A New Self-Report Measure Of Individual Skin Type Through Characterization Of Skin Melanin Using Reflectance Photospectroscopy, Mark Sanchez, Lisa Marriott, Teala Alvord, R. Samatham, S. Chang

Student Research Symposium

In the realm of research and dermatology, the Fitzpatrick Skin Type scale (FST) has been the gold standard of measurement to classify sun sensitivity for human’s skin. This scale is based on an individual’s dermal reaction to ultraviolet exposure (Parrish, et al., 1974; Fitzpatrick, 1975; Pathak, et al., 1976; Fitzpatrick, 1988). It was assumed in science as well as popular culture that individuals with darker skin were less susceptible to issues related to UV damage of their skin. More recent research (Eilers, et al., 2013) suggests that while melanin affords some skin protection, damage can still occur that may result …

A Mathematical Investigation On Tumor-Immune Dynamics: The Impact Of Vaccines On The Immune Response, Jonathan Quinonez, Neethi Dasu, Mahboobi Qureshi

Rowan-Virtua Research Day

Mathematical models analyzing tumor-immune interactions provide a framework by which to address specific scenarios in regard to tumor-immune dynamics. Important aspects of tumor-immune surveillance to consider is the elimination of tumor cells from a host’s cell-mediated immunity as well as the implications of vaccines derived from synthetic antigen. In present studies, our mathematical model examined the role of synthetic antigen to the strength of the immune system. The constructed model takes into account accepted knowledge of immune function as well as prior work done by de Pillis et al. All equations describing tumor-immune growth, antigen presentation, immune response, and interaction …

Deep Neural Network Architectures For Music Genre Classification, Kai Middlebrook, Shyam Sudhakaran, Kunal Sonar, David Guy Brizan

Creative Activity and Research Day - CARD

With the recent advancements in technology, many tasks in fields such as computer vision, natural language processing, and signal processing have been solved using deep learning architectures. In the audio domain, these architectures have been used to learn musical features of songs to predict: moods, genres, and instruments. In the case of genre classification, deep learning models were applied to popular datasets--which are explicitly chosen to represent their genres--and achieved state-of-the-art results. However, these results have not been reproduced on less refined datasets. To this end, we introduce an un-curated dataset which contains genre labels and 30-second audio previews for …

Large Scale Dynamical Model Of Macrophage/Hiv Interactions, Sean T. Bresnahan, Matthew M. Froid

UNO Student Research and Creative Activity Fair

Properties emerge from the dynamics of large-scale molecular networks that are not discernible at the individual gene or protein level. Mathematical models - such as probabilistic Boolean networks - of molecular systems offer a deeper insight into how these emergent properties arise. Here, we introduce a non-linear, deterministic Boolean model of protein, gene, and chemical interactions in human macrophage cells during HIV infection. Our model is composed of 713 nodes with 1583 interactions between nodes and is responsive to 38 different inputs including signaling molecules, bacteria, viruses, and HIV viral particles. Additionally, the model accurately simulates the dynamics of over …

Neural Shrubs: Using Neural Networks To Improve Decision Trees, Kyle Caudle, Randy Hoover, Aaron Alphonsus

SDSU Data Science Symposium

Decision trees are a method commonly used in machine learning to either predict a categorical response or a continuous response variable. Once the tree partitions the space, the response is either determined by the majority vote – classification trees, or by averaging the response values – regression trees. This research builds a standard regression tree and then instead of averaging the responses, we train a neural network to determine the response value. We have found that our approach typically increases the predicative capability of the decision tree. We have 2 demonstrations of this approach that we wish to present as …

Session: 4 Multilinear Subspace Learning And Its Applications To Machine Learning, Randy Hoover, Kyle Caudle Dr., Karen Braman Dr.

SDSU Data Science Symposium

Multi-dimensional data analysis has seen increased interest in recent years. With more and more data arriving as 2-dimensional arrays (images) as opposed to 1-dimensioanl arrays (signals), new methods for dimensionality reduction, data analysis, and machine learning have been pursued. Most notably have been the Canonical Decompositions/Parallel Factors (commonly referred to as CP) and Tucker decompositions (commonly regarded as a high order SVD: HOSVD). In the current research we present an alternate method for computing singular value and eigenvalue decompositions on multi-way data through an algebra of circulants and illustrate their application to two well-known machine learning methods: Multi-Linear Principal Component …

Predicting Unplanned Medical Visits Among Patients With Diabetes Using Machine Learning, Arielle Selya, Eric L. Johnson

SDSU Data Science Symposium

Diabetes poses a variety of medical complications to patients, resulting in a high rate of unplanned medical visits, which are costly to patients and healthcare providers alike. However, unplanned medical visits by their nature are very difficult to predict. The current project draws upon electronic health records (EMR’s) of adult patients with diabetes who received care at Sanford Health between 2014 and 2017. Various machine learning methods were used to predict which patients have had an unplanned medical visit based on a variety of EMR variables (age, BMI, blood pressure, # of prescriptions, # of diagnoses on problem list, A1C, …

Non-Invasive Analysis Of The Sputum Transcriptome Discriminates Clinical Phenotypes Of Asthma, Xiting Yan

Yale Day of Data

Whole transcriptome wide gene expression profiles in the sputum and circulation from 100 asthma patients were measured using the Affymetrix HuGene 1.0ST arrays. Unsupervised clustering analysis based on pathways from KEGG were used to identify TEA clusters of patients from the sputum gene expression profiles. The identified TEA clusters have significantly different pre-bronchodilator FEV1, bronchodilator responsiveness, exhaled nitric oxide levels, history of hospitalization for asthma and history of intubation. Evaluation of TEA clusters in children from Asthma BRIDGE cohort confirmed the identified differences in intubation and hospitalization. Furthermore, evaluation of the TH2 gene signatures suggested a much lower prevalence of …

A Novel Pathway-Based Distance Score Enhances Assessment Of Disease Heterogeneity In Gene Expression, Yunqing Liu, Xiting Yan

Yale Day of Data

Distance-based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and the relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. In this study, we developed a novel computational method to assess the biological differences based on pathways by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both …

Health Risk Tolerance As A Key Determinant Of (Un)Willingness To Behavior Change: Conceptualization And Scale Development, Hyoyeun Jun, Yan Jin

International Crisis and Risk Communication Conference

After the study of testing determinants of risk tolerance affecting information sharing, this study was conducted as a second step to actually develop the scale for risk tolerance. Firstly, this study followed qualitative steps, such as in-depth interview and focus group, to capture how public describes the situation when they are tolerating the risk, when they knew what the recommended behavior is to relieve the risk. Secondly, this study collected 1000 U.S. public sample for the survey questionnaire that are the items generated from the qualitative steps.