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Estimation Of The Representative Elementary Volume Of A Fractured Till: A Field And Groundwater Modeling Approach, Nathan L. Young, William W. Simpkins, Jacqueline E. Reber, Martin F. Helmke 2019 Iowa State University

Estimation Of The Representative Elementary Volume Of A Fractured Till: A Field And Groundwater Modeling Approach, Nathan L. Young, William W. Simpkins, Jacqueline E. Reber, Martin F. Helmke

Geological and Atmospheric Sciences Publications

Fractured till is often represented as an equivalent porous medium (EPM) in groundwater models. Knowledge of the representative elementary volume (REV) is necessary for proper application of an EPM model. While REV estimation and hydraulic conductivity tensor determinations are common in fractured rock studies, they are rarely applied to materials with a permeable matrix, such as fractured till. This study uses field fracture measurements, model simulations, and the FracKFinder toolbox to estimate the REV and determine hydraulic conductivity tensors for the fractured, late Wisconsinan till of the Dows Formation in central Iowa (USA), at depths of 1.0–1.5 ...


A Transactive Energy Approach To Distribution System Design: Household Formulation, Swathi Battula, Leigh Tesfatsion, Zhaoyu Wang 2019 Iowa State University

A Transactive Energy Approach To Distribution System Design: Household Formulation, Swathi Battula, Leigh Tesfatsion, Zhaoyu Wang

Economics Working Papers

A household model is formulated to facilitate careful development and performance testing of bid-based transactive energy system (TES) designs with voluntary customer participation. The optimal general bid-function form for households with thermostatically controlled loads is derived from dynamic programming principles, based solely on general household thermal dynamic and welfare attributes. Quantitative forms are determined for these optimal bid functions, given quantitative forms for these attributes. These quantitative attributes are used to construct representative household types based on clusterings of correlated parameter values. Bid comparison, peak-load reduction, and load-matching test cases conducted for a 123-bus distribution system operating under a generic ...


Evaluation Of Modern Missing Data Handling Methods For Coefficient Alpha, Katerina Matysova 2019 University of Nebraska - Lincoln

Evaluation Of Modern Missing Data Handling Methods For Coefficient Alpha, Katerina Matysova

Public Access Theses, Dissertations, and Student Research from the College of Education and Human Sciences

When assessing a certain characteristic or trait using a multiple item measure, quality of that measure can be assessed by examining the reliability. To avoid multiple time points, reliability can be represented by internal consistency, which is most commonly calculated using Cronbach’s coefficient alpha. Almost every time human participants are involved in research, there is missing data involved. Missing data means that even though complete data were expected to be collected, some data are missing. Missing data can follow different patterns as well as be the result of different mechanisms. One traditional way to deal with missing data is ...


A Note On Propensity Score Weighting Method Using Paradata In Survey Sampling, Seho Park, Jae Kwang Kim, Kimin Kim 2019 Dartmouth College

A Note On Propensity Score Weighting Method Using Paradata In Survey Sampling, Seho Park, Jae Kwang Kim, Kimin Kim

Statistics Publications

Paradata is often collected during the survey process to monitor the quality of the survey response. One such paradata is a respondent behavior, which can be used to construct response models. The propensity score weight using the respondent behavior information can be applied to the final analysis to reduce the nonresponse bias. However, including the surrogate variable in the propensity score weighting does not always guarantee the efficiency gain. We show that the surrogate variable is useful only when it is correlated with the study variable. Results from a limited simulation study confirm the finding. A real data application using ...


Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin 2019 Missouri State University

Seasonal Time Series Models With Application To Weather And Lake Level Data, Mengqing Qin

MSU Graduate Theses

This work studies seasonal time series models with application to lake level and weather data. The thesis includes related time series concepts, integrated autoregressive moving average models (abbreviated as ARIMA), parameter estimation, model diagnostics, and forecasting. The studied time series models are applied to the data of daily lake level in Beaver Lake (1988-2017) and the data of daily maximum temperature in New York Central Park (1870-2017). Due to seasonality of the data, three different approaches are proposed to the modeling: regression method, functional ARIMA method and multiplicative seasonal ARIMA method. The forecasted values of the year 2018 are compared ...


Morphological Identification Of Bighead Carp, Silver Carp, And Grass Carp Eggs Using Random Forests Machine Learning Classification, Carlos A. Camacho, Christopher J. Sullivan, Michael J. Weber, Clay L. Pierce 2019 Iowa State University

Morphological Identification Of Bighead Carp, Silver Carp, And Grass Carp Eggs Using Random Forests Machine Learning Classification, Carlos A. Camacho, Christopher J. Sullivan, Michael J. Weber, Clay L. Pierce

Natural Resource Ecology and Management Publications

Visual identification of fish eggs is difficult and unreliable due to a lack of information on the morphological egg characteristics of many species. We used random forests machine learning to predict the identity of genetically identified Bighead Carp Hypophthalmichthys nobilis, Grass Carp Ctenopharyngodon idella, and Silver Carp H. molitrix eggs based on egg morphometric and environmental characteristics. Family, genus, and species taxonomic-level random forests models were explored to assess the performance and accuracy of the predictor variables. The egg characteristics of Bighead Carp, Grass Carp, and Silver Carp were similar, and they were difficult to distinguish from one another. When ...


Linking Bedrock Discontinuities To Glacial Quarrying, J. B. Woodard, L. K. Zoet, Neal R. Iverson, C. Helanow 2019 University of Wisconsin-Madison

Linking Bedrock Discontinuities To Glacial Quarrying, J. B. Woodard, L. K. Zoet, Neal R. Iverson, C. Helanow

Geological and Atmospheric Sciences Publications

Quarrying and abrasion are the two principal processes responsible for glacial erosion of bedrock. The morphologies of glacier hard beds depend on the relative effectiveness of these two processes, as abrasion tends to smooth bedrock surfaces and quarrying tends to roughen them. Here we analyze concentrations of bedrock discontinuities in the Tsanfleuron forefield, Switzerland, to help determine the geologic conditions that favor glacial quarrying over abrasion. Aerial discontinuity concentrations are measured from scaled drone-based photos where fractures and bedding planes in the bedrock are manually mapped. A Tukey honest significant difference test indicates that aerial concentration of bed-normal bedrock discontinuities ...


Conditional Survival Analysis For Concrete Bridge Decks, Azam Nabizadeh, Habib Tabatabai, Mohammad A. Tabatabai 2019 University of Wisconsin-Milwaukee

Conditional Survival Analysis For Concrete Bridge Decks, Azam Nabizadeh, Habib Tabatabai, Mohammad A. Tabatabai

Civil and Environmental Engineering Faculty Articles

Bridge decks are a significant factor in the deterioration of bridges, and substantially affect long-term bridge maintenance decisions. In this study, conditional survival (reliability) analysis techniques are applied to bridge decks to evaluate the age at the end of service life using the National Bridge Inventory records. As bridge decks age, the probability of survival and the expected service life would change. The additional knowledge gained from the fact that a bridge deck has already survived a specific number of years alters (increases) the original probability of survival at subsequent years based on the conditional probability theory. The conditional expected ...


On The S-Instability And Degeneracy Of Discrete Deep Learning Models, Andee Kaplan, Daniel J. Nordman, Stephen B. Vardeman 2019 Duke University

On The S-Instability And Degeneracy Of Discrete Deep Learning Models, Andee Kaplan, Daniel J. Nordman, Stephen B. Vardeman

Statistics Publications

A probability model exhibits instability if small changes in a data outcome result in large and, often unanticipated, changes in probability. This instability is a property of the probability model, given by a distributional form and a given configuration of parameters. For correlated data structures found in several application areas, there is increasing interest in identifying such sensitivity in model probability structure. We consider the problem of quantifying instability for general probability models defined on sequences of observations, where each sequence of length N has a finite number of possible values that can be taken at each point. A sequence ...


#46 - America's Response To President Trump's Tweets, Amanda Friend 2019 University of West Georgia

#46 - America's Response To President Trump's Tweets, Amanda Friend

Georgia Undergraduate Research Conference (GURC)

Purpose: The purpose of the research throughout this study was to examine Trump’s tweets during the first six months he was in office. Due to Trump using Twitter as his main form of communication it is important for journalists and individuals to follow his tweets.

Research Questions: The analysis covers how many times people shared positive or negative tweets and if people shared more issue based tweets. This study emphasizes President Trump’s most popular tweets and how people responded to his first six months on Twitter.

Method: The tweets were coded with a key using content analysis to ...


Corn Yield Response To Row Spacing And Plant Population In Iowa, Mark A. Licht, M. R. Parvej, Emily E. Wright 2019 Iowa State University

Corn Yield Response To Row Spacing And Plant Population In Iowa, Mark A. Licht, M. R. Parvej, Emily E. Wright

Agronomy Publications

Corn (Zea mays L.) planted in narrow row spacing (<30-inch) with high plant population has become a recent interest in the US Corn Belt to increase grain yield. We evaluated the impact of row spacing and plant population on corn grain yield across 22 site-years in Iowa from 2009 to 2018. Experiments were designed as a split-plot with two row spacings, 20- and 30-inch, as the main-plot and three to four plant populations, ranging from 30,000 to 42,000 plants acre–1, as subplot. Grain yield was affected in 73% of the site-years: 13 site-years by row spacing, six site-years by plant population, and 2 site-years by the interaction of both. Corn in 20-inch rows yielded 5 to 19 bu acre–1 more in 11 site-years and 10 to 14 bu acre–1 less in two site-years compared to 30-inch rows. In both 20- and 30-inch row spacings, corn yield decreased linearly at 0.4 to 1.7 bu acre–1 per thousand increase in plant population in four site-years and responded ...


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 ...


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.


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.


Classification Of Coronary Artery Disease In Non-Diabetic Patients Using Artificial Neural Networks, Demond Handley 2019 Illinois State University

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.


Statistical Modeling And Characterization Of Induced Seismicity Within The Western Canada Sedimentary Basin, Sid Kothari 2019 The University of Western Ontario

Statistical Modeling And Characterization Of Induced Seismicity Within The Western Canada Sedimentary Basin, Sid Kothari

Electronic Thesis and Dissertation Repository

In western Canada, there has been an increase in seismic activity linked to anthropogenic energy-related operations including conventional hydrocarbon production, wastewater fluid injection and more recently hydraulic fracturing (HF). Statistical modeling and characterization of the space, time and magnitude distributions of the seismicity clusters is vital for a better understanding of induced earthquake processes and development of predictive models. In this work, a statistical analysis of the seismicity in the Western Canada Sedimentary Basin was performed across past and present time periods by utilizing a compiled earthquake catalogue for Alberta and eastern British Columbia. Specifically, the frequency-magnitude statistics were analyzed ...


Identifying Marginal Treatment Effects In The Presence Of Sample Selection, Otávio Bartalotti, Desire Kedagni, Vitor Possebom 2019 Iowa State University

Identifying Marginal Treatment Effects In The Presence Of Sample Selection, Otávio Bartalotti, Desire Kedagni, Vitor Possebom

Economics Working Papers

This article presents identification results for the marginal treatment effect (MTE) when there is sample selection. We show that the MTE is partially identified for individuals who are always observed regardless of treatment, and we derive sharp bounds on this parameter under four sets of assumptions. The first identification result combines the standard MTE assumptions without any restrictions to the sample selection mechanism. The second result imposes monotonicity of the sample selection variable with respect to the treatment, considerably shrinking the identified set. Third, we incorporate a stochastic dominance assumption which tightens the lower bound for the MTE. Finally, we ...


Statistical L-Moment And L-Moment Ratio Estimation And Their Applicability In Network Analysis, Timothy S. Anderson 2019 Air Force Institute of Technology

Statistical L-Moment And L-Moment Ratio Estimation And Their Applicability In Network Analysis, Timothy S. Anderson

Theses and Dissertations

This research centers on finding the statistical moments, network measures, and statistical tests that are most sensitive to various node degradations for the Barabási-Albert, Erdös-Rényi, and Watts-Strogratz network models. Thirty-five different graph structures were simulated for each of the random graph generation algorithms, and sensitivity analysis was undertaken on three different network measures: degree, betweenness, and closeness. In an effort to find the statistical moments that are the most sensitive to degradation within each network, four traditional moments: mean, variance, skewness, and kurtosis as well as three non-traditional moments: L-variance, L-skewness, and L-kurtosis were examined. Each of these moments were ...


Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan 2019 University of Missouri

Statistical And Machine Learning Methods Evaluated For Incorporating Soil And Weather Into Corn Nitrogen Recommendations, Curtis J. Ransom, Newell R. Kitchen, James J. Camberato, Paul R. Carter, Richard B. Ferguson, Fabián G. Fernández, David W. Franzen, Carrie A. M. Laboski, D. Brenton Myers, Emerson D. Nafziger, John E. Sawyer, John F. Shanahan

Agronomy Publications

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing ...


Sample Size Requirements And Considerations For Models To Assess Human-Machine System Performance, Jennifer S. G. Lopez 2019 Air Force Institute of Technology

Sample Size Requirements And Considerations For Models To Assess Human-Machine System Performance, Jennifer S. G. Lopez

Theses and Dissertations

Hierarchical Linear Models (HLMs), also known as multi-level models, are an extension of multiple regression analysis and can aid in the understanding of human and machine workloads of a system. These models allow for prediction and testing in systems with hierarchies of two or more levels. The complex interrelated variability of these multi-level models exists in operational settings, such as the Air Force Distributed Common Ground System Full Motion Video (AF DCGS FMV) community which is composed of individuals (Level-1), groups (Level-2), units (Level-3), and organizations (Level-4). Through the development of sample size requirements and considerations for multi-level models, this ...


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