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2021

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Design, Assembly, And Testing Of A Small 3d-Printed Thick-Gem, Jerry Lamar Collins Ii Dec 2021

Design, Assembly, And Testing Of A Small 3d-Printed Thick-Gem, Jerry Lamar Collins Ii

Theses and Dissertations

Thick GEMs are a type of gas electron multiplier, a micropattern gaseous detector, with many applications in research. The subject of this study is whether or not a small 3D printed thick GEM board can be used to form a well-functioning detector. A Thick GEM having three separate board sectors, each having different sized clearance rim annuli around their holes, was designed, printed, and assembled. Several studies to quantify its behavior over both short and long time intervals were conducted, and the results calculated. The THGEM sector with 0.1 mm annulus rims was able to achieve primarily 102 gain, whereas …

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Changes In The Abundance And Distribution Of Benthic Mollusks In Polluted Sediments Of A Shallow Subtropical Estuary, Rachael Holtsberg Stark Dec 2021

Changes In The Abundance And Distribution Of Benthic Mollusks In Polluted Sediments Of A Shallow Subtropical Estuary, Rachael Holtsberg Stark

Theses and Dissertations

The Indian River Lagoon (IRL) has been degraded by decades of eutrophication, and has accumulated an abundance of fine-grained, organic-rich sediments (muck). To quantify the impacts of organic sediments and environmental dredging, benthic mollusks were chosen to serve as bioindicators of environmental change, sediment health, and water quality. Data on species richness, biodiversity, and abundances was collected alongside sediment and water quality data before, during and after dredging. Organic sediment content was found to have an inverse logarithmic relationship benthic mollusk biodiversity, species richness, and abundance. Sediments low in percent organic content (0.7% to 6.1%) were located near the adjacent …

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Development And Optimization Of A Denitrification System To Promote Biological Nutrient Removal In A Subtropical Estuary, Abigail Gering Dec 2021

Development And Optimization Of A Denitrification System To Promote Biological Nutrient Removal In A Subtropical Estuary, Abigail Gering

Theses and Dissertations

Eutrophication impacts coastal ecosystems globally and triggers phytoplankton and algal blooms, leading to an increase in oxygen demand in the water column and sediments due to various biogeochemical processes. Naturally, biological nitrification, denitrification and anammox processes help to remove nitrogen predominately by nitrifying and denitrifying bacteria in the sediments. Eutrophic systems have increased nutrient and organic matter loading and are prone to the accumulation of ‘muddy’, fine-grained, organic-rich sediments (locally termed ‘muck’) that may not be able to support high enough populations of these bacteria, and therefore, may not adequately support nitrification/denitrification processes that would otherwise remove nitrogen. To decrease …

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Application Of Ensemble Methods For Solving Offshore Wind Farm Layout Optimization Problems, Puyi Yang Dec 2021

Application Of Ensemble Methods For Solving Offshore Wind Farm Layout Optimization Problems, Puyi Yang

Theses and Dissertations

Wind energy is considered as one of the most promising renewable energy sources. The intent of this dissertation is to assess the application of ensemble methods on offshore wind farm layout optimization. In this regard, four research questions are considered and addressed accordingly each of which are discussed in detail in a separate chapter (chapter 2 to 5). The first research question is focused on choosing appropriate analytical wake model to be used for the wind farm layout optimization (WFLO) problems in this dissertation. The WFLO problem is a complex and non-convex optimization problem. The accuracy of analytical wake models …

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An Assessment Of Image-Cloaking Techniques Against Automated Face Recognition For Biometric Privacy, Brandon Scott Ledford Dec 2021

An Assessment Of Image-Cloaking Techniques Against Automated Face Recognition For Biometric Privacy, Brandon Scott Ledford

Theses and Dissertations

Over the past two decades, Americans have aggressively increased the amount of facial data uploaded to the internet primarily via social media. This data is largely unprotected due to the dire lack of existing regulations protecting users from large scale face recognition in the United States, where the value of data trade is in the tens of billions. In its current state, facial privacy in the United States depends on American corporations opting not to collect the public data, an option rarely chosen. Much research has been done in the area of suppressing recognition abilities, giving users the ability to …

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Forecasting Sep Events Based On Merged Cme Catalogs Using Machine Learning, Peter William Tarsoly Dec 2021

Forecasting Sep Events Based On Merged Cme Catalogs Using Machine Learning, Peter William Tarsoly

Theses and Dissertations

The lack of preparation for a Solar Energetic Particle (SEP) event may be catastrophic for astronauts and aircraft passengers alike, along with their electronic equipment. It is widely theorized that SEP events are caused by Coronal Mass Ejections (CMEs), some occurring up to a full day beforehand, accompanied by additional space weather conditions. The only significant models for SEP forecasting are statistically or machine learning-based, often developed on imprecise data. We present an enhanced catalog of CMEs, along with other space weather phenomena, and their relationship with the occurrence of SEP events. Using the enhanced CME catalog, we combine machine …

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Smart Glasses Assisting Visually-Impaired People, Ali Delham Algarni Dec 2021

Smart Glasses Assisting Visually-Impaired People, Ali Delham Algarni

Theses and Dissertations

Smart Glasses Assisting Visually-Impaired artificial intelligence system providing speech recognition, object recognition, face recognition, and distance to all features for visually impaired people's enhanced (daily) autonomy. Smart Glasses Assisting Visually-Impaired People is a new integrative process that combines as such, the invention is both a further use of the technologies supporting the features listed above and an improvement to the overall activities carried out by visually impaired people in their daily lives activities such as: providing vocal commands, being able to read text, being able to receive spoken information about various objects in their surroundings, being aware of their location …

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Unveiling Migration Patterns Using Data And Network Science, Firas Aswad Dec 2021

Unveiling Migration Patterns Using Data And Network Science, Firas Aswad

Theses and Dissertations

Many real-world phenomena have recently been witnessed, motivating scientists to provide a comprehensive understanding of worldwide networks, such as the human migration phenomenon. Human migration research is multidisciplinary and has yielded work in social sciences, physics, and the new field of smart city designing and planning based on big data analytics. The importance of the research comes from the impacts on both countries, sources, and distention. Impacts occur at several levels such as economy, city planning, politics, and law enforcement, leading to changes in demographics. Thus, studies on human migration help policymakers prepare and handle crises when they occur in …

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On A Class Of Critical N-Laplacian Problems, Tsz Chung Ho Dec 2021

On A Class Of Critical N-Laplacian Problems, Tsz Chung Ho

Theses and Dissertations

We establish some existence results for a class of critical N-Laplacian problems in a bounded domain in RN. In the absence of a suitable direct sum decomposition, we use an abstract linking theorem based on the Z2-cohomological index to obtain a nontrivial critical point.

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Potential Use For Serosurveillance Of Feral Swine To Map Risk For Anthrax Exposure, Texas, Usa, Rachel M. Maison, Courtney F. Pierce, Izabela K. Ragan, Vienna R. Brown, Michael J. Bodenchuk, Richard A. Bowen, Angela M. Bosco-Lauth Dec 2021

Potential Use For Serosurveillance Of Feral Swine To Map Risk For Anthrax Exposure, Texas, Usa, Rachel M. Maison, Courtney F. Pierce, Izabela K. Ragan, Vienna R. Brown, Michael J. Bodenchuk, Richard A. Bowen, Angela M. Bosco-Lauth

United States Department of Agriculture Wildlife Services: Staff Publications

Anthrax is a disease of concern in many mammals, including humans. Management primarily consists of prevention through vaccination and tracking clinical-level observations because environmental isolation is laborious and bacterial distribution across large geographic areas diffi cult to confi rm. Feral swine (Sus scrofa) are an invasive species with an extensive range in the southern United States that rarely succumbs to anthrax. We present evidence that feral swine might serve as biosentinels based on comparative seroprevalence in swine from historically defi ned anthrax-endemic and non–anthraxendemic regions of Texas. Overall seropositivity was 43.7% (n = 478), and logistic regression revealed county endemicity …

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News From The Bioconductor Project, Bioconductor Core Team Dec 2021

News From The Bioconductor Project, Bioconductor Core Team

The R Journal

Bioconductor provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor 3.14 was released on 27 October, 2021. It is compatible with R 4.1.0 and consists of 2083 software packages, 408 experiment data packages, 904 up-to-date annotation packages, and 29 workflows.

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Rpese: Risk And Performance Estimators Standard Errors With Serially Dependent Data, Anthony-Alexander Christidis, R Douglas Martin Dec 2021

Rpese: Risk And Performance Estimators Standard Errors With Serially Dependent Data, Anthony-Alexander Christidis, R Douglas Martin

The R Journal

The R package RPESE (Risk and Performance Estimators Standard Errors) implements a new method for computing accurate standard errors of risk and performance estimators when returns are serially dependent. The new method makes use of the representation of a risk or performance estimator as a summation of a time series of influence-function (IF) transformed returns, and computes estimator standard errors using a sophisticated method of estimating the spectral density at frequency zero of the time series of IF-transformed returns. Two additional packages used by RPESE are introduced, namely RPEIF which computes and provides graphical displays of the IF of risk …

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Visual Diagnostics For Constrained Optimisation With Application To Guided Tours, H Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez Dec 2021

Visual Diagnostics For Constrained Optimisation With Application To Guided Tours, H Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez

The R Journal

A guided tour helps to visualise high-dimensional data by showing low-dimensional projections along a projection pursuit optimisation path. Projection pursuit is a generalisation of principal component analysis in the sense that different indexes are used to define the interestingness of the projected data. While much work has been done in developing new indexes in the literature, less has been done on understanding the optimisation. Index functions can be noisy, might have multiple local maxima as well as an optimal maximum, and are constrained to generate orthonormal projection frames, which complicates the optimization. In addition, projection pursuit is primarily used for …

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Bssm: Bayesian Inference Of Non-Linear And Non-Gaussian State Space Models In R, Jouni Helske, Matti Vihola Dec 2021

Bssm: Bayesian Inference Of Non-Linear And Non-Gaussian State Space Models In R, Jouni Helske, Matti Vihola

The R Journal

We present an R package bssm for Bayesian non-linear/non-Gaussian state space modeling. Unlike the existing packages, bssm allows for easy-to-use approximate inference based on Gaussian approximations such as the Laplace approximation and the extended Kalman filter. The package also accommodates discretely observed latent diffusion processes. The inference is based on fully automatic, adaptive Markov chain Monte Carlo (MCMC) on the hyperparameters, with optional importance sampling post-correction to eliminate any approximation bias. The package also implements a direct pseudo-marginal MCMC and a delayed acceptance pseudo-marginal MCMC using intermediate approximations. The package offers an easy-to-use interface to define models with linear-Gaussian state …

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Passed: Calculate Power And Sample Size For Two Sample Tests, Jinpu Li, Ryan .. Knigge, Kaiyi Chen, Emily V. Leary Dec 2021

Passed: Calculate Power And Sample Size For Two Sample Tests, Jinpu Li, Ryan .. Knigge, Kaiyi Chen, Emily V. Leary

The R Journal

Power and sample size estimation are critical aspects of study design to demonstrate minimized risk for subjects and justify the allocation of time, money, and other resources. Researchers often work with response variables that take the form of various distributions. Here, we present an R package, PASSED, that allows flexibility with seven common distributions and multiple options to accommodate sample size or power analysis. The relevant statistical theory, calculations, and examples for each distribution using PASSED are discussed in this paper.

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Automatic Time Series Forecasting With Ata Method In R: Ataforecasting Package, Ali Sabri Taylan, Güçkan Yapar, Hanife Taylan Selamlar Dec 2021

Automatic Time Series Forecasting With Ata Method In R: Ataforecasting Package, Ali Sabri Taylan, Güçkan Yapar, Hanife Taylan Selamlar

The R Journal

Ata method is a new univariate time series forecasting method that provides innovative solutions to issues faced during the initialization and optimization stages of existing methods. The Ata method’s forecasting performance is superior to existing methods in terms of easy implementation and accurate forecasting. It can be applied to non-seasonal or deseasonalized time series, where the deseasonalization can be performed via any preferred decomposition method. The R package ATAforecasting was developed as a comprehensive toolkit for automatic time series forecasting. It focuses on modeling all types of time series components with any preferred Ata methods and handling seasonality patterns by …

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Robustbf: An R Package For Robust Solution To The Behrens-Fisher Problem, Gamze Güven, ŞÜkrü Acıtaş, Hatice ŞAmkar, Birdal ŞEnoğLu Dec 2021

Robustbf: An R Package For Robust Solution To The Behrens-Fisher Problem, Gamze Güven, ŞÜkrü Acıtaş, Hatice ŞAmkar, Birdal ŞEnoğLu

The R Journal

Welch’s two-sample t-test based on least squares (LS) estimators is generally used to test the equality of two normal means when the variances are not equal. However, this test loses its power when the underlying distribution is not normal. In this paper, two different tests are proposed to test the equality of two long-tailed symmetric (LTS) means under heterogeneous variances. Adaptive modified maximum likelihood (AMML) estimators are used in developing the proposed tests since they are highly efficient under LTS distribution. An R package called RobustBF is given to show the implementation of these tests. Simulated Type I error rates …

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Volesti: Volume Approximation And Sampling For Convex Polytopes In R, Apostolos Chalkis, Vissarion Fisikopoulos Dec 2021

Volesti: Volume Approximation And Sampling For Convex Polytopes In R, Apostolos Chalkis, Vissarion Fisikopoulos

The R Journal

Sampling from high-dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering, artificial intelligence, and machine learning. In this paper, we present volesti, an R package that provides efficient, scalable algorithms for volume estimation, uniform, and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and provides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language.

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Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network, Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes Dec 2021

Drhotnet: An R Package For Detecting Differential Risk Hotspots On A Linear Network, Álvaro Briz-Redón, Francisco Martínez-Ruiz, Francisco Montes

The R Journal

One of the most common applications of spatial data analysis is detecting zones, at a certain scale, where a point-referenced event under study is especially concentrated. The detection of such zones, which are usually referred to as hotspots, is essential in certain fields such as criminology, epidemiology, or traffic safety. Traditionally, hotspot detection procedures have been developed over areal units of analysis. Although working at this spatial scale can be suitable enough for many research or practical purposes, detecting hotspots at a more accurate level (for instance, at the road segment level) may be more convenient sometimes. Furthermore, it is …

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Maint.Data: Modelling And Analysing Interval Data In R, A Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias Dec 2021

Maint.Data: Modelling And Analysing Interval Data In R, A Pedro Duarte Silva, Paula Brito, Peter Filzmoser, José G. Dias

The R Journal

We present the CRAN R package MAINT.Data for the modelling and analysis of multivariate interval data, i.e., where units are described by variables whose values are intervals of R, representing intrinsic variability. Parametric inference methodologies based on probabilistic models for interval variables have been developed, where each interval is represented by its midpoint and log-range, for which multivariate Normal and Skew-Normal distributions are assumed. The intrinsic nature of the interval variables leads to special structures of the variance-covariance matrix, which are represented by four different possible configurations. MAINT.Data implements the proposed methodologies in the S4 object system, introducing a …

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Matchthem: Matching And Weighting After Multiple Imputation, Farhad Pishgar, Noah Greifer, Clémence Leyrat, Elizabeth Stuart Dec 2021

Matchthem: Matching And Weighting After Multiple Imputation, Farhad Pishgar, Noah Greifer, Clémence Leyrat, Elizabeth Stuart

The R Journal

Balancing the distributions of the confounders across the exposure levels in an observational study through matching or weighting is an accepted method to control for confounding due to these variables when estimating the association between an exposure and outcome and reducing the degree of dependence on certain modeling assumptions. Despite the increasing popularity in practice, these procedures cannot be immediately applied to datasets with missing values. Multiple imputation of the missing data is a popular approach to account for missing values while preserving the number of units in the dataset and accounting for the uncertainty in the missing values. However, …

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Diproperm: An R Package For The Diproperm Test, Andrew G. Allmon, J.S. Marron, Michael G. Hudgens Dec 2021

Diproperm: An R Package For The Diproperm Test, Andrew G. Allmon, J.S. Marron, Michael G. Hudgens

The R Journal

High-dimensional low sample size (HDLSS) data sets frequently emerge in many biomedical applications. The direction-projection-permutation (DiProPerm) test is a two-sample hypothesis test for comparing two high-dimensional distributions. The DiProPerm test is exact, i.e., the type I error is guaranteed to be controlled at the nominal level for any sample size, and thus is applicable in the HDLSS setting. This paper discusses the key components of the DiProPerm test, introduces the diproperm R package, and demonstrates the package on a real-world data set

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Bayessenmc: An R Package For Bayesian Sensitivity Analysis Of Misclassification, Jinhui Yang, Lifeng Lin, Haitao Chu Dec 2021

Bayessenmc: An R Package For Bayesian Sensitivity Analysis Of Misclassification, Jinhui Yang, Lifeng Lin, Haitao Chu

The R Journal

In case–control studies, the odds ratio is commonly used to summarize the association between a binary exposure and a dichotomous outcome. However, exposure misclassification frequently appears in case–control studies due to inaccurate data reporting, which can produce bias in measures of association. In this article, we implement a Bayesian sensitivity analysis of misclassification to provide a full posterior inference on the corrected odds ratio under both non-differential and differential misclassification. We present an R (R Core Team, 2018) package BayesSenMC, which provides user-friendly functions for its implementation. The usage is illustrated by a real data analysis on the association between …

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Passo: An R Package For Assessing Partial Association Between Ordinal Variables, Shaobo Li, Xiaorui Zhu, Yuejie Chen, Dungang Liu Dec 2021

Passo: An R Package For Assessing Partial Association Between Ordinal Variables, Shaobo Li, Xiaorui Zhu, Yuejie Chen, Dungang Liu

The R Journal

Partial association, the dependency between variables after adjusting for a set of covariates, is an important statistical notion for scientific research. However, if the variables of interest are ordered categorical data, the development of statistical methods and software for assessing their partial association is limited. Following the framework established by Liu et al. (2021), we develop an R package PAsso for assessing Partial Associations between ordinal variables. The package provides various functions that allow users to perform a wide spectrum of assessments, including quantification, visualization, and hypothesis testing. In this paper, we discuss the implementation of PAsso in …

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Ngsseml: Non-Gaussian State Space With Exact Marginal Likelihood, Thiago R. Santos, Glaura C. Franco, Dani Gamerman Dec 2021

Ngsseml: Non-Gaussian State Space With Exact Marginal Likelihood, Thiago R. Santos, Glaura C. Franco, Dani Gamerman

The R Journal

The number of packages/software for Gaussian State Space models has increased over recent decades. However, there are very few codes available for non-Gaussian State Space (NGSS) models due to analytical intractability that prevents exact calculations. One of the few tractable exceptions is the family of NGSS with exact marginal likelihood, named NGSSEML. In this work, we present the wide range of data formats and distributions handled by NGSSEML and a package in the R language to perform classical and Bayesian inference for them. Special functions for filtering, forecasting, and smoothing procedures and the exact calculation of the marginal likelihood function …

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Dad: An R Package For Visualisation, Classification And Discrimination Of Multivariate Groups Modelled By Their Densities, Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard Dec 2021

Dad: An R Package For Visualisation, Classification And Discrimination Of Multivariate Groups Modelled By Their Densities, Rachid Boumaza, Pierre Santagostini, Smail Yousfi, Sabine Demotes-Mainard

The R Journal

Multidimensional scaling (MDS), hierarchical cluster analysis (HCA), and discriminant analysis (DA) are classical techniques which deal with data made of n individuals and p variables. When the individuals are divided into T groups, the R package dad associates with each group a multivariate probability density function and then carries out these techniques on the densities, which are estimated by the data under consideration. These techniques are based on distance measures between densities: chi-square, Hellinger, Jeffreys, Jensen-Shannon, and Lp for discrete densities, Hellinger , Jeffreys, L2 , and 2-Wasserstein for Gaussian densities, and L2 for numeric non-Gaussian densities …

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Estimating Social Influence Effects In Networks Using A Latent Space Adjusted Approach In R, Ran Xu Dec 2021

Estimating Social Influence Effects In Networks Using A Latent Space Adjusted Approach In R, Ran Xu

The R Journal

Social influence effects have been extensively studied in various empirical network research. However, many challenges remain in estimating social influence effects in networks, as influence effects are often entangled with other factors, such as homophily in the selection process and the common social-environmental factors that individuals are embedded in. Methods currently available either do not solve these problems or require stringent assumptions. Recent works by Xu (2018) and others have shown that a latent space adjusted approach based on the latent space model has the potential to disentangle the influence effects from other processes, and the simulation evidence has shown …

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G2f As A Novel Tool To Find And Fill Gaps In Metabolic Networks, Daniel Osorio, Kelly Botero, Andrés Pinzón Velasco, Nicolás Mendoza-Mejía, Felipe Rojas-Rodríguez, George Barreto, Janneth González Dec 2021

G2f As A Novel Tool To Find And Fill Gaps In Metabolic Networks, Daniel Osorio, Kelly Botero, Andrés Pinzón Velasco, Nicolás Mendoza-Mejía, Felipe Rojas-Rodríguez, George Barreto, Janneth González

The R Journal

During the building of a genome-scale metabolic model, there are several dead-end metabolites and substrates which cannot be imported, produced, nor used by any reaction incorporated in the network. The presence of these dead-end metabolites can block out the net flux of the objective function when it is evaluated through Flux Balance Analysis (FBA), and when it is not blocked, bias in the biological conclusions increase. In this aspect, the refinement to restore the connectivity of the network can be carried out manually or using computational algorithms. The g2f package was designed as a tool to find the gaps from …

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Rejoinder: Software Engineering And R Programming, Melina Vidoni Dec 2021

Rejoinder: Software Engineering And R Programming, Melina Vidoni

The R Journal

It is a pleasure to take part in such fruitful discussion about the relationship between Software Engineering and R programming, and what could be gain by allowing each to look more closely at the other. Several discussants make valuable arguments that ought to be further discussed

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The R Quest: From Users To Developers, Simon Urbanek Dec 2021

The R Quest: From Users To Developers, Simon Urbanek

The R Journal

R is not a programming language, and this produces the inherent dichotomy between analytics and software engineering. With the emergence of data science, the opportunity exists to bridge this gap, especially through teaching practices.

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