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2021

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Six-Fold Increase Of Atmospheric Pco2 During The Permian–Triassic Mass Extinction, Yuyang Wu, Daoliang Chu, Jinnan Tong, Haijun Song, Jacopo Dal Corso, Paul B. Wignall, Huyue Song, Yong Du, Ying Cui Dec 2021

Six-Fold Increase Of Atmospheric Pco2 During The Permian–Triassic Mass Extinction, Yuyang Wu, Daoliang Chu, Jinnan Tong, Haijun Song, Jacopo Dal Corso, Paul B. Wignall, Huyue Song, Yong Du, Ying Cui

Department of Earth and Environmental Studies Faculty Scholarship and Creative Works

The Permian–Triassic mass extinction was marked by a massive release of carbon into the ocean-atmosphere system, evidenced by a sharp negative carbon isotope excursion. Large carbon emissions would have increased atmospheric pCO2 and caused global warming. However, the magnitude of pCO2 changes during the PTME has not yet been estimated. Here, we present a continuous pCO2 record across the PTME reconstructed from high-resolution δ13C of C3 plants from southwestern China. We show that pCO2 increased from 426 +133/−96 ppmv in the latest Permian to 2507 +4764/−1193 ppmv at the PTME within about 75 kyr, and that the reconstructed …


Understanding The Behavior Of The Covid-19 Pandemic Using Data Analytics, Mohammad Reza Davahli Dec 2021

Understanding The Behavior Of The Covid-19 Pandemic Using Data Analytics, Mohammad Reza Davahli

Electronic Theses and Dissertations, 2020-2023

In December 2019, China announced the breakout of a new virus identified as coronavirus SARS-CoV-2 (COVID-19), which soon grew exponentially and became a global pandemic. Despite strict actions to mitigate the spread of the virus in various countries, the COVID-19 pandemic resulted in a significant loss of human life in 2020 and 2021. To better understand the pandemic, this doctoral research incorporated data analytics to evaluate the behavior and impacts of the virus. The doctoral research contributed to the scientific body of the knowledge in different ways including (1) presenting a systematic literature review of current research and topics about …


Structural Dynamics And Encapsulation Properties Of Polyelectrolyte Complex Micelles, Sachit Shah Dec 2021

Structural Dynamics And Encapsulation Properties Of Polyelectrolyte Complex Micelles, Sachit Shah

Electronic Theses and Dissertations, 2020-2023

Charged therapeutics such as nucleic acids and proteins can treat a vast range of human diseases that are traditionally undruggable. Their broadness in treating disease is due to their ability to influence cellular function. However, their high charge density and physiological barriers such as enzymatic degradation, hinder the deliverability of these molecules to the sites of disease. Polyelectrolyte complex (PEC) micelles are core-corona nanostructures that can encapsulate charged molecules and offer a platform for delivery. PECs form the core, when two oppositely charged polyelectrolytes are mixed in an aqueous solution, and the micelle corona is a neutral hydrophilic polymer that …


A Framework For The Design And Characterization Of Advanced Gaskets, Sannmit Shinde Dec 2021

A Framework For The Design And Characterization Of Advanced Gaskets, Sannmit Shinde

Electronic Theses and Dissertations, 2020-2023

Most industrial manufacturing or processing plants use bolted connections between pipes that transfer media from one location to another. Gaskets are often used to seal these systems as they offer elevated levels of leak mitigation; however, despite their nearly universal usage, the current understanding of gasket mechanics at the meso-scale is still limited. Contemporary gaskets feature viscoelastic materials, fillers, textures, and are fabricated at various thicknesses. They are used in a wide range of thermal, mechanical, chemical, and temporal conditions. The current work characterizes polytetrafluoroethylene (PTFE) gaskets made with several different filler materials and having vastly different geometries. The chemically …


On Mode Transition Phenomenon And Operating Conditions In Rotating Detonation Rocket Engines, Taha Rezzag-Lebza Dec 2021

On Mode Transition Phenomenon And Operating Conditions In Rotating Detonation Rocket Engines, Taha Rezzag-Lebza

Electronic Theses and Dissertations, 2020-2023

The work presented herein consist of first studying the instantaneous properties of the detonation waves in a rotating detonation rocket engine by tracking each individual wave and recording its position, velocity, and peak intensity as it travels around the annulus. Results for a steady portion of a test performed on a rotating detonation rocket engine show that the wave properties exhibit oscillatory behavior. Results obtained from the rotating detonation rocket engine show that the properties are highly dependent on the azimuthal position. In an attempt to understanding the cause of such a behavior, similar investigations were performed on an air-breathing …


Processing And Visualizing Satellite Data, Caleb Collier Dec 2021

Processing And Visualizing Satellite Data, Caleb Collier

Computer Science & Engineering Student Projects

Satellites are a useful way of gathering data at high altitudes. To be able to properly view the data, however, there are many important steps that one must take to ensure the data received is readable and usable. The data must be transmitted from the satellite to the ground, then must be decommuted and can then be used in various ways. This paper is an exploration of various ways of processing and visualizing data received from satellites, as well as various ways of using the data.


New York Public Mapping Project, Marissa Zanfardino Dec 2021

New York Public Mapping Project, Marissa Zanfardino

Redistricting Resources

Over the past few months, the New York Census and Redistricting Institute has conducted the New York Public Mapping Project to coincide with the 2021 state redistricting cycle. Student teams from New York’s higher education institutions have been invited to participate in drafting maps for New York’s congressional, state senate, and state assembly districts and submit them to the State. Teams were able to use free online software and data, along with specialized training, to draw model maps that can be submitted to the state. This Project served as a key civic engagement effort for New York college and law …


Iron Geochemistry Across An Estuary-To-Coastal Gradient, Lissett G. Diaz Dec 2021

Iron Geochemistry Across An Estuary-To-Coastal Gradient, Lissett G. Diaz

Honors Theses

Iron is a ubiquitous earth element that participates in biogeochemical processes that occur in marine sediments. Microorganisms utilize iron for many purposes, including cell growth, conserving energy, and for maintaining metabolic activity. In coastal sedimentary settings, understanding the redox reactions involving ferric iron, Fe3+, and ferrous iron, Fe2+, in its solid phase and pore-water phases, respectively, enable an appreciation of biogeochemical transformations occurring in the coastal zone. In this study, iron concentrations in sediment of ranging permeability were determined at four stations marking an estuary-coast transition zone in Singleton Swash in Myrtle Beach, South Carolina. The …


A Mixed Methods Study Of Undergraduate Research Motivations At The University Of Nebraska Omaha (Uno), Afrah F. Rasheed Dec 2021

A Mixed Methods Study Of Undergraduate Research Motivations At The University Of Nebraska Omaha (Uno), Afrah F. Rasheed

Theses/Capstones/Creative Projects

Undergraduate research provides unique experiences and skill sets that are applicable in various careers. However, there has been an increasing need for greater diversity among researchers, namely undergraduate researchers, and the motivations behind undergraduate research participation need to be further explored. The purpose of this study was to identify the demographic information of undergraduate students at the University of Nebraska at Omaha (UNO), and what motivations were the most prevalent in undergraduate research at UNO. A mixed-methods approach was utilized through a quantitative portion and a qualitative portion. The quantitative portion was a web-based survey which gauged for 22 independent …


Iowa Waste Reduction Center Newsletter, December 2021, University Of Northern Iowa. Iowa Waste Reduction Center. Dec 2021

Iowa Waste Reduction Center Newsletter, December 2021, University Of Northern Iowa. Iowa Waste Reduction Center.

Iowa Waste Reduction Center Newsletter

Contents:

--- End of 2021 Director Address
--- Calendar
--- Welcome, Megan!
--- Saying Goodbye! : David Hindman - Elle Olthoff - Brandon Stenger
--- IGCB Awards New Gold-Level Breweries
--- 2022-2023 Recordkeeping Compliance Calendars for Dry Cleaners
--- 2022-2023 Compliance Calendar/Logbook for Bulk Gasoline Plants
--- Industry News


Notes From The Director, The Mcnair Team Dec 2021

Notes From The Director, The Mcnair Team

McNair Scholars Research Journal

No abstract provided.


Message From The Interim Vice President For Research, The Mcnair Team Dec 2021

Message From The Interim Vice President For Research, The Mcnair Team

McNair Scholars Research Journal

No abstract provided.


Full Issue, The Mcnair Team Dec 2021

Full Issue, The Mcnair Team

McNair Scholars Research Journal

No abstract provided.


Scholarships, The Mcnair Team Dec 2021

Scholarships, The Mcnair Team

McNair Scholars Research Journal

No abstract provided.


Specific Splice Junction Detection In Single Cells With Sicilian, Roozbeh Dehghannasiri, Julia E. Olivieri, Ana Damljanovic, Julia Salzman Dec 2021

Specific Splice Junction Detection In Single Cells With Sicilian, Roozbeh Dehghannasiri, Julia E. Olivieri, Ana Damljanovic, Julia Salzman

All Faculty Articles - School of Engineering and Computer Science

Precise splice junction calls are currently unavailable in scRNA-seq pipelines such as the 10x Chromium platform but are critical for understanding single-cell biology. Here, we introduce SICILIAN, a new method that assigns statistical confidence to splice junctions from a spliced aligner to improve precision. SICILIAN is a general method that can be applied to bulk or single-cell data, but has particular utility for single-cell analysis due to that data’s unique challenges and opportunities for discovery. SICILIAN’s precise splice detection achieves high accuracy on simulated data, improves concordance between matched single-cell and bulk datasets, and increases agreement between biological replicates. SICILIAN …


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.


Changes In R, Tomas Kalibera, Sebastian Meyer, Kurt Hornik, Gennadiy Starostin, Luke Tierney Dec 2021

Changes In R, Tomas Kalibera, Sebastian Meyer, Kurt Hornik, Gennadiy Starostin, Luke Tierney

The R Journal

We present important changes in the development version of R (referred to as R-devel, to become R 4.2) and give a summary of the new search engine interfaced by RSiteSearch(). Some statistics on bug tracking activities in 2021 are also provided.


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 …


The Vote Package: Single Transferable Vote And Other Electoral Systems In R, Adrian E. Raftery, Hana ŠevčÍková, Bernard W. Silverman Dec 2021

The Vote Package: Single Transferable Vote And Other Electoral Systems In R, Adrian E. Raftery, Hana ŠevčÍková, Bernard W. Silverman

The R Journal

We describe the vote package in R, which implements the plurality (or first-past-the-post), two-round runoff, score, approval, and Single Transferable Vote (STV) electoral systems, as well as methods for selecting the Condorcet winner and loser. We emphasize the STV system, which we have found to work well in practice for multi-winner elections with small electorates, such as committee and council elections, and the selection of multiple job candidates. For single-winner elections, STV is also called Instant Runoff Voting (IRV), Ranked Choice Voting (RCV), or the alternative vote (AV) system. The package also implements the STV system with equal preferences, for …


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

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


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 …


Openskies - Integration Of Aviation Data Into The R Ecosystem, Rafael Ayala, Daniel Ayala, Lara Sellés Vidal, David Ruiz Dec 2021

Openskies - Integration Of Aviation Data Into The R Ecosystem, Rafael Ayala, Daniel Ayala, Lara Sellés Vidal, David Ruiz

The R Journal

Aviation data has become increasingly more accessible to the public thanks to the adoption of technologies such as Automatic Dependent Surveillance-Broadcast (ADS-B) and Mode S, which provide aircraft information over publicly accessible radio channels. Furthermore, the OpenSky Network provides multiple public resources to access such air traffic data from a large network of ADS-B receivers. Here, we present openSkies, the first R package for processing public air traffic data. The package provides an interface to the OpenSky Network resources, standardized data structures to represent the different entities involved in air traffic data, and functionalities to analyze and visualize such …


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.


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 …


A New Versatile Discrete Distribution, Rolf Turner Dec 2021

A New Versatile Discrete Distribution, Rolf Turner

The R Journal

This paper introduces a new flexible distribution for discrete data. Approximate moment estimators of the parameters of the distribution, to be used as starting values for numerical optimization procedures, are discussed. “Exact” moment estimation, effected via a numerical procedure, and maximum likelihood estimation, are considered. The quality of the results produced by these estimators is assessed via simulation experiments. Several examples are given of fitting instances of the new distribution to real and simulated data. It is noted that the new distribution is a member of the exponential family. Expressions for the gradient and Hessian of the log-likelihood of the …


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 …


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.


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

Passed: Calculate Power And Sample Size For Two Sample Tests, University Of Missouri Li, Ryan P. 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.


Robust And Efficient Optimization Using A Marquardt-Levenberg Algorithm With R Package Marqlevalg, Viviane Philipps, Boris P. Hejblum, Mélanie Prague, Daniel Commenges, Cécile Proust-Lima Dec 2021

Robust And Efficient Optimization Using A Marquardt-Levenberg Algorithm With R Package Marqlevalg, Viviane Philipps, Boris P. Hejblum, Mélanie Prague, Daniel Commenges, Cécile Proust-Lima

The R Journal

Implementations in R of classical general-purpose algorithms for local optimization generally have two major limitations which cause difficulties in applications to complex problems: too loose convergence criteria and too long calculation time. By relying on a Marquardt-Levenberg algorithm (MLA), a Newton-like method particularly robust for solving local optimization problems, we provide with marqLevAlg package an efficient and general-purpose local optimizer which (i) prevents convergence to saddle points by using a stringent convergence criterion based on the relative distance to minimum/maximum in addition to the stability of the parameters and of the objective function; and (ii) reduces the computation time in …


Emss: New Em-Type Algorithms For The Heckman Selection Model In R, Kexuan Yang, Sang Kyu Lee, Jun Zhao, Hyoung-Moon Kim Dec 2021

Emss: New Em-Type Algorithms For The Heckman Selection Model In R, Kexuan Yang, Sang Kyu Lee, Jun Zhao, Hyoung-Moon Kim

The R Journal

When investigators observe non-random samples from populations, sample selectivity problems may occur. The Heckman selection model is widely used to deal with selectivity problems. Based on the EM algorithm, Zhao et al. (2020) developed three algorithms, namely, ECM, ECM(NR), and ECME(NR), which also have the EM algorithm’s main advantages: stability and ease of implementation. This paper provides the implementation of these three new EM-type algorithms in the package EMSS and illustrates the usage of the package on several simulated and real data examples. The comparison between the maximum likelihood estimation method (MLE) and three new EM-type algorithms in robustness issues …