This paper introduces provenance, a software package within the statistical programming environment R, which aims to facilitate the visualisation and interpretation of large amounts of sedimentary ...provenance data, including mineralogical, petrographic, chemical and isotopic provenance proxies, or any combination of these. provenance comprises functions to: (a) calculate the sample size required to achieve a given detection limit; (b) plot distributional data such as detrital zircon U–Pb age spectra as Cumulative Age Distributions (CADs) or adaptive Kernel Density Estimates (KDEs); (c) plot compositional data as pie charts or ternary diagrams; (d) correct the effects of hydraulic sorting on sandstone petrography and heavy mineral composition; (e) assess the settling equivalence of detrital minerals and grain-size dependence of sediment composition; (f) quantify the dissimilarity between distributional data using the Kolmogorov–Smirnov and Sircombe–Hazelton distances, or between compositional data using the Aitchison and Bray–Curtis distances; (e) interpret multi-sample datasets by means of (classical and nonmetric) Multidimensional Scaling (MDS) and Principal Component Analysis (PCA); and (f) simplify the interpretation of multi-method datasets by means of Generalised Procrustes Analysis (GPA) and 3-way MDS. All these tools can be accessed through an intuitive query-based user interface, which does not require knowledge of the R programming language. provenance is free software released under the GPL-2 licence and will be further expanded based on user feedback.
SUMMARY
The analysis of controlled-source electromagnetic (EM) data recorded with semi-airborne exploration systems requires advanced simulation and inversion tools that are capable of handling ...realistic survey geometries. Semi-airborne EM setups with elongated transmitters deployed in mountainous terrain prohibit the exploitation of secondary-field formulations in numerical approximations without producing hardly quantifiable errors. Building upon the open-source software custEM for forward modeling and pyGIMLi for geophysical inversion, we present an inverse modeling procedure based on highly accurate second-order finite-element forward solutions on irregular grids and fast-converging Gauss–Newton minimization. Using the total-field formulation of the electric field approach in combination with a direct solver enables calculating explicit sensitivities with comparatively cheap back-substitutions for thousands of ground and airborne receiver stations in multiple flight areas. Second-order basis functions show general superiority over first-order basis-functions regarding the accuracy and performance of the forward problem. Beyond that, synthetic and real data inversion studies related to semi-airborne geometries indicate that second-order basis functions help particularly to avoid high modeling errors for the weakest field components and artifacts in the vicinity of transmitters or at the surface. This leads generally to a better convergence and final inversion results of higher robustness and quality. The presented tools are freely available such as the underlying software.
Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the ...many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software’s SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the “how-tos” of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.
refnx is a model‐based neutron and X‐ray reflectometry data analysis package written in Python. It is cross platform and has been tested on Linux, macOS and Windows. Its graphical user interface is ...browser based, through a Jupyter notebook. Model construction is modular, being composed from a series of components that each describe a subset of the interface, parameterized in terms of physically relevant parameters (volume fraction of a polymer, lipid area per molecule etc.). The model and data are used to create an objective, which is used to calculate the residuals, log‐likelihood and log‐prior probabilities of the system. Objectives are combined to perform co‐refinement of multiple data sets and mixed‐area models. Prior knowledge of parameter values is encoded as probability distribution functions or bounds on all parameters in the system. Additional prior probability terms can be defined for sets of components, over and above those available from the parameters alone. Algebraic parameter constraints are available. The software offers a choice of fitting approaches, including least‐squares (global and gradient‐based optimizers) and a Bayesian approach using a Markov‐chain Monte Carlo algorithm to investigate the posterior distribution of the model parameters. The Bayesian approach is useful for examining parameter covariances, model selection and variability in the resulting scattering length density profiles. The package is designed to facilitate reproducible research; its use in Jupyter notebooks, and subsequent distribution of those notebooks as supporting information, permits straightforward reproduction of analyses.
The refnx Python modules for neutron and X‐ray reflectometry data analysis are introduced. A sample analysis illustrates a Bayesian approach using a Markov‐chain Monte Carlo algorithm to understand the confidence in the fit parameters.
The availability of thermodynamic data for geologically relevant phases has made practical the calculation of stable phase relations throughout the mantle and crust of terrestrial planets. GeoPS ...(http://www.geops.org) is a program designed for this purpose in which both input and output are done through an intuitive graphical user interface. GeoPS provides a wide range of phase equilibrium calculations based on a novel Gibbs energy minimization algorithm. The algorithm provides for exceptionally robust and computationally efficient solution to the phase equilibrium problem by successive alternation between a linear programming step to identify stable phase compositions and a non‐linear programming step to refine the compositions estimated during the linear programming. Applications include calculation of various types of phase diagrams and path‐dependent phase fractionation. By combining an easy‐to‐use graphical user interface with a robust and efficient solver, GeoPS makes phase equilibrium modelling accessible to researchers and students with minimal training and provides a powerful tool for understanding natural phase relations and for planning experimental work.
SIR2014
is the latest program of the
SIR
suite for crystal structure solution of small, medium and large structures. A variety of phasing algorithms have been implemented, both
ab initio
(standard or ...modern direct methods, Patterson techniques,
Vive la Différence
) and non-
ab initio
(simulated annealing, molecular replacement). The program contains tools for crystal structure refinement and for the study of three-dimensional electron-density maps
via
suitable viewers.
This book constitutes the proceedings of the 16th International Conference on Informatics in Schools: Situation, Evolution and Perspectives, ISSEP 2023, held in Lausanne, Switzerland, during October ...23–25, 2023. The 14 full papers presented in this book were carefully reviewed and selected from 47 submissions. They are organized in four topical sections named: artificial intelligence and its applications; competitions, problem solving, and computational; robotics and unplugged modalities; and curricula and computer science concepts. This is an open access book.
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