scholarly journals Prediction of chemical composition for callus production in Gyrinops walla Gaetner through machine learning

2020 ◽  
Vol 7 (4) ◽  
pp. 511-522 ◽  
Author(s):  
Sachithri P. Munasinghe ◽  
Seneviratnege Somaratne ◽  
Shyama R. Weerakoon ◽  
Chandani Ranasinghe
Keyword(s):  
Machine Learning ◽  
Chemical Composition ◽  
Callus Production

Integration of Chemofacies and Rock Mechanical Properties Using Machine Learning Algorithms: Implications for Geomechanics and Hydraulic Fracture Stimulations in Paleozoic Formations, Saudi Arabia

2021 ◽  
Author(s):  
Maaruf Hussain ◽  
Abduljamiu Amao ◽  
Khalid Al-Ramadan ◽  
Sunday Olatunji ◽  
Ardiansyah Negara
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Saudi Arabia ◽  
Spatial Distribution ◽  
Chemical Composition ◽  
Rock Strength ◽  
Machine Learning Algorithms ◽  
Unconventional Reservoirs ◽  
Geochemical Properties ◽  
Rock Mechanical Properties

Abstract The knowledge of rock mechanical properties is critical to reducing drilling risk and maximizing well and reservoir productivity. Rock chemical composition, their spatial distribution, and porosity significantly influenced these properties. However, low porosity characterized unconventional reservoirs as such, geochemical properties considerably control their mechanical behavior. In this study, we used chemostratigraphy as a correlation tool to separate strata in highly homogenous formations where other traditional stratigraphic methods failed. In addition, we integrated the chemofacies output and reduced Young's modulus to outline predictable associations between facies and mechanical properties. Thus, providing better understanding of lithofacies-controlled changes in rock strength that are useful inputs for geomechanical models and completions stimulations.

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

2021 ◽  
Author(s):  
Ashwin P. Rao ◽  
Phillip R. Jenkins ◽  
John D. Auxier II ◽  
Michael B. Shattan
Keyword(s):  
Machine Learning ◽  
Chemical Composition ◽  
Machine Learning Techniques ◽  
Composition Analysis ◽  
Learning Techniques ◽  
Chemical Composition Analysis

Enhancing the analytical capabilities of a hand-held LIBS device for chemical composition analysis of a plutonium surrogate using different machine learning paradigms.

Machine learning guided methods in building chemical composition-hardenability model for wear-resistant steel

2020 ◽  
Vol 24 ◽  
pp. 101332
Author(s):  
Guibin Dong ◽  
Xiucheng Li ◽  
Jingxiao Zhao ◽  
Shuai Su ◽  
R.D.K. Misra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Composition ◽  
Resistant Steel ◽  
Wear Resistant

scholarly journals Application of Supervised Machine-Learning Methods for Attesting Provenance in Catalan Traditional Pottery Industry

Minerals ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 8 ◽  
Author(s):  
Anna Anglisano ◽  
Lluís Casas ◽  
Marc Anglisano ◽  
Ignasi Queralt
Keyword(s):  
Machine Learning ◽  
Chemical Composition ◽  
Raw Materials ◽  
Supervised Machine Learning ◽  
Pottery Production ◽  
Learning Methods ◽  
Pottery Industry ◽  
Machine Learning Methods ◽  
Supervised Methods ◽  
Site Discrimination

The traditional pottery industry was an important activity in Catalonia (NE Spain) up to the 20th century. However, nowadays only few workshops persist in small villages were the activity is promoted as a touristic attraction. The preservation and promotion of traditional pottery in Catalonia is part of an ongoing strategy of tourism diversification that is revitalizing the sector. The production of authenticable local pottery handicrafts aims at attracting cultivated and high-purchasing power tourists. The present paper inspects several approaches to set up a scientific protocol based on the chemical composition of both raw materials and pottery. These could be used to develop a seal of quality and provenance to regulate the sector. Six Catalan villages with a renowned tradition of local pottery production have been selected. The chemical composition of their clays and the corresponding fired products has been obtained by Energy dispersive X-ray fluorescence (EDXRF). Using the obtained geochemical dataset, a number of unsupervised and supervised machine learning methods have been applied to test their applicability to define geochemical fingerprints that could allow inter-site discrimination. The unsupervised approach fails to distinguish samples from different provenances. These methods are only roughly able to divide the different provenances in two large groups defined by their different SiO2 and CaCO3 concentrations. In contrast, almost all the tested supervised methods allow inter-site discrimination with accuracy levels above 80%, and accuracies above 85% were obtained using a meta-model combining all the predictive supervised methods. The obtained results can be taken as encouraging and demonstrative of the potential of the supervised approach as a way to define geochemical fingerprints to track or attest the provenance of samples.

scholarly journals Modelling wine astringency from its chemical composition using machine learning algorithms

OENO One ◽  
2019 ◽  
Vol 53 (3) ◽  
Author(s):  
Maria P Sáenz-Navajas ◽  
Sara Ferrero-del-Teso ◽  
Miguel Romero ◽  
Darío Pascual ◽  
David Diaz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chemical Composition ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Wine Production ◽  
Polyphenolic Composition ◽  
Trained Panel ◽  
Production Stages ◽  
The Mean ◽  
Support Vector Regressor

Aims: The present work aims to predict sensory astringency from wine chemical composition using machine learning algorithms.Material and results: Moristel grapes from different vineblocks and at different stages of ripening were collected. Eleven different wines were produced in 75 L tanks in triplicate, and further sensory factors were described by the rate-all-that-apply method with a trained panel of participants. The polyphenolic composition was characterised in wines by measuring the concentration and activity of tannins using UHPLC-UV/VIS, the mean degree of polymerisation (mDP. and the composition of tannins using thiolysis followed by UHPLC-MS. Conventional oenological parameters were analysed using FTIR and UV-Vis. Machine learning was applied to build models for predicting a wines astringency from its chemical composition. The best model was obtained using the support vector regressor (radial kernel) algorithm presenting a root-mean-square error (RMSE) value of 0.190.Conclusions: The main variables of the astringency model were the % of procyanidins constituting tannins and ethanol content, followed by other eight variables related to tannin structure and acidity.Significance of the study: These results increase the knowledge of chemical variables related to the perception of wine astringency and provide tools to control and optimise grape and wine production stages to modulate astringency and maximise quality and the consumer appeal of wines.

The Origin of the Moon

1962 ◽  
Vol 14 ◽  
pp. 149-155 ◽  
Author(s):  
E. L. Ruskol
Keyword(s):  
Chemical Composition ◽  
Solar System ◽  
The Moon ◽  
The Earth ◽  
The Difference ◽  
Origin Of The Moon

The difference between average densities of the Moon and Earth was interpreted in the preceding report by Professor H. Urey as indicating a difference in their chemical composition. Therefore, Urey assumes the Moon's formation to have taken place far away from the Earth, under conditions differing substantially from the conditions of Earth's formation. In such a case, the Earth should have captured the Moon. As is admitted by Professor Urey himself, such a capture is a very improbable event. In addition, an assumption that the “lunar” dimensions were representative of protoplanetary bodies in the entire solar system encounters great difficulties.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

On the Inhomogeneity of Chemical Composition over the Surface of 21 Per

1976 ◽  
Vol 32 ◽  
pp. 343-349
Author(s):  
Yu.V. Glagolevsky ◽  
K.I. Kozlova ◽  
V.S. Lebedev ◽  
N.S. Polosukhina
Keyword(s):  
Chemical Composition ◽  
Spectral Line ◽  
Variable Star ◽  
Radial Velocities ◽  
Crimean Astrophysical Observatory ◽  
Line Intensities ◽  
Magnetic Variable

SummaryThe magnetic variable star 21 Per has been studied from 4 and 8 Å/mm spectra obtained with the 2.6 - meter reflector of the Crimean Astrophysical Observatory. Spectral line intensities (Wλ) and radial velocities (Vr) have been measured.

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