scholarly journals New Approach Combining Molecular Fingerprints and Machine Learning to Estimate Relative Ionization Efficiency in Electrospray Ionization

ACS Omega ◽  
2020 ◽  
Vol 5 (16) ◽  
pp. 9510-9516 ◽  
Author(s):  
Alfred W. Mayhew ◽  
David O. Topping ◽  
Jacqueline F. Hamilton
Keyword(s):  
Machine Learning ◽  
Electrospray Ionization ◽  
Ionization Efficiency ◽  
Molecular Fingerprints ◽  
New Approach

Effects of peptide acetylation and dimethylation on electrospray ionization efficiency

2016 ◽  
Vol 51 (2) ◽  
pp. 105-110 ◽  
Author(s):  
Kyung-Cho Cho ◽  
Jeong Won Kang ◽  
Yuri Choi ◽  
Tae Woo Kim ◽  
Kwang Pyo Kim
Keyword(s):  
Electrospray Ionization ◽  
Ionization Efficiency

Research of actual information on well casing using machine learning and neural networks

2021 ◽  
pp. 108-119
Author(s):  
D. V. Shalyapin ◽  
D. L. Bakirov ◽  
M. M. Fattahov ◽  
A. D. Shalyapina ◽  
V. G. Kuznetsov
Keyword(s):  
Machine Learning ◽  
Probabilistic Models ◽  
Western Siberia ◽  
Network Modeling ◽  
Horizontal Wells ◽  
Categorical Variables ◽  
Neural Network Modeling ◽  
New Approach ◽  
Attachment Process

In domestic and world practice, despite the measures applied and developed to improve the quality of well casing, there is a problem of leaky structures in almost 50 % of completed wells. The study of actual data using classical methods of statistical analysis (regression and variance analyses) doesn't allow us to model the process with sufficient accuracy that requires the development of a new approach to the study of the attachment process. It is proposed to use the methods of machine learning and neural network modeling to identify the most important parameters and their synergistic impact on the target variables that affect the quality of well casing. The formulas necessary for translating the numerical values of the results of acoustic and gamma-gamma cementometry into categorical variables to improve the quality of probabilistic models are determined. A database consisting of 93 parameters for 934 wells of fields located in Western Siberia has been formed. The analysis of fastening of production columns of horizontal wells of four stratigraphic arches is carried out, the most weighty variables and regularities of their influence on target indicators are established. Recommendations are formulated to improve the quality of well casing by correcting the effects of acoustic and gamma-gamma logging on the results.

scholarly journals Process Intensification 4.0: A New Approach for Attaining New, Sustainable and Circular Processes Enabled by Machine Learning

2021 ◽  
pp. 108671
Author(s):  
Enrique A. López-Guajardo ◽  
Fernando Delgado-Licona ◽  
Alejandro J. Álvarez ◽  
Krishna D.P. Nigam ◽  
Alejandro Montesinos-Castellanos ◽  
...  
Keyword(s):  
Machine Learning ◽  
Process Intensification ◽  
New Approach

scholarly journals Introducing the Viewpoint in the Resource Description using Machine Learning

2021 ◽  
Author(s):  
Ouahiba Djama
Keyword(s):  
Machine Learning ◽  
Experimental Study ◽  
Search Engines ◽  
Machine Learning Technique ◽  
New Approach ◽  
Learning Technique ◽  
Resource Description

Search engines allow providing the user with data and information according to their interests and specialty. Thus, it is necessary to exploit descriptions of the resources, which take into consideration viewpoints. Generally, the resource descriptions are available in RDF (e.g., DBPedia of Wikipedia content). However, these descriptions do not take into consideration viewpoints. In this paper, we propose a new approach, which allows converting a classic RDF resource description to a resource description that takes into consideration viewpoints. To detect viewpoints in the document, a machine learning technique will be exploited on an instanced ontology. This latter allows representing the viewpoint in a given domain. An experimental study shows that the conversion of the classic RDF resource description to a resource description that takes into consideration viewpoints, allows giving very relevant responses to the user’s requests.

scholarly journals Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

2018 ◽  
Vol 5 (5) ◽  
pp. 939-945 ◽  
Author(s):  
Grace X. Gu ◽  
Chun-Teh Chen ◽  
Deon J. Richmond ◽  
Markus J. Buehler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Additive Manufacturing ◽  
Convolutional Neural Networks ◽  
New Approach ◽  
Hierarchical Materials

A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.

Sign in / Sign up

Export Citation Format

Share Document