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The California Department of Transportation (Caltrans) published tests methods for concrete admixtures in 2007.
The finite element model has been validated against published tests conducted at elevated temperatures.
As it has been largely published, tests performed as a function of time have as a common characteristic high standard deviation values.
Frame masonry composite was analyzed using neural networks trained on the experimental database that contains results of 113 published tests of one-story one-bay masonry infilled frames.
Analyses have been carried out on a total of 405 previously published tests using back-propagation neural networks, to assess the capacity energy required to trigger liquefaction in sand and silty sands.
Analyses have been carried out on a total of 302 previously published tests using a nonparametric regression procedure known as multivariate adaptive regression splines (MARS), to assess the capacity energy required to trigger liquefaction in sand and silty sands.
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To date, every single published test has been consistent with quantum theory.
Numerical results for previously published test cases are presented to demonstrate attitude estimation accuracy.
In general, soft pivot model predictions are closely predicted when compared to the previously published test data.
The coupled system model was implemented in MATLAB-Simulink, and validated by using a series of published testing tools.
The published test methods were evaluated for their suitability to determine the anaerobic biodegradability of surfactants under use-relevant, i.e. waste water treatment plant conditions.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com