Your English writing platform
Discover LudwigSuggestions(1)
Exact(1)
The optimum uniaxial material dataset for strain-life predictions employing the Chaboche model is determined.
Similar(59)
We compared ARGOT with Blast2GO over the same BLAST results (see supporting material Dataset S5) obtained for 4,000 proteins from the test set YEAST.
This work specifically present a framework for a fast and reliable classification of a large material dataset with respect to desired mechanical properties, and can be used for all materials within the context of materials science and engineering.
The Columbia Utrecht Reflectance and Texture Database (CUReT) [15, 17], the KTH-TIPS [13], the Flickr Material Database (FMD) [22], and the Material-in-context Database (MINC) [25] are open datasets for material recognition.
The complete dataset for the standard sequences is available as supplementary material (see Additional files 3 and 4).
(i) Two texture datasets: the first one is Brodatz32 [33] that is a standard dataset for texture recognition, and the second one is KTH-TIPS 2a [34], a dataset for material categorization [35].
Large datasets allow for analytic flexibility, and it is all too tempting to trawl a dataset for "significant" associations.
We have no such dataset for Tuesday's recall.
Remaining dataset for testing performance (second dataset).
We mined these large datasets for material responses by employing matrix decomposition techniques, such as independent component analysis.
As human judgment represents the ground truth, collecting material for creating datasets is an expensive and time-consuming task.
Write better and faster with AI suggestions while staying true to your unique style.
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