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Review of synergy between machine learning and first principles models for asset integrity management

Overview of attention for article published in Frontiers in Chemical Engineering, July 2023
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1 X user

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13 Mendeley
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Title
Review of synergy between machine learning and first principles models for asset integrity management
Published in
Frontiers in Chemical Engineering, July 2023
DOI 10.3389/fceng.2023.1138283
Authors

Tianxing Cai, Jian Fang, Sharath Daida, Helen H. Lou

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 46%
Student > Doctoral Student 1 8%
Unknown 6 46%
Readers by discipline Count As %
Engineering 3 23%
Unspecified 1 8%
Chemical Engineering 1 8%
Neuroscience 1 8%
Business, Management and Accounting 1 8%
Other 0 0%
Unknown 6 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 July 2023.
All research outputs
#19,562,818
of 24,063,285 outputs
Outputs from Frontiers in Chemical Engineering
#132
of 283 outputs
Outputs of similar age
#127,423
of 175,543 outputs
Outputs of similar age from Frontiers in Chemical Engineering
#1
of 1 outputs
Altmetric has tracked 24,063,285 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 283 research outputs from this source. They receive a mean Attention Score of 2.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 175,543 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them