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Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NOx) Emissions Using Deep Learning

Overview of attention for article published in Frontiers in Mechanical Engineering, March 2022
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  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

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3 X users

Readers on

mendeley
23 Mendeley
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Title
Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NOx) Emissions Using Deep Learning
Published in
Frontiers in Mechanical Engineering, March 2022
DOI 10.3389/fmech.2022.840310
Pubmed ID
Authors

Rinav Pillai, Vassilis Triantopoulos, Albert S. Berahas, Matthew Brusstar, Ruonan Sun, Tim Nevius, André L. Boehman

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 13%
Unspecified 2 9%
Student > Master 2 9%
Student > Bachelor 2 9%
Lecturer > Senior Lecturer 1 4%
Other 3 13%
Unknown 10 43%
Readers by discipline Count As %
Engineering 9 39%
Unspecified 2 9%
Computer Science 1 4%
Energy 1 4%
Physics and Astronomy 1 4%
Other 0 0%
Unknown 9 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 26 March 2022.
All research outputs
#16,106,940
of 25,562,515 outputs
Outputs from Frontiers in Mechanical Engineering
#122
of 608 outputs
Outputs of similar age
#234,926
of 449,727 outputs
Outputs of similar age from Frontiers in Mechanical Engineering
#5
of 29 outputs
Altmetric has tracked 25,562,515 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 608 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 79% of its peers.
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 449,727 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.