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Inverse problem solving in semiconductor photoacoustics by neural networks

Overview of attention for article published in Inverse Problems in Engineering, July 2020
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Mentioned by

twitter
2 tweeters

Citations

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6 Dimensions

Readers on

mendeley
3 Mendeley
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Title
Inverse problem solving in semiconductor photoacoustics by neural networks
Published in
Inverse Problems in Engineering, July 2020
DOI 10.1080/17415977.2020.1787405
Authors

Katarina Lj. Djordjevic, Dragan D. Markushev, Žarko M. Ćojbašić, Slobodanka P. Galović

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Researcher 1 33%
Lecturer 1 33%
Readers by discipline Count As %
Unspecified 1 33%
Mathematics 1 33%
Physics and Astronomy 1 33%

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 25 May 2022.
All research outputs
#17,040,671
of 21,859,856 outputs
Outputs from Inverse Problems in Engineering
#80
of 144 outputs
Outputs of similar age
#230,549
of 338,467 outputs
Outputs of similar age from Inverse Problems in Engineering
#1
of 1 outputs
Altmetric has tracked 21,859,856 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 144 research outputs from this source. They receive a mean Attention Score of 1.8. This one is in the 20th percentile – i.e., 20% 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 338,467 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% 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