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Towards reinforcement learning for vulnerability analysis in power-economic systems

Overview of attention for article published in Energy Informatics, September 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
4 Mendeley
Title
Towards reinforcement learning for vulnerability analysis in power-economic systems
Published in
Energy Informatics, September 2021
DOI 10.1186/s42162-021-00181-5
Authors

Thomas Wolgast, Eric MSP Veith, Astrid Nieße

Twitter Demographics

The data shown below were collected from the profiles of 4 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 50%
Unspecified 1 25%
Student > Master 1 25%
Readers by discipline Count As %
Computer Science 3 75%
Unspecified 1 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 21 February 2022.
All research outputs
#12,090,029
of 21,840,504 outputs
Outputs from Energy Informatics
#14
of 53 outputs
Outputs of similar age
#131,719
of 337,081 outputs
Outputs of similar age from Energy Informatics
#1
of 1 outputs
Altmetric has tracked 21,840,504 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 53 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 73% 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 337,081 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
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