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X Demographics
Attention Score in Context
| Title |
幾何学的特徴量に対する偏微分方程式を用いた型成形製造制約の数理モデルとトポロジー最適化への展開
|
|---|---|
| Published in |
Transactions of the Japan Society for Computational Engineering and Science, November 2021
|
| DOI | 10.11421/jsces.2021.20210018 |
| Authors |
酒井 虹太, 野口 悠暉, 山田 崇恭 |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
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 15 September 2022.
All research outputs
#15,485,623
of 25,852,155 outputs
Outputs from Transactions of the Japan Society for Computational Engineering and Science
#4
of 18 outputs
Outputs of similar age
#210,492
of 446,300 outputs
Outputs of similar age from Transactions of the Japan Society for Computational Engineering and Science
#2
of 2 outputs
Altmetric has tracked 25,852,155 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 18 research outputs from this source. They receive a mean Attention Score of 3.7. This one scored the same or higher as 14 of them.
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 446,300 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 50% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one.