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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Radiation Tolerance Testing Methodology of Robotic Manipulator Prior to Nuclear Waste Handling
|
|---|---|
| Published in |
Frontiers in Robotics and AI, February 2020
|
| DOI | 10.3389/frobt.2020.00006 |
| Pubmed ID | |
| Authors |
Kaiqiang Zhang, Chris Hutson, James Knighton, Guido Herrmann, Tom Scott |
| Abstract |
Dramatic cost savings, safety improvements and accelerated nuclear decommissioning are all possible through the application of robotic solutions. Remotely-controlled systems with modern sensing capabilities, actuators and cutting tools have the potential for use in extremely hazardous environments, but operation in facilities used for handling radioactive material presents complex challenges for electronic components. We present a methodology and results obtained from testing in a radiation cell in which we demonstrate the operation of a robotic arm controlled using modern electronics exposed at 10 Gy/h to simulate radioactive conditions in the most hazardous nuclear waste handling facilities. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Switzerland | 1 | 25% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 25 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 20% |
| Researcher | 4 | 16% |
| Student > Master | 2 | 8% |
| Student > Bachelor | 1 | 4% |
| Unspecified | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 11 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 36% |
| Medicine and Dentistry | 2 | 8% |
| Nursing and Health Professions | 1 | 4% |
| Chemical Engineering | 1 | 4% |
| Unspecified | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 10 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 14 December 2020.
All research outputs
#1,529,476
of 23,305,591 outputs
Outputs from Frontiers in Robotics and AI
#101
of 1,540 outputs
Outputs of similar age
#38,918
of 450,558 outputs
Outputs of similar age from Frontiers in Robotics and AI
#8
of 50 outputs
Altmetric has tracked 23,305,591 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,540 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.6. This one has done particularly well, scoring higher than 93% 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 450,558 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 50 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.