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Mendeley readers
Attention Score in Context
| Title |
Transforming the canonical piecewise-linear model into a smooth-piecewise representation
|
|---|---|
| Published in |
SpringerPlus, September 2016
|
| DOI | 10.1186/s40064-016-3278-y |
| Pubmed ID | |
| Authors |
Victor M. Jimenez-Fernandez, Maribel Jimenez-Fernandez, Hector Vazquez-Leal, Evodio Muñoz-Aguirre, Hector H. Cerecedo-Nuñez, Uriel A. Filobello-Niño, Francisco J. Castro-Gonzalez |
| Abstract |
A smoothed representation (based on natural exponential and logarithmic functions) for the canonical piecewise-linear model, is presented. The result is a completely differentiable formulation that exhibits interesting properties, like preserving the parameters of the original piecewise-linear model in such a way that they can be directly inherited to the smooth model in order to determine their parameters, the capability of controlling not only the smoothness grade, but also the approximation accuracy at specific breakpoint locations, a lower or equal overshooting for high order derivatives in comparison with other approaches, and the additional advantage of being expressed in a reduced mathematical form with only two types of inverse functions (logarithmic and exponential). By numerical simulation examples, this proposal is verified and well-illustrated. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 8 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 50% |
| Other | 1 | 13% |
| Researcher | 1 | 13% |
| Professor > Associate Professor | 1 | 13% |
| Student > Postgraduate | 1 | 13% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 1 | 13% |
| Psychology | 1 | 13% |
| Neuroscience | 1 | 13% |
| Chemistry | 1 | 13% |
| Engineering | 1 | 13% |
| Other | 0 | 0% |
| Unknown | 3 | 38% |
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 11 October 2016.
All research outputs
#14,864,294
of 22,893,031 outputs
Outputs from SpringerPlus
#838
of 1,850 outputs
Outputs of similar age
#192,858
of 320,241 outputs
Outputs of similar age from SpringerPlus
#92
of 172 outputs
Altmetric has tracked 22,893,031 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,850 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one is in the 49th percentile – i.e., 49% 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 320,241 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 172 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.