F. Rivas-Dávalos

482 citations
22 papers · 327 · h-index 11

Impact in

Papers in

F. Rivas-Dávalos

21 papers receiving 315 citations

Peers

F. Rivas-Dávalos
Comparison fields: 5 of 47
  • Safety, Risk, Reliability and Quality 127
  • Finance 67
  • Statistics, Probability and Uncertainty 45
  • Software 23
  • Medical Laboratory Technology 5
Replace V. V. Singh with:
V. V. Singh India
María Luz Gámiz Spain
Jeremy A. Bloom United States
Michael Jong Kim Canada
Paulo H. Ferreira Brazil
Mikhaïl Nikulin France
Zifeng Zhao United States
Jarosław Bartoszewicz Poland
R. Dahlgren United States
F. Rivas-Dávalos relative to V. V. Singh India V. V. Singh's profile →
Citations per field
00.5×3.4×
V. V. Singh · 1×
Citations per year

Countries citing papers authored by F. Rivas-Dávalos

Since Specialization
Citations

This map shows the geographic impact of F. Rivas-Dávalos's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by F. Rivas-Dávalos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites F. Rivas-Dávalos more than expected).

Fields of papers citing papers by F. Rivas-Dávalos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by F. Rivas-Dávalos. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by F. Rivas-Dávalos. The network helps show where F. Rivas-Dávalos may publish in the future.

Co-authors

The 13 scholars most cited alongside F. Rivas-Dávalos, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with F. Rivas-Dávalos Line = papers co-authored together F. Rivas-Dávalos links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201284
2 201256
3 201437
4 201825
5 201518
6 200715
7 200912
8 201412
9 200612
10 200911
11 201711
12 20149
13 20168
14 20074
15 20073
16 20163
17 20152
18 20062
19 20241
20 20241

About F. Rivas-Dávalos

F. Rivas-Dávalos is a scholar working on Safety, Risk, Reliability and Quality, Electrical and Electronic Engineering, Statistics and Probability, Statistics, Probability and Uncertainty and Control and Systems Engineering, having authored 22 papers that have together received 327 indexed citations. Recurring topics across this work include Power System Reliability and Maintenance (11 papers), Optimal Power Flow Distribution (10 papers), Statistical Distribution Estimation and Applications (6 papers), Probabilistic and Robust Engineering Design (5 papers), Electric Power System Optimization (5 papers), Smart Grid Energy Management (4 papers), Reliability and Maintenance Optimization (3 papers) and Advanced Multi-Objective Optimization Algorithms (3 papers). The work is most often cited by research in Safety, Risk, Reliability and Quality (127 citations), Finance (67 citations), Statistics, Probability and Uncertainty (45 citations), Software (23 citations) and Medical Laboratory Technology (5 citations). F. Rivas-Dávalos has collaborated with scholars based in Mexico, United Kingdom and South Korea. Frequent co-authors include Serguei Maximov, J.L. Guardado, Eduardo A. Martínez Ceseña, Joseph Mutale, E. Melgoza, Edgar L. Moreno‐Goytia, M.R. Irving, Junho Song, R. Escarela-Pérez and Juan C. Olivares-Galván. Their work appears in journals such as International Journal of Electrical Power & Energy Systems, Electric Power Systems Research, Reliability Engineering & System Safety, SpringerPlus and Renewable and Sustainable Energy Reviews.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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