D. López-Mancilla

661 citations
29 papers · 474 indexed · h-index 13

D. López-Mancilla

27 papers receiving 456 citations

Peers

D. López-Mancilla
Comparison fields: 5 of 69
  • Statistical and Nonlinear Physics 202
  • Computer Vision and Pattern Recognition 154
  • Computer Networks and Communications 140
  • Transportation 30
  • Internal Medicine 16
Replace Peiyong Duan with:
Peiyong Duan China
Teng Zhang United States
Haojie Lin China
Boudour Ammar Tunisia
H.J.C. Huijberts Netherlands
Zhenbin Du China
Ming‐Wei Wu China
Hideki Kokame Japan
Wen Jiang China
D. López-Mancilla relative to Peiyong Duan China Peiyong Duan's profile →
Citations per field
00.5×4.3×
Peiyong Duan · 1×
Citations per year

Countries citing papers authored by D. López-Mancilla

Since Specialization
Citations

This map shows the geographic impact of D. López-Mancilla'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 D. López-Mancilla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. López-Mancilla more than expected).

Fields of papers citing papers by D. López-Mancilla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by D. López-Mancilla. 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 D. López-Mancilla. The network helps show where D. López-Mancilla may publish in the future.

Co-authorship network

The 19 scholars most cited alongside D. López-Mancilla, 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 D. López-Mancilla Line = papers co-authored together D. López-Mancilla links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20232
2 202213
3 202235
4 20212
5 20201
6 202028
7 201914
8 201919
9 201921
10 201812
11 20163
12 20165
13 20132
14 201331
15 20120
16 20107
17 20088
18 200621
19 200521
20 20057

About D. López-Mancilla

D. López-Mancilla is a scholar working on Statistical and Nonlinear Physics, Internal Medicine and Computer Networks and Communications, having authored 29 papers that have together received 474 indexed citations. Recurring topics across this work include Chaos control and synchronization (16 papers), Nonlinear Dynamics and Pattern Formation (12 papers), Chaos-based Image/Signal Encryption (7 papers), Neural Networks Stability and Synchronization (4 papers), Neural Networks and Applications (3 papers), Neural Networks and Reservoir Computing (2 papers), stochastic dynamics and bifurcation (2 papers) and Advanced Steganography and Watermarking Techniques (2 papers). The work is most often cited by research in Statistical and Nonlinear Physics (202 citations), Computer Vision and Pattern Recognition (154 citations) and Computer Networks and Communications (140 citations). D. López-Mancilla has collaborated with scholars based in Mexico, Spain and Russia. Frequent co-authors include Esteban Tlelo‐Cuautle, Oscar Roberto López-Bonilla, Enrique Efrén García-Guerrero, Everardo Inzunza-González, C. Cruz-Hernández, Roger Chiu, Juan Hugo García-López, Carlos E. Castañeda, R. Jaimes-Reátegui and C. Posadas–Castillo. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Neurocomputing.

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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