Sergio A. Cannas

2.0k total citations
89 papers, 1.5k citations indexed

About

Sergio A. Cannas is a scholar working on Condensed Matter Physics, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics. According to data from OpenAlex, Sergio A. Cannas has authored 89 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Condensed Matter Physics, 30 papers in Atomic and Molecular Physics, and Optics and 20 papers in Statistical and Nonlinear Physics. Recurrent topics in Sergio A. Cannas's work include Theoretical and Computational Physics (44 papers), Magnetic properties of thin films (15 papers) and Physics of Superconductivity and Magnetism (11 papers). Sergio A. Cannas is often cited by papers focused on Theoretical and Computational Physics (44 papers), Magnetic properties of thin films (15 papers) and Physics of Superconductivity and Magnetism (11 papers). Sergio A. Cannas collaborates with scholars based in Argentina, Brazil and Italy. Sergio A. Cannas's co-authors include Francisco A. Tamarit, Daniel A. Stariolo, Diana E. Marco, Orlando V. Billoni, Sergio A. Páez, Leonardo Franco, Marcelo A. Montemurro, Constantino Tsallis, Dante R. Chialvo and S.A. Pighin and has published in prestigious journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Sergio A. Cannas

85 papers receiving 1.4k citations

Peers

Sergio A. Cannas

CMP

AMPO

SNP

Francesca Colaiori Italy

Paolo Sibani Denmark

Gunnar Pruessner United Kingdom

Alexandre Souto Martinêz Brazil

Wolfram Just Germany

Vivien Lecomte France

Maria R. D’Orsogna United States

T. J. Newman United Kingdom

Hernán Larralde Mexico

Werner Horsthemke United States

Francesca Colaiori Italy

Sergio A. Cannas

448 ×0.7

CMP

143 ×0.4

AMPO

570 ×1.6

SNP

94 ×0.5

149 ×0.9

Citations per year, relative to Sergio A. Cannas Sergio A. Cannas (= 1×) peers Francesca Colaiori

Countries citing papers authored by Sergio A. Cannas

Since Specialization

Citations

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

Fields of papers citing papers by Sergio A. Cannas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sergio A. Cannas. 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 Sergio A. Cannas. The network helps show where Sergio A. Cannas may publish in the future.

Co-authorship network of co-authors of Sergio A. Cannas

This figure shows the co-authorship network connecting the top 25 collaborators of Sergio A. Cannas. A scholar is included among the top collaborators of Sergio A. Cannas based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sergio A. Cannas. Sergio A. Cannas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Grigera, Tomás S., et al.. (2024). Mean-field solution of the neural dynamics in a Greenberg-Hastings model with excitatory and inhibitory units. Physical review. E. 110(1). 14130–14130.

Cannas, Sergio A., et al.. (2022). Purified myelin lipids display a critical mixing point at low surface pressure. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1864(5). 183874–183874. 3 indexed citations

Grigera, Tomás S., et al.. (2022). Finite-size correlation behavior near a critical point: A simple metric for monitoring the state of a neural network. Physical review. E. 106(5). 54313–54313. 2 indexed citations

Cannas, Sergio A. & Daniel A. Stariolo. (2019). Three-state model with competing antiferromagnetic and pairing interactions. Physical review. E. 99(4). 42137–42137. 5 indexed citations

Perotti, Juan I., et al.. (2019). Universal and nonuniversal neural dynamics on small world connectomes: A finite-size scaling analysis. Physical review. E. 100(5). 52138–52138. 8 indexed citations

Zamponi, Nahuel, et al.. (2018). Mitochondrial network complexity emerges from fission/fusion dynamics. Scientific Reports. 8(1). 363–363. 74 indexed citations

Guglielmi, Francesco, et al.. (2016). Left Dominant Arrhythmogenic Cardiomyopathy Causing Sustained Ventricular Tachycardia – A Case Report. 2(1). 37–37. 1 indexed citations

Martini, Bortolo, et al.. (2016). Who is the guilty among these two silent killers?. HeartRhythm Case Reports. 3(1). 33–35. 1 indexed citations

Ramsperger, U., et al.. (2016). Critical exponents and scaling invariance in the absence of a critical point. Nature Communications. 7(1). 13611–13611. 11 indexed citations

10.

Pighin, S.A., Orlando V. Billoni, & Sergio A. Cannas. (2012). Finite-temperature phase diagram of ultrathin magnetic films without external fields. Physical Review E. 86(5). 51119–51119. 8 indexed citations

11.

Marco, Diana E., Juan Pablo Carbajal, Sergio A. Cannas, et al.. (2009). An experimental and modelling exploration of the host-sanction hypothesis in legume–rhizobia mutualism. Journal of Theoretical Biology. 259(3). 423–433. 32 indexed citations

12.

Osenda, Omar, Francisco A. Tamarit, & Sergio A. Cannas. (2009). Nonequilibrium structures and slow dynamics in a two-dimensional spin system with competing long-range and short-range interactions. Physical Review E. 80(2). 21114–21114. 4 indexed citations

13.

Hadzibeganovic, Tarik, et al.. (2008). The missing neurocognitive and artificial general intelligence bases of robocup reasearch: What still needs to be done before 2050?. Data Archiving and Networked Services (DANS). 1–4. 1 indexed citations

14.

Hadzibeganovic, Tarik & Sergio A. Cannas. (2008). Measuring and Modeling the Complexity of Polysynthetic Language Learning: A Non-Extensive Neural Network Approach. 1(1). 104–106.

15.

Marco, Diana E., Sergio A. Cannas, Marcelo A. Montemurro, Bo Hu, & Shi‐Yuan Cheng. (2008). Comparable ecological dynamics underlie early cancer invasion and species dispersal, involving self-organizing processes. Journal of Theoretical Biology. 256(1). 65–75. 14 indexed citations

16.

Ferrero, Ezequiel E. & Sergio A. Cannas. (2007). Long-term ordering kinetics of the two-dimensionalq-state Potts model. Physical Review E. 76(3). 31108–31108. 17 indexed citations

17.

Billoni, Orlando V., Francisco A. Tamarit, & Sergio A. Cannas. (2006). Monte Carlo simulations of a ferromagnetic-FeF2 system. Physica B Condensed Matter. 384(1-2). 184–186. 8 indexed citations

18.

Cannas, Sergio A., Diana E. Marco, & Sergio A. Páez. (2003). Modelling biological invasions: species traits, species interactions, and habitat heterogeneity. Mathematical Biosciences. 183(1). 93–110. 46 indexed citations

19.

Gleiser, Pablo M., Sergio A. Cannas, Francisco A. Tamarit, & Bing Zheng. (2001). Long-range effects in granular avalanching. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 63(4). 42301–42301. 5 indexed citations

20.

Tamarit, Francisco A., Daniel A. Stariolo, Sergio A. Cannas, & Pablo Serra. (1996). Effects of refractory periods in the dynamics of a diluted neural network. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 53(5). 5146–5152. 4 indexed citations

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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