Mario E. Inchiosa

1.6k total citations
31 papers, 1.3k citations indexed

About

Mario E. Inchiosa is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Mario E. Inchiosa has authored 31 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Statistical and Nonlinear Physics, 11 papers in Computer Networks and Communications and 7 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Mario E. Inchiosa's work include stochastic dynamics and bifurcation (22 papers), Nonlinear Dynamics and Pattern Formation (11 papers) and Probabilistic and Robust Engineering Design (7 papers). Mario E. Inchiosa is often cited by papers focused on stochastic dynamics and bifurcation (22 papers), Nonlinear Dynamics and Pattern Formation (11 papers) and Probabilistic and Robust Engineering Design (7 papers). Mario E. Inchiosa collaborates with scholars based in United States, Italy and Germany. Mario E. Inchiosa's co-authors include Adi R. Bulsara, William L. Ditto, John F. Lindner, Brian K. Meadows, L. Gammaitoni, Peter Hänggi, Kurt Wiesenfeld, John W. Robinson, M. Löcher and Joseph Neff and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Physical review. B, Condensed matter.

In The Last Decade

Mario E. Inchiosa

30 papers receiving 1.3k citations

Peers

Mario E. Inchiosa

SNP

CNC

GPC

E. Menichella-Saetta Italy

Roberto R. Deza Argentina

A. Venkatesan India

I. Shimada Japan

Tomohiro Nagashima Germany

Masatoshi Shiino Japan

Barbara Gentz Germany

Paul H. Bryant United States

Behnam Kia United States

E. Menichella-Saetta Italy

Mario E. Inchiosa

891 ×0.7

SNP

488 ×0.7

CNC

251 ×0.8

GPC

137 ×0.4

157 ×0.7

Citations per year, relative to Mario E. Inchiosa Mario E. Inchiosa (= 1×) peers E. Menichella-Saetta

Countries citing papers authored by Mario E. Inchiosa

Since Specialization

Citations

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

Fields of papers citing papers by Mario E. Inchiosa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mario E. Inchiosa. 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 Mario E. Inchiosa. The network helps show where Mario E. Inchiosa may publish in the future.

Co-authorship network of co-authors of Mario E. Inchiosa

This figure shows the co-authorship network connecting the top 25 collaborators of Mario E. Inchiosa. A scholar is included among the top collaborators of Mario E. Inchiosa 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 Mario E. Inchiosa. Mario E. Inchiosa 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

Lafaro, Rocco J., et al.. (2015). Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables. PLoS ONE. 10(12). e0145395–e0145395. 50 indexed citations

Inchiosa, Mario E., et al.. (2008). Role of Agent Based Financial Market Models in Global Product Development. 65–73. 1 indexed citations

Bulsara, Adi R., et al.. (2002). An evaluation of the stochastic resonance phenomenon as a potential tool for signal processing. 583–587. 6 indexed citations

Inchiosa, Mario E., et al.. (2001). Stochastic dynamics in a two-dimensional oscillator near a saddle-node bifurcation. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 63(6). 66114–66114. 16 indexed citations

Acebrón, Juan A., Adi R. Bulsara, Mario E. Inchiosa, & Wouter‐Jan Rappel. (2001). Noise-mediated dynamics in a two-dimensional oscillator: Exact solutions and numerical results. Europhysics Letters (EPL). 56(3). 354–360. 9 indexed citations

Robinson, John W., Johan Rung, Adi R. Bulsara, & Mario E. Inchiosa. (2000). General measures for signal-noise separation in nonlinear dynamical systems. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 63(1). 11107–11107. 17 indexed citations

Wiesenfeld, Kurt, Adi R. Bulsara, & Mario E. Inchiosa. (2000). Oscillatory dynamics of a nonlinear amplifier in the high-gain regime: Exploiting a global connection. Physical review. B, Condensed matter. 62(14). R9232–R9235. 11 indexed citations

Hänggi, Peter, et al.. (2000). Nonlinear stochastic resonance: The saga of anomalous output-input gain. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 62(5). 6155–6163. 80 indexed citations

Bulsara, Adi R., Kurt Wiesenfeld, & Mario E. Inchiosa. (2000). Nonlinear dynamics in a high-gain amplifier: the dc SQUID. Annalen der Physik. 9(9-10). 679–688. 4 indexed citations

10.

Gammaitoni, L., M. Löcher, Adi R. Bulsara, et al.. (1999). Controlling Stochastic Resonance. Physical Review Letters. 82(23). 4574–4577. 104 indexed citations

11.

Inchiosa, Mario E., Adi R. Bulsara, Kurt Wiesenfeld, & L. Gammaitoni. (1999). Nonlinear signal amplification in a 2D system operating in static and oscillatory regimes. Physics Letters A. 252(1-2). 20–26. 14 indexed citations

12.

Robinson, John W., et al.. (1998). Information-Theoretic Distance Measures and a Generalization of Stochastic Resonance. Physical Review Letters. 81(14). 2850–2853. 27 indexed citations

13.

Inchiosa, Mario E. & Adi R. Bulsara. (1998). dc signal detection via dynamical asymmetry in a nonlinear device. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 58(1). 115–127. 27 indexed citations

14.

Inchiosa, Mario E., et al.. (1998). Signal Enhancement in a Nonlinear Transfer Characteristic. Physical Review Letters. 80(7). 1381–1384. 31 indexed citations

15.

Inchiosa, Mario E., Adi R. Bulsara, & L. Gammaitoni. (1997). Higher-order resonant behavior in asymmetric nonlinear stochastic systems. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 55(4). 4049–4056. 39 indexed citations

16.

Inchiosa, Mario E., Adi R. Bulsara, John F. Lindner, Brian K. Meadows, & William L. Ditto. (1996). Array enhanced stochastic resonance: Implications for signal processing. AIP conference proceedings. 375. 401–419. 2 indexed citations

17.

Inchiosa, Mario E. & Adi R. Bulsara. (1995). Coupling enhances stochastic resonance in nonlinear dynamic elements driven by a sinusoid plus noise. Physics Letters A. 200(3-4). 283–288. 39 indexed citations

18.

Lindner, John F., Brian K. Meadows, William L. Ditto, Mario E. Inchiosa, & Adi R. Bulsara. (1995). Array Enhanced Stochastic Resonance and Spatiotemporal Synchronization. Physical Review Letters. 75(1). 3–6. 369 indexed citations

19.

Inchiosa, Mario E., et al.. (1990). Convergence of an iterative neural network learning algorithm for linearly dependent patterns. Journal of Physics A Mathematical and General. 23(5). L223–L228. 2 indexed citations

20.

Inchiosa, Mario A., et al.. (1985). Prediction of Thiopental Induction Dose Based on Caffeine Volume of Distribution. The Journal of Clinical Pharmacology. 25(4). 281–284.

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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