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Table representation of search results timeline featuring number of search results per year.

Year Number of Results
1976 1
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1988 1
1991 1
1993 1
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1995 1
1996 2
1998 1
1999 2
2000 2
2001 4
2002 6
2003 9
2004 7
2005 6
2006 5
2007 10
2008 15
2009 3
2010 7
2011 6
2012 3
2013 4
2014 5
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2023 8
2024 1

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160 results

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Page 1
Stochastic cellular automaton model of culture formation.
Klausen FR, Lauritsen AB. Klausen FR, et al. Phys Rev E. 2023 Nov;108(5-1):054307. doi: 10.1103/PhysRevE.108.054307. Phys Rev E. 2023. PMID: 38115445
We introduce a stochastic cellular automaton as a model for culture and border formation. ...
We introduce a stochastic cellular automaton as a model for culture and border formation. ...
A stochastic cellular automaton model to describe the evolution of the snow-covered area across a high-elevation mountain catchment.
Painter KJ, Gentile A, Ferraris S. Painter KJ, et al. Sci Total Environ. 2023 Jan 20;857(Pt 1):159195. doi: 10.1016/j.scitotenv.2022.159195. Epub 2022 Oct 6. Sci Total Environ. 2023. PMID: 36209873 Free article.
To address this information gap, we formulate a novel, fine-scale stochastic Cellular Automaton model that describes snow coverage across a high-elevation catchment. ...
To address this information gap, we formulate a novel, fine-scale stochastic Cellular Automaton model that describes snow cove …
A scalable solver for a stochastic, hybrid cellular automaton model of personalized breast cancer therapy.
Lai X, Taskén HA, Mo T, Funke SW, Frigessi A, Rognes ME, Köhn-Luque A. Lai X, et al. Int J Numer Method Biomed Eng. 2022 Jan;38(1):e3542. doi: 10.1002/cnm.3542. Epub 2021 Nov 14. Int J Numer Method Biomed Eng. 2022. PMID: 34716985
Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel algorithms to link stochastic CA with differential equations (https://bitbucket.org/HTasken/cansim). The algorithms minimize the com …
Building upon FEniCS, a popular and powerful scientific computing platform for solving partial differential equations, we developed parallel …
Fast Variable Structure Stochastic Automaton for Discovering and Tracking Spatiotemporal Event Patterns.
Zhang J, Wang Y, Wang C, Zhou M. Zhang J, et al. IEEE Trans Cybern. 2018 Mar;48(3):890-903. doi: 10.1109/TCYB.2017.2663842. Epub 2017 Apr 5. IEEE Trans Cybern. 2018. PMID: 28391215
Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic automata whose state transition function is fixed and predesigned to guarantee their immunity to noise. ...Clearly, confronting a pattern's …
Existing algorithms for spatiotemporal event pattern recognition in dynamic noisy environment are based on fixed structure stochastic …
A stochastic automaton shows how enzyme assemblies may contribute to metabolic efficiency.
Amar P, Legent G, Thellier M, Ripoll C, Bernot G, Nystrom T, Saier MH Jr, Norris V. Amar P, et al. BMC Syst Biol. 2008 Mar 25;2:27. doi: 10.1186/1752-0509-2-27. BMC Syst Biol. 2008. PMID: 18366733 Free PMC article.
BACKGROUND: The advantages of grouping enzymes into metabolons and into higher order structures have long been debated. To quantify these advantages, we have developed a stochastic automaton that allows experiments to be performed in a virtual bacterium with both a …
BACKGROUND: The advantages of grouping enzymes into metabolons and into higher order structures have long been debated. To quantify these ad …
Estimates of the collective immunity to COVID-19 derived from a stochastic cellular automaton based framework.
Lima I, Balbi PP. Lima I, et al. Nat Comput. 2022;21(3):449-461. doi: 10.1007/s11047-022-09893-3. Epub 2022 Jun 18. Nat Comput. 2022. PMID: 35757184 Free PMC article.
Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designed to emulate the spread of SARS-CoV-2 in a population of static individuals interacting only via a Moore neighbourhood of radius one, with a …
Here we describe the estimates of the collective immunity to COVID-19 from a stochastic cellular automaton based model designe …
Stochastic models for phototaxis.
Levy D, Requeijo T. Levy D, et al. Bull Math Biol. 2008 Aug;70(6):1684-706. doi: 10.1007/s11538-008-9314-5. Epub 2008 Jul 22. Bull Math Biol. 2008. PMID: 18648888
The excitation of each individual bacterium is assumed to change based on the excitation of the neighboring bacteria. Under these assumptions, we propose a (discrete) cellular automaton model and derive an analogous stochastic model for describing the evolution in t …
The excitation of each individual bacterium is assumed to change based on the excitation of the neighboring bacteria. Under these assumption …
Stochastic computing with biomolecular automata.
Adar R, Benenson Y, Linshiz G, Rosner A, Tishby N, Shapiro E. Adar R, et al. Proc Natl Acad Sci U S A. 2004 Jul 6;101(27):9960-5. doi: 10.1073/pnas.0400731101. Epub 2004 Jun 23. Proc Natl Acad Sci U S A. 2004. PMID: 15215499 Free PMC article.
Stochastic computing has a broad range of applications, yet electronic computers realize its basic step, stochastic choice between alternative computation paths, in a cumbersome way. Biomolecular computers use a different computational paradigm and hence afford nove …
Stochastic computing has a broad range of applications, yet electronic computers realize its basic step, stochastic choice bet …
Cellular-automaton model for tumor growth dynamics: Virtualization of different scenarios.
Valentim CA, Rabi JA, David SA. Valentim CA, et al. Comput Biol Med. 2023 Feb;153:106481. doi: 10.1016/j.compbiomed.2022.106481. Epub 2022 Dec 28. Comput Biol Med. 2023. PMID: 36587567 Free article.
Implementation techniques are also explored together with prospective model expansion towards deterministic features. The proposed stochastic cellular automaton model is able to effectively simulate different scenarios regarding tumor growth effectively, figuring as …
Implementation techniques are also explored together with prospective model expansion towards deterministic features. The proposed stocha …
A parameter-free learning automaton scheme.
Ren X, Li S, Ge H. Ren X, et al. Front Neurorobot. 2022 Sep 23;16:999658. doi: 10.3389/fnbot.2022.999658. eCollection 2022. Front Neurorobot. 2022. PMID: 36213147 Free PMC article.
For a learning automaton, a proper configuration of the learning parameters is crucial. ...This intriguing property dramatically reduces the difficulty of applying a learning automaton to an unknown stochastic environment. A rigorous proof of -optimality for …
For a learning automaton, a proper configuration of the learning parameters is crucial. ...This intriguing property dramatically redu …
160 results