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Artigo de periodico
Non-Newtonian incompressible fluids with nonlinear shear tensor and hereditary conditions (2024)
López-Lázaro, Heraclio ; Marín-Rubio, Pedro ; Planas, Gabriela
Fonte: Communications in Nonlinear Science and Numerical Simulation. Unidade: ICMC
Assuntos: ATRATORES, MECÂNICA DOS FLUÍDOS, EQUAÇÕES DIFERENCIAIS PARCIAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LÓPEZ-LÁZARO, Heraclio e MARÍN-RUBIO, Pedro e PLANAS, Gabriela. Non-Newtonian incompressible fluids with nonlinear shear tensor and hereditary conditions. Communications in Nonlinear Science and Numerical Simulation, v. No 2024, p. 1-20, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.cnsns.2024.108204. Acesso em: 28 nov. 2025.
APA
López-Lázaro, H., Marín-Rubio, P., & Planas, G. (2024). Non-Newtonian incompressible fluids with nonlinear shear tensor and hereditary conditions. Communications in Nonlinear Science and Numerical Simulation, No 2024, 1-20. doi:10.1016/j.cnsns.2024.108204
NLM
López-Lázaro H, Marín-Rubio P, Planas G. Non-Newtonian incompressible fluids with nonlinear shear tensor and hereditary conditions [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2024 ; No 2024 1-20.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2024.108204
Vancouver
López-Lázaro H, Marín-Rubio P, Planas G. Non-Newtonian incompressible fluids with nonlinear shear tensor and hereditary conditions [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2024 ; No 2024 1-20.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2024.108204
Artigo de periodico
Diffusion transitions in a 2D periodic lattice (2022)
Lazarotto, Matheus Jean; Caldas, Iberê Luiz ; Elskens, Yves
Fonte: Communications in Nonlinear Science and Numerical Simulation. Unidade: IF
Assuntos: SISTEMAS HAMILTONIANOS, CAOS (SISTEMAS DINÂMICOS)
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LAZAROTTO, Matheus Jean e CALDAS, Iberê Luiz e ELSKENS, Yves. Diffusion transitions in a 2D periodic lattice. Communications in Nonlinear Science and Numerical Simulation, v. 112, 2022Tradução . . Disponível em: https://doi.org/10.1016/j.cnsns.2022.106525. Acesso em: 28 nov. 2025.
APA
Lazarotto, M. J., Caldas, I. L., & Elskens, Y. (2022). Diffusion transitions in a 2D periodic lattice. Communications in Nonlinear Science and Numerical Simulation, 112. doi:10.1016/j.cnsns.2022.106525
NLM
Lazarotto MJ, Caldas IL, Elskens Y. Diffusion transitions in a 2D periodic lattice [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2022 ; 112[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2022.106525
Vancouver
Lazarotto MJ, Caldas IL, Elskens Y. Diffusion transitions in a 2D periodic lattice [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2022 ; 112[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2022.106525
Artigo de periodico
Mathematical model of brain tumour growth with drug resistance (2021)
Trobia, José; Tian, Kun; Batista, Antonio ; Grebogi, Celso; Ren, Hai-Peng; Santos, Moises S.; Protachevicz, R. P. ; Borges, Fernando S.; Szezech, Jose Danilo ; Viana, Ricardo L.; Caldas, Iberê Luiz ; Iarosz, Kelly C.
Fonte: Communications in Nonlinear Science and Numerical Simulation. Unidade: IF
Assuntos: FÍSICA MATEMÁTICA, BIOFÍSICA, GLIOMA, QUIMIOTERAPIA, NEOPLASIAS CEREBRAIS, EQUAÇÕES DIFERENCIAIS DA FÍSICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TROBIA, José et al. Mathematical model of brain tumour growth with drug resistance. Communications in Nonlinear Science and Numerical Simulation, v. 103, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.cnsns.2021.106013. Acesso em: 28 nov. 2025.
APA
Trobia, J., Tian, K., Batista, A., Grebogi, C., Ren, H. -P., Santos, M. S., et al. (2021). Mathematical model of brain tumour growth with drug resistance. Communications in Nonlinear Science and Numerical Simulation, 103. doi:10.1016/j.cnsns.2021.106013
NLM
Trobia J, Tian K, Batista A, Grebogi C, Ren H-P, Santos MS, Protachevicz RP, Borges FS, Szezech JD, Viana RL, Caldas IL, Iarosz KC. Mathematical model of brain tumour growth with drug resistance [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2021 ; 103[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2021.106013
Vancouver
Trobia J, Tian K, Batista A, Grebogi C, Ren H-P, Santos MS, Protachevicz RP, Borges FS, Szezech JD, Viana RL, Caldas IL, Iarosz KC. Mathematical model of brain tumour growth with drug resistance [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2021 ; 103[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2021.106013
Artigo de periodico
Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks (2021)
Lameu, Ewandson Luiz ; Borges, Fernando S.; Iarosz, Kelly ; Protachevicz, R. P. ; Antonopoulos, Chris G. ; Macau, Elbert E.N.; Batista, Antonio
Fonte: Communications in Nonlinear Science and Numerical Simulation. Unidade: IF
Assuntos: BIOFÍSICA, REDES NEURAIS, PLASTICIDADE NEURONAL, SINAPSE, NEUROTRANSMISSORES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LAMEU, Ewandson Luiz et al. Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks. Communications in Nonlinear Science and Numerical Simulation, v. 96, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.cnsns.2020.105689. Acesso em: 28 nov. 2025.
APA
Lameu, E. L., Borges, F. S., Iarosz, K., Protachevicz, R. P., Antonopoulos, C. G., Macau, E. E. N., & Batista, A. (2021). Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks. Communications in Nonlinear Science and Numerical Simulation, 96. doi:10.1016/j.cnsns.2020.105689
NLM
Lameu EL, Borges FS, Iarosz K, Protachevicz RP, Antonopoulos CG, Macau EEN, Batista A. Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2021 ; 96[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2020.105689
Vancouver
Lameu EL, Borges FS, Iarosz K, Protachevicz RP, Antonopoulos CG, Macau EEN, Batista A. Short-term and spike-timing-dependent plasticity facilitate the formation of modular neural networks [Internet]. Communications in Nonlinear Science and Numerical Simulation. 2021 ; 96[citado 2025 nov. 28 ] Available from: https://doi.org/10.1016/j.cnsns.2020.105689

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