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  • Source: ACS Catalysis. Unidades: FFCLRP, IQ

    Subjects: OURO, RÓDIO, CATÁLISE, NANOPARTÍCULAS

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      RODRIGUES, Maria Paula de Souza et al. Gold−rhodium nanoflowers for the plasmon-enhanced CO2 electroreduction reaction upon visible light. ACS Catalysis, v. 13, n. 1, p. 267−279, 2023Tradução . . Disponível em: https://doi.org/10.1021/acscatal.2c04207. Acesso em: 09 nov. 2024.
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      Rodrigues, M. P. de S., Dourado, A. H. B., Oliveira Filho, A. G. S. de, Batista, A. P. de L., Feil, M., Krischer, K., & Torresi, S. I. C. de. (2023). Gold−rhodium nanoflowers for the plasmon-enhanced CO2 electroreduction reaction upon visible light. ACS Catalysis, 13( 1), 267−279. doi:10.1021/acscatal.2c04207
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      Rodrigues MP de S, Dourado AHB, Oliveira Filho AGS de, Batista AP de L, Feil M, Krischer K, Torresi SIC de. Gold−rhodium nanoflowers for the plasmon-enhanced CO2 electroreduction reaction upon visible light [Internet]. ACS Catalysis. 2023 ; 13( 1): 267−279.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1021/acscatal.2c04207
    • Vancouver

      Rodrigues MP de S, Dourado AHB, Oliveira Filho AGS de, Batista AP de L, Feil M, Krischer K, Torresi SIC de. Gold−rhodium nanoflowers for the plasmon-enhanced CO2 electroreduction reaction upon visible light [Internet]. ACS Catalysis. 2023 ; 13( 1): 267−279.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1021/acscatal.2c04207
  • Source: Machine Learning. Unidade: FFCLRP

    Subjects: REDES COMPLEXAS, REDES NEURAIS, SISTEMAS DINÂMICOS

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      OLIVEIRA JUNIOR, Laercio de e STELZER, Florian e LIANG, Zhao. Clustered and deep echo state networks for signal noise reduction. Machine Learning, v. 111, n. 8, p. 2885-2904, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10994-022-06135-6. Acesso em: 09 nov. 2024.
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      Oliveira Junior, L. de, Stelzer, F., & Liang, Z. (2022). Clustered and deep echo state networks for signal noise reduction. Machine Learning, 111( 8), 2885-2904. doi:10.1007/s10994-022-06135-6
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      Oliveira Junior L de, Stelzer F, Liang Z. Clustered and deep echo state networks for signal noise reduction [Internet]. Machine Learning. 2022 ; 111( 8): 2885-2904.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1007/s10994-022-06135-6
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      Oliveira Junior L de, Stelzer F, Liang Z. Clustered and deep echo state networks for signal noise reduction [Internet]. Machine Learning. 2022 ; 111( 8): 2885-2904.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1007/s10994-022-06135-6
  • Source: Journal of Chemical Information and Modeling. Unidade: FFCLRP

    Subjects: APRENDIZADO COMPUTACIONAL, MODELOS MATEMÁTICOS, ESTRUTURA MOLECULAR (QUÍMICA TEÓRICA)

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      SOARES, Thereza A. et al. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. Disponível em: https://doi.org/10.1021/acs.jcim.2c01422. Acesso em: 09 nov. 2024. , 2022
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      Soares, T. A., Alves, A. F. N., Mazzolari, A., Ruggiu, F., Wei, G. -W., & Merz, K. (2022). The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial]. Journal of Chemical Information and Modeling. Washington: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1021/acs.jcim.2c01422
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      Soares TA, Alves AFN, Mazzolari A, Ruggiu F, Wei G-W, Merz K. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 22): 5317-5320.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
    • Vancouver

      Soares TA, Alves AFN, Mazzolari A, Ruggiu F, Wei G-W, Merz K. The (Re)-evolution of Quantitative Structure–Activity Relationship (QSAR) studies propelled by the surge of machine learning methods [Editorial] [Internet]. Journal of Chemical Information and Modeling. 2022 ; 62( 22): 5317-5320.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1021/acs.jcim.2c01422
  • Source: European Physical Journal - Special Topics. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, SISTEMAS DINÂMICOS, ALGORITMOS ÚTEIS E ESPECÍFICOS

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      VERRI, Filipe Alves Neto et al. Network community detection via iterative edge removal in a flocking-like system. European Physical Journal - Special Topics, v. 230, n. 14-15, p. 2843-2855, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00154-5. Acesso em: 09 nov. 2024.
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      Verri, F. A. N., Gueleri, R. A., Qiusheng, Z., Junbao, Z., & Liang, Z. (2021). Network community detection via iterative edge removal in a flocking-like system. European Physical Journal - Special Topics, 230( 14-15), 2843-2855. doi:10.1140/epjs/s11734-021-00154-5
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      Verri FAN, Gueleri RA, Qiusheng Z, Junbao Z, Liang Z. Network community detection via iterative edge removal in a flocking-like system [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2843-2855.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5
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      Verri FAN, Gueleri RA, Qiusheng Z, Junbao Z, Liang Z. Network community detection via iterative edge removal in a flocking-like system [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2843-2855.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00154-5
  • Source: The European Physical Journal Special Topics. Unidade: FFCLRP

    Subjects: ALGORITMOS, APRENDIZADO COMPUTACIONAL, REDES COMPLEXAS

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      VALEJO, Alan Demetrius Baria et al. A review and comparative analysis of coarsening algorithms on bipartite networks. The European Physical Journal Special Topics, v. 230, n. 14-15, p. 2801 - 2811, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00159-0. Acesso em: 09 nov. 2024.
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      Valejo, A. D. B., Santos, W. de O. dos, Naldi, M. C., & Liang, Z. (2021). A review and comparative analysis of coarsening algorithms on bipartite networks. The European Physical Journal Special Topics, 230( 14-15), 2801 - 2811. doi:10.1140/epjs/s11734-021-00159-0
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      Valejo ADB, Santos W de O dos, Naldi MC, Liang Z. A review and comparative analysis of coarsening algorithms on bipartite networks [Internet]. The European Physical Journal Special Topics. 2021 ; 230( 14-15): 2801 - 2811.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00159-0
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      Valejo ADB, Santos W de O dos, Naldi MC, Liang Z. A review and comparative analysis of coarsening algorithms on bipartite networks [Internet]. The European Physical Journal Special Topics. 2021 ; 230( 14-15): 2801 - 2811.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00159-0
  • Source: European Physical Journal - Special Topics. Unidades: FFCLRP, ICMC

    Subjects: REDES COMPLEXAS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES

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      ANGHINONI, Leandro et al. Time series pattern identification by hierarchical community detection. European Physical Journal - Special Topics, v. 230, n. 14-15, p. 2775-2782, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00163-4. Acesso em: 09 nov. 2024.
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      Anghinoni, L., Vega-Oliveros, D. A., Silva, T. C., & Liang, Z. (2021). Time series pattern identification by hierarchical community detection. European Physical Journal - Special Topics, 230( 14-15), 2775-2782. doi:10.1140/epjs/s11734-021-00163-4
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      Anghinoni L, Vega-Oliveros DA, Silva TC, Liang Z. Time series pattern identification by hierarchical community detection [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2775-2782.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00163-4
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      Anghinoni L, Vega-Oliveros DA, Silva TC, Liang Z. Time series pattern identification by hierarchical community detection [Internet]. European Physical Journal - Special Topics. 2021 ; 230( 14-15): 2775-2782.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00163-4
  • Source: Chemosensors. Unidade: FFCLRP

    Subjects: CARBONO, COMPOSTOS ORGÂNICOS, SENSORES QUÍMICOS, ESPECTROSCOPIA, SENSORES BIOMÉDICOS

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      LU, Tianqi et al. Flexible impedimetric electronic nose for high-accurate determination of individual volatile organic compounds by tuning the graphene sensitive properties. Chemosensors, v. 9, n. 12, p. 1-18, 2021Tradução . . Disponível em: https://doi.org/10.3390/chemosensors9120360. Acesso em: 09 nov. 2024.
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      Lu, T., Al-Hamry, A., Rosolen, J. M., Hu, Z., Hao, J., Wang, Y., et al. (2021). Flexible impedimetric electronic nose for high-accurate determination of individual volatile organic compounds by tuning the graphene sensitive properties. Chemosensors, 9( 12), 1-18. doi:10.3390/chemosensors9120360
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      Lu T, Al-Hamry A, Rosolen JM, Hu Z, Hao J, Wang Y, Adiraju A, Yu T, Matsubara EY, Kanoun O. Flexible impedimetric electronic nose for high-accurate determination of individual volatile organic compounds by tuning the graphene sensitive properties [Internet]. Chemosensors. 2021 ; 9( 12): 1-18.[citado 2024 nov. 09 ] Available from: https://doi.org/10.3390/chemosensors9120360
    • Vancouver

      Lu T, Al-Hamry A, Rosolen JM, Hu Z, Hao J, Wang Y, Adiraju A, Yu T, Matsubara EY, Kanoun O. Flexible impedimetric electronic nose for high-accurate determination of individual volatile organic compounds by tuning the graphene sensitive properties [Internet]. Chemosensors. 2021 ; 9( 12): 1-18.[citado 2024 nov. 09 ] Available from: https://doi.org/10.3390/chemosensors9120360
  • Source: The European Physical Journal Special Topics. Unidade: FFCLRP

    Subjects: REDES COMPLEXAS, NEUROCIÊNCIAS, PROCESSOS ESTOCÁSTICOS

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      YANCHUK, Serhiy et al. Dynamical phenomena in complex networks: fundamentals and applications. The European Physical Journal Special Topics, v. 230, p. 2711-2716, 2021Tradução . . Disponível em: https://doi.org/10.1140/epjs/s11734-021-00282-y. Acesso em: 09 nov. 2024.
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      Yanchuk, S., Roque, A. C., Macau, E. E. N., & Kurths, J. (2021). Dynamical phenomena in complex networks: fundamentals and applications. The European Physical Journal Special Topics, 230, 2711-2716. doi:10.1140/epjs/s11734-021-00282-y
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      Yanchuk S, Roque AC, Macau EEN, Kurths J. Dynamical phenomena in complex networks: fundamentals and applications [Internet]. The European Physical Journal Special Topics. 2021 ; 230 2711-2716.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00282-y
    • Vancouver

      Yanchuk S, Roque AC, Macau EEN, Kurths J. Dynamical phenomena in complex networks: fundamentals and applications [Internet]. The European Physical Journal Special Topics. 2021 ; 230 2711-2716.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1140/epjs/s11734-021-00282-y
  • Source: Proceedings. Conference titles: International Joint Conference on Neural Networks - IJCNN. Unidade: FFCLRP

    Subjects: MENSAGEM, CHAT, CRIPTOLOGIA, PRIVACIDADE, FRAMEWORKS

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      COTACALLAPA, Moshe et al. Measuring the engagement level in encrypted group conversations by using temporal networks. 2020, Anais.. Los Alamitos: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo, 2020. Disponível em: https://doi.org/10.1109/IJCNN48605.2020.9207174. Acesso em: 09 nov. 2024.
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      Cotacallapa, M., Berton, L., Ferreira, L. N., Quiles, M. G., Liang, Z., Macau, E. E. N., & Vega-Oliveros, D. A. (2020). Measuring the engagement level in encrypted group conversations by using temporal networks. In Proceedings. Los Alamitos: Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Universidade de São Paulo. doi:10.1109/IJCNN48605.2020.9207174
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      Cotacallapa M, Berton L, Ferreira LN, Quiles MG, Liang Z, Macau EEN, Vega-Oliveros DA. Measuring the engagement level in encrypted group conversations by using temporal networks [Internet]. Proceedings. 2020 ;[citado 2024 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207174
    • Vancouver

      Cotacallapa M, Berton L, Ferreira LN, Quiles MG, Liang Z, Macau EEN, Vega-Oliveros DA. Measuring the engagement level in encrypted group conversations by using temporal networks [Internet]. Proceedings. 2020 ;[citado 2024 nov. 09 ] Available from: https://doi.org/10.1109/IJCNN48605.2020.9207174
  • Source: Computers & Geosciences. Unidade: FFCLRP

    Subjects: INCÊNDIOS FLORESTAIS, QUEIMADA, GEOCIÊNCIAS, MUDANÇA CLIMÁTICA, ANÁLISE DE SÉRIES TEMPORAIS

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      FERREIRA, Leonardo N. et al. Global fire season severity analysis and forecasting. Computers & Geosciences, v. 134, p. 1-9, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.cageo.2019.104339. Acesso em: 09 nov. 2024.
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      Ferreira, L. N., Vega-Oliveros, D. A., Liang, Z., Cardoso, M. F., & Macau, E. E. N. (2020). Global fire season severity analysis and forecasting. Computers & Geosciences, 134, 1-9. doi:10.1016/j.cageo.2019.104339
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      Ferreira LN, Vega-Oliveros DA, Liang Z, Cardoso MF, Macau EEN. Global fire season severity analysis and forecasting [Internet]. Computers & Geosciences. 2020 ; 134 1-9.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1016/j.cageo.2019.104339
    • Vancouver

      Ferreira LN, Vega-Oliveros DA, Liang Z, Cardoso MF, Macau EEN. Global fire season severity analysis and forecasting [Internet]. Computers & Geosciences. 2020 ; 134 1-9.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1016/j.cageo.2019.104339
  • Source: Journal of Chemical Theory and Computation. Unidade: FFCLRP

    Subjects: DIAGNÓSTICO POR IMAGEM, MOLÉCULA, MATERIAIS, METAIS, ELEMENTOS DE TRANSIÇÃO

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      AOTO, Yuri A. et al. How to arrive at accurate benchmark values for transition metal compounds: computation or experiment?. Journal of Chemical Theory and Computation, v. 13, n. 11, p. 5291-5316, 2017Tradução . . Disponível em: https://doi.org/10.1021/acs.jctc.7b00688. Acesso em: 09 nov. 2024.
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      Aoto, Y. A., Batista, A. P. de L., Köhn, A., & Oliveira Filho, A. G. S. de. (2017). How to arrive at accurate benchmark values for transition metal compounds: computation or experiment? Journal of Chemical Theory and Computation, 13( 11), 5291-5316. doi:10.1021/acs.jctc.7b00688
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      Aoto YA, Batista AP de L, Köhn A, Oliveira Filho AGS de. How to arrive at accurate benchmark values for transition metal compounds: computation or experiment? [Internet]. Journal of Chemical Theory and Computation. 2017 ; 13( 11): 5291-5316.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1021/acs.jctc.7b00688
    • Vancouver

      Aoto YA, Batista AP de L, Köhn A, Oliveira Filho AGS de. How to arrive at accurate benchmark values for transition metal compounds: computation or experiment? [Internet]. Journal of Chemical Theory and Computation. 2017 ; 13( 11): 5291-5316.[citado 2024 nov. 09 ] Available from: https://doi.org/10.1021/acs.jctc.7b00688

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