Have a personal or library account? Click to login
Analysis of the Pre-Injection System of a Marine Diesel Engine Through Multiple-Criteria Decision-Making and Artificial Neural Networks Cover

Analysis of the Pre-Injection System of a Marine Diesel Engine Through Multiple-Criteria Decision-Making and Artificial Neural Networks

Polish Maritime Research
Volume 28 (2021): Issue 4 (December 2021)
By:
Open Access
|Jan 2022

References

  1. 1. J. Kowalski and W. Tarelko, “NOx emission from a two-stroke ship engine. Part 1: Modeling aspect,” Appl. Therm. Eng., vol. 29, no. 11–12, pp. 2153–2159, Aug. 2009, doi: <a href="https://doi.org/10.1016/j.applthermaleng.2008.06.032.10.1016/j.applthermaleng.2008.06.032" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.applthermaleng.2008.06.032.10.1016/j.applthermaleng.2008.06.032</a>
    Search in Google ScholarBack to article
  2. 2. J. Kowalski and W. Tarelko, “NOx emission from a two-stroke ship engine: Part 2 – Laboratory test,” Appl. Therm. Eng., vol. 29, no. 11–12, pp. 2160–2165, Aug. 2009, doi: <a href="https://doi.org/10.1016/j.applthermaleng.2008.06.031.10.1016/j.applthermaleng.2008.06.031" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.applthermaleng.2008.06.031.10.1016/j.applthermaleng.2008.06.031</a>
    Search in Google ScholarBack to article
  3. 3. J. Girtler, “A method for evaluating the performance of a marine piston internal combustion engine used as the main engine on a ship during its voyage in different sailing conditions,” Polish Marit. Res., vol. 17, no. 4, Jan. 2010, doi: <a href="https://doi.org/10.2478/v10012-010-0033-0.10.2478/v10012-010-0033-0" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10012-010-0033-0.10.2478/v10012-010-0033-0</a>
    Search in Google ScholarBack to article
  4. 4. R. Zhao et al., “A numerical and experimental study of marine hydrogen–natural gas–diesel tri-fuel engines,” Polish Marit. Res., vol. 27, no. 4, pp. 80–90, Dec. 2020, doi: <a href="https://doi.org/10.2478/pomr-2020-0068.10.2478/pomr-2020-0068" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/pomr-2020-0068.10.2478/pomr-2020-0068</a>
    Search in Google ScholarBack to article
  5. 5. X. Lu, P. Geng, and Y. Chen, “NOx emission reduction technology for marine engine based on Tier-III: A review,” J. Therm. Sci., vol. 29, no. 5, pp. 1242–1268, Oct. 2020, doi: <a href="https://doi.org/10.1007/s11630-020-1342-y.10.1007/s11630-020-1342-y" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s11630-020-1342-y.10.1007/s11630-020-1342-y</a>
    Search in Google ScholarBack to article
  6. 6. S. Lion, I. Vlaskos, and R. Taccani, “A review of emissions reduction technologies for low and medium speed marine Diesel engines and their potential for waste heat recovery,” Energy Convers. Manag., vol. 207, p. 112553, Mar. 2020, doi: <a href="https://doi.org/10.1016/j.enconman.2020.112553.10.1016/j.enconman.2020.112553" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.enconman.2020.112553.10.1016/j.enconman.2020.112553</a>
    Search in Google ScholarBack to article
  7. 7. J. Deng, X. Wang, Z. Wei, L. Wang, C. Wang, and Z. Chen, “A review of NOx and SOx emission reduction technologies for marine diesel engines and the potential evaluation of liquefied natural gas fuelled vessels,” Sci. Total Environ., vol. 766, p. 144319, Apr. 2021, doi: <a href="https://doi.org/10.1016/j.scitotenv.2020.144319.10.1016/j.scitotenv.2020.14431933421776" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.scitotenv.2020.144319.10.1016/j.scitotenv.2020.14431933421776</a>
    Search in Google ScholarBack to article
  8. 8. A. N. Bhatt and N. Shrivastava, “Application of artificial neural network for internal combustion engines: A state of the art review,” Arch. Comput. Methods Eng., May 2021, doi: <a href="https://doi.org/10.1007/s11831-021-09596-5.10.1007/s11831-021-09596-5809092033967576" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s11831-021-09596-5.10.1007/s11831-021-09596-5809092033967576</a>
    Search in Google ScholarBack to article
  9. 9. J. Kowalski, “ANN based evaluation of the NOx concentration in the exhaust gas of a marine two-stroke diesel engine,” Polish Marit. Res., vol. 16, no. 2, Jan. 2009, doi: <a href="https://doi.org/10.2478/v10012-008-0023-7.10.2478/v10012-008-0023-7" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.2478/v10012-008-0023-7.10.2478/v10012-008-0023-7</a>
    Search in Google ScholarBack to article
  10. 10. V. Çelik and E. Arcaklioğlu, “Performance maps of a diesel engine,” Appl. Energy, vol. 81, no. 3, pp. 247–259, Jul. 2005, doi: <a href="https://doi.org/10.1016/j.apenergy.2004.08.003.10.1016/j.apenergy.2004.08.003" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.apenergy.2004.08.003.10.1016/j.apenergy.2004.08.003</a>
    Search in Google ScholarBack to article
  11. 11. E. Siami-Irdemoosa and S. R. Dindarloo, “Prediction of fuel consumption of mining dump trucks: A neural networks approach,” Appl. Energy, vol. 151, pp. 77–84, Aug. 2015, doi: <a href="https://doi.org/10.1016/j.apenergy.2015.04.064.10.1016/j.apenergy.2015.04.064" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.apenergy.2015.04.064.10.1016/j.apenergy.2015.04.064</a>
    Search in Google ScholarBack to article
  12. 12. M. Bietresato, A. Calcante, and F. Mazzetto, “A neural network approach for indirectly estimating farm tractors engine performances,” Fuel, vol. 143, pp. 144–154, Mar. 2015, doi: <a href="https://doi.org/10.1016/j.fuel.2014.11.019.10.1016/j.fuel.2014.11.019" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fuel.2014.11.019.10.1016/j.fuel.2014.11.019</a>
    Search in Google ScholarBack to article
  13. 13. K. Goudarzi, A. Moosaei, and M. Gharaati, “Applying artificial neural networks (ANN) to the estimation of thermal contact conductance in the exhaust valve of internal combustion engine,” Appl. Therm. Eng., vol. 87, pp. 688–697, Aug. 2015, doi: <a href="https://doi.org/10.1016/j.applthermaleng.2015.05.060.10.1016/j.applthermaleng.2015.05.060" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.applthermaleng.2015.05.060.10.1016/j.applthermaleng.2015.05.060</a>
    Search in Google ScholarBack to article
  14. 14. E. Arcaklioglu and İ. Çelikten, “A diesel engine’s performance and exhaust emissions,” Appl. Energy, vol. 80, no. 1, pp. 11–22, Jan. 2005, doi: <a href="https://doi.org/10.1016/j.apenergy.2004.03.004.10.1016/j.apenergy.2004.03.004" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.apenergy.2004.03.004.10.1016/j.apenergy.2004.03.004</a>
    Search in Google ScholarBack to article
  15. 15. K. Nikzadfar and A. H. Shamekhi, “Investigating the relative contribution of operational parameters on performance and emissions of a common-rail diesel engine using neural network,” Fuel, vol. 125, pp. 116–128, Jun. 2014, doi: <a href="https://doi.org/10.1016/j.fuel.2014.02.021.10.1016/j.fuel.2014.02.021" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fuel.2014.02.021.10.1016/j.fuel.2014.02.021</a>
    Search in Google ScholarBack to article
  16. 16. K. Muralidharan and D. Vasudevan, “Applications of artificial neural networks in prediction of performance, emission and combustion characteristics of variable compression ratio engine fuelled with waste cooking oil biodiesel,” J. Brazilian Soc. Mech. Sci. Eng., vol. 37, no. 3, pp. 915–928, May 2015, doi: <a href="https://doi.org/10.1007/s40430-014-0213-4.10.1007/s40430-014-0213-4" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s40430-014-0213-4.10.1007/s40430-014-0213-4</a>
    Search in Google ScholarBack to article
  17. 17. S. Arumugam, G. Sriram, and P. R. S. Subramanian, “Application of artificial intelligence to predict the performance and exhaust emissions of diesel engine using rapeseed oil methyl ester,” Procedia Eng., vol. 38, pp. 853–860, 2012, doi: <a href="https://doi.org/10.1016/j.proeng.2012.06.107.10.1016/j.proeng.2012.06.107" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.proeng.2012.06.107.10.1016/j.proeng.2012.06.107</a>
    Search in Google ScholarBack to article
  18. 18. A. Duran, M. Lapuerta, and J. Rodriguez-Fernandez, “Neural networks estimation of diesel particulate matter composition from transesterified waste oils blends,” Fuel, vol. 84, no. 16, pp. 2080–2085, Nov. 2005, doi: <a href="https://doi.org/10.1016/j.fuel.2005.04.029.10.1016/j.fuel.2005.04.029" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fuel.2005.04.029.10.1016/j.fuel.2005.04.029</a>
    Search in Google ScholarBack to article
  19. 19. S. Gürgen, B. Ünver, and I. Altin, “Prediction of cyclic variability in a diesel engine fueled with n-butanol and diesel fuel blends using artificial neural network,” Renew. Energy, vol. 117, pp. 538–544, Mar. 2018, doi: <a href="https://doi.org/10.1016/j.renene.2017.10.101.10.1016/j.renene.2017.10.101" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.renene.2017.10.101.10.1016/j.renene.2017.10.101</a>
    Search in Google ScholarBack to article
  20. 20. H. Oğuz, I. Saritas, and H. E. Baydan, “Prediction of diesel engine performance using biofuels with artificial neural network,” Expert Syst. Appl., vol. 37, no. 9, pp. 6579–6586, Sep. 2010, doi: <a href="https://doi.org/10.1016/j.eswa.2010.02.128.10.1016/j.eswa.2010.02.128" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.eswa.2010.02.128.10.1016/j.eswa.2010.02.128</a>
    Search in Google ScholarBack to article
  21. 21. P. Shanmugam, V. Sivakumar, A. Murugesan, and M. Ilangkumaran, “Performance and exhaust emissions of a diesel engine using hybrid fuel with an artificial neural network,” Energy Sources, Part A Recover. Util. Environ. Eff., vol. 33, no. 15, pp. 1440–1450, May 2011, doi: <a href="https://doi.org/10.1080/15567036.2010.539085.10.1080/15567036.2010.539085" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1080/15567036.2010.539085.10.1080/15567036.2010.539085</a>
    Search in Google ScholarBack to article
  22. 22. K. Çelebi, E. Uludamar, E. Tosun, Ş. Yildizhan, K. Aydin, and M. Özcanli, “Experimental and artificial neural network approach of noise and vibration characteristic of an unmodified diesel engine fuelled with conventional diesel, and biodiesel blends with natural gas addition,” Fuel, vol. 197, pp. 159–173, Jun. 2017, doi: <a href="https://doi.org/10.1016/j.fuel.2017.01.113.10.1016/j.fuel.2017.01.113" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.fuel.2017.01.113.10.1016/j.fuel.2017.01.113</a>
    Search in Google ScholarBack to article
  23. 23. N. Akkouche, K. Loubar, F. Nepveu, M. E. A. Kadi, and M. Tazerout, “Micro-combined heat and power using dual fuel engine and biogas from discontinuous anaerobic digestion,” Energy Convers. Manag., vol. 205, p. 112407, Feb. 2020, doi: <a href="https://doi.org/10.1016/j.enconman.2019.112407.10.1016/j.enconman.2019.112407" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.enconman.2019.112407.10.1016/j.enconman.2019.112407</a>
    Search in Google ScholarBack to article
  24. 24. S. Javed, R. U. Baig, and Y. V. V. S. Murthy, “Study on noise in a hydrogen dual-fuelled zinc-oxide nanoparticle blended biodiesel engine and the development of an artificial neural network model,” Energy, vol. 160, pp. 774–782, Oct. 2018, doi: <a href="https://doi.org/10.1016/j.energy.2018.07.041.10.1016/j.energy.2018.07.041" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.energy.2018.07.041.10.1016/j.energy.2018.07.041</a>
    Search in Google ScholarBack to article
  25. 25. S. Javed, Y. V. V. Satyanarayana Murthy, R. U. Baig, and D. Prasada Rao, “Development of ANN model for prediction of performance and emission characteristics of hydrogen dual fueled diesel engine with Jatropha Methyl Ester biodiesel blends,” J. Nat. Gas Sci. Eng., vol. 26, pp. 549–557, Sep. 2015, doi: <a href="https://doi.org/10.1016/j.jngse.2015.06.041.10.1016/j.jngse.2015.06.041" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.jngse.2015.06.041.10.1016/j.jngse.2015.06.041</a>
    Search in Google ScholarBack to article
  26. 26. T. F. Yusaf, D. R. Buttsworth, K. H. Saleh, and B. F. Yousif, “CNG-diesel engine performance and exhaust emission analysis with the aid of artificial neural network,” Appl. Energy, vol. 87, no. 5, pp. 1661–1669, May 2010, doi: <a href="https://doi.org/10.1016/j.apenergy.2009.10.009.10.1016/j.apenergy.2009.10.009" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.apenergy.2009.10.009.10.1016/j.apenergy.2009.10.009</a>
    Search in Google ScholarBack to article
  27. 27. E. Uludamar et al., “Evaluation of vibration characteristics of a hydroxyl (HHO) gas generator installed diesel engine fuelled with different diesel–biodiesel blends,” Int. J. Hydrogen Energy, vol. 42, no. 36, pp. 23352–23360, Sep. 2017, doi: <a href="https://doi.org/10.1016/j.ijhydene.2017.01.192.10.1016/j.ijhydene.2017.01.192" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijhydene.2017.01.192.10.1016/j.ijhydene.2017.01.192</a>
    Search in Google ScholarBack to article
  28. 28. J. Syed, R. U. Baig, S. Algarni, Y. V. V. S. Murthy, M. Masood, and M. Inamurrahman, “Artificial neural network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach,” Int. J. Hydrogen Energy, vol. 42, no. 21, pp. 14750–14774, May 2017, doi: <a href="https://doi.org/10.1016/j.ijhydene.2017.04.096.10.1016/j.ijhydene.2017.04.096" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijhydene.2017.04.096.10.1016/j.ijhydene.2017.04.096</a>
    Search in Google ScholarBack to article
  29. 29. H. Taghavifar, H. Taghavifar, A. Mardani, A. Mohebbi, S. Khalilarya, and S. Jafarmadar, “On the modeling of convective heat transfer coefficient of hydrogen fueled diesel engine as affected by combustion parameters using a coupled numerical-artificial neural network approach,” Int. J. Hydrogen Energy, vol. 40, no. 12, pp. 4370–4381, Apr. 2015, doi: <a href="https://doi.org/10.1016/j.ijhydene.2015.01.140.10.1016/j.ijhydene.2015.01.140" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijhydene.2015.01.140.10.1016/j.ijhydene.2015.01.140</a>
    Search in Google ScholarBack to article
  30. 30. S. Tasdemir, I. Saritas, M. Ciniviz, and N. Allahverdi, “Artificial neural network and fuzzy expert system comparison for prediction of performance and emission parameters on a gasoline engine,” Expert Syst. Appl., May 2011, doi: <a href="https://doi.org/10.1016/j.eswa.2011.04.198.10.1016/j.eswa.2011.04.198" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.eswa.2011.04.198.10.1016/j.eswa.2011.04.198</a>
    Search in Google ScholarBack to article
  31. 31. J. Martínez-Morales, H. Quej-Cosgaya, J. Lagunas-Jiménez, E. Palacios-Hernández, and J. Morales-Saldaña, “Design optimization of multilayer perceptron neural network by ant colony optimization applied to engine emissions data,” Sci. China Technol. Sci., vol. 62, no. 6, pp. 1055–1064, Jun. 2019, doi: <a href="https://doi.org/10.1007/s11431-017-9235-y.10.1007/s11431-017-9235-y" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s11431-017-9235-y.10.1007/s11431-017-9235-y</a>
    Search in Google ScholarBack to article
  32. 32. M. M. Etghani, M. H. Shojaeefard, A. Khalkhali, and M. Akbari, “A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel,” Appl. Therm. Eng., vol. 59, no. 1–2, pp. 309–315, Sep. 2013, doi: <a href="https://doi.org/10.1016/j.applthermaleng.2013.05.041.10.1016/j.applthermaleng.2013.05.041" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.applthermaleng.2013.05.041.10.1016/j.applthermaleng.2013.05.041</a>
    Search in Google ScholarBack to article
  33. 33. M. Deb, P. Majumder, A. Majumder, S. Roy, and R. Banerjee, “Application of artificial intelligence (AI) in characterization of the performance–emission profile of a single cylinder CI engine operating with hydrogen in dual fuel mode: An ANN approach with fuzzy-logic based topology optimization,” Int. J. Hydrogen Energy, vol. 41, no. 32, pp. 14330–14350, Aug. 2016, doi: <a href="https://doi.org/10.1016/j.ijhydene.2016.07.016.10.1016/j.ijhydene.2016.07.016" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijhydene.2016.07.016.10.1016/j.ijhydene.2016.07.016</a>
    Search in Google ScholarBack to article
  34. 34. J. K. Dukowicz, “A particle-fluid numerical model for liquid sprays,” J. Comput. Phys., vol. 35, no. 2, pp. 229–253, Apr. 1980, doi: <a href="https://doi.org/10.1016/0021-9991(80)90087-X.10.1016/0021-9991(80)90087-X" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/0021-9991(80)90087-X.10.1016/0021-9991(80)90087-X</a>
    Search in Google ScholarBack to article
  35. 35. L. M. Ricart, J. Xin, G. R. Bower, and R. D. Reitz, “In-cylinder measurement and modeling of liquid fuel spray penetration in a heavy-duty diesel engine,” May 1997, doi: <a href="https://doi.org/10.4271/971591.10.4271/971591" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.4271/971591.10.4271/971591</a>
    Search in Google ScholarBack to article
  36. 36. Y. Ra and R. D. Reitz, “A reduced chemical kinetic model for IC engine combustion simulations with primary reference fuels,” Combust. Flame, vol. 155, no. 4, pp. 713–738, Dec. 2008, doi: <a href="https://doi.org/10.1016/j.combustflame.2008.05.002.10.1016/j.combustflame.2008.05.002" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.combustflame.2008.05.002.10.1016/j.combustflame.2008.05.002</a>
    Search in Google ScholarBack to article
  37. 37. H. Yang, S. R. Krishnan, K. K. Srinivasan, K. C. Midkiff, “Modeling of NOx emissions using a superextended Zeldovich mechanism,” ASME 2003 Internal Combustion Engine and Rail Transportation Divisions Fall Technical Conference, 2003, doi: <a href="https://doi.org/10.1115/ICEF2003-0713.10.1115/ICEF2003-0713" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1115/ICEF2003-0713.10.1115/ICEF2003-0713</a>
    Search in Google ScholarBack to article
  38. 38. J. A. Miller and P. Glarborg, “Modeling the formation of N2O and NO2 in the thermal DeNOx process,” Springer Ser. Chem. Phys., vol. 61, pp. 318–333, 1996.<a href="https://doi.org/10.1007/978-3-642-80299-7_25" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-3-642-80299-7_25</a>
    Search in Google ScholarBack to article
  39. 39. J. Sietsma and R. J. F. Dow, “Creating artificial neural networks that generalize,” Neural Networks, vol. 4, no. 1, pp. 67–79, Jan. 1991, doi: <a href="https://doi.org/10.1016/0893-6080(91)90033-2.10.1016/0893-6080(91)90033-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/0893-6080(91)90033-2.10.1016/0893-6080(91)90033-2</a>
    Search in Google ScholarBack to article
  40. 40. D. Golmohammadi, “Neural network application for fuzzy multi-criteria decision making problems,” Int. J. Prod. Econ., vol. 131, no. 2, pp. 490–504, Jun. 2011, doi: <a href="https://doi.org/10.1016/j.ijpe.2011.01.015.10.1016/j.ijpe.2011.01.015" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.ijpe.2011.01.015.10.1016/j.ijpe.2011.01.015</a>
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/pomr-2021-0051 | Journal eISSN: 2083-7429 | Journal ISSN: 1233-2585
Journal RSS Feed
Language: English
Page range: 88 - 96
Published on: Jan 1, 2022
Published by: Gdansk University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Keywords:
Marine engine,
emissions,
consumption,
artificial neural networks,
multi-criteria decision making,
computational fluid dynamics
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other,
Geosciences,
Atmospheric science and climatology,
Life sciences,
Life sciences, other

© 2022 C.G. Rodriguez, M.I. Lamas, J.D. Rodriguez, A. Abbas, published by Gdansk University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Previous articleVolume 28 (2021): Issue 4 (December 2021)Next article