Designing Socio-Technical Systems Using the System Paradigm in the Context of Nano-, Bio-, Information Technology and Cognitive Science Convergence
Abstract
Purpose: The purpose of the study is to develop a methodology for designing socio-technical systems using the system paradigm in the context of nano-, bio-, information technology and cognitive science convergence.
Methodology/Approach: The systemic paradigm is used. The optimization is carried out according to the integral indicator of resource consumption and system energy efficiency. Fractal socio-technical systems are created that provide the maximum correlation between the needs of individuals and the activities of society, taking into account the dynamics of the formation of needs for the purpose of self-development with restrictions on safety factors and material resources.
Findings: The proposed methodology makes it possible to develop socio-technical systems with a high level of security, ensuring sustainable spiral self-development and integration of scientific knowledge on the basis of adaptive, innovative, intuitive and analytical elements of the system.
Research Limitation/Implication: A general concept for designing socio-technical systems using the system paradigm in the context of nano-, bio-, information technology and cognitive science convergence is presented that requires further research.
Originality/Value of paper: A methodology for designing socio-technical systems using the system paradigm in the context of nano-, bio-, information technology and cognitive science convergence was first proposed.
References
Aithal, P.S. and Aithal, S. 2016. Nanotechnology innovations and commercialization-opportunities, challenges & reasons for delay. International Journal of Engineering and Manufacturing (IJEM), [e-journal] 6(6), pp.15-25. DOI: 10.5815/ijem.2016.06.02.
Di Maio, F., Rem, P.C., Baldé, K. and Polder, M., 2017. Measuring resource efficiency and circular economy: A market value approach. Resources. Conservation and Recycling, [e-journal] 122, pp.163-171. DOI: 10.1016/j.resconrec.2017.02.009.
Erdoğan, Z. and Namlı, E., 2019. A living environment prediction model using ensemble machine learning techniques based on quality of life index. Journal of Ambient Intelligence and Humanized Computing, [e-journal] 10, pp.1-17. DOI: 10.1007/s12652-019-01432-w.
Eurostat, 2017. Final report of the expert group on quality of life indicators. [pdf] Luxembourg: Publications Office of the European Union. Available at:
Girard, L.F., Hudec, O., Kourt, K. and Nijkamp, P., 2017. ‘Science of the City’: Towards a Higher Quality of Urban Life. Quality Innovation Prosperity, [e-journal] 21(1), pp.1-8. DOI: 10.12776/qip.v21i1.851.
Güngör, A. and Alp, G.T., 2019. Cognitive styles affecting the performance of research and development (R&D) employees in the era of Industry 4.0. Industry 4.0, [e-journal] 4(5), pp.203-205. DOI: 10.1051/e3sconf/202127308017.
Javied, T., Bakakeu, J., Gessinger, D. and Franke, J., 2018. Strategic energy management in industry 4.0 environment. In: SysCon, 2018 Annual IEEE International Systems Conference (SysCon). Vancouver, Canada, 23-26 April 2018. IEEE. pp.1-4. DOI: 10.1109/SYSCON.2018.8369610.
Kaverin, S.B., 1987. About the psychological classification of needs. Voprosy psihologii, 875, pp.121-129.
Kolbachev, Е.B., Halász, S. and Fedorchuk, V.Е., 2019. Experience and prospects of application of the system paradigm of J. Kornai in the design of production and technical systems. Bulletin of the South-Russian State Technical University (NPI) Series Socio-Economic Sciences, 4, pp.36-43. DOI: 10.17213/2075-2067-2019-4-36-43.
Maslow, А., 2008. Motivation and person. Saint Petersburg: Piter.
Sydorova, E., Halász, S., Zelenkova, G., Pakhomov, A. and Pahomova, A., 2021. Management tools in the context of NBIC convergence. E3S Web of Conferences, 273, p.08017. DOI: 10.1051/e3sconf/202127308017.
Sydorova, E., Pakhomova, A.A., Halasz, S., Fedorchuk, V.Е. and Nardina, А.А., 2020. Increasing the efficiency of production systems by providing product generation and transformation throughout the PLM cycle within the framework of NBIC-convergence. Drukerovskij vestnik, [e-journal] 4, pp.240-252. DOI: 10.17213/2312-6469-2019-4-240-252.
Winkler-Goldstein, R., Imbault, F., Usländer, T. and de la Gastine, H., 2018. Fractal Production Reprogramming “Industrie 4.0” Around Resource and Energy Efficiency?”. In: EEEIC/I&CPS Europe, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe). Palermo, Italy, 12-15 June 2018. pp.1-5. DOI: 10.1109/EEEIC.2018.8494395.
Authors
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