№2, 2022

THE IMPACT OF INDUSTRY 4.0 ON THE FORMATION OF SCIENCE 4.0

Tahmasib Kh. Fataliyev, Shakir A. Mehdiyev

Technological innovations at the forefront of industrial revolutions are applied not only in industry but also create a need for research and applications, as a result of which they are mastered and penetrate other areas of human activity. In this regard, the latest advances in information technology in the field of data generation, storage, transmission and processing during the 4th industrial revolution have led to the transformation of traditional scientific activity and the rapid development of the concept of “data-based science”. The article analyzes the main development stages in the scientific environment, the organization of scientific activities with the widespread use of Industry 4.0 solutions, and its organic connection with the concept of Science 4.0. The essence of Science 4.0 is revealed through the review of studies in the field of the Internet of Things, cyber-physical systems, artificial intelligence, cloud computing, big data analytics, and other intelligent solutions. Conceptual issues of the formation of Science 4.0 are developed and relevant proposals are made for its implementation (pp.40-47).

Keywords: E-science, Industry 4.0, Science 4.0, Internet of Things, Cyber-Physical Systems, Artificial Intelligence
References
  • Alguliyev, R. M., Alakbarov, R. G., & Fataliyev, T. Kh. (2015). Electronic science: current status, problems, and perspectives. Problems of information technology, 6(2), 4–14.
    https://doi.org/10.25045/jpit.v06.i2.01
  • Baigrie, B. S. (2007). Electricity and magnetism: a historical perspective. Greenwood Publishing Group, 165 p.
  • Banerji, M. et al. (2010). Galaxy Zoo: reproducing galaxy morphologies via machine learning. Monthly Notices of the Royal Astronomical Society, 406(1), 342–353.
    https://doi.org/10.1111/j.1365-2966.2010.16713.x
  • Bartling, S. (2019). Blockchain for science and knowledge creation. in Gesundheit digital. (pp. 159-180), Springer, Berlin, Heidelberg.
    https://doi.org/10.1007/978-3-662-57611-3_10
  • Beregi, J. et al. (2018). Radiology and artificial intelligence: An opportunity for our speciality. Diagnostic and Interventional Imaging, 99(11), 677-678. https://doi.org/10.1016/j.diii.2018.11.002
  • Burger, B. et al. (2020). A mobile robotic chemist. Nature, 583(7815), 237–241.
    https://doi.org/10.1038/s41586-020-2442-2
  • Copeland, B. J., & Sommaruga, G. (2015). The Stored-Program Universal Computer: Did Zuse Anticipate Turing and von Neumann? In Turing’s revolution (pp. 43-101). Birkhäuser, Cham.
    https://doi.org/10.1007/978-3-319-22156-4_14
  • Fataliyev, T. Kh., & Mehdiyev, Sh. A. (2019). Research of the technology for the management and processing of big scientific data. Problems of information society, 10(2), 60–70.
    https://doi.org/jpis.v10.i2.06
  • Fataliyev, T. Kh., & Mehdiyev, Sh. A. (2019). Integration of Cyber-Physical Systems in E-Science Environment: State-of-the-Art, Problems and Effective Solutions. I.J. Modern Education and Computer Science, 11(9), 35-43.
    https://doi.org/10.5815/ijmecs.2019.09.04
  • Fataliyev, T. Kh., & Mehdiyev, Sh. A. (2020). Industry 4.0: The Oil and Gas Sector Security and Personal Data Protection. I.J. Engineering and Manufacturing, 10(2), 1-14. https://doi.org/10.5815/ijem.2020.02.01
  • Fox, J., Arena, D., & Bailenson, J. N. (2009). Virtual reality: A survival guide for the social scientist. Journal of Media Psychology: Theories, Methods, and Applications, 21(3), 95–113.
    https://doi.org/10.1027/1864-1105.21.3.95
  • Gilbert, K. R. (1971). Henry Maudslay 1771–1831. Transactions of the Newcomen Society, 44(1), 49-62.
    https://doi.org/10.1179/tns.1971.003
  • Gorelik, A. (2019). The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science. O'Reilly Media, 205 p.
  • Gráda, C. Ó. (2016). Did science cause the industrial revolution? Journal of Economic Literature. 54(1), 1, 2016, 224-239. http://dx.doi.org/10.1257/jel.54.1.224. 224
  • Harris, J. R. (1988). The British iron industry 1700–1850. Palgrave, London. 90 p.
    https://doi.org/10.1007/978-1-349-06457-1
  • Häse, F., Roch, L., & Aspuru-Guzik, A. (2019) Next-Generation Experimentation with Self-Driving Laboratories. Trends in Chemistry, 1(3), 282–291.
    https://doi.org/10.1016/j.trechm.2019.02.007
  • Hey, T., Tansley, S., & Tolle, K. M. (2009). Jim Gray on eScience: a transformed scientific method.
  • Hey, T., & Trefethen, A. E. (2002). The UK e-science core programme and the grid. In International Conference on Computational Science (pp. 3-21). Springer, Berlin, Heidelberg.
    https://link.springer.com/chapter/10.1007/3-540-46043-8_1
  • Huurdeman, A. A. (2003). The worldwide history of telecommunications. John Wiley & Sons. 638 p.
  • Il'yanovich, E. B. (2021). Nauka i tekhnika na gorizonte chetvertoj tekhnologicheskoj revolyucii sovremennoj tekhnogennoj civilizacii. Vestn. Sev. (Arktich.) feder. un-ta. Ser.: Gumanit. i soc. Nauki, 21(4), 100–110.
    https://doi.org/10.37482/2687-1505-V121
  • Israel, S., & Scoble, R. (2016). The Fourth Transformation: How Augmented Reality & Artificial Intelligence Will Change Everything. Patrick Brewster Press. 208 p.
  • Jardim-Goncalves, R., Romero, D., & Grilo, A. (2017) Factories of the future: challenges and leading innovations in intelligent manufacturing. International Journal of Computer Integrated Manufacturing, 30 (1), 4-14.
    https://doi.org/10.1080/0951192X.2016.1258120
  • Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. 2013.
    https://www.din.de/blob/76902/e8cac883f42bf28536e7e8165993f1fd/recommendations-for-implementing-industry-4-0-data.pdf.
  • Lee, J., Bagheri, B., & Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18- 23.
    https://doi.org/10.1016/j.mfglet.2014.12.001
  • Nichols M. R. How Is Cloud Computing Changing Scientific Research? 2019.
    https://interestingengineering.com/how-is-cloud-computing-changing-scientific-research
  • Radder, H. (2009). The philosophy of scientific experimentation: a review. Automated Experimentation, 1(1), 1-8.
    https://doi.org/10.1186/1759-4499-1-2
  • Roberts, L. (1988). The Arpanet and computer networks. In A history of personal workstations (pp. 141-172).
    https://doi.org/10.1145/61975.66916
  • Senior, A. W. et al. (2020). Improved protein structure prediction using potentials from deep learning. Nature, 577, 706-710. https://doi.org/10.1038/s41586-019-1923-7
  • Shukla, N., Tiwari, M. K., & Beydoun, G. (2019). Next-generation smart manufacturing and service systems using big data analytics. Computers and Industrial Engineering, 128, 905-910.
    https://doi.org/10.1016/j.cie.2018.12.026
  • Sparkes, A. et al. (2010). Towards Robot Scientists for autonomous scientific discovery. Automated Experimentation, 2(1).
    https://doi.org/10.1186/1759-4499-2-1
  • Van Rossum, J. (2017). Blockchain for research. Perspectives on a new paradigm for scholarly communication. Digital Science Report.
  • Villegas-Ch W., X. Palacios-Pacheco, X., & Luján-Mora, S. (2019). Application of a smart city model to a traditional university campus with a Big data architecture: A sustainable smart campus. Sustainability, 11(10), 2857.
    https://doi.org/10.3390/su11102857
  • Voosen, P. (2020). Europe is building a 'digital twin' of Earth to revolutionize climate forecasts.
    https://www.sciencemag.org/news/2020/10/europe-building-digital-twin-earth-revolutionize-climate-forecasts.
  • Zezulka, F., Marcon, P., Vesely, I., & Sajdl, O. (2016). Industry 4.0–An Introduction in the phenomenon. IFAC-PapersOnLine, 49(25), 8-12.
    https://doi.org/10.1016/j.ifacol.2016.12.002
  • Zhong, R. Y., Xu, K. C., Chen, C., & Huang, G. O. (2017). Big data analytics for physical internet-based intelligent manufacturing shop floors. International Journal of Production Research, 55(9), 2610-2621.
    https://doi.org/10.1080/00207543.2015.1086037