Communication

Matching Role of Observation and its Replication Model in Managing Intelligent Paradigms and Monitoring Natural and Artificial Complexities.

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Razek, A. (2024). Matching Role of Observation and its Replication Model in Managing Intelligent Paradigms and Monitoring Natural and Artificial Complexities. Digital Technologies Research and Applications, 3(2), 176–182. https://doi.org/10.54963/dtra.v3i2.280

Authors

This contribution aims to shed light on the character of the observation-modeling link, and the role of the matching of its faces, in the management of different events. These include intelligent theories and digital tools, as well as the complexity of dynamic processes of natural and artificial phenomena. Such matching in the link could be practiced in offline or real-time mode. Offline mode mainly concerns the governance of intelligent theories and digital tools mimicking physical paradigms. Real-time mode concerns dynamic processes involving a significant degree of complexity. This exists in natural events like wildlife and human biology. It is also present in autonomous supervised artificial procedures, which involve complex real phenomena mathematically replicated by coupled multiphysics in the framework of matched real-virtual pairs. This communication involves analyses and discussions of these different pairings and their affected events, supported by examples allied to the literature. This corresponds to cases of intelligent theories, computational tools mimicking physics, real-time matching in natural wildlife and human biology, as well as twins supervising complex artificial procedures.

Keywords:

Observation‑Virtual Duo; Smart Theories; Intelligent Digital Tools; Natural Matched Processes; Com‑ plex Procedures; Matched Twins

References

  1. Bates, H.W. Contributions to an Insect Fauna of the Amazon Valley. Lepidoptera: Heliconidae. Trans. Linn. Soc. Lond. 1862, 23, 495–566.
  2. Schrö dinger, E. An undulatory theory of the mechanics of atoms and molecules. Phys. Rev. 1926, 28, 1049– 1070.
  3. Wineland, D.J.; Monroe, C.; Itano, W.M.; Leibfried, D.; King, B.E.; Meekhof, D.M. Experimental issues in coherent quantum-state manipulation of trapped atomic ions. J. Res. Natl. Inst. Stand. Technol. 1998, 103, 259.
  4. Brune, M.; Haroche, S.; Raimond, J.M.; Davidovich, L.; Zagury, N. Manipulation of photons in a cavity by dispersive atom-field coupling: Quantum-nondemolition measurements and generation of Schrödinger cat states. Phys. Rev. A 1992, 45, 5193–5214.
  5. The Nobel Prize in Physics 1957. The Nobel Foundation. Retrieved November 1, 2014.
  6. Wu, C.S.; Ambler, E.; Hayward, R.W.; Hoppes, D.D.; Hudson, R.P. Experimental Test of Parity Conservation in Beta Decay. Phys. Rev. 1957, 105, 1413–1415.
  7. Maxwell, J.C. A Treatise on Electricity & Magnetism; Dover Publications: New York, NY, USA, 1873; ISBN 0-486-60636-8 (Vol. 1) & 0-486-60637-6 (Vol. 2). Available online: https://www.aproged.pt/biblioteca/MaxwellI.pdf (accessed on 15 August 2024).
  8. Hall, E.H. On a new action of the magnet on electric currents. Am. J. Math. 1879, 2, 287–292.
  9. Laesecke, A. Through measurement to knowledge: The inaugural lecture of Heike Kamerlingh Onnes (1882). J. Res. Natl. Inst. Stand. Technol. 2002, 107, 261–277.
  10. Haykin, S. Neural Networks: A Guided Tour. Soft Comput. Intell. Syst. 2000, 71, 71–80.
  11. Burr, G.W.; Shelby, R.M.; Sebastian, A.; Kim, S.; Kim, S.; Sidler, S.; Virwani, K.; Ishii, M.; Narayanan, P.; Fumarola, A.; et al. Neuromorphic computing using non-volatile memory. Adv. Phys. X 2016, 2, 89–124.
  12. Castelvecchi, D. Quantum computers ready to leap out of the lab in 2017. Nature 2017, 541, 9–10.
  13. Fedorov, A.K.; Kiktenko, E.O.; Lvovsky, A.I. Quantum computers put blockchain security at risk. Nature 2018, 563, 465–467.
  14. Penny, W. Bayesian Models of Brain and Behaviour. ISRN Biomath. 2012, 2012, 785791.
  15. Krasich, K.; O’Neill, K.; De Brigard, F. Looking at Mental Images: EyeTracking Mental Simulation During Retrospective Causal Judgment. Cogn. Sci. 2024, 48, e13426.
  16. Hohwy, J. Priors in perception: Top-down modulation, Bayesian perceptual learning rate, and prediction error minimization. Conscious. Cogn. 2017, 47, 75–85.
  17. Grieves, M.; Vickers, J. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary Perspectives on Complex Systems; Springer: Cham, Switzerland, 2017; pp. 85– 113.
  18. Razek, A. Monitoring Complexity in Clean Energy Systems Applications. Clean Energy Sustain. 2024, 2, 10007.
  19. Razek, A. Coupled Models in Complex Systems Related to Smart Electromagnetic Energy Procedures. J. Mod. Green Energy 2024, 3, 2.
  20. He, B.; Bai, K.J. Digital twinbased sustainable intelligent manufacturing: A review. Adv. Manuf. 2021, 9, 1–21.
  21. Kamel Boulos, M.N.; Zhang, P. Digital twins: From personalised medicine to precision public health. J. Pers. Med. 2021, 11, 745.
  22. Gehrmann, C.; Gunnarsson, M. A digital twin based industrial automation and control system security architecture. IEEE Trans. Ind. Inf. 2020, 16, 669–680.
  23. Bhatti, G.; Mohan, H.; Raja Singh, R. Towards the future of smart electric vehicles: Digital twin technology. Renew. Sustain. Energy Rev. 2021, 141, 110801.
  24. Li, L.; Aslam, S.; Wileman, A.; Perinpanayagam, S. Digital twin in aerospace industry: A gentle introduction. IEEE Access 2022, 10, 9543–9562.
  25. Aydemir, H.; Zengin, U.; Durak, U. The digital twin paradigm for aircraft review and outlook. In Proceedings of the AIAA Scitech 2020 Forum, Orlando, FL, USA, 6–10 January 2020; Part F.
  26. Neethirajan, S.; Kemp, B. Digital twins in livestock farming. Animals 2021, 11, 1008.
  27. Razek, A. Coupled Models in Electromagnetic and Energy Conversion Systems from Smart Theories Paradigm to That of Complex Events: A Review. Appl. Sci. 2022, 12, 4675.
  28. Janssen, L.A.; Besselink, B.; Fey, R.H.; Van de Wouw, N. Modular model reduction of interconnected systems: A robust performance analysis perspective. Automatica 2024, 160, 111423.
  29. Kudela, J.; Matousek, R. Recent advances and applications of surrogate models for computations: A review. Soft Comput. 2022, 26, 13709–13733.