Skip to content
KPI Science News
ISSN 2617-5509 · e‑ISSN 2663-7472 Open Access · CC BY 4.0
Vol. 142 · Issue 1 · 2026 Mar 30, 2026 article

A METHOD FOR ANALYZING MULTICOMPONENT COMPLEX SYSTEMS BASED ON MULTIDIMENSIONAL MATRICES OF ADJACENCY AND ENTROPY RELATIONS BETWEEN SYSTEM COMPONENTS

VV
Vyacheslav Voloshyn Corresponding "Priazovsky State Technical University", Dnipro, Ukraine Ukraine
IT
Illia Tkalenko AMCOM Engineering s., r., o., Prague, , Czechia Ukraine
PublishedMar 30, 2026
KPISN 1 VOL 142 · 1
VOL 142 · ISS 1 · 2026 View issue

Abstract

Background. Existing methods for analysing multicomponent complex systems account only weakly for the relative incompatibility of system parameters, in particular the interplay between physical, chemical, and other mechanisms and the configurational, thermodynamic, and additional properties of the system. The challenge of achieving a stable, practically formalised investigation of the development of complex systems—through a mathematically simplified and conceptually transparent set of indicators enabling comprehensive analysis—remains unresolved. Objective. The development of an alternative method for maximally comprehensive and simplified analysis of multicomponent complex systems, based on their functionality, structural organisation, and inherent properties. Methods. A method for analysing complex systems by means of multidimensional entropy-structured adjacency matrices is proposed as a derivative model for describing their multicomponent nature. Entropy and its variation, which reflects changes in the state of the system itself, are proposed as thermodynamic indicators of multicomponent systems. Results. The first essential feature of the model lies in the partitioning of the total entropy of the entire system into superpositional components corresponding to the parameters of the system’s properties, which forms the basis for a certain universality of the method. The second significant distinction of the model is associated with its multilayered nature within the adjacency matrix. This enables the investigation of a wide spectrum of incomparable properties of a complex system, including its structural organisation, informational characteristics, functionality, physico-chemical features, surface phenomena—including the boundary with the supersystem—as well as its capacity for thermodynamic nonequilibrium and kinetic behaviour, depending on the nature of the system itself. The validation and advantages of the model are demonstrated through several practical examples from the field of metallurgical waste recycling, where mixtures are considered as multicomponent systems used in the production of metallurgical briquettes as secondary raw materials for this industrial sector. Conclusion. A qualitatively new approach has been developed for the study of multicomponent complex systems by means of a multidimensional entropy-containing adjacency matrix, which describes interrelations through the total entropy decomposed into individual components. Its relative universality and practical applicability are demonstrated.

Keywords

References

  1. Mitchell M. Complexity: A Guided Tour. New York/Oxford, Oxford University Press, 2009. 368 p. https://doi.org/10.1093/oso/9780195124415.001.0001
  2. Newman M. E. J. Networks: An Introduction. Oxford University Press, Oxford, 2010. - pp. 371–412. - https://global.oup.com/academic/product/networks-9780198805090
  3. Kivelä M., Porter M. A. at. al, Multilayer Networks. Journal of Complex Networks, 2014, - pp. 203–248. https://doi.org/10.1093/comnet/cnu016 .
  4. Laloux, L., Cizeau, P., Bouchaud, J. P., Potters, M., Random Matrix Theory and Financial Correlations. Random Matrix Theory, Hamburg, 2013. - pp. 1–32. https://doi.org/10.1142/S0219024900000255 .
  5. Kindermann R., Snell J. L., Markov Random Fields and Their Applications. American Mathematical Society, Providence, 1980, - pp. 39–82. - https://books.google.com/books?id=Px9QAQAAIAAJ
  6. Cichocki, A., Mandic, D. at. al. Tensor Decompositions for Signal Processing Applications. IEEE Signal Processing Magazine, 2015. - pp. 145–163. - https://arxiv.org/abs/1403.4462
  7. Shannon C. E. A Mathematical Theory of Communication. Bell System Technical Journal, Vol. 27, 1948. - pp. 379–4231, 623–6562. - https://people.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf
  8. Cover T. M., Thomas J. A. Elements of Information Theory. John Wiley & Sons (2nd ed.), 2006. 461 p. - https://search.worldcat.org/de/title/Elements-of-information-theory/oclc/70862892
  9. Jaynes E. T. Papers on Probability, Statistics and Statistical Physics. Kluwer Academic Publishers/Springer, Dordrecht, Netherlands, vol. 158,
  10. 458 p. - https://link.springer.com/book/10.1007/978-94-017-1577-3
  11. Edwards, S. F., Oakeshott, R. B. S. Theory of Powders. Physica A: Statistical Mechanics and its Applications, Vol. 157, 1989. - pp. 1080–1090. https://doi.org/10.1016/0378-4371(89)90035-6
  12. Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. Multilayer Networks. Journal of Complex Networks, 2014. - pp. 258-274. https://doi.org/10.1093/comnet/cnu016
  13. Jaynes E. T. Probability Theory: The Logic of Science. Cambridge University Press, Cambridge, United Kingdom, 2003. 727 p. - https://www.cambridge.org/core/books/probability-theory/0A76DBF47F7C3D3657174B0A322BE86F
  14. Voloshyn V.S., Tkalenko I. V. Metodyka otsinky vzaiemodii metalurhiinykh vidkhodiv pry yikh retsyklinhu. [Methodology for Assessing the Interaction of Metallurgical Wastes during Their Recycling] Vcheni zapysky Tavriiskoho universytetu im. V. I. Vernadskoho. T.36(75), № 4, Ch.1, 2025.- S.191-199. https://doi.org/10.32782/2663-5941/202 5.4.1/24
  15. Voloshyn V. S. Vykorystannia metalurhiinykh vidkhodiv dlia vyrobnytstva heopolimeriv. [Use of metallurgical waste for the production of geopolymers] Ekolohichna bezpeka: problemy i shliakhy vyrishennia. Zb. nauk. statei UkrNDIEP, Kharkiv, 2025. - S.109-115 - http://www.niiep.kharkov.ua/sites/default/files/sbornik_konf_2025.pdf
  16. Vitikka О., M. Iljana M., Heikkilä F., Tkalenko I., Kovtun J., Koriuchev N., Shehovsov D., Fabritius T. Effect of Biocarbon Addition on Metallurgical Properties of Mill Scale-Based Auger Pressing Briquettes. ISIJ International. The Iron and Steel Institute of Japan; Vol. 64, № 6, 2024. - pp. 964–977.
  17. https://doi.org/10.2355/isijinternational.ISIJINT-2024-0266
  18. Shuo Li, Zihan Zhou, Guo Hu, Chuanqin Yao, Fujun Niu. Jun Wu Using One-Part Geopolymer in Stabilizing High-Water-Content Soft Clay: Towards an Eco-Friendly and Cost-Effective Solution. Buildings, T.5, Vol. 3, 2025. - pp. 477-501. https://doi.org/10.3390/buildings15040289
§ 06 — Related

Similar articles in this journal

Related peer-reviewed studies published in this journal.
View all issues

AN APPROACH TO CONSTRUCTING AN INFORMATIVE FEATURE SPACE OF TECHNICAL INDICATORS FOR FINANCIAL PROCESSES BASED ON NEURAL NETWORK MODELS

Roman Tertychnyi, Denys Chuprin, Petro Bidyuk

Background. Financial time series are characterized by nonlinearity, non-stationarity, and a high level of noise, which significantly complicates their modeling and…

#informative feature space construction approach #technical indicators #Parkinson volatility #neural networks
10.20535/kpisn.2026.3.359610

Similar Articles

1-10 of 63

You may also start an advanced similarity search for this article.