Заглавие:	High-performance technologies of modeling and identification of complex multi-component systems and processes
Автори:	Lupenko, Serhii Petryk, Mykhaylo Legrand, André Pierre Khimich, Oleksandr
Affiliation:	Politechnika Opolska
Bibliographic description (Ukraine):	Serhii Lupenko, Mykhaylo Petryk, André Pierre Legrand, Oleksandr Khimich. High-performance technologies of modeling and identification of complex multi-component systems and processes. Opole : Opole University of Technology, 2024. 202 p.
Дата на Публикуване:	2024
Date of entry:	15-Яну-2025
Издател:	Publishing House of the Opole University of Technology
Country (code):	PL
Place of the edition/event:	Opole
Number of pages:	202
URI:	http://elartu.tntu.edu.ua/handle/lib/47577
ISBN:	978-83-66903-75-3
ISSN:	1429-6063
Copyright owner:	© Copyright by Politechnika Opolska, 2024
References (International):	1. Lupenko S.A. Theoretical bases of modeling and processing of cyclic signals in information systems./S.A. Lupenko. – Lviv: "Magnolia 2006" Publishing House, 2016. P. 344. (Scientific monograph in Ukrainian). 2. Lupenko S.A. Theoretical bases of modeling and processing of cyclic signals in information systems. Second edition. Stereotyped. Lviv: "Magnolia 2006" Publishing House, 2020. p. 340. ISBN 978-617-574- 108-5. (Scientific monograph in Ukrainian). 3. Serhii Lupenko. Rhythm-adaptive statistical estimation methods of probabilistic characteristics of cyclic random processes. Digital Signal Processing, Volume 151, 2024, 104563, ISSN 1051-2004, https://doi.- org/10.1016/j.dsp.2024.104563. 4. Serhii Lupenko. The rhythm-adaptive Fourier series decompositions of cyclic numerical functions and one-dimensional probabilistic characte ristics of cyclic random processes. Digital Signal Processing, 2023, 104104, ISSN 1051-2004, https://doi.org/10.1016/j.dsp.2023.104104. 5. Lupenko, S. The Mathematical Model of Cyclic Signals in Dynamic Systems as a Cyclically Correlated Random Process. Mathematics 2022, 10, 3406. https://doi.org/10.3390/math10183406. 6. Lupenko, S.; Butsiy, R. Isomorphic Multidimensional Structures of the Cyclic Random Process in Problems of Modeling Cyclic Signals with Regular and Irregular Rhythms. Fractal Fract. 2024, 8, 203. https://- doi.org/10.3390/fractalfract8040203. 7. Gardner, W.A.; Napolitano, A.; Paura, L. Cyclostationarity: Half a cen tury of research. Signal Process. 2006, 86, 639–697. 8. Dragan, Y.P.; Yavorskii, I.N. Statistical analysis of periodic random processes. Otbor i Peredacha Inf. 1985, 71, 20–29. (In Russian) 9. Lupenko, S.; Butsiy, R.; Shakhovska, N. Advanced Modeling and Signal Processing Methods in Brain–Computer Interfaces Based on a Vector of Cyclic Rhythmically Connected Random 10. Lupenko, S. Statistical Methods for Common Processing of the Joint Rhythmically Connected Cyclic Random Processes. In Measuring and Computing Technics in Production Processes; Khmelnytsky University: Khmelnytsky, Ukraine, 2005; Volume 2, pp. 80–84. (In Ukrainian) 11. Lupenko, S.; Lytvynenko, I.; Sverstiuk, A.; Horkunenko, A.; Shelesto vskyi, B. Software for statistical processing and modeling of a set of syn chronously registered cardio signals of different physical nature. CEUR Workshop Proceedings, 2021, 2864, pp. 194–205. 77. Kärger Jörg, Ruthven, D.M., & Theodorou, D.N. (2012). Diffusion in nanoporous materials. Hoboken, USA: John Wiley & Sons. 78. Krishna, R. (2019). Thermodynamically consistent methodology for estimation of diffusivities of mixtures of guest molecules in microporous materials. ACS Omega, 4(8), 13520–13529. https://doi.org/10.1021/- acsomega.9b01873 79. Leclerc, S., Petryk, M., Canet, D., & Fraissard, J. (2012). Competitive diffusion of gases in a zeolite using proton NMR and a slice selection procedure. Catalysis Today, 187(1), 104–107. https://doi.org/10.1016/j.- cattod.2011.09.007 80. Sergienko I.V., Petryk M.R., Khimich O.M., Canet D., Mykhalyk D.M., Leclerc S, Fraissard J. The Mathematical Modelling of Mass-transfer in Media of Nano-porous Structure. Kyiv: National Academy of Sciences of Ukraine. Institute V.M. Gluschkov of Cybernetic, 2014, 210 p. ISBN 978-966-02-7098-5. (Scientific monograph in Ukrainian). 81. Petryk, M.R., Khimich, A., Petryk, M.M., & Fraissard, J. (2019). Experimental and computer simulation studies of dehydration on microporous adsorbent of natural gas used as motor fuel. Fuel, 239, 1324–1330. https://doi.org/10.1016/j.fuel.2018.10.134 82. Lebovka N., Petyk M., Tatochenko M. & Vygornitskii N. Two-stage random sequential adsorption of discorectangles and disks on a two dimensional surface. Physical Review E. Vol.108, 024109 (2023) doi.org/10.1103/PhysRevE.108.024109 83. Petryk, M, Leclerc, S., D Canet, S.I. V, Deineka, V.S., & Fraissard, J. (2015). The competitive diffusion of gases in a zeolite bed: NMR and slice procedure, modelling and identification of parameters. The Journal of Physical Chemistry C. ACS, 119(47), 26519–26525. https://doi.org/- 10.1021/acs.jpcc.5b07974 84. Petryk M., Leclerc S., Canet D., Fraissard J. Modeling of gas transport in a microporous solid using a sclice selection procedure: Application to the diffusion of benzene in ZSM5. Catalysis Today. Vol. 139, Issue 3, 234– 240 (2008). 85. Petryk, M.R., Khimich, A.N., & Petryk, M.M. (2018). Simulation of adsorption and desorption of hydrocarbons in nanoporous catalysts of neutralization systems of exhaust gases using nonlinear Langmuir isotherm. Journal of Automation and Information Sciences, 50(10), 18– 33. https://doi.org/10.1615/JAutomatInfScien.v50.i10.20 86. Petryk M.R., Boyko I.V., Khimich A.N, Petryk M.M. High-Performance Supercomputer Technologies of Simulation and Identification of Nanoporous Systems with Feedback for n-Component Competitive Adsorption. Cybernetics and System Analysis, Springer New York, Vol. 57(2), 316–328 (2021) 87. Petryk M., Boyko I., Fessard J.,Lebovka N. Modelling of non-isothermal adsorption of gases in nanoporous adsorbent based on Langmuir equilibrium. Adsorption. Vol 29, 141–150 (2023) doi.org/10.1007/- s10450-023-00389-9www.top500.org 88. Khimich A.N., Molchanov I.N., Mova V.I. etc. Numerical software of the MIMD computer by Inpark0m. – Kyiv: Nauk. Dumka, 2007. 89. Khimich A.N., Molchanov I.N., Popov A.V., Chistyakova T.V., Ya kovlev M.F. Parallel algorithms for solving problems of computa-tional mathematics. – Kyiv: Nauk. dumka, 2008. 90. Nikolaevskaya E., Khimich A., Chistyakova T. Programming with Mul tiple Precision. Studies in Computational Intelligence. Springer-Verlag. Berlin, Heidelberg, 2012. 91. Khimich О.М., Chistyakova T.V, Sidoruk V.A., Ershov P.S. Adaptive computer technologies for solving problems of computational and applied mathematics. Cybernetics and Systems Analysis, 2021, V. 57(6), 990–997. 92. Khimich O.M., Popov O.V., Chistyakov V.A., Kokhanivskyi V.O. Adap tive Algorithms for Solving Eigenvalue Problems in the Variable Com puter Environment of Supercomputers, 2023, V. 59(«3), 480–49 93. Khimich, A.N. Estimates of the total error in the solution of systems of linear algebraic equations for matrices of arbitrary ranks. Computer mathematics. 2002. No. 2, 41–49. 94. Khimich, A.N. Perturbation bounds for the least squares problem. Cybernetics and Systems Analysis. 1996. Vol. 32(3). 434–436. doi: 10.1007/BF02366509 95. Khimich, A.N., Popov, A.V., Polyanko, V.V. Algorithms of parallel computations for linear algebra problems with irregularly structured ma trices. Cybernetics and Systems Analysis. 2011. Vol. 47(6), 973–985. doi: 10.1007/s10559-011-9377-4 96. Khimich, A.N., Yakovlev, M.F. On the solution of systems with matrices of incomplete rank. Computer Mathematics. 2003. No. 1, 1–15. 97. Molchanov, I.N., Popov, A.V. & Khimich, A.N. Algorithm to solve the partial eigenvalue problem for large profile matrices. Cybernetics and Systems Analysis. 1992. Vol. 28(3), 281–286. https://doi.org/10.1007/BF01126215 98. Popov, A.V., Khimich, A.N. Research and solution of the first basic problem of the theory of elasticity. Computer Mathematics. 2003. No. 2, 105–114. 99. Popov, A.V. On an effective method for solving incorrect problems with sparse matrices. Theory of Optimal Solutions.2013, 77-81. 100. Gorodetsky A.S., Evzerov I.D. Computer models of structures. Kyiv: FACT, 2007. 394 p. 101. Popov A.V., Chistyakov O.V. On the effectiveness of algorithms with multilevel parallelism. Physico-mathematical modeling and information technologies. 2021. Issue 33, 133–137. doi: 10.15407/fmmit2021.33.133 102. Popov O.V. On parallel algorithms for factorization of sparse matrices. Computer mathematics. Sat. science labor. 2013. Issue 2, 115–124. http://dspace.nbuv.gov.ua/handle/123456789/84755 103. Khimich O.M., Sydoruk V.A. The use of mixed bit rate in mathematical modeling. Mathematical and computer modeling. Series: Physical and mathematical sciences. 2019. Issue 19, 180–187. http://dspace.- nbuv.gov.ua/handle/123456789/84755 104. Khimich O.M., Sydoruk V.A. A tiling hybrid algorithm for the factorization of structurally symmetric matrices. Theory of optimal solutions. Coll. of science works. 2017, 125–132. 105. Alan George, Joseph W-H Liu Computer Solution of Large Sparse Positive Definite Systems. Prentice-Hall, 1981.. 106. Khimich O.M., Popov A.V. Solving Ill-Posed Problems of the Theory of Elasticity Using High-Performance Computing Systems. Cybernetics and Systems Analysis, Vol. 59(5), 2023, 743–752. doi: 10.1007/s10559- 023-00610-1 12. Lupenko S.A. Mathematical modeling and methods of processing synchronously registered heart signals using cyclic rhythmically related random processes/S.A. Lupenko, А.S. Sverstyuk // Lviv: "Magnolia 2006" Publishing House - 2006, 2020. p. 148. ISBN 978-617-574-184-9. (Scientific monograph in Ukrainian). 13. Lupenko S. Model Of Signals With Double Stochasticity In The Form Of A Conditional Cyclic Random Process / Lytvynenko I., Stadnyk N., Zozulia A.//The 2nd International Workshop Information – Commu nication Technologies & Embedded Systems, Vol-2762. 12 November, 2020 Mykolaiv, Ukraine. pp. 201–208. ISSN 1613-0073. 14. Niroshana, S.M.I.; Kuroda, S.; Tanaka, K.; Chen, W. Beat-wise segmentation of electrocardiogram using adaptive windowing and deep neural network. Sci. Rep. 2023, 13, 11039. https://doi.org/10.1038/- s41598-023-37773-y 15. Chen, Z.; Wang, M.; Zhang, M.; Huang, W.; Gu, H.; Xu, J. Post processing refined ECG delineation based on 1D-UNet. Biomed. Signal Process. Control 2023, 79 (Part 1), 104106. https://doi.org/10.1016/- j.bspc.2022.104106 16. Belkadi, M.A.; Daamouche, A.; Melgani, F.A deep neural network approach to QRS detection using autoencoders. Expert Syst. Appl. 2021, 184, 115528. https://doi.org/10.1016/j.eswa.2021.115528 17. Wang, Z.; Wang, J.; Chen, M.; Yang, W.; Fu, R. Deep Regression Network With Sequential Constraint for Wearable ECG Characteristic Point Location. IEEE Access 2023, 11, 63487–63495. https://doi.- org/10.1109/ACCESS.2023.3288700 18. Karri, M.; Annavarapu, C.S.R. A real-time embedded system to detect QRS-complex and arrhythmia classification using LSTM through hybridized features. Expert Syst. Appl. 2023, 214, 119221. https://doi.- org/10.1016/j.eswa.2022.119221 19. Lupenko, S. The problem of interpolation of the rhythm function of a cyclic function with a known zone structure. In Electronics and Managements Systems; National Aviation University: Kyiv, Ukraine, 2007; pp. 27–35. (In Ukrainian). 20. Lytvynenko, I.; Lupenko, S.; Onyskiv, P. Method of Evaluation of Dis crete Rhythm Structure of Cyclic Signals with the Help of Adaptive In terpolation. In Proceedings of the 2020 IEEE 15th International Scientific and Technical Conference on Computer Sciences and Information Tech nologies, CSIT 2020, Zbarazh, Ukraine, 23–26 September 2020; pp. 155–158. 21. Lupenko, S. Peculiarities of Discretization of Cyclic Functions. In Mea suring and Computing Technics in Production Processes; Khmelnytsky University: Khmelnytsky, Ukraine, 2006; Volume 1, pp. 59–65. (In Ukrainian). 21. Lytvynenko I. Modeling and Methods of Statistical Processing of a Vector Rhytmocardiosignal / Lytvynenko, I., Lupenko, S., Onyskiv, P., Zozulia, A.//Open Bioinformatics Journal, 2021, 14(1), pp. 73–86. 23. Lupenko, S. Scale transformation operator in the tasks of modeling and analysis of cyclic signals. Sci. J. Ternopil State Tech. Univ. 2007, 12, 141–152. (In Ukrainian). 24. Lupenko, S.; Butsiy, R. Express Method of Biometric Person Authentication Based on One Cycle of the ECG Signal. Sci. J. TNTU (Tern.), 2024, 113, 100–110. doi:https://doi.org/10.33108/visnyk_tntu 2024.01.100 25.Vakulenko, D., Vakulenko, L., Zaspa, H., Lupenko, S., Stetsyuk, P., Stovba, V. Components of Oranta-AO software expert system for innovative application of blood pressure monitors. Journal of Reliable Intelligent Environments, 2022. https://doi.org/10.1007/s40860-022- 00191-4. 26. García-González, M.A.; Argelagós-Palau, A. Data from: Combined measurement of ECG, breathing, and Seismocardiograms. PhysioNet 2013, 101, e215–e220. https://doi.org/10.13026/C2KW23 27. Lupenko, S.; Shcherbak, L. Constructive mathematical model of cardiac signals based on linear periodic random processes and fields. Sci. J. Ternopil State Tech. Univ. 2000, 5, 101–110. (In Ukrainian). 28. Haritopoulos, M.; Roussel, J.; Capdessus, C.; Nandi, A.K. Cyclosta tionarity-Based Estimation of the Foetus Subspace Dimension from ECG Recordings. In Advances in Intelligent Systems and Computing; Springer: Cham, Switzerland, 2014; pp. 726–729. 29. Mihandoost, S.; Chehel Amirani, M. Cyclic spectral analysis of electrocardiogram signals based on GARCH model. Biomed. Signal Process. Control 2017, 31, 79–88. 30. Zhang, M.; Haritopoulos, M.; Nandi, A.K. Fetal ECG subspace estimation based on cyclostationarity. In Proceedings of the 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary, 28 August–2 September 2016; pp. 2060–2064. 31. Chang, Y.-C. Deep-Learning Estimators for the Hurst Exponent of Two Dimensional Fractional Brownian Motion. Fractal Fract. 2024, 8, 50. https://doi.org/10.3390/fractalfract8010050 32. Ran, J.; Ostoja-Starzewski, M.; Povstenko, Y. Mach Fronts in Random Media with Fractal and Hurst Effects. Fractal Fract. 2021, 5, 229. https://doi.org/10.3390/fractalfract5040229 33. Park, G. Analysis of authentication technology using bio-signals and con struction of bio-signal database. Korea Internet and Security Agency, Technical Report, Jan. 2016. 34. Kim, J.; Park, G. Personal authentication technology using biosignals and DB construction. TTA J. 2016, 165, 41–46. 35. Vural, E.; Simske, S.; Schuckers, S. Verification of individuals from ac celerometer measures of cardiac chest movements. In Proceedings of the 2013 International Conference of the BIOSIG, Darmstadt, Germany, 5–6 September 2013; pp. 1–8, INSPEC Accession Number: 13826572. 36. Guo, H.; Cao, X.; Wu, J.; Tang, J. Ballistocardiogram-based person iden tification using correlation analysis. In Proceedings of the World Con gress on Medical Physics and Biomedical Engineering, Beijing, China, 26–31 May 2012; Volume 39, pp. 570–573. doi:https://doi.org/- 10.1007/-978-3-642-29305-4_149 37. Plataniotis, K.; Hatzinaks, D.; Lee, J. ECG biometric recognition without fiducial detection. In Proceedings of the Biometrics Symposium (BSYM '06), Baltimore, MD, USA, 19–21 September 2006; pp. 6–11. doi: https://doi.org/10.1109/BCC.2006.4341628 38. Wubbeler, G.; Stavridis, M.; Kreiseler, D.; Bousseljot, R.; Elster, C. Ver ification of humans using the electrocardiogram. Pattern Recognit. Lett. 39. Pereira, T.M.C.; Conceição, R.C.; Sebastião, R. Initial Study Using Elec trocardiogram for Authentication and Identification. Sensors 2022, 22, 2202. doi:https://doi.org/10.3390/s22062202 40. Pawuś, D.; Paszkiel, S. The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery–Pilot Study. Appl. Sci. 2022, 12, 2161. https://doi.org/10.3390/app12042161. 41. Xu, B.; Li, W.; Liu, D.; Zhang, K.; Miao, M.; Xu, G.; Song, A. Continu ous Hybrid BCI Control for Robotic Arm Using Noninvasive Electroen cephalogram, Computer Vision, and Eye Tracking. Mathematics 2022, 10, 618. https://doi.org/10.3390/math10040618. 42. Murphy, D.P.; Bai, O.; Gorgey, A.S.; Fox, J.; Lovegreen, W.T.; Burkhardt, B. W.; Atri, R.; Marquez, J. S.; Li, Q.; Fei, D. -Y. Electroen cephalogram-Based Brain–Computer Interface and Lower-Limb Pros thesis Control: A Case Study. Frontiers in Neurology. 2017, 8. https://doi.org/10.3389/fneur.2017.00696. 43. Alisson, C.; Víctor, A.; Francis, R. L.; Enrique, P.; Diego P. -O. BCI System using a Novel processing Technique Based on Electrodes Selec tion for Hand Prosthesis Control. Escuela Superior Politécnica del Lito ral, IFAC-PapersOnLine. 2021, 54, 364–369. https://doi.org/-10.1016/- j.ifacol.2021.10.283. 44. Rosca, S. D.; Leba, M. Design of a brain-controlled video game based on a BCI system. In MATEC Web of Conferences; EDP Sciences: Les Ulis. 2019, 290, 01019. https://doi.org/10.1051/matecconf/201929001019. 45. Ahn, M.; Lee, M.; Choi, J.; Jun, S.C. A Review of Brain-Computer In terface Games and an Opinion Survey from Researchers, Developers and Users. Sensors. 2014, 14, 14601–14633. https://doi.org/10.3390/- s140814601. 46. Pawuś, D.; Paszkiel, S. BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection. Appl. Sci. 2022, 12, 10385. https://doi.org/- 10.3390/app122010385. 47. Paszkiel, S.; Rojek, R.; Lei, N.; Castro, M.A. A Pilot Study of Game De sign in the Unity Environment as an Example of the Use of Neurogaming on the Basis of Brain–Computer Interface Technology to Improve Con centration. NeuroSci 2021, 2, 109–119. https://doi.org/-10.3390/neuro sci2020007 48. Zając, M.; Paszkiel, S. Using Brain-Computer Interface Technology for Modeling 3D Objects in Blender Software. Journal of Automation, Mo bile Robotics and Intelligent Systems 2021, 14, 18–24. https://doi.- org/10.14313/JAMRIS/4-2020/40 49. Paszkiel, S. Using Neural Networks for Classification of the Changes in the EEG Signal Based on Facial Expressions. In: Analysis and Classifi cation of EEG Signals for Brain–Computer Interfaces. Studies in Com putational Intelligence. Springer 2020, 852, 41–69. https://doi.- org/10.1007/978-3-030-30581-9_7 50. Paszkiel, S.; Dobrakowski, P. The Use of Multilayer ConvNets for the Purposes of Motor Imagery Classification. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Automation 2021: Recent Achievements in Automation, Robotics and Measurement Techniques. Advances in Intel ligent Systems and Computing. Springer 2021, 1390, 10–19. https://doi.org/10.1007/978-3-030-74893-7_2 51. Sarmiento, L.C.; Villamizar, S.; López, O.; Collazos, A.C.; Sarmiento, J.; Rodríguez, J.B. Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods. Sensors 2021, 21, 6503. https://- doi.org/10.3390/s21196503 52. Lupenko, S.; Butsiy, R.; Volyanyk, O.; Stadnyk, N. Advanced Signal Processing and Classification of EEG Patterns in Neurointerface Systems. In Proceedings of the 3rd International Workshop on Information Technologies: Theoretical and Applied Problems (ITTAP 2023), Ternopil, Ukraine, Opole, Poland, 22–24 November 2023, 3628, 156–164. 53. Butsiy, R.; Lupenko, S. Comparison of Modern Methods of Classi fication of EEG Patterns for Neurointerface Systems. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems; Springer, Singapore, 2022, 465, 345– 354. doi:https: //doi.org/10.1007/978-981-19-2397-5_32 54. Haubenberger, Dietrich, and Mark Hallett. "Essential tremor." New England Journal of Medicine 378.19 (2018): 1802-1810. 55. Legrand A.P., Rivals I., Richard A., Apartis E., Roze E., Vidailhet M., Meunier S., Hainque E. New insight in spiral drawing analysis methods – Application to action tremor quantification. J Clinical Neuro physiology, 128 (10) (2017): 1823–1834. 56. Szumilas, Mateusz, et al. "A multimodal approach to the quantification of kinetic tremor in Parkinson’s disease." Sensors 20.1 (2019): 184 57. J.F. Cavanagh, P. Kumar, A.A. Mueller, S.P. Richardson, and A. Mueen, “Diminished EEG habituation to novel events effectively classifies Parkinson’s patients,” Clinical Neurophysiology, vol. 129(2), (2018), 409–418. doi: 10.1016/j.clinph.2017.11.023. 58. Roth, Navit, Orit Braun-Benyamin, and Sara Rosenblum. "Drawing Direction Effect on a Task’s Performance Characteristics among People with Essential Tremor." Sensors 21.17 (2021): 5814. 59. Kim, Christine Y., et al. "Repeated spiral drawings in essential tremor: a possible limb-based measure of motor learning." The Cerebellum 18 (2019): 178–187. 60. Gil-Martín, Manuel, Juan Manuel Montero, and Rubén San-Segundo. "Parkinson’s disease detection from drawing movements using convolu tional neural networks." Electronics 8.8 (2019): 907 61. Khimich O.M.,Petryk M.R., Mykhalyk D.M., Boyko I.V., Popov O.V., Sydoruk V.A. Methods of mathematical modeling and identification of complex processes and systems on the basis of high-performance calculations. Kyiv: National Academy of Sciences of Ukraine. Institute V.M. Gluschkov of Cybernetic, 2019, 176 p. (Scientific monograph in Ukrainian) 62. Mudryk I., Petryk M. High-performance modeling, identification and analysis of heterogeneous abnormal neurological movement’s para meters based on cognitive neuro feedback-influences. Innovative Solutions in Modern Science. 2021. New York: TK Meganom LLC. V1(45). 2021, 235–249. Doi: 10.26886/2414-634X.1(45)2021.16. 63. M. Petryk, M. Bachynskyi, V.Brevus, I.Mudryk, D. Mykhalyk. Analysis technology of neurological movements considering cognitive feedback influences of cerebral cortex signals. Proceedings of the 2nd International Workshop on Information Technologies: Theoretical and Applied Problems (ITTAP 2022). Ternopil, Ukraine, 22–24 November 2022, CEUR Workshop Proceedings, 2022, 45–54. 64.] Lenyuk M., Petryk M. Integral Fourier, Bessel transformations with spectral parameters in problems of mathematical modeling in heterogeneous media. Кyiv: Naukova Dumka, 2000, 3726 p. ISBN 966- 00-0649-7. (Scientific monograph in Ukrainian). 65. Bachynskyi, M., Petryk, M., Brevus, V., Mudryk, I., Glova, B. 3D-hybrid mathematical model for analysis of abnormal neurological movements for the purposes of diagnosis and treatment of limb tremor. 3rd Interna tional Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2023. Ternopil. 22–23 November 2023. CEUR Workshop Proceedings, 2023, 183–196. 66. Ross S. L. Differential Equations, 3rd Ed. Wiley. 2008, 816 p. 67. G. Doetsch Handbuch der Laplace-Transformation: Band I: Theorie der Laplace-Transformation. Springer-Verlang, Basel, 2013. 68. Staines, J. The Heaviside Operational Calculus: The Laplace Transform for Electrical Engineers. Amazon, CreateSpace Independent Publishing Platform, Scotts Valley, California, 2013. 69. Gray, A. and Mathews, G. B. A Treatise on Bessel Functions and Their Applications to Physics, 2nd ed. Ann Abor, Michigan: Scolary Pub. University of Michigan, 2008 70. Euro 5 and Euro 6 standards: reduction of pollutant emissions from light vehicles. Available at: europa.eu/legislation_summaries/environment/ air_pollution/l28186_es.htm (May 5, 2010) 71. Puertolas B., Navarro M.V., Lopez J.M., Murillo R., Mastral A.M., Garcia T. Modelling the heat and mass transfers of propane onto a ZSM 5 zeolite. Separation and Purification Technology. 2012, 86,127-36. 72. Feng, C., Jiaqiang, E., Han, W., Deng, Y., Zhang, B., Zhao, X., & Han, D. (2021). Key technology and application analysis of zeolite adsorption for energy storage and heat-mass transfer process: A review. Renewable and Sustainable Energy Reviews, 144, 110954. https://doi.org/10.1016/- j.rser.2021.110954 73. Qian, Z., Wei, L., Mingyue, W., & Guansheng, Q. (2021). Application of amine-modified porous materials for CO2 adsorption in mine confined spaces. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 629, 127483. https://doi.org/10.1016/j.colsurfa.2021.127483 74. Wang, Y., Wang, C., Wang, L., Wang, L., & Xiao, F.-S. (2021). Zeolite fixed metal nanoparticles: new perspective in catalysis. Accounts of Chemical Research, 54(11), 2579–2590. https://doi.org/10.1021/acs.- accounts.1c00074 75. Sanchez-Varretti, F. O., Bulnes, F. M., & Ramirez-Pastor, A. J. (2019). Adsorption of interacting binary mixtures on heterogeneous surfaces: theory, Monte Carlo simulations and experimental results. Adsorption, 25(7), 1317–1328. https://doi.org/10.1007/s10450-019-00093-7 76. Van Assche, T.R.C., Baron, G.V, & Denayer, J.F. M. (2018). An explicit multicomponent adsorption isotherm model: Accounting for the size effect for components with Langmuir adsorption behavior. Adsorption, 24(6), 517–530.https://doi.org/10.1007/s10450-018-9962-1
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