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Two–Stage Instrumental Variables Identification of Polynomial Wiener Systems with Invertible Nonlinearities

References Al-Duwaish, H., Karim, M. and Chandrasekar, V. (1996). Use of multilayer feedforward neural networks in identification and control of Wiener model, IEE Proceedings: Control Theory and Applications 143 (3): 255–258, DOI: 10.1049/ip-cta:19960376. Aljamaan, I., Westwick, D., Foley, M. and Chandrasekar, V. (2016). Identification of Wiener models in the presence of ARIMA process noise, IFAC-PapersOnLine 49 (7): 1008–1013, DOI: 10.1016/j.ifacol.2016.07.334. Ase, H. and Katayama, T. (2015). A subspace-based identification of two

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Finite Element Analysis of Influence of Non-homogenous Temperature Field on Designed Lifetime of Spatial Structural Elements under Creep Conditions

“continual fracture”, may be fulfilled efficiently using phenomenological scalar damage parameter, proposed in the works of V. Bolotin, L. Kachanov and Yu. Rabotnov [ 7 , 13 ]. This approach is developed and implemented for different loading conditions in the publications of Ukrainian scientists M. Bobyr, V. Golub, G. L’vov, Yu. Shevchenko [ 5 , 6 , 9 , 14 ] and foreign ones (Chen G., Hayhurst D., Lemaitre J., Murakami S., Otevrel I., etc.), particularly in [ 10 – 12 , 15 , 16 ]. It is shown that the damage accumulation process should be taking into account for

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Nonlinear waves in a simple model of high-grade glioma

biological literature, there is a vast range of values for the diffusion and proliferation coefficients. To carry out the estimations, we resort to the following value for the proliferation ρ = 0.2 day −1 , which is in the range [0.01–0.5] day −1 , taken from [ 28 , 54 ] and D = 0.05 mm 2 /day (which is in the range [0.0004–0.1] mm 2 /day) [ 39 ]. Finally, we take α = 1/10 day −1 , L = 85 mm, x 0 = 10 mm, c = c min = 2 ( 1 − β ) , $\begin{array}{} c=c_{\text{min}}=2\sqrt{(1-\beta)}, \end{array} $ M = 0.3, b = 0.005, a = ( c − c 2 − 4 ( 1 − β − V

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Energy characteristic of a processor allocator and a network-on-chip

. 589-596, DOI: 10.1109/ICDCS.1994.302473. Cardarilli, G., Re, A. D., Nannarelli, A. and Re, M. (2002). Power characterization of digital filters implemented on FPGA, IEEE International Symposium on Circuits and Systems (ISCAS 2002) , Vol. 5, pp. 801-804, DOI: 10.1109/ISCAS.2002.1010825. Chmaj, G., Zydek, D. and Koszalka, L. (2004). Comparison of task allocation algorithms for mesh-structured systems, Computer Systems Engineering, Theory & Applications, 4th Polish-British Workshop, Szklarska Poręba, Poland , pp. 39

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A biochemical multi-species quality model of a drinking water distribution system for simulation and design

Applications, Prentice Hall Int, Upper Saddle River, NJ. Bull, R.J., Reckhowb, D.A., Li, X., Humpaged, A.R., Joll, C. and Hrudeyc, S.E. (2011). Potential carcinogenic hazards of non-regulated disinfection by-products: Haloquinones, halo-cyclopentene and cyclohexene derivatives, n-halamines, halonitriles, and heterocyclic amines, Toxicology 286 (1): 1-19, DOI:10.1016/j.tox.2011.05.004. Chowdhury, S., Champagne, P. and McLellan, P.J. (2009). Models for predicting disinfection byproduct (DBP) formation in drinking waters: A chronological

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Stabilization of an epidemic model via an N-periodic approach

: 10.1080/00207160.2013.800864. Cantó, B., Coll, C. and Sánchez, E. (2014). A study on vaccination models for a seasonal epidemic process, Applied Mathematics and Computation 243: 152-160, DOI: 10.1016/j.amc.2015.05.104. Ding, D., Ma, Q. and Ding, X. (2014). An unconditionally positive and global stability preserving NSFD scheme for an epidemic model with vaccination, International Journal of Applied Mathematics and Computer Science 24(3): 635-646, DOI: 10.2478/amcs-2014-0046. Enatsu, Y., Nakata, Y. and Muroya, Y. (2012

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Controllability and Observability of Linear Discrete-Time Fractional-Order Systems

, Mathematical Problems in Engineering , bf 2006 (ID42489): 1-10. Cois O., Oustaloup A., Battaglia E. and Battaglia J.L. (2002). Non integer model from modal decomposition for time domain identification, 41st IEEE CDC'2002 Tutorial Workshop 2, Las Vegas, USA. Debeljković D. Lj., Aleksendrić M., Yi-Yong N. and Zhang Q. L. (2002). Lyapunov and non-Lyapunov stability of linear discrete time delay systems, Facta Universitatis, Series: Mechanical Engineering 1(9): 1147-1160. Dorĉák L., Petras I. and

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Heuristic algorithms for optimization of task allocation and result distribution in peer-to-peer computing systems

References Anderson, D.P. (2004). BOINC: A system for public-resource computing and storage, 5th IEEE/ACM International Workshop on Grid Computing, Pittsburgh, PA, USA , pp. 4-10. Arthur, D. and Panigrahy, R. (2006). Analyzing BitTorrent and related peer-to-peer networks, Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm, SODA’06, ACM, New York, NY, pp. 961-969, DOI: 10.1145/1109557.1109664. BOINC (2011). BOINC poject, Chmaj, G. and Walkowiak, K. (2008

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Estimation of feedwater heater parameters based on a grey-box approach

-486. Flynn, D. (2000). Thermal Power Plants. Simulation and Control , Institution of Electrical Engineers, London. Funkquist, J. (1997). Grey-box identification of a continuous digester a distributed parameter process, Control Engineering Practice (5): 919-930. Gewitz, A. (2005). EKF-based parameter estimation for a lumped, single plate heat exchanger Hangos, K. M. and Cameron, I. (2001). Process Modeling and Model Analysis , Academic Press, London

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Optimization on the Complementation Procedure Towards Efficient Implementation of the Index Generation Function

References Abraham, A., Jain, R., Thomas, J. and Han, S.Y. (2007). D-SCIDS: Distributed soft computing intrusion detection system, Journal of Network and Computer Applications 30(1): 81-98, DOI: 10.1016/j.jnca.2005.06.001. Bache, K. and Lichman, M. (2013). UCI Machine Learning Repository, University of California, Irvine, CA, Bazan, J.G., Szczuka, M.S. and Wróblewski, J. (2002). A new version of Rough Set Exploration System, in J.J. Alpigini et al. (Eds.), Rough Sets and Current

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