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. 4, pp. 25{34, 1987. 12. J. Kennedy and R. Eberhart, Particle swarm optimization, in Proceedings of ICNN'95 International Conference on Neural Networks, vol. 4, pp. 1942{1948, New York: IEEE, 1995. 13. E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Artiffcial Systems. New York: Oxford University Press, 1999. 14. J. A. Carrillo, Y.-P. Choi, C. Totzeck, and O. Tse, An analytical framework for a consensus-basedglobal optimization method, ArXiv e-prints, January 2016. 15. M. Brambilla, E. Ferrante, M. Birattari, and M. Dorigo, Swarm

.-B. (2009). Bayesian networks based rare event prediction with sensor data, Knowledge-Based Systems 22 (5): 336-343. Cherniack, M., Balakrishnan, H., Balazinska, M., Carney, D., Cetintemel, U., Xing, Y. and Zdonik, S. (2003). Scalable distributed stream processing, Proceedings of CIDR-03: 1st Biennial Conference on Innovative Database Systems, Asilomar, CA, USA . Considine, J., Li, F., Kollios, G. and Byers, J. (2004). Approximate aggregation techniques for sensor databases, ICDE-04: 20th IEEE International Conference on Data Engineering, Boston, MA, USA , pp. 449

(2): 25-31. Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., de Velde, W. V. and Wielinga, B. (2000). Knowledge Engineering and Management: The Common-KADS Methodology , MIT Press, Cambridge, MA. Wagner, G. (2002). How to design a general rule markup language?, in R. Tolksdorf and R. Eckstein (Eds.) XML Technologien für das Semantic Web—XSW 2002, Proceedings zum Workshop, 24-25 Juni 2002, Berlin , Lecture Notes in Informatics, Vol. 14, GI, Bonn, pp. 19-37. Wagner, G., Giurca, A. and Lukichev, S. (2006). A usable interchange format for

, vol. 25, no. 03, pp. 565-585, 2015. 19. J. Lorenz, A stabilization theorem for dynamics of continuous opinions, Physica A: Statistical Me- chanics and its Applications, vol. 355, no. 1, pp. 217-223, 2005. 20. J. M. Hendrickx, G. Shi, and K. H. Johansson, Finite-time consensus using stochastic matrices with positive diagonals, IEEE Transactions on Automatic Control, vol. 60, no. 4, pp. 1070-1073, 2015. 21. L. Radford, Three key concepts of the theory of objectification: Knowledge, knowing, and learning, Journal of Research in Mathematics Education, vol. 2, no. 1, pp

-drifting data streams using ensemble classifiers, Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’03, Washington, DC, USA, pp. 226-235. Weinberg, A.I. and Last, M. (2017). Interpretable decision-tree induction in a big data parallel framework, International Journal of Applied Mathematics and Computer Science 27(4): 737-748, DOI: 10.1515/amcs-2017-0051. Zliobaite, I., Bifet, A., Pfahringer, B. and Holmes, G. (2014). Active learning with drifting streaming data, IEEE Transactions on Neural Networks and Learning Systems 25

on CD-ROM. Sanctis G. D., Sarti A. and Tubaro S. (2003). Automatic synthesis strategies for object-based dynamical physical models in musical acoustics, Proceedings of the Conference on Digital Audio Effects (DAFx-03) , London, UK, pp. DAFX-1-DAFX-6. Sarti A. and Sanctis G. D. (2006). Memory extraction from dynamic scattering junctions in wave digital structures, Signal Processing Letters 13 (12): 729-732. Smith J. O. (2007). Physical audio signal processing: For virtual musical instruments and digital audio effects, Technical report, Stanford University Center

and Computational Methods in Genomic Sciences. 38. A. Ciliberto, F. Capuani, and J. J. Tyson, Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation, PLOS Computational Biology , vol. 3, pp. 1–10, 03 2007. 39. M. G. Pedersen and A. M. Bersani, Introducing total substrates simplifies theoretical analysis at non-negligible enzyme concentrations: pseudo first-order kinetics and the loss of zero-order ultrasensitivity, Journal of Mathematical Biology , vol. 60, no. 2, pp. 267–283, 2010. 40. C. Kwang-Hyun, S. Sung-Young, K

, A.P. and Yigit, A.S. (2003). Fully coupled vibrations of actively controlled drillstrings, Journal of Sound and Vibration 267 (5): 1029–1045, DOI: 10.1016/S0022-460X(03)00359-6. Davis, J.E., Smyth, G.F., Bolivar, N. and Pastusek, P.E. (2012). Eliminating stick-slip by managing bit depth of cut and minimizing variable torque in the drillstring, IADC/SPE Drilling Conference and Exhibition, San Diego, CA, USA , pp. 402–410, DOI: 10.2118/151133-MS. Farrar, D.E. and Glauber, R.R. (1967). Multicollinearity in regression analysis: The problem revisited, The Review of

, Proceedings of the European Control Conference ECC99, Karlsruhe, Germany , F0256. Byrski, W. and Fuksa, S. (2000). Optimal identification of continuous systems and a new fast algorithm for on line mode, Proceedings of the International Conference on System Identification, SYSID2000, Santa Barbara, CA, USA , PM 2-5. Byrski, W. and Fuksa, S. (2001). Stability analysis of CLTI state feedback system with simultaneous state and parameter identification, Proceedings of the IASTED International Conference on Applied Simulation and Modelling, ASM01, 2001, Marbella, Spain , pp. 7

References Amenta, N., Bern, M. and Kamvysselis, M. (1998). A new Voronoi-based surface reconstruction algorithm, Proceedings of the 25th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH’98), Orlando, FL, USA, pp. 415-421. Attene, M., Campen, M. and Kobbelt, L. (2013). Polygon mesh repairing: An application perspective, ACM Computing Surveys 45(2): 15:1-15:33. Bajaj, C., Bernardini, F. and Xu, G. (1995). Automatic reconstruction of surfaces and scalar fields from 3D scans, Proceedings of the 22nd Annual Conference on Computer Graphics