[1. Almarashi, A., M. Badr, M. Elgarhy, F. Jamal, C. Chesneau. Statistical Inference of the Half–Logistic–Inverse–Rayleigh Distribution. – Entropy, Vol. 22, 2020.10.3390/e22040449]Search in Google Scholar
[2. Cordeiro, G. M., M. Alizadeh, E. P. R. D. Marinho. The Type I Half-Logistic Family of Distributions. – J. Stat. Comput. Simul., Vol. 86, 2016, pp. 707-728.10.1080/00949655.2015.1031233]Search in Google Scholar
[3. Kyurkchiev, N., A. Iliev, A. Rahnev. On the Verhulst Growth Model with “Polynomial Variable Transfer”. Some Applications. – International Journal of Differential Equations and Applications, Vol. 19, 2020, No 1, pp. 15-32.]Search in Google Scholar
[4. Kyurkchiev, N., A. Iliev, A. Rahnev. On the Half-Logistic Model with “Polynomial Variable Transfer”. Application to Approximate the Specific “Data CORONA VIRUS”. – International Journal of Differential Equations and Applications, Vol. 19, 2020, No 1, pp. 45-61.]Search in Google Scholar
[5. Kyurkchiev, N. Selected Topics in Mathematical Modeling: Some New Trends (Dedicated to Academician Blagovest Sendov (1932-2020)). LAP LAMBERT Academic Publishing, 2020. ISBN: 978-620-2-51403-3.]Search in Google Scholar
[6. Kyurkchiev, N., A. Iliev, A. Rahnev, T. Terzieva. On the Extended Half-Logistic Model by H. Bakouch with “Polynomial Variable Transfer”. Application to Approximate the Specific “Data BG COVID-19”, 2020 (to Appear).]Search in Google Scholar
[7. Bakouch, H. A Family of Extended Half-Distributions: Theory and Applications. Filomat, 2020.10.2298/FIL2001257B]Search in Google Scholar
[8. https://www.worldometers.info/coronavirus/country/south-korea/]Search in Google Scholar
[9. Falliere, N., L. O’Murchu, E. Chien. W32.Stuxnet Dossier. Version 1.4 (February 2011). Symantec Security Response, 2011. 69 p.]Search in Google Scholar
[10. Kyurkchiev, N., A. Iliev, A. Rahnev, T. Terzieva. A New Analysis of Code Red and Witty Worms Behavior. – Communications in Applied Analysis, Vol. 23, 2019, No 2, pp. 267-285.]Search in Google Scholar
[11. Iliev, A., N. Kyurkchiev, A. Rahnev, T. Terzieva. Some New Approaches for Modelling Large-Scale Worm Spreading on the Internet. II. – Neural, Parallel, and Scientific Computations, Vol. 27, 2019, No 1, pp. 23-34.]Search in Google Scholar
[12. Kyurkchiev, N., A. Iliev, A. Rahnev, T. Terzieva. A New Analysis of Cryptolocker Ransomware and Welchia Worm Propagation Behavior. Some Applications. III. – Communications in Applied Analysis, Vol. 23, 2019, No 2, pp. 359-382.]Search in Google Scholar
[13. Terzieva, T., A. Iliev, A. Rahnev, N. Kyurkchiev. The Lomax-D-Generalized-Weibull Cumulative Sigmoid with Applications to the Theory of Computer Viruses Propagation. IV. – Neural, Parallel, and Scientific Computations, Vol. 27, 2019, No 3, 4, pp. 141-150.]Search in Google Scholar
[14. Terzieva, T., A. Iliev, A. Rahnev, N. Kyurkchiev. On a Powerful Transmuted Odd Log-Logistic-Gumbell Model with Applications to the Theory of Computer Viruses Propagation. V. – Communications in Applied Analysis, Vol. 23, 2019, No 3, pp. 441-451.]Search in Google Scholar
[15. Terzieva, T., A. Iliev, A. Rahnev, N. Kyurkchiev. Comments on a New Hyperbolic Sine-Weibull Model with Applications to the Theory of Computer Viruses Propagation. VI. – International Journal of Differential Equations and Applications, Vol. 18, 2019, No 1, pp. 137-146.]Search in Google Scholar
[16. Malinova, A., O. Rahneva, A. Golev, V. Kyurkchiev. Investigations on the Odd-Burr-III-Weibull Cumulative Sigmoid. Some Applications. – Neural, Parallel, and Scientific Computations, Vol. 27, 2019, No 1, pp. 35-45.]Search in Google Scholar
[17. Rahneva, O., T. Terzieva, A. Golev. Investigations on the Zubair-Family with Baseline Ghosh-Bourguignon’s Extended Burr XII Cumulative Sigmoid. Some Applications. – Neural, Parallel, and Scientific Computations, Vol. 27, 2019, No 1, pp. 11-22.]Search in Google Scholar
[18. Angelova, E., A. Golev, T. Terzieva, O. Rahneva. A Study on a Hyper–Power–Logistic Model. Some Applications. – Neural, Parallel and Scientific Computations, Vol. 27, 2019, No 1, pp. 45-57.]Search in Google Scholar
[19. Sendov, B. Hausdorff Approximations. Boston, Kluwer, 1990.10.1007/978-94-009-0673-0]Search in Google Scholar
[20. Sendov, B., A. Andreev, N. Kyurkchiev. Numerical Solution of Polynomial Equations. – In: Handbook of Numerical Analysis. III. P. Ciarlet, J. Lions, Eds. Amsterdam, Elsevier Science Publ., 1994.10.1016/S1570-8659(05)80019-5]Search in Google Scholar
[21. Kyurkchiev, N. Initial Approximation and Root Finding Methods. – In: WILEY-VCH Verlag Berlin GmbH. Vol. 104. 1998.]Search in Google Scholar
[22. Iliev, A., N. Kyurkchiev. Nontrivial Methods in Numerical Analysis: Selected Topics in Numerical Analysis. Saarbrucken, LAP Lambert Academic Publishing, 2010. ISBN: 978-3-8433-6793-6.]Search in Google Scholar
[23. Kyurkchiev, N., S. Markov. On the Hausdorff Distance between the Heaviside Step Function and Verhulst Logistic Function. – J. Math. Chem., Vol. 54, 2016, No 1, pp. 109-119.10.1007/s10910-015-0552-0]Search in Google Scholar
[24. Kyurkchiev, N., S. Markov. Sigmoid Functions: Some Approximation and Modelling Aspects. Saarbrucken, LAP LAMBERT Academic Publishing, 2015. ISBN 978-3-659-76045-7.]Search in Google Scholar
[25. Iliev, A., N. Kyurkchiev, A. Rahnev, T. Terzieva. Some Models in the Theory of Computer Viruses Propagation. LAP LAMBERT Academic Publishing, 2019. ISBN: 978-620-0-00826-8.]Search in Google Scholar
[26. Markov, S. Reaction Networks Reveal New Links between Gompertz and Verhulst Growth Functions. – Biomath, Vol. 8, 2019, No 1.10.11145/j.biomath.2019.04.167]Search in Google Scholar
[27. Kyurkchiev, N. On a Sigmoidal Growth Function Generated by Reaction Networks. Some Extensions and Applications. – Communications in Applied Analysis, Vol. 23, 2019, No 3, pp. 383-400.]Search in Google Scholar
[28. Kyurkchiev, N., A. Iliev, S. Markov. Some Techniques for Recurrence Generating of Activation Functions: Some Modeling and Approximation Aspects. LAP LAMBERT Academic Publishing, 2017. ISBN: 978-3-330-33143-3.]Search in Google Scholar
[29. Kyurkchiev, N., A. Iliev, A. Rahnev. Some Families of Sigmoid Functions: Applications to Growth Theory. LAP LAMBERT Academic Publishing, 2019. ISBN: 978-613-9-45608-6.]Search in Google Scholar
[30. Iliev, A., N. Kyurkchiev, S. Markov. A Note on the New Activation Function of Gompertz Type. – Biomath Communications, Vol. 4, 2017, No 2. 20 p.10.11145/10.11145/bmc.2017.10.201]Search in Google Scholar
[31. Anguelov, R., M. Borisov, A. Iliev, N. Kyurkchiev, S. Markov. On the Chemical Meaning of Some Growth Models Possessing Gompertzian-Type Property. – Math. Meth. Appl. Sci., 2017, pp. 1-12.10.1002/mma.4539]Search in Google Scholar
[32. Anguelov, R., N. Kyurkchiev, S. Markov. Some Properties of the Blumberg’s Hyper-Log-Logistic Curve. – BIOMATH, Vol. 7, 2018, No 1. 8 p.10.11145/j.biomath.2018.07.317]Search in Google Scholar
[33. Markov, S., A. Iliev, A. Rahnev, N. Kyurkchiev. On the Exponential–Generalized Extended Compertz Cumulative Sigmoid. – International Journal of Pure and Applied Mathematics, Vol. 120, 2018, No 4, pp. 555-562.]Search in Google Scholar
[34. Pavlov, N., A. Iliev, A. Rahnev, N. Kyurkchiev. Some Software Reliability Models: Approximation and Modeling Aspects. LAP LAMBERT Academic Publishing, 2018. ISBN: 978-613-9-82805-0.]Search in Google Scholar
[35. Pavlov, N., A. Iliev, A. Rahnev, N. Kyurkchiev. Nontrivial Models in Debugging Theory. Part 2. LAP LAMBERT Academic Publishing, 2018. ISBN: 978-613-9-87794-2.]Search in Google Scholar
[36. Iliev, A., N. Kyurkchiev, S. Markov. On the Approximation of the Cut and Step Functions by Logistic and Gompertz Functions. – Biomath, Vol. 4, 2015, pp. 2-13.10.11145/j.biomath.2015.10.101]Search in Google Scholar
[37. Kyurkchiev, N., A. Iliev, A. Rahnev. A New Class of Activation Functions Based on the Correcting Amendments of Gompertz-Makeham Type. – Dynamic Systems and Applications, Vol. 28, 2019, No 2, pp. 243-257.10.12732/dsa.v28i2.2]Search in Google Scholar
[38. Kyurkchiev, N. Investigations on a Hyper-Logistic Model. Some Applications. – Dynamic Systems and Applications, Vol. 28, 2019, No 2, pp. 351-369.]Search in Google Scholar
[39. Markov, S., A. Iliev, A. Rahnev, N. Kyurkchiev. A Note on the n-Stage Growth Model. Overview. – Biomath Communications, Vol. 5, 2018, No 2, pp. 79-100.10.11145/bmc.2018.11.117]Search in Google Scholar
[40. Kyurkchiev, N., A. Iliev, A. Rahnev. A Look at the New Logistic Models with “Polynomial Variable Transfer”. LAP LAMBERT Academic Publishing, 2020. ISBN: 978-620-2-56595-0.]Search in Google Scholar
[41. Kyurkchiev, N., S. Markov. On a Logistic Differential Model. Some Applications. – Biomath Communications, Vol. 6, 2019, No 1, pp. 34-50.10.11145/bmc.2019.04.307]Search in Google Scholar
[42. Kyurkchiev, N. Some New Classes of Growth Functions Generated by Reaction Networks and Based on “Correcting Amendments” of Bateman-Gompertz and Bateman-Gompertz-Makeham-Type, I. – Communications in Applied Analysis, Vol. 24, 2020, No 1, pp. 13-29.]Search in Google Scholar
[43. Kyurkchiev, N. On a Class of Growth Curves with Exponentially Variable Transfers Generated by Reaction Networks, II. – International Electronic Journal of Pure and Applied Mathematics, Vol. 14, 2020, No 1, pp. 21-29.]Search in Google Scholar
[44. http://portalms.saude.gov.br/images/pdf/2016/maio/24/Informe-Epidemiol-gico-n-27-SE-20-2016-24mai2016-12h00.pdf]Search in Google Scholar
[45. https://virologydownunder.blogspot.com/2016/06/brazils-microcephaly-and-cns-disorder-m.html]Search in Google Scholar
[46. https://www.who.int/csr/disease/swineflu/history_map/InfluenzaAH1N1_maps.html]Search in Google Scholar