Публикации 2026, 2027, 2028

  1. E. Atanassov, A. Karaivanova, S. Yordanov, S. Spasov, M. Durchova, A. Kirilov, Novel Algorithm for Adaptive Circuit Construction for Quantum Kernel Methods. In: Lirkov, I., Margenov, S. (eds.) Large-Scale Scientific Computations. LSSC 2025, Lecture Notes in Computer Science, vol. 16061, pp. 379–387, Springer Cham., 2026 ,doi.org/10.1007/978-3-032-22221-3_42 (SJR:0.393, Q2, 2025)
  2. .E. Atanassov, M. Durchova, S. Ivanovska, A. Karaivanova, A. Kirilov, Supercomputer HEMUS: Benchmark Results and Performance Optimization, Cybernetics and Information Technologies, vol. 26 (2), pp. 217-232, 2026 , DOI: 10.2478/cait-2026-0022, (IF:1.7 (Q3, WoS); SJR: 0.460 (Q2)), paper
  3. E. Atanassov, A. Kirilov, M. Durchova, I. Georgiev, Postprocessing Optimisation Algorithms for QAOA in MaxCut Problem. In: Paszynski, M., Barnard, A.S., Zhang, Y.J. (eds.) Computational Science – ICCS 2026 Workshops. ICCS 2026, Lecture Notes in Computer Science, vol 16789, pp. 419-430, Springer, Cham., 2026 , doi.org/10.1007/978-3-032-29918-5_30 (SJR:0.393, Q2, 2025)
  4. D. Dimov, S. Ivanovska, A. Dimov, Anastylosis of frescoes − optimal parallel performance via RINCCAS in a given HPC resource, Journal of Computational Science, vol.95, pp.102779, 2026, ISSN:1877-7503 doi.org/10.1016/j.jocs.2025.102779
  5. S. Schulze, S. Kucherenko, E. Atanassov , Comparative Analysis of Pseudo-Random and Low-Discrepancy Number Generators: Implications for Normality and Value-at-Risk Estimation in Investment Portfolios Wilmott, vol. 2026, no. 143, 2026 , paper
  6. S.-M. Gurova, T. Gurov, A. Karaivanova, Eigenvalue Estimation in Portfolio Risk: The Role of Skipping and Leaping in Sobol and Halton Sequences. In: Lirkov, I., Margenov, S. (eds.) Large-Scale Scientific Computations. LSSC 2025. Lecture Notes in Computer Science, vol 16061, pp. 413–426, Springer, Cham., 2026, doi.org/10.1007/978-3-032-22221-3_46 (SJR:0.393, Q2, 2025)
  7. S. Tafkov, Z. Minchev, Shared Defense Response in Corporate Networks, The 16th International Conference on Business Information Security (BISEC2025), pp. 110-119, 2026, doi.org/10.46793/BISEC25.110T
  8. Tz. Ostromsky, S.-M. Gurova, M. Lazarova, Efficient Implementation of Sensitivity Analysis Code of a Large Environmental Model on High Performance Supercomputers. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, Springer, vol. 1237, pp. 111–122, 2026, doi.org/10.1007/978-3-032-05711-2_8
  9. Y. Hristov, Z. Minchev, Machine Learning Analysis Advancing Security Planning, The 16th International Conference on Business Information Security (BISEC2025), pp. 98-109, 2026,doi.org/10.46793/BISEC25.098H
  10. Z. Minchev, Cybersecurity Synergies & Gaps Identification in the Age of Digital Cognition, The 16th International Conference on Business Information Security (BISEC2025), pp. 120-133, 2026, doi.org/10.46793/BISEC25.120M