Skip to content

Toggle service links

STEM Faculty

Browse publications from researchers across STEM Faculty.

Reset Advanced Search

Latest staff publications > Displaying [1–20 of 26]

  • Sudakow, I., Myers, C., Spiridonov, A., Stankevič, R., Feulner, G. and Livina, V., 2026, ‘Transitions between persistent climate–carbon regimes coincide with elevated Phanerozoic biosphere vulnerability’, Nature Communications, 17(1), https://oro.open.ac.uk/111554/.
  • Ma, Y., Sudakow, I. and Krapivsky, P. L., 2026, ‘Mixed Phases in Feedback Ising Models’, Physical Review Letters, 137(2), https://oro.open.ac.uk/111483/.
  • Abramian, A. K., Vakulenko, S. A. and Sudakow, I., 2026, ‘Modeling dynamic ice–structure interaction with failure zones: Predicting resonance and chaotic responses’, Chaos, Solitons & Fractals, 204, https://oro.open.ac.uk/107862/.
  • Kondrashov, D., Sudakow, I., Livina, V. and Yang, Q., 2026, ‘Accurate and robust real-time prediction of September Arctic sea ice’, Chaos: An Interdisciplinary Journal of Nonlinear Science, 36, https://oro.open.ac.uk/108601/.
  • Sultana, A., Asari, V. K., Sudakow, I. and Cooper, L. W., 2026, ‘ArcticNet for Semantic Segmentation of Meltpond Regions in the Arctic Sea Ice’, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 19 pp. 70–83., https://oro.open.ac.uk/107236/.
  • Sultana, A., Abouzahra, S. N., Asari, V. K., Aspiras, T., Liu, R., Sudakow, I. and Cooper, L., 2025, ‘Ultralight VisionMamba UNet: a segmentation architecture for meltpond region localization’, in Pattern Recognition and Prediction XXXVI, 13-17 Apr 2025, Orlando, United States., https://oro.open.ac.uk/104936/.
  • Pande, J., Pant, H. V. and Sudakov, I., 2025, ‘Tipping points in educational engagement: Sentiment analysis of online learners’ feedback’, MATEC Web of Conferences, 413, https://oro.open.ac.uk/106903/.
  • Sultana, A., Abouzahra, S. N., Asari, V. K., Aspiras, T. H., Walker, A., Liu, R., Sudakow, I., Cooper, L. W. and Alam, M. S., 2025, ‘Efficient Arctic data generation using a separable convolutional diffusion model’, in Pattern Recognition and Prediction XXXVI, 13-17 Apr 2025, Orlando, United States., https://oro.open.ac.uk/104941/.
  • Sudakow, I., Vakulenko, S. A. and Grigoryev, D., 2025, ‘Non-equilibrium dynamics in complex networks via asymmetric Glauber models’, Journal of Physics A: Mathematical and Theoretical, 58(23), https://oro.open.ac.uk/104621/.
  • Vakulenko, S. A. and Sudakow, I., 2025, ‘Noisy deep networks: chaos, multistationarity, and eternal evolution’, Journal of Physics: Complexity, 6(2), https://oro.open.ac.uk/104008/.
  • Sudakow, I. and Vakulenko, S. A., 2024, ‘New spin models in ecology: Super multi-stationarity and chaos’, Chaos, Solitons & Fractals, 184, https://oro.open.ac.uk/97844/.
  • Sultana, A., Asari, V. K., Aspiras, T., Liu, R., Sudakow, I. and Cooper, L. W., 2024, ‘NABLA-N for meltpond detection’, in SPIE DEFENSE + COMMERCIAL SENSING, 21-26 Apr 2024, National Harbor, Maryland, United States., https://oro.open.ac.uk/98949/.
  • Sultana, A., Abouzahra, S. N., Asari, V. K., Aspiras, T. H., Liu, R., Sudakow, I., Cooper, L. W., Agaian, S. S. and DelMarco, S. P., 2024, ‘Diffusion model-based generation of sea ice data’, in Multimodal Image Exploitation and Learning 2024, 21-26 Apr 2024, National Harbor, Maryland, USA., https://oro.open.ac.uk/98948/.
  • Abramova, I. A., Demchev, D. M., Kharyutkina, E. V., Savenkova, E. N. and Sudakow, I. A., 2024, ‘Utilization of the U-Net Convolutional Neural Network and Its Modifications for Segmentation of Tundra Lakes in Satellite Optical Images’, Atmospheric and Oceanic Optics, 37(2), pp. 205–210., https://oro.open.ac.uk/98937/.
  • Savenkova, E., Vakulenko, S. and Sudakow, I., 2024, ‘Slow-Fast Dynamical Systems with a Load Variation’, in 12th IC-MSQUARE, 28-31 Aug 2023, Belgrade, Serbia., https://oro.open.ac.uk/98403/.
  • Loginov, S., Moraru, E., Kharyutkina, E. and Sudakow, I., 2024, ‘Climatology of Synoptic Non-Gaussian Meteorological Anomalies in the Northern Hemisphere during 1979–2018’, Climate, 12(1), https://oro.open.ac.uk/95471/.
  • Sudakow, I., Reinitz, J., Vakulenko, S. A. and Grigoriev, D., 2024, ‘Evolution of biological cooperation: an algorithmic approach’, Scientific Reports, 14, https://oro.open.ac.uk/95375/.
  • Winkelmann, R., Steinert, N. J., Armstrong McKay, D. I., Brovkin, V., Kääb, A., Notz, D., Aksenov, Y., Arndt, S., Bathiany, S., Burke, E., Garbe, J., Gasson, E., Goelzer, H., Hugelius, G., Kristin Klose, A., Langebroek, P., Marzeion, B., Maussion, F., Nitzbon, J., Robinson, A., Rynders, S. and Sudakow, I., 2023, ‘Chapter 1’, 2: Tipping points in the cryosphere. University of Exeter, Exeter, UK., https://oro.open.ac.uk/94721/.
  • Sudakow, I., Myers, C., Petrovskii, S. V., Sumrall, C. D. and Witts, J., 2023, ‘Mathematical modeling is an efficient research tool to address challenges in mass extinction research: Reply to comments on “Knowledge gaps and missing links in understanding mass extinctions: Can mathematical modeling help?”’, Physics of Life Reviews, 46 pp. 5–7., https://oro.open.ac.uk/90940/.
  • Demchev, D., Sudakow, I., Khodos, A., Abramova, I., Lyakhov, D. and Michels, D., 2023, ‘Recognizing the Shape and Size of Tundra Lakes in Synthetic Aperture Radar (SAR) Images Using Deep Learning Segmentation’, Remote Sensing, 15(5), https://oro.open.ac.uk/87705/.