Lyubov E. Aksenova, Kirill D. Aksenov, Anton V. Prisyazhnyuk, Victoria V. Myasnikova, and Andrey V. Krasov
Development of a retinal pathology classification model using transfer learning and open datasets . . . . . . . . 95
Vanya Barseghyan, Svetlana Solodusha, and Evgeniia Markova
Optimal boundary control of string vibrations with intermediate values of the deflection function under minimization of the boundary energy integral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Anastasia A. Grishchenko, Anastasia S. Dzhafarova, Elena M. Suleymanova, Marina V. Sysoeva, and Ilya V. Sysoev
Nonlinear analysis of interhemispheric connectivity in the pentylenetetrazol rat model of spike-wave discharges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
Anitha J, Sreenivasa Chakravarthi Sangapu
Unified intelligence in ophthalmology: a comprehensive review of a single framework for predicting multiple eye diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
V. Kavitha, Seelam Sunitha Devi, and Balla Chandra Sekhar
Roughness effects on two-dimensional turbulent convection: heat transport, flow reversals, and machine learning analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
Ba Huy Nguyen, The Dong Dang, Igor Furtat, Pavel Gushchin, Anh Quan Dao, Tung Duong Mai
Control of direct current motors with guaranteed speed maintenance in given bounds . . . . . . . . . . . . . . . . . . . . 148
Mokhtar Ouamri
Cubic trigonometric chaotic systems for high-quality pseudo-random number generation . . . . . . . . . . . . . . . . 154
Helder Rojas, Nilton Rojas, J. B. Espinoza, and Luis Huamanchumo
A simple algorithm for output range analysis for deep neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Natarajan Shriethar
Limit cycles and period-doubling bifurcations in a van der pol–type triple pendulum with spring coupling . 174
Andrey Tsyganov and Julia Tsyganova
Parameter identification of discrete-time linear stochastic systems based on decentralized square-root information filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Yuyi Zhang, Andrey Logachov, Aleksandr Smirnov, and Nikita Kazarinov
Artificial neural network surrogates for impact problem simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200

CYBERNETICS AND PHYSICS, VOL. 14, NO. 2, 2025 , 93

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