Numerous students suffer from academic procrastination; it is a common problem and
phenomenon in academic settings. Many previous researchers have analyzed its …

Machine learning has become a widely popular and successful paradigm, especially in data-
driven science and engineering. A major application problem is data-driven forecasting of …

Reservoir computing (RC), a particular form of recurrent neural network, is under explosive
development due to its exceptional efficacy and high performance in reconstruction and/or …

Stochastic resonance (SR) is a fascinating nonlinear phenomenon that uses appropriate
external noise to enhance the specified weak signal. Therefore, it has been widely studied in …

The benefits of noise to applications of nonlinear dynamical systems through mechanisms
such as stochastic and coherence resonances have been well documented. Recent years …

Nonlinear time delay systems produce inherently delay-induced periodic oscillations, which
are, however, too idealistic compared to observations. We exhibit a unified stochastic …

Complex and nonlinear dynamical systems often involve parameters that change with time,
accurate tracking of which is essential to tasks such as state estimation, prediction, and …

This paper focuses on learning the dynamics of time delay systems from trajectory data and
proposes the use of the maximal Lyapunov exponent (MLE) as an indicator to describe the …

High-quality random number generators (RNGs) are essential in many fields. To overcome
the drawbacks in instability of the true RNGs and periodicity of the pseudo-RNGs, based on …

A type of chaos called laminar chaos was found in singularly perturbed dynamical systems
with periodic time-varying delay [Phys. Rev. Lett. 120, 084102 (2018)] 0031-9007 …