Chaos has been one of the most effective cryptographic sources since it was first used in
image-encryption algorithms. This paper closely examines the development process of …

Recurrent neural networks (RNNs) are popular machine learning tools for modeling and
forecasting sequential data and for inferring dynamical systems (DS) from observed time …

We develop a behavioral stock market model in which a market maker adjusts stock prices
with respect to the orders of chartists, fundamentalists and sentiment traders. We analytically …

The existence of strange nonchaotic attractors (SNAs) and the associated mechanisms are
studied in a class of quasiperiodically forced piecewise smooth systems. We show that the …

In this work we construct and analyze non-perturbative stationary and axially-symmetric
black hole solutions in general relativity coupled to an electromagnetic and an axion field …

A main theoretical interest in biology and physics is to identify the nonlinear dynamical
system (DS) that generated observed time series. Recurrent Neural Networks (RNNs) are, in …

This article considers an N-firm oligopoly with abating and non-abating firms and analyses a
dynamic setting in which the environmental regulator sets the tax rate to incentivise firms to …

We consider the well-known Lozi map, which is a 2D piecewise linear map depending on
two parameters. This map can be considered as a piecewise linear analog of the Hénon …

We develop a simple financial market model in which a market maker adjusts the price with
respect to orders placed by chartists and fundamentalists. A novel feature of our model is …

Based on a behavioral exchange rate model, we show that a central bank that conducts
competitive interventions to promote its economy may devalue its currency, albeit at the …