A Lyapunov-Based Analysis on the Almost Periodicity of Impulsive Conformable Reaction-Diffusion Neural Networks with Distributed Delays. [PDF]
Entropy (Basel)Stamova I, Stamov G, Spirova C.
europepmc +1 more source
A practical guide to characterising ecological coexistence
Biological Reviews, Volume 101, Issue 1, Page 195-220, February 2026.ABSTRACT Coexistence is simultaneously one of the most fundamental concepts of ecology, and one of the most difficult to define. A particular challenge is that, despite a well‐developed body of research, several different schools of thought have developed over the past century, leading to multiple independent, and largely isolated, branches of ...
Adam T. Clark +9 more
wiley +1 more source
Dynamic behavior and control analysis in a new chaotic three-tier supply chain system with a sinusoidal modelling uncertainty for resilient manufacturing networks. [PDF]
Sci RepNahar J +7 more
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Endogenous Tax Compliance and Macroeconomic Performance Driven by Satisficing Evolutionary Dynamics
Metroeconomica, Volume 77, Issue 1, Page 36-54, February 2026.ABSTRACT We incorporate tax evasion to a demand‐led macrodynamic model of capacity utilization and output growth rate. The frequency of tax evaders is endogenously time‐varying, driven by imitation‐augmented satisficing evolutionary dynamics involving pecuniary factors, as well as non‐pecuniary elements which are reflected in the distribution of tax ...
Leonardo Barros‐Torres +2 more
wiley +1 more source
Analytical technique and diverse chaos-identifying tools for the time fractional symmetric regularized long wave equation. [PDF]
Sci RepUllah MS, Hasan MM, Mahbub MA.
europepmc +1 more source
Robust Lyapunov Functions for Reaction Networks: An Uncertain System Framework [PDF]
, 2015 M. Ali Al-Radhawi, David Angeli
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Data‐Driven Sliding Mode Control for Partially Unknown Nonlinear Systems
International Journal of Robust and Nonlinear Control, Volume 36, Issue 2, Page 629-638, 25 January 2026.ABSTRACT This paper presents a novel data‐driven control framework for multi‐input multi‐output (MIMO) nonlinear systems with partially unknown dynamics and bounded disturbances. In practical scenarios, exact cancellation of system nonlinearities is often infeasible due to limited model information and unpredictable disturbances.
Jianglin Lan +2 more
wiley +1 more source