Machine learning methods have been remarkably successful for a wide range of application
areas in the extraction of essential information from data. An exciting and relatively recent …

The study of epidemics is useful for not only understanding outbreaks and trying to limit their
adverse effects, but also because epidemics are related to social phenomena such as …

There has been an upsurge of interest in applying machine-learning (ML) techniques to
chemistry, and a number of these applications have achieved impressive predictive …

Contagion dynamics on networks are used to study many problems, including disease and
virus epidemics, incarceration, obesity, protests and rebellions, needle sharing in drug use …

Neighborhood effects have an important role in evacuation decision-making by a family.
Owing to peer influence, neighbors evacuating can motivate a family to evacuate …

Large-scale population displacements arising from conflict-induced forced migration
generate uncertainty and introduce several policy challenges. Addressing these concerns …

In this work, we provide a procedure that allows us to transform certain kinds of deterministic
Boolean networks on minterm or maxterm functions into symmetric ones, so inferring that …

Discrete dynamical systems serve as useful formal models to study diffusion phenomena in
social networks. Several recent articles have studied the algorithmic and complexity aspects …

In this work, we solve the problem of the coexistence of periodic orbits in homogeneous
Boolean graph dynamical systems that are induced by a maxterm or a minterm (Boolean) …

In this paper, we study the dynamics of synchronous Boolean networks and extend
previously obtained results for binary Boolean networks to networks with state variables in a …