There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …

The fractal property is one of the most important properties in complex networks. It describes
the power law relationship between characteristics of the box and the box size. There are …

Batteries with conformal shape and multiple functionalities could provide new degrees of
freedom in the design of robotic devices. For example, the ability to provide both load …

Understanding the human brain and its functions has always been an interesting and
challenging problem. Recently, a significant progress on this problem has been achieved on …

Hybrid nanostructures between biomolecules and inorganic nanomaterials constitute a
largely unexplored field of research, with the potential for novel applications in bioimaging …

The world of biology created a wealth of complex materials intertwining order, disorder, and
hierarchy. They are produced with minimal energy expenditures and display combinations …

Accurate prediction of nucleic binding residues is essential for the understanding of
transcription and translation processes. Integration of feature-and template-based strategies …

The notion of curvature on graphs has recently gained traction in the networks community,
with the Ollivier–Ricci curvature (ORC) in particular being used for several tasks in network …

Describing networks geometrically through low-dimensional latent metric spaces has helped
design efficient learning algorithms, unveil network symmetries and study dynamical …

Understanding the mechanisms by which neurons create or suppress connections to enable
communication in brain-derived neuronal cultures can inform how learning, cognition and …