English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper we propose a methodology for deriving a model of a complexsystem by exploiting the information extracted from Topological Data Analysis.Central to our approach is the S[B] paradigm in which a complex system isrepresented by a two-level model. One level, the structural S one, is derivedusing the newly introduced quantitative concept of Persistent Entropy. Theother level, the behavioral B one, is characterized by a network of interactingcomputational agents described by a Higher Dimensional Automaton. Themethodology yields also a representation of the evolution of the derivedtwo-level model as a Persistent Entropy Automaton. The presented methodology isapplied to a real case study, the Idiotypic Network of the mammal immunesystem.

Topological characterization of S[B] systems: From data to models of complexity