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

Evidence-based Prescriptive Analytics (EbPA) is necessary to determineoptimal operational set-points that will improve business productivity. EbPAresults from what-if analysis and counterfactual experimentation on CAUSALDigital Twins (CDTs) that quantify cause-effect relationships in the DYNAMICSof a system of connected assets. We describe the basics of Causality and CausalGraphs and develop a Learning Causal Digital Twin (LCDT) solution; ouralgorithm uses a simple recurrent neural network with some innovativemodifications incorporating Causal Graph simulation. Since LCDT is a learningdigital twin where parameters are learned online in real-time with minimalpre-configuration, the work of deploying digital twins will be significantlysimplified. A proof-of-principle of LCDT was conducted using real vibrationdata from a system of bearings; results of causal factor estimation, what-ifanalysis study and counterfactual experiment are very encouraging.

Evidence-based Prescriptive Analytics, CAUSAL Digital Twin and a Learning Estimation Algorithm