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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The A.I. disruption and the need to compete on innovation are impactingcities that have an increasing necessity to become innovation hotspots.However, without proven solutions, experimentation, often unsuccessful, isneeded. But experimentation in cities has many undesirable effects not only forits citizens but also reputational if unsuccessful. Digital Twins, so popularin other areas, seem like a promising way to expand experimentation proposalsbut in simulated environments, translating only the half-baked ones, the oneswith higher probability of success, to real environments and thereforeminimizing risks. However, Digital Twins are data intensive and need highlylocalized data, making them difficult to scale, particularly to small cities,and with the high cost associated to data collection. We present an alternativebased on synthetic data that given some conditions, quite common in SmartCities, can solve these two problems together with a proof-of-concept based onNO2 pollution.

The use of Synthetic Data to solve the scalability and data availability problems in Smart City Digital Twins