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 expansion of network infrastructure in Greece has raised concerns aboutthe possible negative health effects on sensitive groups, such as children,from exposure to long-term radiofrequency electromagnetic fields (RF-EMFs). Theobjective of this study is to apply Unsupervised Machine Learning methods suchas hierarchical clustering, in order to establish patterns of EMF radiation inschools. To this end we performed measurements in the majority schools units inthe region of Thessaly in order to calculate the mean value for RF - EMFexposure in the 27 MHz - 3 GHz range, which covers the whole spectrum of RF -EMF sources. Hierarchical clustering dendrogram analysis shows that populationdensity in urban areas of Thessaly bears no relation to the level of EMFexposure in schools. Furthermore, in $97.5\%$ of schools found in the Thessalyregion, the exposure level is at least 3500 times below the Greek exposurelimits while in $2.5\%$ it is a little less than 500 times below the limit.

Unsupervised Machine Learning and EMF radiation in schools: a study of 205 schools in Greece