#recurrence_quantification
Live, measured metrics for the hashtag #recurrence_quantification from the open social web. Every number carries a named source and the time it was fetched. Nothing is estimated.
Own #recurrence_quantification
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Day-by-day usage
measured · mastodon.online (Mastodon public tags API) · fetched 2026-07-27 08:55 UTC0 uses by 0 unique accounts across the window. Real per-day counts, not estimates. Newest bar is today so far.
Related hashtags
measured · mastodon.online (Mastodon public search API) · fetched 2026-07-27 08:55 UTCLive pulse
measured · mastodon.online (Mastodon tag timeline) · fetched 2026-07-27 08:55 UTCEverything below is measured over the latest 4 public posts (spanning ~22435 hours).
Posting hours (UTC)
Languages: English (2) · German (2)
Avg boosts / post: 0
Used together with
Top of the latest posts
Rotary machine faults classified via #recurrence_plot and #recurrence_quantification features; 94% accuracy maintained under noise vs. 85% for statistical features across ML methods https://pubs.aip.org/aip/cha/article/36/4/043112/3386395/R
Now #recurrence_quantification helps to make flights more safe (automated flutter detection of fan rotor baldes during aero engine monitoring) https://www.degruyter.com/document/doi/10.1515/tjj-2022-0066/html
Interesting combination of #recurrence_quantification measures, joint #recurrence_plot and network approach to compare a number of different time series for pathological heartbeat dynamics https://doi.org/10.1063/5.0167477
Every number above is measured from a named public API at the shown fetch time. Nothing is estimated or extrapolated. Platforms that lock their data behind paid APIs are not shown. Agents: the same numbers, as JSON, at /api/hashtags/recurrence_quantification