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

Data collected from a bike-sharing system exhibit complex temporal andspatial features. We analyze shared-bike usage data collected in Seoul, SouthKorea, at the level of individual stations while accounting forstation-specific behavior and covariate effects. For this, we adopt a penalizedregression approach with a multilayer network fused Lasso penalty. These fusionpenalties are imposed on networks which embed spatio-temporal linkages, andcapture the homogeneity in bike usage that is attributed to intricatespatio-temporal features without arbitrarily partitioning the data. On thereal-life datasets, we demonstrate that the proposed approach yieldscompetitive predictive performance and provides a new interpretation of thedata.

Capturing usage patterns in bike sharing system via multilayer network fused Lasso