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

Traditional random-walk particle-tracking (PT) models of advection anddispersion do not track entropy, because particle masses remain constant. Newermass-transfer particle tracking (MTPT) models have the ability to do so becausemasses of all compounds may change along trajectories. Additionally, theprobability mass functions (PMF) of these MTPT models may be compared tocontinuous solutions with probability density functions, when a consistentdefinition of entropy (or similarly, the dilution index) is constructed. Thisdefinition reveals that every numerical model incurs a computational entropy.Similar to Akaike's entropic penalty for larger numbers of adjustableparameters, the computational complexity of a model (e.g., number of nodes)adds to the entropy and, as such, must be penalized. The MTPT method can use aparticle-collision based kernel or an SPH-derived adaptive kernel. The latteris more representative of a locally well-mixed system (i.e., one in which thedispersion tensor equally represents mixing and solute spreading), while theformer better represents the separate processes of mixing versus spreading. Weuse computational means to demonstrate the viability of each of these methods.

Entropy: The former trouble with particles (including a new numerical model computational penalty for the Akaike information criterion)