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

A neural network with two hidden layers is developed for nuclear massprediction, based on the finite-range droplet model (FRDM12). Differenthyperparameters, including the number of hidden units, the choice of activationfunctions, the initializers, and the learning rates, are adjusted explicitlyand systematically. The resulting mass predictions are achieved by averagingthe predictions given by several different sets of hyperparameters withdifferent regularizers and seed numbers. It can provide us not only the averagevalues of mass predictions but also reliable estimations in the mass predictionuncertainties. The overall root-mean-square deviations of nuclear mass havebeen reduced from $0.603$ MeV for the FRDM12 model to $0.200$ MeV and $0.232$MeV for the training set and validation set, respectively.

Nuclear mass predictions based on deep neural network and finite-range droplet model (2012)