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

We present a Bayesian approach to probabilistically infer vertical activityprofiles within a radioactive waste drum from segmented gamma scanning (SGS)measurements. Our approach resorts to Markov chain Monte Carlo (MCMC) samplingusing the state-of-the-art Hamiltonian Monte Carlo (HMC) technique and accountsfor two important sources of uncertainty: the measurement uncertainty and theuncertainty in the source distribution within the drum. In addition, ourefficiency model simulates the contributions of all considered segments to eachcount measurement. Our approach is first demonstrated with a synthetic example,after which it is used to resolve the vertical activity distribution of 5nuclides in a real waste package.

Bayesian inference of 1D activity profiles from segmented gamma scanning of a heterogeneous radioactive waste drum