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In this article we reflect on the life and work of Andrew Fry, a renowned molecular cell biologist and a cherished member of the scientific community at the University of Leicester, UK, who passed away on 30th April 2024 at the age of 57. His groundbreaking work on the cellular mechanisms of Never in Mitosis gene-A related kinases (Neks) made an indelible mark on the field. Alongside his scientific achievements, Andrew was an exceptional mentor, a thoughtful academic leader and a dependable collaborator. To understand what motivated Andrew, we first need to look into his background. [ABSTRACT FROM AUTHOR]

ANSWERS® is developing a set of uncertainty quantification (UQ) tools for use with its major physics codes: WIMS/PANTHER (reactor physics), MONK (criticality and reactor physics) and MCBEND (shielding and dosimetry). The Visual Workshop integrated development environment allows the user to construct and edit code inputs, launch calculations, post-process results and produce graphs, and recently uncertainty quantification and optimisation tools have been added. Prior uncertainties due to uncertainties in nuclear data or manufacturing tolerances can be estimated using the sampling method or using the sensitivity options in the physics codes combined with appropriate covariance matrices. To aid the user in the choice of appropriate validation experiments, the MONK categorisation scheme and/or a similarity index can be used. An interactive viewer has been developed which allows the user to search through, and browse details of, over 2,000 MONK validation experiments that have been analysed from the ICSBEP and IRPhE validation sets. A Bayesian updating approach is used to assimilate the measured data with the calculated results. It is shown how this process can be used to reduce bias in calculated results and reduce the calculated uncertainty on those results. This process is illustrated by application to a PWR fuel assembly.

The traditional approach to estimating systems of share equations is to append a multivariate normal stochastic component to the deterministic component. The adding up restrictions are then exploited to delete one equation and estimation is carried out by maximum likelihood. Such an approach leads to an implied model which violates economic theory, as there remains a nonzero probability of shares being negative or greater than unity. In this paper we propose the use of a method from the statistics literature, compositional data analysis, and a distribution familiar to statisticians, the additive logistic normal, for the estimation of a system of demand share equations. Key words: Stochastic; Demand share; Simplex; Additive logistic normal; Dirichlet distribution; Multivariate normality

The paper suggests two ways to model the long term effects of advertising. One simply uses adstocks with a long half life. The other uses a new expression: cumulative adstock [...]