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The onset of necking in dynamically expanding ductile rings is delayed due to the stabilizing effect of inertia, and with increasing expansion velocity, both the number of necks incepted and the number of fragments increase. In general, neck retardation is expected to delay fragmentation as necking is often the precursor to fracture. However, in porous ductile materials, it is possible that fracture can occur without significant necking. Thus, the objective of this work is to unravel the complex interaction of initial porosity and inertia on the onset of necking and fracture. To this end, we have carried out a series of finite element calculations of unit cells with sinusoidal geometric perturbations and varying levels of initial porosity under a wide range of dynamic loading conditions. In the calculations, the material is modeled using a constitutive framework that includes many of the hardening and softening mechanisms that are characteristics of ductile metallic materials, such as strain hardening, strain rate hardening, thermal softening, and damage-induced softening. The contribution of the inertia effect on the loading process is evaluated through a dimensionless parameter that combines the effects of loading rate, material properties, and unit cell size. Our results show that low initial porosity levels favor necking before fracture, and high initial porosity levels favor fracture before necking, especially at high loading rates where inertia effects delay the onset of necking. The finite element results are also compared with the predictions of linear stability analysis of necking instabilities in porous ductile materials. ; J.A.R.-M. acknowledges the financial support provided by the European Research Council under the European Union's Horizon 2020 research and innovation programme (Project PURPOSE, Grant agreement 758056).

Tapadoir (from the Irish ´ tapa 'fast' and the nominal suffix -oir ´ ) is a statistical machine translation (SMT) project, funded by the Irish government. This work was commissioned to help government translators meet the translation demands which have arisen from the Irish language's status as an official EU and national language. The development of this system, which translates English into Irish (a morphologically rich, low-resourced minority language), has produced an interesting set of challenges. These challenges have inspired a creative response to the lack of data and NLP tools available for the Irish language and have also resulted in the development of new resources for the Irish linguistic and NLP community. We show that our SMT system out-performs Google TranslateTM (a widely used general-domain SMT system) as a result of steps we have taken to tailor translation output to the user's specific needs.