Stable perovskite and metastable post-perovskite NaCoF3 were deformed in pure-shear geometry in a deformation-DIA press with radiographic monitoring of the sample strain. In isothermal experiments where there was no transformation, post-perovskite was found to be 5 times weaker than perovskite. In temperature-ramping experiments where post-perovskite transformed to perovskite during the deformation experiment the initial post-perovskite sample was 5–10 times weaker than perovskite under comparable conditions and their strengths converged during the transformation, being equal on completion of the transformation. These results confirm recent findings which show that postperovskite is weaker than perovskite, regardless of the prior history of the sample.

During nuclear or radiological emergency preparedness and response a processing pipelineis put in place at the Finnish Radiation and Nuclear Safety Authority to produceassessment reports of the current situation, its likely evolution with time, andrecommendations on protective actions. Within the processing pipeline raw data is more andmore refined towards these end products as experts, advisors, and decision makers collect,analyze, portray, annotate, filter, arrange and rearrange, share, approve or dismiss, andpublish content. Emergency procedures and guidelines assist and guide them through theprocess, as do information and communication technologies. The inner working of theprocessing pipeline is in constant flux as information and communication technologiesdevelop and allow better facilitation of the process, and as social and political demandschange. The present article describes the overall working of the pipeline, and how it hasrecently been improved by the KETALE collaborative software. The focus then shifts to thenew ICRP recommendations and how the new national emergency guidelines are planned to beimplemented within the KETALE software. The KETALE software has greatly increased thethroughput of our emergency processing pipeline and improved product quality.

Here, the state of our knowledge regarding the interior dynamics and evolution of habitable terrestrial planets including Earth and super-Earths is reviewed, and illustrated using state-of-the-art numerical models. Convection of the rocky mantle is the key process that drives the evolution of the interior: it causes plate tectonics, controls heat loss from the metallic core (which generates the magnetic field) and drives long-term volatile cycling between the atmosphere/ocean and interior. Geoscientists have been studying the dynamics and evolution of Earth's interior since the discovery of plate tectonics in the late 1960s and on many topics our understanding is very good, yet many first-order questions remain. It is commonly thought that plate tectonics is necessary for planetary habitability because of its role in long-term volatile cycles that regulate the surface environment. Plate tectonics is the surface manifestation of convection in the 2900-km deep rocky mantle, yet exactly how plate tectonics arises is still quite uncertain; other terrestrial planets like Venus and Mars instead have a stagnant lithosphere- essentially a single plate covering the entire planet. Nevertheless, simple scalings as well as more complex models indicate that plate tectonics should be easier on larger planets (super-Earths), other things being equal. The dynamics of terrestrial planets, both their surface tectonics and deep mantle dynamics, change over billions of years as a planet cools. Partial melting is a key process influencing solid planet evolution. Due to the very high pressure inside super-Earths' mantles the viscosity would normally be expected to be very high, as is also indicated by our density function theory (DFT) calculations. Feedback between internal heating, temperature and viscosity leads to a superadiabatic temperature profile and self-regulation of the mantle viscosity such that sluggish convection still occurs.

Decision makers must react in a prompt and appropriate manner in various emergency situations. The bases for decisions are often predictions produced with decision support systems (DSS). Actual radiation measurement data can be used to improve the reliability of the predictions. Data assimilation is an important link between model calculations and measurements and thus decreases the overall uncertainty of the DSS predictions. However, different aspects have to be taken into account for the optimal use of the data assimilation technique: different countries may have differing measurement strategies and systems as well as differing calculation models. The scenario and the amount and composition of radionuclides released may vary. In this paper we analyse the situation during and after an accident and draw up a list of recommendations that can help modellers to take into account the measurements that are best suited for data assimilation.

In 2003 IAEA launched the EMRAS Programme aiming at evaluating the predictive power of radiological models. The programme continued work of previous international radioecological modelling programmes and comprised several working groups focusing on different aspects of environmental modelling. The Iodine Working Group reassessed the impact of the release of 131I during the Chernobyl accident with the aim of comparing model predictions with environmental data and inter-comparing the model predictions. Measurement data and detailed geographic and demographic descriptions were available for three regions: Plavsk, Warsaw and Prague. As for the Prague Scenario, milk supply regions of three big dairies were chosen for the model validation. Apart from geographic, demographic and agricultural descriptions (e.g. gathering regions of the dairies, feeding regime), the modellers were provided with information on the weather conditions and measurement data of iodine contamination. The most important peculiarities of Prague Scenario were keeping milk cattle in sheds and a special feeding regime during May 1986. The modellers were asked to assess the 131I content in the thyroid of the local population and the resulting dose. The assessments were compared with measurement data. The results of these model calculations and their comparison with experimental data are presented.