Adaptation of FDMT to include process-based models and consider regional aspects following radionuclide deposition events.

Experience from earlier nuclear accidents has clearly shown the need for maintaining and developing appropriate modelling capabilities. Dealing with complex issues such as human exposure following a nuclear accident necessitates the implementation of a set of interconnected models such as FDMT. FDMT is an integrated module within the two main European decision support systems for radiological emergency preparedness, ARGOS and JRODOS, to simulate the transfer of radionuclides along terrestrial food chains and to predict their activity concentrations in foodstuffs. In order to make the module more fit-for-purpose, FDMT has been implemented in a new modelling platform (ECOLEGO) which provides a high degree of flexibility with regard to conducting developmental work. This paper presents improvements in FDMT further through either the incorporation of new models or further elaboration of existing ones, as well as updates in default parameters. Models have also been made more fit-for-purpose through consideration of regional-specific parameters. Specific improvements include modelling developments related to dry deposition, radioactive particle weathering, radiocaesium transfer influenced by soil characteristics and, for a region-specific case, animal uptake. In addition, the paper presents new pathways and parameters (and updated values) to be considered for making FDMT more adapted for Norwegian conditions. Overall, the improvements made in the present work should significantly reduce the uncertainties associated with the outputs of the FDMT models.

A study of particle dry deposition parameterizations in an atmospheric radioactive preparedness model: Application to the Chernobyl case.

Parameterization of dry deposition is key for modelling of atmospheric transport and deposition of radioactive particles. Still, very simple parameterizations are often encountered in radioactive preparedness models such as the SNAP model (SNAP=Severe Nuclear Accident Program) of the Norwegian Meteorological Institute. In SNAP a constant dry deposition velocity (=0.2 cm/s) neglecting aerodynamic and surface resistances, is presently used. Therefore, two new dry depositions schemes (the Emerson scheme and the EMEP (European Monitoring and Evaluation Programme) scheme) have been implemented in SNAP to evaluate the benefits of including aerodynamic and surface resistances codes with respect to model prediction skills. The three dry deposition schemes are evaluated using 137Cs total deposition from soil sample data (n = 540) for a 60 km radial zone out from the Chernobyl Nuclear Power Plant (ChNPP) collected during the months after the accident. The present study capitalizes on high resolution meteorological data (2.5 km horizontal resolution), a detailed land-use data set with 273 sub-classes and the hitherto most comprehensive source term description for the Chernobyl accident. Based on our findings it is recommended to replace the present simple SNAP scheme with the Emerson or EMEP dry deposition scheme.

Comparing model skills for deterministic versus ensemble dispersion modelling: The Fukushima Daiichi NPP accident as a case study.

Atmospheric dispersion models are crucial for nuclear risk assessment and emergency response systems since they rapidly predict air concentrations and deposition of released radionuclides, providing a basis for dose estimations and countermeasure strategies. Atmospheric dispersion models are associated with relatively large and often unknown uncertainties that are mostly attributed to meteorology, source terms and parametrisation of the dispersion model. By developing methods that can provide reliable uncertainty ranges for model outputs, decision makers have an improved basis for handling nuclear emergency situations. In the present work, model skill of the Severe Nuclear Accident Programme (SNAP) model was quantified by employing an ensemble method in which 51 meteorological realisations from a numerical weather prediction model were combined with 9 source term descriptions for the accidental 137Cs releases from Fukushima Daiichi Nuclear Power Plant during 14th-17th March 2011. The meteorological forecast was compared to observations of wind speed from 30 meteorological stations. The 459 dispersion realisations were compared with hourly observations of activity concentrations from 100 air filter stations. Exclusive use of deterministic meteorology resulted in most members of the dispersion ensemble showing too low concentration values, however this was mitigated by applying ensemble meteorology. Ensemble predictions, including both the meteorological and source term ensemble, show an overall higher prediction skill compared to individual meteorology and source term runs, with true predictive rate accuracy increasing from 30%-50% to 70%-90%, with a decrease in positive predictive rate accuracy from 75%-80% to 65%-75%. Skill scores and other ensemble indicators also showed improvements in using ensembles of source terms and meteorology. From the present study on the Fukushima accident there are strong indications that ensemble predictions improve the basis for decision making in the early phase after a nuclear accident, which emphasises the importance of including ensemble prediction in nuclear preparedness tools of the future.

Virtual reality training of lucid dreaming.

Metacognitive reflections on one's current state of mind are largely absent during dreaming. Lucid dreaming as the exception to this rule is a rare phenomenon; however, its occurrence can be facilitated through cognitive training. A central idea of respective training strategies is to regularly question one's phenomenal experience: is the currently experienced world real, or just a dream? Here, we tested if such lucid dreaming training can be enhanced with dream-like virtual reality (VR): over the course of four weeks, volunteers underwent lucid dreaming training in VR scenarios comprising dream-like elements, classical lucid dreaming training or no training. We found that VR-assisted training led to significantly stronger increases in lucid dreaming compared to the no-training condition. Eye signal-verified lucid dreams during polysomnography supported behavioural results. We discuss the potential mechanisms underlying these findings, in particular the role of synthetic dream-like experiences, incorporation of VR content in dream imagery serving as memory cues, and extended dissociative effects of VR session on subsequent experiences that might amplify lucid dreaming training during wakefulness. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

PTSD relapse in Veterans of Iraq and Afghanistan: A systematic review.

This systematic review examines studies published between 2003, the initial invasion of Iraq, and 2018 related to the long-term treatment outcomes for Veterans of Iraq and Afghanistan suffering from combat-related posttraumatic stress disorder (PTSD). More specifically this review attempts to estimate the rate at which Veterans experience the return of symptoms after completing treatment. The review was conducted by the authors in accordance with the Cochrane Handbook for Systematic Reviews of Interventions. The literature search identified eight eligible studies, which met the predefined inclusion criteria. Of the included studies a majority were deemed to be at a high risk of attrition bias. In addition, few studies comprehensively reported relevant relapse or recurrence related outcome statistics. The implications of the available evidence base on long-term treatment outcomes are discussed. Recommendations for future studies on relapse and recurrence of PTSD symptoms among Veterans of Iraq and Afghanistan are also presented.