Recent years have seen a surge in interest in building deep learning-based fully data-driven models for weather prediction. Such deep learning models, if trained on observations can mitigate certain biases in current state-of-the-art weather models, some of which stem from inaccurate representation of subgrid-scale processes. However, these data-driven models, being over-parameterized, require a lot of training data which may not be available from reanalysis (observational data) products. Moreover, an accurate, noise-free, initial condition to start forecasting with a data-driven weather model is not available in realistic scenarios. Finally, deterministic data-driven forecasting models suffer from issues with long-term stability and unphysical climate drift, which makes these data-driven models unsuitable for computing climate statistics. Given these challenges, previous studies have tried to pre-train deep learning-based weather forecasting models on a large amount of imperfect long-term climate model simulations and then re-train them on available observational data. In this article, we propose a convolutional variational autoencoder (VAE)-based stochastic data-driven model that is pre-trained on an imperfect climate model simulation from a two-layer quasi-geostrophic flow and re-trained, using transfer learning, on a small number of noisy observations from a perfect simulation. This re-trained model then performs stochastic forecasting with a noisy initial condition sampled from the perfect simulation. We show that our ensemble-based stochastic data-driven model outperforms a baseline deterministic encoder–decoder-based convolutional model in terms of short-term skills, while remaining stable for long-term climate simulations yielding accurate climatology.

Brushite (CaHPO4.2H2O) is an important calcium phosphate encountered in bone tissue engineering and bone cement formulation. There are many studies on the synthesis and characterization of brushite, but full-scale substitution and replacement of Ca by Sr in brushite as a key element in medical and environmental applications has not yet been explored systematically. Therefore, this study aims to evaluate the effects of substitution of Ca by Sr on the microstructural and thermal properties of brushite, including the chemical phases present, crystallization, structural water and phase stability. The chemical phases were determined by means of powder X-ray diffraction. The thermal properties were studied by thermogravimetric analysis. Crystallization and surface morphology were analysed using scanning electron microscopy. Various properties were dependent on the incorporated Sr ions. The replacement percentage of Sr may be divided into two major stages: the first from 0% to 50%; and the second from 50% up to 100%. The (CaxSr1–x)HPO4.nH2O shows that micro-scale crystals of platy brushite formed in the first stage of Sr replacement, from 0% up to 50%. As Sr might inhibit the formation of crystals, crystal nucleation rates were reduced as the Sr percentage increased. An amorphous product formed as a result of 50% Sr replacement. The second stage of Sr replacement with Sr contents >50% yielded a new crystal morphology corresponding mainly to SrHPO4.nH2O. The complete replacement of Ca by Sr transforms the brushite with platy microcrystals into SrHPO4 nanosheets.

There is no doubt that stability studies are of great importance in the fascinating research of extrasolar planetary systems. Even if most of the more than 300 extrasolar planets orbit their host stars as single giant planet and build simple two-body systems, we should not exclude the possibility that these systems could host other (small) planets that have not yet been detected due to obsevational limits. Another aspect to carry out stability studies is the growing interest in the search for extraterrestrial life in the universe. The long-term stability of a planetary system is one of the basic requirements for the evolution of life on a terrestrial planet. In this paper the dynamical behaviour of Earth-like planets will be discussed in single-star single-giant-planet systems, multi-planet systems and binary systems.

Tropical moist forests in Africa are concentrated in the Congo Basin. A variety of animals in these forests, in particular mammals, are hunted for their meat, termed bushmeat. This paper investigates current and future trends of bushmeat protein, and non-bushmeat protein supply, for inhabitants of the main Congo Basin countries. Since most bushmeat is derived from forest mammals, published extraction (E) and production (P) estimates of mammal populations were used to calculate the per person protein supplied by these. Current bushmeat protein supply may range from 30 g person−1 day−1 in the Democratic Republic of Congo, to 180 g person−1 day−1 in Gabon. Future bushmeat protein supplies were predicted for the next 50 years by employing current E:P ratios, and controlling for known deforestation and population growth rates. At current exploitation rates, bushmeat protein supply would drop 81% in all countries in less than 50 years; only three countries would be able to maintain a protein supply above the recommended daily requirement of 52 g person−1 day−1. However, if bushmeat harvests were reduced to a sustainable level, all countries except Gabon would be dramatically affected by the loss of wild protein supply. The dependence on bushmeat protein is emphasized by the fact that four out of the five countries studied do not produce sufficient amounts of non-bushmeat protein to feed their populations. These findings imply that a significant number of forest mammals could become extinct relatively soon, and that protein malnutrition is likely to increase dramatically if food security in the region is not promptly resolved.