Local socio-economic effects of protected area conservation: The case of Maromizaha forest, Madagascar

The vision Madagascar Naturally aimed to triple the size of protected areas in Madagascar from 1.7 million hectares to 6 million hectares before 2008, in order to ensure the safe guarding of Madagascar’s natural heritage and the human well-being that depends on it. In 2008, Maromizaha forest was selected  by the Ministry of Environment and Forests tobecome a New Protected Area where the delegated manager is the Groupe d’Etude et de Recherche sur les Primates de Madagascar (GERP). One of GERP’s strategies is to provide support to the livelihoods of the local people around the Maromizaha protected area in order to reduce the dependency on natural resources. During April 2014, GERP organized a rapid socio-economic survey of 70 households across six villages, in order to make a preliminary, comparison and assessment of this development support and its impact on the main income generating activities of the local people, their highest level of formal education in 2008 and 2014, and their thinking about conservation offsetting. The results showed that in 2014, 70% of local people were engaged in agriculture and less than 40% in cattle farming. Some villagers have benefited from pilot development projects organized by financial and environmental organizations. Other local people benefited from other livelihood activities related to the conservation management of the forest. Most participants were aware of the ecosystem services of the forests (94.3%) and the education level has increased from 2008 to 2014,  although even in 2014, 56% of the survey participants were educated only to primary school level; the rate of illiteracy is at 15.6%. We summarize some strengths, weaknesses and recommendations in order to improve the management of the Maromizaha Protected Area

Semi-supervised generative approach to point-defect formation in chemically disordered compounds: application to uranium-plutonium mixed oxides

Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical compounds. An especially challenging problem arises in the modeling of chemically disordered solid solutions, for which some properties depend on the distribution of chemical species in the crystal lattice. This is the case of defect properties of uranium-plutonium mixed oxides nuclear fuels. The number of possible configurations is so high that the problem becomes intractable if treated with direct sampling. We thus propose a machine learning approach, based on generative modeling, to optimize the exploration of this large configuration space. A probabilistic, semi-supervised approach using Mixture Density Network is applied to estimate the concentration of thermal defects in (U, Pu)O2. We show that this method, based on the prediction of the density of states of formation energy of a defect, is computationally much more cost-efficient compared to other approaches available in the literature.Comment: The performed calculations require re-verificatio