The Non-smooth Dynamics of Multiple Leaf Masonry Walls of the Arquata Del Tronto Fortress

The progressive damage of the multiple leaf masonry walls of the Arquata del Tronto medieval fortress, inside the epicentral zone of the last Centre Italy earthquakes of August and October 2016, has been investigated by means of the Non-Smooth Contact Dynamics method (NSCD). According to this model, the masonry structure has been modelled as a system of rigid blocks, and since the contacts between blocks are governed by the Signorini’s impenetrability condition and by dry-friction Coulomb’s law, the building exhibits discontinuous dynamics. The NSCD method has proved to be a powerful tool for investigating the dynamics induced by ground seismic accelerations. Indeed, the numerical results have given a deep insight into the seismic vulnerability of this damaged medieval fortress, confirming several possible failure mechanisms

The DeepFaune initiative: a collaborative effort towards the automatic identification of European fauna in camera trap images

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep-learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often >0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model