Abstract: The present disclosure related to a dog food composition comprising from about 10% by weight to about 20% by weight of fat, from about 5% by weight to about 15% by weight of fibers and from about 30% by weight to about 60% by weight proteins, the weight percentages being based on the total weight of dry matter of the composition.

Abstract: A method for identifying emitting species (S1-SN) emitting X- or gamma radiation in a scene, wherein a spectrum of the radiation is supplied as input of a first set of a plurality of convolutional neural networks, each convolutional neural network of the first set being associated with at least one atomic species to be identified and having at least one output indicative of the presence or the absence of the atomic species in the scene. Advantageously, a second set of a plurality of convolutional neural networks makes it possible to determine a signal proportion of each emitting species present in the X- or gamma radiation emanating from the scene. Also disclosed is a device for implementing such a method.

Abstract: The present disclosure relates to a dog food composition comprising from about 10% by weight to about 20% by weight of fat, from about 5% by weight to about 15% by weight of fibers and from about 30% by weight to about 60% by weight proteins, the weight percentages being based on the total weight of dry matter of the composition.