Abstract: A model training method comprises: determining an initial multi-channel residual neural network model; acquiring a multi-channel residual neural network model training data set, the multi-channel residual neural network model training data set comprises multi-channel bone radio-frequency data obtained by a QUS device, and a fracture evaluation value label corresponding to the multi-channel bone radio-frequency data; and training the initial multi-channel residual neural network model by taking the multi-channel bone radio-frequency data as an input and taking the fracture evaluation value label as an output, so as to obtain a multi-channel residual neural network model. Features of an ultrasonic radio-frequency signal can be extracted, and a multi-channel residual neural network model is obtained by training, which model can be applied to the field of fracture risk prediction, such that an effective prevention measure is taken in a timely manner.

Abstract: The invention discloses an apparatus and a method for identifying nodes in a node-based data source, the apparatus being unique node identifiers, or series of unique node identifiers. The identifiers remain unchanged despite transformations of the data source. Any modification made to data derived from the transformations is also effected at the data source by means of the identifiers, the identifiers mapping the data in the transformed version of the data source to the data in the data source.