Abstract: The present application disclosures a double production line and a rapid prefabrication process of a segmental beam. The double production line including two production machine and a track system provided on the construction ground; the production machine includes a fixed end mold, two side molds, a bottom mold trolley, a middle internal mold trolley and two side internal mold trolley; two side molds are positioned on two sides of the fixed end mold respectively, the fixed end mold and two side molds together define a pouring position with an end opening, two openings of the pouring position are arranged facing each other; the track system includes a transverse track and a longitudinal track communicated with each other, two pouring positions are both positioned in the extension path of the transverse track, and the longitudinal track is positioned between two pouring positions.

Abstract: An integrated device, a semiconductor device, and an integrated device manufacturing method are provided, to improve capacitor integration density of the integrated device. The integrated device includes: A first dielectric layer is disposed on a first metal layer; the first metal layer, the first dielectric layer, and a gate metal layer on the first dielectric layer form a first capacitor; the gate metal layer, a second dielectric layer on the gate metal layer, and a second metal layer on the second dielectric layer form a second capacitor; and the first metal layer is connected to the second metal layer through a first conductor structure, so that the first capacitor and the second capacitor are connected in parallel.

Abstract: A method for compressing a machine learning model by an electronic device. The method may comprise determining a compression parameter of a set hidden layer in a model based on a pruning number of respective channels included in the set hidden layer and a pruning loss of each hidden layer of the model; and compressing the model based on the compression parameter of the set hidden layer. The compression parameter may be related to a pruning of the model.