Abstract: The present disclosure provides a metal wire and a method for manufacturing the same. The method for manufacturing the metal wire includes: forming a metal bar on a substrate; forming a mask above the metal bar, a width of the mask being smaller than a width of the metal bar, and an orthographic projection of the mask on the substrate is within an orthographic projection of the metal bar on the substrate; and wet etching the metal bar to a saturation state under a protection of the mask to form a metal wire, a width of the metal wire being smaller than the width of the mask. The above method can form the metal wire with a high thickness and a narrow line width.

Abstract: The present invention discloses a perceptual representation learning method for protein conformations based on a pre-trained language model, including: obtaining a protein made up of an amino acid sequence, building different data sets according to protein conformations, and defining a prompt for each type of protein conformation; building, based on a pre-trained language model, a representation learning module for fusing an embedding representation of each type of the prompt into an embedding representation of the protein, so as to obtain a protein embedding representation under a prompt identifier; building a task module for performing task prediction on a task corresponding to each type of protein conformation based on the protein embedding representation under the prompt identifier; building a loss function for each type of task based on a task prediction result and a tag, and updating model parameters of the representation learning module and the task module in combination with loss functions of all type

Abstract: A tape includes: a substrate, a conductive adhesive layer disposed on the substrate, and an insulating layer disposed on a part of a bonding surface of the conductive adhesive layer. The insulating layer is configured to insulate the conductive adhesive layer from a conducting part of an object to be bonded.

Abstract: The present disclosure relates to a computer implemented method for applying changes into tables of a target database system using a data synchronization system, the data synchronization system being configured to automatically use at least one configurable parameter for applying a requested change in the target database system. The method provides a trained machine learning model, the machine learning model being configured to adjust the at least one configurable parameter based on a workload level. The method determines a current workload level at the target database system and uses the machine learning model for adjusting the at least one configurable parameter according to the determined workload level.

Abstract: The present disclosure relates to a computer implemented method for applying changes into tables of a target database system using a data synchronization system, the data synchronization system being configured to automatically use at least one configurable parameter for applying a requested change in the target database system. The method provides a trained machine learning model, the machine learning model being configured to adjust the at least one configurable parameter based on a workload level. The method determines a current workload level at the target database system and uses the machine learning model for adjusting the at least one configurable parameter according to the determined workload level.