Abstract: A display substrate and a display device are provided. The display substrate includes a base substrate, at least one group of contact pads, a plurality of first light-emitting elements, a plurality of first pixel driving circuits, a plurality of connecting traces, a plurality of data lines and a plurality of leads. The display area includes a first display area and a second display area; the first light-emitting elements are located in the first display area; the first pixel driving circuits are located in the second display area; the leads are located in the first display area and the peripheral area, and connect the data lines and the at least one group of contact pads; orthographic projections of the first light-emitting elements on a substrate surface of the base substrate are at least partly overlapped with orthographic projections of the leads on the substrate surface of the base substrate.

Abstract: Disclosed is a prediction method for tool remaining life of a numerical control machine tool based on a hybrid neural model, including: constructing a hybrid neural network model, specifically including the following steps: constructing sample data according to the sampling frequency of tool data; obtaining a first feature vector representing the tool life by utilizing a convolutional neural network and a long short-term memory network; generating working condition signals of sampling points into a second feature vector representing the tool life by utilizing an NFM neural network; and inputting a current working time of a tool and the acquired feature vectors into a multi-layer perceptron for fusion to predict the tool life.

Abstract: Disclosed is a prediction method for tool remaining life of a numerical control machine tool based on a hybrid neural model, including: constructing a hybrid neural network model, specifically including the following steps: constructing sample data according to the sampling frequency of tool data; obtaining a first feature vector representing the tool life by utilizing a convolutional neural network and a long short-term memory network; generating working condition signals of sampling points into a second feature vector representing the tool life by utilizing an NFM neural network; and inputting a current working time of a tool and the acquired feature vectors into a multi-layer perceptron for fusion to predict the tool life.

Abstract: Example methods include receiving training data comprising a plurality of audio clips and a plurality of textual descriptions of audio. The methods include generating a shared representation comprising a joint embedding. An audio embedding of a given audio clip is within a threshold distance of a text embedding of a textual description of the given audio clip. The methods include generating, based on the joint embedding, a conditioning vector and training, based on the conditioning vector, a neural network to: receive (i) an input audio waveform, and (ii) an input comprising one or more of an input textual description of a target audio source in the input audio waveform, or an audio sample of the target audio source, separate audio corresponding to the target audio source from the input audio waveform, and output the separated audio corresponding to the target audio source in response to the receiving of the input.

Abstract: A method for predicting a remaining life of a tool of a computer numerical control machine is provided. In the method, indirect measurement indicators of the tool are selected based on monitoring and analyzing a current state of the tool, a prediction model for the remaining life of the tool is established based on data de-noising, feature extraction and a multi-kernel W-LSSVM algorithm. Thereby, a method for predicting a remaining life of a tool of a computer numerical control machine is provided.

Abstract: A method for predicting a remaining life of a tool of a computer numerical control machine is provided. In the method, indirect measurement indicators of the tool are selected based on monitoring and analyzing a current state of the tool, a prediction model for the remaining life of the tool is established based on data de-noising, feature extraction and a multi-kernel W-LSSVM algorithm. Thereby, a method for predicting a remaining life of a tool of a computer numerical control machine is provided.

Abstract: According to the application, by measuring mass spectra of urine samples and/or serum samples in a healthy group, a negative control group, a positive control group and a treatment group of a drug to be evaluated, the mass spectra of the samples of the treatment group of the drug to be evaluated and the mass spectra of the samples of the negative control group are compared to calculate a difference between peak areas of characteristic fragment particle peaks of metabolic markers in the samples of the treatment group of the drug to be evaluated and in the samples of the negative control group, and a difference value is used to evaluate multidrug resistance of the drug to be evaluated. The larger the difference value is, the larger a callback value is, and the better an effect of reversing the tumor multidrug resistance is.

Abstract: A composite nanoparticle, a preparation method thereof and preparation method of a composite nano preparation using thereof, wherein the composite nanoparticle is a polymer-lipid nanoparticle encapsulating psoralen, isopsoralen and paclitaxel simultaneously, and the preparation method thereof comprises the following steps of: S1. dissolving soybean lecithin and DSPE-PEG2000 in an aqueous phase, subjected to blending and preheating; and S2. dissolving psoralen, isopsoralen, paclitaxel and PLGA in an oleic phase, subjected to blending and injecting into the aqueous phase of S1 to obtain a mixture, and then heating and blending the mixture to obtain the composite nanoparticle.