Abstract: This application discloses a negative electrode plate and a preparation method thereof, a secondary battery, a battery pack, and an electric apparatus. The negative electrode plate includes a negative electrode current collector and a negative electrode material layer disposed on at least one surface of the negative electrode current collector. The negative electrode material layer includes a negative electrode active substance, and the negative electrode material layer has a structure in which high-compacted-density regions and low-compacted-density regions are consecutively and alternately spaced apart, a density difference of the negative electrode active substances in the high-compacted-density regions and low-compacted-density regions being 0.1 g/cm3-0.6 g/cm3.

Abstract: The present disclosure is a three-way U-Net method for accurately segmenting an uncertain boundary of a retinal blood vessel, includes: describing an uncertainty of a blood vessel boundary label, constructing an upper bound and a lower bound of the uncertain boundary based on the dilation operator and the erosion operator respectively to obtain a maximum value and a minimum value for the blood vessel boundary, and mapping the boundary with uncertain information into one range; combining an uncertainty representation of the boundary with a loss function, and designing a three-way loss function; training network parameters by adopting a stochastic gradient descent algorithm and utilizing a total loss of the three-way loss function; and designing and implements an auxiliary diagnosis application system for intelligently segmenting the retinal blood vessel with functions of the fundus data acquisition, the intelligent accurate segmentation and the auxiliary diagnosis for the retinal blood vessel.

Abstract: The present disclosure is a three-way U-Net method for accurately segmenting an uncertain boundary of a retinal blood vessel, includes: describing an uncertainty of a blood vessel boundary label, constructing an upper bound and a lower bound of the uncertain boundary based on the dilation operator and the erosion operator respectively to obtain a maximum value and a minimum value for the blood vessel boundary, and mapping the boundary with uncertain information into one range; combining an uncertainty representation of the boundary with a loss function, and designing a three-way loss function; training network parameters by adopting a stochastic gradient descent algorithm and utilizing a total loss of the three-way loss function; and designing and implements an auxiliary diagnosis application system for intelligently segmenting the retinal blood vessel with functions of the fundus data acquisition, the intelligent accurate segmentation and the auxiliary diagnosis for the retinal blood vessel.

Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.

Abstract: A method for classifying multi-granularity breast cancer genes based on a double self-adaptive neighborhood radius includes large-scale gene locus data are read and normalized, and a data analysis is performed on the large-scale gene loci. An optimum value K is selected by adopting a combination of contour coefficients and a PCA dimensionality reduction visualization, and a model of information granulation is adjusted. A heuristic reduction algorithm is used to implement a multi-granularity attribute reduction of a self-adaptive neighborhood radius based on a cluster center distance and a multi-granularity attribute reduction of a neighborhood radius based on an attribute inclusion degree, and big data for breast cancer genes are classified and predicted by adopting a machine learning classification algorithm based on a SVM support vector machine.