Abstract: The present invention discloses a pharmaceutical composition comprising azvudine and an EGFR/TKI inhibitor. The pharmaceutical composition of the present invention shows a good synergistic effect in antitumor, and can delay the generation of drug resistance and improve the therapeutic effect and safety, thereby achieving the goal of prolonging the survival of patients.

Abstract: Provided is an automatic classification system. The automatic classification system is designed based on a YOLOV3 technical framework, so that garbage can be classified and recycled, garbage pollution is reduced, and waste is recycled at the maximum cost. The system is designed with a box of a double-layer structure, wherein an upper box is provided with a garbage throwing-in opening, a camera is mounted at the upper box, and the upper top of the upper box is provided with an partition layer for the installation of a solar panel and a storage battery, so as to meet the power supply requirement of the system. The camera captures thrown-in garbage and uses the recognition algorithm to recognize and classify the garbage classes.

Abstract: Provided is an automatic classification system. The automatic classification system is designed based on a YOLOV3 technical framework, so that garbage can be classified and recycled, garbage pollution is reduced, and waste is recycled at the maximum cost. The system is designed with a box of a double-layer structure, wherein an upper box is provided with a garbage throwing-in opening, a camera is mounted at the upper box, and the upper top of the upper box is provided with an partition layer for the installation of a solar panel and a storage battery, so as to meet the power supply requirement of the system. The camera captures thrown-in garbage and uses the recognition algorithm to recognize and classify the garbage classes.

Abstract: The present invention provides a fault prediction and condition-based repair method of an urban rail train bogie. An optimum service life distribution model of a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, and a basic brake device of a bogie is determined by adopting a method based on survival analysis; a reliability characteristic function of each subsystem is obtained; then, a failure rate of each subsystem of the bogie is calculated by adopting a neural network model optimized by an evolutionary algorithm; and finally, proportional risk modelling is conducted by taking the failure rate and safe operation days of each subsystem of the bogie as concomitant variables; and on the basis of cost optimization, thresholds and control limits for condition-based repair of a bogie system are obtained.

Abstract: The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

Abstract: The invention discloses a complex network-based high speed train system safety evaluation method. The method includes steps as follows: (1) constructing a network model of a physical structure of a high speed train system, and constructing a functional attribute degree of a node based on the network model; (2) extracting a functional attribute degree, a failure rate and mean time between failures of a component as an input quantity, conducting an SVM training using LIBSVM software; (3) conducting a weighted kNN-SVM judgment: an unclassifiable sample point is judged so as to obtain a safety level of the high speed train system. For a high speed train system having a complicated physical structure and operation conditions, the method can evaluate the degree of influences on system safety when a state of a component in the system changes. The experimental result shows that the algorithm has high accuracy and good practicality.

Abstract: The present invention provides a fault prediction and condition-based repair method of an urban rail train bogie. An optimum service life distribution model of a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, and a basic brake device of a bogie is determined by adopting a method based on survival analysis; a reliability characteristic function of each subsystem is obtained; then, a failure rate of each subsystem of the bogie is calculated by adopting a neural network model optimized by an evolutionary algorithm; and finally, proportional risk modelling is conducted by taking the failure rate and safe operation days of each subsystem of the bogie as concomitant variables; and on the basis of cost optimization, thresholds and control limits for condition-based repair of a bogie system are obtained.