Abstract: A device for preparing ultra-high purity zinc based on intelligently-controlled zone melting, including a slide platform connected with a screw through a servo control system to control movement of a heating-cooling device. A quartz tube is provided inside an induction heater to protect a melting zone. An infrared thermometer is connected to the heater, and configured to monitor temperature within the melting zone, and control power of the heater. A ring magnetic stirrer with non-contact circumferential rotation cooperates with coil to stir zinc melt. A water-cooling copper jacket is connected to two ends of the heater to cool a zinc bar, and its water inlet and outlet are connected with a water chiller. The infrared thermometer monitors temperature of the zinc bar and controls water flow of the cooling system. A lifting device is connected with a base cabinet to change inclined angle of the zinc bar.

Abstract: A high-thermal conductivity composite material is AlNp/ZA27 composite material, including 2%, 4%, 6%, or 8% by volume of aluminum nitride (AlN) ceramic particles and zinc-aluminium-27 (ZA27) alloy. The ZA27 alloy includes 70.52-71.08% by weight of Zn, 25.58˜27.65% by weight of Al, 1.27˜3.45% by weight of Cu, and 0.50% or less by weight of Mg. In the preparation of the high-thermal conductivity composite material, an as-cast AlNp/ZA27 composite material is subjected to homogenizing annealing and reciprocating extrusion.

Abstract: Disclosed are a Nested Named Entity Recognition method based on part-of-speech awareness, system, device and storage medium therefor. The method uses a BiLSTM model to extract a feature of text word data in order to obtain a text word depth feature, and each text word of text to be recognized is initialized into a corresponding graph node, and a text heterogeneous graph of the text to be recognized is constructed according to a preset part-of-speech path, the text word data of the graph nodes is updated by an attention mechanism, and the features of all graph nodes of the text heterogeneous graph are extracted using the BiLSTM model, and a nested named entity recognition result is obtained after decoding and annotating. The present disclosure can recognize ordinary entities and nested entities accurately and effectively, and enhance the performance and advantages of the nested named entity recognition model.

Abstract: Disclosed is a system for classifying encrypted traffic based on a data packet. The system includes a traffic capture module, a traffic analysis module, and a traffic classification module. The system collects data packets from a network flow to construct a machine learning model, so as to classify encrypted traffic and identify normal traffic and malicious traffic. In a process of constructing a feature matrix, basic spatial-temporal features, header features, load features, and statistical features are obtained. In addition, behavioral features of the data packets are obtained and used to demonstrate differences between the normal traffic and the malicious traffic. Moreover, the present disclosure focuses on a difference between different versions of an encryption protocol, especially a transport layer security (TLS) protocol, and introduces the difference into a model for analysis, so that the system classifies encrypted traffic in a more efficient manner.