Abstract: The present invention relates to a preparation method of an edible and biodegradable environmental-friendly tableware, and the present invention provides a preparation method of a natural macromolecule-based edible and degradable tableware, where the principles of endogenous diffusion and polymer crosslinking to prepare an edible tableware such as a straw, a cup and a bowl from a microscopic state. The tableware material of the present invention may degrade rapidly under natural conditions and requires no composting. The tableware prepared by the method of the present invention has an excellent water stability performance. In terms of material acquisition, carrageenan, sodium alginate and other raw materials are widely available and stable, and may constitute a good substitute for grain starch, wood, etc., and the material cost is low.

Abstract: A transaction model of a general model generates a target transaction vector for a target transaction record. The general model also generates account vectors for accounts. A match score is generated between the account vectors and the transaction vector. The general model selects a first account identifier of an account using the match score. The transaction model also generates historical transaction vectors for historical transaction records. Further, a comparison score is generated between the historical transaction vectors and the target transaction vector. A second account identifier of an historical transaction is selected according to the comparison score. One of the first account identifier and the second account identifier is selected as the account identifier for the transaction record, and the transaction record is stored with the account identifier.

Abstract: The present disclosure provides a data processing method performed by an electronic device. The method includes: obtaining first and second service behavior features, and a service scenario feature of an object from service history data associated with the object; generating first and second service embedding vectors and a scenario representation vector according to the first and second service behavior features and the service scenario feature; obtaining first and second weights of the first and second service embedding vectors according to the scenario representation vector and the first and second service embedding vectors; generating first and second service feature vectors according to the first and second service embedding vectors and the first and second weights of the first and second service embedding vectors, respectively; and obtaining an object embedding vector according to the first service feature vector and the second service feature vector.

Abstract: A method for combined characterization of pore structure includes steps as follows. Firstly, CO2, N2 and high-pressure mercury intrusion porosimetry characterization curves are plotted based on actual measurement data, then, average values of the overlapping range of the CO2 and N2 characterization curves are calculated, and a function yi?=ƒ(x) is fitted. Each pore volume yi? corresponding to each pore diameter xi is calculated, and a curve is plotted with xi as a horizontal coordinate and yi? as a vertical coordinate, thereby obtaining a characterization curve of the overlapping range between CO2 and N2 adsorptions. The same data processing is used to process the overlapping range data of the N2 and high-pressure mercury intrusion porosimetry characterization curves, to obtain the characterization curve between them. The characterization curves are spliced with the original CO2, N2, and high-pressure mercury intrusion porosimetry characterization curves to obtain a combined characterization curve.