Abstract: This disclosure provides an isotonic lipid emulsion for enteral administration comprising Omega-3 fatty acids and medium-chain triglyceride (MCT) fatty acids. The lipid particles administered through the disclosed lipid emulsion can be efficiently absorbed by animal and human body and rapidly enriched on organ and tissue cell membranes.

Abstract: This disclosure provides a method of administrating to a subject a two-portion solution system in a specific ingesting order to yield a water-insoluble cross-linked hydrogel mass in the stomach. The volume of the formed water-insoluble cross-linked hydrogel mass is at least 50% v/v of the total volume of the two-portion solution system. The method is useful for preventing and treating overweight and obesity.

Abstract: The present disclosure provides a network traffic prediction method, an apparatus and an electronic device. In the present disclosure, the network traffic prediction method includes establishing a higher-level network traffic prediction model, where the higher-level network traffic prediction model includes a linear model; using independent variables of the linear model in the higher-level network traffic prediction model as dependent variables, establishing at least one layer of a lower-level network traffic prediction model, and decomposing independent variables in the lower-level network traffic prediction model till all independent variables of each lower-level network traffic prediction model are acquired naturally; and substituting a calculation model of the lower-level network traffic prediction model into the higher-level network traffic prediction model to obtain a complete network traffic prediction model.

Abstract: The present disclosure provides a network traffic prediction method, an apparatus and an electronic device. In the present disclosure, the network traffic prediction method includes establishing a higher-level network traffic prediction model, where the higher-level network traffic prediction model includes a linear model; using independent variables of the linear model in the higher-level network traffic prediction model as dependent variables, establishing at least one layer of a lower-level network traffic prediction model, and decomposing independent variables in the lower-level network traffic prediction model till all independent variables of each lower-level network traffic prediction model are acquired naturally; and substituting a calculation model of the lower-level network traffic prediction model into the higher-level network traffic prediction model to obtain a complete network traffic prediction model.