Abstract: A preparation method of sodium hyaluronate with a full molecular weight distribution (MWD) is provided, including: step 1): spraying hydrogen peroxide on a sodium hyaluronate solid raw material, and conducting an ultraviolet (UV) irradiation treatment; step 2): dissolving a sodium hyaluronate degradation material in water, and adjusting a pH to higher than 7.0; step 3): subjecting a sodium hyaluronate alkaline solution to an ultrasonic treatment; step 4): preparing the sodium hyaluronate solid raw material into a sodium hyaluronate solution with a concentration of 0.1% to 1% (w/v), and thoroughly mixing the sodium hyaluronate solution in an addition proportion of 20% to 60% (v/v) with the sodium hyaluronate alkaline solution obtained after the ultrasonic treatment; and step 5): subjecting a resulting mixed solution to an adsorption treatment with diatomaceous earth and activated carbon, filtering for concentration, and drying a resulting concentrate to obtain the sodium hyaluronate with a full MWD.

Abstract: A preparation method of sodium hyaluronate with a full molecular weight distribution (MWD) is provided, including: step 1): spraying hydrogen peroxide on a sodium hyaluronate solid raw material, and conducting an ultraviolet (UV) irradiation treatment; step 2): dissolving a sodium hyaluronate degradation material in water, and adjusting a pH to higher than 7.0; step 3): subjecting a sodium hyaluronate alkaline solution to an ultrasonic treatment; step 4): preparing the sodium hyaluronate solid raw material into a sodium hyaluronate solution with a concentration of 0.1% to 1% (w/v), and thoroughly mixing the sodium hyaluronate solution in an addition proportion of 20% to 60% (v/v) with the sodium hyaluronate alkaline solution obtained after the ultrasonic treatment; and step 5): subjecting a resulting mixed solution to an adsorption treatment with diatomaceous earth and activated carbon, filtering for concentration, and drying a resulting concentrate to obtain the sodium hyaluronate with a full MWD.

Abstract: An approach for a non-factoid question answering framework across tasks and domains may be provided. The approach may include training a multi-task joint learning model in a general domain. The approach may also include initializing the multi-task joint learning model in a specific target domain. The approach may include tuning the joint learning model in the target domain. The approach may include determining which task of the multiple tasks is more difficult for the multi-task joint learning model to learn. The approach may also include dynamically adjusting the weights of the multi-task joint learning model, allowing the model to concentrate on learning the more difficult learning task.

Abstract: An approach for a non-factoid question answering framework across tasks and domains may be provided. The approach may include training a multi-task joint learning model in a general domain. The approach may also include initializing the multi-task joint learning model in a specific target domain. The approach may include tuning the joint learning model in the target domain. The approach may include determining which task of the multiple tasks is more difficult for the multi-task joint learning model to learn. The approach may also include dynamically adjusting the weights of the multi-task joint learning model, allowing the model to concentrate on learning the more difficult learning task.