Abstract: The present disclosure relates to a method for carrying out dose delivery quality assurance for high-precision radiation treatment, in which parameters affecting a pass rate of dose delivery quality assurance can be derived through regression analysis, which is a known statistical analysis method, and a pass rate prediction model capable of predicting each parameter and the pass rate of dose delivery quality assurance can be derived, and accordingly, it can be predicted in advance whether dose delivery quality assurance will be passed according to the parameters through the above prediction model, without repeatedly carrying out dose delivery quality assurance according to a patient's treatment plan, and as a result, the efficiency of dose delivery quality assurance can be enhanced, and the time or capacity required for such quality assurance is reduced, such that radiation treatment for an actual patient can be quickly and precisely carried out.

Abstract: The present invention relates to a user knowledge tracking method with more improved accuracy. An operation method of a user drop-out rate prediction system including a plurality of encoder neural networks and a plurality of decoder neural networks may include the steps of: inputting question information to a kth encoder neural network and inputting response information to a kth decoder neural network; generating query data, which is information about a question of which the correct answer probability is desired to be identified by the user, by reflecting a weight to the response information, and generating attention information to be used as a weight for the query data by reflecting a weight to the question information; and learning the user drop-out rate prediction system using the attention information as a weight for the query data.

Abstract: The present disclosure relates to a system capable of increasing the accuracy of a prediction result when predicting a user drop-out rate or predicting a correct answer probability of a user in an online learning environment, and further relates to an operation method of the system. A system according to the present disclosure may include a plurality of encoder neural networks and a plurality of decoders, and may input question information to a kth encoder neural network and input response information to kth decoder neural network to learn the system, thereby predicting the user drop-out rate information and the users correct answer probability information with higher accuracy on the basis of the learned system.

Abstract: The present invention is to predict a correct answer probability of a user for a specific question with higher accuracy, and provide learning content having more increased efficiency. A method for operating a learning content recommendation system includes transmitting question information including information on a plurality of questions to a user, receiving solving result information that is the user's response for the plurality of questions, and training a user characteristic model based on the question information and the solving result information, wherein the training of the user characteristic model includes assigning a weight to the user characteristic model based on a degree of influence on a correct answer probability in a sequence of questions input to the user characteristic model.

Abstract: Disclosed herein is a composition for gene delivery which forms into liquid crystals in an aqueous fluid including chitosan and a liquid crystal formation material. The composition of the present invention including chitosan and a liquid crystal formation material improves the relatively low binding strength between chitosan and a gene, and also considerably increases the stability of a chitosan nanocomposite in blood serum, thereby performing highly efficient gene delivery and providing excellent stability, thus being useful as a gene therapeutic agent.

Abstract: The present invention relates to a user knowledge tracing method with more improved accuracy, and an operating method for a user knowledge tracing system including a plurality of encoder neural networks and a plurality of decoder neural networks includes: inputting exercise information to a k-th encoder neural network and inputting response information to a k-th decoder neural network; generating query data, which is information on an exercise for which a user is to predict a correct answer probability, by reflecting a weight to the response information and generating attention information to be used as a weight for the query data by reflecting the weight to the exercise information; and training the user knowledge tracing system by using the attention information as the weight for the query data.