The paper presents the results of experiments on measuring cross sections for the neutron transfer channels 9Be(3He,α)8Begs,3.03 in the reaction of the 3He (30 MeV) ions with the 9Be target. To describe the angular distributions, we use the Distorted Wave Born Approximation (DWBA) method (FRESCO code). The results of the theoretical analysis are in agreement with the experimental data. In addition, we perform calculations based on the solution of the time-dependent Schrödinger equation (TDSE) for the weakly bound neutron of the 9Be nucleus. The TDSE approach allows us to determine the dynamics of the neutron transfer process and calculate the probabilities for the transfer and removal of the neutron of the 9Be nucleus in the 3He+9Be reaction.

The unique characteristics of frescoes on overseas Chinese buildings can attest to the integration and historical background of Chinese and Western cultures. Reasonable analysis and preservation of overseas Chinese frescoes can provide sustainable development for culture and history. This research adopts the image analysis technology based on artificial intelligence, and proposes a ResNet-34 model and method integrating transfer learning. This deep learning model can identify and classify the source of the frescoes of the emigrants, and can effectively deal with the problems such as the small number of fresco images on the emigrants' buildings, poor quality, difficulty in feature extraction, and similar pattern text and style. The experimental results show that the training process of the model proposed in this article is stable. On the constructed Jiangmen and Haikou fresco JHD datasets, the final accuracy is 98.41%, and the recall rate is 98.53%. The above evaluation indicators are superior to classic models such as AlexNet, GoogLeNet, and VGGNet. It can be seen that the model in this article has strong generalization ability and is not prone to overfitting. It can effectively identify and classify the cultural connotations and regions of frescoes.