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We build and train an artificial neural network (ANN) model based on experimental α-decay energy (Qα) data. In addition to decays between the ground states of parent and daughter nuclei, decays from the ground states of parent nuclei to the excited states of daughter nuclei are also included. In this way, the number of samples is increased dramatically. The α particle is assumed to have a spherical symmetric shape. The root-mean-square deviation between the calculated results obtained from the ANN model and the experimental data is 0.105 MeV. It shows a good predictive power for α-decay energy with the ANN model. The influence of different inputs is investigated. It is found that both the shell effect and the pairing effect result in an obvious improvement of the predictive power of the ANN model, and the shell effect plays a more important role. The optimal result can be obtained when both the shell and pairing effects are considered simultaneously. The application of the ANN model in predicting α-decay energy indicates a neutron magic number at N=184 in the superheavy nuclei mass region.

Concerning the Fukushima nuclear accident, many radionuclides were released into the marine environment, which caused contamination of many parts of the world through ocean circulation. Regarding inland nuclear power plants, freshwater habitats such as reservoirs and rivers could also be polluted by the radioactive effluents. Several models were developed to track radionuclide transport in the water environment. However, the applicability and characteristics of these models were not fully identified and compared, particularly for the different water environments. In this study, the research progress and application examples of radionuclide transport models in rivers and oceans in recent years are systematically summarized, and the methods for simulating radionuclide transport behavior in various water environments are compared. An adequate model is expected to be instrumental in assessing radioactive pollution in various water environments.