Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the ...Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.展开更多
Satisfiability problem of authorization require- ments in business process asks whether there exists an as- signment of users to tasks that satisfies all the requirements, and methods were proposed to solve this probl...Satisfiability problem of authorization require- ments in business process asks whether there exists an as- signment of users to tasks that satisfies all the requirements, and methods were proposed to solve this problem. However, the proposed methods are inefficient in the sense that a step of the methods is searching all the possible assignments, which is time-consuming. This work proposes a method to solve the satisfiability problem of authorization requirements with- out browsing the assignments space. Our method uses im- proved separation of duty algebra （ISoDA） to describe a sat- isfiability problem of qualification requirements and quan- tification requirements （Separation of Duty and Binding of Duty requirements）. Thereafter, ISoDA expressions are re- duced into multi-mutual-exclusive expressions. The satisfia- bilities of multi-mutual-exclusive expressions are determined by an efficient algorithm proposed in this study. The experiment shows that our method is faster than the state-of-the-art methods.展开更多
基金the National Key R&D Program of China(No.2023YFA1606503)the National Natural Science Foundation of China(Nos.12035011,11975167,11947211,11905103,11881240623,and 11961141003).
文摘Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.
文摘Satisfiability problem of authorization require- ments in business process asks whether there exists an as- signment of users to tasks that satisfies all the requirements, and methods were proposed to solve this problem. However, the proposed methods are inefficient in the sense that a step of the methods is searching all the possible assignments, which is time-consuming. This work proposes a method to solve the satisfiability problem of authorization requirements with- out browsing the assignments space. Our method uses im- proved separation of duty algebra （ISoDA） to describe a sat- isfiability problem of qualification requirements and quan- tification requirements （Separation of Duty and Binding of Duty requirements）. Thereafter, ISoDA expressions are re- duced into multi-mutual-exclusive expressions. The satisfia- bilities of multi-mutual-exclusive expressions are determined by an efficient algorithm proposed in this study. The experiment shows that our method is faster than the state-of-the-art methods.
