The first 2^(+)excited states of the nucleus directly reflect the interaction between the shell structure and the nucleus,providing insights into the validity of the shell model and nuclear structure characteristics.A...The first 2^(+)excited states of the nucleus directly reflect the interaction between the shell structure and the nucleus,providing insights into the validity of the shell model and nuclear structure characteristics.Although the features of the first 2^(+)excited states can be measured for stable nuclei and calculated using nuclear models,significant uncertainty remains.This study employs a machine learning model based on a light gradient boosting machine(LightGBM)to investigate the first 2^(+)excited states.Specifically,the training of the LightGBM algorithm and the prediction of the first 2^(+)properties of 642 nuclei are presented.Furthermore,detailed comparisons of the LightGBM predictions were performed with available experimental data,shell model calculations,and Bayesian neural network predictions.The results revealed that the average difference between the LightGBM predictions and the experimental data was 18 times smaller than that obtained by the shell model and only 70%of the BNN prediction results.Considering Mg,Ca,Kr,Sm,and Pb isotopes as examples,it was also observed that LightGBM can effectively reproduce the magic number mutation caused by shell effects,with the energy being as low as 0.04 MeV due to shape coexistence.Therefore,we believe that leveraging LightGBM-based machine learning can profoundly enhance our insights into nuclear structures and provide new avenues for nuclear physics research.展开更多
Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive mode...Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.展开更多
基金supported by the National Key R&D Program of China (No. 2022YFA1603300)the Romanian Ministry of Research,Innovation and Digitalization under Contract PN 23.21.01.06+1 种基金The ELI-RO project with Contract ELI-RORDI-2024-008 (AMAP)a grant from the Romanian Ministry of Research,Innovation and Digitization,CNCS-UEFIS-CDI,with project numbers PN-Ⅲ-P4-PCE-2021-1014, PN-Ⅲ-P4-PCE-2021-0595, and PN-Ⅲ-P1-1.1-TE2021-1464 within PNCDI Ⅲ
文摘The first 2^(+)excited states of the nucleus directly reflect the interaction between the shell structure and the nucleus,providing insights into the validity of the shell model and nuclear structure characteristics.Although the features of the first 2^(+)excited states can be measured for stable nuclei and calculated using nuclear models,significant uncertainty remains.This study employs a machine learning model based on a light gradient boosting machine(LightGBM)to investigate the first 2^(+)excited states.Specifically,the training of the LightGBM algorithm and the prediction of the first 2^(+)properties of 642 nuclei are presented.Furthermore,detailed comparisons of the LightGBM predictions were performed with available experimental data,shell model calculations,and Bayesian neural network predictions.The results revealed that the average difference between the LightGBM predictions and the experimental data was 18 times smaller than that obtained by the shell model and only 70%of the BNN prediction results.Considering Mg,Ca,Kr,Sm,and Pb isotopes as examples,it was also observed that LightGBM can effectively reproduce the magic number mutation caused by shell effects,with the energy being as low as 0.04 MeV due to shape coexistence.Therefore,we believe that leveraging LightGBM-based machine learning can profoundly enhance our insights into nuclear structures and provide new avenues for nuclear physics research.
基金supported by the National Natural Science Foundation of China(Nos.62172287,62102273)。
文摘Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.
