The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE ...The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model.展开更多
基金Supported by Basic and Applied Basic Research Project of Guangdong Province(2021B0301030006)。
文摘The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model.
文摘针对单一传感器及单一蓝藻提取方法用于太湖蓝藻水华长时序监测的局限性,本文基于2014—2023年高分一号(GF-1)与Landsat 8多源影像数据,采用归一化植被指数(NDVI)方法、随机森林(RF)方法、基于最大类间方差确定样本(大津法)的随机森林(Otsu-RF)方法提取太湖蓝藻,通过对比分析确定蓝藻最优提取方法,揭示近10年太湖蓝藻水华的时空变化特征。结果表明:①Otsu-RF方法在不同影像下提取蓝藻水华的精度最高,且能够更有效地提取零星分布的蓝藻;②与GF-1图像相比,Landsat 8融合影像上的蓝藻像元纹理更加清晰,藻华提取结果更为精确;③2014—2023年太湖夏、秋季蓝藻水华爆发强度较高,春冬季较弱,其中2017、2020年太湖藻华爆发尤为严重,全域年平均蓝藻面积都超过了300 km 2;④太湖蓝藻水华春、夏、秋季多爆发在竺山湖湾、梅梁湖湾、西部湖区沿岸区域,冬季多发生在南部湖区沿岸区域。
