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| 1 | Machine learning in nuclear physics at low and intermediate energies显示文摘Machine learning(ML)is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing tasks.In this review,we first briefly introduce the different methodologies used in ML algorithms and techniques.As a snapshot of many applications by ML,some selected applications are presented,especially for low-and intermediate-energy nuclear physics,which include topics on theoretical applications in nuclear structure,nuclear reactions,properties of nuclear matter,and experimental applications in event identification/reconstruction,complex system control,and firmware performance.Finally,we present a summary and outlook on the possible directions of ML use in low-intermediate energy nuclear physics and possible improvements in ML algorithms. | Wanbing He Qingfeng Li Yugang Ma Zhongming Niu Junchen Pei Yingxun Zhang | 2023 | Science China(Physics,Mechanics & Astronomy)2023，66，8： | 3 |
| 2 | Collective rotations of fission isomers in actinide nuclei显示文摘Using the total-Routhian-surface(TRS)method,the rotational behaviors of fission isomers in the second well of actinide nuclei234 242U,236 244Pu and238 246Cm were investigated.The pairing-deformation-frequency self-consistent TRS calculations reproduced reasonably the experimental moments of inertia extracted from spectroscopic data.It is calculated that,in these largelyelongated(β2≈0.65 andβ4≈0.03)fission isomers,theν12[981]neutron andπ12+[651]proton align simultaneously at rotational frequencyω≈0.4 0.6 MeV(corresponding to spin I≈80),which leads to clear upbending in moments of inertia(MoI’s).Our calculations have indicated that the hexadecapole deformationβ4influenced significantly the frequency of the rotational alignment of the proton12+[651]orbit. | XIE KePing KE WeiYao LIANG WuYang FU XiMing JIAO ChangFeng PEI JunChen XU FuRong | 2014 | Science China(Physics,Mechanics & Astronomy)2014，57，2： | 2 |
| 3 | Relationship of Microchannels and Plaque Erosion in Patients with ST-Segment Elevation Myocardial Infarction: An Optical Coherence Tomography Study显示文摘Objective:Microchannels are associated with the progression of atherosclerotic vulnerable plaques.However,in patients with culprit optical coherence tomography(OCT)-defined plaque erosion,the knowledge of microchannels and culprit lesion vulnerability is limited.The aim of this study was to investigate culprit lesion characteristics in patients with ST-segment elevated myocardial infarction(STEMI)caused by plaque erosion with and without microchannels using OCT.Methods:In all,348 STEMI patients with plaque erosion who underwent OCT of the culprit lesion at the 2nd Affiliated Hospital of Harbin Medical University(Harbin,China)from August 2014 to December 2017 were included and divided into the microchannel group(n=116,33.3%)and no-microchannel group(n=232,66.7%).The clinical characteristics and OCT-derived plaque features were compared between both groups.Results:Among the 348 STEMI patients with plaque erosion,culprit lesions with microchannels had higher incidence of lipid plaque(59.5%vs.45.3%,P=0.012);calcification(41.4%vs.24.6%,P=0.002);spotty calcification(30.2%vs.18.1%,P=0.014);macrophages accumulation(72.4%vs.45.7%,P<0.001);and cholesterol crystals(32.8%vs.14.2%,P<0.001)than those without microchannels.In addition,minimal lumen area was smaller((1.9±0.9)mm2 vs.(2.8±2.3)mm2,P<0.001)and lumen area stenosis was greater((71.3%±13.4%)vs.(65.3%±19.3%),P=0.001)in the microchannel group than in the no-microchannel group.Conclusion:In patients with STEMI caused by plaque erosion,one-third manifested typical microchannel characteristics,and those with microchannels were associated with more severe luminal stenosis and more vulnerable plaque features than those without microchannels. | Senqing Jiang Junchen Guo Yanwei Yin Chao Fang Jifei Wang Yidan Wang Fangmeng Lei Sibo Sun Xueying Pei Ruyi Jia Shaotao Zhang Lulu Li Yini Wang Lei Xing Huai Yu Huimin Liu Maoen Xu Xuefeng Ren Lijia Ma Guo Wei Jingbo Hou Jiannan Dai Bo Yu | 2022 | Cardiology Discovery2022，2，2： | 0 |
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