The lifetime of the isomeric state in fully stripped^(94)Ru^(44+)ions has been measured using isochronous mass spectrometry(IMS)at the experimental Cooler Storage Ring(CSRe)of the Heavy Ion Research Facility in Lanzho...The lifetime of the isomeric state in fully stripped^(94)Ru^(44+)ions has been measured using isochronous mass spectrometry(IMS)at the experimental Cooler Storage Ring(CSRe)of the Heavy Ion Research Facility in Lanzhou(HIRFL).Previously,the isomeric lifetime was determined by analyzing the decay time points of individual decay events.In this paper,we present a novel approach to determine the isomeric lifetime based on the survival time of ions obtained from IMS.The survival lifetimes of the ground and isomeric states of^(94)Ru^(44+)were measured to be s and s in the laboratory,respectively.Given that the ground state of^(94)Ru^(44+)has a natural lifetime of approximately 75 min,its survival lifetime in the experimental setup was predominantly determined by the beam-loss lifetime,including interactions with residual gas in the storage ring and carbon foil of the detector.In contrast,the survival lifetime of^(94m)Ru^(44+)was governed by its intrinsic nuclear lifetime and additional beam-loss effects.The nuclear decay lifetime of^(94m)Ru^(44+)was extracted through differential survival lifetime analysis between ground and isomeric states,under the assumption that the beam-loss lifetimes for both quantum systems are identical.Using this novel methodology,the lifetime measured in the laboratory frame was s.After relativistic time-dilation corrections,the corresponding rest-frame half-life was calculated to be s.This result demonstrates excellent consistency with previous experimental results,validating the reliability of the new method.This method is suitable for determining half-lives of highly charged ions in the range of several tens of microseconds to milliseconds using IMS.展开更多
Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and public safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep le...Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and public safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep learning-based automatic gust front identification algorithm is proposed in this study.The algorithm utilizes Mask Region-based Convolutional Neural Network(Mask R-CNN),a state-of-the-art instance segmentation model,trained on a large dataset of 2623 gust front samples from S-band weather radar volume scans in East China and the North China Plain between 2009 and 2016.Extensive data preprocessing and manual annotation are performed to prepare the training dataset.The optimized model achieves impressive performance on a test set of 604 samples,with a detection probability of 93.21%,a false alarm rate of 3.60%,a missed alarm rate of 6.79%,and a critical success index of 90.08%.The algorithm demonstrates robust identification capabilities across gust fronts of varying scales,types,and parent thunderstorm systems,highlighting its operational applicability.展开更多
基金Supported by the National Natural Science Foundation of China(11805032,12275050,12305151,12322507)Jiangxi Provincial Natural Science Foundation(20224BAB211023)high-level and high-skilled leading talent training project of Jiangxi Province。
文摘The lifetime of the isomeric state in fully stripped^(94)Ru^(44+)ions has been measured using isochronous mass spectrometry(IMS)at the experimental Cooler Storage Ring(CSRe)of the Heavy Ion Research Facility in Lanzhou(HIRFL).Previously,the isomeric lifetime was determined by analyzing the decay time points of individual decay events.In this paper,we present a novel approach to determine the isomeric lifetime based on the survival time of ions obtained from IMS.The survival lifetimes of the ground and isomeric states of^(94)Ru^(44+)were measured to be s and s in the laboratory,respectively.Given that the ground state of^(94)Ru^(44+)has a natural lifetime of approximately 75 min,its survival lifetime in the experimental setup was predominantly determined by the beam-loss lifetime,including interactions with residual gas in the storage ring and carbon foil of the detector.In contrast,the survival lifetime of^(94m)Ru^(44+)was governed by its intrinsic nuclear lifetime and additional beam-loss effects.The nuclear decay lifetime of^(94m)Ru^(44+)was extracted through differential survival lifetime analysis between ground and isomeric states,under the assumption that the beam-loss lifetimes for both quantum systems are identical.Using this novel methodology,the lifetime measured in the laboratory frame was s.After relativistic time-dilation corrections,the corresponding rest-frame half-life was calculated to be s.This result demonstrates excellent consistency with previous experimental results,validating the reliability of the new method.This method is suitable for determining half-lives of highly charged ions in the range of several tens of microseconds to milliseconds using IMS.
基金Supported by the National Key Research and Development Program of China(2023YFC3007501)Open Fund of China Meteorological Administration Key Laboratory for Aviation Meteorology(HKQXZ-2024005)Innovation Ability Promotion Plan of Chengdu University of Information Technology(KYQN202307).
文摘Gust fronts,which are characterized by strong winds and intense wind shear,pose a threat to both aviation and public safety.To aid forecasters in issuing timely warnings for this hazardous weather phenomenon,a deep learning-based automatic gust front identification algorithm is proposed in this study.The algorithm utilizes Mask Region-based Convolutional Neural Network(Mask R-CNN),a state-of-the-art instance segmentation model,trained on a large dataset of 2623 gust front samples from S-band weather radar volume scans in East China and the North China Plain between 2009 and 2016.Extensive data preprocessing and manual annotation are performed to prepare the training dataset.The optimized model achieves impressive performance on a test set of 604 samples,with a detection probability of 93.21%,a false alarm rate of 3.60%,a missed alarm rate of 6.79%,and a critical success index of 90.08%.The algorithm demonstrates robust identification capabilities across gust fronts of varying scales,types,and parent thunderstorm systems,highlighting its operational applicability.
