The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation.It is found that the initial thermal s...The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation.It is found that the initial thermal stability and earliest stage evolution of the local atomic clusters show no strong correlation with their initial short-range orders,and this leads to an observation of a novel symmetry convergence phenomenon,which can be understood as an atomic structure manifestation of the ergodicity.Furthermore,in our system we have quantitatively proved that the crucial factor for the thermal stability against crystallization exhibited by the metallic glass is not the total amount of icosahedral clusters,but the degree of global connectivity among them.展开更多
Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important rese...Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.展开更多
Atomic magnetometers operated in the spin-exchange relaxation-free(SERF)regime are the promising sensor to replace superconducting quantum interference devices(SQUIDs)in the biomagnetism field.The SERF magnetometer wi...Atomic magnetometers operated in the spin-exchange relaxation-free(SERF)regime are the promising sensor to replace superconducting quantum interference devices(SQUIDs)in the biomagnetism field.The SERF magnetometer with compact size and good performance is crucial to the new generation of wearable magnetoencephalography(MEG)system.In this paper,we developed a compact and closed-loop SERF magnetometer with the dimensions of 15.0×22.0×30.0 mm^(3)based on a single-beam configuration.The bandwidth of the magnetometer was extended to 675 Hz while the sensitivity was maintained at 22 f T/Hz^(1/2).A nearly 3-fold enhancement of the bandwidth was obtained in comparison with the open-loop control.The implementation of the closed-loop control also greatly improved the dynamic range,enabling the magnetometer to be robust against the disturbance of the ambient field.Moreover,the magnetometer was successfully applied for the detection of humanα-rhythm and auditory evoked fields(AEFs),which demonstrated the potential to be extended to multi-channel MEG measurements for future neuroscience studies.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 52031016 and 11804027)the China Scholarship Council for financial support during part of this work
文摘The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation.It is found that the initial thermal stability and earliest stage evolution of the local atomic clusters show no strong correlation with their initial short-range orders,and this leads to an observation of a novel symmetry convergence phenomenon,which can be understood as an atomic structure manifestation of the ergodicity.Furthermore,in our system we have quantitatively proved that the crucial factor for the thermal stability against crystallization exhibited by the metallic glass is not the total amount of icosahedral clusters,but the degree of global connectivity among them.
基金Project supported by the Xuzhou Key Research and Development Program (Social Development) (Grant No. KC21304)the National Natural Science Foundation of China (Grant No. 61876186)。
文摘Electroencephalogram(EEG) signals contain important information about the regulation of brain system. Thus, automatic detection of epilepsy by analyzing the characteristics obtained from EEG signals has important research implications in the field of clinical medicine. In this paper, the horizontal visibility graph(HVG) algorithm is used to map multifractal EEG signals into complex networks. Then, we study the structure of the networks and explore the nonlinear dynamics properties of the EEG signals inherited from these networks. In order to better describe complex brain behaviors, we use the angle between two connected nodes as the edge weight of the network and construct the weighted horizontal visibility graph(WHVG). In our studies, fractality and multifractality of WHVG are innovatively used to analyze the structure of related networks. However, these methods only analyze the reconstructed dynamical system in general characterizations,they are not sufficient to describe the complex behavior and cannot provide a comprehensive picture of the system. To this effect, we propose an improved multiscale multifractal analysis(MMA) for network, which extends the description of the network dynamics features by focusing on the relationship between the multifractality and the measured scale-free intervals.Furthermore, neural networks are applied to train the above-mentioned parameters for the classification and identification of three kinds of EEG signals, i.e., health, interictal phase, and ictal phase. By evaluating our experimental results, the classification accuracy is 99.0%, reflecting the effectiveness of the WHVG algorithm in extracting the potential dynamic characteristics of EEG signals.
基金Project supported by Ji Hua Laboratory(Grant No.X190131TD190)the Research and Development Project for Equipment of Chinese Academy of Sciences(Grant No.YJKYYQ20210051)+1 种基金the Suzhou pilot project of basic research(Grant No.SJC2021024)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20200215)。
文摘Atomic magnetometers operated in the spin-exchange relaxation-free(SERF)regime are the promising sensor to replace superconducting quantum interference devices(SQUIDs)in the biomagnetism field.The SERF magnetometer with compact size and good performance is crucial to the new generation of wearable magnetoencephalography(MEG)system.In this paper,we developed a compact and closed-loop SERF magnetometer with the dimensions of 15.0×22.0×30.0 mm^(3)based on a single-beam configuration.The bandwidth of the magnetometer was extended to 675 Hz while the sensitivity was maintained at 22 f T/Hz^(1/2).A nearly 3-fold enhancement of the bandwidth was obtained in comparison with the open-loop control.The implementation of the closed-loop control also greatly improved the dynamic range,enabling the magnetometer to be robust against the disturbance of the ambient field.Moreover,the magnetometer was successfully applied for the detection of humanα-rhythm and auditory evoked fields(AEFs),which demonstrated the potential to be extended to multi-channel MEG measurements for future neuroscience studies.
