In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and a...In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.展开更多
This article describes the transient models of the neutronics code VITAS that are used for solving time-dependent,pinresolved neutron transport equations.VITAS uses the stiffness confinement method(SCM)for temporal di...This article describes the transient models of the neutronics code VITAS that are used for solving time-dependent,pinresolved neutron transport equations.VITAS uses the stiffness confinement method(SCM)for temporal discretization to transform the transient equation into the corresponding transient eigenvalue problem(TEVP).To solve the pin-resolved TEVP,VITAS uses a heterogeneous variational nodal method(VNM).The spatial flux is approximated at each Cartesian node using finite elements in the x-y plane and orthogonal polynomials along the z-axis.Angular discretization utilizes the even-parity integral approach at the nodes and spherical harmonic expansions at the interfaces.To further lower the computational cost,a predictor–corrector quasi-static SCM(PCQ-SCM)was developed.Within the VNM framework,computational models for the adjoint neutron flux and kinetic parameters are presented.The direct-SCM and PCQ-SCM were implemented in VITAS and verified using the two-dimensional(2D)and three-dimensional(3D)exercises on the OECD/NEA C5G7-TD benchmark.In the 2D and 3D problems,the discrepancy between the direct-SCM solver’s results and those reported by MPACT and PANDAS-MOC was under 0.97%and 1.57%,respectively.In addition,numerical studies comparing the PCQ-SCM solver to the direct-SCM solver demonstrated that the PCQ-SCM enabled substantially larger time steps,thereby reducing the computational cost 100-fold,without compromising numerical accuracy.展开更多
Objective The study aimed to estimate the benchmark dose(BMD)of coke oven emissions(COEs)exposure based on mitochondrial damage with the mitochondrial DNA copy number(mtDNAcn)as a biomarker.Methods A total of 782 subj...Objective The study aimed to estimate the benchmark dose(BMD)of coke oven emissions(COEs)exposure based on mitochondrial damage with the mitochondrial DNA copy number(mtDNAcn)as a biomarker.Methods A total of 782 subjects were recruited,including 238 controls and 544 exposed workers.The mtDNAcn of peripheral leukocytes was detected through the real-time fluorescence-based quantitative polymerase chain reaction.Three BMD approaches were used to calculate the BMD of COEs exposure based on the mitochondrial damage and its 95%confidence lower limit(BMDL).Results The mtDNAcn of the exposure group was lower than that of the control group(0.60±0.29 vs.1.03±0.31;P<0.001).A dose-response relationship was shown between the mtDNAcn damage and COEs.Using the Benchmark Dose Software,the occupational exposure limits(OELs)for COEs exposure in males was 0.00190 mg/m^(3).The OELs for COEs exposure using the BBMD were 0.00170 mg/m^(3)for the total population,0.00158 mg/m^(3)for males,and 0.00174 mg/m^(3)for females.In possible risk obtained from animal studies(PROAST),the OELs of the total population,males,and females were 0.00184,0.00178,and 0.00192 mg/m^(3),respectively.Conclusion Based on our conservative estimate,the BMDL of mitochondrial damage caused by COEs is0.002 mg/m^(3).This value will provide a benchmark for determining possible OELs.展开更多
This work illustrates the innovative results obtained by applying the recently developed the 2<sup>nd</sup>-order predictive modeling methodology called “2<sup>nd</sup>- BERRU-PM”, where the ...This work illustrates the innovative results obtained by applying the recently developed the 2<sup>nd</sup>-order predictive modeling methodology called “2<sup>nd</sup>- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2<sup>nd</sup>-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2<sup>nd</sup>-order response sensitivities must always be included to quantify the need for including (or not) the 3<sup>rd</sup>- and/or 4<sup>th</sup>-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1<sup>st</sup>- and 2<sup>nd</sup>-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3<sup>rd</sup>- and even 4<sup>th</sup>-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1<sup>st</sup>-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2<sup>nd</sup>-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).展开更多
Atomic radiative data such as excitation energies, transition wavelengths, radiative rates, and level lifetimes with high precision are the essential parameters for the abundance analysis, simulation, and diagnostics ...Atomic radiative data such as excitation energies, transition wavelengths, radiative rates, and level lifetimes with high precision are the essential parameters for the abundance analysis, simulation, and diagnostics in fusion and astrophysical plasmas. In this work, we mainly focus on reviewing our two projects performed in the past decade. One is about the ions with Z■30 that are generally of astrophysical interest, and the other one is about the highly charged krypton(Z = 36)and tungsten(Z = 74) ions that are relevant in research of magnetic confinement fusion. Two different and independent methods, namely, multiconfiguration Dirac–Hartree–Fock(MCDHF) and the relativistic many-body perturbation theory(RMBPT) are usually used in our studies. As a complement/extension to our previous works for highly charged tungsten ions with open M-shell and open N-shell, we also mainly focus on presenting and discussing our complete RMBPT and MCDHF calculations for the excitation energies, wavelengths, electric dipole(E1), magnetic dipole(M1), electric quadrupole(E2), and magnetic quadrupole(M2) transition properties, and level lifetimes for the lowest 148 levels belonging to the 3l3configurations in Al-like W61+. We also summarize the uncertainties of our systematical theoretical calculations, by cross-checking/validating our datasets from our RMBPT and MCDHF calculations, and by detailed comparisons with available accurate observations and other theoretical calculations. The data are openly available in Science Data Bank at https://doi.org/10.57760/sciencedb.10569.展开更多
基金the National Natural Science Foundation of China(Nos.41804047 and 42111540260)Fundamental Research Funds of the Institute of Geophysics,China Earthquake Administration(NO.DQJB19A0114)the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(No.IGGCAS-201904).
文摘In recent years,artificial intelligence technology has exhibited great potential in seismic signal recognition,setting off a new wave of research.Vast amounts of high-quality labeled data are required to develop and apply artificial intelligence in seismology research.In this study,based on the 2013–2020 seismic cataloging reports of the China Earthquake Networks Center,we constructed an artificial intelligence seismological training dataset(“DiTing”)with the largest known total time length.Data were recorded using broadband and short-period seismometers.The obtained dataset included 2,734,748 threecomponent waveform traces from 787,010 regional seismic events,the corresponding P-and S-phase arrival time labels,and 641,025 P-wave first-motion polarity labels.All waveforms were sampled at 50 Hz and cut to a time length of 180 s starting from a random number of seconds before the occurrence of an earthquake.Each three-component waveform contained a considerable amount of descriptive information,such as the epicentral distance,back azimuth,and signal-to-noise ratios.The magnitudes of seismic events,epicentral distance,signal-to-noise ratio of P-wave data,and signal-to-noise ratio of S-wave data ranged from 0 to 7.7,0 to 330 km,–0.05 to 5.31 dB,and–0.05 to 4.73 dB,respectively.The dataset compiled in this study can serve as a high-quality benchmark for machine learning model development and data-driven seismological research on earthquake detection,seismic phase picking,first-motion polarity determination,earthquake magnitude prediction,early warning systems,and strong ground-motion prediction.Such research will further promote the development and application of artificial intelligence in seismology.
基金supported by the National Natural Science Foundation of China (Nos. 12175138, U20B2011)the Young Talent Project of the China National Nuclear Corporation
文摘This article describes the transient models of the neutronics code VITAS that are used for solving time-dependent,pinresolved neutron transport equations.VITAS uses the stiffness confinement method(SCM)for temporal discretization to transform the transient equation into the corresponding transient eigenvalue problem(TEVP).To solve the pin-resolved TEVP,VITAS uses a heterogeneous variational nodal method(VNM).The spatial flux is approximated at each Cartesian node using finite elements in the x-y plane and orthogonal polynomials along the z-axis.Angular discretization utilizes the even-parity integral approach at the nodes and spherical harmonic expansions at the interfaces.To further lower the computational cost,a predictor–corrector quasi-static SCM(PCQ-SCM)was developed.Within the VNM framework,computational models for the adjoint neutron flux and kinetic parameters are presented.The direct-SCM and PCQ-SCM were implemented in VITAS and verified using the two-dimensional(2D)and three-dimensional(3D)exercises on the OECD/NEA C5G7-TD benchmark.In the 2D and 3D problems,the discrepancy between the direct-SCM solver’s results and those reported by MPACT and PANDAS-MOC was under 0.97%and 1.57%,respectively.In addition,numerical studies comparing the PCQ-SCM solver to the direct-SCM solver demonstrated that the PCQ-SCM enabled substantially larger time steps,thereby reducing the computational cost 100-fold,without compromising numerical accuracy.
基金supported by the National Natural Science Foundation of China[grant numbers:NSFC81872597,81001239]。
文摘Objective The study aimed to estimate the benchmark dose(BMD)of coke oven emissions(COEs)exposure based on mitochondrial damage with the mitochondrial DNA copy number(mtDNAcn)as a biomarker.Methods A total of 782 subjects were recruited,including 238 controls and 544 exposed workers.The mtDNAcn of peripheral leukocytes was detected through the real-time fluorescence-based quantitative polymerase chain reaction.Three BMD approaches were used to calculate the BMD of COEs exposure based on the mitochondrial damage and its 95%confidence lower limit(BMDL).Results The mtDNAcn of the exposure group was lower than that of the control group(0.60±0.29 vs.1.03±0.31;P<0.001).A dose-response relationship was shown between the mtDNAcn damage and COEs.Using the Benchmark Dose Software,the occupational exposure limits(OELs)for COEs exposure in males was 0.00190 mg/m^(3).The OELs for COEs exposure using the BBMD were 0.00170 mg/m^(3)for the total population,0.00158 mg/m^(3)for males,and 0.00174 mg/m^(3)for females.In possible risk obtained from animal studies(PROAST),the OELs of the total population,males,and females were 0.00184,0.00178,and 0.00192 mg/m^(3),respectively.Conclusion Based on our conservative estimate,the BMDL of mitochondrial damage caused by COEs is0.002 mg/m^(3).This value will provide a benchmark for determining possible OELs.
文摘This work illustrates the innovative results obtained by applying the recently developed the 2<sup>nd</sup>-order predictive modeling methodology called “2<sup>nd</sup>- BERRU-PM”, where the acronym BERRU denotes “best-estimate results with reduced uncertainties” and “PM” denotes “predictive modeling.” The physical system selected for this illustrative application is a polyethylene-reflected plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation (involving 21,976 uncertain parameters), the solution of which is representative of “large-scale computations.” The results obtained in this work confirm the fact that the 2<sup>nd</sup>-BERRU-PM methodology predicts best-estimate results that fall in between the corresponding computed and measured values, while reducing the predicted standard deviations of the predicted results to values smaller than either the experimentally measured or the computed values of the respective standard deviations. The obtained results also indicate that 2<sup>nd</sup>-order response sensitivities must always be included to quantify the need for including (or not) the 3<sup>rd</sup>- and/or 4<sup>th</sup>-order sensitivities. When the parameters are known with high precision, the contributions of the higher-order sensitivities diminish with increasing order, so that the inclusion of the 1<sup>st</sup>- and 2<sup>nd</sup>-order sensitivities may suffice for obtaining accurate predicted best- estimate response values and best-estimate standard deviations. On the other hand, when the parameters’ standard deviations are sufficiently large to approach (or be outside of) the radius of convergence of the multivariate Taylor-series which represents the response in the phase-space of model parameters, the contributions stemming from the 3<sup>rd</sup>- and even 4<sup>th</sup>-order sensitivities are necessary to ensure consistency between the computed and measured response. In such cases, the use of only the 1<sup>st</sup>-order sensitivities erroneously indicates that the computed results are inconsistent with the respective measured response. Ongoing research aims at extending the 2<sup>nd</sup>-BERRU-PM methodology to fourth-order, thus enabling the computation of third-order response correlations (skewness) and fourth-order response correlations (kurtosis).
基金the support from the National Natural Science Foundation of China (Grant Nos. 12074081 and 12104095)。
文摘Atomic radiative data such as excitation energies, transition wavelengths, radiative rates, and level lifetimes with high precision are the essential parameters for the abundance analysis, simulation, and diagnostics in fusion and astrophysical plasmas. In this work, we mainly focus on reviewing our two projects performed in the past decade. One is about the ions with Z■30 that are generally of astrophysical interest, and the other one is about the highly charged krypton(Z = 36)and tungsten(Z = 74) ions that are relevant in research of magnetic confinement fusion. Two different and independent methods, namely, multiconfiguration Dirac–Hartree–Fock(MCDHF) and the relativistic many-body perturbation theory(RMBPT) are usually used in our studies. As a complement/extension to our previous works for highly charged tungsten ions with open M-shell and open N-shell, we also mainly focus on presenting and discussing our complete RMBPT and MCDHF calculations for the excitation energies, wavelengths, electric dipole(E1), magnetic dipole(M1), electric quadrupole(E2), and magnetic quadrupole(M2) transition properties, and level lifetimes for the lowest 148 levels belonging to the 3l3configurations in Al-like W61+. We also summarize the uncertainties of our systematical theoretical calculations, by cross-checking/validating our datasets from our RMBPT and MCDHF calculations, and by detailed comparisons with available accurate observations and other theoretical calculations. The data are openly available in Science Data Bank at https://doi.org/10.57760/sciencedb.10569.
