This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their ...In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.展开更多
In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of posit...In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process.To avoid these issues,we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices.In their full specifications,the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts.Moreover,the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation.An application to two crude oils,WTI and Brent,and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations.In addition,the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.展开更多
A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-sc...A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-scale(SGS)turbulent fluxes associated with convective clouds in idealized tropical cyclone(TC)simulations.To evaluate the capability of the modified scheme in simulating real TCs,two sets of simulations of TC Soudelor(2015),one with the modified scheme and the other with the original scheme,are conducted.Comparisons with observations and coarse-grained results from large eddy simulation benchmarks demonstrate that the modified scheme improves the forecasting of the intensity and structure,as well as the SGS turbulent fluxes of Soudelor.Using the modified turbulence scheme,a TC with stronger intensity,smaller size,a shallower but stronger inflow layer,and a more intense but less inclined convective updraft is simulated.The rapid intensification process and secondary eyewall features can also be captured better by the modified scheme.By analyzing the mechanism by which turbulent transport impacts the intensity and structure of TCs,it is shown that accurately representing the turbulent transport associated with convective clouds above the planetary boundary layer helps to initiate the TC spin-up process.展开更多
Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving mod...Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.展开更多
Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast res...Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.展开更多
Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy...Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.展开更多
The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed wit...The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.展开更多
Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in we...Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.展开更多
In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In t...In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
In this study, we mainly introduce two salinity parameterization schemes used in Sea Ice Simulator （SIS）, that is, isosaline scheme and salinity profile scheme. Comparing the equation of isosaline scheme with that o...In this study, we mainly introduce two salinity parameterization schemes used in Sea Ice Simulator （SIS）, that is, isosaline scheme and salinity profile scheme. Comparing the equation of isosaline scheme with that of salinity profile scheme, we found that there was one different term between the two schemes named the salinity different term. The thermodynamic effect of the salinity difference term on sea ice thickness and sea ice concentration showed that： in the freezing processes from November to next May, the sea ice temperature could rise on the influence of the salinity difference term and restrain sea ice freezing; at the first melting phase from June to August, the upper ice melting rate was faster than the lower ice melting rate. Then sea ice temperature could rise and accelerate the sea ice melting; at the second melting phase from September to October, the upper ice melting rate was slower than the lower ice melting rate, then sea ice temperature could decrease and restrain sea ice melting. However, the effect of the salinity difference term on the sea ice thickness and sea ice concentration was weak. To analyze the impacts of the salinity different term on Arctic sea ice thickness and sea ice concentration, we also designed several experiments by introducing the two salinity parameterizations to the ice-ocean coupled model, Modular Ocean Model （MOM4）, respectively. The simulated results confirmed the previous results of formula derivation.展开更多
We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
Oxygenated volatile organic compounds(OVOCs) are key intermediates in the atmospheric photooxidation process. To further study the primary and secondary sources of OVOCs,their ambient levels were monitored using a pro...Oxygenated volatile organic compounds(OVOCs) are key intermediates in the atmospheric photooxidation process. To further study the primary and secondary sources of OVOCs,their ambient levels were monitored using a proton-transfer reaction mass spectrometer(PTR-MS) at an urban site in the Pearl River Delta of China. Continuous monitoring campaigns were conducted in the spring, summer, fall, and winter of 2016. Among the six types of OVOC species, the mean concentrations of methanol were the highest in each season(up to 13–20 ppbv), followed by those of acetone, acetaldehyde and acetic acid(approximately 2–4 ppbv), while those of formic acid and methyl ethyl ketone(MEK) were the lowest(approximately 1–2 ppbv). As observed from a diurnal variation chart, the OVOCs observed in Shenzhen may have been affected by numerous factors such as their primary and secondary sources and photochemical consumption. The photochemical age-based parameterization method was used to apportion the sources of ambient OVOCs. Methanol had significant anthropogenic primary sources but negligible anthropogenic secondary sources during all of the seasons. Acetone, MEK and acetic acid were mostly attributed to anthropogenic primary sources during each season with smaller contributions from anthropogenic secondary sources. Acetaldehyde had similar contributions from both anthropogenic secondary and anthropogenic primary sources throughout the year.Meanwhile, anthropogenic primary sources contributed the most to formic acid.展开更多
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
基金supported by the National Natural Science Foundation of China(No.52374264)the National Key Technologies Research and Development Program of China(No.2024YFC2909600)the Major Science and Technology Projects in Yunnan Province(No.202402AB080010).
文摘In recent years,the study of chalcopyrite and pyrite flotation surfaces using computational chemistry methods has made significant progress.However,current computational methods are limited by the small size of their systems and insufficient consideration of hydration and temperature effects,making it difficult to fully replicate the real flotation environment of chalcopyrite and pyrite.In this study,we employed the self-consistent charge density functional tight-binding(SCC-DFTB)parameterization method to develop a parameter set,CuFeOrg,which includes the interactions between Cu-Fe-C-H-O-N-S-P-Zn elements,to investigate the surface interactions in large-scale flotation systems of chalcopyrite and pyrite.The results of bulk modulus,atomic displacement,band structure,surface relaxation,surface Mulliken charge distribution,and adsorption tests of typical flotation reagents on mineral surfaces demonstrate that CuFeOrg achieves DFT-level accuracy while significantly outperforming DFT in computational efficiency.By constructing large-scale hydration systems of mineral surfaces,as well as large-scale systems incorporating the combined interactions of mineral surfaces,flotation reagents,and hydration,we more realistically reproduce the actual flotation environment.Furthermore,the dynamic analysis results are consistent with mineral surface contact angle experiments.Additionally,CuFeOrg lays the foundation for future studies of more complex and diverse chalcopyrite and pyrite flotation surface systems.
文摘In popular Baba-Engle-Kraft-Kroner(BEKK)and dynamic conditional correlation(DCC)multivariate generalized autoregressive conditional heteroskedasticity models,the large number of parameters and the requirement of positive definiteness of the covariance and correlation matrices pose some difficulties during the estimation process.To avoid these issues,we propose two modifications to the BEKK and DCC models that employ two spherical parameterizations applied to the Cholesky decompositions of the covariance and correlation matrices.In their full specifications,the introduced Cholesky-BEKK and Cholesky-DCC models allow for a reduction in the number of parameters compared with their traditional counterparts.Moreover,the application of spherical transformation does not require the imposition of inequality constraints on the parameters during the estimation.An application to two crude oils,WTI and Brent,and the main exchange rate prices demonstrates that the Cholesky-BEKK and Cholesky-DCC models can capture the dynamics of covariances and correlations.In addition,the Kupiec test on different portfolio compositions confirms the satisfactory performance of the proposed models.
基金supported by the National Key Research and Development Program of China(Grant No.2021YFC3000803)the National Natural Science Foundation of China(Grant Nos.42375149,41975133 and 42205070)the Shanghai Pujiang Program(Grant No.22PJ1415900)。
文摘A modified three-dimensional turbulence parameterization scheme,implemented by replacing the conventional eddydiffusivity formulation with the H-gradient model,has shown good performance in representing the subgrid-scale(SGS)turbulent fluxes associated with convective clouds in idealized tropical cyclone(TC)simulations.To evaluate the capability of the modified scheme in simulating real TCs,two sets of simulations of TC Soudelor(2015),one with the modified scheme and the other with the original scheme,are conducted.Comparisons with observations and coarse-grained results from large eddy simulation benchmarks demonstrate that the modified scheme improves the forecasting of the intensity and structure,as well as the SGS turbulent fluxes of Soudelor.Using the modified turbulence scheme,a TC with stronger intensity,smaller size,a shallower but stronger inflow layer,and a more intense but less inclined convective updraft is simulated.The rapid intensification process and secondary eyewall features can also be captured better by the modified scheme.By analyzing the mechanism by which turbulent transport impacts the intensity and structure of TCs,it is shown that accurately representing the turbulent transport associated with convective clouds above the planetary boundary layer helps to initiate the TC spin-up process.
基金The National Key Research and Development Program of China under contract No.2021YFC3101602the National Natural Science Foundation of China under contract Nos 42176017 and 41976019.
文摘Mesoscale eddies play a pivotal role in deciphering the intricacies of ocean dynamics and the transport of heat,salt,and nutrients.Accurate representation of these eddies in ocean models is essential for improving model predictions.In this study,we propose a convolutional neural network(CNN)that combines data-driven techniques with physical principles to develop a robust and interpretable parameterization scheme for mesoscale eddies in ocean modeling.We use a highresolution reanalysis dataset to extract subgrid eddy momentum and then applying machine learning algorithms to identify patterns and correlations.To ensure physical consistency,we have introduced conservation of momentum constraints in our CNN parameterization scheme through soft and hard constraints.The interpretability analysis illustrate that the pre-trained CNN parameterization shows promising results in accurately solving the resolved mean velocity and effectively capturing the representation of unresolved subgrid turbulence processes.Furthermore,to validate the CNN parameterization scheme offline,we conduct simulations using the Massachusetts Institute of Technology general circulation model(MITgcm)ocean model.A series of experiments is conducted to compare the performance of the model with the CNN parameterization scheme and high-resolution simulations.The offline validation demonstrates the effectiveness of the CNN parameterization scheme in improving the representation of mesoscale eddies in the MITgcm ocean model.Incorporating the CNN parameterization scheme leads to better agreement with high-resolution simulations and a more accurate representation of the kinetic energy spectra.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130608 and 42075142)the National Key Research and Development Program of China(Grant No.2020YFA0608000)the CUIT Science and Technology Innovation Capacity Enhancement Program Project(Grant No.KYTD202330)。
文摘Existing traditional ocean vertical-mixing schemes are empirically developed without a thorough understanding of the physical processes involved,resulting in a discrepancy between the parameterization and forecast results.The uncertainty in ocean-mixing parameterization is primarily responsible for the bias in ocean models.Benefiting from deep-learning technology,we design the Adaptive Fully Connected Module with an Inception module as the baseline to minimize bias.It adaptively extracts the best features through fully connected layers with different widths,and better learns the nonlinear relationship between input variables and parameterization fields.Moreover,to obtain more accurate results,we impose KPP(K-Profile Parameterization)and PP(Pacanowski–Philander)schemes as physical constraints to make the network parameterization process follow the basic physical laws more closely.Since model data are calculated with human experience,lacking some unknown physical processes,which may differ from the actual data,we use a decade-long time record of hydrological and turbulence observations in the tropical Pacific Ocean as training data.Combining physical constraints and a nonlinear activation function,our method catches its nonlinear change and better adapts to the oceanmixing parameterization process.The use of physical constraints can improve the final results.
基金supported by the National Natural Science Foundation of China(Grant No.82151302)the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-B-113)+1 种基金the National High Level Hospital Clinical Research Funding(Grant No.2022-PUMCH-A-019)the CAMS Innovation Fund for Medical Sciences(Grant No.2021-12M-1-014).
文摘Achieving high-resolution intracranial imaging in a safe and portable manner is critical for the diagnosis of intracranial diseases,preoperative planning of craniotomies and intraoperative management during craniotomy procedures.Adaptive waveform inversion(AWI),a variant of full waveform inversion(FWI),has shown potential in intracranial ultrasound imaging.However,the robustness of AWI is affected by the parameterization of the Gaussian penalty matrix and the challenges posed by transcranial scenarios.Conventional AWI struggles to produce accurate images in these cases,limiting its application in critical medical settings.To address these issues,we propose a stabilized adaptive waveform inversion(SAWI)method,which introduces a user-defined zero-lag position for theWiener filter.Numerical experiments demonstrate that SAWI can achieve accurate imaging under Gaussian penalty matrix parameter settings where AWI fails,perform successful transcranial imaging in configurations where AWI cannot,and maintain the same imaging accuracy as AWI.The advantage of this method is that it achieves these advancements without modifying the AWI framework or increasing computational costs,which helps to promote the application of AWI in medical fields,particularly in transcranial scenarios.
基金Supported by the National Natural Science Foundation of China(No.42276019)the Guangdong Provincial Observation and Research Station for Tropical Ocean Environment in Western Coastal Waters(No.GSTOEW)。
文摘The uncertainty of ocean turbulent mixing parameterization comprises a significant challenge in ocean and climate models. A depth-dependent deep learning ocean turbulent mixing parameterization scheme was proposed with the hydrological and microstructure observations conducted in summer 2012 in the shelf sea east of Hainan Island, in South China Sea(SCS). The deep neural network model is used and incorporates the Richardson number Ri, the normalized depth D, the horizontal velocity speed U, the shear S^(2), the stratification N^(2), and the density ρ as input parameters. Comparing to the scheme without parameter D and region division, the depth-dependent scheme improves the prediction of the turbulent kinetic energy dissipation rate ε. The correlation coefficient(r) between predicted and observed lgε increases from 0.49 to 0.62, and the root mean square error decreases from 0.56 to 0.48. Comparing to the traditional physics-driven parameterization schemes, such as the G89 and MG03, the data-driven approach achieves higher accuracy and generalization. The SHapley Additive Explanations(SHAP) framework analysis reveals the importance descending order of the input parameters as: ρ, D, U, N^(2), S^(2), and Ri in the whole depth, while D is most important in the upper and bottom boundary layers(D≤0.3&D≥0.65) and least important in middle layer(0.3<D<0.65). The research shows applicability of constructing deep learning-based ocean turbulent mixing parameterization schemes using limited observational data and well-established physical processes.
基金supported by the National Natural Science Foundations of China(Grant Nos.42305163 and U22A20577)the Construction Project of Weather Modification Ability in Central China(Grant No.ZQC-H22256)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0760300)the Projects of the Earth System Numerical Simulation Facility(Grant Nos.2024-EL-PT-000707,2023-ELPT-000482,2023-EL-ZD-00026,and 2022-EL-PT-00083)the STS Program of the Inner Mongolia Meteorological Service,Chongqing Institute of Green and Intelligent Technology,Chinese Academy of Sciences,and Institute of Atmospheric Physics,Chinese Academy of Sciences(Grant No.2021CG0047)。
文摘Accurate descriptions of cloud droplet spectra from aerosol activation to vapor condensation using microphysical parameterization schemes are crucial for numerical simulations of precipitation and climate change in weather forecasting and climate prediction models.Hence,the latest activation and triple-moment condensation schemes were combined to simulate and analyze the evolution characteristics of a cloud droplet spectrum from activation to condensation and compared with a high-resolution Lagrangian bin model and the current double-moment condensation schemes,in which the spectral shape parameter is fixed or diagnosed by an empirical formula.The results demonstrate that the latest schemes effectively capture the evolution characteristics of the cloud droplet spectrum during activation and condensation,which is in line with the performance of the bin model.The simulation of the latest activation and condensation schemes in a parcel model shows that the cloud droplet spectrum gradually widens and exhibits a multimodal distribution during the activation process,accompanied by a decrease in the spectral shape and slope parameters over time.Conversely,during the condensation process,the cloud droplet spectrum gradually narrows,resulting in increases in the spectral shape and slope parameters.However,these double-moment schemes fail to accurately replicate the evolution of the cloud droplet spectrum and its multimodal distribution characteristics.Furthermore,the latest schemes were coupled into a 1.5D cumulus model,and an observation case was simulated.The simulations confirm that the cloud droplet spectrum appears wider at the supersaturated cloud base and cloud top due to activation,while it becomes narrower at the middle altitudes of the cloud due to condensation growth.
基金support of the National Natural Science Foundation of China(No.12372221)is acknowledged.
文摘In this study,we introduce a deep generative model,named Multi-Species Generative Adversarial Network(MS-GAN),which is developed to extract the low-dimensional manifold of three-dimensional multi-species surfaces.In the development of MS-GAN,we extend the freeform deformation by incorporating principal component analysis to increase the non-linear deformation ability while maintaining geometric smoothness.The implicit information of multiple baselines is embedded in the feature extraction layers,to enhance the diversity and parameterization of multi-species dataset.Furthermore,Wasserstein GAN with a gradient penalty is used to ensure the stability and convergence of the training networks.Two experiments,ruled surfaces and propeller blade surfaces,are performed to demonstrate the advantages and superiorities of MS-GAN.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
基金supported by the National Natural Science Foundation of China(No.41075030,41106004,41106159 and 41206013)the Ocean Public Welfare Science Research Project,State Oceanic Administration,People's Republic of China(No.201005019)
文摘In this study, we mainly introduce two salinity parameterization schemes used in Sea Ice Simulator （SIS）, that is, isosaline scheme and salinity profile scheme. Comparing the equation of isosaline scheme with that of salinity profile scheme, we found that there was one different term between the two schemes named the salinity different term. The thermodynamic effect of the salinity difference term on sea ice thickness and sea ice concentration showed that： in the freezing processes from November to next May, the sea ice temperature could rise on the influence of the salinity difference term and restrain sea ice freezing; at the first melting phase from June to August, the upper ice melting rate was faster than the lower ice melting rate. Then sea ice temperature could rise and accelerate the sea ice melting; at the second melting phase from September to October, the upper ice melting rate was slower than the lower ice melting rate, then sea ice temperature could decrease and restrain sea ice melting. However, the effect of the salinity difference term on the sea ice thickness and sea ice concentration was weak. To analyze the impacts of the salinity different term on Arctic sea ice thickness and sea ice concentration, we also designed several experiments by introducing the two salinity parameterizations to the ice-ocean coupled model, Modular Ocean Model （MOM4）, respectively. The simulated results confirmed the previous results of formula derivation.
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
基金supported by the Ministry of Science and Technology of China (Nos.2017YFC0210004,2014BAC21B01)the Science and Technology Plan of Shenzhen Municipality (Nos.JCYJ20170412150626172,JCYJ20160122105855253)
文摘Oxygenated volatile organic compounds(OVOCs) are key intermediates in the atmospheric photooxidation process. To further study the primary and secondary sources of OVOCs,their ambient levels were monitored using a proton-transfer reaction mass spectrometer(PTR-MS) at an urban site in the Pearl River Delta of China. Continuous monitoring campaigns were conducted in the spring, summer, fall, and winter of 2016. Among the six types of OVOC species, the mean concentrations of methanol were the highest in each season(up to 13–20 ppbv), followed by those of acetone, acetaldehyde and acetic acid(approximately 2–4 ppbv), while those of formic acid and methyl ethyl ketone(MEK) were the lowest(approximately 1–2 ppbv). As observed from a diurnal variation chart, the OVOCs observed in Shenzhen may have been affected by numerous factors such as their primary and secondary sources and photochemical consumption. The photochemical age-based parameterization method was used to apportion the sources of ambient OVOCs. Methanol had significant anthropogenic primary sources but negligible anthropogenic secondary sources during all of the seasons. Acetone, MEK and acetic acid were mostly attributed to anthropogenic primary sources during each season with smaller contributions from anthropogenic secondary sources. Acetaldehyde had similar contributions from both anthropogenic secondary and anthropogenic primary sources throughout the year.Meanwhile, anthropogenic primary sources contributed the most to formic acid.
