Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations a...Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.展开更多
Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color phot...Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color photography(FCP)or optical coherence tomography(OCT).These methods can only reflect retinal diseases to a certain extent,ignoring the speci ficity of modalities between different imaging modalities.In this research,a newmulti-scale feature fusion network(MSFF-Net)model for multi-modal retinal image diagnosis is proposed.The MSFF-Net model employs a dualbranch architecture design,enabling efficient learning and extraction of multi-modal feature information related to retinal diseases from CFP and OCT images.MSFF-Net improves disease diagnosis by combining multi-scale features of CFP and OCT images.When evaluated on challenging datasets,the model achieved an accuracy of 95.00%and an F1-score of 95.24%for retinal disease diagnosis.Even under low-quality dataset conditions,it maintained robust performance,with diagnostic accuracy and F1-scores of 71.50%and 71.73%,respectively.In addition,the MSFFNet model outperformed eight state-of-the-art single and multi-modal models in the comparison experiments.The proposed MSFF-Net model provides ophthalmologists with a more accurate and efficient diagnostic pathway that helps them detect and treat retinal diseases earlier.展开更多
文摘基于广义约化R矩阵理论,使用RAC程序(R-matrix analysis code)综合分析了^(6)He系统中所有可以利用的实验数据,给出了氚核入射10-2—20 MeV能量范围内主要反应道的评价核数据.其中积分截面包括T(t,2n)^(4)He,T(t,n)^(5)He,T(t,d)^(4)H;微分截面包括T(t,2n)^(4)He,T(t,n)^(5)He,T(t,d)^(4)H,T(t,t)T.结果表明,RAC的评价结果与实验数据和ENDF/B-Ⅷ.1的评价数据整体符合良好.重点关注T(t,2n)^(4)He反应,评价值在10^(-2)—20 MeV范围内与已有实验数据一致,在2.9 Me V附近出现由2+能级主导的共振,在1.9 Me V处,已有实验同时测量了积分截面和角分布,本工作的评价结果在两类数据上均表现出良好的一致性,积分截面与微分截面的联合约束有效提升了R矩阵参数的稳定性和评价结果的可靠性.基于6He系统的整体评价,进一步补充了T(t,n)^(5)He和T(t,d)^(4)H反应的截面数据.本工作完善了聚变反应相关的数据基础,并为后续与镜像系统6Be系统的联合分析奠定了基础.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3209504)Natural Science Foundation of Wuhan(Grant No.2024040801020271)the Fundamental Research Funds for Central Public Welfare Research Institutes(Grant No.CKSF2025718/YT).
文摘Wetting deformation in earth-rockfill dams is a critical factor influencingdam safety.Although numerous mathematical models have been developed to describe this phenomenon,most of them rely on empirical formulations and lack prior knowledge of model parameters,which is essential for Bayesian parameter inversion to enhance accuracy and reduce uncertainty.This study introduces a datadriven approach to establishing prior knowledge of earth-rockfill dams.Driving factors are utilized to determine the potential range of model parameters,and settlement changes within this range are calculated.The results are iteratively compared with actual monitoring data until the calculated range encompasses the observed data,thereby providing prior knowledge of the model parameters.The proposed method is applied to the right-bank earth-rockfilldam of Danjiangkou.Employing a Gibbs sample size of 30,000,the proposed method effectively calibrates the prior knowledge of the wetting model parameters,achieving a root mean square error(RMSE)of 5.18 mm for the settlement predictions.By comparison,the use of non-informative priors with sample sizes of 30,000 and 50,000 results in significantly larger RMSE values of 11.97 mm and 16.07 mm,respectively.Furthermore,the computational efficiencyof the proposed method is demonstrated by an inversion computation time of 902 s for 30,000 samples,which is notably shorter than the 1026 s and 1558 s required for noninformative priors with 30,000 and 50,000 samples,respectively.These findingsunderscore the superior performance of the proposed approach in terms of both prediction accuracy and computational efficiency.These results demonstrate that the proposed method not only improves the predictive accuracy but also enhances the computational efficiency,enabling optimal parameter identificationwith reduced computational effort.This approach provides a robust and efficientframework for advancing dam safety assessments.
基金supported by the National Natural Science Foundation of China(Nos.82472104 and U24B2053)the Natural Science Basic Research Program of Shaanxi(No.2025JC-JCQN-023)+2 种基金the Key Core Technology Research and Development of Shaanxi(No.2024QY2-GJHX-03)the Innovation Capability Support Program of Shaanxi(Program No.2023-CX-TD-54)the Xidian University Specially Funded Project for Interdisciplinary Exploration(No.TZJHF202510).
文摘Retinal diseases are a serious threat to human visual health and their early diagnosis is crucial.Currently,most of the retinal disease diagnostic algorithms are based on a single imaging modality of fundus color photography(FCP)or optical coherence tomography(OCT).These methods can only reflect retinal diseases to a certain extent,ignoring the speci ficity of modalities between different imaging modalities.In this research,a newmulti-scale feature fusion network(MSFF-Net)model for multi-modal retinal image diagnosis is proposed.The MSFF-Net model employs a dualbranch architecture design,enabling efficient learning and extraction of multi-modal feature information related to retinal diseases from CFP and OCT images.MSFF-Net improves disease diagnosis by combining multi-scale features of CFP and OCT images.When evaluated on challenging datasets,the model achieved an accuracy of 95.00%and an F1-score of 95.24%for retinal disease diagnosis.Even under low-quality dataset conditions,it maintained robust performance,with diagnostic accuracy and F1-scores of 71.50%and 71.73%,respectively.In addition,the MSFFNet model outperformed eight state-of-the-art single and multi-modal models in the comparison experiments.The proposed MSFF-Net model provides ophthalmologists with a more accurate and efficient diagnostic pathway that helps them detect and treat retinal diseases earlier.
