Significant wave period is an important parameter in coastal and offshore engineering design.Traditional spectral wave models do not directly calculate this parameter,which means that it needs to be estimated from the...Significant wave period is an important parameter in coastal and offshore engineering design.Traditional spectral wave models do not directly calculate this parameter,which means that it needs to be estimated from the spectral periods using empirical formulas.The wave energy period is one of the wave periods directly output by many wave models and is often used in studies of wave energy.This study investigated the relationship between significant wave period and wave energy period using wave data measured at three stations in the coastal waters of China.The observations recorded at these stations in the South China Sea,the East China Sea,and the Bohai Sea covered a wide range of surface wave conditions.Analysis indicated that the ratio of significant wave period to wave energy period is closely related to the Goda peakedness parameter of the wave spectra.Therefore,we proposed an empirical formula in which significant wave period is a function of wave energy period and the Goda peakedness parameter.Evaluation results showed that the performance of this formula is substantially better than that of fitting formulas that use constant coefficients.展开更多
BACKGROUND Clinically significant portal hypertension(CSPH)is a crucial prognostic deter-minant for liver-related events(LREs)in patients with compensated viral cir-rhosis.Liver stiffness measurement(LSM)-related mark...BACKGROUND Clinically significant portal hypertension(CSPH)is a crucial prognostic deter-minant for liver-related events(LREs)in patients with compensated viral cir-rhosis.Liver stiffness measurement(LSM)-related markers may help to predict the risk of LREs.AIM To evaluate the value of LSM and its composite biomarkers[LSM-platelet ratio(LPR),LSM-albumin ratio(LAR)]in predicting LREs.METHODS This study retrospectively enrolled compensated viral cirrhosis patients with CSPH.The Cox regression model was employed to examine the prediction of LSM,LPR,and LAR for LREs.The model performance was assessed through receiver operating characteristic,decision curve,and time-dependent area under the curve analysis.The Kaplan-Meier curve was used to evaluate the cumulative incidence of LREs,and further stratified analysis of different LREs was per-formed.RESULTS A total of 598 patients were included,and 319 patients(53.3%)developed LREs during follow-up.Multivariate proportional hazards modeling demonstrated that LSM,LPR,and LAR were independent predictors of LREs.LPR had better performance in predicting LREs than LAR and LSM(area under the curve=0.780,0.727,0.683,respectively,all P<0.05).The cumulative incidence of LREs in the high-risk group were significantly higher than that in the low-risk group(P<0.001).Among the different LREs,LPR was superior to LSM and LAR in predicting liver decompensation,while the difference in predicting hepatocellular carcinoma and liver-related death was relatively small.CONCLUSION LPR is superior to LSM and LAR in predicting LREs in compensated viral cirrhosis patients with CSPH,especially in predicting liver decompensation.展开更多
Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in S...Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting.However,these methods do not adapt well to dynamic seasonal variations in wave data.In this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural network.This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion.First,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern perspectives.Second,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics.Finally,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories.The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction.Furthermore,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.展开更多
基金The National Natural Science Foundation of China under contract No.41821004the Basic Scientific Fund for National Public Research Institutes of China under contract No.2020Q08the Fund of Laoshan Laboratory under contract No.LSKJ202201600.
文摘Significant wave period is an important parameter in coastal and offshore engineering design.Traditional spectral wave models do not directly calculate this parameter,which means that it needs to be estimated from the spectral periods using empirical formulas.The wave energy period is one of the wave periods directly output by many wave models and is often used in studies of wave energy.This study investigated the relationship between significant wave period and wave energy period using wave data measured at three stations in the coastal waters of China.The observations recorded at these stations in the South China Sea,the East China Sea,and the Bohai Sea covered a wide range of surface wave conditions.Analysis indicated that the ratio of significant wave period to wave energy period is closely related to the Goda peakedness parameter of the wave spectra.Therefore,we proposed an empirical formula in which significant wave period is a function of wave energy period and the Goda peakedness parameter.Evaluation results showed that the performance of this formula is substantially better than that of fitting formulas that use constant coefficients.
基金Supported by the High-Level Chinese Medicine Key Discipline Construction Project,No.zyyzdxk-2023005Capital’s Funds for Health Improvement and Research,No.2024-1-2173+2 种基金National Natural Science Foundation of China,No.82474419 and No.82474426Beijing Municipal Natural Science Foundation,No.7232272Beijing Traditional Chinese Medicine Technology Development Fund Project,No.BJZYZD-2023-12.
文摘BACKGROUND Clinically significant portal hypertension(CSPH)is a crucial prognostic deter-minant for liver-related events(LREs)in patients with compensated viral cir-rhosis.Liver stiffness measurement(LSM)-related markers may help to predict the risk of LREs.AIM To evaluate the value of LSM and its composite biomarkers[LSM-platelet ratio(LPR),LSM-albumin ratio(LAR)]in predicting LREs.METHODS This study retrospectively enrolled compensated viral cirrhosis patients with CSPH.The Cox regression model was employed to examine the prediction of LSM,LPR,and LAR for LREs.The model performance was assessed through receiver operating characteristic,decision curve,and time-dependent area under the curve analysis.The Kaplan-Meier curve was used to evaluate the cumulative incidence of LREs,and further stratified analysis of different LREs was per-formed.RESULTS A total of 598 patients were included,and 319 patients(53.3%)developed LREs during follow-up.Multivariate proportional hazards modeling demonstrated that LSM,LPR,and LAR were independent predictors of LREs.LPR had better performance in predicting LREs than LAR and LSM(area under the curve=0.780,0.727,0.683,respectively,all P<0.05).The cumulative incidence of LREs in the high-risk group were significantly higher than that in the low-risk group(P<0.001).Among the different LREs,LPR was superior to LSM and LAR in predicting liver decompensation,while the difference in predicting hepatocellular carcinoma and liver-related death was relatively small.CONCLUSION LPR is superior to LSM and LAR in predicting LREs in compensated viral cirrhosis patients with CSPH,especially in predicting liver decompensation.
基金The National Key R&D Program of China under contract No.2021YFC3101604。
文摘Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting.However,these methods do not adapt well to dynamic seasonal variations in wave data.In this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural network.This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion.First,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern perspectives.Second,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics.Finally,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories.The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction.Furthermore,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
