This article provides a comprehensive review of the advancements in the application of artificial intelligence(AI)technology in the modernization of traditional Chinese medicine(TCM)compound prescriptions,and emphasiz...This article provides a comprehensive review of the advancements in the application of artificial intelligence(AI)technology in the modernization of traditional Chinese medicine(TCM)compound prescriptions,and emphasizes recent research developments,including intelligent design,prediction of mechanisms of action,and precise application of TCM compound prescriptions.The integration of multi-omics data,deep learning algorithms,and knowledge graph technologies has established novel technical avenues for the modernization research of TCM.This study systematically analyzes the advantages and challenges associated with AI technologies in the research of TCM compound prescriptions,highlighting issues such as data heterogeneity,limited interpretability of AI models,and the absence of standardized procedures.Furthermore,this article examines the prospective developmental trajectories within this field,highlighting the importance of synergistic collaboration between AI and traditional pharmacology to improve the clinical applicability and effectiveness of TCM.The objective is to offer valuable insights into the modernization of TCM driven by AI and to stimulate further research in this area.展开更多
Using the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface ...Using the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature （SST）, singular value decomposition （SVD） analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin （20~0S-20~0N, 120~0E- 80~0W）, respectively starting in four seasons from 1982 to 2005. On the basis of SVD analysis, the simulated pattern was replaced with the corresponding observed pattern to reconstruct SST anomaly fields to improve the ability of the simulation. The predictive skill, anomaly correlation coefficients （ACC）, after systematic error correction using the first five modes was regarded as potential predictability. Results showed that： 1） the statistical postprocessing approach was effective for systematic error correction; 2） model error sources mainly arose from mode 2 extracted from the SVD analysis-that is, during the transition phase of ENSO, the model encountered the spring predictability barrier; and 3） potential predictability （upper limits of predictability） could be high over most of the tropical Pacific basin, including the tropical western Pacific and an extra 10-degrees region of the mid and eastern Pacific.展开更多
This article presents a systematic direct approach to carry out effective optimization of a wide range of continuous-thrust Earth-orbit transfers with intermediate-level thrust acceleration,including minimum-time (wit...This article presents a systematic direct approach to carry out effective optimization of a wide range of continuous-thrust Earth-orbit transfers with intermediate-level thrust acceleration,including minimum-time (with a single burn arc) and mini-mum-fuel (with multiple burn arcs) transfers. With direct control parameterization,in which the control steering programs of burn arcs are interpolated through a finite number of nodes,the optimal control problem is converted into the parameter optimi-zation proble...展开更多
AIM To explore the risk factors of developing chronic pan-creatitis （CP） in patients with acute pancreatitis （AP） and develop a prediction score for CP.METHODS Using the National Health Insurance Research Database...AIM To explore the risk factors of developing chronic pan-creatitis （CP） in patients with acute pancreatitis （AP） and develop a prediction score for CP.METHODS Using the National Health Insurance Research Database in Taiwan, we obtained large, population-based data of 5971 eligible patients diagnosed with AP from 2000 to 2013. After excluding patients with obstructive pancreatitis and biliary pancreatitis and those with a follow-up period of less than 1 year, we conducted a multivariate analysis using the data of 3739 patients to identify the risk factors of CP and subsequently develop a scoring system that could predict the development of CP in patients with AP. In addition, we validated the scoring system using a validation cohort.RESULTS Among the study subjects, 142 patients （12.98%） developed CP among patients with RAP. On the other hand, only 32 patients （1.21%） developed CP among patients with only one episode of AP. The multivariate analysis revealed that the presence of recurrent AP （RAP）, alcoho-lism, smoking habit, and age of onset of 〈 55 years were the four important risk factors for CP. We developed a scoring system （risk score 1 and risk score 2） from the derivation cohort by classifying the patients into low-risk, moderate-risk, and high-risk categories based on similar magnitudes of hazard and validated the performance using another validation cohort. Using the prediction score model, the area under the curve （AUC） [95% confdence interval （CI）] in predicting the 5-year CP incidence in risk score 1 （without the number of AP episodes） was 0.83 （0.79, 0.87）, whereas the AUC （95%CI） in risk score 2 （including the number of AP episodes） was 0.84 （0.80, 0.88）. This result demonstrated that the risk score 2 has somewhat better prediction performance than risk score 1. However, both of them had similar performance between the derivation and validation cohorts.CONCLUSIONIn the study,we identifed the risk factors of CP and devel-oped a prediction score model for CP.展开更多
As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Pr...As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.展开更多
Optimization of energy consump-tion for ecast model based on big data platform and parallel random forest
A healthy data set is acquired through data collection and preprocessing based on the construction of distribu...Optimization of energy consump-tion for ecast model based on big data platform and parallel random forest
A healthy data set is acquired through data collection and preprocessing based on the construction of distributed big data analysis platform such as Hadoop,Spark and Hbase.Re-gression forecasting model of energy consumption based on the parallel random forest algorithm is built to comprehensively ana-lyze and compare the relationship between input based on ran-dom forest prediction model,model parameters and output.The emphasis lies on comparative analysis of the decision tree num-ber,depth of the decision tree and maximumnumber of split,which will affect the training model accuracy,running time and complexity.Optimization of the prediction model canachieve ac-curate prediction on the coal consumption for power supply and soft measurement calculation.展开更多
The Hong Kong Observatory （HKO） has been developing a suite of nowcasting systems to support op- erations of the forecasting center and to provide a variety of nowcasting services for the general public and speciali...The Hong Kong Observatory （HKO） has been developing a suite of nowcasting systems to support op- erations of the forecasting center and to provide a variety of nowcasting services for the general public and specialized users. The core system is named the Short-range Warnings of Intense Rainstorm of Localized Systems （SWIRLS）, which is a radar-based nowcasting system mainly for the automatic tracking of the movement of radar echoes and the short-range Quantitative Precipitation Forecast （QPF）. The differential, integral （or variational）, and object-oriented tracking algorithms were developed and integrated into the nowcasting suite. In order to predict severe weather associated with intense thunderstorms, such as high gust, hail, and lightning, SWIRLS was enhanced to SWIRLS-II by introduction of a number of physical models, especially the icing physics as well as the thermodynamics of the atmosphere. SWIRLS-Ⅱ was further enhanced with non-hydrostatic, high resolution numerical models for extending the forecast range up to 6h ahead. Meanwhile, SWIRLS was also modified for providing nowcasting services for aviation community and specialized users. To take into account the rapid development of lightning events, ensemble nowcasting techniques such as time-lagged and weighted average ensemble approaches were also adopted in the nowcasting system. Apart from operational uses in Hong Kong, SWIRLS/SWIRLS-Ⅱ was also exported to other places to participate in several international events such as the WMO/WWRP Forecast Demon- stration Project （FDP） during the Beijing 2008 Olympics Games and the Shanghai Expo 2010. Meanwhile, SWIRLS has also been transferred to various regional meteorological organizations for establishing their nowcasting infrastructure. This paper summarizes the history and the technologies of SWIRLS/SWIRLS-Ⅱ and its variants and the associated nowcasting applications and services provided by the HKO since the mid 1990s.展开更多
文摘This article provides a comprehensive review of the advancements in the application of artificial intelligence(AI)technology in the modernization of traditional Chinese medicine(TCM)compound prescriptions,and emphasizes recent research developments,including intelligent design,prediction of mechanisms of action,and precise application of TCM compound prescriptions.The integration of multi-omics data,deep learning algorithms,and knowledge graph technologies has established novel technical avenues for the modernization research of TCM.This study systematically analyzes the advantages and challenges associated with AI technologies in the research of TCM compound prescriptions,highlighting issues such as data heterogeneity,limited interpretability of AI models,and the absence of standardized procedures.Furthermore,this article examines the prospective developmental trajectories within this field,highlighting the importance of synergistic collaboration between AI and traditional pharmacology to improve the clinical applicability and effectiveness of TCM.The objective is to offer valuable insights into the modernization of TCM driven by AI and to stimulate further research in this area.
基金supported by the National Natural Science Foundation of China (NSFC) (Grant Nos. 40975065 and 40821092)the National Basic Research Program (NBRP) "Ocean–atmosphere interaction over the joining area of Asia and the Indian-Pacific Ocean (AIPO) and its impact on the short-term climate variation in China" project(2006CB403605)
文摘Using the Flexible Global Ocean-Atmosphere-Land System model （FGOALS） version g1.11, a group of seasonal hindcasting experiments were carried out. In order to investigate the potential predictability of sea surface temperature （SST）, singular value decomposition （SVD） analyses were applied to extract dominant coupled modes between observed and predicated SST from the hindcasting experiments in this study. The fields discussed are sea surface temperature anomalies over the tropical Pacific basin （20~0S-20~0N, 120~0E- 80~0W）, respectively starting in four seasons from 1982 to 2005. On the basis of SVD analysis, the simulated pattern was replaced with the corresponding observed pattern to reconstruct SST anomaly fields to improve the ability of the simulation. The predictive skill, anomaly correlation coefficients （ACC）, after systematic error correction using the first five modes was regarded as potential predictability. Results showed that： 1） the statistical postprocessing approach was effective for systematic error correction; 2） model error sources mainly arose from mode 2 extracted from the SVD analysis-that is, during the transition phase of ENSO, the model encountered the spring predictability barrier; and 3） potential predictability （upper limits of predictability） could be high over most of the tropical Pacific basin, including the tropical western Pacific and an extra 10-degrees region of the mid and eastern Pacific.
基金National Natural Science Foundation of China (10603005)Foundation of President of the Academy of Opto-Electro-nics ( AOE-CX-200601)
文摘This article presents a systematic direct approach to carry out effective optimization of a wide range of continuous-thrust Earth-orbit transfers with intermediate-level thrust acceleration,including minimum-time (with a single burn arc) and mini-mum-fuel (with multiple burn arcs) transfers. With direct control parameterization,in which the control steering programs of burn arcs are interpolated through a finite number of nodes,the optimal control problem is converted into the parameter optimi-zation proble...
文摘AIM To explore the risk factors of developing chronic pan-creatitis （CP） in patients with acute pancreatitis （AP） and develop a prediction score for CP.METHODS Using the National Health Insurance Research Database in Taiwan, we obtained large, population-based data of 5971 eligible patients diagnosed with AP from 2000 to 2013. After excluding patients with obstructive pancreatitis and biliary pancreatitis and those with a follow-up period of less than 1 year, we conducted a multivariate analysis using the data of 3739 patients to identify the risk factors of CP and subsequently develop a scoring system that could predict the development of CP in patients with AP. In addition, we validated the scoring system using a validation cohort.RESULTS Among the study subjects, 142 patients （12.98%） developed CP among patients with RAP. On the other hand, only 32 patients （1.21%） developed CP among patients with only one episode of AP. The multivariate analysis revealed that the presence of recurrent AP （RAP）, alcoho-lism, smoking habit, and age of onset of 〈 55 years were the four important risk factors for CP. We developed a scoring system （risk score 1 and risk score 2） from the derivation cohort by classifying the patients into low-risk, moderate-risk, and high-risk categories based on similar magnitudes of hazard and validated the performance using another validation cohort. Using the prediction score model, the area under the curve （AUC） [95% confdence interval （CI）] in predicting the 5-year CP incidence in risk score 1 （without the number of AP episodes） was 0.83 （0.79, 0.87）, whereas the AUC （95%CI） in risk score 2 （including the number of AP episodes） was 0.84 （0.80, 0.88）. This result demonstrated that the risk score 2 has somewhat better prediction performance than risk score 1. However, both of them had similar performance between the derivation and validation cohorts.CONCLUSIONIn the study,we identifed the risk factors of CP and devel-oped a prediction score model for CP.
文摘As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.
文摘Optimization of energy consump-tion for ecast model based on big data platform and parallel random forest
A healthy data set is acquired through data collection and preprocessing based on the construction of distributed big data analysis platform such as Hadoop,Spark and Hbase.Re-gression forecasting model of energy consumption based on the parallel random forest algorithm is built to comprehensively ana-lyze and compare the relationship between input based on ran-dom forest prediction model,model parameters and output.The emphasis lies on comparative analysis of the decision tree num-ber,depth of the decision tree and maximumnumber of split,which will affect the training model accuracy,running time and complexity.Optimization of the prediction model canachieve ac-curate prediction on the coal consumption for power supply and soft measurement calculation.
文摘The Hong Kong Observatory （HKO） has been developing a suite of nowcasting systems to support op- erations of the forecasting center and to provide a variety of nowcasting services for the general public and specialized users. The core system is named the Short-range Warnings of Intense Rainstorm of Localized Systems （SWIRLS）, which is a radar-based nowcasting system mainly for the automatic tracking of the movement of radar echoes and the short-range Quantitative Precipitation Forecast （QPF）. The differential, integral （or variational）, and object-oriented tracking algorithms were developed and integrated into the nowcasting suite. In order to predict severe weather associated with intense thunderstorms, such as high gust, hail, and lightning, SWIRLS was enhanced to SWIRLS-II by introduction of a number of physical models, especially the icing physics as well as the thermodynamics of the atmosphere. SWIRLS-Ⅱ was further enhanced with non-hydrostatic, high resolution numerical models for extending the forecast range up to 6h ahead. Meanwhile, SWIRLS was also modified for providing nowcasting services for aviation community and specialized users. To take into account the rapid development of lightning events, ensemble nowcasting techniques such as time-lagged and weighted average ensemble approaches were also adopted in the nowcasting system. Apart from operational uses in Hong Kong, SWIRLS/SWIRLS-Ⅱ was also exported to other places to participate in several international events such as the WMO/WWRP Forecast Demon- stration Project （FDP） during the Beijing 2008 Olympics Games and the Shanghai Expo 2010. Meanwhile, SWIRLS has also been transferred to various regional meteorological organizations for establishing their nowcasting infrastructure. This paper summarizes the history and the technologies of SWIRLS/SWIRLS-Ⅱ and its variants and the associated nowcasting applications and services provided by the HKO since the mid 1990s.
