鼻咽癌作为放射治疗可达根治效果的肿瘤之一,受到全球放射治疗医师的密切关注。放射治疗的临床靶区和放疗剂量是影响鼻咽癌治疗预后的重要因素。随着对鼻咽癌生物学特性以及周围邻近组织结构研究的日益深入,原来的临床靶区勾画指南可能...鼻咽癌作为放射治疗可达根治效果的肿瘤之一,受到全球放射治疗医师的密切关注。放射治疗的临床靶区和放疗剂量是影响鼻咽癌治疗预后的重要因素。随着对鼻咽癌生物学特性以及周围邻近组织结构研究的日益深入,原来的临床靶区勾画指南可能难以满足目前临床精准治疗的需求。因此来自全球各地的鼻咽癌专家进行了充分思考和深入探讨,于2018年在Radiotherapy and Oncology刊登了International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma一文。该指南建议鼻咽癌放射靶区CTVp1=GTVp+5mm,CTVp2=CTVp1+5mm+整个鼻咽,CTVn1=GTVn+5mm,CTVn2=CTVn1+5mm;并根据T分期对邻近组织结构的靶区勾画提出建议;同时针对危及器官的勾画以及其他争议项阐明各方观点,为临床实践提供参考。本文就该指南的具体细节进行解读。展开更多
The hydrologic model HEC-HMS （Hydrologic Engineering Center, Hydrologic Modeling System）, used in combination with the Geospatial Hydrologic Modeling Extension, HEC-GeoHMS, is not a site-specific hydrologic model. A...The hydrologic model HEC-HMS （Hydrologic Engineering Center, Hydrologic Modeling System）, used in combination with the Geospatial Hydrologic Modeling Extension, HEC-GeoHMS, is not a site-specific hydrologic model. Although China has seen the applications of many hydrologic and hydraulic models, HEC-HMS is seldom applied in China, and where it is applied, it is not applied holistically. This paper presents a holistic application of HEC-HMS. Its applicability, capability and suitability for flood forecasting in catchments were examined. The DEMs （digital elevation models） of the study areas were processed using HEC-GeoHMS, an ArcView GIS extension for catchment delineation, terrain pre-processing, and basin processing. The model was calibrated and verified using historical observed data. The determination coefficients and coefficients of agreement for all the flood events were above 0.9, and the relative errors in peak discharges were all within the acceptable range.展开更多
Since 2011,certain advances have been made through the resource investigation,metallogenesis research,mining supervision and environmental protection of ion-adsorption type rare earth element (REE) deposit in South Ch...Since 2011,certain advances have been made through the resource investigation,metallogenesis research,mining supervision and environmental protection of ion-adsorption type rare earth element (REE) deposit in South China.Firstly,some progress has been made in REE prospecting in Jiangxi,Guangdong,Guangxi and Yunnan.REE deposits are not only found within the weathering crusts of granites and felsic volcanic rocks,but also within the weathering crusts of epimetamorphic rocks and basic magmatic rocks.Secondly,the methods of exploration,delineating ore bodies and calculation of reserves have been improved,which intuitively reflect the thickness,REE composition and value of weathering crust.Thirdly,the relationship between REEs and weathering degree and the rule of distribution,migration and enrichment of REEs in the weathering profile was summarized through the analysis of big data,which can predict the metallogenetic horizon of REEs.Fourthly,a method for quick,accurate and dynamic investigation of the REE deposit has been established by using high resolution remote sensing technology.Finally,the relation between the production status of REE mines and water pollution has been revealed based on long-term hydrochemical monitoring data of rivers and wells in mines and surrounding areas.展开更多
This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network r...This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.展开更多
文摘鼻咽癌作为放射治疗可达根治效果的肿瘤之一,受到全球放射治疗医师的密切关注。放射治疗的临床靶区和放疗剂量是影响鼻咽癌治疗预后的重要因素。随着对鼻咽癌生物学特性以及周围邻近组织结构研究的日益深入,原来的临床靶区勾画指南可能难以满足目前临床精准治疗的需求。因此来自全球各地的鼻咽癌专家进行了充分思考和深入探讨,于2018年在Radiotherapy and Oncology刊登了International guideline for the delineation of the clinical target volumes (CTV) for nasopharyngeal carcinoma一文。该指南建议鼻咽癌放射靶区CTVp1=GTVp+5mm,CTVp2=CTVp1+5mm+整个鼻咽,CTVn1=GTVn+5mm,CTVn2=CTVn1+5mm;并根据T分期对邻近组织结构的靶区勾画提出建议;同时针对危及器官的勾画以及其他争议项阐明各方观点,为临床实践提供参考。本文就该指南的具体细节进行解读。
文摘The hydrologic model HEC-HMS （Hydrologic Engineering Center, Hydrologic Modeling System）, used in combination with the Geospatial Hydrologic Modeling Extension, HEC-GeoHMS, is not a site-specific hydrologic model. Although China has seen the applications of many hydrologic and hydraulic models, HEC-HMS is seldom applied in China, and where it is applied, it is not applied holistically. This paper presents a holistic application of HEC-HMS. Its applicability, capability and suitability for flood forecasting in catchments were examined. The DEMs （digital elevation models） of the study areas were processed using HEC-GeoHMS, an ArcView GIS extension for catchment delineation, terrain pre-processing, and basin processing. The model was calibrated and verified using historical observed data. The determination coefficients and coefficients of agreement for all the flood events were above 0.9, and the relative errors in peak discharges were all within the acceptable range.
文摘Since 2011,certain advances have been made through the resource investigation,metallogenesis research,mining supervision and environmental protection of ion-adsorption type rare earth element (REE) deposit in South China.Firstly,some progress has been made in REE prospecting in Jiangxi,Guangdong,Guangxi and Yunnan.REE deposits are not only found within the weathering crusts of granites and felsic volcanic rocks,but also within the weathering crusts of epimetamorphic rocks and basic magmatic rocks.Secondly,the methods of exploration,delineating ore bodies and calculation of reserves have been improved,which intuitively reflect the thickness,REE composition and value of weathering crust.Thirdly,the relationship between REEs and weathering degree and the rule of distribution,migration and enrichment of REEs in the weathering profile was summarized through the analysis of big data,which can predict the metallogenetic horizon of REEs.Fourthly,a method for quick,accurate and dynamic investigation of the REE deposit has been established by using high resolution remote sensing technology.Finally,the relation between the production status of REE mines and water pollution has been revealed based on long-term hydrochemical monitoring data of rivers and wells in mines and surrounding areas.
基金supported by the National Natural Science Foundation of China (Grant Nos.42271483,42071364)the Postgraduate Research&Practice Innovation Program of Jiangsu Province (Grant No.KYCX23_1696).
文摘This study presents a novel method for optimizing parameters in urban flood models,aiming to address the tedious and complex issues associated with parameter optimization.First,a coupled one-dimensional pipe network runoff model and a two-dimensional surface runoff model were integrated to construct an interpretable urban flood model.Next,a principle for dividing urban hydrological response units was introduced,incorporating surface attribute features.The K-means algorithm was used to explore the clustering patterns of the uncertain parameters in the model,and an artificial neural network(ANN)was employed to identify the sensitive parameters.Finally,a genetic algorithm(GA) was used to calibrate the parameter thresholds of the sub-catchment units in different urban land-use zones within the flood model.The results demonstrate that the parameter optimization method based on K-means-ANN-GA achieved an average Nash-Sutcliffe efficiency coefficient(NSE) of 0.81.Compared to the ANN-GA and K-means-deep neural networks(DNN) methods,the proposed method better characterizes the runoff generation and flow processes.This study demonstrates the significant potential of combining machine learning techniques with physical knowledge in parameter optimization research for flood models.
