Based on the complex correlation between the geochemical element distribution patterns at the surface and the types of bedrock and the powerful capabilities in capturing subtle of machine learning algorithms,four mach...Based on the complex correlation between the geochemical element distribution patterns at the surface and the types of bedrock and the powerful capabilities in capturing subtle of machine learning algorithms,four machine learning algorithms,namely,decision tree(DT),random forest(RF),XGBoost(XGB),and LightGBM(LGBM),were implemented for the lithostratigraphic classification and lithostratigraphic prediction of a quaternary coverage area based on stream sediment geochemical sampling data in the Chahanwusu River of Dulan County,Qinghai Province,China.The local Moran’s I to represent the features of spatial autocorrelations,and terrain factors to represent the features of surface geological processes,were calculated as additional features.The accuracy,precision,recall,and F1 scores were chosen as the evaluation indices and Voronoi diagrams were applied for visualization.The results indicate that XGB and LGBM models both performed well.They not only obtained relatively satisfactory classification performance but also predicted lithostratigraphic types of the Quaternary coverage area that are essentially consistent with their neighborhoods which have the known types.It is feasible to classify the lithostratigraphic types through the concentrations of geochemical elements in the sediments,and the XGB and LGBM algorithms are recommended for lithostratigraphic classification.展开更多
目的:对比观察胸部硬膜外给予利多卡因对双腔气管插管患者血流动力学和唤醒水平的影响。方法:选择40例美国麻醉医师协会(American Society of Anesthesiologists,ASA)Ⅰ~Ⅱ级、年龄19~66岁拟在经口双腔气管插管全身麻醉下施择期手术的...目的:对比观察胸部硬膜外给予利多卡因对双腔气管插管患者血流动力学和唤醒水平的影响。方法:选择40例美国麻醉医师协会(American Society of Anesthesiologists,ASA)Ⅰ~Ⅱ级、年龄19~66岁拟在经口双腔气管插管全身麻醉下施择期手术的胸外科患者,分别为常规全身麻醉诱导下直接喉镜双腔气管插管组(T组,20例)和常规全身麻醉诱导复合胸部硬膜外给予利多卡因后实施双腔气管插管组(E组,20例)。麻醉诱导后分别采用Macintosh直接喉镜实施经口气管插管操作,观察两组患者麻醉诱导前后及气管插管时和气管插管后5 min内的血压(blood pressure,BP)、心率(heart rate,HR)、二重指数(rate pressure product,RPP)和脑电双频指数(bispectral index ,BIS)的变化,并记录气管插管时间。结果:麻醉诱导后,两组患者的BP和RPP均较麻醉诱导前明显降低。与麻醉诱导后相比较,气管插管后两组患者的BP、HR和RPP明显升高。与麻醉诱导前相比较,气管插管后E组患者BP明显降低,T组患者收缩压(systolic blood pressure,SBP)、舒张压(diastolic blood pressure,DBP)和平均动脉压(mean arterial pressure,MAP)明显升高,且持续时间约1 min。两组患者气管插管后HR均明显升高,T组患者HR增快持续约4 min,E组患者HR增快持续约1 min。与E组相比较,观察期内气管插管后T组SBP、DBP、MAP、HR和RPP均明显升高。与基础值相比,两组患者麻醉诱导后和气管插管后的BIS值均明显降低,且两组之间差异无统计学意义。与E组比较,观察期T组SBP大于基础值30%和RPP大于22 000的发生率明显较高,且E组中未见SBP大于基础值30%和RPP大于22 000的患者。结论:在双腔气管插管期间,硬膜外给予利多卡因可明显减轻插管导致的剧烈血流动力学变化,但对唤醒反应无影响。展开更多
基金Projects(41772348,42072326)supported by the National Natural Science Foundation of ChinaProject(2017YFC0601503)supported by the National Key Research and Development Program,China。
文摘Based on the complex correlation between the geochemical element distribution patterns at the surface and the types of bedrock and the powerful capabilities in capturing subtle of machine learning algorithms,four machine learning algorithms,namely,decision tree(DT),random forest(RF),XGBoost(XGB),and LightGBM(LGBM),were implemented for the lithostratigraphic classification and lithostratigraphic prediction of a quaternary coverage area based on stream sediment geochemical sampling data in the Chahanwusu River of Dulan County,Qinghai Province,China.The local Moran’s I to represent the features of spatial autocorrelations,and terrain factors to represent the features of surface geological processes,were calculated as additional features.The accuracy,precision,recall,and F1 scores were chosen as the evaluation indices and Voronoi diagrams were applied for visualization.The results indicate that XGB and LGBM models both performed well.They not only obtained relatively satisfactory classification performance but also predicted lithostratigraphic types of the Quaternary coverage area that are essentially consistent with their neighborhoods which have the known types.It is feasible to classify the lithostratigraphic types through the concentrations of geochemical elements in the sediments,and the XGB and LGBM algorithms are recommended for lithostratigraphic classification.
