Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonl...Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.展开更多
目的探讨早期肌少症筛查对于缺血性脑卒中患者预后康复效果的影响。方法选取河北中石油中心医院收治的缺血性脑卒中患者200例,根据是否伴有肌少症分为卒中伴肌少症组(100例)和脑卒中组(100例),比较2组四肢骨骼肌质量/身高、小腿围、体...目的探讨早期肌少症筛查对于缺血性脑卒中患者预后康复效果的影响。方法选取河北中石油中心医院收治的缺血性脑卒中患者200例,根据是否伴有肌少症分为卒中伴肌少症组(100例)和脑卒中组(100例),比较2组四肢骨骼肌质量/身高、小腿围、体重指数(body mass index,BMI)、脑卒中后6个月患者的预后结局;并根据患者康复效果将其分为预后良好组(128例)和预后不良组(72例),对患者康复效果的影响因素采用Logistic数据模型进行分析。结果卒中伴肌少症组的四肢骨骼肌质量/身高、小腿围、BMI值均低于脑卒中组(P<0.05),卒中伴肌少症组的预后不良率高于脑卒中组(P<0.05),预后良好组入院时国立卫生研究院卒中量表(National Institute of Health Stroke Scale,NIHSS)评分、入院时格拉斯哥昏迷(Glasgow Coma Scale,GCS)评分、平均动脉压变异性、合并高血压、合并糖尿病、合并心房颤动的情况与预后不良组比较(P<0.05),Logistic多因素分析,入院时NIHSS评分越高、入院时GCS评分越低、平均动脉压变异性越大、伴有心房颤动、肌少症是缺血性脑卒中患者预后不良的独立危险因素(P<0.05)。结论缺血性脑卒中患者伴有肌少症对于患者预后恢复不利,早期筛查患者是否伴有肌少症具有一定的临床价值。展开更多
基金Supported by the National Natural Science Foundation of China(No.41405097)the Fundamental Research Funds for the Central Universities of China in 2017
文摘Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.
文摘目的探讨早期肌少症筛查对于缺血性脑卒中患者预后康复效果的影响。方法选取河北中石油中心医院收治的缺血性脑卒中患者200例,根据是否伴有肌少症分为卒中伴肌少症组(100例)和脑卒中组(100例),比较2组四肢骨骼肌质量/身高、小腿围、体重指数(body mass index,BMI)、脑卒中后6个月患者的预后结局;并根据患者康复效果将其分为预后良好组(128例)和预后不良组(72例),对患者康复效果的影响因素采用Logistic数据模型进行分析。结果卒中伴肌少症组的四肢骨骼肌质量/身高、小腿围、BMI值均低于脑卒中组(P<0.05),卒中伴肌少症组的预后不良率高于脑卒中组(P<0.05),预后良好组入院时国立卫生研究院卒中量表(National Institute of Health Stroke Scale,NIHSS)评分、入院时格拉斯哥昏迷(Glasgow Coma Scale,GCS)评分、平均动脉压变异性、合并高血压、合并糖尿病、合并心房颤动的情况与预后不良组比较(P<0.05),Logistic多因素分析,入院时NIHSS评分越高、入院时GCS评分越低、平均动脉压变异性越大、伴有心房颤动、肌少症是缺血性脑卒中患者预后不良的独立危险因素(P<0.05)。结论缺血性脑卒中患者伴有肌少症对于患者预后恢复不利,早期筛查患者是否伴有肌少症具有一定的临床价值。
