Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology...Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology laboratory. Using the sigma metric, this study assessed the performance of the Biochemistry analytical system of a medical biology laboratory in Côte d'Ivoire. Methods: Six Sigma methodology was applied to 3 analytes (alanine aminotransferase, glucose and creatinine). Performance indicators such as measurement imprecision and bias were determined based on the results of internal and external quality controls. The sigma number was calculated using the total allowable error values proposed by Ricos et al. Results: For both control levels, ALT had a sigma number greater than 6 (7.6 for normal control and 7.9 for pathological control). However, low sigma numbers, less than or equal to 2 for creatinine (1.4 for normal control and 2 for pathological control) and less than 1 for glucose were found. Conclusion: This study revealed good analytical performance of ALT from the point of view of 6 sigma analysis. However, modifications to the overall quality control procedure for glucose and creatinine are needed to improve their analytical performance. The study should be extended to the entire laboratory’s analytes in order to modify the strategies of quality control procedures based on metric analysis for an overall improvement in analytical performance.展开更多
In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this pr...In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.展开更多
文摘Introduction: The Six Sigma methodology is an opportunity for a better understanding of the performance of analytical methods and for a better adaptation of the quality control management policy of the medical biology laboratory. Using the sigma metric, this study assessed the performance of the Biochemistry analytical system of a medical biology laboratory in Côte d'Ivoire. Methods: Six Sigma methodology was applied to 3 analytes (alanine aminotransferase, glucose and creatinine). Performance indicators such as measurement imprecision and bias were determined based on the results of internal and external quality controls. The sigma number was calculated using the total allowable error values proposed by Ricos et al. Results: For both control levels, ALT had a sigma number greater than 6 (7.6 for normal control and 7.9 for pathological control). However, low sigma numbers, less than or equal to 2 for creatinine (1.4 for normal control and 2 for pathological control) and less than 1 for glucose were found. Conclusion: This study revealed good analytical performance of ALT from the point of view of 6 sigma analysis. However, modifications to the overall quality control procedure for glucose and creatinine are needed to improve their analytical performance. The study should be extended to the entire laboratory’s analytes in order to modify the strategies of quality control procedures based on metric analysis for an overall improvement in analytical performance.
基金supported by the National Natural Science Foundation of China(Grant No.41331174)
文摘In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.
