目的以风险问题为导向,持续改进医院新冠肺炎疫情防控措施,阻断医院感染传播链,筑牢防线。方法运用失效模式与效应分析(failure mode and effect analysis,FMEA)方法结合PDCA管理工具,对院内新冠肺炎疫情防控环节进行风险评估,查找高风...目的以风险问题为导向,持续改进医院新冠肺炎疫情防控措施,阻断医院感染传播链,筑牢防线。方法运用失效模式与效应分析(failure mode and effect analysis,FMEA)方法结合PDCA管理工具,对院内新冠肺炎疫情防控环节进行风险评估,查找高风险因素,分析原因,制定有效的整改措施。结果实施整改后,院内新冠肺炎疫情防控环节9项高风险因素的总RPN值下降了82.98%。结论将FMEA方法与PDCA管理工具结合应用于院内新冠肺炎疫情防控的风险管理,可及时发现高风险隐患,有助于精准防控、持续改进,不断完善制度、流程,降低医院感染风险,保障医疗安全。展开更多
Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam(CFRD) and its displacements, the harmony search(HS) algorithm is used to optimize the back propagation n...Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam(CFRD) and its displacements, the harmony search(HS) algorithm is used to optimize the back propagation neural network(BPNN), and the HS-BPNN algorithm is formed and applied for the inversion analysis of the parameters of rock-fill materials. The sensitivity of the parameters in the Duncan and Chang's E-B model is analyzed using the orthogonal test design. The case study shows that the parameters φ0, K, Rf, and Kb are sensitive to the deformation of the rock-fill dam and the inversion analysis for these parameters is performed by the HS-BPNN algorithm. Compared with the traditional BPNN, the HS-BPNN algorithm exhibits the advantages of high convergence precision, fast convergence rate, and strong stability.展开更多
文摘目的以风险问题为导向,持续改进医院新冠肺炎疫情防控措施,阻断医院感染传播链,筑牢防线。方法运用失效模式与效应分析(failure mode and effect analysis,FMEA)方法结合PDCA管理工具,对院内新冠肺炎疫情防控环节进行风险评估,查找高风险因素,分析原因,制定有效的整改措施。结果实施整改后,院内新冠肺炎疫情防控环节9项高风险因素的总RPN值下降了82.98%。结论将FMEA方法与PDCA管理工具结合应用于院内新冠肺炎疫情防控的风险管理,可及时发现高风险隐患,有助于精准防控、持续改进,不断完善制度、流程,降低医院感染风险,保障医疗安全。
基金supported by the National Natural Science Foundation of China(Grant Nos.51579086,51479054,51379068&51139001)Jiangsu Natural Science Foundation(Grant No.BK20140039)the Priority Academic Program Development of Jiangsu Higher Education Institutions(Grant No.YS11001)
文摘Considering the complex nonlinear relationship between the material parameters of a concrete faced rock-fill dam(CFRD) and its displacements, the harmony search(HS) algorithm is used to optimize the back propagation neural network(BPNN), and the HS-BPNN algorithm is formed and applied for the inversion analysis of the parameters of rock-fill materials. The sensitivity of the parameters in the Duncan and Chang's E-B model is analyzed using the orthogonal test design. The case study shows that the parameters φ0, K, Rf, and Kb are sensitive to the deformation of the rock-fill dam and the inversion analysis for these parameters is performed by the HS-BPNN algorithm. Compared with the traditional BPNN, the HS-BPNN algorithm exhibits the advantages of high convergence precision, fast convergence rate, and strong stability.
