目的:分析并确定ARCO 2-4期股骨头坏死(osteonecrosis of femoral head,ONFH)对Harris评分有影响意义的MR征象。方法:回顾性分析2019年1月至2020年6月34例行常规MR、T2 mapping、3D-SPACE序列检查及Harris评分的ARCO 2-4期ONFH患者,排除...目的:分析并确定ARCO 2-4期股骨头坏死(osteonecrosis of femoral head,ONFH)对Harris评分有影响意义的MR征象。方法:回顾性分析2019年1月至2020年6月34例行常规MR、T2 mapping、3D-SPACE序列检查及Harris评分的ARCO 2-4期ONFH患者,排除3例,最终纳入31例,男23例,女8例,年龄18~62(40.0±10.8)岁;其中21例为双侧ONFH,共计52个ONFH,ARC02期17个,ARCO 3期24个,ARCO 4期11个。在医院数字影像信息系统(picture archiving and communication system,PACS)对MR影像征象(股骨头塌陷深度、ONFH指数、骨髓水肿、股骨头骨质增生、软骨损伤分级、软骨T2值及关节积液)进行评估及测量,在Siemens后处理工作站计算软骨定量参数T2值并测量。采用Pearson相关分析评估MR各征象与Harris评分的相关性,采用多重线性回归分析评估与Harris评分有相关性的MR征象对Harris评分的影响。结果:Pearson相关分析显示股骨头塌陷深度(r=-0.563,P=0.000)、软骨损伤分级(r=-0.500,P=0.000)及关节积液(r=-0.535,P=0.000)与Harris评分呈负相关。多重线性回归分析显示关节积液(β=-6.198,P=0.001)、股骨头塌陷深度(β=-4.085,P=0.014)对Harris评分呈负相关。结论:关节积液、股骨头塌陷深度对Harris评分有显著的负向影响关系,建议影像医师常规对股骨头塌陷深度、关节积液进行定量及等级评估,以高效精准地辅助临床诊疗。展开更多
Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been...Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.展开更多
文摘目的:分析并确定ARCO 2-4期股骨头坏死(osteonecrosis of femoral head,ONFH)对Harris评分有影响意义的MR征象。方法:回顾性分析2019年1月至2020年6月34例行常规MR、T2 mapping、3D-SPACE序列检查及Harris评分的ARCO 2-4期ONFH患者,排除3例,最终纳入31例,男23例,女8例,年龄18~62(40.0±10.8)岁;其中21例为双侧ONFH,共计52个ONFH,ARC02期17个,ARCO 3期24个,ARCO 4期11个。在医院数字影像信息系统(picture archiving and communication system,PACS)对MR影像征象(股骨头塌陷深度、ONFH指数、骨髓水肿、股骨头骨质增生、软骨损伤分级、软骨T2值及关节积液)进行评估及测量,在Siemens后处理工作站计算软骨定量参数T2值并测量。采用Pearson相关分析评估MR各征象与Harris评分的相关性,采用多重线性回归分析评估与Harris评分有相关性的MR征象对Harris评分的影响。结果:Pearson相关分析显示股骨头塌陷深度(r=-0.563,P=0.000)、软骨损伤分级(r=-0.500,P=0.000)及关节积液(r=-0.535,P=0.000)与Harris评分呈负相关。多重线性回归分析显示关节积液(β=-6.198,P=0.001)、股骨头塌陷深度(β=-4.085,P=0.014)对Harris评分呈负相关。结论:关节积液、股骨头塌陷深度对Harris评分有显著的负向影响关系,建议影像医师常规对股骨头塌陷深度、关节积液进行定量及等级评估,以高效精准地辅助临床诊疗。
文摘Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms.
