目的:观察损伤控制骨科(damage control orthopaedic,DCO)策略对下肢长骨干骨折伴发脂肪栓塞综合征(fat embolism syndrome,FES)的干预效果。方法:回顾性分析2015年1月至2021年5月收治的163例下肢长骨干骨折伴发FES患者临床资料,以2018...目的:观察损伤控制骨科(damage control orthopaedic,DCO)策略对下肢长骨干骨折伴发脂肪栓塞综合征(fat embolism syndrome,FES)的干预效果。方法:回顾性分析2015年1月至2021年5月收治的163例下肢长骨干骨折伴发FES患者临床资料,以2018年1月实施DCO策略为时间点分为两组,2015年1月至2017年12月收治92例为对照组,2018年1月至2021年5月收治71例为干预组。观察并比较两组患者院内死亡率、动脉血氧饱和度(arterial oxygen saturation,SaO2)、动脉血氧分压(arterial partial pressure of oxygen,PaO2)和氧合指数(oxygenation index,OI)、血红蛋白(hemoglobin,Hb)、血小板计数(platelet count,PLT)、髋关节Harris评分、美国特种外科医院膝关节评分(Hospital for Special Surgery Knee Score,HSS)评分、美国骨科足与踝关节协会(American Orthopaedic Foot and Ankle Society,AOFAS)评分、临床疗效及并发症。结果:163例患者获随访,时间12~18(16.91±1.22)个月。干预组院内死亡率为2.82%(2/71),对照组院内死亡率为16.30%(15/92),两组差异有统计意义(χ^(2)=6.455,P<0.05);两组干预后SaO2、PaO2和OI均较干预前升高(P<0.05),且两组干预后SaO2、PaO2和OI比较差异有统计意义(P<0.05)。两组干预后Hb、PLT均较干预前升高(P<0.001),且两组干预后Hb、PLT比较差异有统计意义(P<0.05)。两组患者治疗3个月后髋关节Harris评分、膝关节HSS评分、踝关节AOFAS评分均优于治疗前(P<0.05)。干预组临床总有效率高于对照组(χ^(2)=4.194,P<0.05)。干预组并发症总发生低于对照组(χ^(2)=4.747,P<0.05)。结论:DCO策略有助于降低下肢长骨干骨折伴发FES患者院内死亡率,有利于消除FES症状和平稳生命体征,可为Ⅱ期确定性手术争取时间优势,临床干预效果显著,值得推广应用。展开更多
为解决中央处理器(Central Processing Unit, CPU)性能分析所面临的分析指标复杂、分析过程不具有可解释性、分析结果不可追溯的问题,提出了一种融合ER(Evidence Reasoning)和分层BRB(Belief Rule Base)的CPU性能分析模型.首先,利用ER...为解决中央处理器(Central Processing Unit, CPU)性能分析所面临的分析指标复杂、分析过程不具有可解释性、分析结果不可追溯的问题,提出了一种融合ER(Evidence Reasoning)和分层BRB(Belief Rule Base)的CPU性能分析模型.首先,利用ER算法从不同层面对处理器影响因素进行指标评估,其次,通过分层BRB实现对CPU性能的综合分析,最后,采用鲸鱼优化算法(Whale Optimization Algorithm, WOA)对模型参数优化.通过UCI数据库(University of California Irvine, UCI)计算机硬件数据集验证了模型的有效性.整个分析模型建立在ER算法上,保证了模型推理的可解释性,而分层BRB方法解决了传统BRB的组合规则爆炸问题,同时结合优化算法有效的提高模型的准确度.展开更多
The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can i...The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.展开更多
文摘目的:观察损伤控制骨科(damage control orthopaedic,DCO)策略对下肢长骨干骨折伴发脂肪栓塞综合征(fat embolism syndrome,FES)的干预效果。方法:回顾性分析2015年1月至2021年5月收治的163例下肢长骨干骨折伴发FES患者临床资料,以2018年1月实施DCO策略为时间点分为两组,2015年1月至2017年12月收治92例为对照组,2018年1月至2021年5月收治71例为干预组。观察并比较两组患者院内死亡率、动脉血氧饱和度(arterial oxygen saturation,SaO2)、动脉血氧分压(arterial partial pressure of oxygen,PaO2)和氧合指数(oxygenation index,OI)、血红蛋白(hemoglobin,Hb)、血小板计数(platelet count,PLT)、髋关节Harris评分、美国特种外科医院膝关节评分(Hospital for Special Surgery Knee Score,HSS)评分、美国骨科足与踝关节协会(American Orthopaedic Foot and Ankle Society,AOFAS)评分、临床疗效及并发症。结果:163例患者获随访,时间12~18(16.91±1.22)个月。干预组院内死亡率为2.82%(2/71),对照组院内死亡率为16.30%(15/92),两组差异有统计意义(χ^(2)=6.455,P<0.05);两组干预后SaO2、PaO2和OI均较干预前升高(P<0.05),且两组干预后SaO2、PaO2和OI比较差异有统计意义(P<0.05)。两组干预后Hb、PLT均较干预前升高(P<0.001),且两组干预后Hb、PLT比较差异有统计意义(P<0.05)。两组患者治疗3个月后髋关节Harris评分、膝关节HSS评分、踝关节AOFAS评分均优于治疗前(P<0.05)。干预组临床总有效率高于对照组(χ^(2)=4.194,P<0.05)。干预组并发症总发生低于对照组(χ^(2)=4.747,P<0.05)。结论:DCO策略有助于降低下肢长骨干骨折伴发FES患者院内死亡率,有利于消除FES症状和平稳生命体征,可为Ⅱ期确定性手术争取时间优势,临床干预效果显著,值得推广应用。
文摘为解决中央处理器(Central Processing Unit, CPU)性能分析所面临的分析指标复杂、分析过程不具有可解释性、分析结果不可追溯的问题,提出了一种融合ER(Evidence Reasoning)和分层BRB(Belief Rule Base)的CPU性能分析模型.首先,利用ER算法从不同层面对处理器影响因素进行指标评估,其次,通过分层BRB实现对CPU性能的综合分析,最后,采用鲸鱼优化算法(Whale Optimization Algorithm, WOA)对模型参数优化.通过UCI数据库(University of California Irvine, UCI)计算机硬件数据集验证了模型的有效性.整个分析模型建立在ER算法上,保证了模型推理的可解释性,而分层BRB方法解决了传统BRB的组合规则爆炸问题,同时结合优化算法有效的提高模型的准确度.
基金This work is supported in part by the Postdoctoral Science Foundation of China under Grant No.2020M683736in part by the Teaching reform project of higher education in Heilongjiang Province under Grant No.SJGY20210456in part by the Natural Science Foundation of Heilongjiang Province of China under Grant No.LH2021F038.
文摘The prediction of processor performance has important referencesignificance for future processors. Both the accuracy and rationality of theprediction results are required. The hierarchical belief rule base (HBRB)can initially provide a solution to low prediction accuracy. However, theinterpretability of the model and the traceability of the results still warrantfurther investigation. Therefore, a processor performance prediction methodbased on interpretable hierarchical belief rule base (HBRB-I) and globalsensitivity analysis (GSA) is proposed. The method can yield more reliableprediction results. Evidence reasoning (ER) is firstly used to evaluate thehistorical data of the processor, followed by a performance prediction modelwith interpretability constraints that is constructed based on HBRB-I. Then,the whale optimization algorithm (WOA) is used to optimize the parameters.Furthermore, to test the interpretability of the performance predictionprocess, GSA is used to analyze the relationship between the input and thepredicted output indicators. Finally, based on the UCI database processordataset, the effectiveness and superiority of the method are verified. Accordingto our experiments, our prediction method generates more reliable andaccurate estimations than traditional models.
