目的分析新生儿脐动脉血气指标与阿普加(Apgar)评分的相关性。方法选取294例新生儿作为研究对象,根据出生1 min Apgar评分不同分为研究组(Apgar评分≤7分,22例)、对照组(Apgar评分8~10分,272例)。比较两组出生1 min Apgar评分、脐动脉...目的分析新生儿脐动脉血气指标与阿普加(Apgar)评分的相关性。方法选取294例新生儿作为研究对象,根据出生1 min Apgar评分不同分为研究组(Apgar评分≤7分,22例)、对照组(Apgar评分8~10分,272例)。比较两组出生1 min Apgar评分、脐动脉血气指标[pH值、血乳酸(Lac)、剩余碱(BE)],采用Pearson相关性分析法分析脐动脉血气指标与Apgar评分的相关性。结果研究组出生1 min Apgar评分(5.78±0.39)分、pH值(7.18±0.16)、BE(-10.14±1.13)mmol/L低于对照组的(8.59±0.41)分、(7.26±0.02)、(-3.24±1.02)mmol/L,Lac(8.46±1.14)mmol/L高于对照组的(5.02±0.21)mmol/L(P<0.05)。Pearson相关性分析结果显示,pH值、BE与出生1 min Apgar评分呈正相关(r=0.607、0.626,P<0.05);Lac与出生1 min Apgar评分呈负相关(r=-0.780,P<0.05)。结论相较于出生1 min Apgar评分8~10分的新生儿,Apgar评分≤7分的新生儿pH值、BE更低,Lac更高,新生儿脐动脉血气指标(pH值、BE、Lac)与Apgar评分关系密切,将脐动脉血气指标与Apgar评分同时应用可为后续开展临床诊疗新生儿窒息工作提供参考。展开更多
针对电动汽车单一电源电流冲击大的问题,构建了电容半主动拓扑的复合电源,并提出一种基于小波变换与遗传算法(genetic algorithm,GA)优化模糊控制的能量管理策略。利用Haar小波分解需求功率,以高频分量及储能元件荷电状态(state of char...针对电动汽车单一电源电流冲击大的问题,构建了电容半主动拓扑的复合电源,并提出一种基于小波变换与遗传算法(genetic algorithm,GA)优化模糊控制的能量管理策略。利用Haar小波分解需求功率,以高频分量及储能元件荷电状态(state of charge,SOC)为输入,通过GA优化隶属度函数参数实现功率精准分配。在新欧洲驾驶循环(new European driving cycle,NEDC)及中国轻型汽车行驶工况-乘用车(China light-duty vehicle test cycle-passenger car,CLTC-P)等工况下的仿真表明,相比单一电源策略,所提策略峰值电流平均降低约28.0%,均方根(root mean square,RMS)电流平均降低约21.0%,并将高电流区间占比压缩至6%以内,电池温升幅度降低34.8%和37.8%;在不同SOC条件下均表现出鲁棒性,对电流指标的优化幅度稳定保持在20%~28%区间,有效延长了电池循环寿命。展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
文摘目的分析新生儿脐动脉血气指标与阿普加(Apgar)评分的相关性。方法选取294例新生儿作为研究对象,根据出生1 min Apgar评分不同分为研究组(Apgar评分≤7分,22例)、对照组(Apgar评分8~10分,272例)。比较两组出生1 min Apgar评分、脐动脉血气指标[pH值、血乳酸(Lac)、剩余碱(BE)],采用Pearson相关性分析法分析脐动脉血气指标与Apgar评分的相关性。结果研究组出生1 min Apgar评分(5.78±0.39)分、pH值(7.18±0.16)、BE(-10.14±1.13)mmol/L低于对照组的(8.59±0.41)分、(7.26±0.02)、(-3.24±1.02)mmol/L,Lac(8.46±1.14)mmol/L高于对照组的(5.02±0.21)mmol/L(P<0.05)。Pearson相关性分析结果显示,pH值、BE与出生1 min Apgar评分呈正相关(r=0.607、0.626,P<0.05);Lac与出生1 min Apgar评分呈负相关(r=-0.780,P<0.05)。结论相较于出生1 min Apgar评分8~10分的新生儿,Apgar评分≤7分的新生儿pH值、BE更低,Lac更高,新生儿脐动脉血气指标(pH值、BE、Lac)与Apgar评分关系密切,将脐动脉血气指标与Apgar评分同时应用可为后续开展临床诊疗新生儿窒息工作提供参考。
文摘针对电动汽车单一电源电流冲击大的问题,构建了电容半主动拓扑的复合电源,并提出一种基于小波变换与遗传算法(genetic algorithm,GA)优化模糊控制的能量管理策略。利用Haar小波分解需求功率,以高频分量及储能元件荷电状态(state of charge,SOC)为输入,通过GA优化隶属度函数参数实现功率精准分配。在新欧洲驾驶循环(new European driving cycle,NEDC)及中国轻型汽车行驶工况-乘用车(China light-duty vehicle test cycle-passenger car,CLTC-P)等工况下的仿真表明,相比单一电源策略,所提策略峰值电流平均降低约28.0%,均方根(root mean square,RMS)电流平均降低约21.0%,并将高电流区间占比压缩至6%以内,电池温升幅度降低34.8%和37.8%;在不同SOC条件下均表现出鲁棒性,对电流指标的优化幅度稳定保持在20%~28%区间,有效延长了电池循环寿命。
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
