Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and development.In this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most networks.According to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the nodes.In most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree information.Finally,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.
目的设计一种生命体征采集系统,实现非联网体征采集设备测量数据的自动识别与记录。方法利用移动手持终端(Personal Digital Assistant,PDA)作为硬件载体,以卷积神经网络算法为基础,结合YOLOv5目标检测算法开发图像识别功能,设计具有图像识别能力的生命体征采集系统。通过PDA实现体温、血压、血氧、血糖测量设备体征数据的快速识别,并按照护理管理规则自动记录至医疗系统。结果生命体征采集系统应用后,体征数据采集正确率由99.67%提高至100.00%,次均采集时间缩短29.21%,医护满意度提升5.74%,且差异均有统计学意义(P<0.05)。结论该系统以真实体征数据保障有效诊断,进一步提高了病区护理工作效率,提升了护理质量水平与准确性。
