ID 原文 译文
53047 以部署成本和网络时延为优化目标， optimizes deployment costs and network delays,
53048 划分基站集群， divides base station clusters,
53049 并使用整数线性规划(ILP)建立模型。 and builds models using integer linear programming(ILP).
53050 为了获得运行效率更高的边缘服务器部署方案，本文使用分支定界算法和启发式贪婪算法获得优化模型的近似最优解。 In order to obtain a more efficient edge server deployment scheme, this paper uses branch and bound algorithm and heuristic greedy algorithm to obtain the approximate optimal solution of the optimization model.
53051 实验评估结果显示，分支定界算法和启发式贪婪算法最高可以把边缘服务器部署算法运行时间减少37. 6%。 The experimental evaluation results show that the branch and bound algorithm and the heuristic greedy algorithm can reduce the running time of the edge server deployment algorithm by up to 37. 6%.
53052 此外，本文分析了用户服务器请求数量和用户服务优先级对算法运行时间和边缘服务器运行成本的影响。 Moreover, the paper analyzes the impact of the number of user server requests and user service priority on the algorithm running time and edge server running cost.
53053 利用复杂网络分析方法对脑网络的拓扑结构和属性进行分析，探讨高特质焦虑个体情绪注意偏向的脑功能机制。 The topological structure and attributes of brain network are analyzed by the complex network analysis method to explore the brain function mechanism of emotional attention bias in individuals with high trait anxiety.
53054 选取高特质性焦虑(HTA)和低特质性焦虑(LTA)各15名被试，进行3种情绪面孔表情(高兴、中性和愤怒)的搜索任务，同步记录64导脑电(EEG)。 Fifteen volunteers with high trait anxiety(HTA) and 15 volunteers with low trait anxiety(LTA) are selected to perform the task of searching for three emotional faces(happy, neutral and angry), and 64-channel electroencephalogram(EEG) is recorded simultaneously.
53055 对EEG数据进行同步似然分析，计算网络的整体属性参数(全局效率、聚类系数和特征路径长度)及节点属性参数(节点度)。 Synchronous likelihood analysis is performed on EEG, the overall attribute parameters(global efficiency, clustering coefficient and characteristic path length) and node attribute parameters(node degree) of the network are calculated.
53056 组内统计分析显示，HTA和LTA两组被试均表现为，愤怒面孔搜索任务下额叶与颞叶节点度、聚类系数及全局效率明显大于其高兴面孔和中性面孔的值，而特征路径长度小于高兴面孔和中性面孔的值。 The intra-group statistical analysis shows that in both the HTA and LTA groups, the node degrees of frontal and temporal lobe, clustering coefficient and global efficiency under angry face search task are significantly larger than the values of happy and neutral faces, while the characteristic path length is smaller than those of happy and neutral faces.