重叠社团在社交网络大数据中普遍存在.针对现有重叠社团挖掘算法易将重叠区域错误地划分为独立的社团且计算复杂的问题,提出了一种基于局部信息度量的快速重叠社团挖掘算法(Local information based Fast Over-lapped Communities Detec...重叠社团在社交网络大数据中普遍存在.针对现有重叠社团挖掘算法易将重叠区域错误地划分为独立的社团且计算复杂的问题,提出了一种基于局部信息度量的快速重叠社团挖掘算法(Local information based Fast Over-lapped Communities Detection,Li-FOCD).首先,为节点定义局部信息度量指标——社团连接度和邻居连接度,建模节点与社团的关系,缩小了计算范围;然后,每次并行地迭代执行缩减、扩展、去重等操作,并更新局部度量指标,通过松弛每次迭代的终止条件,发现近似最优社团集合而不是最优社团,最终算法复杂度为O(m+n).基于真实的大规模社交网络数据的试验分析表明:与当前流行的重叠社团挖掘算法相比,Li-FOCD在不损失检测质量的前提下,大幅提升了计算效率.展开更多
目的构建甘草黄酮类化合物系统性分离制备方法。方法采用特异性吸附材料富集甘草中的黄酮类化合物,以自主研发的制备色谱工厂系统,采用色谱分离专家系统软件优化分离制备条件,通过上样量和富集次数等参数的考察,建立了基于分离富集模式...目的构建甘草黄酮类化合物系统性分离制备方法。方法采用特异性吸附材料富集甘草中的黄酮类化合物,以自主研发的制备色谱工厂系统,采用色谱分离专家系统软件优化分离制备条件,通过上样量和富集次数等参数的考察,建立了基于分离富集模式的反相二维色谱制备甘草黄酮有效部位及单体化合物的方法。结果建立了以C18为分离、富集填料,甲醇-水、乙腈-水为一维、二维分离流动相,水为富集稀释液,梯度洗脱体积流量和稀释富集液的体积流量均为21 m L/min,上样量300 mg,富集次数3次的二维色谱分离制备甘草黄酮的方法,其分离过程具有良好的重复性。应用该方法分离制备,可重复获得16个甘草黄酮部位和甘草苷、甘草素、芒柄花黄素、刺甘草查耳酮、7,4′-二羟基黄酮、4′-O-[β-D-apio-D-furanosyl-(1→2)-β-D-glucopyranosyl]liquiritigenin、异甘草素、甘草酚、甘草香豆素共9个单体化合物。结论建立的甘草黄酮的制备方法为甘草资源的综合利用和甘草黄酮活性药物开发奠定了基础。展开更多
The current network-on-chip （NoC） topology cannot predict subsequent switch node status promptly. Switch nodes have to perform various functions such as routing decision, data forwarding, packet buffering, congestio...The current network-on-chip （NoC） topology cannot predict subsequent switch node status promptly. Switch nodes have to perform various functions such as routing decision, data forwarding, packet buffering, congestion control and properties of an NoC system. Therefore, these make switch architecture far more complex. This article puts forward a separating on-chip network architecture based on Mesh （S-Mesh）, S-Mesh is an on-chip network that separates routing decision flow from the switches. It consists of two types of networks： datapath network （DN） and control network （CN）. The CN establishes data paths for data transferring in DN. Meanwhile, the CN also transfers instructions between different resources. This property makes switch architecture simple, and eliminates conflicts in network interface units between the resource and switch. Compared with 2D-Mesh, Toms Mesh, Fat-tree and Butterfly, the average packet latency in S-Mesh is the shortest when the packet length is more than 53 B. Compared with 2D-Mesh, the areas savings of S-Mesh is about 3%-7/% and the power dissipation is decreased by approximate 2%.展开更多
文摘重叠社团在社交网络大数据中普遍存在.针对现有重叠社团挖掘算法易将重叠区域错误地划分为独立的社团且计算复杂的问题,提出了一种基于局部信息度量的快速重叠社团挖掘算法(Local information based Fast Over-lapped Communities Detection,Li-FOCD).首先,为节点定义局部信息度量指标——社团连接度和邻居连接度,建模节点与社团的关系,缩小了计算范围;然后,每次并行地迭代执行缩减、扩展、去重等操作,并更新局部度量指标,通过松弛每次迭代的终止条件,发现近似最优社团集合而不是最优社团,最终算法复杂度为O(m+n).基于真实的大规模社交网络数据的试验分析表明:与当前流行的重叠社团挖掘算法相比,Li-FOCD在不损失检测质量的前提下,大幅提升了计算效率.
文摘目的构建甘草黄酮类化合物系统性分离制备方法。方法采用特异性吸附材料富集甘草中的黄酮类化合物,以自主研发的制备色谱工厂系统,采用色谱分离专家系统软件优化分离制备条件,通过上样量和富集次数等参数的考察,建立了基于分离富集模式的反相二维色谱制备甘草黄酮有效部位及单体化合物的方法。结果建立了以C18为分离、富集填料,甲醇-水、乙腈-水为一维、二维分离流动相,水为富集稀释液,梯度洗脱体积流量和稀释富集液的体积流量均为21 m L/min,上样量300 mg,富集次数3次的二维色谱分离制备甘草黄酮的方法,其分离过程具有良好的重复性。应用该方法分离制备,可重复获得16个甘草黄酮部位和甘草苷、甘草素、芒柄花黄素、刺甘草查耳酮、7,4′-二羟基黄酮、4′-O-[β-D-apio-D-furanosyl-(1→2)-β-D-glucopyranosyl]liquiritigenin、异甘草素、甘草酚、甘草香豆素共9个单体化合物。结论建立的甘草黄酮的制备方法为甘草资源的综合利用和甘草黄酮活性药物开发奠定了基础。
基金sponsored by the Hi-Tech Research and Development Program of China (2009AA01Z105)the Research Foundation of the Ministry of Education of China, and the Intel Information Technique (MOE-INTEL-08-05)
文摘The current network-on-chip （NoC） topology cannot predict subsequent switch node status promptly. Switch nodes have to perform various functions such as routing decision, data forwarding, packet buffering, congestion control and properties of an NoC system. Therefore, these make switch architecture far more complex. This article puts forward a separating on-chip network architecture based on Mesh （S-Mesh）, S-Mesh is an on-chip network that separates routing decision flow from the switches. It consists of two types of networks： datapath network （DN） and control network （CN）. The CN establishes data paths for data transferring in DN. Meanwhile, the CN also transfers instructions between different resources. This property makes switch architecture simple, and eliminates conflicts in network interface units between the resource and switch. Compared with 2D-Mesh, Toms Mesh, Fat-tree and Butterfly, the average packet latency in S-Mesh is the shortest when the packet length is more than 53 B. Compared with 2D-Mesh, the areas savings of S-Mesh is about 3%-7/% and the power dissipation is decreased by approximate 2%.
