In this paper we investigate the potential of Subset Multiple Correspondence Analysis (s-MCA), a variant of MCA, to visually explore two-mode networks. We discuss how s-MCA can be useful to focus the analysis on inter...In this paper we investigate the potential of Subset Multiple Correspondence Analysis (s-MCA), a variant of MCA, to visually explore two-mode networks. We discuss how s-MCA can be useful to focus the analysis on interesting subsets of events in an affiliation network while preserving the properties of the analysis of the complete network. This unique characteristic of the method is also particularly relevant to address the problem of missing data, where it can be used to partial out their influence and reveal the more substantive relational patterns. Similar to ordinary MCA, s- MCA can also alleviate the problem of overcrowded visualizations and can effectively identify associations between observed relational patterns and exogenous variables. All of these properties are illustrated on a student course-taking affiliation network.展开更多
We report a bioinformatic analysis of the datasets of sequences of all ten genes from the 2009 H1N1 influenza A pandemic in the state of Wisconsin. The gene with the greatest summed information entropy was found to be...We report a bioinformatic analysis of the datasets of sequences of all ten genes from the 2009 H1N1 influenza A pandemic in the state of Wisconsin. The gene with the greatest summed information entropy was found to be the hemagglutinin (HA) gene. Based upon the viral ID identifier of the HA gene sequence, the sequences of all of the genes were sorted into two subsets, depending upon whether the nucleotide occupying the position of maximum entropy, position 658 of the HA sequence, was either A or U. It was found that the information entropy (H) distributions of subsets differed significantly from each other, from H distributions of randomly generated subsets and from the H distributions of the complete datasets of each gene. Mutual information (MI) values facilitated identification of nine nucleotide positions, distributed over seven of the influenza genes, at which the nucleotide subsets were disjoint, or almost disjoint. Nucleotide frequencies at these nine positions were used to compute mutual information values that subsequently served as weighting factors for edges in a graph net-work. Seven of the nucleotide positions in the graph network are sites of synonymous mutations. Three of these sites of synonymous mutation are within a single gene, the M1 gene, which occupied the position of greatest graph centrality. It is proposed that these bioinformatic and network graph results may reflect alterations in M1-mediated viral packaging and exteriorization, known to be susceptible to synonymous mutations.展开更多
为了进一步实现无线传感器网络生命周期的最大化,针对网络中能量均匀且均衡覆盖问题展开研究,提出覆盖率均衡区域覆盖算法BRACA(Balanced Rate Area Coverage Algorithm)。该算法引入覆盖率均衡思想,将各传感器节点对目标区域覆盖率的...为了进一步实现无线传感器网络生命周期的最大化,针对网络中能量均匀且均衡覆盖问题展开研究,提出覆盖率均衡区域覆盖算法BRACA(Balanced Rate Area Coverage Algorithm)。该算法引入覆盖率均衡思想,将各传感器节点对目标区域覆盖率的均衡性与节点剩余能量的均衡性作为筛选因子,且通过调节传感器节点的剩余能量与其平均覆盖率的比例关系,筛选出最大不相关且代价最小的网络覆盖子集,以尽可能少的节点实现对区域的覆盖。经对比实验验证,算法BRACA具有更高的计算效率,所生成的ε-覆盖子集,以更少且更均衡的能量消耗,保证了网络覆盖率≥90%,有效地延长了网络生命周期。展开更多
文摘In this paper we investigate the potential of Subset Multiple Correspondence Analysis (s-MCA), a variant of MCA, to visually explore two-mode networks. We discuss how s-MCA can be useful to focus the analysis on interesting subsets of events in an affiliation network while preserving the properties of the analysis of the complete network. This unique characteristic of the method is also particularly relevant to address the problem of missing data, where it can be used to partial out their influence and reveal the more substantive relational patterns. Similar to ordinary MCA, s- MCA can also alleviate the problem of overcrowded visualizations and can effectively identify associations between observed relational patterns and exogenous variables. All of these properties are illustrated on a student course-taking affiliation network.
文摘We report a bioinformatic analysis of the datasets of sequences of all ten genes from the 2009 H1N1 influenza A pandemic in the state of Wisconsin. The gene with the greatest summed information entropy was found to be the hemagglutinin (HA) gene. Based upon the viral ID identifier of the HA gene sequence, the sequences of all of the genes were sorted into two subsets, depending upon whether the nucleotide occupying the position of maximum entropy, position 658 of the HA sequence, was either A or U. It was found that the information entropy (H) distributions of subsets differed significantly from each other, from H distributions of randomly generated subsets and from the H distributions of the complete datasets of each gene. Mutual information (MI) values facilitated identification of nine nucleotide positions, distributed over seven of the influenza genes, at which the nucleotide subsets were disjoint, or almost disjoint. Nucleotide frequencies at these nine positions were used to compute mutual information values that subsequently served as weighting factors for edges in a graph net-work. Seven of the nucleotide positions in the graph network are sites of synonymous mutations. Three of these sites of synonymous mutation are within a single gene, the M1 gene, which occupied the position of greatest graph centrality. It is proposed that these bioinformatic and network graph results may reflect alterations in M1-mediated viral packaging and exteriorization, known to be susceptible to synonymous mutations.
文摘为了进一步实现无线传感器网络生命周期的最大化,针对网络中能量均匀且均衡覆盖问题展开研究,提出覆盖率均衡区域覆盖算法BRACA(Balanced Rate Area Coverage Algorithm)。该算法引入覆盖率均衡思想,将各传感器节点对目标区域覆盖率的均衡性与节点剩余能量的均衡性作为筛选因子,且通过调节传感器节点的剩余能量与其平均覆盖率的比例关系,筛选出最大不相关且代价最小的网络覆盖子集,以尽可能少的节点实现对区域的覆盖。经对比实验验证,算法BRACA具有更高的计算效率,所生成的ε-覆盖子集,以更少且更均衡的能量消耗,保证了网络覆盖率≥90%,有效地延长了网络生命周期。
