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Abstract: We have investigated the temperature dependence of electrical conductivity and thermoelectric power (TEP) at 1.7K< T <300K in an organo metallic complex, the iodine doped poly(vinyl alcohol)–Cu2+ chelate. We observed intrinsic metallic temperature dependence of resistivity from room temperature to 68K with a broad minimum [ρ(68K)/ρ(300K) ∼0.75], which has not been observed previously in similar organo metallic complexes. There occurs an unusual metal-insulator transition at T ∼68K and the resisitivity increases upon cooling below 68K. However, the low temperature resistivity becomes finite (instead of going to infinity), [ρ(1.7K)/ρ(300K) ∼0.98] indicating that a quantum mechanical tunneling conduction is dominant at this low temperature. It is remarkable that the resistivity at 1.7K is as small as that of room temperature. Such unusual temperature dependence of conductivity could be understood as thermally assisted hopping conduction between metallic islands. However, the observed intrinsic metallic temperature dependence of resistivity implies that such hopping conduction barrier is not important at high temperature (T >68K). The intrinsic metallic characteristics are confirmed by the quasi-linear temperature dependence of TEP for the whole measured temperature range (1.7K< T <300K) with a small slope change at low temperature, T <68K, which is understood as an effect of variable range hopping (VRH) conduction at low temperature. The results of magneto resistance (MR) and magneto thermoelectric power (MTEP) are consistent with the above interpretation. [Copyright &y& Elsevier]

• We propose a novel approach to emulate the clustering results of co-citation (CC) and bibliographic coupling (BC). • The proposed method simply replicates a node into two distinct nodes: a citing node and a cited node. • Citing nodes emulates BC and cited nodes imitates CC with very low computational loads. • The proposed method generally outperformed BC and CC regarding cluster accuracy measured by normalized mutual information. • The analysis of intracluster and intercluster geographical proximity suggests that the proposed method retains additional information than BC and CC. Scholars frequently employ relatedness measures to estimate the similarity between two different items (e.g. , documents, authors, and institutes). Such relatedness measures are commonly based on overlapping references (i.e. , bibliographic coupling) or citations (i.e. , co-citation) and can then be used with cluster analysis to find boundaries between research fields. Unfortunately, calculating a relatedness measure is challenging, especially for a large number of items, because the computational complexity is greater than linear. We propose an alternative method for identifying research fronts that uses direct citation inspired by relatedness measures. Our novel approach simply replicates a node into two distinct nodes: a citing node and cited node. We then apply typical clustering methods to the modified network. Clusters of citing nodes should emulate those from the bibliographic coupling relatedness network, while clusters of cited nodes should act like those from the co-citation relatedness network. In validation tests, our proposed method demonstrated high levels of similarity with conventional relatedness-based methods. We also found that the clustering results of the proposed method outperformed those of conventional relatedness-based measures regarding similarity with natural language processing-based classification. [ABSTRACT FROM AUTHOR]