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This paper presents that mineral oil and linear alkylbenzene with dispersed metal oxide nanoparticles were investigated to clarify the effect of nanoparticles on AC breakdown strength and relative permittivity characteristics. The materials of nanoparticles were selected at TiO 2 (crystal structure of anatase and rutile) and ZnO. The nanoparticle concentrations of samples were prepared varying from 0 to 0.5 vol% into mineral oil or linear alkylbenzene by ultrasonic dispersion. AC (60 Hz) breakdown test was performed in a sphere-sphere electrode (diameter φ=12.5 mm, stainless steel) with gap length 2.0 mm, and also relative permittivity was measured at 60 Hz at room temperature. The breakdown strength increased by about 5 to 15% with the addition of nanoparticles. Relative permittivity increased with the addition of nanoparticles. With some combinations of insulating oil and nanoparticle, the measured relative permittivity agreed with the calculation with the random arranged model. But there are some combinations that the measured relative permittivity and the calculated one aren't coincident. These instructions give basic guidelines for illuminating their insulating characteristics.

We propose Distributed Neighbor Expansion (Distributed NE), a parallel and distributed graph partitioning method that can scale to trillion-edge graphs while providing high partitioning quality. Distributed NE is based on a new heuristic, called parallel expansion, where each partition is constructed in parallel by greedily expanding its edge set from a single vertex in such a way that the increase of the vertex cuts becomes local minimal. We theoretically prove that the proposed method has the upper bound in the partitioning quality. The empirical evaluation with various graphs shows that the proposed method produces higher-quality partitions than the state-of-the-art distributed graph partitioning algorithms. The performance evaluation shows that the space efficiency of the proposed method is an order-of-magnitude better than the existing algorithms, keeping its time efficiency comparable. As a result, Distributed NE can partition a trillion-edge graph using only 256 machines within 70 minutes.