A large number of people download music files easily from web sites. But rare music sites provide personalized services. So, we suggest a method for personalized services. We extract the properties of music from music's sound wave. We use STFT (Shortest Time Fourier Form) to analyze music's property. And we infer users' preferences from users' music list. To analyze users' preferences we propose a dynamic K-means clustering algorithm. The dynamic K-means clustering algorithm clusters the pieces in the music list dynamically adapting the number of clusters. We recommend pieces of music based on the clusters. The previous recommendation systems analyze a user's preference by simply averaging the properties of music in the user's list. So those cannot recommend correctly if a user prefers several genres of music. By using our K-means clustering algorithm, we can recommend pieces of music which are close to user's preference even though he likes several genres. We perform experiments with one hundred pieces of music. In this paper we present and evaluate algorithms to recommend music.

The purpose of this study was to investigate the effect of 7-MEGA™ 500 on the improvement of skin aging in an UVB-induced photo-aging model of hairless mice. The dorsal skin of hairless mice was exposed to UVB three times a week for 12 weeks to induce skin wrinkle. After inducing the wrinkle, 7-MEGA™ 500 was orally administered once a day for 4 weeks. Skin thickness, skin barrier function, and wrinkle indicators were improved by treatment with 7-MEGA™ 500. Both gene and protein expression levels of MMP-3 and c-Jun in skin were significantly decreased by 7-MEGA™ 500. Therefore, the intake of 7-MEGA™ 500 is thought to have a positive effect on the improvement of skin aging, although further studies are needed.

No abstract

No abstract

No abstract