Because suicide is increasingly becoming a public health threat in Japan, it is necessary to identify high-risk groups to develop effective preventive measures. The suicide mortality trends from 1985 to 2006 for Japanese aged between 15 and 79 years were analyzed by a Bayesian age-period-cohort analysis to evaluate the independent effects of age, period, and birth cohort. Age-specific effect showed an overall increase with age in both genders, but a distinct increase was noted only among men aged between 50 and 64 years. The period effect exhibited a sudden rise in 1998; this effect was more apparent in men than in women. The cohort-specific effect increased in male birth cohorts born after 1926 and in female birth cohorts born after 1956. In conclusion, a gender difference was detected in the effects of age, period, and cohort on suicide risk among Japanese.

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Recently, research about man-machine interface has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigenface method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing.In this paper, in order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied compress the dimensionality of portions that constitute a face. A value of cos0 is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using Neural Networks (NN), the value of cos0 between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification metod for true or false smile, computer simulations are done.

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