For a complex reaction such as glucose production from maltose by hydrolysis in subcritical water, neural network analysis was applied to determine the optimum combination of the reaction conditions. As input parameters for neural network analysis, the preset temperature of the reactor, the residence time, the initial concentration of maltose, and the pressure of the subcritical water were selected. As the output parameter, the reactive index I, the product of the yield and selectivity of glucose, was selected. In the first demonstration, the optimum reactive condition of residence time and temperature was determined at constant concentration and pressure. The neural network model was built from the various experimental data, and the reactive indices I_P at unknown conditions were predicted from the model. The neural network model was corrected by the addition of experimental data around the lowest I_P. The addition of data and the rebuild of the model were repeated. The final model was completed when the reactive conditions giving the lowest I_P and the experimental I_E were identical. To determine the optimum reactive condition, 1 or 3 rebuilds of the neural network model were required. The errors between the predicted and experimental conditions were 2°C in the reactor temperature and 1.89 min in the residence time. Also, in the expansion to a 4-dimensional reactive condition, that is, temperature, residence time, concentration and pressure, the optimum reactive condition was well presented by neural network analysis.

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We compare the CPU time and the wall clock time of the Raffenetti's P file algorithm with the usual algorithm on the two electron integrals storing with four suffixes of the ab initio Hartree-Fock calculations. The calculations are performed with the flutoprazepam, triazolam, clotiazepam, etizolam, and flutazolam molecules. These molecules are all minor-tranquilizers with the benzodiazepine or thienodiazepine backbone. The 3-21G basis sets are employed. Almost in all cases, P file algorithm gave slower speed than the usual algorithm. The number of two electron integrals increases almost two times larger than the usual algorithms. In a large molecule, the matrix of the two electron integrals becomes very sparse and the recombination of the integrals just increases the total number of the integrals. It is concluded that the P method sometimes calculates faster but sometimes does not. In a large scale calculation, it should be suggested to perform a test calculation to confirm which method is faster prior to the real calculations.

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We propose a method to get information about the SPM density and the characters in the sky. We use the spectrum of the moonlight for night observations, and adopt color indexes that are calculated from RGB-data of digital photography for the daylight cases. We show a diffractive optic to get the spectrum, and make correlations between the color index and the SPM density. Using these approaches, we observed the SPM density in the sky between east latitude 120 and 130 degree along the north latitude 32 degree. We discovered the following:
1. SPM clusters were often found in the upper sky on the west area from 120 degree of east latitude. Physically considering the particle density, it is natural to find the concentration in the lower sky. However, if there is a jet stream in the upper air-layer, and the SPM cluster moves with the stream; the upper SPM concentration would be found. On the Lider observations, we often find such SPM clusters.
2. Absorptions were found clearly in the blue band of the moonlight spectrum. It shows a localized SPM cluster in the upper sky. The absorptions are round in the case of the lower sky. Since the ray passes through long distances, the absorptions were averaged about many kinds of different SPM clusters.
3. On the west area from 120 degree of east latitude, there is no relation between the SPM density on the ground and the intensity of the red-band of the moonlight spectrum. On the east 131 degree, the relation is found obviously. Thus, it is certain that we find orange moons in every lower sky.
4. There is invisible mist in the stratosphere from 120 degree of the east latitude.
From these observations, we believe that the characters of the SPM are changed in moving 10 degrees of the east latitude.

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In this note, we present a method to draw a distribution map of materials observed at a few, sparse and localized data points, a situation frequently appeared in the problem of chemistry and environmental science. In this situation, it is usually difficult to draw a distribution map because data on triangular or square lattice are required. We generate additional points where the amount of materials is estimated by CQSAR: Compensation Quantitative Structure-Activity Relationships based on perceptron type neural network. Then the data were used for drawing a distribution map. The distribution map using the additional points generated by CQSAR is useful for understanding an overview of material distributions.

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