R Adachi

Bone resorption was measured directly in flight and synchronous control rats during COSMOS 1129. Continuous tracer administration techniques were used, with replacement of dietary calcium with isotopically enriched 40Ca and measurement by neutron activation analysis of the 48Ca released by the skeleton. There is no large change in bone resorption in rats at the end of 20 days of spaceflight as has been found for bone formation. Based on the time course of changes, the measured 20-25% decrease in resorption is probably secondary to a decrease in total body calcium turnover. The excretion of sodium, potassium, and zinc all increase during flight, sodium and potassium to a level four to five times control values.

Studies with dogs were undertaken to determine whether nitrogen metabolism during aerobic treadmill running is affected by recency of food intake. Over a 6-hr period after intake of a standard meal, the percentage of energy derived from carbohydrate, fat, and protein oxidation was relatively constant at 70, 24, and 6, respectively. With postabsorptive dogs, the percentages of energy derived from carbohydrate, fat, and protein were significantly different ( P <.01) and in the ratio of 45:53:2. Urinary nitrogen, amino acids, 17-hydroxycorticosteroids, Na, and K were examined in postabsorptive animals at rest and at work. Over a 3-hr period, the urinary NPN and Na were 0.49 g and 5.8 mEq, respectively, and unaffected by work; K and hydroxycorticosteroids were 4.6 mEq and 228 μg, respectively, and significantly elevated during work. Of 18 amino acids measured, only cystathionine excretion was significantly altered during work. In running trials of 88–600 min duration, both NPN and 17-hydroxycorticosteroids showed a tendency to increase with calorie expenditure. Submitted on December 22, 1961

Continuous mixing and conveying technology for solid-liquid mixtures is required in the manufacturing process of foods and medicines. To achieve this, we develop a peristaltic mixing conveyor that simulates the function of the human intestines. This device can mix and convey food and medicinal contents by inflating a rubber tube using air pressure. Currently, we are working on a system of content condition estimation using measurement data from the pressure and flow rate sensors installed in the device. However, these measurement methods use air supplied to the device as the measurement target, and the compressibility of air limits the conditions of contents that can be estimated. So, the generalizability of the estimation is low. In this study, a thin pressure-sensitive sensor is installed that can measure the mechanical responses of device contents due to mixing by the device. We also construct a multisensing system that combines conventional pressure/flow rate and pressing force measurements. Sensor data acquired when solid-liquid mixtures are fed into the device are applied to machine learning to distinguish the mixing ratios of the mixtures. Results show that the accuracy of mixing ratio discrimination is improved from $96.7 \%$ to $98.9 \%$ when pressure and flow rate data are combined with pressing force data. The results thus confirm the improved accuracy of content identification when pressure/flow rate and pressing force measurements are combined.