New Methods to Evaluate Diesel Smoke from Inspection/Maintenance Vehicles Using Integration of Smoke Emissions over a Measurement Cycle of Snap-Acceleration Test at no Load

Each country regularly inspects smoke emissions from diesel vehicles. The most common method is a snap acceleration test at no load. Currently, the representative smoke value of a tested vehicle is decided by averaging three peak values which correspond to three consecutive measurement cycles. The biggest problem of the current method of determining the representative smoke of tested vehicles based on the averaging 3 consecutive peak values is that it varies enormously according to how the driver pushes the accelerator. In this study, new methods based on integration were researched to calculate representative smoke values from tested vehicles using the same data obtained by a snap acceleration test at no load. The smoke emissions for each measurement cycle were integrated with respect to time, and the average of the integrated values was obtained. Also, the smoke value of the acceleration range in each measurement cycle was integrated and the average was obtained. The representative value of smoke emissions was analyzed according to both the old and new calculation method. A total of 20 vehiclesof the same model with 2 liter diesel engineswere tested using snap acceleration at no load. It was found that the fluctuation of the representative smoke values of the tested vehicles was reduced when the average of the integrated values over either one measurement cycle or the acceleration range was used as the representative value.

Analysis of Nurse Calls for Residents and Workers Condition Understanding in Nursing Homes

Nursing-care facilities, such as specialized facility of fee-based homes for the elderly, have nurse call buttons in residents’ rooms. Nurse call systems are usually used by residents to call workers at the facilities. In this paper, we regarded the nurse calls as sensors that form a sensor network installed in the nursingcare facilities. We then conducted a long-term consecutive measurement of nurse calls at 15 facilities measuring nurse calls by residents for seven months, and analyzed monthly and diurnal variations in the number of nurse calls activated. As a result, we found no seasonal variation in average numbers but did find diurnal variations related to the schedules of facilities. We also clarified four kinds of characteristics of residents.

The thermal response of sheep to a hot environment in different years

SUMMARYThe aim of this experiment was to determine whether the thermal responses ofsheep exposed to a hot environment were similar in different years. Rectal temperaturesofsheep were measured during the hot months (between November and March) of three consecutive years (1990/91, 1991/92, 1992/93) in a flock of Merino sheep (n = 151) in the semi–arid tropics of northern Australia. Mean daily maximum temperatures in these months were 36±0·4, 39±0·3 and 37±0·3°C in Years 1, 2 and 3, respectively. In Year 1, rectal temperatures were measured on 3 consecutive days;the repeatability was 0·47 at 08·00 h and 0·64 at 16·00 h. In Year 1, two sub-groups were identified according to measurements of rectal temperature, low body temperature(LBT; n = 35) sheep, whose rectal temperature was ≤ 39·8°C at 16·00 h on each of the 3 consecutive measurement days, and high body temperature (HBT; n = 32) sheep, whose rectal temperature was ≥ 39·9°C. The rectal temperatures of LBT and HBT sheep were then measured during the subsequent 2 years. Rectal temperatures of HBT sheep were significantly higher than those of LBT sheep at both 08·00 and 16·00 h, in both Year 2 and Year 3. Rectal temperatures at 16·00 h in Year 1 were correlated (P < 0.001) with rectal temperatures at 16·00 h in both Year 2 (r = 0·79) and Year 3 (r = 0.82), respectively. It was concluded that measurement of rectal temperature in sheep exposed to a hot environment in any oneyear is an accurate index of their rectal temperatures during subsequent years.