• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.2
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• pp.420-427
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• 2006

Immunological activities of the combined-administration of Ginseng Radix Rubra and Vitis Fructus were examined in C57BL/6 mice. Ginseng Radix Rubra and Vitis Fructus were extracted with distilled water or 40% ethyl alcohol. Ginseng Radix Rubra water extracts (GW), the mixture (1:1) of Ginseng Radix Rubra and Vitis Fructus water extracts [GVW(1:1)], the mixture (1:3) of Ginseng Radix Rubra and Vitis Fructus water extracts [GVW(1:3)], 40% ethyl alcohol extracts of Ginseng Radix Rubra (GE), the mixture (1:1) of Ginseng Radix Rubra and Vitis Fructus 40% ethyl alcohol extracts [GVE(1:1)] and the mixture (1:3) of Ginseng Radix Rubra and Vitis Fructus 40% ethyl alcohol extracts [GVE(1:3)] were administered p.o. once a day for 7 days, respectively. GVW(1:1) and GVW(1:3) decreased the viability of thymocytes increased by GW, but GVE(1:1) and GVE(1:3) increased the viability of thymocytes decreased by GE. GVW(1:1) and GVW(1:3) increased the viability of splenocytes decreased by GW or GE. Also, GVW(1:1) and GVE(1:1) enhanced the population of helper T cell in thymocytes, and GVE(1:1) and GVE(1:3) decreased the population of cytotoxic T cells increased by GE. Furthermore, GVW(1:1), GVW(1:3), GVE(1:1) and GVE(1:3) enhanced the population of $B220^+$ cells decreased by GW or GE, and decreased the population of $Thyl^+$ cells increased by GW or GE, and decreased the population of splenic $CD4^+$ cells increased by GW or GE. In addition, GVW(1:1) and GVW(1:3) decreased the phagocytic activity and the production of nitric oxide in peritoneal macrophages increased by GW, but GVE(1:1) and GVE(1:3) enhanced the phagocytic activity and the production of nitric oxide in peritoneal macrophages decreased by GE. These results suggest that Vitis Fructus has an regulative action on immune response of Ginseng Radix Rubra.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1731-1740
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• 2013

The design life and durability of the bridges are strongly affected by the Gross Vehicle Weight(GVW) of heavyweight trucks. The Weigh-In-Motion(WIM) systems are typically used to collect information on truck total weight and speed. The statistical analysis of the GVW measured using High Speed WIM systems showed that most of heavy vehicles were from Vehicle Type 7, 10, and 12. The analysis was also carried out to determine goodness of fit with theoretical probability distributions. The normal distribution was shown to best describe the overall distribution of GVW. The top 10% of the GVW appeared to best fit by the Weibull 3 probability distribution.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.133-140
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• 2016

The Ministry of Land, Infrastructure and Transport has introduced low-floor buses, which are convenient for passengers getting on and off the bus and for the handicapped. The standard bus model is 11 m long and uses compressed natural gas (CNG). However, this model has drawbacks in narrow rural road conditions such as those in farming and fishing villages and mountainous areas, as well as difficulty in refueling since CNG facilities are not readily available. In this study, running resistance values were obtained by coasting performance tests on actual roads using a Tata Daewoo LF-40 model with three different weight conditions: curb vehicle weight (CVW), half vehicle weight (HVW), and gross vehicle weight (GVW).The test methods include WHVC, NIER-06, and constant-speed driving at 60 km/h. These tests were used to measure the fuel economy of vehicles other than the target vehicles to obtain the combined fuel economy. The energy efficiency was highest in the case of CVW. In the WHVC mode, the fuel consumption rates of HVW and GVW were typically 3.5% and 12% higher than that of CVW, respectively. In constant-speed driving, the fuel efficiency of HVW was higher than that of CVW. Further research is required to analyze the exhaust gas data.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.307-314
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• 2015

The purpose of this study is know the fuel economy of difference tractive resistance calculation methods on light duty low-floor bus. Two tractive resistance calculation methods(coastdown test and JFCM conversion formula) are tested to understand the difference of fuel economy. JFCM was developed for fuel economy regulations of heavy duty vehicle. That show a big difference as a result of the calculation using coastdown test and JFCM conversion formula. The difference of the tractive resistance affects the fuel economy.

• Journal of ILASS-Korea
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• v.24 no.1
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• pp.21-26
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• 2019

In Korea, Medium-duty trucks are classified into GVW (Gross Vehicle Weight) 3.5~10tons. MDTs are mostly used for logistics or delivery between regions. There have been studied on diesel fuel vehicles for SUVs(Sports Utility Vehicle) or light-duty trucks. But MDTs have been not studied. Therefore, this study have been used MDTs for characteristic exhaust emission. Test was carried out using the certification test mode (NEDC, New European Driving cycle) and the NIER mode in chassis dynamometer of the MDTs. And emission gas was analyzed for PN (Particulate Number), PN size distribution and aldehydes, VOCs (Volatile Organic Compounds), PAHs (Polycyclic Aromatic Hydrocarbons). This paper concluded that EURO-IV trucks produced more MSATs than EURO V trucks. Depending on the engine temperature, more MSATs were generated in cold temperature than in the hot start operation. However, the driving speed, the opposite results was obtained.

• Structural Engineering and Mechanics
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• v.76 no.1
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• pp.1-15
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• 2020

The aim of this paper is to simply present live load factor calculation methodology formulation with the addition of a simple new future load projection procedure to previously proposed two methods. For this purpose, Oregon Weigh-in-Motion (WIM) data were used to calculate live load factors by using WIM data. These factors were calculated with two different approaches and by presenting new simple modifications in these methods. A very simple future load projection method is presented in this paper. Using four different WIM sites with different average daily truck traffic (ADTT) volume, and all year data, live load factors were obtained. The live load factors, were proposed as a function of ADTT. ADTT values of these sites correspond to three different levels which are approximately ADTT= 5,000, ADTT = 1,500 and ADTT ≤ 500 cases. WIM data for a full year were used from each site in the calibration procedure. Load effects were projected into the future for the different span lengths considering five-year evaluation period and seventy-five-years design life. The live load factor for ADTT=5,000, AASHTO HS20 loading case and five-year evaluation period was obtained as 1.8. In the second approach, the methodology established in the Manual for Bridge Evaluation (MBE) was used to calibrate the live load factors. It was obtained that the calculated live load factors were smaller than those in the MBE specifications, and smaller than those used in the initial calibration which did not convert to the gross vehicle weight (GVW) into truck type 3S2 defined by AASHTO equivalents.