목적: 열수 추출한 차즈기(Perilla frutescens var. acuta)의 4주간 섭취에 의한 굴절이상과 시기능 변화에 미치는 영향을 알아보고자 연구하였다. 방법: 의료기기 임상시험심사위원회(IRB 허가번호: DSGOH-042)허가를 받은 후, 성인남녀 30명을 대상 으로 하였다. 차즈기추출물과 위약을 각각 섭취하고 2시간 후 스마트기기로 근거리 작업(VDT)을 2시간동안 실시하여 섭취 전과 후 결과를 비교하였다. 결과: 등가구면 굴절이상도 변화 비교 결과 PFA군에서는 유의하게 개선되는 경향을 보였다(p=0.001, p=0.000). 또한, PFA군은 위약에 비해 폭주근점과 조절근점이 유의한 차이로 개선효과를 보였다(p=0.007, p=0.001, p=0.001, p=0.000). 양안조절용이성 결과 PFA군은 유의하게 증가하였으나(p=0.021), 위약군에 서는 유의한 차이가 없었고, 두 섭취군 간 유의한 차이도 나타나지 않았다(p=0.876). 안전성 평가 결과에서 는 두 섭취군에서 이상반응 등이 관찰되지 않아 PFA 섭취가 인체에 안전함을 확인하였다. 결론: PFA는 위약에 비해 모양체근 활성도를 증가시켜 굴절이상도 증가를 억제하고 폭주력과 조절력, 안정피로 개선에 영향을 미치는 것으로 사료된다.

In this paper, we investigate the trend of injector waveform change due to failure of air flow sensor and intake air temperature sensor of CRDI engine. Changes in the injector opening time can be detected by the failure of the associated sensor, and the extension of the reaction time is closely related to fuel consumption. Thus, the proper maintenance time of the vehicle will affect the fuel economy and reduce the exhaust gas.

The purpose of this study was to assess the influence of the duration of smartphone usage on cervical and lumbar spine flexion angles and reposition error in the cervical spine. The study included 18 healthy smartphone users (7 males and 11 females). We measured the kinematics of the upper and lower cervical and lumbar spine flexion angles and the reposition error of the upper and lower cervical spine after 3 s and 300 s smartphone use in sitting. A paired t-test was used to compare the effects of the duration of smartphone usage on the kinematics of cervical and lumbar spine flexion angles and reposition error. The flexion angles of the lower cervical and lumbar spine and the reposition error in the upper and lower cervical spine were significantly increased after 300 s smartphone of use (p<.05). However, the flexion angle of the upper cervical spine was not significantly different between the 3 s and 300 s smartphone of use (p>.05). These findings suggest that prolonged use of smartphones can induce changes in cervical and lumbar spine posture and proprioception in the cervical spine.

This study is aimed at investigating the influence of different quantitative knowledge of results on the measurement error during lumbar proprioceptive sensation training. Twenty-eight healthy adult men participated and subjects were randomly assigned into four different feedback groups(100% relative frequency with an angle feedback, 50% relative frequency with an angle feedback, 100% relative frequency with a length feedback, 50% relative frequency with a length feedback). An electrogoniometer was used to determine performance error in an angle, and the Schober test with measurement tape was used to determine performance error in a length. Each subject was asked to maintain an upright position with both eyes closed and both upper limbs stabilized on their pelvis. Lumbar vertebrae flexion was maintained at for three seconds. Different verbal knowledge of results was provided in four groups. After lumbar flexion was performed, knowledge of results was offered immediately. The resting period between the sessions per block was five seconds. Training consisted of 6 blocks, 10 sessions per one block, with a resting period of one minute. A resting period of five minutes was provided between 3 blocks and 4 blocks. A retention test was performed between 10 minutes and 24 hours later following the training block without providing knowledge of results. To determine the training effects, a two-way analysis of variance and a one-way analysis of variance were used with SPSS Ver. 10.0. A level of significance was set at .05. A significant block effect was shown for the acquisition phase (p<.05), and a significant feedback effect was shown in the immediate retention phase (p>.05). There was a significant feedback effect in the delayed retention phase (p<.05), and a significant block effect in the first acquisition phase and the last retention phase (p<.05). In conclusion, it is determined that a 50% relative frequency with a length feedback is the most efficient feedback among different feedback types.

Radars have been widely employed to detect precipitation and to predict rainfall. However, the radar-based estimate of rainfall is affected by uncertainties or errors such as mis-calibration, beam blockage, anomalous propagation, and ground cutter. Even though these uncertainties of radar rainfall estimate (RRE) have been studied, their effect on a runoff simulation especially to the peak discharge and peak time have not been much focused. Therefore, the objective of current study is to analyze the effect of the RRE uncertainties or errors based on synthetic simulation of RRE and its effect on peak discharge. First of all, mean of modeled radar rainfall is fixed (e.g., 100mm) and its error variance was set as ±10mm, ±20mm, ±40mm, and ±50mm independent to each grid cell. This independent simulation is based on white-noise process. The second simulation included a spatial-correlation between grid cells in simulating the error variance. The relationship between the distances of rain gauges and the corresponding correlations was modeled with the power law function. The parameters of the function were estimated through meta-heuristic method (specifically harmony search). Moreover, in order to find the correlation of observed data, the whole data from 27 rain gauges in the basin and the corresponding RRE from the dual polarization radar on Mt. Bisl in Korea were employed. The results of the former simulation (independent errors to each grid cell) show that the bias of the peak discharge is increased along with the variance increased, which is caused by influence of zero values. In the latter simulation (spatially correlated errors between grid cells), the results show that the peak discharge variance from the latter presents much larger than that of the former. Furthermore, the spatial distribution pattern of the modeled radar rainfall exhibited very similar to that of the real rainfall. Finally, we concluded that the error variance of RRE on runoff simulation leading bias and high uncertainty.