• Journal of Preventive Medicine and Public Health
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• 제27권3호
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• pp.581-595
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• 1994

To investigate the effect of carbon disulfide on blood pressure, the BP measurements in the periodic health examination results and the medical records of factory clinic were reviewed. The study subjects were composed of 1336 male and 544 female workers, who were categorized into three groups by the exposure status-highly exposed, moderately exposed and non-exposed group. The results of the study were as follows; 1. The age-adjusted mean systolic and diastolic BP of male workers were 122.35 mmHg/79.11 mmHg in highly exposed, 121.57mmHg/79.05mmHg in moderately exposed and 122.67mmHg/82.27mmHg in non-exposed group. For female workers, BPs were 115.13mmHg/74.49mmHg in moderately exposed and 113.48mmHg/74.30mmHg in non-exposed group. 2. In multiple regression analysis of maximum BP against Age and tenure, the slope coefficients of age and tenure on the systolic BP were 0.379, 0.667 respectively and those on the diastolic BP were 0.331, 0.405 respectively in highly exposed male workers. Tenure was a significant variable in this study. For female workers, however the slope coefficients of tenure on BP were significant only for systolic BP of moderately-exposed group. 3. In multiple regression analysis of Bp against age, cumulative exposure index (CEI), cholesterol, all the variables showed significant slope coefficients in male, but age and CEI on systolic BP were significant for female workers (p<0.05). 4. In the multiple analysis of the amount of Bp change and the velocity of Bp change among male workers, the slope coefficients of tenure tended to increase as exposure level increased. Among female workers, the slope coefficients of tenure were significant on the amount of Bp change and the velocity of Bp change in moderately exposed group.

• 한국습지학회지
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• 제13권3호
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• pp.547-565
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• 2011

수위예측을 위해 개념적, 물리적 모형들을 포함한 다양한 유형의 기법들이 사용되고 있다. 그럼에도 불구하고 이러한 기법들 중 수위예측을 위해 단일의 우수한 모형을 선정하는 것은 매우 어려운 일이다. 최근에는 수문학적 과정의 복잡성으로 인해 기존 물리적 기반의 강우-유출 모형이 가지고 있는 단점들을 극복하고자 자료 지향형 수위예측 모형이 널리 도입되고 있다. 본 연구의 목적은 이러한 자료 지향형 모형 중 뉴로-퍼지와 회귀분석 모형의 수위예측에 대한 성능을 비교하는 것이다. 제안된 두 모형을 한강수계의 왕숙천에 대해 적용하였다. 제안된 두 모형의 성능을 평가하기 위해 평균제곱근오차, Nash-Suttcliffe 효율계수, 평균절대오차, 수정 결정계수와 같이 4개의 통계지표들을 사용하였다. 모의결과 뉴로-퍼지 수위예측 모형이 다중선형회귀 수위예측 모형보다 좀 더 나은 예측 결과를 나타내는 것을 확인할 수 있었다. 본 연구결과는 향후 중소하천에서 충분한 선행시간을 확보한 정확도 높은 홍수정보시스템의 구축에 활용할 수 있을 것으로 판단된다.

• Asian-Australasian Journal of Animal Sciences
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• 제25권9호
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• pp.1237-1247
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• 2012

The objective of this study was to evaluate the predictions of dry matter intake (DMI) and average daily gain (ADG) of Vietnamese Yellow (Vang) purebred and crossbred (Vang with Red Sindhi or Brahman) bulls fed under Vietnamese conditions using two levels of solution (1 and 2) of the large ruminant nutrition system (LRNS) model. Animal information and feed chemical characterization were obtained from five studies. The initial mean body weight (BW) of the animals was 186, with standard deviation ${\pm}33.2$ kg. Animals were fed ad libitum commonly available feedstuffs, including cassava powder, corn grain, Napier grass, rice straw and bran, and minerals and vitamins, for 50 to 80 d. Adequacy of the predictions was assessed with the Model Evaluation System using the root of mean square error of prediction (RMSEP), accuracy (Cb), coefficient of determination ($r^2$), and mean bias (MB). When all treatment means were used, both levels of solution predicted DMI similarly with low precision ($r^2$ of 0.389 and 0.45 for level 1 and 2, respectively) and medium accuracy (Cb of 0.827 and 0.859, respectively). The LRNS clearly over-predicted the intake of one study. When this study was removed from the comparison, the precision and accuracy considerably increased for the level 1 solution. Metabolisable protein was limiting ADG for more than 68% of the treatment averages. Both levels differed regarding precision and accuracy. While level 1 solution had the least MB compared with level 2 (0.058 and 0.159 kg/d, respectively), the precision was greater for level 2 than level 1 (0.89 and 0.70, respectively). The accuracy (Cb) was similar between level 1 and level 2 (p = 0.8997; 0.977 and 0.871, respectively). The RMSEP indicated that both levels were on average under-or over-predicted by about 190 g/d, suggesting that even though the accuracy (Cb) was greater for level 1 compared to level 2, both levels are likely to wrongly predict ADG by the same amount. Our analyses indicated that the level 1 solution can predict DMI reasonably well for this type of animal, but it was not entirely clear if animals consumed at their voluntary intake and/or if the roughness of the diet decreased DMI. A deficit of ruminally-undegradable protein and/or a lack of microbial protein may have limited the performance of these animals. Based on these evaluations, the LRNS level 1 solution may be an alternative to predict animal performance when, under specific circumstances, the fractional degradation rates of the carbohydrate and protein fractions are not known.

• 농업과학연구
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• 제48권2호
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• pp.299-310
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• 2021

Glycerol is a non-volatile compound with no aromatic properties that contributes significantly to the quality of wine by providing sweetness and richness of taste. In addition, it is also the third most significant byproduct of alcoholic fermentation in terms of quantity after ethanol and carbon dioxide. In this study, Fourier transform infrared (FT-IR) spectroscopy was employed as a fast non-destructive method in conjugation with multivariate regression analysis to build a model for the quantitative analysis of glycerol concentration in wine samples. The samples were prepared by using three varieties of red wine samples (i.e., Shiraz, Merlot, and Barbaresco) that were adulterated with glycerol in concentration ranges from 0.1 to 15% (v·v^-1), and subjected to analysis together with pure wine samples. A net analyte signal (NAS)-based methodology, called hybrid linear analysis in the literature (HLA/GO), was applied for predicting glycerol concentrations in the collected FT-IR spectral data. Calibration and validation sets were designed to evaluate the performance of the multivariate method. The obtained results exhibited a high coefficient of determination (R²) of 0.987 and a low root mean square error (RMSE) of 0.563% for the calibration set, and a R² of 0.984 and a RMSE of 0.626% for the validation set. Further, the model was validated in terms of sensitivity, selectivity, and limits of detection and quantification, and the results confirmed that this model can be used in most applications, as well as for quality assurance.

• Safety and Health at Work
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• 제10권4호
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• pp.452-460
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• 2019

Background: Monitoring and control of PM_2.5 are being recognized as key to address health issues attributed to PM_2.5. Availability of low-cost PM_2.5 sensors made it possible to introduce a number of portable PM_2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM_2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM_2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM_2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM_2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM_2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM_2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R²) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R² and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM_2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

• Journal of Microbiology and Biotechnology
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• 제15권6호
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• pp.1323-1329
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• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Biomolecules & Therapeutics
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• 제9권4호
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• pp.291-297
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• 2001

The bioequivalence of two triflusal products was evaluated with 20 healthy volunteers following single oral dose according to the guidelines of Korea Food and Drug Administration (KFDA). Trisa $l^{R}$ capsule (Whanin Pharm. Corp., Korea) and Disgre $n^{R}$ capsule (Myung-In Pharm. Corp., Korea) were used as test product and reference product, respectively. Both products contain 300 mg of trifusal. One capsule of test product or reference product was orally administered to the volunteers, respectively, by randomized two period crossover study (2$\times$2 Latin square method). Blood samples were taken at predetermined time intervals for 4 hours and the determination of trifusal was accomplished using semi-microbore HPLC equipped with automated column switching system. The analytical method with HPLC was validated according to the Bioanalytic Method Validation guideline by F7A prior to determining the plasma samples. The pharmacokinetic parameters (AU $C_{0-4h}$ $C_{max}$ and $T_{max}$) were calculated and ANOVA test was utilized for statistical analysis of parameters. As a result of the assay validation, the limit of quantification of trifusal in human plasma by current assay procedure was 50 ng/ml using 500 $\mu$l of plasma. The accuracy of the assay was from 97.76% to 116.51% while the intra-day and inter-day coefficient of variation of the same concentration range was less than 15%. Average drug concentration at the designated time intervals and pharmacokinetic parameters calculated were not significantly different between two products (p>0.05). The difference of mean AU $C_{olongrightarrow4hr}$, $C_{max}$, and $T_{max}$ between the two products (2.92, 4.39, and -2.44%, respectively) were less than 20%. The power (1-$\beta$) and treatment difference ($\Delta$) for AU $C_{olongrightarrow4hr}$ and $C_{max}$ were more than 0.8 and less than 0.2, respectively. Although the power for $T_{max}$ was under 0.8, $T_{max}$ of the two products was not significantly different from each other (p>0.05). These results satisfied the criteria of KFDA guideline for bioequivalence, indicating the two products of triflusal were bioequivalent.quivalent.ent.ent.

• 대한토목학회논문집
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• 제31권5D호
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• pp.633-639
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• 2011

적정수준의 택시규모를 산정하기 위해 본 연구에서는 타코미터 데이터 분석을 통해 법인택시와 개인택시의 운행실태를 분석하였다. 대상예시지역으로 평택시를 선정하여, 도시특성을 기반으로한 택시총량산정 수리모형을 개발하였다. 개발된 도시특성기반 모형의 분석결과 결정계수(수정된 R제곱) 값이 0.970로 모형이 유의한 것으로 나타났다. 모형적용결과 평택시에 적합한 택시총량은 2014년까지 총1,794대로 이는 2009년 기준년도보다 214대 많은 것으로 분석되었다. 또한 실제 택시의 운영데이터를 기반으로한 실차율에 의한 택시총량산정 모형을 분석하였다. 실차율모형 적용결과 2014년까지 총1,224대로 이는 2009년 보다 356대 적은 것으로 분석이 되었다. 결론적으로 평택시의 택시총량은 실차율모형과 도시특성을 반영한 두 모델을 적용한 결과 2014까지 평택시의 택시총량공급계획은 2009년 1,580보다 71대 적은 1,509대로 분석되었다.

• 대한원격탐사학회지
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• 제37권5_2호
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• pp.1307-1315
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• 2021

매년 여름철 양자강에서 유출되는 저염분수는 동중국해 뿐만 아니라 제주도 주변 해역의 염분 변화에 큰 영향을 미치며 때때로 그 영향은 한반도 연안에 국한되지 않고 대한해협을 통과하여 동해 외해 까지 확장되기도 한다. 한반도 주변으로 확장된 양자강 유출수는 해양 물리 및 생태학적으로 많은 영향을 끼치며 어업 및 양식업에 큰 피해를 유발하기도 한다. 그러나 현장조사의 한계점 때문에 동중국해에서 확산되는 저염분수를 지속적으로 관측하기에는 현실적으로 어려움이 있다. 이러한 이유로 양자강 유출수의 확산을 실시간으로 모니터링하기 위해 인공위성을 활용한 표층 염분 산출 연구가 많이 진행되어 왔다. 본 연구에서는 시간 및 공간 해상도가 상대적으로 좋은 GOCI(Geostationary Ocean Color Imager)를 활용한 동중국해 표층 염분 산출 알고리즘을 개발하였다. 알고리즘 개발을 위해 기계학습 기법 중 하나인 MPNN(Multilayer Perceptron Neural Network)을 이용하였으며, 출력층에는 SMAP(Soil Moisture Active Passive) 위성의 표층 염분 자료를 활용하였다. 이전 연구에서 2016년 자료를 이용한 표층 염분 산출 알고리즘이 개발되었으나 본 연구에서는 연구 기간을 2015년 부터 2020년까지로 확장하여 알고리즘 성능을 개선하였다. 2011년부터 2019년까지 동중국해에서 관측된 국립수산과학원의 정선조사자료를 이용하여 알고리즘 성능을 검증한 결과로 R²는 0.61과 RMSE는 1.08 psu로 나타났다. 본 연구는 GOCI를 이용한 동중국해 표층 염분 모니터링 알고리즘 개발을 위해 수행되었으며, 향후 GOCI-II의 표층 염분 산출 알고리즘 개발에 많은 기여를 할 것으로 기대된다.

• 토지주택연구
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• 제12권4호
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• pp.93-101
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• 2021

국내 소규모 가구가 점차적으로 증가함에 따라 소규모 가구를 위한 주거공급 정책에 대한 중요성이 높아지고 있다. 이러한 중요성에 따라 정부에서는 소규모 가구를 위한 도시형 생활주택을 지속적으로 공급해오고 있다. 도시형 생활주택은 공동주택, 일반 업무시설과 동일하게 분양 및 임대 사업이므로 발주자는 프로젝트 기획단계에서 적정공사기간을 산정하는 것은 중요하다. 그러나, 선행연구에서는 대규모 건축물의 공사기간을 산정할 수 있는 모델이 존재하나 도시형 생활주택과 같은 소규모 건축물에 대한 적정 공사기간 산정 모델은 부재한 것으로 분석되었다. 따라서 본 연구의 목적은 기획단계에서 발주자가 적정 공사기간을 산정할 수 있는 다중 회귀분석 기반 도시형 생활주택의 공사기간 산정 모델을 개발 및 검증하는 것이다. 개발된 모델에 입력되는 독립변수는 연면적, 수도권, 지하층수, 지상층수, 주 건축물 수, 강원권의 총 6개이며, 개발된 모델의 수정된 결정계수(R_a²)는 0.547로 분석되었다. 개발된 모델의 성능은 RMSE의 경우 171.26일, MAPE의 경우 26.53%로 도출되었다. 본 연구를 통해 개발된 모델은 발주자에게 신뢰성 있는 공사기간 산정 결과를 제공할 수 있을 것으로 기대된다.