• Asian Pacific Journal of Cancer Prevention
• /
• 제13권10호
• /
• pp.5105-5111
• /
• 2012

Background: Published data on the association between single nucleotide polymorphisms (SNPs) in the ESR1 gene and breast cancer susceptibility are inconclusive or controversial. The aim of this Human Genome Epidemiology (HuGE) review and meta-analysis was to derive a more precise estimation of this relationship. Methods: A literature search of Pubmed, Embase, Web of science and CBM databases was conducted from inception through September 1th, 2012. Crude odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association. Results: A total of five studies including 1,678 breast cancer cases and 1,678 general population controls in Asian populations were involved in this meta-analysis. When all the eligible studies were pooled into the meta-analysis, the higher transcriptional activity variant allele T of ESR1 PvuII (C>T) (rs2234693) in pre-menopausal breast cancer women showed a significant relation to increased risk (OR = 1.13, 95%CI: 1.01-1.28, P = 0.040) in contrast to their post-menopausal counterparts which showed non-significant increased risk (OR = 1.01, 95%CI: 0.87-1.18, P = 0.858). Nevertheless, no significant association between ESR1 XbaI (A>G) (rs9340799) polymorphism and the risk of breast cancer was observed in pre-menopausal and post-menopausal individuals. Conclusion: Based on a homogeneous Asian population, results from the current meta-analysis indicates that the ESR1 PvuII (C>T) polymorphism places pre-menopausal breast cancer women at risk for breast cancer, while ESR1 XbaI (A>G) polymorphism is not likely to predict the risk of breast cancer.

• Asian Pacific Journal of Cancer Prevention
• /
• 제15권2호
• /
• pp.663-669
• /
• 2014

Background: With recent progress in health science administration, a huge amount of data has been collected from thousands of subjects. Statistical and computational techniques are very necessary to understand such data and to make valid scientific conclusions. The purpose of this paper was to develop a statistical probability model and to predict future survival times for male breast cancer patients who were diagnosed in the USA during 1973-2009. Materials and Methods: A random sample of 500 male patients was selected from the Surveillance Epidemiology and End Results (SEER) database. The survival times for the male patients were used to derive the statistical probability model. To measure the goodness of fit tests, the model building criterions: Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) were employed. A novel Bayesian method was used to derive the posterior density function for the parameters and the predictive inference for future survival times from the exponentiated Weibull model, assuming that the observed breast cancer survival data follow such type of model. The Markov chain Monte Carlo method was used to determine the inference for the parameters. Results: The summary results of certain demographic and socio-economic variables are reported. It was found that the exponentiated Weibull model fits the male survival data. Statistical inferences of the posterior parameters are presented. Mean predictive survival times, 95% predictive intervals, predictive skewness and kurtosis were obtained. Conclusions: The findings will hopefully be useful in treatment planning, healthcare resource allocation, and may motivate future research on breast cancer related survival issues.

• Journal of Biosystems Engineering
• /
• 제39권3호
• /
• pp.184-193
• /
• 2014

Purpose: The aim of this study was to develop a technique for the non-destructive and rapid prediction of the moisture content in red pepper powder using near-infrared (NIR) spectroscopy and a partial least squares regression (PLSR) model. Methods: Three red pepper powder products were separated into three groups based on their particle sizes using a standard sieve. Each product was prepared, and the expected moisture content range was divided into six or seven levels from 3 to 21% wb with 3% wb intervals. The NIR reflectance spectra acquired in the wavelength range from 1,100 to 2,300 nm were used for the development of prediction models of the moisture content in red pepper powder. Results: The values of $R{_V}{^2}$, SEP, and RPD for the best PLSR model to predict the moisture content in red pepper powders of varying particle sizes below 1.4 mm were 0.990, ${\pm}0.487%$ wb, and 10.00, respectively. Conclusions: These results demonstrated that NIR spectroscopy and a PLSR model could be useful techniques for measuring rapidly and non-destructively the moisture content in red pepper powder.

• 한국농공학회지
• /
• 제33권2호
• /
• pp.120-129
• /
• 1991

This study refers to temporal variation of physical properties of tilled soil under natural rainfalls. Field measurements of porosity, average hydraulic conductivity and average capillary pressure head on a tilled soil were conducted by soil sampler and air-entry permeameter respectively at regular intervals after tillage. Temporal variation of these physical properties were analysed by cumulative rainfall energy since tillage. Field experiment was conducted on a sandy loam soil at Suwon durging April~July in 1989. The followings are a summary of this study results ; 1. Average porosity just after tillage was 0.548cm$^3$/cm$^3$. As cumulative rainfall energy were increased in 0.1070, 0.1755, 0.3849 J/cm$^2$, average porosity were decreased in 0.506, 0.4]95, 0.468m$^3$/cm$^3$ respectively. 2. Average hydraulic conductivity just after tillage was 45.42cm/hr. As cumulative rainfall energy were increased in 0.1755, 0.2466, 0.2978, 0.3849J/cm$^2$ average hydraulic conductivity were decreased in 15.34, 13.47, 9.58, 8.65cm/hr respectively. 3. As average porosity were decreased in 0.548, 0.506, 0.495, 0.468cm$^3$/cm$^3$ average capillary pressure head were increased in 6.1, 6.7, 6.9, 7.4cm respectively. 4. It was found that temporal variation of porosity, average hydraulic conductivity on a tilled soil might be expressed as a function of cumulative rainfall energy and average capillary pressure head might be expressed as a function of porosity. 5. The results of this study may be helpful to predict infiltration into a tilled soil more accurately by considering Temporal variation of physical properties of soil.

• 농업과학연구
• /
• 제47권3호
• /
• pp.471-483
• /
• 2020

Flood prediction is an important issue to prevent damages by flood inundation caused by increasing high-intensity rainfall with climate change. In recent years, machine learning algorithms have been receiving attention in many scientific fields including hydrology, water resources, natural hazards, etc. The performance of a machine learning algorithm was investigated to predict the water elevation of a river in this study. The aim of this study was to develop a new method for securing a large enough lead time for flood defenses by predicting river water elevation using the a long- short-term memory (LSTM) technique. The water elevation data at the Oisong gauging station were selected to evaluate its applicability. The test data were the water elevation data measured by K-water from 15 February 2013 to 26 August 2018, approximately 5 years 6 months, at 1 hour intervals. To investigate the predictability of the data in terms of the data characteristics and the lead time of the prediction data, the data were divided into the same interval data (group-A) and time average data (group-B) set. Next, the predictability was evaluated by constructing a total of 36 cases. Based on the results, group-A had a more stable water elevation prediction skill compared to group-B with a lead time from 1 to 6 h. Thus, the LSTM technique using only measured water elevation data can be used for securing the appropriate lead time for flood defense in a river.

• 한국화재소방학회논문지
• /
• 제31권4호
• /
• pp.20-25
• /
• 2017

본 연구에서는 화염 내에 생성된 그을음 입자가 방사하는 복사 강도 분포를 측정하고, 이를 바탕으로 Jet A1 액적확산 화염의 온도 분포를 예측하였다. 이를 위해서 700 nm와 900 nm 각각의 파장에 대해서 화염 내 그을음 입자가 방사하는 복사 강도를 CCD 카메라로 측정하였고 Abel 변환을 통해 얻어진 국소 복사 강도 분포를 이색법(Two Color Method)에 적용하여 최종 화염의 온도 분포를 계산하였다. 그 결과 이색법에 의한 측정은 그을음의 복사 강도와 투영된 시각선의 간격에 따라서 약 2% 정도 이내의 deconvolution 오차가 발생할 수 있으며, 본 연구 결과에서 제시한 측정 방법을 통해 2000 K 기준 약 18 K 오차 범위 이내에서 화염온도 예측이 가능함을 확인하였다.

• Earthquakes and Structures
• /
• 제2권4호
• /
• pp.357-376
• /
• 2011

Ground motion models predict the mean and standard deviation of the logarithm of spectral acceleration, as a function of predictor variables such as earthquake magnitude, distance and site condition. Such models have been developed for a variety of seismic environments throughout the world. Some calculations, such as the Conditional Mean Spectrum calculation, use this information but additionally require knowledge of correlation coefficients between logarithmic spectral acceleration values at multiple periods. Such correlation predictions have, to date, been developed primarily from data recorded in the Western United States from active shallow crustal earthquakes. This paper describes results from a study of spectral acceleration correlations from Japanese earthquake ground motion data that includes both crustal and subduction zone earthquakes. Comparisons are made between estimated correlations for Japanese response spectral ordinates and correlation estimates developed from Western United States ground motion data. The effect of ground motion model, earthquake source mechanism, seismic zone, site conditions, and source to site distance on estimated correlations is evaluated and discussed. Confidence intervals on these correlation estimates are introduced, to aid in identifying statistically significant differences in correlations among the factors considered. Observed general trends in correlation are similar to previous studies, with the exception of correlation of spectral accelerations between orthogonal components, which is seen to be higher here than previously observed. Some differences in correlations between earthquake source zones and earthquake mechanisms are observed, and so tables of correlations coefficients for each specific case are provided.

• Clinical and Experimental Pediatrics
• /
• 제62권8호
• /
• pp.301-306
• /
• 2019

Purpose: Coronary artery abnormalities (CAA) are the most important complications of Kawasaki disease (KD). Iron deficiency anemia (IDA) is a prevalent micronutrient deficiency and its association with KD remains unknown. We hypothesized that presence of IDA could be a predictor of CAA. Methods: This retrospective study included 173 KD patients, divided into 2 groups according to absence (group 1) and presence (group 2) of CAA. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a logistic regression model to estimate the association between CAA and other indicators. Due to collinearity between indicators of IDA, each indicator was paired with anemia in 3 models. Results: Serum iron, iron saturation, and ferritin concentration, the 3 indicators of IDA, were significantly higher in group 1 than in group 2. Three sets of models including anemia with iron indicators produced the OR of CAA of 3.513, 3.171, and 2.256, respectively. The 3 indicators of IDA were negatively associated with CAA, by OR of 0.965, 0.914, and 0.944, respectively. The areas under the curve (AUCs) of ferritin concentration, iron saturation, serum iron, anemia, and Kobayashi score were 0.907 (95% CI, 0.851-0.963), 0.729 (95% CI, 0.648-0.810), 0.711 (95% CI, 0.629-0.793), 0.638 (95% CI, 0.545-0.731), and 0.563 (95% CI, 0.489-0.636), respectively. Conclusion: Indicators of IDA, especially ferritin, were highly associated with CAA; therefore, they were stronger predictors of CAA than Kobayashi scores. IDA indicators can be used to predict CAA development and to suggest requirements for early interventions.

• Korean Chemical Engineering Research
• /
• 제59권3호
• /
• pp.393-398
• /
• 2021

리튬이온전지의 성능을 최적화하기 위해서는 여러 열화 요소들을 고려한 성능 예측 모델링 기술이 필요하다. 본 연구에서는 리튬이온전지의 사이클 노화로 인한 방전 거동 및 사이클 수명 변화를 수학적으로 모델링하였다. 모델링의 신뢰성을 검증하기 위해 0.25C로 사이클 시험을 진행했으며, 30 사이클 간격으로 진행한 RPT (Reference performance test)를 통해 전기적 거동을 파악하였다. 기존의 리튬이온전지의 사이클 수명 예측 모델에 BOL (Beginning of life)에서 일어나는 현상 중 하나인 Break-in 메커니즘을 반영하여 수명예측 정확도를 개선시켰다. 모델에 근거하여 예측된 사이클 수명 변화는 실제 시험 결과와 잘 일치하였다.

• 한국산학기술학회논문지
• /
• 제22권3호
• /
• pp.234-241
• /
• 2021

군에서는 수요예측에 대한 중요성을 인식하여 수리부속에 대해 예측 정확도 향상을 위한 많은 연구가 이루어지고 있다. 수리부속 수요예측은 예산 운영과 장비 가동률 측면에서 매우 중요한 요소가 되고 있다. 그러나 현재 군에서 적용중인 시계열 모형으로는 수요량의 변동과 발생주기가 일정하지 않은 간헐적 수요에 대해서는 예측에 한계가 있는 실정이다. 따라서, 본 연구는 공군 패트리어트 수리부속의 간헐적 수요에 대한 예측 정확도를 제고하는 방법을 제시하고자 하였다. 이를 위해서 2013년부터 2019년까지의 701개의 수리부속 소모개수를 토대로 수요 유형을 구분하여 수리부속의 간헐적 수요 자료를 수집하였다. 또한, 장비 고장에 영향을 줄 수 있는 외부 요인으로는 기온, 장비운영시간을 식별하여 입력변수로 선정하였다. 그 후, 소모개수와 외부 요인을 통해 군에서 적용하는 시계열 모형과 제안하는 데이터 마이닝 모형으로 예측을 실시하여 모형별 예측 정확도를 판단했다. 예측 결과로 기존의 시계열 모형과 비교하여 데이터 마이닝 모형의 예측 정확도가 높았으며, 그 중 다층 퍼셉트론 모형이 가장 우수한 성능을 보였다.