• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2847-2852
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• 2016

Background: Although the breast cancer incidence rate in Japan is lower than in western countries, the age-specific rates have markedly increased in recent years, along with the problems of declining birth rate and an aging population. Materials and Methods: We examined past trends of age-specific breast cancer incidence using data from the Osaka Cancer Registry from 1976 to 2010, and estimated future trends until 2025 based on the changes observed and population dynamics using a log linear regression model. Results: The age-specific breast cancer incidence rate has increased consistently from the 1970s, and the rates have caught up with those of Japanese-Americans in the US. Assuming the increasing tendency of age-specific breast cancer incidence to be constant, the average annual incidence of breast cancer will increase 1.7-fold from 2006-2010 to 2021-2025. Furthermore, the number of patients aged 80 years should increase 3.4-fold. Conclusions: The medical demand for breast cancer care in Japan may increase explosively in the future, particularly among the elderly. We need to prepare for such a future increase in demand for care, although careful monitoring is needed to confirm these results.

• Journal of Information Science Theory and Practice
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• v.7 no.3
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• pp.6-20
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• 2019

Learning analytics, or educational data mining, is an emerging field that applies data mining methods and tools for the exploitation of data coming from educational environments. Learning management systems, like Moodle, offer large amounts of data concerning students' activity, performance, behavior, and interaction with their peers and their tutors. The analysis of these data can be elaborated to make decisions that will assist stakeholders (students, faculty, and administration) to elevate the learning process in higher education. In this work, the power of Excel is exploited to analyze data in Moodle, utilizing an e-learning course developed for enhancing the information computer technology skills of school teachers in primary and secondary education in Greece. Moodle log files are appropriately manipulated in order to trace daily and weekly activity of the learners concerning distribution of access to resources, forum participation, and quizzes and assignments submission. Learners' activity was visualized for every hour of the day and for every day of the week. The visualization of access to every activity or resource during the course is also obtained. In this fashion teachers can schedule online synchronous lectures or discussions more effectively in order to maximize the learners' participation. Results depict the interest of learners for each structural component, their dedication to the course, their participation in the fora, and how it affects the submission of quizzes and assignments. Instructional designers may take advice and redesign the course according to the popularity of the educational material and learners' dedication. Moreover, the final grade of the learners is predicted according to their previous grades using multiple linear regression and sensitivity analysis. These outcomes can be suitably exploited in order for instructors to improve the design of their courses, faculty to alter their educational methodology, and administration to make decisions that will improve the educational services provided.

• Journal of Environmental Impact Assessment
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• v.30 no.6
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• pp.355-360
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• 2021

As the domestic water management policy shifted from concentration-oriented water management to load management-centered Total Maximum Daily Load (TMDL), water quality and aquatic ecosystems brought changed. However, it was difficult to determine whether the water quality and the health of the aquatic ecosystem improved after the implementation of the TMDL due to changes in pollutant sources and discharge fluctuations ect, so the effect was analyzed using a log-linear model and biological indicators (Benthic Macroinvertebrates). As a result, BOD and T-P concentrations in the Jinwi River Watershed were reduced by 30% and 35%, showed the effect of improving water quality, however the benthic macroinvertebrates index (BMI) downgraded from grade D to grade E. Therefore, efforts to cultivate ecologicalrivers are necessary to upgrade the health of the aquatic ecosystem in the river watershed.

• Tuberculosis and Respiratory Diseases
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• v.84 no.3
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• pp.182-187
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• 2021

Background: Fractional exhaled nitric oxide (FeNO) is a non-invasive marker for eosinophilic airway inflammation and a good predictor of response to corticosteroids. There is a need for a reliable and accurate measurement method, as FeNO measurements have been widely used in clinical practice. Our study aimed to compare two FeNO analyzers and derive a conversion equation for FeNO measurements in adults. Methods: We included 99 participants who had chief complaints of chronic cough and difficulty in breathing. The participants underwent concurrent FeNO measurement using NIOX VERO (Circassia AB) and NObreath (Bedfont). We compared the values of the two devices and analyzed their correlation and agreement. We then formulated an equation to convert FeNO values measured by NObreath into those obtained by NIOX VERO. Results: The mean age of the participants was 51.2±17.1 years, with a female predominance (58.6%). Approximately 60% of the participants had asthma. The FeNO level measured by NIOX VERO (median, 27; interquartile range [IQR], 15-45) was significantly lower than that measured by NObreath (median, 38; IQR, 22-58; p<0.001). There was a strong positive correlation between the two devices (r=0.779, p<0.001). Additionally, Bland-Altman plots and intraclass correlation coefficient demonstrated a good agreement. Using linear regression, we derived the following conversion equation: natural log (Ln) (NObreath)=0.728×Ln (NIOX VERO)+1.244. Conclusion: The FeNO values of NIOX VERO and NObreath were in good agreement and had positive correlations. Our proposed conversion equation could help assess the accuracy of the two analyzers.

• Animal Bioscience
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• v.34 no.2
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• pp.163-171
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• 2021

Objective: Considering the importance of dairy farming and the negative effects of heat stress, more tolerant genotypes need to be identified. The objective of this study was to investigate the effect of heat stress via temperature-humidity index (THI) and diurnal temperature variation (DTV) in the genetic evaluations for daily milk yield of Holstein dairy cattle, using random regression models. Methods: The data comprised 94,549 test-day records of 11,294 first parity Holstein cows from Brazil, collected from 1997 to 2013, and bioclimatic data (THI and DTV) from 18 weather stations. Least square linear regression models were used to determine the THI and DTV thresholds for milk yield losses caused by heat stress. In addition to the standard model (SM, without bioclimatic variables), THI and DTV were combined in various ways and tested for different days, totaling 41 models. Results: The THI and DTV thresholds for milk yield losses was THI = 74 (-0.106 kg/d/THI) and DTV = 13 (-0.045 kg/d/DTV). The model that included THI and DTV as fixed effects, considering the two-day average, presented better fit (-2logL, Akaike information criterion, and Bayesian information criterion). The estimated breeding values (EBVs) and the reliabilities of the EBVs improved when using this model. Conclusion: Sires are re-ranking when heat stress indicators are included in the model. Genetic evaluation using the mean of two days of THI and DTV as fixed effect, improved EBVs and EBVs reliability.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.411-425
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• 2021

Accelerated failure time (AFT) model represents a linear relationship between the log-survival time and covariates. We are interested in the inference of covariate's effect affecting the variation of survival times in the AFT model. Thus, we need to model the variance as well as the mean of survival times. We call the resulting model mean and variance AFT (MV-AFT) model. In this paper, we propose a variable selection procedure of regression parameters of mean and variance in MV-AFT model using penalized likelihood function. For the variable selection, we study four penalty functions, i.e. least absolute shrinkage and selection operator (LASSO), adaptive lasso (ALASSO), smoothly clipped absolute deviation (SCAD) and hierarchical likelihood (HL). With this procedure we can select important covariates and estimate the regression parameters at the same time. The performance of the proposed method is evaluated using simulation studies. The proposed method is illustrated with a clinical example dataset.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.2
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• pp.163-173
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• 2021

Objective: This study aimed to characterize a validated model for predicting oocyte retrieval in controlled ovarian stimulation (COS) and to construct model-based nomograms for assistance in clinical decision-making regarding the gonadotropin protocol and dose. Methods: This observational, retrospective, cohort study included 636 women with primary unexplained infertility and a normal menstrual cycle who were attempting assisted reproductive therapy for the first time. The enrolled women were split into an index group (n=497) for model building and a validation group (n=139). The primary outcome was absolute oocyte count. The dose-response relationship was tested using modified Poisson, negative binomial, hybrid Poisson-E_max, and linear models. The validation group was similarly analyzed, and its results were compared to that of the index group. Results: The Poisson model with the log-link function demonstrated superior predictive performance and precision (Akaike information criterion, 2,704; λ=8.27; relative standard error (λ)=2.02%). The covariate analysis included women's age (p<0.001), antral follicle count (p<0.001), basal follicle-stimulating hormone level (p<0.001), gonadotropin dose (p=0.042), and protocol type (p=0.002 and p<0.001 for short and antagonist protocols, respectively). The estimates from 500 bootstrap samples were close to those of the original model. The validation group showed model assessment metrics comparable to the index model. Based on the fitted model, a static nomogram was built to improve visualization. In addition, a dynamic electronic tool was created for convenience of use. Conclusion: Based on our validated model, nomograms were constructed to help clinicians individualize the stimulation protocol and gonadotropin doses in COS cycles.

• Journal of Yeungnam Medical Science
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• v.39 no.2
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• pp.108-115
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• 2022

Background: This study was aimed at comparing and analyzing the results of FractionLab (Varian/Mobius Medical System) with those of portal dosimetry that uses an electronic portal imaging device. Portal dosimetry is extensively used for patient-specific quality assurance (QA) in intensity-modulated radiotherapy (IMRT). Methods: The study includes 29 patients who underwent IMRT on a Novalis-Tx linear accelerator (Varian Medical System and Brain-LAB) between June 2019 and March 2021. We analyzed the multileaf collimator DynaLog files generated after portal dosimetry to evaluate the same condition using FractionLab. The results of the recently launched FractionLab at various gamma indices (0.1%/0.1 mm-1%/1 mm) are analyzed and compared with those of portal dosimetry (3%/3 mm). Results: The average gamma passing rates of portal dosimetry (3%/3 mm) and FractionLab are 98.1% (95.5%-100%) and 97.5% (92.3%-99.7%) at 0.6%/0.6 mm, respectively. The results of portal dosimetry (3%/3 mm) are statistically comparable with the QA results of FractionLab (0.6%/0.6 mm-0.9%/0.9 mm). Conclusion: This paper presents the clinical performance of FractionLab by the comparison of the QA results of FractionLab using portal dosimetry with various gamma indexes when performing patient-specific QA in IMRT treatment. Further, the appropriate gamma index when performing patient-specific QA with FractionLab is provided.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.171-180
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• 2021

Places exposed to mass media induce behavior by forming curiosity and expectations for potential travelers. The places reported through mass media influence viewers. Among TV programs, the most influential genre is drama, and reality programs that provide immersion with different characteristics from dramas influence viewers' choice of destination. CVM is mainly used for estimating the value of objects that cannot be evaluated in the market, such as tourist destinations. This study conducted an economic valuation of filming locations for dramas and travel entertainment programs using CVM, and then compared and analyzed the research results of the two filming locations. Linear and log logit analysis were performed to measure the willingness to pay for the filming location of the drama/travel entertainment program, and the payment amount was derived. The conclusion of the study is that as the travel cost required to visit the filming location of the drama/travel entertainment program increased, the intention to visit decreased. The amount payable when visiting the filming location of the drama/travel entertainment program was higher than the average consumption amount for a day trip, and the amount payable for the drama was higher than that of visiting the filming location of the travel entertainment program.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.326-326
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• 2021

장기간에 걸쳐 넓은 지역에 대해 발생하는 가뭄을 예측하기위해 많은 학자들의 기술적, 학술적 시도가 있어왔다. 본 연구에서는 복잡한 시계열을 가진 가뭄을 전망하는 방법 중 시나리오에 기반을 둔 가뭄전망 방법과 실시간으로 가뭄을 예측하는 비시나리오 기반의 방법 등을 이용하여 미래 가뭄전망을 실시했다. 시나리오에 기반을 둔 가뭄전망 방법으로는, 3개월 GCM(General Circulation Model) 예측 결과를 바탕으로 2009년도 PDSI(Palmer Drought Severity Index) 가뭄지수를 산정하여 가뭄심도에 대한 단기예측을 실시하였다. 또, 통계학적 방법과 물리적 모델(Physical model)에 기반을 둔 확정론적 수치해석 방법을 이용하여 비시나리오 기반 가뭄을 예측했다. 기존 가뭄을 통계학적 방법으로 예측하기 위해서 시도된 대표적인 방법으로 ARIMA(Autoregressive Integrated Moving Average) 모델의 예측에 대한 한계를 극복하기위해 서포트 벡터 회귀(support vector regression, SVR)와 웨이블릿(wavelet neural network) 신경망을 이용해 SPI를 측정하였다. 최적모델구조는 RMSE(root mean square error), MAE(mean absolute error) 및 R(correlation Coefficient)를 통해 선정하였고, 1-6개월의 선행예보 시간을 갖고 가뭄을 전망하였다. 그리고 SPI를 이용하여, 마코프 연쇄(Markov chain) 및 대수선형모델(log-linear model)을 적용하여 SPI기반 가뭄예측의 정확도를 검증하였으며, 터키의 아나톨리아(Anatolia) 지역을 대상으로 뉴로퍼지모델(Neuro-Fuzzy)을 적용하여 1964-2006년 기간의 월평균 강수량과 SPI를 바탕으로 가뭄을 예측하였다. 가뭄 빈도와 패턴이 불규칙적으로 변하며 지역별 강수량의 양극화가 심화됨에 따라 가뭄예측의 정확도를 높여야 하는 요구가 커지고 있다. 본 연구에서는 복잡하고 비선형성으로 이루어진 가뭄 패턴을 기상학적 가뭄의 정도를 나타내는 표준강수증발지수(SPEI, Standardized Precipitation Evapotranspiration Index)인 월SPEI와 일SPEI를 기계학습모델에 적용하여 예측개선 모형을 개발하고자 한다.