This study aimed to improve the accuracy of road pavement design by comparing and analyzing various statistical and machine-learning techniques for predicting asphalt layer thickness, focusing on regional roads in Pakistan. The explanatory variables selected for this study included the annual average daily traffic (AADT), subbase thickness, and subgrade California bearing ratio (CBR) values from six cities in Pakistan. The statistical prediction models used were multiple linear regression (MLR), support vector regression (SVR), random forest, and XGBoost. The performance of each model was evaluated using the mean absolute percentage error (MAPE) and root-mean-square error (RMSE). The analysis results indicated that the AADT was the most influential variable affecting the asphalt layer thickness. Among the models, the MLR demonstrated the best predictive performance. While XGBoost had a relatively strong performance among the machine-learning techniques, the traditional statistical model, MLR, still outperformed it in certain regions. This study emphasized the need for customized pavement designs that reflect the traffic and environmental conditions specific to regional roads in Pakistan. This finding suggests that future research should incorporate additional variables and data for a more in-depth analysis.

암은 오랫동안 한국인의 주된 사망원인이었으며, 주로 환경적 요인이 암 발생에 영향을 주고 있다. 암으로 인한 높은 사망률에도 불구하고, 어떠한 지리적 및 환경적 요인이 암 발생에 영향을 주는지에 대한 국내 사례 연구는 부족한 실정이다. 본 연구의 목적은 공간적 관점에서 갑상선암, 대장암, 위암, 폐암, 간암, 전립선암, 유방암을 포함한 주요 암의 발생률이 지리･환경적 요인에 따라 어떻게 다르게 나타나는지를 파악하는 것이다. 이를 위하여, 시군구 수준에서 계산된 암 발생률을 종속변수로 활용하였고, 암 발병에 영향을 미칠 것으로 기대되는 13가지 독립변수를 선정하였다. 종속변수의 공간적 의존성 및 군집을 확인하기 위하여 전역적, 국지적 Moran ’ s I 통계량을 이용하였고, 암 발병에 영향을 미치는 요인을 파악하기 위하여 OLS 및 공간회귀모형들을 활용하였다. 전역적 Moran 통계량과 LISA Cluster map을 통하여 암 발생의 공간적 패턴에서 공간적 자기상관을 확인할 수 있었다. 다음 OLS 회귀 분석을 통하여 암 발생에 영향을 미치는 변수들을 파악하였다. 이때 대부분의 암에서 연령 효과가 강하게 나타났기 때문에 이를 통제하였다. OLS 모형의 잔차에서 공간적 의존성이 명확하게 나타남을 확인한 후, 공간적 의존성을 모형화하기 위하여 공간회귀분석을 수행하였다. 분석 결과 모든 유형 별 암에 대한 발생률을 설명하는데 있어 공간회귀분석이 적합한 것으로 나타났다. 특히 공간적 의존성을 고려할 경우 암별로 영향을 주는 요인이 OLS 분석 결과와 상이할 수 있음을 확인하였다. 본 연구 결과는 공간통계분석을 통하여 암 발생에 영향을 주는 지리 환경적 요인을 보다 정확하게 식별할 수 있음을 보여주었다. 이러한 결과는 암을 예방하기 위하여 지역의 환경을 요인을 어떻게 개선할 것인지에 대한 유용한 정보를 제공한다.

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of α and β. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.

The risk assessment for heavy metal pollutions were analyzed by using statistical techniques including correlation and cluster analyses. The contamination data in this investigation obtained were from the Chungcheongnam-do abandoned mines. The descriptive statistical analysis showed that the values of Pb and Zn were relatively higher than other heavy metal values. The detection of heavy metals by distance from abandoned mines within 1,000m were mostly As, Cd, Pb, and Zn. It was noted, especially, that Zn was even detected at 4,000m The results of coefficient correlation showed that Zn to Cd was the highest values. The cluster and dendogram analyses were generated. The results showed the two clear groups by heavy metal characteristics.

Engineer and quality statistician use different terms and techniques but they have same meaning and concept. The terms accuracy and precision are frequently used by engineer and scientist in the department of R&D and production technology. This paper makes a contribution to the interpretation of statistical quality techniques by the use of accuracy and precision. Engineer and scientist who have six sigma black belt and statistical background can use efficiently and effectively accuracy and precision in a manner consistent with statistical usage according to this study.

In this study, water quality data of eight main sites in the Geumho River watershed were collected and analyzed for long-term changes in water quality over the period from 2005 to 2015. The results showed that BOD concentration was gradually improved by the Total Maximum Daily Load (TMDL), stages 1 and 2. Recently, a tendency of increasing BOD concentration was observed in the downstream section of the river. The concentration of COD was analyzed to be contaminated throughout the water system regardless of the water quality improvement project, and the TN concentration tended to increase in the midstream of the river from 2013. The TP concentration has clearly decreased from 2012 after the second stage of TMDL. For the statistical analysis of PCA ordination, monthly water qualities (pH, DO, Electrical Conductivity (EC), Water Temperature (WT), BOD, COD, TN, TP, TOC, and SS) and flow rate data for 5 years from 2012 to 2016 were used. Seasonally the Geumho River showed an increase in the TN concentration at point sources during the dry season (December to February). TP showed the effect of non-point sources in the summer, because rainfall has caused a rise in flow rate in the upstream. Besides, the origin of pollution source was changed from non-point sources with BOD, COD, and TOC.